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==== Front
Bioinformatics
Bioinformatics
bioinformatics
Bioinformatics
1367-4803
1367-4811
Oxford University Press
32449747
10.1093/bioinformatics/btaa542
btaa542
Original Papers
Genome Analysis
AcademicSubjects/SCI01060
TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
Sharma Divya Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada M5T 3M7
Paterson Andrew D Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada M5T 3M7
Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada, M5G 1X8
Xu Wei Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada M5T 3M7
Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada, M5G 2C1
Luigi Martelli Pier Associate Editor
To whom correspondence should be addressed. E-mail: [email protected]
01 9 2020
25 5 2020
25 5 2020
36 17 45444550
10 3 2020
08 5 2020
19 5 2020
© The Author(s) 2020. Published by Oxford University Press.
2020
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Motivation
Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations.
Results
We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) Cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes (T2D) with 170 cases, 174 controls; to demonstrate the model’s effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, Cirrhosis and T2D data, respectively, against the next best performing method, Random Forest.
Availability and implementation
https://github.com/divya031090/TaxoNN_OTU.
Supplementary information
Supplementary data are available at Bioinformatics online.
Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038 RGPIN-2017-06672 Crohn’s and Colitis Canada CCC-GEMIII Helmsley Charitable Trust 10.13039/100007028 NSERC 10.13039/501100000038 RGPIN-2017-06672
==== Body
pmc1 Introduction
The human microbiome comprises a collection of microbes which live on and inside the human body. The microbiome data are usually quantified into Operational Taxonomic Units (OTUs), based on their sequence similarity to reference datasets (Blaxter et al., 2005). The risk of some diseases has been found to be associated with the host’s microbiome (Jackson et al., 2018), making prediction of risk of disease based on microbiome analysis an important problem. In this regard, machine learning can efficiently understand the relationship between the microbiomes and between microbiomes and diseases (Sommer et al., 2017).
The role of the microbiome has been examined in subjects with a variety of diseases such as Inflammatory Bowel Diseases (Gevers et al., 2014), Cirrhosis (Schnabl and Brenner, 2014) and type 2 diabetes (T2D) (Hartstra et al., 2015) justifying the potential use of the microbiome as a disease risk prediction tool. A sparse distance-based learning method for multiclass classification of human microbiota is proposed by Liu et al. (2011); Pasolli et al. (2016) proposed a computational framework for prediction tasks using species-level relative abundances and strain-specific markers. Whereas, Bokulich et al. (2018) presented a comparison of supervised learning classifiers and regressors for microbiomes using a Python-based machine-learning library. Ananthakrishnan et al. (2017) incorporated clinical and microbiome data to classify treatment response and Lo and Marculescu (2019) proposed a neural network framework for disease prediction with data augmentation to mitigate over-fitting. However, the role of taxonomy in prediction using OTU data is often unclear, wherein similar OTUs are often correlated across samples.
Convolutional Neural Networks (CNNs) (Krizhevsky et al., 2012) have been successfully applied to diversified areas such as face recognition (Yang et al., 2016), optical character recognition (Bai et al., 2014) and medical diagnosis (Sun et al., 2016). CNNs perform well in capturing spatial and temporal dependencies in the input data. CNNs are also capable to capture interactions in the data during prediction (Tsang et al., 2017). Ensemble learning has also garnered a lot of attention in the field of bioimage classification (Nanni et al., 2018) and scene-text recognition (Park et al., 2016) wherein multiple neural networks are combined together to enhance model performance as well as incorporate multiple inputs. However, we observed that CNNs have not been widely applied in the area of microbiome analysis to predict disease risk. One reason could be that OTU relative abundance data in itself (without any re-arrangement) does not show any spatial similarity that the CNNs can capture.
Motivated by the inherent correlation shared by the OTUs in the same taxonomy level and the non-linear relationship between the OTUs during disease prediction (Tsai et al., 2015; Xiao et al., 2018), we propose a novel deep learning model taxoNN (taxonomy-based Neural Network). taxoNN stratifies input OTU data into various clusters based on their phylum information. Further, as ensemble learning is effective, hence, we propose an ensemble of CNNs over the stratified clusters containing OTUs sharing the same phylum. The rationale is that OTUs after the phylum level division share similarity and hence, some correlation with each other. Moreover, to introduce spatial relationship in the input OTUs for the CNNs to capture, we order the OTUs on the basis of correlation with each other and Euclidean distance from the centre of the cluster.
2 Materials and methods
2.1 Proposed neural network framework: taxoNN
We experimented with using three types of CNN models. To begin with, we used a basic convolutional framework (CNN_basic), where the input OTUs were arranged in an alphabetical order of their taxonomic label and hence, their order did not represent a biological relationship.
We then experimented with shuffling the OTUs (CNN_shuffle) in the input on each iteration of the neural network, in the assumption that the various iterations of shuffling would in turn lead to correlated microbiomes arrange in one window. However, this assumption might limit the prediction accuracy.
Hence, we finally examined incorporating the inherent phylogenetic relationship in the OTU data before providing it as an input to the neural network model. Supplementary Figure S1, shows a sample taxonomy tree containing various taxonomic levels and illustrates that hierarchy in OTU data is complex and clusters corresponding to the different phyla can contain a varied number of OTUs.
Let there be ‘I’ subjects in the whole study, the OTU data for ith subject (where, i∈ I), was presented in a 1-D vector format to the network, as, OTUith subject={o1,o2,…,oN}, where, N was the total number of OTUs in a subject. These OTUs were then stratified into four clusters based on their phyla such that each cluster had different number of OTUs. For example the first cluster contained ‘p’ OTUs, second contained ‘q’ OTUs, third contained ‘r’ OTUs and fourth contained ‘s’ OTUs (where p + q+r + s = N), and CNN was applied to each cluster individually. To order and place correlated OTUs together, we adopted two approaches:
Approach 1: Ordering based on distance to the cluster centre: In this approach, for a cluster, we took ‘p’ OTUs of I-dimension each (corresponding to ‘I’ number of subjects), inside the cluster. We then calculated the medoid of that cluster. A medoid is a representative object of a dataset whose average dissimilarity to all the objects in the cluster is minimal. A medoid in a cluster containing OTUs of the same phyla, is calculated using the formula: (1) OTUmedoid=argminy∈{OTU1,OTU2,…,OTUp}∑j=1pd(y,OTUj)
As can be seen in Supplementary Figure S12a, for ease of representation, we took a few OTUs and considered their OTU vectors to contain only three subjects. OTUs are shown as blue dots representing relative abundance of that particular OTU and medoid of these OTUs was then calculated (shown as red dot Supplementary Fig. S12b) using Equation 1. Further, Euclidean distances di, dj and dk of three sample OTUs (i, j and k) from the medoid were calculated (Supplementary Fig. S12c) and OTUs were ordered on the basis of their increasing distance to the medoid. In this way, we obtained di<dj<dk, therefore, OTUi was ordered before OTUj and OTUk in the OTU vector that was provided as an input to the CNN. This idea was extended to all the ‘p’ OTU vectors in the cluster.
This ordering combined with the convolutional sliding window helped to combine OTUs which were closely located and shared more similarity in the cluster. OTUs in the same sliding window, combined with the weight vector in the neural network led to creating non-linear terms that were sent to the next layer of the neural network and hence, this helped in understanding the non-linear relationship between them. We named this variation of taxoNN as taxoNNdis.
Approach 2: Ordering based on correlation: The second approach that we used was to order the OTUs based on their correlation with each other using Spearman rank. This gave us a p × p matrix for p OTUs in a cluster as shown in Figure 1a. Next, each row of this correlation matrix was reduced to a cumulative correlation coefficient, calculated with respect to all the OTUs in a single row using the formula: (2) ρOTUrowj=|ρOTUj1|·|ρOTUj2|⋯|ρOTUjp|p
for j∈[1,p]
Fig. 1. An illustration of correlation-based ordering in the OTUs in a cluster. (a) Example heatmap obtained by plotting Spearman rank coefficients between positively correlated OTUs in a cluster. (b) Cumulative coefficient obtained with respect to each row of the heatmap matrix. (c) Vector of cumulative coefficients arranged in a decreasing order where, ρOTUrow5>ρOTUrow3>ρOTUrowp>ρOTUrow1>ρOTUrowk. (d) The cumulative coefficients are renamed as ρOTUrowi* to represent that they are now arranged in a decreasing order. (e) Heatmap sorted based on the new order of cumulative coefficients, making the correlated terms concentrate in a space and arrange closer in the matrix
The set of these cumulative coefficients is represented as POTU (Fig. 1b) as: (3) POTU={ρOTUrow1,ρOTUrow2,…,ρOTUrowp}
Thus, we obtained a vector of correlation coefficients, POTU based on Equation 3, with each value representing a cumulative correlation coefficient for each row. The values in the set POTU were then arranged in a decreasing order and a new vector POTU* was created containing cumulative correlation coefficients in decreasing order which were further re-indexed from 1 to p. The asterisk here represents re-indexing. (4) POTU*={ρOTUrow5,ρOTUrow3,…,ρOTUrowk}
(5) POTU*={ρOTUrow1*,ρOTUrow2*,…,ρOTUrowp*}
Subsequently, the heatmap obtained by the correlations in the OTU data is reordered based on the decreasing order of the cumulative correlation coefficients. Through this ordering the correlation structure between the OTUs was used to establish a similarity in the neighbouring OTUs before being provided to the neural network model. We named this variation of taxoNN as taxoNNcorr.
The broad overview of our CNN framework is presented in Figure 2a. Figure 2b illustrates various layers in the neural network acting on each cluster of the OTU data. We defined the model with two 1-D convolutional layers, each followed by a pooling layer. The data has been pre-processed in such a way that each vector contains N OTUs. These OTUs are then divided into clusters based on their phylum level with first cluster containing ‘p’ OTUs, second containing ‘q’ OTUs, third containing ‘r’ OTUs and fourth containing ‘s’ OTUs. The first convolutional layer (Conv1) defines 32 filters (feature detectors) of height 5 (window size) and stride size (number by which sliding window slides) of 1. For activation, we use Rectified Linear Unit (ReLU) (Glorot et al., 2011) and after the convolution operation (Krizhevsky et al., 2012) in the first layer, the extracted features were forwarded to the pooling layer (Pool1). A pooling layer is often used after a CNN layer in order to reduce the complexity of the output and prevent overfitting of the data. Similarly, a second set of convolutional (with 64 filters) and pooling Layer (Conv2 and Pool2) were used to extract features. Finally, the feature vectors obtained were flattened to a single vector. In a similar manner features were learned and flattened from each cluster.
Fig. 2. Illustration of the layers in the CNN framework. (a) Detailed illustration of the phylum-based stratification and ensemble learning of CNNs for disease prediction. The four different clusters are color coded with different colours and after phyla stratification are input to the four neural networks (N1, N2, N3 and N4). Later the features extracted are flattened and stacked during the concatenation step to further lead to prediction of disease outcome. (b) Illustration of the layers in a single neural network (N1/N2/N3/N4) acting on one particular cluster of the input data. (Color version of this figure is available at Bioinformatics online.)
Next, ensemble learning (Hansen and Salamon, 1990) was used, where, features from each cluster were combined. The flattened vectors obtained from each cluster were merged via concatenation to make one very long vector that was then interpreted and sent to two fully connected layers before a prediction was made. In the two fully connected layers the first layer had 100 nodes followed by a ReLU activation while the second layer had only a binary node with a softmax activation (Goodfellow et al., 2016) to predict the two classes according to the disease status (Disease/Control). The details about the input and output processing through each layer are shown in Supplementary Figure S19. We also experimented with adding variables such as age and sex as input along with the OTU data in the model. In this scenario, two separate vectors, one containing age values and other, the sex values were given as input to the individual CNNs along with the OTU vectors in each cluster.
2.2 Simulated studies
We designed simulation studies using the microbiome data available in the ‘Genetic, Environmental, Microbial’ (GEM) project (Turpin et al., 2016). Subjects were first-degree relatives of subjects with Crohn’s disease between 6 and 35 years of age and recruited between 2008 and 2015. This project aimed to identify microbial, genetic and environmental factors responsible for the initiation of Crohn’s disease. Stool samples were collected for 16S ribosomal DNA sequencing at a minimum depth of 30 000 reads/sample. Samples with fewer than 30 000 reads and OTUs with prevalence of <5% were removed from the analysis. Analysis was restricted to merged OTUs with the same taxonomic assignment.
Our simulated datasets were created using 1796 subjects provided in the GEM study data. Each sample contained values for 168 OTUs. The OTUs in this simulated dataset were categorized into taxonomy levels with 12 phyla, 15 classes, 20 orders, 37 families and 60 genera. The three dominant bacterial phyla in terms of the number of OTUs were Firmicutes, Proteobacteria and Actinobacteria.
We used this data to create a population with 100 000 samples. Instead of a simple replication we added noise to each OTU using a normally distributed function with mean equal to a random number in the range [1×10−6,2×10−6] and standard deviation of 10−6 to create new samples. While doing so we ensured that we preserve the zeroes and also considered that the relative abundance is equal to one, by adding and subtracting the noise term in equal proportion in each OTU set, keeping the zeroes. We then generated the disease status (y = 1 for case; y = 0 for control) using the formula: (6) p(y=1)= exp(α+∑i=132βi·OTUi+∑i=12∑j=i+13βij·OTUi·OTUj)1+exp(α+∑i=132βi·OTUi+∑i=12∑j=i+13βij·OTUi·OTUj)
where βi were the regression coefficients associated with OTUs, α was the base prevalence, βij were the regression coefficients for the pairwise interaction terms, y was the outcome variable and p(y=1) was the probability of the outcome variable to be 1, i.e. disease status positive. In general, the OTUs that are potentially associated with risk of disease, in a microbiome dataset are unknown and their number can range from zero to a very large value. Carefully choosing the number of these OTUs during simulating data, thus, becomes a challenge. Therefore, based on a trade-off between the model performance upon analysis with various number of OTUs (Supplementary Table S1) and the realistic estimation of OTUs potentially associated with risk of disease in a real microbiome dataset, we selected 32 OTUs randomly as the OTUs that were potentially associated with risk of disease, also ensuring that all clusters contribute to these OTUs. We set the value of α as -2.5, βi in 1st cluster ranging from [1,1.5], 2nd cluster ranging from [1,2], 3rd cluster ranging from [1.5,2] and 4th cluster [0.5,1]. Interaction terms were added to introduce non-linearity in the data. Out of the 32 OTUs potentially associated with risk of disease, 3 OTUs were randomly picked and three pairwise interactions between them were generated (as shown in Equation 6), where, βij was taken as [1,1.5,2]. In this way, we generated 2000 samples as cases and 98 000 as controls from the 100 000 samples. For the simulation data to evaluate our algorithm, we then randomly selected 200 cases from theses 2000 case samples and randomly selected 200 matched controls based on age and sex. We performed 1:1 matching of cases to controls for age in the range of ±5 years and exact match for sex. Hence, obtained a case-control dataset of 200 cases and 200 controls. 100 simulation datasets were generated following the same strategy.
The phyla-based stratification on the OTUs in the simulated dataset was done in the following manner: for 168 OTUs, after phyla-based stratification, 1st cluster contained 92 OTUs, 2nd contained 28 OTUs, 3rd contained 27 OTUs and 4th contained 21 OTUs. Each cluster was provided as an input to an individual CNN to understand the relationships between OTUs inside each phyla and later the extracted features were used for making the predictions.
2.3 Real studies: T2D study and Cirrhosis study
To assess the prediction power of taxoNN on linking the gut microbiome with disease risk, we implemented our algorithm on a T2D (Qin et al., 2012) study containing 174 cases and 170 controls and a liver Cirrhosis study (Qin et al., 2014), containing 118 cases and 114 controls. OTUs at the genus level in the kingdom ‘Bacteria’ were used as an input. The T2D data was based on deep next-generation shotgun sequencing of DNA extracted from the stool samples from Chinese subjects. The subjects in the Cirrhosis data were of Han Chinese origin. In both studies Proteobacteria, Actinobacteria and Firmicutes emerged as the phyla with majority of OTUs, leading to forming three major clusters for taxoNN. Supplementary Tables S2 and S3 give more details about the OTUs in each cluster in the T2D study and Cirrhosis study, respectively. Details of variables like age and sex of the subjects provided with both studies are given in Supplementary Table S4. The box-plots containing relative abundance percentages of OTUs in each phylum of T2D and Cirrhosis studies are presented in Supplementary Figures S3 and S7, respectively. Supplementary Figures S4–S6 and Supplementary Figures S8–S10 provide box-plots for relative abundance percentages of genera in each cluster of the T2D and Cirrhosis studies.
2.4 Model specification and evaluation criteria
For training the neural network model on the simulated study, 70% of the subjects were considered in the training data and 30% in the test data. Therefore, out of 200 controls and cases which were pair-matched for age and sex as described in Section 2.2, 140 controls and 140 cases were used for training the network, and 60 controls and 60 cases were used for testing the network. Similarly, for the T2D and Cirrhosis studies, 70% of the subjects were considered in the training data and, 30% in the test data. Thereby, in the T2D study 119 cases and 119 controls were used for training and 55 cases and 50 controls were used for the test set. In Cirrhosis study, 83 controls and 83 cases were used for training and 31 controls and 35 cases were used to test the model. We also performed an internal validation using 10 times 10-fold cross validation on the training set itself, to analyze model performance before testing and to eliminate overfitting. For the cross-validation, we used 90% of the total training set selected at random for training, and the remaining 10% as a hold out set for testing. We obtained 10 AUC values corresponding to initial 10-folds in the training set. We repeated this process 10 times in order to generate corresponding 100 AUC values. We then calculated the 95% confidence intervals using these 100 AUC values. 400 epochs were run for the neural network model with a stride size of 1, window size of 5, number of OTUs related to disease outcome set as 32 for the first layer and number of filters in the CNN network as 32. Each network was trained using stochastic gradient descent with a learning rate of 0.001. We trained our network on an NVIDIA Tesla P100 GPU with 16GB of RAM using tensorflow library in Python alongwith some data analysis using R version 3.5.3.
The performance of our technique was evaluated through a Receiver Operating Characteristics curve (ROC curve) using specificity, sensitivity and thereafter calculating mean Area Under Curve (AUC), where a larger AUC meant a better classification model. Given the number of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN), the measures are mathematically expressed as follows: Sensitivity=TP/(TP+FN) and Specificity=TN/(TN+FP).
We compared the results obtained by our proposed model taxoNN in its two variations taxoNNdis and taxoNNcorr against conventional machine learning models like Random Forests (RFs) (Liaw et al., 2002), Gaussian Bayes Classifier (GBC) (Hand and Yu, 2001), Naive Bayes (NB) (Rish et al., 2001), Ridge regression (Hoerl and Kennard, 1970), Lasso regression (Tibshirani, 1996) and Support Vector Machines (SVM) (Suykens and Vandewalle, 1999).
3 Results
3.1 Simulation results
3.1.1 Type 1 error performance
In the simulated datasets, first, we tested for taxoNN under the null, i.e. where none of the OTUs in the input data were related to the outcome i.e. disease status. We obtained an AUC value of 0.513 using taxoNNcorr and 0.504 with taxoNNdis model. Comparing the AUC values obtained from our model with RF (AUC = 0.502), SVM (AUC = 0.523), Ridge (AUC = 0.517) and Lasso (AUC = 0.510) we observed that our model was stable under the null and shows that the prediction of disease status was not governed by the OTUs in the case of non-causal relationship between the OTUs and disease.
3.1.2 Comparison of predictive performance
For the simulated datasets under the situation of association, the ROC curves obtained are presented in Figure 3. As can be seen in Figure 3, the blue and the brown plot lines in the graph depict the ROC curve for taxoNNcorr and taxoNNdis, respectively. The area under the curve was highest for our proposed models, taxoNNcorr and taxoNNdis with AUC values, 0.883 and 0.874, respectively, followed by RF technique (AUC = 0.846). As discussed, we initially experimented with predicting disease status using a basic CNN model. As the arrangement of input OTU data in this case did not signify any relationships, therefore, the AUC obtained was equal to 0.753. The second variation we tried was to shuffle the input data on each iteration of the CNN (CNN_shuffle) so that, we can approximate OTU correlations by making them fall in the same CNN window for combination into the next layer. We observed that, in this case the performance of the CNN improved (AUC = 0.822), as compared to the basic CNN. However, the performance in this method is highly dependent upon the OTU combinations resulting due to the shuffling and thus, might vary upon shuffling the OTUs. The other machine learning methods like RF and SVM with AUCs 0.846 and 0.825, respectively, performed relatively better than GBC and NB (AUC = 0.792, 0.789, respectively) due to their tree-based structure, rendering their ability to capture non-linearity in the data. However, there was a clear under-performance by these methods as compared to taxoNNcorr with a difference in AUC ranging from about 0.038 for RF and increasing to about 0.094 for the least efficient performing method GBC. The computation time taken by our method on an NVIDIA Tesla P100 GPU with 16GB of RAM for each iteration of the ensemble of neural networks was 9.35 s. The initial ordering of the input OTU data took 1.27 s. Therefore, each iteration took about 10.62 s. The neural networks ran simultaneously for each cluster and took 400 epochs to learn, therefore, the overall time taken for taxoNN to train was about 70.8 min for the simulated dataset. Details of the performance of taxoNN in case of change in parameters associated with the neural network, in presence of interaction terms and in case of imbalance of case and controls is shown in Supplementary Table S1, Supplementary Table S5 and Supplementary Figure S11, respectively.
Fig. 3. ROC curve obtained on the test set of the simulated study. The test set comprised 60 controls and 60 cases. The red dotted line corresponds to AUC equal to 0.5, indicating a random classification model
3.2 Results for T2D and Cirrhosis studies
In this section, we present results on the training and test sets of the T2D and Cirrhosis studies. We filtered the data in both the studies, eliminating OTUs that had a zero proportion in all individuals and thereby obtained 184 OTUs for the Cirrhosis study and 208 for the T2D study after this filtering. Supplementary Figure S2 illustrates pie-charts corresponding to the OTU distribution in the T2D and Cirrhosis studies. Illustration of how the heatmaps are sorted and rearranged based on the correlations between the OTUs in each cluster are provided in Supplementary Figures S13–S15 for T2D and Supplementary Figures S16–S18 for Cirrhosis study. An additional analysis on an external validation cohort (Karlsson et al., 2013) is presented in Supplementary Tables S7 and S8.
3.2.1 Results for T2D study
The results for T2D dataset taking 10-fold cross validation on the training set are presented in Figure 4a (also, Supplementary Table S6). We plotted the 95% confidence intervals (CI) for each of the methods. The mean AUC values obtained for taxoNN and taxoNN were 0.753 (95% CI: 0.741–0.761) and 0.741 (95% CI: 0.731–0.750), respectively, followed by RF (AUC = 0.740), CNN_shuffle (AUC = 0.736), SVM (AUC = 0.721), Ridge regression (AUC = 0.699), Lasso regression (AUC = 0.687), GBC (AUC = 0.684) and NB (AUC = 0.682). We also calculated the results on the test set of the T2D study (tabulated in Table 1 second column), and obtained a mean AUC value of 0.733 using taxoNNcorr which was considerably higher than the other machine learning methods on the test set.
Fig. 4. 95% confidence intervals obtained for the mean AUC values for 10 times 10-fold cross validation on the training set for the (a) T2D study and the (b) Cirrhosis study
Table 1. AUC values tabulated for various machine learning methods on test set of T2D and Cirrhosis studies
AUC T2D AUC Cirrhosis
Method w/o age+sex w age+sex w/o age+sex w age+sex
RF 0.703 0.708 0.893 0.901
GBC 0.642 0.648 0.816 0.825
SVM 0.701 0.704 0.877 0.882
Lasso regression 0.665 0.670 0.823 0.831
Ridge regression 0.700 0.705 0.842 0.848
NB 0.682 0.685 0.802 0.807
CNN_basic 0.643 0.647 0.799 0.801
CNN_shuffle 0.712 0.718 0.844 0.852
taxoNN dis 0.720 0.725 0.903 0.908
taxoNN corr 0.733 0.762 0.911 0.938
Note: The results are reported on both studies considering model performance without (w/o) including age and sex and with (w) age and sex. Note that the last row (values in bold) shows the consistent improvement in the performance of the proposed model taxoNNcorr for both studies.
3.2.2 Results for Cirrhosis study
The results for Cirrhosis study taking 10 times 10-fold cross validation by creating 10-folds in the training set and using 1 out of the 10-folds for testing each time are presented in Figure 4b (also, Supplementary Table S6). The 95% confidence interval over the 100 mean AUC values were calculated for the other machine learning methods in comparison to taxoNN. We obtained a mean AUC value as high as 0.921 (95% CI: 0.911–0.930) for the proposed taxoNNcorr model closely followed in performance by the taxoNNdis model with a mean AUC of 0.919 (95% CI: 0.909–0.925). An improvement of 0.025 was noted when comparing the AUC value of taxoNNcorr to the next best performing method of RF (AUC = 0.892) followed by the SVM method which was observed to give a mean AUC of 0.881. The GBC, NB and Ridge regression performed comparably with mean AUC values of 0.874, 0.870 and 0.877, respectively. It was observed that the least efficient method in this case was the basic CNN model with AUC as low as 0.832. Results on the test set for Cirrhosis study are reported in Table 1, fourth column, showing the effectiveness of taxoNN on the Cirrhosis study.
3.2.3 Incorporating clinical variables
As tabulated in Supplementary Table S4, we observed that in both studies cases were significantly older than the controls. In the T2D study the cases had a significantly greater proportion of males than controls. Whereas, for Cirrhosis study, there were no significant differences in sex between cases and controls. To analyze further, we evaluated the prediction power of our model including age and sex data. We observed an AUC value of 0.592 given the age and sex for the Cirrhosis dataset using logistic regression. Similar, observation was made for the T2D dataset where we obtained an AUC value of 0.613 using just the age and sex. When we combined these two variables along with the OTU training set (performing 10 times 10-fold validation) and provided it as input (Table 1 third column) to taxoNNcorr for the T2D study, we obtained an improved AUC of 0.762 as compared to 0.738 previously obtained using only the OTUs. The same held valid for the Cirrhosis study, where the AUC after combining environmental variables increased from 0.921 to 0.938 (Table 1 fifth column). We also observed that when age and sex were provided to other machine learning models of the T2D study, enhanced their performance a little, with an increase of 0.008, 0.009, 0.005, 0.008, 0.006, 0.005 in the AUC values of RFs, SVM, GBC, NB, Lasso Regression, Ridge Regression, respectively (Table 1). A similar trend was observed for the performance in Cirrhosis study, with an increase of ∼0.005 in AUC values for other machine learning methods. However, it is to be noted that in taxoNN, inclusion of age and sex enhanced the performance to a larger degree as compared to other machine learning methods (increase of 0.017 and 0.009 in the AUC in T2D and Cirrhosis studies, respectively).
4 Discussion
Extensive analysis on three datasets establish that stratifying OTU data into clusters and using ensembles of CNN models on the clusters to predict disease status as proposed in taxoNN leads to efficiently capturing OTU data. We observed that taxoNN performs consistently better across all the three datasets. Other methods like RFs which have a record of working well with non-linear data (Ryo and Rillig, 2017), performed slightly better than NB and GBC methods while predicting the risk of disease (Table 1). We also observed that in general, the AUC values obtained by performing 10 times 10-fold validation on the training set (Supplementary Table S6) were higher than the one obtained by working on the test set (Table 1).
By changing the parameters associated with the CNN (Supplementary Table S1) such as window size and the number of filters in each layer, we observed a trend of dropping in performance upon increasing these parameters beyond a certain level. We inferred that up to window size of five the performance was good, but increasing the window size further resulted in adding unnecessary amount of correlations between the OTUs in the input data which might not truly reflect the scenario in the real data. Similarly, when we increased the number of filters from 32 to 64 we observed that the performance dropped.
We also analyzed the methods in the literature that propose machine learning techniques for disease prediction for T2D and Cirrhosis studies. Qin et al. (2014) used an SVM method with training set (AUC of 0.918) and leave-one-out cross-validation set (AUC of 0.838) for the Cirrhosis data. In comparison, taxoNNcorr using the 10-fold cross validation outperformed by a significant margin giving an AUC value of 0.921 and similarly, taxoNNdis also gave a much higher AUC of 0.919 suggesting our model’s efficiency. Qin et al. (2012) propose a T2D classifier system based on the 50 gene markers through a minimum redundancy–maximum relevance (mRMR) feature selection method, to exploit the potential ability of T2D classification by gut microbiota. An AUC of 0.81 was reported using SVM for classification through the gene markers. As our model focused on relative abundance of the OTUs, therefore, a straight comparison to the results provided by Qin et al. (2012) was not feasible.
However, there are a few assumptions and limitations of our method. Microbiomes can reside in various sites in the body such as skin, mammary glands, uterus, ovarian follicles, oral mucosa and gut. However, for the scope of this article, we implemented our algorithm only on gut microbiome data, limiting our analysis to predicting diseases caused by gut microbiomes. As discussed earlier, the OTUs that are potentially associated with risk of disease in a microbiome dataset are unknown and their number in a study can be arbitrary, ranging from zero to a very large value. We experimented with taking 8, 16 and 32 OTUs associated to disease outcome in the simulation study, which ranges from 5 to 15% of the total OTUs in the study. We then selected 32 OTUs as the OTUs associated with risk of disease based on their performance in taxoNN (Supplementary Table S1). However, we might be under or over estimating the number of OTUs and it would be interesting to consider different number of OTUs in the future to evaluate the model better. Also, we simulated the data, taking three interaction terms w.r.t three randomly selected OTUs to add non-linearity in our OTU data. However, just three pairs of OTUs might not be enough to approximate the complex relationship presented within real OTU data. Hence, a better analysis by varying the number of interacting OTUs needs to be done to evaluate model performance. For our analysis, we consider phylum level stratification in taxoNN in all the three studies, due to presence of adequate number of OTUs in phylum level which is required for efficient model training. However, in the future, it will be interesting to observe studies which have adequate OTUs in other taxonomy levels like class and order along with phylum level (Supplementary Table S9).
As tabulated in Supplementary Table S4, age has been identified to be associated with the disease outcome for both T2D and Cirrhosis, whereas sex has been identified to be associated with T2D. This may represent poorly matched subjects in these studies. If these factors are causally associated with disease, then when used along with OTU data, they can enhance the performance of the model. However, our model is currently limited to just these two variables alongside the OTU data. A more comprehensive analysis taking other environmental variables like ethnicity, smoking status, dietary habits and medication can be conducted to evaluate their effects in disease prediction alongside microbiome data. We also observed that our method performs fairly robustly with respect to imbalance in the number of cases and controls up to a certain level (Supplementary Fig. S11), but the performance dropped considerably when the imbalance increased (1:4 ratio between cases and controls). Hence, better techniques to handle data imbalance need to be examined.
5 Conclusion
We propose a technique to predict disease status through gut microbiome data using a novel ensemble of neural networks. Using the inherent biological information in the OTU data, we divided the OTUs into clusters based on their phylum and trained on each cluster individually and later ensembled features from each neural network to predict disease status. We also proposed two novel ordering methods based on correlation and cluster centre distance to arrange input OTUs based on their similarity to help capture the spatial similarity in the input as required by the CNN. We obtained encouraging results on simulation data, Cirrhosis and T2D studies and consistent improvement in performance across both test and training sets compared to competing methods.
From our analysis we can infer, that non-linearity in the OTU data can be captured well using a CNN and relationships provided by the taxonomy in OTU data can help to improve accuracy of disease prediction. In the future, we would like to apply taxoNN for predicting continuous and time-to-event outcomes in addition to the current binary outcome and potentially implement our model on pathway analysis in genetic data. We would aim to identify specific microbiomes which play an important role for causing a particular disease. The limitations discussed in Section 4, pertaining to dealing with imbalance in input data and experimenting with more interaction terms also provide a good scope for future studies.
Funding
Wei Xu was funded by Natural Sciences and Engineering Research Council of Canada (NSERC Grant RGPIN-2017-06672), Crohn’s and Colitis Canada (CCC Grant CCC-GEMIII), and Helmsley Charitable Trust. Divya Sharma was supported by NSERC Grant RGPIN-2017-06672 and CCC Grant CCC-GEMIII.
Financial Support: none declared.
Conflict of Interest: none declared.
Supplementary Material
btaa542_Supplementary_Data Click here for additional data file.
==== Refs
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PMC007xxxxxx/PMC7750935.txt |
==== Front
Bioinformatics
Bioinformatics
bioinformatics
Bioinformatics
1367-4803
1367-4811
Oxford University Press
32614393
10.1093/bioinformatics/btaa602
btaa602
Applications Notes
Systems Biology
AcademicSubjects/SCI01060
RSSALib: a library for stochastic simulation of complex biochemical reactions
Thanh Vo Hong Department of Computer Science, Aalto University, Finland
Wren Jonathan Associate Editor
To whom correspondence should be addressed. E-mail: [email protected]
15 9 2020
02 7 2020
02 7 2020
36 18 48254826
15 2 2020
24 4 2020
20 6 2020
© The Author(s) 2020. Published by Oxford University Press.
2020
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Motivation
Stochastic chemical kinetics is an essential mathematical framework for investigating the dynamics of biological processes, especially when stochasticity plays a vital role in their development. Simulation is often the only option for the analysis of many practical models due to their analytical intractability.
Results
We present in this article, the simulation library RSSALib, implementing our recently developed rejection-based stochastic simulation algorithm (RSSA) and a wide range of its improvements, to accelerate the simulation and analysis of biochemical reactions. RSSALib supports reactions with complex kinetics and time delays, necessary to model complexities of reaction mechanisms. Our library provides both an application program interface and a graphic user interface to ease the set-up and visualization of the simulation results.
Availability and implementation
RSSALib is freely available at: https://github.com/vo-hong-thanh/rssalib.
Supplementary information
Supplementary data are available at Bioinformatics online.
Academy of Finland 10.13039/501100002341 311639 Algorithmic Designs for Biomolecular Nanostructures (ALBION)
==== Body
pmc1 Introduction
Biochemical processes at the cellular level are intrinsically stochastic due to the discreteness of species and the randomness of reaction firings, leading to significant fluctuation in the cellular response. Stochastic chemical kinetics describes the stochastic dynamics of biochemical reactions through the chemical master equation. Stochastic simulation in many cases is the only approach to study the temporal dynamics of biological systems due to the high-dimensional state space (Marchetti et al., 2017). Stochastic simulation of biochemical reactions poses many computational challenges not only due to the size of networks but also due to complex reaction mechanisms. Reactions with non-linear rate laws, such as enzymatic kinetics, often applied to model biochemical reactions to better match the experiments. In addition, reactions always take a certain time, called delay, from their initiation to finish. For many biological processes, such as the transcription and translation, where their completion time is slow, it is necessary to consider time delays to accurately describe the system dynamics. Finally, due to the stochastic nature of the approach, many simulation runs must be performed to obtain a reasonable statistical estimation of the expected behaviour of the system dynamics.
We present in this article, the simulation library RSSALib to offer the computational advantages of our recently developed rejection-based simulation algorithm (RSSA) (Thanh et al., 2014). We implement RSSA and a wide range of improvements to cope with different aspects of biological processes. RSSALib supports reactions with complex kinetics, including Michaelis–Menten and Hill kinetics. It also allows reactions with time delays required to model complicated biological phenomena. The biological network can be described in our reaction format or imported directly from an SBML model. Our simulation library provides both an application program interface (API) for stand-alone applications and a graphic user interface (GUI) to ease the set up of simulation and visualization of results. We validate our implementation and demonstrate its applications on real biological models (see Supplementary Material for more details) to highlight its applicabilities and computational improvements in simulation performance of our computational tool.
2 RSSALib
The simulation library RSSALib provides a full implementation of all known RSSA formulations to offer their computational advantages in dealing with varying complexities of biological networks.
2.1 Theoretical background
RSSA is an exact simulation (see Thanh et al., 2014, for a formal proof) that accelerates performance by reducing the average computations of reaction propensities. We consider a biological network consisting of N species Si, i=1,…,N, interacting through M reactions Rj, j=1,…,M. The populations of species constitute the state X(t). The probability that reaction Rμ fires in the infinitesimal time [t,t+τ+dτ), given the state X(t), is p(τ,μ)dτ in which the probability density function (pdf) p(τ,μ)=aμ exp {−a0τ} where aj is called reaction propensity and a0=∑j=1Maj. Instead of directly sampling pdf p(τ,μ) using the propensity aj, RSSA uses the bounds [aj¯,aj¯] and the rejection-based technique. The selection of the next reaction firing in RSSA consists of two steps. First, a candidate reaction Rμ is selected with probability aμ¯/a0¯ where a0¯=∑j=1Maj¯. The candidate reaction Rμ then enters a rejection-based test for validation with success probability aμ/a¯μ. RSSA avoids computing propensity aμ in this step as much as possible using the fact that if Rμ is accepted with probability a¯μ/a¯μ, then it is also accepted with probability aμ/a¯μ. If Rμ is accepted, its firing time is then computed. The firing time τ of the accepted reaction Rμ is chosen following an Erlang distribution to ensure exactness of the selection specified by pdf p(τ,μ).
2.2 Usage and implementation
Figure 1 shows the use of RSSALib’s GUI to simulate and visualize the simulation result. The GUI allows one to load the model and perform simulation with a click-and-run. RSSALib can also be used as a developer API for building stand-alone applications. For this usage, we manually load the model, and call the runSim() method of the simulator to execute the simulation. In the following, we briefly describe the simulation algorithms implemented in RSSALib and their time complexities.
Fig. 1. GUI of RSSALib for setting up simulation and visualization
RSSA and its extension for reactions with time delays (DelayedRSSA) realize the candidate reaction Rμ by linearly accumulating propensity upper bounds until it finds the reaction. We also provide the cache-friendly search, which reuses the previously computed sum of propensities in the last step. The time complexity of the search is O(M).
Partial-propensity RSSA (PRSSA) uses the factorization of the mass-action propensity to factorize the propensity bounds aj¯/aj¯ of reactions, which are then grouped by the common reactant species into the so-called partial propensity structure. The selection of candidate reaction in the PRSSA is performed in two consecutive steps in which the first search selects a group, and the second one locates the reaction in that group. The time complexity of the search in PRSSA is proportional to the number of species, i.e. O(N).
RSSA with tree-based search (RSSA-Binary) uses the tree-based search to reduce the time complexity for selecting the candidate reaction. First, a tree is built in which its leave store propensities aj¯ of reactions and internal nodes store the sum value of their children. The search for the next reaction will travel from the root to a leaf to discover the next reaction. The search depth is equal to the height of the tree, which is O(log (M)), hence its time complexity.
RSSA with composition–rejection search (RSSA-CR) implements the composition–rejection method to reduce the time complexity of the search for the candidate reaction to be independent with the number of reactions. Reactions are partitioned into L groups Gi, i=1,…,L so that a reaction Rj is put into a group Gi if its propensity aj¯ satisfies 2qi−1≤aj¯≤2qi. The selection of the candidate reaction is made in two steps. First, a group Gl is selected proportional to the sum of propensity bounds of reactions in the group. Then, the reaction Rμ in the group Gl is located by applying the acceptance–rejection with hat function 2ql. The selection of the candidate by the composition–rejection search depends only on the number of groups, i.e. O(L).
RSSA with table lookup search (RSSA-Lookup) reduces the time complexity of the search to be constant, i.e. O(1); however, it requires to build the lookup tables which take linear time O(M). The M probabilities aj¯/a0¯, for j=1…M, are partitioned into an equi-probable mixture of M two-point distributions and store these values in two tables, called cut-off table, storing the probability of the first values of the two-point mixtures, and alias table, containing the alias to the second parts of the mixtures. For the selection of the next reaction, a random number r1 is first used to lookup the position of the equi-probable mixture. It is then rescaled to select which part of the two-point.
3 Conclusion
We presented the simulation library RSSALib, providing the implementation of the RSSA and a wide range of its improvements, to accelerate the simulation and analysis of biochemical reactions. Our computational tool enables investigating large, complex biological systems.
Funding
The research was supported by Academy of Finland [311639], ‘Algorithmic Designs for Biomolecular Nanostructures (ALBION)’.
Conflict of Interest: none declared.
Supplementary Material
btaa602_supplementary_data Click here for additional data file.
==== Refs
References
Marchetti L. et al (2017) Simulation Algorithms for Computational Systems Biology. Springer International Publishing, AG.
Thanh V.H. et al (2014) Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays. J. Chem. Phys., 141 , 134116.25296793
|
PMC007xxxxxx/PMC7750936.txt |
==== Front
Bioinformatics
Bioinformatics
bioinformatics
Bioinformatics
1367-4803
1367-4811
Oxford University Press
32437529
10.1093/bioinformatics/btaa530
btaa530
Original Papers
Systems Biology
AcademicSubjects/SCI01060
Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study
http://orcid.org/0000-0001-6613-2530
Rodosthenous Theodoulos Department of Mathematics, Imperial College London, London SW7 2AZ, UK
http://orcid.org/0000-0002-4013-5458
Shahrezaei Vahid Department of Mathematics, Imperial College London, London SW7 2AZ, UK
Evangelou Marina Department of Mathematics, Imperial College London, London SW7 2AZ, UK
Wren Jonathan Associate Editor
To whom correspondence should be addressed. E-mail: [email protected] or [email protected]
01 9 2020
21 5 2020
21 5 2020
36 17 46164625
05 12 2019
22 4 2020
16 5 2020
© The Author(s) 2020. Published by Oxford University Press.
2020
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Motivation
Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets.
Results
Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets.
Availability and implementation
https://github.com/theorod93/sCCA.
Supplementary information
Supplementary data are available at Bioinformatics online.
==== Body
pmc1 Introduction
Nowadays, it is becoming a common practice to produce multiple OMICS (e.g. Transcriptomics, Metabolomics, Proteomics, etc.) datasets from the same individuals (Hasin et al., 2017; Hass et al., 2017; TCGA, 2012) leading to research questions involving the in-between relationships of the datasets as well as with the complex traits (responses). The datasets obtained through different mechanisms lead to different data distributions and variation patterns. The statistical challenge is how can these heterogeneous and high-dimensional datasets be analysed to understand their in-between relationships. A follow up question to address is how can these relationships be used for understanding the aetiology of complex traits.
Over the past years, a number of data integration approaches have been proposed for finding in-between dataset relationships (Li et al., 2018; Sathyanarayanan et al., 2019; Subramanian et al., 2020; Wu et al., 2019). These approaches can be split by their strategy: (A) Early: combining data from different sources into a single dataset on which the model is built; (B) Intermediate: combining data through inference of a joint model; and (C) Late: building models for each dataset separately and combining them to a unified model (Gligorijević and Pržulj, 2015). Huang et al. (2017) present a review of available methods for data integration and argue the need for direct comparisons of these methods for aiding investigators choosing the best approach for the aims of their analysis. A number of data integration approaches have been proposed in the literature for clustering disease subtypes (Mariette and Villa-Vialaneix, 2018; Swanson et al., 2019), whereas only few approaches have been proposed for supervised learning, i.e. for predicting the disease outcome (Jiang et al., 2016; Zhao et al., 2015). Van Vliet et al. (2012) investigated early, intermediate and late integration approaches by applying nearest mean classifiers for predicting breast cancer outcome. Their findings suggest that multiple data types should be exploited through intermediate or late integration approaches for obtaining better predictions of disease outcome.
The focus of this paper is on canonical correlation analysis (CCA), an intermediate integrative approach proposed by Hotelling (1936). CCA and its variations have been applied in various disciplines, including personality assessment (Sherry and Henson, 2005), material science (Rickman et al., 2017), photogrammetry (Vestergaard and Nielsen, 2015), cardiology (Jia et al., 2019), brain imaging genetics (Du et al., 2018, 2019) and single-cell analysis (Butler et al., 2018).
In the case of integrating two datasets CCA produces two new sets of latent variables, called canonical (variate) pairs. Suppose that there are two datasets with measurements made on the same samples (X1∈Rn×p1 and X2∈Rn×p2, assuming w.l.o.g. p1>p2). For every ith pair, where i=1,…,min(p1,p2), CCA finds two canonical vectors, wi(1) and wi(2), such that Cor(X1wi(1),X2wi(2)) is maximized based on the constraints described below. For the first pair, the only constraint to satisfy is Var(X1w1(1))=Var(X2w1(2))=1. In computing the rth canonical pair, the following three constraints need to be satisfied:
Var(X1wr(1))=Var(X2wr(2))=1 .
Cor(X1wc(1),X1wr(1))=Cor(X2wc(2),X2wr(2))=0, ∀c=1,…,r−1 .
Cor(X1wc(1),X2wr(2))=Cor(X2wc(2),X1wr(1))=0, ∀c=1,…,r−1 .
Orthogonality among the canonical variate pairs must hold: that is, not just between the elements of each feature space, but also among all combinations of the canonical variates; except the ones in the same pair, for which the correlation must be maximized. Complete orthogonality is attained when these constraints are satisfied.
In other words, CCA finds linear combinations of X1 and X2 that maximize the correlations between the members of each canonical variate pair (X1wi(1),X2wi(2)), where X1wi(1)=wi1(1)X11+⋯+wip1(1)X1p1 and X2wi(2)=wi1(2)X21+⋯+wip2(2)X2p2. By assuming that there exists some correlation between the datasets, we can look at the most expressive elements of canonical vectors (and indirectly, features) to find relationships between datasets.
CCA can be considered as an extension of principal component analysis (PCA) applied on two datasets rather than one dataset. Similarly to PCA, CCA can be applied for dimension reduction purposes, as the maximum size of the new sets of latent variables is k=min{p1,p2}.
A solution to CCA can be obtained through singular value decomposition (Hsu et al., 2012). The canonical vector wi(1) is an eigenvector of ΣX1X1−1ΣX1X2ΣX2X2−1ΣX2X1, while wi(2) is proportional to ΣX2X2−1ΣX2X1wi(1). Each pair i of canonical vectors corresponds to the respective eigenvalues in a descending order.
In the case of high-dimensional data (p≫n), the covariance matrix is not invertible and a CCA solution cannot be obtained. Over the years, a number of different methods have been proposed in the literature for finding solutions to this problem. These variations are called sparse CCA (sCCA) methods.
In recent years, several sCCA methods have been proposed, using different approaches, formulations and penalizations. Chu et al. (2013) implemented a trace formulation to the problem, Waaijenborg et al. (2008) applied Elastic-Net, while Fang et al. (2016) used a general fused lasso penalty to simultaneously penalize each individual canonical vector and the difference of every two canonical vectors. Other authors proposed methods based on sparse partial least squares. Lê Cao et al. (2009) assume symmetric relationships between the two datasets, Hardoon and Shawe-Taylor (2011) focus on obtaining a primal representation for the first dataset, while having a dual representation of the second, and the method proposed by Mai and Zhang (2019) that does not impose any sparsity on the covariance matrices.
In this paper, three sCCA methods that share similar characteristics in their formulation and optimizing criteria are discussed, investigated and extended. The first method, penalized matrix decomposition CCA (PMDCCA) proposed by Witten and Tibshirani (2009) obtains sparsity through l1 penalization, widely known as least absolute shrinkage and selection operator (LASSO; Tibshirani, 1996). The bound form of the constraints is used in order to reach a converged solution by iteratively updating w(1) and w(2). One of the assumptions of the PMDCCA approach is that the two datasets are independent, i.e. the covariance matrix of each input dataset is assumed to be the identity matrix. The second method proposed by Suo et al. (2017) relaxes the assumption of independence by allowing dependent data to be analysed through proximal operators [for the rest of the article, we are referring to this method as RelPMDCCA (relaxed PMDCCA)]. Even though, the additional restrictions make RelPMDCCA practically more applicable, it is computationally more expensive than PMDCCA. The third sCCA method we investigated is conventional CCA (ConvCCA) proposed by Parkhomenko et al. (2009). Similarly to PMDCCA sparsity is obtained through l1 penalization. ConvCCA estimates the singular vector of X1TX2 while iteratively applies the soft-thresholding operator. Chalise and Fridley (2012) extended ConvCCA to allow for other penalty functions and through a comparative study found the smoothly clipped absolute deviation (SCAD) penalty to produce the most accurate results. Motivated by this finding, in our work we have modified both ConvCCA and RelPMDCCA to be penalized through SCAD.
Section 2 starts with a description of the three sCCA approaches: PMDCCA, RelPMDCCA, ConvCCA, followed by a description of our proposed extension of ConvCCA and RelPMDCCA to allow for multiple input datasets. A comprehensive simulation study has been conducted for comparing the performance of the three methods and their extensions on integrating two and multiple datasets. To our knowledge, a comparison of the three sCCA methods has not been made elsewhere. The simulated datasets and scenarios considered are presented in Section 3.1.
We have further addressed the second important question; how can data integration be used for linking the multi-OMICS datasets with complex traits/responses? For addressing this question, we have looked the problem as both a supervised and an unsupervised one. For the supervised model, we have used the computed canonical pairs as input predictors in regression models for predicting the response. On the flip side, we have explored adding the response vector as an input matrix in the setting of multiple datasets integration. We have found both these approaches to have a better predictive accuracy than conventional machine learning methods that use either one or both of the input datasets. Section 4 presents the analysis of real datasets with the aim of predicting traits through sCCA.
2 Materials and methods
All sCCA methods share a common objective function, given by: (1) minw(1),w(2)−Cor(X1w(1),X2w(2))+pτw1(w(1))+pτw2(w(2)),
where pτw1(w(1)) and pτw2(w(2)) represent penalty functions on w(1) and w(2), respectively. It is a bi-convex optimization where, if w(1) is fixed, then Equation (1) is strictly convex in w(2) and vice versa. Hence, one can find a solution through an iterative algorithm.
In this section, the computation of the first canonical pair is presented. To derive additional pairs, we have extended an approach proposed by Suo et al. (2017), which is presented in Section 2.6.
2.1 Conventional CCA
Parkhomenko et al. (2009) proposed a solution of sCCA based on approximating the sample correlation matrix and applying l1 penalization through the soft-thresholding operator proposed by Tibshirani (1996). An iterative procedure updates both canonical vectors, w(1) and w(2), at each iteration k. The procedure is illustrated in the following steps where one of the vectors (e.g. w(1)) is updated while the second (e.g. w(2)) is kept fixed: where S((wl(1))k+1,12τwj)=(|(wl(1))k+1|−12τwj)+Sign((wl(1))k+1) and (x)+={xif x≥00if x<0,Sign(x)={−1if x<00if x>01if x=0,∀l={1,…,p1}
τwj represents the tuning parameter for each dataset Xj (j = 1, 2) and (w(1))k is the value of w(1) at the kth iteration. To update w(2), the same procedure is followed with the difference of replacing the second step with (w(2))k+1←K12T(w(1))k+1.
Compute sample correlation matrix K12=ΣX1X1−12ΣX1X2ΣX2X2−12
(w(1))k+1←K12(w(2))k
Normalize (w(1))k+1=(w(1))k+1||(w(1))k+1||
Apply soft-thresholding: (wl(1))k+1=S((wl(1))k+1,12τw1)
Normalize (w(1))k+1=(w(1))k+1||(w(1))k+1||,
2.2 Penalized matrix decomposition CCA
Witten and Tibshirani (2009) formulated the problem as: (2) minw(1),w(2)−Cor(X1w(1),X2w(2))+τw1||w(1)||1+τw2||w(2)||1subject to ||w(1)||2≤1;||w(2)||2≤1.
An iterative algorithm based on penalized matrix decomposition (PMD) is applied with the update formula: (3) w(i)←S(XiTXjw(j),Δi)||S(XiTXjw(j),Δi)||2,
where Δi>0 is chosen such that ||w(i)||1=1 holds, or Δi=0 if ||w(i)||1≤1, for i,j=1,2, i≠j. Although the PMDCCA solution is different from the ConvCCA solution, both approaches assume that the features are independent within each dataset (i.e. XiTXi=Ipi,i=1,2).
2.3 Relaxed PMDCCA
Suo et al. (2017) proposed a solution that relaxes the independence assumption and applies penalization through proximal operators. Their formulation of the problem is similar to (2): (4) minw(1),w(2)−Cor(X1w(1),X2w(2))+τw1||w(1)||1+τw2||w(2)||1subject to Var(X1w(1))≤1;Var(X2w(2))≤1.
The solution to this optimization is obtained through linearized alternating direction method of multipliers (Boyd, 2010; Parikh and Boyd, 2014). The iterative updates on the canonical variate pairs are computed through proximal algorithms. Due to space limitations, to view the updates, we refer the readers to the original paper by Suo et al. (2017).
2.4 Implementing SCAD penalty
The SCAD (Fan and Li, 2001) penalty with tuning parameter τ applied on w is given as follows: (5) pτSCAD(w)={τ||w||1if ||w||1≤τ−||w||12−2aτ||w||1+τ22(a−1)if τ<||w||1≤aτ(a+1)τ22if ||w||1>aτ
where a is fixed and suggested by Fan and Li to be set as a = 3.7. Motivated by the findings of Chalise and Fridley (2013), we have modified RelPMDCCA to perform penalization through SCAD.
Even though SCAD is a non-convex penalty function, Mazumder et al. (2011) argue that if the optimizing function is strictly convex, then penalization via SCAD is well-behaved and converges to a stationary point. In the objective functions of ConvCCA [Equation (1)] and RelPMDCCA [Equation (4)], τw1||w(1)||1+τw2||w(2)||1 is replaced by pτw1SCAD(w(1))+pτw2SCAD(w(2)). As a result the iterative updates of the canonical vectors are different. In ConvCCA, the algorithm is adjusted accordingly by replacing the soft-thresholding operator in Step 4 with: (6) (wl(1))i+1={(|(wl(1))i+1|−τw1)+Sign((wl(1))i+1)if |(wl(1))i+1|≤2τw1(a−1)(wl(1))i+1−Sign((wl(1))i+1)aτw1a−2if 2τw1<|(wl(1))i+1|≤aτw1(wl(1))i+1if (wl(1))i+1>aτw1.
The updates of canonical vectors in RelPMDCCA with SCAD penalty are performed by: (7) proxμf(ωj)={ωj+μcj−μτw1if μτw1<ωj+μcj≤τw1+μτw1ωj+μcj=+μτw1if−τw1−μτw1≤ωj+μcj<−μτw1ωj+μcj−μη21+2μη1if ψ1+μη2<ωj+μcj≤aψ1+μη2ωj+μcj+μη21+2μη1if−aψ1−μη2≤ωj+μcj<−ψ1−μη2ωj+μcjif |ωj+μcj|>aτw10else,
where η1=−12(a−1), η2=2aτw12(a−1), ψ1=τw1(1+2μη1), c=X1TX2w(2) and ω=((w(1))k−μλ(X1T(X1(w(1))k−zk+ξk))). The parameter τw1 controls the sparseness level while the algorithm parameters μ and λ must satisfy 0<μ≤λ||X1||22 (Parikh and Boyd, 2014). w(2) is updated through the same proximal operators, with τw2 acting as the tuning parameter. The update functions of z and ξ remain the same.
2.5 Multiple sCCA
In OMICS studies, it is common for a study to have more than two datasets (such as transcriptomics, genomics, proteomics and metabolomics) on the same patients. Incorporating all available data simultaneously through an integrative approach can reveal unknown relationships between the datasets. This section presents extensions of the three sCCA methods we have seen, for the integration of multiple (more than two) datasets simultaneously.
Suppose we have M separate datasets denoted by X1,…,XM, where Xm∈Rn×pm, ∀m={1,…,M}. The problem of Equation (1) is then generalized as: (8) minw(1),…,w(M)∑i<j−Cor(Xiw(i),Xjw(j))+∑m=1Mpτwm(wm)
As sCCA is bi-convex, multiple sCCA is multi-convex, i.e. if w(j), ∀j≠i are fixed, then the problem is convex in w(i). Instead of producing maximal correlated canonical variate pairs (2-tuple), multiple sCCA produces canonical variate list (M-tuple), e.g. for i=1,…,min(p1,…,pM), the ith canonical variate list would be (X1wi(1),…,XMwi(M)). Each Xmw(m) is taken such that, it is maximally correlated with the rest of the latent features ∑j≠mXjw(j).
In multiple ConvCCA, we propose to update w(i) iteratively, by keeping w(j), ∀j≠i and computing Kij, ∀j≠i. On the kth iteration, w(i) is updated by (w(i))k+1←∑j≠iKij(w(i))k (Algorithm 1 in Supplementary Material).
Witten and Tibshirani (2009) proposed an extension to their solution, by assuming that XmTXm=Ipm, ∀m={1,…,M}. To update w(i), the canonical vectors w(j), ∀j≠i are kept fixed. Multiple PMDCCA can then be performed by minimizing w(i)TXiT(∑j≠iXjw(j)), ∀i={1,…,M}, with constraint functions ||w(m)||2≤1.
We have extended RelPMDCCA for multiple datasets by following the approach of PMDCCA. The constraint functions Var(Xmw(m))≤1, ∀m=(1,…,M) and the proximal operators remain the same. If w(j) ∀j≠1 are kept fixed, we can update w(1), by replacing −w(1)TX1TX2w(2) in Equation (4) with −w(1)TX1T∑j≠1Xjw(j).
The tuning parameters in sCCA and multiple sCCA are selected as the ones producing maximal correlation through cross-validation. A detailed description of the procedure is presented in the Supplementary Material.
In this work, we have explored the performance of the methods in predicting the response of interest when the response is included as one of the datasets being integrated.
2.6 Computing the additional canonical vectors
We have only addressed the computation of the first canonical variate pair so far but this might not be adequate for capturing the variability of the datasets and of their relationships. Similarly to PCA as the number of computed principal components is increased the total amount of variability explained is increased. By computing the additional canonical vectors of CCA additional constraints must be satisfied as illustrated below.
Suo et al. (2017) compute the remaining canonical vectors by adding the second constraint to the optimization. Let W1=(w1(1),…,wr−1(1)) and W2=(w1(2),…,wr−1(2)) define the r - 1 canonical vectors which were computed. The rth canonical vector of w(1) is found through the optimization problem: (9) minw(1)−w(1)TX1TX2w(2)+pτw1(w(1))+1{w(1):||X1w(1)||2≤1}subject to W1TX1TX1w(1)=0,
where pτw1(w(1)) and pτw2(w(2)) represent the penalty functions on w(1) and w(2) with parameters τw1 and τw2, respectively. This solution would successfully result in producing latent features that are uncorrelated within the new datasets of canonical vectors, although the correlation is not restricted between the two new datasets.
In an attempt to include the additional constraint to the optimization problem, we propose the following extension to Equation (9): (10) minw(1)−w(1)TX1TX2w(2)+pτw1(w(1))+1{w(1):||X1w(1)||2≤1}subject to X1w(1)=z;W1TX1Tz=0r−1;W2TX2Tz=0r−1.
The solution to this optimization problem is obtained by letting X˜=[X1W1TX1TX1W2TX2TX1], and Y˜=[YW2TX2TX2W1TX1TX2]. The rth canonical vector is then computed by applying an sCCA method on X˜ and Y˜ to obtain wr(j), j=1,2, respectively. The exact algorithm in computing the additional canonical vectors is presented in Algorithm 2 in Supplementary Material.
Witten and Tibshirani (2009) proposed to update the cross-product matrix Y=X1TX2 after the computation of each canonical pair, by Yj+1←Yj−(wj(1)TYjwj(2))wj(1)wj(2)T, where wj(1) and wj(2) are the jth canonical vectors. The authors of ConvCCA proposed to take the residual of K by removing the effects of the first canonical vectors and repeat the algorithm in order to obtain the additional canonical pairs.
3 Results
3.1 Simulation studies
Simulated datasets were generated for assessing the performance of the three methods on (i) integrating two datasets, (ii) the orthogonality attained by each method and (iii) integrating multiple datasets. PMDCCA was implemented by using the existing functions in R package PMA. We used our own code for ConvCCA and RelPMDCCA, as they were not found publicly available; our code is available in the github link provided in the abstract.
3.1.1 Models, scenarios and evaluation measures
3.1.1.1 Models
Three models were used for simulating data with similar characteristics as OMICS datasets. All three data-generating models are based on five parameters, n,p1,p2,p1(cc),p2(cc), where n represents the number of samples, pi is the total number of features in Xi and pi(cc) represents the number of features in Xi which are cross-correlated with the rest of datasets (pi(cc)≤pi). Different types of scenarios were examined covering a range of possible data characteristics. The data were generated based on the assumption of having high canonical correlation. Further, a separate null scenario was designed in which canonical correlation was taken to be low.
Simple model. A simple data-generating model that generates data for M≥2 datasets: (11) Xi=uw(i)T+ϵi, i={1,…,M},
where u∈Rn, w(i)∈Rpi and ϵij∼N(0,1), j=1,…,pi. Only the first pi(cc) elements of w(i) are non-zero, representing the cross-correlated features that we seek to identify.
Single-latent variable model. Parkhomenko et al. (2009) proposed a single-latent variable model in assessing ConvCCA. An extension of this model is presented here, allowing the generation of multiple datasets. M datasets, Xm∈Rn×pm,m={1,…,M}, are generated, such that the first pm(cc) features of each Xm would be cross-correlated. In other words, w.l.o.g. the first pm(cc) of Xm will be correlated with the first pj(cc) of Xj,∀j≠m. These groups of features are associated with each other according to the same (single-latent variable) model. A latent variable, w(i), explains a subset of observed variables in Xi, i.e. {Xi,1,…,Xi,pi(cc)}. Through a common higher-level latent variable, μ, w(i) ∀i, are correlated. The rest of the features are independent within their respective datasets.
After simulating a random variable μ∼N(0,σμ2), the data are generated as follows:
For the cross-correlated variables:(xm)ij=αj(m)μi+exmij for i=1,…,n, j=1,…,pm(cc), m=1,…,Mwhere we assume ∑i=1αi(m)=1,∀m, and exmij∼N(0,σe2), ∀i,j,m.
For the independent variables:(xm)ij=exmij for i=1,…,n, j=pm(cc)+1,…,p1, m=1,…,M where again we assume exmij∼N(0,σe2), ∀i,j,m.
Fig. 1. sCCA performance on simulated data for integrating two datasets. (a) ROC curve plots on all five sCCA methods after averaging over all data-generating models and all scenarios. (b) Box-plots of the overall loss of the first canonical vector (X1w(1)) averaged over all data-generating models and scenarios, and (c) canonical correlation in the simulation studies for sCCA. (d) ROC curve plots, showing averaged results (over the models) for each scenario on X1w(1). (Results on X2w(2) can be seen in the Supplementary Material). (e) ROC curve plots, showing averaged (over the scenarios) results for each model on X1w(1)
Supplementary Figure S1 paints a picture of the single-latent variable data-generating model.
Covariance-based model. Suo et al. (2017) proposed simulations based on the structure of the covariance matrices of both datasets (X1 and X2). Three types of covariance matrices were considered in this study: (i) Identity, (ii) Toeplitz and (iii) Sparse. We have utilized this model for generating two datasets. Details regarding this data-generating model can be found in Supplementary Material.
3.1.1.2 Scenarios and evaluation measures
In comparing the three sCCA methods for integrating two datasets, six scenarios of different data characteristics were examined (Table 1). The first scenario acts as a baseline for the rest. A single parameter is changed for each additional scenario. In addition to the six scenarios, a null scenario was implemented, at which the two datasets were generated through the covariance-based model with true canonical correlation, ρ=0.1. The purpose of this null simulation model is to understand better how the methods work, and determine the likelihood of obtaining a high correlation by chance.
Table 1. Data characteristics and simulation scenarios used to evaluate the three sCCA methods for integrating two or three datasets
Scenarios Data characteristics
Integrating two datasets
Null n=100,1000,10000, p1=80, ,p2=60, p1(cc)=5, p2(cc)=15
1 n=40, p1=80, ,p2=60, p1(cc)=5, p2(cc)=15
2 n=150, p1=80, ,p2=60, p1(cc)=5, p2(cc)=15
3 n=40, p1=200, ,p2=60, p1(cc)=5, p2(cc)=15
4 n=40, p1=80, ,p2=200, p1(cc)=5, p2(cc)=15
5 n=40, p1=80, ,p2=60, p1(cc)=50, p2(cc)=15
6 n=40, p1=80, ,p2=60, p1(cc)=5, p2(cc)=50
Integrating three datasets
1 n=40, p1=80, ,p2=60, p3=40, p1(cc)=15, p2(cc)=10, p3(cc)=5
2 n=40, p1=200, ,p2=60, p3=40, p1(cc)=15, p2(cc)=10, p3(cc)=5
3 n=150, p1=80, ,p2=60, p3=40, p1(cc)=15, p2(cc)=10, p3(cc)=5
Note: n represents the number of samples, while pi(cc) and pi represent the cross-correlated and total number of features, respectively, in dataset i, for i = 1, 2.
We assessed the performance of the sCCA methods for integrating multiple datasets by generating three datasets through three scenarios as shown in Table 1.
The sCCA methods in both simulation studies were evaluated by measuring: (i) the canonical correlation; (ii) the correct identification of sparsity in the data, by computing accuracy, precision and recall of the true non-zero elements of the estimated canonical vectors; and (iii) the loss between true and estimated canonical vectors. A detailed description of the evaluation measures is presented in the Supplementary Material.
An additional simulation study was conducted to evaluate orthogonality. Two datasets were generated, with each of the following scenarios used in all three data-generating models: (i) n=500,p1=100,p2=200,p1(cc)=20,p2(cc)=40, (ii) n=150,p1=100,p2=200,p1(cc)=20,p2(cc)=40 and (iii) n=50,p1=100,p2=200,p1(cc)=20,p2(cc)=40.
3.1.2 Simulation outcomes
3.1.2.1 Integrating two datasets
In the conducted simulation study, the performance of sCCA methods was assessed on the three data-generating models and six scenarios shown in Table 1. The results are based on the first canonical pair.
Figure 1a depicts the resulting ROC curves and their area under the curve (AUC) values, averaged over all data-generating models and scenarios. ConvCCA with SCAD had the best performance in identifying correctly the sparseness and the non-zero elements of canonical vectors, as it produced the highest AUC. RelPMDCCA with SCAD obtained the lowest AUC value, which shows that the optimal choice of penalty function depends on the sCCA method. While the second dataset (and latent features X2w(2)) obtained slightly reduced sensitivity, the sCCA methods performed in a similar fashion as with the first dataset.
RelPMDCCA produced the highest loss between the true and estimated canonical vectors (Fig. 1b), but it also provided the highest canonical correlation (Fig. 1c). Its overall averaged correlation is close to 1, while for the other two methods, it is closer to 0.9.
Figure 1d shows the performance of the first canonical vector (X1w(1)) averaged over all data-generating models. As expected, by increasing the number of samples in a case where n > p (Scenario 2), the AUC values on all sCCA increased, with RelPMDCCA showing the largest improvement. However, a decrease in the performance of all sCCA methods is observed when the number of non-zero elements is increased. This can be seen on Scenarios 5 and 6, for w(1) and w(2), respectively. We can argue that in cases where the non-zero elements of a canonical vector are at least half of its length (total number of elements), the methods fail to correctly identify some of them. That might be due to the fact that sCCA methods force penalization and expect a sparser outcome. Furthermore, since the performance on w(2) in Scenario 6 is worse than that on w(1) in Scenario 5, we can argue that the higher the ratio of non-zero elements over the total number of elements, the less accurate the identification. On Scenarios 3 and 4, where the total number of features is increased while the non-zero elements are not, sCCA methods performed as well as on the baseline scenario.
After averaging over the scenarios, the methods’ performance on each data-generating model is shown in Figure 1e. The methods’ performance seems to be overall influenced by the choice of data-generating model. In the case of single-latent variable model, ConvCCA clearly produced the least errors, while in the simple model, PMDCCA produced the highest AUC values. In the covariance-based models, all sCCA methods performed equally well in estimating correctly non-zero elements of the canonical vectors. Methods on Toeplitz model produced higher AUC values than on Identity model, where Sparse model contained the most errors out of all data-generating models. Based on this observation, we can argue that the sparser the data, the less accurate the methods.
3.1.2.2 Null simulation model
To conclude our simulation study in integrating two datasets, we applied the three sCCA methods on two datasets, simulated to have low canonical correlation. Two datasets were generated by following the covariance-based model and setting the true correlation ρ=0.1 on different sample sizes, n = 100, 1000, 10 000. The rest of the simulation parameters remained constant as presented in Table 1.
As sample size increases, the correlations obtained by all methods are decreasing (Table 2). Even though the datasets were simulated to have low correlation, it is possible that a certain combination of their respective features could have high correlation by chance due to the small sample size. ConvCCA and RelPMDCCA captured this relationship and produced high canonical correlation on low-sampled simulations, while PMDCCA did a better a job in avoiding it even with n = 100. PMDCCA produced the highest AUC on simulations with small sample size, while on n = 10 000, RelPMDCCA captured the true non-zero features more accurately than PMDCCA and ConvCCA (Fig. 2).
Fig. 2. sCCA performance on Null scenario. ROC curves of the first canonical vector by all three sCCA on Null scenario with sample sizes n = 100, 1000, 10 000
Table 2. Null simulation model
Canonical correlation on Null simulation model
PMDCCA ConvCCA ConvCCA RelPMDCCA RelPMDCCA
Sample size LASSO LASSO SCAD LASSO SCAD
n = 100 0.55 (0.08) 0.81 (0.05) 0.80 (0.02) 0.96 (0.05) 0.98 (0.02)
n = 1000 0.22 (0.03) 0.48 (0.02) 0.48 (0.04) 0.51 (0.01) 0.50 (0.02)
n = 10 000 0.12 (0.03) 0.26 (0.05) 0.24 (0.03) 0.26 (0.04) 0.26 (0.04)
Note: Canonical correlations of PMDCCA, ConvCCA and RelPMDCCA averaged across 100 runs on the null scenarios.
The results of this simulation, along with the results of Section 3.2.1, suggest that although ConvCCA and RelPMDCCA are capable in producing a larger canonical correlation than PMDCCA, the likelihood of that correlation being due to chance is greater. The same conclusions were reached after repeating this process on two independent and uncorrelated datasets.
3.1.2.3 Orthogonality and sparsity
A third simulation study was conducted, with the aim of evaluating orthogonality of the three sCCA methods. For each method, orthogonality was enforced differently. For RelPMDCCA the solution proposed in Section 2.6 was applied, while for ConvCCA and PMDCCA we applied the solutions proposed by Parkhomenko et al. (2009) and Witten and Tibshirani (2009), respectively.
The scenarios in this study are split into three cases, based on the data sparsity: (i) n>p2>p1, (ii) p2>n>p1 and (iii) p2>p1>n. Table 3 shows the classification of each case with one out of three classes: (A) Full (Orthogonality): all pairs were found to be orthogonal; (B) Partial: most, but not all pairs were found to be orthogonal; and (C) None: none, or limited pairs were found to be orthogonal. Different colours in Table 3 refer to the different simulation models: simple model, single-latent variable model, identity covariance-based model.
Table 3. Orthogonality of sCCA methods
Orthogonality of all simulations with five canonical variates
Methods −> PMDCCA ConvCCA RelPMDCCA
n>p2>p1 None Orthogonality
Partial Orthogonality
Partial Full Orthogonality
Partial
Full Partial
None
None
p2>n>p1 None
Partial
Full None
None
None Full
Full
Partial
p2>p1>n Full
Full
Full None
None
None Full
Full
Full
Note: The table shows whether the algorithms succeed in obtaining orthogonal pairs. None refers to not obtaining orthogonality at all; Full refers to obtaining orthogonality between all pairs; Partial for some, but not all. For each scenario, simulations via the simple simulation model, single-latent variable model and covariance-based model are represented by the first, second and third rows, respectively.
Table 4. A summary on the performance of sCCA methods based on both the simulation studies conducted and the analysis of real data
Summary on the performance of sCCA methods
On two datasets ConvCCA Great performance on simulation studies, especially on single-latent model
Over-fitted cancerTypes data and performed well on nutriMouse
Low time complexity
PMDCCA Good performance on simulation studies, especially on simple model
Over-fitted cancerTypes data and performed well on nutriMouse
Low time complexity
RelPMDCCA Moderate to good performance on simulation studies
Had the best performance in analysing two real datasets
High time complexity
Multiple datasets ConvCCA Good performance on simulation studies
Avoided over-fitting and improved performance in both data studies
Low time complexity
PMDCCA Very good performance on simulation studies
Avoided over-fitting and improved performance in both data studies
Low time complexity
RelPMDCCA Moderate to good performance on simulation studies
Overall obtained the best results in both data studies
High time complexity
Note: It is an intuitive evaluation of the methods, split into having two datasets or multiple.
As summarized in Table 3, orthogonality is not always preserved, and that depends on the sCCA method, as well as on the data characteristics. The choice of data-generating model did not have a high impact in attaining orthogonality. All sCCA methods were penalized through l1 during this simulation study.
In case (i), ConvCCA has attained full orthogonality on the first five canonical variate pairs, where PMDCCA and RelPMDCCA failed to do so (except between some pairs). In the other two cases, none of the canonical variates obtained from ConvCCA were orthogonal. PMDCCA preserved complete orthogonality in p2>p1>n case and most when p2>n>p1. Complete orthogonality was attained in both of those cases when RelPMDCCA was implemented. Parkhomenko et al. (2009) and Witten and Tibshirani (2009) only consider the first canonical pair in their examples and do not explicitly discuss the performance of their respective methods on additional canonical pairs.
3.1.2.4 Integrating three datasets
A simulation study on multiple sCCA was performed by generating three datasets through the (i) simple model and (ii) single-latent variable model. The same evaluating measures as with the case with the two datasets were computed. Canonical correlation was evaluated by computing the average canonical correlation of each dataset with the rest.
Figure 3a presents the averaged (arithmetic) canonical correlation observed by each method. RelPMDCCA produced the highest, as it did in with the case of two datasets. PMDCCA produced the least correlated and least sparse solution, suggesting that it is not performing very well with multiple datasets where the objective of sCCA is to maximize the correlation between the datasets. Figure 3b shows the sparsity obtained by each method with RelPMDCCA providing the sparsest solution.
Fig. 3. Multiple sCCA performance on simulated data for integrating three datasets. (a) Box-plots showing the canonical correlation along the ConvCCA, RelPMDCCA and PMDCCA methods in a multiple setting. (b) An example of a scatter plot for the first estimated canonical vector. (c) ROC curves on multiple sCCA simulations
Overall, PMDCCA produces the highest AUC values. RelPMDCCA was superior only when the number of samples was increased (Scenario 3). RelPMDCCA and ConvCCA showed a decrease in their performance when the number of non-zero elements was increased, but PMDCCA was able to maintain its good performance.
3.2 Real datasets
3.2.1 NutriMouse
Martin et al. (2007) have performed a nutrigenomic study, with gene expression (X1∈Rn×p1) and lipid measurements (X2∈Rn×p2) on n = 40 mice, with p1=120 genes and concentrations of p2=21 lipids were measured. Two response variables are available: diet and genotype of mice. Diet is a five-level factor: coc, fish, lin, ref, sun and genotype is recorded as either Wild-type (WT), or Peroxisome Proliferator-Activated Receptor-α (PPARα). NutriMouse data are perfectly balanced in both responses, in which an equal number of samples is available for each class.
Through the analysis of the nutriMouse datasets we aimed to (i) evaluate which of three considered sCCA approaches performs better and (ii) determine whether data integration of datasets can improve prediction over conventional approaches that only analyse a single dataset. For addressing the second question, we have implemented the following off the shelf statistical machine learning approaches: (A) Principal Components Regression (PCR)—logistic model with first 10 principal components acting as predictors; (B) Sparse Regression (SpReg)—penalized (through LASSO and SCAD) logistic and multinomial models, when the response was diet and genotype, respectively; (C) k-Nearest Neighbours (kNN) for supervised classification; and (D) k-means for unsupervised clustering—acting as a benchmark in splitting the data by ignoring the labels. Since two datasets are available, these four methods were implemented on the following three cases of input data: (i) only X1, (ii) only X2 and (iii) Xboth=(X1,X2)∈Rn×pboth, where pboth=p1+p2. The aforementioned machine learning methods and the three sCCA approaches were applied on 100 bootstrap samples of the datasets, taking separate training and test sets at each repetition, for assessing the predictive accuracy of the methods. For the machine learning methods applied, the version with the input dataset obtaining the smallest error was compared with the sCCA approaches. In predicting the genotype response using only X1 (i.e. gene expression data) was preferred, but Xboth was chosen with diet acting as the response (Supplementary Material).
Figure 4 shows the predictive accuracy of the methods on both diet (Fig. 4a) and genotype (Fig. 4b). PCR and k-means were the least accurate methods (Supplementary Material). In predicting either response, sCCA methods outperformed the conventional machine learning methods. Both the sCCA approaches and the conventional machine learning approaches had very high accuracy for predicting the genotype response whereas their accuracy was lower for predicting the diet response. All the methods had an accuracy between 0.7 and 0.86, except RelPMDCCA that had the highest accuracy 0.92. The precision and recall measures showed similar patterns with RelPMDCCA obtaining the highest values for both measures against all other methods (Supplementary Material).
Fig. 4. sCCA performance on nutriMouse data. Box-plots presenting the accuracy of sCCA methods, k-NN and SpReg with LASSO and SCAD with the response being (a) diet and (b) genotype. (c) Scatter plots of the canonical vectors from the first canonical variate pair of a random nutriMouse test set, after applying sCCA and multiple sCCA
Multiple sCCA with response matrix. We applied our proposed extensions of the sCCA approaches for multiple datasets, where one of the input datasets is the matrix of the two response vectors.
Figure 4c presents the canonical vectors of the first canonical variate pair. The first column of plots shows the canonical vectors obtained without considering a response matrix. On a two-setting integration, the data are not separated well for neither response. However, by including the response matrix, multiple sCCA methods separated clearly the samples between WT and PPARα mice, as shown in the second column of Figure 4c. A slight improvement in their separation between diets was also observed. All multiple sCCA methods performed equally well, although visually RelPMDCCA indicates the clearest separation in terms of genotype.
3.2.2 CancerTypes
Due to the abundance of data in the Cancer Genome Atlas (TCGA) database, a lot of researchers have applied various integrative algorithms for cancer research (Lock et al., 2013; Parimbelli et al., 2018; Poirion et al., 2018; Wang et al., 2014). Gene expression, miRNA and methylation data from three separate cancer types were taken: (i) breast, (ii) kidney and (iii) lung. For each patient, we also have information about their survival status. Thus, the goal of this analysis was to assess whether (multiple) sCCA can improve conventional classification methods in determining the cancer type, and survival status.
The data consist of 65 patients with breast cancer, 82 with kidney cancer and 106 with lung cancer, from which 155 patients are controls. The data in this study cover 10 299 genes (X1), 22 503 methylation sites (X2) and 302 mi-RNA sequences (X3). Data cleaning techniques such as removing features with low gene expression and variance were used, leaving us with a remaining of 2250 genes and 5164 methylation sites. Similarly to Section 4.1, k-NN, PCR and SpReg were applied, along with sCCA algorithms. After investigating the best combination, miRNA expression and methylation datasets were selected for integrating two datasets. Multiple sCCA was implemented on all three available datasets.
Figure 5a presents the accuracy, precision and recall of each method in predicting the patients’ survival status. SpReg, ConvCCA and PMDCCA produced perfect recall, while their precision was recorded around 60%. Such finding suggests over-fitting as a single class is favoured. Thus, a solution providing good results while avoiding over-fitting would be preferable.
PCR and k-NN did not show any signs of over-fitting, but did not perform well (PCR had accuracy below 0.5 and k-NN had consistently low values on all three measures). RelPMDCCA did not over-fit the data and produced high measure values, especially with LASSO being the penalty function. Multiple RelPMDCCA produced the most accurate and precise solution out of all methods applied. Multiple PMDCCA and ConvCCA improved the results of their respective integration method on two datasets, as they avoided over-fitting, with precision and recall values being more balanced. Regardless of the response, same conclusions were reached, i.e. implementing multiple sCCA can avoid over-fitting.
Figure 5b presents the scatter-plots of the test set of the canonical vectors of the first canonical variate pair. In contrast with the nutriMouse study, visually there is no clear separation observed between cancer types or survival status of the patients. Since the objective of sCCA is to maximize canonical correlation, it is important to preserve it. The canonical vectors of the test set are computed by linearly combining the estimated canonical vectors (through training), with the original test datasets. RelPMDCCA produced the highest correlation in all three combinations of canonical vectors (Fig. 5b).
Fig. 5. sCCA performance on cancerTypes data. (a) Model performance for the prediction of samples’ survival status. The best overall performed model is shown with bold. (b) Scatter-plots of canonical variates in cancerTypes analysis through multiple sCCA
4 Discussion
The increasing number of biological, epidemiological and medical studies with multiple datasets on the same samples calls for data integration techniques that can deal with heterogeneity and high-dimensional datasets.
Over the years, a lot of methods for sCCA have been proposed that integrate high-dimensional data. In this study, we have focused on ConvCCA, PMDCCA and RelPMDCCA, as these methods penalize the same optimizing function [Equation (1)]. We modified RelPMDCCA to penalize canonical vectors through SCAD and we compared its performance against LASSO penalty. Further, we proposed an extension in computing the additional canonical pairs. The extension satisfies necessary conditions in enforcing orthogonality among them. Finally, we extended ConvCCA and RelPMDCCA for integrating more than two datasets instead of just two as their original version.
By collectively reducing the dimensions of the datasets, while obtaining maximal correlation between the datasets, sCCA methods were found to have better accuracy in predicting complex traits than other conventional machine learning methods. Through our proposed extensions of the ConvCCA and RelPMDCCA approaches for integrating more than two datasets and for incorporating the response matrix as one of the integrated datasets, we have showed that over-fitting can be avoided and higher predictive accuracy can be obtained.
Through the analysis of the two real datasets, we illustrated that the sCCA methods can improve our predictions of complex traits in both cases: (i) when a regression model is built with the new canonical matrices as input matrices and (ii) when the response matrix is one of the input matrices in the data integration. For both cases, the sCCA methods can improve the predictions of the response.
Table 4 summarises our conclusions on each of the three sCCA methods applied on two or multiple datasets. Even though both nutriMouse and cancerTypes datasets have small sample sizes, the latter has a large number of features, providing an indication of a true genomic-wide scale analysis. We found that RelPMDCCA obtained the best results, but it is also more computationally expensive than the other methods. We conclude that in cases where datasets with large number of features or samples, PMDCCA might be a more appropriate method to consider due to its advantage regarding computational time. This observation calls for further optimizing RelPMDCCA in reducing its complexity and increasing its feasibility on large-scale analysis. The computation times of the methods are presented in the Supplementary Material.
Our simulation study findings are in agreement with Chalise and Fridley (2012) that showed that ConvCCA has better results with SCAD penalty rather than LASSO. When analysing two uncorrelated datasets both ConvCCA and RelPMDCCA had a greater likelihood of obtaining high correlation compared to PMDCCA, when the number of samples is small. With larger sample sizes, all methods obtained smaller correlations. With no exception, RelPMDCCA provides the highest canonical correlation in all simulation studies and all real-data analyses performed in this paper.
To preserve orthogonality, the results of our simulation study suggest different methods based on data characteristics. If the data satisfy n>p1>p2, then ConvCCA is a more sensible choice. In the other cases (p1>n>p2 or p1>p2>n), ConvCCA failed to provide orthogonal canonical pairs, while PMDCCA and RelPMDCCA, attained orthogonality on synthetic data from all three data-generating models. We observed the performance of the sCCA methods to depend on the structure of the input data. For sparse datasets, we recommend the use of the RelPMDCCA approach as it is the one that performed better for such datasets.
Huang et al. (2017) argued the need for a comparison of data integration approaches. This paper has addressed this by evaluating the performance of three sCCA approaches. We have further illustrated that integrating datasets through multiple sCCA could improve the prediction power, suggesting that researchers with access to two or more datasets should aim for an integrative analysis.
Financial Support: none declared.
Conflict of Interest: none declared.
Supplementary Material
btaa530_Supplementary_Data Click here for additional data file.
Acknowledgements
The authors would like to thank Sarah Filippi, Philipp Thomas and Takoua Jendoubi for useful conversations.
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PMC007xxxxxx/PMC7750938.txt |
==== Front
Bioinformatics
Bioinformatics
bioinformatics
Bioinformatics
1367-4803
1367-4811
Oxford University Press
32573679
10.1093/bioinformatics/btaa580
btaa580
Original Papers
Data and Text Mining
AcademicSubjects/SCI01060
ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins
http://orcid.org/0000-0002-8507-7191
Yang Shu Department of Computer Science, University of British Columbia, Vancouver, BC V6T1Z4, Canada
Liu Xiaoxi RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan
Ng Raymond T Department of Computer Science, University of British Columbia, Vancouver, BC V6T1Z4, Canada
Ponty Yann Associate Editor
To whom correspondence should be addressed. E-mail: [email protected] or [email protected]
15 9 2020
23 6 2020
23 6 2020
36 18 47974804
12 12 2019
18 5 2020
18 6 2020
© The Author(s) 2020. Published by Oxford University Press.
2020
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Motivation
The interaction between proteins and nucleic acids plays a crucial role in gene regulation and cell function. Determining the binding preferences of nucleic acid-binding proteins (NBPs), namely RNA-binding proteins (RBPs) and transcription factors (TFs), is the key to decipher the protein–nucleic acids interaction code. Today, available NBP binding data from in vivo or in vitro experiments are still limited, which leaves a large portion of NBPs uncovered. Unfortunately, existing computational methods that model the NBP binding preferences are mostly protein specific: they need the experimental data for a specific protein in interest, and thus only focus on experimentally characterized NBPs. The binding preferences of experimentally unexplored NBPs remain largely unknown.
Results
Here, we introduce ProbeRating, a nucleic acid recommender system that utilizes techniques from deep learning and word embeddings of natural language processing. ProbeRating is developed to predict binding profiles for unexplored or poorly studied NBPs by exploiting their homologs NBPs which currently have available binding data. Requiring only sequence information as input, ProbeRating adapts FastText from Facebook AI Research to extract biological features. It then builds a neural network-based recommender system. We evaluate the performance of ProbeRating on two different tasks: one for RBP and one for TF. As a result, ProbeRating outperforms previous methods on both tasks. The results show that ProbeRating can be a useful tool to study the binding mechanism for the many NBPs that lack direct experimental evidence. and implementation
Availability and implementation
The source code is freely available at <https://github.com/syang11/ProbeRating>.
Supplementary information
Supplementary data are available at Bioinformatics online.
Genome Canada, and Natural Sciences and Engineering Research Council (NSERC) of Canada
==== Body
pmc1 Introduction
Knowledge about the binding profiles of nucleic acid-binding proteins (NBPs) is a vital prerequisite to detecting potential NBP binding targets (i.e. RNAs and DNAs) in the cell, and understanding gene regulation and evolution (Dong et al., 2018; Tak Leung et al., 2019; Yang et al., 2011). Currently, the binding profiles are mainly determined computationally (Lambert et al., 2018; Pan et al., 2019) from high-throughput experimental data such as protein-binding microarray (PBM) (Berger et al., 2006; Weirauch et al., 2014) or chromatin immunoprecipitation (ChIP)-seq (Barski and Zhao, 2009; Park, 2009) for transcription factors (TFs), and RNAcompete assay (Ray et al., 2009, 2013) or crosslinking immunoprecipitation (CLIP) (Konig et al., 2012; Wang et al., 2015) for RNA-binding proteins (RBPs). Despite the continuous advances in these large-scale experimental technologies, binding data generated from them still only cover a limited portion of the known NBPs across different species due to the high cost. This is especially the case for the RBPs, which are less studied than the TFs. For example, the most extensive compendium (Ray et al., 2013) for RBPs (207 unique proteins) by far was generated by RNAcompete assays. The single largest RBP family in the compendium, the RNA recognition motif (RRM) family, has 171 entries. But, there are more than 5000 known and predicted RRM RBPs according to the CISBP-RNA database (Ray et al., 2013), mostly unexplored. Therefore, predicting the binding profiles for unexplored NBPs by leveraging the limited experimental data is needed. Especially, since most of the unexplored NBPs only have their sequences information available, predicting the binding profiles for NBPs directly from their sequences is critically desired.
There have been various researches seeking to determine an NBP’s binding preference throughout the years. The models focusing on sequence specificity have evolved from consensus motif or position weight matrix (PWM) in the early days (Bailey et al., 2009; Stormo, 2000), to classic machine learning models like SVM with k-mer features and so on (Ghandi et al., 2014; Orenstein et al., 2016; Pelossof et al., 2015), to the latest deep neural network realm (Alipanahi et al., 2015; Ghanbari and Ohler, 2019; Pan and Shen, 2018b; Zeng et al., 2016). Since RNA has secondary structures that DNA does not have, many approaches dealing with RBP-RNA interactions also incorporate RNA structure information to binding specificity (Gandhi et al., 2018; Hiller et al., 2006; Kazan et al., 2010). Despite the numerous efforts that have been devoted in this field, previous researches on binding preference modeling have several limitations.
First and foremost, the majority of the works are protein specific, i.e. they construct a binding preference model of a single protein, without considering the relationship among different proteins. Since the model is built on RNA or DNA targets of each protein independently, patterns learned from protein A typically cannot be efficiently transferred to protein B. One key reason for the previous methods being protein specific is that the available experimental data are highly limited and imbalanced: very few NBPs compared to the number of nucleic acids. For example, the RNAcompete assay typically contains >200 000 RNA probes; yet only a few hundred RBPs have been gauged so far (Ray et al., 2013). Other experimental data types are in similar situations. Therefore, for an experimentally unexplored NBP, it is still difficult to know its binding preference at this moment.
In addition, most existing methods predict the NBP binding preference as a highly reduced summarization of the actual binding data, like a consensus or PWM motif (Kazan et al., 2010; Stormo, 2000) conventionally, or a convolutional neural network filter (Alipanahi et al., 2015; Pan and Shen, 2018b) recently. These so called ‘binding specificity’ prediction approaches are very useful and have been intensively studied. However, using these reduced summarizations, one may not capture all the details of the binding data so that if the binding preference of protein A is transferred to protein B, information that is important to protein B but not A may be lost.
Lastly, a substantial body of research for nucleic acid–protein interactions have been focusing on structural data like RNA–protein/DNA–protein complex structures from the Protein Data Bank (PDB) (Berman, 2000); while some more recent studies focus on CLIP or ChIP-seq data which are high-throughput in vivo data. The former group includes a wide range of studies from identifying nucleic acids-binding amino acid residues in protein sequences (Jung et al., 2018; Peng and Kurgan, 2015; Walia et al., 2017; Yan et al., 2016; Zhang et al., 2019) to predicting DNA/RNA–protein interaction pairs (Bellucci et al., 2011; Suresh et al., 2015; Yi et al., 2018), and so on. These are related tasks to predicting an NBP’s binding preference. However, for a protein in interest, structural complexes in PDB often only cover limited individual interactions between fragments of nucleic acids and fragments of that protein, which are not diverse enough to determine the nucleic acids binding preference of the protein. In contrast, ChIP-seq and CLIP experiments generate high-throughput binding data, which contains a much larger number of diverse nucleic acid targets for a given protein. Computational methods focusing on ChIP or CLIP data essentially formulate nucleic acid–protein binding as a classification task and determine a protein’s binding preference model through binary labeled data (positive: bound, negative: unbound) (Li et al., 2017; Maticzka et al., 2014; Pan and Shen, 2018b). However, a subtle problem is that there are no defined unbound cases, i.e. the ChIP or CLIP experiments only report positive nucleic acids that are putatively bound by the given protein. Strategies like shuffling nucleotides in the positive sequences and so on are often used as a rough workaround to generate negative samples.
A few existing studies try to address the above limitations (Pelossof et al., 2015; Ray et al., 2013; Yang et al., 2018). One study (Ray et al., 2013) determines binding preference motifs as PWMs from numerically labeled RNAcompete data, and it proposes to infer the binding motif of an unexplored RBP to be the same as the PWM of that RBP’s well-studied nearest neighbor. It uses binding domain sequence similarity to define the nearest neighbor, and it predicts PWMs for thousands of unexplored RBPs in the CISBP-RNA database. Following this line, a later study (Yang et al., 2018) utilizes the co-evolution assumption between RNA and protein, and it combines K nearest neighbors’ PWMs to make the prediction. We refer to it as Co-Evo method. It reports better performance than the first study (Ray et al., 2013). Although the sequence similarity used by both studies is a simple metric to compare two proteins, and the PWMs are reduced summarizations of the real binding motifs, the two studies do provide feasible solutions for the unexplored NBPs problem. By far, to our knowledge, the best solution in terms of prediction quality is supplied by AffinityRegression (Pelossof et al., 2015). AffinityRegression utilizes a recommender system (Ricci et al., 2011) formulation, where NBPs are like users, and RNAs or DNAs are like products to be recommended. Given a family of TF or RBP domains and their binding profile data (PBM or RNAcompete assay), AffinityRegression learns protein family-level binding patterns from amino acid and nucleotide k-mer features, through bilinear regression and matrix factorization. For an unexplored NBP in the same family, AffinityRegression directly recommends its full binding profile, i.e. predicted binding affinity values of the NBP against each RNA or DNA probe in the assay, instead of a reduced summarization of the binding profile. However, although AffinityRegression has made significant progress, it assumes linearity in its regression model, which is unlikely held in practice. Its use of k-mer frequency features to represent the NBPs and nucleic acids is also disputable since the context information around potential binding sites would be lost and the feature dimension goes too quickly as k grows. For example, 3-mer amino acid features would be 8000 dimensions; while the number of proteins in the largest PBM or RNAcompete dataset is less than 300.
To close the gaps, here, we present a new method called ProbeRating to infer binding profiles for unexplored NBPs, using only sequence information. ProbeRating extends AffinityRegression by incorporating non-linearity to the model and improving features with more sophisticated representation techniques. ProbeRating does its job via a two-stage framework (Fig. 1). It first adapts the word embedding method FastText (Bojanowski et al., 2017) to extract distributed representations from the NBP sequences and the nucleic acid probes, respectively. It then takes the new representations as input features to train a supervised recommender system to recommend probes to unexplored NBPs. We implement the functionality of representation learning in a package called FastBioseq. For the recommender, we develop a novel neural network-based approach to incorporate the non-linearity. We show that ProbeRating significantly outperforms AffinityRegression for both RBP binding prediction and TF binding prediction tasks. Same as AffinityRegression, ProbeRating is also capable of recommending a full binding profile directly for a test NBP, instead of a reduced summarization.
Fig. 1. Schematic diagram of our workflow. (a) Protein and RNA sequences are embedded as numerical feature vectors by FastBioseq. (b) The protein and RNA features are fed into ProbeRating’s recommender system to infer binding intensity profiles for unexplored proteins. Performance is evaluated to compare with the nearest-neighbor methods and AffinityRegression
2 Materials and methods
2.1 Input features
Instead of using k-mer features as AffinityRegression does, we adapt the FastText (Bojanowski et al., 2017) method which is an extension of the widely used Word2Vec (Mikolov et al., 2013) method in the field of natural language processing, and we extract features from protein and nucleic acid sequences. Word2Vec encodes words to numerical vectors, through training on a corpus of text documents for a classification task of predicting a center word based on its nearby words. It assumes the meaning of a word is characterized by its context, and it thus could capture semantics in the vectors. FastText extends Word2Vec by decomposing each word into subword n-grams and treats each n-gram (instead of each word) as a unit during the learning of representations. By doing this, FastText can deal with word that is not seen in the training corpus (out-of-vocabulary word) or get better embedding vectors for rare words, which cannot be done by Word2Vec. These properties make FastText very attractive for biological sequence embedding since: (i) mutations occur through the evolution, and (ii) the predefined biological ‘word’ (will explain later) may not precisely match the actual binding sites. Although the idea of using Word2Vec to encode proteins has been explored before and shown effective (Asgari and Mofrad, 2015; Pan and Shen, 2018a), to our knowledge, we are the first one that uses the more advanced FastText in binding preference prediction.
Here, we briefly introduce how our FastBioseq package utilizes FastText to generate protein and nucleic acid feature vectors. The details can be found in Supplementary Note. If we treat each k-mer of amino acids or nucleotides as a word, then a protein or DNA/RNA sequence can be decomposed as k sentences of non-overlapping k-mers, as shown in Figure 1a (taking RBP sequence pi and RNA sequence rj as an example). Different sentences correspond to different reading frames on the sequence, denoted with different colors in Figure 1a. For each sentence, FastBioseq uses the continuous bag-of-words (CBOW) algorithm (Bojanowski et al., 2017) of FastText to train on every k-mer’s contextual information, which is considered important for NBP binding sites recognition. After training, FastBioseq can embed the k-mers onto a vector space with user-defined dimensions. The resultant k-mer vectors are then combined to construct the sentence vectors which in turn produce the sequence vectors. In this way, FastBioseq solves both the high dimensionality and loss of context problems that the conventional k-mer frequency feature suffers from.
In natural language word embedding, a large corpus of sentences is typically required to provide enough coverage of different contexts for different words. This is also true for biological sequence embedding. So, we compiled several large corpora to train our FastBioseq embedding models, which will be described in Section 2.3. Moreover, we also implemented a Word2Vec version of sequence embedding as well as a Doc2Vec (Le and Mikolov, 2014) version which is another popular extension of Word2Vec to explicitly embed the entire document (i.e. entire biological sequence) to a vector instead of combining word vectors. These implementations can be easily used from our FastBioseq package.
2.2 The recommender model
Once we get the protein and nucleic acid features extracted from FastBioseq, we input them to our ProbeRating model, as Figure 1b shown. To explain the ProbeRating model, here we take RBPs as an example again. The model works the same for the TFs. Like several previous studies (Corrado et al., 2016; Pelossof et al., 2015), ProbeRating uses a recommender system setting to model the interaction of RBP features and RNA features. In this setting, given a list of users with their ratings over a list of products, it is known as the ‘cold start’ problem when the recommender is used to predict a new user’s ratings to the products. Here, we treat RBPs as users, RNA probes as products and their binding intensity scores as ratings.
2.2.1 The naïve model
A natural approach to address the cold start problem is to incorporate content information from the interacting objects themselves (i.e. content features of users-products, or RBPs–RNAs), based on matrix trifactorization. Formally, let P∈RM×S be the RBP feature matrix containing M RBPs each has S features, D∈RN×Q be the RNA probe feature matrix containing N probes with Q features, and Y∈RN×M be the binding intensity matrix corresponding to all the RBP–RNA pairs. The trifactorization approximates Y by DWPT, where W∈RQ×S is the weight matrix that explains the interaction between the RBP features and the RNA features. In our naïve model, we mimic this approach but formulate a feedforward neural network to add non-linearity. Namely, (1) Y^=HDWHHPT
where HD and HP are outputs from two subneural networks, one for RNA features and one for protein features. More details of this naïve model can be found in Supplementary Note.
However, for the binding profile prediction task, we are facing, a practical issue for this naïve model is that there are too few NBPs in the dataset to be learned from. As mentioned in earlier sections, the most extensive RBP compendium (Ray et al., 2013) available is generated by RNAcompete assays. In the compendium, the number of RBPs with binding profile data available is only <250 and is 103 times smaller than the number of RNAs probes in the RNAcompete assay. TFs are in a similar situation. If we used conventional sequence features like k-mer frequencies, the model could easily overfit since the high dimensionality of the features. Even if we alleviated overfitting with regularizations, or with low-dimensional features extracted by FastBioseq, we could hardly learn a sophisticated parametric model given the small number of RBP instances. Our results showed that this naïve model did not perform well (Supplementary Note).
2.2.2 The final model
To address the issue in the naïve model, we adapt and extend a strategy used by AffinityRegression to convert the ‘binding intensity prediction’ problem to a ‘similarity prediction’ problem, solve it and then convert back. AffinityRegression solves the ‘similarity prediction’ problem with a regular bilinear regression model, and it does the conversion through a series of linear transformation and matrix factorization operations. We solve with the more expressive neural network model and use a more straightforward conversion approach, which can be interpreted as a non-parametric tweak to our naïve model.
To convert the RBP ‘binding intensity prediction’ problem to an RBP ‘similarity prediction’ problem, the original intensity matrix Y∈RN×M is transformed to YTY∈RM×M, as shown in Figure 2. Each column of Y is a vector of normalized binding intensity scores of an RBP against all N RNA probes. So YTY becomes the cosine similarity matrix for all pairs of RBPs. We now predict the similarity value Y:,iTY:,j of RBPs pi and pj by Si,j^ from our neural network model: (2) Si,j^=wMTMhP,hE+bM ∈R
where wM∈RKL and bM∈R are parameters. MhP,hE is a function to merge hP and hE, and it corresponds to a merge layer with no trainable parameters in the neural network. hP and hE are outputs from two shallow subneural networks: (3) hP=aWPTPj,:+bP ∈RL
(4) hE=aWETEi,:+bE ∈RK
where a is a sigmoid function. Pj,: is a row vector in the RBP feature matrix P. Ei,: is a row vector from the matrix E=YTD ∈RM×Q. E is used to match YTY, and can be considered as a compression of D. WP∈RS×L and bP∈RL are parameters to project protein features to a latent feature space with L dimensions. Similarly, WE∈RQ×K and bE∈RK are parameters for RNA features. All the above parameters, denoted by Θ={wM,bM,WP,bP,WE,bE}, are solved by the Adam optimizer to minimize the regularized sum of squared loss: (5) argminΘ∑i∑j||Si,j^-Y:,iTY:,j||22+λr(Θ)
where rΘ is a regularization function (we use L2 norm), with coefficient λ. After we solve this ‘similarity prediction’ problem, we need to convert it back to the original problem. For a given testing RBP x, we feed its FastBioseq feature vector px to the neural network and output its predicted similarity values sx^∈RM against each training RBPs. To reconstruct the binding intensity values yx^ from sx^, we implement two simple yet effective options: (6) yx^=Ysx^
(7) yx^=YT-1sx^
Fig. 2. The architecture of the neural network model. Note that, the left multiplication with YT on the input RNA feature matrix and the output binding intensity matrix transforms the intensity prediction problem into a similarity prediction problem. With the similarity output from the model, we convert it back to the final intensity value by doing a non-parametric reconstruction
The first reconstruction treats the similarity values in sx^ as weights and does a weighted sum to obtain yx^. The second reconstruction multiplies a Moore–Penrose pseudoinverse of YT to sx^. It is inspired by AffinityRegression’s approach, and it comes directly from the above fact that Si,j^≈Y:,iTY:,j. We found the first option generally performed better in our preliminary experiments and used it in this study. The reconstruction is non-parametric since the prediction needs the entire binding intensity matrix of the training data, i.e. Ytrain. Intuitively, these approaches work because we first solve a much easier similarity prediction problem requiring much fewer data from the RBP–RBP pairs, and then leverage the original training data to reconstruct the prediction for the harder RBP–RNA binding intensity problem.
Furthermore, we explore different structures for the neural network. As shown in Figure 2, there could be deeper network architectures in the two subnets and after merging them by stacking more layers. Additionally, for the merge layer MhP,hE, we develop four different types of merging (please refer to Supplementary Note for details). For the results reported below, we use the shallow architecture and the merge layer with the fewest parameters.
2.3 Datasets
The primary datasets of our study include an RNAcompete dataset for the RBPs and a PBM dataset for the TFs. Both datasets are commonly used benchmark datasets from previous studies (Alipanahi et al., 2015; Gandhi et al., 2018; Koo et al., 2018; Orenstein et al., 2016; Yang et al., 2018), including AffinityRegression. The first dataset, called RRM162 (as shown in Table 1), is derived from the largest compendium of RBP binding assay (Ray et al., 2013) and the AffinityRegression paper. It contains 162 binding domains from the RNA Recognition Motif (RRM) family. We choose the RRM family since it is the largest family in the compendium and is also one of the most abundant RBP families in nature (Maris et al., 2005). Each RRM domains in RRM162 are measured against 241 357 RNA probes, which results in 162*241 357 binding intensity scores in total. Similarly, the second dataset, called Homeo215 (as shown in Table 1), is derived from the AffinityRegression paper, and it contains 215 Homeodomain sequences and their binding intensity Z-scores (Berger et al., 2008) against >30 000 DNA 8-mers. The reason we use 8-mer Z-scores instead of probe intensities in Homeo215 is described in Supplementary Note and later in Section 3.
Table 1. Summary of datasets in this study
Datasets # proteins # nucleic acids Type
RRM162 162 241 357 RNAcompete binding data
Homeo215 215 32 896 PBM binding data
Uniprot400k 428 109 – Diverse protein sequences
RRM3k 3213 – RRM sequences
Homeo8k 8302 – Homeo sequences
In addition, to pretrain the FastBioseq protein embedding models as mentioned in Section 2.1, we compile three large corpora of protein sequences. The first dataset is Uniprot400k (Table 1), which contains >400 000 protein sequences from diverse families and species, downloaded from the Uniprot database (https://www.uniprot.org/). The second dataset, RRM3k (Table 1), contains >3000 RRM domain sequences extracted from the CISBP-RNA database (http://cisbp-rna.ccbr.utoronto.ca/). The third dataset, Homeo8k (Table 1), contains >8000 Homeodomain sequences obtained from the CISBP database (http://cisbp.ccbr.utoronto.ca/). Note that here, the protein embedding models are pretrained on these three datasets and are used later to extract protein features on a different dataset (RRM162 or Homeo215). This simple pretraining procedure involves the idea of transfer learning (Pan and Yang, 2010) that knowledge from one task is transferred to another task that usually has much fewer data.
The two primary datasets are used to train and test ProbeRating and the other methods to be compared with. We perform a series of preprocessing procedures to remove redundancy, normalize the intensity scores, etc., for each of the datasets (details in Supplementary Note). The three pretraining datasets are used to pretrain the FastBioseq embedding model so that the model can be used to convert protein sequences in the primary datasets to numerical vectors. We also remove the redundant proteins in each of the pretraining datasets, respectively, and we further remove the proteins overlapping with those in the primary datasets so that the pretraining datasets have no intersections with the two primary datasets. During evaluation, for each of ProbeRating and the other methods, we perform the training and testing as following: we first randomly divide the proteins in the dataset of RRM162 or Homeo215 into 10 folds and leave one fold out as an independent test set. For the remaining ninefolds, we do cross validation to use 90% of them as the training set to tune the model parameters and the rest 10% as the validation set to tune the model hyperparameters. By doing this, the test sets are completely independent from the training and validation process. The same procedure is applied to test on all proteins. We repeat the process 20 times with different random divisions of the dataset and report the average test performance. Same as the AffinitRegression paper, Spearman correlation coefficient (SCC) is used to assess the regression performance since the binding intensity scores are quantile normalized. The details of the redundancy removal, the experimental setup, the implementation, the hardware specification and the runtime summarization can be found in Supplementary Note.
3 Results
In this section, the capability of ProbeRating will be demonstrated by first showing that it is better than three baseline methods, and then showing it also outperforms the more sophisticated AffinityRegression.
3.1 ProbeRating outperformed nearest-neighbor baselines
Since we use a non-parametric reconstruction approach to leverage the training data to predict for unexplored NBPs, a natural sanity check is to compare ProbeRating with the simple but often effective nearest-neighbor approach. Given a testing NBP, if we find its nearest-neighbor by some similarity/distance metric, then the neighbor’s binding profile will be the prediction for this NBPs.
The most common similarity metric is the sequence similarity percent identity (PID), which can be obtained from the BLOSUM amino acid similarity matrix using sequence alignment algorithms. The BLOSUM nearest-neighbor approach has been used by previous studies (Ray et al., 2013; Yang et al., 2018) to infer the PWM binding motifs for unexplored NBPs and has also been compared with AffinityRegression. We evaluated the performance of ProbeRating against the BLOSUM nearest-neighbor baseline on both the RRM162 and the Homeo215 datasets. As shown in Table 2, ProbeRating achieved an average SCC of 0.864 across all RBPs on the RRM162 dataset and an average SCC 0.772 on the Homeo215 dataset. The SCCs were significantly better than BLOSUM baseline’s 0.771 and 0.676 with both P-values<10-10 based on the two-tailed Wilcoxon signed-rank test. Moreover, as we could see from Figure 3A and B, the blue dots are mostly above the x = y line in both plots, which indicates ProbeRating almost always outperformed BLOSUM baseline on the two datasets.
Fig. 3. Performance of ProbeRating compared to three nearest-neighbor baselines. (A, B) ProbeRating against the BLOSUM nearest-neighbor baseline. The results for RRM162 are shown on the left in (A) and Homeo215 on the right in (B). The x-axis indicates the SCC between the BLOSUM baseline predicted intensity and the true intensity values for each protein. The y-axis indicates the SCC between the ProbeRating predicted intensity and the true intensity values. Each blue dot in the scatter plot represents the performance of a protein. The straight black line represents x = y. P-values are computed based on a two-tailed Wilcoxon signed-rank test. (C, D) ProbeRating against the k-mer feature nearest-neighbor baseline. (E, F) ProbeRating against the FastBioseq feature nearest-neighbor baseline
Table 2. Summary of the performance of different methods
Method Performancea
RRM162 Homeo215
BLOSUM nearest-neighbor baseline 0.771 0.676
k-mer nearest-neighbor baseline 0.795 0.671
FastBioseq nearest-neighbor baseline 0.804 0.647
AffinityRegression 0.823 0.739
AffinityRegression with FastBioseq feature 0.827 0.747
Co-Evo 0.211 0.410
ProbeRating 0.864 0.772
a Spearman correlation averaged over all tested proteins.
In addition to the BLOSUM nearest neighbor, we further explored the idea of the nearest neighbor by incorporating two other similarity metrics: the Euclidean similarity of the k-mer frequency features used by AffinityRegression and the cosine similarity of the FastBioseq generated embedding features. These two metrics, together with BLOSUM PID, captured related but different aspects of protein sequence information, and thus gave related but different nearest neighbors. We compared ProbeRating with the resultant k-mer nearest-neighbor baseline (Fig. 3C, D) and the FastBioseq nearest-neighbor baseline (Fig. 3E, F), as also shown in Table 2. Again, ProbeRating outperformed the two baselines with P-values<10-10 on both RRMs (average SCCs for k-mer and FastBioseq feature baselines were 0.795 and 0.804) and Homeos (average SCCs for k-mer and FastBioseq feature baselines were 0.671 and 0.647) tasks. And ProbeRating was better than the two baselines on majority of the proteins.
Therefore, in general, we observed a clear advantage of ProbeRating over the three different nearest-neighbor baselines. It showed that the neural network and reconstruction approaches used inside ProbeRating were non-trivial, and that they were better than simply taking the nearest neighbor by a large margin.
3.2 ProbeRating outperformed AffinityRegression on RBP binding preference prediction task
After testing the performance over the baselines, we then sought to compare ProbeRating with the AffinityRegression method. First, we considered the RBP case. As shown in Figure 4A where proteins in the x-axis are sorted in ascending order based on AffinityRegression’s performance, ProbeRating outperformed AffinityRegression for most of the 162 RRM proteins. When focusing on the first 15 proteins (Fig. 4B), i.e. the proteins that AffinityRegression performed the poorest (lowest SCCs), we observed that ProbeRating generally agreed with AffinityRegression on the set of the hardest proteins but performed slightly better. In both plots, the blue line is almost always above the red line. If we look at Table 2 and Figure 4C, the overall average SCC of the 162 RRM proteins for AffinityRegression was 0.823, which was much lower than ProbeRating’s 0.864 (P < 0.001). Besides, AffinityRegression did have a higher mean value than the three nearest-neighbor baselines in this case (Table 2), and it was significantly better than them based on the Wilcoxon test.
Fig. 4. Performance of ProbeRating compared to AffinityRegression on the RRM162 dataset. (A) Each dot represents the SCC between the predicted and true RNAcompete probe intensities for a protein. The solid blue line indicates the performance of ProbeRating with the FastBioseq embedded features (PR), the dashed red line indicates the original AffinityRegression with its k-mer frequency features (AR) and the dotted yellow line indicates feeding AffinityRegression with the FastBioseq embedded features (AR-F). Proteins in the x-axis are sorted in ascending order based on their original AffinityRegression’s SCCs. (B) Similar plot to (A), zoom in on the first 15 proteins. (C) Boxplot for the performance of the three methods for all 162 RRM proteins. In each box, the dashed white line denotes the mean, and the solid green line denotes the median. The significance bar represents the P-value from a two-tailed Wilcoxon signed-rank test, with *** P < 0.001
Moreover, we were wondering whether using FastBioseq features alone would lead to better performance. Since our FastBioseq was trained based on the context information of each k-mer, it may implicitly embed some sequence conservation and local structural information into the final feature vectors. To separate the effect of the features from that of the models, we fed the FastBioseq features to the AffinityRegression model replacing its original k-mer frequency features. As denoted by AR-FastBioseq in Figure 4, we compared this approach with the original AffinityRegression and our ProbeRating. As a result, AR-FastBioseq did show slightly better mean (0.827 versus 0.823 in Table 2) than AffinityRegression. But no statistical significance was detected, due to a larger variance of AR-FastBioseq than AffinityRegression (Fig. 4C). Also, AR-FastBioseq’s result was significantly worse than the ProbeRating’s result (P < 0.001).
In summary, similar to the baselines case in the last section, ProbeRating significantly outperformed the more sophisticated method AffinityRegression. A consistent advantage of ProbeRating was shown.
3.3 ProbeRating outperformed AffinityRegression on TF binding preference prediction task
Next, we asked whether the good performance of ProbeRating could generalize to TFs by considering the Homeo binding preference prediction task. This task was different from the above RRM binding preference prediction task not only because one was RBP–RNA interaction and the other was TF–DNA interaction, but also because the Homeo task worked on 8-mer DNA segments and Z-scores instead of ordinary probes and intensity scores. As mentioned earlier in Section 2.3 and in Supplementary Note, this setting has practical usage: unlike in the RRM162 case where there is only one large-scale RNAcompete assay available right now, there exist several large-scale PBM experiments with very different probe designs. 8-mer Z-score is a way to integrate the data from different sources. Thus, if our ProbeRating method could also succeed in this case, the strength of the method would be more convincing.
As a result, ProbeRating outperformed AffinityRegression on the Homeo215 dataset (in Fig. 5), just like on the previous RRM162 dataset. As shown in Figure 5A, ProbeRating was better than AffinityRegression for the majority of the Homeo domains. The mean SCC across all 215 Homeo proteins for ProbeRating was 0.772. It was again significantly better than AffinityRegression’s 0.739 with P < 0.001 (Fig. 5C and Table 2), even though AffinityRegression was significantly better than all the three baselines. When zooming in on the first 15 proteins, as shown in Figure 5B, although these proteins were also hard for ProbeRating (among the lowest SCCs across all proteins), ProbeRating generally performed better than AffinityRegression except for a few cases.
Fig. 5. Performance of ProbeRating compared to AffinityRegression on the Homeo215 dataset. The plots here are represented in the same way as in Figure 4. In (A) and (B), proteins in the x-axis are again sorted in ascending order based on their original AffinityRegression’s SCCs. In (C), the significance bars indicate the P-values from a two-tailed Wilcoxon signed-rank test, with *** P < 0.001
Moreover, we also tested the AR-FastBioseq approach on the Homeo215 dataset. Interestingly, its mean SCC (0.747 as in Table 2) was again in the middle of ProbeRating’s and the original AffinityRegression’s mean SCCs, similar to the RRM162 case in the last section. AR-FastBioseq significantly outperformed the original AffinityRegression with P < 0.001 this time, and it was also significantly worse than ProbeRating with P < 0.001. This result showed that the FastBioseq features alone improved the performance in the AffintyRegression model, and the neural network approach building on top of that further elevated the performance in ProbeRating.
3.4 ProbeRating was compared with the binding specificity prediction method
Finally, as mentioned in Section 1, most existing methods focus on determining the NBP binding preference as a simplified summarization, like PWM or CNN filter, instead of predicting the full binding profile as AffinityRegression and ProbeRating do. Although the focuses and goals are different, the binding-specificity method Co-Evo (Yang et al., 2018) that was mentioned in Section 1 is also capable of inferring the nucleic acids preferences of an unexplored protein, and it is relatively more recent than the other methods. So, we evaluated Co-Evo on the same datasets to compare it with ProbeRating, to get a sense of where ProbeRating stands when compared with binding specificity prediction methods. As a result, we observed the SCCs of Co-Evo were much worse than ProbeRating and AffinityRegression (Table 2), which was not surprising since Co-Evo was designed to predict a PWM motif to summarize the binding preference instead of to predict the binding profile directly. The details of the Co-Evo results can be found in Supplementary Note.
4 Discussion
In this study, we introduced a new method ProbeRating to predict the binding profiles for NBPs that are experimentally unexplored. We showed that predicting the binding profile for unexplored NBPs is a critical but challenging task given is the limited data available. Thus, the task is less studied compared to the other task of directly determining the binding preference for an NBP from its experimental data. Extending the previous work of AffinityRegression, we developed a two-stage framework to tackle the task utilizing modern techniques from deep learning and word embedding. The first stage involved encoding the protein and nucleic acid sequences into distributed feature vectors. We contributed a tool FastBioseq, which essentially wrapped the famous FastText method from natural language processing to extract high-level features from biological sequences. The second stage involved recommending binding preferences for new proteins. We contributed a feedforward neural network with a non-parametric reconstruction step to leverage the training data. Our method was evaluated on the benchmark RBP and TF binding datasets. It performed well on both datasets and showed significant improvements over AffinityRegression and three baselines.
While the significant performance advancement of our method shows the advantage of using more expressive neural network models and word embedding features to study NBP–nucleic acid interactions, we see several potential improvements to this study. Here, ProbeRating propagates binding information from experimentally characterized NBPs to those unexplored ones within the same protein family. It would be interesting to investigate whether predicting for proteins from another family also works, or how similar the unexplored protein is to those already explored ones to get ProbeRating to work. As we mentioned earlier in the nearest-neighbor baselines section, the metrics to define ‘similar’ can be different, depending on what features we are using. Additionally, when investigating RBP–RNA interactions, although RNAs are known to fold themselves into secondary and tertiary structures, we do not consider this information. It is because the probes in our RNAcompete dataset were intentionally designed to be unstructured or weak structured (Ray et al., 2013). However, our two-stage framework could easily incorporate RNA structure as well as protein structure as input features when appropriate data become available in further researches. Moreover, the highly modular subnetwork structures in ProbeRating provide a lot of flexibility to be extended by other neural network models, too.
Overall, the strength of ProbeRating suggests promising capacity to the field. It is especially desired by RBPs that do not have much experimental evidence available at this moment. ProbeRating could be applied to learn binding patterns in those crucial RBP-related problems, like lncRNA regulation (Quinn and Chang, 2016; Zhao et al., 2016) or CRISPR/CAS systems (Liu et al., 2016; Wang et al., 2016). Also, similar to AffinityRegression, the prediction output from ProbeRating is the entire binding intensity profile instead of a simplified representation. So, when dealing with an unexplored NBP, the output binding profile from ProbeRating could be further fed as input to those intensively studied protein-specific methods for downstream analysis. Furthermore, besides of NBP–nucleic acid paired prediction, ProbeRating could be applied to other scenarios. For example, AffinityRegression has been used for protein–protein interaction in a tumor-related signaling pathway study (Osmanbeyoglu et al., 2017). ProbeRating could also be used in such a case to see if better performance is achieved. ProbeRating’s component FastBioseq can be used as a standalone package in other scenarios, too. It provides a general and flexible tool of biological sequence embedding for DNA, RNA and proteins.
Supplementary Material
btaa580_supplementary_data Click here for additional data file.
Acknowledgements
The authors thank Wan Lam and Junwen Wang for the valuable discussions.
Funding
This work was supported by Genome Canada, and Natural Sciences and Engineering Research Council (NSERC) of Canada.
Conflict of Interest: none declared.
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==== Front
Bioinformatics
Bioinformatics
bioinformatics
Bioinformatics
1367-4803
1367-4811
Oxford University Press
32579213
10.1093/bioinformatics/btaa586
btaa586
Original Papers
Sequence Analysis
AcademicSubjects/SCI01060
Detecting and correcting misclassified sequences in the large-scale public databases
http://orcid.org/0000-0003-1540-7797
Bagheri Hamid Department of Computer Science, Ames, IA 50011, USA
Severin Andrew J Genome Informatics Facility, Iowa State University, Ames, IA 50011, USA
Rajan Hridesh Department of Computer Science, Ames, IA 50011, USA
Elofsson Arne Associate Editor
To whom correspondence should be addressed. E-mail: [email protected]
15 9 2020
24 6 2020
24 6 2020
36 18 46994705
02 4 2020
10 6 2020
16 6 2020
© The Author(s) 2020. Published by Oxford University Press.
2020
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Motivation
As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity.
Results
We found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases.
Availability and implementation
Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr.
Supplementary information
Supplementary data are available at Bioinformatics online.
National Science Foundation 10.13039/100000001 CCF-15-18897 CNS-15-13263 CCF-19-34884 VPR office at Iowa State University
==== Body
pmc1 Introduction
Researchers use BLAST on the non-redundant (NR) database on a daily basis to identify the source and function of a protein/DNA sequence. The NR database encompasses protein sequences from non-curated (low quality) and curated (high quality) databases. It contains NR sequences from GenBank translations (i.e. GenPept) together with sequences from other databases [Refseq (Pruitt et al., 2007), PDB (Berman et al., 2003), SwissProt (Boeckmann et al., 2003), PIR (Wu et al., 2003) and PRF]. NR removes 100% identical sequences and merges the annotations and sequence IDs.
We have identified three root causes for annotation errors in the public databases: user metadata submission, contamination error in the biological samples and computational methods. NCBI relies on the accuracy of the metadata provided by researchers that are depositing sequencing data into the database. Data are deposited into NCBI into Biosamples and Bioprojects as raw data, genome assemblies and transcriptomes. Biosamples contain metadata describing the data type, scope, organism, publication, authors and attributes, which include cultivar, biomaterial provider, collection date, tissue, developmental stage, geographical location, coordinates and additional notes. This metadata is then propagated to the sequences that are deposited. For example, if data for DNA sequences were deposited by a plant researcher studying soybeans obtained from a soybean roots, then all sequences tied to that metadata will be labeled with the organism name Glycine max. If the researcher had in fact been working on Glycine soja then this would result in a misassignment of all Glycine max sequences.
The second key challenge that all large databases have is the issue of contamination (Schnoes et al., 2009). For example, if the aforementioned hypothetical soybean research did not remove the soybean root nodules during sample processing, then the tissue sample could also contain DNA from Nitrogen fixating soil bacteria that infect nodules leading to contamination of the sequences and ultimately the sequence database. NCBI is aware of the potential for contamination in sequence databases and describes potential sources of contamination that include: DNA recombination techniques (vectors, adaptors, linkers and PCR primers, transposon and insertion sequences) and sample impurities (organelle, DNA/RNA and multiple organisms). NCBI encourages the use of programs to try to reduce issues with contamination. Specifically, they recommend screening for contamination using VecScreen (VecScreen; https://www.ncbi.nlm.nih.gov/tools/vecscreen/) and BLAST for the sequences used in sequencing library preparation. More broadly, they recommend BLAST to screen out bacterial, yeast and Escherichia coli sequences and BLASTing against the NR database to identify potential contaminating sequences. Unfortunately, despite efforts to reduce contamination, sequences still end up in the NR database that is incorrectly taxonomically classified. This can limit our ability to identify contamination of future sequence submissions, as BLASTing against the database could propagate these types of errors as the database grows in size (Schnoes et al., 2009). The contamination problem is not unique to NCBI but can be found in all large databases. A large-scale study of complete and draft bacterial and archea genomes in the NCBI RefSeq database revealed that 2250 genomes are contaminated by human sequences (Breitwieser et al., 2019). Breitwieser et al. reported 3437 spurious protein entries that are currently present in NR and TrEMBL protein databases.
The third key challenge is that there are errors in the annotations due to the computational error in tools that are based on homology to existing sequences to predict the annotations (Schnoes et al., 2009). These errors have caused annotation accuracy and database quality issues over the years. Annotation errors are not limited to contamination or computationally predicted one. For instance, there exists evidence that suggests some of the experimentally derived annotations may be incorrect (Schnoes et al., 2009).
Therefore, it will be beneficial for researchers to utilize a quality control method to detect misclassified sequences and propose the most probable taxonomic assignment.
To address these well-known problems, there are two approaches in the literature: phylogenetic-based approach and functional approach. For the first approach, Kozlov et al. (2016) have proposed a phylogeny-aware method to detect and correct misclassified sequences in public databases. They utilized the Evolutionary Placement Algorithm (EPA) to identify mislabeled taxonomic annotation. Edgar (2018) has studied taxonomy annotation error in rRNA databases. They showed that the annotation error rate in SILVA and Greengenes databases is about 17%. They also used the phylogenetic-based approach.
In the second approach, it is a common technique for quality control and data cleaning to utilize domain knowledge in the form of ontologies (Chu et al., 2015). Gene Ontology (Ashburner et al., 2000) has been suggested to infer aspects of protein function based on sequence similarity (Holliday et al., 2017). The MisPred Nagy and Patthy (2013) and FixPred (Nagy and Patthy, 2014) programs are used to address the identification and correction of misclassified sequences in the public databases. The FixPred and MisPred methods are based on the principle that an annotation is likely to be erroneous if its feature violates our knowledge about proteins (Nagy et al., 2008). MisPred (Nagy and Patthy, 2013) is a tool developed to detect incomplete, abnormal, or mispredicted protein annotations. There is a web interface to check the protein sequence online. MisPred uses protein-coding genes and protein knowledge to detect erroneous annotations at the protein function level. For example, they have found for a subset of protein databases that violation of domain integrity accounts for the majority of mispredictions. Modha et al. (2018) have proposed a pipeline to pinpoint taxonomic error as well as identifying novel viral species. There is another web-server for exploratory analysis and quality control of proteome-wide sequence search (Medlar et al., 2018) that requires a protein sequence in a FASTA format. European Bioinformatics Institute (EMBL-EBI) developed InterPro (InterPro; http://www.ebi.ac.uk/interpro/) to classify protein sequences at the superfamily, family and subfamily levels. UniProt has also developed two prediction systems, UniRule and the Statistical Automatic Annotation System (SAAS) (SAS; https://www.uniprot.org/help/saas), to annotate UniProtKB/TrEMBL protein database automatically. CDD is a Conserved Domain Database for the functional annotation of proteins (Marchler-Bauer et al., 2011).
Exploring public sequence databases and curating annotations at large-scale are challenging. Previous research on the NR database focused on a small subset of the NR database and analyzed annotation error due to the computational requirements. There has been a study (Schnoes et al., 2009) on misclassification levels for molecular function for a model set of 37 enzyme families. To the best of our knowledge, the amount of misclassification in the entire database has not been well quantified.
Here, we attempt to address these limitations in detecting and correcting annotations at large-scale and make the following contributions:
We utilize a genomics-specific language, BoaG, that uses the Hadoop cluster (Bagheri et al., 2019), to explore annotations in the NR database that is not available in other works.
We also present a heuristic-based method to detect misclassified taxonomic assignments in the NR database that is low-cost and easy to use. We automatically generate a phylogenetic tree from a list of taxonomic assignments and use the tree, along with frequency, the provenance (database of origin) of each taxonomic annotation and clustering information from NR at 95% similarity to identify potential misclassification and propose the most probable taxonomic assignment.
The technique proposed in this work could be generalized to apply to other public databases and different kinds of annotations like protein functions. In this work, we address the taxonomic annotation error in protein databases. We also tested our approach on the RNA dataset introduced in the literature.
We have identified ‘29 175 336’ proteins in the NR database that have more than one distinct taxonomic assignments, among which ‘2 238 230’ (7.6%) are potentially taxonomically misclassified. We also found that the total number of potential misclassifications in clusters at 95% similarity, above the genus level, is ‘3 689 089’ out of 88 M clusters, which are 4% of the total clusters. This percentage of misclassifications in NR has a significant impact due to the potential for error propagation in the downstream analysis (Mukherjee et al., 2015).
The rest of the paper is organized as follows. In Section 2, we present methods and materials for dataset generation and our approach. In Section 3, we discuss the results of taxonomically misclassified proteins within sequences and in NR 95%. We also present the correcting approach for detected sequences. In Section 4, we conclude with suggestions for the future.
2 Materials and methods
In this section, we will describe the overview architecture of our detection and correction approach. Then, we describe the dataset generation and how we generate a phylogenetic tree from taxonomic assignments. Next, we discuss our detection algorithm to find misclassified sequences. Then, we describe our approach to propose taxonomic assignments for the sequences identified as misclassified. Finally, we will describe the sensitivity analysis on changing the different parameters to propose the taxonomic assignments.
2.1 An overview of the method
Figure 1 shows an overview of our approach. The NCBI’NR database files were downloaded from (ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/) on October 20, 2018. Taxonomic information was obtained from XML files on NCBI (https://ftp.ncbi.nlm.nih.gov/blast/temp/DB\_XML/). CD-HIT (Fu et al., 2012) (version v4.6.8-2017-1208) was used to cluster NR protein sequences into clusters at 95% similarity using the following parameters (-n 5 -g 1 -G 0 -aS 0.8 -d 0 -p 1 -T 28 -M 0). These parameters use a word length of 5 and require that the alignment of the short sequences is at least 80% of its length. The data acquisition, preprocessing and clustering took about 3 days. The detection and correction part took about 8 h.
Fig. 1. Overview architecture of the proposed method to detecting taxonomically misclassified sequences in the NR database. Diagram shows the raw dataset and steps for the proposed work
We took the NR protein FASTA files that have the definition lines containing annotations from different databases and generate the BoaG format that took about 2 h. Each definition line in the raw data includes protein ID, protein name followed by an organism name in square brackets, e.g. ‘>AAB18559 unnamed protein product [E. coli str. K-12 substr. MG1655]’. BoaG is a domain-specific language that uses a Hadoop-based infrastructure for biological data (Bagheri et al., 2019). A BoaG program is submitted to the BoaG infrastructure. It is compiled and executed on a distributed Hadoop cluster to execute a query on the BoaG-formatted database of the raw data. BoaG has aggregators that can be run on the entire database or a subset of the database taking advantage of protobuf-based schema design optimized for a Hadoop cluster for both the data and the computation. These aggregators are similar to but not limited to aggregators traditionally found in SQL databases and NoSQL databases like MongoDB. A BoaG script requires fewer lines of code, provides storage efficiency and automatically parallelized large-scale analysis.
2.1.1 Dataset generation
To describe our dataset, let D denotes the protein and clustering dataset in our study: D={P,C,τ,Ϝ}. Here, P={P1,P2,…,Pm} is a set of all the proteins in the NR database. C={C1,C2,…,Cn} represents a set of all clusters at 95% similarity. |P| and |C| in our dataset are about 174 M and 88 M, respectively. τ is a set of taxonomic assignment for proteins, and Ϝ is a set of functions in the NR database. In this work, we focus on exploring taxonomic assignments.Definition 1. Cluster. We define cluster as a set of protein sequences such that their sequence are 95% similar and their sequence length is 80% similar. Every particular cluster, Cj, has k members: (1) Cj={P1,P2,…,Pk}, and k∈[1,|P|]
In Definition 1, each protein sequence belongs to exactly one cluster at 95% similarity, and each cluster has one representative sequence. If a protein is not identical in sequence and length, it will fall into a cluster with no other member.
2.1.2 Generating phylogenetic tree from taxonomic assignments
We get the list of taxonomic assignments that originate from different databases (manually reviewed and computationally created) and build a phylogenetic tree by utilizing the ETE3 library (Huerta-Cepas et al., 2016). This library utilizes the NCBI taxonomy database that is updated frequently.Definition 2. Annotation List. Each phylogeny tree is associated with one particular protein, Pi and has the set of taxonomic assignments that originate from different databases. Here, Ai,j denotes annotation number j for protein Pi: (2) τ(Pi)={Ai,1,Ai,2,…,Ai,j}, j∈[1,|τ|]
For example, the protein sequence AAB18559 has taxonomic assignments of ‘511 145’ and ‘723 603’ that each appeared once.Definition 3. Provenance. For the particular protein Pi, we define prov(Ai,a) the provenance of annotation Ai,a as a set of databases that the annotation Ai,a originates from: (3) prov(Ai,a)∈{GenBank,trEMBL,PDB,RefSeq,SwissProt}
In Definition 3, annotations from GenBank, trEMBL and PDB are calculated computationally, while annotations from RefSeq and SwissProt are manually reviewed. For example, prov(511145)=GenBank meaning that the tax id ‘511 145’ for the sequence AAB18559 originates from the GenBank database.Definition 4. Annotation Probability
We define probability for each taxonomic assignment based on the frequency of each annotation divided by total taxonomic assignments from different databases as follows: (4) prob(Ai,a)=freq(Ai,a∈Comp)+w×freq(Ai,a∈Rev)∑j∈Compfreq(Ai,j)+∑j∈Revw×freq(Ai,j)
In Definition 4, Ai,a∈Comp represents the annotation that calculated computationally (Comp) from databases i.e. GenBank, trEMBL, PDB and Ai,a∈Rev denotes the reviewed (Rev) one from RefSeq, SwissProt. One annotation might originate from both reviewed and computational created databases. We use a conservative weighting factor, w, to denote the importance of the experimental annotation (manually reviewed) in which w is an integer number and w≥1.
The upper bound for total proteins, i.e. |P|, is 174 M at the time we downloaded NR. Each leaf node, Va, in the phylogenetic tree is annotated with the information described in the Definitions 2, 3 and 4. There are list of frequencies and provenances, shows as top bar, since one particular taxonomic annotation might originate from different databases: (5) Va={prob(Ai,a),freq(Ai,a),prov(Ai,a)¯}
For particular protein Pi, we define most probable annotation (MPA) as MPA(Pi)=Ai,j as an annotation with the highest probability among the set of annotations. In addition, we define least probable annotation (LPA), with the lowest probability, that potentially might be misclassified as LPA(Pi)=Ai,k, in which i≠j.Definition 5. Conserved Proteins. We define a conserved protein as a protein that has more than 10 distinct taxonomic assignment. List of these conserved proteins are shown in our repository (https://github.com/boalang/nr). (6) Pi such that |τ(Pi)|≥10
2.2 Approach to detect taxonomic misclassification
Algorithm 1 The NR misassignment detection algorithm. Input comes from the BoaG query (Supplementary Material)
1: procedure DetectMisassignments D
2: NR Length←|P|▹ m = 174 M proteins
3: while i≤NRLength do
4: phylo←PhyloTree(Pi).
5: If misassigned(phylo) && not conserved(Pi) then
6: print (misassignment found in Pi)
7: procedure PhyloTreePi
8: ncbi←ncbiTAXA() ▹ used to generate phylogeny tree
9: phyloTree←ncbi.get_topology(Pi) ▹ From taxa list
10: for Ai,a in τ(Pi) do
11: Va←prob(Ai,a),list(freq(Ai,a),prov(Ai,a))
12: return phyloTree.
Our approach is as follows: first, we run a BoaG query (Supplementary Fig. S1) on the NR database. This query runs on the full NR database in the Hadoop cluster. The Algorithm 1 describes the detection approach for misclassified sequences. It iterates over the entire NR database. In line 4, it takes a protein Pi and generates a phylogeny tree from the set of taxonomic assignments for Pi. Then, in line 5, it checks if it has a misclassification. If the lowest common ancestor (LCA) is the root level, it means there is a considerable distance between taxonomic assignments for that particular protein sequence. Therefore, there is a potential misassignment among the list of the taxonomic assignments due to the contamination in the sample, error in the computational method, or data entry by the researchers who deposited the sequence. We call this a root violation or conflict. We also consider superkingdom, phylum, class, order and family violations. In addition, we looked at the highly conserved proteins to remove false positives because conserved proteins might appear in species that are far from each other, i.e. belong to different domains in the phylogeny tree. We did not remove the list of conserved proteins in the dataset, since they contain taxonomic information that were utilized for proposing taxonomic assignment for the misclassified sequences. Assume Pi belongs to Cj. Once we detected the violation in Pi, we look at the cluster Cj and consider the most frequent taxonomic assignment as the correct taxa. Details are shown in Section 2.3.
The Algorithm 1 requires O(|P|*|τ|) time. Here, |P| is the size of proteins in the NR database and |τ| is the upper bound of number of taxonomic assignments per proteins. In line 5, misassigned(phylo) verifies if the LCA of the generated tree shows a root violation or any other violations. The conserved(Pi) expression checks if the protein sequence is a conserved one (Equation 6). This requires O(1) time because this is a straight-forward fetch, and we have the pointer to the root of the tree to check the LCA. In line 5, to check that a protein is not in a conserved list, Definition 5, it requires a membership test and takes O(1) time. This conserved list is a precomputed list from our dataset that is shown in our repository. We wrote a multi-threaded Python code, and the total run time for the algorithm was 7 h for the entire NR database on an iMac (Retina 5 K, 27-inch, Late 2015) with core i7 and 32 GB RAM. For the second procedure, in line 11, the algorithm requires O(|τ|) to calculate the probability of each leaf in the generated phylogenetic tree.Algorithm 2 Annotation correction: The MPA for the misclassified sequences. Input from the BoaG query (Supplementary Material)
1: procedure mostProbablePi,p,c
2: top_ann←max(prob(τ(Pi))) ▹ Most probable taxa
3: if prob(top_ann)≥p then
4: return (top_ann).
5: else
6: cluster←Cj in which Pi∈Cj
7: top_ann←ClusterMostProbable(cluster,p,c).
8: return top_ann.
9: procedure ClusterMostProbableclustr, p, c
10: if size(cluster)≥c then
11: for Ai,a in τ(cluster) do
12: Va←prob(Ai,a),list(freq(Ai,a),prov(Ai,a))
13: top_ann←max(prob(τ(cluster))) ▹ Most probable taxa
14: if prob(top_ann)≥p then
15: return top_ann
16: else
17: return null ▹ Cannot fix misclassification
2.3 The most probable taxonomic assignment for detected misclassifications
For the detected misclassified sequences, we defined criteria to propose the most probable taxonomic assignment (MPA). First, we ran a BoaG query (Supplementary Fig. S2) to retrieve the annotations and clustering information at 95% similarity. As shown in Definition 4, we considered provenance or database of origin, frequency of annotations to calculate the probable taxonomic assignment (MPA), which is the highest probability. Let’s assume that Pi belongs to cluster Cj. If the algorithm does not find the MPA within a certain threshold, probability p, then we look at the cluster of 95% similarity that the sequence belongs to. Second, we found the most probable taxonomic assignment in Cj. If a particular taxonomic assignment was the most frequent one in Cj then we return that annotation as the MPA for the protein sequence Pi. For example, in cluster Cj, 7 sequences out of 10 sequences have a specific annotation. Then, we consider this annotation to be the MPA protein sequence Pi with 70% confidence.
Details are shown in the Algorithm 2. In line 2, for a particular protein Pi, it returns the most frequent taxonomic assignment within a certain threshold p. Let’s assume we want a taxonomic assignment that appears more than 70% of the time. If the algorithm does not find the MPA, it checks the cluster Cj with 95% similarity that this sequence belongs to and finds the one with a certain probability, p and a cluster size, c (line 7). In line 9, ClusterMostProbable takes the cluster id and finds the most probable taxonomic assignment in the cluster (line 13).
The Algorithm 2 requires O(|τ(P)|) time, Definition 2, to find the top(1) or maximum probability of an annotation in the list of annotations.
2.4 Simulated and literature dataset
To evaluate the performance of our taxonomic misclassification approach, we generated a simulated dataset. We took a subset of one million proteins of the reviewed dataset, i.e. RefSeq database and randomly misclassified 50% of the proteins in the sample by adding a taxonomic assignment from another phylum or kingdoms. Then, we tested if the approach can detect these sequences. We also tested our approach for detecting misclassified sequences and correcting them on the real-world data, presented in the literature (Edgar, 2018; Kozlov et al., 2016). These works have focused on the RNA dataset, and they quantified misclassified RNA sequences. We also used CD-HIT to cluster RNA databases based on 95% sequence similarity. Further details on the simulated dataset, scripts and data files can be accessed from https://github.com/boalang/nr.
2.5 Sensitivity analysis
We define sensitivity analysis as a way that an input parameter affects the output of the proposed approach. Here, probability based on annotation frequencies and the cluster size are the two input parameters that affect what percentages of detected misclassified sequences that we can fix, i.e. MPA, as shown in Algorithm 2 on the NR dataset. The algorithm will not give the same suggestion for changes in parameters. For example, if we change the cluster size, number of proteins in the cluster, it may or may not find correct taxa. We conducted a sensitivity analysis based on the probability of each annotation that we defined in Definition 4 and the size of the cluster of 95% that the sequence belongs to. We run the algorithm to find the most probable taxonomic assignments (MPA) with different clusters size, c and with different probabilities, p. As it is shown in (Supplementary Fig. S3), with a probability of 0.4 and without giving more weight to the annotations that verified experimentally, we could provide a most probable taxonomic assignment to about 60% of the proteins that we detected as misclassified. We also extended sensitivity analysis by giving more weight to the experimental taxonomic assignment with the probability of 0.4 we could provide the most probable taxonomic assignment for more than 80% of the sequences that were identified as a misclassification.
3 Results
In this section, we present the number of proteins that are misclassified taxonomically. We also present the performance of our work on the simulated dataset and the datasets presented in the literature. Then, we describe our findings on misassignments in the clusters. Next, we present correcting taxonomic misclassification. Finally, we discuss a case study that we explored deeply to identify a subset of clusters that contain sequences with a taxonomic misclassification.
3.1 Detected taxonomically misclassified proteins
We found ‘29 175 336’ proteins in the NR database that have more than one distinct taxonomic assignments. The rest of the proteins have identical taxonomic assignments, even though they originate from different databases. The total number of potential taxonomically misclassified sequences is ‘2 238 230’ out of ‘29 175 336’ (7.6%) at the time of download. This percentage of NR is significant because of the error propagation in the downstream analysis (Mukherjee et al., 2015). Table 1 shows the number of violations in the protein sequences in NR at the superkingdom to the family level that have been detected by applying distance in the phylogenetic tree. The second column shows the number of total proteins that have a certain number of taxonomic assignments. For example, there are ‘17 496 167’ protein sequences in NR that have 2 taxonomic assignments in which ‘30 237’ of them have potential root violations and ‘47 271’, ‘202 205’, ‘59 606’, ‘177 132’, ‘290 065’ have kingdom, phylum, class, order and family violations, respectively. For the NR datasets, we did a sample study of 1000 samples and manually found 5.5% misassignment. The potentially misclassified sequences detected by the approach was around 7.6% that is consistent with the total number that was manually found, i.e. 5.5%.
Table 1. Detected misclassified taxonomic proteins in the NR database
taxa Total root Kingdom Phylum Class Order Family
2 17 496 167 30 237 47 271 202 205 59 606 177 132 290 065
3 5 921 066 14 376 19 666 107 705 38 575 104 709 236 515
4 2 132 971 4673 21 587 64 801 17 662 47 914 94 054
5 1 022 482 3143 9469 34 322 10 050 27 295 53 276
6 642 760 2509 5662 24 136 7333 23 324 37 998
7 388 794 1572 3959 12 972 5905 13 488 27 221
8 262 682 1121 2803 5988 5375 10 075 16 340
9 190 756 783 2647 3825 3173 7557 12 681
10 156 767 667 1843 3805 2451 6413 11 327
>10 960 891 10 940 23 232 30 048 38 679 46 391 107 679
The first two bold rows show the highest potential misassignments because if a protein has two or three taxonomic assignments and shows a root or kingdom violation, it is more likely to be misclassified.
Table 1 shows proteins that have less than 10 taxonomic assignments. The last row shows all other proteins with more than 10 assignments. The first two bold rows show the highest potential misassignments because if a protein has two or three taxonomic assignments and shows a root or kingdom violation, it is more likely to be misclassified. The full list of detected misclassified proteins, and detailed analysis are shown in our GitHub repository. We did not report the genus conflict since the probability of a false-positive misclassification is much higher compared to other taxonomic levels of conflict, such as root and superkingdom.
Figure 2 shows one example of a detected misclassified protein, with an id of NP_001026909. Since the LCA in this tree is the root, it means those taxonomic assignments belong to a different kingdom. Leaves are annotated with a frequency of each taxonomic assignment as a bar chart from all reviewed and unreviewed databases i.e. RefSeq (Pruitt et al., 2007), GenBank (Benson et al., 2009), PDB (Berman et al., 2003), UniProt\SwissProt (Boeckmann et al., 2003) and UniProt\TrEMBL (Consortium, 2014), respectively. As it is shown in the annotations, there are potential misassignments even though the key IDs originate from the reviewed databases, i.e. RefSeq and SwissProt. In this example, synthetic construct is the misassignment and the MPA for this protein is Homo sapiens.
Fig. 2. Phylogenetic tree generated for sequence ID NP_001026909. Taxonomic assignments originate from GenBank, trEMBL, PDB, RefSeq and SwissProt database
We also explored some clusters in depth as a case study and identified proteins that are taxonomically misclassified as Glycine, which are in fact contamination in the sample (Supplementary Section S1.6).
3.2 Performance on simulated and real-world dataset
Our approach to detecting taxonomically misclassified proteins on the simulated dataset showed 87% recall and 97% precision. We define true positive (TP) as sequences that misclassified in the sample, and our approach retrieves those sequences. False positives (FP) are sequences that do not have misassignments, but our approach classified them as misclassified sequences. False negative (FN) is a reviewed sequence which the algorithm incorrectly classifies as correct (not misclassified), while it is misclassified. Some of these false negatives are due to changes in the taxonomies over time. Some taxonomic IDs might be obsolete, deleted, or get merged into other tax ids. We also found that some of the trees generated by NCBI API have the root named ‘Cellular Organisms’ with rank equal to ‘no rank’, that did not fall in any of the taxonomic ranking. We use the following formula to calculate precision and recall (precision = TPTP+FP; recall = TPTP+FN).
We extended our experiment and added more than two random assignments to the proteins and the precision increased. The reason is that adding more random assignments increases the distance among tax IDs in the phylogeny tree and hence increases the chance of detection by the approach. We also tested our approach on the dataset presented by (Edgar, 2018) in which they explored the Greengenes and the SILVA database for taxonomic error. Our methods reproduced their finding on annotation conflicts among SILVA and Greengenes (McDonald et al., 2012) database. We did not run their approach on the simulated dataset since it was designed to detect misassignments in rRNA sequences, not proteins. For evaluating our work, we looked for similar works that focused on detecting taxonomic misassignments. However, their approach was hard-coded for RNA sequences. Therefore, we modified our approach to test on their dataset. The proposed work focuses on inconsistencies among the list of taxonomies, and it can be applied to the RNA sequences as well. We clustered their dataset at 95% similarity and used the same consensus-based technique to detect conflicts between sequences and clusters. The phylogeny-aware technique proposed by Kozlov et al. (2016), called SATIVA, identifies and corrects misclassified sequences for RNA databases . They utilized the EPA to detect misclassified sequences. In their approach, a reference tree is created. Then, to estimate the most likely placements of the query sequence in the reference tree, they use EPA. We took their RNA dataset and cluster the sequences at 95% similarity, then utilized our technique to check if the annotation of each sequence has a conflict with a cluster that the sequence belongs to. There is a difference between the NR dataset and the RNA dataset presented by Kozlov et al. in terms of the number of taxonomic annotation. In their experiment, they have one taxonomic label for each sequence; however, in the NR database, there are several annotations for each protein sequence. Therefore, their technique is not designed to detect misclassification in a set of given annotations. In terms of running time, the clustering at 95% is less expensive than running sequence alignment and generating phylogeny-tree and verifying each query sequence. Therefore, our approach is scalable for large-scale sequence databases. In general, examining the distance on the phylogenetic tree of multiple annotations for the shorter sequences performs better compared to the alignment-based approaches with the reference databases. Table 2 shows the standard values for precision and recall, as well as the running time comparison. Our approach to detect misassignments on the sample RNA dataset has a lower recall. This is due to the relatively smaller datasets that caused some clusters to have few members and made it challenging to detect misclassified sequences.
Table 2. Accuracy of detecting misassignments and the comparison with work presented in SATIVA (Kozlov et al., 2016)
Precision Recall Runtime
SATIVA Proposed SATIVA Proposed SATIVA Proposed
0.93 0.98 0.98 0.90 116 min 12 min
Note: The best values are highlighted.
3.3 Detected misassignments in clusters
There are ‘12 960 476’ clusters at 95% similarity that have two taxonomic assignments in which ‘17 099’ of them have potential root violations and ‘92 526’, ‘263 844’, ‘100 560’, ‘267 251’ and ‘461 795’ have kingdom, phylum, class, order and family violations, respectively. The number of root violations for 2 tax assignments in clusters is less than sequences because there are protein sequences that do not belong to any clusters at 95% similarity. In total 64 M out of 174 M proteins (36%) in the NR database are unclustered (Supplementary Table S1). The total number of potential misclassifications for clusters at 95% similarity, without genus level, is ‘3 689 089’ out of ‘25 159 866’ clusters that have more than 1 taxa, which are 15% of total clusters. Detail numbers of misclassified sequences in the clusters along with an example of detected taxonomically assigned annotations in the cluster are shown in the Supplementary Materials.
3.4 Correcting taxonomic misclassification
Each protein sequence belongs to one and only one cluster. We analyzed the set of top three taxonomic annotations of each sequence and compared them with the top three taxonomic annotations of the cluster the sequence belongs to. For example, top three taxonomic assignment for sequence with id AAA32344 is ’10 743’, ‘1 182 665’ and ‘656 390’. This sequence falls in the cluster-id 8 461 728, and the top tax ids in this cluster are ‘562’, ‘83 334’ and ‘621’. We consider this as a conflict between sequence AAA32344 and cluster 8 461 728. All three annotations are different; therefore, we consider this case as three conflicts. If two annotations out of three are different, we classify this as two conflicts. If one taxonomic annotation is different from the two sets, we classify it as one conflict. Finally, if the three annotations are identical, there is no conflict. Different percentages of conflicts from the subset of one million sequences are shown in Supplementary Fig. S5.
Table 3 shows several examples of the protein sequences that we have found to be misclassified in the NR database. The first column represents the sequence id, and the second column is the cluster id corresponds to the sequence. The third column shows the original taxonomic assignment, and the forth column is the proposed taxonomy based on the consensus information from the clusters of the NR database at 95% similarity. The last column is Confidence Score (CS), a number between 0 and 1, shows how confident we are in proposing new taxonomic assignment based on the consensus information from the clusters at 95% similarity. This score calculated from the clusters’ information as top taxonomic assignment, i.e. most frequent one, in the cluster divided by total taxa in the cluster. The assumption here is that the consensus of multiple independent sequence annotations can catch simple misannotation errors. For example, protein sequence with id YP_950729 has Staphylococcus virus PH15 as its taxonomic assignment. It falls in cluster id 83178931 and the recommended annotation is firmicutes. We also conducted similar analysis on the dataset by SATIVA, and could reproduce the proposed taxa based on the consensus information from the clusters. For the dataset by Edgar (2018) since the number of sequences was small, we could not get clusters with enough members to suggest annotations.
Table 3. Proposed taxa for the detected misclassified sequences in NR
Protein ID Cluster ID Original taxa Proposed taxa CS
AAB18559 18 982 245 Uncultured actinobacterium Escherichia coli 1
AAT83007 21 005 513 Mycobacteroides abscessus Cutibacterium acnes 0.8
CCW09133 9 901 357 Streptococcus pneumoniae Bacillus cereus 0.5
KFV03115 13 041 247 Tauraco erythrolophus Pelodiscus sinensis 0.4
YP_950729 83 178 931 Staphylococcus virus PH15 firmicutes 0.8
Note: Last column shows the confidence score (CS).
3.5 Running time
We conducted an analysis of the RNA dataset presented by SATIVA with different samples of sequences. Firstly, we took 100 sequences and ran SATIVA in the sample. Next, we took 500 sequences. In two other experiments, we took 1000 and 2000 additional sequences and recorded the running time. Figure 3 shows the comparison in terms of running time between proposed work and the SATIVA method. The most time-consuming part of our approach is the clustering time (run by CD-HIT). By adding more sequences, the runtime slightly increased. In contrast, for the SATIVA method, as we increase the number of sequences, the running time increases significantly. The computational expensive part of the SATIVA approach is the phylogenetic methods (EPA) it uses. The comparison between the proposed approach and SATIVA method has been made on the local system iMac (Retina 5 K, 27-inch and Late 2015) with core i7 and 32 GB RAM.
Fig. 3. Compare running time of the proposed work with the SATIVA method. We used dataset from the SATIVA paper
4 Discussion and conclusion
In this work, we addressed taxonomically misclassified sequences in the large publicly available databases by utilizing our domain-specific language and Hadoop-based infrastructure. We focused on the misassignments at the taxonomic level, and similar to MisPred (Nagy and Patthy, 2013), we utilize the current knowledge of organismal classification, to detect annotation errors. Similar to (Holliday et al., 2017), we utilized this form of knowledge-based reasoning for quality control and detect annotation errors. Compared to other works, our work differs in that we do not need to run sequence similarity to explore annotations and find taxonomic inconsistency for each query sequence in the NR database. Instead, first, we clustered the NR proteins at the data generation phase and this is a one-time cost and used the clustering information later to detect annotation error and propose the MPAs. In this work, we proposed a heuristic method to find inconsistencies in the metadata, i.e. taxonomic assignments. In our method, we proposed the most probable taxonomic assignment for each protein sequence. We applied this method to the entire database. We also provided a Python implementation that could be used for analyzing a list of annotations for any protein of interest and find the misclassification. The violations reported in this paper in Table 1 are the upper bound of the misassignments. The more stringent filter includes hypothetical protein and membrane protein functions in the list of conserved protein, which will lower the number of identified misclassification. We use open-source CD-HIT clustering software only at the data generation phase, and we could utilize any other clustering software. Steinegger and Söding, (2018) have built a novel clustering tool that clusters a huge protein database in linear time . Since this one-time cost happens only in the data generation phase, our approach to detect misassignments and propose the most probable taxonomic assignment is scalable.
4.1 Applications and limitations
At 95% similarity, 64 M sequences in the NR remain unclustered. Therefore, if a particular protein remains unclustered, there is not enough consensus information to correct annotation for that protein. A solution for this might be to take the EPA approach (Kozlov et al., 2016) for these sequences that remains as future work. The proposed technique to detect misassignments may fail with recent horizontal gene transfer (HGT) events since HGT is not transferred from parent to offspring. However, the consensus information from the clusters might reveal annotation errors. The proposed heuristic technique and findings could also be applied to other databases. Current work focuses on detecting and correcting misassignments at the level of taxonomic assignments, and we do not address protein function annotations.
5 Conclusion
Misclassification can lead to significant error propagation in the downstream analysis. In this work, we proposed a heuristic approach to detect misclassified taxonomic assignments and find the MPAs for misclassified sequences. This method will be a valuable tool in cleaning up on large-scale public databases. The technique we proposed could be extended in the form of ontologies to address other annotation errors like protein functions.
Supplementary Material
btaa586_supplementary_data Click here for additional data file.
Acknowledgements
This study was supported by the National Science Foundation under Grant CCF-15-18897, CNS-15-13263 and CCF-19-34884 and the VPR office at Iowa State University. The listed funders played no role in the design of the study, data generation, implementation, or in writing the manuscript. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the NSF grant number ACI-1548562.
Funding
This study was supported by the National Science Foundation under Grant CCF-15-18897, CNS-15-13263 and CCF-19-34884 and the VPR office at Iowa State University
Financial Support: none declared.
Conflict of Interest: none declared.
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PMC007xxxxxx/PMC7863719.txt |
==== Front
J Gen Physiol
J Gen Physiol
jgp
The Journal of General Physiology
0022-1295
1540-7748
Rockefeller University Press
33533890
jgp.202012760
10.1085/jgp.202012760
Article
Myofilament Special Issue, 2020
Membrane transport
Systems physiology
Cellular physiology
Intercellular signaling
Dynein regulates Kv7.4 channel trafficking from the cell membrane
Dynein regulates Kv7 channels
van der Horst Jennifer 1
https://orcid.org/0000-0003-3276-5887
Rognant Salomé 1
https://orcid.org/0000-0003-4552-496X
Abbott Geoffrey W. 2
https://orcid.org/0000-0002-4620-8507
Ozhathil Lijo Cherian 1
https://orcid.org/0000-0002-6627-7518
Hägglund Per 1
Barrese Vincenzo 34
Chuang Christine Y. 1
Jespersen Thomas 1
https://orcid.org/0000-0002-5196-6919
Davies Michael J. 1
Greenwood Iain A. 3
https://orcid.org/0000-0002-8631-3539
Gourdon Pontus 15
https://orcid.org/0000-0003-2618-4015
Aalkjær Christian 16
https://orcid.org/0000-0002-9903-714X
Jepps Thomas A. 1
1 Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
2 Bioelectricity Laboratory, Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, CA
3 St. George’s, University of London, London, UK
4 Department of Neuroscience, Reproductive Science and Dentistry, University of Naples “Federico II,” Naples, Italy
5 Department of Medical Sciences, Lund University, Lund, Sweden
6 Department of Biomedicine, Aarhus University, Aarhus, Denmark
Correspondence to Thomas A. Jepps: [email protected]
This work is part of a special collection on myofilament function and disease.
01 3 2021
03 2 2021
153 3 e20201276020 9 2020
21 12 2020
08 1 2021
© 2021 van der Horst et al.
2021
https://creativecommons.org/licenses/by-nc-sa/4.0/ http://www.rupress.org/terms/ This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
van der Horst et al. show that dynein, a microtubule motor protein, carries the voltage-gated potassium channel Kv7.4 away from the cell membrane in vascular smooth muscle cells, thereby reducing its functional impact. These data have implications for our understanding of arterial contractility.
The dynein motor protein transports proteins away from the cell membrane along the microtubule network. Recently, we found the microtubule network was important for regulating the membrane abundance of voltage-gated Kv7.4 potassium channels in vascular smooth muscle. Here, we aimed to investigate the influence of dynein on the microtubule-dependent internalization of the Kv7.4 channel. Patch-clamp recordings from HEK293B cells showed Kv7.4 currents were increased after inhibiting dynein function with ciliobrevin D or by coexpressing p50/dynamitin, which specifically interferes with dynein motor function. Mutation of a dynein-binding site in the Kv7.4 C terminus increased the Kv7.4 current and prevented p50 interference. Structured illumination microscopy, proximity ligation assays, and coimmunoprecipitation showed colocalization of Kv7.4 and dynein in mesenteric artery myocytes. Ciliobrevin D enhanced mesenteric artery relaxation to activators of Kv7.2–Kv7.5 channels and increased membrane abundance of Kv7.4 protein in isolated smooth muscle cells and HEK293B cells. Ciliobrevin D failed to enhance the negligible S-1–mediated relaxations after morpholino-mediated knockdown of Kv7.4. Mass spectrometry revealed an interaction of dynein with caveolin-1, confirmed using proximity ligation and coimmunoprecipitation assays, which also provided evidence for interaction of caveolin-1 with Kv7.4, confirming that Kv7.4 channels are localized to caveolae in mesenteric artery myocytes. Lastly, cholesterol depletion reduced the interaction of Kv7.4 with caveolin-1 and dynein while increasing the overall membrane expression of Kv7.4, although it attenuated the Kv7.4 current in oocytes and interfered with the action of ciliobrevin D and channel activators in arterial segments. Overall, this study shows that dynein can traffic Kv7.4 channels in vascular smooth muscle in a mechanism dependent on cholesterol-rich caveolae.
Carlsberg Foundation http://dx.doi.org/10.13039/501100002808 CF16-0136 Lundbeck Foundation http://dx.doi.org/10.13039/501100003554 R323-2018-3674 Danmarks Frie Forskningsfond http://dx.doi.org/10.13039/501100011958 9039-00409B Horizon 2020 http://dx.doi.org/10.13039/501100007601 Marie Skłodowska-Curie http://dx.doi.org/10.13039/100010665 801199 Novo Nordisk Foundation http://dx.doi.org/10.13039/501100009708 NNF13OC0004294 NNF18OC0031634 National Institute of General Medical Sciences http://dx.doi.org/10.13039/100000057 GM130377 National Institute of Neurological Disorders and Stroke http://dx.doi.org/10.13039/100000065 NS107671
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pmcIntroduction
The repertoire of ion channels found in the membrane of any given cell determines the functionality of the cell. Thus, the trafficking of ion channels into and away from the cell membrane must be carefully controlled for the cell to maintain an appropriate physiological response. Ion channels found in the cell membrane will be ultimately internalized and either recycled back into the cell membrane or degraded. In heterologous expression systems, the internalization and overall surface expression of several potassium channels is reported to be influenced by the dynein motor protein, including the voltage-dependent Kv1.5, Kv2.1, and Kv3.1 channels (Choi et al., 2005; Loewen et al., 2009; Steele and Fedida, 2014).
Dynein is a motor protein bound to the microtubule network that transports “cargo,” including membrane proteins, along cellular paths created by microtubules. This motor protein complex utilizes ATP hydrolysis to drive retrograde movement toward the minus ends of microtubules, i.e., away from the cell membrane (Franker and Hoogenraad, 2013; Bhabha et al., 2016).
Recently, our laboratory showed that disruption of microtubules (with colchicine and nocodazole) increased the membrane levels and function of the voltage-gated potassium channel Kv7.4 (Lindman et al., 2018). In rat mesenteric and renal arteries, where Kv7.4 channels are important physiologically (Stott et al., 2014; van der Horst et al., 2020), microtubule disruption increased Kv7 channel function, which was associated with increased Kv7.4 membrane expression in vascular myocytes. The microtubule-dependent mechanisms that orchestrate this novel regulation pathway are yet to be determined.
The aim of this study was to investigate the influence of dynein on the microtubule-dependent trafficking of the Kv7.4 channel. Herein, we show that dynein binds to Kv7.4 channels to regulate their trafficking away from the cell membrane, which has important implications in vascular physiology. Furthermore, we identify the cholesterol-rich domains of caveolae as being important for this process in vascular smooth muscle cells.
Materials and methods
In silico analysis
Human Kv7.1–Kv7.5, encoded by KCNQ1 to KCNQ5 genes, respectively (UniProt accession nos. P51787, O43526, O43525, P56696, and Q9NR82) and Xenopus laevis Kv7.1 (UniProt accession no. P70057), were aligned using Clustal Omega (Sievers et al., 2011), yielding the full-sequence alignment. We identified similarity to previously identified dynein-binding motifs (Rodríguez-Crespo et al., 2001). The cryo-EM structure of Xenopus Kv7.1 (PDB accession no. 5VMS; Sun and MacKinnon, 2017) was visualized using PyMOL (version 1.8; Schrödinger, LLC).
Animals
All animal experiments were performed in accordance with Directive 2010/63EU on the protection of animals used for scientific purposes and were approved by the National Ethics Committee, Denmark. Male Wistar rats were purchased from Janvier Labs, group-housed in clear plastic containers, and underwent at least 1 wk of habituation before use. All experiments were performed using 14–17-wk-old male Wistar rats euthanized by cervical dislocation.
Reagents
Some the main reagents used in this study are listed below.
Ciliobrevins (Tocris) are a group of small molecules that inhibit the motor activity of dynein by acting as ATP competitors for the dynein adenosine triphosphatase, without disturbing dynein binding to microtubules and without affecting other motor proteins, such as kinesin (Firestone et al., 2012; Roossien et al., 2015). These inhibitory effects are observed between 10 and 40 µM (Firestone et al., 2012; Eyre et al., 2014).
Methyl-β-cyclodextrin (M-βCD; Sigma-Aldrich) depletes cholesterol (Kilsdonk et al., 1995) and disrupts caveolae in arterial preparations while maintaining the contractile phenotype at concentrations <10 mM (Dreja et al., 2002).
Filipin III (Sigma-Aldrich) is a polyene antibiotic that forms complexes with cholesterol to reduce its ability to interact with proteins at concentrations <10 µM, as used in this study (Norman et al., 1972; Delgado-Ramírez et al., 2018).
S-1 and NS15370 (kind gift from NeuroSearch, Ballerup, Denmark) are activators of Kv7.2–Kv7.5 channels with no effect on Kv7.1 channels. At the concentrations used in this study, the effects of these compounds can be fully prevented by Kv7 blockers, such as XE991 or linopirdine (Bentzen et al., 2006; Dalby-Brown et al., 2013; Chadha et al., 2014; Jepps et al., 2014).
Plasmid construction and site-directed mutagenesis
Human KCNQ4 was subcloned into the mammalian expression vector pEGFP-N2 (GenBank accession no: U57608), with the gene cloned into a pEGFP-N2 vector so that it is in frame with the enhanced GFP (EGFP) coding sequences, enabling the expression of KCNQ4 as fusion to the N terminus of EGFP. Mutant KCNQ4 cDNA was generated by PCR-based site-directed mutagenesis using the Quick Change II Site-Directed Mutagenesis Kit (Agilent Technologies). Human p50 (Dynamitin/DCTN2) in pCMV3 was purchased from Sino Biological (HG14449-UT). Overexpression of p50 in HEK293B cells was used as a tool to disrupt the dynein motor system by dissociating the dynactin complex, a complex required for dynein activity (Echeverri et al., 1996; Schroer, 2004; Loewen et al., 2009).
HEK293B cell culture and transfection
HEK293B cells were cultured to 80% confluency before passaging and used for experimentation between P10 and P30. Cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) containing, glutamax, 1% penicillin/streptomycin, and 10% FBS (Substrate Department, the Panum Institute) in an incubator with 5% CO2. For proximity ligation assay (PLA) experiments, 500 µl cell solution was plated on 12-mm coverslips in 24-well plates 1 d before transfection and stored at 37°C with 5% CO2. The cells on the coverslips were transfected at 60% confluency with 50 ng GFP-tagged Kv7.4 or Kv7.4-Q580A mixed with 0.15 µl Lipofectamine 2000 (Invitrogen) and 40 µl Opti-MEM (GIBCO). The cells were incubated for 24 h at 37°C with 5% CO2. Cells were used for PLA experiments 24 h after transfection. For Western blotting and coimmunoprecipitation experiments, HEK293B cells were cultured in a T25 flask and transiently transfected with 1 µg GFP-tagged Kv7.4 or Kv7.4-Q580A mixed with 3 µl Lipofectamine 2000 (Invitrogen) and 50 µl Opti-MEM (GIBCO). Cells were harvested 24 h after transfection in radioimmunoprecipitation assay buffer (in mM): 50 Tris, pH 8.0, 150 NaCl, 1% NP-40, 1% SDS, 0.5% sodium deoxycholate, and protease inhibitor cocktail (Roche) or in immunoprecipitation-lysis buffer (in mM): 50 Tris, pH 8.5, 5 EDTA, 150 NaCl, 10 KCl, 1% Nonidet P40, and protease inhibitor cocktail (Roche) and used for Western blotting or coimmunoprecipitation experiments, respectively.
Electrophysiology
HEK293B cells, cultured at 37°C in DMEM supplemented with 10% FBS and 4 mM glutamine, were transiently transfected with 1 µg EGFP-tagged Kv7.4 or Kv7.4-Q580A in a T25 flask mixed with 3 µl Lipofectamine 2000 (Invitrogen) and 50 µl Opti-MEM (GIBCO). Kv7.4 and Kv7.4-Q580A were coexpressed with 1 µg p50/dynamitin (catalog: HG14449-UT; SinoBiological). The cells were incubated for 24 h at 37°C with 5% CO2. Cells were used 24 h after transfection.
Electrophysiological recordings were performed in whole-cell patch-clamp configuration with patch pipettes pulled from 1.5-mm borosilicate glass capillaries (World Precision Instruments, Inc.) using micropipette puller P97 (Sutter Instruments Co.). Pipette tips were polished to have a pipette resistance of 4–6 MΩ in the bath solution. The pipette solution contained (in mM) 110 KCl, 10 EGTA, 5.17 CaCl2, 1.42 MgCl2, 4 K2ATP, and 10 HEPES (pH 7.4 with KOH). The bath solution contained (in mM) 140 NaCl, 4 KCl, 2 CaCl2, 1 MgCl2, 10 HEPES, and 10 glucose (pH 7.4 with NaOH). Whole-cell currents were recorded with a Multiclamp 700B amplifier (Molecular Devices) controlled by Clampex 10 via digidata 1550 (Molecular Devices). Data were low-pass filtered at 5 kHz and sampled at 10 kHz. All the experiments were performed at room temperature (21°C). Stock solutions of ciliobrevin D were prepared in DMSO and diluted to the appropriate concentration (3 µM) in the bath solutions. The cells were incubated with ciliobrevin D for 15 min before recording currents. For I-V relations, currents were recorded using a stimulation protocol consisting of voltage steps of 2 s from holding potential of −80 mV, ranging from −60 to +40 mV. Currents were measured from the end of the 2-s voltage step and were normalized to the cell capacitance. Steady-state activation curves were constructed by dividing the steady-state current at each voltage to the maximum current at +40 mV. To calculate V1/2 for each given condition, the steady-state activation curves were fitted with a Boltzmann equation.
All analyses and preparation of the figures were done in Igor Pro 6 (WaveMetrics Inc.).
In silico docking
The ciliobrevin D molecule was docked in silico to the human Kv7.1 (Sun and MacKinnon, 2020) and human Kv7.4 (Li et al., 2021) cryo-EM structures that were available at the time of writing. Mutant channels were created by substitution in UCSF Chimera. The unguided docking was performed with CHARMM force fields (Grosdidier et al., 2011a) using SwissDock (Grosdidier et al., 2011b).
Immunoprecipitation and Western blotting
For immunoprecipitation experiments using rat mesenteric artery lysate, three animals’ worth of mesenteric arteries were pooled for protein extraction and homogenized in 200 µl lysis buffer (in mM): 50 Tris, pH 8.5, 5 EDTA, 150 NaCl, 10 KCl, 1% Nonidet P40, and protease inhibitor cocktail (Roche) for 10 min at 4°C. After centrifugation at 11,000 g for 10 min at 4°C to remove cell debris, the supernatant was collected followed by protein quantitation using a bicinchoninic acid Protein Assay kit (Thermo Fisher Scientific). For immunoprecipitation of heterologously expressed Kv7.4, HEK293B cells were maintained in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin. Transient DNA transfections were performed with Lipofectamine using 1 µg Kv7.4 plasmid DNA/T25 flask. The transfected HEK293B cells were harvested 24 h after transfection by centrifugation, and then the cell pellet was incubated on ice for 30 min with 200 µl lysis buffer. After centrifugation at 11,000 g for 10 min at 4°C, the supernatant was collected followed by protein quantitation using a bicinchoninic acid Protein Assay kit (Thermo Fisher Scientific).
Total cell protein lysate was immunoprecipitated using a Dynabeads Protein G Immunoprecipitation Kit (Thermo Fisher Scientific). Briefly, 1.5 mg magnetic dynabeads were transferred to 1.5-ml tubes and placed on a magnet to separate the beads from the solution. The supernatant was removed, and the beads were resuspended with 3 µg anti-dynein antibody (ab23905; Abcam) or anti–caveolin-1 antibody (C3237; Sigma-Aldrich) diluted in 200 µl antibody binding buffer. Tubes containing Dynabeads–Ab complex were incubated with rotation for 30 min at 21°C and placed on a magnet to remove the supernatant. After the Dynabeads–Ab complex was washed in Ab binding and washing buffer, the supernatant was removed, and the Dynabeads–Ab complex was resuspended with 1 mg total rat mesenteric artery protein lysate or 50 µg HEK cell lysate in 200 µl PBS (pH 7.4) and incubated with rotation overnight at 4°C. Subsequently, the Dynabeads–Ab–Ag complex was washed three times in PBS; thereafter, the supernatant was removed, and the Dynabeads–Ab–Ag complex was denatured at 70°C for 10 min in the presence of 20 µl elution buffer (50 mM glycine, pH 2.8), NuPAGE sample buffer, and reducing agent (Thermo Fisher Scientific) to elute Ab–Ag complex from the beads. Samples of the total supernatant were then loaded onto SDS-PAGE gels (4–12% bis–tris; Invitrogen), subjected to electrophoresis, and then transferred onto a polyvinylidene fluoride membrane (Immobilon-FL; Sigma-Aldrich). The membrane was probed with an anti-dynein (1:1,000; ab23905; Abcam), anti–caveolin-1 (1:2,000; C3237; Sigma-Aldrich), anti-Kv7.4 (1:200; sc-50417; Santa Cruz Biotechnology), or anti-GFP (1:2,000; A-11122; Thermo Fisher Scientific) antibody. Protein bands were visualized using fluorescently conjugated secondary antibodies raised in mouse and rabbit (both at 1:10,000; Li-Cor Biosciences) and imaged and analyzed on the Odyssey Infrared Imaging System (Li-Cor Biosciences; version 5.2.5).
Mass spectrometry (MS)
Immunoprecipitated samples eluted from Dynabeads (see above) were subjected to trypsin digestion following the filter-aided sample preparation procedure (Wiśniewski et al., 2009). Briefly, samples were diluted in 8 M urea and 0.1 M Tris, pH 8.5, to 500 µl, and then concentrated on Vivaspin 500 ultrafiltration devices (Sartorius) at 14,000 g (21°C) to a volume of <20 µl. Following addition of 400 µl of 40 mM dithiothreitol in 8 M urea, 0.1 M Tris, pH 8.5, samples were incubated at 21°C for 30 min and centrifuged as above. Samples were then treated with 400 µl of 50 mM iodoacetamide in 8 M urea and 0.1 M Tris, pH 8.5, and incubated for 30 min at 21°C in the dark. The samples were then centrifuged as above before 8 M urea and 0.1 M Tris, pH 8.0, was added (repeated twice). Finally, the samples received 400 µl of 1.6 M urea in 0.1 M Tris, pH 8.0, and were centrifuged to a volume of <20 µl followed by the addition of 100 µl of 1.6 M urea in 0.1 M Tris, pH 8.0, and 1 µl sequence grade trypsin (Promega; 0.1 µg/µl). Following overnight incubation (21°C), samples were centrifuged as above, and the flow through was subjected to solid-phase extraction on Empore C18 discs, as described previously (Rappsilber et al., 2003). Peptide samples were analyzed on an Impact II Q-TOF mass spectrometer (Bruker) coupled to a Dionex Ultimate 3000RSnano chromatography system (Thermo Fisher Scientific). Peptides were loaded onto a Nanoelute C18 column (75 µm × 15 cm, 1.9-µm particle size; Bruker) and eluted over 65 min using a gradient elution system consisting of mobile phase A (0.1% formic acid in H2O) and B (80% acetonitrile in H2O containing 0.1% formic acid) at a flow rate of 0.3 µl/min. The mass spectrometer was operated with cycles of a MS scan followed by up to 12 tandem MS scans of the most intense precursor ions. MaxQuant version 1.6.1.0 (www.maxquant.org) was used as a database search engine for identification of peptides with the following parameters: carbamidomethylation of Cys (fixed modification); Met oxidation and N-terminal acetylation (variable modifications); allowed number of missed cleavages, two; first search mass tolerance, 0.07 D; and main search mass tolerance, 0.006 D. The output data were filtered to exclude proteins that did not match the acceptance criteria of at least two confidently matched peptides in four out of four replicates, which were absent in control samples from immunoprecipitations without IgG and in the presence of normal mouse control IgG.
Immunocytochemistry
Freshly isolated rat mesenteric artery myocytes were isolated as previously described (Jepps et al., 2015). Briefly, dissected mesenteric arteries were placed in smooth muscle dissection solution (SMDS) containing (in mM) 60 NaCl, 80 sodium glutamate, 5 KCl, 2 MgCl2, 10 glucose, and 10 HEPES, pH 7.4, at 37°C for 10 min. Mesenteric arteries were then placed in SMDS containing BSA, papain, and dithiothreitol for 8 min at 37°C, before being washed several times in ice-cold SMDS and incubated again at 37°C in SMDS containing calcium, BSA, collagenase type F, and collagenase type H. Following several washes in ice-cold SMDS, the tissue was triturated gently with a glass pipette. Cells were allowed to adhere to coverslips, then fixed in 4% paraformaldehyde in PBS at 21°C for 30 min and incubated with a blocking solution consisting of 0.2% fish skin gelatin in PBS containing 0.1% Triton X-100 (PBST). Cells were then incubated with primary antibodies diluted in PBST overnight at 4°C. Primary antibodies used were Kv7.4 (1:200; ab65797; Abcam), dynein (1:500; ab23905; Abcam), β-tubulin (1:500; A1126; Invitrogen) and caveolin-1 (1:500; ab17052; Abcam), and sodium-calcium exchanger (NCX; 1:500; R3F1; Swant). Cells were visualized with structured illumination microscopy using a Zeiss Elyra PS.1 Super Resolution Microscope (Carl Zeiss). Cells stained with the NCX antibody were visualized using a Carl Zeiss LSM900 Confocal Super Resolution Microscope with Airyscan 2. Midcell xy sections were selected and analyzed. Total cell fluorescence and intracellular fluorescence signals were quantified using Zen 2012 confocal software.
In a subset of experiments, HEK293B cells transfected with Kv7.4-EGFP (as described above) were allowed to adhere to coverslips, the nuclei were stained with 4′,6-diamidino-2-phenylindole (Thermo Fisher Scientific), and the cells were visualized on an LSM780 confocal microscope (Carl Zeiss).
PLA
Colocalization of dynein with Kv7.4 or caveolin-1 and caveolin-1 with Kv7.4 was studied with PLA in HEK293B cells stably expressing Kv7.4 and Kv7.4-Q580A or freshly isolated rat mesenteric artery myocytes using the Duolink in situ (PLA) detection kit 563 (Olink) per the manufacturer’s instructions. Similar to previous studies (Zhong et al., 2010a; Brueggemann et al., 2014; Chadha et al., 2014; Jepps et al., 2015; Stott et al., 2016; Barrese et al., 2020), cells were allowed to adhere to coverslips and fixed in 4% paraformaldehyde in PBS. Cells were permeabilized in PBST, blocked in Duolink blocking solution, and incubated with pairs of primary antibodies. The primary antibodies employed were dynein (ab23905; Abcam), Kv7.4 (ab65797; Abcam), caveolin-1 (1:500 ab17052; Abcam), caveolin-1 (C3237; Sigma-Aldrich), and NCX (R3F1; Swant). Combinations of secondary anti-rabbit or anti-mouse antibodies of PLA PLUS and MINUS probes were used followed by hybridization, ligation, and amplification steps. Colocalization signals (proteins located within 40 nm of each other) were visualized with a standard Zeiss LSM710 upright laser-scanning confocal microscope.
Myography
Third-order branches of rat mesenteric artery were removed from the animals and cleaned of adherent tissue in physiological salt solution (PSS) containing (in mM) 121 NaCl, 2.8 KCl, 1.6 CaCl2, 25 NaHCO3, 1.2 KH2HPO4, 1.2 MgSO4, 0.03 EDTA, and 5.5 glucose. Following dissection, vessels were cut into 2-mm segments and mounted in a wire myograph (Danish Myo Technology) for isometric tension recordings. The chambers of the myograph contained PSS maintained at 37°C and aerated with 95% O2/5% CO2. Changes in tension were recorded continuously by PowerLab and Chart software (ADInstruments). Arterial segments were equilibrated for 30 min and normalized to passive force (Mulvany and Halpern, 1977). Subsequently, the arteries were contracted with 10 µM of α1-adrenergic receptor agonist methoxamine to assess viability. The chambers were washed with PSS before artery segments were incubated with or without 10 µM ciliobrevin D, 5 mM M-βCD for 1 h, or 3 µM Filipin III for 30 min. After incubation, the arteries were precontracted with 10 µM methoxamine. Subsequently, Kv7.2–Kv7.5 specific activators S-1 (0.1 µM–10 µM) or NS15370 (1 nM–1 µM) were applied to the arteries cumulatively.
Morpholino transfections
To determine the functional impact of Kv7.4 channels in the ciliobrevin D–enhanced vasorelaxations, we employed a morpholino-induced Kv7.4 knockdown technique that was shown previously to efficiently knock down Kv7.4 channels in rat mesenteric arteries (Jepps et al., 2015; Stott et al., 2018). We transfected whole mesenteric artery segments with either a Kv7.4-targeted morpholino or a miss-match control morpholino (with five bases altered from the targeted sequence). Morpholino oligonucleotides (100 nM; Gene Tools Inc.) were mixed with Lipofectamine 2000 (Life Technologies) in Opti-MEM and left at 21°C for 2 h. The Opti-MEM mix was then added to DMEM containing HEPES, glutamax, and NaHCO3 (Substrate Department, the Panum Institute), and the third-order mesenteric arteries were placed in this solution at 37°C. After 36 h of incubation, arteries transfected with miss-match or Kv7.4-targeted morpholino were mounted in the wire myograph for isometric tension recordings, and viability was tested using 20 µM methoxamine. This technique was used due to a lack of a Kv7.4-specific inhibitors.
Two-electrode voltage clamp (TEVC)
Complimentary RNA (cRNA) encoding human Kv7.4 was generated by in vitro transcription using the T7 polymerase mMessage mMachine kit (Thermo Fisher Scientific) after linearization of a vector incorporating Xenopus β-globin 5′ and 3′ untranslated regions flanking the human KCNQ4 coding region to enhance translation and cRNA stability. We quantified cRNA by spectrophotometry. Defolliculated stage V and VI Xenopus oocytes (Xenoocyte) were injected with Kv7.4 cRNA (10 ng total per oocyte). Oocytes were incubated at 16°C in Barth’s saline solution containing penicillin and streptomycin and were washed daily for 3 d before TEVC recording. TEVC was performed at room temperature using an OC-725C amplifier (Warner Instruments) and pClamp11 software (Molecular Devices). For recording, oocytes were placed in a small-volume oocyte bath (Warner Instruments) and viewed with a dissection microscope. Chemicals were sourced from Sigma Aldrich. Bath solution was (in mM) 96 NaCl, 4 KCl, 1 MgCl2, 1 CaCl2, and 10 HEPES, pH 7.6. M-βCD was prepared fresh in bath solution each experimental day and introduced into the oocyte recording bath by gravity perfusion at a constant flow of 1 ml per minute for at least 3 min before recording. Pipettes were of 1–2 MΩ resistance when filled with 3 M KCl. We recorded currents in response to voltage pulses to +40 mV from a holding potential of −80 mV. Electrophysiology data analysis was performed with Clampfit (Molecular Devices) and Origin software (OriginLab Corporation); values are stated as mean ± SEM.
Statistical analysis
All statistical analysis was performed using GraphPad Prism 7. Mean logEC50 (half-maximal effective concentration) and maximal relaxation (Rmax) values were calculated from individual experiments and compared by an unpaired t test or, when more than two groups were compared, a one-way ANOVA followed by a Sidak or Tukey posttest. When it was not possible to calculate logEC50 values, concentration-effect curves were tested with two-way ANOVAs followed by the Bonferroni posttest with correction for multiple comparisons. Membrane expression data were compared with a one-way ANOVA followed by a Tukey posttest or an unpaired t test, depending on the number of groups in the comparison. Mean puncta in PLAs were compared by an unpaired t test. All data are presented as means ± SEM.
Results
Dynein inhibition enhances Kv7.4 currents in HEK cells
As the dynein motor protein is critical for retrograde microtubule network transport, we investigated whether a dynein-recognition site was present in the Kv7.4 channel protein. Similarity to two established dynein-binding motifs was identified, KSTQT and GIQVDR (Rodríguez-Crespo et al., 2001), separated by a single arginine (R; Fig. 1 A). These sites are located in the intracellular C terminus of the protein at the junction between the so-called C and D helices, distal to the membrane. Further analysis using the structure of the homologous Kv7.1 protein (Howard et al., 2007) suggests that several residues in the equivalent sequence-stretch of Kv7.4, specifically the underscored residues in KSLQT-R-VDQIVG, are accessible to the intracellular environment (Fig. 1 B). The high agreement of these exposed amino acids with the established recognition sites, in particular for the first motif, likely allow contact with dynein and suggest that this interaction is based on charge complementation. We note that the motifs are rather conserved among the Kv7 protein family, suggesting that other Kv7 channel isoforms may also be governed by dynein.
Figure 1. Dynein binds to the Kv7.4 C terminus. (A) Amino acid alignment of the end of the c-helix of Xenopus Kv7.1 (used for the modeling in B) and human Kv7.1–Kv7.5 channels. (B) Close view of the Kv7 channel c-helix using the structure of Xenopus Kv7.1 suggests that several residues (highlighted in green) in the dynein-binding motifs indeed are accessible to the intracellular environment. Those in red are not exposed to the intracellular environment and are therefore unlikely to be required for dynein recognition. (C, i–iii) Representative whole-cell voltage clamp recordings and respective I-V relations compared between Kv7.4 and Kv7.4-Q580A (i); when cotransfected with p50/dynamitin (ii); or incubation with 3 µM ciliobrevin D (iii). Statistical comparisons were made with a two-way ANOVA, followed by a Bonferroni multiple comparisons test, where P < 0.05, P < 0.01, and P < 0.001 are depicted by *, **, and ***, respectively. (C iv) Mean V1/2 for steady-state activation was compared for each condition with a one-way ANVOA. (D) Docking simulations performed with SwissDock on Kv7.1, Kv7.4, Kv7.1-Q560A, and Kv7.4-Q580A showing ciliobrevin D binding to both mutant channels but neither WT channel. Each of the four spirals (pink, green, orange, and gold) represent an intercellular C terminus of each of the four Kv7 protein α subunits that multimerize to form a functional channel. Mean values are shown with error bars depicting the SEM.
To test the hypothesis that these dynein-recognition sites in Kv7.4 are crucial for the binding of dynein and regulation of the channel, we substituted one of the intracellularly exposed amino acids in the first motif, Q580, with alanine, which is expected to interrupt the channel interaction with dynein. Voltage-clamp electrophysiology experiments on HEK cells overexpressing Kv7.4-Q580A (n = 6) showed increased Kv7.4 currents at +30 and +40 mV compared with control Kv7.4 currents (n = 10; Fig. 1 C). Coexpression of p50/dynamitin, to interfere with dynein function, increased Kv7.4 currents at voltages positive to 0 mV (n = 5) but had no effect on the Kv7.4-Q580A currents (n = 6; Fig. 1 C). In addition, a dynein-specific inhibitor, 3 µM ciliobrevin D, enhanced the Kv7.4 currents at voltages positive to 10 mV but inhibited the Kv7.4-Q580A currents (n = 5–7; Fig. 1 C). The voltage of half-maximal activation was not affected in the Q580A mutant nor with cotransfection with p50 or ciliobrevin application (Fig. 1 C).
To investigate the inhibition of the Kv7.4-Q580A current by ciliobrevin, we performed in silico docking simulations (Fig. 1 D). For these simulations, we used the known structure of Kv7.1 and the recently published Kv7.4 structure (Sun and MacKinnon, 2020; Li et al., 2021). Ciliobrevin D was unable to bind to the WT Kv7.1 and Kv7.4 channels, whereas both the Kv7.1-Q560A and Kv7.4-Q586A channels allowed ciliobrevin D to bind (Fig. 1 D). In the simulation with Kv7.4, it appears the glutamine (Q580) provides steric hindrance to prevent ciliobrevin D binding, but the smaller alanine in Kv7.4-Q580A allows binding. We suggest that binding of ciliobrevin D to this part of the C terminus would either prevent channel multimerization or forward trafficking of the channel from the ER (Howard et al., 2007; Haitin and Attali, 2008; Wiener et al., 2008), thereby reducing the number of functional channels in the cell membrane, which would result in a reduced current.
Dynein is colocalized with Kv7.4 in HEK293B cells
Western blot analysis showed that the WT Kv7.4 and Kv7.4-Q580A channels were expressed at similar levels in HEK293B cells (Fig. 2 A). PLA experiments showed colocalization of Kv7.4 and dynein in HEK293B cells transfected with Kv7.4. In cells transfected with Kv7.4-Q580A, the number of PLA puncta was reduced, suggesting reduced interaction of dynein with the Kv7.4-Q580A protein (Kv7.4-WT: 15 cells, Kv7.4-Q580A: 20 cells; P < 0.0001; Fig. 2 B).
Figure 2. The Kv7.4-Q580A mutant is less able to bind dynein. (A i) Representative Western blot with HEK293B cells transfected with WT-Kv7.4-EGFP or Q580A-Kv7.4-EGFP showing Kv7.4-EGFP bands (∼104 kD). (A ii) Mean data comparing the Kv7.4-EGFP band intensity in the lysates from HEK293B cells transfected with WT-Kv7.4-EGFP or Q580A-Kv7.4–EGFP. (B i) Representative images of PLAs in HEK293B cells transfected with Kv7.4 (left) or Kv7.4-Q580A (right) using Kv7.4 and dynein antibodies. The EGFP is visualized (green) in the insets to show Kv7.4 expression. Each red punctum is representative of the Kv7 channel and dynein protein localizing within 40 nm of one another. (B ii) Quantification of puncta in HEK293B cells (Kv7.4-WT: 15 cells, Kv7.4-Q580A: 20 cells) with Kv7.4 and dynein and the respective antibodies alone as control, according to an unpaired t test. ***, P < 0.0001. Scale bars, 5 µm. Mean values are shown with error bars depicting the SEM.
Dynein is expressed in vascular smooth muscle and colocalizes with Kv7.4
The presence of dynein in rat mesenteric artery lysates was evidenced by Western blotting (Fig. 3 A). Expression of dynein in intact smooth muscle cells was confirmed using structured illumination microscopy, which showed dynein expression throughout freshly isolated mesenteric artery smooth muscle cells (Fig. 3 B).
Figure 3. Dynein colocalizes with Kv7.4 proteins in vascular smooth muscle cells. (A) Representative Western blot showing dynein (∼74 kD) expression in protein lysates from three different samples of rat small mesenteric artery (rMA) and in one protein lysate from untransfected HEK293B cells. (B) Representative structured illumination microscopy images of an isolated smooth muscle cell from a rat small mesenteric artery, stained with antibodies against β-tubulin (green) and dynein (magenta). Scale bar, 10 µm. (C) Representative Kv7.4 bands from a Western blot following coimmunoprecipitation of dynein in HEK293B cells overexpressing Kv7.4. No bands were detected for the nonspecific binding sample (NS) or for the negative control sample (Neg ctr), where normal mouse control IgG was used instead of the specific pulldown antibody. (D) Representative Kv7.4 bands from a Western blot with samples of rat mesenteric artery protein lysate (rMA) that were immunoprecipitated (IP) with a dynein antibody. Each rMA sample contains n = 3 rats’ worth of mesenteric arteries. (E) Representative structured illumination microscopy image of an isolated smooth muscle cell from a rat small mesenteric artery stained with antibodies against Kv7.4 (magenta) and dynein (green). Scale bar, 10 µm. (F i) Representative image of PLAs with Kv7.4 and dynein antibodies in mesenteric artery myocytes. Each red punctum is representative of the Kv7 channel and dynein protein localizing within 40 nm of one another. (F ii) Quantification of puncta in 34 cells (n = 4 rats) with Kv7.4 and dynein and the respective antibodies alone as control. Mean values are shown with error bars depicting the SEM.
Before investigating whether Kv7.4 coimmunoprecipitated with dynein in rat mesenteric artery protein lysate, we confirmed that Kv7.4 is associated directly with dynein in HEK cells. Immunoprecipitation with an anti-dynein antibody followed by a Western blot using a GFP antibody showed an interaction between dynein and Kv7.4 in HEK293B cells transfected with Kv7.4 (n = 3; Fig. 3 C). Having established that this technique allows for coimmunoprecipitation of these two proteins, we identified Kv7.4 protein in rat mesenteric artery lysate immunoprecipitated with an anti-dynein antibody (n = 3; Fig. 3 D).
Structured illumination microscopy showed the respective localization of dynein and Kv7.4 in freshly isolated mesenteric artery smooth muscle cells (Fig. 3 E). Colocalization (within 40 nm) between dynein and Kv7.4 in freshly isolated mesenteric artery smooth muscle cells was revealed by PLA (34 cells; n = 4 rats; Fig. 3 F). Taken together, these results show dynein colocalizes with Kv7.4 in isolated rat small mesenteric artery myocytes.
Kv7 channel function is enhanced after dynein inhibition
Enhancing Kv7 channels leads to membrane hyperpolarization, thereby reducing the open probability of voltage-dependent calcium channels relaxing precontracted arteries. Activators of Kv7.2–Kv7.5 channels, such as S-1 and NS15370, elicit vasorelaxations, which can be fully prevented by Kv7 blockers such as XE991 or linopirdine (Bentzen et al., 2006; Dalby-Brown et al., 2013; Chadha et al., 2014; Jepps et al., 2014). Using these pharmacological tools, we identified the functional role of dynein by incubating freshly isolated mesenteric artery segments with the specific dynein inhibitor ciliobrevin D and stimulating the precontracted arteries with increasing concentrations of the Kv7.2–Kv7.5 activator NS15370. Incubation with 10 µM ciliobrevin D increased the relaxation to NS15370 in vessels precontracted with methoxamine (n = 4 or 5; EC50 P = 0.0048; Fig. 4, A and B). We also tested an additional Kv7 channel activator, S-1, and found that ciliobrevin D (n = 4) was able to enhance the S-1–mediated relaxation (control n = 7; EC50 P = 0.0278; Fig. 4 C). Incubation with ciliobrevin D had no effect on the initial contractions induced by methoxamine (data not shown).
Figure 4. Kv7 channel function is enhanced following dynein inhibition. (A) Representative isometric tension recordings of rat mesenteric artery segments preconstricted with methoxamine (•) before sequentially increasing concentrations of Kv7.2–Kv7.5–specific activator NS15370 were applied in control (left) and ciliobrevin D–treated (right) arteries. (B) Mean concentration-effect curves and EC50 values to the Kv7.2–Kv7.5–specific activator NS15370 showing the effect of NS15370 in rat mesenteric artery segments before and after 10 µM ciliobrevin D incubation. Mean EC50 values were compared according to an unpaired t test. **, P < 0.01. (C) 10 µM ciliobrevin D enhanced the relaxation to another Kv7.2–Kv7.5 channel activator, S-1, according to an unpaired t test. *, P < 0.05. (D) Rat mesenteric arteries were transfected with either a Kv7.4-targeted or the control (miss-match) morpholino. Relaxations to S-1 were inhibited in arteries transfected with the Kv7.4-targeted morpholino compared with arteries transfected with the control morpholino. Ciliobrevin D enhanced relaxations to S-1 in control arteries, but ciliobrevin D was unable to enhance S-1–mediated relaxation in Kv7.4 knockdown arteries. A one-way ANOVA followed by a Sidak multiple comparisons test was performed, with ** and *** denoting P < 0.01 and P < 0.001, respectively. Error bars show the mean and SEM.
To inhibit Kv7.4 channels specifically, we knocked down Kv7.4 protein in isolated mesenteric arteries using a targeted morpholino to block its translation (Jepps et al., 2015). Functional confirmation of Kv7.4 knockdown was observed in vessels transfected with the Kv7.4-targeted morpholino, where the response to S-1 (1 µM) was inhibited compared with the miss-match control (n = 5; P = 0.01; Fig. 4 D). S-1 relaxation was enhanced significantly in miss-match control arteries by ciliobrevin D (n = 5; Rmax 74.8 ± 7.2%; P = 0.009), whereas morpholino-induced knockdown of Kv7.4 prevented ciliobrevin D from enhancing S-1–mediated relaxation (Fig. 4 D). Thus, ciliobrevin D is unable to enhance S-1 relaxation in the absence of Kv7.4 channels.
Next, we determined whether ciliobrevin D increased the membrane abundance of Kv7.4 channels in isolated mesenteric myocytes. Fig. 5 A shows that incubation with 3 µM ciliobrevin D increased the expression of Kv7.4 channels in the plasma membrane, relative to the total cell expression (n = 9; P = 0.002). As a control, we investigated whether the expression of the NCX was affected by ciliobrevin D treatment. In these experiments, ciliobrevin D had no effect of the location of the NCX in mesenteric artery smooth muscle cells (n = 10 or 11; Fig. 5 A). We confirmed the ciliobrevin-induced increase of Kv7.4 membrane expression in HEK293B cells (Fig. 5 B). In these experiments, 3 µM ciliobrevin D increased Kv7.4 membrane expression (P = 0.0026). The ciliobrevin D–induced increase in Kv7.4 membrane expression was similar to the effect of 100 µM colchicine treatment (n = 6 or 7; P < 0.001; Fig. 5 B), which we reported previously (Lindman et al., 2018).
Figure 5. Dynein inhibition increases membrane expression of Kv7.4 protein. (A, i–iii) Representative midcell z-section of a mesenteric artery myocyte treated with or without ciliobrevin D and stained for Kv7.4 (top) or NCX (bottom; i). Scale bars, 10 μm. Mean membrane intensity of Kv7.4 (n = 9; ii) and NCX (n = 10 or 11; iii) relative to total intensity in cells treated with ciliobrevin D compared with nontreated cells, calculated from midcell z section. Significance was determined by an unpaired t test. **, P < 0.001. (B i) Representative midcell z section of HEK293B cells transfected with Kv7.4-EGFP (green) in nontreated and ciliobrevin D– or colchicine-treated cells. The nuclei are stained with 4′,6-diamidino-2-phenylindole (blue). Scale bars, 10 μm. (B ii) Mean membrane intensity of Kv7.4 relative to total intensity in cells treated with ciliobrevin D (n = 5) or colchicine (n = 7) compared with nontreated cells (n = 6), calculated from midcell z section. Significance was determined by a one-way ANOVA. **, P < 0.001; ***, P < 0.0001. Error bars show mean and SEM.
Dynein is associated with caveolae proteins
We wanted to investigate whether dynein targeted particular vesicles in vascular smooth muscle to create a better understanding of how Kv7.4 is removed by dynein from the cell membrane. Thus, we performed mass spectrometry on dynein immunoprecipitates from rat mesenteric artery lysate. We were unable to identify Kv7.4 as a positive hit in the dataset, with this likely due to the low abundance of Kv7.4 protein. However, we identified caveolin-1 as well as caveolae-associated protein 1 (cavin-1; n = 4; Table 1) as proteins interacting with dynein, suggesting that dynein binds to proteins forming the caveolae microdomains in the cell membrane.
Table 1. Proteins identified following dynein immunoprecipitation, trypsin digestion, and liquid chromatography–tandem MS analysis
Protein UniProt accession no.
Caveolae-associated protein 1 G3V8L9
Caveolin-1 Q2IBC6
Cytoplasmic dynein 1 heavy chain 1 F1LRT9
Cytoplasmic dynein 1 intermediate chain 2 D3ZU74
The identifications were based on at least two peptides matched confidently per protein in four of four replicates, which were absent in control samples from immunoprecipitations without IgG and in the presence of normal mouse control IgG.
To verify this interaction, we performed coimmunoprecipitation assays with rat mesenteric artery lysate using antibodies for dynein and caveolin-1. Dynein was identified in the caveolin-1 immunoprecipitate (Fig. 6 A), and in the reverse experiment, caveolin-1 was identified in the dynein immunoprecipitate (Fig. 6 B). To further validate this interaction, we performed PLA with antibodies against dynein and caveolin-1. Fig. 6 C shows PLA puncta were detected with the dynein- and caveolin-1–specific antibodies. Cholesterol depletion with M-βCD reduced the number of interactions between dynein and caveolin-1 (Fig. 6 C; P = 0.004).
Figure 6. Dynein colocalizes with caveolin-1 proteins. (A) Representative dynein (∼74 kD) bands from a Western blot with rat mesenteric artery protein lysate (rMA) that was immunoprecipitated (IP) with a caveolin-1 (Cav-1) antibody (n = 3) and the total protein lysate as positive control. (B) Representative caveolin-1 (∼22 kD) bands from a Western blot with rMA that was immunoprecipitated with a dynein antibody (n = 3) and the total protein lysate as positive control. Each rMA sample contains n = 3 rats’ worth of mesenteric arteries. No bands for dynein or Cav-1 were detected for the nonspecific binding sample (NS) or for the negative control sample (Neg ctr) where normal rabbit/mouse control IgG was used instead of the specific pulldown antibody. (C i) Representative images of PLA in smooth muscle cells from rat mesenteric arteries with dynein and caveolin-1 antibodies in control (left) and M-βCD–treated (right) cells. Red puncta indicate that target proteins are in close proximity (<40 nM). Scale bars, 10 μm. (C ii) Quantification of the number of PLA puncta in mesenteric artery myocytes (34 cells, n = 4 rats) showing significant decrease of dynein and caveolin-1 colocalization in M-βCD treated cells (**, P = 0.004 according to an unpaired t test). Error bars represent SEM.
Kv7.4 channels are found in caveolae
It is not known if Kv7.4 channels localize in cholesterol-rich regions such as caveolae. To investigate whether Kv7.4 channels are localized to the cholesterol-rich caveolae, we performed structured illumination microscopy on freshly isolated mesenteric artery smooth muscle cells and found Kv7.4 channels to be closely localized with caveolin-1 (Fig. 7 A). To determine colocalization between caveolin-1 and Kv7.4 channels, we performed PLA, which showed that these proteins were localized within 40 nm in smooth muscle cells (27 cells, n = 3 rats; Fig. 7 B). When we incubated rat mesenteric artery myocytes with M-βCD, there was a decrease in the number of associations between caveolin-1 and Kv7.4 (29 cells, n = 3 rats; P = 0.008; Fig. 7 B). For a control interaction with caveolin-1, we also tested whether the NCX protein colocalized with caveolin-1 and if any interaction was affected by cholesterol depletion. Similar to Kv7.4, the NCX colocalized with caveolin-1, but unlike Kv7.4, this interaction was not affected by M-βCD treatment (Fig. 7 C). Overall, these data show colocalizations between Kv7.4 and caveolin-1 proteins, which are dependent on cholesterol, suggesting at least some of the Kv7.4 channels found in the cell membrane are localized in caveolae of vascular smooth muscle cells.
Figure 7. Kv7.4 channels colocalize with caveolin-1. (A) Representative midcell z section of an isolated smooth muscle cell from a rat small mesenteric artery stained with antibodies against caveolin-1 (Cav-1; magenta) and Kv7.4 (green). Scale bar, 10 µm. Insets show the brightfield images of the cells. (B i) Representative images of PLAs in smooth muscle cells from rat mesenteric arteries with Kv7.4 and caveolin-1 antibodies in control (left) and M-βCD–treated (right) cells. Red puncta indicate target proteins are in close proximity (<40 nM). (B ii) Quantification of the number of PLA puncta in mesenteric artery myocytes (control: 27 cells, n = 3 rats; M-βCD: 29 cells, n = 3 rats) showing significant decrease of Kv7.4 and caveolin-1 colocalization in M-βCD–treated cells (**, P = 0.008 according to an unpaired t test). (C i) Representative images of PLAs in smooth muscle cells from rat mesenteric arteries with NCX and caveolin-1 antibodies in control (left) and M-βCD–treated (right) cells. Red puncta indicate target proteins are in close proximity (<40 nM). (C ii) Quantification of the number of PLA puncta in mesenteric artery myocytes (control, 10 cells; M-βCD, 10 cells) showing equal colocalization of NCX and caveolin-1 in M-βCD–treated cells compared with nontreated cells. Error bars represent SEM.
Dynein trafficking of Kv7.4 is dependent on cholesterol
Dynein has been reported to cluster in cholesterol-rich microdomains. Therefore, we tested whether cholesterol depletion reduced the interaction of Kv7.4 with dynein. In mesenteric artery smooth muscle cells, PLA with antibodies against Kv7.4 and dynein showed a reduced number of interactions following M-βCD treatment compared with control (18–20 cells, n = 3 rats; P < 0.0001; Fig. 8 A). Structured illumination microscopy showed that Kv7.4 membrane expression was increased following M-βCD treatment (n = 8–10 cells; P = 0.0001; Fig. 8 B), which was similar to the increase observed with ciliobrevin D treatment. As a control, we observed that the membrane expression of the NCX was unaffected by M-βCD treatment (Fig. 8 B).
Figure 8. Dynein trafficking of Kv7.4 is dependent on cholesterol. (A i) Representative images of PLAs in smooth muscle cells from rat mesenteric arteries with Kv7.4 and dynein antibodies in control (left) and M-βCD–treated (right) cells. Red puncta indicate target proteins are in close proximity (<40 nM). Scale bars, 10 μm. (A ii) Quantification of the number of PLA puncta in mesenteric artery myocytes (control, 20 cells, n = 2 rats; M-βCD, 18 cells, n = 2 rats) showing significant decrease of Kv7.4 and caveolin-1 colocalization in M-βCD–treated cells (***, P < 0.0001 according to an unpaired t test). (B i) Representative midcell z section of a mesenteric artery myocyte treated with or without M-βCD and stained for caveolin-1 (Cav-1; magenta) and Kv7.4 or NCX (green). (B ii) Mean membrane intensity of Kv7.4 relative to total intensity in cells increased with M-βCD (n = 10) compared with nontreated control cells (n = 8; according to a one-way ANOVA. ***, P < 0.0001). Scale bars, 5 μm. (B iii) Mean membrane intensity of NCX relative to total intensity in cells was equal in M-βCD–treated cells (n = 10) and nontreated control cells (n = 9–11). The intensity of caveolin-1 was the same for both groups. Error bars show mean values and SEM.
Cholesterol depletion inhibits Kv7 channel function in mesenteric arteries
To determine the effect of cholesterol depletion on Kv7.4 currents, we performed TEVC experiments on oocytes overexpressing Kv7.4. Application of 1 mM M-βCD inhibited the Kv7.4 current at 40 mV in four out of five oocytes (P = 0.0029; Fig. 9 A). Incubation of rat mesenteric arteries with M-βCD inhibited NS15370 relaxation (n = 8–14; EC50 P < 0.0001; Fig. 9, B and C). Saturation of M-βCD with cholesterol restored the vasodilation to NS15370 (n = 5; Fig. 9, B and C). Interestingly, ciliobrevin D was unable to enhance the NS15370 relaxation in the presence of M-βCD (n = 6; Fig. 9, B and C). To confirm the effect of cholesterol depletion on Kv7 channel function in arteries, we also tested Filipin III. Similar to M-βCD, Filipin III inhibited NS15370 relaxation in rat mesenteric artery segments (n = 7; EC50 P = 0.0078; Fig. 9 D). Taken together, these experiments suggest that arterial Kv7 channels require cholesterol to maintain their normal function.
Figure 9. Cholesterol depletion inhibits Kv7 channel function in mesenteric arteries. (A i) Representative recording of a Kv7.4 current from a Xenopus oocyte before (black) and after (red) application of 1 mM M-βCD. (A ii) Mean data showing the effect of M-βCD on the Kv7.4 current in Xenopus oocytes at 40 mV (n = 5). **, P < 0.01 according to a paired t test. (B) Representative isometric tension recordings of rat mesenteric artery segments preconstricted with methoxamine (•) before sequentially increasing concentrations of Kv7.2–Kv7.5–specific activator NS15370 were applied in (from left to right) control, M-βCD–treated, cholesterol-saturated M-βCD–treated, and M-βCD + ciliobrevin D–treated arteries. (C) Mean concentration-effect curves and EC50 values for the Kv7.2–Kv7.5–specific activator NS15370 in isometric tension recordings from either segments of mesenteric artery treated with 5 mM M-βCD (blue, n = 8), 5 mM M-βCD supplemented with cholesterol (green, n = 5), 10 µM ciliobrevin D (red, n = 10), or 5 mM M-βCD + 10 µM ciliobrevin D (orange, n = 6) or from control vessels (black, n = 14) when methoxamine was used to preconstrict the artery segments. M-βCD significantly attenuates the relaxation for NS15370, while cholesterol-saturated M-βCD had no effect compared with control vessels. Ciliobrevin D was unable to enhance relaxation in M-βCD–treated vessels. (D) Mean concentration-effect curves and EC50 values for the Kv7.2–Kv7.5–specific activator NS15370 in isometric tension recordings from segments of mesenteric artery treated with 3 µM Filipin III. 3 µM Filipin III significantly attenuates relaxation for NS15370. Statistical comparisons on the mean EC50 values were performed with a one-way ANOVA followed by a Tukey multiple comparisons test. *, P < 0.05; **, P < 0.001; ***, P < 0.0001. Error bars show mean values and SEM.
Discussion
Our laboratory showed previously that microtubule disruption enhanced Kv7 channel–dependent vasorelaxation, which was mediated by an increase in Kv7.4 channel levels in the membrane (Lindman et al., 2018). In the current study, we investigated the mechanisms behind this microtubule-dependent regulation of Kv7.4 channels. Our data not only provide the first evidence of dynein influencing Kv7.4 channel trafficking but also show that this dynein-dependent process regulates vascular function and depends on cholesterol-rich caveolae.
In heterologous expression systems, dynein influences the expression of several potassium channels (Choi et al., 2005; Loewen et al., 2009); but none had investigated the Kv7 channel family. Given our previous findings (Lindman et al., 2018), we focused on the Kv7.4 channel, and similar to previous studies investigating the role of dynein on Kv channels, we adopted an electrophysiological approach. Both overexpression of p50/dynamitin, to interfere with dynein function, and ciliobrevin D, to inhibit the movement of dynein, increased the Kv7.4 current in HEK293B cells. In silico analysis of the Kv7.4 channel identified two dynein recognition sites in the C terminus of the channel. Mutating an exposed glutamine residue (580) confirmed dynein’s ability to bind to this region, since the currents produced by this channel were larger than the Kv7.4 WT currents, increasing to a similar degree as ciliobrevin D treatment on the WT channel, and neither p50 overexpression nor ciliobrevin D treatment was able to increase the mutant Kv7.4 current density. Overexpression of p50 had a greater effect on the Kv7.4 current than ciliobrevin D treatment and the Kv7.4-Q580A mutation did. Coexpression of p50 is known to disrupt many dynein-dynactin–dependent processes. The exact mechanism of disruption is not completely clear but probably involves disruption of the dynein–dynactin interaction (Schroer, 2004). This disruption to the dynein–dynactin complex is known to alter dynein-dependent retrograde, as well as anterograde, transport of membrane proteins and multiple organelles by disrupting the binding of the dynein complex to its cargo (Burkhardt et al., 1997; Utrilla et al., 2017). Overall, such treatment can completely rearrange the organelles in the cells, including the Golgi, ER, and lysosomes (Burkhardt et al., 1997). With such a multitude of effects on cell function, it is not possible, within the scope of this study, to determine why p50 overexpression had an enhanced effect on the WT Kv7.4 current compared with ciliobrevin D treatment and the Kv7.4-Q580A mutation. Nevertheless, these data highlight the importance of the dynein recognition sequence in the C terminus of Kv7.4 channels for dynein-dependent regulation since the Kv7.4-Q580A mutant was completely unaffected by p50 overexpression. Given that this glutamine residue is conserved throughout the Kv7 channel family, this dynein-dependent transport mechanism may be a common mechanism in several organs where these channels are also important physiologically, such as the heart, brain, pancreas, and smooth muscle cells.
One striking effect from our electrophysiological studies was the inhibition of the Kv7.4-Q580A currents by ciliobrevin D. To investigate this effect, we performed in silico docking experiments with the known structures for Kv7.1 and Kv7.4. These data showed that ciliobrevin D could bind to the mutant channel, but not the WT Kv7.1 or Kv7.4 channels, in an area important for channel multimerization and trafficking from the ER (Howard et al., 2007; Haitin and Attali, 2008; Wiener et al., 2008). We propose, therefore, that the binding of ciliobrevin D to the mutant channel prevents the functional formation of Kv7.4 channels in the cell membrane, thereby inhibiting the current. Given this off-target effect of ciliobrevin D on the mutant Kv7.4 channel, it is not possible to interpret the effect of ciliobrevin D on the dynein-dependent regulation of the Kv7.4-Q580A channel.
It is well established that vascular voltage-gated Kv7 potassium channels are important regulators of arterial tone, with activation of the Kv7 channels hyperpolarizing the membrane, leading to a relaxation of the smooth muscle cells and concomitant vasodilatation (Zhong et al., 2010b; Ng et al., 2011; Jepps et al., 2014; Stott et al., 2014). Kv7.4 and Kv7.5 are the two predominant functional isoforms in vascular smooth muscle cells, where they mostly form heteromeric Kv7.4/7.5 channels (Brueggemann et al., 2014; Chadha et al., 2014; Jepps et al., 2015). Therefore, we investigated whether the Kv7.4–dynein interaction observed in HEK293B cells was physiologically relevant in vascular smooth muscle.
Using PLA and coimmunoprecipitation assays, we identified dynein colocalization with Kv7.4 in isolated vascular smooth muscle cells and mesenteric artery lysate. Following this, we found that the relaxations to two different Kv7.2–Kv7.5 activators, S-1 and NS15370 (Jepps et al., 2014), were enhanced by dynein inhibition, suggesting an increase in Kv7 channel function. To test this interpretation, we knocked down Kv7.4 protein in mesenteric artery segments (Jepps et al., 2015). Since the targeted morpholino produced a knockdown of Kv7.4 by inserting a splice site, thereby rendering the expressed protein nonfunctional, we assessed successful knockdown using the functional myography responses. As such, there was no relaxation to S-1 following incubation with the Kv7.4-targeted morpholino. Dynein inhibition enhanced the S-1 response in control morpholino-transfected arteries but had no effect in arteries where Kv7.4 was knocked down. We confirmed that Kv7.4 protein was increased in the membrane of both mesenteric artery smooth muscle cells and HEK293B cells following ciliobrevin D treatment and used the expression of the NCX as a negative control for these experiments. These results confirm that dynein is involved in the removal of the channels from the cell membrane of vascular smooth muscle cells and that inhibition of dynein increases the number of Kv7.4 channels in the membrane, thereby increasing the functional impact of the channel in the arteries.
Since their discovery, ciliobrevin compounds have been used by several groups to investigate the functional roles of dynein (Eyre et al., 2014; Fu et al., 2014; Sikirzhytski et al., 2014; Cao et al., 2015); however, this is the first study to exploit these inhibitors to reveal a cardiovascular role for dynein. Although the use of such inhibitors can be complicated by off-target effects, our electrophysiological data show that ciliobrevin D can increase Kv7.4 channel currents, and our Kv7.4 morpholino data provide compelling evidence that the enhanced S-1 effects with ciliobrevin D arise from increased Kv7.4 channel function.
Although we identified a direct binding site for dynein on Kv7.4, blocking dynein function does not prevent endocytosis and recycling of early endosomes (Granger et al., 2014; Rezaul et al., 2016). Therefore, we speculated that the Kv7.4 protein was internalized in a vesicle that was targeted by dynein for removal away from the cell membrane, perhaps through binding to the Kv7.4 channel directly. We performed mass spectrometry on mesenteric artery protein lysates to investigate dynein-bound proteins that could comprise a functional vesicle in which the Kv7.4 protein was being trafficked. Strikingly, caveolae proteins were identified in the mass spectrometry experiments. We confirmed the interaction of dynein with caveolin-1 by coimmunoprecipitation and PLA.
Caveolae are cholesterol-rich regions of the smooth muscle cell plasma membrane formed by membrane proteins called caveolins and cavins, in particular caveolin-1, which is essential for caveolae formation (Root et al., 2015; Parton et al., 2018). These flask-shaped plasma membrane microdomains (50–100 nm) are enriched in lipids such as cholesterol and sphingolipids, where cholesterol is the major component and is crucial for the structural integrity of the microdomain. Several membrane proteins localize in caveolae (Dart, 2010; Villar et al., 2016), and Kv7.1–Kv7.3 channels are known to localize in cholesterol-rich domains (Oldfield et al., 2009; Roura-Ferrer et al., 2010; Zhang et al., 2013; Oliveras et al., 2014; Delgado-Ramírez et al., 2018). Although caveolae provide important microdomains in the cell membrane, they are also internalized as motile vesicles important for the transport of certain proteins within or across polarized cells (Mundy et al., 2002). Several studies have shown that intracellular caveolae movement is regulated by microtubules, with disruption of the microtubule network increasing membrane expression of caveolae (Mundy et al., 2002; Tagawa et al., 2005; Head et al., 2006, 2014). Importantly, dynein motors cluster around cholesterol-rich microdomains in various cells types, which is thought to be an important regulatory mechanism for this motor, allowing it to function efficiently (Johansson et al., 2005; Rocha et al., 2009; Rai et al., 2016; Wijdeven et al., 2016).
Our data show that dynein and Kv7.4 proteins can interact with the caveolin-1 protein of caveolae. Depleting cholesterol in the cells reduced the number of interactions between Kv7.4 and dynein, as well as dynein interactions with caveolin-1. This depletion increased membrane localization of Kv7.4 proteins to a similar extent as ciliobrevin D treatment. In line with previous studies showing that dynein clusters to cholesterol-rich microdomains (Johansson et al., 2005; Rocha et al., 2009; Rai et al., 2016; Wijdeven et al., 2016), our data show that cholesterol is an integral component of this dynein-dependent trafficking mechanism. Thus, when Kv7.4 proteins are located in the cholesterol-rich caveolae, they can be targeted by dynein for retrograde trafficking from the membrane.
Since Kv7.4 membrane expression was enhanced after cholesterol depletion, we speculated that M-βCD would enhance Kv7.4 channel currents and Kv7-specific arterial relaxations to a similar extent as ciliobrevin D treatment did. Surprisingly, M-βCD inhibited currents arising from oocyte overexpression of Kv7.4, and in myography experiments, the relaxation to NS15370 was attenuated after cholesterol depletion with M-βCD. This effect was confirmed using the cholesterol-binding agent Filipin III. In addition, ciliobrevin D incubation had no effect on the M-βCD–induced inhibition of NS15370 relaxation, suggesting that the inhibition was not related to cell tracking or the number of channel proteins in the plasma membrane and functionally confirming the need for cholesterol to target dynein-dependent trafficking of Kv7.4 channels. Previously, Kv7.2/7.3 channel activity was inhibited after cholesterol depletion (Delgado-Ramírez et al., 2018), and we suggest that vascular Kv7 channels require cholesterol and/or caveolae not only to be trafficked but also for correct function within the cell membrane. M-βCD has been used in several studies investigating smooth muscle function, where it has affected multiple ion channels and proteins (Löhn et al., 2000; Dreja et al., 2002; Smith et al., 2005; Morikage et al., 2006; Shmygol et al., 2007; Sones et al., 2010), which may also account for the reduced Kv7 channel function in our arterial preparations.
As the focus of this study was on understanding Kv7.4 trafficking, we have not characterized the implications of the dynein–caveolin-1 interaction further. In addition, the precise molecular interactions regulating dynein binding to the caveolae–Kv7.4 complex have not been investigated. For example, oxysterol-binding protein-related protein 1L (ORP1L) is a cholesterol-sensing switch essential for dynein tethering and the minus-end transport of certain vesicles (Johansson et al., 2005; Wijdeven et al., 2016; Rout et al., 2018). Investigating these proteins and their associations with dynein and Kv7.4 in smooth muscle cells may provide further insights into this trafficking mechanism. Finally, we have shown that despite increased membrane expression, cholesterol depletion inhibits the Kv7-dependent relaxation of arteries. One limitation of our study is the lack of whole-cell Kv7 currents from mesenteric artery smooth muscle cells; however, the direct inhibitory effect of M-βCD on Kv7.4 currents in oocytes, taken together with previous findings that Kv7.2/3 channels are inhibited by M-βCD (Delgado-Ramírez et al., 2018), is good evidence that Kv7.4 channels require cholesterol to function normally. Additional experiments are required to fully elucidate the role of cholesterol and the caveolae on Kv7.4 channel function.
The importance of caveolae in orchestrating signaling pathways is highlighted by their role in hypertension, where a reduction in the number of caveolae is a hallmark feature of arteries from hypertensive animals (Li et al., 2005; Albinsson et al., 2007; Rahman and Swärd, 2009; Swärd et al., 2013; Lian et al., 2019). Additionally, Kv7.4 channels are down-regulated and attenuated functionally in arteries from hypertensive animals (Jepps et al., 2011; Chadha et al., 2012). The results of this study indicate that future experiments should determine if the Kv7.4 down-regulation in arteries from hypertensive animals is associated with a reduced number of caveolae, with this possibly driven by a dysregulated microtubule network.
In summary, this study set out to determine if the microtubule-dependent regulation of Kv7.4 channels was through the dynein motor protein. We identified a dynein-binding domain in the C terminus of the Kv7.4 protein, which allows dynein to regulate Kv7.4 channel membrane expression. This interaction is relevant physiologically in vascular smooth muscle, where it is likely to underlie the microtubule-dependent regulation of Kv7.4 described previously (Lindman et al., 2018). Furthermore, we have shown that dynein binds to caveolae proteins, where Kv7.4 channels appear to reside. Overall, this study provides the first evidence for a physiological role for dynein in vascular smooth muscles and outlines a complex regulatory trafficking mechanism for the Kv7.4 channel. Further work is required to fully understand the details of this association, as well as the potential role of other proteins regulated by dynein in smooth muscle cells.
Acknowledgments
Henk L. Granzier served as editor.
We thank the staff at the Core Facility of Integrated Microscopy (University of Copenhagen) for technical support.
This study was funded by the Carlsberg Foundation (grant CF16-0136 to T.A. Jepps), the Lundbeck Foundation (grant R323-2018-3674 to T.A. Jepps), Danmarks Frie Forskningsfond (grant 9039-00409B to T.A. Jepps), the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie scheme (grant agreement no. 801199 to J. van der Horst), the Novo Nordisk Foundation (grant NNF13OC0004294 to M.J. Davies and NNF18OC0031634 to T. Jespersen), and the US National Institutes of Health, National Institute of General Medical Sciences (GM130377 to G.W. Abbott), and National Institute of Neurological Disorders and Stroke (NS107671 to G.W. Abbott).
Author contributions: J. van der Horst, S. Rognant, L.C. Ozhathil, V. Barrese, C.Y. Chuang, P. Hägglund, P. Gourdon, G.W. Abbott, C. Aalkjær, and T.A. Jepps all acquired and analyzed data. L.C. Ozhathil, T. Jespersen, P. Gourdon, G.W. Abbott, P. Hägglund, I.A. Greenwood, M.J. Davies, C. Aalkjær, and T.A. Jepps contributed to the design of the experiments and interpretation of the results. All authors contributed to the drafting and revision of the manuscript and have approved the final version prior to submission.
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PMC007xxxxxx/PMC7933985.txt |
==== Front
J Gen Physiol
J Gen Physiol
jgp
The Journal of General Physiology
0022-1295
1540-7748
Rockefeller University Press
33656557
jgp.202012655
10.1085/jgp.202012655
Communication
Biophysics
Molecular Pharmacology
DEG/ENaC/ASIC channels vary in their sensitivity to anti-hypertensive and non-steroidal anti-inflammatory drugs
Pharmacology of selected C. elegans DEG/ENaC/ASIC channels
https://orcid.org/0000-0003-4440-6611
Fechner Sylvia 1
D’Alessandro Isabel 1
Wang Lingxin 1
https://orcid.org/0000-0002-1284-6228
Tower Calvin 1
Tao Li 2
https://orcid.org/0000-0002-5810-1272
Goodman Miriam B. 1
1 Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA
2 Department of Biology, Stanford University, Stanford, CA
Correspondence to M.B. Goodman: [email protected]
S. Fechner: [email protected]
05 4 2021
03 3 2021
153 4 e20201265508 5 2020
12 1 2021
© 2021 Fechner et al.
2021
https://creativecommons.org/licenses/by-nc-sa/4.0/ http://www.rupress.org/terms/ This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
Animal physiology depends on degenerin, epithelial sodium, and acid-sensing ion channels (DEG/ENaC/ASICs). By measuring the sensitivity of three C. elegans DEG/ENaC/ASICs to amiloride analogs and NSAIDs, Fechner et al. show that individual channels have distinct pharmacological footprints.
The degenerin channels, epithelial sodium channels, and acid-sensing ion channels (DEG/ENaC/ASICs) play important roles in sensing mechanical stimuli, regulating salt homeostasis, and responding to acidification in the nervous system. They have two transmembrane domains separated by a large extracellular domain and are believed to assemble as homomeric or heteromeric trimers. Based on studies of selected family members, these channels are assumed to form nonvoltage-gated and sodium-selective channels sensitive to the anti-hypertensive drug amiloride. They are also emerging as a target of nonsteroidal anti-inflammatory drugs (NSAIDs). Caenorhabditis elegans has more than two dozen genes encoding DEG/ENaC/ASIC subunits, providing an excellent opportunity to examine variations in drug sensitivity. Here, we analyze a subset of the C. elegans DEG/ENaC/ASIC proteins to test the hypothesis that individual family members vary not only in their ability to form homomeric channels but also in their drug sensitivity. We selected a panel of C. elegans DEG/ENaC/ASICs that are coexpressed in mechanosensory neurons and expressed gain-of-function or d mutants in Xenopus laevis oocytes. We found that only DEGT‑1d, UNC‑8d, and MEC‑4d formed homomeric channels and that, unlike MEC‑4d and UNC‑8d, DEGT‑1d channels were insensitive to amiloride and its analogues. As reported for rat ASIC1a, NSAIDs inhibit DEGT‑1d and UNC‑8d channels. Unexpectedly, MEC‑4d was strongly potentiated by NSAIDs, an effect that was decreased by mutations in the putative NSAID-binding site in the extracellular domain. Collectively, these findings reveal that not all DEG/ENaC/ASIC channels are amiloride-sensitive and that NSAIDs can both inhibit and potentiate these channels.
Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 313913559 Stanford Summer Research Program National Institutes of Health http://dx.doi.org/10.13039/100000002 R01NS047715 R35NS105092 Howard Hughes Medical Institute http://dx.doi.org/10.13039/100000011
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pmcIntroduction
The degenerin, epithelial sodium, and acid-sensing ion channels (DEG/ENaC/ASICs) are present in most if not all metazoan genomes and expressed in diverse tissues, including the epithelia of several organs and in the central and peripheral nervous systems (Eastwood and Goodman, 2012; Kellenberger and Schild, 2002). At least two DEG proteins are known to be mechanosensitive, ENaCs are constitutively active and can be regulated by shear stress, and ASICs are activated by proton binding (Eastwood and Goodman, 2012). The DEGs were identified in Caenorhabditis elegans by virtue of their role in mechanosensation and by gain-of-function mutations that cause neuronal degeneration (Chalfie and Wolinsky, 1990; Driscoll and Chalfie, 1991; Huang and Chalfie, 1994). The ENaCs were identified via expression of rodent capped RNAs (cRNAs) in Xenopus laevis oocytes followed by functional screening (Canessa et al., 1995). The proteins that form ASICs were based on their homology to DEGs and ENaCs (García-Añoveros et al., 1997; Kellenberger and Schild, 2002; Waldmann et al., 1997).
All of these proteins have short intracellular amino and carboxy termini and two transmembrane domains linked by a large extracellular domain that is divided into structures described by a hand holding a ball: wrist, finger, ball, and knuckle. This view has emerged from high-resolution structures derived from x-ray diffraction of protein crystals (Baconguis et al., 2014; Dawson et al., 2012; Gonzales et al., 2009; Jasti et al., 2007; Noreng et al., 2018) and cryo-electron microscopy of chicken ASIC1a (Sun et al., 2018; Yoder et al., 2018) and human ENaC (Noreng et al., 2018). Individual proteins assemble into trimers and form a pore along a common threefold axis at the center of the complex. The extracellular finger domain exhibits more sequence variation than other domains and the general topology of all family members is assumed to share the same fold as ASIC1a. Although ENaCs are formed from three distinct proteins, many DEGs and ASICs can form both homomeric and heteromeric channels. Thus, the ensemble of functional channels is expanded not only by genetic variation of individual channel subunits but also by the formation of heteromeric channels.
The ENaC channels are crucial for salt homeostasis and are blocked by amiloride (Garty and Benos, 1988; Palmer, 1992), a classic anti-hypertension drug that functions as an open-channel blocker (Schild et al., 1997). Sensitivity to amiloride and its analogues is not limited to mammalian family members or to ENaCs, however, but is also seen in DEG/ENaC/ASIC channels expressed in invertebrates. Amiloride is a potent (sub-micomolar half-maximal inhibitory concentration [IC50]) blocker of DEG and ENaC channels, but is at least 100-fold less potent as a blocker of ASIC channels (Canessa et al., 1994; Goodman et al., 2002; Vullo and Kellenberger, 2019). Despite widespread findings of amiloride as an inhibitor, amiloride is also reported to potentiate the activity of two DEG/ENaC/ASIC channels (Adams et al., 1999; Elkhatib et al., 2019). It is not known whether amiloride inhibition and potentiation arise from binding to similar or distinct sites. Among channels inhibited by amiloride, variations in sensitivity could reflect differences in binding affinity or in the efficacy of inhibition. It remains unknown whether or not sensitivity to amiloride or its analogues is a universal feature of DEG/ENaC/ASIC channels.
The ASIC channels are implicated in neurological disease and in pain sensation, but there are no potent and selective small molecule inhibitors of ASICs available (Boscardin et al., 2016; Hanukoglu and Hanukoglu, 2016; Kellenberger and Schild, 2015; Vullo and Kellenberger, 2019). Evidence is emerging that ASICs are targets of nonsteroidal anti-inflammatory drugs (NSAIDs). In particular, ibuprofen is an effective (micromolar IC50) allosteric inhibitor of H+-evoked ASIC1a currents, and mutations in the wrist and the first transmembrane domain reduce the apparent affinity for ibuprofen (Lynagh et al., 2017). This finding implicates NSAIDs as an additional class of small molecules affecting the function of DEG/ENaC/ASIC channels.
Whereas mammalian genomes have nine genes encoding ENaC and ASIC proteins (Hanukoglu and Hanukoglu, 2016; Kellenberger and Schild, 2002), the C. elegans genome harbors more than two dozen genes encoding DEG/ENaC/ASIC proteins (Goodman and Schwarz, 2003; Hobert, 2013). Thus, the C. elegans set of DEG/ENaC/ASIC proteins offers an excellent opportunity to examine variations in the biophysical properties within this superfamily. As an entry point for exploration, we expressed five DEG/ENaC/ASIC proteins in Xenopus oocytes individually, tested their ability to form functional channels, and examined their response to amiloride and its analogues as well as a set of NSAIDs. The five DEG/ENaC/ASIC proteins we studied, DEGT‑1, DEL‑1, UNC‑8, MEC-10, and MEC‑4, are expressed in touch receptor neurons (MEC‑4, MEC‑10, and DEGT‑1; Driscoll and Chalfie, 1991; Huang and Chalfie, 1994; Chatzigeorgiou et al., 2010), mechanical nociceptors (UNC‑8, MEC‑10, and DEL‑1; Tavernarakis et al., 1997; Chatzigeorgiou et al., 2010), and motor neurons (UNC‑8 and DEL‑1; Tavernarakis et al., 1997). All five proteins are either known or proposed to contribute to the formation of mechanosensitive ion channels (O’Hagan et al., 2005; Chatzigeorgiou et al., 2010; Tao et al., 2019; Liu et al., 2020). We used constitutively active gain-of-function or d mutant channel isoforms throughout the study. Here, we found that DEL-1d fails to generate any detectable current on its own and confirmed prior work showing that MEC-10d (Goodman et al., 2002) is also not sufficient to generate current. By contrast, DEGT-1d forms a homomeric channel that is insensitive to amiloride and its analogues, has a more negative reversal potential than other channels, and is blocked by NSAIDs. Both MEC-4d and UNC-8d formed channels blocked by amiloride and carried currents, consistent with prior work (Goodman et al., 2002; Wang et al., 2013). Unexpectedly, MEC‑4d current was strongly potentiated by NSAIDs, and sensitivity to these drugs was decreased by mutations in the extracellular domain that affect inhibition of ASIC1a by ibuprofen, a frontline NSAID drug. Collectively, these findings reveal that not all DEG/ENaC/ASIC channels are amiloride-sensitive and that NSAIDs can both potentiate and inhibit these channels.
Materials and methods
Expression constructs and molecular biology
Plasmids carrying native cDNAs encoding MEC-4, MEC-10, and other C. elegans DEG/ENaC/ASIC proteins derived from the C. elegans genome are subject to deletions and recombination when propagated in standard bacterial strains (Goodman et al., 2002; Chalfie et al., 2003). Previously, we circumvented this outcome using SMC4, a bacterial strain specifically derived for this purpose (Goodman et al., 2002; Chalfie et al., 2003). Here, we used an alternative strategy that enables the propagation of expression plasmids in standard bacterial strains (5-α Competent E. coli, High Efficiency; NEB): synthetic cDNAs codon-optimized for expression in insect cells. Accordingly, we obtained plasmids containing synthesized codon-optimized cDNAs encoding full-length MEC‑4, MEC‑10, and DEGT‑1 (GenScript) in the pGEM-HE oocyte expression vector (Liman et al., 1992). DEL‑1 was codon-optimized for expression in C. elegans (IDT) based on the predicted sequence reported in wormbase release WS250. The predicted isoforms encoded by the del-1 locus have been modified in a more recent database releases (WS274), and these changes are evident only in the amino-terminal domains. WS274 predicts three isoforms, and the isoform we used from WS250 encodes 18 amino acids that are not represented in the updated predictions. Unlike MEC‑4, MEC‑10, DEL‑1, and DEGT‑1, the UNC-8 isoform was not codon-optimized. This plasmid was obtained from L. Bianchi (University of Miami, Miami, FL), was used in prior studies (Matthewman et al., 2018, 2016; Miller-Fleming et al., 2016; Wang et al., 2013), and encodes the shortest of four predicted splice variants (R13A1.4d).
We studied the expressed channels as constitutively active or degeneration isoforms based on gain-of-function mutations identified in forward genetic screens or engineered into homologous residues. Because coexpressing MEC-2 yields larger currents than expressing MEC-4d alone and coexpressing MEC-2 with UNC-8d is either indifferent or may yield to larger currents than expressing UNC-8d alone (Brown et al., 2007; Goodman et al., 2002; Matthewman et al., 2016), we coexpressed MEC-2 with all DEG/ENaC/ASIC channels studied here.
We used in vitro mutagenesis (Q-5 Site-directed Mutagenesis Kit; NEB) to introduce the mutations creating d isoforms using the following primers. All mutations were introduced into plasmids encoding wild-type protein, except for the mutants affecting the extracellular domain in MEC-4, which were introduced into plasmids encoding MEC-4(A713T) or MEC-4d. The primers used for mutagenesis are as follows: MEC‑4(A713T), 5′-CTCTTGACTGACTTCGGTGG-3′, 5′-GTTCACGAAACCGTAGGCTT-3′; MEC‑10(A676V), 5′-AAGATGATGGTTGACTTCGGC-3′, 5′-CACGATACCGTAGGCCTC-3′; DEGT‑1(A813T), 5′-CTCTTGACTGAGATCGGAGG-3′, 5′-CAGGAACAAGTTGTAGGAGC-3′; UNC‑8(G347E), 5′-AAAGATGCGGAAGCCATCACA-3′, 5′-GAGGTCGCTCAATCCAAAAG-3′; DEL‑1(A603V), 5′-AATTTGATGGTCGATATGGGAG-3′, 5′-GAACCATGAATATGACTCG-3′; MEC‑4(A713T, E704K), 5′-GGCCTACGGTTTCGTGAACCTCT-3′, 5′-TTGGACTCGGTGAGCATCTCGAA-3′; MEC‑4(A713T, E704A), 5′-GCGGACTCGGTGAGCATCTC-3′, 5′-AGCCTACGGTTTCGTGAACCTC-3′.
RNA preparation, validation, and oocyte injection
For each channel isoform, we generated cRNAs using in vitro transcription (mMESSAGE mMACHINE T7 kit; Ambion) and quantified cRNA concentration spectroscopically (NanoDrop2000; Thermo Fisher Scientific). We validated the size and integrity of cRNAs using gel electrophoresis (ReliantRNA Gels, 1.25% SeaKem Gold AgaroseSKG; Lonza). To each cRNA sample, we added buffer (2 µl 10× MOPS buffer; Lonza) and loading dye (8 µl, B0363S; NEB) and loaded denatured (70°C, 10 min) samples alongside an RNA ladder (2–4 µl, single-strand RNA ladder, N0362S; NEB). The resulting gels were stained with ethidium bromide for 30 min (0.5 µg/ml double distilled H2O; Thermo Fisher Scientific), washed in double distilled H2O (30 min), and visualized with UV light.
Xenopus oocytes were isolated from gravid females (NASCO) modified from standard procedures (Liu and Liu, 2006). Briefly, frogs were anesthetized with MS-222 (0.5% buffered with pharmaceutical-grade sodium bicarbonate, pH 7–7.5, 1 h). Follicles were removed, opened with forceps, and transferred to OR‑2 solution. For defolliculation, oocytes were incubated (45 min) in OR-2 containing 3 mg/ml collagenase type IV (C-9891; Sigma-Aldrich), washed in fresh OR-2, and incubated in collagenase solution again until follicles were visibly separated from the cells. Defolliculated oocytes were stored in ND96 solution containing (in mM) 96 NaCl, 2.5 KCl, 1 MgCl2, 1.8 CaCl2, and 5 HEPES, pH 7.6, adjusted with NaOH, containing 10 mg/ml penicillin-streptomycin solution (P0781; Sigma-Aldrich). The OR-2 solution contained (in mM) 82.5 NaCl, 2.5 KCl, 1 MgCl2, and 5 HEPES, pH 7.6. We injected cRNA encoding a single DEG/ENaC/ASIC isoform (5 ng) and MEC‑2 cRNA (15 ng) in each oocyte. We reduced cRNA amounts to 3 ng (MEC‑4d isoforms) and 9 ng (MEC‑2) for cells used to collect ibuprofen dose–response curves. We maintained oocytes at 18°C in modified Leibovitz medium (L-15; Sigma-Aldrich) supplemented with gentamicin (144 µM; Gibco) and amiloride (300 µM) for 2–9 d, as described (Brown et al., 2007). Oocytes expressing UNC-8d were maintained in the same solution with an additional 100 µM benzamil. To minimize the impact of variation in the expression of endogenous ion channels and the efficiency of the expression of heterologous channels, we report data from oocytes derived from at least three donor frogs for each channel isoform.
Whole-cell recordings and external solutions
Membrane current was measured by two-electrode voltage clamp (OC-725C; Warner Instruments, LLC) at room temperature (21–24°C). Electrodes (∼0.3 MΩ) were fabricated from borosilicate glass (G100TF‑4; Warner Instruments, LLC) on a horizontal puller (P-97; Sutter Instruments) and filled with 3 M KCl. Analogue signals (current, voltage) were digitized (ITC-16; Instrutech), filtered at 200 Hz (eight-pole Bessel filter), and sampled at 1 kHz. A 60-Hz notch filter (FLA-01; Cygnus Technology, Inc.) was used to reduce line (60 Hz) noise. This equipment was controlled by Patchmaster software (HEKA) on a Windows PC.
Unless otherwise indicated, oocytes were superfused with control saline containing (in mM) 100 Na-gluconate, 2 KCl, 2 MgCl2, 1 CaCl2, and 10 HEPES, adjusted to pH 7.4 with NaOH. Drugs were diluted from stock solutions and added to control saline. For pH 6.4 and 8.4 solution, we replaced HEPES in the control saline with 10 mM PIPES and 10 mM TAPS, respectively.
We purchased drugs from the indicated suppliers and established stock solutions in DMSO. We used stock solutions of 0.1 M, except for phenamil (0.01 M) and for ibuprofen and aspirin dose–response experiments (0.24 M). Drugs were obtained from these suppliers: amiloride (A7410; Sigma-Aldrich), benzamil (B2417; Sigma-Aldrich), 5-(N-ethyl-N-isopropyl)amiloride (EIPA; A3085; Sigma-Aldrich), phenamil (14308; Cayman Chemical), benzamidine (12072; Fluka), ibuprofen (SLBR3566V; Sigma-Aldrich), R-ibuprofen (16794; Cayman Chemical), S-ibuprofen (375160; Cayman Chemical), aspirin (A2093; Sigma-Aldrich), salicylic acid (S5922; Sigma-Aldrich), diclofenac (D6899; Sigma-Aldrich), and flurbiprofen (F8514; Sigma-Aldrich).
Measuring current, drug sensitivity, and reversal potential
We used two voltage protocols to measure membrane current and its response to amiloride analogues and NSAIDs (Fig. 1 A): a voltage ramp (from −100 to +100 mV in 1 s) and voltage steps (from −100 to +40 mV or +60 mV, in 20-mV increments). Both protocols included a conditioning step to −85 mV, which we used to measure current amplitude. In all cases, the holding potential was −60 mV. We applied these protocols repetitively during the application of drugs and solutions with modified ion composition and used voltage ramps to measure reversal potentials.
Figure 1. The DEG/ENaC/ASIC subunits DEGT-1d, UNC-8d, and MEC-4d form homomeric channels in Xenopus oocytes, while DEL-1d and MEC-10d likely do not. (A) Graphical representation of the voltage-clamp protocol (top) and total current measured in an oocyte expressing MEC-4d (bottom). Shaded areas are epochs at a holding potential of −85 mV (aqua) and a voltage ramp from −100 to 100 mV (pink) used to measure current amplitude and reversal potential, respectively. (B–G) Current–voltage curves measured in oocytes coexpressing MEC-2 and DEGT-1d (B), UNC-8d (C), MEC-4d (D), DEL-1d (E), MEC-10d (F), and uninjected (Uninj) oocytes (G). (H) Total current, I (at −85 mV) as a function of uninjected and expressed proteins (top) and estimation plot (bottom) showing the effect of channel protein expression relative to uninjected oocytes (ΔI). (I) Reversal potential (Vrev) as a function of uninjected and expressed proteins (top) and estimation plot (bottom) showing the effect of channel protein expression relative to uninjected oocytes (ΔVrev). Mean values, 95% confidence intervals, and statistical tests for the data in B–I are in Table 1. (J) Change in current (at −85 mV) induced by a pH shift from 8.4 to 6.4 (IΔpH) in uninjected oocytes and oocytes expressing DEGT-1d, UNC-8d, and MEC-4d (top), and estimation plot (bottom) showing the effect size relative to uninjected oocytes (ΔIΔpH). Mean values, 95% confidence interval (in µA), and the two-sided P value of the Mann–Whitney test for J are as follows: DEGT-1d: 0.56 µA (0.43–0.76), P = 4.70E-09 (n = 27); UNC-8d: −1.11 µA (−1.64 to −0.76), P = 4.99E-07 (n = 11); and MEC-4d: 1.03 µA (0.–1.78), P = 4.14E-06 (n = 20).
To determine which channels were indifferent, inhibited, or activated by each of the 10 drugs we tested, we applied each drug a final concentration of 30 µM and measured the difference or drug-sensitive current, Idrug = I(+drug) – I(−drug), at −85 mV. Average values were taken from epochs during step and ramp protocols when cells were held at −85 mV (Fig. 1 A, top) and averaged over three voltage presentations in the absence and presence of each drug. Next, we compared Idrug in experimental and control (uninjected) oocytes, computing the distribution of ΔIdrug using estimation statistics (Ho et al., 2019). We used an analogous strategy to assess which channels were indifferent, inhibited, or activated by protons. Specifically, we measured IΔpΗ by subtracting the current measured at −85 mV in the presence of pH 6.4 saline from the current measured at pH 8.4, a 100-fold increase in H+ concentration. We compared IΔpΗ in experimental and control oocytes, computing ΔIΔpH. We measured reversal potentials from voltage ramps (Fig. 1 A, top, −100 to +100 mV in 1 s). Drug dose–response curves were measured at voltage steps between −100 and either +40 or +60 mV in 20-mV increment steps. Three replicates of the voltage step protocol were averaged to derive current amplitude in the presence of each drug concentration.
Data analysis, curve fitting, and figure generation
Mean values and reversal potentials were calculated using MATLAB (R2014b/R2020b; https://github.com/wormsenseLab/AnalysisFunction.git). ANOVA (two-way) followed by multiple comparison with the Holm–Sidak method (P < 0.05) was performed in Sigma Plot 12.5. Estimation statistics were performed in Python using Data Analysis and Bootstrap-Coupled ESTimation or DABEST (Ho et al., 2019). Using IgorPro 6.37 (Wavemetrics), individual dose response curves were fitted with the Hill equation, EC50 = Imax*[xn/(EC50n+xn)] and IC50=Imax*IC50n/IC50n+xn, where EC50 is the half-maximal concentration for potentiation and IC50 is the half-maximal concentration for inhibition, x is the drug concentration, n is the Hill coefficient which was set to 1, and Imax is the maximum current. We used the mean values for EC50 and IC50 to compute an average fit to the pooled and averaged data. Figures were prepared in python jupyter notebooks (https://github.com/wormsenseLab/JupyterNotebooksDEGENaCPharm.git).
Sequence analysis
The following ion channel sequences (accession nos. in parentheses) were used to generate a phylogenetic tree (see Fig. 7 A) and alignments (Fig. 7 C): ACD‑1 (C24G7.2), ACD‑2 (C24G7.4), ACD‑3b (C27C12.5b), ACD‑4 (F28A12.1), ACD‑5 (T28F2.7), ASIC‑1 (ZK770.1), ASIC‑2 (T28F4.2), DEG‑1 (C47C12.6.1), DEGT‑1 (F25D1.4), DEL‑1 (E02H4.1), DEL‑2a (F58G6.6a), DEL‑3 (F26A3.6), DEL‑4 (T28B8.5), DEL‑5 (F59F3.4), DEL‑6 (T21C9.3), DEL‑7 (C46A5.2), DEL‑8 (C11E4.3), DEL‑9 (C18B2.6), DEL‑10 (T28D9.7), DELM‑1 (F23B2.3), DELM‑2 (C24G7.1), EGAS‑1 (Y69H2.11), EGAS‑2 (Y69H2.12), EGAS‑3 (Y69H2.2), EGAS‑4 (F55G1.13), FLR‑1 (F02D10.5), MEC‑4 (T01C8.7), MEC‑10 (F16F9.5), UNC‑8d (R13A1.4d), UNC‑105e (C41C4.5e), rASIC‑1 (NP_077068), rASIC‑2 (Q62962.1), rASIC-3 (NP_775158.1), rASIC‑4 (Q9JHS6.1), rαENaC (NP_113736.1), rβENaC (NP_036780.1), rγENaC (NP_058742.2), and hδENaC (001123885.2). The alignment for calculating the phylogentic tree was generated with MUSCL and http://www.phylogeny.fr (Dereeper et al., 2010, 2008) and visualized with figtree v1.4.4 (Rambaud, 2018). The alignment was generated with Clustal Omega (Fig. 7 C).
Online supplemental material
Fig. S1 shows how DEGT-1d is affected by amiloride ibuprofen concentrations >30 µM as well as how amiloride sensitivity is independent of the presence or absence of MEC-2. Fig. S2 shows the dose-dependence and voltage sensitivity of UNC-8d inhibition by benzamil and MEC-4d inhibition by EIPA.
Results
We expressed five C. elegans DEG/ENaC/ASIC proteins in Xenopus oocytes to determine which could form homomeric ion channels. The proteins we studied, DEGT-1d, DEL-1d, UNC-8d, MEC-10d, and MEC-4d, are expressed in three classes of mechanoreceptor neurons: touch receptor neurons, polymodal nociceptors, and stretch-sensitive motor neurons. To increase the likelihood of detecting DEG/ENaC/ASIC-dependent currents, we used mutant or d isoforms of each of these proteins linked to neuronal degeneration. We also coexpressed all channel subunits with MEC-2, based on prior studies showing that this yields larger currents than expressing MEC-4d alone (Brown et al., 2007; Goodman et al., 2002; Matthewman et al., 2016; Zhang et al., 2004). Individual proteins were deemed capable of forming homomeric ion channels if total membrane current (measured at −85 mV) and its reversal potential differed from those measured in uninjected control oocytes. We sought additional evidence that each protein formed active channels by measuring the response to a change in pH, sensitivity to amiloride and its analogues, and sensitivity to a panel of NSAIDs.
DEGT-1d, but not DEL-1d, forms homomeric channels
Previous research showed that the DEG/ENaC/ASIC subunits MEC‑4d and UNC‑8d, but not MEC‑10d, can form homomeric channels when exogenously expressed in Xenopus oocytes (Goodman et al., 2002; Wang et al., 2013). Coexpression with MEC‑2 increases MEC-4d (Brown et al., 2008; Goodman et al., 2002; Matthewman et al., 2016) but not UNC‑8d currents (Matthewman et al., 2016). On average, oocytes expressing MEC‑4d generated inward currents at −85 mV that were approximately sevenfold larger than those expressing UNC‑8d and 17-fold larger than those expressing DEGT‑1d. Oocytes expressing MEC‑10d and DEL‑1d, by contrast, generated currents that were indistinguishable from those recorded in uninjected oocytes (Fig. 1, A–I; and Table 1). Consistent with prior reports that oocytes expressing DEG/ENaC/ASIC channels become sodium-loaded during the incubation period (Brown et al., 2007; Canessa et al., 1994; Goodman et al., 2002), membrane current reversed polarity near 0 mV in oocytes expressing MEC‑4d and UNC‑8d (Fig. 1, C, D, and I; and Table 1). UNC‑8d–expressing oocytes had more positive resting membrane potentials (Table 1) following incubation in a medium containing benzamil (100 µM) in addition to amiloride (300 µM). With these findings, we replicate prior work showing that MEC‑4d and UNC‑8d, but not MEC‑10d, form homomeric channels in oocytes (Goodman et al., 2002; Wang et al., 2013) and show that DEL‑1d is not likely to form ion channels on its own.
Table 1. Resting membrane potential and properties of membrane current in oocytes expressing DEG/ENaC/ASIC subunits
Channel subunit Vrest (mV) Vrev (mV) ΔVrev (mV) 95% CI (mV) P Current, I (µA) ΔI (µA) 95% CI (µA) P
Mean ± SEM (n) Mean ± SEM (n) (min, max) Mean ± SEM (n) (min, max)
MEC-4d +13.61 ± 0.2 (95) −1.85 ± 0.114 (93) 47.20 (38.4, 54.7) 2.38e-25 −10.81 ± 0.085 (95) −10.7 (−12.4 to −9.14) 4.53E-39
UNC-8d +6.93 ± 0.16 (56) −3.67 ± 0.163 (59) 52.80 (44.2, 60.0) 2.85e-20 −1.68 ± 0.017 (59) −1.57 (−1.83 to −1.33) 1.15E-27
DEGT-1d −19.31 ± 0.1 (109) −23.31 ± 0.140 (111) 25.80 (17.2, 33.3) 9.05e-13 −0.61 ± 0.004 (113) −0.50 (−0.58 to −0.42) 3.08e-34
DEGT-1d alone −20.87 ± 3.1 (15) −23.99 ± 0.717 (15) 30.8 (21.8, 40.5) 6.56e-05 −0.53 ± 0.019 (15) −0.419 (−0.588 to −0.298) 5.23e-09
DEL-1d −38 0.94 ± 0.9 (18) −60.68 ± 1.563 (19) −11.60 (−26.0, 4.43) 0.166 −0.16 ± 0.008 (20) −0.04 (−0.13 to 0.02) 0.239
MEC-10d −27.31 ± 0.92 (19) −42.12 ± 1.089 (19) 6.96 (−5.24, 18.7) 0.251 −0.21 ± 0.009 (19) −0.09 (−0.19 to −0.03) 0.004
MEC-2 alone −48 ± 7.7 (11) −67.28 ± 1.686 (11) −12.5 (−24.8, 1.2) 0.162 −0.11 ± 0.004 (11) −0.000626 (−0.0325 to 0.0257) 0.236
Uninjected −36 0.22 ± 0.12 (143) −49.08 ± 0.396 (95) - - - −0.12 ± 0.001 (144) - - -
Mean ± SEM for the resting membrane voltage (Vrest) under current clamp conditions and reversal potential (Vrev) and current (current, I) under voltage clamp conditions at the beginning of each recording session. Vrev was calculated from the ramp protocol in Fig. 1 A, and current, I, was measured at −85 mV. Numbers in parentheses are the total number of oocytes analyzed from at least three donor frogs. Confidence intervals (CIs) are derived from estimation statistics to indicate the effect of channel expression on reversal potential (ΔVrev) and current (ΔI), and exact P values from Mann–Whitney test indicate which conditions generated currents that differed from uninjected control oocytes. Except for MEC‑2 and DEGT‑1d alone (shown in Fig. S1), these data are drawn from the same data set displayed in graphical form in the lower panels of Fig. 1, I and J.
Oocytes expressing DEGT‑1d generated small currents at −85 mV, and these currents reversed polarity at significantly more positive potentials than those recorded from uninjected cells and those expressing MEC‑10d and DEL‑1d. The reversal potential of DEGT‑1d currents was also significantly more negative than those recorded from cells expressing MEC‑4d and UNC‑8d (Fig. 1, B–D and I; and Table 1). Together, these findings suggest that DEGT‑1d is sufficient to generate an ion channel whose ion permeability appears to differ from channels formed by MEC‑4d and UNC‑8d. These properties are not conferred by MEC‑2, since DEGT‑1 currents were indifferent to its presence (Fig. S1 A and Table 1). Similarly, the currents are not conferred by MEC‑2, since oocytes expressing MEC‑2 alone were indistinguishable from uninjected oocytes (Fig. S1 A and Table 1; Goodman et al., 2002).
Figure S1. Sensitivity of DEGT‑1d, DEGT‑1 alone, and MEC‑2 alone to higher concentrations of amiloride and ibuprofen. (A) Top: Paired dots show the current at −85 mV in uninjected oocytes (black), in oocytes expressing MEC-2 alone (gray), in DEGT-1d alone (light red), and in oocytes coexpressing MEC-2 and DEGT-1d (red) before and after treatment with 100 µM or 300 µM amiloride (Amil). Drug-sensitive current (Idrug; middle) at −85 mV in uninjected oocytes (black), in oocytes expressing MEC‑2 alone (gray), in DEGT-1d alone (light red), and in oocytes coexpressing MEC‑2 and DEGT‑1d (red). Estimation plots (bottom) showing the effect of each drug (ΔIdrug) on MEC‑2 alone (gray), DEGT‑1d alone (light red), and DEGT‑1d (together with MEC-2; red) relative to the drug effect on uninjected oocytes. Subsequent estimation statistics are described as a change in current compared with the change in current to uninjected oocytes, the 95% confidence interval (in µA), and the two-sided P value of the Mann–Whitney test: 100 µM Amil DEGT‑1d: 0.111 µA (−0.00403 to 0.388), P = 0.279 (n = 12); 300 µM Amil DEGT‑1d: 0.0623 µA (0.0383–0.0922); 300 µM Amil DEGT-1d: 0.0623 µA (0.0383–0.0922), P = 1.32e-06 (n = 36); DEGT‑1 alone: 0.0288 µA (0.00847–0.0617), P = 0.0528 (n = 15); MEC-2 alone: −0.0067 µA (−0.0176 to 0.00437), P = 0.19 (n = 11). (B) Paired dots (top) show the current at −85 mV in uninjected oocytes (black) and in oocytes coexpressing MEC‑2 and DEGT‑1d (red) before and after treatment with 100 µM or 300 µM ibuprofen (Ibu). Drug-sensitive current (Idrug; middle) at −85 mV in uninjected oocytes (black) and in oocytes coexpressing MEC-2 and DEGT-1d (red). Estimation plots (bottom) showing the effect of each drug (ΔIdrug) on DEGT‑1d (red) relative to the drug effect on uninjected oocytes. Subsequent estimation statistics are as follows: 100 µM Ibu DEGT‑1d: 0.19 µA (0.0446–0.329), P = 0.0229 (n = 17); 300 µM Ibu DEGT‑1d: 0.322 (0.213–0.465), P = 2.48e-06 (n = 30).
To verify that DEGT‑1d is able to form homomeric channels, we sought recording conditions in which the current could be potentiated or blocked. As other members of this superfamily form acid-sensitive ion channels, we tested the effect of increasing the extracellular proton concentration on currents carried by DEGT‑1d, MEC‑4d, and UNC‑8d switching from solutions from pH 8.4 and 6.4. This maneuver decreased current carried by DEGT‑1d and, to a lesser degree, MEC‑4d, leading to a positive IΔpH (Fig. 1 J, top). By contrast, acidification appeared to potentiate UNC‑8d currents (Fig. 1 J, top). To determine whether these effects differed from that found in control oocytes, we used an estimation approach to compute the distribution of ΔIΔpH (effect size and 95% confidence intervals) for each channel type (Fig. 1 J, bottom). Collectively, these findings indicate that DEGT‑1d forms a homomeric channel with properties that differ from most other DEG/ENaC/ASIC channels and suggest that alkalization could enhance and acidification could suppress DEGT‑1–dependent currents in vivo.
Unlike MEC-4d and UNC-8d, DEGT-1d is insensitive to amiloride analogues
The DEG/ENaC/ASIC ion channel family is also known as the amiloride-sensitive ion channel family (Goodman and Schwarz, 2003; Hanukoglu and Hanukoglu, 2016; Kellenberger and Schild, 2002), suggesting that channels formed by these proteins are sensitive to the diuretic amiloride and its derivatives. Indeed, both MEC‑4d and UNC‑8d are known to be blocked by amiloride (Goodman et al., 2002; Wang et al., 2013). MEC‑4d has a micromolar affinity for amiloride, and UNC‑8d is approximately eightfold less sensitive to amiloride (Brown et al., 2007; Goodman et al., 2002; Wang et al., 2013). To learn more about amiloride block as a shared but potentially variable property of DEG/ENaC/ASIC channels, we tested DEGT‑1d, UNC‑8d, and MEC‑4d for sensitivity to amiloride and four analogues: benzamil, EIPA, phenamil, and benzamidine (Figs. 2 and 3). These analogues were developed in an effort to generate specificity for ENaCs and Na+/H+ antiporters, both of which play critical roles in the mammalian kidney and are inhibited by amiloride (Frelin et al., 1987).
Figure 2. Structures of amiloride analogues and representative current-voltage curves in the absence (pre) and presence of EIPA. (A) Structures of amiloride analogues used in this study. (B–E) Current-voltage curves of uninjected oocytes (B) and oocytes coexpressing MEC-2 and DEGT‑1d (C), UNC‑8d (D), or MEC‑4d (E) in the absence (lighter color, pre) and presence (darker color, EIPA) of 30 µM EIPA.
Figure 3. Unlike MEC-4d and UNC-8d, DEGT-1d currents are insensitive to amiloride analogues. (A–D) Paired dots show the current at −85 mV in uninjected oocytes (A) and in oocytes coexpressing MEC‑2 and DEGT‑1d (B), UNC‑8d (C), or MEC‑4d (D) before and after treatment with 30 µM amiloride (Amil), benzamil (Bmil), EIPA, phenamil (Phen), or benzamidine (Bzd). (E–G) Drug-sensitive current (Idrug; top) at −85 mV in oocytes expressing DEGT‑1d (E), UNC‑8d (F), or MEC-4d (G; in color) compared with uninjected oocytes (black). Estimation plots (bottom) showing the effect of each drug (ΔIdrug) on DEGT‑1d (E), UNC‑8d (F), or MEC‑4d (G) relative to the drug effect on uninjected oocytes. Mean values, 95% confidence intervals, and statistical analyses related to E–G are in Table 2, Table 3, and Table 4.
For simplicity, we exposed oocytes expressing DEGT‑1d, UNC‑8d, and MEC‑4d to a single concentration (30 µM) of each amiloride analogue. Fig. 2, B–E, shows representative current–voltage curves recorded in the presence and absence of one amiloride analogue, EIPA, in uninjected (control) oocytes and in oocytes expressing each channel. EIPA had no detectable effect on currents in control oocytes or in those expressing DEGT-1d (Fig. 2, A and B). By contrast, EIPA inhibited both UNC‑8d and MEC‑4d current (Fig. 2, D and E). During each recording, we also measured current amplitude at −85 mV (see voltage protocol, Fig. 1 A, top). Fig. 3, A–D, shows the average current measured in the presence and absence of each analogue for individual control, DEGT‑1d, UNC‑8d, and MEC‑4d oocytes. Next, we determined the drug-sensitive current, Idrug = I(+drug) – I(−drug), for each channel isoform and for control oocytes (Fig. 3, E–G, top). Finally, we adopted an estimation statistics approach (Ho et al., 2019) to determine the size of the drug effect, ΔIdrug, on DEGT‑1d, UNC‑8d, and MEC‑4d relative to control (Fig. 3, E–G, bottom). See also Table 2, Table 3, and Table 4. In this representation, negligible effects result in a distribution centered near 0, and inhibitory effects shift the distribution to more positive values.
Table 2. Effect of amiloride analogs and NSAIDs on DEGT-1d currents relative to uninjected oocytes: estimation statistics supporting Fig. 3 and Fig. 5
DEGT-1d ΔIdrug (µA) 95% CI (µA) P
Amiloride analogs
Amiloride 0.0033 (20) (−0.02 to 0.025) 0.36
Benzamil 0.0073 (18) (−0.009 to 0.035) 0.61
EIPA 0.0073 (13) (−0.008 to 0.028) 0.43
Phenamil −0.0043 (13) (−0.024 to 0.016) 0.32
Benzamidine 0.0041(13) (−0.012 to 0.026) 0.77
NSAIDs
Ibuprofen 0.02 (18) (0.0068 to 0.036) 0.001
Flurbiprofen 0.15 (5) (0.084 to 0.22) 0.003
Diclofenac −0.037 (5) (−0.075 to −0.015) 0.003
Aspirin 0.037 (5) (0.0075 to 0.064) 0.08
Salicylic acid 0.073 (4) (0.042 to 0.094) 0.006
Estimation statistics for Fig. 3 E and Fig. 5 E are described as a change in current (ΔIdrug) compared to uninjected oocytes, the 95% CI (in µA), and a two-sided P value of the Mann–Whitney test. Numbers of experiments are given in parentheses (n).
Table 3. Effect of amiloride analogs and NSAIDs on UNC-8d currents relative to uninjected oocytes: estimation statistics supporting Fig. 3 and Fig. 5
UNC-8d ΔIdrug (μA) 95% CI (µA) P
Amiloride analogs
Amiloride 0.22 (5) (0.026 to 0.29) 0.03
Benzamil 0.87 (18) (0.64 to 1.13) 2.08e-09
EIPA 0.61 (9) (0.45 to 0.83) 4.92e-06
Phenamil 0.18 (10) (0.12 to 0.26) 2.86e-06
Benzamidine 0.32 (9) (0.17 to 0.58) 1.29e-05
NSAIDs
Ibuprofen 0.026 (15) (0.000518 to 0.0492) 0.024
Flurbiprofen 0.22 (10) (0.123 to 0.382) 0.00033
Diclofenac 0.24 (13) (0.156 to 0.352) 5.25e-06
Aspirin −0.071 (9) (−0.162 to 0.00864) 0.39
Salicylic acid −0.062 (10) (−0.197 to 0.00931) 0.16
Estimation statistics for Fig. 3 F and Fig. 5 F are described as a change in current (ΔIdrug) compared to uninjected oocytes, the 95% CI (in µA), and a two-sided P value of the Mann–Whitney test. Numbers of experiments are given in parentheses (n).
Table 4. Effect of amiloride analogs and NSAIDs on MEC-4d currents relative to uninjected oocytes: estimation statistics supporting Fig. 3 and Fig. 5
MEC-4d ΔIdrug (µA) 95% CI (µA) P
Amiloride analogs
Amiloride 6.71 (50) (5.1 to 8.87) 2.07e-15
Benzamil 7.81 (32) (5.43 to 11.0) 2.03e-12
EIPA 5.53 (27) (3.77 to 8.11) 2.07e-11
Phenamil 5.18 (26) (3.1 to 8.08) 2.02e-09
Benzamidine 0.61 (25) (−0.065 to 1.68) 0.0022
NSAIDs
Ibuprofen −4.39 (9) (−6.43 to −2.69) 0.0015
Flurbiprofen −3.7 (7) (−4.66 to −2.92) 0.0008
Diclofenac −2.78 (24) (−4.44 to −1.66) 9.91e-08
Aspirin −4.75 (6) (−7.73 to −3.45) 0.0014
Salicylic acid −5.39 (7) (−7.26 to −3.89) 0.0008
Estimation statistics for Fig. 3 G and Fig. 5 G are described as a change in current (ΔIdrug) compared to uninjected oocytes, the 95% CI (in µA), and a two-sided P value of the Mann–Whitney test. Numbers of experiments are given in parentheses (n).
Unlike MEC‑4d, UNC‑8d, and most other DEG/ENaC/ASIC channels, DEGT‑1d was insensitive to all amiloride analogues we tested at our reference concentration of 30 µM (Fig. 3, B and E). To differentiate between a reduced affinity and a lack of sensitivity, we tested concentrations up to 300 µM for amiloride. At this concentration, amiloride reduced current in DEGT‑1d–expressing oocytes by 60 nA, on average, relative to its effect on control oocytes (Fig. S1 A).
UNC‑8d is inhibited not only by amiloride and benzamil (Wang et al., 2013; Miller-Fleming et al., 2016) but also by EIPA, phenamil, and benzamidine (Fig. 3, C and F). Amiloride analogues block UNC‑8d currents with different degrees of potency. In ascending order of potency, UNC‑8d was blocked by amiloride, phenamil, benzamidine, EIPA, and benzamil. The reported IC50 values for UNC‑8d channels for amiloride at −100 mV are 7.8 µM in divalent-containing and 106 µM in divalent-free solution (Wang et al., 2013). The reported IC50 values for UNC‑8d channels for benzamil at −100 mV are 47 µM in divalent-containing and 119 µM in divalent-free solution (Miller-Fleming et al., 2016). In this study, we determined an IC50 for benzamil in control saline (divalent-containing solution) of 14.8 ± 1.6 µM (n = 4) at −60 mV (15.1 µM at −100 mV; Fig. S2, A–C). In contrast to amiloride inhibition, the apparent affinity to benzamil was indistinguishable at voltages between −100 and −20 mV, indicating that the block by benzamil is not voltage-dependent.
Figure S2. Sensitivity of UNC-8d channels to benzamil and MEC-4d channels to EIPA. (A) Representative traces of currents of oocytes coexpressing MEC-2 and UNC-8d channels in the absence (left) and presence (right) of 300 µM Benzamil (Bmil). Current responses to voltage steps from −100 mV to +40 mV in 20-mV increments. (B) Dose–response curves of UNC-8d channels to Bmil at −60 mV. The mean IC50 ± SEM for Bmil at −60 mV was 14.8 ± 1.6 µM (n = 4). (C) IC50 values for different voltages (n = 4). Mean values for dose–response curves were derived from a step protocol similar to Fig. 1 A. Instead of the ramp (red background), voltage steps from −100 mV to +40 mV in 20-mV increments were applied . (D) Representative traces of currents of oocytes coexpressing MEC‑2 and MEC‑4d channels in the absence (left) and presence (right) of 300 µM EIPA. Voltage pulses were applied between −100 and 40 mV (20-mV increments) from a holding potential of −60 mV. Similar results were obtained in a total of 11 oocytes isolated from three frogs. (E) EIPA dose–response relationship of MEC-4d current at −60 mV (normalized to Imax and baseline current). Points are the mean ± SEM analyzed from at least three donor frogs, and the smooth line is fit to these points using a single-binding site curve. (F) IC50 as a function of voltage (points are the mean ± SEM, n = 11, except 0 + 20 mV, n = 2, and +40 mV, n = 1). Smooth lines in B and E are fits of the data to the Hill equation (see Materials and methods).
Similar to UNC‑8d, MEC‑4d is inhibited by amiloride, benzamil, and benzamidine (Brown et al., 2007) and by EIPA and phenamil (Fig. 3, D and G). However, the order of potency differs from UNC‑8d. In ascending order, MEC-4d was blocked by benzamidine < amiloride ≈ EIPA ≈ phenamil < benzamil. The similar potency of amiloride and EIPA was unexpected, and we analyzed this drug further by collecting full dose–response curves for EIPA inhibition of MEC-4d (Fig. S2, D–F). The half-blocking dose or IC50 for EIPA was 3.06 ± 0.6 µM (n = 11) at −60 mV (Fig. S2, E and F), which is indistinguishable from the IC50 for amiloride (2.35 ± 0.39 µM [n = 7] at −60 mV; Brown et al., 2007). It has been shown that the block through amiloride in ENaC, MEC‑4d, and UNC‑8d depends on the transmembrane potential difference such that hyperpolarization of the membrane increases channel block (Kellenberger and Schild 2002; Brown et al., 2007; Wang et al., 2013). By comparison, the apparent affinity to EIPA was insensitive to voltage (Fig. S2 F). Collectively, these results show that MEC‑4d is blocked by many amiloride analogues, in this order of potency: benzamidine (196 µM; Brown et al., 2007) < EIPA (3.06 µM, this study) ≈ amiloride (2.35 µM; Brown et al., 2007) < benzamil (0.83 µM; Brown et al., 2007).
If all of these drugs were to function as open channel blockers like amiloride (Brown et al., 2007; Waldmann et al., 1995), then the dramatic difference in their potency among DEGT‑1d, UNC‑8d, and MEC‑4d channels implies that these channels differ in the molecular pathways by which drugs access a common binding site in the pore or that they possess distinct binding sites. We favor the former idea for two reasons. First, the conserved second transmembrane domain of DEG/ENaC/ASIC proteins has long been thought to line the ion conduction pore and to contribute to a conserved amiloride-binding site (Kellenberger et al., 1999; Snyder et al., 1999). Second, access to this site in the pore would be influenced by differences in the open-state conformation of the extracellular domain, and this is the region that is most divergent among the channels we studied.
Some NSAIDs block DEGT-1d and UNC-8d, but all potentiate MEC-4d
Because NSAIDs have been reported to block ASIC channels with IC50 values in the high micromolar range (90–350 µM; Lingueglia and Lazdunski, 2013; Voilley, 2004), we examined sensitivity to five NSAID drugs (Fig. 4 A). This provides an additional window into shared but variable properties of DEG/ENaC/ASIC channels. Current–voltage curves of uninjected (control), DEGT‑1d, UNC‑8d, and MEC‑4d currents (Fig. 4, B–E) show that ibuprofen has little if any effect on control currents, modestly inhibits DEGT‑1d and UNC‑8d, and seems to potentiate MEC‑4d. None of the NSAIDs tested affected currents in control oocytes at 30 µM (Fig. 5 A), providing a simple background for assessing their effect on DEGT‑1d, UNC‑8d, and MEC‑4d.
Figure 4. Structures of NSAIDs and representative current-voltage curves in the absence (pre) and presence of flurbiprofen (Fibu). (A) Chemical structures of NSAIDs used in this study. (B–E) Current-voltage curves of uninjected oocytes (B) and in oocytes coexpressing MEC-2 and DEGT-1d (C), UNC-8d (D), or MEC-4d (E) in the absence (lighter color) and presence (darker color) of 30 µM Fibu.
Figure 5. NSAIDs potentiate MEC-4d current and inhibit or are ineffective on DEGT-1d and UNC-8d. (A–D) Paired dots show the current at −85 mV in uninjected oocytes (A) and in oocytes coexpressing MEC-2 and DEGT-1d (B), UNC-8d (C), or MEC-4d (D) before and after treatment with 30 µM ibuprofen (Ibu), flurbiprofen (Fibu), diclofenac (Diclo), aspirin (Asp), salicylic acid (SA). (E–G) Drug-sensitive current (Idrug; top) at −85 mV in oocytes expressing DEGT-1d (E), UNC-8d (F), or MEC-4d (G; in color) compared with uninjected oocytes (black). Estimation plots (bottom) showing the effect of each drug (ΔIdrug) on DEGT-1d (E), UNC-8d (F), or MEC-4d (G) relative to the drug effect on uninjected oocytes. Mean values, 95% confidence intervals, and statistical analyses related to E–G are in Table 2, Table 3, and Table 4.
Similar to our strategy for analyzing the effect of amiloride analogues, we measured current (at −85 mV) in the absence and presence of each NSAID and plotted these paired values for control, DEGT‑1d, UNC‑8d, and MEC‑4d currents (Fig. 5, A–D). Next, we used these data to determine the drug-sensitive current, Idrug, in control and channel-expressing oocytes (Fig. 5, E–G, top) and estimation statistics to determine if the effects exceeded those expected for control oocytes, ΔIdrug (Fig. 5, E–G, bottom). Collectively, this analysis indicates that 30 µM flurbiprofen and salicylic acid partially inhibit DEGT‑1 (Fig. 5, B and E) and that 30 µM flurbiprofen and diclofenac partially inhibit UNC‑8d (Fig. 5, C and F). Higher concentrations of ibuprofen also blocked DEGT‑1d currents (Fig. S1 B). Surprisingly, all five NSAIDs potentiated MEC‑4d currents (Fig. 5, D and G). See also Table 2, Table 3, and Table 4. These findings demonstrate that NSAIDs can function as antagonists or agonists of DEG/ENaC/ASIC channels, depending on the channel target.
Next, we applied two NSAIDs (ibuprofen and aspirin) to cells expressing MEC‑4d channels and analyzed the dose–response relationship as a function of membrane voltage (Fig. 6, A–C). To improve the sensitivity of these measurements, we reduced the baseline currents by injecting 1.6-fold less cRNA encoding MEC‑4d for these experiments. Fig. 6 A shows MEC-4d current evoked by a family of voltage steps in the absence (left) and presence (right) of aspirin. The mean ± SEM EC50 values for ibuprofen and aspirin at −100 mV are 34.6 ± 0.9 µM (n = 12) and 79.9 ± 3.7 µM (n = 9), respectively (Fig. 6 B). Neither drug showed evidence of voltage dependence (Fig. 6 C), suggesting that the binding site for these drugs lies outside the pore region. Ibuprofen is an enantiomer containing two chiral molecules, and the S-isoform is the preferred ligand for its primary targets, the cyclo-oxygenase enzymes COX-1 and COX-2 (Orlando et al., 2015; Selinsky et al., 2001). In contrast, MEC-4d potentiation is equally sensitive to both ibuprofen enantiomers (Fig. 6 D) and is less sensitive to ibuprofen than COX-1 and COX-2 (Blobaum and Marnett, 2007). Collectively, these findings suggest that the binding sites for NSAIDs differ in DEG/ENaC/ASIC channels and COXs.
Figure 6. Sensitivity of MEC-4d channels to NSAIDs ibuprofen and aspirin. (A) MEC-4d current in the absence (left) and presence of aspirin (Asp, 700 µM; right). (B) Ibuprofen (Ibu; circles) and aspirin (Asp; triangles) dose–response relationships at −100 mV (normalized to Imax and baseline current). Points are mean ± SEM (Ibu, n = 12; Asp, n = 9); error bars are smaller than the points in most cases. Smooth lines are fits of the data to the Hill equation (see Materials and methods). (C) EC50 values for different voltages for Ibu (circle) and Asp (triangle). (D) Drug-sensitive MEC-4d current (Idrug; top) and estimation plots (bottom) in the presence and absence of ibuprofen isomers (R-Ibu) and (S-Ibu) applied at 30 µM compared with uninjected (uninj) oocytes (black; ΔIdrug). Estimation plots in D (bottom) illustrate the 95% confidence interval (in µA), and the two-sided P value of the Mann–Whitney test. The difference for MEC-4d for 30 µM R-Ibu is 5.27 µA (3.49–8.62), P = 0.000197 and for 30 µM S-Ibu is 4.7 µA (3.39–5.91), P = 0.00028.
MEC‑4d is not the only member of the large DEG/ENaC/ASIC ion channel family (Fig. 7 A) affected by NSAIDs. rASIC1a is inhibited by ibuprofen, and this allosteric effect depends on three positively charged and two hydrophobic residues near the first and second transmembrane domains (Fig. 7, B and C; Lynagh et al., 2017). The putative binding site for ibuprofen is proposed to include these five residues. To learn more, we aligned and compared the sequences of seven C. elegans DEG/ENaC/ASIC channels with rASIC1a (Fig. 7 C). We found that MEC‑4 differs from rASIC1a at the three of the five residues linked to inhibition by ibuprofen (Fig. 7 C, arrowheads). Here, we focused on K422 in rASIC1a and E704, the homologous position in MEC-4 (Fig. 7 C, black arrowhead). Comparing the effect of ibuprofen on MEC‑4d and MEC‑4d(E704K), we found that ibuprofen potentiated both isoforms (Fig. 7, D and E). We quantified this effect by collecting dose–response curves for ibuprofen and aspirin (Fig. 7 G and H). Sensitivity to ibuprofen was modestly increased in MEC‑4d(E704K), but unaffected in MEC‑4d(E704A): the mean ± SEM EC50 values to ibuprofen for MEC‑4d(E704K) and MEC‑4d(E704A) at −100 mV are 135 ± 40 µM (n = 13) and 19.5 ± 8.84 µM (n = 14), respectively (Fig. 7 G). A similar shift was observed for potentiation for MEC‑4d(E704K) by aspirin with a mean ± SEM EC50 value of 179 ± 56 µM (n = 9; Fig. 7 H). This finding differs from allosteric inhibition of rASIC1a, which is significantly impaired by introducing alanine into this position (Lynagh et al., 2017). In an effort to discover the domains responsible for these differences between rASIC1a and MEC-4d, we designed constructs encoding chimeras of these two channels. These chimeras did not generate any detectable current, however, even when coexpressed with MEC‑2. The ability of ibuprofen to potentiate MEC‑4d and to inhibit rASIC1a could reflect the existence of distinct binding sites in the two channel isoforms or a common, conserved binding site and distinct energetic coupling between ibuprofen binding and channel gating. Based on the similar effects of mutagenesis on apparent ibuprofen affinity, we favor a simple model in which NSAIDs share a similar binding site in MEC‑4d and rASIC1a. Future studies to directly determine the ibuprofen-binding sites will be required to differentiate between these classes of models, however.
Figure 7. Amino acid in the wrist close to TM2 in MEC-4d changes sensitivity to ibuprofen and aspirin. (A) Phylogenetic tree of C. elegans DEG/ENaC/ASIC subunits and mammalian ENaC and ASIC subunits. Accession nos. are given in Materials and methods. (B) Ribbon diagram of trimeric cASIC1a (PDB 4NTW) rendered in PyMOL. Residues shown in space-filling mode are linked to ibuprofen binding in rASIC1a (Lynagh et al., 2017). (C) Amino acid alignment of rASIC1, rαENaC, DEGT‑1, UNC‑8, MEC‑4, DEL‑1, MEC‑10, DEG‑1, and DEL‑10 made with Clustal Omega. Left: Amino-terminal domain and transmembrane domain 1 (TM1). Right: β9-α4 and transmembrane domain 2 (TM2). Secondary structure motifs numbered as in Jasti et al. (2007). rASIC1a(K422) (black arrowhead) implicated in Ibu sensitivity. The gray d indicates the site that mutates to cause degeneration in C. elegans. Amino acids implicated in rASIC1 ibuprofen responses are highlighted as follows: red (positively charged), blue (negatively charged), and yellow (hydrophobic). (D and E) MEC-4d current traces (D) and MEC‑4d(E704K) current (E) in the absence (left) and presence of 700 µM (right) ibuprofen (Ibu). (F) Current amplitude (current, I; top) at −85 mV in the presence and absence of ibuprofen (Ibu) and Aspirin (Asp) for the MEC‑4d isoform (green) and the MEC‑4d(E704K) isoform (right). Estimation plots (bottom) showing the drug-induced change in current (Idrug) for MEC‑4d (green) or MEC‑4d(E704K) (gray) in the presence of 700 µM Ibu or Asp compared with the absence of the drugs. Estimation plots show the 95% confidence interval (in µA), and the two-sided P value of the Mann–Whitney test. The effect of 700 µM Ibu is 4.63 µA (1.29–7.73), P = 0.0102 (n = 12) and 10.7 µA (7.58–13.9), P = 5.09e-05 (n = 13) for MEC‑4d and MEC‑4d(E704K), respectively. The effect of 700 µM Asp is 5.11 µA (0.74–9.92), P = 0.073 (n = 7) and 6.53 µA (1.28–11.8), P = 0.0521 (n = 9) for MEC‑4d and MEC‑4d(E704K), respectively. (G) Ibuprofen dose–response relationships for MEC‑4d (green), MEC‑4d(E704K) (gray), and MEC‑4d(E704A) (black) currents at −100 mV (normalized to Imax and baseline current). (H) Aspirin dose–response relationships for MEC‑4d (green) and MEC‑4d-E704K (gray) currents at −100 mV (normalized to Imax and baseline current). Smooth lines in G and H are fits of the data to the Hill equation (see Materials and methods).
Discussion
Sensitivity to amiloride and its analogues
Developed in 1967 to treat hypertension, amiloride is listed an essential medicine by the World Health Organization (2019). Many amiloride derivatives have been developed, and we leveraged this collection to evaluate the sensitivity of DEGT‑1d, UNC‑8d, and MEC‑4d to a panel of five amiloride analogues (Figs. 2 and 3). Whereas both MEC‑4d and UNC‑8d were inhibited by at least one amiloride analogue, DEGT-1d was not obviously affected by any of the five amiloride analogues we tested. UNC‑8d currents differed from MEC‑4d in their sensitivity to amiloride analogues. In particular, UNC‑8d currents are more sensitive to inhibition by benzamil and EIPA than to either amiloride or phenamil. Four of the five compounds inhibited currents carried by MEC‑4d (benzamidine had little or no effect on MEC‑4d at 30 µM, but does block MEC‑4d currents at higher doses [Brown et al., 2007]). Benzamil was the most potent inhibitor of MEC‑4d currents, followed by amiloride and EIPA (Fig. 3 G; Brown et al., 2007). Single-channel recordings demonstrate that amiloride functions as an open channel blocker of MEC‑4 channels (Brown et al., 2007), indicating that amiloride binds within the ion conduction pore. This idea is reinforced by three-dimensional cocrystal structures of cASIC1a and amiloride revealing an amiloride molecule lodged near the external vestibule of the central pore (Baconguis et al., 2014). Together, our findings suggest that DEGT‑1d, UNC‑8d, and MEC‑4d proteins form homomeric channels that differ in the structure of the amiloride-binding site or in the accessibility of compounds to this site.
Sensitivity to NSAIDs and their analogues
The mechanism by which NSAIDs generate analgesia is by inhibiting COX-1 and COX-2 enzymes (Day and Graham, 2013; Weissmann, 1991). These compounds also inhibit DEG/ENaC/ASIC channels (Lingueglia and Lazdunski, 2013; Lynagh et al., 2017; Voilley, 2004; Voilley et al., 2001) and P2X channels (Lynagh et al., 2017). We built on these observations and tested C. elegans DEG/ENaC/ASICs for sensitivity to a panel of five NSAIDs (Figs. 4 and 5). Two of the five NSAIDs modestly inhibited DEGT-1d (Fig. 5 E and Fig. S1) and UNC-8d (Fig. 5 F). In contrast with our finding and the well-characterized inhibition of ASIC1a by NSAIDs, all five compounds strongly activated MEC-4d currents (Fig. 5 G).
Ibuprofen potentiates MEC‑4d currents in a dose-dependent manner (Fig. 6 B) and functions as a negative allosteric modulator of proton-gated ASIC1a currents (Lynagh et al., 2017). Based on our finding that mutating E704 in MEC‑4d decreases the apparent affinity for ibuprofen (Fig. 7 G), we propose that MEC‑4d shares an ibuprofen-binding site with ASIC1a. This raises the question of how ibuprofen binding might enhance MEC‑4d current and suppress ASIC1a current. In ASIC1a, ibuprofen and protons elicit opposing conformational changes at the top of the pore-lining second transmembrane domain (Lynagh et al., 2017), supporting the idea that ibuprofen is a negative allosteric modulator of proton-dependent ASIC1a gating. If a similar conformational change were associated with NSAID binding to MEC‑4d, then it would be uncoupled to proton binding (MEC‑4d is not activated by protons), and we would infer that the motion is associated with an increase in channel gating. Future work will be needed to resolve the exact nature of the allosteric interactions between ibuprofen binding and channel gating, however. The differential response of MEC‑4d and ASIC1a presents an avenue for further study.
Concluding remarks
The DEG/ENaC/ASIC channels differ from most if not all other classes of ion channels: they are only present in metazoan genomes (Katta et al., 2015; Liebeskind et al., 2015). Phylogenetic studies indicate that this gene superfamily has undergone expansions within certain animal lineages, including nematodes and insects (Liebeskind et al., 2015). By analyzing a subset of C. elegans DEG/ENaC/ASIC proteins, we extend understanding of the functional diversification of this ion channel superfamily. In particular, we show that DEGT-1d appears to lack sensitivity to amiloride and four of its derivatives. To our knowledge, this is the first member of this family found to have these properties. This finding suggests that using only amiloride might well obscure the contribution of DEG/ENaC/ASIC channels to cell and tissue function. From the subset of DEG/ENaC/ASIC channels studied, DEGT-1 is phylogenetically distant from the others (Fig. 7 A). We also identified NSAIDs as potential inhibitors of DEGT‑1d and UNC‑8d currents and positive activators of MEC‑4d currents. Thus, ibuprofen might serve as a tool to screen for the activity of other DEG/ENaC/ASICs in heterologous cells or in their native tissue. Collectively, we demonstrate that each of the proteins able to form homomeric channels in Xenopus oocytes exhibits a unique pharmacological footprint within two drug families. This property opens the door to using sensitivity to amiloride and ibuprofen to determine the composition of heterotrimeric DEG/ENaC/ASIC channels either in heterologous cells or in their native tissues.
Acknowledgments
Jeanne M. Nerbonne served as editor.
We thank Z. Liao for excellent technical support, including Xenopus oocyte isolation and molecular biology; and L. Bianchi for the gift of the UNC-8–encoding plasmid.
This work was funded by a fellowship to S. Fechner (Deutsche Forschungsgemeinschaft), the Amgen Scholars Program to I. D’Alessandro (Stanford Summer Research Program), grants from the National Institutes of Health to M.B. Goodman (R01NS07715 and R35NS105092), and support from Howard Hughes Medical Institute to L. Tao.
The authors declare no competing financial interests.
Author contributions: S. Fechner and M.B. Goodman conceived the project. S. Fechner, I. D’Alessandro, and M.B. Goodman designed research. S. Fechner, I. D’Alessandro, L. Wang, and C. Tower performed research. L. Tao contributed new reagents. S. Fechner, I. D’Alessandro, and C. Tower analyzed data. S. Fechner created figures. S. Fechner and M.B. Goodman wrote the paper. I. D’Alessandro, L. Wang, C. Tower, and L. Tao edited the paper.
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PMC008xxxxxx/PMC8291076.txt |
==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
34316717
10.1093/narcan/zcab030
zcab030
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Survey and Summary
Ligand-mediated delivery of RNAi-based therapeutics for the treatment of oncological diseases
Abdelaal Ahmed M Department of Biological Sciences, Purdue University, West Lafayette, IN 47906, USA
https://orcid.org/0000-0002-9602-7827
Kasinski Andrea L Department of Biological Sciences, Purdue University, West Lafayette, IN 47906, USA
Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA
To whom correspondence should be addressed. Tel: +1 765 496 1658; Email: [email protected]
9 2021
20 7 2021
20 7 2021
3 3 zcab03028 6 2021
21 6 2021
25 3 2021
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Cancer.
2021
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
RNA interference (RNAi)-based therapeutics (miRNAs, siRNAs) have great potential for treating various human diseases through their ability to downregulate proteins associated with disease progression. However, the development of RNAi-based therapeutics is limited by lack of safe and specific delivery strategies. A great effort has been made to overcome some of these challenges resulting in development of N-acetylgalactosamine (GalNAc) ligands that are being used for delivery of siRNAs for the treatment of diseases that affect the liver. The successes achieved using GalNAc-siRNAs have paved the way for developing RNAi-based delivery strategies that can target extrahepatic diseases including cancer. This includes targeting survival signals directly in the cancer cells and indirectly through targeting cancer-associated immunosuppressive cells. To achieve targeting specificity, RNAi molecules are being directly conjugated to a targeting ligand or being packaged into a delivery vehicle engineered to overexpress a targeting ligand on its surface. In both cases, the ligand binds to a cell surface receptor that is highly upregulated by the target cells, while not expressed, or expressed at low levels on normal cells. In this review, we summarize the most recent RNAi delivery strategies, including extracellular vesicles, that use a ligand-mediated approach for targeting various oncological diseases.
Graphical Abstract
Graphical Abstract Ligands developed and tested for delivery of RNAi therapeutics.
National Cancer Institute 10.13039/100000054 R01CA226259 R01CA205420 Purdue University 10.13039/100006377 Purdue Center for Cancer Research
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pmcINTRODUCTION
Several diseases including cancer are characterized by aberrant gene expression including upregulation, constitutive activation, or mutations that contribute to disease progression. Ideal therapeutic strategies for modulating these genes include RNA interference (RNAi)-based approaches, which include short interfering RNAs (siRNAs) and microRNAs (miRNAs). siRNAs and miRNAs can be easily designed and synthesized for modulating various genes, even those that are challenging to target with traditional small molecule inhibitors.
miRNAs are small non-coding RNAs that regulate gene expression through imperfect base pairing to various target messenger RNAs (mRNAs), most commonly resulting in mRNA cleavage or translation repression (1). The imperfect base pairing gives miRNAs the unique ability to bind to and regulate multiple target mRNAs. Thus, it is perhaps not surprising that miRNA dysregulation is a common occurrence in human diseases, including cancers where tumor suppressive miRNAs such as miR-34a or let-7 are downregulated and oncogenic miRNAs such as miR-21 and miR-155 are upregulated (2,3).The dysregulated miRNAs contribute to various hallmarks of cancer, such as sustained cancer cell proliferation, induction of angiogenesis, metastatic and invasion phenotypes, and resistance to cell death and anti-cancer agents (4,5) (Figure 1). Based on these intriguing properties of miRNAs, miRNA-based therapeutics, including restoring tumor suppressive miRNAs or antagonizing oncogenic miRNAs, have been developed and will continue to evolve. While miRNA restoration depends mainly on exogenous delivery of miRNA duplexes to restore the levels of downregulated tumor suppressive miRNAs, antagonizing oncogenic miRNAs aims to sequester or inhibit abundantly expressed oncogenic miRNAs through multiple approaches, including use of antisense oligonucleotides or small molecules inhibitors (6) (Figure 1).
Figure 1. miRNA dysregulation in cancer and miRNA-based therapeutics strategies. In cancer, oncogenic miRNAs are upregulated while tumor suppressive miRNAs are downregulated, both of which contribute to disease progression (sustained proliferation, activation of metastasis and invasion, induction of angiogenesis, and resistance to death). To overcome miRNA dysregulation, two miRNA-based therapeutic strategies have been developed. Bottom left, when a tumor suppressive miRNA is downregulated, miRNA levels can be upregulated through exogenous delivery of synthetic miRNAs or through small molecules that can restore normal miRNA biogenesis. Restoring tumor suppressive miRNAs levels downregulates the expression of target cancer oncogenes, inhibiting disease progression. Bottom right, conversely, when an oncogenic miRNA is upregulated, the miRNA can be inhibited using antisense oligonucleotides, miRNA sponges, or small molecule inhibitors. Oncogenic miRNAs inhibition upregulates the expression of tumor suppressor proteins which helps to inhibit disease progression.
MiRNAs inhibit the expression of target genes through imperfect base pairing with target messenger RNAs (mRNAs), which allows a single miRNA the ability to regulate the expression of multiple genes, potentially acting as a multi-drug cocktail. For example, the tumor suppressive miRNA, miR-34 can downregulate genes involved in proliferation (c-MYC, Androgen Receptor), angiogenesis (VEGF), anti-apoptosis (BCL2) and immune response (PD-L1) resulting in a potent antitumor response (7–11). Indeed, several preclinical studies have validated that miR-34, or other tumor suppressive miRNAs such as let-7, can inhibit proliferation, survival, and metastasis of various tumors grown in vivo (12–19). In addition to tumor suppressive miRNAs, siRNAs have shown potential for treating various cancer types such as breast and prostate cancers, both of which have been treated with a PLK1 siRNA (20,21), and blood cancers that have been treated with a STAT3 siRNA (22,23). While miRNAs do not require perfect base pairing with their targets, siRNAs downregulate gene expression through perfect base pairing with a complementary mRNA sequence resulting in mRNA degradation. Various comprehensive reviews have been written over the years on the mechanisms involved in miRNA and siRNA targeting and their use as anti-cancer agents (see (24–27)). Here, we focus on the successes and challenges that still remain with attempting to achieve efficient and toxic-free in vivo delivery of therapeutically-relevant miRNAs and siRNAs.
The development of RNAi-based therapeutics has been limited by several challenges including delivery-associated toxicity, reduced stability, and immunogenic effects related to unmodified RNAi molecules (28). The sensitivity of unmodified RNAi molecules to degradation by nucleases results in decreased RNAi activity, requiring repetitive and high dosing to achieve the intended therapeutic response. Several chemical modifications can overcome these issues. For example, chemical modifications that include replacing the phosphodiester backbone with phosphorothioate (PS) bonds and introducing 2′-O-methyl (2′-OMe) and 2′-fluoro (2′-F) modifications in place of the unstable 2′-OH of the ribose sugar, reduce immunogenic effects, and enhance both stability and activity of siRNAs (29) (Figure 2A). Efforts are ongoing to identify additional modifications and to determine how the modifications affect targeting. Since chemical modifications in the 2′-position of the ribose sugar enhance the binding affinity of siRNA to its RNA target (30), they may induce unintended off-target effects. Although it has been shown that siRNA containing 2′-O-methyl modifications at various positions along the siRNA could reduce off-target effects (31,32), it is critical to determine how various modification patterns affect global gene targeting. Beyond the stability and immunogenicity issues, the main challenge that limits the development of RNAi-based cancer therapeutics is lack of safe, specific, and effective delivery strategies.
Figure 2. Design of ligand-targeted miRNA or siRNA conjugates and the critical characteristics for each component. (A) Various targeting moieties and chemical modifications commonly used for miRNA or siRNA delivery. (B) Ligand-targeted miRNA or siRNA conjugates include a targeting ligand, linker, and miRNA or siRNA. The ligand should have high affinity and specificity for a receptor that is upregulated by the diseased cells but not normal cells. Rapid binding and uptake of the ligand is also important to avoid clearance from circulation. In the case of ligands with low affinity, multivalent designs can enhance binding of the ligand to target cells. Ligand size should be considered carefully, as small ligands often penetrate the dense architecture of the tumor to reach target cells, yet larger ligands are likely to be retained in circulation longer. Ligands can be attached to the RNAi molecule using a cleavable or non-cleavable linker. Optimizing the linker chemistry can enhance ligand binding affinity and can be used to attach an endosomal escape agent to the delivery system. In addition, the miRNA or siRNA should be designed to ensure stability and activity, low immunogenicity, and preferential loading of the guide strand to minimize any potential off-target effects.
A great effort has been made to overcome delivery issues and to ensure the selective targeting of RNAi molecules to diseased cells. For example, the development of N-acetylgalactosamine (GalNAc)-siRNA conjugates by Alnylam Pharmaceuticals is considered a breakthrough in RNAi delivery to the liver (33) (see Figure 4A for the chemical structure). GalNAc is a high affinity ligand that binds the asialoglycoprotein receptor (ASGPR) which is upregulated by hepatocytes resulting in ∼106 receptors per cell (34,35). Upon binding to the receptor, GalNAc-siRNA conjugates are rapidly internalized through receptor-mediated endocytosis. Due to endosomal acidification that causes disruption of ionic interactions, GalNAc-siRNAs are released from ASGPRs which rapidly recycle back to the cell surface every 10–15 min (34,35). The siRNAs are slowly released from the endosomes into the cytosol where loading into the RNA-induced silencing complex (RISC) for subsequent gene targeting takes place (34). The stability of the chemically modified siRNA in the acidic compartments and the slow release rate contribute to the robust and sustained activity of GalNAc-siRNA conjugates in vivo (34). In addition, the unique features of ASGPR in hepatocytes, including significant overexpression and rapid internalization and recycling rate, in addition to the ability of GalNAc to disengage from its receptor, are important factors that have resulted in the exceptional success of GalNAc–siRNA conjugates. Furthermore, GalNAc-siRNA conjugates are chemically modified using 2′-F, and 2′-OMe modifications, and include PS linkages at certain positions which enhances both stability and activity of the siRNA (30,33,36). For any receptor-targeted delivery approach, including GalNAc conjugates, the concentration of the receptor on the cell surface and the internalization kinetics are critical for achieving efficient uptake and targeting. Indeed, the overall level of ASGPR on the surface of hepatocytes is sufficient to achieve robust delivery and activity of GalNAc-siRNAs conjugates, as a reduction in ASGPR by 50% still supports a similar siRNA response (37). In contrast to the liver, delivery of RNAi molecules for targeting non-hepatic diseases is much more difficult. Ligand-RNAi conjugates must accumulate at a sufficient rate in target cells to achieve silencing. However, barriers including the endothelial barrier, renal clearance, inadequate receptor expression, and endosomal entrapment need to be overcome to achieve sufficient silencing (35,38).
In addition to enhancing the stability and activity of siRNAs, considerations need to be made regarding unintended targeting at the molecular level, so-called off-target effects. Alnylam developed a design that limits the off-target effect of siRNAs (39). In the seed region of the siRNA antisense strand a thermally destabilizing modification, such as a glycol nucleic acid (GNA) is introduced. GNA modifications thermally destabilize non-specific interactions, while preserving on-target knockdown. Since sequence-specific RNA-based off-target effects might cause potential hepatotoxicity (39), the broad use of thermally destabilizing modifications will need to be explored in more detail, which could help overcome many of the off-target toxicities observed with other RNAi approaches.
After a great effort in the siRNA therapeutics field, Alnylam Pharmaceuticals has achieved US FDA approval for three siRNAs-based drugs – ONPATTRO® (Patisiran) in 2018, GIVLAARI® (Givosiran) in 2019, and most recently Oxlumo™ (Lumasiran) in 2020. Patisiran targets transthyretin (TTR) for the treatment of hereditary transthyretin amyloidosis (40), Givosiran targets aminolevulinic acid synthase 1 (ALAS1) for the treatment of acute hepatic porphyria (41), and Lumasiran treats primary hyperoxaluria type 1 (PH1) by targeting glycolate oxidase (GO) (42). While Patisiran uses lipid nanoparticles for delivery of siRNA, Givosiran and Lumasiran capitalize on direct conjugation of the siRNA to a tri-GalNAc ligand (40–42) (see Figure 4A for chemical structure). The success of these siRNA-based drugs offers the promise of developing other RNAi-based therapeutics for targeting extrahepatic diseases such as cancers. Even though targeting tissues beyond the liver is more complicated and additional effort is still needed, great progress has been made to overcome some of the limitations that stand against delivering RNAi molecules to these tissues. In this review, we summarize the strategies that have been developed to achieve specific delivery of miRNAs and siRNAs for targeting oncological and survival signaling in tumor cells as well as in tumor-associated immunosuppressive cells.
LIGAND-BASED STRATEGIES FOR RNAi DELIVERY
Several RNAi-targeted delivery strategies have been developed to achieve selective delivery of RNAi molecules to tumors. These strategies include direct conjugation of the RNAi molecule to a targeting ligand in the absence of a delivery vehicle or packaging the RNAi molecules into a delivery vehicle that is engineered to display a targeting ligand on its surface (Figure 3). Various ligands used for both delivery strategies are described in this review and are summarized in Table 1.
Figure 3. Proposed internalization mechanisms of ligand-targeted miRNA or siRNA. Left panel: Internalization of ligand decorated synthetic lipids or natural extracellular vesicles (EVs) packaged with tumor suppressive miRNA/siRNA. After binding of the ligand to its receptor on the surface of cancer cells, ligand displaying delivery vehicles (synthetic vehicles or natural EVs) undergo receptor-mediated endocytosis or fusion with the cell membrane followed by release of miRNAs or siRNAs into the cytosol. Right panel: Internalization of vehicle free ligand-conjugated miRNA or siRNA. The ligand, conjugated to the miRNA or siRNA, binds to its receptor on the cell surface and is internalized via receptor-mediated endocytosis followed by release of the miRNA or siRNA into the cytosol while the receptor is recycled back to the cell surface. In the cytosol, tumor suppressive miRNAs or siRNAs engage with the RNA-induced silencing complex (RISC) and modulate gene expression resulting in downregulation of target genes and thus, reducing tumorigenesis.
Table 1. Ligand-targeted miRNA or siRNA delivery systems for cancer therapy
Ligand/receptor miRNA/siRNA RNA chemical modification Condition Outcome Ref.
Ligand-targeted (vehicle-free) delivery
Folate/FR miR-34a 2′-O-Methyl nucleotides Breast and lung cancer Inhibition of tumor growth (71)
Folate-nigericin/FR miR-34a 2′-O-Methyl nucleotides Breast cancer Downregulation of cell proliferation (73)
Folate/FR siLuciferase 2′-O-Methyl nucleotides Breast cancer Downregulation of Luciferase activity (71)
Folate-ODN/ FR siαV integrin, siSurvivin Unmodified FR+ HUVECs/ KB cells Significant downregulation of target genes (69)
Folate/FR siLuciferase, siBCL-2 Unmodified * FR+ HeLa cells Significant downregulation of target genes (70)
DUPA-dsRBD/ PSMA siPLK1 2′-O-Methyl or 2′-F nucleotides, PS bonds Prostate cancer Inhibition of tumor growth (43)
Trivalent DUPA/ PSMA siPLK1 2′-O-Methyl nucleotides, PS bonds Prostate cancer Inhibition of tumor growth (78)
GL21.T aptamer/ AXL receptor let-7g 2′-F pyrimidines Lung cancer Inhibition of tumor growth (83)
GL21.T aptamer/ AXL receptor miR-212 2′-F pyrimidines Lung cancer Sensitization of lung cancer to TRAIL therapy (84)
GL21.T aptamer/ AXL receptor miR-34c 2′-F pyrimidines Lung cancer Synergistic inhibition of cell proliferation by GL21.T-miR-34c and erlotinib (86)
GL21.T aptamer/ AXL receptor miR-148b 2′-F pyrimidines Breast cancer and melanoma Apoptosis and necrosis in breast tumor, prevented tumor cell dissemination (87)
anti-KIT aptamer/ KIT receptor miR-26a 2′-F uridines Basal-like breast cancer cells and HSPCs Inhibition of tumor growth and protection against chemotherapy induced myelosuppression (88)
apt69.T aptamer/ BCMA miR-137 2′-F pyrimidines Multiple myeloma Downregulation of cell viability (89)
EpCAM aptamer/ EpCAM receptor siPLK1 2′-F pyrimidines Breast cancer Inhibition of tumor growth (21)
A10 aptamer/PSMA siPLK1 and siBCL-2 2′-F pyrimidines Prostate cancer Inhibition of tumor growth by A10-siPLK1 (20,90)
CpG-ODN/SR, TLR9 miR-146a 2′-O-Methyl nucleotide Del(5q) MDS and AML Inhibition of NF-κB inflammatory activity and disseminated leukemia progression (104)
CpG-ODN/ SR, TLR9 siSTAT3 Deoxyribonucleotides Melanoma, multiple myeloma, and AML Induction of antitumor immunity and Inhibition of tumor growth (22,23,108)
CpG-ODN/ SR, TLR9 siSTAT3 Deoxyribonucleotides MDSCs from Prostate cancer patients Abrogation of MDSC immunosuppressive activity (109)
ScFv/Her2 siPLK1 2′-O-Methyl nucleotides Breast cancer Inhibition of tumor growth and metastasis and prolonged survival (113)
mAb/Transferrin receptor siLuciferase Unmodified Brain tumor Significant reduction of luciferase expression (115)
mAb (Hu3S193) /Lewis-Y siSTAT3 Unmodified Lewis-Y+ A431 cells Significant silencing of STAT3 (116)
mAb/EGFR siKRAS Not reported Colon Cancer Significant inhibition of tumor growth (117)
ScFv/PSMA siNotch1 Unmodified Prostate cancer Significant inhibition of tumor growth (118)
mAb/PSMA siTRIM24 2′-O-Methyl nucleotide Prostate cancer Significant inhibition of tumor growth (119)
Dual variable domain antibody/ BCMA, SLAMF7 or CD138 siCTNNB1 2′-O-Methyl, 2′-F nucleotides, PS bonds Multiple myeloma Significant downregulation of CTNNB1 (120)
DARPin/EpCAM siBCL-2 2′-O-Methyl nucleotides Breast cancer Sensitization to doxorubicin treatment (123)
Centyrins/EGFR or EpCAM siCTNNB1 2′-O-Methyl, 2′-deoxy-2′-F nucleotides,PS bonds A431 tumor or colorectal cancer cell lines Significant downregulation of CTNNB1 and reduction of colorectal cancer cell viability in vitro (124)
Targeted extracellular vesicles (EVs)
GE11 peptide/ EGFR let-7a Unmodified Breast cancer Inhibition of tumor growth (140)
AS1411 aptamer/ nucleolin let-7a Not reported Breast cancer Inhibition of tumor growth (141)
A15/integrin αvβ3 miR-159 Cholesterol-modified Breast cancer Inhibition of tumor growth (142)
IL3/IL3-R siBCR-ABL Not reported Chronic myeloid leukemia Inhibition of tumor growth (145)
Folate/FR siSurvivin 2′-F nucleotides Colorectal cancer (PDX-CRC) Inhibition of tumor growth (147)
EGFR aptamer/EGFR siSurvivin 2′-F nucleotides Breast cancer Inhibition of tumor growth (147)
PSMA aptamer (A9g)/PSMA siSurvivin 2′-F nucleotides Prostate cancer Inhibition of tumor growth (147)
FR: folate receptor; PSMA: prostate specific membrane antigen; dsRBD: double stranded RNA binding domain; DUPA: (2- [3-(1,3-dicarboxy propyl) ureido] pentanedioic acid); BCMA: B cell maturation antigen; ODN: oligodeoxynucleotides; SR: scavenger receptor; TLR: Toll-like receptor; A15: disintegrin and metalloproteinase 15; IL3: interleukin 3; IL3-R: interleukin 3-receptor; EGFR: epidermal growth factor receptor; ScFv: single-chain fragmented antibody; mAb: Monoclonal antibody; Si: siRNA (short interfering RNA); miR: microRNA; Bcl-2: B-cell lymphoma 2; Plk1: polo-like kinase 1; STAT3: signal transducer and activator of transcription 3; TRIM24: Tripartite motif-containing protein 24; 2′-F: 2′- fluoro; PS: phosphorothioate; HSPCs: hematopoietic stem/progenitor cells; Del(5q) MDS: chromosome-5q deletion myelodysplastic syndrome; AML: acute myeloid leukemia; MDSCs: myeloid derived suppressor cells; NF-κB: nuclear factor-κB; PDX-CRC: patient-derived colorectal cancer xenograft; Asterisk *, siRNA with internal folic acid modification.
Vehicle-free delivery of RNAi molecules
Due to lack of specificity and poor cellular uptake of unconjugated miRNAs and siRNAs, as well as the potential toxicity of lipid transfecting agents, translating small RNAs into the clinic has been challenging (28). To overcome some of these disadvantages, while capitalizing on the power of small RNA therapeutics, various miRNA and siRNA delivery approaches have been developed and tested. A major advance includes direct conjugation of a targeting ligand to the RNA in the absence of a delivery vehicle. Several targeting ligands have been developed, including those based on small molecules, antibodies, aptamers, or synthetic CpG oligodeoxynucleotides (CpG-ODN) (Figure 2A). When using these so-called vehicle-free delivery approaches certain important features need to be considered (Figure 2B). Firstly, to obtain specificity, the ligand of choice needs to interact with a high affinity receptor that is upregulated on the surface of the intended target or diseased cells. The same receptor should not be expressed, or expressed at a relatively low level, on normal cells, or should be inconsequential for targeted delivery—for example, the location of the receptor on normal cells should be inaccessible to blood-born ligands. Secondly, to maximize internalization, binding and uptake of the ligand conjugates by the target cells must occur rapidly before clearance of the ligand conjugate from circulation. Of course, a high affinity ligand can support this feature; however, for certain ligands, engineering a divalent or multivalent ligand may further enhance both binding and uptake (43). Thirdly, to reduce the possibility of RNA degradation by serum nucleases and to increase intracellular half-life, chemical modification of the RNA is critical. However, modification positions within the RNA and the type of modification chemistry must be optimized carefully to prevent interference with the silencing activity of the RNA and/or unintended off-target effects. In fact, several recently developed chemically modified ligand-siRNA conjugates can enhanced both siRNA stability and silencing activity of the siRNA, resulting in long-lasting downregulation of target genes as discussed below (29,33). Fourthly, to promote the most robust interaction between the ligand and the receptor, inclusion of a linker between the ligand and the RNA should be considered. The linker itself can be modified to enhance binding to the receptor and the pharmacokinetics properties of the targeting ligand. This strategy has been employed for ligands that target Prostate-Specific Membrane Antigen (PSMA) (44,45). In general, the RNA can be linked to the ligand via a cleavable or non-cleavable linker depending on the stability of the conjugate and the activity of the conjugated RNA. Finally, since vehicle-free delivery approaches depend mainly on internalization of a targeting ligand by a specific receptor, they are subjected to endosomal entrapment, which is a rate-limiting factor in RNAi activity. Some targeting ligands have recently been modified to contain an endosomal escape agent to overcome this problem (43,46). This section summarizes some of the more commonly used, and recently developed vehicle-free delivery approaches, including the use of small molecule ligands, aptamers, CpG-ODNs, antibodies, and high-affinity scaffold proteins as effective ligands for delivery of miRNAs and siRNAs.
Small molecule-mediated delivery of RNAi
Several small molecule ligands have been developed for delivery of miRNAs or siRNAs specifically and robustly to diseased cells. Either naturally occurring or synthetic small molecules are identified, designed, and/or synthesized to bind to particular receptors with high affinity and specificity. After binding to the receptor on cell surface, the small molecule-RNA conjugates are internalized through receptor-mediated endocytosis, ultimately releasing some of the therapeutic RNA into the cytosol for target gene silencing (Figure 3, right panel). Small molecule ligands have many advantages that make them promising molecules for RNAi-mediated delivery which include low cost and feasibility of synthesis, and the ability to penetrate the dense architecture of the tumor microenvironment allowing them to effectively reach the intended cancer cells (47). Also, most small molecules contain derivatizable functional groups that make them amenable for facile conjugation to an RNA or for adding additional modifications (47).
Folate
Folate, vitamin B9, is an essential vitamin that is required by almost all cells and is a high affinity binding partner of the folate receptor (FR) which has four isoforms (α, β, γ and δ) (48–50). In non-diseased tissue, FR-α is expressed on the apical surface of epithelial cells in the lungs, mammary ducts, choroid plexus, and kidneys (50), whereas FR-β is expressed by immunosuppressive myeloid cells in the tumor microenvironment (51,52). Unlike other FR isomers, FR-γ is a secreted protein produced by certain hematopoietic cells (53) while the FR-δ isoform is expressed by transforming growth factor-beta (TGF-β) induced regulatory T-cells (Treg) as well as naturally arising Treg cells (54,55). After binding to the FR on the cell surface, folate molecules are rapidly internalized by the cells via receptor-mediated endocytosis (48). In addition to the high-affinity FRs, mammalian cells use other pathways to transport folate into cells, including the reduced folate carrier (RFC) and the proton-coupled folate transporter (PCFT). The RFC is ubiquitously expressed and is considered the major transporter of reduced folate in mammals (56). Importantly, the affinity of the RFC for folic acid is approximately 50-100-fold lower than the affinity for reduced folate, which is the major folate metabolite found in circulation (56). Thus, uptake of folate-conjugates for therapeutic intervention is not expected to occur in cells that only express the RFC. PCFT, the primary transporter system that facilitates folate absorption in the intestine, also has reduced affinity for folate in comparison to FR-α (57). Thus, due to the high affinity of FR-α and FR-β for folate, and their upregulation in cancer, it is unlikely that folate-conjugated drugs would be significantly taken up by normal cells.
While expression of the high-affinity FRs is limited on normal cells, FR expression is highly upregulated in numerous cancer types including tumors arising in the lung, breast, ovary, colon, and kidney (48,49,58–62). This high expression allows for substantial delivery of conjugated cargo to these cancerous tissues. For targeted agents, such as folate conjugates, typically a three-fold increase in therapeutic delivery between a normal and targeted cell is considered a major improvement in case of extrahepatic delivery over non-targeted agents, far less than the orders of magnitude observed when delivering GalNAc-siRNA conjugates to the liver. For ligands with a reduced delivery rate, incorporation of an endosomal escape agent is often required to achieve a therapeutic response. For FR expressing tumors the level of FR expression can easily be 10–100-fold higher than the level in normal tissues (60,62), which is more than adequate for delivering a biologically relevant concentration of RNAi molecules. While overexpression of the FR is essential, there are multiple attributes of the folate ligand that make it suitable for delivery as well. Folate is inexpensive and easy to synthesize, nonimmunogenic, and due to its small size, it is able to access solid tumors easily (47,63). These features make folate a promising ligand for the delivery of imaging and therapeutic agents specifically to cancer cells (64,65).
The path to using folate as a targeting ligand began in 1991 when Dr. Philip Low's group from Purdue University successfully used folate conjugates for delivery of macromolecules to KB cervical carcinoma cells (66). This work paved the way for several medical applications using folate conjugates. With regard to siRNA delivery, folate was conjugated to an siRNA labeled with a DY647 fluorophore to monitor its uptake and biodistribution (67). Folate-siRNA-DY647 conjugates were specifically internalized in FR-expressing cells in comparison to the non-targeted siRNA duplex. When folate-siRNA was injected into nude mice bearing KB tumor xenografts, folate accumulated specifically at the tumor relative to other organs indicating specific binding of folate–siRNA conjugates by FR-expressing cells in vivo. However, following the intracellular trafficking of the siRNA in cells, it was observed that folate-siRNA conjugates were entrapped in the endosomes, likely hindering siRNA target gene silencing.
Approximately ten years later, through intramolecular conjugation, the same group incorporated an endosomal escape agent, nigericin, into their folate-siRNA conjugates (folate–nigericin–siRNA) to overcome the endosomal entrapment issue (68). This strategy depends on the difference in solute concentration between the endosome which is rich in sodium ions (Na+) and the cytoplasm which is rich in potassium ions (K+). Following endosomal uptake of folate-nigericin-siRNA, nigericin is liberated from the rest of the compound and translocates into the endosomal membrane where it acts as a K+/H+ antiporter facilitating the exchange of osmotically active K+ for osmotically inactive H+ causing a build-up of osmotic pressure that leads to endosomal swelling and bursting. Folate-nigericin-siRNA activity was assessed in FR+ MDA-MB-231 cells that stably express a luciferase 2 reporter gene (Luc2). The Luc2 reporter was more robustly silenced following treatment with folate-nigericin-siluc2, indicating that inclusion of nigericin enhances target gene silencing.
Following the initial conjugation of folate to an siRNA, other strategies were evaluated including non-covalently tethering of folate to an siRNA through nucleic acid base pairing between the siRNA and a random oligodeoxynucleotide (F-ODN-siRNA) (69). In vitro treatment of human vein endothelial cells (HUVECs) with folate conjugated to an siRNA targeting ITGAV, an mRNA encoding αV integrin resulted in 80% inhibition of ITGAV mRNA in KB cells. Also, a reduction in the survivin transcript was observed in KB cells following treatment with F-ODN conjugated to an siRNA targeting survivin. This strategy has many advantages including simple synthesis and preparation that is cost-effective, and it has the potential to achieve a synergistic effect through the ability to deliver multiple siRNAs or miRNAs using a single oligodeoxynucleotide.
In addition to conjugating folate through a linker at the 3′ or 5′ ends of siRNA, a centrally-modified folic acid was recently reported for siRNA delivery (70) (Figure 4C). In this case, an siRNA was synthesized that allowed folic acid to be conjugated at various base positions along the length of the sense strand, including the central region that spans the Ago2 cleavage site. Indeed, siRNAs delivered with a centrally located folic acid modification resulted in enhanced silencing activity in comparison to siRNAs conjugated to folic acid at or near the 3′ end of the sense strand. Despite the potential of these newly developed centrally modified siRNAs, in vivo evaluation of safety and efficacy has yet to be conducted.
Figure 4. Examples of chemical structures of some common ligand-conjugates used to deliver miRNAs or siRNAs to diseased cells. The ligand structure is indicated in blue followed by the linker and the RNA. (A) Alnylam Pharmaceuticals: tri-GalNAc ligand conjugated to an siRNA to target the asialoglycoprotein receptor (ASGPR) on liver hepatocytes. (B) Kasinski group: folate conjugated to miR-34a through an unreleasable (top) or releasable (bottom, shown in red) linker for targeting folate-receptor (FR) expressing cancer cells (C) Desaulniers group: folate conjugated to the center of an siRNA for targeting FR expressing cancer cells. (D) Lieberman group: an EpCAM aptamer linked to an siRNA for targeting EpCAM+ epithelial breast cancers; U-U-U, linker. (E) Kortylewski group: CpG (D19) oligodeoxynucleotide (ODN) conjugated to miR-146a for treatment of inflammatory disorders and inhibition of leukemia progression; Asterisks, phosphorothioated bonds; x, C3 units of a carbon linker.
While delivery of siRNAs tends to be the benchmark, our group was the first to successfully conjugate folate directly to a miRNA, which we termed FolamiRs (71) (Figure 4B). Using the tumor suppressive miRNA, miR-34a, it was determined that FolamiR-34a was rapidly internalized only by FR-expressing cancer cells and that internalized conjugates were active and induced target gene silencing. The therapeutic safety and efficacy of FolamiR-34a were observed in breast cancer xenografts and in a genetically engineered model of non-small cell lung cancer. Efficacy was achieved at doses 10–150-fold lower than what was observed in the same models using MRX34, an encapsulated version of miR-34a that made its way into clinical trials (17,72). Although FolamiRs therapeutic effect is promising, like most ligand-conjugates, endosomal entrapment of the FolamiR molecules is a rate-limiting step. In a follow-up study, intramolecular conjugation of the FolamiR to nigericin enhanced the silencing activity of the delivered miRNA by facilitating endosomal escape (73). The cytosolic enrichment of miR-34a by nigericin induced downregulation of the miR-34a targets, oncogenic MET, and the programmed death-ligand 1 (PD-L1).
Although folate is a promising ligand for the delivery of miRNAs and siRNAs specifically to cancer cells. It would be beneficial to evaluate the effect of folate-conjugated miRNAs and siRNAs on cells in the tumor microenvironment such as tumor-associated macrophages and myeloid-derived suppressor cells (MDSCs) that are known to express FRβ (51).
DUPA (2- [3-(1,3-dicarboxy propyl) ureido] pentanedioic acid)
DUPA is a synthetic urea-based ligand that binds to PSMA with nanomolar affinity resulting in saturation of the receptor in a short period of time (74). PSMA, also known as glutamate carboxypeptidase II, is a type II membrane protein that is localized to the plasma membrane (75). PSMA is expressed at low levels in normal prostate tissue but is highly upregulated in prostate cancer (74,76). It is also expressed in the neovasculature of several solid tumors and is associated with the progression of prostate cancer (74,76). PSMA is constitutively internalized and the internalization rate increases after binding to anti-PSMA antibodies (77). After internalization, PSMA rapidly recycles back to the surface of the cell providing additional internalization rounds (74).
DUPA conjugates were first used to deliver siRNAs selectively to PSMA expressing prostate cancer cells in 2009 (67). In vitro treatment of LNCaP cells with a fluorescently tagged siRNA directly conjugated to DUPA (DUPA-siRNA-cy5) resulted in significant uptake 1-h post-treatment. Similarly, intravenous injection of DUPA-siRNA-cy5 into nude mice bearing LNCaP xenografts led to significant accumulation of DUPA-siRNA in LNCaP tumors. Although this approach provides a novel way to specifically deliver siRNAs to LNCaP cells, and delivery of the fluorescent siRNA was robust, target gene silencing using DUPA-siRNA conjugates needs to be evaluated in vitro and in vivo.
While still directly conjugated to DUPA, Tai et al. developed a novel DUPA-targeted siRNA delivery strategy (referred to as RNP8) that results in the formation of a DUPA-siRNA complex (43). In this case, DUPA-siRNA conjugates were mixed with a double strand RNA-binding domain (dsRBD) octamer which results in docking of the siRNA portion of the conjugate into the dsRBD while the DUPA ligands remained exposed on the surface. To facilitate endosomal escape of the siRNAs, an endosomolytic peptide (poly-histidine) was inserted into the C-terminus of the dsRBD. One important feature of this approach is the multivalency, which enhanced the binding affinity of DUPA to PSMA over one order of magnitude, from a KD of 82.1 nM for monovalent DUPA (DUPA-siRNA) to a KD of 0.728 nM for RNP8.
Both activity and biodistribution of the RNP8 complex were enhanced over DUPA-siRNA conjugates. Activity of the two complexes was assessed in vitro using LNCaP cells and an siRNA targeting Plk1. Treatment of LNCaP cells with RNP8 downregulated the Plk1 transcript and protein in comparison to DUPA-siRNA, which suppressed Plk1 only following transfection. The lack of silencing activity of DUPA-siRNA (without the dsRBD octamer) could be attributed to endosomal entrapment or the lower internalization rate of DUPA-siRNA conjugates. To evaluate in vivo biodistribution, Alexa Fluor 680-labeled siRNA was used in the preparation of DUPA-siRNA and RNP8. Following intravenous injection into mice bearing LNCaP xenografts, RNP8 accumulated at a higher rate in LNCaP tumors in comparison to DUPA-siRNA. Although this was attributed to lower binding affinity and rapid clearance of DUPA-siRNA from circulation relative to RNP8, other possibilities should also be considered. For example, for DUPA-siRNA, the siRNA was linked to DUPA through a cleavable disulfide bond which could have been reduced prematurely in the circulation leading to reduced signal in the tumor. A similar phenomenon was observed using folate as a delivery ligand (71). In this case, inclusion of a cleavable bond worked in vitro; however, in vivo the disulfide was reduced prematurely in circulation resulting in folate, but not the RNA, reaching the tumor tissue (71). The discrepancy in DUPA-siRNA accumulation within LNCaP tumors between this study and the study by the Low group is likely due to difference in linker chemistries resulting in different affinities (45,67). The KD of DUPA-siRNA used in this study was 82.1 nM while the KD of the DUPA ligand used in the study conducted by the Low group was 14 ± 1 nM.
The antitumor activity of RNP8 was assessed in vivo where RNP8 induced stronger inhibition of tumor growth in comparison to DUPA-siRNA. Despite that, protein-based delivery approaches require extensive evaluation for proper protein folding and could be expensive to synthesize (78). Recognizing this, the same group designed a new siRNA delivery approach that replaced the RNA-binding domains with an ethidium dimer (Et2) that binds to small RNAs. The polyhistidine peptide was also substituted with polyvinylimidazole (PVIm) to facilitate endosomal escape. A multivalent DUPA trimer (DUPA3) was conjugated to the sense strand of the siRNA and the resulting complex was mixed with Et2–PVIm to generate the DUPA3–siRNA/Et2–PVIm complex. Cellular binding and uptake studies indicated that DUPA3–siRNA with or without Et2–PVIm was taken up by LNCaP cells. Following intracellular trafficking using confocal microscopy, it was observed that DUPA3–siRNA/Et2–PVIm successfully escaped from the endosomes while DUPA3–siRNA (without Et2–PVIm) remained trapped. In vivo, DUPA3–siRNA/Et2–PVIm, but not DUPA3–siRNA, significantly inhibited LNCaP tumor growth. Despite the nice antitumor effect of DUPA3–siRNA/Et2–PVIm, the safety and efficacy of DUPA3–siRNA/Et2–PVIm needs to be evaluated in immunocompetent mice before considering clinical applications.
Aptamer-mediated delivery of RNAi molecules
Aptamers are short single-stranded oligonucleotide sequences that can be designed and developed to bind to any target protein, including surface receptors, with high affinity and specificity (79). In comparison to conventional antibodies, aptamers are easier to manufacture, have no or minimal toxicity and immunogenicity, and are smaller in size (80). Based on these properties, several aptamers have been developed for selective delivery of RNAi therapeutics to cancer cells.
Aptamer-mediated delivery of miRNAs
In addition to the use of aptamers for delivery of chemotherapeutics (81,82), a great effort has been made towards the use of aptamers for the delivery of miRNAs. For example, an aptamer, GL21.T that binds to the AXL tyrosine kinase receptor was used to restore tumor suppressive miRNAs in cancer cells that overexpress the AXL receptor (83). To accomplish this, a multifunctional therapeutic agent was developed that included covalent conjugation of the let-7g miRNA to GL21.T (referred to as GL21.T-let). In this case, the ribose sugars of the RNA were modified with 2′-fluoro pyrimidines (2′F-Py) to provide protection against nucleases, and to reduce potential immunogenicity. GL21.T-let selectively delivered let-7g to AXL+ A549 cells and downregulated HMGA2 and N-Ras, two let-7-g targets through a Dicer-mediated mechanism. In vivo biodistribution studies determined that GL21.T-let conjugates accumulated in A549 (Axl+) xenografts but not in MCF-7 (Axl–) tumors, with some targeting to the kidneys. Therapeutically, intravenous injection of GL21.T-let only inhibited tumor growth of Axl+ A549. Quantification of immune-related genes, 2′-5′ oligoadenylate synthetase 1, interferon-inducible IFIT1 (p56), and interleukin-8 in the spleen and the liver, suggested that GL21.T-let is well tolerated.
Multiple additional studies also validated use of the GL21.T aptamer for miRNA delivery. For example, miR-212 levels were restored in AXL+ cells through GL21.T-mediated delivery resulting in downregulation of the anti-apoptotic protein, PED, and restoration of TNF-related apoptosis-inducing ligand (TRAIL)-mediated cytotoxicity (84). Although the efficacy of this strategy needs to be validated in vivo, it provides a new approach for combinatorial therapy using aptamer-miR-212 to sensitize lung cancer to TRAIL therapy. The GL21.T aptamer was also used to overcome resistance to the commonly administered tyrosine kinase inhibitor, erlotinib. One mechanism of acquired resistance to erlotinib is through AXL overexpression and activation (85). Downregulation of AXL by a miR-34c mimic restored non-small cell lung cancer sensitivity to erlotinib (86). Thus, Russo et al. conjugated GL21.T to miR-34c (GL21.T/miR-34c) followed by co-treatment of erlotinib-resistant HCC827 cells with GL21.T/miR-34c and erlotinib (86). Significant inhibition of cell proliferation was observed in cells treated with GL21.T/miR-34c. Recently, the GL21.T aptamer was used to deliver miR-148b to AXL expressing breast cancer and melanoma cells, which also resulted in inhibition of tumor progression through downregulation of miR-148b targets, ALCAM and ITGA5 (87).
Additional aptamer-miRNA conjugates have also shown promise for cancer treatment. For example, a c-Kit targeting aptamer was used to deliver miR-26a (miR-26a chimera) to basal-like breast cancer cells and hematopoietic stem cells (HSCs), both expressing the KIT receptor (88). Delivery of the miR-26a chimera resulted in significant inhibition of cell growth in KIT receptor positive cells and had a striking combinatorial effect when combined with the chemotherapeutic agent, 5-fluorouracil (5-FU). Since chemotherapeutic drugs are associated with hematopoietic toxicity (88), the study also tested whether the miR-26a chimera could protect against chemotherapy-induced myelosuppression. Although mice treated with 5-FU only showed significant leukopenia and thrombocytopenia, the number of leukocytes and thrombocytes was nearly doubled in mice treated with the combination (5-FU and miR-26a chimera). Similar results were also obtained when the miR-26a chimera was used in combination with carboplatin. Although the study showed therapeutic potential of miR-26a chimera against basal-like breast cancer cells, additional support for using the miR-26a chimera could be gained by validating these effects in primary cells and other breast cancer cell types.
Targeting cancerous plasma cells has also been reported with aptamer-miRNAs (89). In this case, the RNA aptamer, apt69.T was designed to bind to B Cell Maturation Antigen (BCMA), which is highly upregulated by plasma cells in multiple myeloma (MM). Conjugating apt69.T to miR-137 (apt69.T-miR-137) and treating U266 cells resulted in a reduction in cell viability. Despite that, further verification that targeting occurs in bone marrow-accumulated malignant plasma cells in vivo would be beneficial.
Aptamer-mediated delivery of siRNAs
Several aptamers have been developed for the delivery of siRNAs to tumor cells. For example, an EpCAM aptamer was used by the Lieberman group to target epithelial breast cancers that highly upregulate EpCAM (21). In this case, the EpCAM aptamer-siRNA chimeras (AsiC) were synthesized by linking the 5′ end of the siRNA sense strand to the 3′ end of the aptamer via a U–U–U linker followed by annealing of the siRNA antisense strand (Figure 4D). The long strand of the AsiC (i.e. EpCAM aptamer + linker + siRNA sense strand) was synthesized with 2′-fluoropyrimidines, which enhances stability of the RNA in 50% serum (t1/2 >> 36 h).
Cellular uptake studies indicate that the EpCAM aptamer was taken up by EpCAM+ MDA-MB-468 cells. Importantly, the binding of EpCAM-AsiC was evaluated using normal tissue and breast tumor biopsies from breast cancer patients. EpCAM-AsiC significantly accumulated in the tumor biopsies in comparison to normal tissue samples further confirming the selectivity of EpCAM-AsiC to tumors that have upregulated EpCAM. The antitumor activity of PLK1 EpCAM-AsiC was assessed in vivo using an EpCAM+ MB468 reporter cell line that stably expresses luciferase. Treating these cells with PLK1 EpCAM-AsiC for 24 h before implanting them into nude mice, completely inhibited tumor formation. Subcutaneous injection of PLK1 EpCAM-AsiC into nude mice implanted with EpCAM+ MB468-luc cells in one flank and EpCAM– MB231 cells in the other induced regression of MB468-luc cells, but not MB231 cells. Similar results were also obtained in mice bearing Her2+ MCF10CA1a cells.
In addition to the use of aptamers to deliver small RNAs to breast cancer tissues, aptamers have been used to deliver siRNAs to prostate cancer cells with high PSMA expression. For example, the A10 RNA aptamer was used to deliver siRNAs targeting PLK1 or BCL-2 to PSMA expressing prostate cancer cells (90). In vitro studies indicated that A10-PLK1 and A10-BCL-2 bound specifically to PSMA expressing LNCaP cells and induced downregulation of PLK1 or BCL-2, respectively. Silencing of PLK1 or BCL-2 using A10-siRNAs reduced cell proliferation and induced apoptosis specifically in PSMA expressing cells. In vivo treatment resulted in a significant reduction in LNCaP tumor volume following A10-Plk1 administration. No effect was observed for PSMA– PC-3 tumors indicating specificity and efficacy of A10-Plk1 in targeting PSMA expressing tumors. As a preliminary evaluation of immune response, the level of IFN-β was measured in LNCaP cells, resulting in no IFN-β production following treatment with A10-siRNA chimeras.
A second-generation PSMA-PLK1 chimera was developed by the same group to enhance the silencing activity, specificity, and stability of the chimeras (20). In this design, several aspects of the A10-Plk1 chimera were modified. The aptamer was reduced to 39 nucleotides instead of the original 71 nucleotides to facilitate chemical synthesis. Also, to enhance the silencing activity and specificity of aptamer-siRNA chimera, an siRNA with a two nucleotide (UU)-overhang at the 3′ end of the siRNA was synthesized, and the duplex structure was further optimized to favor guide strand processing. Second-generation PSMA-PLK1 chimeras induced enhanced PLK1 silencing in comparison to the first-generation A10-Plk1 chimera. One possible explanation for the enhanced silencing by the second-generation chimeras is that the modified siRNAs are better substrates for Dicer, which resulted in more processed duplexes. In vivo therapeutic efficacy studies using the second-generation PSMA-PLK1 chimeras showed complete regression of tumor growth. Clearly, aptamer and siRNA modifications can greatly affect efficacy and need to be considered wisely for each target. This study also determined that addition of a polyethylene glycol (PEG) group to the PSMA-PLK1 chimera enhances the antitumor activity and circulation half-life. Overall, this strategy has several advantages including increased serum retention and stability, prolonged target gene silencing in vivo, and stronger inhibition of tumor growth at low siRNA doses.
Although aptamers bind their targets with high affinity and specificity and have been extensively used for delivery of RNAi molecules for targeting various cancers, there are some limitations with the use of aptamers including high cost involved in large-scale production and sensitivity of unmodified aptamers to nucleases (91). Developing shorter aptamers and inclusion of chemical modifications that do not affect affinity and specificity are important considerations for the future development of aptamers for delivery of RNAi-based therapeutics.
CpG oligodeoxynucleotide (ODN)-mediated delivery of RNAi molecules
Another prominent synthetic oligonucleotide used for delivery of RNAi-based therapeutics is the CpG oligodeoxynucleotides (ODN), a Toll-like receptor 9 (TLR9) ligand that is rapidly internalized by certain cells and induces immune responses (92,93). Under normal physiological conditions, human TLR9 is mainly expressed by plasmacytoid dendritic cells (DCs) and B cells. However, under inflammatory and tumor conditions, certain cells, including tumor-associated macrophages and polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) upregulate TLR9 (93). The expression of TLR9 is not restricted to immune cells as it is also upregulated by various tumors, including prostate cancer, glioma stem cells, acute myeloid leukemia (AML), multiple myeloma (MM), and B cell lymphoma (22,94–97). TLR9 is an intracellular receptor, therefore, it is not directly implicated in the uptake of CpG-ODN (93). Instead, surface-localized receptors have been involved in the uptake of CpG-ODN such as SR-A1 (93), RAGE (98), CXCL16 (99), CD205 (100), CD14 (101) or SR-BI (102). However, once internalized, TLR9 is required for effective siRNA-mediated silencing when siRNAs are conjugated to a CpG-ODN (22). One possible explanation is that TLR9 activation might be important for mediating rapid siRNA release from the endosomes (22,103). Based on these features, CpG-ODNs are considered a promising approach for delivery of therapeutics including miRNAs and siRNAs to cancer cells as well as immunosuppressive cells in the tumor microenvironment.
CpG oligodeoxynucleotide (ODN)-mediated miRNA delivery
Lately, CpG-ODNs have been used to deliver miRNA to non-malignant myeloid and malignant leukemic cells (104). For example, CpG-miR-146a conjugates (C-miR146a) were used to restore miR-146a levels in chromosome-5q deletion myelodysplastic syndrome [del(5q) MDS] and AML. In both of these diseases, miR-146a loss, through derepression of IRAK1 and TRAF6 leads to NF-κB activation (24,104–106). Thus, C-miR146a was developed to modulate NF-κB inflammatory and tumorigenic activity (104). In this study, C-miR146a was synthesized by conjugating the 5′ end of the miR-146a sense strand to the 3′ end of CpG-A/D19-ODN using a carbon linker followed by hybridization of the miR-146a guide strand (Figure 4E). The sense strand was minimally modified with a single 2′-O-methyl-modification at the 3′ end. In vitro, the C-miR146a duplex was rapidly taken up by multiple human immune cells, mouse RAW264.7 macrophages, human MDSL, HL-60 leukemia cells, and human Raji lymphoma cells. C-miR146a uptake was mediated by scavenger receptor A and was dependent on clathrin-mediated endocytosis. In vivo, intravenous injection of C-miR146a into mir-146a-deficient mice restored miR-146a in the bone marrow and spleen resulting in a reduction in Irak1 and Traf6 up to 24-h post-injection and corrected aberrant myeloproliferation in mir-146a-deficient mice. The ability of C-miR146a in dampening cytokine release syndrome (CRS) induced by CD19-specific chimeric antigen receptor (CAR) T-cells was also assessed in vivo (104). Intraperitoneal injection of C-miR146a three days prior to CAR T-cell transfer, upregulated miR-146a in peritoneal myeloid cells and reduced the level of CRS-related cytokines, IL-6, and granulocyte colony-stimulating factor (G-CSF). This suggests that C-miR146a could be used to overcome adverse effects associated with CD19-CAR T-cell therapy without compromising its antitumor activity.
The antitumor activity of C-miR146a in HL-60, MDSL del(5q) leukemia cells, and MV4-11 AML cells was also evaluated. Although, treatment with C-miR146a induced cell death in all the cells, C-miR146a induced more robust cell death in miR-146a-deficient HL-60, MDSL cells. In vivo therapeutic efficacy studies using C-miR146a (10 mg/kg) resulted in inhibition of disseminated HL-60 leukemia progression. The effect was likely due to targeting of NF-κB mediated survival signaling by miR-146a. This work suggests that the C-miR146a strategy has therapeutic potential to target myeloproliferative disorders as well as myeloid leukemia. These studies also set the stage for using the CpG strategy for delivery of other tumor suppressive miRNAs or anti-cancer antagomirs.
CpG oligodeoxynucleotide (ODN)-mediated siRNA delivery
Activation of Signal Transducer and Activator of Transcription 3 (STAT3) is associated with oncogenesis, survival, and proliferation of cancer cells (107). Activated STAT3 also promotes production of several angiogenic and immunosuppressive factors in myeloid cells, and inhibits expression of certain T-helper cell 1 (Th1) costimulatory molecules (107). Since pharmacological targeting of STAT3 is challenging, several studies have used siRNAs conjugated to CpG ligands to selectively inhibit STAT3 in tumor cells and tumor-associated immunosuppressive cells (93).
In 2009, Kortylewski et al. developed a strategy that links a Stat3 siRNA to a CpG oligonucleotide for targeting TLR9 expressing cells in the tumor microenvironment (108). In this case, the CpG oligonucleotide was linked to the antisense strand of a Stat3 siRNA followed by hybridization to the sense strand to generate a CpG-Stat3 siRNA duplex (CpG-Stat3). After confirming uptake and silencing activity using TLR9 expressing cells in vitro, the therapeutic efficacy of CpG-Stat3 siRNA was confirmed in vivo using B16 tumor-bearing mice. Efficient uptake and Stat3 gene silencing were observed in tumor-associated macrophages, DCs, and B cells. In addition, CpG-Stat3 siRNA conjugates induced stronger inhibition of B16 tumor growth in comparison to the negative control. This effect was mainly immune-mediated as treatment with CpG-Stat3 siRNA conjugates led to an increase in tumor-infiltrating neutrophils and CD8+T cells, and a reduction in immunosuppressive CD4+FoxP3+ regulatory T cells. These results indicated that combining STAT3 inhibition with TLR9 stimulation might enhance the antitumor immune response. Afterward, the CpG-Stat3 siRNA strategy was further optimized for targeting hematologic malignancies (23). In this study, the CpG-Stat3 siRNA induced regression in the syngeneic Cbfb/Myh11/Mpl (CMM) AML model (23). CpG-Stat3 conjugates induced potent antitumor immunity and eradicated disseminated AML cells in immunocompetent mice. Although the safety and efficacy of this strategy needs to be validated using humanized mouse models of AML, it provides a potential solution for targeting leukemia-initiating cells and for eradicating disseminated AML in vivo.
Based on the success of the CpG-Stat3 siRNA strategy in murine tumor models, the strategy was further optimized for targeting STAT3 in human TLR9+ immune cells and blood cancer cells (22). Human-specific CpG type A (CpG(A)/D19) was linked to the antisense strand of the STAT3 siRNA through a flexible carbon chain linker followed by annealing of the sense strand to generate CpG(A)-STAT3 siRNA conjugates. In vitro uptake and activity studies indicated that CpG(A)-STAT3 siRNA conjugates were internalized by human myeloid dendritic cells, plasmacytoid dendritic cells (pDCs), and B cells resulting in 60% knockdown of STAT3 in DCs. The CpG(A)-STAT3 siRNA induced stronger upregulation of the HLA-DR complex and the costimulatory molecule CD86 in DCs in comparison to control CpG(A)-Luc siRNA conjugates. In addition, treatment of pDCs with CpG(A)-STAT3 enhanced their ability to induce T cell proliferation. Overall, these results indicated that the CpG(A)-STAT3 siRNA has strong immunostimulatory properties in human immune cells. This study also evaluated the ability of CpG(A) to deliver STAT3 siRNA to TLR9+ hematologic malignancies, MM and AML. Although CpG(A)-STAT3 conjugates initially accumulated within early endosomes, CpG(A)-STAT3 downregulated STAT3 by approximately 50% suggesting that at least some of the siRNA escaped the endosome. In vivo gene silencing efficacy of CpG(A)-siRNAs in AML xenografts produced effective targeting of BCL-XL, an anti-apoptotic protein, and STAT3, and significantly inhibited tumor growth. Similar results were obtained using myeloma or leukemia xenografts.
The CpG-STAT3 siRNA strategy was further adapted for targeting tumor-associated immunosuppressive cells, such as MDSCs (109). MDSCs are a heterogeneous population of cells that expand during cancer progression and are associated with poor patients' survival (109–111). Hossain et al. found that TLR9 is overexpressed by a subset of granulocytic-MDSC (G-MDSC) that accumulates in the peripheral blood of prostate cancer patients during disease progression (109). This population of G-MDSC had elevated STAT3 activity and could inhibit the proliferation and activity of CD8+ T cells. Treatment of G-MDSC with the CpG-STAT3 siRNA successfully induced STAT3 silencing and restored T cell functions. Additional studies that evaluate the effect of CpG-STAT3 on MDSC differentiation are needed to further understand their response to CpG-STAT3. Despite that, the CpG-STAT3 strategy is considered a novel therapeutic approach for targeting immunosuppressive cells in prostate cancer.
Antibody-mediated delivery of RNAi molecules
Antibodies have been successfully used for delivery of anticancer therapeutics including RNAi-molecules to cancer cells. This approach involves conjugating an antibody that binds a certain receptor expressed by cancer cells to a cytotoxic payload through a linker. Several antibody-drug conjugates have been used and approved as cancer therapeutics and more are in various clinical stages (47). While not initially cancer-directed, in 2005 the Lieberman group used a protamine-antibody fusion protein to deliver an siRNA to cells infected with HIV or transfected with the HIV-1 envelope (112). In vitro, HIV replication was inhibited in HIV-infected primary T cells following treatment with an antibody Fab fragment-protamine fusion protein (F105-P) linked to an siRNA targeting the HIV-1 capsid gene gag. To highlight use of the antibody approach for delivery to tumors in vivo, intravenous or intratumoral injection of F105-P-siRNAs targeting c-Myc, Vegf, and Mdm2 in B16 tumors that stably express the HIV envelope specifically suppressed tumor growth. Importantly, there was insignificant induction of IFN-β, Stat-1 or 2′-5′ oligoadenylate synthetase 1 expression following treatment. This body of work also determined that a single-chain antibody directed against ErbB2 (Her2) and fused with protamine specifically delivered siRNAs into ErbB2-expressing cancer cells resulting in silencing of target gene in ErbB2+ cells indicating that this approach could be generalized.
A similar single-chain fragmented antibody (ScFvs) was used for targeting Her2+ human breast cancer cells in vivo (113). Using these cells, the therapeutic potential of Her2-ScFvs-protamine fusion protein (F5-P) complexed to an siRNA targeting PLK1 (F5-P/PLK1-siRNA) was evaluated. Intravenous injection of F5-P/PLK1-siRNA significantly reduced PLK1 expression and inhibited xenograft growth. F5-P/PLK1-siRNA suppressed metastasis and led to prolonged survival of mice bearing Her2+ breast tumors. For future applications and to gain a more comprehensive understanding of the biology, it is important to understand how siRNAs delivered using F5-P/PLK1 are released from the endosomes and what cytosolic concentrations are needed for achieving a response. Nonetheless, an advantage of this approach is that it could be modified for targeting other cancer types by simply changing the fusion protein antibody.
Antibody–siRNA conjugates were developed for targeting transferrin receptor (TfR, or CD71) expressing cells. The transferrin receptor is constitutively internalized by cells which allows the transport of transferrin (Tf) into early endosomes (114). In the acidic pH of endosomes, Tf is dissociated from the TfR which rapidly and repeatedly recycles back to the cell surface (114). A transferrin receptor monoclonal antibody (mAb) was used for delivery of siRNA specifically to brain tumors (115). In this case, TfR–mAb–siRNA conjugates were prepared by conjugating a luciferase-targeting biotin-labeled siRNA to a streptavidin-tagged TfR–mAb. Intravenous injection of TfR–mAb–siRNA targeting luciferase into rat-bearing C6 or RG-2 tumors that stably express the luciferase gene resulted in a 69–81% reduction in luciferase expression in these tumors (115).
Additional studies were conducted to evaluate the use of covalent and noncovalent conjugation between the antibody and siRNA, specifically focusing on endosomal escape. For these studies, a STAT3 siRNA was targeted to Lewis-Y expressing cancer cells using an anti-Lewis-Y monoclonal antibody (Hu3S193) (116). The STAT3 siRNA was covalently linked to the antibody using a cleavable disulfide bond (hu3S193-siRNA) or was noncovalently complexed with an antibody modified with a (D-arginine)9 peptide (9r) using electrostatic interactions (hu3S193-9r(1):siRNA). Activity studies indicated that the covalent conjugate (hu3S193-siRNA) generated a significant reduction in STAT3 expression only when cells were co-treated with an endosomal escape agent such as chloroquine or the arginine peptide (9r). In contrast, the noncovalent conjugate (hu3S193-9r(1):siRNA) alone induced efficient silencing of STAT3 in Lewis-Y-expressing cancer cells, but not in control cells, indicating the importance of the arginine peptide (9r) in mediating siRNA endosomal escape.
Additional antibody-siRNA conjugates were developed to overcome drug resistance, in this case to anti-EGFR antibodies. One mechanism that drives anti-EGFR antibody resistance involves mutations in KRAS, which is downstream of EGFR (117). To overcome KRAS-mediated resistance, an EGFR antibody-KRAS siRNA complex was developed and tested both in vitro and in vivo (117). The EGFR antibody-KRAS siRNA complex was internalized by EGFR-expressing cells, strongly suppressed KRAS expression, and inhibited clonogenic growth of mutant KRAS cells. In vivo, intraperitoneal injection of the EGFR antibody-KRAS siRNA complex significantly inhibited tumor growth in mice-bearing anti-EGFR-resistant cells in comparison to control groups.
To specifically target prostate cancer, an anti-PSMA single chain antibody was engineered to deliver two independent constructs (118). One contained an anti-PSMA single chain antibody fused to a truncated protamine while the other included an endosomal escape peptide HA2 and a furin cleavage site. Both constructs successfully delivered Notch1 siRNA into LNCaP cells, induced efficient knockdown of Notch1, and inhibited LNCaP cell proliferation in vitro and in vivo. However, the inhibition was more robust when the construct containing the HA2 peptide was used. A similar study by Shi et al. successfully delivered a TRIM24 siRNA using a human monoclonal PSMA antibody fused with protamine to target castration-resistant prostate cancer with high PSMA expression resulting in significant suppression of tumor growth (119).
Recently, an antibody-siRNA conjugate has been developed which depends on using dual variable domain (DVD) antibodies that contain an outer variable fragment (Fv) for selective antigen targeting and an inner catalytic Fv which has a uniquely reactive lysine (Lys) for conjugation to a β-lactam-functionalized siRNA (120). Treatment of a multiple myeloma cell line with a SLAMF7, CD138 or BCMA targeting DVD–Antibody conjugated to a β-catenin (CTNNB1) siRNA induced significant knockdown of CTNNB1 expression. This method generates highly homogenous antibody–siRNA conjugates that have a defined structure and are easy to assemble. Despite that, future studies that aim to understand how siRNAs delivered using this approach reach the cytoplasm would be beneficial.
These studies highlight the potential use of antibody-siRNA conjugates as anti-cancer agents. Antibodies have several advantages for delivery of RNAi molecules including clinical relevance, high binding affinity to their antigens, and the ability to deliver an active siRNA. In contrast, slow penetration of solid tumors due to large molecular weight antibodies and potential immune activation could affect the efficacy and safety of the treatment (121). To overcome these issues, several studies as discussed above used Fab fragments or scFv which are smaller and lack the immune activating Fc region. The small size facilitates additional penetration of solid tumors, while removing the Fc region reduces unintended interactions with non-target cells. Additional work to understand, optimize, and enhance the activity of antibody–RNAi conjugates will lend further support to the use of antibody–siRNA conjugates.
High affinity-scaffold proteins
Novel scaffold proteins such as DARPins and Centyrins have been used for delivery of RNAi molecules for targeting extrahepatic diseases. DARPins (designed ankyrin repeat protein) are small non-immunoglobulin proteins that can be selected to bind with high affinity and specificity to virtually any target protein (122). In 2009, an EpCAM-specific DARPin fused to truncated protamine and conjugated to a Bcl-2 siRNA for targeting EpCAM-expressing breast carcinoma cells was generated (123). Cellular uptake studies using FITC-labeled siRNA conjugated to EpCAM-specific DARPin indicated localization of the siRNA in the endosomal compartments, but a diffused cytosolic signal was also detected. Treatment of MCF-7 cells with EpCAM-specific DARPin complexed with the BCL-2 siRNA induced a significant downregulation of BCL2 expression and sensitized the cells to doxorubicin treatment. This indicates that the amount of siRNA released from the endosomes is enough to induce sufficient target gene silencing to influence a combinatorial effect; however, more optimization, including incorporating an endosomal escape agent could result in further enhancement of the therapeutic effect.
Centyrins are small, engineered protein that are based on consensus fibronectin (FN3) domains found in human Tenascin C (124). Centyrins can be engineered to bind any target antigen with high specificity and affinity similar to that of the antibodies (124). For example, an EGFR-binding Centyrin was conjugated to a beta-catenin (CTNNb1) targeting siRNA (EGFR-Cent-CTNNb1) followed by evaluation of its activity in EGFR-expressing cancer cells both in vitro and in vivo (124). Treatment of EGFR-expressing A431 cells with EGFR-Cent-CTNNb1 siRNA conjugates induced a significant downregulation of beta-catenin expression at both mRNA and protein levels. Intravenous injection of EGFR-Cent-CTNNb1 siRNA into mice bearing A431 xenografts resulted in a significant knockdown of beta-catenin expression in the tumor. Similar results were also obtained when the CTNNb1 siRNA was conjugated to Centyrins that bind to PSMA, BCMA, or EpCAM indicating that this approach could be generalized for targeting other cancer types. In addition, when two siRNAs targeting different genes were conjugated to a single EGFR-binding Centyrin, both genes were simultaneously silenced in vitro. The ability to target two genes with the same conjugate provides a way to develop siRNA-based therapeutics that could produce a synergistic antitumor effect or overcome potential resistance mechanisms.
Lipid conjugates for RNAi delivery
The uptake of naked RNAi molecules by cells is limited, in part, by the hydrophilic nature of RNA; thus, conjugating RNAs to a hydrophobic molecule, such as cholesterol, could enhance the uptake of RNAi-molecules by the cells (125). Indeed, cholesterol has been extensively used for delivery of RNAi molecules to various cells and tissues. The uptake of cholesterol-siRNA conjugates by cells occurs rapidly by a selective endocytic process after insertion of the cholesterol conjugates into the plasma membrane (126). Binding of cholesterol with circulating lipoproteins could also facilitate uptake of cholesterol-siRNA conjugates by lipoprotein receptors (126). In 2004, Soutschek et al. conjugated cholesterol to a chemically stabilized siRNA that targets apolipoprotein B (apoB, Chol-apoB-1-siRNA) (127). Intravenous administration of Chol-apoB-siRNA into mice significantly reduced apoB expression in the liver and jejunum as well as total cholesterol levels.
With regard to using cholesterol-siRNA conjugates for treating cancer, cholesterol was conjugated to an siRNA targeting MDR1 (Ch-siMDR) followed by evaluating its activity using KB-8-5 tumor-bearing mice (128). Biodistribution studies using Cy5.5-Labeled Ch-siRNA indicated that conjugation of siRNA to cholesterol enhanced tumor and liver accumulation while reducing retention in the kidney. Intravenous, intraperitoneal, or peritumoral administration of Ch-siMDR into KB-8-5 tumor-bearing mice significantly downregulated the P-glycoprotein level in the tumors. Since siRNAs delivered using this approach might accumulate in other organs in addition to the tumor, genes that are essential for tumor cell growth, while not essential for normal cells, would be preferred targets.
Similarly, cholesterol-siRNA conjugates have been developed for targeting glioblastoma (129). In this case, a fully modified siRNA was conjugated to cholesterol (Chol-hsiRNA) followed by evaluating uptake and activity using primary GBM8 cells. Chol-hsiRNA targeting Cyclophilin B (PPIB) or Huntingtin (HTT) mRNA were rapidly taken up by cells and induced significant target gene silencing in vitro. To evaluate the efficacy of Chol-hsiRNA in vivo, GBM8 cells that stably express firefly luciferase were injected orthotopically into the brains of mice. A single intratumoral injection of Chol-hsiRNAs targeting HTT or firefly luciferase induced 45% reduction in human HTT mRNA and about 90% reduction in firefly luciferase activity seven days post-injection.
In addition to siRNAs, cholesterol, and other lipids such as eicosapentaenoic acid (EPA) and docosanoic acid (DCA) have recently been used to deliver a chemically modified version of the let-7b miRNA for treating non-small cell lung cancer (NSCLC) (130). Uptake of lipid-conjugated let-7b was evaluated using Cy3-labeled let-7b in a NSCLC cell line. Cy3-labeled let-7b conjugated to cholesterol or DCA was significantly taken up by the cells while EPA-let-7b conjugates were not, indicating that the lipid structure might affect conjugate uptake. Significant enrichment of let-7b was detected following treatment of NSCLC cells with different lipid-let-7b conjugates, which was correlated with downregulation of the let-7 target gene HMGA2. The biodistribution of lipid-conjugated let-7b was evaluated in vivo using NSCLC tumor-bearing mice following a single subcutaneous injection. let-7b was enriched in the tumors following treatment with EPA and DCA conjugates. However, the highest level of let-7b was detected in the liver and the spleen. And, while the EPA-hmiR-let-7b treatment downregulated HMGA2 mRNA levels and Ki-67 expression in the tumor, the effect on tumor size was insignificant, which requires further exploration.
Overall, cholesterol and other lipids have been successfully used for delivery of RNAi molecules to various tissues providing an opportunity to modulate gene expression in these tissues, which provides an advantage over naked RNAi molecules. Despite the benefits, the main limitations of lipid or cholesterol-conjugated RNAi molecules include lack of specificity and significant accumulation in the liver, kidney, and spleen. Keeping that in mind, further optimization of the lipid structure could help to minimize non-specific uptake by normal tissue. In addition, careful selection of RNAi-molecules that are only essential for the growth of diseased cells is important for these less-specific delivery approaches. While potentially achievable for siRNA-based therapeutics, this might be difficult in case of miRNAs that have a vast range of targets.
Targeted extracellular vesicles for RNAi delivery
Although synthetic delivery vehicles, such as liposomes, are successfully used for delivery of miRNAs to cancer cells, several limitations stand against routine clinical use, including delivery associated toxicity, non-specific uptake, immunogenicity, and accelerated blood clearance (6,131,132). A great effort has been made to overcome some of these limitations, which was previously reviewed by our group (6) as well as by others (131,132). As an alternative approach, natural extracellular vesicles (EVs) have gained significant scientific attention as potential delivery vehicles. In this section, we discuss recent studies that tested and developed ligand targeted EVs for cancer therapy.
EVs are membrane bound vesicles produced by a variety of cell types, including cancer cells, as a means of intercellular communication. Based on their size and biogenesis, EVs are divided into various subpopulations referred to as exosomes, microvesicles, and apoptotic bodies (133) with exosomes being the most commonly used subpopulation for RNAi delivery. However, due to the inability to selectively purify exosomes, most studies use the more general term, EVs. EVs have emerged as alternative vehicles for delivery of therapeutic miRNAs and siRNAs to diseased cells for many reasons: (i) EVs isolated from normal cells have minimal toxicity and immunogenicity (134,135); (ii) EVs are able to cross natural barriers (136); (iii) compared to liposomes, EVs from certain cell types have enhanced retention in circulation due to CD47 expression, which protects them from phagocytosis (137) and (iv) EVs can be engineered to express a cell-surface ligand that can be used to achieve specific delivery to target cells as discussed below and shown in Figure 3.
Internalization of EVs is mediated by multiple mechanisms, including direct membrane fusion and receptor-mediated endocytosis (138,139) (Figure 3). When ligand decorated EVs are internalized by receptor-mediated endocytosis, they back-fuse with the endosomal membrane and release their content into the cytosol (139). Alternatively, EVs can directly enter the cells through fusion with the outer plasma membrane resulting in the release of their content into the cytosol (139). For instance, folate decorated EVs are mainly taken up by FR expressing cells through membrane fusion (139). After binding to FRs on the cell surface, EVs fuse with the cell membrane and release their content, in this case, siRNAs into the cytosol resulting in target gene silencing (139). Although folate might not be directly involved in the uptake of the EVs in this case, it facilitates the binding of EVs to FR expressing cells.
Peptide-mediated EV delivery was achieved using the GE11 peptide (YHWYGYTPQNVI) that binds selectively to the epidermal growth factor receptor (EGFR) (140). Since EGFR is overexpressed by a variety of tumor cells, including breast cancer cells, GE11-tagged EVs have the potential to specifically deliver tumor suppressive miRNAs or siRNAs to these cancer cells. In this study, HEK293 cells were used as factories to generate the GE11-EVs followed by loading of the EVs with let-7a or a control miRNA. In vitro experiments confirmed the specific uptake of GE11-EVs by EGFR expressing breast cancer cells. In vivo, intravenous injection of let-7a loaded GE11-EVs (1 μg, 1 time weekly for 4 weeks) into mice bearing HCC70 xenografts inhibited tumor growth in comparison to control EVs. Although high EV accumulation was detected in the liver 24 h post-injection, no major tissue damage was observed. Future studies that evaluate the safety and efficacy of this strategy in immunocompetent mice would help advance this technology clinically.
Aptamer-targeted EVs were developed for delivery of miRNAs to breast cancer cells overexpressing the nucleolin receptor (141). In this case, the AS1411 aptamer, which has high affinity for the nucleolin receptor, was displayed on the outer surface of EVs secreted by immature dendritic cells using cholesterol affinity. Afterward, using electroporation, AS1411-modified EVs were loaded with let-7 tagged with a Cy3 fluorophore. In vitro uptake studies indicated efficient uptake of AS1411-EVs-let-7-Cy3 by nucleolin expressing MDA-MB-231 cells resulting in reduced proliferation and migration. Biodistribution was evaluated using nude mice bearing MDA-MB-231 xenografts. Treatment of animals with AS1411-EVs-let-7-Cy3 resulted in a strong fluorescence signal in the tumor tissue in comparison to non-targeting EVs. Both types of EVs accumulated in non-cancerous tissues, including the liver and the brain which might be a concern for generating off-target effects. An additional in vivo study assessed the therapeutic efficacy of AS1411-EVs-let-7. Intravenous injection of AS1411-EVs-let-7 (150 μg/every other day, 12 injections) induced a regression in MDA-MB-231 tumor growth in comparison to mice treated with free let-7 or control EVs. The study showed no signs of tissue damage or immune activation in mice injected with AS1411-EVs-let-7 every other day for 4 days; however, it would be valuable to monitor the safety of this strategy over the course of treatment that led to a therapeutic response.
Another EV targeting strategy was developed for delivery of miRNA and chemotherapeutic drugs using disintegrin and metalloproteinase 15 (A15)-expressing EVs (A15-Exo) to triple-negative breast cancer (142). A15 has high binding affinity to integrin αvβ3, which is overexpressed on the surface of several cancer types including breast, ovarian, glioblastoma, melanoma, and prostate cancer (143,144). This strategy relies on stimulation of THP-1 monocytes with phorbol 12-myristate 13-acetate (PMA) which leads to release of A15-Exo into the cell culture supernatant. After purification from supernatant, A15-Exo were loaded with doxorubicin and cholesterol-modified miR-159 (Co-A15-Exo) followed by evaluating in vivo therapeutic efficacy. Intravenous injection of Co-A15-Exo into MDA-MB-231 tumor-bearing mice resulted in synergistic inhibition of tumor growth in comparison to control groups.
In addition to miRNAs, modified EVs have been used for delivery of siRNAs to Chronic Myeloid Leukemia (CML), capitalizing on upregulation of the interleukin 3 receptor (IL3-R) (145). To achieve selective targeting, HEK293 cells were engineered to produce exosomes that express the exosomal protein Lamp2b, fused to the interleukin 3 fragment (IL3-Lamp2b). Uptake of IL3-Lamp2b exosomes (IL3L Exo) was confirmed in vitro using IL3-R expressing LAMA84 and K562R cell lines. Subsequently, IL3-Lamp2b exosomes were loaded with an siRNA targeting BCR-ABL, a tyrosine kinase which is constitutively active in CML cells (146). In vitro activity assays indicated that IL3L Exo loaded with BCR-ABL siRNA (IL3L Exo BCR-ABL siRNA) inhibited the growth of LAMA84, K562, and Imatinib-resistant K562 cells. Intraperitoneal injection of IL3L Exo BCR-ABL siRNA into NOD/SCID mice bearing Imatinib-resistant K562 xenografts inhibited tumor growth in comparison to mice injected with the negative control.
Another study engineered EVs that displayed folate molecules, PSMA-targeting aptamers, or EGFR-targeting aptamers on their surface for delivery of survivin siRNA to cancer cells overexpressing FR, PSMA, or EGFR, respectively (147). The therapeutic efficacy of PSMA aptamer/EVs, EGFR aptamer/EVs, or folate/EVs loaded with survivin siRNA was evaluated using nude mice bearing prostate cancer, breast cancer, or patient-derived colorectal cancer xenografts, respectively. Significant inhibition of tumor growth was observed in all three cancer models. This work provides an effective strategy for targeting various tumor types by changing the ligand on the EVs surface without affecting EVs composition or integrity.
Overall, these studies indicated that modified EVs can be used to deliver miRNAs/siRNAs either alone or in combination with other drugs for cancer therapy. Despite the therapeutic promise of EVs as delivery vehicles, additional effort is still needed to enhance the therapeutic applicability. Most of these studies loaded the EVs with synthetic miRNA or siRNA through transfecting them into the exosome-secreting cells, through directly transfecting the exosomes, or by electroporation. All these methods have certain limitations and none can be scaled for production (148,149). Transfecting exosome-secreting cells that overexpress miRNA or siRNA may affect the packaging process or the behavior of the EVs. In addition, the loading efficiency could vary greatly based on the RNA sequence (149). Electroporation could be associated with siRNA aggregation or precipitation causing an overestimation of the loaded siRNA (149,150). Direct transfection of EVs with miRNAs has also been used, instead of transfecting the donor cells (151). However, contaminating transfection reagents could remain associated with the EVs resulting in unintended side effects (152). Indeed, poor loading could result in the need to deliver more EVs which may lead to toxicity. To enhance loading, Reshke et al. incorporated an siRNA sequence into the backbone of pre-miR-451, a miRNA that is significantly enriched in EVs of most cell types (150). Significant siRNA enrichment was detected inside the EVs when the siRNA was integrated into the pre-miR-451 backbone but not the pre-miR-16 backbone. In vitro and in vivo uptake and activity studies indicated that this strategy facilitated siRNA delivery to certain cell types and induced efficient gene knockdown at doses lower than lipid nanoparticles or electroporated EVs. Other studies showed efficient loading when cholesterol-modified siRNAs were incubated with the EVs without affecting vesicle integrity (142,148). For example, a study from the Khvorova lab determined that siRNAs that are chemically stabilized, as well as hydrophobically-modified (hsiRNAs), are efficiently loaded into the EVs (148). When these EVs were loaded with an siRNA targeting Huntingtin mRNA, significant silencing was achieved in vitro as well as in vivo. In addition to effective loading, another advantage of this method is the use of chemically stabilized siRNA which can reduce the effective dose required to achieve silencing. Without question, novel, more efficient, and more consistent loading approaches are needed prior to translating EVs into the clinic. Another challenge with using EVs as a delivery vehicle is that secreted EVs are not easy to scale up for production. One strategy that aims to enhance EVs secretions is the immortalization of primary cells (153). For example, mesenchymal stem cells (MSC) can be immortalized by inducing c-Myc overexpression which enhances their expansion and thus EVs secretion (153). Regardless, EVs are considered a promising approach for miRNA and siRNA delivery. However, further understanding of EVs biology such as how different molecules are naturally loaded into the EVs and how EVs are taken up by different cell types will advance EVs as delivery vehicles for RNAi-based therapeutics.
LIMITATIONS AND OPTIMIZATIONS
Ligand-mediated RNAi delivery provides a novel way for targeting various cancer types as discussed above. This approach bypasses many of the limitations of passive targeting, including non-specific delivery and off-target effect on normal cells. In addition, the specific targeting to the diseased cells achieved with ligand-mediated delivery will result in higher accumulation of RNAi molecule in these cells which could enhance effectiveness and potentially reduce dosing. All the methods described also support the ability to deliver various anti-cancer RNAs at the same time resulting in a synergistic effect once potential synergistic RNAs are identified (154).
The major bottleneck limiting the utility of a ligand-targeted approach for RNAi delivery is endosomal entrapment. The internalized RNA must be released into the cytosol or it will be degraded when the late endosomes fuse with the lysosomes. The prevailing hypothesis is that chemically modified RNAi-molecules will be more stable in the acidic compartments and slowly release into the cytosol, similar to what happens in the case of GalNAc-siRNA conjugates. Nonetheless, several strategies have been developed to facilitate more rapid release of RNAi molecules into the cytosol including cell-penetrating or fusogenic peptides such as the EB1 endosomolytic peptide, the influenza-derived fusogenic peptide diINF-7 (34,155), or small molecules such as chloroquine (156). However, some of these techniques are often toxic to cells or stimulate an immune response. Recently, we used the small molecule, nigericin to facilitate endosomal escape of ligand-conjugated miRNAs, which significantly enhanced miRNA activity in the absence of toxicity (73). Regardless, more effort needs to be placed on developing additional novel, safe, and effective endosomal escape agents to further reduce dosing and enhance the activity of RNAi-based therapeutics.
An additional concern includes the inability of the ligand-siRNA conjugate to be released from the receptor, especially if the conjugate does not contain a cleavable linker. Some ligands bind their receptor with so-called ‘excess affinity’ which could lead to continuous binding of the ligand to its receptor. Instead of disengaging from the receptor, the ligand will be recycled back with the receptor to the cell surface reducing cytosolic distribution and subsequent binding and internalization of additional conjugates (114). GalNAc, the successful ligand used for siRNA delivery binds ASGPR with nanomolar affinity (157); thus, ligands with comparable affinity should be considered. However, other ligands might behave differently when bound to their receptor under different endosomal conditions. For example, the level of acid endonucleases in normal cells, such as hepatocytes, could be completely different than that of cancer cells. Further understanding of the endosome environment such as the acidic pH that favors the dissociation of ligand from its receptor will be beneficial to determine when, and in which stage, RNAi molecule will be free in the endosomal lumen for release into the cytosol, which will ultimately enhance the efficacy of ligand-mediated RNAi delivery strategies.
Receptor saturation and rapid clearance following treatment are other issues that greatly influence intracellular RNAi concentration (158). In general, while the binding of high affinity ligands (such as folate) to their receptor occur rapidly after intravenous injection and saturate the receptor in a short amount of time, excess conjugates that did not bind the receptor will be rapidly cleared from the body, perhaps even before the unoccupied receptor recycles back to the surface (47). This will reduce the duration in which the receptor is exposed to the ligand making repetitive dosing a necessity to achieve a therapeutic response.
While there are still multiple hurdles to overcome, significant progress has been made to overcome some of the challenges associated with delivery of RNAi molecules to extrahepatic tissues and will continue. The success of GalNac conjugates is helping to pave the way and the recent use of RNA-based vaccines for protecting individuals from SARS-Cov2 suggests that an exponential increase in RNA therapeutics is on the horizon (159,160). Efforts in achieving specific and efficient delivery, including identifying and developing additional ligands and unique strategies to promote endosomal escape are major areas of focus that need to be tackled in the upcoming years.
CONCLUSION AND FUTURE DIRECTIONS
RNAi-based therapeutics including miRNAs and siRNAs have evolved as ideal therapeutic approaches for the treatment of various diseases, which is not surprising as these molecules can modulate most disease-related genes. However, advancing them as therapeutics for human diseases has been limited by challenges, including non-specific delivery, rapid RNA degradation, and poor cellular uptake (28). Various chemical modifications have been developed that enhanced stability and activity of siRNAs and reduced immune response. These modifications include addition of PS bonds at certain positions of the RNA backbone and replacing the unstable 2′-OH of the ribose sugar by 2′-OMe and 2′-F (29). Despite that, RNAi-based therapeutics for treating oncological diseases have not reached their full potential yet due to lack of safe and specific delivery approaches. Indeed, identifying an efficient delivery system is as important as enhancing the properties of RNAi molecules – both are required for developing RNAi-based therapeutics. GalNAc-siRNA conjugates are an exceptional example that demonstrates how consideration of both features can lead to rapid advances in RNAi-based therapeutics from preclinical to clinical stage. GalNAc binds the ASGPR which is expressed by liver hepatocytes and depends mainly on receptor targeting to achieve selective delivery of the conjugated siRNAs (34). Thus, expanding RNAi-based applications for targeting other tissues requires identifying novel ligands and their receptors. Once identified, RNAi molecules could be directly conjugated to a targeting ligand or packaged into a delivery vehicle that is engineered to display a ligand on its surface. Extensive efforts to identify cancer-targeting ligands have been successful and have resulted in identifying several small molecules, aptamers, antibodies, and peptides that deliver RNAi-molecules specifically to cancer cells as discussed.
Delivery of RNAi molecules using ligand decorated EVs is an emerging area that shows promise in overcoming endosomal entrapment due to the ability of EVs to directly fuse with the cell membrane or back-fuse with endosomal membranes (139). Another advantage of using EVs is that EVs provide a way to deliver RNAi molecules for targeting diseased cells in the central nervous system due to their ability to cross natural barriers (148,161). In addition, EVs are naturally released from cells, and thus, can be obtained from the cells of the patient improving safety. However, it is important to identify optimal EVs secretion and purification conditions, as well as efficient packaging techniques that enable large scale production and reproducible loading conditions prior to clinical applications.
Overall, a great effort has been made to identify various ligand-targeted approaches for delivery of miRNAs and siRNAs specifically to tumor cells and to overcome several delivery challenges. Validating the efficacy and safety of these approaches using immunocompetent and humanized mouse models is required prior to any clinical applications. In addition, developing efficient and non-toxic endosomal escape agents and utilizing these agents to enhance RNAi activity could finally allow us to realize the power of RNAi-based therapeutics for the treatment of human cancers.
ACKNOWLEDGEMENTS
Figures 1–3 and the graphical abstract were created using BioRender.com. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
FUNDING
National Cancer Institute [R01CA226259 (A.L.K.) and R01CA205420 (A.L.K)] (in part); A.M.A. was supported by an Andrew's Fellowship from Purdue University; SIRG Graduate Research Assistantship from the Purdue Center for Cancer Research. Funding for open access charge: The open access publication charge for this paper has been waived by Oxford University Press - NAR Editorial Board members are entitled to one free paper per year in recognition of their work on behalf of the journal.
Conflict of interest statement. None declared.
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PMC008xxxxxx/PMC8364333.txt |
==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
34409299
10.1093/narcan/zcab032
zcab032
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Survey and Summary
A novel view on an old drug, 5-fluorouracil: an unexpected RNA modifier with intriguing impact on cancer cell fate
Chalabi-Dchar Mounira Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Fenouil Tanguy Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Institut de pathologies multi-sites des Hospices Civils de Lyon, Groupement Hospitalier Est, F-69677 Bron, France
Machon Christelle Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Laboratoire de chimie analytique, Faculté de pharmacie de Lyon, Université de Lyon, 8 avenue Rockefeller, F-69373 Lyon, France
Laboratoire de biochimie et de pharmaco-toxicologie, Centre hospitalier Lyon-Sud – Hospices Civils de Lyon, F-69495 Pierre Bénite, France
Vincent Anne Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Catez Frédéric Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
https://orcid.org/0000-0002-9557-8221
Marcel Virginie Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Mertani Hichem C Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Saurin Jean-Christophe Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Département d’Endoscopie et de Gastroentérologie, Pavillon L, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69008 Lyon, France
Bouvet Philippe Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Ecole Normale Supérieure de Lyon, F-69007 Lyon, France
Guitton Jérôme Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
Laboratoire de biochimie et de pharmaco-toxicologie, Centre hospitalier Lyon-Sud – Hospices Civils de Lyon, F-69495 Pierre Bénite, France
Laboratoire de toxicologie, Faculté de pharmacie de Lyon, Université de Lyon, 8 avenue Rockefeller, F-69373 Lyon, France
Venezia Nicole Dalla Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
https://orcid.org/0000-0002-7914-4319
Diaz Jean-Jacques Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, Centre Léon Bérard, F-69373 Lyon Cedex 08, France
To whom correspondence should be addressed. Tel: +33 478 782 819; Email: [email protected]
Correspondence may also be addressed to Nicole Dalla Venezia. Tel: +33 426 556 745; Email: [email protected]
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
9 2021
14 8 2021
14 8 2021
3 3 zcab03209 8 2021
01 7 2021
18 3 2021
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Cancer.
2021
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
5-Fluorouracil (5-FU) is a chemotherapeutic drug widely used to treat patients with solid tumours, such as colorectal and pancreatic cancers. Colorectal cancer (CRC) is the second leading cause of cancer-related death and half of patients experience tumour recurrence. Used for over 60 years, 5-FU was long thought to exert its cytotoxic effects by altering DNA metabolism. However, 5-FU mode of action is more complex than previously anticipated since 5-FU is an extrinsic source of RNA modifications through its ability to be incorporated into most classes of RNA. In particular, a recent report highlighted that, by its integration into the most abundant RNA, namely ribosomal RNA (rRNA), 5-FU creates fluorinated active ribosomes and induces translational reprogramming. Here, we review the historical knowledge of 5-FU mode of action and discuss progress in the field of 5-FU-induced RNA modifications. The case of rRNA, the essential component of ribosome and translational activity, and the plasticity of which was recently associated with cancer, is highlighted. We propose that translational reprogramming, induced by 5-FU integration in ribosomes, contributes to 5-FU-driven cell plasticity and ultimately to relapse.
Graphical Abstract
Graphical Abstract 5-FU integration in ribosomes contributes to cell plasticity through translational reprogramming.
Institut National de la Santé et de la Recherche Médicale 10.13039/501100001677 Centre National de la Recherche Scientifique 10.13039/501100004794 Université Claude Bernard Lyon 1 10.13039/501100006687 Agence Nationale de la Recherche 10.13039/501100001665 ANR-13-BSV8-0012-01 RIBOMETH ANR-19-CE12-0004 ACTIMETH Institut National Du Cancer 10.13039/501100006364 PLBIO 2018-131 FluoRib PAIR Sein program ARC INCa LNCC 7625 RiboTEM SIRIC program INCa-DGOS-Inserm 12563 LYriCAN Labex 10.13039/501100004100 DEVCAN2UMAN Fondation ARC pour la Recherche sur le Cancer 10.13039/501100004097 20161204686 MARACAS Ligue Contre le Cancer 10.13039/501100004099
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pmcINTRODUCTION
Based on the observation that tumoral tissues used the uracil more rapidly than normal tissues (1,2), 5-Fluorouracil (5-FU) was identified as an antimetabolite chemotherapy as early as 1957 and was approved by the FDA in 1962 for the treatment of colorectal cancer (CRC) (3). Since its first approval, 5-FU has been widely used either alone or in combination with other drugs to treat many solid cancers of digestive origin (colorectal, anal, pancreatic, oesophageal, gastric and ampullary tumours) and those arising in other organs (i.e. breast, cervix, and head and neck cancers). Today, 5-FU is used to treat several of the deadliest cancers, including CRC and pancreatic ductal adenocarcinoma (PDAC) (Table 1). Even though treatment modalities (dose, timing and administration) vary according to the origin of the tumour, 5-FU remains one of the essential drugs used for cancer management (4).
Table 1. Most frequent cancers treated with 5-Fluorouracil (5-FU) worldwide in 2018. The top 2 of each column are presented in bold (5,6) – (https://seer.cancer.gov - https://gco.iarc.fr/today/home)
Most frequent cancer site treated with 5-FU Cancer subtype treated with chemotherapy including 5-FU 5-year survival (%) Ranking by Mortality Mortality/ Incidence (%) 5-Year Prevalence
Colorectal and anal carcinoma High risk stage II, stage III (locally advanced) and stage IV (metastatic) 64 4 48 4,789,635
Pancreatic adenocarcinoma Every stage 9 9 94 282,574
Esophageal carcinoma Every stage except some stage IA 20 6 65 446,522
Gastric adenocarcinoma Every stage except some stage IA 32 3 51 1,175,507
Hepatocellular carcinoma Advanced multifocal hepatocellular carcinoma 18 2 70 565,028
Cholangiocarcinoma Stage IV cholangiocarcinoma 19 17 44 175,086
Cervical carcinoma Stage IB3 to stage IVA 66 8 55 1,474,265
Breast carcinoma Stage II to stage IV and some aggressive stage I triple negative carcinoma 91 5 30 6,875,099
Head and Neck carcinoma Stage II to IV 58 to 65 7 42 1,842,306
Inclusion of 5-FU into the regimen used to treat CRC, the second most common cause of cancer-related deaths in western countries, is mandatory. The annual incidence and mortality rates of CRC exceed one and half a million of cases worldwide, respectively (5,6). 5-FU is used as the first-line treatment for CRC patients. However, the rate of response to 5-FU does not exceed 40–60% when 5-FU is used in combination with other molecules such as folinic acid, irinotecan or oxaliplatin, or with targeted therapies such as those based on anti-vascular endothelial growth factor (VEGF) (bevacizumab) or anti-epidermal growth factor (EGFR) (cetuximab) antibodies (7,8). Nevertheless, between 20% and 25% of patients with stage II or stage III cancers do not respond to protocols containing 5-FU, and in these cases, patient overall survival only reaches 50% within 5 years following CRC diagnosis (9) (Table 1). Hence, CRC represents an opportunity for biomedical programs that aim at investigating the mode of action of 5-FU and its secondary effects, as well as resistance mechanisms.
PDAC represents another major type of cancer in which 5-FU treatment has received considerable attention. Indeed, not only 5-FU is administered to patients with resectable pancreatic adenocarcinoma, but it also constitutes the first-line treatment for most PDAC patients. 5-FU is mainly given in association with other systemic agents (e.g. folinic acid, oxaliplatin, irinotecan or gemcitabine), but it can also be combined with radiotherapy or surgery (10). PDAC has fatal outcome in most cases with a 5-year survival rate of only 9%, and an annual estimated death rate of 430 000 worldwide. Moreover, its mortality-to-incidence ratio is close to 1 (Table 1), meaning that the number of patients diagnosed and dying from PDAC each year is almost the same (11,12). Because its incidence has increased by 55% over the last 25 years, PDAC is expected to be the second most frequent cause of cancer-related deaths over the next 10 years (13,14). Consequently, the number of patients suffering from PDAC and exposed to 5-FU will continue to increase in the future. Although several studies have addressed the mechanisms of resistance to 5-FU in PDAC (15,16), further investigation into this resistance is necessary to ensure better patient management.
As described in Table 1, a variety of cancers, aside from CRC and PDAC, are currently treated with 5-FU, generally in combination with systemic and targeted chemotherapy, radiotherapy and/or surgery. However, for such locations, data on resistance mechanisms are even scarcer than for digestive adenocarcinomas (17,18). It is, therefore, crucial to improve the understanding of the mode of action of 5-FU to identify the molecular mechanisms that may be involved in long-term 5-FU based treatment failure (recurrence and metastasis) (19) and to optimize the use of 5-FU for the management of patients with advanced digestive or extra-digestive cancers.
To exert its cytotoxic effect, 5-FU has to reach the tumour site, enter into cells, and be phosphorylated into its three active metabolites that are 5-fluorodeoxyuridine monophosphate (5-FdUMP), 5-fluorodeoxyuridine triphosphate (5-FdUTP) and 5-fluorouridine triphosphate (5-FUTP). Thus, 5-FU follows multistep pathways from transport into the cell to complete metabolism.
5-FU enters into human cells using several types of trans-membrane proteins known as human nucleoside transporters (hNT). The hNT include three human concentrative nucleoside transporters proteins (hCNT1, 2 and 3) and four human equilibrative nucleoside transporters proteins (hENT1, 2, 3 and 4) (20). While uridine uptake is performed by all hNTs cited above except hENT4 (20,21), 5-FU transport is achieved by hENT1 and hENT2 only (22–24) (Figure 1). In addition, uptake of 5-FU by organic anionic transporter 2 (hOAT2) has been reported. hOAT2 is highly expressed in the liver and kidney proximal tubules and may be responsible, at least in part, for the hepatic uptake of 5-FU (25–27). Finally, passive transport of 5-FU has also been demonstrated through paracellular and transcellular routes (28). Multidrug resistance-associated protein-5 (MRP-5) and MRP-8, two organic anion transporters ubiquitously expressed in tissues, mediate the efflux of 5-FU through the efflux of 5-FdUMP (29–31).
Figure 1. Overview of the uptake and efflux of 5-FU in human cells. Uptake (blue) of 5-FU is controlled by the human equilibrative nucleoside transporters hENT1 and hENT2 and by the organic anionic transporter hOAT2. Efflux (red) of 5-fluorodeoxyuridine monophosphate (5-FdUMP), a 5-FU metabolite, is mediated by the multidrug resistance-associated proteins MRP-5 and MRP-8.
After administration in patients, approximately 80% of 5-FU is catabolized in the liver into pharmacologically inactive metabolites, 5–20% are excreted in the urine, and only 1–3% contribute to the anabolism pathway responsible for clinical and cytotoxic effects (32). The effects of 5-FU are strongly dependent on the balance between anabolism and catabolism, and therefore, on the various factors involved in these two phases, such as the substrate concentration, the level of enzymes in the various tissues and their enzymatic activities. It is important to emphasize that the substitution of a hydrogen atom with a fluorine atom modifies the conformation of 5-FU only slightly in comparison to uracil and, therefore, the cellular enzymes can metabolize the two substrates indistinctively (33). 5-FU catabolism in the human body is particularly rapid, since the half-life of 5-FU is <20 min (34). 5-FU is mainly degraded by reductive pathways in three steps, leading to pharmacologically inactive metabolites (35,36) (Figure 2). The three enzymes dihydropyrimidine dehydrogenase (DPD), dihydropyrimidinase (DHP) and β-ureidopropionase (BUP-1) are also involved in the degradation of endogenous uracil and thymine (35). Due to its central role in the catabolism of 5-FU and the existence of a genetic polymorphism, DPD has been extensively studied for its role in the efficacy as well as in the toxicity of 5-FU (37). 5-FU anabolism comprises two pathways to produce three active metabolites: 5-FdUMP, 5-FdUTP and 5-FUTP (7,38) (Figure 2). The main pathway converts 5-FU to 5-fluorouridine monophosphate (5-FUMP) either directly by orotate phosphoribosyltransferase (OPRT) or indirectly via 5-fluorouridine (5-FUrd) using uridine phosphorylase (UP) and uridine kinase (UK). An alternative pathway involves the conversion of 5-FU into 5-fluorodeoxyuridine (5-FdUrd) and then into 5-FdUMP.
Figure 2. Overview of the anabolism and catabolism of 5-FU in human cells. The balance between anabolism and catabolism is crucial for the effect of 5-FU on cells. Catabolism is the most rapid process through which 5-FU is degraded in three steps. Anabolism leads to the production of three active metabolites (green): 5-fluorodeoxyuridine monophosphate (5-FdUMP), 5-fluorodeoxyuridine triphosphate (5-FdUTP) and 5-fluorouridine triphosphate (5-FUTP); 5-FdUrd, 5-fluorodeoxyuridine; 5-FdUDP, 5-fluorodeoxyuridine diphosphate; 5-FUrd, 5-fluorouridine; 5-FUMP, 5-fluorouridine monophosphate; 5-FUDP, 5-fluorouridine diphosphate; 5-FUDP-HexNAc, 5-FUDP-N-acetylhexosamine; 5-FUDP-Hex, 5-FUDP-hexose; DHFU, dihydrofluorouracil; FUPA, α-fluoro-β-ureidopropionic acid; FBAL, α-fluoro-β-alanine; TP, thymidine phosphorylase; TK, thymidine kinase; UMPK, UMP kinase; UDPK, UDP kinase; dUH, dUTP hydrolase; UP, uridine phosphorylase; UK, uridine kinase; OPRT, orotate phosphoribosyltransferase; RNR, ribonucleotide reductase; UDPNAP, UDP-N-acetylhexosamine-pyrophosphorylase; UDPGP, UDP-glucose-pyrophosphorylase; DPD, dihydropyrimidine dehydrogenase; DHP, dihydropyrimidinase; BUP-1, β-ureidopropionase. The structural formulas of 5-FU and its metabolites were drawn using the PubChem database (https://pubchem.ncbi.nlm.nih.gov/edit3/index.html).
From all this knowledge about the usage of 5-FU for cancer treatments and of its metabolism, in this review, we point out how integration of 5-FU into RNA and particularly into ribosomal RNA (rRNA) may provide novel mechanistic features that need to be taken into account in order to identify more precisely the limits of this drug that is still widely used. First, after an historical review of data regarding the integration of 5-FU into DNA, we expose the intrinsic ability of 5-FU to incorporate into RNA molecules including rRNA. Second, we uncover how, from recent improvements of analytical technologies, the concept of the F-ribosome emerged. Finally, we discuss how F-ribosomes could constitute a breakthrough for understanding 5-FU resistance and thus, how this innovative concept might help in improving patient management in the future.
BACKGROUND AND NOVELTIES OF 5-FU INTEGRATION INTO RNA, A REVISITED INTERPLAY
As described above, in cells, 5-FU is converted into active metabolites, namely 5-FdUMP, 5-FdUTP and 5-FUTP. While the first two metabolites have long been associated with some of the cytotoxic effects caused by 5-FU, it is only recently that it has been accepted that 5-FUTP, through its integration into RNA, contributes intensely to 5-FU’s anti-proliferative activity. In this section, after a brief overview of the impact of 5-FU on DNA metabolism, we focus on findings linking 5-FU and RNA. We present data demonstrating that 5-FU incorporation into RNA is associated with anti-proliferative effects. Then, we discuss the alteration of RNA features induced by 5-FU integration in RNA. Finally, we highlight the most abundant RNA species, namely ribosomal RNA (rRNA), which provides promising future avenues.
Cytotoxic effects of 5-FU due to modifications of DNA metabolism
5-FU was first used in clinic due to the anti-proliferative activity of two of its metabolites, 5-FdUMP and 5-FdUTP, through their deleterious effects on DNA (reviewed in (39)).
One of the impacts of 5-FU on DNA relies on its capacity to induce DNA damage. 5-FdUTP is a substrate for DNA polymerases and is incorporated into DNA. Following 5-FU treatment, the increased concentrations of dUTP and 5-FdUTP overwhelm dUTPase, which hydrolyses dUTP to dUMP. As a consequence, dUTP and 5-FdUTP can be mis-incorporated into DNA instead of dTTP, causing DNA damage (7). While the incorporation of 5-FU into DNA has largely been demonstrated in human tumour cells (40–43), mouse models such as the murine mammary carcinoma model (44), and CRC patients (45), its contribution to cytotoxicity is not compelling.
Another deleterious effect of 5-FU relies on its capacity to arrest DNA replication, notably via 5-FdUMP, which inhibits thymidylate synthase (TS). TS is an enzyme that acts as the de novo source of thymidine by reducing one carbon to a methyl group of dUMP to produce dTMP, which is further phosphorylated to form dTTP. Thymidine is necessary for DNA replication and repair. By interacting with the nucleotide-binding site of TS, 5-FdUMP inhibits binding of the normal substrate dUMP and, consequently, inhibits dTMP synthesis, resulting in abnormally low levels of dTTP and a massive increase in the level of dUTP, which is responsible for DNA replication arrest (7,19).
Finally, to correct 5-FU-induced errors that occur during replication, DNA repair mechanisms take place and contribute to 5-FU cytotoxicity. For example, thymidine DNA glycosylase (TDG) can excise incorporated 5-FU through the base excision repair (BER) mechanism, resulting in DNA strand break accumulation and cell death (46). Another DNA repair mechanism, DNA mismatch repair (MMR), has also been reported to influence 5-FU cytotoxicity in CRC cells (47). A recent study on human colon and ovarian cancer cells demonstrated that the initiation of the BER mechanism by UNG enzymes is the main mechanism of 5-FU-associated DNA repair that sensitizes cells to 5-FU (48). Overall, the partial efficacy of the repair mechanism, mainly because of 5-FU-induced depletion of the dTTP nucleotide pools, contributes to 5-FU cytotoxicity. In addition, 5-FU excised from DNA might increase the intracellular concentration of the drug and thus favour a cytotoxic effect (47).
Altogether, these studies reveal that 5-FU dysregulates a variety of DNA-based mechanisms, namely the inhibition of TS activity, mis-incorporation into DNA and defective DNA repair. While these types of dysregulation undoubtedly contribute to the cytotoxic effects of 5-FU (49), a growing body of evidence suggests that 5-FU cytotoxicity is also a substantial consequence of defects in RNA metabolism.
Integration of 5-FU into RNA is implicated in 5-FU driven cytotoxicity
By using approaches that rely on the quantification of radioactively labelled 5-FU metabolites and competition with unlabelled metabolites, a number of studies provided evidence of the incorporation of 5-FU into RNA and unveiled its implication in the 5-FU-driven anti-proliferative effects.
In vivo quantification of labelled 5-FU metabolites conducted in mice injected intraperitoneally with solutions of [3H]-labelled fluorinated pyrimidines showed that incorporation of 5-FU into RNA accounts for gastrointestinal toxicity in mice (50). Experiments conducted in cell lines using exponentially growing mammary epithelial MCF7 cells treated with [3H]5-FU confirmed, through total RNA extraction and nucleoside analysis by high pressure liquid ion exchange chromatography, that incorporation of 5-FU into RNA induces toxicity. Indeed correlation was established between the loss of clonogenic survival of mammary epithelial MCF7 cells and the incorporation of 5-FU into RNA (51).
Further evidence of 5-FU RNA integration driven toxicity has been unveiled using various models, such as mice, yeast and human cell lines. In mice treated with 5-FU, microscopic observations of small intestinal and crypt epithelial cells showed that 5-FU induces apoptosis. Interestingly, administration of 5-FU with uridine to relieve the incorporation of 5-FU into RNA was shown to reduce 5-FU-induced apoptosis in crypt epithelial cells. Moreover, uridine, but not thymidine, was able to inhibit the cytotoxicity of 5-FU observed in the intestinal cells of mice treated with 5-FU regiments (52). In a model of yeast grown in the presence of 5-FU, supplementation of the cultures with UMP reversed the inhibitory effect of 5-FU on cell growth, while the addition of dTMP only showed a slight effect, thus demonstrating the prevalence of RNA-based over a DNA-based toxicity (53). In cell lines, DNA and RNA were isolated from cells treated with 5-FU and hydrolysed to nucleosides. 5-FUrd and 5-FdUrd were quantified by liquid chromatography coupled to mass spectrometry. The incorporation of 5-FU into RNA was up to 15 000-fold higher than that into DNA. Moreover, for a panel of human cell lines, media complementation with uridine, which restored normal RNA metabolism through the reduction of 5-FU incorporation into RNA, was shown to compensate for most of the 5-FU-induced cytotoxic effects (54).
Thus, despite the fact that 5-FU was initially described for its deleterious action on the synthesis and integrity of DNA, numerous studies have clearly established that 5-FU cytotoxicity is mainly associated with its incorporation into RNA. However, methodologies used at the time in which these studies were performed assessed the 5-FU integration into the bulk of cellular RNA, without delineating every RNA species.
Integration of 5-FU into RNA may affect RNA structural and functional features
Because RNA is central to 5-FU toxicity, several studies have explored the consequences of 5-FU integration on the molecular features of different RNA species including messenger RNA (mRNA), transfer RNA (tRNA) and small nuclear RNA (snRNA).
It has been reported that 5-FU impacts mRNA splicing. Indeed, an in vitro experiment using β-globin mRNA minigene constructs showed that integration of 5-FU alters pre-mRNA splicing (55). In yeast, transcriptome modifications induced by 5-FU were analysed by high-density DNA microarray technology. Global disruption of pre-mRNA splicing characterized by intron retention was found to affect numerous mRNAs (56). However, these data were challenged by Zhao et al (57). These authors revealed that the defect in mRNA splicing does not originate from the incorporation of 5-FU into mRNA since fluorinated mRNA synthesized in vitro and injected into Xenopus oocytes was not found to be aberrantly spliced. Instead, the incorporation of 5-FU into the spliceosomal U2 snRNA at pseudouridylated sites was shown to inhibit U2 snRNA pseudouridylation and subsequent pre-mRNA splicing (57). Integration of 5-FU not only affects snRNA pseudouridylation but also affects its structure and turnover. Indeed, in sarcoma murine cells, the comparison of electrophoretic migration of snRNA in non-denaturing and denaturing polyacrylamide gels showed that 5-FU alters the structure of U4 and U6 snRNAs, and a reduced turnover of the U1 snRNA was observed in cells treated with 5-FU (58). The impact of 5-FU on mRNA translational efficiency has also been investigated. In vitro translation assays using total mRNA or the purified dihydrofolate reductase mRNA isolated from cells grown in presence of 5-FU revealed no major impact of 5-FU (59,60). Moreover in vitro translation of the synthetic mRNA encoding TS and containing substitution of 100% of the uracil bases with 5-FU, confirmed that the integration of 5-FU into mRNA does not affect its translation (61).
Regarding tRNA, integration of 5-FU was also shown to affect post-transcriptional RNA modifications, including formation of pseudouridine (ψ) and methylation (62,63). In vitro binding assays between in vitro synthesized 5-FU substituted tRNA (5-FU-tRNA) and yeast purified pseudouridine synthase revealed that 5-FU-tRNA interacts with the enzyme at its catalytic site. Using the release of radioactivity from a tritium labeled pseudouridine synthase RNA substrate as a measure of the enzyme activity, it was further shown that 5-FU-tRNA limited tritium release, indicative of an inhibition of the enzyme activity (62,63). The interaction between 5-FU-tRNA and the catalytic site of the pseudouridine synthase was later shown to be a covalent complex (64). In vitro methylation assay of tRNA by tRNA methyltransferases purified from mice liver and tumours showed that 5-FU inhibits specifically the tRNA uracil-5-methyltransferase (62,63).
More recently, the effect of exposure to 5-FU on the expression of miRNAs and lncRNA was investigated. miRNA microarray technology revealed that 5-FU promotes profound transcriptional reprogramming, leading to altered expression of miRNA in human breast and colorectal cancer cells (65–67). Lately, the role of miRNAs and lncRNAs in 5-FU resistance of CRC was intensively reviewed (68–70). Nevertheless, the depicted underlying mechanism through which 5-FU may impact these non-coding RNAs relies on the capability of 5-FU to alter their expression, leaving the potential 5-FU incorporation into miRNA and lncRNA still undocumented.
Thus, these results demonstrate that 5-FU is incorporated into several RNA species (Figure 3). Whether this incorporation affects their features remains to be consolidated and extended to all RNA species in the future. Nevertheless, how 5-FU-induced alteration of RNA metabolism impacts cell responses and behaviours or toxicity remains largely unknown.
Figure 3. Overview of all RNA that potentially incorporate 5-FU, with a focus on Ribosome Biogenesis (RiBi). RiBi requires the initial transcription of rDNA genes to a single precursor of ribosomal RNA (pre-rRNA), 47S, in the nucleolus, whereas 5S rRNA is transcribed in the nucleoplasm. 47S is subsequently cleaved in several pre-rRNA to ultimately provide 18S, 5.8S and 28S rRNAs. During this processing step, rRNAs are post-transcriptionally modified by protein-processing complexes including fibrillarin (FBL) complexed with C/D box snoRNAs and DKC1 complexed with H/AHA box snoRNAs, which are responsible for 2′-O-methylation and pseudouridylation modifications, respectively. RiBi also requires the transcription of mRNAs encoding ribosomal proteins (RP, not shown here), which, after being translated in the cytoplasm and imported into the nucleus, are assembled into small pre-40S (18S and 32 RPS) and large pre-60S (5S+5.8S+28S and 47 RPL) ribosomal subunits. The two mature subunits are then exported into the cytoplasm and assembled into mature ribosomes (80S), ready to achieve translation with mRNA and tRNA. The extrinsic RNA epitranscriptome created by 5-FU (red star) enforces the expansion of the DNA-based initial view of the deleterious effect of 5-FU on phenotype.
5-FU is incorporated into rRNA and affects ribosome biogenesis (RiBi)
In cells, rRNA is the most abundant RNA species making it the easiest RNA to be technologically apprehended. Thus, for over 50 years, interest in the impact of 5-FU integration into rRNA emerged, although findings remained limited in terms of molecular and phenotypic consequences. Using bacterial models, numerous studies have reported that 5-FU affects ribosome synthesis. Although ribosome biogenesis (RiBi) in bacterial cells differs from that in mammalian cells (71), interest in the impact of 5-FU on mammalian ribosome production has been evaluated given the use of 5-FU as a potent chemotherapy.
RiBi is one of the most energy-consuming processes in the cell that requires hundreds of factors. Synthesis of the different ribosomal components also involves the three RNA Polymerases (RNA Pol) in humans. RNA Pol I is responsible for the synthesis of three of the four rRNAs (28S, 18S and 5.8S). RNA Pol III synthesizes 5S rRNA, and RNA Pol II synthesizes mRNAs encoding for ribosomal proteins (RP). The biogenesis of ribosomes is thus a multi-step process, the limiting step of which is the transcription of rDNA genes by RNA Pol I, which generates a precursor ribosomal RNA (pre-rRNA): the 47S. This pre-rRNA contains the sequences of 18S, 5.8S and 28S rRNAs, separated by internal transcribed spacer sequences (ITS) and surrounded by external transcribed spacer sequences (ETS). This pre-rRNA is cleaved at both ends, generating a subsequent pre-rRNA: 45S. This pre-rRNA is then sequentially processed to produce mature 5.8S, 18S and 28S rRNAs (72).
By analysing the impact of several concentrations of 5-FU on the incorporation of [3H]uracil into RNA of rat hepatoma cells, Wilkinson et al. showed that 5-FU integration does not inhibit the synthesis of 45S pre-rRNA, but rather affected its maturation (73,74). Indeed, after the electrophoresis of RNA on polyacrylamide-agarose gels, gels were scanned at 260 nm and sliced for [3H] radioactivity counting. The amount of [3H] labelled 45S pre-rRNA increased compared with the amount of downstream [3H] labelled 38S intermediate. In addition, 18S and 28S rRNAs, constitutive of the mature ribosome, were produced in lower amounts, with 18S rRNA being more sensitive to 5-FU than 28S rRNA (73,74). In human CRC cells treated with [3H]5-FU, the incorporation of labelled 5-FU into 45S pre-rRNA and the production of labelled mature 18S and 28S rRNAs were analysed by RNA fractionation by polyacrylamide-agarose electrophoresis followed by [3H] fluorography to demonstrate that 5-FU is incorporated into rRNA. In human CRC cells treated with 5-FU, northern blotting, using a set of [32P] labelled probes, confirmed that 5-FU affects 45S pre-rRNA maturation (75). In a large-scale study aiming at describing the impact of a panel of 36 chemotherapeutic drugs on rDNA transcription and rRNA processing, human fibrosarcoma cells were cultured with drugs, phosphate depleted and labelled with [32P] orthophosphate. The isolated RNAs were separated by agarose gel electrophoresis for quantification by phosphorimager analysis of all rRNA species. This approach confirmed that 5-FU does not impact rDNA transcription, but it does affect pre-rRNA processing (76).
Since 5-FU affects RiBi, it cannot be excluded that 5-FU may cause what is recognized in the literature as ribosomal stress (77). Ribosomal stress occurs when inhibition of rRNA processing triggers a disequilibrium between the quantity of rRNA and that of RPs. In this process, two key RPs, namely RPL5 and RPL11, bind to MDM2, thus prevent the interaction between MDM2 and P53, and consequently stabilize and activate P53, leading to cell cycle arrest and apoptosis (78). Nevertheless, providing that P53 needs to be free of any mutations to be functional, this potential 5-FU induced cytotoxic effect, if occurring in some circumstances, cannot account for a generalized mode of action of 5-FU since many cancers are characterized by p53 mutations.
Hence, these studies demonstrated, using approaches available at their time of publication, that 5-FU is incorporated into rRNA precursors and affects RiBi (Figure 3). However, no demonstration of the presence of 5-FU into ribosomes could be done.
5-FU INTEGRATION IN rRNA GENERATES THE FLUORINATED RIBOSOME (F-RIBOSOME)
To improve our understanding of the consequences of 5-FU incorporation into rRNA, the development of technologies aimed at detecting and quantifying the 5-FU molecules incorporated into RNA turned out to be crucial. Here, we discuss how progress in the detection of 5-FU anabolites in RNA allowed to identify and quantify the 5-FU molecules integrated in the ribosomes, and propose that, by creating an epitranscriptomic-like source of rRNA modification, 5-FU creates a novel biological object called F-ribosome.
Analytical detection of 5-FU anabolites in RNA from old to new technologies
The study of the distribution of 5-FU metabolites in different biological samples (cells, cellular compartments, DNA, RNA, etc.) requires advanced analytical tools to reach greater sensitivity and selectivity. Several technologies enabling such analyses have been described: radiolabelled assay, nuclear magnetic resonance (NMR), capillary electrophoresis, gas chromatography coupled with mass spectrometry (GC-MS), and, more recently, liquid chromatography coupled with mass spectrometry (LC-MS).
Radiolabelled assays were mostly used in the 1980s, particularly to study the incorporation of 5-FU into DNA and RNA (79). For these assays, cells were incubated with [3H]FU, RNA and DNA were separated by differential extraction and then radioactivity was measured in each extract. This approach is highly sensitive; however, it is limited to laboratories authorised to conduct radiolabelling. In addition, since the signal measured is identical, irrespective of the metabolite, this technology relies on pure DNA and RNA extracts. Another drawback of this approach is that the treatment of samples with radiolabelled [3H]5-FU is necessary, preventing its use on patient samples. It thus became obsolete and has been replaced by methods using chromatographic separation.
19F nuclear magnetic resonance (19F NMR) is a non-invasive spectroscopic method that is used to identify and quantify all 5-FU anabolites in a single run (80,81). It was applied to several biological samples such as intracellular extracts from cultured cells (80), tumour tissues (82,83) and RNA (82). To the best of our knowledge, it has not been used to analyse DNA. 19F NMR is specific to fluorinated compounds and has to be coupled with 31P NMR to study endogenous nucleotides and nucleosides (81).
Two capillary electrophoresis methods coupled with UV detection have been reported for the quantification of 5-FU, 5-FUrd, 5-FdUrd, 5-FdUMP and 5-FUTP (84,85). The most recently applied method to quantify 5-FU and 5-FdUMP in cells and tissues failed to detect 5-FdUMP (85), emphasizing the lack of sensitivity of the method.
GC-MS methods reported in the literature were used to study the incorporation of the 5-FU metabolite into RNA and DNA (45,83). While 5-FU can be analysed by GC, 5-FUMP and 5-FdUMP cannot be determined as such for physicochemical reasons. In these studies, both metabolites had to undergo two enzymatic steps to be transformed into 5-FU. The procedure was time-consuming and presented the following major drawback: since 5-FU was quantified as a surrogate for 5-FUMP and 5-FdUMP, RNA had to be free of any DNA contamination and vice versa during sample preparation, due to the risk of overestimating the concentrations of metabolites.
LC-MS represents the method of choice well described in the literature for the analysis of anabolic metabolites of 5-FU. Detection by MS provides both high sensitivity and selectivity. LC-MS technology is able to quantify nucleosides as well as nucleotide anabolites, although the quantification of nucleotides, particularly tri-phosphates, remains challenging. Thus, few assays have described the analysis of both types of compounds (86–89). Methods have been applied on cultured cells (86,87,89), peripheral blood mononuclear cells (88), and DNA and RNA (54,86).
Recently, our laboratory set up a highly sensitive technology based on LC coupled with high resolution mass spectrometry (LC-HRMS) using Orbitrap technology for the simultaneous determination of the ten anabolic metabolites (nucleosides, nucleotides and sugar nucleosides) of 5-FU (86). The method was applied to measure the proportion of each anabolic 5-FU metabolite in cellular contents and to study their incorporation into RNA and DNA. After extraction and digestion, a dephosphorylation step was added in order to quantify 5-FUrd in RNA and 5-FdUrd in DNA rather than 5-FUMP and 5-FdUMP since a better sensitivity was achieved for nucleosides rather than nucleotides (86). This method also provided the measurement of endogenous nucleosides and nucleotides, thus enabling measuring the consequences of inhibition of enzymes, such as TS or pseudouridine synthase, in the same analytical run. More importantly, this method allowed us to report for the first time the incorporation rate of 5-FUrd into rRNA, namely 28S and 18S (86).
The F-ribosome, a novel ribosome species, created by 5-FU
While incorporation of 5-FU into RNA has been studied since the early seventies using methodologies that roughly assess the quantity of 5-FU integrated into RNA, these methodologies were rather inadequate for distinguishing rRNA from all species of RNA when measuring the incorporation of labelled 5-FU metabolites.
The recent technological developments from our laboratory not only pushed the LC-HRMS technology up to measuring the incorporation rate of 5-FUrd in 28S and 18S rRNAs as detailed above, but further allowed the detection and quantification of 5-FUrd present in the whole ribosome (86). From cells treated with 5-FU, cytoplasmic ribosomes were isolated and their rRNA subsequently purified as a pool of 28S, 18S, 5.8S and 5S rRNA. Since the nucleotide sequence, and thus the number of each endogenous nucleoside is known for human 28S, 18S, 5.8S and 5S rRNAs, the quantification of the amount of 5-FUrd relative to that of endogenous nucleosides provides the number of 5-FUrd molecules per rRNA, and per ribosome accordingly.
Quantification of the amount of 5-FUrd from ribosomes in several CRC cell lines treated for 24 h with 10 μM 5-FU showed that 5-FU treatment results in the production of fluorinated ribosomes that we called F-ribosomes (i.e., ribosomes with 5-FU incorporated in their rRNA) (90). We thus demonstrated that 5-FU-treated cells produce F-ribosomes bearing extrinsic chemical rRNA modifications (Figure 3). To support the notion that 5-FU creates novel ribosomes, we recently investigated the activity of F-ribosomes using an in vitro hybrid translational assay (91). We found that 5-FU incorporation in the ribosome modifies its intrinsic translational activity (90).
Quite interestingly, recent findings showed that 5-FU treatment is associated with a full translational reprogramming. These data support the intriguing possibility that the F-ribosome intrinsic translational activity could contribute to such translational reprogramming. Two early large-scale studies proposed that 5-FU modulates the translation of a subset of mRNAs. However, the alteration of translational efficiency was not directly investigated, and 5-FU treated CRC cells underwent additional modifications, i.e. stable modifications of TS expression (92) or Hsp70 immunoprecipitation (93). Recently, by coupling polysome profiling with mRNA expression microarray, we showed that 5-FU induces a global translational reprogramming in three CRC cell lines (94). We identified 313 genes coding for mRNA, the translational efficiency of which is selectively modulated following 5-FU treatment. Enrichment analysis revealed that translationally down-regulated mRNAs are mainly implicated in DNA replication, as usually observed in response to 5-FU reinforcing the notion that translational control also contributes to this 5-FU induced effect. Interestingly, while 5-FU decreases global translation slightly, it promotes the translational efficiency of over 280 genes (the full list is available in (94) Supplementary Table S2). These translationally up-regulated genes are implicated in transcription regulation and have roles in the translational machinery including translation initiation and elongation factors, tRNA maturation factors and ribosomal proteins (the genes are indicated in (94) Supplementary Table S3). Among the mRNAs whose translation is up-regulated in response to 5-FU, some are involved in pathways controlling cell proliferation and tumorigenesis such as Wnt and integrin signalling pathways also known as pathways involved in 5-FU resistance (49,95) (Table 2). Again, this suggests that translational control could be part of the cell response mechanisms that finally lead to reshape gene expression regulation favouring emergence of resistant cells. In parallel, we unveiled that a miRNA-based mechanism was involved in the 5-FU-dependent translation, reduced miR-155 expression increasing the translation of HIVEP2 mRNA translation in response to 5-FU. Whether this miRNA-based mechanism works in concert with the rRNA-based mechanism remains to be determined. Nevertheless, whatever the molecular mechanisms engaged in this translational reprogramming, gaining access to the translatome deeply remodelled by 5-FU provides a reservoir of potentially actionable protein targets, the modulation of which could increase the sensitivity of cancer cells to 5-FU (Figure 4).
Table 2. Signalling pathways affected by translational reprogramming in response to 5-FU. Bioinformatic non-statistical analysis using the list of translationally up-regulated genes identified in (94) using PANTHER (http://pantherdb.org, Functional classification viewed in graphic charts’ using ‘pathways’ parameters) (124)
Pathway name (ID) Number of genes involved in the pathway Gene hit against total number of gene in pathway (%) Genes
Wnt signalling pathway (P00057) 6 5.0 FZD1, FZD5, DACT1, MYH6, GNG7, GNG10
Inflammation mediated by chemokine and cytokine signalling pathway (P00031) 6 5.0 MYH6, SOCS6, GNG7, GNG10, RHOQ, SOCS7
Gonadotropin-releasing hormone receptor pathway (P06664) 4 3.3 ACVR2B, NR3C1, MAP3K13, MAP4K4
Integrin signalling pathway (P00034) 4 3.3 RAP2B, FRK, MEGF9, RHOQ
Angiogenesis (P00005) 4 3.3 FZD1, FZD5, PDGFC, PDGFA
Figure 4. Model showing that translational reprogramming, induced by 5-FU integration in ribosomes, contributes to 5-FU-driven cell plasticity and relapse. 5-FU is generally recognized as a disruptor of DNA metabolism contributing to its cytotoxicity. However, 5-FU is mainly incorporated into RNA. Through its integration in rRNA, the most abundant RNA, 5-FU produces active F-ribosomes which induce translational reprogramming accompanied by an incomplete cytotoxicity. Instead, cell plasticity takes place and will ultimately contribute to relapse.
Still, whether translational reprogramming is a direct consequence of 5-FU incorporation into ribosomes and into the other RNA being part of the translational apparatus or involved in its fine tuning remains to be fully demonstrated.
CONCLUSION AND PERSPECTIVES
Biological epitranscriptomic rRNA modifications (2′-O-ribose-methylation, pseudouridylation, base modification) are implicated in the control of ribosome translation and cancer cell fate (96–98).
2′-O-ribose-methylation (2′-O-Me) is the most abundant modification, with 106 sites mapped in human rRNA (99). While ribosomes were usually considered to be constitutively methylated at each 2′-O-Me site in healthy proliferating cells (100–102), our laboratory reported that rRNA 2′-O-Me can be altered at some specific sites during mammary tumorigenesis (103–105), and that alteration of 2′-O-Me rRNA directly affects the intrinsic translational activity of ribosomes (106). These data support the importance of ribosome epitranscriptomics in regulating translation and contributing to the phenotypes of breast tumours.
Pseudouridylation, which involves the isomerisation of uridine into ψ, accounts for 95 modified sites in human RNA (107). Defects in ψ have been linked with X-linked dyskeratosis congenita (X-DC) associated with an increased risk of developing tumours (108), and rRNA ψ defects are responsible for alterations of ribosomal translational activity (109,110) and cell fate (111–114).
Methylation and acetylation of bases that are responsible for >12 additional epitranscriptomic rRNA modifications in humans (96,115) also contribute to the modulation of ribosome activity and human cell fate. For example, deficiency of NSUN5 (28S rRNA (cytosine-C(5))-methyltransferase) or ZCCHC4 (rRNA N6-adenosine-methyltransferase), which introduces the m5C3782 or m6A4220 modification on 28S rRNA respectively, induces translational reprogramming and affects cell proliferation (116,117).
Taken together, these studies highlight the ribosome, and more particularly, epitranscriptomic rRNA modifications, as key players in translational regulation and tumorigenesis. Any alteration in biological rRNA epitranscriptomics leads to profound changes in the translational activity of the ribosome and is associated with new phenotypes (103–105,118,119). Through its stable integration into rRNA, 5-FU induces extrinsic post-transcriptomic modifications of the ribosomes, thus creating a novel biological object, which is an artificially modified ribosome, the F-ribosome. Following this notion, it can be proposed that rRNA modification by 5-FU creates a source of epitranscriptomic-like rRNA modification which may affect cell fate and notably contribute to the emergence of 5-FU resistant cells (Figure 4).
The ribosome is a complex macromolecular machinery that has a pivotal role in the growth and proliferation of mammalian cells. However, the ribosome is rarely envisaged when studying the mechanisms of resistance to anti-cancer therapies, such as 5-FU-based treatments. Here, we placed the ribosome as well as RNA-mediated translational regulation at the heart of the action of 5-FU and propose that a novel view of this old drug can contribute to managing the impact of 5-FU in therapy by the medical community. In this biological model, combination of 5-FU with RiBi inhibitors and/or drugs targeting proteins encoded by translationally deregulated mRNAs could be evaluated to counteract cell plasticity and relapse as a consequence (Figure 4).
At present one of the most extensively studied issues in oncology is the drug resistance, as this accounts for the vast majority of cancer-related deaths. Significant changes in tumour cells occur upon treatment and the resulting resistance process is currently modelled as a succession of three phases, named sensitivity, tolerance and recurrence (120,121). A wide majority of studies explore the molecular mechanisms underlying the drug tolerance through analysis of genetic as well as non-genetic mechanisms such as epigenetic and transcriptomic reprograming (121–123). However, it appears obvious that being the last step of gene expression, translation arises as an inescapable layer of gene expression regulation that, although underestimated for a long time, should now be fully considered to elucidate the molecular mechanisms underlying drug-tolerance, drug-resistance and recurrence following anti-cancer treatments containing 5-FU.
ACKNOWLEDGEMENTS
We thank Brigitte Manship for editing the manuscript.
FUNDING
Institut National de la Santé et de la Recherche Médicale (Inserm); Centre National de la Recherche Scientifique (CNRS); Université Claude Bernard Lyon 1 (UCBL1); Agence Nationale de la Recherche (ANR) [ANR-13-BSV8-0012-01 RIBOMETH, ANR-19-CE12-0004 ACTIMETH]; Institut National du Cancer (INCa) [PLBIO 2018-131 FluoRib]; PAIR Sein program [ARC INCa LNCC 7625 RiboTEM]; SIRIC program [INCa-DGOS-Inserm 12563 LYriCAN]; Laboratoire d’Excellence (Labex) [DEVCAN2UMAN]; Fondation ARC pour la Recherche sur le Cancer [20161204686 MARACAS]; Ligue contre le Cancer (Drôme, Rhône, Isère, Puy-de-Dôme, Allier).
Conflict of interest statement. None declared.
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PMC008xxxxxx/PMC8445368.txt |
==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
34541538
10.1093/narcan/zcab036
zcab036
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Survey and Summary
Regulatory roles of RNA modifications in breast cancer
Kumari Kanchan Department of Molecular Biology, Umeå University, SE-901 85 Umeå, Sweden
Wallenberg Centre for Molecular Medicine, Umeå University, SE-901 85 Umeå, Sweden
Groza Paula Department of Molecular Biology, Umeå University, SE-901 85 Umeå, Sweden
Wallenberg Centre for Molecular Medicine, Umeå University, SE-901 85 Umeå, Sweden
https://orcid.org/0000-0002-2374-2045
Aguilo Francesca Department of Molecular Biology, Umeå University, SE-901 85 Umeå, Sweden
Wallenberg Centre for Molecular Medicine, Umeå University, SE-901 85 Umeå, Sweden
To whom correspondence should be addressed. Fax: +46 90772630; Email: [email protected]
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
9 2021
16 9 2021
16 9 2021
3 3 zcab03625 8 2021
07 8 2021
05 7 2021
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Cancer.
2021
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Collectively referred to as the epitranscriptome, RNA modifications play important roles in gene expression control regulating relevant cellular processes. In the last few decades, growing numbers of RNA modifications have been identified not only in abundant ribosomal (rRNA) and transfer RNA (tRNA) but also in messenger RNA (mRNA). In addition, many writers, erasers and readers that dynamically regulate the chemical marks have also been characterized. Correct deposition of RNA modifications is prerequisite for cellular homeostasis, and its alteration results in aberrant transcriptional programs that dictate human disease, including breast cancer, the most frequent female malignancy, and the leading cause of cancer-related death in women. In this review, we emphasize the major RNA modifications that are present in tRNA, rRNA and mRNA. We have categorized breast cancer-associated chemical marks and summarize their contribution to breast tumorigenesis. In addition, we describe less abundant tRNA modifications with related pathways implicated in breast cancer. Finally, we discuss current limitations and perspectives on epitranscriptomics for use in therapeutic strategies against breast and other cancers.
Graphical Abstract
Graphical Abstract Modifications of RNA known in breast cancer.
Knut and Alice Wallenberg Foundation 10.13039/501100004063 Cancerfonden 10.13039/501100002794 Pj:190337 Cancer Research Foundation in Northern Sweden 10.13039/501100004886 AMP 21-1030
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pmcINTRODUCTION
RNA modifications, collectively termed the epitranscriptome, are crucial regulators of temporal and spatial gene expression programs. Currently, over 170 RNA modifications decorating all RNA species and in all three kingdoms of life have been described (1,2). Although modification to RNA has been documented for over 50 years, the functions of most of these modifications are largely unknown. Recent development of more sensitive and specific technologies, such as high-throughput sequencing and improved mass spectrometry, shed light on to the exciting new field of RNA epitranscriptomics.
In general, the fate of modified RNAs is determined by the coordinated actions of writers, erasers and readers that impose, remove and recognize the chemical mark. Some of the writers are stand-alone enzymes, whereas others act as multiprotein writer complexes that also comprise accessory subunits. In addition, some of the RNA modifications are reversible i.e. removed by the erasers, whereas others are irreversible. Most of the known RNA modifications map to abundant RNAs such as transfer RNA (tRNA) and ribosomal RNA (rRNA), tRNA being the most extensively modified RNA type in the cell (3) (Figure 1).
Figure 1. Chemical structures of common RNA modifications known to have a role in breast cancer. The modified groups are highlighted in red. The type of RNA in which the modification is detected is indicated using the symbols of rRNA, tRNA and mRNA.
In eukaryotes, the nuclear-encoded tRNAs contain on average 11–13 modifications per molecule whereas mitochondrial tRNAs are modified to a lesser extent, with an average of 5 modifications per molecule (4). The molecular consequence of the modification depends on the type of chemical modification and the location within a tRNA. Hence, modifications occurring at the wobble position (position 34) and to the residue adjacent to the anticodon loop (position 37) are highly conserved and lead to the strongest effects in optimization of codon usage, regulating translational efficiency and fidelity (5–7). However, modifications along the whole L-shape affect tRNA stability, localization and functional folding (8,9). Such modifications include but are not limited to 5-methylcytosine (m5C), N1-methyladenosine (m1A), pseudouridine (Ψ), 5-methyluridine (m5U), 1-methylguanosine and 7-methylguanosine (m1G and m7G, respectively), and inosine (I), and complex multistep chemical modifications, such as N6-threonylcarbamoyladenosine (t6A) and 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) (10) (Figure 2).
Figure 2. tRNA modifications. Clover leaf model representing the structure of a human tRNA. Nucleotides that undergo modification are shown in pink. Distinct chemical modifications are represented in different colors. The enzymes known to mediate tRNA modifications linked to breast cancer are capitalized and highlighted in blue.
Human ribosomes contain four rRNA types, i.e. 28S, 5S and 5.8S rRNAs in the 60S subunit, and 18S rRNA in the 40S subunit. In each subunit, the rRNAs form the core of the translation machinery whereas ribosomal proteins stabilize the structure and fine-tune the function of the ribosome (11,12). During ribosome biogenesis, rRNAs are extensively modified, expanding the topological properties of RNAs and optimizing the ribosome functionality (13). Recently, >130 individual rRNA modifications have been visualized in the three-dimensional structure of the human ribosome, being several of the modifications associated with degenerate states in cancer (14). In eukaryotic ribosomes, the most abundant rRNA modifications are ribose 2′-O-methylation (2′O-Me or Nm) and Ψ catalyzed by box C/D and box H/ACA ribonucleoprotein (RNP) enzymes, respectively, using small nucleolar RNAs (snoRNAs) for the recognition of specific rRNA target sites (15–17). Conversely, base modifications, such as methylations and acetylations, are catalyzed by conventional protein enzymes, most of which have only recently been identified (18–21), and thereby, their function is largely unknown (22).
Shortly after the discovery of the 5′ cap and 3′ polyadenylation, N6-methyladenosine (m6A) was identified in mRNA (18–21). m6A is the most abundant internal mRNA modification with on average 3–5 adenines methylated per mRNA (23,24). Other less abundant modifications within eukaryotic mRNA include m1A, N6,2′-O-dimethyladenosine (m6Am), m5C, 5-hydroxymethylcytosine (hm5C) and Ψ (Figure 3) (3). These modifications are not randomly distributed in the mRNA, and depending on the modification type and the deposition site, they can virtually affect all aspects of RNA fate including RNA processing, RNA export, mRNA translation and degradation. All these molecular events shape the transcriptome in a spatiotemporal manner to tightly regulate gene expression programs (25).
Figure 3. Internal mRNA modifications. Schematic representation of internal mRNA modifications and the predicted location on the mRNA are shown. The main writers of the respective modifications and their functions in breast cancer are depicted.
The deposition of chemical modifications into RNA is dynamic allowing the rapid adaptation to changing environmental cues and to various stresses. Such adaptation is crucial for cellular homeostasis. Hence, alterations in the expression levels of RNA modifiers and thereby, dysregulated RNA modification pathways, have been linked to tumorigenesis as well as other human diseases (26). In this review, we describe the current understanding on how these epitranscriptomic marks are implicated in breast tumorigenesis, the most prevalent cancer among women worldwide. Specifically, we highlight several critical modifications, namely m6A, m1A, m5C, 2′O-Me, RNA editing and Ψ (Table 1). We describe which RNA species they have been identified in, their molecular and cellular functions (where known) and the evidence linking them to the development, maintenance and progression of breast cancer.
Table 1. List of RNA modifications in breast cancer. RNA modifications, enzymes and associated effect in breast cancer
Type of RNA Enzymes Function in breast cancer Ref
m6A
mRNA ALKBH5, IGF2BP1 Promoted BCSC phenotype (69,70,73)
mRNA METTL3 Promoted BCSC phenotype, induces metastasis (201)
mRNA IGF2BP2 Promoted BCSC phenotype (72)
mRNA METTL3, METTL14, WTAP, FTO, ALKBH5 Inhibited colony formation and migration (202)
mRNA YTHDF3 Promoted breast cancer, induces brain metastasis and angiogenesis (203)
mRNA METTL14, ALKBH5, YTHDF3 Promoted growth, proliferation and angiogenesis (56)
mRNA METTL14, ZC3H13 Correlation with unfavorable prognosis in breast cancer patients (49)
mRNA METTL3 Increased cell proliferation and tumor progression (57)
mRNA METTL3 Increased proliferation and decreased apoptosis in breast cancer cells in vitro and in vivo (58)
mRNA METTL3, FTO, IGF2BP1, YTHDF1 Promoted lung metastasis and clinical progression in breast cancer, induction of EMT (59)
mRNA METTL3 Inhibited migration and invasive capacities of the cells in TNBC (60)
mRNA METTL14 Promoted breast cancer initiation and progression (62)
mRNA FTO Promoted cell proliferation, colony formation, tumor growth and metastasis (63)
mRNA METTL3 Enhanced expression of oncogenes, induces acquired chemoresistance (74)
mRNA METTL3 Induced breast cancer cell proliferation, associated with drug sensitivity (76)
mRNA YTHDF2 Promoted breast cancer progression (204)
mRNA ALKBH3 Increased breast cancer cell invasiveness (83)
miRNA METTL14 Promoted migration and invasion in breast cancer cells (50)
miRNA FTO Promoted cell invasion and migration (52)
miRNA METTL3 Associated with acquired chemoresistance (75)
circRNA METTL3 Promoted cell proliferation (61)
rRNA METTL5 p70-S6K activation and translation initiation, increased breast cancer cell growth (77)
m5C
Type of RNA Enzymes Function in breast cancer Ref
mRNA NSUN2-NSUN7, DNMT1, DNMT3A, DNMT3B, ALYREF TET2 Affected tumor development, tumor immune microenvironment and potential markers for TNBC patients (110)
2’-O-Me
Type of RNA Enzymes Function in breast cancer Ref
rRNA Fibrillarin Promoted BCSC phenotype (128)
rRNA Undefined Associated with breast cancer subtypes and tumor grade, linked with overall patient survival (127)
Ψ
Type of RNA Enzymes Function in breast cancer Ref
rRNA DKC1 Telomerase activity, linked with better clinical outcome (143)
A-to-I
Type of RNA Enzymes Function in breast cancer Ref
mRNA ADAR Resistance to methotrexate (167)
mRNA ADAR Cell viability, drug sensitivity clinically relevant editing events in breast tumors than normal tissues (161)
mRNA ADAR1 Cell cycle control, DNA damage response, increased breast cancer cell progression (163)
mRNA ADAR1 DNA damage, immunity, DNA replication, increased breast cancer cell progression (159)
mRNA ADAR1p110 Increased proliferation and breast cancer metastasis (164)
C-to-U
Type of RNA Enzymes Function in breast cancer Ref
mRNA APOBEC3 Improved breast cancer survival (173)
mRNA Apobec-1 complementation factor (A1CF) Increased breast cancer progression (174)
Breast cancer
According to a global cancer statistics study, breast cancer is the most commonly diagnosed cancer in women and among the leading cause of cancer-related death in females (27) (Figure 4). Although there is an evident increase in breast cancer incidence and mortality among pre- and post-menopausal women, females of younger age are also at risk every year, with higher emphasis in developing countries (28). Hence, although advanced therapies and early detection have improved the survival rate (29), the causal factors of breast carcinoma still remain elusive.
Figure 4. Global cancer statistics. Pie chart depicting the percentage of new cases in each tumor type in 2020 for women globally. The data used to prepare the chart were taken from GLOBOCAN 2020 (27).
The clinical behavior and the treatment outcomes in breast cancer are highly influenced by tumor heterogeneity consisting in increased morphological variability and fluctuating therapy response (30). Based on the expression of the hormone receptors (estrogen (ER) and progesterone (PR)) and the human epidermal growth factor receptor 2 (HER2), there are four molecular subtypes of breast cancer, namely luminal A, luminal B, HER2 positive and basal-like or triple-negative (TNBC) (31). Although both luminal A and B are ER and PR positive, the later one has a worst prognosis and is either HER positive or negative with high levels of the proliferation marker Ki-67 (30,32). The HER2 subtype is ER and PR negative but positive for HER2 and the TNBC presents a triple negative immunophenotype (ER, PR and HER2 negative), increased proliferation rate and the highest incidence of relapse (30,33,34). Hence, in addition to providing prognostic information, the molecular subtypes can be used to evaluate clinical behaviors and response to treatments. However, the intrinsic heterogeneity of breast cancer impedes the full characterization based on the aforementioned histopathologic parameters. Thus, recent evidence supports the presence of multiple subtypes within a tumor (32) and the existence of even more than four subtypes (30), manifesting the complex molecular landscape of breast cancer cells. In addition, it has been shown that the microenvironment can dictate plasticity of breast cancer cells (35). For instance, the mammary stroma can induce basal differentiation in MCF7 cells, a luminal cellular model, and the site of injection of these cells into mice models can confer different tumor phenotypes, with injection in the milk ducts leading to an increased mimicry of the original tumor compared with the injection into the fat pads (35). Therefore, finding an effective cure for this heterogeneous and multifactorial disease is still a major challenge.
Such complexity and heterogeneity of breast cancer cells can be better understood through integration of multi-omics approaches that provide resourceful information of the different layers of gene expression regulation. Although genomic, epigenomic and transcriptomic datasets have added new insights into the true biological landscape of breast cancer, epitranscriptomic analysis are still in their infancy. Given that many of the RNA modifications are dysregulated in human cancers, the epitranscriptome represents a hot-spot of interest in the quest of elucidating the transition from a normal physiological to a pathological state, hoping that its study will enable the development of more efficient and effective therapies against cancer. For breast cancer, recognition of the function of RNA modifications in its development may represent the missing piece of the puzzle in deciphering the complex pathogenesis of these tumors.
N6-methyladenosine
m6A is the most studied and abundant internal modification on eukaryotic mRNA. It also appears on tRNAs and other non-coding RNAs (ncRNAs) and more recently, it has been found on rRNA (2,36,37). Due to its large impact on RNA metabolism at multiple levels i.e. splicing, nuclear export, mRNA stability, translation and even RNA and protein interactions, it plays a critical role not only in proper functioning of all sorts of biological processes but also in acquired pathologies such as cancer (26,38,39).
m6A is co-transcriptionally deposited on mRNA by a stable methyltransferase complex consisting of two core components, i.e. methyltransferase-like 3 (METTL3) and 14 (METTL14), and other proteins that ensure m6A specificity (40,41). The demethylases include fat mass and obesity-associated protein (FTO) and α-ketoglutarate dependent dioxygenase alk B homolog 5 (ALKBH5) (42,43). The most representative readers are members of the YTH domain-containing protein family (YTHDF1/2/3 and YTHDC1) (44,45).
In recent years, the expression of m6A regulators has been correlated with hallmarks of cancer and it was shown that breast cancer cells exhibited higher m6A methylation levels compared to healthy mammary epithelial cells (Table 1) (46,47). However, most of the studies seem controversial as a defined gene expression pattern of writers, erasers and readers is missing, reflecting the complexity and heterogeneity of breast pathogenesis. For instance the writer METTL14 was downregulated in TNBC, with METTL14 expression being correlated with favorable outcomes (48). Similarly, Gong et al. reported that low expression levels of METTL14 led to poor prognosis and tumor progression in TNBC (49). On the contrary, overexpression of METTL14 was observed in studies that compared breast cancer patients with healthy controls, METTL14 acting as an oncogene that promoted invading and migrating capacities of the cells (46,50). Dong et al. have attributed METTL14 overexpression to luminal A and B subtypes, being negatively correlated with tumor grade and not exerting influence over prognosis (51). In addition, the expression of the erasers of m6A has also been shown to be dysregulated in breast cancer patients, being the increased expression of ALKBH5 correlated with poor prognosis in TNBC (47), whereas the expression of the demethylase FTO appeared to be decreased in breast cancer patients compared to normal controls (47). However, Xu et al. observed upregulation of FTO in HER2+ subtype and a strong connection with unfavorable prognosis in this subtype (52). The expression of readers correlated as well with different outcomes. For instance, increased expression of YTHDF3 was linked with unfavorable survival, nodal metastasis and poor prognosis in breast cancer patients (53,54). Other readers such as YTHDF1 and YTHDF1 displayed increased expression in various analyses of breast cancer patient samples (48,54,55), while YTHDC1 and YTHDC2 were shown to be downregulated in TNBC (48). These studies reflect that the same writer protein, for example METTL14, can display distinct expression patters depending on the breast cancer subtype. They also show how the expression of distinct erasers or readers can follow opposite patterns of gene expression. Therefore, dysregulated expression of m6A writer, eraser and reader, either high or low, might lead to aberrant gene expression programs that promote breast tumorigenesis. Indeed, it has been shown that the interplay among m6A regulators determines the m6A levels and, consequently, the stability of several transcripts that are known to play a critical role in cell cycle, epithelial–mesenchymal transition (EMT) and angiogenesis in breast cancer (56).
Several mechanisms involving METTL3 in breast cancer progression have been described (Figure 5). METTL3 was found to participate in a feedback loop with Hepatitis B virus X-interacting protein (HBXIP) and the microRNA (miRNA) let-7g, in which METTL3 expression was positively regulated by HBXIP through inhibiting let-7g miRNA, while HBXIP expression was increased by METTL3. This positive feedback loop resulted in increased cell proliferation and, ultimately, in cancer progression (57). In addition, METTL3-mediated increased methylation levels promoted the translation of Bcl-2, a major regulator of cell death modulating cellular proliferation and apoptosis of cancerous cells (58). Furthermore, it has been shown that m6A can induce breast cancer lung metastasis by increasing the stability of a mRNA duplex formed from the keratin 7 (KRT7) transcript, encoding a regulator of EMT, cytoskeleton programming and cellular transformation, and its antisense long noncoding RNA (lncRNA) KRT7-AS (59). On the contrary, in TNBC cell lines, METTL3 halts metastatic progression by hypermethylation of the Collagen Type III Alpha 1 Chain (COL3A1) transcript which trigger its degradation (60). Recently, it was shown that METTL3 is the host gene of a circRNA, so called circMETTL3 whose expression was regulated via a m6A-dependent mechanism (61). This circRNA was upregulated and impacted cell proliferation, migration and invasion in breast cancer through upregulation of cyclin-dependent kinases (CDK1), recently identified as a breast cancer prognosis indicator (61). Moreover, METTL14 was reported to enhance cell proliferation by facilitating the deposition of m6A on the transcripts of the oncogenes CXCR4 and CYP1B1 (62). Mechanisms showing the implications of FTO in cell proliferation, colony formation and metastasis of breast cancer both in vitro and in vivo were also described. Hence, FTO-mediated demethylation of Bcl-2/adenovirus E1B mRNA (BNIP3), a pro-apoptotic tumor suppressor gene, favored tumor progression (63). Recently, the reader of m6A YTHDF2 was found to sustain MYC-driven cell growth and survival in TNBC cell lines by facilitating the turnover of mRNAs belonging to MAPK/ERK signaling pathways. Hence, YTHDF2 was limiting the endoplasmic reticulum stress response and therefore contributed to EMT and breast tumorigenesis (64).
Figure 5. Functions of m6A in breast cancer. m6A modification is dynamically regulated by its writers (METTL3, METTL14, WTAP, HAKAI and VIRMA) erasers (FTO and ALKBH5) and readers (YTHDFC3 and IGF2BP2). Only enzymes with reported function in breast cancer are depicted for simplicity. Deposition of m6A on distinct transcripts can enhance (HBXIP, p21, CXCR4, CDK1, CYP1B1, KRT7, Bcl2; red) or diminish (COL3A1; green) breast cancer initiation and progression and affect therapy outcome (AK4; blue). Demethylation of BNIP3 also enhances breast cancer (red) whereas demethylation of Nanog, KLF4 and Sox2 promotes the breast cancer stem cell phenotype. Similarly, recognition of m6A-modified DROSHA and c-Myc mRNAs leads to the acquisition of breast cancer stem cell characteristics. YTHDFC3 binds to ST6GALNAC5, EGFR and GJA1 to induce breast cancer metastasis. YTHDF2 recognizes mRNAs involved in MAPK/ERK signaling. Deposition of m6A on pri-miRNA allows DGCR8 recognition and further processing. Increased expression of METTL14, FTO and METTL3 alters the expression of miRNA, enhancing the breast cancer phenotype (red) or induces drug resistance (blue) in breast cancer cells. Enhanced METTL5 mediated methylation at adenosine 1832 of mammalian 18S rRNA promotes p70-S6K activation and an increased translation initiation thus stimulating breast cancer cell growth.
Another way by which the m6A machinery can contribute to breast cancer formation and development is through its roles in miRNA biogenesis. METTL3 has been shown to deposit m6A on primary miRNAs (pri-miRNAs) enabling their recognition by the RNA binding protein DGCR8 and thereby facilitating miRNAs biogenesis (65). Particularly, in breast cancer, METTL14 reshaped the miRNA profile (50). Modulation of the expression of miR-146a-5p by METTL14 promoted cell migration and invasion underlying the predominant control that m6A may have over multiple aspects of breast tumorigenicity. In addition, inhibition of miR-181b-3p by FTO resulted in upregulation of ADP ribosylation factor like GTPase 5B (ARL5B) (52). ARL5B is responsible for promoting lysosome motility facilitating cell migration. Therefore, through this mechanism FTO enhanced the invasive and migratory capabilities of the breast cancer cells (52). Considering the data obtained in studying other cancer types, further studies will most certainly reveal an even higher impact of m6A on miRNA in breast carcinogenesis.
m6A plays a significant role in the formation of cancer stem cells (CSCs), tumorigenic cells with stem cell properties, which are known for their capabilities of facilitating the carcinogenic process and the resistance to therapy (66). Hypoxic environments, through the action of hypoxia-inducible factors (HIF), have been linked with EMT and thus with acquisition of stem cell-like properties that favor migration and invasion in breast cancer (67,68). Expression levels of ALKBH5 and the oncogenic factor Zinc-Finger Protein 217 (ZNF217) are increased in these environments (69,70). Notably, the mouse ortholog ZFP217 has been shown to interact with METTL3 and restrict the m6A deposition on pluripotency factors (71), increasing their mRNA stability. Thus, ZNF217- and ALKBH5-mediated demethylation of pluripotency factors, i.e. Nanog and KLF4, induced the acquisition of stem cell-like properties and therefore, BCSC specification (69,70). IGF2BP2, a reader of m6A, was also linked to mechanisms enhancing BCSC by stabilization of the mRNA of two oncogenes, namely DROSHA and c-Myc (72,73). All these results point toward a significant role of this mark in elucidating means that can provide new therapeutic strategies in both drug resistant and refractory hypoxic breast tumors.
Recent evidence indicates that m6A also plays important roles in treatment outcomes, being either a vector of acquired resistance or a perfect target for more efficient therapies. On one hand, resistance to tamoxifen and doxorubicin were modulated by METTL3 through methylation of adenylate kinase 4 (AK4) transcripts, and miRNA-221–3p, respectively (74,75). AK4 is a mitochondrial matrix protein involved in energy metabolism homeostasis whose expression is linked to the progression of multiple cancers. In tamoxifen resistant MCF-7 cells, m6A deposition on AK4 mRNA led to increased expression which in turn resulted in higher reactive oxygen species and p38 levels while depletion of METTL3 and AK4 resensitized the cells to tamoxifen (74). METTL3 regulation of miRNA-221–3p through methylation of pri-miRNA-221–3p resulted in chemoresistance to doxorubicin in breast cancer cells, due to the suppression of the tumor suppressor HIPK2 that led to overexpression of Che-1, a transcription regulator known for its role in development of anticancer drug resistance (75). On the other hand, metformin, a drug used in the treatment of type 2 diabetes mellitus, has been shown to suppress m6A through downregulation of METTL3 (76). Metformin inhibited METTL3 and thus the m6A deposition on p21 (also known as cyclin-dependent kinase inhibitor 1 (CDKN1A)) exhibiting an antiproliferative effect in breast cancer cells (76).
Lastly, m6A on 18S rRNA at position 1832 has also been linked to breast cancer cell growth. This modification is catalyzed by METTL5 which must form a heterodimeric complex with TRMT112 to perform its methyltransferase activity (77). METTL5-mediated 1832 methylation at 18S rRNA fine-tuned the conformation of the ribosome decoding center, increasing its interaction with mRNAs (78). METTL5 has been shown to be overexpressed in breast cancer samples and its loss led to a reduction in proliferation of different breast cancer cell lines and of the S6K phosphorylation needed for the cells to initiate translation and to undergo growth (42). Similarly, enhanced expression of ZCCHC4, which catalyzes the m6A deposition of human 28S rRNA at position A4220 (79), is also evidenced in breast tumor tissues although the function of this modification in breast tumorigenesis is yet to be investigated (47). Taken together, this research topic is still in its infancy and more studies are needed to contour a better picture of the role of m6A on rRNA in breast cancer.
N1-methyladenosine
Methylation of adenosine at position N1 or m1A has the capacity to disrupt the Watson–Crick base-pairing specificity affecting RNA structure and protein–RNA interaction (80–82). It was originally discovered in tRNA and rRNA, and later studies detected its presence in mRNA although its ubiquity is still controversial. Hence, whereas original studies suggested that thousands of mRNAs showed m1A enrichment in the 5′UTR, usually in GC-rich regions and that m1A modification played an important role in translation and in environmental stress response (80,83), later studies revealed few transcripts with this modification (84). In addition, the original authors themselves conceded that m1A distribution showed no 5′UTR bias (85).
Therefore, although there is not enough evidence to suggest that m1A is present at appreciable levels on mRNA, one study has explored its role in breast cancer. Cytokine macrophage colony-stimulating factor (CSF-1) is an oncogene that promotes metastatic dispersion in breast and ovarian carcinomas. It has been shown that CSF-1 transcripts decorated with m1A were targeted for mRNA decay. Hence, ALKBH3-mediated demethylation increased the stability of CSF-1 mRNA, thereby increasing its expression. Given that ALKBH3 is overexpressed in many types of tumors, it is plausible that this is a general mechanism and not exclusively for breast or ovarian cancers (86).
5-methylcytosine
The methylation of the carbon 5 in cytosine (m5C) was originally found in rRNA and tRNA; however, recent studies have also detected m5C on mRNA (87,88). In eukaryotes, the large ribosomal subunit contains two m5C residues (22) that are essential to maintain ribosomal structure and fidelity during translation, playing a significant role in lifespan and stress resistance (89). On tRNAs, m5C protected from angiogenin-mediated endonucleolytic cleavage, and thereby avoided the biogenesis of tRNA-derived small RNA fragments (tRFs) (90,91). Such tRFs play a key role in regulating gene expression programs participating in various physiological processes such as cell stress, cell growth and cell differentiation (92). tRFs also play significant roles in various human diseases, including cancer (93). On mRNA, m5C promotes export (87), stabilization (88) and translation (94,95).
Depending on the RNA species, distinct m5C methyltransferase from the NOL1/NOP2/SUN domain (NSUN) family of proteins (NSUN1 to NSUN7) and the DNA methyltransferase member 2 (DNMT2) catalyze the m5C modification (95). Hence, NOP2 (NSUN1) and NSUN5 methylate 28S rRNA, while NSUN4 modify mitochondrial rRNA (96–99). DNMT2, NSUN2, NSUN3 and NSUN6 all methylate cytoplasmic tRNAs, with different specificity and at different residues, and NSUN3 targets mitochondrial tRNA (100–104). Moreover, NSUN6 also targets site-specific deposition of m5C in mRNA (105), NSUN2 in ncRNAs and mRNA (87,106), and NSUN7 targets enhancer RNAs (eRNAs) (107).
Several m5C methyltransferases, including NOP2, NSUN2 and NSUN4, are up-regulated in breast cancer although the molecular consequences of this overexpression are not well characterized (108–110). In addition, the genomic region containing NSUN2 (5p15.31–33) was associated with a strong risk for the development of breast cancer (111). Hence, both amplification and overexpression of NSUN2 has therapeutic potential as a drug target (109). Additionally, overexpression of NSUN2 by DNA hypomethylation has been associated with proliferation, migration and invasion while NSUN2 knockdown inhibited these processes in vitro and in vivo (112). These results were corroborated in another pan-cancer study showing that NSUN2, among other RNA methyltransferases, was amplified or mutated in breast cancer, and its expression was associated with poor prognosis in these patients (113). Notably, as NSUN2 can catalyze the deposition of m5C on distinct RNA species, i.e. of tRNAs, mRNAs and ncRNAs (87,106,114) additional work will be required to address the molecular mechanism that leads to breast carcinogenesis. Recently, Huang et al. reported that all eleven m5C regulators (NSUN2-NSUN7, DNMT1, DNMT3A, DNMT3B, ALYREF and TET2) were differentially expressed in TNBC and can potentially predict clinical prognostic risk in patients. Whereas upregulated expression of NSUN2 was found to be closely associated with cell cycle signaling pathways, RNA degradation and RNA polymerase, reduced expression of NSUN6 was linked with cell adhesion, metabolism and extracellular matrix receptor interaction (115).
Y-box binding protein 1 (YBX1) is a specific reader of m5C in mRNA which stabilizes the oncogene HDGF in urothelial carcinoma of the bladder (88). YBX1 is a multifunctional protein that is frequently overexpressed in breast cancer regardless of the subtype and its expression was correlated with poor survival, drug resistance and relapse (116,117). Therefore, these studies suggest that dysregulated YBX1-mediated decoding of m5C can lead to not only urothelial carcinoma but also to other cancers, including breast cancer.
2′-O-methylation
2′O-Me is the addition of a methyl group at the 2′ hydroxyl of the ribose moiety of all four nucleosides. This mark can be catalyzed by stand-alone enzymes (118) or by the methyltransferase fibrillarin in association with the C/D box family snoRNAs and the conserved proteins Nop56p, Nop58p and 15.5K or NHP2L1, collectively known as box C/D RNP complex (119). 2′O-Me is a predominant mark in rRNA, where >100 sites exist (120), but it has also been found in tRNA, mRNA (121–126) and other small RNAs (127).
Internal modification of 2′O-Me at 3′-terminal protects terminal ribose, inhibits the function of the RNA ligase and negatively impacts the efficiency of the polyA-polymerase (128,129), directly reducing the translational capacities of ribosomes (130). A Pan-cancer analysis highlighted that several RNA methyltransferases are either amplified or mutated in different cancers (68). Among these, FtsJ RNA 2′-O-methyltransferase 3 (FTSJ3), that modifies both rRNA and mRNA is among the stand-alone enzymes that has been correlated with cell growth and survival of breast cancer cells (113).
2′O-Me is linked to ribosome biogenesis and deregulated ribosome biogenesis is found to be associated with breast cancer cell progression (131). Therefore, studying the signature of 2′O-Me sites in both patient samples or cell lines is of therapeutic importance. This can be performed by RiboMethSeq which maps 2′O-Me sites based on the principle that methylation of ribose 2′OH makes 3′-adjacent phosphodiester bond resistance to alkaline hydrolysis and nuclease cleavage (132). Indeed, RiboMethSeq in 195 primary breast tumor samples showed that rRNA 2′O-Me levels differed between breast cancer subtypes and tumor grades (133). Particularly, TNBC patients displayed a signature where 2′O-Me levels at 18S-Am576 and at 18S-Gm1447 sites were increased and decreased, respectively. In addition, modification at the 18S-Gm1447 site was found to be guided by SNORD127 at 18S of rRNA, suggesting that studies evaluating the mechanism of SNORD127 in TNBC might be of therapeutic importance.
Recently, a distinct 2′O-Me rRNA pattern was observed upon exposing normal breast epithelial and breast cancer cell lines, i.e. MCF10A and T47D cells, to hypoxic conditions (134). In hypoxic conditions both cell lines displayed high 2′O-Me levels at 1858 and 4436 sites and reduced methylation at 390, 1612, 2848, 4806 and 1803 sites, compared to normoxia. Noteworthy, in low oxygen conditions genes such as vascular endothelial growth factor (VEGF-C) are translated in a cap-independent manner through internal ribosome entry site (IRES). Hence, a specific pool of ribosomes with a distinct 2′O-Me pattern facilitated the IRES recognition enhancing the translation of VEGF-C and most probably, other oncogenes (135). Although mechanistic evaluation of 2′O-Me in breast tumorigenesis is lacking, several groups have shown that deregulated expression of components of 2′O-Me RNP complex, such as C/D box snoRNAs and fibrillarin, led to breast tumorigenesis (136,137). For instance, increased fibrillarin expression was associated with aberrant 2′O-Me rRNA pattern and thus impaired translational fidelity. In addition, Marcel et al. showed that p53 acted as a safeguard of protein synthesis by repressing the expression of fibrillarin in breast cancer (138). Moreover, 58 differentially expressed snoRNAs were identified in 26 TNBC cell lines (139) being SNORD78, SNORD93, SNORD62A, SNORD2 and SNORD57 among the most highly upregulated snoRNAs in the invasive breast cancer MDA-MB-231 cell line. Small RNA sequencing in normal and primary breast tumor tissues discovered thirteen snoRNAs which were associated with overall survival and relapse free survival of breast cancer patients (140). In another study, Kothari et al. performed a transcriptome array in patient samples from different breast cancer subtypes and found SNORD114 and SNORD115 as important regulators of breast tumorigenesis (141).
The snoRNA host genes can be of great therapeutic importance as they mostly show altered expression in multiple cancers, and are known to regulate cell growth, tumor progression, metastasis and chemoresistance (142–144). ZFas1, host RNA for SNORD12, SNORD12b and SNORD12c (ZNFX1 Antisense RNA 1) regulated alveolar development and epithelial cell differentiation during normal mammary development and thus is a potential biomarker acting as a tumor suppressor in breast cancer (145). However, not all snoRNAs are involved in 2′O-Me. A combined analysis of high-throughput sequencing protocols and computational methods (146) suggested alternative roles for snoRNAs beyond 2′O-Me. Therefore, for a better understanding of correlation between snoRNAs expression and 2’O-Me, more research is needed in this area.
Pseudouridylation
Pseudouridylation is the isomerization of 1-ribosyluracil (uridine) to 5-ribosyluracil (pseudouridine, ψ) within the ribonucleoside. Known to be present in all kinds of RNA species, pseudouridylation is the second most common RNA modification of rRNA and has important roles in protein translation and pre-mRNA splicing (37,147). The mechanism of isomerization of uridine to pseudouridine can be either RNA independent or RNA dependent. RNA independent pseudouridylation is performed by enzymes having both substrate recognition and catalytic properties known as pseudouridine synthases (148). However, RNA dependent pseudouridylation is carried out by the RNP complex that consists of catalytic protein dyskerin (DKC1), box H/ACA snoRNAs (SNORAs) (substrate recognition), non-histone protein 2 (Nhp2), nucleolar protein 10 (Nop10) and glycine-arginine-rich protein-1 (Gar1) (149).
In breast cancer patients and cell lines, reduced expression of DKC1 was found to be associated with reduced telomerase activity, rRNA pseudouridylation and better clinical outcome (150,151). Similarly, depletion of DKC1 led to p53-inactivation and increased IRES-mediated VEGF mRNA translation in breast cancer cell lines (152,153). Moreover, enhanced DKC1 expression increased ribosome efficiency and stimulated the aggressive phenotype in the normal breast epithelial MCF10A cell line (154). In addition, expression of Nop10 was found to be associated with aggressive breast cancer (155) and coding missense variants of Nhp2 gene were detected in hereditary breast cancer patients although no pathogenic role was elucidated (156). Levels of small nucleolar RNAs (SNORNAs) in cancer are deregulated (157), being SNORA3, SNORA18, SNORA7B, SNORA13 SNORA2A among others highly expressed in metastatic breast cancer patients and cell lines (139,158,159). Inhibition of oncogenic SNORA7B impaired cell growth, migration and apoptosis in breast cancer cells (160). Relative pattern of pseudouridylation in normal and breast cancerous cases is yet to be evaluated and hence needs more research to elucidate its direct role in breast tumor initiation and progression.
RNA editing
RNA editing refers to modifications of specific nucleotides in RNA sequence differentiating it from its corresponding DNA sequence that may lead to pathological consequences. The most common form of RNA editing is from adenosine (A) to inosine (I) (161) and is catalyzed by specific enzymes namely adenosine deaminases acting on RNA (ADARs). In mammals, three ADAR genes encode for four different isoforms known as ADAR1p150, ADAR1p110, ADAR2 and ADAR3 (162). A-to-I editing within the protein-coding region of an mRNA can result in an amino acid change in the encoded protein as the resulting inosine is interpreted as guanosine by the translational machinery leading to an amino acid substitution in the protein product (163). In addition, A-to-I can alter regulatory motifs, binding of RNA-binding proteins, or RNA secondary structures influencing pre-mRNA alternative splicing (164–166). Furthermore, RNA editing within 3′UTRs of target transcripts can alter miRNA targeting an perturb miRNA-mediated regulation of oncogenes and tumor-supressors (167). The enzymes involved in A-I editing on tRNAs are known as Adenosine deaminases that act on tRNAs (ADATs). Deregulated ADATs-mediated RNA editing is associated with diseases such as type 2 diabetes, neurological disorders, mitochondria-related disorders and cancer (168). Less frequent cytidine (C) to uridine (U) mRNA editing is carried out by cytidine deaminases belonging to the family of enzymes known as activation induced cytidine deaminase/apolipoprotein B editing complex (AID/APOBEC) or AADs (169), eleven of which are found in humans (170).
A-to-I editing in breast cancer
A-to-I editing is known to be the major source of mRNA variability in breast cancer (171), and it is controlled by type-I interferon response and high ADAR DNA copy number (172). Hence, >76 000 RNA editing sites were identified by using a bioluminescent reporter system and upon interferon treatment of the breast cancer MCF7 cell line (173). Breast tumors harbor amplified levels of ADAR (174) and inhibiting its activity has been shown to induce apoptosis. Indeed, A-to-I mRNA editing regulated breast cancer cell proliferation by modulating the stability and expression of genes involved in cell cycle and DNA damage response being ATM, MDM2, MDM4, CENPN and XPO1 among the affected transcripts (175). In another study, increased A-to-I mRNA editing was found on the mRNA of ATM, GINS4 and POLH affecting their stability and/ or expression in primary breast tumors when compared to the non-cancerous tissues (171). In addition, Gumireddy et al. reported that A-to-I mRNA editing of γ-amino butyric acid receptor alpha 3 (GABRA3), one of the subunits of GABA (A) receptor, changed its role from a metastasis promoter to a tumor suppressor by suppressing the ERK pathway activation (176). In an attempt to assess the role of A-to-I RNA editing on proteomic diversity, Peng et al. showed that A-to-I RNA editing contributed to protein heterogeneity in breast cancer (177). Hence, nine unique RNA editing sites with variant peptide evidence were identified such as COPA_I164V and IGFBP_R78G which were detected across 11 out of 36 BRCA mass spectrometry datasets (177). These peptide variants were shown to be presented as self-antigens by human leukocyte antigen (HLA) molecules, and thus recognized by the immune system. For instance, CD8+ effector T cells were evidenced in tumors in response to peptide variants generated from cyclin I (178). A-to-I RNA editing has also been linked to resistance to chemotherapy. Specifically, RNA editing at 26 sites of 3′UTR modulated the expression of dihydrofolate reductase resulting in highly proliferative breast cancer cells which were resistant to methotrexate (179). To sum up, existing data suggests an important role of A-to-I RNA editing in genes associated with breast cancer-relevant pathways and treatment outcomes, suggesting an important role of ADAR1 function in breast tumorigenesis.
C-to-U editing in breast cancer
APOBEC-dependent somatic mutation confers increased susceptibility for breast cancer (180) and is found to be enriched in the HER2 subtype (181,182). Overexpression of cytidine deaminase APOBEC3A in HEK293T cells caused mRNA alterations in several tumor associated genes including PTEN, KMT2A, ATM, BRCA1 and BRCA2 (183); however, their significance in breast tumorigenesis is yet to be evaluated. mRNA expression of APOBEC3B was evaluated in ductal carcinoma in situ (DCIS), invasive breast cancer (IBC) and normal breast cells (184). Normal breast cells expressed reduced levels of APOBEC3B, while increased expression was evident in DCIS and IBC. Recently, APOBEC3-mediated C-to-U RNA editing in breast cancer was shown to be associated with improved patient survival and enhanced immune activity (185). In another study, using a bioinformatics approach, C-to-U RNA editing levels were estimated in 1040 primary breast tumor tissues and 93 adjacent normal tissues. More than 5000 APOBEC3-mediated RNA editing sites were identified using TCGA sequencing data. For 440 sites, editing was found on 411 transcripts among which most prevalent were on GATA Zinc Finger Domain Containing 2B (GATAD2B), Serpin Family A Member 1 (SERPINA1) and Adenosine Monophosphate Deaminase 3 (AMPD3) (185). Moreover, Apobec-1 complementation factor (A1CF), another mRNA editing enzyme, promoted proliferation of basal-like breast cancer cells by targeting interleukin-6 (186). Overall, the existing data on C-to-U editing suggests important roles in breast cancer progression and further research may identify targets with therapeutic importance.
Other RNA modifications related to breast cancer
Growing evidence in recent years has pointed out that tRNAs and their derivatives are dysregulated in breast and other cancers (187). However, the functional role of tRNA modifications in tumorigenesis is still elusive. As mentioned before, several modifications are shared by the distinct RNA species but others are unique to tRNAs, as is the case of cm5U34 and mcm5U34 at the wobble position. These marks have been shown to influence the translational efficiency and the accuracy of the reading frame (188). In addition, the catalytic enzymes for mcm5s2 deposition are up-regulated in breast cancer sustaining metastasis (189). They enhanced the translation of DEK and LEF1, two oncogenes, enabling breast cancer cells to migrate and invade other tissues (189). Another modification that occurs at the wobble anticodon position is queuosine which is dependent on the gut microbiome (190). Thus, the microbiome product queuine is the substrate for the enzymes tRNA guanine transglycosylases (TGTs). In eukaryotes, the active TGT is a heterodimer formed of a catalytically active queuine tRNA-ribosyltransferase subunit 1 (QTRT1) and a catalytically inactive QTRT2 subunit (191). Knockout of QTRT1 in MCF7 and MDA-MB-231 cells produced changes in the functions of genes involved in cell proliferation, junction formation and migration (190). These results have been validated in mouse models, thus enforcing the significant role that this modification is playing in breast cancer development (190).
TRMT12, a tRNA methyltransferase that mediates posttranscriptional modifications on tRNAPhe has been reported to be consistently amplified and overexpressed in cell lines and breast cancer patient samples (192). In yeast, it catalyses the formation of wybutosine (yW) on the 37th residue of tRNAPhe, modification that helps in stabilization of the codon-anticodon interaction and in maintenance of the reading frame (192). Thus, disruption at the level of this modification can lead to translational errors and is therefore of great interest to establish the role of these enzymes, together with its effect on tRNAs, in tumorigenesis of breast cells.
It is noteworthy that many of the methyltransferases that catalyze the methylation of different types of RNAs have been shown to be deregulated in breast cancer. Although the lack of any association between the deposited marks and the expression of these proteins made us filter out these studies, there are some that are worth mentioning. For instance, tRNA methyltransferase 2 homolog A (TRMT2A) which catalyzes the methylation of the U5 of tRNAs and regulates cell cycle has been associated with increased recurrence risk in HER2+ breast cancer patients (193). Human tRNA methyltransferase 9-like (hTRM9L) protein, an another tRNA methyltransferase, is downregulated in breast cancer and was shown to suppress tumor growth in vivo by decreasing proliferation, cell cycle arrest in G0/G1, upregulation of LIN9, and blocking the hypoxia response (194) in colorectal cancer. Whether this mechanism is the same in breast cancer and if it is mediated though methylation remains to be further investigated.
Despite being less explored than other RNA marks, many of these modifications convey to be promising clues in deciphering the molecular enigma behind the development of breast cancer. Thus, it will be of increased interest to see how extensive studies of these marks on the various species of RNA can contribute to a more precise picture of breast cancer tumorigenic process.
Perspectives
In the last decade, and as a result of the advent of more reliable methodology, the number of epitranscriptomic studies has dramatically increased. The boost and excitement of this emerging field has led to a tremendous progress in identifying the machinery and the role of RNA modifications. Hence, RNA marks have emerged as important regulators of a variety of cellular processes and their roles in human disease, including cancer, have extensively been described. It has been shown that writers, erasers and readers can act as tumor suppressors or as oncogenes depending on the cellular context. Thus, a single enzyme can have opposing roles in distinct cancer types. However, as a consequence of this fast-growing discipline, many erroneous conclusions have been drawn, and a large number of controversial studies has emerged. In addition, most of the works have just focused on few RNA modifications, and thereby the functions of most RNA modifications remain to be characterized.
During these years, RNA modifications have been comprehensively mapped transcriptome-wide by coupling antibody immunoprecipitation or chemical probing with next-generation sequencing. Yet, tools for the simultaneous identification of distinct chemical marks in the same RNA are currently not available. Hence, it is unknown how the different RNA modifications interplay to influence cancer development. Aberrant expression of such RNA-modifying machinery has been acknowledged for most aspects of breast and other cancer types. One particular challenge is to attribute a phenotype to a particular chemical mark as there is a crosstalk between rRNA, tRNA and mRNA modifications which involves common modification factors. For example, NSUN2 can impose m5C on both mRNA and tRNA species (195). In addition, several previous conclusions might have to be revised as some of these key players are moonlighting proteins which perform multiple autonomous and often unrelated functions. Therefore, it is also challenging to distinguish between the canonical, i.e. directly related to RNA modifications and secondary protein functions. For instance, knockouts of IME4, the yeast homolog of METTL3, display more severe phenotypes than those derived by a catalytic mutant, suggesting that IME4 has methylation-independent functions (196,197). Indeed, it has been shown that in lung cancer, cytoplasmic METTL3 promotes the translation of oncogenes by a mechanism independent of its methyltransferase activity (198).
Because of the inherent reversibility of some of the RNA modifications, inhibitors targeting the RNA-modification machinery are promising therapeutic targets for breast and other tumors. As m6A is the most studied RNA modification, it is not surprising that lead biotech companies have developed drugs to target the writer complex, specifically METTL3 (199). Hence, STORM Therapeutics has developed the first catalytic inhibitor of METTL3. Pharmacological inhibition of METTL3 led to strong antitumor effects in vitro and in relevant mouse models of acute myeloid leukemia (AML) (200).
Overall, as highlighted in this review, considerable advance has been made in recent years in the research field of epitranscriptomics which has provided a link between RNA modifications and breast cancer. Yet, the epitranscriptome remains vastly unexplored. Thus, it appears that we just perceive the tip of the iceberg and that the number of RNA modifications is much higher than previously estimated. The exhaustive characterization of RNA modifications and the molecular mechanism in which they are involved holds a new avenue for novel cancer therapies.
ACKNOWLEDGEMENTS
We sincerely apologize to authors whose work could not be included due to space limitations.
FUNDING
Knut and Alice Wallenberg Foundation (through the Wallenberg Centre for Molecular Medicine Umeå), Cancerfonden (Pj:190337), Cancer Research Foundation in Northern Sweden (AMP 21-1030).
Conflict of interest statement. None declared.
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PMC008xxxxxx/PMC8476162.txt |
==== Front
Bioinformatics
Bioinformatics
bioinformatics
Bioinformatics
1367-4803
1367-4811
Oxford University Press
32573714
10.1093/bioinformatics/btaa584
btaa584
Applications Notes
Structural Bioinformatics
AcademicSubjects/SCI01060
Molywood: streamlining the design and rendering of molecular movies
Wieczór Miłosz Department of Physical Chemistry, Gdańsk University of Technology, Gdańsk 80-233, Poland
Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
Hospital Adam Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
Bayarri Genis Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
Czub Jacek Department of Physical Chemistry, Gdańsk University of Technology, Gdańsk 80-233, Poland
Orozco Modesto Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona 08028, Spain
Elofsson Arne Associate Editor
To whom correspondence should be addressed. E-mail: [email protected] or [email protected]
01 9 2020
23 6 2020
23 6 2020
36 17 46604661
04 3 2020
04 6 2020
15 6 2020
© The Author(s) 2021. Published by Oxford University Press.
2020
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Motivation
High-quality dynamic visuals are needed at all levels of science communication, from the conference hall to the classroom. As scientific journals embrace new article formats, many key concepts—particularly, in structural biology—are also more easily conveyed as videos than still frames. Notwithstanding, the design and rendering of a complex molecular movie remain an arduous task. Here, we introduce Molywood, a robust and intuitive tool that builds on the capabilities of Visual Molecular Dynamics (VMD) to automate all stages of movie rendering.
Results
Molywood is a Python-based script that uses an integrated workflow to give maximal flexibility in movie design. It implements the basic concepts of actions, layers, grids and concurrency and requires no programming experience to run.
Availability and implementation
The script is freely available on GitLab (gitlab.com/KomBioMol/molywood) and PyPI (through pip), and features an extended documentation, tutorial and gallery hosted on mmb.irbbarcelona.org/molywood.
Polish National Agency for Academic Exchange 10.13039/501100014434 Instituto Nacional de Bioinformática European Union’s Horizon 2020 Biomolecular and Bioinformatics Resources Platform ISCIII PT 13/0001/0030 MINECO Severo Ochoa Award of Excellence
==== Body
pmc1 Introduction
Since the groundbreaking resolution of the 3-dimensional structure of myoglobin in 1960 (Kendrew et al., 1960), our understanding of processes of life has increasingly relied on the visualization of molecules. The two revolutions in high-resolution structural biology—first in X-ray crystallography, second in cryogenic electron microscopy (cryo-EM) imaging (Stuart et al., 2016)—resulted in tens of thousands of unique protein structures being deposited in public databases, ultimately providing us with an atomistic-level insight into the peculiar world of molecular shapes and conformational changes. However, the scientific communication of these findings primarily relies on still images and figures, often obscuring or distorting the structural insights.
This situation could be rectified through widespread adoption of molecular movies as a complementary visual aid in the dissemination of scientific findings (McGill, 2008). Not only are publishers starting to actively encourage the use of multimedia in the new enhanced, web-only article formats, but also science communication has become increasingly visual due to the proliferation of science-oriented blogs, vlogs and social media channels (Welbourne and Grant, 2016). On the other hand, researchers often find existing tools inadequately suited to these challenges. In fact, a very limited choice of such tools exists today: VMD’s Movie Maker remains limited to simplest cases, BioBlender and ePMV require programming and/or Blender skills (Andrei et al., 2012; Johnson et al., 2011), while PyMOL’s eMovie effectively vanished from the Web (Hodis et al., 2007; Hsin et al., 2008). Simultaneously, platforms such as Molecular Maya are commercial and thus not accessible to many researchers. While molecular movies of excellent quality do exist in the literature, they are usually a result of a lengthy work on integrating workflows, automatizing and post-processing.
These shortcomings prompted us to create Molywood, a Python tool capable of managing complex moviemaking workflows across a range of utilities, including the open-source utilities FFmpeg and Imagemagick, Python’s Matplotlib and VMD with its extended Tcl scripting capabilities (Humphrey et al., 1996). With an easy-to-follow syntax and flexible composition rules, the tool greatly simplifies movie design, allowing the user to focus on conceptual rather than technical issues. Finally, we provide a library of minimal working examples and featured full-fledged movies—hosted at mmb.irbbarcelona.org/molywood—to guide users through the supported options.
Fig. 1. The conceptual organization of Molywood. A Script is divided into possibly multiple Scenes, which in turn consist of instantaneous, consecutive or simultaneous Actions. Individual Actions can access external resources, such as numeric data, figures or other movies. A number of simple global parameters control video quality
2 Application
Molywood is organized around three hierarchical concepts: Scripts, Scenes and Actions (Fig. 1). A Script is a collection of Scenes, and defines the entire movie; similarly, a Scene is a collection of Actions and can be thought of as a single panel. Most movies will consist of a single Scene, so here we focus on Actions first.
An Action is the main primitive that abstracts a single event—a rotation, zoom, highlight, etc.—either with a specific duration or effected instantaneously. In Molywood, many Actions can overlap in time, e.g. one can rotate the view while simultaneously zooming in and animating the trajectory. Moreover, Actions can be asynchronous (i.e. overlap only partially with one another): following the above example, it is possible to start the rotation when the zoom-in and animation are already underway. All Actions are controlled by key-value pairs that specify all tunable parameters, such as action duration, transition smoothness or color of the highlight. For more complex behavior, they can interact with other Actions: for instance, a structural highlight can be labeled upon creation, so that the same highlight can be later altered or removed. Arbitrarily long extracts from audio files (e.g. voice narratives) can also be easily aligned with specific Actions.
A large portion of Molywood is structured around Overlays—graphical elements that can be overlaid on the basal scene. Overlays support dynamically changing text, 1D and 2D graphs with custom formatting, external figures and movies, as well as other Scene objects (i.e. one molecular movie can be used as an inset in another one). One can also dynamically change the position, relative size and opacity of the Overlays, with no limit to their number or the stacking of layers. Overlays are hence indispensable in augmenting the bare visuals with additional data, such as labels, analysis results, insets or zooms, logos or ending credits.
In turn, the concept of Scenes was primarily introduced to enable multi-panel movies, e.g. to contrast-related systems side-by-side. Their use, however, is very flexible: they might as well be treated as overlays, or joined consecutively to yield movie-like cuts and transitions. Moreover, different Scenes can visualize different molecular systems. This flexibility in design fosters users’ creativity in making appealing videos targeted at the general public.
Finally, several global options exist that make the tool even more easily customized. Each Scene can be loaded from a saved VMD visualization state, preserving user-defined viewpoints, selections and settings. Alternatively, a quick start is also possible from an existing PDB structure and/or trajectory, with an option to download PDB structures on-the-fly from the online database. Movie quality can be easily set by specifying frame-per-second values and Scene resolution. In case of resource-intensive renders, frames can be preserved to allow for restarting when only minor changes are introduced. Last but not least, four hierarchical rendering modes exist to speed up the design process: (i) no rendering, with frames only displayed in VMD; (ii) quick-and-dirty rendering via VMD’s Snapshot; (iii) proper rendering with Tachyon, possibly on an external machine; (iv) professional rendering with shading and Tachyon’s Ambient Occlusion. By changing render modes, one can quickly get through the design process and offload the most resource-heavy rendering step to an external workstation, where all dependencies can be conveniently batch-installed in a separate conda environment.
3 Discussion
Due to the evolving character of Molywood, finer details of the implementation, an extended documentation of all keywords, a set of mini-tutorials and the examples gallery are hosted on https://mmb.irbbarcelona.org/molywood and regularly updated. The gallery, in particular, is intended as an ever-growing collection of highlights to serve other users as both guidelines and inspiration; users may submit their movies along with self-contained input datasets if they want to share their work. Meanwhile, the tutorials provide a detailed overview of all available keywords, parameters and defaults, serving as an extended and more intuitive documentation. We also encourage user feedback to help Molywood better address the needs of the community, hoping the tool will eventually define new standards in movie generation, as well as inspire further developments of open-source visualization software. With this common goal, we shall see the future of the field as yet more visually appealing.
Funding
This work was supported by the Polish National Agency for Academic Exchange, the Instituto Nacional de Bioinformática, the European Union’s Horizon 2020 research and innovation program [BioExcel-2 project], the Biomolecular and Bioinformatics Resources Platform (ISCIII PT 13/0001/0030) as well as the MINECO Severo Ochoa Award of Excellence (Government of Spain) (awarded to IRB Barcelona). M.O. is an ICREA Research Professor.
Conflict of Interest: none declared.
==== Refs
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Kendrew J.C. et al (1960) Structure of myoglobin: a three-dimensional Fourier synthesis at 2 Å. Resolution. Nature, 185 , 422–427.18990802
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PMC008xxxxxx/PMC8480672.txt |
==== Front
J Exp Med
J Exp Med
jem
The Journal of Experimental Medicine
0022-1007
1540-9538
Rockefeller University Press
34554188
jem.20201656
10.1084/jem.20201656
Article
Innate Immunity and Inflammation
BTK operates a phospho-tyrosine switch to regulate NLRP3 inflammasome activity
BTK phosphorylates NLRP3, regulating its activity
https://orcid.org/0000-0002-1499-9375
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https://orcid.org/0000-0001-8569-8169
Hantschel Oliver Conceptualization Resources Supervision Writing - review & editing 20
https://orcid.org/0000-0002-6729-1469
Scharl Michael Formal analysis Investigation Methodology Resources 6
https://orcid.org/0000-0002-7281-8579
Wu Hao Supervision Writing - review & editing 23
https://orcid.org/0000-0003-1488-5666
Latz Eicke Conceptualization Investigation Writing - review & editing 421
https://orcid.org/0000-0002-8627-7056
Weber Alexander N.R. Conceptualization Formal analysis Funding acquisition Investigation Project administration Resources Supervision Validation Visualization Writing - original draft Writing - review & editing 1101422
1 Interfaculty Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
2 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA
3 Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
4 Institute of Innate Immunity, University Hospital Bonn, Bonn, Germany
5 German Center for Neurodegenerative Diseases, Bonn, Germany
6 Department for Gastroenterology and Hepatology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
7 Interfaculty Institute of Biochemistry, University of Tübingen, Tübingen, Germany
8 Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Tübingen, Germany
9 Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany
10 Cluster of Excellence 2180, Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
11 Institute of Organic Chemistry, University of Tübingen, Tübingen, Germany
12 Division of Pediatric Rheumatology and Autoinflammation Reference Center Tübingen, Department of Pediatrics, University Hospital Tübingen, Tübingen, Germany
13 Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
14 Cluster of Excellence 2124, Controlling Microbes to Fight Infection, University of Tübingen, Tübingen, Germany.
15 Centre of Chronic Immunodeficiency, University Hospital Freiburg, Freiburg, Germany
16 Institute for Immunodeficiency, Center for Chronic Immunodeficiency, Medical Center, Faculty of Medicine, Albert-Ludwigs University, Freiburg, Germany
17 German Center for Infection Research, Freiburg, Germany
18 Center for Integrative Biological Signaling Studies, Albert-Ludwigs University, Freiburg, Germany
19 Cluster of Excellence 2155, Resolving Infection Susceptibility, Hanover Medical School, Freiburg, Germany
20 Institute of Physiological Chemistry, Faculty of Medicine, Philipps University of Marburg, Marburg, Germany
21 Division of Infectious Diseases and Immunology, University of Massachusetts, Worcester, MA
22 German Cancer Consortium, Tübingen, Germany
Correspondence to Alexander N.R. Weber: [email protected]
Disclosures: J.B. Kümmerle-Deschner reported grants from Novartis, personal fees from Novartis, grants from SOBI, and personal fees from SOBI outside the submitted work. B. Grimbacher reported grants from BMBF, grants from DFG, grants from several pharmaceutical companies, personal fees from several pharmaceutical companies, and grants from foundations outside the submitted work. E. Latz is co-founder and consultant to IFM Therapeutics. No other disclosures were reported.
Z.A. Bittner’s present address is Prime Vector Technologies GmbH, Tübingen, Germany.
O.-O. Wolz’s present address is CureVac AG, Tübingen, Germany.
01 11 2021
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© 2021 Bittner et al.
2021
https://creativecommons.org/licenses/by-nc-sa/4.0/ http://www.rupress.org/terms/ This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
Bittner et al. discovered that direct phosphorylation of the inflammasome sensor NLRP3, by the well-established drug target Bruton’s tyrosine kinase (BTK), promotes inflammasome assembly and boosts IL-1β release. NLRP3 phosphorylation by BTK may thus represent a novel target for therapeutic intervention in inflammation.
Activity of the NLRP3 inflammasome, a critical mediator of inflammation, is controlled by accessory proteins, posttranslational modifications, cellular localization, and oligomerization. How these factors relate is unclear. We show that a well-established drug target, Bruton’s tyrosine kinase (BTK), affects several levels of NLRP3 regulation. BTK directly interacts with NLRP3 in immune cells and phosphorylates four conserved tyrosine residues upon inflammasome activation, in vitro and in vivo. Furthermore, BTK promotes NLRP3 relocalization, oligomerization, ASC polymerization, and full inflammasome assembly, probably by charge neutralization, upon modification of a polybasic linker known to direct NLRP3 Golgi association and inflammasome nucleation. As NLRP3 tyrosine modification by BTK also positively regulates IL-1β release, we propose BTK as a multifunctional positive regulator of NLRP3 regulation and BTK phosphorylation of NLRP3 as a novel and therapeutically tractable step in the control of inflammation.
Else-Kröner-Fresenius Stiftung http://dx.doi.org/10.13039/501100003042 Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 CRC TR156 We-4195/15-1 University Hospital Tübingen http://dx.doi.org/10.13039/100012940 2310-0-0 2615-0-0 IFM Therapeutics http://dx.doi.org/10.13039/100015621 E-Rare program http://dx.doi.org/10.13039/100017732 German Research Foundation http://dx.doi.org/10.13039/501100001659 GR1617/14-1/iPAD Federal Ministry of Education and Research http://dx.doi.org/10.13039/501100002347 GAIN_01GM1910A Damon Runyon Cancer Research Foundation http://dx.doi.org/10.13039/100001021 University of Tübingen http://dx.doi.org/10.13039/501100002345 University Hospital Tübingen http://dx.doi.org/10.13039/100012940 Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 EXC 2180 EXC 2124 EXC 2189 EXC 2155 EXC 2151 Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 EXC 2180 (390900677) EXC 2124 EXC 2189 (390939984) EXC 2155 (39087428) EXC 2151 (390873048)
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pmcIntroduction
Inflammation mediated via the NLRP3 inflammasome not only supports the resolution of infections and sterile insults but also contributes to pathology in multiple human diseases, such as cryopyrin-associated periodic fever syndrome, gout, stroke, Alzheimer’s disease, and atherosclerosis (Duewell et al., 2010; Broderick et al., 2015; Magupalli et al., 2020; Franke et al., 2021). Thus, the activity of the NLRP3 inflammasome, a powerful molecular machine maturing IL-1 family cytokines via the activity of caspase-1, is tightly controlled at several levels. At the structural level, recent cryo-electron microscopy (EM) studies demonstrated that the three-dimensional conformation of NLRP3 is critical for NLRP3 oligomerization and may depend on ADP/ATP binding (Sharif et al., 2019). In addition, NLRP3 binding proteins, such as NEK7, have been shown to have an impact on inflammasome activity (He et al., 2016; Schmid-Burgk et al., 2016). Moreover, posttranslational modifications of NLRP3 enhance or reduce its activity by only partially elucidated mechanisms (Song and Li, 2018). Finally, NLRP3 interacts dynamically with subcellular organelles, such as the trans-Golgi network: On the one hand, a polybasic region (PBR) in the linker between the NLRP3 pyrin domain (PYD) and NAIP, CIITA, HET-E and TEP1 (NACHT) domain controls interaction of NLRP3 with negatively charged phosphatidylinositol phosphates (PIPs) at organelles, such as the Golgi, which were shown to disperse upon cell stimulation (Chen and Chen, 2018). On the other hand, dissociation from membranes into the cytosol was proposed as a requirement for the nucleation of larger NLRP3 oligomers (Zhang et al., 2017). The subsequent dynein/HDAC6-dependent trafficking of NLRP3 to the microtubule organizing center (MTOC) prompts the formation of complete inflammasome complexes, which include the adaptor apoptosis-associated speck-like protein containing a caspase activation and recruitment domain (ASC), NEK7, and the IL-1β maturation enzyme caspase-1 (Magupalli et al., 2020). However, cues instigating these shifts in localization remain to be resolved. Generally, how multiple layers of NLRP3 regulation are related or even integrated at the cellular as well as at the molecular level is unclear. If individual regulators were to provide this integration, they could be valuable targets to modulate inflammasome activity.
We and others have recently identified Bruton’s tyrosine kinase (BTK) as a novel and therapeutically relevant NLRP3 regulator (Ito et al., 2015; Liu et al., 2017) that is rapidly activated upon NLRP3 inflammasome stimulation and interacts with NLRP3 and ASC in overexpression systems. Its genetic ablation led to reduced IL-1β secretion in vitro and, importantly, in human ibrutinib-treated patients ex vivo (Liu et al., 2017). BTK is a well-known cancer target for which Food and Drug Administration–approved inhibitors, such as ibrutinib, exist (reviewed in Weber et al., 2017). Furthermore, BTK activation in macrophages was described in COVID-19–related lung inflammation, which was sensitive to BTK kinase inhibitors (Roschewski et al., 2020), and several in vivo models have illustrated BTK’s relevance for promoting NLRP3-mediated inflammation (O’Riordan et al., 2019, 2020; Purvis et al., 2020). Based on the molecular mechanisms and chronic inflammatory processes observed in the pathology of many disorders and cancers, targeting NLRP3 via BTK also appears to be an attractive therapeutic option in other diseases (Banoth and Cassel, 2017; Henrickson, 2017; Weber, 2021)—provided that the molecular basis for BTK’s involvement in the inflammasome process is elucidated.
Here, we report that BTK directly modifies four NLRP3 tyrosine (Y) residues in the PYD-NACHT polybasic linker, altering the charge of the PBR peptide sequence. Tyrosine mutagenesis alters phospholipid interactions, and BTK promotes the shift of NLRP3 from heavy (e.g., intact Golgi) to light (e.g., dispersed Golgi) membranes. Consequently, ablation of BTK kinase activity or tyrosine mutation decreased the formation of cytosolic NLRP3 oligomers, complexes with ASC, and IL-1β release, respectively. Our data suggest that BTK-mediated tyrosine phosphorylation affects the activity of the NLRP3 inflammasome by modifying PBR charge, subcellular localization, inflammasome assembly, and ultimately IL-1β secretion. BTK thus emerges as an important regulation hub for the activation of the NLRP3 inflammasome at multiple levels, which could be simultaneously targeted via BTK kinase inhibitors.
Results
BTK deficiency coincides with reduced NLRP3 tyrosine phosphorylation in vitro and in vivo
Based on previous work (Liu et al., 2017), we hypothesized that BTK and NLRP3 may engage in a direct kinase–substrate relationship, whose elucidation might unravel novel molecular aspects of NLRP3 inflammasome regulation. As it is the best studied NLRP3 agonist, we primarily used the pore-forming K+-dependent stimulant, nigericin. We sought to test this in Btk-deficient primary murine bone marrow–derived macrophages (BMDMs) and in peripheral blood mononuclear cells (PBMCs) from patients with the genetic BTK deficiency X-linked agammaglobulinemia (XLA). As expected, IL-1β release upon nigericin stimulation was significantly reduced in Btk-deficient BMDMs and patient-derived PBMCs, respectively (Fig. 1, A and B). Interestingly, in BMDMs, endogenous NLRP3 precipitated and interacted with endogenous BTK in WT but not Btk or Nlrp3 KO BMDMs (Fig. 1 C), irrespective of the BTK kinase inhibitor ibrutinib. Similarly, BTK coimmunoprecipitated with NLRP3 in PBMCs from healthy donors (HDs; Fig. 1 D). Thus, in both human and murine primary immune cells, BTK and NLRP3 interact independently of nigericin stimulation. This was also confirmed with a cell-free in vitro pulldown of recombinant purified NLRP3, which precipitated BTK and NEK7 proteins (Fig. 1 E). Being a late NLRP3 interactor (Magupalli et al., 2020), NEK7 was considered of less importance for the early activation events of NLRP3 that were in focus here, and thus only served as a positive binding control for recombinant NLRP3 (Sharif et al., 2019; also see Materials and methods). We next tested whether BTK is able to phosphorylate NLRP3 upon nigericin treatment. In murine BMDMs (Fig. 1 F), immunoprecipitated NLRP3 became rapidly phospho-tyrosine (p-Y) positive in cells expressing Btk but not in Btk or Nlrp3 KO cells. Similarly, NLRP3 phosphorylation was also observed in HD PBMCs (Fig. 1 G), and lower in XLA patients’ PBMCs (Fig. S1, A and B). Treatment with λ-phosphatase abolished p-Y reactivity, further confirming the phospho-antibody specificity. Notably, endogenous NLRP3–BTK interaction and NLRP3 time-dependent tyrosine phosphorylation were also observed upon stimulation with monosodium urate (MSU) crystals, another NLRP3 agonist, in vivo. In brief, when MSU was injected intraperitoneally and lavages analyzed at different time points for IL-1β release (cell-free supernatant, control for stimulation conditions) and NLRP3 immunoprecipitation (lavage cells), an interaction of endogenous NLRP3 with BTK and NLRP3 tyrosine phosphorylation were observed, and these were specific for lavages from WT but not Nlrp3 KO mice, starting at 1 h and peaking at 2 h after injection (Fig. 1, H and I; and Fig. S1, C–E). Thus, BTK and NLRP3 interact endogenously in primary immune cells, in vitro and in vivo, and BTK promotes NLRP3 tyrosine phosphorylation upon NLRP3 stimulation.
Figure 1. NLRP3 directly interacts with and is tyrosine phosphorylated by BTK. (A and B) IL-1β release (triplicate ELISA) from WT versus Btk KO BMDMs (A; n = 5 each) or XLA versus HD PBMCs (B; n = 3 or 6 per group). (C and D) Coimmunoprecipitation (Co-IP) of NLRP3 from WT, Btk KO, or Nlrp3 KO BMDM (C; n = 3) or ibrutinib-treated PBMC lysates (D; n = 2). Black lines indicate that intervening lanes have been spliced out. (E) In vitro pulldown of Flag-tagged BTK or His-SUMO–tagged NEK7 by MBP-tagged NLRP3 (n = 3). Arrowheads denote copurified NEK7 and BTK in the eluates. The BTK band was further confirmed by Flag-IP. (F) Co-IP from WT, Btk KO, or Nlrp3 KO BMDMs using anti–p-Y antibodies (n = 3). (G) Co-IP from PBMCs using anti–p-Y (n = 5). λ-Phosphatase was added as a dephosphorylation control where indicated (n = 2). (H and I) Co-IP from peritoneal lavage cells harvested 2 h after MSU i.p. injection (n = 4; quantified in I relative to background in lane 1). (J) In vitro kinase assay using two different commercial suppliers, A and B, of recombinant BTK. Posi-Tag = specificity control. Quantification relative to background in lane 1 (n = 2). (K) Co-IP from PBMCs using anti–p-Y antibodies with prior ibrutinib pretreatment (n = 3). (L and M) IPs from HEK293T cells transfected with NLRP3 and BTK WT or KD constructs or treated with inhibitors (n = 2 each). A, B, and I represent combined data (mean + SD) from n biological replicates (each dot represents one mouse or patient/HD). C–H and J–M are representative of n biological (HD or mouse) or technical replicates. *, P < 0.05 using Student’s t test (A), one-way ANOVA with Dunnett’s correction (B), or Mann–Whitney U test (I). Acalabru, acalabrutinib; ctrl, control; IB, immunoblot; Ibru, ibrutinib; Nig, nigericin; Quant., quantification.
Figure S1. BTK-dependence of NLRP3 tyrosine phosphorylation. (A and B) Coimmunoprecipitation (Co-IP) of NLRP3 from HD or XLA patient PBMC lysates (n = 2 each), quantified in A relative to lane 1 or 5, respectively, and in B, relative to GAPDH in the respective LPS-only control across experiments. Black lines indicate that intervening lanes have been spliced out. (C–E) In vivo MSU peritonitis model (n = 4 in each group). (C) IL-1β release by triplicate ELISA from peritoneal lavage supernatant 0 or 8 h after MSU-treatment in vivo. (D) Analysis of representative peritoneal lavage cell lysates at different times after MSU-treatment in vivo. (E) Quantification of p-Y–NLRP3 immunoblot (IB) relative to background in NLRP3 IPs from peritoneal lavage cells (n = 3). (F) pNLRP3 occurrence in the in vitro kinase assay with BTK or KD BTK upon incubation with ATP for the indicated time periods, with and without ibrutinib (n = 3). (G) HEK293T cells were transfected with the indicated NLRP3 and BTK WT or mutant constructs and treated with inhibitors, and lysates were subjected to HA-IP and IB (n = 3 each). In A, D, F, and G, one representative example of n biological replicates is shown. B, C, and E represent combined data (mean + SD) from n biological replicates (each dot represents one donor or mouse). *, P < 0.05 according to one-way ANOVA with Šidák (C) or Dunnett’s (E) correction. Acalabru, acalabrutinib; Ibru, ibrutinib; Incub., incubation; Nig, nigericin; recomb., recombinant.
BTK kinase activity is required for NLRP3 tyrosine phosphorylation
We next tested whether BTK kinase activity was required for NLRP3 tyrosine phosphorylation. Two independent cell-free in vitro setups showed that the presence of BTK was necessary and sufficient for NLRP3 p-Y modification (Fig. 1 J and Fig. S1 F). Furthermore, in both PBMCs and the in vitro setup, NLRP3 tyrosine phosphorylation was blocked by ibrutinib treatment (Fig. 1 K and Fig. S1 F) and, thus, was dependent on BTK kinase activity. Next, human embryonic kidney (HEK) 293T cells were transfected with NLRP3 and BTK and treated with or without BTK inhibitors. In this cellular system, NLRP3 tyrosine phosphorylation was abrogated in the presence of both BTK kinase inhibitors (Fig. 1 L), consistent with results in primary BMDMs (Fig. 1 C). In contrast, the NLRP3-specific inhibitor MCC950 (Coll et al., 2015; Tapia-Abellán et al., 2019) failed to prevent BTK-specific interaction and NLRP3 p-Y modification (Fig. 1 L). Interestingly, the presence of NLRP3 seemed to promote BTK phosphorylation, an indicator of activity (Duarte et al., 2020). Notably, the expression of kinase-dead (KD) BTK (K430E mutation; see Weber et al., 2017) was not able to induce NLRP3 tyrosine phosphorylation, despite an intact interaction (Fig. 1 M), ruling out other kinases in this system. Similar results were obtained for the in vitro cell-free setup (Fig. S1 F). Thus, BTK kinase activity appears essential and sufficient for NLRP3 p-Y modification using primary immune cells, the HEK293T system, or purified recombinant proteins, indicative of a direct kinase–substrate relationship.
BTK phosphorylates four conserved tyrosine residues in the NLRP3 PYD-NACHT linker domain
To map the modified tyrosine residues in NLPR3, we compared Flag-tagged full-length with truncated NLRP3 constructs (Mayor et al., 2007) of only the PYD (1–93); the extended NACHT domain (94–534), which includes an N-terminal linker domain (94–219; Sharif et al., 2019); and the leucine-rich repeat (LRR) domain (535–1,036; see Fig. 2 A and Fig. S2). BTK exclusively phosphorylated the extended NACHT construct (Fig. 2, B and C), ruling out Y861 (first identified by Spalinger et al., 2016) as the phosphosite. Individual mutation of the nine tyrosines in the core NACHT domain (220–534; see Fig. 2 D) to phenylalanine (F) did not impact the level of phospho-NLRP3 detected upon BTK coexpression (Fig. S3, A and B). However, when the linker (94–219) tyrosines were targeted (Fig. 2 E), mutated Y168 showed partial, but significant reduction of the p-Y signal, as shown by conventional immunoblotting (Fig. 2, F and G) and automated capillary electrophoresis analysis (Fig. 2 H and Fig. S3 C), respectively. Thus, Y168 emerged as a novel putative p-Y site in NLRP3 specifically modified by BTK. Unfortunately, the linker region is not accessible to mass spectrometric analysis (data acquired from Stutz et al., 2013 and replotted in Fig. S3 D). Therefore, to assess the phosphorylation of Y168 by alternative means, 15-mer peptides covering all linker tyrosines (Fig. 2 E and Table S1) were incubated with His-tagged BTK to assess peptide phosphorylation in a cell-free system. Following BTK removal by anti-His beads, tyrosine phosphorylation of the peptides was visualized by dot blot analysis. The majority of Y-containing peptides and all F-containing corresponding peptides showed little or no phosphorylation (e.g., a weakly phosphorylated Y123; Fig. S3 E). However, the Y168-containing peptide (black square in Fig. 2 I) showed strong tyrosine phosphorylation (Fig. 2 I and Fig. S3 E). Of note, peptides containing Y136, Y140, or Y143—either in combination (black circle) or as single tyrosines—were also phosphorylated (Fig. 2 I), similar to peptides containing the corresponding sequences in mouse NLRP3 (Y132, Y136, Y145, and Y164; see Fig. S3 F). That the linker region contained altogether at least four BTK-modified tyrosines explained the only partial effect of single Y168 mutation observed above (Fig. 2, F–H). In line with this, the p-Y NLRP3 signal in HEK293T cells was reduced slightly in expression constructs containing an individual Y168 mutation (black square) but drastically so upon simultaneous mutation of Y136, Y140, and Y143 to phenylalanine (“3Y>F,” black circle) or the combination of Y136, Y140, and Y143 with Y168 mutation (“4Y>F,” black square + circle) both in an FL-NLRP3 construct and when only the linker sequence was fused to an mCitrine YFP–hemagglutinin (HA) sequence (here termed “hLinker-Cit-HA”; Fig. 2 J). BTK thus appears to be able to specifically modify not only one but at least four tyrosines—Y136, Y140, Y143, and Y168—in the PYD-NACHT linker of human (and murine) NLRP3 in vitro. To gain structural insights, we mapped the sites in a recent cryo-EM structure of an NLRP3–NEK7 complex (Fig. 2 K and Fig. S4 A; Sharif et al., 2019). Interestingly, Y136, Y140, and Y143 were found in a helical region proposed to contact the PYD for ASC recruitment (Sharif et al., 2019). Furthermore, Y168, which maps to the vicinity of several likely pathogenic cryopyrin-associated periodic fever syndrome mutations (Fig. S2), was adjacent to the putative ADP binding site and might thus influence nucleotide binding (Fig. 2, L and M; and Fig. S4 A). Interestingly, all the BTK-modified Y residues were strongly conserved in other NLRP3 sequences, further highlighting their potential functional relevance (Fig. S4 B). BTK and many other SH3–SH2-containing kinases are thought to select substrate proteins by binding via their kinase domain (KinD) and subsequent phosphorylation. The phosphorylated substrate may, at the same time, allosterically activate kinase activity by binding the SH3–SH2 module (Duarte et al., 2020). We therefore sought to test whether the conserved NLRP3 linker region may bind and affect BTK kinase activity and thereby explore the reason for unequal capacities of the NLRP3 mutants to immunoprecipitate BTK (Fig. 2 J) and enhance BTK Y-phosphorylation in the presence of NLRP3 (Fig. 1 L). BTK activation (as evidenced by BTK Y-phosphorylation) was assessed upon coincubation of NLRP3-derived PBR Y-containing substrate peptides first with a purified truncated BTK protein consisting only of the SH3–SH2 and the KinD (SH3–SH2–BTK construct). In line with an activity-promoting effect of NLRP3 on BTK, WT NLRP3 peptides increased BTK phosphorylation over time (Fig. S4 C, top panels; quantified in D). An F-containing peptide induced much lower BTK phosphorylation, as allosteric activation by binding the SH2 domain is not possible. p-Y–containing peptides failed to induce BTK activity probably because they compete with BTK autophosphorylation (Duarte et al., 2020), which is in line with the evidence that none of the peptides had a direct effect on BTK activity in the context of a purified KinD-only truncated BTK protein (Fig. S4, C and D). This indicates that processive binding and phosphorylation of the NLRP3 linker substrate promote BTK activity via BTK’s SH3–SH2 module. Collectively, our results suggest that BTK directly modifies multiple highly conserved tyrosines within a functionally important domain in NLRP3, which could impact downstream steps in the assembly of the oligomeric inflammasome complexes.
Figure 2. BTK phosphorylates the PYD-NACHT linker. (A) NLRP3 domains (UniProt ID Q96P20). (B) Flag immunoprecipitation (Flag-IP) from HEK293T cells transfected with Flag-tagged NLRP3 truncation and/or Flag–BTK constructs (n = 3). (C) As in B but including ibrutinib (n = 3). (D) Positions of targeted tyrosine residues. (E) Linker region including polybasic motif. (F) As in B but using NLRP3 Y>F point mutants or WT NLRP3, with WT or KD BTK plasmids (n = 4). (G) Quantification of F (n = 4). (H) WES capillary electrophoresis of NLRP3 Flag-IP from HEK293T cells transfected with WT or mutant Flag-NLRP3 and WT or KD BTK (n = 3). (I) Dot blot of BTK assay with 15-mer NLRP3-derived WT or mutant synthetic peptides (n = 3). After BTK removal using anti-His beads, peptide mixtures were directly spotted and stained with a total peptide stain (input) or anti–p-Y. A circle denotes peptides containing the three PBR tyrosines, and a square denotes Y168-containing peptides. (J) As in F but also NLRP3 linker (WT or Y mutated) fused to mCitrine (mCit)-HA (n = 3). (K) Tyrosines (red) highlighted in the model of NLRP3 (blue)–NEK7 (yellow) complex (PDB: 6NPY). (L and M) Close-up view on dimer interface (L) and putative nucleotide binding site (M). G represents combined data (mean + SD) from n biological replicates (each dot represents one replicate). B, C, F, and H–J are representative of n technical replicates. *, P < 0.05 according to one-sample t test (G). IB, immunoblot; Ibru., ibrutinib; RLU, relative light unit.
Figure S2. Position of tyrosine residues and disease-associated mutations in the linker and/or NACHT domain of human NLRP3. Annotation of NLRP3 sequence (UniProt accession no. Q96P20).
Figure S3. Positional mapping of BTK-modified tyrosine residues in NLRP3. (A) Position of all mutated tyrosine residues in linker–NACHT construct and phosphorylation analysis of core-NACHT tyrosine NLRP3 mutants. HEK293T cells were transfected with the indicated NLRP3 mutant constructs and a BTK WT construct as indicated, and lysates were subjected to HA-immunoprecipitation (IP) and immunoblot (IB) as indicated (n = 4 each). (B) Quantification of A combined from n = 4 experiments as the ratio of p-Y–NLRP3 to total NLRP3 in the IP fraction normalized to transfection with WT NLRP3. (C) Quantification of WES capillary electrophoresis of NLRP3 p-Y IPs from HEK293T cells (Fig. 2 H) from n = 3 experiments. (D) Data from MS analysis of purified murine NLRP3 (mNLRP3), digested with different proteases, showing combined (top) and separate coverage (bottom) information extracted from Stutz et al. (2013) and replotted here. (E) Dot blot of in vitro kinase assay of His-BTK and 15-mer synthetic peptides derived from human NLRP3, containing the indicated tyrosines stained with a total protein stain (top grid; input) or anti–p-Y antibodies (bottom grid; n = 3). (F) As in E but with peptides derived from murine NLRP3 ((n = 3). B and C represent combined data (mean + SD) from n biological replicates. In E and F, one representative example of n biological replicates is shown. *, P < 0.05 according to one-sample t test (B and C). AOC, antioxidant capacity.
Figure S4. Structural positioning, sequence conservation of BTK-modified tyrosine residues in NLRP3, and effect of NLRP3 on BTK kinase activity. (A and B) Structural aspects and conservation of BTK-modified tyrosine residues in NLRP3. (A) NLRP3 model 6NPY showing NLRP3 linker-NACHT-LRR (blue) and NEK7 C-terminal lobe (yellow). A putative bound ADP molecule and selected tyrosines are highlighted. (B) ClustalW multiple sequence alignments of NLRP3 sequences from other species. Coloring according to similarity (black, conserved). BTK-modified tyrosines are highlighted (residue numbering according to human NLRP3). (C and D) Effect of NLRP3 peptides (WT Y sequence, p-Y sequence, or Y>F) on phosphorylation (assessed by p-Y blot) of the SH3-SH2-KinD or KinD-truncated BTK proteins purified from insect cells. BTK phosphorylation as investigated by immunoblot (IB) is indicative of BTK activation (n = 3). C shows one representative example of n technical replicates, combined in D. *, P < 0.05 according to two-way ANOVA (D). M, MW ladder; quant., quantification; rec., recombinant; rel. relative.
BTK-mediated phosphorylation sites affect linker charge and PIP binding
Chen and Chen (2018) identified a PBR (including K127–130 in mouse NLRP3 and K127 and K129–130 in human NLRP3) as critical for NLRP3 charge-dependent binding to organelle PIPs and inflammasome nucleation. Unexpectedly, Y136, Y140, and Y143 precisely mapped to this PBR in the NLRP3 PYD-NACHT linker and interdigitated with the basic residues mediating the proposed NLRP3 interaction with negatively charged membrane phospholipids (Fig. 3 A). The mutation of three positively charged residues (K127, K129, and K130) in the mNLRP3 PBR to alanine (K>A) was shown to abrogate binding (Chen and Chen, 2018) to PI4P, a phospholipid found at the trans-Golgi network and secretory granules (Vicinanza et al., 2008). We therefore hypothesized that BTK phosphorylation of Y136, Y140, and Y143 might generally alter the proposed charge attraction of NLRP3 with membrane phospholipids. Charge computations suggested that at cytoplasmic pH 7.4, the net charge of the isolated PBR sequence of NLRP3 shifts from +7.28 (unphosphorylated) to +2.01 when Y136, Y140, and Y143 are phosphorylated in human NLRP3 (Fig. 3 B) and from +8.33 (unphosphorylated) to +3.06 (3× phosphorylated) in mouse NLRP3. Likewise, in the NLRP3–NEK7 cryo-EM structure, the 3× phospho-modification causes a significant change in the computed surface charge of a “hypothetically active” NLRP3 (Sharif et al., 2019) toward less-positive values (Fig. 3 C). To confirm this charge difference experimentally, synthetic phospho and non-phospho versions of the Y136/Y140/Y143-containing peptides from both human and mouse NLRP3 were studied in pH titrations, demonstrating the p-Y peptide to be significantly less positively charged (Fig. 3 D). We therefore hypothesized that tyrosine phosphorylation by BTK might alter interactions of NLRP3 with PIPs, possibly shifting the binding from monophosphorylated (PI3P, PI4P) toward the more negatively charged diphosphorylated PIPs (PI(3,4)P2, PI(4,5)P2). Taking the same approach as Chen and Chen (2018), the binding of 4xY>E, i.e., phosphomimetic, mutant human NLPR3 linker-Cit-HA fusion protein, to beads coated with a singly charged phospholipid, PI4P, was reduced compared with the corresponding WT fusion protein construct (Fig. 3, E and F). This was further confirmed by the fusion of the murine NLRP3 PBR to GFP-Flag (mPBR-GFP-Flag, as in Chen and Chen, 2018). Despite equal expression, the Y>E construct bound PI4P beads less strongly than WT (Fig. 3 G). Reduced binding was also observed for the corresponding K>A mutant, which was known to be defective in PI4P binding based on charge neutralization (Chen and Chen, 2018). In line with these data, our results indicate that the BTK-modified Y positions could affect NLRP3–PIP interactions. It was beyond the scope of the present study to explore the binding to other PIPs shown to bind NLRP3, namely PI3P, PI3, 5P2, PI4, 5P2, and phosphatidic acid (Chen and Chen, 2018), which differ in charge density, abundance, and distribution across different organelles (Vicinanza et al., 2008). Nevertheless, it seemed important to know whether BTK activity might represent a cue to shift the subcellular localization of NLRP3. To test this by another technique and in primary immune cells, we stimulated WT and Btk KO primary BMDMs with lipopolysaccharide (LPS) and nigericin and fractionated their lysates into P5 (heavy membrane, intact Golgi, and mitochondria) and P100 (light membrane, dispersed Golgi, ER, and polysomes) fractions and probed them for NLRP3 (Fig. S5 A). BTK, whose pleckstrin homology domain also binds PIPs (references in Li et al., 1997), was also analyzed and found to localize similarly to NLRP3 (Fig. S5 A), in keeping with earlier interaction analyses (Fig. 1, C and F). In both WT and Btk KO BMDMs, NLRP3 was detectable in the P5 fraction upon LPS treatment and before nigericin stimulation, indicating that BTK is not essential for initial NLRP3 localization toward P5 membranes (Fig. 3, H and I). However, nigericin stimulation in WT BMDMs coincided with progressive and significant depletion of NLRP3 from the P5 fraction 20–45 min after treatment, consistent with the concepts of Golgi fragmentation and NLRP3 relocalization (Chen and Chen, 2018; Magupalli et al., 2020; Zhang et al., 2017), and the timing of phosphorylation in these cells (Fig. 1 F). Conversely, Btk KO BMDMs did not show significant P5 depletion within this time frame (Fig. 3, H and I), suggesting that the NLRP3 interaction with Golgi membranes may remain more stable in the absence of BTK. Collectively, these experiments show that both the BTK-modified Y positions in the NLRP3 PBR and the presence of BTK may have an impact on NLRP3 PBR charge, PIP binding, and subcellular fractionation of NLRP3.
Figure 3. BTK phosphorylation of the NLRP3 polybasic motif enables Golgi/PI4P dissociation. (A and B) Charge distribution (A) and ProtPi net charge computation (B) of unmodified and 3× phosphopeptide human NLRP3 PBR. (C) CHARMM surface charge predictions of linker–NACHT–LRR structure in the putative nonphosphorylated (left) and 4× phosphorylated (right) form. Blue, positive charge; red, negative charge. Gray boxes indicate that the area of charge alterations in the monomers maps to a contact area in the hypothetical dimer (center, rotated by 90°; see relative position in oligomer below). (D) pH titration of peptides encompassing the polybasic motifs of human or murine NLRP3 as phospho (blue) or non-phosphorylated control (ctrl; red) peptide (n = 3). (E and F) Human NLRP3 linker-Cit-HA constructs precipitated with PI4P beads (n = 2; quantified in F relative to immunoprecipitation [IP] HA signal in WT transfection [lane 1]). (G) As in E but murine NLRP3 PBR fused to GFP-Flag (mPBR-GFP-Flag; n = 2). (H and I) Subcellular fractionation of nigericin-treated WT or Btk KO BMDM lysates into P5 (heavy membranes) and S5 (light membranes and cytosol; n = 3; quantified relative to untreated [lane 1] in the experiment shown in H or across experiments in I). D, F, and I represent combined data (mean + SD) from n biological replicates. In E, G, and H, one representative example of n technical replicates is shown. *, P < 0.05 according to one-way ANOVA with Šidák correction (I; relative to respective LPS only) or two-way ANOVA (D). Citr. synth., citrate synthetase; Ctrl, control; IB, immunoblot; Nig, nigericin; Quant., quantification.
Figure S5. Subcellular fractionation of NLRP3 and BTK, effect of tyrosine mutation on NLRP3 activity and conformation and graphical abstract. (A) Sucrose gradient fractionation of WT and Btk KO BMDM lysates upon LPS + nigericin treatment for 5 min. Fractions were analyzed by SDS-PAGE and immunoblot (IB) as indicated (n = 1; pilot fractionation experiment). (B and C) Low concentration (50 ng/ml) LPS priming, nigericin stimulation, and subsequent IL-1β (B) or TNF (C) release from WT or 4xY>F human NLRP3-expressing reconstituted Nlrp3 KO iMacs, measured by ELISA (n = 3). (D and E) Response of NLRP3 BRET sensors to nigericin in HEK293T cells treated 24 h after transfection with 10 μM nigericin for 30 min or left mock treated before subsequent BRET measurement. BRET signals (D) were also normalized to the mean obtained for each construct mock treated (E; both n = 6–8). (F) Graphical summary illustrating multiple roles of BTK as NLRP3 regulator. B–E represent combined data (mean + SD) from n biological replicates. In A, one representative example of n biological replicates is shown. *, P < 0.05 according to one-way ANOVA with (B) or without (C) Šidák correction. def., deficient; EV, empty vector; Nig, nigericin; PBM, polybasic motif; reconst., reconstituted; unstim., unstimulated.
BTK kinase activity affects NLRP3 oligomerization, ASC interaction, and IL-1β release
A recent study proposed that NLRP3 release from the mitochondria-associated ER membranes was required for ASC engagement and full inflammasome assembly (Zhang et al., 2017). As depletion of NLRP3 from the Golgi was lower in the absence of BTK (Fig. 3, H and I), we explored whether BTK kinase activity also affects subsequent full inflammasome assembly, e.g., at the level of NLRP3 oligomerization, and ASC recruitment into the complex. Indeed, native PAGE of nigericin-stimulated WT and Btk KO BMDM lysates (Fig. 4 A) or BTK inhibitor- versus vehicle-treated Pycard (ASC) KO (Fig. 4 B) showed reduced NLRP3 oligomers (typically detectable as nondiscrete “smears”; Green et al., 2018) in Btk-deficient or Btk-inhibited samples. Confocal microscopy of LPS- and nigericin-stimulated primary BMDM showed a lower fraction of cells forming NLRP3 specks costaining positively for the Golgi marker RCAS1 in Btk KO BMDM versus WT BMDM (Fig. 4, C and D). NLRP3 was not detectable in its dispersed state before nigericin addition in all analyzed BMDMs independent of genotype, but nigericin-stimulated Nlrp3 KO showed no staining/specks, confirming the specificity of the staining. The recruitment of ASC into the complex was also assessed in primary BMDM by analyzing ASC cross-linking in whole-cell lysate (WCL) pellets. Evidently, compared with WT BMDM cross-linked pellets, in Btk KO pellets, higher-order oligomers were absent, and in WT BMDM, ibrutinib pretreatment reduced the intensity of recovered ASC staining (Fig. 4 E). Consistent with earlier results, BTK inhibition also reduced speck formation in nigericin or the additional NLRP3 agonist LeuLeu-OMe ASC-mCerulean–expressing immortalized murine macrophages (iMacs; Fig. 4, F and G). Moreover, size-exclusion chromatography of untreated WT cell lysates showed that BTK and NLRP3 coeluted in the high-molecular-weight (MW) fraction (>1,100 kD; Fig. 4 H). Consistent with native PAGE in Btk KO or WT lysates from ibrutinib-treated cells, elution shifted to lower-MW complexes (Fig. 4, H and I). Ablation of BTK activity thus appeared to reduce the subsequent ability of NLRP3 to oligomerize into high MW inflammasomes and to assemble with ASC (Magupalli et al., 2020). To show that BTK-modified Y residues—and the effects of BTK described so far—also had an impact on IL-1β release, Nlrp3-deficient iMacs were retrovirally transduced with WT or tyrosine-mutated (4xY>F) human NLRP3–T2A–mCherry constructs, allowing for cell sorting for equal protein expression (Fig. 4 J). Compared with WT-transduced cells, cells with the 4xY>F construct failed to restore nigericin- and R837 (imiquimod)-dependent IL-1β release (Fig. 4 K). Conversely, TNF release, which is NLRP3 independent (Liu et al., 2017), was comparable between cell lines (Fig. 4 L). To test specificity for the NLRP3 inflammasome and confirm equal capacities of these different cell lines for IL-1β release via another NLRP3- and BTK-independent pathway (Liu et al., 2017), the AIM2 inflammasome, we also stimulated the cells with poly(dA:dT). The resulting IL-1β release was assessed and found to be comparable between WT and mutant cells (Fig. 4, K and L). Of note, the effect of NLRP3 Y mutation was independent of LPS concentration (Fig. S5, B and C; Mao et al., 2020) as 4xY>F cells showed consistently lower IL-1β release when primed with either 50 or 200 ng/ml LPS. To check the folding integrity of the 4xY>F mutant, bioluminescence resonance energy transfer (BRET) was performed (Tapia-Abellán et al., 2019) in HEK293T cells. Compared with WT NLRP3, the 4xY>F mutant NLRP3 showed lower absolute BRET signals (Fig. S5 D) in line with a functional impairment, but similar relative conformational changes to nigericin (Fig. S5 E), indicating that folding was intact. The data show that the BTK-modified Y positions identified here play an important role for full NLRP3 inflammasome activity and subsequent IL-1β release.
Figure 4. BTK modification affects NLRP3 oligomerization and IL-1β release. (A and B) WT, Btk KO, Nlrp3 KO, or Pycard (ASC) KO BMDMs stimulated (45 min nigericin) and respective lysates analyzed directly by native PAGE (A, n = 2; B, n = 4). (C) Representative fluorescence microscopy images of NLRP3 specks in WT, Btk KO, and Nlrp3 KO primed with LPS, stimulated (30 min nigericin), and stained as indicated (n = 2). Blue, nuclei (Hoechst); green, NLRP3; red, Golgi (RCAS1). Scale bar = 10 μm; arrowheads mark NLRP3 specks. (D) Quantification from multiple 3 × 3 tiles per strain per experiment. (E) As in A or B but ASC in WCLs was cross-linked upon stimulation, with or without ibrutinib pretreatment (n = 4). (F) Representative fluorescence microscopy images of ASC specks in Nlrp3 KO iMacs reconstituted with NLRP3-Flag ASC-mCerulean and stimulated as indicated. Blue, nuclei (DRAQ5); yellow, ASC (mCerulean). Scale bar = 50 μm; arrowheads mark only one ASC speck per overview image for the sake of clarity, but in the insets, all specks are marked. (G) Quantification from multiple images per treatment per experiment (n = 2). (H) As in A but lysates were applied to size-exclusion chromatography (SEC) before fractions were analyzed (n = 3). (I) As in H comparing inhibitor-treated WT BMDM or Btk KO BMDM lysates (n = 3). (J–L) NLRP3 expression levels determined by immunoblot (IB), IL-1β, or TNF release quantified by triplicate ELISA in/from WT or 4xY>F NLRP3-reconstituted NLRP3-deficient iMacs (n = 3). D, G, K, and L represent combined data (mean + SD) from n technical replicates. A–C, E, F, H, and I are representative of n biological (mice) or technical replicates. *, P < 0.05 according to Mann–Whitney U test (D), Student’s t test (G), and one-way ANOVA with (K) or without (L) Šidák correction. def., deficient; EV, empty vector; FOV, field of view; Ibru, ibrutinib; Nig, nigericin; reconst., reconstituted; Ve, elution volume; w, with.
Discussion
The mechanism of activation of the NLRP3 inflammasome has been intensely studied for some time, and recent work has unraveled a critical role of the dynamic localization of NLRP3 to and from organelles in the regulation of its activity (Chen and Chen, 2018; Seoane et al., 2020; Zhang et al., 2017). By using both biochemical and cellular assays in vitro and in human and murine cell lines and primary cells, we found that BTK directly participated in these processes at the level of NLRP3, providing the following novel molecular insights: Direct phosphorylation of four conserved and functionally important tyrosine residues in the NLRP3 polybasic linker motif affected the charge of the PBR sequence, and phosphomimetic mutation altered NLRP3–PIP bead interactions, presumably by neutralization of the linker net surface charge. In line with this, in the presence of BTK—whose subcellular localization overlaps with NLRP3—NLRP3 retention at the Golgi appeared lower, whereas NLRP3 inflammasome oligomerization, ASC association, and IL-1β secretion were higher. Finally, mutation of modified NLRP3 tyrosines to the non-phosphorylatable phenylalanine abrogated full IL-1β release, highlighting the importance of these tyrosines. Our data suggest that BTK-mediated phosphorylation of multiple NLRP3 tyrosines may thus serve as a kind of molecular switch, tuning NLRP3 charge and, subsequently, localization and inflammasome assembly. Modification, localization, and oligomerization of NLRP3 have been recognized to be important. However, they were supposed to be hierarchically separate layers of NLRP3 inflammasome regulation and, hence, of inflammation. Our data indicate that some of these layers may be integrated and interconnected by a multifunctional BTK: By decoding the most basal determinants, such as protein sequence, BTK appears to integrate posttranslational modifications, surface charge, interaction with membranes, and ultimately assembly of a highly oligomeric molecular machinery (Fig. S5 F).
Such a dense and interconnected network of regulation would be in line with the critical need to tightly control excessive IL-1β release to prevent pathologies. As multiple organelles (Seoane et al., 2020; Weber et al., 2020b) and other regulatory proteins (e.g., PKD; Zhang et al., 2017) may also participate in this process, we cannot formally rule out effects of BTK at the level of ASC. Furthermore, the cues that activate BTK activity remain to be deciphered. As NLRP3 and BTK interact before nigericin addition (Fig. 1 D) and show the same localization (Fig. S5 A), we speculate that NLRP3 itself, e.g., upon undergoing functional changes in the wake of K+ efflux (Muñoz-Planillo et al., 2013), may render BTK active through protein–protein interactions. The observation that coexpression of NLRP3 with BTK (Fig. 1 J) and the presence of NLRP3 PBR peptides (Fig. S4, C and D) increase BTK phosphorylation as an indicator of kinase activity (Duarte et al., 2020) is thus in line with the fact that BTK kinase activity is highly dependent on three-dimensional conformation and protein–protein interactions (Duarte et al., 2020). If conformationally “active” NLRP3 (Sharif et al., 2019) itself instigated a positive feed-forward loop by switching BTK on at the Golgi, this would restrict BTK activity to already membrane-associated and oligomerized NLRP3, thereby promoting release of NLRP3 oligomers while not preventing unmodified NLRP3 to associate with PIPs. Furthermore, positioning of BTK at the same level as NLRP3 would be in line with the observation that IL-1β release is boosted by the presence of BTK but is not entirely dependent on it (Fig. 1, A and B; and Fig. 4 I; Ito et al., 2015; Liu et al., 2017). Studies by Chen and Chen (2018), Zhang et al. (2017), and Magupalli et al. (2020) illustrated the staggering complexity of NLRP3 trafficking and its regulation, but due to multiple assays, constructs, and cell types that have been used, a uniform “trafficking-activation mechanism” cannot be established yet. Resolving the remaining ambiguities was outside the scope of this study, but our observations suggest that concerted modification of charge would have an impact on NLRP3–PIP interactions and could be a mechanism for BTK to promote NLRP3 relocalization. Generally, our results highlight BTK as a positive NLRP3 regulator and are congruent with earlier (Ito et al., 2015; Liu et al., 2017) and current studies (O’Riordan et al., 2019, 2020; Purvis et al., 2020). A recent report by Mao et al. (2020) also proposed BTK to act as a positive regulator in murine and human immune cells at LPS priming concentrations of <100 ng/ml, which are physiological (Copeland et al., 2005; Zweigner et al., 2001). However, under nonphysiologically high TLR4 or TLR2 priming, which may blur the lines between priming and actual NLRP3 activation, the lack or inhibition of BTK increased NLRP3 inflammasome IL-1β output. Other results of Mao et al. have already been discussed elsewhere (Weber et al., 2017, 2020a, 2020b), and in our hands and at least in iMacs, LPS concentration in the range of 50–200 ng/ml did not influence the loss-of-function phenotype of NLRP3 phospho mutants (Fig. 4, I and J; and Fig. S5, B and C). Ours and their studies warrant further exploration of a possible “rheostat role” of BTK that may tune NLRP3 activation, depending on TLR signaling intensity. However, our data clearly indicate that BTK is primarily involved in licensing maximal IL-1β release via the regulatory events characterized here.
Due to the complexity of the system, we focused on nigericin as the most widely used NLRP3 agonist. We therefore cannot extrapolate the role of BTK in response to all the many known activators of NLRP3. However, individual datasets shown here or in our previous work (Liu et al., 2017) for the other NLRP3 agonists (ATP, LLoMe, leukocidin A/B, and MSU) indicate that BTK is likely to have similar roles in these pathways, especially since relocalization seems to be a unifying concept of these and even K+-independent NLRP3 stimuli (Chen and Chen, 2018). An additional question for further study is the relationship among NLRP3, BTK, and the additional regulator NEK7, although the precise role of NEK7 in NLRP3 activation remains enigmatic (Schmacke et al., 2019 Preprint).
Nevertheless, our work suggests that NLRP3 phosphorylation may represent an interesting novel biomarker, and possibly a therapeutic target, for early NLRP3 activation. NLRP3 inhibition via BTK blockade may be an unwanted side effect in the treatment of B cell malignancies for which BTK kinase inhibitors have been Food and Drug Administration approved and successfully used (Byrd et al., 2013; reviewed in Weber, 2021). On the other hand, recent investigations showed that macrophage BTK activation in SARS-CoV2–infected patient PBMCs and a beneficial effect of ibrutinib on COVID-19–related lung inflammation are probably attributable to blocking the NLRP3 inflammasome (Roschewski et al., 2020). Additionally, in an experimental in vivo model of sepsis-related heart dysfunction and metabolic inflammation, BTK inhibition directly ameliorated NLRP3-mediated inflammation and pathophysiology (O’Riordan et al., 2019, 2020; Purvis et al., 2020). Based on these studies, our work points to a molecular rationale for targeting strategies that may be applied to block excess IL-1β production in these and other acute inflammasome-related disease states (Weber, 2021).
Materials and methods
Reagents
Nigericin and LPS (from Escherichia coli, namely LPS-EK, catalog no. tlrl-peklps) were purchased from InvivoGen, ATP from Sigma, ibrutinib and acalabrutinib from Selleckchem, recombinant GM-CSF from PeproTech, and Ficoll from Merck Millipore. Coelenterazine h and Lipofectamine 2000 were from Life Technologies. The composition of the physiological (E-Total [ET]) buffer used in BRET experiments was 147 mM NaCl, 10 mM Hepes, 13 mM D-glucose, 2 mM KCl, 2 mM CaCl2, and 1 mM MgCl2 (pH 7.4). Peptides (synthesized in-house) and antibodies are listed in Table S1 and Table S2, respectively.
Peptides
Synthetic peptides were produced by standard 9-fluorenylmethyloxycarbonyl/tert-butyl strategy using peptide synthesizers P11 (Activotec) or Liberty Blue (CEM Corporation). Purity was assessed by reversed phase HPLC (e2695; Waters) and identity affirmed by nano-UHPLC (UltiMate 3000 RSLCnano) coupled online to a hybrid mass spectrometer (LTQ Orbitrap XL; both Thermo Fisher Scientific). Lyophilized peptides were purified by standard HPLC. For certain peptides, a pH titration with 0.1 M NaOH was performed using standard procedures. For in vitro assays, peptides were dissolved at 10 mg/ml in DMSO and diluted 1:10 in bidistilled H2O. Frozen aliquots were further diluted in cell culture medium and sterile filtered if necessary.
Plasmid constructs
ASC, NLRP3, and BTK coding sequences in pENTR clones were generated as described in Wang et al. (2015). BRET sensor constructs consisting of NLRP3 fused N terminally with YFP and C terminally with Renilla luciferase or NLRP3 C terminally fused only to Renilla luciferase as a control in all BRET assays were as described in Tapia-Abellán et al. (2019). Truncated Flag-tagged NLRP3 constructs were a gift of F. Martinon (University of Lausanne, Faculty of Biology and Medicine, Department of Biochemistry, Lausanne, Switzerland; Mayor et al., 2007). Constructs for the human PYD-NACHT linker (residues 94–219) fused to mCitrine-HA or the murine polybasic motif (residues 127–146) in the context of Flag-GFP (as in Chen and Chen, 2018) were synthesized by GENEWIZ. Point mutations in BTK and NLRP3 were subsequently introduced using the QuikChange II Site-Directed Mutagenesis Kit (Agilent Technologies) according to the manufacturer’s instructions. Presence of the desired mutation and absence of unwanted regions in the entire coding DNA sequence was confirmed by automated DNA sequencing. Maltose-binding protein (MBP)–fused NLRP3 dPYD (ΔPYD; deletion of the PYD domain) and NEK-7 constructs used for protein purification were described in Sharif et al. (2019). Full-length SH3–SH2–KinD and KinD constructs used for protein purification were described in Duarte et al. (2020).
Study subjects and blood sample acquisition
XLA patients were recruited at the Centre of Chronic Immunodeficiency, University Hospital Freiburg, and healthy blood donors at the Interfaculty Institute of Cell Biology, Department of Immunology, University of Tübingen. All patients and healthy blood donors included in this study provided their written informed consent before participation. Approval for use of their biomaterials was obtained by the respective local ethics committees in accordance with the principles laid down in the Declaration of Helsinki as well as applicable laws and regulations. XLA patients were clinically identified and genetically characterized as described in Liu et al. (2017).
Mice
Btk KO (originally generated by Khan et al., 1995), NLRP3 KO (stock no. 021302; The Jackson Laboratory), and WT mice, all on a C57BL/6J (The Jackson Laboratory) background, were maintained locally in specific pathogen–free conditions under regular hygiene monitoring. All animal experiments were approved by local authorities and performed in accordance with local institutional guidelines and animal protection laws, including specific locally approved protocols for sacrificing.
In vivo peritonitis model
Experiments were done as described in Spalinger et al. (2016). In brief, mice were injected with 1.8 mg MSU for the indicated time. Peritoneal cells were collected by intraperitoneal injection of 3 ml PBS and subsequent retrieval of the solution into a sterile conical tube and centrifugation at 300 ×g for 5 min at 4°C. Supernatants were used for IL-1β measurements using a commercially available ELISA kit (#DY401; R&D Systems). The cells were lysed in M-PER lysis buffer (Thermo Fisher Scientific) supplemented with proteinase and phosphatase inhibitors (Roche) and processed for immunoprecipitation as described below.
Cell culture
All cells were cultured at 37°C and 5% CO2 in DMEM or Roswell Park Memorial Institute medium supplemented with 10% fetal calf serum, L-glutamine (2 mM), penicillin (100 U/ml), and streptomycin (100 μg/ml; all from Thermo Fisher Scientific). They were free of mycoplasma contamination and monitored regularly using a PCR-based assay.
Isolation and stimulation of primary human immune cells
PBMCs from HDs and patients were isolated from whole blood using Ficoll density gradient purification; primed with 10 ng/ml LPS for 3 h; and in some cases, treated with 60 μM ibrutinib for 15 min before stimulation with 15 μM nigericin for the indicated time periods.
Generation of primary BMDMs and NLRP3 stimulation
BM cells were isolated from femurs and tibiae of 8–12-wk-old mice, grown, and differentiated using GM-CSF (M1 polarization) as described in Liu et al. (2017). BMDMs were primed with 100 ng/ml LPS for 3 h and either treated with 60 μM ibrutinib for 15 min or stimulated directly with 5 μM nigericin for the indicated time periods.
ASC speck formation assay and confocal microscopy
As described in Stutz et al. (2013) and Liu et al. (2017), Nlrp3 KO iMacs overexpressing NLRP3-FLAG and ASC-mCerulean were pretreated with ibrutinib or solvent control for 10 min before stimulation with either 5 μM nigericin (Life Technologies) for 60 min or 1 mM Leu-Leu-OMe⋅HCl (Chem-Impex) for 90 min. After stimulation, cells were fixed with 4% formaldehyde, and nucleic acids were stained with DRAQ5 (eBioscience). Cells were imaged with a Zeiss Observer.Z1 epifluorescence microscope using a 20× objective as described in Stutz et al. (2013). The number of cells and the number of specks were counted for 10 images per condition using CellProfiler (Carpenter et al., 2006).
NLRP3 speck formation confocal microscopy
BMDM from WT, Btk KO, and Nlrp3 KO mice were generated as described and reseeded in 250,000 cells/well in a 24-well plate containing a 12-mm round coverslip (1.5H thickness, #CG15NH; Thorlabs). The cells were primed with 100 ng/ml LPS (LPS-EK, Ultrapure; InvivoGen) followed by 10 μM nigericin (Life Technologies) for 30 min. The cells were briefly washed with PBS and fixed with freshly prepared 4% paraformaldehyde in prewarmed PEM buffer (80 mM PIPES, 5 mM EGTA, and 1 mM MgCl2 and titrated to pH 6.9 using 5 M KOH) for 15 min at room temperature. To ensure permeabilization, the cells were incubated in PEM + 0.05% saponin for 5 min. This was followed by blocking using 5% BSA in PBS + 0.05% saponin for 1 h at room temperature. The cells were washed three times, with a 5-min interval between the washes, with PBS + 0.05% saponin and followed by primary antibody incubation for 1 h at room temperature (goat polyclonal anti-NLRP3, 1:100, ab4207; Abcam) and rabbit anti-RCAS1 (1:100, #12290; Cell Signaling Technology) and then by secondary antibody (anti-goat Alexa Fluor 488, #A21467, and anti-rabbit Alexa Fluor 647, #A21443; Invitrogen) incubation for 45 min at room temperature. This was followed by staining of the nuclei with Hoechst (1:10,000, #62249; Thermo Fisher Scientific) in PBS. The cells were washed three times for 5 min in PBS + 0.05% saponin after the primary and the secondary antibody incubations. The coverslips were mounted using ProLong Diamond Antifade (#P36965; Thermo Fisher Scientific) and stored for 24 h at room temperature in the dark before imaging. Cells were imaged with a Zeiss LSM 800 Airyscan fluorescence microscope using a 40× objective. Several 3 × 3 tiles were imaged from which the number of cells and the number of specks were counted for three tiles per condition using blinded manual counting.
Expression and purification of recombinant BTK, NEK7, and NLRP3
The plasmids encoding NLRP3 with the deleted PYD (amino acids 134–1034) for MBP fusion protein expression in baculovirus (Bac)-to-Bac system (Thermo Fisher Scientific) and human NEK7 for His-SUMO fusion protein expression in E. coli BL21 (DE3) were described in Sharif et al. (2019). For NLRP3 expression, the baculovirus of NLRP3 was prepared using the Bac-to-Bac system (Thermo Fisher Scientific). Protein expression was induced by infection of Sf9 cells with 1% vol/vol of baculovirus. 48 h after infection, cells were lysed by sonication in buffer containing 30 mM Hepes, 200 mM NaCl, 2 mM 2-mercaptoethanol, and 10% glycerol at pH 7.5 with freshly added protease inhibitor cocktail (Sigma-Aldrich). The supernatant was incubated with 3 ml amylose resin at 4°C for 1 h and subjected to gravity flow. NLRP3 protein was eluted with 50 mM maltose and further purified with size-exclusion chromatography on a Superose 6 10/300 GL column (GE Healthcare) equilibrated with buffer containing 30 mM Hepes, 150 mM NaCl, and 2 mM β-mercaptoethanol at pH 7.5. NEK7 was overexpressed in E. coli BL21 (DE3) overnight at 18°C after induction with 0.1 mM isopropyl-β-d-thio-galacto-pyranoside after optical density at 600 nm reached 0.8. Cells were lysed by sonication in buffer containing 50 mM Hepes, 500 mM NaCl, 5 mM MgCl2, 10 mM imidazole, 10% glycerol, and 2 mM β-mercaptoethanol at pH 7.5 with freshly added protease inhibitor cocktail (Sigma-Aldrich). The His-SUMO-fusion NEK7 was purified by affinity chromatography using Ni-NTA beads (QIAGEN) followed by size-exclusion chromatography on a Superdex 200 10/300 GL column (GE Healthcare), equilibrated with buffer containing 30 mM Hepes, 150 mM NaCl, and 2 mM β-mercaptoethanol at pH 7.5. SH3–SH2–KinD, SH2–KinD, and KinD BTK constructs were expressed in Sf9 cells for 48 h upon infection with 1% vol/vol baculovirus. Cells were lysed by sonication in buffer containing 50 mM Hepes, 150 mM NaCl, 5 mM MgCl2, 10 mM imidazole, 10% glycerol, and 2 mM β-mercaptoethanol at pH 7.5 with freshly added protease inhibitor cocktail. Proteins were purified using Ni-NTA beads, followed by size-exclusion chromatography on a Superdex 200 10/300 GL column equilibrated with buffer containing 30 mM Hepes, 150 mM NaCl, and 2 mM β-mercaptoethanol at pH 7.5. Full-length WT and KD mutant BTK were overexpressed in Expi293 cells (Thermo Fisher Scientific) using transient transfection with polyethylenimine 25K (Polysciences). Cells were harvested 96 h after transfection and lysed in buffer containing 50 mM Hepes, 150 mM NaCl, 2 mM 2-mercaptoethanol, and 10% glycerol at pH 7.5 with freshly added protease inhibitor cocktail. The FLAG-fusion proteins were subjected to affinity chromatography using anti-FLAG M2 affinity gel (Millipore Sigma), eluted with 3×FLAG peptide (Millipore Sigma), and further purified by size-exclusion chromatography on a Superdex 200 10/300 GL column equilibrated with buffer containing 30 mM Hepes, 150 mM NaCl, and 2 mM β-mercaptoethanol at pH 7.5. Proteins were concentrated to 2–7 mg/ml, flash-frozen in liquid nitrogen, and stored at −80°C.
In vitro pulldowns
MBP-tagged NLRP3 (2 μM) was mixed with 4 μM His-SUMO-NEK7 or WT or mutant FLAG-BTK in buffer containing 30 mM Hepes, 150 mM NaCl, and 2 mM β-mercaptoethanol at pH 7.5 and incubated for 30 min at 30°C. The mixture was further incubated for 1 h with 40 μl amylose resin and washed twice with 500 μl of the same buffer, followed by 1-h elution with 50 mM maltose. 30% and 70% of the sample was loaded as input and elution fractions, respectively, and analyzed by SDS-PAGE and immunoblot using monoclonal ANTI-FLAG M2-Peroxidase (HRP) or anti–p-Y antibody (Sigma-Aldrich).
ELISA
Human and murine IL-1β, IL-6, or TNF in supernatants were determined by ELISA using half-area plates and kits by R&D Systems and BioLegend, determining triplicate measurements on a standard plate reader.
Coimmunoprecipitation and immunoblot from primary cells
PBMCs or BMDMs were primed with LPS and stimulated with nigericin, washed with cold PBS, and immediately lysed in radioimmunoprecipitation assay (RIPA) lysis buffer containing protease/phosphatase inhibitors (Roche). A sample of the cleared lysate was taken before addition of the primary antibody (see Table S2). To selected samples, 2,000 U λ-phosphatase (P0753; New England Biolabs) and MnCl2 to 1 mM were added and incubated for 30 min at 30°C. After 18 h of rotation at 4°C, magnetic bead–coupled secondary antibody (Protein G Dynabeads; Thermo Fisher Scientific) was added for another 90 min. The beads were then washed three times with lysis buffer, resuspended in SDS loading buffer, and boiled. Standard SDS-PAGE was performed on Thermo Fisher Scientific precast gels followed by immunoblot according to the antibody manufacturer’s instructions. Membranes were exposed using Fusion FL camera and FusionCapt Advance software (PEQLAB). Quantification was conducted using the same software.
Coimmunoprecipitation and immunoblot from HEK293T cells
HEK293T were transfected using CaPO4 and 24 h later, treated with 1 μM MCC950 or 60 μM acalabrutinib for 6 h or with 60 μM ibrutinib for 2 h followed by 4-h incubation with medium without ibrutinib, where indicated. Cells were lysed 48 h after transfection in RIPA buffer supplemented with protease/phosphatase inhibitors (Roche). Lysates were subjected to immunoprecipitation of the NLRP3-HA or NACHT-FLAG fusion protein with Dynabeads (Sigma-Aldrich) or with agarose beads covered with PI4P (P-B004a; Echelon Biosciences). Washed beads were boiled in loading buffer and applied to standard SDS-PAGE on Thermo Fisher Scientific precast gels, followed by immunoblot according to the antibody manufacturer’s instructions. Membranes were exposed using Fusion FL camera and FusionCapt Advance software (PEQLAB). Quantification was conducted using the same software.
Coimmunoprecipitation and immunoblot from peritoneal cells
Samples from peritoneal cells were first precleared with Sepharose G beads (GE Healthcare) for 1 h, incubated at 4°C with 5 μg mouse anti-NLRP3 (Enzo Life Sciences) under constant agitation overnight, and precipitated with Sepharose G beads. The pellets were washed three times in PBS, 10 μl 1× loading buffer (Thermo Fisher Scientific) was added, the solution was boiled for 10 min at 95°C, beads were pelleted by centrifugation at 300 ×g for 3 min, and the supernatants were loaded on PAGE for electrophoresis. WCLs were mixed with 4× loading buffer. The gels were blotted on nitrocellulose membranes and blocked with 3% milk in washing buffer (Tris-buffered saline with 0.05% Tween), and the membranes were incubated with mouse anti-NLRP3 (Enzo Life Sciences) mouse anti–p-Y (Cell Signaling Technology), rabbit anti-Btk (Cell Signaling Technology), and mouse anti–β-actin antibody (Millipore) overnight followed by incubation with HRP-labeled secondary antibodies for 2 h at room temperature and detection of immunoreactive proteins using an enhanced chemiluminescence kit (Enzo Life Sciences) and exposure on x-ray films (GE Healthcare).
BRET
HEK293T cells were transfected with YFP–NLRP3–Renilla luciferase or NLRP3–Renilla luciferase fusion constructs for WT and 4xY>F mutant using Lipofectamine 2000 according to manufacturer’s instructions for 24 h. 105 cells per well were then reseeded in a poly-L-lysine–coated white opaque 96-well plates; after adhesion, cells were incubated in ET buffer in the presence or absence of nigericin and upon addition of 5 μM coelenterazine h, and BRET signals were detected sequentially with a two-filter setting (Renilla luciferase filter [475 ± 30 nm] and YFP filter [530 ± 30 nm] at 37°C using a FLUOstar OPTIMA microplate reader from BMG LABTECH). In these experiments, a stable BRET signal (15 cycles) was recorded and averaged, and each reading was integrated for 0.5 s. Every condition was measured in duplicate. The BRET ratio in milliBRET units (mBU) was defined as shown below (for details, see Tapia-Abellán et al., 2019). Data were then normalized to the mean mBU for each construct in the unstimulated state. As a control of relative levels of expression for each BRET construct, the same cells were also plated in black, clear-bottom 96-wells to measure YFP fluorescence using an excitation filter at 510 nm and an emission filter at 540 nm.
BRET (mBU)=[(Lum (530±30nm)donor+acceptorLum (475±30nm)donor+acceptor)−(Lum (530±30nm)Luc−onlyLum (475±30nm)Luc−only)]×1,000
Whole-exome sequencing (WES) capillary electrophoresis
3 μl of the prepared immunoblot lysates were run on a ProteinSimple WES instrument according to the manufacturer’s instructions. Data were analyzed with Compass for Simple Western software to compare the p-NLRP3 signal with the heavy-chain signal from the same run as an internal control.
Native PAGE
BMDMs were stimulated as described and lysed in RIPA lysis buffer without SDS. Lysates were centrifuged at 2,300 ×g for 10 min to pellet DNA. Supernatant was centrifuged at 16,100 ×g for 25 min, and the pellet was resuspended in native PAGE sample buffer (Thermo Fisher Scientific). The samples were loaded onto NuPage 3%–8% Tris-acetate gels (Thermo Fisher Scientific) without boiling, and native PAGE was conducted using Tris-glycine running buffer (Thermo Fisher Scientific). The gel was soaked in 10% SDS solution for 10 min before performing semidry transfer and continuing with conventional immunoblot.
Cross-linking of ASC oligomers
BMDMs were primed with LPS and treated with ibrutinib and nigericin as described. Cells were lysed in RIPA lysis buffer, and pellets were cross-linked using disuccinimidyl suberate and analyzed as described in Khare et al. (2016).
Size-exclusion chromatography
BMDMs were stimulated and lysed in 50 mM Tris-HCl, pH 7.4, 1% NP-40, and 150 mM NaCl. 100 μl cleared lysate was loaded on a Superdex 200 10/300 GL column (GE Healthcare), and proteins were eluted using an ÄKTA Purifier (GE Healthcare) and buffer (50 mM Tris-HCL, pH 7.4, 150 mM NaCl) with 0.25 ml/min flow. 200 μl fractions were collected and analyzed via Western blot.
In vitro kinase assay
For results in Fig. 1 H, recombinant NLRP3 from Novus Biologicals (H00114548-P01) and BTK from Sino Biological (10578-H08B) or Abcam (ab205800) were incubated at 30°C for 3 h using Cell Signaling Technology kinase buffer (#9802) in the presence of 2 mM ATP. As a negative control, recombinant Posi-Tag Epitope Tag Protein (BioLegend) was used. Before and after kinase assay, samples were boiled and analyzed via SDS-PAGE and Western blot. For results in Fig. S1 F, NLRP3 and BTK (WT or KD) were purified as described above. For reactions, 2 μM MBP-tagged NLRP3 was mixed with or without 0.2 μM purified FLAG-tagged BTK in buffer containing 30 mM Hepes, 150 mM NaCl, 12.5 mM MgCl2, 2.5 mM ATP, and 2 mM β-mercaptoethanol at pH 7.5 in the presence or absence of ibrutinib (catalog no. S2680; Selleckchem). The mixture was incubated at 30°C, and equal aliquots were taken at indicated time points. Samples were analyzed by SDS-PAGE and immunoblot using anti–p-Y antibody (#8954S; Cell Signaling Technology). For results in Fig. S4, C and D, 0.5 μM purified BTK constructs were mixed with 1 μM NLRP3 peptides with WT Y sequence, p-Y, or with Y>F in buffer containing 30 mM Hepes, 150 mM NaCl, 5 mM MgCl2, 2.5 mM ATP, 2 mM β-mercaptoethanol and protease inhibitor cocktail (Sigma) at pH 7.5. The reaction was performed at 30°C, and equal aliquots were taken at indicated time points. For each BTK construct, samples were analyzed by immunoblot using anti–p-Y antibody (Cell signaling, cat. 8954S) at equal exposure times. The quantification was performed with ImageJ software.
Dot blot analysis
Peptides synthetized in-house were incubated with recombinant BTK (Sino Biologicals) for 3 h in Cell Signaling Technology kinase buffer (#9802) supplemented with 2 mM ATP. Next, the samples were boiled, and anti-His magnetic beads (Dynabeads His-Tag Isolation and Pulldown; Thermo Fisher Scientific) were added to deplete the samples of phosphorylated BTK. The samples were cleared from the magnetic beads, and the supernatants were manually spotted on a nitrocellulose membrane. The dried spots were stained using Pierce reversible protein stain to visualize total peptide amounts. The membrane was then blocked with 5% BSA in Tris-buffered saline with 0.1% Tween, and conventional anti–p-Y primary and secondary antibody incubation steps followed.
Subcellular fractionation
Cells were homogenized using a 10-ml syringe and 27G × 19-mm needles in homogenization buffer (0.25 M sucrose, 10 mM Tris HCl, pH 7.5, 10 mM KCl, 1.5 mM MgCl2, protease inhibitor [Roche] and PhosStop [Roche]). Homogenized cells (“homogenate” in Fig. 3 H) were centrifuged at 1,000 ×g for 5 min to remove the nucleus. The supernatant was centrifuged at 5,000 ×g for 10 min to obtain a heavy membrane fraction (pellet, P5, analyzed directly as in Fig. 3 H or fractionated further as described below). The supernatant (S5) was either analyzed directly as in Fig. 3 H or centrifuged 100,000 ×g for 20 min to separate a light membrane fraction (P100) from the cytosol (S100). For Fig. S5 A, P5 and P100 pellets were washed once with homogenization buffer and then used for further sucrose gradient ultracentrifugation separately. For sucrose gradient ultracentrifugation, a continuous 15%–45% (wt/wt) sucrose gradient was prepared in 10 mM Tris-HCl (pH 7.5), 20 mM KCl, and 3 mM MgCl2 using Gradient Station (Biocomp Instruments). P5 or P100 was loaded on top of the gradient and centrifuged at 170,000 ×g for 3 h. The gradient was fractionated into 12 fractions of 1.1 ml using the fraction collector module of Gradient Station.
PI4P bead binding assays
HEK293T cells were transfected with HA-tagged human WT or Y>E PYD/NACHT linker (residues 94–219)–mCitrine–HA constructs. Cells were lysed in RIPA buffer, and PI4P beads (P-B004A; Echelon Biosciences) or the same amount of control beads (P-B000; Echelon Biosciences) were added to cleared lysates and incubated for 1.5 h at 4°C while rotating. Beads were then washed three times with RIPA buffer and boiled, and bound proteins were analyzed via immunoblot. Alternatively, cells were transfected with WT, Y>E, or K>A Flag-tagged murine PBR (residues 127–146)–GFP–Flag constructs adopted from Chen and Chen (2018). PI4P beads or control beads were blocked beforehand in 2% BSA, 0.5% NP-40, and 200 μg/ml Flag peptide (F3290; Sigma-Aldrich) for 2 h at 4°C. Transfected cells were then lysed in RIPA buffer, and the expressed proteins were purified using Anti-FLAG M2 Magnetic Beads (M8823; Merck). Beads were washed three times with RIPA buffer and boiled to elute the purified PBR. Blocked PI4P beads or the same amount of control beads were added to the eluted protein and incubated for 1.5 h at 4°C while rotating. Beads were then washed three times for 8 min with RIPA buffer, resuspended in lithium dodecyl sulfate lithium dodecyl sulfate sample buffer, and boiled, and bound protein was analyzed using immunoblot.
Reconstitution and analysis of Nlrp3-deficient iMacs
Nlrp3-deficient iMacs (Hornung et al., 2008) were retrovirally transduced with human NLRP3 (WT or 4xY>F)–Flag–T2A–mCherry constructs as described in Hornung et al. (2008) and subsequently sorted for similar expression of mCherry. Similar NLRP3 expression was confirmed by anti-Flag immunoblot of cell lysates. Cells were seeded at 105 cells/well in a 96-well plate in 100 μl and primed with LPS (50 or 200 ng/ml) for 3 h, and inflammasome stimuli in OptiMEM were added as follows: nigericin at 8 μM for 1.5 h, R837 (imiquimod) at 20 μg/ml for 2 h, or poly(dA:dT) at 200 ng per well with 0.5 μl lipofectamine 2000 for 4 h. IL-1β and TNF were subsequently determined by IL-1β and TNF homogeneous time-resolved fluorescence assay, respectively (62MIL1BPEH and 62MTNFAPEG; Cisbio).
NLRP3 sequence analysis, structure inspection, and charge prediction
Nod-like receptor sequences were retrieved from UniProt and ClustalW aligned within Geneious R6 software. A hypothetical active conformation of NLRP3 was modeled based on NLRP3–NEK7 structure in an inactive state (Protein Data Bank accession no. 6NPY; Sharif et al., 2019). NACHT domain reorganization and hypothetical NLRP3 oligomerization was generated based on the NLRC4 oligomer (Protein Data Bank accession no. 3JBL) as a homology model template by introduction of a 90° rotation of NBD-HD1 module (Sharif et al., 2019). Phosphorylation of tyrosine residues of interest was performed in PyMOL (Schrödinger) using the PyTMs plugin (Warnecke et al., 2014). Electrostatic potential of the solvent-accessible surface of phosphorylated and nonphosphorylated NLRP3 models was calculated with the PBEQ-Solver online visualization tool (https://www.charmm-gui.org; Jo et al., 2008a; Jo et al., 2008b; MacKerell et al., 1998) and visualized with PyMOL. Protein net charges of the Y136-, Y140-, and Y143-containing linker were conducted with ProtPi (https://www.protpi.ch).
Statistics
Experimental data were analyzed using Excel 2010 (Microsoft) and/or GraphPad Prism 6, 7, or 8; microscopy data with ImageJ/Fiji; and flow cytometry data with FlowJo 10. Normal distribution in each group was always tested using the Shapiro–Wilk test first for the subsequent choice of a parametric (ANOVA, Student’s t test) or nonparametric (e.g., Friedman, Mann–Whitney U, Wilcoxon) test. P values (α = 0.05) were then calculated, and multiple testing was corrected for in Prism, as indicated in the figure legends. P < 0.05 was generally considered as statistically significant and denoted by an asterisk. Comparisons were made to unstimulated control, unless indicated otherwise, and denoted by brackets.
Online supplemental material
Fig. S1 displays additional data related to Fig. 1, showing that in cells from patients with BTK deficiency, NLRP3 phosphorylation is decreased; that in an experimental in vivo mouse model of MSU peritonitis, IL-1β release and NLRP3 phosphorylation are time and BTK dependent; and that in vitro BTK kinase activity is required for NLRP3 phosphorylation. Figs. S2, S3, and S4 show additional data related to Fig. 2. Fig. S2 displays the position of tyrosines in the NLRP3 FISNA and NACHT domains relative to positions of known pathological significance. Fig. S3 shows that the NACHT domain tyrosine mutation does not have an impact on NLRP3 phosphorylation and that for recombinant NLRP3, the linker region is not amenable to mass spectrometric analysis; furthermore, using peptide scanning, individual tyrosines in the linker of human and murine NLRP3, but not tyrosines in the NACHT domain, were found to be BTK modified. Fig. S4 illustrates the position of BTK-modified tyrosines in a recent NEK7-NLRP3 cryo-EM structure and in multiple sequence alignments of different NLRP3 sequences; furthermore, the effect of linker NLRP3 peptides on BTK activity is shown and quantified. Fig. S5 shows data related to Figs. 3 and 4, most notably the fractionation approach employed to probe subcellular localization of NLRP3 in dependence on BTK and the effect of p-Y mutation in reconstituted iMacs primed with different LPS concentrations. Finally, the responsiveness of NLRP3 phospho mutant forms as assessed by BRET and a graphical summary of the entire paper are shown. Table S1 summarizes the peptides used in this study. Table S2 summarizes the antibodies used in this study.
Supplementary Material
Table S1 shows peptides used in this study.
Click here for additional data file.
Table S2 shows antibodies used in this study.
Click here for additional data file.
Acknowledgments
We gratefully acknowledge Pablo Pelegrin (Instituto Murciano de Investigacion Biosanitaria, Murcia, Spain) for provision of the WT NLRP3 BRET construct; Ulrich Wulle for help with peptide synthesis; Teerithveen Pasricha and Jacqueline Berner for help with in vitro kinase assays and BRET measurement, respectively; and Libero Lo Presti for helpful comments. We thank Xiaowu Zhang and Felix Meissner for helpful advice on kinase target residue identification and mass spectrometry, respectively. We thank all study subjects and their families as well as voluntary healthy blood donors for participating in the study.
The study was supported by the Else-Kröner-Fresenius Stiftung (to A.N.R. Weber); Deutsche Forschungsgemeinschaft grants CRC TR156 “The Skin as an Immune Sensor and Effector Organ–Orchestrating Local and Systemic Immunity” (to Z.S. Bittner, F. Herster, F. Bork, C.L. Greve, and A.N.R. Weber) and We-4195/15-1 (to A.N.R. Weber); University Hospital Tübingen (Fortüne Grants 2310-0-0 to X. Liu and A.N.R. Weber, and 2615-0-0 to M. Mateo Tortola and A. Tapia-Abellán); IFM Therapeutics (to S. Shankar and A.N.R. Weber); the E-Rare program of the European Union, managed by the German Research Foundation, grant code GR1617/14-1/iPAD (to B. Grimbacher); the Netzwerke Seltener Erkrankungen of the Federal Ministry of Education and Research (GAIN_01GM1910A, to B. Grimbacher); and the Damon Runyon Cancer Research Foundation (to L. Andreeva). Infrastructural funding was provided by the University of Tübingen; the University Hospital Tübingen; and the Deutsche Forschungsgemeinschaft Clusters of Excellence “iFIT–Image-Guided and Functionally Instructed Tumor Therapies” (EXC 2180 to A.N.R. Weber and M.W. Löffler), “CMFI–Controlling Microbes to Fight Infection (EXC 2124 to X. Liu, A.N.R. Weber, and A. Tapia-Abellán), “CIBSS–Centre for Integrative Signalling Studies” (EXC 2189 to B. Grimbacher), “RESIST–Resolving Infection Susceptibility” (EXC 2155 to B. Grimbacher), “ImmunoSensation2” (EXC 2151, to E. Latz). This work was also supported by (Gefördert durch die) Deutsche Forschungsgemeinschaft im Rahmen der Exzellenzstrategie des Bundes und der Länder–EXC 2180 (390900677), EXC 2124, EXC 2189 (390939984), EXC 2155 (39087428), and EXC 2151 (390873048).
Author contributions: Z.A. Bittner, X. Liu, M. Mateo Tortola, M. Spalinger, Y. Cardona Gloria, T.-H. Chang, F. Bork, C.L. Greve, S. Dickhöfer, H. Kalbacher, K. Bosch, L. Andreeva, A. Marcu, M. Mangan, P. Düwell, M. Lovotti, F. Herster, S. Shankar, A. Tapia-Abellán, O.-O. Wolz, N.A. Schilling, S. Wagner, and A.N.R. Weber performed experiments; Z.A. Bittner, X. Liu, S. Dickhöfer, H. Kalbacher, L. Andreeva, S. Stevanoić, M. Lovotti, M. Mangan, M. Mateo Tortola, M. Spalinger, M. Scharl, O. Hantschel, P. Düwell, F. Herster, N.A. Schilling, S. Wagner, and A.N.R. Weber analyzed data; M.W. Löffler, J.B. Kümmerle-Deschner, M. Scharl, A. Delor, and B. Grimbacher were involved in patient recruitment and sample acquisition; Z.A. Bittner and A.N.R. Weber wrote the manuscript; and L. Andreeva, P. Düwell, M.W. Löffler, S. Shankar, M. Mateo Tortola, A. Tapia-Abellán, O. Hantschel, S. Wagner, H. Wu, and E. Latz provided valuable comments. All authors approved the final manuscript. A.N.R. Weber and Z.A. Bittner conceived and coordinated the study.
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==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
34734185
10.1093/narcan/zcab043
zcab043
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Short Review
Can the epigenome contribute to risk stratification for cancer onset?
https://orcid.org/0000-0001-6606-2501
Lelièvre Sophie A Institut de Cancérologie de l’Ouest (ICO)-Western Cancer Institute, Scientific Direction for Translational Research, Angers, France
To whom correspondence should be addressed. Tel: +33 2 41 35 28 56; Email: [email protected]
12 2021
01 11 2021
01 11 2021
3 4 zcab04327 10 2021
10 10 2021
21 7 2021
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Cancer.
2021
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.
Graphical Abstract
Graphical Abstract The accumulation of alterations in the epigenome and chromatin organization via additive effects of risk factors participates in risk stratification in populations.
Pays-de-la-Loire Connect
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pmcINTRODUCTION
Our ability to prevent cancer development relies on the detection and mitigation of risk. An important challenge is to identify people at the highest risk for a particular cancer through a risk stratification process. Although epidemiologically the risk has been defined on multiple levels (e.g. family, lifestyle, genetic), biological investigations have shown that for a cancer to occur, changes in gene transcription prevail. But chemical modifications on the DNA and histones that direct gene transcription and define the epigenome are not yet part of a risk stratification scheme.
The need for risk stratification
Humans are not equal when dealing with cancer onset. It is of utmost importance to stratify the risk in order to develop targeted interventions. For most types of cancer there is not one causative factor but rather a multiplicity of factors, and their relative contribution to building 100% risk is not understood. A striking example is the increase of breast cancer incidence in young women (1). Although hormone levels might be useful to include for risk stratification in postmenopausal women, it does not seem to be the case for younger women (2), and the reasons for increased premenopausal cancer risk are still mostly unknown.
Genetics has been the driver for risk stratification, encompassing high risk variants (e.g. BRCA), intermediate risk variants (e.g. PALB2, ATM, CHEK2) and low risk single nucleotide polymorphisms (SNPs). The use of polygenic risk scores (PRS) linked to SNPs may stratify breast cancer risk regardless of the family history (3). However, even when integrating all the known genetic factors it only explains 45% of observed familial aggregation (4). It was proposed that associating genetic risk (including 313 SNPs from European ancestry (5) in addition to known mutations (6)) and other risk factors might improve risk stratification for a population with known breast cancer incidence (4). The other risk factors included lifestyle, reproductive information and mammographic density. This combination of factors revealed that for the population of women in the United Kingdom, 1.1% might have a lifetime risk of at least 30% (high risk) and 14.7% might have a risk below 30% but at least 17%. Women at high risk for breast cancer also included those with a 10-year risk at age 40 of at least 8%. Interestingly, the biggest contributors to this risk stratification model were PRS and mammographic density (7), both having relationships with the epigenome (8,9).
The participation of mammographic density in risk stratification reinforces the importance of tissue architecture in directing cancer onset. More than two decades ago, it was demonstrated that even in the presence of genetic alterations linked to cancer behavior, a tumor does not develop when the architecture of the normal tissue is maintained (10–12). Tissue architecture that encompasses the organization of cells and their components is controlling the epigenome (13), and reciprocally, it is controlled by the epigenome, as we initially demonstrated for the establishment of breast epithelial polarity (14). Thus, the architecture within which the epigenome is exposed to risk factors is likely to contribute to the transcriptomic response.
The nuclear origins of risk
Easily accessible in the blood, the DNA is currently at the center of risk stratification, especially in light of the contributions of SNPs identified via genome-wide association studies (GWAS). Initially, 170 breast cancer susceptibility loci were identified (5), accounting for 40% of heritability explained by all common variants on genome-wide SNP arrays. The SNPs are often within regulatory regions, like enhancers and sequences for transcription factor (TF) binding, hence potentially affecting gene transcription. The addition of functional studies to GWAS are revealing potential molecular mechanisms of risk through transcription profiles and redistribution of proteins like CCCTC-binding factor (CTCF) that affect chromosomal architecture (15). CTCF is a major organizer of higher order chromatin via anchoring and insulation of DNA loops and interactions with chromatin remodeling proteins, histone modifying enzymes and TFs.
It is important to link SNPs associated with cancer risk with specific molecular mechanisms to identify targets for interventions, especially since genetic variants confer risks that depend on breast cancer subtypes, at least estrogen receptor negative (ER−) versus ER positive (ER+) (5). Considering the subtypes of cancers in risk stratification underlies the importance of focusing on subgroups within the general population in which certain forms of breast cancer are preponderant. The subtyping of cancers in relation to molecular pathways and individuals is part of molecular pathological epidemiology (16). Since cancer subtypes are distinguishable via gene transcription patterns, themselves enabled by epigenetic modifications that respond to environmental exposures, an understanding of epigenetics is imperative to improve disease management.
The investigation of the epigenetic risk of cancer is challenging. The results of epigenome-wide association studies (EWAS) depend on the sources of normal tissues because the epigenome is not only tissue-specific but also condition-specific. Normal looking tissue adjacent to tumors is subject to field effect and its confounding impact, and breast tissue donated by cancer-free women needs to be accompanied with sufficient information to estimate cancer risk. Ultimately, the identified risk markers will have to be present in the blood or saliva for routine assessment. For this reason, DNA methylation, a stable epigenetic mark on circulating DNA, is a major target of investigation (17–19). However, most reports highlight different sets of genes when looking downstream of epigenetic changes associated with risk. This apparent heterogeneity in possible gene targets is most likely the illustration of the interaction between the individuals or groups of individuals and their specific environment, which makes it difficult to pinpoint the pathways to cancer onset.
Nevertheless, there are commonalities in epigenetic changes on a global chromatin organization scale. Higher mean DNA methylation at CpG islands and epigenetic age-acceleration have been associated with an increased risk of breast cancer (20). Interestingly, classes of risk modulators like aging, environmental exposure and diet trigger few but major changes in the tissue microenvironment that are all conducive to global alterations in chromatin. Tissue aging is linked to oxidative stress, in part generated by the accumulation of reactive oxygen species in the extracellular matrix (ECM) (21); increased body mass index (BMI) is associated with high leptin production by the adipose tissue, leading to a proinflammatory microenvironment and oxidative stress; mammographic density is related to increased ECM stiffness. Combining the different physiological and molecular consequences of epidemiologically confirmed risks might lead to additive effects on the epigenome.
In this short-review, using the example of breast cancer, we are placing epigenetic alterations in the multiparametric consideration of risk escalation. The integration of the epigenome in the notion of risk is discussed via evidence of the interplay between genetic and epigenetic alterations, the plasticity of higher order chromatin organization and the emerging investigations of how given risk factors specifically target epigenetic pathways.
INTERPLAY BETWEEN GENETIC AND EPIGENETIC ALTERATIONS TO DEFINE CANCER RISK
Tissue architecture is capable of taming cancer cells even in the presence of a plethora of genetic alterations (10). This seminal demonstration was performed in 3D cell culture that permits the production of specific tissue architectures. Induction of partial acinus differentiation leading to the quiescence of cancer cells was accompanied with a redistribution of epigenetic domains in the cell nucleus towards patterns observed in the phenotypically normal epithelium (22). Assuming that differentiation characterized by a specific tissue architecture controls higher order chromatin organization and its epigenetic structures, there might be protective epigenome features associated with normal differentiation that are progressively disrupted by the accumulation of risk factors.
Influence of SNPs on gene transcription control
Candidate causal variants (CCV) are noncoding in majority and primarily located in regulatory regions of gene transcription (23,24). Functional testing of the variants of interest involves mapping to epigenomic datasets that identify histone marks, like H3K27ac for enhancers (25). The combination of epigenetic mark H3K27ac, mediator complex subunits (MED) and epigenetic reader bromodomain containing protein 4 (BRD4) has been used to define super-enhancers that contribute to cell identity. These marks provided higher enrichment (i.e. lower P values compared to those expected by chance) of cancer specific-risk SNPs than binding profiles of TFs involved in cancer onset (26). By confirming a relation between enhancer activity and CCVs, these results bridge epigenetic and genetic fields for cancer risk assessment.
The consequences of risk-associated genetic variants on gene transcription control are multiple; however, all the mechanisms involved seem to alter chromatin interactions. There might be changes in chromatin-TF interactions as revealed for FOXA1, at distal regulatory elements, which leads to allele-specific gene expression (27). FOXA1 is labeled as pioneer TF since it can interact with compact DNA and make it accessible to other factors. Here, the cistromes of FOXA1 were enriched with risk-associated SNPs. FOXA1 was also among a cluster of four risk-TF that share regulatory mechanisms and would favor ER+ tumors (28). Such mechanistic studies have required new computational methodologies with the combination of epigenomics and genotype imputation with cistromics (i.e., the investigation of cis-acting targets of trans-acting factors), as well as grouping putative target genes of TF into regulons and assessing enrichments in risk loci to identify regulatory clusters. The impact of SNPs has also been abundantly identified for single genes. For instance, SNPs associated with a decrease in risk were identified within the promoter of TERT where they reduce promoter activity, without the involvement of distant regulatory elements (29). However, CCVs often modulate DNA looping. Locus 8p12 CCVs of the enhancer of the tumor suppressor gene DUSP4 either reduce its activity via looping to the gene's promoter region or prevent looping to the promoter (30).
SNPs associated with breast cancer risk are also found in the distribution maps of H3K4me1, a histone modification found in regulatory regions of genes (27). A similar observation has been made for many diseases, hence confirming that an alteration of enhancer function is one of the mechanisms associated with genetic risk.
Other categories of genes affected by variants associated with cancer risk include DNA integrity checkpoints, which would trigger further loss of homeostasis. For instance, SNPs in a distal enhancer of the intergenic region 11q13 decrease the expression of estrogen-regulated long noncoding (lnc)RNAs CUPID1&2 by reducing chromatin looping between the enhancer and the gene bidirectional promoter. Normally, these lncRNAs are regulating the choice of pathways used for double-strand break repair (31).
It is not surprising that SNPs impact transcription control, these alterations residing mostly in regulatory regions of the DNA. However, the accumulating evidence that SNPs are related to downstream modifications in a cell type specific manner, as shown by the examples of FOXA1, BRD4, H3K4me1 and DNA methylation supports the essential role that the epigenome might play in cancer risk.
Epigenetic marks of risk
DNA methylation at specific CpGs is envisioned as a source for risk markers since these alterations might be found in circulating DNA. However, it is important to establish that these methylation patterns exist in the breast at risk for cancer. Nested case-control studies from a prospective cohort of patients with ER+ breast cancer revealed that methylation changes in the CpGs of genes in the normal appearing epithelium mapped metabolic processes linked to fatty acids, although there was no difference in global methylation between cases (cancer developed a few years later in the contralateral breast) and controls. Three genes with methylation changes were validated in breast tissue and blood and might provide markers to improve risk stratification for the development of sporadic breast cancer (32). It was also observed that women at higher risk for breast cancer based on the Gail model are more likely to present methylation of tumor suppressor genes APC and RASSF1 in the breast tissue (33). The interest in a possible marker may be strengthened by additional studies that pinpoint this marker under different conditions, as shown for APC, since the possible protection of increasing parity is associated with decreased methylation of this gene in the breast (34). Like with SNPs, epigenetic modifications that influence DNA integrity may contribute to cancer risk. For instance, hypermethylation of the ATM gene detected in the blood stream was associated with breast cancer risk and proposed to be included in risk stratification studies (35).
The partnership between epigenetic modifications and SNPs highlighted by integrative bioinformatics analysis is useful to further refine cancer risk. For instance, the effect of SNPs on methylation may be defined as methylation quantitative traits loci (36). This method was used to identify prognostic gene signatures in breast cancer with the aim of reducing false positives via multiple sources pointing to the same genes. These CpG-SNP pairs might be particularly useful in risk stratification for primary prevention approaches.
From a perspective standpoint, the combination of genetic variants and epigenetic marks might be particularly useful for risk information on specific cancer subtypes. In a study with women of European and African ancestry, 53 genetic variants were associated with ER+ breast cancer and 37 variants were associated with ER− cancers (25). Risk variants found in exons of multi-exonic ncRNA genes have been linked to specific cancer subtypes (37). Some of these ncRNAs are expressed depending on risk variants, and the promoters of many of these RNAs loop to regions that contain risk variants, which suggests that genetic variants control these RNAs. The inclusion of epigenetic reader BRD4, that appears to identify risk loci and is cell type specific (26), might enable the identification of the cellular origin of risk for cancer subtypes and open new directions for epigenetic intervention on select cell populations (38). Overall, the flexibility of the epigenome makes it an attractive compartment to identify mechanisms by which specific factors lead to risk escalation for different cancer subtypes.
EPIGENETIC ESCALATION OF RISK
The multiplicity of epigenetic modifications occurring within higher order chromatin organization suggests the possibility of an epigenetic escalation of risk. Timing is particularly important in risk escalation for breast cancer, with windows of susceptibility that account for acute exposure leading to increased risk or protection over a defined period of life (e.g. childhood, pregnancy, menopause) and through chronic impact (e.g. hormonal, aging). Thus, exposure to environmental factors and lifestyle will have distinct influences throughout life.
Lifelong risk of aging
Aging is the strongest risk factor that is likely responsible for the majority of postmenopausal breast cancers. It is promoted in part by an imbalance in anti- and pro-oxidative factors leading to chronic oxidative stress (21). Differences in breast aging, in light of its epigenome, compared to the person's chronological age have been linked with heightened breast cancer risk (20,39). The epigenetic clock is based on DNA methylation at 353 CpG sites calculated by elastic net regression or 385 CpG sites giving an estimate of mitotic division (9,39). Intrinsic epigenetic age acceleration (i.e. the difference between epigenetic [biological] and chronological ages) is the risk factor associated with aging; its elevation by one unit has been linked with a 4% increase odds of developing breast cancer, with age acceleration detected 10 years prior to diagnosis (20). Logically, epigenetic aging interferes with the addition and the removal of methyl groups and it is likely to modify the impact of other influential factors.
Aside from the epigenetic clock, DNA methylation in breast tissues of aging women has been shown to be enriched at enhancer regions and binding sites for chromatin remodelers Myc and CTCF (40). Moreover, hypomethylation represented 30.7% of the modified CpG sites and was identified primarily at binding sites for transcriptional activators c-Fos and Stat-3. The CpG regions in tissues of aging women only partially overlapped with those used to estimate tissue age (only 3 and 17 of the 787 CpGs identified in this study were found in the two epigenetic clock CpG lists), demonstrating a discrepancy between chronological (person's age) and biological (tissue age) that might be important for disease risk. The fact that these CpG regions were further modulated in cancer led to the proposition that they are involved in risk (40). This study performed on a limited number of individuals (100) highlights the preponderance of aging in DNA methylation changes associated with breast cancer risk. Strikingly, no CpG sites were significantly modified for their methylation level in relation to BMI, parity and family history that are factors involved in risk modulation. Whether these other risk factors act on different epigenetic pathways will be important to determine for risk escalation associated to the combination of different risk factors.
Accelerated tissue aging may have consequences on the impact of TFs since methylation usually prevents their binding to DNA (41). Moreover, the expression of TF changes with age hence, so does their binding to regulatory SNPs. Age-interacting SNPs have been analyzed via a novel SNiPAge concept to better understand age-associated phenotypes (9). In the breast, 536 interaction triplets (TF-SNP-gene) linked to aging were detected based on TFs that show reduction in expression with age. Overall, the SNPs identified were significantly related to breast cancer and located in open chromatin, often in regulatory regions like enhancers and promoters, as shown by epigenetic marks H3K27ac and H3K4me3, respectively. Interestingly, these SNPs were enriched in regions that negatively regulate the stress response, highlighting a potential means for the accumulation of alterations leading to increased risk, since an altered response to stress decreases DNA repair capabilities.
The participation of SNPs in premature aging and breast cancer onset is not a new observation. In 2007, it was reported that one SNP as well as combinations of multiple SNPs in the WRN gene controlling life span and aging were associated with breast cancer risk. Very interestingly, the level of significance increased if there was a longer time between onset of menarchy and first pregnancy, which confirms the additive effects of two different risk factors (here aging and a reproductive pattern that influences estrogen impact) (42). Also, a rare homozygous CC genotype in WRN was associated with increased breast cancer risk in premenopausal women (43).
SNPs associated with aging and their biological functions appear to include DNA methylation that is an essential modulator of tumorigenesis (9). However, the exact contribution of these SNPs to epigenetic aging remains to be understood. In the case of the classical WRN gene alteration, differentially methylated regions are different from those identified in epigenetic aging. These regions are enriched in genes that control transcription factor activity and sequence-specific DNA binding to promoters, possibly leading to transcriptional misregulation (44). Thus, instead of furthering standard epigenetic aging, SNPs might bring cells further away from their normal homeostasis via epigenetic changes that complement those linked to accelerated aging.
Epigenetic risk and life events
The nongenetic factors that contribute to risk stratification according to epidemiologists encompass BMI, reproductive history and mammographic density (45). These factors might have a determinant impact on a specific period in life, and all appear to induce epigenetic changes. Here we are discussing these factors in light of the major physiological disturbances with which they are associated, namely metabolic disturbances (e.g. pro-inflammatory, pro-oxidative).
Diet and metabolism
The metabolism has been implicated in the development of cancers, including breast cancer and metabolic pathways are modulated by aging, notably via the relation TF-SNPs (9). High BMI is both the result and a source of metabolic imbalance leading to pro-inflammatory and oxidative conditions (21); it acts as a breast cancer risk inducer among postmenopausal women and a mediator of aggressive breast cancers among premenopausal women. High BMI has been associated with DNA methylation changes in breast tissue and blood (46,47). Moreover, free fatty acids linked to obesity influence ERα and mTOR pathways, which could impact gene transcription (48). Importantly, an increase in BMI appears to precipitate epigenetic aging as shown for the liver (49) and can be related to an increase in age based on DNA methylation in breast tissues (50). LINE-1 hypomethylation has been proposed to mediate more than 80% of the effects of BMI on breast cancer risk (51). Thus, high metabolic disturbance associated with BMI might contribute to loss of normal epigenetic balance via both the accumulation of epigenetic aging alterations and specific changes in transcription control.
In addition to metabolic disorders, specific components of diets have been studied early on for their impact on the epigenome or at least their relation to epigenetic changes (52); however, attempting to link the degree of methylation of CpGs, nutrient level and breast cancer risk is a difficult task. The threshold at which the concentration of the nutrient studied has biological relevance for cancer risk, as it was discussed for Vitamin D, is paramount to determine. Such considerations are essential for elements like the vitamin D/VDR complex known to control gene transcription involved in cancer risk and the immune system (53) and for which there is still a debate regarding its exact role in breast cancer risk.
Parity and inflammation
Certain diets and environmental stresses like pollutants have a proinflammatory effect in the tissue microenvironment that is considered abnormal and the source of disorders like cancers. However, a natural consequence of parity in the breast is the elevation of the expression of genes related to inflammation (54). This inflammatory effect is limited in time, but a slight increase in breast cancer risk has been documented within two years following pregnancy. The epigenetic origin of this phenomenon is being progressively unraveled. Genes active during cancer development and coding for proteases MMP9 and calpains are targeted by p65/p300 during mammary gland involution (55). The p65/p300 complex is associated with the NF-kappaB inflammatory pathway, the activity of which has been known to be tightly controlled by the balance between histone acetyl transferases and histone deacetylases (56). In addition to modifications in the ECM, Calpain 1 modulates preadipocytes through chromatin remodeling, which suggests a profound impact on tissues that control mammary homeostasis. However, parity is also associated with hypermethylation of CpG islands of FOXA1 (57), which might account for the ‘delayed’ protective effects of increasing parity. The TF FOXA1 facilitates chromatin binding of ERα and downstream transcriptional activation. Thus, it is envisioned that by favoring epigenetic silencing of FOXA1, parity would attenuate the estrogen impact on the mammary gland and decrease breast cancer risk over a long period.
Breast density and ECM stiffness
High mammographic density has been recognized as an important element for risk stratification, and SNPs associated with increased breast density and breast cancer risk have been identified in GWAS meta-analysis (58). High mammographic density is partially linked to increased ECM stiffness (via stromal collagen (59), which is one of the consequences of an inflammatory condition (60,61). Thus, breast density is related to epigenetic mechanisms of risk via inflammatory pathways. Few studies have been performed on the direct epigenetic impact of ECM stiffness. However, 3D cell culture revealed an effect on chromatin organization, notably via nuclear wrinkling and increased heterochromatin bundles at the nuclear periphery (8). There was also a significant decrease in acetylated H4 and an increase in acetylated H3. The change in nuclear morphometry was related to histone deacetylase (HDAC) activity. Increased stiffness also induced chromatin opening, rendering regulatory elements in the genome more accessible to certain TFs, and primarily Sp1, a regulator of malignant transformation and interactor of chromatin modifying enzymes like class I HDACs 3 and 8 (8). This study brings another dimension to epigenetic risk escalation via a global reorganization of chromatin that affects certain categories of TFs. It is expected that a change of this magnitude would alter the impact on the epigenome of any additional risk factor, especially since ECM stiffness is influenced early and later in life by pollutants and nutrition, respectively (62,63).
Overall, many of the risk factors may be related to central metabolic pathways in cells like inflammatory and oxidative pathways for which epigenetic mechanisms are being progressively understood. Nevertheless, other effects specific to each risk factor ought to be studied for a possible deleterious influence on the epigenome that would add to that of inflammatory and oxidative pathways.
CONCLUSIONS
Placing the information highlighted in this short-review in perspective, risk escalation depends on timing of exposure and the combination of genetic and epigenetic alterations. It is recognized that prepuberty is a window of susceptibility, notably because of the epigenetic status of the mammary cells that are primed but not fully differentiated, making them more susceptible to undergo protective or deleterious permanent epigenetic changes. Risk escalation might also depend on ancestry. Many risk loci are common among women of European, Asian and African ancestry (25). However, only a small portion of variants common to African and European ancestries have the same directionality, and in the same risk regions, variants with significance in terms of risk might be different (25). Moreover, increased epigenetic aging has been associated with race (40). Ethnic difference in age-associated diseases is suggested by the genomic proximity of ethnicity-associated SNPs and SNPs linked with aging (9). Whether these relationships could explain differences in thresholds for the effective impacts of nutrients and BMI levels on risk among ethnic groups remains to be determined.
Measurable markers are necessary for risk stratification. A multiparameter, medical biology approach to risk stratification should combine genetic and epigenome-related markers, since reaching a risk level necessary for tumor onset is likely to entail the accumulation of nuclear changes. We propose that risk escalation in the cell nucleus encompasses the reciprocal influence of genetic and epigenetic alterations on DNA integrity and the targeting of different features of chromatin organization (Figure 1).
Figure 1. Cancer risk escalation in the cell nucleus. It is essential to study changes in the epigenome and its related parameters that are associated with cancer risk in the normal tissue of origin. For the breast, the luminal epithelium is organized as a monolayer (internal to the layer of myoepithelial cells- not shown) within which cell heterogeneity is represented by different phenotypes and the presence of a small percentage of progenitor cells. These cells have a different organization of chromatin and its epigenetic content that might be detectable via a different shape and size of the nucleus. The source of this tissue might be from reduction mammoplasty (left) or from biopsies from normal-appearing tissue adjacent to tumors (right). The association of certain epigenetic alterations with risk is sometime confirmed because of an increase or an extension of these alterations in the same DNA regions as cancer develops and progresses. The breast epithelium may be reproduced in 3D cell culture to enable the mechanistic investigation of risk factors. The nucleus (pale blue magnified circle) of an epithelial cell at risk may display different types of chromatin alterations, like DNA methylation at CpG sites (often increased), changes in chromatin loops and anchorage/condensation. The presence of SNPs associated with risk at DNA regulatory regions (e.g. super enhancers marked by BRD4, TF binding regions) modifies gene transcription via a direct impact on ligand binding or through changes in chromatin looping. Depending on the risk factors (red bubbles) one or more effect on chromatin has been identified so far such as changes in DNA methylation, TF binding or expression, and chromatin compaction. Importantly, risk factors may also feed into others’ effects and could strengthen the extent of alterations as shown by the dashed red arrows. There are at least three possible measurable means (written in blue) to build up risk and that could help identify epigenome-related thresholds to cancer onset: DNA methylation profiles, genetic alterations and gene expression profiles. Moreover, SNPs and DNA methylation influence each other leading to the accumulation of genetic alterations. As illustrated in the ‘risk escalation’ box, how the risk is built in the chromatin is likely to influence the speed at which the epigenome loses control of normal differentiation and the resulting subtype of cancer. The blood stream is an important compartment to routinely measure cancer risk because of the presence of circulating DNA, ncRNAs and exosomes. Circulating DNA and ncRNAs might be specific or nonspecific indicators of risk, whereas exosomes might be traceable to a specific tissue at risk. The combination of different measurable parameters might be necessary to identify a risk of cancer in a specific tissue and how the risk was built (i.e., the epigenetic pathways altered, the type of cells involved) so that interventions may be tailored to the origin(s) of the increase in risk.
The essential participation of the epigenome in risk escalation is linked to the possibility of increasing or decreasing the degree of DNA methylation at CpGs of regulatory regions depending on risk and protective factors. Moreover, in light of studies with the type of microenvironmental alterations that the risk factors generate, changes in gene expression leading to tumor onset rely heavily on DNA methylation, as well as alterations in DNA looping and chromatin packing.
Many more investigations are necessary to understand how the combination of major risk factors builds up genetic and epigenetic alterations, not only to identify risk markers but also to design targeted interventions to decrease cancer risk with a precision prevention approach. For instance, knowing the specific epigenetic pathway responsible for changes in DNA methylation is paramount to apply proper preventive treatment (17).
Models for such investigations need to be carefully selected. The organization of the epigenome of the noncancerous cells of the tissue of origin appears to determine the mutation patterns of cancers for this tissue (64). This finding means that there is a continuity in the accumulation of risk from cancer onset to progression via an interplay between the genome and the epigenome. It also suggests that it is paramount for in vitro models of cancer risk with human cells to reproduce proper tissue architecture since it is responsible for higher order chromatin organization (22). These models ought to include the type of cells (e.g. luminal progenitor) and the differentiation stages corresponding to specific windows of risk susceptibility, since these elements appear essential to determine the types of breast cancer (at least ER– or ER+) (65) for which there are also corresponding SNPs and epigenetic changes (66).
Future investigations ought to determine how epigenome-related parameters specific for an increased risk in a specific organ might be measurable in blood samples. The organization of the epigenome appears specific to each tissue; thus, tissue-specific risk markers, if present in the blood through free DNA or exosomes, are within reach.
DATA AVAILABILITY
There are no unpublished data from the author reported in this short-review.
ACKNOWLEDGEMENTS
Due to limitations in the number of references allowed, the author apologizes if additional important citations are missing. SL is part of the International Breast Cancer & Nutrition (IBCN) network.
FUNDING
Pays-de-la-Loire Connect Talent to SL.
Conflict of interest statement. None declared.
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PMC008xxxxxx/PMC8634572.txt |
==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
34870206
10.1093/narcan/zcab045
zcab045
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Survey and Summary
Misregulation of the expression and activity of DNA methyltransferases in cancer
Mensah Isaiah K Department of Biochemistry, Purdue University, West Lafayette, IN 47907, USA
https://orcid.org/0000-0001-8551-0269
Norvil Allison B Eli Lilly & Co, IN, USA
AlAbdi Lama Department of Zoology, Collage of Science, King Saud University, Riyadh, Saudi Arabia
McGovern Sarah Department of Biochemistry, Purdue University, West Lafayette, IN 47907, USA
Petell Christopher J Cook Research Institute, West Lafayette, IN, USA
He Ming Department of Biochemistry, Purdue University, West Lafayette, IN 47907, USA
https://orcid.org/0000-0002-1250-9571
Gowher Humaira Department of Biochemistry, Purdue University, West Lafayette, IN 47907, USA
To whom correspondence should be addressed. Tel: +1 765 494 3326; Email: [email protected]
12 2021
01 12 2021
01 12 2021
3 4 zcab04510 11 2021
29 10 2021
20 9 2021
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Cancer.
2021
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Abstract
In mammals, DNA methyltransferases DNMT1 and DNMT3’s (A, B and L) deposit and maintain DNA methylation in dividing and nondividing cells. Although these enzymes have an unremarkable DNA sequence specificity (CpG), their regional specificity is regulated by interactions with various protein factors, chromatin modifiers, and post-translational modifications of histones. Changes in the DNMT expression or interacting partners affect DNA methylation patterns. Consequently, the acquired gene expression may increase the proliferative potential of cells, often concomitant with loss of cell identity as found in cancer. Aberrant DNA methylation, including hypermethylation and hypomethylation at various genomic regions, therefore, is a hallmark of most cancers. Additionally, somatic mutations in DNMTs that affect catalytic activity were mapped in Acute Myeloid Leukemia cancer cells. Despite being very effective in some cancers, the clinically approved DNMT inhibitors lack specificity, which could result in a wide range of deleterious effects. Elucidating distinct molecular mechanisms of DNMTs will facilitate the discovery of alternative cancer therapeutic targets. This review is focused on: (i) the structure and characteristics of DNMTs, (ii) the prevalence of mutations and abnormal expression of DNMTs in cancer, (iii) factors that mediate their abnormal expression and (iv) the effect of anomalous DNMT-complexes in cancer.
Graphical Abstract
Graphical Abstract Modulation of DNMT activity in cancer.
National Institutes of Health 10.13039/100000002 R01GM118654-01
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pmcDNA METHYLATION
DNA methylation involves the covalent addition of a methyl group (–CH3) to the 5′ position of cytosine (5mC) by DNA methyltransferases (DNMT) (1,2). The structures and catalytic mechanisms of DNA methyltransferases are highly conserved from bacteria to mammals; thus, DNA methylation is the most prevalent DNA modification (3). In mammals, it is predominantly found at CpG dinucleotides, 70–80% of which are methylated (4). DNA methylation is critical for mammalian development, differentiation, and its defects are implicated in several human diseases, including cancer (5).
In mammalian genomes, the non-uniform distribution of DNA methylation is strongly influenced by the overall depletion of CpG dinucleotides. Regions in mammalian genomes which contain expected or slightly higher levels of CpG are called CpG islands (CGI). DNA methylation shows the highest density at the repetitive and transposable elements and lowest levels at CGIs (6–8). CGIs are further categorized as high, intermediate, and low CG content. Interestingly, high CG content CGIs (>60%) are least methylated and are present at over two-thirds of all mammalian promoters, including housekeeping genes and a few developmental genes. The transcriptionally active state of housekeeping genes, which are critical for cellular function, is maintained by their unmethylated CGI promoters (9–11). Methylation changes associated with changes in gene expression are mostly found in intermediate and low CG content CGIs, which reside at tissue-specific promoters, differentially methylated regions (DMRs), enhancers, and super-enhancers (12–15). Interestingly, in cancer, enhancer DNA methylation was more closely associated with the aberrant gene expression profiles than the gene promoters, emphasizing the regulatory role of DNA methylation at low CG content sites (16–18). Regulation of CGI DNA methylation is also critical because aberrant CGI hypermethylation is prevalent in almost all cancers (19). Several mechanisms have been shown to regulate DNMT activity at CGI, including interactions with H3K4me, H3K36me and H4K20me3 histone tail modifications (20–23). Protection of CpGs from methylation can be achieved either directly by transcription factor (TF) binding or indirectly by TF-mediated targeting of chromatin remodeling enzymes (12,24,25). On the other hand, DNA methylation at regulatory elements can result in loss or gain of TF binding, affecting gene expression (26–28). In any case, aberrant methylation in cancer is either a direct consequence of DNMT mutations or indirectly due to misregulation of DNMTs, the enzymes that catalyze this modification.
Contrary to their regulatory elements, highly transcribed genes have abundant intragenic or gene body methylation (6). At intragenic regions, the predominant presence of the histone mark H3K36me3 recruits DNA methyltransferases (29,30). Correlation studies have demonstrated that gene body methylation antagonizes the activity of polycomb repressive complex (PRC2), hence, promoting transcription (31). Furthermore, DNA methylation plays a role in alternative promoter usage, alternative splicing, and in precluding cryptic transcription initiation (3,6,32–35). Aberrant hypermethylation of CGI’s, which reside in the gene body, has been shown to correlate with increased expression of the corresponding genes and is predictive of elevated oncogene expression in cancer (36).
DNA methylation contributes to heterochromatin formation at repetitive and transposable elements by creating a target motif for methyl-binding domain (MBD) proteins (37,38). The histone modifiers such as methyltransferases and deacetylases in a complex with MBD proteins create a condensed chromatin state that prevents transcription (39,40). Aberrant hypomethylation and loss of heterochromatin are highly prevalent in cancer, leading to an increase in DNA recombination and loss of genome integrity. DNA methylation defects have been used as a sensitive marker for cancer diagnosis. Alone or in combination with other therapies, DNA demethylation therapy has been successfully used for some leukemias and myeloblastic syndromes (19). However, these methods are nonspecific and have global effects on DNA methylation, which can potentially have strong side effects and may even foster metastasis (41–44).
DNA METHYLTRANSFERASES
The DNA methyltransferases (DNMTs) are a family of enzymes that catalyze the transfer of the methyl moiety from the donor S-adenosylmethionine (SAM) to DNA (45). Mammalian DNMTs specialize in de novo methylation and maintenance methylation (46). The de novo methyltransferases (DNMT3A and DNMT3B) establish methylation patterns during embryonic development, while the maintenance methyltransferase DNMT1 copies methylation patterns from parent to daughter strand during DNA replication (47–49).
Structure/Function relationship of the DNMT1 methyltransferase
DNMT1 is a maintenance methyltransferase that is highly expressed in dividing cells (50). DNMT1 knockout in mice results in a 90% loss of methylation and death mid-gestation (51). Inducible knockout of DNMT1 in hESCs and human colon cancer cells HCT116 results in a rapid loss of methylation genome-wide accompanied by substantial cell death (52,53).
DNMT1 is a multi-modular enzyme comprising an N-terminus that contains a DNA-binding CXXC domain, a replication foci-targeting sequence (RFTS), two Bromo-adjacent homology (BAH) domains, and a C-terminal catalytic domain with an innate preference for hemimethylated DNA (54,55). The N-terminal domain of DNMT1 interacts with proteins that guide the methylation activity of DNMT1 (56). These interaction partners include PCNA (proliferating cell nuclear antigen), which targets DNMT1 to replication foci (57), the histone methyltransferase G9a (58) and HP1 (heterochromatin protein 1) (59). The DNMT1 RFTS domain, which constitutes the homodimer interface (60), interacts with the catalytic domain, preventing DNA binding (61,62). At hemimethylated sites, this autoinhibition is relieved by the interaction of the RFTS domain with UHRF1 (63). Additionally, the interaction of DNMT1 RFTS domain with H3K9me3 and H3Ub was recently shown to recruit DNMT1 to specific sites and increase its activity (64). The specificity of DNMT1 is further influenced by the interaction of its CXXC domain with unmethylated CpG sites. This interaction triggers a conformational change, positioning an autoinhibitory linker between the catalytic site and the DNA (65). Little is known about the role of the two tandem BAH domains, though they are conserved in all mammalian DNMT1 homologs (66). New insights, however show that the interaction of DNMT1 BAH domain with the repressive H4K20me3 is involved in heterochromatin formation (23).
The DNMT1 catalytic domain shares similarity in catalytic motifs with DNMT3 enzymes and is responsible for substrate binding, DNA binding, and catalysis (56). Though mammalian DNA methyltransferases utilize the same catalytic mechanism, they have unique catalytic parameters (46,55). DNMT1 is a highly processive enzyme and can methylate up to 30 CpG sites before dissociating from the DNA, a well-adapted property for its role in maintenance methylation (67,68). Although DNMT1 has a high preference for hemimethylated DNA, it also performs de novo DNA methylation at unmethylated CCGG sites and targets transposable elements for de novo methylation during development (67,69–72). DNMT1 can also perform de novo methylation by cooperating with the DNMT3 de novo methyltransferases (73).
Structure/Function relationship of the DNMT3 methyltransferases
The DNMT3 family consists of two catalytically active members, DNMT3A and DNMT3B, and a catalytically-inactive member, DNMT3L (48,74). The DNMT3s are expressed primarily during embryonic development and in adult stem cells (75). DNMT3A and DNMT3B are highly homologous with roughly 40% sequence identity and a similar domain organization (76). DNMT3B knockout in mice is embryonic lethal, while DNMT3A knockout mice survive to term but die within six weeks after birth (47). In addition to unique targets, DNMT3 enzymes methylate many sites redundantly (52,77). DNMT3L is expressed explicitly during male and female germ cell development and plays an essential role in establishing methylation imprints (78–82). Knockout of DNMT3L in mice results in male sterility and defects in female oocytes (83–86).
The N-terminal regulatory regions of DNMT3A and DNMT3B consist of two domains, a Pro-Trp-Trp-Pro (PWWP) domain, and a cysteine-rich zinc-binding region called the ARTX-DNMT3-DNMT3L (ADD) domain (87). The PWWP domain interacts with both DNA and histone H3 methylated at the lysine 36 residue (H3K36me2/3), directing DNMT3 activity to intergenic regions (88–90). Studies show that DNMT3A interacts with H2AK119ub through an uncharacterized amino-terminal ubiquitin-dependent recruitment region targeting DNMT3A to specific regions (91). The ADD domain recruits DNMT3 enzymes to gene regulatory elements by binding to unmethylated histone H3 lysine 4 (H3K4) (92,93). When not bound to H3K4, the ADD domain interacts with the DNA binding region of DNMT3A, resulting in auto-inhibition (20,92).
The mammalian DNMTs contain ten conserved motifs in their C-terminal methyltransferase (MTase) domain. Motifs I – III are involved in SAM binding, while motifs IV and VI are required for catalysis (94,95). The target recognition domain (TRD), responsible for DNA binding, spans the region between the start of motif VII and the end of motif IX. The MTase domains of DNMT3A and DNMT3B share about 80% sequence similarity and can function independently of their N-terminal regions (96). DNMT3L lacks the motifs IX, X, and TRD, making it catalytically inactive (92). However, DNMT3L interacts with the catalytic domain of DNMT3A and DNMT3B, and allosterically stimulates their catalytic activity (80,84,97).
DNMT3A and DNMT3L co-crystallize as a hetero-tetrameric complex, with two DNMT3A monomers at the center, flanked by two DNMT3L monomers on either side (98). The two active sites of the central DNMT3A dimer are ∼40 Å apart, allowing for concurrent methylation of two CpGs separated by one helical turn of the DNA (87). In the absence of DNMT3L, DNMT3A can form homo-tetramers, which further oligomerize in the presence of DNA (32,99). The oligomerization of DNMT3A also supports its cooperative catalytic mechanism (99,100). The dimer interface of DNMT3A primarily constitutes the electrostatically interacting Arg and Asp residues (101). Given that DNMT3A monomers have low catalytic activity, the residues at the dimer interface are critical for optimal catalysis. This is further highlighted by a high prevalence of somatic substitution of the dimer interface residue, Arg882His, in acute myeloid leukemia (AML), resulting in about 80% loss of the catalytic activity of the mutant enzyme (102,103). Unlike DNMT3A, DNMT3B performs DNA methylation in a processive manner independent of interactions at the dimer interface (104). Although recent co-crystallization of DNMT3B with DNMT3L revealed a similar hetero-tetramer mediated by conserved residues, mutations of these residues in DNMT3B have little to no effect on the catalytic activity of the enzyme, emphasizing the specific role of dimer interface in the catalytic activity of DNMT3A (105). Recent structural analysis of the ternary complex of DNMT3A2, DNMT3B3 and a nucleosome core particle flanked by linker DNA indicates that the catalytically inactive accessory, DNMT3B3 binds to the acidic path of the nucleosome core, orienting the DNMT3A2 enzyme to bind to the linker DNA (106). These studies explain the observed methylation of linker DNA positioned nucleosomes by the DNMT3 enzymes (107).
CANCER-ASSOCIATED DNMT MUTATIONS
Recent large-scale cancer genomics consortia such as The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) identified common somatic mutations across cancers. Whereas many epigenetic regulators carry somatic mutations, relatively few occur in the DNMT enzymes (reviewed in (108)). Mutations to DNMT1 have been identified in a small proportion of patients with colorectal carcinoma (109), while DNMT3B mutations have only been identified in patients with the genetic disorder immunodeficiency, centromeric instability, and facial anomalies (ICF) syndrome (110). Conversely, there is a high prevalence of DNMT3A somatic mutations in patients with acute myeloid leukemia (AML) (111). Some of these mutations have been extensively studied to understand their impact on DNMT catalytic activity. These data suggest a critical role of DNMTs in development, weak redundancy in their function, and lack of synonymity in their sequence.
DNMT3A mutations in acute myeloid leukemia
DNMT3A plays an important role in somatic stem cell differentiation in addition to its role in establishing methylation patterns during development (112). Knocking out DNMT3A in hematopoietic stem cells (HSCs) leads to decreased levels of cell differentiation and increased self-renewal (113). DNMT3A−/− HSCs display significant genome-wide hypomethylation with focal areas of hypermethylation (114,115). The phenotype of these cells is similar to what is observed in human hematological malignancies harboring DNMT3A loss-of-function mutations (116).
Genomic studies identified somatic DNMT3A mutations associated with poor prognosis in 22% of adult patients with acute myeloid leukemia (AML) (111,117). Similar DNMT3A mutations are also observed in chronic myelomonocytic leukemia (118). The PWWP and ADD domains harbor nonsense and frameshift mutations, while the MTase domain mostly acquires missense mutations (119). Most DNMT3A MTase domain mutations are located in the conserved motifs, dimer interface, and the TRD (119).
In AML, about 65% of DNMT3A heterozygous missense mutations affect codon Arg882, with the majority occurring as an Arg-to-His substitution (111,120). The mutation typically arises during the early stages of cancer development and is associated with significantly lower rates of overall and disease-free survival (121). This substitution disrupts intermolecular interactions at the dimer interface and decreases DNA binding, resulting in a 40–80% loss of catalytic activity (99,101,103,122). Additionally, the DNMT3A Arg882His variant has altered flanking sequence preference around the target CpG site compared to wild-type DNMT3A (123). The flanking sequence preference of the DNMT3A Arg882His variant resembles DNMT3B more closely than that of WT DNMT3A, which may lead to aberrant methylation of DNMT3B targets in AML (124) (Figure 1A). In addition to altered flanking sequence preference, the Arg882His variant has weak interface interactions that disrupt the cooperative mechanism (124) (Figure 1B). Through its interaction with the WT enzyme, the Arg882His variant is suggested to have a dominant-negative effect on cooperativity (103,122,125). Other variants of Arg882, such as Arg882Cys, Arg882Ser and Arg882Pro are also found in AML patients and were shown to have reduced catalytic activity similar to Arg882His, with some differences in catalytic properties (126).
Figure 1. Catalytic properties of DNMT3 enzymes and the DNMT3A R882H mutant. (A) DNMT3A and DNMT3B preferentially methylate specific targets. The DNMT3A R882H is a somatic mutation found predominantly in AML patients, accounting for about 65% of all DNMT3A mutations in AML, and at lower frequencies in other cancers such as MDS, MPN, T-ALL and AITL (116,268). The DNMT3A R882H mutation ablates the ability of DNMT3A to target DNMT3A-preferred sites, while its activity at DNMT3B-preferred sites is unaffected. (B) DNMT3B methylates CpG sites in a processive manner, whereas DNMT3A methylates cooperatively by recruiting additional DNMT3A subunits. The DNMT3A R882H mutation also disrupts DNMT3A cooperativity that reduces the overall catalytic activity of the enzyme. AITL: angioimmunoblastic T- cell lymphoma; MDS: myelodysplastic syndrome; MPN: myeloproliferative neoplasms; T-ALL: T-cell acute lymphoblastic leukemia.
Aberrant expression of MEIS1 has been previously associated with poor prognosis in AML (127), and DNMT3A mutation can activate the MEIS1-mediated transcription program following MEIS1 promoter hypomethylation (128). In vitro studies using MEIS1 enhancer as a substrate showed significantly lower activity of DNMT3A Arg882His variant at all except one CpG site with flanking sequence preferred by DNMT3B, emphasizing the influence of this AML mutation on the substrate specificity of the enzyme (124). These in vitro experiments suggest that besides causing genome-wide hypomethylation due to loss of catalytic activity, the AML mutation can result in a gain of function activity by which the variant DNMT3A enzyme could preferentially methylate DNMT3B target sites. Although biological outcomes of a change in the substrate preference have not been described, the data suggest a potential occurrence of aberrant methylation leading to changes in gene expression. Hypomethylation resulting from loss of DNMT3A catalytic activity causes widespread gene dysregulation, including the overexpression of the Hox family genes and Idh1 (129). Conditional expression of the mouse DNMT3A R878H mutant (the mouse equivalent of R882H) initiates AML and mimics features of human leukemia (130). The mechanism of pathogenesis in this mouse model was found to be related to aberrant mTOR activation resulting from DNA hypomethylation (130).
Over ten additional missense DNMT3A mutations have been identified in AML patients, but they occur at lower frequencies than the Arg882His. In vitro studies of DNMT3A variants such as Arg635Gly, Ser714Cys, Trp893Ser, Pro904Leu, Arg736His and Arg771Gln/Pro/Gly displayed reduced catalytic stimulation by DNMT3L and a substrate-dependent decrease in catalytic activity, suggesting biological effects similar to Arg882His variant (131).
DNMT GENE EXPRESSION CHANGES IN CANCER
Alterations in DNA methylation patterns can result in changes in oncogene and tumor suppressor gene expression (132). DNA methylation is maintained through a myriad of factors, including the DNA methyltransferases, TET methylcytosine dioxygenases and histone-modifying enzymes. As the proteins chiefly responsible for establishing and maintaining methylation patterns, the DNMTs have been widely implicated in methylation changes in cancer cells. Significant focus has been placed on studying the consequences of DNMT expression changes observed in various cancers.
Mouse models of cancer have proven extremely useful in ascertaining the role of DNMTs in cancer pathogenesis. In a mouse model of pancreatic cancer, DNMT1 hypomorphic mice had a reduction in tumor burden coupled with decreased DNA methylation at a subset of cancer-associated genes in the pancreas (133). These findings are relevant to the observation that aberrant DNA hypermethylation is observed in tumors from early- and late-stage pancreatic cancer [120, 121], indicating that DNMT1 may drive some of these initial altered methylation patterns. In a mouse model of intestinal neoplasia, overexpression of DNMT3B1 resulted in enhanced colon tumorigenesis and tumor size (134). DNMT3A inhibition in a mouse model of melanoma inhibited tumor growth and affected the expression of various tumor-related genes, including class I and II MHC genes and various chemokines (135). These studies cumulatively imply a role for DNMTs in enhancing the pathologic characteristics of cancer cells.
Some studies also point to DNMTs as having roles as tumor suppressors in cancer. In mouse models of lung adenocarcinoma, DNMT3A knockout negatively impacts angiogenesis and cell adhesion (136). This observation is in line with a study showing that increased DNMT3A expression in lung adenocarcinoma is associated with a favorable prognosis (137). Similarly, DNMT3A deficiency results in attenuated progression of peripheral T cell lymphoma (138), and DNMT3B loss hastens the progression of MLL-AF9 leukemia (139). These studies exemplify that DNMT expression level changes can have broad effects that lead to pathogenesis.
Effect on the global methylation pattern
Altered genome-wide methylation has long been related to genome instability, increased chromosomal translocations, and widespread gene dysregulation in cancers (132,140–142). Therefore, overall changes in methylation related to DNMT expression variation are critical in cancer pathogenesis.
Publicly available data from The Cancer Genome Atlas (TCGA) illustrates that significant alterations in DNMT mRNA expression are observed in a wide variety of cancers (Figure 2). Although the gene expression data in Figure 2 are not normalized to proliferation-specific control (143), the overexpression of DNMTs in most of these cancers has been verified in several studies (reviewed in (144,145)). High DNMT3B expression levels correlate with a particular colorectal cancer phenotype characterized by high global DNA methylation levels, particularly at CpG islands (146). In colorectal and gastric cancers, overexpression of DNMT3B was also correlated with disease progression and increased levels of methylation in tumor cells (147,148). Irrespective of changes in DNMT expression, genome-wide hypomethylation coupled with focal hypermethylation is a feature of almost all cancers (reviewed in (149)). Hypomethylation itself has been tied to the pathogenicity of various cancers. For example, increased chromosomal rearrangement is associated with DNA hypomethylation in hepatocellular carcinoma (150) and prostate cancer (151). Dnmt1 knockout mice show genomic instability, hypomethylation of repetitive elements, and increased macroadenoma load in intestinal cancer (152).
Figure 2. Differential expression of DNMTs from TCGA data. Box plots of RNA-seq data comparing TCGA tumor samples (red) to TCGA normal and GTEx samples (blue). P-value cutoff = 0.01. Box plots were generated using GEPIA 2 (269). BLCA – bladder urothelial carcinoma, CHOL – cholangio carcinoma, DLBC – lymphoid neoplasm diffuse large B-cell lymphoma, ESCA – esophageal carcinoma, HNSC – head and neck squamous cell carcinoma, LAML – acute myeloid leukemia, LUSC – lung squamous cell carcinoma, PAAD – pancreatic adenocarcinoma, SARC – sarcoma, SKCM – skin cutaneous melanoma, TGCT – testicular germ cell tumors, THYM – thymoma, UCEC – uterine corpus endometrial carcinoma, UCS – uterine carcinosarcoma.
Tumor suppressor gene silencing
TSG silencing resulting in cancer pathogenesis is a pervasive feature, making the mechanisms resulting in this silencing an intriguing avenue for cancer research. In many cases, the altered expression of a DNMT can result in aberrant methylation at TSG promoters, resulting in TSG silencing in cancer cells.
DNMT3A overexpression in gastric cancer leads to silencing the p18INK4C gene, a cyclin-dependent kinase inhibitor that regulates cell cycle progression (153). The silencing of p18INK4C by DNMT3A leads to cell cycle dysregulation and accelerated G1/S transition, promoting cancer progression (153). In hepatocellular carcinoma (HCC), 64% of tissue samples overexpressed DNMT3A, and it was implicated in the methylation and subsequent silencing of the PTEN gene, which encodes a phosphatase critical for cell cycle regulation (154). In lung squamous cell carcinoma, DNMT1, DNMT3A and DNMT3B are expressed at higher levels in cancer tissue relative to healthy tissue controls (155). DNMT1 and DNMT3B expression in cancerous lung tissue is correlated with gene promoter hypermethylation at multiple TSG promoters, including FHIT and p16INK4a (155). A recent study found that in endometrial cancer, concurrent DNMT3B and EZH2 upregulation in cancer cells results in the epigenetic silencing of TCF3 (156). TCF3 is a transcriptional activator of CCKN1A (p21WAF1/Cip1), and its silencing results in accelerated endometrial cancer cell proliferation (156).
Gene body methylation of oncogenes
Though DNA methylation at gene promoters is associated with transcriptional silencing, methylation in gene bodies is associated with the active transcription of genes (36). Gene body methylation of oncogenes, therefore, can result in oncogene overexpression, driving cancer pathogenesis. A broad analysis of seven solid tumor types found a correlation between gene body hypomethylation and overexpression of ∼43% of homeobox genes, many of which are oncogenes (157). Researchers found that this overexpression could be recapitulated by targeting DNMT3A to specific homeobox gene bodies for hypermethylation (157). In liver cancer, gene body and 5′-UTR methylation of oncogenes are associated with their overexpression (158). This pattern of oncogene gene body methylation and increased gene expression was observed in 56% of patients (158). Treatment of HCC cells with the DNMT inhibitor decitabine reduced gene body methylation, gene expression levels and transiently reduced the tumorigenic properties (158). In renal clear cell carcinoma and lung adenocarcinoma, the CARD11 gene is significantly overexpressed concurrent with increased CARD11 gene body methylation relative to healthy cells (159). In the context of these cancers, CARD11 aberrantly activates the mTOR pathway and suppresses autophagy (159). Again, treatment of cells from both cancer types with decitabine decreased CARD11 expression (159), suggesting a direct role DNMTs in hypermethylation and cancer-specific changes in gene expression.
Cancer cell growth and proliferation
Aside from DNA methylation and gene expression changes, DNMT expression level alterations have been linked to various other oncogenic properties in cancer cells, including cancer stem cell maintenance and proliferation.
The ability of colon cancer cells to initiate tumors was significantly reduced upon DNMT1 knockout, but their ability to grow otherwise was not affected (160). This observation is partially attributed to a reduced proportion of cancer-initiating cells in the DNMT1 knockout colon cancer cell line (160). DNMT1 was also found to be necessary for the maintenance of bilinear myeloid-B lymphoid leukemia stem cells (161). In mammary cancer, DNMT1 loss results in limitation of the cancer stem cell population (162). These studies indicate a potentially larger role for DNMT1 in cancer stem cell maintenance, reminiscent of DNMT’s critical role in maintaining several human stem cell lineages (52,162,163).
Other than stem cell maintenance, DNMTs have also been found to affect cancer cell pathogenic properties, including proliferation and migration. In Burkitt lymphoma, both DNMT1 and DNMT3B are overexpressed, and treatment of Burkitt lymphoma cell lines with the DNMT inhibitor decitabine decreases DNMT levels and inhibits cell growth (164). DNMT3L expression supports the growth of embryonal carcinoma cells and is known to be significantly overexpressed in testicular germ cell tumors (165).
Prognostic value
Given its high prevalence in various cancers, DNMT expression level changes can predict patient prognosis and stage cancers. For example, in glioblastoma, DNMT3B and DNMT1 are highly overexpressed, and their expression levels can be used as markers for cancer staging (166). Investigation into these expression changes revealed hypomethylation at the DNMT1 and DNMT3B gene promoters along with a distinct euchromatin signature at the DNMT1 promoter in tumors (167). Similarly, in AML, DNMT3B expression at high levels independently carries an unfavorable patient prognosis (168), in addition to the known poor prognosis for patients carrying mutations in DNMT3A discussed above (169,170). Pancreatic ductal adenocarcinoma (PDAC) patients with higher levels of DNMT1 have a lower survival rate than those with lower expression (171). In PDAC, high DNMT1 levels were also correlated with nerve infiltration, TNM staging, degree of cell differentiation, and advanced stages of the disease (171). In chronic pancreatitis, DNMT3A and DNMT3B expression is correlated with tumor size, and patients with higher DNMT3A and DNMT3B expression have a lower survival rate (172). DNMT3B is also overexpressed in endometrial cancer and is even more highly expressed in poorly-differentiated than well-differentiated endometrial cancer cell lines (173). In agreement with the role of DNMT3B in endometrial cancer pathogenesis, treatment of an endometrial cancer cell line with a DNMT inhibitor inhibited cell proliferation and increased apoptosis (174). Cumulatively, research points toward DNMT levels being a physiologically relevant readout of cancer progression and attractive as a means of deducing patient prognosis.
MECHANISMS CONTROLLING DNMT EXPRESSION IN CANCER
Since changes in DNMT expression levels can have severe consequences in terms of cancer patient prognosis, the mechanisms by which DNMT expression is dysregulated in cancer have also been thoroughly investigated. DNMT expression can be altered through many mechanisms, including aberrant DNMT pre-mRNA splicing, polymorphisms within DNMT promoters, and epigenetic mechanisms at DNMT gene regulatory elements.
Transcription factor complexes
Alterations in DNMT expression levels are often attributed to aberrantly expressed transcription factor complexes or epigenetic remodelers acting upon the DNMT promoter regions. In non-small cell lung cancer, overexpression of DNMT1 along with the transcription factor Sp1 is associated with poor patient prognosis (175). The overexpression of DNMT1 was found to be partly mediated by p53 mutations that abrogate its interaction with Sp1 at the DNMT1 promoter, which would normally repress DNMT1 expression (175). DNMT3A and DNMT1 are both overexpressed in pancreatic cancer, in which GLI1 promotes the expression of both DNMT3A and DNMT1 (176). GLI1 is an effector protein in the Hedgehog signaling pathway, which is aberrantly activated in pancreatic cancer (177). In lung adenocarcinoma, the HOXB3 transcription factor binds the DNMT3B gene and increases DNMT3B expression (178). This increased DNMT3B expression leads to the epigenetic silencing of the RASSF1A tumor suppressor gene, which could be reversed using DNMT inhibitor treatment (178). In breast cancer, DNMT3A expression is repressed by the MTA1/HDAC1/YY1 co-repressor complex (179). High expression of MTA1 coupled with low DNMT3A expression predicts a poor prognosis for breast cancer patients (179).
Viral proteins
Multiple cancer-associated viral proteins have been implicated in DNMT expression alterations, leading to genome-wide methylation pattern changes and gene dysregulation (Reviewed in (180)). In hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC), the overexpression of the viral-encoded HBx regulator protein can upregulate DNMT1, DNMT3A1 and DNMT3A2 (181). The upregulation of these DNMTs is coincident with the overall hypomethylation coupled with focal hypermethylation phenotype observed in HCC tumor cells (182). The hepatitis delta virus (HDV) enhances the development of hepatocellular carcinoma in infected patients (183). The HDV-encoded delta antigen upregulates DNMT3B by activating the STAT3 pathway, resulting in hypermethylation at specific loci, including the TSG transcription factor E2F1 (184). Hepatitis C virus (HCV) -positive HCCs have a unique DNA methylation signature consisting of both DNA hypo- and hypermethylation (185,186). Consistently, the HCV core protein was shown to upregulate both DNMT1 and DNMT3B, resulting in promoter hypermethylation of the CDH1 TSG (187). These studies indicate that DNMT overexpression downstream of hepatitis viral proteins is an overarching pathogenic mechanism in hepatitis-associated HCC.
Promoter polymorphisms
Polymorphisms in the DNMT promoter regions that alter promoter activity have been characterized in multiple cancers. The DNMT3B promoter −149C→T single-base transition, correlated with increased promoter activity, is associated with more than a two-fold increased risk of developing lung cancer (188). This same promoter polymorphism is also associated with an earlier onset of hereditary non-polyposis colorectal cancer in patients with this polymorphism than those carrying the wild-type allele (189). The DNMT3B promoter −579G→T polymorphism is associated with a higher risk of developing thymoma in myasthenia gravis patients (190), along with a higher risk of developing multiple other cancers, including lung cancer and head and neck squamous cell carcinoma (191,192). The DNMT3A promoter −448A→G polymorphism is a common SNP associated with alterations in DNMT3A promoter activity (193). The −448A allele increases promoter activity and has been associated with an increased risk of developing gastric cancer (193).
Alternative splicing
Aberrant alternative splicing can also be a component of altered DNMT expression in cancer, especially in the case of DNMT3B, which has over 30 described splice isoforms resulting from alternative splicing events and alternative promoter usage (194,195). In Myc-induced lymphoma, expedited lymphomagenesis is associated with increased expression of a truncated catalytically-inactive dominant-negative isoform, DNMT3B7 (196). This observation is corroborated by a recent study that showed that mice expressing a catalytically-inactive DNMT3B isoform from one or both alleles develop B-cell lymphomas among other hematologic malignancies (197). In non-small cell lung cancer (NSCLC), the ΔDNMT3B subfamily was described, consisting of at least seven DNMT3B variants resulting from alternative splicing, some lacking enzymatic activity (198). These different ΔDNMT3B isoforms can differentially regulate methylation of specific genes, including the tumor suppressor RASSF1A, which is specifically methylated by the ΔDNMT3B family member ΔDNMT3B4 (199). A recent study found that DNMT3B isoforms lacking catalytic activity were highly expressed in HCC relative to healthy liver tissue (200). Upon hepatocyte-specific DNMT3B deletion, mice exhibit a higher incidence of HCC relative to control mice, suggesting that catalytically-active 3B may play a protective role against hepatocarcinogenesis (200).
INTERACTIONS OF DNMTS AND THEIR MISREGULATION IN CANCERS
The interactions of DNMTs with proteins and post-translationally modified histones regulate DNA methylation at specific genomic regions in various cell types (6,97,201,202). Spurious DNA methylation in cancer may be caused by the disruption of conventional DNMT interacting partners or the formation of new complexes due to the aberrant expression of developmental factors that misdirect DNMTs to atypical genomic sites (Table 1).
Table 1. List of DNMT interacting partners in human cancers. This table shows a list of DNMT interactions with proteins which play important roles in several human cancers as described in the listed references
Interacting partner (s) DNMT Evidence in human cancer Ref.
DNA-bound proteins CFP1 DNMT1 Disruption of this interaction reduces tumor growth in glioma cells. (214)
p53 DNMT3A Represses p53-mediated gene expression (221)
DAXX DNMT1 DAXX recruits DNMT1 to specific genomic loci to regulate autophagy programs in prostate cancer (270)
MECP2 DNMT1 Interaction mediates hypermethylation at ESR1, survivin and cdc25c genes in breast cancer. (203)
PCNA DNMT1 Disruption of this interaction is an oncogenic event in tumorigenesis (220)
MYC DNMT3A Induces promoter methylation and miR-200b silencing in breast cancer (271)
Rb DNMT1 Rb inhibits the methyltransferase activity of DNMT1, which may lead to global hypomethylation in osteosarcoma (212)
MAFG DNMT3B Silencing of CIMP genes in colorectal cancer (209)
ZNF304 DNMT1 Silencing of CIMP genes in colorectal cancer (210)
PU.1 DNMT3A/B Recruits DNMTs to silence TSGs in NIH3T3 cells (205)
PML-RARα DNMT3A/ DNMT1 Silences the RARβ2 gene in acute promyelocytic leukemia (207)
ISGF3γ DNMT3A Disruption of this complex enhances the efficiency of chemotherapy in mice tumors (208)
UHRF1 DNMT1 Disruption of this complex induces tumorigenesis in astrocytes, breast, lung, and mesothelial cells (219)
STAT3 DNMT1 May be involved in STAT3 mediated transcriptional repression of tumor suppressor genes (272)
Chromatin-modifiers SETDB1 DNMT3A Mediates transcriptional repression of tumor suppressor genes (231)
hNaa10p DNMT1 Recruits DNMT1 to suppress TSG expression and enhances DNMT1 activity (273)
HDAC1/2 DNMT1/DNMT3A/B Mediates maintenance of heterochromatin in normal and cancer cells (211,233,274)
HP1 DNMT1 Disruption of this interaction promotes tumorigenesis in mice (227)
LSH DNMT3B Depletion of LSH reduces DNMT3Bs’ association with DNA in erythroleukemia (245)
MBD3 DNMT3A/B Mediates gene silencing on the MT-1 promoter in lymphosarcoma cells (241)
NsPc1 DNMT1 Silences HOX genes (232)
DMAP1 DNMT1 Disruption of this interaction increased sensitivity of cancer cells to chemotherapy in colorectal cancer (227,234)
USP7 DNMT1 Interacts with and stabilizes DNMT1, promoting the catalytic activity of DNMT1 (275,276)
EZH2 DNMT1 Mediates silencing of miR-484 and contributes to cervical cancer progression (277)
KDM1A DNMT1 Localized to heterochromatin in a cell cycle-dependent manner in cancer cells (237)
G9a DNMT1 Coordinates DNA and histone methylation during replication and has been implicated in small cell lung cancer (278,279)
PRC1 DNMT3A R882H Silences differentiation in a DNA-methylation independent manner (230)
Suv39H1 DNMT1/3A/3B May be responsible for Snail-mediated E-cadherin repression in breast cancer (280)
Interaction of DNMTs with DNA binding factors
Site-specific DNA methylation is regulated by transcription factor-mediated recruitment of DNMTs to their binding sites, leading to specific changes in gene expression (Figure 3). For example, p53, a tumor-suppressing transcription factor, interacts and stimulates DNMT1 activity, which consequently represses the SURVININ promoter in human fibroblasts (203). In osteosarcoma cells, MYC targets DNMT3A to MYC-specific gene promoters to mediate gene repression (204). In NIH3T3 cells, PU.1, a master regulator for myeloid and B-cell lineage development, recruits DNMT3A/B to methylate promoters of the TSGs p16 and p27 (205). Further, in HCT116 cells, DNMT3A interacts with DAXX and functions as a co-repressor for DAXX target genes independent of its methyltransferase activity (206). PML-RARα, a chimeric oncoprotein formed by an aberrant translocation of PML gene next to RARα, recruits DNMT3A and DNMT1 to silence the promoter of the tumor suppressor RARβ2 gene in acute promyelocytic leukemia (207). DNMT3A also interacts with ISGF3γ, a transcription activator induced upon interferon α stimulation (202). High levels of DNMT3A/ISGF3γ complex indicate a poor prognosis in tumors, and subsequent disruption of this interaction enhances the efficiency of chemotherapy in mice tumors (208). It is proposed that MAFG, a transcriptional repressor, recruits a co-repressor complex that includes DNMT3B to methylate and silence CpG island methylator phenotype (CIMP) genes in BRAF mutant colorectal tumors (209). Similarly, promoter-bound ZNF304 recruits DNMT1 as part of a co-repressor complex to methylate and silence transcription of CIMP genes in KRAS mutant colorectal cancer (210).
Figure 3. Schematic representation of transcription factor-mediated recruitment of DNMTs in cancer. (A) Unmethylated promoter regions typically promote transcription. Transcriptional repressors potently recruit DNMTs to mediate site-specific DNA methylation at promoter or enhancer regions that attenuate transcription of genes, especially TSGs in cancer. (B) Gene repression by recruitment of DNMTs by transcription factors can be independent of the methyltransferase activity of DNMTs. TR-bound DNMTs may function as an artificial co-repressor or recruit chromatin-modifiers that mediate gene repression at target sites. DNMT – DNA methyltransferase; -Me – methyl group on methylated DNA; CM – chromatin modifier. TR – Transcriptional repressor .
DNMT1, via its CXXC domain, directly interacts with the Rb TSG and represses reporter constructs containing E2F binding sites without detectable changes in promoter methylation (211). On the other hand, in osteosarcoma cells, the interaction of Rb with DNMT1 was shown to inhibit its methyltransferase activity by disrupting DNA/DNMT1 complexes, contributing to global hypomethylation defects, which is a general phenomenon in most cancers (212). CXXC finger protein 1 (CFP1), a component of the Setd1A and Setd1B methyltransferase complexes, directly interacts with DNMT1. CFP1-deficient ESCs show global hypomethylation and loss of DNMT1 protein suggesting the role of CFP1 in DNMT1 protein stability. (213). Disruption of the DNMT1/CFP1 interaction strongly increases the sensitivity of tumors to chemotherapy in mice and reduces tumor growth of glioma cells (214). Based on more recent studies demonstrating a robust allosteric regulation of DNMT1 activity (215), it will be interesting to study the impact of the Rb/DNMT1 and CFP1/DNMT1 interaction on the activity and specificity of DNMT1 in normal and cancer cells.
DNMT1 also interacts with PCNA, a processivity factor of DNA polymerase, to mediate post replication maintenance of DNA methylation (216,217). p21, a cell cycle regulator, and DNMT1 interact with PCNA in a mutually exclusive manner (216). It is speculated that the loss of p21 in most cancers may result in an opportunistic interaction of PCNA with DNMT1 leading to mistargeting and spurious DNA methylation (218). However, the disruption of DNMT1/PCNA interactions promotes carcinogenesis and tumorigenesis in several human cancer cells (219,220). Thus, it is imperative to study the mechanisms underlying the Ying Yang effects caused by DNMT interactions in cancer. Additionally, DNMT3A directly interacts with p53 to repress p53-mediated transactivation of p21 in breast cancer cells, independent of its methyltransferase activity (221). Altogether, with or without affecting their methyltransferase activity, the interactions of DNMTs with other DNA binding complexes are essential for the proper regulation of gene expression and genome stability, misregulations of which are implicated in cancer.
Interaction of DNMTs with chromatin modifiers
Anomalous interactions between chromatin-modifying proteins with DNMTs have been implicated in cancer. Underscoring a functional relationship that facilitates the cooccurrence of deacetylated histones and DNA methylation, early studies showed interactions between histone deacetylases HDAC1 and HDAC2 with DNMT3A/B and DNMT1 in mammalian cells (222–224). In human cancer cells, the combined activity of these enzymes has been targeted by a combination of inhibitors which leads to the re-expression of densely hypermethylated and transcriptionally silenced TSGs. (225). Loss of DNMT1 in HCT116 cells is associated with an increase in H3K9 acetylation and a decrease in H3K9 methylation with concomitant loss of HDACs and HP1, suggesting that DNMT1 mediates the maintenance of heterochromatin in human colon cancer cells (226). Additionally, the disruption of DNMT1 interaction with DNMT3B or HP1 promoted tumorigenesis in mice (227). Repressive chromatin is also induced by an interaction of DNMTs with the SUV39H1 and EZH2 histone methyltransferases, which methylate histone H3K27 (46,59,228). Studies using peptides to disrupt DNMT3B interaction with HDAC1 and EZH2 enhanced tumorigenesis in a mouse glioma model (227). Although DNMT3A interacts with polycomb group proteins to mediate gene repression through its DNA methylation activity (229) the DNMT3A R882H AML variant interacts and recruits PRC1 to the PU.1 URE region, suggesting a DNA methylation-independent silencing of cell differentiation and lineage commitment in leukemogenesis (230).
Other examples of a collaboration between DNMTs and chromatin-modifying enzymes include the specific interaction of DNMT3A with the histone methyltransferase, SETDB1, that localizes DNMT3A to methylate and inactivate the promoter of RASSF1A, a TSG commonly silenced in human cancers (231). DNMT1 associates with a neural-specific polycomb, NSPc1 and EzH2 to form a complex that silences HOX genes (232). Some interactions were shown to affect the DNMT activity, including DMAP1−DNMT1 interaction, which enhanced the enzymatic activity of DNMT1 (233,234). DMAP1 participates in the TIP60-p400 histone acetyltransferase (HAT) complex, which acetylates histone H4 at lysine 16 (H4K16) to relax condensed chromatin (235). Disrupting DMAP1-DNMT1 interaction resulted in an increased sensitivity of glioma cancer cells to chemotherapy and irradiation-induced cell death potentially due to repression of TSGs (227). Correspondingly, the reduction in DMAP1 protein by lentiviral shRNA showed a decrease in DNA methylation at the p16 promoter with a concomitant reduction in cell proliferation (234). Additionally, DNMT1 activity is affected by its interaction with LSD1. Demethylation of DNMT1 protein by LSD1 was shown to be essential for its stability. Although the loss of LSD1 in embryonic stem cells resulted in progressive loss of DNA methylation (236), depletion of LSD1 in cancer cells had no such effect (237). In cancer cells, however, LSD1–DNMT1 interaction is highest during the S-phase, suggesting a role of this interaction in cell cycle progression and pathogenesis (237–239).
At repetitive elements, DNMT3A and DNMT3B interact with specific histone modifications and the heterochromatin binding protein 1 (HP1), thus accumulating DNA methylation at these genomic elements (47,88,240). DNMTs also interact with MBD2/3 and MeCP2 methyl CpG binding proteins, leading to gene repression in normal and cancer cells (46). For instance, in mouse lymphosarcoma cells, DNMT3A/B interacts with MBD3 to mediate gene silencing on the MT-I promoter (241). An ATP-dependent chromatin remodeling protein, LSH, associates with DNMTs to mediate DNA methylation at specific genome sites and repress transcription (242,243). Consistent with the observation that DNA hypomethylation is prevalent in cancer (244), the deletion of LSH in mice resulted in the reduced association of DNMT3B with DNA and the global loss of DNA methylation leading to the development of erythroleukemia (245).
Interaction of DNMTs with modified histone tails
DNMTs interact with specific post-translational modifications on histones through their N terminal motifs, thus potentially targeting DNA methylation and regulating the regional specificity of DNMTs (reviewed in (246)). DNMT3 enzymes have two chromatin interacting domains, the ADD (ATRX–DNMT3–DNMT3L) domain, and the PWWP domain, both of which mediate interactions of DNMTs with chromatin and regulate their activity on nucleosomal DNA. (247). The PWWP domain of DNMT3B interacts with histone H3K36me3, and its catalytic domain allosterically interacts with DNMT3A catalytic domain. Thus a concerted interaction of DNMT3B3, a catalytically inactive isoform of DNMT3B, with DNMT3A and histone H3K36 was shown to recruit and enhance the activity of DNMT3A at H3K36me3 gene regions and CpG islands in colorectal cancer cells (248,249). Similarly, DNMT3A via its PWWP domain interacts with H3K36me2 to mediate intergenic DNA methylation (22). The ADD domains of DNMT3L and DNMT3A specifically interact with unmethylated H3K4, thereby triggering de novo DNA methylation at these sites (92,93,250). An interaction between the ADD domain of DNMT3A with H3K4me0 releases DNMT3A from an autoinhibited conformation (20,92,93,251) (Figure 4). These observations are consistent with findings that DNA methylation inversely correlates to H3K4 methylation in embryonic stem and somatic cells (11,252,253). This mechanistic feature allows dynamic regulation of DNMT3A activity wherein loss of histone H3K4me can potentially recruit/trigger the activity of DNMT3A via the interaction of its ADD domain with unmethylated histone H3K4. Indeed recent studies elucidated such a mechanism that involves the interaction of DNMT3A with the LSD1–Mi2/NuRD complex and targets DNA methylation to the enhancers of pluripotency genes during ESC differentiation. As pluripotency is turned off post differentiation, repression of PpGs is orchestrated by a series of chromatin-associated enzymatic activities of the LSD1–Mi2/NuRD – DNMT3A complex. Histone deacetylation and demethylation by LSD1–Mi2/NuRD complex facilitates the interaction of DNMT3A-ADD domain with histone H3 leading to subsequent activation of DNA methyltransferase activity at PpG enhancers (254) (Figure 4). These observations are crucial given that about a third of all cancers abnormally express PpGs. A disruption in the enhancer repression mechanism could potentially allow spurious expression of PpGs in cancer. In line with this hypothesis, a subsequent study showed incomplete PpG repression in differentiating embryonal carcinoma cells, owing to a failure in LSD1 and DNMT3A activity at their respective enhancers. Interestingly, it was shown that in differentiating ECC, high OCT4 expression leads to its continued interaction with LSD1 and that OCT4 inhibits LSD1 activity. This inhibits DNA methylation through the retention of H3K4me at the PpG enhancers in ECCs (255).
Figure 4. Illustration of cross-talk between DNMT3A and the LSD1-Mi2/NuRD complex in normal versus cancer cells. The ADD domain allosterically inhibits the methyltransferase activity of DNMT3A via direct interaction with the catalytic domain. Inactive DNMT3A interacts with the LSD1/MI2/NuRD complex and is recruited to active enhancers via interactions with transcription factors such as OCT4. Notably, the demethylase activity of LSD1 is inhibited by OCT4, and its loss thereof activates LSD1 to demethylate H3K4. This releases the ADD domain to interact with unmodified H3K4, which relieves the activity of DNMT3A to methylate DNA. The deacetylation of H3K27 by the MI2/NuRD complex in concert with DNA methylation creates a repressive environment that hinders gene expression. However, in cancer cells with elevated OCT4 expression, OCT4 remains bound to enhancer regions and inhibits the activity of LSD1, thus, retaining H3K4 methylation. The methylated H3K4 maintains DNMT3A in an autoinhibited state, preventing its participation in mediating a repressive chromatin environment, leading to spurious expression of genes such as PpGs in cancer.
Besides the canonical histone tail modifications that interact with DNMTs, the association of DNMT1 with ubiquitinated H3 activates its enzymatic activity (256,257). Interaction of DNMTs with modified histone tails has been implicated in cancer. Particularly, DNMTs are enriched at high CpG density class genes in embryonic carcinoma cells (ECC). Notably, this high enrichment of DNMTs correlates with the increased enrichment of H3K27me3, H2AK119ub and H3K36me3 histone modifications in EC cells (258).
CONCLUSIONS/OUTLOOK
Here we review the enzymatic and biological properties of DNMTs, and the effects of their activity in normal and cancer cells. We discuss the known effects of DNMT3A mutations in Acute Myeloid Leukemia in light of the structural and enzymatic properties of DNMT3A and regulation of DNMT function by transcriptional, post-transcriptional mechanisms, and protein–protein interactions.
Our understanding of the regulatory processes that control the activity of DNMTs is far from complete. This is partly because the effect of DNA methylation on gene expression is ‘context’ dependent, and ‘context’ could be defined by DNA sequence, position relative to genes, and chromatin environment. For example, whereas DNA methylation at gene promoters coincides with gene repression, highly transcribed genes have high DNA methylation in their gene bodies. As a result, loss of DNMT regulation has varying effects in cancer, resulting in both hyper- and hypomethylation of the genome.
DNMTs sustain one of the most well-studied epigenetic regulatory mechanisms, putting them at the forefront of research related to TSG silencing, oncogene expression, and cancer cell proliferation. Expression changes in DNMTs, particularly overexpression of DNMT3B, are reported in many cancers. DNMT expression can be upregulated by diverse factors, including Sp1 and Sp3 zinc finger proteins, Wilms tumor 1, Homeobox B3 and various human viruses (259–261). Both losses of transcriptional repression control and gain of spurious induction have been reported to explain high DNMT expression in cancers. Nucleoside analogs, azacytidine, and decitabine, which target DNMTs are used to treat myeloid malignancies. However, the effectiveness of these drugs is limiting due to their low bioavailability, relative toxicity, and non-specific effects at high doses. (reviewed in (262)). A new class of non-nucleoside drugs targeting DNMTs in cancers is currently being developed to potentially mitigate these challenges (263,264).
Targeting of DNA methylation to regulatory elements is orchestrated by a complex interplay of DNA and chromatin associating factors that associate with DNMTs. A list of factors directly or indirectly interacting with DNMTs is compiled in Table 1. For example, a cross-talk between LSD1/Mi2NuRD complex with DNMT3A at the PpG enhancers ensures deposition of DNA methylation at these sites when the genes are turned off. Disruption of this mechanism leads to incomplete gene repression in embryonal carcinoma cells (255). Similarly, repression of NY-ESO1 gene in glioma and mesothelioma cells occurs through sequential recruitment of three chromatin-modifying complexes: (i) HDAC1/mSIN3a/NCOR complex deacetylates the promoter, (ii) DNMT3B/HDAC1/EGR1 complex establishes site-specific DNA methylation and histone deacetylation, and (iii) DNMT1/PCNA/UHRF1/G9a complex maintains DNA methylation and initiates heterochromatinization by introducing H3K9me2 repressive mark (265). A similar strategy at repetitive elements establishes and maintains heterochromatin, preventing chromosomal aberrations and ensuring centromere maintenance. The process involves cross-talk between DNMT3A/3B with HP1 and H3K9me3 (266). The importance of the discovery of cross-talk mechanisms is emphasized by the success of combination therapies targeting DNMTs and other chromatin-modifying enzymes, such as HDACs in various cancers. (41,267).
Although DNA methylation is pervasive across the mammalian genome, in cancer cells, an aberrant increase in DNA methylation occurs at defined sites, suggesting mistargeting rather than increased enzymatic activity. Therefore, in the simplest model, inhibitors that disrupt the interactions mistargeting DNMTs in cancer cells can potentially prevent aberrant targeting and hypermethylation. For more complex mechanisms, the cascades of regulatory reactions that affect DNMT activity can be modeled to design inhibitors that disrupt DNMT activity during cancer development. A thorough analysis of similarities and differences in the allosteric regulation and structure-function relationship of DNMTs will enable the development of inhibitors that selectively target these enzymes. Furthermore, owing to the tissue-specific expression of DNMTs, their pathways may also vary in different cancers. Understanding the tissue-specific mechanism of DNMTs could potentially lead to an effective cancer-specific therapeutic strategy.
In conclusion, an in-depth understanding of DNMTs, their enzymatic control, interaction partners, chromatin localization, and interaction with various signaling pathways is needed for the discovery of new cancer drug targets and to deepen our understanding of mechanisms that define cell identity.
FUNDING
National Institutes of Health (NIH) [R01GM118654-01].
Conflict of interest statement. None declared.
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==== Front
J Pain Res
J Pain Res
jpr
Journal of Pain Research
1178-7090
Dove
34880668
338716
10.2147/JPR.S338716
Original Research
Sodium Bicarbonate Sub-Diaphragmatic Irrigation Relieves Shoulder Pain After Total Laparoscopic Hysterectomy: A Randomized Controlled Trial
Liu et al
Liu et al
http://orcid.org/0000-0001-8854-523X
Liu Li 1 *
Xia Tian 2 *
Ji Haiyan 1
Guo Yaxin 1
http://orcid.org/0000-0002-4322-6857
Liu Junfeng 1
Du Liping 1
http://orcid.org/0000-0001-9105-9021
Lei Daoyun 3
http://orcid.org/0000-0002-6869-0701
Han Chao 1
Ma Tieliang 4
1 Department of Anesthesiology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, People’s Republic of China
2 Department of Gynaecology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, People’s Republic of China
3 Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, Jiangsu, People’s Republic of China
4 Central Laboratory, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, People’s Republic of China
Correspondence: Chao Han Tel +86 0510-87921000 Email [email protected]
* These authors contributed equally to this work
30 11 2021
2021
14 36153622
10 9 2021
19 11 2021
© 2021 Liu et al.
2021
Liu et al.
https://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
Study Objective
To determine whether sub-diaphragmatic irrigation with sodium bicarbonate would relieve post-laparoscopic shoulder pain (PLSP) after total laparoscopic hysterectomy.
Design
Randomized double-blinded trial.
Setting
Teaching hospital.
Patients
Seventy patients undergoing total laparoscopic hysterectomy (TLH) for benign indications.
Intervention
We randomly allocated patients to intervention or control groups where sodium bicarbonate containing flushing liquid or normal saline was irrigated sub-diaphragm before sewing.
Measurement & Main Results
The primary outcome was PLSP following surgery measured by a numerical rating scale (NRS) (0 = no pain; 10 = worst pain imaginable). Secondary outcomes were abdominal incisional and visceral pain, analgesic use, and sodium bicarbonate related side effects. The incidence of PLSP in intervention group was significantly lower than that in control group (P < 0.05). Contrarily, incisional and visceral pain was similar in both groups (P = 0.1). The consumption of rescue analgesics in the intervention group was lower than that in the control group. Side effects were comparable in both study groups.
Conclusion
Sub-diaphragmatic irrigation with sodium bicarbonate could effectively reduce shoulder pain, but not abdominal incisional and visceral pain, in patients undergoing TLH without an increase in side effects.
Registration information
Clinical trial registry number: http://www.chictr.org.cn/ (ChiCTR2100041765)
Registration findings
http://www.chictr.org.cn/showproj.aspx?proj=66721 Link to clinical trial page and data repository: http://www.medresman.org.cn/pub/cn/proj/projectshshow.aspx?proj=2992
Keywords
total laparoscopic hysterectomy
post-laparoscopic shoulder pain
sodium bicarbonate
==== Body
pmcIntroduction
Laparoscopic surgery has been widely accepted in the diagnosis and management of gynecologic organ-related diseases, as it provides minimization of surgical wound, less postoperative pain, shorter hospital stay, rapid recovery, and a better cosmetic outcome, compared to conventional laparotomy.1,2 However, there are still 35%–80% of patients complaining of moderate to severe pain affects the quality of life within 3 days to a week after gynecologic laparoscopic surgery, resulting in delayed discharge or interference of coming back to normal activities.3,4
Laparoscopic gynecological surgery-related pain consists of skin incision (well localized), visceral organs (diffuse and poorly localized), and shoulder pain.5 The abdominal incisional and visceral pain appears immediately after the operation and non-steroidal anti-inflammatory drugs6 or local incisional infiltration7,8 are generally considered to be effective to alleviate the pain. However, post-laparoscopic shoulder pain (PLSP) is supposed to be referred pain due to carbon dioxide [CO2] pneumoperitoneum9 and occurs approximately 8 hours postoperatively, later than incisional pain,10 and displays less responsiveness to analgesics.4 Therefore, it is of great clinical significance to prevent PLSP.
Substantial evidence has shown that CO2 pneumoperitoneum be the main source of PLSP.9 Carbonic anhydrase located on the moist surface of the peritoneum and diaphragm converts CO2 to carbonic acid, resulting in a reduction in the peritoneal pH which irritates the peritoneal and diaphragmatic nerves, thus leading to PLSP.9,11 Elevating the pH value of the diaphragm might block the phrenic nerve stimulation induced by CO2 theoretically, which has been proved by the evidence of carbonic anhydrate inhibitor-acetazolamide in the reduction of PLSP dramatically.12
Sodium bicarbonate, a well-known alkaline liquid, is also used for peritoneal lavage in some specific cases, such as diffuse peritonitis-induced acid environment.13 Here, we hypothesize that sodium bicarbonate might neutralize the acid environment induced by CO2 pneumoperitoneum, and then relieve PLSP. The study was designed to determine whether sodium bicarbonate sub-diaphragmatic irrigation could reduce PLSP in patients undergoing total laparoscopic hysterectomy (TLH).
Materials and Methods
Patients
This randomized, double-blind, controlled trial was conducted at the Department of Anesthesiology, Affiliated Yixing Hospital of Jiangsu University, Yixing, China between December 2020 to April 2021. The study was approved by the ethics committee of the affiliated Yixing Hospital of Jiangsu University and registered as a clinical trial (trial registration number [chictr.org.cn] ID: ChiCTR2100041765). Raw data is available in medresman, http://www.medresman.org.cn/pub/cn/proj/projectshshow.aspx?proj=2992. This study has been performed consistent with the principles of the Helsinki Declaration on Human Experimentation.
Participants were recruited from a single-center gynecologic department at our unit. Consecutive patients scheduled for elective TLH under general anesthesia for benign indications (leiomyomas, endometriosis, abnormal uterine bleeding) were invited to participate in the study.
Eligible women were aged 20 to 80 years, American Society of Anesthesiologists (ASA) physical status classification I-II, no history of trauma or surgery, and be able to provide written informed consent.
Exclusion criteria were known allergy or contraindications to sodium bicarbonate, clinically significant cardiovascular or central nervous system disease, impaired renal or hepatic function, presence of pre-surgical shoulder pain or any acute or chronic pain syndromes, cognitive impairment or communications disorder, previous prescription of analgesic treatment, anxiety or depression (score >8 using Hospital Anxiety and Depression Scale (HADS) in a Chinese version), conversion to laparotomy, decline to participate. Furthermore, the patients with abdominal drainage, malignancy found on final pathology, and severe complications were also excluded.
Sample Size Calculation
We estimated the sample size at http://powerandsamplesize.com/, an open-source calculator. According to our pilot study, the incidence of PLSP in patients with sodium bicarbonate sub-diaphragmatic irrigation was 5%, while 30% in control (data not published). To indicate a clinically significant reduction, a difference of 25% in the incidence of PLSP was considered appropriate for the sample size calculation. Given a power of 80% and a significance level of 5%, the sample size was determined to be 66, with 33 subjects in each arm using a 1:1 ratio for the groups. We added 15% for missing data or attritions, so a final sample size of 78 patients (39 patients per group) was recruited.
Randomization
The women were assigned randomly to the intervention/control groups on the day the surgery took place by a research nurse on the basis of a randomization code developed by a computerized random number generators in balanced blocks of 6. The allocation was concealed using opaque sequentially numbered sealed envelopes. The intervention group received sub-diaphragmatic Irrigation with 500mL 1% sodium bicarbonate solution before suture, while control group equal volume normal saline.
Anesthesia and Surgery Protocol
All patients received the same anesthetic scheme by a designated senior anesthesiologist. Standard monitoring was used including heart rate, respiratory rate, continuous ECG, SpO2, and non-invasive arterial blood pressure. General intravenous anesthesia was induced with midazolam (0.3 mg/kg), etomidate (0.3 mg/kg), fentanyl (5 μg/kg) and vecuronium (0.15 mg/kg). All patients were intubated and mechanically ventilated with 100% oxygen, VT 8–10 mL/kg, frequency 10–14/min, with an end-tidal CO2 of 30–40 mmHg during the surgery procedure. Fentanyl 0.1mg was added before skin incision. The anesthesia was maintained with a continuous infusion of propofol 4–12 mg/kg/h and remifentanil 15–40 μg/kg/h, with the concentration titrated to hemodynamics stability and enough depth of anesthesia (BIS value between 40 and 60). The CO2 pneumoperitoneum pressure was maintained at 12mmHg with an inflation flow rate of 15L/min. Ketorolac 30mg was given intravenously 5 minutes before sewing and the intravenous administration of anesthetics was stopped 5min before the end of the operation. Reversal of residual neuromuscular blockade was done with a mixture of neostigmine (0.02 mg/kg) and atropine (0.01 mg/kg) and the patient was extubated. Patients were subsequently transferred to the ward after 15min monitoring without a continuous postoperative analgesia program proposed by the anesthesiologist. Rescue analgesia was provided with ketorolac 30mg IV injection whenever the patient complained of a numerical rating scale (NRS) score equal to or more than 4, and the maximum total daily was not allowed to exceed 120mg.
A team of gynecologists experienced in laparoscopic surgery performed all procedures (4-port technique and vaginal cuff closed) and irrigated sub-diaphragm with blinded liquid before sewing in Trendelenburg position, while not pulmonary recruitment maneuvers but abdomenal compression was used to expel out of gas to minimize the residual gas within the abdominal cavity. Shoulder bracket was applied to support the shoulders during the procedure as well.
Measures
An independent research investigator, not involved in the operation and blinded to the patient’s group, recorded NRS score of shoulder, incisional and visceral pain at fixed intervals, ie, immediately after extubation, 2, 6, 12, 24, 48, 72h postoperatively. Peri-operative vital signs, the dose of anesthetics, recovery time, tracheal extubation time, and rescue ketorolac were collected from the hospital database. As there were cases of critical alkalosis following intra-peritoneal irrigation with sodium bicarbonate,14 we diluted the concentration of sodium bicarbonate in the flushing solution and performed blood gas analysis 10min after extubation. The results of blood gas analysis were also recorded. Any possible symptoms of alkalosis such as dyspnea, agitation, paresthesia, convulsions were collected as well.
Statistical Analysis
The normality assumption was assessed using the Kolmogorov–Smirnov test in all analyses. Continuous variables were presented as a mean (SD) or median (interquartile range), and categorical variables were presented as count and percentage. Group comparisons were made using 2 independent sample t tests for continuous variables with a normal distribution, the Mann–Whitney U-test for continuous variables with a nonnormal distribution, or the χ2 test or Fisher exact test for dichotomous and ranked data. A two-tailed probability of <0.05 was considered statistically significant. We used SPSS (Statistical Package for the Social Science; SPSS Inc., Chicago, IL, USA) version 25 for all statistical analyses.
Results
A study flow diagram is shown in Figure 1. Of the 78 patients, eight participants were excluded due to conversion to laparotomy (1), abdominal drainage (4), malignancy outcome (2), and severe infection (1), and seventy cases were finally enrolled in this study, thirty-five in each group. No significant difference was found between the two groups in terms of demographic and clinic data including age, body mass index (BMI), smoking, alcohol, comorbidities (hypertension, diabetes mellitus), indications, pre-surgical hypogastralgia, duration of surgery and anesthesia and pneumoperitoneum, consumption of intra-operative propofol and remifentanil, blood loss, recovery time, tracheal extubation time, and stay of length (P > 0.05; Table 1), except for the cumulative rescue ketorolac consumption in the intervention group lower than in the control group (P < 0.05; Table 1).Table 1 Demographic and Clinic Data in the Two Groups
Variable Intervention Group (n = 35) Control Group (n = 35) P value
Preoperative demographic data
Age(years) 50.7±6.2 50.5±5.8 0.6
BMI(kg/m2) 23.8±2.9 24.5±3.9 0.3
ASA classification 0.8
I 16(45.6%) 17(48.6%)
II 19(54.4%) 18(51.4%)
Education 0.8
Illiteracy 1(2.9%) 2(5.7%)
Elementary education 31(88.6%) 30(85.7%)
High school and above 3(8.6%) 3(8.6%)
Employment 0.9
Employed 23(65.7%) 22(62.9%)
Housewife 8(22.9%) 9(25.7%)
Retired 4(11.4%) 4(11.4%)
Smoking 2(5.7%) 1(2.9%) 0.6
Alcohol 1(2.9%) 1(2.9%) 1
Hypertension 8(22.9%) 7(20.0%) 0.7
DM 2(5.7%) 2(5.7%) 1
Indications
Leiomyomas 21(60.0%) 24(68.6%) 0.08
Endometriosis 11(31.4%) 10(28.6%) 0.2
Abnormal uterine bleeding 3(8.6%) 1(2.8%) 0.3
Pre-surgical hypogastralgia 8(22.9%) 11(31.4%) 0.4
Perioperative variables
Duration of surgery(min) 135.3±63.2 122.7±55.7 0.4
Duration of anesthesia(min) 145.9±62.8 136.1±56.1 0.5
Duration of pneumoperitoneum(min) 116.6±63.2 111.9±54.9 0.7
Consumption of propofol(mg) 86.6±24.6 75.4±30.0 0.1
Consumption of remifentanil(μg) 241.9±67.5 220.1±86.0 0.3
Loss of blood(mL) 69.4±39.3 75.7±34.6 0.5
Recovery time(min) 13.2±5.4 12.4±4.9 0.5
Tracheal extubation time(min) 14.7±5.0 13.9±4.6 0.5
Rescue ketorolac(mg) 36.0±21.3 55.7±33.0 0.005
Stay of length(d) 9.6±1.4 9.9±1.6 0.4
Note: Values are presented as mean ± SD or n (%).
Abbreviations: BMI, body mass index; DM, diabetes mellitus.
Figure 1 Flowchart showing the enrollment of participants to the study groups.
Eleven patients in the control group presented with PLSP and three in the intervention group during 3 days after surgery. The incidence of PLSP in the intervention group was significantly lower than that in the control group (8.6% vs 31.4%, P < 0.05; Table 2). The median interquartile range (in centiles) of shoulder pain score immediately after extubation, 2, 6, 12, 24, 48, and 72 h postoperatively in the intervention group was 0.0(0.0–0.0), 0.0(0.0–0.0), 0.0(0.0–0.0), 0.0(0.0–0.0), 0.0(0.0–0.0), 0.0(0.0–0.0), and 0.0(0.0–0.0) respectively. The value in the control group were 0.0(0.0–0.0), 0.0(0.0–0.0), 0.0(0.0–1.0), 0.0(0.0–2.0), 0.0(0.0–1.0), 0.0(0.0–1.0), 0.0(0.0–0.0), respectively. The median interquartile range of shoulder pain score 6, 12, 24, and 48 h postoperatively was significantly low in the intervention group as compared to control group (p = 0.018, 0.015, 0.019, 0.006 at 6, 12, 24 and 48 h, respectively, Table 2). In addition, the pain distribution was dominated in right shoulder and no difference was found in both groups (intervention group vs control group, 63.6% vs 66.7%, P > 0.05). The postoperative abdominal incisional and visceral pain at all time-points were comparable between the two groups (Table 2).Table 2 Shoulder, Incisional and Visceral Pain Score in Both Groups
Variable Intervention Group (n = 35) Control Group (n = 35) P value
Shoulder pain
NRS score immediately after extubation 0.0(0.0–0.0) 0.0(0.0–0.0) 1
NRS score 2 h after surgery 0.0(0.0–0.0) 0.0(0.0–0.0) 0.3
NRS score 6 h after surgery 0.0(0.0–0.0) 0.0(0.0–1.0) 0.018
NRS score 12 h after surgery 0.0(0.0–0.0) 0.0(0.0–2.0) 0.015
NRS score 24 h after surgery 0.0(0.0–0.0) 0.0(0.0–1.0) 0.019
NRS score 48 h after surgery 0.0(0.0–0.0) 0.0(0.0–1.0) 0.006
NRS score 72 h after surgery 0.0(0.0–0.0) 0.0(0.0–0.0) 0.07
Incisional pain
NRS score immediately after extubation 3.0(1.0–5.0) 4.0(2.5–5.0) 0.8
NRS score 2 h after surgery 3.0(0.0–5.0) 3.0(2.0–5.0) 0.7
NRS score 6 h after surgery 3.0(1.5–5.0) 3.0(1.5–3.0) 0.1
NRS score 12 h after surgery 2.0(0.0–3.0) 2.0(0.0–3.0) 0.8
NRS score 24 h after surgery 1.0(0.0–3.0) 1.0(0.0–3.0) 1
NRS score 48 h after surgery 0.0(0.0–1.0) 0.0(0.0–0.5) 0.8
NRS score 72 h after surgery 0.0(0.0–0.0) 0.0(0.0–0.0) 0.8
Visceral pain
NRS score immediately after extubation 0.0(0.0–3.0) 0.0(0.0–0.0) 0.3
NRS score 2 h after surgery 0.0(0.0–3.0) 0.0(0.0–2.5) 0.8
NRS score 6 h after surgery 0.0(0.0–2.5) 0.0(0.0–2.0) 0.8
NRS score 12 h after surgery 0.0(0.0–2.5) 0.0(0.0–3.0) 0.5
NRS score 24 h after surgery 0.0(0.0–2.0) 0.0(0.0–2.5) 0.7
NRS score 48 h after surgery 0.0(0.0–2.0) 0.0(0.0–2.5) 0.6
NRS score 72 h after surgery 0.0(0.0–1.0) 0.0(0.0–1.0) 0.8
Note: Values are presented as Median (IQR) 50th percentile (25th–75th).
Abbreviation: NRS, numerical rating scale.
There were also no significant differences in arterial blood gas analyses (P > 0.05; Table 3). One patient in the intervention group presented with symptoms of weakness within 6h after the surgery, and spontaneously resolved without any specific treatment within 12 h. We did not observe any other complications.Table 3 Marker of Blood Gas Analysis in the Two Groups
Variable Intervention Group (n = 35) Control Group (n = 35) P value
PH 7.41±0.07 7.35±0.05 0.5
HCO3- 25.0±1.4 23.9±1.6 0.7
K+ 3.4±0.2 3.4±0.2 0.8
PO2 94.2±10.5 100.2±13.6 0.3
PCO2 38.4±4.8 42.5±6.9 0.9
Abbreviations: PH, potential of hydrogen; HCO3-, bicarbonate ion; K+, kalium anion; PO2, partial pressure of oxygen; PCO2, partial pressure of carbon dioxide.
Discussion
In the current study, sodium bicarbonate sub-diaphragmatic irrigation reduced the incidence and severity of PLSP during 3 days after TLH. The result is consistent with the study by Saadati K et al,15 in which peritoneal irrigation with sodium bicarbonate reduced the intensity of PLSP in patients undergoing laparoscopic cholecystectomy. On the other hand, the administration of sodium bicarbonate did not show an impact on incisional and visceral pain following surgery. Additionally, we did not found a significant difference in blood gas analysis and any serious complications. Therefore, sodium bicarbonate sub-diaphragmatic irrigation is an effective and safe method to prevent shoulder pain following laparoscopic hysterectomy.
Five aetiologies have been proposed for the underlying cause of PLSP including carbonic acid production, microvascular peritoneal hemorrhages, peritoneal dehydration and damage, visceral ligament traction, and neuropraxia.11 Besides, arm abduction and muscle pain associated with succinylcholine, a depolarizing muscle relaxant, might be relevant to shoulder pain following follow laparoscopy.9 However, none of the above-mentioned reasons can alone explain the phenomenon.
Previous studies mostly focused on the reversion of the physical effects of CO2 pneumoperitoneum. Increasing intra-abdominal pressure during procedure causes the loss of a “suction” effect between the liver and diaphragm, allowing traction on the triangular and coronary ligaments of the liver that leads to PLSP.9,11,16 The patients undergoing low-pressure laparoscopic surgery had significantly lower PLSP scores.17 Moreover, the volume of residual pneumoperitoneum has been proved as a contributing factor and positively correlated with the intensity of PLSP.18,19 Therefore, evacuating residual CO2 to prevent PLSP is likely the first precaution. Varied approaches based on removing CO2 include pulmonary recruitment maneuver (PRM),20–22 intraperitoneal normal saline infusion,23 and both combined.24,25 Furthermore, the application of body position change to facilitate gas drain is also effective to reduce PLSP.26–28 However, the therapeutic potential of all these strategies needs support from the evidence from large-size sample clinical studies.
Some other studies investigated the role of local intraperitoneal anesthesia in the reduction of PLSP.29–31 Local intraperitoneal administration of anesthetic might cause a reversible interruption of nervous conduction, subsequently, inhibit the visceral afferent signaling, and reduce the shoulder pain. However, the results of these studies was not consistent.31 Local anesthetic toxicity and post-operative nausea and vomiting remain the main reason limiting its further application.32 Phrenic nerve block targeted on the irritation of CO2 insufflation to the sub-diaphragmatic intra-peritoneal surface has also been shown to prevent PLSP effectively.33
In our study, the incidence of PLSP in the saline group was 31.4%, lower than 80% reported in some known literature.4 We contributed it to strict patient selection, excellent anesthesia and analgesia program, experienced gynecologists, and appropriate pneumoperitoneum pressure. Intra-peritoneal normal saline infusion could also remove CO2 from the abdominal cavity to decrease PLSP.23 The different distribution of pain between left and right shoulders indicated that the acid intra-peritoneal environment caused by CO2 insufflation was not the only cause of shoulder pain. PLSP is therefore assumed as a multifactorial result in nature.
Homogenizing patients to highlight the role of sodium bicarbonate sub-diaphragmatic irrigation was the merit of the study design. However, there were some limitations in the study. First of all, we did not analyze the sub-population of age and duration of operative time, which had been reported as the risk factors of PLSP.34 Secondly, TLH related perineal pain be not included in the present study despite of causing discomfort to patients to some extent. Furthermore, the sample size was still small, although the number had been calculated in a statistical manner.
In summary, sub-diaphragmatic irrigation with sodium bicarbonate could decrease the intensity of shoulder pain during 3 days following TLH, but it had no impact on incisional and visceral pain without an increase in side effects. Our study indicated that sodium bicarbonate sub-diaphragmatic irrigation might be an effective and safe method to reduce PLSP.
Acknowledgments
This work was supported by the General Program of Health Commission of Wuxi (grant number, MS201934) and Double hundred top talent project of Wuxi (grant number, HB2020109).
Disclosure
The authors report no conflicts of interest in this work.
==== Refs
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15. Saadati K, Razavi MR, Salman DN, et al. Postoperative pain relief after laparoscopic cholecystectomy: intraperitoneal sodium bicarbonate versus normal saline. Gastroenterol Hepatol Bed Bench. 2016;9 (3 ):189–196.27458511
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17. Sroussi J, Elies A, Rigouzzo A, et al. Low pressure gynecological laparoscopy (7mmHg) with AirSeal ® System versus a standard insufflation (15mmHg): a pilot study in 60 patients. J Gynecol Obstet Hum Reprod. 2017;46 (2 ):155–158. doi:10.1016/j.jogoh.2016.09.003 28403972
18. Song T, Kim KH, Lee KW. The intensity of postlaparoscopic shoulder pain is positively correlated with the amount of residual pneumoperitoneum. J Minim Invasive Gynecol. 2017;24 (6 ):984–989. doi:10.1016/j.jmig.2017.06.002 28602786
19. Sabzi Sarvestani A, Zamiri M. Residual pneumoperitoneum volume and postlaparoscopic cholecystectomy pain. Anesth Pain Med. 2014;4 (4 ):e17366. doi:10.5812/aapm.17366 25599023
20. Pergialiotis V, Vlachos DE, Kontzoglou K, et al. Pulmonary recruitment maneuver to reduce pain after laparoscopy: a meta-analysis of randomized controlled trials. Surg Endosc. 2015;29 (8 ):2101–2108. doi:10.1007/s00464-014-3934-7 25361653
21. Lee J, Park C, Kim J, et al. Effect of low-pressure pulmonary recruitment maneuver on postlaparoscopic shoulder pain: randomized controlled trial. J Minim Invasive Gynecol. 2020;27 (1 ):173–177. doi:10.1016/j.jmig.2019.03.020 30930211
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25. Kumari A, Rajaram S, Gupta B, et al. Assessment of a combination of clinical maneuvers in evaluation of post-laparoscopic pain: a randomized clinical trial. J Obstet Gynaecol India. 2019;69 (5 ):444–450. doi:10.1007/s13224-019-01224-4 31598048
26. Kiyak H, Yilmaz G, Necmiye A. Semi-Fowler positioning in addition to the pulmonary recruitment manoeuvre reduces shoulder pain following gynaecologic laparoscopic surgery. Wideochir Inne Tech Maloinwazyjne. 2019;14 (4 ):567–574. doi:10.5114/wiitm.2019.84384 31908704
27. van Dijk JEW, Dedden SJ, Geomini PMAJ, et al. POstLAparoscopic Reduction of pain By combining intraperitoneal normal salinE And the pulmonary Recruitment maneuver (POLAR BEAR trial). RCT to estimate reduction in pain after laparoscopic surgery when using a combination therapy of intraperitoneal normal saline and the pulmonary recruitment maneuver. BMC Womens Health. 2017;17 (1 ):42. doi:10.1186/s12905-017-0397-8 28610572
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29. Kaushal-Deep SM, Anees A, Khan S, Khan MA, Lodhi M. Randomized controlled study of intraincisional infiltration versus intraperitoneal instillation of standardized dose of ropivacaine 0.2% in post-laparoscopic cholecystectomy pain: do we really need high doses of local anesthetics-time to rethink! Surg Endosc. 2018;32 (7 ):3321–3341. doi:10.1007/s00464-018-6053-z 29340809
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==== Front
Acta Paediatr
Acta Paediatr
10.1111/(ISSN)1651-2227
APA
Acta Paediatrica (Oslo, Norway : 1992)
0803-5253
1651-2227
John Wiley and Sons Inc. Hoboken
35150457
10.1111/apa.16287
APA16287
Brief Report
Brief Reports
Covid‐19
Seroprevalence and T‐cell response in 32 children 10 months after COVID‐19
MORITZ et al.
Moritz David 1
Krifors Anders 2 3 4
Freyhult Elisabeth 5
Månsson Emeli https://orcid.org/0000-0003-2867-1044
2 3 6 [email protected]
1 Department of Paediatrics Uppsala University Uppsala Sweden
2 Department of Infectious Diseases Västmanlands Hospital Västerås Sweden
3 Centre of Clinical Research Region Västmanland Uppsala University Uppsala Sweden
4 Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden
5 Department of Microbiology Västmanlands Hospital Västerås Sweden
6 School of Medical Sciences Örebro University Örebro Sweden
* Correspondence
Emeli Månsson, Department of Infectious Diseases, Västmanlands Hospital Västerås, 721 89 Västerås, Sweden.
Email: [email protected]
15 2 2022
5 2022
111 5 10.1111/apa.v111.5 10421043
16 1 2022
02 11 2021
10 2 2022
© 2022 The Authors. Acta Paediatrica published by John Wiley & Sons Ltd on behalf of Foundation Acta Paediatrica.
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Region Västmanland ‐ Uppsala University, Centre for Clinical Research, Västmanlands hospital Västerås source-schema-version-number2.0
cover-dateMay 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
==== Body
pmcAbbreviations
anti‐N IgG immunoglobulin G directed at SARS‐CoV‐2 nucleocapsid protein
anti‐S IgG immunoglobulin G directed at SARS‐CoV‐2 spike protein
BAU binding antibody unit
CMIA chemiluminescent microparticle immunoassay
COVID‐19 coronavirus disease 2019
ELISpot enzyme‐linked immunospot
IgG immunoglobulin G
INF interferon
nAb neutralising antibody
number n
ORF open‐reading frame
PCR polymerase chain reaction
SARS‐CoV severe acute respiratory syndrome coronavirus
SNMO spike nucleocapsid membrane open‐reading frame
Protective cellular and humoral immune responses to SARS‐CoV‐2 have been found to persist 1 year after recovery from COVID‐19 in adults. 1 Less is known about the kinetics of SARS‐CoV‐2 antibodies and T‐cell memory response in children. We determined IgG antibodies against spike protein (anti‐S), nucleocapsid protein (anti‐N) and IFN‐γ T‐cell response 10 months after PCR‐confirmed COVID‐19 in 32 children aged 1–18 years.
All patients, <19 years of age, with a PCR‐confirmed SARS‐CoV‐2 infection May–July 2020 in Region Västmanland, Sweden, were invited to participate in the study. After written informed consent, blood samples were collected 10 months (±7 days) after the positive PCR test. A short questionnaire was completed at the time of blood sampling. For qualitative detection of anti‐N IgG, a chemiluminescent microparticle immunoassay (CMIA) was used (SARS‐CoV‐2 IgG, Abbott). An index of the absorbance of the specimen and calibrator ≥1.1 was reported as positive. For semi‐quantitative detection of anti‐S IgG, a CMIA was used (SARS‐CoV‐2 IgG II Quant assay, Abbott). A signal/cut‐off ratio ≥14 BAU (binding antibody unit)/ml was reported as positive. The T‐cell responses to peptide pools from spike protein (S1), spike (S1 and S2), nucleoprotein, membrane protein and open‐reading frame proteins (SNMO) were assessed using an enzyme‐linked immunospot assay (ELISpot Confirm kit: SARS‐CoV‐2 (T‐cells and INF‐ƴ), Mabtech, Sweden) following manufacturers’ instruction, using freshly prepared peripheral blood monocytes. A positive control (anti‐CD3 mAb) and a negative control well was set up for each individual. A positive T‐cell response (towards either S1 or SNMO) was defined as a well with >7 spots after subtraction of the number of spots in the negative control well and with at least two times the number of spots in the negative control well. T‐cell analysis was performed by abclabs, Solna, Sweden, whereas serology was performed at the Department of Microbiology, Region Västmanland, Sweden. The study was approved by the Swedish Ethical Review Board (Dnr 2020–06283).
Thirty‐two out of 52 eligible children were included in the study. The median age was 13.5 years (range 1.5–18 years). 19/32 (62%) participants were female. The most commonly reported symptoms were fever (23/32), fatigue (13/32), cough (12/32) and anosmia/ageusia (12/32). No child was hospitalised due to COVID‐19. No re‐infections were encountered during follow‐up (10 months ± 7 days). The presence of anti‐N IgG, anti‐S IgG (including binding antibody unit [BAU] levels) and T‐cell response towards SARS‐CoV‐2 antigens is presented by age‐group in Table S1. Anti‐S IgG was detected in 25/32 (78%) of the children and anti‐N IgG in 2/32 (6%). A T‐cell response towards SARS‐CoV‐2 S1 was demonstrated in 17/32 (53%) and towards the combined SMNO‐peptide pool in 20/32 (63%), with a concordance rate of 78%. All but two children with a T‐cell response (to either the S1 peptide pool or the SMNO‐peptide pool) were positive for anti‐S IgG (Figure 1).
FIGURE 1 Overlapping immune responses in a cohort of 32 children with PCR‐confirmed COVID‐19. anti‐N, directed against SARS‐CoV‐2 nucleocapsid protein; anti‐S, directed against SARS‐CoV‐2 spike protein; IgG, immunoglobulin G; S1, spike protein domain 1; SNMO, spike nucleocapsid membrane open‐reading frame
In this study, 78% of the children had positive anti‐S IgG 10 months after SARS‐CoV‐2 infection, but only 2/32 were positive for anti‐N IgG. A T‐cell response was detected in two‐thirds of the children and almost completely overlapped the presence of anti‐S IgG.
It has been shown that SARS‐CoV‐2 infected children predominantly develop antibodies towards S1, and that the level of anti‐S IgG correlates with neutralising activity. 2 , 3 Neutralising antibodies (nAbs) persist 8 months after COVID‐19 in children, whereas antibodies directed at a combined target of spike and nucleocapsid proteins are largely undetectable after 7–9 months. 2 This is in line with the almost nonexistence of positive anti‐N IgG in this cohort of PCR‐confirmed mild cases of COVID‐19 and favours anti‐S IgG assays in children when investigating humoral immunity months after presumed infection. However, the discrepancy between anti‐S and anti‐N could also be related to low sensitivity of the anti‐N assay in late convalescent sera.
The nature of long‐term T‐cell response has, to our knowledge, not previously been presented after PCR‐confirmed SARS‐CoV‐2 infection in children. Robust T‐cell response to SARS‐CoV has been found to persist 17 years after the SARS outbreak in 2003, which suggested that even though antibody levels to SARS‐CoV‐2 are waning with time, T‐cell immunity may still persist. In adults, the frequency of T‐cell response to SARS‐CoV‐2 decreased six months posthospital discharge but then remained stable at 79% at 12 months. 1 A Swedish study on young adults found that 67% of IgG positive individuals demonstrated a positive T‐cell response up to 6–7 months after presumed infection. 4 Available data indicate that children to a lesser extent than adults demonstrate T‐cell response towards SARS‐CoV‐2 early (weeks) after infection (83% vs. 100%). 5 As previously suggested, this difference could be related to age and/or disease severity. 4 We found that 68% of children demonstrated T‐cell response to SARS‐CoV‐2 antigen at ten months, indicating long‐lasting cellular immune response.
Limitations of this study include the small number of participants, the lack of a control group and the absence of an assay to determine nAbs.
To summarise, this study indicates a persistent immunity of at least ten months after natural infection for most children and suggests that a long‐lasting T‐cell response may be present in the majority of children after mild infection.
CONFLICT OF INTEREST
The authors have no conflicts of interest to disclose.
Supporting information
Table S1
Click here for additional data file.
ACKNOWLEDGEMENT
The authors would like to thank the paediatric nurses at the Department of Peadiatrics, Hospital of Västmanland, Västerås.
==== Refs
REFERENCES
1 Feng C , Shi J , Fan Q , et al. Protective humoral and cellular immune responses to SARS‐CoV‐2 persist up to 1 year after recovery. Nat Commun. 2021;12 (1 ):4984.34404803
2 Bonfante F , Costenaro P , Cantarutti A , et al. Mild SARS‐CoV‐2 infections and neutralizing antibody titers. Pediatrics. 2021;148 (3 ):e2021052173.34158312
3 Weisberg SP , Connors TJ , Zhu Y , et al. Distinct antibody responses to SARS‐CoV‐2 in children and adults across the COVID‐19 clinical spectrum. Nat Immunol. 2021;22 (1 ):25‐31.33154590
4 Bjorkander S , Du L , Zuo F , et al. SARS‐CoV‐2‐specific B‐ and T‐cell immunity in a population‐based study of young Swedish adults. J Allergy Clin Immunol. 2022;149 (1 ):65‐75.e8.34695490
5 Kaaijk P , Pimentel VO , Emmelot ME , et al. Children and adults with mild COVID‐19 symptoms develop memory T cell immunity to SARS‐CoV‐2. medRxiv. 2021. doi:10.1101/2021.09.10.21263333
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==== Front
Environ Plan B Urban Anal City Sci
Environ Plan B Urban Anal City Sci
spepb
EPB
Environment and Planning. B, Urban Analytics and City Science
2399-8083
2399-8091
SAGE Publications Sage UK: London, England
10.1177_23998083221113332
10.1177/23998083221113332
Special Issue Articles
Impact of COVID-19 policies on pedestrian traffic and walking patterns
Yao X. Angela
Crooks Andrew
Jiang Bin
Krisp Jukka
Liu Xintao
Huang Haosheng
https://orcid.org/0000-0001-5005-0100
Angel Avital
26747 Faculty of Architecture and Town Planning, Technion–Israel Institute of Technology , Haifa, Israel
https://orcid.org/0000-0002-1357-8296
Cohen Achituv
https://orcid.org/0000-0002-5639-8009
Dalyot Sagi
26747 Faculty of Civil and Environmental Engineering, Technion–Israel Institute of Technology , Haifa, Israel
Plaut Pnina
26747 Faculty of Architecture and Town Planning, Technion–Israel Institute of Technology , Haifa, Israel
Avital Angel, Urban and Regional Planning Department, Faculty of Architecture and Town Planning, Technion - Israel Institute of Technology, Haifa, Israel. Email: [email protected]
11 7 2022
6 2023
11 7 2022
50 5 Special issue: Urban Analytical Approaches to Combatting Covid-19 11781193
© The Author(s) 2022
2022
SAGE Publications
https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
The spread of COVID-19 pandemic provoked new policies and restrictions, which had an unprecedented impact on urban mobility and traffic on local and global scales. While changes in motorized traffic were investigated and monitored throughout the recent pandemic crisis in many cities around the world, not much was done on the changes in pedestrian street-traffic and walking patterns during this time. This study aims to identify, quantify, and analyze the changes in pedestrian traffic and walking patterns induced by COVID-19 policies. The “first wave” period of COVID-19 policies in Tel-Aviv, Israel, is used as a case study in this work. The analysis includes over 116 million pedestrian movement records documented by a network of 65 Bluetooth sensors, between 1.2.2020 and 26.7.2020, with a comparison to the equivalent time in 2019 that signifies “normal” pre-COVID-19 conditions. The results show clear correlation between the various COVID-19 policy restrictions and pedestrian count. The shifts to work-from-home and closure of businesses were highly correlated with changes in walking patterns during weekdays, while distinguishing changes in commercial and residential street segments. Nevertheless, while the restrictions dramatically influenced pedestrian movement volume and time of walking, it did not significantly change where people chose to walk, signifying the essentialness of attractive streets, parks and squares for citizens living in urban areas. This study shows how policy affects walking behavior in cities, demonstrating the potential of passive crowdsourced sensing technologies to provide urban planners and decision makers an efficient tool for monitoring and evaluating pedestrian infrastructure implementation in cities.
COVID-19 policy
pedestrian traffic
walking patterns
crowdsourced big data
Bluetooth sensor network
The Israeli Smart Transportation Research Center 2029717 typesetterts10
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pmcIntroduction
The spread of COVID-19 pandemic around the globe is one of the most impactful events of the 21st century. In early stages, as medical interventions were limited to treat the virus and a vaccine was yet to be developed, a variety of non-pharmaceutical policy decisions were applied by governments around the world. These varied from advisory recommendations to legal enforcements, such as social distancing, lockdowns, closure of educational institutes and businesses, bans on gatherings, restrictions on local and international travel—and more (Shakibaei et al., 2020). Among other profound impacts of these restrictions on all areas of life, the new policies have had an unprecedented effect on people’s mobility on local and global scales (Bian et al., 2021; Parady et al., 2020).
While changes in motorized traffic were investigated throughout the recent pandemic crisis in many cities around the world (De Haas et al., 2020; Parady et al., 2020; Shakibaei et al., 2020; Shamshiripour et al., 2020), research on changes in pedestrian street-traffic and walking patterns during this time is limited. Few studies explored the effect of the pandemic on physical activity in general, including—to some extent—on the walking activity (Ács et al., 2020; Alomari et al., 2020; Askarizad et al., 2021; Dunton et al., 2020). Yet, the investigation of the magnitude and spatio-temporal aspects of these changes on pedestrian traffic is still limited (Hunter et al., 2020).
This paper aims at scrutinizing the impact of COVID-19 pandemic policies on pedestrian traffic and walking patterns, addressing two main research questions: (1) How, and to what extent, did the COVID-19 policies influence pedestrian traffic volumes? (2) What changes have occurred in walking hours and spatial distribution of pedestrians during the time of policy restrictions? Accordingly, we identify, quantify, and analyze the changes in pedestrian movement during the “first wave” period of COVID-19 policies in Tel-Aviv, Israel. The underlying principle of this study is that governmental policies, aiming at controlling and limiting the spread of the virus, have significant impacts on urban mobility in general, and particularly on pedestrian mobility in the public realm. The analyzed dataset is consisted of more than 116 million pedestrian records, collected by a network of 65 Bluetooth (BT) sensors. The data was recorded between 1.2.2020 and 26.7.2020 and was compared to “normal” pre-COVID-19 conditions for the equivalent period in 2019.
An epochs division was performed according to the varying legal enforcements and policy decisions given by the Israeli government, and the results showed a prominent association between these and the pedestrian traffic (volume) and walking patterns (frequency peaks). Additionally, the spatial distribution of pedestrians in the study area was analyzed for each policy phase while identifying popular streets. Accordingly, we discuss the influence of policy on how many, when and where people walk in the city, and suggest that decision makers can use policy as a powerful tool to affect walking behavior. Moreover, this work illustrates the great potential of ubiquitous BT technology in monitoring pedestrians’ behavior and preferences and in supporting planning decisions.
Background
Research on pedestrian movement in the built environment is of growing interest in recent years, mainly due to increasing awareness of sustainability and health matters (Sharmin and Kamruzzaman, 2018). Studies in the field have used mostly surveys, manual observations, accelerometers, and pedometers to document walking (Ferrer and Ruiz, 2018; Kim et al., 2019; Mitra et al., 2015); however, these methods are limited in scale, time, and financial resources. The emergence of new technologies in recent years holds great potential for pedestrian documentation and research, yet relatively few studies have used crowedsourced data collection methods for pedestrian monitoring. For example, Bongiorno et al. (2019) used an activity-based mobile phone application to compare pedestrian and bicycling mobility patterns in Boston, US. The results of pedestrian traffic volume during the day showed frequency peaks at morning commute time, afternoon commute time, and at lunch time. Compared to cycling, pedestrian trips were found to be shorter, and mostly used to access locations in business and university districts. Similarly, Malleson et al. (2018) used a smartphone application to understand the asymmetry in pedestrian behavior and route choice in Boston, and showed a typical “9-to-5” working pattern with trip frequency peaks in the morning, at lunch, and again in the afternoon.
Moreover, Lee (2020) used hourly automated pedestrian count (PC) data of seven locations in New York City to analyze pedestrian walking patterns in the city. Unique patterns of foot traffic were associated with land use, development intensity, and the characteristics of the neighborhood. Additionally, different walking patterns, as well as pedestrian traffic volume, were found between weekday and weekend, indicating the divide between office and residential uses of the neighborhood. Finally, Salazar Miranda et al. (2020) used pedestrian trajectories obtained from GPS signals to construct a desirability index for streets in Boston. The study included an investigation of pedestrians’ willingness to deviate from their shortest path and found that desirable streets have better access to public amenities, have fewer complex facades, and have more diverse business establishments.
The technology used in this study for pedestrian monitoring is BT sensors. This technology has been vastly used for motorized-traffic monitoring in the past few years (Mercader and Haddad, 2019), while introducing advantages, such as the ability to collect large quantities of real-time data, the ease of sensor installation and privacy protection (Kurkcu and Ozbay, 2017; Lesani and Miranda-Moreno, 2019). However, only few studies used BT technology for pedestrian tracking, all in small-scale systems (comprised of several detectors), and often in areas with limited transportation modes (Lesani and Miranda-Moreno, 2019; Malinovskiy et al., 2012; Zhao et al., 2019).
The effect of COVID-19 pandemic on walking in urban spaces was investigated in recent studies, mostly evaluating the pandemic’s impact on physical activity in general (Dahlberg et al., 2020; Stockwell et al., 2021). These studies are usually based on survey data. For example, Dunton et al. (2020) used surveys and a built-in accelerometer application to observe early effects of the COVID-19 pandemic on physical activity locations and behaviors of 268 adults in the United States, during April to May 2020. The results showed a reduction in walking activity, ranging between 20.7% and 45.7%, during the mentioned period; still, the researchers observed an increase in steps count by people who were active on the roads in their neighborhood or at parks/trails. Another study, by Alomari et al. (2020), surveyed changes in physical activity and sedentary behavior induced by COVID-19 lockdown among 1844 participants in Jordan, and found similar results, as the majority of the participants reported a “decrease” in walking, jogging, and sports during April and May 2020. Changes in physical activity due to COVID-19 pandemic were also examined by observing the activity of 827 university students in Hungary, by Ács et al. (2020), who found a reduction in reported physical activity during April and May 2020.
Various studies have investigated COVID-19 impact on travel behavior in general (Yabe et al., 2020), and more often specifically on car and public transportation usage. When compared to pre-COVID-19 period, car usage was reported with remarkable reductions in many cities around the world, as some cities experienced declines of over 80% (e.g. Milan, Rome, Barcelona, Madrid, Paris) and around 70% (e.g. Moscow, New York, London, Boston, Lisbon) in car traffic in March 2020 (Shakibaei et al., 2020). Others, for example, Australian cities, experienced 35% drop in car use (Beck and Hensher, 2020). A similar trend was reported in public transport use. Jenelius and Cebecauer (2020), for example, found 40%–60% decrease in public transport ridership in three regions of Sweden, based on ticket validation data, during spring 2020. In Budapest, Hungary, public transportation ridership was reported with 80% reduction during the time of mobility restrictions (Bucsky, 2020). Moreover, a substantial decline of ∼90% in public transport usage was observed across many European countries, such as the Netherlands (De Haas et al., 2020), France and Italy (Falchetta and Noussan, 2020), with some variations across cities.
A different trend was reported on cycling during the pandemic. Jenelius and Cebecauer (2020), for example, found no clear decline in bike flows in Sweden during the COVID-19 pandemic (compared with the previous year), and even an increase in Stockholm’s outer city. Similarly, Bucsky (2020) reported only 2% reduction in bike sharing demand in Budapest, with a 23% decrease in cycling.
Little research was done on the effect of COVID-19 pandemic specifically on pedestrian traffic and walking patterns, yet few studies observed pedestrian movement in small-scale areas with a limited data sample. One example is Askarizad et al. (2021), who counted (using the gate count method) pedestrians in seven locations of urban public spaces in Rasht, Iran, before and after the onset of COVID-19, and showed no significant difference between people’s use of urban spaces. At a larger scale, a study by Hunter et al. (2020) investigated walking patterns of users using their mobile devices in 10 metropolitan areas in the United States before, during and after the lockdown policy. Declines at a rate of ∼30% in the number of walks were found, as well as changes in the common walking patterns.
Methodology
Study area and data
The study area is situated in Tel-Aviv, the second largest city in Israel, with over 450,000 citizens (Tel Aviv-Jaffa Municipality, 2019). The city constitutes an important financial, cultural, and commercial hub and the center of the largest metropolitan area in Israel (Reznik et al., 2019). The study area (Figure 1), approximately an area of 10 sq km, is part of the central district of the city, bordered by Rokah boulevard and Shai Agnon st. in the north, Shaul Hamelech st. and Frishman st. in the south, Ayalon road in the east and Hayarkon st. in the west.Figure 1. Study area—left: general location of Tel-Aviv city center; right: the 65 Bluetooth sensors and 77 operating network links (street segments).
The BT network used in this research consists of 65 BT sensors, placed at fixed locations at road intersections in the study area. The sensors record and monitor encrypted BT Media-Access-Control (MAC) addresses (sent from mobile phones, headsets, etc.) 24\h a day on 77 street segments, referred to as network “links.” Data were collected from 1.2.2020 to 26.7.2020 and for the equivalent time in 2019 (1.2.2019–30.7.2019); the latter is used for baseline comparison to “normal” pre-COVID-19 conditions.
Pedestrian classification
The raw data file recorded by the BT sensors contains travel time information of all BT users traversing the street. To identify pedestrians and filter all other transportation modes, a classification algorithm was applied, which showed high accuracy rate of 89% in classifying pedestrian movement across the BT network. The classification algorithm relies on the statistical approximation of velocity values, and includes these stages:1. System calibration: The detection range and direction for each sensor in the system is different and changes; hence, the active length (traveled distance) between two adjacent sensors cannot be accurately determined. To overcome this, we established an Effective Link Length (ELL) calculation for all network links, by performing a one-time system calibration. This included walking and driving passes (measurements) on the different network links, while collecting GNSS data, compared with the time stamp (TS) data recorded by the BT sensor system. Accordingly, the traveled distance between adjacent sensors was calculated, resulting in average and standard deviation (STD) ELL values for each link, later used for the calculation of traveling velocity values.
2. Effective travel time calculation: For calculating the traveler’s travel time, that is, considering movement time on the link while minimizing wait (stop) time near intersections, we used two TSs recorded by the adjacent sensors: TS of leaving the sensor detection range of one intersection (i) and TS of arriving to the sensor detection range of the adjacent intersection (j) (see equation (1)).
(1) ΔTij(MACx)=Enj(MACx)−Exi(MACx)
where Enj(MACx) stands for the “enter” TS of MACx recorded by sensor j, Exi(MACx) stands for the “exit” TS of MACx recorded by sensor i, and ΔTij is the travel time calculated for link ij for the current record.3. Classification by velocity: For each record, we calculate the velocity and its STD value as a function of the corresponding ELL (see section 1-) and TS—according to the error propagation law 1 . Moreover, we considered an error size, which was subtracted from the calculated velocity. The error size is defined by the velocity’s STD value multiplied by a constant parameter (defined as k). Accordingly, the velocity range calculated is (velocity-(STD*k), velocity). After calculating the record’s velocity range, we classified it according to the presumable velocity of pedestrians. Based on the vast literature on travel mode classification (Fitzpatrick et al., 2007; Pinna and Murrau, 2018; Zhang et al., 2012; Zheng et al., 2016), we used the value of 1.5 m/s as the maximum traveling speed of pedestrians.
4. Traffic jam probability: Finally, to avoid bias resulting from slow-moving vehicles in traffic jams wrongly classified as pedestrians, we removed records with low velocity value with the probability of vehicular traffic.
After applying the classification process, we remain with a workable log-file of pedestrian records only. Thus, we calculate the PC value per link for any given date and time (Table 1). It should be noted that the PC value represents the pedestrians recorded by the BT network.Table 1. An example of hourly calculated PC value on several links between 00:00 a.m. and 10:00 a.m. on 02.03.2020.
Link ID Hour
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00
TA105TA83 391 246 174 160 159 258 702 1140 1222 1184 1248
TA122TA251 933 869 848 864 845 871 921 1188 1331 1278 1250
TA124TA151 28 11 6 5 13 39 130 503 632 530 449
Note: PC: Pedestrian count.
Epochs division
To understand how government policies impact pedestrian traffic and walking patterns, we suggested looking at specific epochs that define the policy evolution derived by the COVID-19 spread. Accordingly, we divided the data into eight phases, defined based on the beginning or ending enforcement time of policy restrictions directed by the Israeli government and the Israeli Ministry of Health, as specified in Table 2. These include time periods consist of pre-COVID-19 (phase I), gradual restriction policies (phases II–III), quarantine (phases IV–VI), and exit-quarantine (phases VII–VIII). Additionally, weekends and holidays are considered separately because pedestrian traffic and walking patterns may be affected differently on these days than during the week.Table 2. Policy changes according to the proposed epochs division.
Phase Dates (in 2020) Policy changes
I Before COVID-19 outbreak in Israel Feb. 1 - March 10
II COVID-19 precaution policy March 11–14 Limited gatherings of up to 100 people
III COVID-19 advanced prevention policy March 15–24 limited gatherings of up to 10 people in closed spaces
Closure of all educational institutions
Closure of all leisure venues (malls, restaurants, pubs, clubs, gyms, pools, beaches, and nature reserves)
IV Lockdown policy March 25 – April 7 and April 11–27 Prohibition to stay more than 100 m (330 ft) from home, except for defined “essential” purposes, such as purchasing food, medicine, essential products, and receiving essential services
V Nationwide Passover Seder curfew April 8–11 Citizens were forbidden to leave their home
VI Nationwide Independence Day curfew April 28–29 Citizens were forbidden to leave their home
VII COVID-19 exit-quarantine policy, ease of most restrictions April 30–May 26 Restrictions on mobility were removed
Businesses were gradually reopened
Children and youth have gradually returned to educational institutions under restrictions
Malls, libraries, parks, nature reserves and zoos were reopened
VIII Witnessing a second COVID-19 pandemic wave May 26–July 26 Gatherings were limited to 50 people in open spaces and to 20 people indoors
Clubs, event venues, and gyms were closed again, while restaurants were open with a limited number of customers
Data analysis
The analysis focuses on:1. Finding correlations between COVID-19 restriction policies and PCs by examining the daily average PC over the predefined periods for each network link and for the entire study area. In addition, we compare the resulting values to the equivalent period in the previous year prior to the COVID-19 onset. This is also perceived by defining five Level(s) of Change (LC) in PC, which represents the ratio (in percentage) between PCs in the observed period and those in the equivalent period (equation (2)):
(2) x=pedestrian counts of the examined periodpedestrian counts of the equivalent period * 100
LC(x)={significant LC, x < 25%major LC, 25% ≤ x < 50%medium LC, 50% ≤ x < 75%minor LC, 75% ≤ x < 100%reverse LC, 100%≤
2. Studying the different walking patterns by calculating separately for each link the average PC and LC values per hour and per restriction policy phase. Particularly, we aim to highlight the differences between commercial and residential links due to policy changes. We expect to find more significant changes in commercial links (compared with residential links) during the lockdown policy, as businesses were closed during that time, and maybe even a reverse LC in some residential links when movement was restricted to 100 m from home. The definition of a “commercial street” appears in previous studies mostly as a street designed primarily for commercial uses. For example, Li (2014) defined it as “where the space and buildings on the street are mainly for commercial uses”; Fan (2016) defined a commercial street as “a major street with commercial activities for a long time”; more recently, Young Hong and Yong Jeon (2020) used a definition of a street that is “primarily composed of retail stores and restaurants.” For analysis purposes, we chose to define links with active businesses covering 50% and more of its street facade as “commercial”, whereas the rest are classified as “residential”. Finally, the Mann–Whitney U test is used to determine whether there is a statistical significance between commercial and residential links in relation to the LC and PC values.
Results and discussion
Study area analysis
A total of 116,107,592 pedestrian records were classified and analyzed in the network for the defined phases. Figure 2(a)–(h) shows the calculated average PC values identified per network link in the study area according to the eight phases—2020 compared to the equivalent time in 2019—distinguishing between weekdays and weekends. LC values are given for each phase.Figure 2. (a–h). Daily average pedestrian count per network link in the study area, according to the eight analyzed policy phases in 2020 and 2019.
Pedestrian count
According to the Tel-Aviv census of 2021, the population change in Tel-Aviv in 2020 compared to 2019 was less than 0.7% 2 , mostly attributed to natural reproduction. Moreover, no new residence projects and industrial and commercial complexes were built during this period in the study area. Thus, we presume that there is no evident population change in terms of residents and travel to work. In relation to age distribution of smartphone users, according to the Israeli census, Israel shows similar values to North American and European countries, where there is a bias toward younger ages: in 2019, under the age of 64, 84–90% own a smartphone and above 65 this value is reduced to 73% 3 , while in 2020, these values are 90% and 82% 4 , respectively, showing an evident increase of smartphone usage for ages 65 and up. All other demographics, for example, gender and education, show similar smartphone usage.
The graphs in Figure 2 show a clear association between the different COVID-19 restriction policies and PC values in the study area. Before the initialization of the pandemic in Israel (phase I, Figure 2(a)), compared to 2019, the PC has increased by 9% and 12% during weekdays and weekends, respectively. We assume the increase in PC value is associated with the growing number of pedestrians who use BT-enabled devices over the course of the year, which is also age associated (more ages 65 and up who use smartphones); in general, we characterize changes of less than 10% as negligible. When the first restrictions are enforced (phase II, Figure 2(b)), there is still no discernible change in PC values during weekdays; however, a slight decrease of 8% on average during the weekends exists when compared to 2019. In the following time-period (phase III, Figure 2(c)), as an advanced prevention policy was enforced, a significant decrease in PC value is evident: 41% decrease over weekdays and 55% decrease over the weekends. Over a week, the daily average of PCs on weekdays has reduced to almost half.Figure 3. Pedestrian count values (hourly average for network link) in the study area, from 1.2.20 to 26.7.20 (weekdays).
Pedestrian count continued to decrease (52% over weekdays and weekends) in the following period (phase IV, Figure 2(d)), as all citizens were prohibited to stay more than 100 m away from their homes. In the middle of this period (phase V, Figure 2(e)), a nationwide curfew was enforced, with the aim of minimizing potential gatherings during the Passover holiday. During the curfew, a decrease of 63% in PC is observed, when compared to 2019. Moreover, while in the previous year a significant increase in PC was observed in the evening hours, as this is the time of gathering for the big “Seder”—feast of Passover, in 2020, the results show no change in PC at this time-of-day. This corresponds with the fact that, due to the curfew policy, families were not able to gather and celebrate the holiday together as they normally do every year. A second curfew was imposed during Israel’s Independence Day (phase VI, Figure 2(f)) and the PC resulted in a 48% decrease when compared to 2019. We suspect that the difference in counts between the two curfews is to some extent derived by the significant change in new positive patients in Israel during the first curfew (338–359 cases per day), compared with the time of the second curfew (82–160 cases per day 5 ). Accordingly, it is possible that stress among the public was lessened as the number of newly verified patients decreased, allowing people to feel more confident to wonder the streets.
In the following period (phase VII, Figure 2(g)), while restrictions were gradually removed, the results show a rapid increase in PC values to the ones in 2019, although still not as the values evident in phase I. Subsequently, Israel started to witness a second wave of the pandemic, as the number of verified COVID-19 patients increased again, and the government decided to impose new restrictions. Accordingly, in the next 2 months (phase VIII, Figure 2(h)), we observe once again a decrease in PC: 21% decrease during weekdays and 31% during the weekends, compared to 2019. This might indicate that despite the improved situation of controlling the virus spread at that time, the societal impact of the pandemic was evident as people were more concerned, restricting their mobility due to the “new reality” conditions.
Figure 3 shows the PC values in the study area (hourly average per network link) during weekdays for all eight analyzed phases. It is evident that as restrictions instituted (from Phase II), a significant decline in PC occurred, with further ongoing decrease during the curfews. During ease of most restrictions and the exit-quarantine policy, PC values increased; however, while it seems as there was a major increase in PC toward July, we note that it is lower than the PC in the equivalent time of 2019 (see Figure 2(h)), meaning that the numbers do not resume their original values of before the pandemic spread in the previous year corresponding to the summer time, which to some extent might show tourism activity in 2019 that is missing in 2020.
Compared with other transport modes in Israel, our results show a similar trend to car traffic reduction during the lockdown policy. For example, in Ayalon Road, which is the busiest highway in Israel, car traffic was minimized by 60% during the lockdown policy 6 . Larger decreases were reported in public transportation usage in Israel, as according to Hopon 7 company’s data, the usage during the lockdown minimized by 83%; however, it should be emphasized that during that time public transportation services operated in only 25% capacity. A different trend was reported during the first curfew policy, as Netivei Israel company reported a remarkable drop of 99% in road traffic across 8500 km of intercity roads 8 . During that time, police forces set up checkpoints between cities to prevent residents from moving between localities. To the best of our knowledge, changes in cyclist traffic during that time in Israel were not reported.
Our results show substantial reductions in PC compared with U.S. cities. Hunter et al. (2020) studied walking patterns in 10 metropolitan areas in the US and reported the largest reductions in Miami (−33.7%), Los Angeles (−33.3%), New York (−25.1%), and San Francisco (−24.8%), whereas we found reductions at a rate of almost twice in Tel-Aviv. The significant difference in numbers might be related to—among others—the different policies enforced at each city, as well as the concern among the public across diverse cultures. Previous studies that were based on self-reports reported small reductions in walking activity (e.g., De Haas et al., 2020) or general reductions without a quantitative measure (e.g., Alomari et al., 2020), while our work provides empirical evidence of pedestrian movement revealing substantial reductions of more than 60% in PC. Moreover, by dividing and distinguishing data over the different policy phases, this study enables witnessing changes at micro-level and observe its effectiveness on walking mode of travel.
Walking patterns
The patterns of pedestrian traffic can provide insights into how people use streets. One prominent observation found in the results is that walking patterns in the study area were significantly affected by the quarantine policy during COVID-19 pandemic. In 2019 (“normal” times), two distinct walking patterns are seen during the day, depicted in Figure 4 (left). The first pattern, over the weekdays, is usually characterized by a curve with “two-peaks,” in which one peak occurs during the morning commute time and the second (a longer one) occurs in the evening commute time. The second pattern, over the weekends, is of a curve with “one-peak” at mid-day. As policy restrictions intensified, these two distinct patterns are almost undistinguishable and close to being identical, depicted in Figure 4 (right). More specifically, as the restrictions tightened, the walking pattern over weekdays gradually turned into a curve with “one-pick,” similar to the one on the weekends (see for example the graphs of phase IV in Figure 2(d)). A similar trend was found in (Hunter et al., 2020), who showed that at the time of policy restrictions most of the walking activity in U.S. cities during weekdays resembled that of weekends. During that time, education institutes in Israel were closed, and many workplaces shifted to work from home or sent employees to a non-paid vacation, making them registered as “unemployed.” This corresponds with the rate of unemployed citizens in the city that leaped from 2.66% in February to 21.61% in March and 25.24% in April 2020 (Data Gov, 2020). Finally, when most of the restrictions were removed, we observed a fast return to “regular” walking patterns (see Figure 2(h)).Figure 4. Walking patterns during the day (on average): before the quarantine policy (left) and during the quarantine policy (right).
This observation indicates the significant and immediate impact of the policy restrictions on pedestrians’ traffic: not only in how many people walk the streets, but also in when they choose to walk them. The restrictions have logically modified the purposes behind people’s journeys, where education and work being the most prominent ones.
Street-level analysis
Following the study area-level analysis, PC was analyzed per link, shown in Figure 5 9 . The reduction in PC values is apparent in many links corresponding to the enforcement of new restrictions; however, some links maintain a relatively large PC value through all phases. Furthermore, when calculating for all phases the top 20 links with the largest PC values, we found that 15 of them appear in all, potentially pointing to these locations that continue to be highly attractive for pedestrians. These are mostly links that are within 200 m proximity to public open spaces (e.g., parks and squares) in the city. The most prominent example of such a place is Rabin Square 10 , an urban plaza surrounded by commercial facades with many eateries, which constitutes a space for leisure activities and gatherings. During policy enforcements, the number of pedestrians in the streets surrounding the plaza decreased; however, it remained large compared with other links in the study area, even though businesses around the plaza were closed during that time. This observation suggests that the plaza plays a significant role as an open and spacious public space in the heart of the city, which is within walking distance for many residents, allowing to meet other people and breathing “fresh air” while maintaining social distancing and adherence to the restrictions.Figure 5. Pedestrian count values during the different periods (hourly average during weekdays).
Accordingly, while the new restriction policy significantly influenced the volume of pedestrians and time they used the streets, it did not have the same impact on where they chose to walk, showing that policies aiming to avoid crowdedness did not seem to emanate even distributions of pedestrian traffic. This finding corresponds with existing literature suggesting that human mobility patterns and traffic distribution are mainly attributed to the underlying street network configuration (Jiang et al., 2009).
Figure 6(a) depicts the LC values per link during the lockdown policy (phase IV), showing that our expectation to find larger changes in PC in commercial links (compared with residential links) during this phase was partly realized. While close to 80% of the links with medium LC values on weekdays are residential, both commercial and residential links resulted with significant LC. Complementary to our expectation, two residential links were found with reverse LC. To statistically evaluate the difference in LC and PC between commercial and residential links, we used the Mann–Whitney U test and found no statistical significance at the p < 0.05 level (p = 0.37). Moreover, the test showed no statistical significance for all the other phases as well.Figure 6. LC values during phase IV on weekdays (a) and daily average of PC values per policy phase in commercial and residential links (b). *Commercial link = link with more than 50% of its façade is commercial. PC: Pedestrian count; LC: Level(s) of Change.
Nevertheless, PC values were found to be consistently higher in commercial links, compared with residential links, during all restriction policy phases (Figure 6(b)). Interestingly, the differences between the PC values in commercial and residential links were found to be insignificant during phases IV to VI, when the restrictions were most strict. In other words, while commercial links are regularly characterized by a larger number of pedestrians, when compared to residential links, the difference in counts between both street types minimized as the policy restrictions tightened, indicating the highest influence on PC values in commercial links during the lockdown policy.
The above might be explained by the fact that the streets in the study area are highly mixed land-use. Another reason might be that several commercial streets, such as Ibn-Gabirol and Ben-Yehuda, are artery links in the road network in the area, also serving as boulevards, and hence they were still used by pedestrians. Accordingly, while closure of businesses might reduce pedestrian traffic in commercial streets, a mobility distance restriction and navigation preferences might increase walking among these streets; therefore, it is difficult to determine what had a greater influence on the LC values.
Summary and conclusions
The spread of the COVID-19 pandemic provoked new policies and restrictions, which had an unprecedented impact on urban mobility and traffic on local and global scales. During the first wave of the virus spread in Israel, the Israeli government has pursued new policies, which included limited gatherings, travel distance restrictions, closure of businesses and leisure venues, lockdown, curfews—and more. This study investigated the impact of these policies on pedestrian street traffic and walking patterns in Tel-Aviv, while using over 116 million records of pedestrian movement recorded by BT sensor technology.
The first part of this work included a division to eight key policy phases that should have diverse effects on pedestrian movement. In the second part of this work, pedestrian traffic data and walking patterns were analyzed for each policy phase, both at area and street levels. The results showed a considerable impact of the COVID-19 policies on PC and walking patterns. Significant reductions in PC, ranging between 41% and 63%, were observed as the policy restrictions acted and tightened, together with a “recovery” in PC as soon as restrictions were removed. These prove that restriction policies were followed by the public, showing high correlation between the restriction type and the volume of pedestrian traffic.
A notable change in the walking pattern was observed during the weekdays, which transformed to behave similarly to weekends—from two evident daily peaks to one, meaning that pedestrians changed their common daily activity. Interestingly, while the policy affected walking volumes and patterns, it did not change the preferable locations of walking in the study area. Attractive streets, mainly around parks and squares, remained popular in relation to other links in the study area, proving their essentialness to citizens living in urban areas. These findings prompt further research on the characteristics of popular locations for pedestrians in the city during pandemic enforcements. Additionally, we documented the changes in pedestrian traffic during lockdown policies, while accounting for commercial and residential land-uses, and found an overall bigger influence on PC values in commercial links.
The administrative response to COVID-19 pandemic, which threatened the public health, has been reflected in decisive and swift restriction actions of governments around the world, which, as this study shows, have significantly affected pedestrian movement. Thus, the current reality creates an important momentum and an opportunity for the same governmental determination to make fundamental decisions to encourage walking and promote sustainable communities in cities. For example, at the peak of the first epidemic wave, some cities in Europe took immediate action to adapt the public realm to the new reality by, for instance, converting parking lanes to sitting areas or additional pavement area for pedestrians—as this research showed is sought by the public.
Ubiquitous BT sensor technology introduces advantages, such as the ease of sensor installation, individuals’ privacy protection and large-scale monitoring, both in area and time. Nevertheless, it introduces some limitations worth mentioning. First, BT sensors cannot distinguish between traffic on both sides of the street. Second, as all MAC addresses are anonymous, personal information (e.g., age and socio-economic data) is not included in the analysis. Finally, while the data is restricted to BT users, a penetration rate to deduce the number of pedestrians should be measured, yet it might change over time and therefor requires repeated measurements in future studies. Moreover, some minor age biases might be evident in terms of users who use BT-enabled devices.
To the best of our knowledge, this is the first study aimed at analyzing pedestrian traffic and walking patterns using a network of BT sensor technology on a city scale. The methodology and data provided in this study are valuable to inform urban planners and decision-makers on the magnitude of COVID-19 policies’ impact on pedestrian movement, as well as on the potential that automated PC technologies hold for monitoring walking behavior in real-time.
Acknowledgment
The BT data used in this work was provided by the Sustainable Mobility and Robust Transportation (T-SMART) Laboratory, Faculty of Civil and Environmental Engineering, The Technion – Israel Institute of Technology.
ORCID iDs
Avital Angel https://orcid.org/0000-0001-5005-0100
Achituv Cohen https://orcid.org/0000-0002-1357-8296
Sagi Dalyot https://orcid.org/0000-0002-5639-8009
Notes
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by The Israeli Smart Transportation Research Center, funding number 2029717.
1 See Kubáček, 2012
2 https://www.tel-aviv.gov.il/Transparency/Documents/%D7%A2%D7%99%D7%A7%D7%A8%D7%99%20%D7%94%D7%A0%D7%AA%D7%95%D7%A0%D7%99%D7%9D%20%D7%95%D7%94%D7%9E%D7%92%D7%9E%D7%95%D7%AA%202021.pdf (in Hebrew)
3 https://www.cbs.gov.il/he/publications/doclib/2020/17.shnatonsciencetechnologycommunication/st17_19x.pdf (in Hebrew)
4 https://www.cbs.gov.il/he/publications/doclib/2021/17.shnatonsciencetechnologycommunication/st17_18x.pdf (in Hebrew)
5 https://newmedia.calcalist.co.il/data_journalism/corona/index.html
6 https://www.ynet.co.il/news/article/B1G00z74eu
7 https://hopon.co.il/about
8 https://www.israelhayom.co.il/article/751627
9 Since some links did not operate during the time of phase VIII, to overcome bias, when compared to 2019, we used average number of PC values per network link instead of PC sum.
10 https://www.google.com/maps/@32.0795588,34.7803439,3a,75y,3.57h,90.01t/data=!3m6!1e1!3m4!1s-uwPNLOgKBqLRX0_quTAJQ!2e0!7i13312!8i6656
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Stockwell S Trott M Tully M , et al. (2021) Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: a systematic review. BMJ Open Sport & Exercise Medicine 7 : e000960. DOI: 10.1136/bmjsem-2020-000960.
Yabe T Tsubouchi K Fujiwara N , et al. (2020) Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Scientific Reports 10 (1 ): 1–9. DOI: 10.1038/s41598-020-75033-5.31913322
Young Hong J Yong Jeon J (2020) Comparing associations among sound sources, human behaviors, and soundscapes on central business and commercial streets in Seoul, Korea. Building and Environment 186 : 107327. DOI: 10.1016/j.buildenv.2020.107327.
Zhang L Dalyot S Eggert D , et al. (2012) Multi-stage approach to travel-mode segmentation and classification of gps traces. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-4/W25 : 87–93. DOI: 10.5194/isprsarchives-xxxviii-4-w25-87-2011.
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Zheng Y Elefteriadou L Chase T , et al. (2016) Pedestrian traffic operations in Urban networks.pdf. Transportation Research Procedia 15 : 137–149.
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PMC009xxxxxx/PMC9295306.txt |
==== Front
Tob Control
Tob Control
tobaccocontrol
tc
Tobacco Control
0964-4563
1468-3318
BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR
34907090
tobaccocontrol-2021-056838
10.1136/tobaccocontrol-2021-056838
AD Watch
1506
Targeted tobacco marketing in 2020: the case of #BlackLivesMatter
http://orcid.org/0000-0003-3074-2294
Heley Kathryn 12
http://orcid.org/0000-0002-3450-4579
Popova Lucy 3
Moran Meghan Bridgid 2
http://orcid.org/0000-0001-6894-5535
Ben Taleb Ziyad 4
Hart Joy L 5
Wackowski Olivia A 6
Westling Erika 7
http://orcid.org/0000-0003-3202-7534
Smiley Sabrina L 8
http://orcid.org/0000-0001-5329-6261
Stanton Cassandra A 910
1 Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
2 Department of Health, Behavior & Society, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
3 School of Public Health, Georgia State University, Atlanta, Georgia, USA
4 Department of Kinesiology, College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, Texas, USA
5 Department of Communication, University of Louisville, Louisville, Kentucky, USA
6 Center for Tobacco Studies, Rutgers University, New Brunswick, New Jersey, USA
7 Oregon Research Institute, Eugene, Oregon, USA
8 Tobacco Center of Regulatory Science, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
9 Behavioral Health & Health Policy, Westat, Rockville, Maryland, USA
10 Georgetown University Medical Center, Washington, District of Columbia, USA
Correspondence to Kathryn Heley, Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; [email protected]
7 2023
14 12 2021
32 4 530533
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
2023
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
advertising and promotion
media
social marketing
tobacco industry
non-cigarette tobacco products
http://dx.doi.org/10.13039/501100000850 National Heart and Lung Institute P50HL120163 U54HL120163 http://dx.doi.org/10.13039/100000861 Burroughs Wellcome Fund U54DA046060-01 http://dx.doi.org/10.13039/100010628 Center for Tobacco Products P50HL120163 R01DA047397 U54HL120163 http://dx.doi.org/10.13039/100000054 National Cancer Institute T31RP2083 U54CA22997 special-featureunlocked
==== Body
pmcIntroduction
2020 saw the continued impact of systemic racism and racial inequity in the United States (US). Inequities of COVID-19 highlighted the convergence of social risk factors, racism and health,1 while the police killing of George Floyd and others sparked international protests for racial justice.2 The Black Lives Matter (BLM) movement brought racial equity central to media and public dialogue in the US and worldwide.3
We provide examples of how tobacco companies and retailers, ever ready to capitalise, used BLM and racial equity in their marketing and messaging. Examples were identified by reviewing social media accounts (Facebook, Twitter, Instagram) for leading brands of tobacco products including cigars, e-cigarettes, cigarettes, smokeless tobacco, heated tobacco products and hookah products; select manufacturer and retailer websites; and advertisements archived on Trinkets & Trash (trinketsandtrash.org), a tobacco advertising surveillance website. Sources were searched in December 2020 and included materials circulated between January and December 2020.
Signaling support: social media expressions, products and initiatives
We identified several examples of cigar/cigarillo and hookah manufacturers integrating BLM and racial equity themes in their advertising and merchandise. Social media posts included expressions of support, solidarity, ‘listening’, remembrance and togetherness, exemplified by social media black squares, hashtags such as ‘#GeorgeFloyd’, and captions (eg, ‘We’re all in this together! Black Lives Matter!’; see figures 1–4 for additional examples).4–7 Several hookah retailers used social media and/or retail websites to promote BLM-branded products and accessories (eg, mouthpiece decorated with jewelery spelling BLM) (figure 5).8
Figure 1 An Instagram carousel posted by Swisher in June 2020 featuring: (1) an empty black square posted on 2 June 2020 as part of ‘Blackout Tuesday’, a social media action where supporters posted similar posts in solidarity across various platforms; (2) a statement posted on 4 June 2020 from the company president about racial inequity in America, without mentioning BLM specifically; (3) a promise to take action to address systemic racism, posted on 15 June 2020. (image source: https://www.trinketsandtrash.org/detail.php?artifactid=15409&page=1)
Figure 2 A 1 June 2020 Instagram post from cigar/cigarillo brand Dutch Masters featuring a black square, the phrase ‘Black Lives Matter’ and a hashtag reference to George Floyd. (image source: https://www.instagram.com/p/CA5WqHkg2Zu/)
Figure 3 A 19 June 2020 Instagram post from cigar/cigarillo brand Swisher Sweets referencing Juneteenth and stating ‘Reflective and resolute’. (image source: https://www.instagram.com/p/CBn19YDnAjv/)
Figure 4 Facebook post featuring a black square and a quote from Dr Martin Luther King Jr, posted by hookah company Starbuzz Tobacco on ‘Blackout Tuesday’ (2 June 2020), a day in which individuals and organisations posted black squares on their social media accounts as a gesture of solidarity with the Black Lives Matter movement and the protests against the police killing of George Floyd. (image source: https://www.facebook.com/StarbuzzTobacco/photos/a.376580699062/10159078516924063/?type=3&theater)
Figure 5 BLM-branded hookah products available for sale in 2020 from Blazze, a commercial website selling hookah products. (image source: https://www.blazzehookah.com/products/blm-hookah-tip)
Cigar/cigarillo companies used social media and email to highlight initiatives purporting to address systemic racism, promote economic equality and create ‘positive social impact’ (figure 6).9 Companies promising these initiatives included Swisher International and John Middleton Co., makers of Swisher Sweets and Black & Mild cigarillos, products disproportionately used by young, Black consumers.10 11 Swisher advertised a new ‘inclusion, diversity and transformation strategy,’ which would partner with multicultural groups and community organisations, ‘develop a talent pipeline with Historically Black Colleges and Universities,’ and create career opportunities for minorities within their own corporate ranks and economic opportunities for Black-owned businesses, artists, and workers (figure 6).9 John Middleton Co. advertised a new initiative which included advertising of Black-owned businesses and an initial commitment of $1 million to non-profits supporting them (figure 7).12
Figure 6 An Instagram carousel posted by Swisher Sweets in July 2020 with an update about the company's ‘Inclusion, Diversity & Transformation Strategy’ to support Black Lives Matter. The posts describe how the brand will create economic opportunities for Black-owned businesses, artists, and workers, as well as introduce the manager who will lead this initiative. (image source: http://www.trinketsandtrash.org/detail.php?artifactid=15408&page=4)
Figure 7 Email from cigar/cigarillo company Black & Mild, sent 27 July 2020, with a message from their parent company, John Middleton Co., supporting Black Lives Matter. The email outlines their Black-Owned Business Initiative plan to address economic inequality. (image source: http://www.trinketsandtrash.org/detail.php?artifactid=15404&page=1)
Discussion
The use of BLM and racial equity messaging by the tobacco industry is noteworthy for several reasons. First, the products in these targeted messages—cigars/cigarillos and hookahs—are disproportionately used by and cause harm to non-white communities.13 14 We did not find similar examples for other tobacco products, such as smokeless tobacco. Second, this marketing of products that exacerbate disparities in tobacco-related harms15–17 is in contrast to the work of the BLM movement, which pursues equity and the eradication of the targeting and harming of Black individuals.18
Racial inequities in tobacco impact are well documented. In the US, Black individuals assume an inordinate burden of tobacco-related diseases.15–17 An extensive literature documents the tobacco industry’s aggressive targeting of Black communities, such as elevated rates of advertising in Black neighbourhoods,15 19 price promotions,19 and the use of culturally-tailored messages and focused marketing in publications with a primarily Black readership to sell menthol cigarettes.20–22 Tobacco companies also have a history of making contributions to minority community organisations, scholarship programmes, higher education institutions, media, and elected officials and cultivating relationships with Black leaders and civic groups to promote their public image and further their business interests.15 23 24 The examples identified in this review illustrate a recent form of this long-standing pattern of tobacco industry-targeted marketing of the Black community, and reinforce previous studies25 26 that have established the impact of this targeted marketing on use. As the Food and Drug Administration (FDA) weighs the public health harms and benefits of new tobacco products seeking marketing authorisation, the role of targeted marketing on increasing appeal and attracting/addicting vulnerable populations should be an essential factor in the equation.
Finally, the BLM-related industry messages observed have particular salience amid the FDA’s announced intentions to ban flavoured cigars and menthol cigarettes, policies that have the potential to save countless Black lives,27 and that can follow similar international policy actions.28 29 Groups such as the African American Tobacco Control Leadership Council have actively advocated for such policies and made use of community organising strategies to inform and mobilise those in communities likely to be impacted by them.30 History has shown though that previous US efforts to restrict tobacco products, including menthol cigarettes, have been met with industry resistance. In the face of such resistance, the examples highlighted are clearly yet another example of industry exploitation of Black communities—and not the support the industry purports. The language of BLM support put forth by the tobacco industry (eg, ‘it is time to go beyond words and actively accelerate true and lasting change’ figure 1)4 is meaningless unless companies correspondingly align their actions with these words as policy efforts move forward.
Ethics statements
Patient consent for publication
Not required.
Contributors: KH, LP, CS and MM conceptualised the study. KH conducted the analysis of the data. All authors contributed to data collection; interpretation of the results; and manuscript reviews and revisions.
Funding: This research was supported, in part, by the National Institute of Drug Abuse (NIDA) and the Food and Drug Administration’s Center for Tobacco Products (FDA CTP) award R01DA047397 (LP); National Heart, Lung, and Blood Institute (NHLBI) and FDA CTP awards P50HL120163 and U54HL120163 (JH); NCI and CTP award U54CA22997 (OW); NCI and CTP awards T31RP2083–California Tobacco-Related Disease Research Program (TRDRP) (SS); an Innovation in Regulatory Science Award from the Burroughs Wellcome Fund (MM); and NIDA and FDA CTP for the Center for Coordination of Analytics, Science, Enhancement and Logistics (CASEL) in Tobacco Regulatory Science U54DA046060-01 (CS).
Disclaimer: The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the co-authors’ institutions, the NIH or the FDA.
Competing interests: MM has served as a paid expert witness in litigation sponsored by the Public Health Advocacy Institute against RJ Reynolds. This arrangement was reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. No potential perceived conflicts were declared by the other co-authors.
Provenance and peer review: Not commissioned; externally peer reviewed.
==== Refs
References
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3 Gramlich J . Americans have heard more about clashes between police and protestors than other recent news stories. Pew Research Center, 2020. Available: https://www.pewresearch.org/fact-tank/2020/09/16/americans-have-heard-more-about-clashes-between-police-and-protesters-than-other-recent-news-stories/ [Accessed 21 Sep 2021].
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5 Dutch Masters . Black lives matter. Available: https://www.instagram.com/p/CA5WqHkg2Zu/ [Accessed 3 Dec 2020].
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9 Swisher Sweets . Our new inclusion, diversity & transformation strategy. Available: http://www.trinketsandtrash.org/detail.php?artifactid=15408&page=4 [Accessed 3 Dec 2020].
10 Creamer MR , Wang TW , Babb S , et al . Tobacco product use and cessation indicators among adults — United States, 2018. MMWR Morb Mortal Wkly Rep 2019;68 :1013–9. 10.15585/mmwr.mm6845a2
11 Chang CM , Corey CG , Rostron BL , et al . Systematic review of cigar smoking and all cause and smoking related mortality. BMC Public Health 2015;15 :390. 10.1186/s12889-015-1617-5 25907101
12 John Middleton Co . Closing the gap: Black-owned business initiative. Available: http://www.trinketsandtrash.org/detail.php?artifactid=15404&page=1 [Accessed 3 Dec 2020].
13 Chen-Sankey JC , Mead-Morse EL , Le D , et al . Cigar-smoking patterns by race/ethnicity and cigar type: a nationally representative survey among U.S. adults. Am J Prev Med 2021;60 :87–94. 10.1016/j.amepre.2020.07.005 33341182
14 Assari S , Chalian H , Bazargan M . Social determinants of Hookah smoking in the United States. J Ment Health Clin Psychol 2020;4 :21–7. 10.29245/2578-2959/2020/1.1185 32285045
15 U.S. Department of Health and Human Services . Tobacco use among U.S. racial/ethnic minority groups—African Americans, American Indians and Alaska natives, Asian Americans and Pacific Islanders, and Hispanics: a report of the surgeon General. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 1998.
16 U.S. Department of Health and Human Services . The health consequences of smoking. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2004.
17 Tsai J , Homa DM , Gentzke AS , et al . Exposure to secondhand smoke among nonsmokers - United States, 1988-2014. MMWR Morb Mortal Wkly Rep 2018;67 :1342–6. 10.15585/mmwr.mm6748a3 30521502
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20 Food and Drug Administration . Preliminary scientific evaluation of the possible public health effects of menthol versus nonmenthol cigarettes, 2013. Available: https://www.fda.gov/media/86497/download [Accessed 19 Sep 2021].
21 National Cancer Institute . The role of the media in promoting and reducing tobacco use. Bethesda, MD: U.S. department of health and human services, National Institutes of Health, National cancer Institute tobacco control monograph No. 19, 2008. Available: https://cancercontrol.cancer.gov/sites/default/files/2020-06/m19_complete_0.pdf [Accessed 18 Sep 2021].
22 Balbach ED , Gasior RJ , Barbeau EM . R.J. Reynolds' targeting of African Americans: 1988-2000. Am J Public Health 2003;93 :822–7. 10.2105/AJPH.93.5.822 12721151
23 Yerger VB , Malone RE . African American leadership groups: smoking with the enemy. Tob Control 2002;11 :336–45. 10.1136/tc.11.4.336 12432159
24 McCandless PM , Yerger VB , Malone RE . Quid pro quo: tobacco companies and the black press. Am J Public Health 2012;102 :739–50. 10.2105/AJPH.2011.300180 21852652
25 Boley Cruz T , Rose SW , Lienemann BA . Pro-tobacco marketing and anti-tobacco campaigns aimed at vulnerable populations: a review of the literature. Tob Induc Dis 2019;17 :68.31582956
26 Marion H , Garner W , Estrada A , et al . Online pro-tobacco marketing exposure is associated with dual tobacco product use among underage US students. Am J Health Promot 2020;34 :648–51. 10.1177/0890117120905231 32096411
27 American Medical Association . FDA agrees to ban menthol to protect African Americans, 2021. Available: https://www.ama-assn.org/press-center/press-releases/fda-agrees-ban-menthol-protect-african-americans [Accessed 19 Sep 2021].
28 Government of Canada . Order amending the schedule of the tobacco act (menthol), 2017. Available: http://www.gazette.gc.ca/rp-pr/p2/2017/2017-04-05/html/sor-dors45-eng.php [Accessed 22 Sep 2021].
29 World Health Organization . Banning menthol in tobacco products, 2016. Available: https://apps.who.int/iris/bitstream/handle/10665/205928/9789241510332_eng.pdf;jsessionid=8715961F4B835518FFB276BCC7CF3256?sequence=1 [Accessed 22 Sep 2021].
30 Mills SD , McGruder CO , Yerger VB . The African American tobacco control leadership Council: advocating for a Menthol cigarette ban in San Francisco, California. Tob Control 2021;30 :e150–3. 10.1136/tobaccocontrol-2020-056215 33483378
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PMC009xxxxxx/PMC9415097.txt |
==== Front
Ultrasound Obstet Gynecol
Ultrasound Obstet Gynecol
10.1002/(ISSN)1469-0705
UOG
Ultrasound in Obstetrics & Gynecology
0960-7692
1469-0705
John Wiley & Sons, Ltd. Chichester, UK
34931729
10.1002/uog.24832
UOG24832
UOG-2021-0888.R1
Original Paper
Original Papers
Changes in structural brain development after selective fetal growth restriction in monochorionic twins
Brain development in selective FGR
Groene et al.
Groene S. G. https://orcid.org/0000-0002-8832-045X
1 2 [email protected]
de Vries L. S. 1
Slaghekke F. 3
Haak M. C. https://orcid.org/0000-0002-0443-1997
3
Heijmans B. T. 2
de Bruin C. 4
Roest A. A. W. https://orcid.org/0000-0002-0153-5934
5
Lopriore E. https://orcid.org/0000-0002-3513-5066
1
van Klink J. M. M. https://orcid.org/0000-0001-9480-1164
1
Steggerda S. J. 1
1 Neonatology, Willem‐Alexander Children's Hospital, Department of Pediatrics Leiden University Medical Center Leiden The Netherlands
2 Molecular Epidemiology, Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands
3 Fetal Therapy, Department of Obstetrics Leiden University Medical Center Leiden The Netherlands
4 Pediatric Endocrinology, Willem‐Alexander Children's Hospital, Department of Pediatrics Leiden University Medical Center Leiden The Netherlands
5 Pediatric Cardiology, Willem‐Alexander Children's Hospital, Department of Pediatrics Leiden University Medical Center Leiden The Netherlands
* Correspondence to: S. G. Groene, Department of Pediatrics, Leiden University Medical Center, J6‐S, Albinusdreef 2, 2333 ZA Leiden, The Netherlands (e‐mail: [email protected])
01 6 2022
6 2022
59 6 10.1002/uog.v59.6 747755
19 11 2021
22 9 2021
05 12 2021
© 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Abstract
Objectives
Fetal growth restriction (FGR) may alter brain development permanently, resulting in lifelong structural and functional changes. However, in studies addressing this research question, FGR singletons have been compared primarily to matched appropriately grown singletons, a design which is inherently biased by differences in genetic and maternal factors. To overcome these limitations, we conducted a within‐pair comparison of neonatal structural cerebral ultrasound measurements in monochorionic twin pairs with selective FGR (sFGR).
Methods
Structural cerebral measurements on neonatal cerebral ultrasound were compared between the smaller and larger twins of monochorionic twin pairs with sFGR, defined as a birth‐weight discordance (BWD) ≥ 20%, born in our center between 2010 and 2020. Measurements from each twin pair were also compared with those of an appropriately grown singleton, matched according to sex and gestational age at birth.
Results
Included were 58 twin pairs with sFGR, with a median gestational age at birth of 31.7 (interquartile range, 29.9–33.8) weeks and a median birth weight of 1155 g for the smaller twin and 1725 g for the larger twin (median BWD, 32%). Compared with both the larger twin and the singleton, the smaller twin had significantly smaller cerebral structures (corpus callosum, vermis, cerebellum), less white/deep gray matter and smaller intracranial surface area and volume. Intracranial‐volume discordance and BWD correlated significantly (R 2 = 0.228, P < 0.0001). The median intracranial‐volume discordance was smaller than the median BWD (19% vs 32%, P < 0.0001). After correction for intracranial volume, only one of the observed differences (biparietal diameter) remained significant for the smaller twin vs both the larger twin and the singleton.
Conclusions
In monochorionic twins with sFGR, neonatal cerebral ultrasound reveals an overall, proportional restriction in brain growth, with smaller cerebral structures, less white/deep gray matter and smaller overall brain‐size parameters in the smaller twin. There was a positive linear relationship between BWD and intracranial‐volume discordance, with intracranial‐volume discordance being smaller than BWD. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
brain development
monochorionic twins
neonatal cerebral ultrasound
selective FGR
Hartstichting 10.13039/501100002996 2017T075 source-schema-version-number2.0
cover-dateJune 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
==== Body
pmc CONTRIBUTION
What are the novel findings of this work?
This is an extensive overview of structural cerebral measurements on neonatal cerebral ultrasound in monochorionic twin pairs with selective fetal growth restriction and a matched, appropriately grown singleton. It shows that the smaller twin presents with an overall restriction in brain growth, with smaller cerebral structures (corpus callosum, vermis, cerebellum), less white/deep gray matter and smaller overall brain‐size parameters.
What are the clinical implications of this work?
Our results reinforce the hypothesis that FGR has significant implications for brain development.
Introduction
Approximately 10% of all pregnancies are affected by fetal growth restriction (FGR), characterized by the failure of the fetus to reach its growth potential 1 . FGR in singletons is multifactorial in origin, by way of maternal, fetal or placental determinants, and is responsible for a large proportion of both perinatal morbidity and mortality 2 . It is hypothesized that FGR can alter permanently fetal development, including brain development, resulting in lifelong structural and functional changes.
The hemodynamic adaptation of the brain to suboptimal growth conditions can be detected antenatally as ‘brain sparing’, a redistribution of blood flow to the brain indicated by a decreased cerebroplacental ratio (CPR) 3 . Despite this supposedly protective mechanism, deficits in brain structures are prevalent in FGR singletons, and include reduced intracranial volume, corpus callosal size and cerebellar diameter 4 , 5 . These structural deficits are known to have significant consequences for brain function in childhood, being associated with, for example, lower cognitive test scores and impaired motor skills 6 .
So far, in studies on the impact of FGR on brain structure and function, FGR singletons have been compared primarily to matched appropriate‐for‐gestational‐age singletons 4 , 7 . However, this study design is inherently biased by differences in genetic and maternal factors, which potentially influence outcomes and thereby limit comparability. These limitations are not present when research is performed in an identical‐twin model with discordance in fetal growth 8 .
Monochorionic (MC) twins have a single placenta that can be shared unequally, resulting in an unbalanced nutrient and oxygen supply and a subsequent discordant growth pattern, known as selective FGR (sFGR) 9 . These twins allow comparison of a growth‐restricted twin with its genetically identical appropriately grown cotwin, with identical maternal characteristics. To date, no study has evaluated cerebral ultrasound (cUS) parameters in this specific twin population. The aim of this study was to conduct a within‐pair comparison of neonatal structural cUS measurements in MC twin pairs with sFGR.
Methods
This study was approved and the requirement for written informed consent was waived by the ethics committee of the Leiden University Medical Center (LUMC), as it was a retrospective analysis of clinically indicated ultrasound examinations (protocol G21.011). All consecutive MC twin pairs with sFGR, defined as a birth‐weight discordance (BWD) ≥ 20%, born in our center (the national referral center for complicated MC twin pregnancies) between 2010 and 2020 were eligible for inclusion. BWD was calculated as 10 : (birth weight of larger twin − birth weight of smaller twin)/birth weight of larger twin × 100. Cases with twin–twin transfusion syndrome and those with twin anemia–polycythemia sequence were excluded due to the likely additional effect of these complications on brain development 11 , 12 . We also excluded MC triplet pregnancies and cases with twin reversed arterial perfusion and/or other congenital abnormalities 12 . Structural measurements could not be performed when no cUS was available for either one or both of the neonates. Each twin pair was matched to one appropriate‐for‐gestational‐age singleton without cerebral injury, to account for differences between twins and singletons. The singletons were selected from our neonatology patient database and were born in the same time period as the included twins. Per twin pair, a singleton was selected with the same sex and gestational age at birth. In order to minimize factors that might influence cerebral outcome for this group, singletons with asphyxia, congenital abnormality or infection, and singletons born after alloimmunization (with or without fetal therapy) during pregnancy were not included.
Clinical characteristics
The following maternal and obstetric baseline characteristics were retrieved from patient files: maternal age, gravidity, parity, Gratacós classification for sFGR 13 (Type I defined as positive end‐diastolic flow, Type II defined as persistent absent or reversed end‐diastolic flow and Type III defined as intermittent absent or reversed end‐diastolic flow), presence of brain sparing (defined as CPR < 1 for at least 2 weeks, with CPR calculated as the pulsatility index of the middle cerebral artery divided by the pulsatility index of the umbilical artery), and, in this case, gestational age at start and duration of brain sparing 14 , monoamnionicity and mode of delivery. The following neonatal baseline characteristics were retrieved: gestational age at birth, sex, BWD, birth weight (in g) and whether neonates were born small‐for‐gestational age (defined as birth weight < 10th centile) 15 . Placental share was calculated and expressed as a percentage of the total placental area, based on the margins of the twin‐specific dyes after standard colored dye injection of MC twin placentae 16 . The percentages were calculated using Image J version 1.57 17 .
cUS measurements
Before 2015, cUS was performed using an Aloka α ultrasound system (Hitachi Medical Systems Holding AG, Zug, Switzerland). From 2015 onwards, a Canon Aplio 400 or Aplio i700 system (Canon Medical Systems B.V., The Netherlands) was used. Between 1 and 3 days after birth, cUS was performed by the attending neonatologist, all of whom had extensive experience with this imaging modality as it is part of standard care in LUMC. Head circumference at birth and corresponding z‐score were documented 18 . Cerebral measurements were performed offline using retrieved images from the first available cUS examination after birth (Clinical Assistant, RVC B.V., The Netherlands). The resistance index of the anterior cerebral artery was recorded and calculated as: (peak systolic velocity − end‐diastolic velocity)/peak systolic velocity. The following structural measurements were performed by a single researcher with expertise in neonatal neuroimaging (S.G.G.) to avoid interobserver variability 4 : anterior horn width, ventricular index (VI), ventriculoatrial width (VAW), thalamo‐occipital distance (TOD), interhemispheric fissure width, corpus callosal length, corpus callosal height, callosum–fastigium length, vermis height, vermis width, transverse cerebellar diameter, frontal white matter height, deep gray matter width, deep gray matter surface area, biparietal diameter, intracranial fronto‐occipital diameter (FOD), intracranial height, axial intracranial surface area and intracranial volume 19 (Table S1 and Figure 1). Intracranial‐volume discordance was calculated as: (intracranial volume of larger twin − intracranial volume of smaller twin)/intracranial volume of larger twin × 100. The researcher was not blinded to the group (smaller twin, larger twin or singleton).
Figure 1 Ultrasound images giving overview of neonatal cerebral measurements: A and G, biparietal diameter; B, width of deep gray matter; C, height of frontal white matter; D, anterior horn width; E, ventricular index; F, ventriculoatrial width; G and H, used in calculation of intracranial surface area; I, transverse cerebellar diameter; J, corpus callosal length; K, corpus callosal height; L, callosum–fastigium length; M, vermis height; N, vermis width; O, intracranial fronto‐occipital diameter; P, intracranial height; Q, thalamo‐occipital distance; R and S, used in calculation of deep gray matter surface area.
UOG-24832-FIG-0001-c
Measurements were compared between the smaller and larger twin, the smaller twin and the matched singleton, and the larger twin and the singleton. In order to examine whether certain structures were affected to a greater extent than others, the analyses were also corrected for intracranial volume 19 . Both uncorrected and corrected measurements are presented herein, because having a smaller brain in itself might have consequences for future neurodevelopment. To evaluate reliability, measurements were repeated by the same researcher in a random sample of 18 neonates (10% of the population), and the intraclass correlation coefficient (ICC) was calculated for every measurement. ICC values < 0.50 were indicative of poor reliability and values between 0.50–0.75 indicated moderate reliability 20 .
Brain lesions seen on cUS
We recorded the presence of brain lesions, including pseudocysts, germinolytic cysts, subependymal or choroid plexus cysts, lenticulostriate vasculopathy, intraventricular hemorrhage (IVH) Grade 1–4 21 , periventricular leukomalacia (PVL) Grade 1–4 22 , ventricular dilatation > 97th percentile 23 and parenchymal hemorrhage. Severe cerebral injury was defined as IVH ≥ Grade 3, cystic PVL (c‐PVL) ≥ Grade 2; ventricular dilatation > 97th percentile, arterial or venous infarction or porencephalic or parenchymal cysts.
Brain maturation
Brain maturation in the twin pairs was assessed by two other researchers (L.S.d.V. and S.J.S.) with expertise in neonatal neuroimaging. These researchers did not perform any structural measurements and were blinded to the group (smaller or larger twin) and gestational age at birth. Maturation was scored in three planes according to the appearance and increasing complexity of the principal sulci, as described by Murphy et al. 24 . Overall maturity was determined on the first cUS examination after birth and based on the comparison of actual gestational age at birth with the maturation score for at least two out of three planes, and was categorized as either normal, 2–4 weeks behind or > 4 weeks behind.
Statistical analysis
Statistical analyses were performed using IBM Statistics Version 25.0 (SPSS, IBM Corp, Armonk, NY, USA). Data are presented as median (interquartile range (IQR)), n/N (%) or n (%). Given the nature of the study population (twin pairs), the analyses took into account that observations between cotwins are not independent, by using the Wilcoxon signed‐rank test (non‐parametric test for related samples) and generalized estimating equations (GEE). To test for association between sFGR and the structural cerebral measurements, the Wilcoxon signed‐rank test was used. A GEE was used to test for association between sFGR and the structural cerebral measurements, corrected for intracranial volume. A GEE was also used to test for association between sFGR and the presence of brain lesions. As the GEE cannot be used when an outcome event does not occur in one of the groups, under these circumstances an adjustment to the data was applied, in which one unaffected twin was considered as an affected twin for both groups (smaller/larger twin) for the purpose of the statistical analysis; this results in more conservative P‐values.
Intracranial‐volume discordance was tested for correlation with BWD and placental‐share discordance and plotted against BWD and placental‐share discordance for each type of sFGR, using sRStudio Version 2021.9.2.382 (RStudio, PBC, Boston, MA, USA). The ICC of each structural measurement was calculated in a two‐way mixed‐effects model based on a single measurement.
P < 0.05 was considered statistically significant. For every structural measurement, three comparisons were performed, i.e. smaller twin vs larger twin, smaller twin vs singleton and larger twin vs singleton. Therefore, a Bonferroni adjustment was applied to correct for multiple testing, resulting in a significance level set at P < 0.017 (i.e. 0.05/3) for the structural measurements.
Results
Of the 653 liveborn MC twin pairs delivered at the LUMC between 2010 and 2020, pairs which did not have sFGR (n = 292) or which met the aforementioned exclusion criteria (n = 296) were excluded. Of the remaining pairs, seven did not have a cUS available for either one or both of the twins. Thus, 58 twin pairs with sFGR and an available cUS were included in the analyses (Figure 2). Hence, 58 appropriate‐for‐gestational‐age singletons without cerebral injury and matched for sex and gestational age at birth were included as well.
Figure 2 Flowchart summarizing inclusion of neonates in the study. cUS, cerebral ultrasound; LUMC, Leiden University Medical Center; MC, monochorionic; sFGR, selective fetal growth restriction; TAPS, twin anemia–polycythemia sequence; TRAP, twin reversed arterial perfusion; TTTS, twin–twin transfusion syndrome.
UOG-24832-FIG-0002-b
Clinical characteristics
Baseline maternal, obstetric and neonatal characteristics are presented in Table 1. As expected, antenatal brain sparing was observed primarily in the smaller twin (76.8% (43/56)), for a median of 7 (IQR, 4–9) weeks, as a sign of hemodynamic adaptation of the brain to suboptimal growth conditions. Brain sparing was observed in only 1.8% (1/56) of larger twins, with a duration of 4 weeks. Of the 58 twin pregnancies, 39.7% (23/58) were classified as Gratacós Type I, 17.2% (10/58) as Type II and 43.1% (25/58) as Type III. The median gestational age at birth was 31.7 (IQR, 29.9–33.8) weeks and nearly 80% of twins were delivered by Cesarean section. The median BWD was 31.5% (IQR, 26.7–38.1%), with the smaller twin weighing 1155 (IQR, 886–1433) g and the larger twin weighing 1725 (IQR, 1386–2145) g. In line with the difference in birth weight, the proportion of neonates born small‐for‐gestational age was 94.8% (55/58) for the smaller twin and 13.8% (8/58) for the larger twin. Conforming to the pathophysiology of sFGR, the smaller twin had a smaller placental share compared with that of the larger twin: 30.0% (IQR, 25.3–34.7%) vs 70.0% (IQR, 65.3–74.7%). For the matched singletons, the median gestational age at birth was 31.7 (IQR, 29.9–33.8) weeks and the birth weight was 1758 (IQR, 1528–2164) g respectively.
Table 1 Baseline maternal, obstetric and neonatal characteristics for twin pregnancies with selective fetal growth restriction (sFGR) and matched singletons*
Characteristic sFGR twins (n = 116; 58 pregnancies) Matched singleton (n = 58)
All Smaller twin (n = 58) Larger twin (n = 58)
Maternal age (years) 31 (28–34) — — —
Gravidity 1 (1–2) — — —
Parity 0 (0–1) — — —
Gratacós type
Type I 23/58 (39.7) — — —
Type II 10/58 (17.2) — — —
Type III 25/58 (43.1) — — —
Brain sparing — 43/56 (76.8) 1/56 (1.8) —
GA at start (weeks) — 19.6 (17.4–21.4) 15.9 —
Duration (weeks) — 7 (4–9) 4 —
Monoamniotic 6/58 (10.3) — — —
GA at birth (weeks) 31.7 (29.9–33.8) — — 31.7 (29.9–33.8)
Female neonate 52/116 (44.8) — — 26/58 (44.8)
Cesarean delivery 92/116 (79.3) — — —
BWD (%) 31.5 (26.7–38.1) — — —
Birth weight (g) — 1155 (886–1433) 1725 (1386–2145) 1758 (1528–2164)
Small‐for‐gestational age — 55/58 (94.8) 8/58 (13.8) 0 (0.0)
Placental share (%) — 30.0 (25.3–34.7) 70.0 (65.3–74.7) —
Data are presented as median (interquartile range), n/N (%) or absolute values.
* Matched for gestational age and sex.
BWD, birth‐weight discordance; GA, gestational age.
cUS measurements
Structural cUS measurements are summarized in Table 2. As expected, based on the difference in birth weight, head circumference at birth and corresponding z‐score were lower for the smaller twin compared with both the larger twin and the singleton: the median (IQR) was 27.1 (25.0–29.3) cm with z‐score of –1.3 (–1.9 to –0.1) for the smaller twin, 29.0 (27.5–30.0) cm with z‐score of 0.5 (–0.5 to 1.2) for the larger twin and 29.0 (27.5–30.0) cm with z‐score of 0.1 (–0.5 to 0.9) for the singleton (P < 0.0001 for both).
Table 2 Neonatal cerebral ultrasound (cUS) parameters in twins with selective fetal growth restriction (sFGR) and matched singletons*
Parameter Smaller twin (n = 58) Larger twin (n = 58) P (small vs large) Matched singleton (n = 58) P (small vs singleton) P (large vs singleton)
GA at cUS (weeks) 31.9 (29.9–34.0) 31.9 (29.9–34.0) 31.7 (30.0–34.0) 0.615 0.608
Postnatal age at cUS (days) 2 (1–2) 2 (1–2) 2 (1–3) 0.063 0.060
Head circumference (cm) 27.1 (25.0–29.3) 29.0 (27.5–30.0) < 0.0001 29.0 (27.5–30.0) < 0.0001 0.435
Head circumference z‐score –1.3 (–1.9 to –0.1) 0.5 (–0.5 to 1.2) < 0.0001 0.1 (–0.5 to 0.9) < 0.0001 0.481
RI‐ACA 0.7 (0.6–0.8) 0.8 (0.7–0.8) 0.062 0.7 (0.6–0.8) 0.441 0.177
Ventricular
AHW (mm)
Right 0.7 (0.3–1.4) 0.6 (0.3–1.0) 0.136 0.6 (0.0–1.1) 0.048 0.820
Left 0.8 (0.3–1.4) 0.6 (0.3–1.4) 0.797 0.6 (0.0–1.3) 0.382 0.593
VI (mm)
Right 9.6 (8.6–10.9) 9.5 (8.8–10.7) 0.991 9.8 (8.8–10.8) 0.462 0.341
Left 9.5 (8.8–10.5) 9.6 (8.8–10.4) 0.486 9.9 (9.2–10.5) 0.241 0.188
VAW (mm)
Right 6.1 (5.2–7.4) 6.4 (5.5–7.6) 0.809 6.4 (5.6–7.5) 0.554 0.874
Left 6.2 (5.2–7.4) 6.8 (6.1–7.8) 0.036 7.0 (5.9–8.0) 0.105 0.863
TOD (mm)
Right 15.6 (13.5–18.4) 16.0 (12.7–18.0) 0.385 12.8 (10.7–15.9) < 0.0001† 0.007†
Left 16.0 (14.1–18.1) 16.2 (13.7–18.9) 0.750 14.6 (11.8–18.7) 0.078 0.109
IFW (mm) 0 (0–0) 0 (0–0) 0.347 0 (0–0) 0.386 0.875
Brain structures
Corpus callosum (mm)
Length 37.6 (35.6–41.1) 39.8 (37.7–43.1) < 0.0001 40.8 (38.4–42.0) 0.001 0.461
Height 2.1 (1.8–2.4) 2.3 (2.0–2.6) 0.003 1.8 (1.5–2.0) < 0.0001† < 0.0001†
Callosum–fastigium length (mm) 42.0 (39.8–45.0) 43.2 (41.6–46.0) < 0.0001 43.4 (42.1–45.1) 0.014 0.585
Vermis (mm)
Height 18.3 (16.6–20.3) 19.2 (18.1–21.1) < 0.0001 18.7 (17.2–19.8) 0.364 0.003†
Width 11.7 (10.3–13.1) 12.0 (10.2–14.2) < 0.0001 11.1 (10.2–12.4) 0.215† 0.132
TCD (cm) 3.5 (3.1–4.0) 3.8 (3.5–4.3) < 0.0001 3.8 (3.5–4.1) < 0.0001 0.851
White/deep gray matter
Frontal white matter height (mm)
Right 18.5 (16.9–20.2) 19.4 (18.1–20.6) 0.002† 19.9 (18.3–21.0) 0.006 0.429
Left 18.8 (16.8–20.1) 19.4 (17.7–21.0) < 0.0001 19.8 (18.4–20.7) 0.001 0.530
Deep gray matter width (mm)
Right 22.4 (20.8–24.9) 24.0 (22.4–27.2) < 0.0001 24.0 (23.0–26.2) < 0.0001 0.993
Left 22.8 (21.1–24.7) 24.3 (22.3–26.7) < 0.0001 24.4 (22.5–25.8) < 0.0001 0.969
Deep gray matter surface area (mm2)
Right 379 (330–460) 436 (393–499) < 0.0001 417 (372–466) 0.003 0.001
Left 378 (331–452) 447 (403–486) < 0.0001 418 (385–448) 0.106 < 0.0001†
Overall brain size
Biparietal diameter (cm) 6.6 (6.1–7.0) 7.0 (6.5–7.7) < 0.0001† 7.2 (6.8–7.5) < 0.0001† 0.706
Intracranial
Surface area (cm2) 34.9 (30.9–43.3) 41.1 (37.1–47.5) < 0.0001 40.5 (36.3–44.1) < 0.0001 0.088
FOD (cm) 8.3 (7.5–9.0) 8.7 (8.3–9.2) < 0.0001 8.7 (8.3–9.1) < 0.0001† 0.718
Height (cm) 6.7 (6.3–7.3) 7.1 (6.8–7.7) < 0.0001 7.3 (6.9–7.6) < 0.0001 0.619
Axial surface area (cm2) 42.6 (37.2–49.1) 49.3 (43.4–55.3) < 0.0001 49.3 (45.5–52.6) < 0.0001 0.794
Volume (cm3) 191 (155–240) 231 (199–283) < 0.0001 245 (210–266) < 0.0001 0.730
Data are presented as median (interquartile range).
* Matched for gestational age and sex.
† Significant after correction for intracranial‐volume discordance (Table S3).
AHW, anterior horn width; FOD, fronto‐occipital diameter; GA, gestational age; IFW, interhemispheric fissure width; RI‐ACA, resistance index in anterior cerebral artery; TCD, transverse cerebellar diameter; TOD, thalamo‐occipital distance; VAW, ventriculoatrial width; VI, ventricular index.
The structural measurements could be divided into four categories: ventricular, brain structures, white/deep gray matter and overall brain‐size parameters. Ventricular parameters did not differ between groups, except for the right TOD which was smaller for the singleton (12.8 (IQR, 10.7–15.9) mm) in comparison with both the smaller (15.6 (IQR, 13.5–18.4) mm, P < 0.0001) and the larger (16.0 (IQR, 12.7–18.0) mm, P = 0.007) twin. This difference was independent of intracranial volume (Table S2).
All other structural measurements (brain structures, white/deep gray matter and overall brain‐size parameters) were significantly smaller for the smaller twin compared with the larger twin, in line with the difference in head circumference. Thus, there was overall restriction in brain growth of the smaller twin. When corrected for intracranial volume, only the right frontal white matter height (P = 0.003) and biparietal diameter (P < 0.0001) remained significantly different.
The measurements of brain structures, white/deep gray matter and overall brain‐size parameters that differed between the smaller and larger twin also differed between the smaller twin and the singleton (indicating comparability of the larger twin and the singleton), except for the vermis height (P = 0.364) and width (P = 0.215) and left deep gray matter surface area (P = 0.106). When corrected for intracranial volume, the differences that persisted were in the corpus callosal height (P < 0.0001), biparietal diameter (P < 0.0001) and FOD (P = 0.014). Vermis width became significantly different after correction (P = 0.007).
Measurements that were significantly different between the larger twin and the singleton (thereby indicative of possible incomparability of these groups) were in two brain structures (corpus callosal height (P < 0.0001) and vermis height (P = 0.003)) and both right and left deep gray matter surface area (P = 0.001 and P < 0.0001, respectively). Three of these differences between the larger twin and singleton persisted after correction for intracranial volume: corpus callosal height (P < 0.0001), vermis height (P = 0.005) and left deep gray matter surface area (P < 0.0001).
Intracranial‐volume discordance and BWD correlated significantly, with a Pearson's correlation coefficient of 0.477 (R 2 = 0.228, P < 0.0001) (Figure 3a). The median intracranial‐volume discordance was smaller than the median BWD (19.3% vs 31.5%, P < 0.0001). Intracranial‐volume discordance and placental‐share discordance did not correlate significantly (Pearson's correlation coefficient of 0.198 (R 2 = 0.039, P = 0.144)) (Figure 3b). There was no significant difference in intracranial‐volume discordance between the three sFGR types (P = 0.080).
Figure 3 Scatterplots, with regression lines and 95% CIs, in 58 twin pregnancies with selective fetal growth restriction (sFGR), depicting the association of intracranial‐volume discordance on neonatal cerebral ultrasound (cUS) with: (a) birth‐weight discordance (R 2 linear = 0.228); and (b) placental‐share discordance (R 2 linear = 0.039). Regression lines are based on the group as a whole. Intracranial‐volume discordance was missing in two cases due to insufficient cUS quality. Cases were categorized according to Gratacós type as: Type I (), Type II () or Type III ().
UOG-24832-FIG-0003-b
The majority of the structural measurements had an ICC indicative of good‐to‐excellent reliability, the exception being the left VI, right VAW and vermis width, which had moderate reliability (Table S3).
Brain lesions seen on cUS
The brain lesions observed on neonatal cUS are presented in Table 3. If PVL was present, the smaller twin presented more often with PVL Grade 1 (transient periventricular densities > 7 days after birth) than did the larger twin (100% (10/10) vs 86% (12/14), P < 0.0001). Severe cerebral injury was present in 7% (4/58) of larger twins and 0% (0/58) of smaller twins (P = 0.065). These four twins presented as follows. The first had c‐PVL Grade 3, 15 days after birth (gestational age at birth, 28.9 weeks; birth weight, 1262 g; severe respiratory morbidity and patent ductus arteriosus; died 15 days after birth, following redirection of care because of severity of cerebral injury). The second had a periventricular hemorrhagic infarction with ventricular dilatation 3 days after birth (gestational age at birth, 28.4 weeks; birth weight, 1210 g; severe respiratory and cardiovascular morbidity; died 6 days after birth, following redirection of care because of severity of cerebral injury). The third had a periventricular hemorrhagic infarction 3 days after birth (gestational age at birth, 30.4 weeks; birth weight, 1740 g; severe respiratory morbidity). The fourth had c‐PVL Grade 3 and IVH Grade 2, 7 days after birth (gestational age at birth, 29.6 weeks; birth weight, 1450 g; severe respiratory morbidity). The first case was from a pregnancy classified antenatally as sFGR Type II and the other three cases were from pregnancies classified as Type III.
Table 3 Neonatal brain lesions seen on cerebral ultrasound in twins with selective fetal growth restriction
Lesion Smaller twin (n = 58) Larger twin (n = 58) P
Pseudocyst 9/58 (16) 3/58 (5) 0.065
Lenticulostriate vasculopathy 1/58 (2) 2/58 (3) 0.571
Intraventricular hemorrhage 6/58 (10) 6/58 (10) 1.000
Grade 1 5/6 (83) 2/6 (33) 0.519
Grade 2 1/6 (17) 2/6 (33) 0.683
Grade 3 0/6 (0) 0/6 (0) 1.000
Grade 4 (venous infarction) 0/6 (0) 2/6 (33) 0.190
Periventricular leukomalacia 10/53 (19) 14/53 (26) 0.333
Grade 1 10/10 (100) 12/14 (86) < 0.0001
Grade 2 0/10 (0) 0/14 (0) 1.000
Grade 3 0/10 (0) 2/14 (14) 0.482
Grade 4 0/10 (0) 0/14 (0) 1.000
Ventricular dilatation > 97th centile 0/58 (0) 1/58 (2) 0.323
Parenchymal hemorrhage 0/58 (0) 1/58 (2) 0.323
Severe cerebral injury 0/58 (0) 4/58 (7) 0.065
Data are presented as n/N (%).
Brain maturation
Overall brain maturation was categorized as 2–4 weeks behind in 9% (5/55) of smaller twins as opposed to 16% (9/57) of larger twins (P = 0.281) and it was > 4 weeks behind in 2% (1/55) of smaller twins as opposed to 2% (1/58) of larger twins (P = 0.979). Maturation could not be scored in three cases due to insufficient quality of the cUS. In two twin pairs (both born at a gestational age of 31 weeks), the maturation of both the smaller and the larger twin was behind.
Discussion
This is the first study evaluating neonatal cerebral measurements in a unique identical‐twin population affected by sFGR, controlling for genetic and maternal factors. Our study shows that the smaller twin presents with an overall restriction in brain growth, with smaller cerebral structures (corpus callosum, vermis, cerebellum), less white/deep gray matter and smaller overall brain‐size parameters. The measurements were different primarily for the smaller twin in comparison with both the larger twin and the matched singleton, demonstrating that the larger twin has similar outcomes to those of the matched singleton. After correction for intracranial volume, only one of the observed differences (biparietal diameter) remained significant for the smaller twin vs both the larger twin and the singleton, indicating a global, proportional decrease in brain growth. Furthermore, there was a positive linear relationship between BWD and intracranial‐volume discordance. The intracranial volume discordance appeared to be smaller than the BWD, indicating a certain degree of brain preservation.
The observed structural deficits have been linked previously to functional consequences in singletons. Small head circumference can be considered a strong predictor of adverse neurodevelopmental outcome, with increased rates of cerebral palsy and impaired cognitive and motor development 6 , 25 . Smaller corpus callosal size in preterm infants has been associated with speech and language difficulties, motor delay, cerebral palsy and a lower full‐scale intelligence quotient in adolescence 26 , 27 , 28 . Moreover, a study in monozygotic twins has shown that even subtle differences in birth weight within the normal range in full‐term twins is associated with alterations in brain structure which persist into adolescence and can be correlated with neurodevelopmental outcome 29 . It is important to realize that any neurodevelopmental consequences of (s)FGR are influenced by the time of onset and severity, as well as the gestational age at birth. Preterm birth is generally considered to exacerbate any deficits.
Our results are similar to those of a pilot study on cUS measurements in singletons, reporting that FGR neonates with antenatal brain sparing have a smaller corpus callosum and cerebellum 4 . In our study, no additional effect of brain sparing was found. However, only 77% of the smaller twins presented with antenatal brain sparing. Brain sparing can be considered a marker of the severity of FGR, as there is redistribution of blood to vital organs in response to unfavorable intrauterine circumstances, and has been linked to adverse neurobehavioral outcome 14 . The term ‘brain sparing’ is therefore a misnomer, as it appears to be an indication of a type of FGR with an even greater impairment of brain growth 4 . This is also illustrated by the fact that the sole parameter that remained significantly different between the smaller twin and both the larger twin and the singleton after correction for intracranial volume was the biparietal diameter, indicative of smaller head growth and associated with adverse neurodevelopmental outcome 30 .
With regard to cerebral injury, a systematic literature review reported an incidence of approximately 8% in MC twins with sFGR, with a particularly increased risk for the larger twin 31 . We found a similar incidence of severe cerebral injury in larger twins (7% (4/58)). Each of these four larger twins was born between 28 and 30 weeks of gestation, were from pregnancies with sFGR Type II or III, presented with severe respiratory morbidity, did not have cerebral abnormalities in utero and developed cerebral injury 3–15 days after birth. It has been suggested that the larger twin is already at increased risk of cerebral injury in utero, due to fetofetal shifts in blood volume through large anastomoses, resulting in hypoxic injury 13 , 32 , 33 . Hence, one would expect the injury to be visible antenatally or within 1 or 2 days after birth. As this was not the case, our data are more in line with the hypothesis that (iatrogenic) prematurity plays an additional role in the development of cerebral injury in the larger twin 31 . As yet, there is no consensus on the timing of delivery for sFGR twins. The benefits of prolonging the pregnancy, with the risk of fetal demise of the smaller twin and subsequent demise or neurological damage of the larger twin, must be weighed against the risks of prematurity 34 .
Our study is limited by its retrospective design. As cUS was performed for the detection of cerebral injury, the quality was not always optimal to conduct all measurements or to score maturation. Moreover, it would have been desirable to include a control group of uncomplicated MC twin pregnancies, as these can also present with brain injury and may show cerebral growth alterations without any known antenatal complications. Lastly, we were unable to find differences in cerebral maturation on cUS, possibly because we used a scoring system that uses a number of rough markers of maturation. A more detailed, validated scoring system for cUS scans is currently unavailable. Nevertheless, our results are strengthened by the unique population of identical twins discordant in birth weight, inherently controlling for sex, gestational age at birth and genetic and maternal factors. By including matched singletons, we were able to investigate changes that are specific to MC twins and may also be present in the larger twin.
More research is necessary to investigate the effects of the observed structural differences on brain function. A systematic review published by our group concluded that the smaller twin of MC pairs with sFGR is at increased risk of neurodevelopmental impairment 35 . However, this was based on just five articles with varying degrees of validity. Long‐term follow‐up with neurodevelopmental testing is needed to provide more conclusive evidence. Ideally, magnetic resonance imaging at term should be performed to achieve a better understanding of alterations in brain growth, maturation and connectivity. Longitudinal neuroimaging beyond the neonatal period should be used to assess whether these alterations are permanent or whether there is catch‐up growth over time.
To conclude, sFGR in MC twins is associated with an overall restriction in brain growth on neonatal cUS. The severity of BWD and intracranial‐volume discordance are positively correlated, suggesting that the BWD translates into a discordance in brain size as well (this size discordance being smaller than BWD). Our results reinforce the hypothesis that FGR has significant implications for brain development.
Supporting information
Table S1 Definitions of the structural measurements on neonatal cerebral ultrasound
Table S2 Neonatal cerebral ultrasound parameters in twins with selective fetal growth restriction and matched singletons, corrected for intracranial volume
Table S3 Intraclass correlation coefficients for the neonatal cerebral ultrasound parameters
Click here for additional data file.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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PMC009xxxxxx/PMC9415131.txt |
==== Front
J Comp Neurol
J Comp Neurol
10.1002/(ISSN)1096-9861
CNE
The Journal of Comparative Neurology
0021-9967
1096-9861
John Wiley and Sons Inc. Hoboken
35015905
10.1002/cne.25297
CNE25297
Research Article
Research Articles
Distribution of neurogranin‐like immunoreactivity in the brain and sensory organs of the adult zebrafish
ALBA‐GONZÁLEZ et al.
Alba‐González Anabel https://orcid.org/0000-0003-1627-3006
1 2
Folgueira Mónica https://orcid.org/0000-0003-2927-7516
1 2 [email protected]
Castro Antonio 1 2
Anadón Ramón https://orcid.org/0000-0003-3260-1209
3
Yáñez Julián https://orcid.org/0000-0002-4559-8398
1 2 [email protected]
1 Department of Biology, Faculty of Sciences University of A Coruña A Coruña Spain
2 Centro de Investigaciones Científicas Avanzadas (CICA) University of A Coruña A Coruña Spain
3 Department of Functional Biology, Faculty of Biology University of Santiago de Compostela Santiago de Compostela Spain
* Correspondence
Mónica Folgueira and Julián Yáñez, Department of Biology, Faculty of Sciences. University of A Coruña. Campus da Zapateira. 15008 A Coruña, Spain.
Email: [email protected]; [email protected]
09 2 2022
7 2022
530 10 10.1002/cne.v530.10 15691587
30 12 2021
30 7 2021
03 1 2022
© 2022 The Authors. The Journal of Comparative Neurology published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Abstract
We studied the expression of neurogranin in the brain and some sensory organs (barbel taste buds, olfactory organs, and retina) of adult zebrafish. Database analysis shows zebrafish has two paralog neurogranin genes (nrgna and nrgnb) that translate into three peptides with a conserved IQ domain, as in mammals. Western blots of zebrafish brain extracts using an anti‐neurogranin antiserum revealed three separate bands, confirming the presence of three neurogranin peptides. Immunohistochemistry shows neurogranin‐like expression in the brain and sensory organs (taste buds, neuromasts and olfactory epithelium), not being able to discern its three different peptides. In the retina, the most conspicuous positive cells were bipolar neurons. In the brain, immunopositive neurons were observed in all major regions (pallium, subpallium, preoptic area, hypothalamus, diencephalon, mesencephalon and rhombencephalon, including the cerebellum), a more extended distribution than in mammals. Interestingly, dendrites, cell bodies and axon terminals of some neurons were immunopositive, thus zebrafish neurogranins may play presynaptic and postsynaptic roles. Most positive neurons were found in primary sensory centers (viscerosensory column and medial octavolateral nucleus) and integrative centers (pallium, subpallium, optic tectum and cerebellum), which have complex synaptic circuitry. However, we also observed expression in areas not related to sensory or integrative functions, such as in cerebrospinal fluid‐contacting cells associated with the hypothalamic recesses, which exhibited high neurogranin‐like immunoreactivity. Together, these results reveal important differences with the patterns reported in mammals, suggesting divergent evolution from the common ancestor.
The expression and distribution of mammalian neurogranin‐like peptides were studied in the brain and sensory organs of adult zebrafish by means of immunocytochemical methods. Results indicate the relation of these neuropeptides with primary and highly integrative centers and suggest a divergent evolution in teleosts.
Danio rerio
neurogranin
protein kinase C
RC3
teleost
zebrafish
Predoctoral Fellowship from Xunta de GaliciaED481A‐2019/003 source-schema-version-number2.0
cover-dateJuly 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
Alba‐González, A. , Folgueira, M. , Castro, A. , Anadón, R. , & Yáñez, J. (2022). Distribution of neurogranin‐like immunoreactivity in the brain and sensory organs of the adult zebrafish. J Comp Neurol. 530 , 1569–1587. 10.1002/cne.25297 35015905
[Correction added on 25 May 2022, after first online publication: The copyright line was changed.]
==== Body
pmcAbbreviations
A anterior thalamic nucleus
APN accessory pretectal nucleus
ca anterior commissure
CC cerebellar crest
Ccb cerebellar corpus
cg gustatory commissure
CiL central nucleus of the hypothalamic lobe
CON caudal octavolateral nucleus
CP central posterior thalamic nucleus
cven rhombencephalic ventral commissure
D dorsal telencephalic area (pallium)
Dc central zone of D
Dd dorsal zone of D
DiL diffuse nucleus of the hypothalamic lobe
Dl lateral zone of D
Dlv ventral part of Dl
Dm medial zone of D
Dp posterior zone of D
DP dorsal posterior thalamic nuclei
dV descending trigeminal root
EG eminentia granularis
fr fasciculus retroflexus
gc central gray
GC retinal ganglion cell layer
GCL granular cell layer
GL glomerular layer
Hb habenulae
Hc caudal hypothalamic nucleus
Hd dorsal nucleus of the periventricular hypothalamus
Hv ventral nucleus of periventricular hypothalamus
IL inferior lobe / hypothalamic lobe
INL retinal inner nuclear layer
IO inferior olive
Ip interpeduncular nucleus
IPL retinal inner plexiform layer
IRF intermediate reticular formation
lfb lateral forebrain bundle
LIX glossopharyngeal lobe
ll lateral lemniscus
LVII facial lobe
LX vagal lobe
M Mauthner cell
Ma Mauthner axon
MB mammillary body
mfb medial forebrain bundle
MFN medial funicular nucleus
mlf medial longitudinal fascicle
MON medial octavolateral nucleus
NAT anterior tuberal nucleus
nC commissural nucleus of Cajal
NI nucleus isthmi
nIII oculomotor nucleus
NLV lateral valvular nucleus
Nm neuromast
NMLF nucleus of the medial longitudinal fascicle
nV trigeminal nerve
OB olfactory bulb
OLM retinal outer limiting membrane
OPL retinal outer plexiform layer
OSL photoreceptor outer segment layer
OT optic tectum
ot optic tract
PC layer of outer and inner segments of photoreceptor cells
PGl lateral preglomerular nucleus
PGm medial preglomerular nucleus
PO posterior pretectal nucleus
Ppa parvocellular preoptic nucleus
Ppm magnocellular preoptic nucleus
PSm magnocellular superficial pretectal nucleus
PSp parvocellular superficial pretectal nucleus
Pth prethalamus
PTN posterior tuberal nucleus
Pvo paraventricular organ
Q optic chiasm
rIII oculomotor nerve root
RL nucleus rostrolateralis
SAC stratum album centrale
SFGS stratum fibrosum et griseum superficiale
SGC stratum griseum centrale
sgt secondary gustatory tract
SGVN secondary gustatory/visceral nucleus
SM stratum marginale
SO stratum opticum
SPV stratum periventriculare
SRF superior reticular formation
STN sensory trigeminal nucleus
sy sulcus ypsiloniformis
tbc tectobulbaris cruciatus tract
tbr tectobulbaris rectus tract
TGN tertiary gustatory nucleus (of Yáñez et al., 2017)
Th thalamus
tip isthmo‐pretectal tract
TLa torus lateralis
TLo torus longitudinalis
tob torobulbar tract
TPp periventricular nucleus of the posterior tubercle
TS torus semicircularis
V ventral telencephalic area
Val lateral division of the cerebellar valvula
Vam medial division of the cerebellar valvula
Vc central nucleus of V
Vcb cerebellar valvula
Vd dorsal nucleus of V
VIII octaval nerve
Vl lateral nucleus of V
Vm motor root of the trigeminal nerve
Vs supracommissural nucleus of V
Vv ventral nucleus of V
X vagal nerve
Xm vagal motor nucleus
1 INTRODUCTION
The neuronal protein p17/RC3/neurogranin was initially purified and sequenced from bovine brain extracts (p17; Baudier et al., 1989, 1991) and, almost in parallel, identified based on the analysis of cDNA clones that showed differential expression between rat forebrain and cerebellum (RC3; Watson et al., 1990). Shortly after, Deloulme et al. (1991) showed that p17 and RC3 were the same protein, named neurogranin (Nrgn). This 78 amino acids peptide contains a consensus IQ‐motif that has at least a binding domain for calmodulin (CaM) (Baudier et al., 1989, 1991; Deloulme et al., 1991; Prichard et al., 1999) and a serine site for protein kinase C (PKC) phosphorylation (Baudier et al., 1991). The distribution of neurogranin in the rodent forebrain has been shown based on in situ hybridization and immunohistochemistry, in particular, in neurons of the cortex and hippocampus, striatum, amygdala, subthalamus, and hypothalamus, with high expression in postsynaptic structures (dendrites, somas) (Represa et al., 1990; Watson et al., 1990, 1992). Various studies have revealed that nonphosphorylated neurogranin appears to regulate neuronal activity via postsynaptic binding to the Ca2+ free form of CaM, modulating phosphorylation of proteins by Ca2+/CaM‐dependent PKCs and resulting in synaptic plasticity (Díez‐Guerra, 2010; Slemmon et al., 2000; Zhong & Gerges, 2012). A well‐characterized effect is long‐term potentiation (LTP) induction, which requires the activation of NMDA receptors and an increase in the Ca2+ concentration within dendritic spines (Zhong & Gerges, 2012). Neurogranin appears involved in higher neural processing such as learning, adaptation and memory formation (Zhong et al., 2015). Several studies also suggested a potential relation of neurogranin with neurodegenerative processes (see Xiang et al., 2020).
Zebrafish (Danio rerio) is a major model vertebrate for studies of developmental biology, including brain development, pharmacology, and neurobiology (Barros et al., 2008; Cassar et al., 2020; Eisen, 1991; Fulwiler & Gilbert, 1991; Schmidt et al., 2013). Adult zebrafish is also an important model for adult neuroanatomy, including a number of cytoarchitectonic and neurochemical studies (Barreiro‐Iglesias et al., 2013; Castro et al., 2006a, 2006b, 2009; Kaslin & Panula, 2001; Kim et al., 2004; Mathieu et al., 2002; Mueller & Guo, 2009; Mueller et al., 2004; Wullimann et al., 1996; K. Yamamoto et al., 2010, Yamamoto et al., 2011) as well as hodological studies (Folgueira et al., 2020; Rink & Wullimann, 2004; Turner et al., 2016; Yáñez et al., 2017, 2018, 2021). Whereas the expression of different neurotransmitters (e.g., serotonin, dopamine, histamine, GABA, glycine), neurotransmitter‐synthesizing enzymes (e.g., GAD, TH1, TH2, ChAT), and neuropeptides (e.g., GHRH, NPY) were the subject of various studies, regulatory proteins such as neurogranin have not been studied in the adult zebrafish brain. Thus, here we analyze in silico the sequences of the three neurogranin peptides predicted in zebrafish and compare them with those of other vertebrates. Western blot analysis, using a commercial neurogranin antiserum, confirms the presence of three “neurogranin‐like” (Nrgn‐like) peptides in zebrafish brain extracts, likely to be the products from paralog genes nrgna and nrgnb (Gómez et al., 2012; Wong et al., 2009; Zada et al., 2014). By immunohistochemical methods with the same antiserum, we describe the distribution of Nrgn‐like immunoreactivity in the adult brain, olfactory organ, retina, and taste buds, showing that the expression is cell‐type specific. Our results reveal some important differences between the distribution patterns reported in zebrafish and mammals, suggesting divergent evolution of the neurogranin systems of ray‐finned fishes and land vertebrates from the last common ancestor.
2 MATERIAL AND METHODS
2.1 Animals
Wild‐type zebrafish adults (Danio rerio) were kept in standard conditions (Aleström et al., 2020) at 28.0 ± 1.0°C and 14 h light/10 h dark periods. Fish were fed a mixture of dry flakes and Artemia sp. twice a day. All procedures were carried out following the Spanish Royal Decree 53/2013 and the European Union Directive 2010/63/EU regarding the protection of animals used for scientific research purposes.
2.2 Neurogranin sequence analysis
Protein sequences for reedfish (bichir; Erpetoichthys calabaricus), zebrafish (D. rerio), Atlantic salmon (Salmo salar), spotted green pufferfish (Tetraodon nigroviridis), coelacanth (Latimeria chalumnae), xenopus (Xenopus tropicalis), green anole (Anolis carolinensis), easter brown snake (Pseudonaja textilis), zebrafinch (Taeniopygia guttata), platypus (Ornithorhynchus anatinus), rat (Rattus norvegicus) and human (Homo sapiens) neurogranins were obtained from Uniprot (www.uniprot.org) or Ensembl (https://www.ensembl.org/index.html) (Table 1). Protein sequences were aligned using NCBI Blastp (NIH; https://blast.ncbi.nlm.nih.gov/Blast.cgi) with the simplest adjustment (“composition‐based statistics”; Schäffer et al., 2001). Fully conserved amino acids (aa) were marked with an asterisk (*), groups of strongly similar properties with a colon (:), and weakly similar properties with periods (.). The IQ domain (20 aa; AAAAKIQASFRGHMARKKIK) was annotated manually following the sequence described by Prichard et al. (1999).
TABLE 1 Protein sequence identifiers for neurogranin in several vertebrates
Species Sequence identifier Length (aa)
Reedfish (Erpetoichthys calabricus) Ensembl‐ENSECRT00000017376.1 74
Zebrafish (Danio rerio) UniProtKB‐F6NQC4 (F6NQC4_DANRE) 60
UniprotKB‐F8W4I3 (F8W4I3_DANRE) 68
UniProtKB‐B7TWQ3 (B7TWQ3_DANRE) 92
UniProtKB‐A1L278 (A1L278_DANRE) 188
Atlantic salmon (Salmo salar) UniProtKB‐B5XEB8 (B5XEB8_SALSA) 94
Spotted green pufferfish (Tetraodon nigroviridis) UniProtKB‐H3BZW5 (H3BZW5_TETNG 84
Coelacanth (Latimeria chalumnae) UniProtKB ‐ H3ACE7 (H3ACE7_LATCH) 75
Xenopus (Xenopus tropicalis) Ensembl‐ENSXETG00000028009 495
Green anole (Anolis carolinensis) Ensembl‐ENSACAG00000025439 62
Easter brown snake (Pseudonaja textilis) Ensembl‐ENSPTXP00000015095 71
Zebrafinch (Taeniopygia guttata) Ensembl‐ENSTGUP00000034709 124
Platypus (Ornithorhynchus anatinus) Ensembl‐ENSOANP00000054267 73
Rat (Rattus norvegicus) UniProtKB‐Q04940 (NEUG_RAT) 78
Human (Homo sapiens) UniProtKB ‐ Q92686 (NEUG_HUMAN) 78
Neurogranin sequence identifiers and lengths (number of amino acids: aa) were taken from UniProt and Emsembl databases.
John Wiley & Sons, Ltd.
2.3 Western blot assay
Brains (80 mg) and spleens (10 mg) of five adult zebrafish were mechanically homogenized separately in lysis buffer solution (pH 7.6) containing 50 mM Tris, 5 mM EDTA, 5 mM 2‐mercaptoethanol, 150 mM NaCl, and a complete protease inhibitor (Thermo Scientific™, PI88266). The homogenized tissues were then centrifuged at 4°C (12,000 rpm; 5 min). Proteins and peptides in the supernatant were centrifuged again for 40 min in the same conditions. Finally, supernatant of this fraction was collected, and total protein concentrations were determined using the Protein Assay Dye Reagent Concentrate (Bio‐Rad, #5000006). Supernatants were aliquoted and stored at −20°C until further used.
After defrosting the aliquots, proteins and peptides were denatured for 5 min at 90°C and separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE). For this, 10–20 μg of protein in 4× sample buffer (Sigma‐Aldrich, G2526) was applied to each lane of a 16% polyacrylamide gel. A standard molecular weight marker (Spectra Multicolor Broad Range Protein Ladder 10–260 kDa; ThermoFisher Scientific, Product #SM26634) was also included in an additional lane. A constant electric current of 100 V was then applied to the gel for 2 h (Mini‐Protean II PAGE system, Bio‐Rad, Richond, CA, USA). Homogenates of Wistar rat (R. norvegicus) and gray mullet (Chelon labrosus) brains, prepared for previous studies (Lamas et al., 2007), were subjected to identical protocol. Separated proteins and peptides were then electroblotted onto a nitrocellulose membrane (0.2 μm pore size; Bio‐Rad 162‐0146, #G5961143) at 100 V for 1 h.
For immunoblotting, nonspecific binding sites were blocked by incubation in 5% milk powder in 0.01 M saline phosphate buffer (PBS; 0.14 M NaCl, pH 7.4) with 0.2% Tween‐20 (PBS‐MT) overnight at 4°C. Then, membranes were incubated with anti‐Nrgn antiserum (Rabbit Anti‐Neurogranin Polyclonal Antibody, Chemicon, AB5620, Lot #3091673) in PBS‐MT (1:1000 dilution) overnight at 4°C. According to the supplier, this antibody was generated in rabbits against the complete sequence of recombinant rat neurogranin. Next, membranes were washed three times (10 min each) in PBS‐MT and then incubated for 1.5 h at room temperature with goat‐anti‐rabbit IgG antibody conjugated to horseradish peroxidase (Sigma Aldrich, A9169, Lot #023M4757, 1:800 dilution) in PBS‐MT. After three rinses in PBS‐MT, antibody bindings were visualized by incubation in diaminobenzidine (Sigma‐Aldrich, 077K36641) with 0.001 % hydrogen peroxide in PBS for 5–10 min at room temperature, intensified with 0.03 % cobalt chloride.
2.4 Immunohistochemistry
For immunohistochemical assays, nine adult zebrafish were euthanized in 0.1% ethyl 3‐aminobenzoate methanesulfonate (MS‐222) and next perfused with 4% paraformaldehyde (PFA) in PBS. After perfusion, brains, retinas, olfactory organs, and one oral barbel were dissected out and postfixed overnight in the same fixative at room temperature. Then, samples were rinsed in PBS and kept at 4°C until use.
For immunocytochemistry in cryosections, brains and retinas were cryoprotected in 30% sucrose in phosphate buffer (PB; 0.1 M pH 7.4) overnight at 4°C. Then, brains, olfactory organs, and retinas were embedded in Tissue‐TEK OCT medium (Sakura Finetek, Torrance, CA, USA) and frozen by immersion in liquid nitrogen‐cooled methyl butane. Blocks were stored at −20°C until sectioning in a cryostat (transverse sections; 12 μm thick). In order to perform the immunostaining, sections were first incubated in 0.2% sodium borohydride in PBS (30 min). Nonspecific binding sites were blocked by incubating sections in 10% normal goat serum (Sigma Aldrich, G6767‐19B409) in 0.5% Triton‐X‐100 in 0.1 M PBS (PBST; pH 7.4) for 1 h. Sections were then incubated overnight at 4°C with the neurogranin antiserum (Rabbit Anti‐Neurogranin Polyclonal Antibody, Chemicon, AB5620, Lot#3091673, 1:500–1:750 dilution). For immunofluorescence, samples were incubated with Alexa Fluor 488‐coupled goat anti‐rabbit IgG antibody (Sigma Aldrich, A11008, 1:500 dilution) for 1 h at room temperature. Slides were mounted using 50% glycerol in PB.
For immunofluorescence in vibratome sections, fixed brains and retinas were embedded in 3% agarose and sectioned using a vibratome (Vibroslice, Campden Instruments, UK; 50–60 μm thick). Then, transverse sections were transferred to small Eppendorf tubes and stored overnight in 100% methanol at −20°C. Sections were rehydrated, washed three times (5 min each) in PBST, permeabilized with proteinase K in PBS (20 min; Sigma Aldrich, P2308), and then postfixed with 4% PFA (20 min). After three washes in PBST (10 min each), sections were incubated with 10% normal goat serum (Sigma Aldrich, G6767‐19B409) in PBST for 1 h. Then, sections were incubated with the primary antibody solution (Rabbit Anti‐Neurogranin Polyclonal Antibody; Chemicon, AB5620, Lot#3091673, 1:500 dilution) for 3 days at 4°C. After, sections were washed four times (30 min each) and incubated with Alexa Fluor 488‐coupled goat anti‐rabbit antibody (Sigma Aldrich, A11008, 1:500 dilution) overnight at 4°C. Sections were washed in PBST (30 min) and mounted on gelatinized slides with 50% glycerol in PB for imaging. The oral barbel of an adult zebrafish and the trunk of two specimens of 5 days old were also processed in toto in a similar way as the vibratome sections.
As complementary material, paraffin sections of two additional adult zebrafish were also used for double immunolabeling against neurogranin and zebrin II monoclonal antibody (hybridoma supernatant from Prof. Hawkes, dilution 1:100; see also Lannoo et al., 1991a, 1991b).
Sections of adult zebrafish brains and retinas were analyzed with an Epifluorescence microscope (Nikon Eclipse 90i) coupled to Olympus DP71 digital camera. The maxillary barbel was also imaged using a laser scanning confocal microscope Nikon A1R equipped with Nikon Plan Fluor 20× (0.50 NA) objective lens.
3 RESULTS
3.1 Comparative sequence analysis of the neurogranin polypeptide
Zebrafish have two neurogranin paralog genes: nrgna (ZFIN ID: ZDB‐GENE‐090710‐4; Zada et al., 2014) and nrgnb (ZFIN ID: ZDB‐GENE‐070112‐1912; spa17 in Gómez et al., 2012; “neurogranin‐similar” in Wong et al., 2009) located in chromosomes 5 and 15, respectively. From these two genes, four peptides of different lengths have been predicted based on evidence at a transcript or sequence level: (a) from nrgna, three isoforms of 60 aa (sequence identifier: UniProtKB‐F6NQC4), 68 aa (UniprotKB‐F8W4I3), and 92 aa (UniProtKB‐B7TWQ3); (b) from nrgnb, a peptide of 188 aa (UniProtKB‐A1L278). The transcript for the 68 aa peptide seems to be susceptible to “nonsense‐mediated decay” (see Emsembl), so it is not likely to be translated (Karousis & Mühlemann, 2019). Thus, the two neurogranin genes in zebrafish seem to translate into three peptides, referred to here onwards as Nrgna‐60, Nrgna‐92, and Nrgnb‐188.
The alignment of the deduced zebrafish neurogranin sequences (Nrgna‐60, Nrgna‐92, and Nrgnb‐188) with homolog sequences in other species is shown in Figure 1. Overall, zebrafish neurogranin sequences show a high degree of conservation (Figure 1a), with Nrgnb‐188 having the highest identity with the rat. Manual annotation showed that all zebrafish predicted peptides contain an IQ domain with over 80% amino acid identity (Figure 1b).
FIGURE 1 (a) Results of NCBI Blastp showing amino acid identity (%) of full‐length sequences of neurogranin from rat (R. norvegicus), spotted green pufferfish (T. nigroviridis), Atlantic salmon (S. salar), and zebrafish (D. rerio) variants a (derived from nrgna > Nrgna‐60 and Nrgna‐92) and variant b (nrgnb > Nrgnb‐188). (b) Alignment of neurogranin sequences containing IQ domain between organisms. Right column indicates the position of the last amino acid shown in the selected portion of the whole sequence. Twenty‐five amino acids were fully conserved (*), six amino acids were strongly similar (:) and only two amino acids were weakly similar (.). IQ domain is highly conserved in all sequences [16 (*), 2 (:), and 1 (.)]. Serine (S) located in position 36 (outlined box) in basal actinopterigyans (E. calabaricus), reptiles, birds, and mammals, is substituted by glycine (G) in teleosts and X. tropicalis, and by asparagine (N) in sarcopterygii (L. chalumnae)
We compared the IQ domain of zebrafish with that of other species of actinopterygians (Erpetoichthys calabricus, S. salar, and T. nigroviridis), sarcopterygians (L. chalumnae), amphibian (X. tropicalis), reptiles (A. carolinensis and P. textilis), bird (T. guttata), and mammals (O. anatinus, R. norvegicus, and H. sapiens). This showed differences in position 36, which is phosphorylated by PKC in mammals (Baudier et al., 1991; Díez‐Guerra, 2010; Domínguez‐González et al., 2007; Gerendasy & Sutcliffe, 1997; Huang et al., 1993; Koob et al., 2014; Yang et al., 2015). A glycine (G) is present in this position in teleosts and Xenopus, while an asparagine (N) is present in Latimeria and a serine (S) in cladistians (basal actinopterygians), reptiles, birds, and mammals (Figure 1b).
3.2 Western blot analysis
We investigated the specificity of the neurogranin primary antibody used in this study by western blot analysis with brain homogenates of adult zebrafish, rat and gray mullet (Figure 2). In addition, zebrafish spleen homogenates were also used (Figure 2). In the rat lane (Ra), we observed a band of around 14 kDa that matches results from previous studies (Baudier et al., 1989, 1991; Represa et al., 1990; Watson et al., 1990, 1992). In the case of the zebrafish brain, our results revealed three different bands of approximately 13, 20 and 37 kDa in two homogenates (ZF1 and ZF3). Based on their predicted molecular mass and neurogranin behavior in SDS–PAGE (Baudier et al., 1989, 1991; Coggins et al., 1993; Deloulme et al., 1991; Díez‐Guerra, 2010; Tejero‐Díez et al., 1999), these three bands seem to correspond to the three polypeptides of 60 aa, 92 aa, and 188 aa predicted for zebrafish. One brain homogenate (ZF2), which did not contain the telencephalon, did not reveal the 37‐kDa band. In zebrafish spleen homogenate (S), we did not observe any labeled band. Similar to zebrafish, we also observed three bands of about 12, 19, and 39 kDa in the gray mullet protein extract (Mu) (Figure 2). Our results indicate that the primary antibody used here, raised against rat neurogranin, cross‐reacts with zebrafish, and gray mullet neurogranin or “Nrgn‐like” proteins.
FIGURE 2 Western blot results for the different protein homogenates employed from zebrafish (ZF and S), Wistar rat (Rat) and gray mullet (Mu). Two zebrafish brain extracts (ZF1 and ZF3) show three Nrgn‐ir bands of about 13, 20 and 37 kDa. Note the absence of the 37‐kDa band in the zebrafish brain extract lacking the telencephalic lobes (ZF2). In zebrafish spleen homogenate (S), no Nrgn‐ir bands were observed. Immunoblotting with anti‐Nrgn‐antiserum was also analyzed in parallel in brain extracts of Wistar rat (R. norvegicus) and gray mullet (Mu; C. labrosus)
3.3 Neurogranin expression in the brain and sensory organs
In the adult zebrafish, the presence of Nrgn‐like immunoreactivity was studied in an entire oral barbel and sections of the olfactory organ, retina and brain. We observed immunoreactivity mainly located in the cytosol of cell bodies, sometimes extending along thick dendrites, but also in fiber bundles and axon terminals (Figures 3, 4, 5, 6, 7, 8). In the brain, all positive cells were putatively identified as neurons, whereas the ependymoglia (the most abundant and easily identifiable macroglial cell type in zebrafish and other teleosts) was always Nrgn‐like negative. Since no experiment with double labeling of neurogranin with oligodendrocyte markers was performed, we cannot rule out the possibility that some cells were oligodendrocytes, although positive cells were lacking in the major white matter tracts where oligodendrocytes typically abound in teleosts (Díaz‐Regueira & Anadón, 1998). In one specimen, grainy fluorescence was observed in some endothelial cells (not shown).
FIGURE 3 (a–d) Nrgn‐like distribution in the surface of a barbel (a), skin (b), the olfactory organ (c) and the retina (e, f). (a) Nrgn‐like immunoreactivity in the apical region of sensory cells in taste buds (arrowheads); detailed in the inset. (b) Transverse section of two Nrgn‐like immunoreactive taste buds (arrowheads) at the dorsal surface of the head. Arrow points to apical microvilli. (c) Transverse section of the olfactory rosette showing Nrgn‐like‐ir in cell bodies (arrowheads) and apical dendrites (arrows) of olfactory receptor cells. Note the very low immunoreactivity in the olfactory axon bundles (open arrows). (d) Detail of a supraorbital canal neuromast showing weak Nrgn‐like immunoreaction in hair cells (arrowheads). (e) Vertical section of the retina showing Nrgn‐like‐ir cell bodies (arrowhead), puncta (black arrows) and comma‐like structures (white arrows). (f) Oblique section through the retina showing Nrgn‐like‐ir cell bodies in the INL (arrowheads) and details of the Nrgn‐like‐ir puncta arranged in a ring pattern around the base of the inner segment of photoreceptors (black arrows). For abbreviations, see the list. Scale bars: 100 μm (a–c), 50 μm (b,d), 25 μm (e–f), and 10 μm (inset in a)
FIGURE 4 (a–p) Schematic drawings of transverse sections of the zebrafish brain from rostral (a) to caudal (p) showing on the left the Nrgn‐like‐ir cell bodies (big dots), neuronal processes and tracts (thin lines and small dots), while on the right the annotations of different nuclei and tracts. The levels of the sections are indicated by lines in the lateral diagram of the brain on the left. For abbreviations, see the list. Scale bar for sections: 200 μm (a–p)
FIGURE 5 (a–i) Photomicrographs of transverse sections of the olfactory bulb (a–d), telencephalic lobes (e–h) and preoptic area (i) of the zebrafish brain showing Nrgn‐like‐ir cell bodies (arrowheads) and fibers (arrows). Medial is to the right. (a) General view of a vibratome section of the olfactory bulb showing Nrgn‐like‐ir fibers in the glomerular layer. (b) Detail of an olfactory glomerulus showing Nrgn‐like‐ir fibers. (c) Detail of a cryostat section showing Nrgn‐like‐ir cell bodies in the glomerular layer. (d) Detail of Nrgn‐like‐ir granule cells. (e) General view of a telencephalic lobe at the rostral level. (f) Vibratome section of a telencephalic lobe at precommissural level. (g) Detail of a vibratome section showing Nrgn‐like‐ir cell bodies. Note the absence of labeled cell bodies in the Dlv and Dc. (h) Vibratome section through the subpallium showing positive cell bodies in Vd and Vv. (i) Detail of positive cell bodies in the anterior parvocellular preoptic region (Ppa). Asterisk, ventricle. For abbreviations, see the list. Scale bars: 200 μm (e–f), 100 μm (a, g–h), 50 μm (b,c, i), and 20 μm (d)
FIGURE 6 (a–f) Vibratome (a–c, e–f) and cryostat (d) cross sections of the zebrafish brain through the hypothalamus (a,b) and diencephalon (c–f), showing Nrgn‐like‐ir labeled cell bodies (arrowheads) and processes (arrows). Medial is to the right. (a) Section through the rostral hypothalamic region showing lightly labeled Nrgn‐like‐ir cell bodies in the NAT. (b) Detail of the lateral inferior lobe showing immunopositive cells in the torus lateralis (TLa), diffuse nucleus (DiL) and around the lateral recess (LR). (c) Detail of Nrgn‐like‐ir immunoreaction in the posterior lobe. (d) Section showing immunoreactive cell bodies in the nucleus rostrolateralis (RL). (e) Section through the thalamus. (f) Detail of the pretectal area. Asterisk, ventricle. For abbreviations, see the list. Scale bars: 100 μm (b,c, e–f), 50 μm (a,d)
FIGURE 7 (a–c) Photomicrographs of transverse sections through the mesencephalon of the zebrafish showing Nrgn‐like‐ir immunoreaction in cell bodies (arrowheads) and processes (arrows). (a) Cross section through the OT showing immunoreactive cell bodies (arrowheads). Note the SM is crowded with immunoreactive dendritic branches. (b) Transverse vibratome section through the mesencephalic tegmentum. The midline is to the right. (c) Detail of Nrgn‐like‐ir axonal endings (arrows) in the NLV. For abbreviations, see the list. Scale bars: 200 μm (b), 50 μm (a), 20 μm (c)
FIGURE 8 (a–m) Photomicrographs of transverse sections of the zebrafish rhombencephalon showing Nrgn‐like‐ir cell bodies (arrowheads) and processes (arrows). Medial is to the right except in (d). (a,b) Nrgn‐like‐ir in Purkinje cells in the medial division of the valvula (a, b) and corpus (b) of the cerebellum. Only in the cerebellar valvula (Vcb) the apical dendrites of these cells (arrows) can be well observed. (c) Detail of the NI and the secondary gustatory‐visceral nucleus (SGVN) showing intensely and lightly labeled immunoreactive cell bodies, respectively. (d) Detail of the interpeduncular nucleus (Ip). (e) Nrgn‐like‐ir neurons in the sensory nucleus of the trigeminal nerve (STN) and dorsal to the sensory root entrance of the trigeminal nerve (nV). (f) General view of a cryostat section at the level of the octaval nerve (VIII) entrance. Note the high amount of Nrgn‐like‐ir dendrites in the CC and the Nrgn‐like‐ir in two fiber bundles, the secondary gustatory tract (sgt) and the bundle at the ventrolateral margin of the tegmentum (thick arrow). (g) Vibratome cross section showing a detail of the Nrgn‐like‐ir cells of the MON. (h) Vibratome section showing a discrete group of Nrgn‐like‐ir cells close to the Mauthner cell. (i) Nrgn‐like‐ir cells (arrowheads) in the caudal octavolateral region and the viscerosensory lobes (IX, VII). Note the Nrgn‐like‐ir dendrites of the CON (arrows) entering the CC. (j) Detail of the vagal lobe showing Nrgn‐like‐ir cell bodies mostly at its periphery. (k) Vibratome cross section at the level of the inferior reticular formation showing immunoreactivity in a large reticular cell and a couple of small cells. Note also the sgt intensely labeled. (l) Detail of Nrgn‐like‐ir cell bodies in the IO. (m) Section at the level of the commissural nucleus of Cajal (nC). Asterisk, ventricle. For abbreviations, see the list. Scale bars: 500 μm (f), 200 μm (k, m), 100 μm (a,b,e, i,j), 50 μm (c,d, g,h),and20 μm (l)
3.3.1 Chemosensory organs
Nrgn‐like immunoreactive (‐ir) cell bodies were observed in taste buds of the oral barbel and in the olfactory organ of the adult zebrafish. Taste buds in zebrafish are distributed in the mouth and oropharyngeal cavities and the skin of the head, lips, oral barbels, and other regions of the body (Hansen et al., 2002). Of all these, we analyzed only the maxillary barbels present in a sexually mature fish. We observed Nrgn‐like immunoreactivity in apical dendrites of some taste cells (6–10 cells in each taste bud) (Figure 3a,b).
In the olfactory organ, goblet cells located peripherally in the non sensory epithelium of the olfactory lamellae appeared intensely immunolabeled. In the sensory epithelium, weak Nrgn‐like immunoreactivity was observed in bipolar cell bodies located at basal and intermediate levels, and in some apical dendrites. In addition, very low immunoreactivity was observed in axon bundles of the olfactory nerve coursing under the epithelium (Figure 3c).
3.3.2 Mechanosensory organs
Although a search was made only along the head of the adult zebrafish, Nrgn‐like‐ir cells were observed in hair cells of cranial canal neuromast (Figure 3d). No Nrgn‐like immunoreactive superficial neuromasts of the lateral line could be confirmed in the 5‐day‐old larvae analyzed in toto.
3.3.3 Retina
In the adult retina, Nrgn‐like‐ir cells were identified as bipolar, amacrine and ganglion cells (Figure 3e). In these cells, Nrgn‐like immunoreactivity seems to be located in the cytosol, labeling the whole cell. Nrgn‐like‐ir bipolar cells showed a characteristic morphology: a fusiform cell body extending a thick apical dendrite that branches in the outer plexiform layer and a thin axon that branches in the inner plexiform layer (IPL) (Connaughton et al., 2004). By their morphology, most of these cells probably correspond to rod bipolar cells. Cells identified as amacrine neurons showed weakly labeled round cell bodies located in the proximal tier of the inner nuclear layer (INL). A number of ganglion cell somas were faintly labeled in the ganglion cell layer, although no immunoreaction could be observed in the optic fiber layer. At the level of the outer limiting membrane, we observed aggregates of 7–12 brightly fluorescent puncta that are arranged in a round pattern, located at the base of the myoid of the inner segment of the short‐single ultraviolet‐wavelength sensitive cones (UV cones) of the adult retina (see Robinson et al., 1993; Salbreux et al., 2012; Takechi et al., 2003). Furthermore, barrels of comma‐like structures were also labeled associated with the base of the inner segment of long outer cones (Figure 3f).
3.3.4 Brain
Telencephalon, preoptic area, and hypothalamus (secondary prosencephalon)
In the olfactory bulbs, Nrgn‐like immunoreactivity was observed mostly in olfactory fibers located in the dorsal and ventrolateral glomeruli (Figures 4a and 5a,b). In some cryostat sections, some Nrgn‐like‐ir round cell bodies were also seen at the margin of the olfactory bulb among the immunoreactive processes (Figure 5c). Occasionally, larger positive somas were also observed within the glomerular layer. Also in cryostat sections, cytoplasmic Nrgn‐like immunoreaction was observed in a number of small cells of the granular layer (Figures 4a and 5d).
In the pallium (dorsal telencephalic area, D) (Figures 4b,c and 5e–h), Nrgn‐like‐ir cell bodies were mostly observed in the periventricular region, but also sparsely distributed in deeper areas of the dorsal (Dd) and lateral (Dl) zones and, but not exclusively, in the ventral area of the medial zone (Dm). In cryostat sections, no positive cells were observed in the posterior zone of D (Dp), although it was traversed by Nrgn‐like‐ir bundles of the lateral telencephalic tract. In addition, at the level of the anterior commissure, Nrgn‐like positive cell bodies and fibers were also observed in the central zone of D (Dc). Note that these zebrafish pallial zones correspond to those described in Yáñez et al. (2021).
In the subpallium (ventral telencephalic area, V) (Figures 4b,c and 5f–h), some Nrgn‐like‐ir perikarya were observed in the dorsal (Vd) and central (Vc) nuclei, as well as a few faintly labeled cells dorsally in the ventral nucleus (Vv). At precommissural levels, Nrgn‐like‐ir fiber bundles mainly originating from Dd converge in the lateral telencephalic tract, which courses along the lateral margin of subpallium. In the same region, a few Nrgn‐like‐ir cells were observed in the lateral nucleus of V (Vl). At commissural levels, Nrgn‐like‐ir fibers of the lateral forebrain bundle decussate at the anterior commissure (ca) (Figure 4c).
In the preoptic area, Nrgn‐like immunoreactivity was seen in many cells located periventricularly in the anterior part of the parvocellular preoptic nucleus (Ppa) and also deeper in some larger neurons that send their processes laterally (Figures 4c and 5i). Occasional Nrgn‐like‐ir cells were also observed in the magnocellular preoptic nucleus (Ppm). A few Nrgn‐like‐ir cells could be also observed in the suprachiasmatic nucleus (Figure 4d).
In the hypothalamus, abundant Nrgn‐like‐ir cell bodies displaying cerebrospinal fluid‐contacting (CSF‐c) morphology were observed in the paraventricular organ (Pvo) and posterior recess organ (Figures 4f,g and 6c). Moreover, a dense mat of Nrgn‐positive fibers is closely associated with these organs, probably formed by the axons of these cells. Nrgn‐like‐ir cells were also observed in the anterior tuberal nucleus (NAT) and the ventral (Hv) and dorsal (Hd) nuclei of the periventricular hypothalamus (Figures 4e,f and 5a,c). Scattered Nrgn‐like‐ir cell bodies could also be observed mainly in the torus lateralis (TLa), and the diffuse (DiL) and central (CiL) nuclei of the hypothalamic inferior lobes (Figures 4e–i and 6a–c). A few positive cell bodies were seen in the region of the tertiary gustatory nucleus (TGN) proper sense of Yáñez et al. (2017) (Figures 4 g and 6b) (see the Discussion).
Diencephalon
In the dorsal (alar) region of the diencephalon, some Nrgn‐like‐ir fibers and a few periventricular cell bodies were located in the prethalamus (Pth; formerly ventral thalamus) (Figures 4d–f and 6d,e). The nucleus rostrolateralis (RL), considered epithalamic by Saidel (2013), showed many Nrgn‐like‐ir cell bodies and fibers (Figures 4d and 6d). In the epithalamus, many positive cell bodies were observed along the pineal tract and in the parapineal organ (not shown). In the habenulae (Hb), a few faintly Nrgn‐like‐ir cell bodies were scattered in the ventral nucleus (Figure 4d,e). In the thalamus (Th; formerly dorsal thalamus), Nrgn‐like‐ir cells were located in the anterior (A) and dorsal posterior thalamic nuclei (Figures 4d–f and 6d–f). In the pretectal area, the parvocellular superficial pretectal nucleus (PSp) showed a number of Nrgn‐like‐ir fibers, whereas the accessory pretectal nucleus (APN) and magnocellular superficial (PSm) pretectal nuclei showed some Nrgn‐like‐ir cell bodies. The posterior pretectal nucleus (PO) showed both Nrgn‐like‐ir cell bodies and fibers (Figures 4d–f and 6f).
In the ventral (basal) diencephalic region, some Nrgn‐like‐ir cell bodies were observed in the ventral part of the periventricular nucleus of the posterior tubercle (TPp), the medial preglomerular nucleus (PGm), and a few periventricular Nrgn‐like‐ir cell bodies in the posterior tuberal nucleus (Figures 4e–h and 6b,c). In addition, both the lateral (lfb) and the medial (mfb) forebrain bundles showed Nrgn‐like‐ir fibers (Figures 4, 5, and 6e,f). Caudally, a few cell bodies were faintly immunostained in the nucleus of the medial longitudinal fascicle (Figure 4h).
Mesencephalon
In the alar region of the mesencephalon, numerous Nrgn‐like‐ir cell bodies were observed in the optic tectum (OT), and in lesser proportion in the torus semicircularis (TS) (Figures 4e–j and 7a,b). In the OT, many round or piriform cell bodies were found in the stratum periventriculare (SPV), which appeared organized in two separated depths (Figure 7a): (1) one periventricular band of four to six rows of pale Nrgn‐like‐ir cell bodies and (2) another apical thinner band of one to three rows of more intensely stained cell bodies next to the stratum album centrale (SAC). Most neurons of the latter band showed a long apical radial dendrite and likely correspond to type XIV cells of Meek and Schellart (1978). In addition, some Nrgn‐like‐ir cell bodies were observed in the stratum fibrosum griseum superficiale (SFGS), a few in the stratum griseum centrale (SGC), and occasionally in the SAC. The cell bodies of the SGC and SFGS are round or fusiform, with a thick apical dendrite branching profusely in the stratum marginale (SM). Thus, these cells appear to correspond with pyramidal cells (Laufer & Vanegas, 1974; Vanegas et al., 1974) or type I cells of Meek and Schellart (1978). Nrgn‐like‐ir fibers were distributed in the stratum opticum, in the SFGS, and three sublaminae in the SGC (Figure 7a). No Nrgn‐like immunoreactivity was observed in the torus longitudinalis (TLo), located along the medial border of the OT.
In the TS, most Nrgn‐like‐ir cell bodies were observed in a periventricular location (Figure 7b). Nrgn‐like‐ir tracts reach the TS running between the OT and the tegmentum.
In the basal mesencephalon, Nrgn‐like immunoreactivity was observed mainly in fiber tracts. Light immunoreactivity was also seen in cell bodies of the oculomotor nucleus (nIII) region (Figures 4i and 7b). However, no immunoreactivity was seen in the oculomotor nerve root or the medial longitudinal fascicle. In the precerebellar lateral valvular nucleus (NLV), thick Nrgn‐like‐ir fiber endings, at least some of them showing a cup‐ or basket‐shaped morphology, were observed around cells of this nucleus (Figures 4h and 7b,c). Based on present results, part of these fibers may come from the ipsilateral hypothalamic lobe, as loose bundles of Nrgn‐like‐ir fibers were observed connecting the lateral lobes of the hypothalamus and the mesencephalic tegmentum (Figures 4i and 7b). Moreover, Nrgn‐like‐ir fibers were observed in the lateral lemniscus (ll), the torobulbar tracts, the isthmo‐pretectal tract (tip) of Yáñez et al. (2017) (see the Discussion), and in the tectobulbaris rectus (tbr) and cruciatus (tbc) tracts (Figures 4 g and 7b) that course caudally.
Rhombencephalon
In the cerebellum, very intense Nrgn‐like immunoreactivity was observed in large cell bodies of the medial (Vam) and lateral (Val) divisions of the cerebellar valvula (Vcb) (Figures 4, 7, and 8a–e). However, only in the medial valvula the characteristic thick dendrites of Purkinje cells, branching into the molecular layer, were clearly immunostained (Figures 7h–j and 8a,b). In the cerebellar corpus (Ccb), fairly numerous large cell bodies also showed Nrgn‐like immunofluorescence (Figures 4i–k and 8b). Double immunostaining against the specific Purkinje cell marker “zebrin II” (see Bae et al., 2009) and neurogranin confirmed colocalization of both markers in many cells, indicating that they were Purkinje cells. In the absence of double immunostaining experiments with markers for eurydendroid cells, we cannot rule out the possibility of neurogranin expression in these cells. In the rostral part of the cerebellar corpus of one specimen, small cell bodies, sparsely distributed mainly in the molecular and granule layer, were also lightly immunostained, but the cell type could not be identified (Figure 8b).
At the rostral rhombencephalic tegmentum, numerous immunoreactive cell bodies were observed dorsally in the nucleus isthmi (NI) and some zones of the secondary gustatory/visceral nucleus (SGVN), as well as many fibers crossing the gustatory commissure (cg) (Figures 4j and 8c). In the ventral midline, the interpeduncular nucleus (Ip) also showed some immunoreactive cell bodies and a few immunoreactive fibers mainly in the dorsal neuropil (Figures 4i and 8d). Nrgn‐like‐ir cell bodies were also sparsely distributed in the superior reticular formation, the central gray (gr) and the raphe (r) (Figure 4j). Some Nrgn‐like‐ir fibers were seen in the secondary gustatory tract (sgt) and also crossing the midline through the rhombencephalic ventral commissure (cven) (Figures 4k and 8e). At this level, Nrgn‐like positive fiber bundles appeared at the margin of the ventromedial and the ventrolateral tegmentum, likely the tectobulbaris rectus (tbr) and cruciatus (tbc) tracts (Figure 4i–j).
At the level of entrance of the trigeminal nerve (nV), many Nrgn‐like positive cell bodies were observed in two groups: (1) a group located just dorsal to the sensory trigeminal nerve root entrance and ventral to the cerebellar eminentia granularis and (2) another group located near the ventrolateral border of the fourth ventricle and that likely corresponds with the principal sensory trigeminal nucleus (STN) (Figures 4k and 8e). Related to a small immunoreactive bundle coursing at the ventrolateral margin of the tegmentum (apparently continuous with the torobulbar tract described above), immunopositive fibers appear to course in the trigeminal area and to the lateral lemniscus (Figures 4k and 8e). Sparser Nrgn‐like‐ir fibers innervate the ventromedial tegmentum and cross in the rhombencephalic ventral commissure (Figures 4k and 8e).
At the level of the octaval nerve entrance, the medial octavolateral nucleus (MON) showed Nrgn‐like‐ir crest cells extending thick apical dendrites to the overlying cerebellar crest (CC), which is continuous with the molecular layer of the cerebellum (Figures 4l,m and 8f,g). Basal dendrites of these cells form a dense plexus within the nucleus among crest cell bodies (Figure 8g). A group of small Nrgn‐like‐ir cell bodies was also seen close to the lateral dendrite of the giant Mauthner cell (M) (Figures 4l and 8h). Caudally, some Nrgn‐like‐ir cell bodies were observed in the facial (LVII), glossopharyngeal (LIX) and vagal (LX) lobes, and a few in the commissural nucleus of Cajal (nC) (Figures 4 m–o and 8i,j). Most of the Nrgn‐like‐ir neurons of the LX were pear‐shaped cells located in the cortex of the lobe and with conspicuous processes directed radially to the core region (Figure 8j). Vagal lobes appeared bilaterally related to positive fibers coursing in the commissural tract (arcuate fibers) (Figure 4n). Furthermore, compact Nrgn‐like‐ir fiber bundles arising from the facial and vagal lobes entered the ipsilateral sgt (Figure 8k). In the caudal rhombencephalon, immunoreactive cell bodies were observed in tegmental regions, both in the intermediate reticular formation and in the inferior olive (IO) (Figures 4l–n and 8i–m). At the transition with the spinal cord, numerous Nrgn‐like‐ir cell bodies and fibers were seen in the medial funicular nucleus (Figures 4o,p and 8m ).
4 DISCUSSION
4.1 Neurogranin‐like peptides in zebrafish
Our western blot results seem to confirm the expression of three neurogranin peptides in zebrafish, as predicted based on gene and transcript sequences. SDS–PAGE protein separation and western blot analysis showed three different bands of approximately 13, 20 and 37 kDa, which likely correspond to the three neurogranin peptides, Nrgna‐60, Nrgna‐92, and Nrgnb‐188, predicted from zebrafish sequences. Two of these peptides (Nrgna‐60 and Nrgna‐92) would be translated from nrgna, while the third larger peptide Nrgnb‐188 would be so from nrgnb. The molecular weights for these peptides deduced from the amino acid sequences are almost half (6.9, 10.6, 20.9 kDa) of those showed in the SDS–PAGE/western blot, but this apparent discrepancy has also been shown for rat neurogranin and neuromodulin (Baudier et al., 1991; Huang et al., 1993; Represa et al., 1990; Watson et al., 1990). We also ruled out the possibility that the three bands observed were the result of neurogranin dimerization and/or oligomerization, as this is prevented by using a reducing agent (Baudier et al., 1991; Wu et al., 2003). Interestingly, the other teleost included in the analysis, the gray mullet, also showed three bands of similar molecular weights to those in zebrafish.
Our protein sequence analysis showed that zebrafish Nrgna‐60 and Nrgna‐92 have a higher identity with rat neurogranin than Nrgnb‐188. It has been shown that nrgnb is related to vascular development and hematopoiesis (Gómez et al., 2012; Wong et al., 2009), being also referred to as “sperm auto antigen 17″ (spa17; Gómez et al., 2012). However, nrgnb shows much less identity with rat spa17 (UniProtKB ‐ Q9Z1K2‐spa17) than with rat neurogranin (46.15% and 61.54%, respectively).
Analysis of the IQ domain among different species (Clayton et al., 2009; present results) showed a glycine substitution in teleosts, instead of the serine (Ser) present in cladistia and tetrapods (apart from Xenopus). The presence of this Ser in the bichir (E. calabaricus), a representative of the basal actinopterygii and sister lineage of all other ray‐finned fish, suggests the derived character of the glycine substitution observed in teleosts. Phosphorylation of neurogranin by PKC has been shown to occur precisely in this serine located in the IQ domain (Ser36) in mammals (Gerendasy et al., 1995, 1994). Likely because of the functional role of this serine, it has been conserved in various species of mammals including prototherians, reptiles and birds (Baudier et al., 1989, 1991; Clayton et al., 2009; Deloulme et al., 1991; Gerendasy et al., 1994; Piosik et al., 1995; Watson et al., 1990; present results). Mammalian variant proteins in which Ser36 was substituted for other amino acids vary in their biochemical properties and failed to serve as a substrate for PKC phosphorylation (Gerendasy et al., 1995; Watson et al., 1996), not being the case for other proteins of the family (calpacitin proteins) (Slemmon et al., 1996). The glycine (Gly) substitution in teleosts could not only affect PKC phosphorylation but also its alpha‐helical structure and affinity for calmodulin (Gerendasy et al., 1994). Thus, further biochemical studies will be necessary to determine how the Ser36 > Gly substitution in teleosts affects neurogranin structure and molecular interactions. Despite the Ser36 > Gly substitution, the teleostean IQ domain conserves the arginine in position 38, which seems to have an essential role in the IQ domain–calmodulin interaction (Kumar et al., 2013). This interaction is also possible because the IQ domain in zebrafish conserved most of the crucial amino acids involved in it (I33, F37, R38, H40, M41, R43, K44, K45, and K47; Kumar et al., 2013).
4.2 Neurogranin immunohistochemistry
Our results indicate that Nrgn‐like immunoreactivity is widely distributed in neurons of the brain and cells of sensory systems of the adult zebrafish. Based on cell morphologies and location, it seems that immunoreactivity in the brain is observed in neurons. The ependymoglia, the most abundant and easily identifiable macroglial cell type in zebrafish and other teleosts (Díaz‐Regueira & Anadón, 1998), was always Nrgn‐like negative. Although neurogranin expression in other glial cells cannot be ruled out without additional experiments, positive cells were lacking in the major white matter tracts where oligodendrocytes typically abound in teleosts (Díaz‐Regueira & Anadón, 1998), which strongly suggests that these cells do not express this peptide in adult zebrafish. Since neurogranins are present in sensory organs and most brain systems (i.e., sensory, motor, integrative), they must have a basic function, not determined by cell type or structure. This basic role is compatible with previous studies showing that neurogranin may play roles in the regulation of synaptic plasticity (Li et al., 2020; Zhong et al., 2011; Zhong & Gerges, 2020), likely by favoring LTP over long‐term depression (Zhong & Gerges, 2020). The exact mechanism by which this regulation is achieved is still under investigation (Li et al., 2020; Zhong et al., 2011; Zhong & Gerges, 2020). We observe that there are three Nrgn‐like peptides in zebrafish and mullet, which could mediate different functions in specific cell types and brain regions through interaction with particular signaling pathways. Further studies will be needed to clarify this point, analyzing the expression of each specific peptide.
Although very broadly distributed, we do observe that neurogranins show expression in specific cell types in the brain, as we will discuss in the following paragraphs.
4.2.1 Distribution of Nrgn‐like immunoreactivity in sensory organs
Neurogranin immunoreactivity was observed in cells of sensory structures such as the gustatory, the olfactory and the visual systems of the adult zebrafish. In our literature searchs, we found no mention of neurogranin expression in these sensory organs. In zebrafish, barbels with taste buds develop in the juvenile stage, at least 1 month after the appearance of the other taste buds in the body surface and oropharyngeal cavity (Hansen et al., 2002). The distribution of neurogranin immunofluorescence observed in barbel taste buds suggests that at least the cells with a single large microvillus express neurogranin. These cells correspond with the light taste bud cells described based on electron microscopy (Hansen et al., 2002).
In the olfactory organ, bipolar neurons with perikarya in the middle of the sensory epithelium are the most common Nrgn‐like positive cells, but the subtype(s) of sensory neuron (ciliary, microvillous, crypt: see Gayoso et al., 2011, 2012) was not assessed.
In the retina of adult zebrafish, we observed Nrgn‐like immunoreactivity mainly in bipolar cells but also some photoreceptors, amacrine and ganglion cells. Different types of bipolar cells were described based on morphology, connections and neurochemical features in zebrafish and other cyprinids (Connaughton et al., 2004; Li et al., 2012; Marc & Cameron, 2001; Sherry & Yazulla, 1993). PKC, which may use neurogranin as substrate (Baudier et al., 1991), was described by immunocytochemical techniques in bipolar cells of the vertebrate retina, including cyprinids (Caminos et al., 2000; Yazulla & Studholme, 2001), which are likely to correspond to ON‐bipolar cells (Haug et al., 2019). Based on shape and location, the PKC‐ir bipolar cells described by these authors resemble the Nrgn‐like‐ir bipolar cells described here. Whereas PKC immunoreactivity intensely labels bipolar cell terminals in three sublaminae of the IPL (Nevin et al., 2008), only weak immunoreactivity against neurogranin was observed in the IPL, suggesting it has a postsynaptic location in these bipolar cells.
4.2.2 Distribution of Nrgn‐like immunoreactivity in the zebrafish brain
Studies on the general distribution of neurogranin in the brain are limited to a few in mammals (Represa et al., 1990), lacking data in nonmammalian species. Here, we report that Nrgn‐like immunoreactivity is expressed in numerous brain regions of zebrafish, extending from the olfactory bulb to the hindbrain. The pattern observed presents numerous differences with that reported in mammals (discussed below) and, as far as we are aware, represents the first description in a nonmammalian vertebrate.
In the olfactory bulb, occasional immunoreactive cell bodies (likely periglomerular cells) were observed among positive fibers of the glomerular layer. However, most Nrgn‐like expression in the bulbs appears in axon terminals of a part of the glomeruli, suggesting a presynaptic distribution of this protein. The almost absence of positive fibers in the olfactory tracts and the main pallial olfactory target (Dp; Gayoso et al., 2011, 2012; Yáñez et al., 2021) suggests that secondary olfactory projections do not use neurogranin. Neurogranin expression has been described in a subpopulation of GABAergic granule cells of the main and accessory olfactory bulbs of the mouse but not in mitral cells (Gribaudo et al., 2009). GABA expression has also been demonstrated in granule cells of the olfactory bulb in zebrafish (Kim et al., 2004; Mueller & Guo, 2009), in which we also observe Nrgn‐like expression.
Our results showed that Nrgn‐like‐ir cell bodies are present in most areas of the pallium of adult zebrafish, although with slight differences in their intensity of immunoreaction. Strong expression of the nrgn gene in the dorsal telencephalon was also observed in 6‐day‐old larvae (Zada et al., 2014). In this regard, expression in zebrafish is similar to the cortex of mammals (neocortex and hippocampus), which expresses neurogranin in numerous principal neurons (Represa et al., 1990; Singec et al., 2004). Neurogranin has been related to synaptic plasticity and learning in higher brain regions (Díez‐Guerra, 2010; Pak et al., 2000; Zhong et al., 2009, 2015), but to date no functional studies have been performed in zebrafish. The dorsal (Dd) and some subdivisions of the medial area of D (Dm) showed the highest intensity of immunoreaction at pre‐ and postcommissural levels, as some cells of the central area (Dc). Most authors consider areas of the actinopterygian pallium homologous to the mammalian hippocampus (Dld), neocortex (Dc), and pallial amygdala (Dm), although it is still a matter of debate (Folgueira et al., 2004b, 2012; Ganz et al., 2014; Nieuwenhuys, 2009; Northcutt, 2008, 2011; Porter & Mueller, 2020; Wullimann & Mueller, 2004; N. Yamamoto et al., 2007; K. Yamamoto et al., 2017; Yáñez et al., 2021). Interestingly, Dm and Dl are involved in different learning tasks in carps (Portavella et al., 2002, 2004), which suggests that the Nrgn‐like abundance in these regions may be related to adaptation in learning circuits. On the other hand, the olfactory pallium (Dp) in zebrafish is devoid of Nrgn‐like‐ir cells and fibers, which is unlike the abundance in the pyriform cortex, the mammalian homologous olfactory region (Álvarez‐Bolado et al., 1996; Gribaudo et al., 2009; Represa et al., 1990). Unlike in mammals, where immunoreactivity is located in dendrites and somas (Singec et al., 2004), bundles of Nrgn‐like positive fibers coming from Dd and other D areas coursed in the lateral and the medial telencephalic tracts, and can be followed caudally to tuberculo‐hypothalamic levels. In the mammalian cortex, neurogranin is considered part of the postsynaptic molecular networks (Zhong et al., 2015). In teleosts, it is possible that neurogranins may act both in postsynaptic and presynaptic processes, as reported in the mammalian spinal cord (Houben et al., 2000). To elucidate the possible presynaptic or postsynaptic roles of each of the zebrafish neurogranins in different circuits, it would be necessary to analyze separately their brain distribution, which was not possible with the method used here.
In the zebrafish subpallium, Nrgn‐like immunoreactivity was observed in some cell bodies of several subdivisions at precommissural (Vv, Vd, Vc) and commissural (Vs) levels. Following the proposed homologies between the tetrapod subpallium and the noneverted ventral area of the telencephalon of teleosts (Folgueira et al., 2004a; Ganz et al., 2012; Northcutt, 1995; Rink & Wullimann, 2004), Nrgn‐ir cells are mainly distributed in the striatum and pallidum homologs. These regions are part of the basal ganglia and show neurogranin expression in the rat (Represa et al., 1990). Neurogranin is involved in the regulation of sensory‐motor responses via cortico‐striatal circuits (Sullivan et al., 2019), but these circuits have not been analyzed in detail in zebrafish.
The hypothalamus and diencephalic regions (prethalamus, thalamus, posterior tubercle and pretectum) have some nuclei with Nrgn‐like‐ir cells. Noteworthy, we observed a number of neurogranin positive cells in the nucleus rostrolateralis, whose neurochemistry and organization have been poorly characterized. This nucleus is considered an epithalamic visual center present in a discrete number of actinopterygians, including zebrafish (Butler & Saidel, 2003; Saidel, 2013; Saidel & Butler, 1997). Nrgn‐like immunoreactivity is also expressed intensely in some thalamic, pretectal and preglomerular nuclei that are considered relay centers between major sensory systems and the pallium or other major systems (Yáñez et al., 2018). However, the rat diencephalon shows very poor neurogranin immunolabeling (Represa et al., 1990), representing a major difference with results in zebrafish.
It is noteworthy the presence of numerous Nrgn‐like‐ir neurons with CSF‐contacting morphology, in the hypothalamic paraventricular organ and the posterior recess organ (Hc of Wullimann et al., 1996; posterior part of the paraventricular organ of Kaslin & Panula, 2001). These circumventricular organs also show numerous monoaminergic CSF‐contacting neurons (Kaslin & Panula, 2001; Xavier et al., 2017; K. Yamamoto et al., 2010). We observed an Nrgn‐positive mat of fibers associated with these organs that strongly resemble the monoaminergic tracts reported with histofluorescence, which relate all the hypothalamic circumventricular organs of teleosts (see Gómez‐Segade et al., 1989). Immunochemical studies show CSF‐contacting neurons of hypothalamic circumventricular organs of teleosts having perikarya with a simple bipolar appearance, but transmission electron microscopy reveals that the apical region of the cell dendrites, including the supraventricular part, receives a surprising number of synaptic contacts, forming together a complex intraventricular mat of processes (Gómez‐Segade et al., 1989). This may explain the Nrgn‐like expression in dendrites of these cells, which are actually postsynaptic to a number of thin intraventricular axons.
The OT is a multisensory integrative midbrain center in zebrafish and other nonmammalian vertebrates, involved in visually guided behavior and being homologous to the mammalian superior colliculus (Meek & Nieuwenhuys, 1998). In zebrafish, Nrgn‐like‐ir perikarya are abundant in different tectal layers, mostly in the SPV, but also in the SAC, SGC and SFGS. This is in contrast with the poor expression of neurogranin in the rodent's superior colliculus (Represa et al., 1990). These differences may stress that the retinotectal pathway is less important than the retinogeniculate pathway in the visual behavior of these rodents, that is, the shift toward the visual telencephalon for different tasks performed by the OT in fishes (Knudsen, 2020). The apical dendrites from SGC and SFGS neurons can occasionally extend to the SM of the OT, which is part of a cerebellum‐like circuit that receives parallel fibers from the TLo (Bell et al., 2008; Folgueira et al., 2020). This tectal parallel fiber system is Nrgn‐like negative, as it is the case of parallel fibers in the cerebellum (see below). In the midbrain, too, many Nrgn‐like‐ir cell bodies and fibers were located in the TS, the homologous of the mammalian inferior colliculus. It is involved in processing mechanosensory information (octaval, lateral line and trigeminal) in nonelectroreceptive teleosts (McCormick & Hernández, 1996; N. Yamamoto et al., 2010). In the rat, the inferior colliculus also shows some neurogranin expression (Represa et al., 1990).
A notable difference between our results with neurogranin and those in mammals (Represa et al., 1990) is the presence of this protein in numerous hindbrain structures of zebrafish (interpeduncular nucleus, NI, cerebellum and cerebellum‐like structures, gustatory/visceral sensory nuclei, etc.). This is also supported by in situ hybridization studies that reveal abundant nrgn gene expression in the hindbrain of 6‐day‐old zebrafish larvae (Zada et al., 2014). In the interpeduncular nucleus (Ip), neurogranin expression was observed in cell bodies and fibers, mainly in the dorsal neuropil. The high proportion of Nrgn‐like positive structures in the dorsal plexus is related to the asymmetrical innervation from the habenulae and probably with the plasticity of fear responses and other behaviors (Agetsuma et al., 2010; Cherng et al., 2020). No expression of neurogranin has been reported in the Ip of mammals (Represa et al., 1990), which indicates differential adaptation roles.
In teleosts, the cerebellum consists of three main regions: the cerebellar valvula, the corpus cerebelli and the vestibulolateralis lobe (Meek & Nieuwenhuys, 1998; Nieuwenhuys, 1967). In the adult zebrafish, Nrgn‐like immunoreactivity was observed mainly in Purkinje cells, although their dendritic arbors of these cells were only evident in the medial division of the cerebellar valvula. In this cerebellar region, Purkinje cell dendrites extend in the molecular layer in a pattern similar to that in mammals (Miyamura & Nakayasu, 2001). Zebrafish Purkinje cells use GABA as neurotransmitter and express parvalbumin7 and zebrin II (Bae et al., 2009; Delgado & Schmachtenberg, 2008; Lannoo et al., 1991b). In teleosts, most Purkinje cells project onto neighbor efferent neurons, the eurydendroid cells, which are glutamatergic and calretinin positive (Bae et al., 2009; Castro et al., 2006b; Ikenaga et al., 2005, 2006a; Murakami & Morita, 1987). Without labeling experiments with markers of eurydendroid cells and neurogranin, we cannot rule out the possibility that some eurydendroid cells also express neurogranin. Results in the zebrafish cerebellum are similar to adult songbirds, in which high neurogranin expression was also described in Purkinje cells (Clayton et al., 2009). In the adult mammalian cerebellum, neurogranin is strongly expressed by inhibitory cells, mostly in a subpopulation of Golgi cells in rodents and, during development, in some bands of Purkinje cells (Larouche et al., 2006; Simat et al., 2007; White & Sillitoe, 2013). In monkeys, neurogranin is also expressed in the excitatory brush cells (Singec et al., 2004). With regard to the major precerebellar centers of zebrafish, the IO contains Nrgn‐like‐ir neurons, while the NLV shows Nrgn‐like immunoreactivity in the characteristic cup‐shaped afferents embracing the neurons of the nucleus (Yang et al., 2004). These results suggest that neurogranins are implicated presynaptically in the NLV and postsynaptically in Purkinje cells, pretectum, and IO.
Prominent Nrgn‐like immunoreactivity is also observed in the characteristic crest cells of the MON, which are the principal cells of this nucleus, which receives primary mechanosensory fibers from the lateral line nerves (see Fame et al., 2006; McCormick & Hernández, 1996; Meek & Nieuwenhuys, 1998). Crest cells are Purkinje‐like complex neurons with apical dendrites that extend in the CC and synapse with parallel fibers arising from the cerebellar eminentia granularis (Díaz & Anadón, 1989; Meredith, 1984). Cerebellar Purkinje cells and MON crest cells also share expression of the GABAergic markers genes (gad1 and gad2), parvalbumin7 (Bae et al., 2009) and, some of them, also the Nrgn positivity in apical dendrites.
The primary and secondary centers of the gustatory and general visceral sensory system are highly specialized in some teleosts, as cyprinids and silurids (Finger, 2009; Ikenaga et al., 2009; Yáñez et al., 2017). We found that Nrgn‐like‐ir cell bodies were abundant in the facial, glossopharyngeal and vagal lobes (primary centers), the secondary gustatory/visceral centers (SGVN), and also in the so‐called TGN proper by us (Yáñez et al., 2017). Note that this TGN nucleus does not correspond to the TGN of Wullimann et al. (1996), which does not receive TGN fibers at all (see Yáñez et al., 2017). If the TGN proper is the homolog of the big carp TGN, however, needs to be assessed with other molecular markers. In the goldfish vagal lobe, NMDA glutamate receptors facilitate short‐term amplification of bursts of inputs coming from the vagal nerve (Smeraski et al., 1999), and calcium‐binding proteins and channels are abundant (Ikenaga et al., 2006b). The abundance of Nrgn‐like immunoreactivity in taste centers in zebrafish suggests that neurogranin may be implicated in plasticity and adaptation in taste processing. These results in zebrafish are in contrast with results in the rat indicating that the hindbrain mostly lacks neurogranin expression (Represa et al., 1990).
5 CONCLUSIONS
Database analysis indicates the presence of three neurogranin peptides in zebrafish with a conserved IQ domain that differs from that in mammals and birds in a serine to glycine substitution in position 36, which is phosphorylated by PKC. Thus comparison with Nrgn in amniotes suggests possible differences in the regulation by PKC between actinopterygians and land vertebrates. Western blots of protein extracts from zebrafish brain using a specific anti‐neurogranin antiserum revealed three protein bands, in good agreement with predictions from database analysis. Since the antibody used binds to the three neurogranin peptides, immunohistochemical results do not allow to differentiate the distribution of the individual peptides in the brain. In our immunohistochemical analysis, we have studied the brain and some sensory organs (barbel taste buds, neuromasts, olfactory rosette and retina) revealing positive cell bodies in all these structures. Positive cells in the barbel taste buds appear to be sensory taste cells, while in the other places examined positivity was observed in neurons, and the expression was cell‐type specific. In the retina, the most conspicuous positive cells are bipolar neurons, probably rod bipolar cells. In the brain, we observed immunopositive neuronal populations in all major brain regions (telencephalon, preoptic area, hypothalamus, diencephalon, mesencephalon and rhombencephalon, including the cerebellum), a more extended distribution than that reported in mammals. Interestingly, in several brain regions the dendrites, cell bodies and axon terminal of neurons were immunopositive. This suggests that neurogranins may play both presynaptic and postsynaptic roles in zebrafish. Most Nrgn‐like immunoreactive neurons were found either in primary sensory centers (viscerosensory column, MON) or in highly integrative centers (such as the pallium and subpallium, OT, and cerebellum). This may suggest that neurogranins are mainly expressed in centers with complex synaptic circuitry in zebrafish. However, there are exceptions, as some apparently simple neuronal populations, as the positive CSF‐contacting cells associated with the hypothalamic recesses, exhibited high Nrgn‐like immunoreactivity. Together, these results reveal some important differences with the patterns reported in mammals, suggesting divergent evolution from an early common ancestor.
CONFLICT OF INTEREST
The authors declare no conflict of interest. All authors had access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
AUTHOR CONTRIBUTIONS
Study concept and design: AA‐G, MF, and JY. Database sequence retrieval and sequence comparison: AA‐G and MF. Western blot data: AA‐G and AC. Immunohistochemistry data: AA‐G and JY. Analysis and interpretation of data: AA‐G, JY, RA and MF. Drafting of the manuscript and final version: AA‐G, JY, RA and MF.
PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/cne.25297.
ACKNOWLEDGMENTS
AA‐G was funded by a Predoctoral Fellowship from Xunta de Galicia (grant number ED481A‐2019/003).
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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PMC009xxxxxx/PMC9555910.txt |
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JB JS Open Access
JB JS Open Access
JBJSOA
JBJS Open Access
2472-7245
Journal of Bone and Joint Surgery, Inc.
36245951
JBJSOA-D-22-00069
10.2106/JBJS.OA.22.00069
00003
0090
0160
Scientific Articles
Cost and Cost Driver Analysis of Anterior Cruciate Ligament Reconstruction Using Time-Driven Activity-Based Costing
Bone-Tendon-Bone Autograft Versus Hamstring Autograft
https://orcid.org/0000-0001-6690-2240
Huyke-Hernández Fernando A. BS 1 2
https://orcid.org/0000-0002-3654-4291
Siljander Breana MD 3
Flagstad Ilexa MD 3
https://orcid.org/0000-0001-9552-2685
Only Arthur MD 1 2
https://orcid.org/0000-0003-0212-2944
Parikh Harsh R. MPH 2
https://orcid.org/0000-0002-8641-3150
Tompkins Marc MD 1 3 4
Nelson Bradley MD 1 3 4
https://orcid.org/0000-0001-6864-4599
Kweon Christopher MD 5
https://orcid.org/0000-0002-6653-2451
Cunningham Brian MD 1 2 a
1 Department of Orthopedic Surgery, TRIA Orthopedic Center, Bloomington, Minnesota
2 Department of Orthopaedic Surgery, Methodist Hospital, St. Louis Park, Minnesota
3 Department of Orthopaedic Surgery, M Health Fairview University of Minnesota Medical Center, University of Minnesota, Minneapolis, Minnesota
4 Department of Orthopaedic Surgery, Regions Hospital, St. Paul, Minnesota
5 Department of Orthopaedic Surgery, University of Washington, Seattle, Washington
a Email for corresponding author: [email protected]
Oct-Dec 2022
11 10 2022
7 4 e22.00069Copyright © 2022 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.
2022
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
Background:
As health care transitions toward value-based care, orthopaedics has started to implement time-driven activity-based costing (TDABC) to understand costs and cost drivers. TDABC has not previously been used to study cost drivers in anterior cruciate ligament reconstruction (ACLR). The purpose of this study was to use TDABC to (1) calculate bone-tendon-bone (BTB) and hamstring ACLR total costs of care and (2) evaluate the impact of graft choice and other factors on ACLR costs.
Methods:
Data were collected from electronic medical records for primary ACLR from the institutional patient-reported outcome registry between 2009 and 2016 in 1 ambulatory surgery center. Patients receiving allograft, revision ACLR, or concomitant meniscal repair or ligament reconstruction were excluded. The total cost of care was determined using TDABC. Multivariate regression analysis was conducted between ACLR cost and group characteristics.
Results:
A total of 328 patients were included; 211 (64.3%) received BTB autograft and 117 (35.7%) received hamstring autograft. The mean cost was $2,865.01 ± $263.45 (95% confidence interval: $2,829.26, $2,900.77) for BTB ACLR versus $3,377.44 ± $320.12 ($3,318.82, $3,436.05) for hamstring ACLR (p < 0.001). Operative time was 103.1 ± 25.1 (99.7, 106.5) minutes for BTB ACLR versus 113.1 ± 27.9 (108.0, 118.2) minutes for hamstring ACLR (p = 0.001). The total implant cost was $270.32 ± $97.08 ($257.15, $283.50) for BTB ACLR versus $587.36 ± $108.78 ($567.44, $607.28) for hamstring ACLR (p < 0.001). Hamstring graft (p = 0.006) and suspensory fixation on the femoral side (p = 0.011) were associated with increased costs.
Conclusions:
The mean cost of care and operative time for BTB autograft ACLR are less than those for hamstring autograft ACLR. Operative time, implant choice, and graft choice were identified as modifiable cost drivers that can empower surgeons to manage primary ACLR costs while maximizing the value of the procedure.
Level of Evidence:
Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence.
OPEN-ACCESSTRUE
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pmcAnterior cruciate ligament reconstruction (ACLR) is a common procedure in the United States, with an estimated 300,000 procedures performed annually as of 20171 and a 22% increase from 2002 to 20142. ACLRs are also increasingly costly procedures, having generated $850,000,000 in health-care expenditures annually in 20003. Mean costs increased from $7,634.19 in 2005 to $10,780.03 in 2013, and they are projected to continue rising4. ACLR costs have also been found to be highly variable4-6. This variability stems from the surgical fixation techniques and graft options available for ACLR5-14. Bone-tendon-bone (BTB) autograft and hamstring autograft are the most common graft choices in the United States15. Orthopaedic surgeons play a substantial role in ACLR cost variability5,16,17.
In the face of excessive and continuously rising national health-care expenditures, the United States health-care system and the field of orthopaedic surgery are shifting toward incentivizing value-based care, where value is defined as the change in patient-reported outcome per dollar spent throughout an episode of care18. Given their influence on cost variability, orthopaedic surgeons have the opportunity to maximize value by modifying their practice to mitigate costs while preserving or improving patient care. Surgeons thus need to have access to accurate cost information and understand which surgeon-controlled variables drive costs. Accurate cost data can be hard to determine because traditional hospital accounting systems are variable19 and, while useful for balancing an institutional budget, have been shown to overestimate the costs involved per episode of patient care20-22. In contrast, time-driven activity-based costing (TDABC) is a methodology that more accurately measures costs and identifies cost drivers at the patient and provider level18,20,21,23. It has been employed in multiple orthopaedic subspecialties such as trauma21, hand24, pediatrics25, and arthroplasty20,26-28. TDABC was also recently employed in the setting of ACLR, revealing significantly different costs compared with those estimated by traditional accounting methods29.
Despite this recent use of TDABC, there is still a paucity of accurate cost information in the ACLR literature. These data are even more limited for BTB and hamstring autograft ACLRs, with no prior reports on their TDABC-derived costs. Given the substantial amount of available information on outcomes of BTB autograft and hamstring autograft ACLRs, it would be useful to accurately calculate and share their associated costs in order to empower surgeons to maximize patient value. The purposes of this study were to use TDABC to (1) calculate total costs of care for BTB and hamstring ACLR, and (2) evaluate the impact of graft choice and other factors on ACLR surgery costs. Our hypothesis was that overall costs would be higher for hamstring ACLR compared with BTB ACLR.
Materials and Methods
Following institutional review board approval, the patient-reported outcome registry for a single ambulatory orthopaedic surgery center in the United States was queried for all patients treated with ACLR from 2009 to 2016 using the Current Procedural Terminology (CPT) code 29888, and a retrospective cost analysis was performed using TDABC. The patients included in the study were skeletally mature and immature individuals who received primary ACLR with either BTB or hamstring autograft following acute ACL injury. Patients who received allograft or underwent revision ACLR, concomitant meniscal repair, or concomitant ligament reconstruction were also excluded. All patients were treated in a single outpatient orthopaedic surgery center and required no inpatient stay. All patients underwent spinal anesthesia and received care according to an institutional postoperative pain protocol.
The included patients were divided into 2 groups for data collection and analysis according to whether they received BTB autograft or hamstring autograft during the ACLR surgery. Patient and surgical characteristics were retrospectively collected via the electronic medical record (EMR). Patient characteristics included age, gender, body mass index (BMI), and the presence of anxiety or depression diagnoses. Surgical characteristics included American Society of Anesthesiologists (ASA) score, primary surgeon, operative time, whether concomitant meniscectomy was performed, graft type, femoral fixation implant type used, and tibial fixation implant type used. Implant types were classified as either aperture or suspensory fixation.
The total cost of care was determined using TDABC. Briefly, TDABC calculates the total cost of a care episode, defined as the sum of all direct and indirect costs incurred within the episode, at the patient level using 2 variables: (1) time spent by a personnel resource to perform an activity, and (2) cost per unit time of the resource under a practical capacity assumption23. Process maps to identify the personnel and time involved in performing each activity within the episode of care were generated based on observations and stopwatch times of a single observer during 10 ACLR procedures performed by a variety of surgeons21,30 (Fig. 1). Preoperative and postoperative times displayed low variability and thus were extrapolated to the sample based on these observations. Operative time and personnel time in the operating room displayed more significant variability and were thus extracted directly from the EMR. Direct costs included the personnel (surgeon, nurses, surgical technician, etc.) and supplies (implants, sutures, drugs, etc.) directly involved in patient care. Personnel costs were derived from salary information, and supply costs were gathered from vendor records and the surgical center accounting department. Indirect costs included services that, while not directly used by the patient, are required for care. These include human resources (HR) management, information technology (IT) support, employee benefits, hospital administration, hospital operations infrastructure, operating theater operations, and other infrastructure costs21,31,32. Indirect costs were estimated to be 29.5% of direct costs21,31-33. To account for indirect costs at the personnel level (benefits, vacation time, sick days, etc.), a practical work capacity of 80% was assumed for all involved personnel with the exception of the surgeon21,23,31. Table I details the methods for obtaining these costs.
Fig. 1 Diagram displaying the process map for an ACLR episode of care. Each box represents a stage in the episode of care, with arrows representing transitions from one stage to the other. The personnel and mean time (in minutes) involved are listed for each stage. ACLR = anterior cruciate ligament reconstruction, RN = registered nurse, CRNA = certified registered nurse anesthetist, MDA = anesthesiologist, CST = certified surgical technician, PA-C/OA = certified physician assistant/orthopaedic assistant, MD = surgeon, OR = operating room.
TABLE I TDABC Formulae for Calculating Costs of an Episode of Care*
Variable Formula
Direct costs Personnel costs + implant cost
Personnel costs Timetocompleteativity×PersonnelannualsalaryTimeworkedperyear×80%
Indirect costs Indirectcostratio×directcosts
Indirect cost ratio Σ FTE indirect servicesΣ FTE direct and indirect services
* TDABC = time-driven activity-based costing, and FTE = full-time equivalents.
Statistical analysis was conducted using SAS version 9.4 (SAS Institute). Categorical data are reported using frequencies and proportions, and continuous data are reported using means and accompanying standard deviations and 95% confidence intervals (CIs). Chi-square tests and Student 2-sample t tests between the BTB group and the hamstring group were used to determine significance for categorical and continuous variables, respectively. Cost drivers were further assessed using multivariable regression analysis; a general linear model (GLM) was constructed to evaluate the impact of patient and surgical factors on the primary outcome of total ACLR cost. Significance was set at alpha = 0.05 (2-tailed).
Source of Funding
No funding was received for this work.
Results
A total of 328 patients were included in the analysis. The patients had a mean age of 24.1 years, and 59.5% were female. Of these patients, 211 (64.3%) received BTB autograft and 117 (35.7%) received hamstring autograft. Gender, BMI, and the rate of depression were comparable between the 2 groups. The BTB group was significantly younger (22.8 ± 9.0 [95% CI: 21.6, 24.0] versus 26.5 ± 12.2 [24.2, 28.7] years, p < 0.001) and had a lower rate of anxiety (9.1% versus 23.1%, p < 0.001) than the hamstring group (Table II).
TABLE II Demographics, Surgical Characteristics, and Costs of Care for the Sample Population (N = 328)*
BTB (N = 211; 64.3%) Hamstring (N = 117; 35.7%) P Value
Gender Female: 132 (62.6%)
Male: 79 (37.4%) Female: 63 (53.9%)
Male: 54 (46.1%) 0.124†
Age (yr) 22.8 ± 9.0 (21.6, 24.0) 26.5 ± 12.2 (24.2, 28.7) <0.001 ‡
BMI (kg/m2) 24.5 ± 4.1 (24.0, 25.1) 25.1 ± 4.2 (24.4, 25.9) 0.211‡
Depression 20 (9.5%) 12 (10.3%) 0.831†
Anxiety 19 (9.1%) 27 (23.1%) <0.001 †
ASA score 1: 183 (87.6%)
2: 26 (12.4%) 1: 99 (85.3%)
2: 17 (14.7%) 0.572†
Operative time (min) 103.1 ± 25.1 (99.7, 106.5) 113.1 ± 27.9 (108.0, 118.2) 0.001 ‡
Meniscectomy 71 (33.7%) 35 (29.9%) 0.488†
Femoral fixation Aperture: 211 (100%)
Suspensory: 0 (0%) Aperture: 9 (7.7%)
Suspensory: 108 (92.3%) <0.001 †
Tibial fixation Aperture: 211 (100%)
Suspensory: 0 (0%) Aperture: 101 (86.3%)
Suspensory: 16 (13.7%) <0.001 †
Implant cost $270.32 ± $97.08 ($257.15, $283.50) $587.36 ± $108.78 ($567.44, $607.28) <0.001 ‡
TDABC costing $2,865.01 ± $263.45 ($2,829.26, $2,900.77) $3,377.44 ± $320.12 ($3,318.82, $3,436.05) <0.001 ‡
* The values are given as either the count (proportion) or mean ± standard deviation (95% confidence interval). Proportions in parentheses are calculated based on the number of patients with data. Significant p values are bolded. BTB = bone-tendon-bone, BMI = body mass index, ASA = American Society of Anesthesiologists, and TDABC = time-driven activity-based costing.
† Chi-square test.
‡ Student 2-sample t test.
A total of 14 surgeons performed BTB and/or hamstring ACLRs in the study period. Operative time in the BTB group was 103.1 ± 25.1 (99.7, 106.5) minutes, which was lower than the time of 113.1 ± 27.9 (108.0, 118.2) minutes in the hamstring group (p = 0.001) (Table II). Surgeon case volume and operative time varied substantially within each type of ACLR (Fig. 2). Comparable proportions of concomitant meniscectomies were performed in the 2 groups (p = 0.488). The BTB group used exclusively aperture fixation (100.0% for both femoral and tibial fixation), whereas the hamstring group used suspensory fixation for the femur in 92.3% of ACLRs and for the tibia in 13.7% of ACLRs (p < 0.001).
Fig. 2 Box-and-whisker box plots demonstrating the range in operative time (in minutes) for BTB (blue) and hamstring (red) ACLR according to the primary surgeon (identified as A through N). A box indicates the interquartile range, the line within the box indicates the median, and the whiskers indicate the range. Case volume of BTB ACLR and its percentage in relation to total ACLRs are reported under the x axis for each surgeon. BTB = bone-tendon-bone, ACLR = anterior cruciate ligament reconstruction.
The mean total cost of care for BTB ACLR was $2,865.01 ± $263.45 ($2,829.26, $2,900.77), which was significantly lower than the cost for hamstring ACLR at $3,377.44 ± $320.12 ($3,318.82, $3,436.05) (p < 0.001) (Table II). The total cost of care displayed a wide range when stratified by surgeon and graft type (Fig. 3). Total implant costs were significantly lower for the BTB group at $270.32 ± $97.08 ($257.15, $283.50) than for the hamstring group at $587.36 ± $108.78 ($567.44, $607.28) (p < 0.001). Multivariable regression analysis identified that increased costs associated with use of hamstring autograft (β = $272.76 [$78.61, $466.90], p = 0.006) and use of suspensory fixation on the femur (β = $253.45 [$57.75, $449.15], p = 0.011) were associated with increased total ACLR costs (Table III). No patient factors, including age and diagnosis of anxiety, were found to increase ACLR costs (all p ≥ 0.209). Performance of concomitant meniscectomy was also not associated with increased ACLR costs (p = 0.297).
Fig. 3 Box-and-whisker box plots demonstrating the range of total cost of care (USD) for BTB (blue) and hamstring (red) ACLR according to primary surgeon (identified as A through N). A box indicates the interquartile range, the line within the box indicates the median, and the whiskers indicate the range. Case volume of BTB ACLR and its percentage in relation to total ACLRs are reported under the x axis for each surgeon. BTB = bone-tendon-bone, ACLR = anterior cruciate ligament reconstruction.
TABLE III GLM Evaluating the Association of the TDABC-Derived Cost of ACLR with Potential Procedural Covariates and Patient Comorbidities (N = 328)*
Parameter β 95% CI P Value
Hamstring graft type† $272.76 $78.61, $466.90 0.006
Suspensory femoral fixation‡ $253.45 $57.75, $449.15 0.011
Suspensory tibial fixation‡ $68.60 −$86.90, $224.09 0.386
Age −$2.17 −$5.56, $1.22 0.209
BMI −$2.97 −$10.83, $4.89 0.458
ASA score −$11.93 −$106.83, $82.97 0.805
Depression −$7.59 −$133.81, $118.63 0.906
Anxiety $57.88 −$51.67, $167.43 0.299
Meniscectomy $35.83 −$31.59, $103.25 0.297
* Significant p values are bolded. GLM = general linear model, β = regression beta coefficient, CI = confidence interval, TDABC = time-driven activity-based costing, ACLR = anterior cruciate ligament reconstruction, BMI = body mass index, and ASA = American Society of Anesthesiologists.
† Bone-tendon-bone was the reference group.
‡ Aperture fixation was the reference group.
Discussion
As the United States health-care system and reimbursement models transition toward prioritizing value-based care, it is crucial for orthopaedic surgeons to gather accurate cost information and relate it to patient outcome data in order to identify interventions that optimize value. ACLR is a common procedure, but the investigation and reporting of costs are highly limited. This study retrospectively applied TDABC to patients undergoing BTB autograft ACLR and hamstring autograft ACLR. BTB autograft ACLR costs less than hamstring autograft ACLR ($2,865.01 versus $3,377.44, p < 0.001). The BTB group was found to have a shorter mean operative time (103.1 versus 113.1 minutes, p = 0.001; post-hoc power, 89.6%) as well as lower mean implant costs ($270.32 versus $587.36, p < 0.001; post-hoc power, 100%) per ACLR than did the hamstring group (Fig. 4). Multivariable linear regression analysis revealed that use of hamstring autograft (β = $272.76, p = 0.006) and use of femoral suspensory fixation (β = $253.45, p = 0.011) were variables that significantly increased costs and contributed to the cost discrepancies between BTB ACLR and hamstring ACLR.
Fig. 4 Comparison of BTB versus hamstring autograft ACLR with respect to operative time and TDABC costs. Overall, BTB autograft ACLR took less time and cost less than hamstring autograft ACLR. The values are shown as the mean and 95% CI. BTB = bone-tendon-bone, ACLR = anterior cruciate ligament reconstruction, TDABC = time-driven activity-based costing.
Current ACLR cost literature is limited and diverse, which can make it difficult for surgeons to gain an accurate understanding of ACLR costs. Most of the existing literature reports a wide range of costs using reimbursement information from traditional hospital accounting systems, which inaccurately represent costs of care. Recent analyses have reported mean procedural costs per ACLR as low as $2,039.09 and as high as $11,431.57, with minimum to maximum costs of individual procedures ranging from $392.80 to >$14,157.304-6. More recently, a study reported an overall mean cost of $5,242.25 per ACLR using TDABC compared with $10,318 using the institution’s accounting system; the latter is almost twice as high as the cost determined using TDABC29. In addition to this variability, ACLR costs in the literature are presented in diverse ways such as inpatient versus outpatient costs12, costs based on timing of surgery34, and costs based on graft type7-14, which may further complicate the dissemination of accurate cost information.
Only a few studies have reported costs for both BTB autograft and hamstring autograft ACLRs, and these have all had limitations similar to those in the general ACLR cost literature12-14. Bonsell reported a mean of $7,459 for total hospital charges (including both inpatient and outpatient procedures) per BTB autograft ACLR, which was significantly higher than the mean of $6,444 for hamstring autograft ACLR12. Forssblad et al. reported a higher mean cost of €436 for hamstring autograft ACLR versus €197 for BTB autograft ACLR ($523.20 versus $236.406)13. Genuario et al. conducted a cost-effectiveness analysis that estimated ACLR costs according to graft type from prior literature and reported a lower mean of $5,375 per hamstring autograft ACLR versus $5,582 per BTB autograft ACLR14. In contrast, the present TDABC analysis revealed a significantly lower mean cost per BTB autograft ACLR ($2,865.01) versus hamstring autograft ACLR ($3,377.44). The present analysis also demonstrated TDABC-derived ACLR costs that were notably lower than those reported by studies using traditional accounting systems, consistent with the findings of Koolmees et al.29.
Prior studies have associated longer operative time8,12,13,16,17, certain particular fixation implants5,11,13,14,16,35, patient demographic factors17, and additional meniscal procedures16,17,36, including meniscectomies36, with higher costs. Contrary to conventional thought, the present study demonstrated that, on average, performing a hamstring ACLR required a longer time than a BTB ACLR did. In line with TDABC principles, this longer operative time likely contributed to the significant cost differences between the groups. The hamstring group also had higher implant costs compared with the BTB group, which is consistent with prior reports13,35. The hamstring group exhibited a notably higher frequency of suspensory fixation implant use compared with the BTB group, which was found to significantly impact total ACLR cost in the multivariable regression analysis. Implant contracts can vary greatly between institutions and facilities, which may greatly affect the overall cost of ACLR, and this study represents only a single facility and may therefore not be broadly generalizable. Although prior studies have considered patient demographics17 and meniscectomies36 as factors that impact ACLR costs, the GLM revealed no significant cost contributions from these variables. In the present analysis, operative time, fixation implant type, and graft type were found to be cost drivers in ACLR surgery. More specifically, the use of suspensory implants associated with the hamstring autograft, alongside the longer operative time associated with hamstring ACLR, rendered use of hamstring autograft a cost driver, resulting in higher total costs of care associated with hamstring ACLR. These 3 variables are all under surgeon influence, representing an example of how TDABC can be used not only to derive accurate costs but also to identify provider-level factors that can lead to variability in costs, and hence in patient value. The wide ranges in surgeon-specific case volume, operative time, and total costs of care that were observed in this study similarly suggest that the 3 variables are under surgeon influence and can be impacted by the culture and practices that different institutions endorse.
Providers should seek accurate costing at the patient level in order to identify and perform interventions of high value. Traditional hospital accounting methods that govern most of the data in the literature use a top-to-bottom approach that distributes the total cost incurred by hospitals to patient services and assumes maximum efficiency of resources, enabling variable facility-dependent costs to distort the interventional costs at the level of the patient21,23. In contrast, TDABC provides a bottom-to-top costing methodology that uses the time required to complete an activity and cost per unit of time to itemize direct costs per intervention, all while assuming a more realistic practical resource capacity that is neglected in traditional systems18,21,23. In other words, TDABC focuses on providing more accurate and detailed cost data that are directly influenced by the patient, provider, and personnel factors per episode of care, rendering it an ideal methodology for patient-level value analysis (PLVA)18,30,37. While cost-effectiveness is frequently assessed via cost-utility analysis at the population level, as has been done for ACLR14,34,38, PLVA provides a more granular value analysis on a per-patient basis37 that better approximates the methods used by newer reimbursement systems to incentivize value in health care. TDABC and PLVA thus help reveal areas amenable to cost containment and afford orthopaedic surgeons the opportunity to inform themselves regarding, and improve, the value and cost-effectiveness of their procedures.
This study has limitations. It is a retrospective review involving out-of-date dollar values from >6 years ago. Inherent limitations of TDABC methodology include the inability to predict societal costs outside of the episode of care as well as difficulty in calculating indirect costs21,31-33. The study was unable to assess the impact of implant variable details (e.g., type of screw used, fixed versus adjustable loop, use of hybrid fixation, single versus double fixation) on costs. Factors such as operative time and subsequent costs are dependent on surgeon training and volume, which vary substantially among surgeons and institutions. The study institution contracted with multiple implant suppliers, increasing cost variability and limiting the generalizability of the results. The study also excluded patients receiving concomitant procedures and was limited to a single center whose available resources may differ from those of other institutions, further limiting generalizability. The study did not evaluate costs or outcomes after surgery. Despite its limitations and the caution that is warranted regarding the interpretation of its results, however, the study accurately quantified institutional costs, and it presents a model of cost and cost-driver analysis that could benefit individual surgeons and health-care centers and systems regardless of the location at which it is implemented.
Conclusions
BTB autograft ACLR exhibited lower mean total TDABC-derived costs of care and shorter operative time compared with hamstring autograft ACLR. Longer operative time and use of suspensory fixation implants, and thus use of hamstring autograft, were identified as cost drivers associated with higher total cost of care per ACLR, whereas patient factors and concomitant meniscectomy did not impact costs. This is the first study to use the more accurate costing methodology of TDABC to provide cost and cost driver data on the 2 most common types of ACLR performed in the United States, allowing surgeons to maximize patient value and adapt to the value-centric shift in health-care economics.
Investigation performed at TRIA Orthopedic Center, Bloomington, Minnesota
Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSOA/A424).
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PMC009xxxxxx/PMC9615149.txt |
==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
36325577
10.1093/narcan/zcac031
zcac031
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Survey and Summary
Translational alterations in pancreatic cancer: a central role for the integrated stress response
Shin Sauyeun Centre de Recherche en Cancérologie de Toulouse (CRCT), INSERM U1037, Université Toulouse III Paul Sabatier, ERL5294 CNRS, Toulouse, France
Equipe labellisée Ligue Contre le Cancer
Solorzano Jacobo Centre de Recherche en Cancérologie de Toulouse (CRCT), INSERM U1037, Université Toulouse III Paul Sabatier, ERL5294 CNRS, Toulouse, France
Equipe labellisée Ligue Contre le Cancer
Liauzun Mehdi Centre de Recherche en Cancérologie de Toulouse (CRCT), INSERM U1037, Université Toulouse III Paul Sabatier, ERL5294 CNRS, Toulouse, France
Equipe labellisée Ligue Contre le Cancer
Pyronnet Stéphane Centre de Recherche en Cancérologie de Toulouse (CRCT), INSERM U1037, Université Toulouse III Paul Sabatier, ERL5294 CNRS, Toulouse, France
Equipe labellisée Ligue Contre le Cancer
https://orcid.org/0000-0002-2501-0593
Bousquet Corinne Centre de Recherche en Cancérologie de Toulouse (CRCT), INSERM U1037, Université Toulouse III Paul Sabatier, ERL5294 CNRS, Toulouse, France
Equipe labellisée Ligue Contre le Cancer
https://orcid.org/0000-0002-0575-4085
Martineau Yvan Centre de Recherche en Cancérologie de Toulouse (CRCT), INSERM U1037, Université Toulouse III Paul Sabatier, ERL5294 CNRS, Toulouse, France
Equipe labellisée Ligue Contre le Cancer
To whom correspondence should be addressed. Email: [email protected]
12 2022
28 10 2022
28 10 2022
4 4 zcac03129 9 2022
27 9 2022
29 7 2022
© The Author(s) 2022. Published by Oxford University Press on behalf of NAR Cancer.
2022
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
mRNA translation is a key mechanism for cancer cell proliferation and stress adaptation. Regulation of this machinery implicates upstream pathways such as PI3K/AKT/mTOR, RAS/MEK/ERK and the integrated stress response (ISR), principally coordinating the translation initiation step. During the last decade, dysregulation of the mRNA translation process in pancreatic cancer has been widely reported, and shown to critically impact on cancer initiation, development and survival. This includes translation dysregulation of mRNAs encoding oncogenes and tumor suppressors. Hence, cancer cells survive a stressful microenvironment through a flexible regulation of translation initiation for rapid adaptation. The ISR pathway has an important role in chemoresistance and shows high potential therapeutic interest. Despite the numerous translational alterations reported in pancreatic cancer, their consequences are greatly underestimated. In this review, we summarize the different translation dysregulations described in pancreatic cancer, which make it invulnerable, as well as the latest drug discoveries bringing a glimmer of hope.
LNCC (Labellisation Ligue Nationale Contre le Cancer) French National Institute of Cancer INCA_16076 2018–080 2018–082 ANR-18-EURE-0003
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pmcINTRODUCTION
Among all organs, the pancreas has the biggest protein synthesis capacity (1). Translation is the most energy-consuming process in cells (2) and thus requires active control to maintain energetic balance. As translational control occurs mostly at the level of the initiation step, molecular details of this stage have been extensively studied in general (3) and in the pancreas (reviewed in 4). Similarly, in pancreatic cancer (e.g. pancreatic ductal adenocarcinoma, hereafter referred as PDA), numerous findings over the past two decades have illustrated that translational control can favor cancer initiation, development, resistance to hypoxia, nutrient starvation and chemotherapies. Dysregulation of protein synthesis is considered as a hallmark of cancer cells, together with proliferation, survival and metastatic progression (5). This review synthesizes current knowledge on important translation dysregulations in PDA, mainly focusing on the initiation step, and highlights potential underlying therapeutic vulnerabilities.
PANCREATIC DUCTAL ADENOCARCINOMA
With an overall 5-year survival rate not exceeding 9%, PDA represents the seventh most common cause of cancer-related death in the world, and is predicted to become the second most common cause of cancer-related death by 2040 in the USA (6). Tobacco, alcohol, family history and genetic factors are known risk factors (7) and, more importantly, new-onset diabetes mellitus and obesity have been associated with PDA (8). PDA initiation and development is driven by KRAS oncogenic mutation (present in >90% of cases) and by functional alterations of tumor suppressor genes p16INK4A, p53 and SMAD4 (9). To date, surgery remains the only potential cure for this cancer; however, 80% of PDA tumors are unresectable, as patients are diagnosed at an advanced stage, due to a late diagnosis. First-line treatments for PDA patients include FOLFIRINOX (combination of 5FU, Leucovirin, Oxaliplatin and Irinotecan), Gemcitabine alone or in combination with nab-Paclitaxel, which are selected solely based on the patient’s physical condition. Nonetheless, patient survival remains very low: respectively 11.1 versus 6.8 versus 8.5 months, showing the urgency in discovering new therapeutic strategies (7). PDA initiation and development have been explored in genetically engineered murine models (GEMMs). These models, expressing mutated KRAS specifically in the pancreas (hereafter referred to as KC), recapitulate key features of the human disease, including histological architecture and chemoresistance. Association of mutated TP53 with KRAS accelerates PDA development kinetics in GEMMs (hereafter referred to as KPC) (10).
THE TRANSLATION INITIATION MACHINERY
Most nuclear-encoded mRNAs are translated via a cap-dependent process in eukaryotic cells. The process starts by the recognition of the 5′ cap structure (7-methyl-GTP) of the mRNA by the eukaryotic translation initiation factor (eIF) 4F complex, which facilitates subsequent recruitment of the small ribosomal subunit 40S to the mRNA (3). eIF4F is composed of three proteins: eIF4E, the cap-binding subunit; eIF4G, a scaffolding protein; and the ATP-dependent RNA helicase eIF4A (see Figure 1). eIF4E is considered as the least abundant component and therefore the limiting factor for eIF4F complex assembly. eIF4E, eIF4G and eIF4A abundance controls translation of a distinct subset of mRNAs (including many oncogenes and cell cycle regulators) rather than global protein synthesis per se. eIF4A constitutes the only enzyme of eIF4F which unwinds RNA secondary structures through an ATP-dependent mechanism (3). The 40S ribosomal subunit is associated with the eIF3 complex (composed of 13 subunits: 3a to 3j) (11), eIF1, eIF1A, eIF5 and the ternary complex (TC) comprising eIF2 with an initiator methionyl-tRNA (Met-tRNAi) to form the 43S pre-initiation complex. The 43S complex can then be tethered to the mRNA, through eIF4F, to form the 48S complex (see Figure 1), which will finally allow scanning to occur. Importantly, eIF5A1/2, also involved in translation elongation and mRNA transport, are regulated by hypusine modification, a specific and rare post-translational modification. Once the ribosome recognizes the start codon, initiation factors are released for recycling, allowing the recruitment of the 60S ribosomal subunit to form the 80S complex.
Figure 1. Overview of the translation initiation mechanism. The 5′ cap-dependent translation implies recognition of the START codon by a ribosome carrying the initiator methionyl-tRNA (Met-tRNAi). First, the ternary complex (TC) is formed by the association between Met-tRNAi and eIF2. The main role of the TC is the transfer of Met-tRNAi to the 40S ribosomal subunit associated with numerous initiation factors (eIF1, eIF1A, eIF5 and eIF3). The 43 pre-initiation complex (43S PIC) is formed by the association of the TC with the 40S ribosome, through the interaction between eIF2β and eIF5. The eIF4F complex, composed of the cap-binding protein eIF4E, eIF4G and the RNA helicase eIF4A, is assembled at the cap structure. The eIF4F complex recruits the 43S PIC through the interaction between eIF3 and eIF4G to constitute the 48S complex. This complex scans the mRNA from 5′ to 3′ until the START codon, then eIF5 hydrolyzes GTP-bound eIF2γ to dissociate the 48S complex and allow association of the 60S with the 40S in order to form the 80S ribosomal subunit. Therapeutic agents under ongoing investigation, targeting this machinery, are noted in green boxes.
DYSREGULATION OF THE TRANSLATION INITIATION MECHANISM IN PDA
Up-regulation of translation initiation factors is a common alteration in cancers (5,12). Immunohistochemistry revealed that 85% of PDA samples have high expression of eIF4E without being correlated with either Tumor/Node/Metastasis stage (T/N/M) or overall survival (13). However, another study suggests that eIF4E expression is increased in poorly differentiated PDA and in metastasis (14). Similarly, eIF4G1 mRNA was found to be up-regulated in PDA as compared with healthy tissue and was associated with poor survival in the TCGA cohort and GEO database (15,16). High eIF4A1 protein abundance was also correlated with poor patient survival and a mesenchymal phenotype (17). In addition, eIF4E and eIF4A have been demonstrated to play critical roles in PDA metabolism. Upon KRAS stimulation, eIF4A and eIF4E were shown to activate the translation of mRNAs encoding the small GTPase ARF6 and its downstream effector AMAP1, thus promoting tumor invasion and metastasis (18). Expression and activity of initiation factors are regulated through multiple mechanisms. For example, Myc is activated or amplified in a high proportion of PDA patients (19) and can favor expression of eIF4E mRNA in a feed-forward manner (20), which suggests a potential enhancement of protein synthesis in this context. Curiously, phosphoglycerate dehydrogenase (PHGDH), the first enzyme in the serine de novo biosynthesis pathway, was shown to favor mRNA translation through a direct interaction with eIF4E and eIF4A, and to enhance eIF4F complex formation. Moreover, depleting or inhibiting PHGDH led to a reduced cap-bound eIF4F and a global decrease in protein synthesis (21). This last example highlights the close connection between mRNA translation and metabolism. Overall, these findings indicate a general overexpression of eIF4F components in PDA, similar to other cancers.
Alterations in the expression of specific eIF3 subunits are also found during PDA development. The eIF3a subunit is up-regulated in PDA whereas eIF3f is down-regulated compared with normal tissues (22–24). Silencing eIF3a, 3b or 3c markedly reduced pancreatic cancer cell proliferation and motility, favoring apoptosis (24–26). In contrast to the core subunit of eIF3, eIF3f silencing was shown to enhance translation and to reduce staurosporine-induced apoptosis (27). Recently, the eIF3 complex was implicated in a special mode of translation of WT1 Associated Protein (WATP) mRNA, involving m6-A modification, and leading to an enhanced WT signaling and tumor growth (28). Finally, the essential role of eIF3 in mRNA translation in PDA was reinforced in KPC GEMMs deleted for the master redox transcription factor, NRF2. KPC mice lacking NRF2 showed enhanced reactive oxygen species (ROS) production and oxidation of the translation machinery (including eIF3), associated with impaired protein synthesis. The lack of NRF2 is also associated with a reduced autocrine EGFR/Akt/4E-BP1 signaling, further decreasing the assembly of the translation initiation complex. Overall, these data indicate that protein synthesis sustains PDA growth (29).
Another factor, eIF5A, was found to be up-regulated and activated (by hypusination) in human PDA and KC GEMMs through a mechanism involving RAS mutation (30). The importance of eIF5A expression for PDA cancer cell growth was demonstrated in vitro and in orthotopic tumors. In addition, inhibitors of hypusination also suppressed PDA cell growth (31). Aside from a recent immunohistochemistry-based study, which identified down-regulation of eIF1, eIF2α and eIF6 to be associated with good prognosis (23), other initiation factors of the 48S pre-initiation complex have not been explored in PDA. All these examples illustrate how modification of translation initiation factors can modulate pancreatic cancer cell capacities and reinforce interest in targeting the protein synthesis machinery.
Targeting the translation mechanism in PDA
Considered as the first step of protein synthesis, targeting the assembly and/or the activity of the eIF4F complex has always been an exciting challenge, which began by limiting the activity of eIF4E, the cap-binding protein. Antisense oligonucleotide (ASO) targeting eIF4E induced the reduction of eIF4E by affecting both mRNA stability and translation. Originally, OGX-427, an ASO leading to eIF4E down-regulation, demonstrated the ability to enhance Gemcitabine activity both in vitro and in xenograft models (14). Unfortunately, a subsequent clinical trial with OGX-427 in combination with Gemcitabine and nab-Paclitaxel failed to demonstrate benefits for the patients (NCT01844817). An alternative to eIF4E silencing is to limit eIF4F formation at the cap structure. Using 4E2RCat, a small molecule which blocks eIF4E–eIF4G interaction, our work demonstrated that the growth of mTOR inhibitor-resistant PDA cancer cells can be reduced (32). Furthermore, this molecule was also well tolerated in vivo and was showed to sensitize tumors to chemotherapies in other cancers (33).
Targeting eIF4A activity has recently demonstrated an impressive efficacy in blocking protein synthesis and PDA tumor growth. Translatome analysis of KPC-derived organoids treated with CR-1-31-B, a synthetic rocaglate, revealed an oncogenic translation program supported by eIF4A, favoring expression of enzymes from glutathione metabolism, glucose uptake, oxidative phosphorylation and glycolysis (34). As a result, treatment with CR-1-31-B suppressed tumor growth and extended survival of KPC GEMMs (34), and, more recently, blocked PDA tumor progression in orthotopic and metastatic models (35). In both cases, c-MYC expression was strongly reduced following eIF4A inhibition (17,35). A reduced translation rate of KRAS and c-Myc mRNA via CR-1-31-B could be mediated by the presence of G-quadruplex structures (35). Nonetheless, many reports have pointed out that eIF4A-sensitive transcripts have larger RNA structures due to an increased 5′-untranslated region (UTR) length (36). Similarly, our data indicated that other rocaglate derivatives, EC143.29 and EC143.69, effectively reduce mRNA translation of CDC6, a core component of pre-replicative complexes, leading to DNA replication arrest in vitro, and blocking tumor growth in vivo (37). Supporting these encouraging results, a clinical trial using Zotatifin (eFT226), an inhibitor of eIF4A, is ongoing on solid tumors, including PDA (NCT04092673). Altogether, translation initiation factors have been widely studied, and have been shown to be globally overexpressed in PDA, contributing to cancer development, metastasis and survival. Reducing the expression of the limiting factor eIF4E has failed, but has to be carefully considered, as ASO technology is probably not the most potent approach as opposed to small molecules acting on enzymatic activity or protein interaction. The expression of eIF4E partners and regulators, such as MNK or 4E-BPs, should also be taken into account (see next paragraphs). Conversely, inhibiting the formation of the eIF4F complex through the helicase eIF4A seems to have promising results. Correlations between IHC-analyzed expression of initiation factors and patient survival remain to be functionally and molecularly deciphered in order to reveal their therapeutic potential.
Oncogenic signaling pathways regulating translation in PDA
Oncogenic mutation of KRAS is present in >90% of PDA, and thus it is considered as the driver mutation of this cancer (9). Mutated KRAS can activate two pathways: the mitogen-activated protein kinase (MAPK) pathway including MEK/ERK and p38/MAPK; and the PI3K/AKT/mTOR pathway (see Figure 2). In other cancers, activation of these pathways has been largely described to induce dysregulation of protein synthesis through translation of a subset of mRNAs encoding tumor-promoting and survival factors (5,12).
Figure 2. Upstream signaling pathways regulating translation initiation. The eIF4F complex is tightly regulated through PI3K/mTORC1 and MNKs. PI3K generates PIP3 leading to the phosphorylation of PDK1 and subsequently AKT. PIP3 can be reversed into PIP2 though PTEN phosphatase activity. AKT phosphorylates TSC1/2 to hinder their dimerization, inhibiting the Rheb GTPase which activates mTORC1. mTORC2 can also lead to subsequent activation of AKT. Upon energy deprivation, AMPK activates TSC1/2 dimerization, inhibiting mTORC1 activity to attenuate energy-consuming translation. 4E-BPs and p70S6Ks are substrates of mTORC1. Phosphorylation of 4E-BP releases eIF4E, allowing eIF4F formation. Phosphorylation of S6K leads to a phosphorylation cascade, including the elongation factor kinase eEF2K, the ribosomal protein S6, PDCD4 and eIF4B. The last two are an inhibitor and an activator of eIF4A, respectively. KRAS mutation leads to the activation of MNKs downstream of RAS/ERK and p38/MAPK pathways to regulate eIF4E through phosphorylation. Therapeutic agents under ongoing investigation, targeting these pathways, are noted in green boxes. MEK/ERK inhibitors are described in (54).
Surprisingly, few publications have looked in detail into the global protein synthesis inhibition or specific modifications of mRNA translation upon modulation of mTOR and MAPK pathways in PDA.
mTORC1 phosphorylates many fundamental factors involved in translational control, including p70-S6 Kinase 1 and 2 (p70S6K1/2), and eIF4E-binding proteins (4E-BPs) (see Figure 2), which have been poorly explored in PDA. Nearly 75% of PDA presents activation of the mTORC1 pathway as evidenced by immunohistochemical analysis of p70S6K or RPS6 phosphorylation (38). This activation of mTOR seems critical for PDA development, as evidenced by slow tumor progression in KC GEMMs harboring non-phosphorylatable sites of RPS6 (39). On the other hand, 4E-BPs (4E-BP1, 2 and 3), more specifically 4E-BP1, the best-characterized and prototypical factor, are inhibitors of eIF4F complex formation and thus of translation initiation. Upon mTORC1 inhibition, these factors are dephosphorylated and therefore sequester eIF4E away from eIF4G, thus inducing mRNA translation inhibition (40) (see Figure 2). Like p70S6Ks, 4E-BP1 phosphorylation is often monitored as a proxy of mTORC1 activity and serves to estimate the cap-dependent translation inhibition. Interestingly, our data indicate a loss of 4E-BP1 expression in PDA cancer cells from KC GEMMs as well as in 50% of human PDA samples (32). So far, this observation has only been described for PDA and head and neck squamous cell carcinomas (41). 4E-BP1 loss takes place during early PDA development and favors proliferation through uncontrolled translation of cyclin D1 mRNA, which is insensitive to mTOR inhibitors (32). A broader analysis of the impact of 4E-BP1 loss on genome-wide mRNA translation revealed enhanced DNA replication and repair processes, mediated by CDC6 and RRM2, two components of the replication machinery. Mechanistically, CDC6 and RRM2 mRNA translation became uncontrolled upon 4E-BP1 down-regulation, as well as insensitive to mTOR inhibitors (37). Other reports have correlated 4E-BP1 expression and dephosphorylation to the efficacy of TRAIL-induced apoptosis, alone or in combination with Gemcitabine (42,43). These effects were associated with the decrease of global protein synthesis rate rather than with translation inhibition of specific mRNA targets.
KRAS leads to the activation of MNK1/2, downstream of MEK/ERK and p38/MAPK, which phosphorylates eIF4E, allowing translation of a subset of mRNAs implicated in tumor development, epithelial to mesenchymal transition and migration (see Figure 2) (44,45). In the absence of MNK1 (and consecutive absence of eIF4E phosphorylation), mouse pancreata display normal histology, despite an impaired homeostatic response to acute pancreatitis (46), one major risk factor for PDA development (7). Curiously, mice carrying non-phosphorylatable eIF4E alleles (eIF4ES209A/S209A) showed normal response to experimental pancreatitis (generated by serial caerulein injections in mice), indicating that MNK1 kinase acts in acute pancreatitis via another substrate (47). In PDA cancer cells, eIF4E phosphorylation is induced in response to Gemcitabine treatment through the expression of specific MNK2 splice variants (48). Moreover, irradiation was also shown to induce eIF4E phosphorylation, leading to enhanced translation of Sox2 mRNA. In turn, the Sox2 transcriptional program favors repopulation after irradiation (49). Importantly, eIF4E phosphorylation was also associated with poor prognosis in PDA patients (48) as observed in other malignancies including melanoma or prostate cancer (44,50).
Targeting oncogenic pathways regulating translation in PDA
This section briefly summarizes the importance of targeting RAS/MAPK or PI3K/AKT/mTOR pathways in regard to PDA development and their impact on protein synthesis. These pathways are crucial for PDA development. This was demonstrated in KC GEMMs by partial or complete inactivation of p110α PI3K, PDK1, MEK or ERK, which efficiently prevents RAS-driven tumors (51–53). Thus, a large panel of inhibitors targeting either MEK/ERK or PI3K/AKT/mTOR (see examples in Figure 2) has been developed, but showed limited efficacy in human subjects, in part due to toxicities (reviewed in 54). A glimmer of hope is now coming from KRAS G12C inhibitors [such as AMG510 (sotorasib) and MRTX849 (adagrasib)], and from a newly identified KRAS G12D inhibitor which could be administered to >30% of PDA patients (55).
Rapamycin, a first-generation and allosteric mTOR inhibitor, was shown to be efficient on p70S6K and 4E-BP1 dephosphorylation, and to arrest PDA cell growth in vitro (56). Unfortunately, Everolimus, a Rapamycin analog, showed minimal clinical activity in Gemcitabine-refractory patients and metastatic PDA (57). Nonetheless, Rapamycin analogs remain first-line treatment for metastatic neurodendocrine tumors. Thus, second-generation mTOR inhibitors, which are kinase inhibitors, were developed to circumvent the activation of the mTORC2 complex, consecutive to the Rapamycin-mediated inhibition of mTORC1 (58). Ink128 mTOR kinase inhibitor (also referred to as MLN0128 or Sapanisertib) has shown ability to reduce phosphorylation of mTORC1/2 target 4E-BP1 in vitro on PDA cancer cells, as well as on xenografts 6 h after treatment. In addition, Ink128 enhances sensitivity to radiotherapy through inhibition of cap-bound eIF4F formation in vitro and in vivo (59). Importantly, 4E-BP1 expression loss observed in PDA cells over-rides the protein synthesis suppression mediated by mTOR inhibition and led to increased resistance, independently of the mTOR inhibitors used in our studies (32,37). Aside from the involvement of mTOR in translational control, it is important to point out that mTOR inhibition could also favor autophagy induction and survival of cancer cells, especially under nutrient-poor conditions typical of the PDA microenvironment (60).
The development of MNK inhibitors, including CGP57380, Galeterone or EFT508/Tomivosertib, was thought to counteract the deleterious effect of eIF4E phosphorylation in cancer. CGP57380 was reported to favor Gemcitabine-induced apoptosis (48). Importantly, transient pharmacological or genetic inhibition of MNK was also reported to impact tumor phenotype, reducing the frequency of the mesenchymal phenotype, which is a known factor in chemoresistance (61). Galeterone and analogs have also shown similar properties in vitro and displayed a stronger antitumoral activity in vivo as compared with CGP57380. Galeterone reduced not only eIF4E phosphorylation but also MNK1/2 expression (62). Nonetheless, expression levels of 4E-BP1 should be considered when using MNK inhibitors. In fact, our work demonstrated that 4E-BP1 expression loss in PDA cells increased eIF4E phosphorylation, independently of MNK expression. In addition, disrupting the eIF4E–eIF4G interaction (which mimic 4E-BP1 action) can reduce eIF4E phosphorylation (32,63). The relevance of targeting eIF4E phosphorylation in the clinic will hopefully confirm these encouraging in vitro results. Clinical trials assessing the efficacy of MNK inhibitors such as Tomivosertib (NCT02605083) or Galeterone (NCT04098081) for the treatment of PDA as well as other solid tumors are currently ongoing.
TRANSLATION REGULATION BY THE INTEGRATED STRESS RESPONSE PATHWAY
During stress conditions, cancer cells differentially regulate the protein synthesis process in order to survive. This modulation occurs at the translation initiation step through the TC comprising eIF2-GTP and Met-tRNAi (see Figure 1), and is called the integrated stress response (ISR; see Figure 3).
Figure 3. Integrated stress response. The ISR is a cellular response to different environmental stresses in order to promote cell adaptation and recovery. PERK, GCN2, HRI or PKR are activated upon endoplasmic reticulum (ER) stress, amino acid deprivation, heme deficiency or viral infection, respectively. Activation of any of these kinases induces the phosphorylation of eIF2 at the α-subunit. eIF2α can be dephosphorylated by two phosphatases, GADD34 and CReP, bound to PP1. eIF2α phosphorylation sequesters eIF2B, a guanine exchange factor which hinders the formation of the TC, attenuating the global cap-dependent translation initiation, and favoring the translation of a subset of mRNAs with several upstream open reading frames (uORFs) such as ATF4. The latter regulates the expression of a large panel of genes implicated in cell adaptation and survival, contributing to tumor growth. ISR-activating agents are noted in red boxes, while ISR inhibitors are noted in green boxes (105–107).
Upon stress, eIF2α phosphorylation plays a key role in cell fate, triggering either a cell survival program or cell death. Activation of any of the four kinases PERK (Protein kinase R-like endoplasmic reticulum kinase), GCN2 (General control nonderepressible), HRI (Heme-regulated inhibitor) or PKR (protein kinase R) upon ER stress, amino acid deprivation, heme deficiency or viral infection, respectively, phosphorylates eIF2α. As a consequence, global cap-dependent translation is attenuated, while the translation of specific mRNAs is triggered following eIF2α phosphorylation (64). Most of these specific transcripts harbor at least one efficiently translated upstream open reading frame (uORF) that represses translation of the main coding ORF under normal conditions. The transcription factor ATF4 is currently the best-characterized factor induced in response to eIF2α phosphorylation, and it was shown to regulate expression of genes implicated in diverse stress responses. This mechanism of the ISR also includes the PERK-branch of the unfolded protein response (UPR) (65) which was shown to be importantly deregulated in many cancers including PDA, contributing to tumorigenesis and resistance.
ISR, a risk factor promoting PDA tumor development and survival
ISR was shown to be constituvely activated in PDA. The eIF2 kinase PERK, as well as eIF2α phosphorylation, and the protein chaperone GRP78 (BiP) were shown to be significantly increased in PDA compared with normal tissues, and to be associated with worse survival rates (66,67). In fact, experimental pancreatitis induced eIF2α phosphorylation (68). Moreover, inhibiting the ISR through PERK deletion (69,70) or ATF4 deletion (71) showed pancreatic damage induction. In addition, Salubrinal, an inhibitor of GADD34 and CReP (eIF2α phosphatases), was shown to favor pancreatitis in C57BL/6 mice by promoting the ISR (72), similarly to the PERK inhibitor GSK2606414 (73), showing that the ISR pathway is important for both pancreatic homeostasis and PDA development.
ISR implication in PDA adaptation to environmental stress
Pancreatic tumors are particularly exposed to high hypoxia and nutrient stress due to poor vascularization and a dense microenvironment (74). Therefore, PDA set up mechanisms to overcome these nutrient and oxygen deficiencies, especially through ISR-mediated transcriptional and metabolic reprogramming to allow adaptation to these conditions. Importantly, a tight regulation of eIF2α phosphorylation allows rapid protein synthesis recovery in PDA upon stress. Recently, overexpression of NUPR1 in PDA (75) was shown to play a crucial role in protein synthesis restoration through its interaction with eIF2α (76). This protein was shown to be implicated in the development of PDA as well (77).
Amino acid and redox homeostasis are highly regulated through constitutively active ISR in order to sustain tumor growth while increasing antioxidant defense (64). Among diverse effectors of an antioxidant response overexpressed in PDA, the X−C cysteine/glutamate exchanger (xCT) appears as a main regulator of redox homeostasis, as cysteine is a major intermediate for the production of the antioxidant glutathione (GSH). This transporter was shown to be up-regulated in PDA through ATF4 together with the ETS-1 transcription factor, activated downstream of the RAS–MEK pathway, which in turn contributes to RAS transformation by regulating the intracellular redox balance (78). Moreover, the transcription factor NRF2, a ROS regulator (which can be activated through phosphorylation by PERK), which protects components of the translation machinery against oxidation (29), was also found to be overexpressed in PDA (79).
To face nutrient deficiency, PDA cancer cells were reported to undergo a metabolic reprogramming orchestrated by KRAS through PI3K–AKT-mediated up-regulation of ATF4 mRNA and the GCN2/eIF2α/ATF4 axis (80). The cross-talk between the ISR and the nutrient-sensing pathways enables regulation of protein synthesis depending on the availability of energy and building blocks. ATF4 plays a central role in increasing nutrient availability through up-regulation of transporters such as LAT1, xCT, SLC1A4/5/7 or GLUT1 to favor amino acid and glucose uptake (81,82). ATF4 also controls expression of enzymes implicated in amino acid biosynthesis such as PHGDH, PSAT1, SHMT1 and ASNS for serine, glycine and asparagine biosynthesis, respectively (21,80,83). Sufficient availability of intracellular amino acids triggers protein synthesis by activating mTORC1 (84). Serine and glycine are crucial amino acids feeding one-carbon metabolism (85), responsible for nucleotide and glutathione production, and, together with asparagine, they have been reported to be essential for PDA tumorigenesis. Interestingly, depletion of asparagine synthetase (ASNS) combined with inhibition of AKT (80) or MAPK (83) pathways was shown to suppress tumor growth. Similarly, targeting ASNS in combination with a GCN2 inhibitor, GCN2i, was reported to induce P38MAPK which favors apoptosis (86). Serine dependency was also reported in specific PDA tumors lacking PHGDH. Despite the induction of ATF4 in the absence of serine, cells failed to induce PHGDH expression (87,88). This phenotype was associated with ribosome stalling on mRNAs enriched in specific Serine codons and selective translation of nerve growth factor mRNA (87). Our data reported a sustained ISR activation in Serine dependent PDA cells, allowing cell survival in the absence of Serine as well as a pronounced chemoresistance (88). Additionally, cellular mechanisms such as autophagy for macromolecule recycling have been reported to be regulated through ATF4 to increase intracellular nutrient availability (89). Therefore, PDA highly relies on the ISR and its effector ATF4 to adapt and sustain proliferation in a highly stressful microenvironment.
ISR implication in PDA chemoresistance
The ISR was shown to be implicated in PDA development by adapting to diverse stresses, including chemotherapies. The basal phosphorylation of eIF2α, as well as a low protein synthesis rate, were shown to inversely correlate with sensitivity to drugs such as Bortezomib, a proteasome inhibitor. Moreover, ATF4 is responsible for the expression of chemoresistance-related factors promoting rapid protein synthesis recovery in PDA such as NUPR1 (76), but also via increased antiapoptotic factors such as BEX2 and Bcl2a1 (90). PDA standard chemotherapies have been largely reported to induce the ISR, in contrast increasing its capacity to regulate protein and redox homeostasis (91,92). Interestingly, inhibition of HRI kinase sensitized PDA cancer cells to Bortezomib through apoptotic cell death and impaired translation inhibition (93). Furthermore, the UPR sensor BiP was shown to regulate the expression of NRF2 and the ATP-binding cassette (ABC) transporters, increasing chemoresistance. Silencing BiP sensitized PDA cell lines to diverse chemotherapies including Gemcitabine, Paclitaxel and 5FU, and reduced tumor growth through apoptosis in cell xenografts (92). As a result, modulating the ISR seems to be an interesting strategy to overcome chemoresistance.
Targeting the ISR as therapeutic potential in PDA
Combining chemotherapies with ISR-targeting agents appears an interesting therapeutic approach to prevent chemoresistance development (94). One of the most attractive strategies is to hinder the activation of the ISR to suppress the adaptive mechanisms in response to drugs. Targeting eIF2α kinases directly blocked the ISR and its adaptive response, leading to PDA tumor growth inhibition. PERK inhibitor GSK2656157 showed interesting pre-clinical results, but failed due to pancreatic and neuronal toxicity in patients (95–97). Similarly, Bortezomib was shown to inhibit PERK and to synergize with Cisplatin (98), but failed in a phase II clinical trial (NCT00416793) due to high toxicity. More recently, GCN2 inhibitor was shown to enhance the action of asparaginase, leading to apoptosis of resistant cancer cells (86). Interestingly, a recent phase II clinical trial (NCT02195180) showed increased PDA patient survival upon treatment with erythrocyte-encapsulated asparaginase in combination with either Gemcitabine or mFOLFOX (99). This highlights the potential of amino acid limitation as a therapeutic approach. On the other hand, to encounter high toxicity due to complete blockade of the ISR, ISRIB, a molecule partially hindering the ISR downstream eIF2α phosphorylation, has been developed (73). Mechanistically, ISRIB has been described (100) to increase the GEF activity of eIF2B, restoring sufficient protein synthesis for normal neuronal function to avoid toxicity, and decreasing ATF4 expression. Interestingly, alleviating the ISR through ISRIB also hindered induction of experimental pancreatitis (73), and decreased chemoresistance in combination with Gemcitabine in vitro and in vivo (90). Similar to observations made for PDA, reducing the ISR was shown to favor chemoresistance in other cancers including breast cancer, melanoma or acute myeloid leukemia (101–103).
Hence, although ISR activation has been demonstrated to promote cancer cell survival, prolonged activation of eIF2α phosphorylation was shown to trigger stress-induced apoptosis leading to cell death. Indeed, overexpressing the eIF2α kinase PERK in PDA cell lines led to apoptotic cell death (104), as opposed to PERK inhibition. Moreover, different molecules that trigger the ISR, such as ER stress or oxidative stress inducers, showed inhibition of PDA progression by inducing apoptosis and autophagy (105–108). Recently, ONC212, an ISR activator, showed reduced PDA progression and a synergistic effect with chemotherapies (109). ONC201, a close analog of ONC212, also activated ATF4 through PKR and HRI, leading to increased expression of TRAIL and DR5 (110). Furthermore, many pharmacological molecules that target protein chaperones, such as HA15 and IT-139, were also shown to increase PDA sensitivity to standard chemotherapies. This includes resistance to Gemcitabine, Paclitaxel and 5FU in vitro and in vivo by inhibiting the protein folding capacity upon stress (92,111–113).
The ISR is at the center of cancer development, survival and resistance through a cross-talk between adaptation to stress and cell proliferation. Therefore, targeting this chemoprotective pathway seems to be a promising strategy in combination with standard chemotherapies. However, as the ISR may trigger apoptotic or survival fate in PDA cancer depending on the nature, intensity and duration of the stress, targeting this pathway without complementary cytotoxic drugs remains challenging. This dual role of the ISR is not limited to PDA but extends to many other cancers [for a review, see (94)].
DISCUSSION AND FUTURE DIRECTIONS
Active protein synthesis is considered as a hallmark of cancer cells. It is required for cellular growth and doubling of cellular organelles prior to division. Many alterations in translation regulatory processes have been described over the last decades in PDA, especially at the initiation step. PI3K/AKT/mTOR and RAS/MEK/ERK pathways are crucial in the control of protein synthesis. Therefore, targeting those upstream signals regulating translation initiation led to the development of a large panel of molecules inhibiting cancer progression in pre-clinical PDA mouse models including GEMMs, orthotopic grafts and PDX. However, among the few drugs that reached clinical trials, most failed to show higher efficacy than the current first-line chemotherapies. Therefore, patient stratification, based on specific tumor biomarkers, will be crucial to reveal the full potential of these pharmaceutical agents as personalized treatments for PDA patients (114).
Recent studies on targeting the ISR in combination with chemotherapies showed promising results. However, entirely suppressing the adaptive stress response pathway seems to enhance side effects on healthy tissues beside cancer cells, such as neurotoxicity. In fact, depleting PERK kinase or ATF4 was shown to highly impact pancreatic function (69,71). Moreover, ISRIB, which did not show any toxicity in mice, was shown to be only efficient within a defined window of stress activation (115), which renders its activity unpredictable in patients. Therefore, enhancing the ISR seems to be more adequate in combination with other chemotherapies. Along the same lines, ONC201, which induces the ISR, has shown encouraging results in patients with glioblastoma (116). Although the mechanism of shifting the ISR from stress adaptation to stress-induced cell death remains elusive, identifying other mRNAs translationally regulated by eIF2α phosphorylation is now urgent to understand the mechanism of ISR-mediated cell survival, as ATF4 was shown to regulate <40% of the ISR downstream genes in mouse embryonic fibroblasts (MEFs) (81). The ISR also regulates various cellular mechanisms such as autophagy, metabolism and immunity, which must be taken into account to avoid unwanted side effects and choose the best therapeutic combinations.
Among pharmaceutical targets of the protein synthesis apparatus, inhibitors of the translation elongation process remain rare or poorly efficient, apart from the only FDA-approved drug, Homoharringtonine (SynRibo). The methyltransferase METTL13, which targets eEF1A and enhances its activity, has been recently identified as essential for both protein synthesis and PDA growth (117). Pharmacological inhibitors remain to be developed to deepen the implication of METTL13 in PDA development and resistance. In addition, CG7, the inhibitor of eIF5A hypusination, and A484954, an inhibitor of eEF2K, have limited efficacy (millimolar range). Pharmacological improvement of these compounds might reveal the full therapeutic potential of targeting translation elongation in PDA cancer cells.
The PDA microenvironment represents a substantial proportion of the tumor volume (50–80%) where cancer-associated fibroblasts (CAFs) are the most abundant cells. CAFs have been described to support tumor growth through the massive secretion of extracellular matrix (ECM) as well as pro-inflammatory and chemo-protective cytokines (118). This important protein synthesis capacity has been poorly characterized despite some papers highlighting the interest in controlling mRNA translation in CAFs. The requirement of ATF4 for the massive type I collagen production supports that notion (119). Our group identified somatostatin analogs as a potential companion of chemotherapy in PDA. Somatostatin receptors are absent in PDA tumor cells but are expressed in CAFs. Mechanistically, we showed that a somatostatin analog, SOM230, could massively reduce protein synthesis via inhibition of the PI3K/AKT/mTOR/4E-BP1 axis. SOM230 diminishes interleukin-6 secretion by CAFs and blocks its pro-invasive and chemoprotective effect on cancer cells (120). Finally, combination of Gemcitabine with SOM230 blocks tumor growth and metastasis in vivo (121). These last examples illustrate one of the most important and active research axes on PDA, the stroma. Strategies to attack PDA tumor cells, such as chemotherapies or targeted therapies, have so far led to development of adaptive resistance mechanisms of tumor cells, as illustrated in this review. Essential crutches from the stroma further support the resistance mechanism. These include the presence of a dense ECM forming a physical barrier to drug delivery (122) and producing a hypoxic environment that selects the most plastic tumor cells, as well as CAFs and macrophages participating in chemotherapy titration or pro-survival cytokine secretion (123). Targeting translational control in PDA should encompass an integrative view of PDA biology and incorporate stroma weakness to make PDA tumors falter and finally fall.
LIMITATIONS
Several limitations apply to this review. Some relevant studies may have been missed although we aimed toward the most systematic review. Finally, due to space limitations or the existence of other valuable review articles, some sections were simplified. This includes the involvement of elongation factors in PDA (124) and targeting of the MAPK pathway in RAS-mutated cancer (54) which have been recently reviewed.
ACKNOWLEDGEMENTS
Author contributions: S.S. and Y.M. wrote the manuscript, S.S. designed the figures. S.S., J.S., M.L., S.P., C.B. and Y.M. edited the manuscript.
FUNDING
This work was supported by LNCC (Labellisation Ligue Nationale Contre le Cancer) and the French National Institute of Cancer [INCA_16076 to Y.M., 2018–080 and 2018–082 to C.B.]. S.S. and M.L. were recipients of a fellowship from the LNCC and J.S. from Fondation Toulouse Cancer Sante/Region Occitanie. J.S. has been partially supported through the grant EUR CARe [ANR-18-EURE-0003] in the framework of the Programme des Investissements d’Avenir.
Conflict of interest statement. None declared.
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PMC009xxxxxx/PMC9624440.txt |
==== Front
JB JS Open Access
JB JS Open Access
JBJSOA
JBJS Open Access
2472-7245
Journal of Bone and Joint Surgery, Inc.
36338797
JBJSOA-D-22-00059
10.2106/JBJS.OA.22.00059
00006
0020
AOA Critical Issues in Education
A Pilot Program: Remote Summer Program to Improve Opportunity and Mentorship Among Underrepresented Students Pursuing Orthopaedic Surgery
https://orcid.org/0000-0002-4880-2411
Hastings Katherine G. MPH 1 a
https://orcid.org/0000-0002-6058-7275
Freiman Halle D. MS, MD 1 [email protected]
https://orcid.org/0000-0002-6203-5853
Amanatullah Derek F. MD, PhD, FAOA 1 [email protected]
https://orcid.org/0000-0001-5375-5157
Gardner Michael J. MD, FAOA 1 [email protected]
https://orcid.org/0000-0002-0282-3663
Frick Steven MD, FAOA 1 [email protected]
https://orcid.org/0000-0002-4160-0163
Shea Kevin G. MD, FAOA 1 [email protected]
1 Department of Orthopaedic Surgery, Stanford University School of Medicine Stanford, California 94305 USA
a E-mail for corresponding author: [email protected]
Oct-Dec 2022
2 11 2022
7 4 e22.00059Copyright © 2022 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.
2022
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
Background:
The purpose of this study was to evaluate the impact of an 8-week remote summer program in supporting underrepresented students interested in orthopaedic surgery.
Methods:
We received 115 applications, and a total of 17 students participated in the program (14.8%). Nine faculty mentors were matched with 1 or 2 students each. The program delivered a curriculum from June-August 2021 consisting of (1) weekly instructional courses on research-related topics led by a content expert; (2) weekly faculty lectures discussing topics including orthopaedic topics, diversity in medicine, leadership, and work-life balance; and (3) a research experience paired with a faculty mentor and peer mentor. We surveyed students to measure skill progression, satisfaction, and overall program evaluation. Preprogram/postprogram evaluation, midprogram check-in, and student feedback surveys were collected.
Results:
Program participants represented a range of race and ethnic backgrounds, research experience levels, and various geographic locations across the United States. The cohort included a high rate of female (42%) and Black (35%) participants. On average, postprogram survey scores indicated that participants believed that the summer program improved their research skills (9.6 of 10), improved their orthopaedic interest (8.9 of 10), and improved mentorship and networking (9.1 of 10). For feedback surveys, 14 respondents of 15 total responses (93%) felt they were adequately matched to their faculty mentor. Twelve (80%) felt they had realistic deliverables for research projects within the 8-week program. Thirteen (87%) indicated they contributed to an abstract or manuscript as a coauthor.
Conclusion:
Our findings indicate that students improved their research skills, interest, and confidence to pursue orthopaedic residency and mentorship/networks in the field. The long-term goal is to improve the accessibility and quality of mentorship for underrepresented students in order to foster an equitable pathway into the field of orthopaedic surgery.
OPEN-ACCESSTRUE
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pmcIntroduction
In December 2018, the American Academy of Orthopaedic Surgeons Board of Directors approved a 5-year strategic plan that included diversity as a strategic goal1. The organization released the American Academy of Orthopaedic Surgeons Governance Diversity Report in June 2020 indicating a baseline for improving diversity among the volunteer structure, member engagement, and applicant selection2. Although there has been a small increase in women and underrepresented minorities (URMs) holding leadership and member positions, these groups comprise less than 15% of the total positions. A recent report from an orthopaedic residency program indicated that the greatest barrier to improving diversity programs is the lack of representation from URMs among their faculty3.
The need for equity and inclusion in orthopaedics to improve diversity and representation has been well documented4. Nonprofit organizations such as Nth Dimensions, the Perry Initiative, and Women in Sports Tech were founded to bridge the gap between access and opportunity for underrepresented groups as a pathway into the field of orthopaedic surgery5-7. After the COVID-19 pandemic and shelter-in-place orders, many institutions had to abruptly cancel summer programs, which left hundreds of medical students without an experience to bolster their background for competitive residency programs. Students from disadvantaged backgrounds were likely disproportionately affected by this lack of opportunities, further widening the diversity gap in orthopaedics. With a robust research infrastructure, our institution is in a privileged and unique position to positively influence equity and inclusion efforts because research opportunities are a common entry point for students pursuing a career in the field.
The goal of this pilot program was to determine the feasibility of implementing and executing an 8-week remote summer program for women and URM students interested in orthopaedic surgery. The program aims to improve access and exposure to orthopaedic research opportunities for underrepresented students, in addition to increasing the availability of structured remote research opportunities for students during summer periods8-11. The long-term goal is to improve the accessibility and quality of mentorship for underrepresented students in order to foster an equitable pathway into the field of orthopaedic surgery.
Materials and Methods
Screening and Interviewing
The program description and application were advertised on the department website along with various social media accounts from January to March 2021. For targeted outreach, we worked with colleagues who are alumni of historically Black colleges and universities to distribute program details. The application form consisted of a brief online survey (using REDCap) requiring demographic and contact information, a resume, and a short essay detailing qualifications and interests. Students were selected based on a set of basic criteria: identified as an underrepresented student within orthopaedics (i.e., women, URM, and/or first-generation college), research experience level (i.e., novice, intermediate, or advanced), ability to access courses and materials remotely, research interest fit with faculty mentors, and ability to legally work in the United States. All applications were evaluated, and those meeting the criteria were followed up by email with a set of additional details and questions. We interviewed a total of 27 students through Zoom using a standardized set of interview questions, and 18 students were offered placement in the program by April 2021. A total of 17 students accepted and participated from June to August 2021.
Program Design
Our program consists of 3 main components: (1) weekly instructional courses on research-related topics, (2) weekly faculty guest lectures discussing various orthopaedic topics, and (3) an 8-week research experience with an assigned faculty mentor and a peer mentor. Each student received a stipend (approximately $3,000) for participating. Research experiences involved a range of activities such as conducting literature reviews, study design, data collection, curation, and/or analysis. A copy of the pilot program's 2021 curriculum, including a complete list of the weekly instructional courses and faculty lecture topics, can be accessed in the Appendix. Other program activities included Q&A sessions with “student ambassadors” (i.e., medical students who worked in the department) and orthopaedic surgery residents within our institution and ad hoc training sessions conducted by research coordinators on resources such as REDCap and working with “big data sources.”
Mentorship Matching
Program administrators distributed a department-wide email requesting faculty mentors for the pilot program. A total of 9 faculty mentors agreed to participate and were followed up with a handout detailing the program goals, mentorship expectations, and deliverables. Each faculty mentor was matched to 2 students based on research projects available and overall fit with the student's experience and orthopaedic interests. The student pairs were matched based on varying experience levels (one early learner and one advanced learner) to promote peer-to-peer mentorship, with the “advanced learner” taking on an additional mentorship role for the “early learner.” An advanced learner was defined as a student with over 2 years of research experience in any field and/or experiences with conducting independent research. Early learners were defined as any student with less than 2 years of experience, with most of them having little to no research experience before the program. Once faculty-student matches were assigned, student participants met one on one with their faculty mentor ahead of the program start date to discuss goals and potential projects. Faculty mentors were expected to meet at least weekly with student participants throughout the 8-week program.
Program Delivery
All components of the program were delivered fully remotely from June to August 2021. For those requiring access to secure data for their research projects, encrypted laptops were mailed to students for use throughout the program. The program hosted a Welcome Orientation session for all participants. Orientation topics included timecard duties and required hiring documentation, email access and library resources, department contact information, and program goals and expectations. The program materials were presented using handouts and/or PowerPoint slides and conducted over Zoom video. The style of course delivery varied by instructor (i.e., lecture-based, discussion, breakout rooms with activities). All course materials could be assessed within a shared Google Drive, including video recordings of the instructional courses and lectures. To optimize scheduling and participation, courses and lectures were held on the same day and time every week. For example, instructional courses occurred each week on Mondays from 12 to 1 pm and faculty lectures (1-2× per week) on Wednesdays and Fridays from 12 to 1 pm. Faculty lectures were scheduled at least 3 months in advance. If a scheduling conflict arose, we were able to reschedule with the faculty mentor within 1 week. On the last day of the program, the program manager held a 1-hour “offboarding” virtual meeting to discuss student feedback and experiences.
Data Collection and Analysis
Student demographic information was obtained from the application surveys (Table I). Other surveys were developed and disseminated to participants and faculty mentors at various time points. Student participants were surveyed to measure skill progression and satisfaction. Faculty mentors were surveyed to assess productivity and student skill building, program satisfaction, and feedback. Preprogram/postprogram evaluation, midprogram check-ins, and student feedback surveys were all collected and analyzed. All surveys were collected anonymously and completed within 1 week.
TABLE I Participant Demographics (n=17); Responses Obtained From the Application Survey Before the Start of the Program
Student Participants (n = 17) Count Percentage (%)
Race/ethnicity*
Asian or Pacific Islander 2 12
Black or African American 6 35
Hispanic/Latinx 4 24
Native American or Alaskan Native 1 6
White 2 12
Multiracial or Biracial 2 12
Prefer not to answer 1 6
Female 7 41
First-generation college student 6 35
Highest level of education completed
High school degree 1 6
Bachelor's degree 13 77
Graduate degree 3 18
US Geographic location (at the time of the program)
Northeast 2 12
Midwest 5 29
South 5 29
West 5 29
Research experience level (self-identified)
Advanced 3 18
Intermediate 9 53
Novice 5 29
* May be several responses per student.
Funding and Total Spending
The pilot program was awarded a departmental grant ($50,000 for one year). Most of the funding was allocated to participant stipends. We partnered with the College of Idaho, contributing $5,000 as a scholarship opportunity for one of their undergraduate students to also participate in the pilot program. This partnership allowed us to assess the feasibility and appropriateness of including undergraduate students in the program. Other programmatic costs included a total of 10 laptops, FedEx shipping costs, costs related to onboarding and background checks, and participant gifts. Total program spending for the 1-year pilot program was $52,490.37 (U.S. Dollars (USD)).
Results
Participant Demographics
We received 115 applications from January to March 2021 and selected 17 students to participate in the program (15%). Student participants represented a range of race/ethnic backgrounds, research experience levels, and a high rate of female (42%) and Black (35%) participation and came from various geographic locations across the United States (Table I). A total of 9 faculty members within the department at our single institution participated as mentors. The faculty mentors ranged in subspecialty areas (e.g., spine, pediatrics, trauma, sports medicine) and years in practice (range: 3-20 years). A total of 3 of 9 faculty mentors were women.
Participant Outcomes
All 17 participants completed the preprogram/postprogram evaluation surveys. In postsurvey findings, participants rated program outcomes (research skills, mentorship, interest in orthopaedics) favorably. All participants indicated that they improved research skills, with scientific writing and literature search/review being the skill that they improved the most. There is a strong interest in continuing to improve “presentation” and “grant writing” skills, which could be a course to build out in future years. A majority of student participants believed that the program further increased their interest and candidacy in orthopaedic surgery and strengthened their likelihood of applying to an orthopaedic residency. Several indicated they are now considering taking a gap year to conduct research based on this experience.
Participant Feedback
Fifteen of 17 students completed the feedback survey (88%). All respondents reported high satisfaction levels with the program. Fourteen of 15 students felt they were adequately matched to their faculty mentor. Of the 3 program components, most indicated that the research experience contributed greatest to their overall learning, then faculty lectures, and then instructional courses. Participants indicated that the greatest room for improvement was increased access to course materials (i.e., all materials located in an online learning platform such as Canvas vs. email), more time in the program (increase to 10 vs. 8 weeks), and improvements to specific course content. Regarding course improvements, there was a desire to make content more interactive and to include more advanced research topics. Course delivery (through Zoom video) was highly effective in learning, but participants would have enjoyed additional opportunities to interact with their peers. The top 3 instructional courses were Literature Search and Review, Citation Manager and Bibliography, and Good Presentation Practice. The top ranked faculty lectures were “Diversity and Decision Making,” “Applying to Orthopaedic Residency” hosted by our residency director, and a tie for “Diversity in Orthopaedic Surgery” and “Balancing Family, Volunteer, and Professional Life.” Thirteen of 15 respondents said that they will have contributed to a publication through this program, and most of them (12 of 15) felt that research expectations were realistic12.
We also received many positive comments from students regarding the program:“He [faculty] was incredibly encouraging and motivating and I left that session feeling very inspired about my path to orthopaedics. Immediately after that session, I became much more intentional about seeking out orthopaedic shadowing/mentorship opportunities in my home institution throughout the summer.”
“I think with my mentor it was stressed that it's a 2-way relationship and that as the mentor, they are also looking to learn from the mentee, so having confidence to speak up and bring your own idea to a relationship can help create a strong mentorship.”
“…before having written the manuscript to our paper, it seemed like an insurmountable task. I am leaving this program with much more confidence in my ability to write.”
Faculty Mentor Feedback
All 9 faculty mentors completed feedback surveys. Majority of the faculty (8 of 9) mentored a total of 2 program participants this summer, with 1 faculty mentoring 1 participant. Nearly all faculty mentors were able to meet weekly with their student, and if not, at least one member of their team was able to meet with participants 1 time per week. Majority (7 of 9) indicated they also worked with other summer students outside this program, and only one mentioned that the workload was “not manageable.” All faculty mentors, except one, indicated they have worked with students previously and felt adequately prepared to take on students this summer. All faculty mentors felt they were appropriately matched to their participants. In addition, all faculty mentors indicated that they would participate again, with only one indicating they could only take one participant in the future. Majority of faculty mentors (5 of 8 responses) felt that 8 weeks was enough time to complete a meaningful research experience. Any negative feedback was directly related to the desire to work with students in-person for additional clinical experiences. However, faculty recognized that remote accessibility is a strength overall and indicated that it did not affect their satisfaction and willingness to participate in the future.
Discussion
Medical students are increasingly challenged with a limited number of research opportunities to prepare for an exceptionally competitive process for orthopaedic residencies. Unfortunately, the financial burden and time constraints to taking on low or unpaid summer internship experiences prohibits those from disadvantaged backgrounds and perpetuates an uneven playing field for applications. Without equitable pathways into the field, URM students are less likely to be admitted and/or pursue orthopaedics as a career, further contributing to the lack of diversity in orthopaedics. Our findings indicate that not only did students gain foundational research skills and orthopaedic knowledge but also felt that their experience made them a more competitive applicant for residency applications and, importantly, gave them a newfound confidence to pursue the field.
Based on student and faculty mentor feedback, we plan to incorporate several improvements. First, many students indicated that they would like increased opportunities for informal peer-to-peer interactions. This will be incorporated through designated “office hours,” which will be optional weekly meetings through Zoom hosted by the program manager to answer any questions among participants. We also plan to implement biweekly “coffee chats” where participants have the opportunity to get to know each other, play games, and engage with peer mentors in a less formal setting. We will also host a virtual student poster session at the end of the summer to allow students to showcase their work among colleagues and faculty in a low-stakes environment. Course materials will be organized and delivered through Canvas. Canvas is an online learning platform, where students can access weekly schedules and assignments, lecture recordings, and graded assignments in a single location. Other no-cost learning/cloud-based platforms (e.g., Google Drive) can also be used to organize course materials.
There were some operational and logistical challenges worth mentioning. First, it is advisable to offer a small stipend ($2-3,000 each) to prevent exclusion of students unable to take unpaid opportunities over the summer. Program administrators surveyed students at the end of the program and found that at least a quarter of respondents indicated that they may not have been able to participate if it was unpaid. Second, there was a general interest to increase program length from 8 to 10 weeks. This may be challenging with varying summer schedules across medical schools, which overlapped with our start and end dates. Third, we emphasize the need to appoint a program manager to spearhead various programmatic logistics before, during, and after the program launch. Tasks may include interviewing, scheduling courses and lectures, building a curriculum into an online course delivery platform, and more. Frequent check-ins and/or monitoring attendance throughout the program were essential to maintain engagement among students and faculty. Fourth, ensuring there were institutional means to acquire secure data remotely was fundamental to research productivity. To do so, we purchased laptops that were encrypted and shipped directly to students. Laptops were shipped back at the end of the program. Laptops will then be reused for several cohorts over the years. Finally, we suggest prioritizing medical students over undergraduate students. A baseline level of medical proficiency was required to be productive remotely and with minimal supervision (i.e., magnetic resonance imaging measurements, anatomical knowledge).
Overall, we believe that this work serves as a stepping stone in building effective and evidence-based remote programs that can be adopted to continue to expand opportunities for URM students in medicine. Our pilot program leveraged digital resources, a team of strong mentors, and a robust research infrastructure with the goal to meaningfully engage students in orthopaedic research. While remote opportunities are becoming increasingly popular, this pilot program underscored the necessity of a thoughtfully curated program that not only includes a research experience but also in combination with instructional courses and lectures for structured learning and mentorship. Findings from this study serve as preliminary evidence of the effectiveness of this program, which will then lead to additional grant opportunities for sustained funding. Our future steps are to partner with other equity-oriented organizations to develop long-standing relationships that serve as a network of diverse faculty for research and professional mentorship among participants. Our team plans to longitudinally track our program alumni and evaluate the impact of this program on student progression toward careers in medicine and orthopaedics.
Appendix
Supporting material provided by the authors is posted with the online version of this article as a data supplement at jbjs.org (http://links.lww.com/JBJSOA/A426). This content was not copyedited or verified by JBJS.
Note: The authors thank the executive leadership in the Department of Orthopaedic Surgery at Stanford University for their administrative and financial support for this pilot program: Dr. William Maloney (Department Chair), Morisa Guy (Director of Finance and Administration), and Nicole Parish (Associate Director of Human Resources). Without their contributions, this program would not be possible. The authors thank our collaborators Drs. Henry Ellis, Lee Pace, Nathan Grimm, and Marc Tompkins for their thorough contributions to the program design, planning, and participation in guest faculty lectures. The authors also thank Dr. Bonnie Mason and Sonya Seymour of Nth Dimensions for taking the time to provide guidance and feedback on the design and implementation of this program, as their incredible diversity initiatives in the field have been a long-standing inspiration for this program.
Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSOA/A425).
==== Refs
References
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2. AAOS Governance Diversity Report. American Association of Orthopaedic Surgeons website. Available at: https://www.aaos.org/about/diversity-in-orthopaedics/strategic-plan/aaos-governance-diversity-report/. Accessed October 29, 2020.
3. McDonald TC Drake LC Replogle WH Graves ML Brooks JayssonT . Barriers to increasing diversity in orthopaedics: the residency program perspective. JBJS Open Access. 2020;5 (2 ):e0007.32832828
4. Alliston T Foucher KC Frederick B Hernandez CJ Iatridis JC Kozloff KM Lewis KJ Liu XS Mercer DM Ochia R Queen RM Rimnac CM van der Meulen MCH Westendorf JJ . The importance of diversity, equity, and inclusion in orthopedic research. J Orthopaedic Res. 2020;38 (8 ):1661-5.
5. Mason BS Ross W Ortega G Chambers MC Parks ML . Can a strategic pipeline initiative increase the number of women and underrepresented minorities in orthopaedic surgery? Clin Orthop Relat Res. 2016;474 :1979-85.27113596
6. Lattanza LL Meszaros-Dearolf L O'Connor MI Ladd A Bucha A Trauth-Nare A Buckley JM . The Perry initiative's medical student outreach program recruits women into orthopaedic residency. Clin Orthop Relat Res. 2016;474 :1962-6.27245771
7. Women in Sports Tech (WiST). Available at: https://www.womeninsportstech.org/wist-next-gen. Accessed on October 29, 2020.
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PMC009xxxxxx/PMC9742735.txt |
==== Front
Environ Plan B Urban Anal City Sci
Environ Plan B Urban Anal City Sci
spepb
EPB
Environment and Planning. B, Urban Analytics and City Science
2399-8083
2399-8091
SAGE Publications Sage UK: London, England
10.1177_23998083221142863
10.1177/23998083221142863
Special Issue Articles
Deploying geospatial visualization dashboards to combat the socioeconomic impacts of COVID-19
Yao X. Angela
Crooks Andrew
Jiang Bin
Krisp Jukka
Liu Xintao
Huang Haosheng
https://orcid.org/0000-0002-7383-2751
Praharaj Sarbeswar PhD
https://orcid.org/0000-0003-1374-9400
Solis Patricia PhD
https://orcid.org/0000-0002-2881-0668
Wentz Elizabeth A PhD
Knowledge Exchange for Resilience, School of Geographical Sciences and Urban Planning, 7864 Arizona State University , Tempe, AZ, USA
Sarbeswar Praharaj, Knowledge Exchange for Resilience, School of Geographical Sciences and Urban Planning, Arizona State University, 976 S Forest Mall, Tempe, AZ 85287-1004, USA. Email: [email protected]
7 12 2022
6 2023
7 12 2022
50 5 Special issue: Urban Analytical Approaches to Combatting Covid-19 12621279
© The Author(s) 2022
2022
SAGE Publications
https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
COVID-19 dashboards with geospatial data visualization have become ubiquitous. There is a growing sense of responsibility to report public health data pushing governments and community organizations to develop and share web-based dashboards. While a substantial body of literature exists on how these GIS technologies and urban analytics approaches support COVID-19 monitoring, their level of social embeddedness, quality and accessibility of user interface, and overall decision-making capabilities has not been rigorously assessed. In this paper, we survey 68 public web-based COVID-19 dashboards using a nominal group technique to find that most dashboards report a wealth of epidemiologic data at the state and county levels. However, these dashboards have limited emphasis on providing granular data (city and neighborhood level) broken down by population sub-groups. We found severe inadequacy in reporting social, behavioral, and economic indicators that shape the trajectory of the pandemic and vice versa. Our survey reveals that most COVID-19 dashboards ignore the provision of metadata, data download options, and narratives around visualizations explaining the data’s background, source, and purpose. Based on these lessons, we illustrate an empirical experiment of building a dashboard prototype—the COVID-19 Economic Resilience Dashboard in Arizona. Our dashboard project demonstrates a model that can inform decision-making (beyond plain information sharing) while being accessible by design. To achieve this, we provide localized data, drill-down options by geography and sub-population, visualization narratives, open access to the data source, and accessible features on the interface. We exhibited the value of linking pandemic-related information with socioeconomic data. Our findings suggest a pathway forward for researchers and governments to incorporate more action-oriented data and easy-to-use interfaces as they refine existing and develop new information systems and data analytics dashboards.
COVID-19
dashboards
public health
GIS
urban analytics
Virginia G. Piper Charitable Trust https://doi.org/10.13039/100014024 typesetterts10
==== Body
pmcIntroduction
The rise in geospatial data availability in cities has encouraged authorities to invest in ways to explore, operationalize, and visually communicate vast amounts of information. There has been a growing trend of public-facing dashboards as instruments that policymakers use to integrate and visualize multi-agency data to monitor and respond to emergency events and organizational performance issues (Lee et al., 2015; Mattern, 2015). Dashboards are software tools enabling information visualization and data storytelling through maps, graphs, diagrams, indicators, and other interactive widgets, consolidated and arranged on a single screen or a webpage (Few, 2006; Rojas et al., 2020). Dashboards are dynamic visualizations because they are programmed to update as new data are released (Batty, 2015), helping the users track and compare over time and space to support real-time decision-making. The COVID-19 pandemic triggered a deluge of data dashboards that visualize cases of infection and fatalities over time and space. These dashboards emphasize the importance of spatial thinking and the effectiveness of geospatial technologies in understanding Covid-19 (Dangermond et al., 2020; Praharaj et al., 2022). However, all these dashboards are not created equal—in their layout, structure, visual flair, content, and navigability, nor do all communities access the data equally (Fareed et al., 2021; Lan et al., 2021). For all the COVID-19 data, graphs, and maps available to the decision-makers, the global response has been chaotic, inconsistent, and in places disastrous, implying that the mass visualization of inputs does not necessarily lead to better outputs (Budd et al., 2020).
Stephen Few (2006) defines dashboards as a “visual display of the most important information needed to achieve one or more objectives.” Dashboard embodies the many ways of visualizing a variety of data that is representable, contextualizable, and intelligible to a non-expert target audience and can be used as a decision-support tool by governments, corporations, urban stakeholders, and communities (Batty, 2015; Kitchin, 2014; Mattern, 2015). A Scopus database search within article titles and abstracts reveals that 321 documents were published with the “COVID-19 dashboard” keyword as of 5th December 2021. The core focus of these projects has been on providing information to the public, enabling place comparisons, and monitoring public health scenarios. Ivanković et al. (2021), in an assessment of 158 COVID-19 dashboards, found that while these visualizations overwhelmingly reported public health indicators (e.g., cases, deaths, and hospitalizations), only a handful revealed the pandemic’s impact on the social and economic profile of communities. Lack of local and disaggregated data (by age, sex, socioeconomic status, and ethnic or racial groups) restricts dashboards’ utility for decision-making purposes and poses a risk of the public not being informed about these critical (and modifiable) differences. Furthermore, Pietz et al. (2020) state that less than 10% of dashboards explicitly stated the purpose and intended user, which raises questions about their efficacy. Previous research also shows the underuse of explanatory narratives in the widely used single-screen dashboards (Chiang, 2011) that are proven to clarify complex data for less-data-savvy users to use the information in their decision-making confidently. Overall, existing literature points to the need for dashboards to explore the connections between public health indicators and socioeconomic patterns to improve healthcare response mechanisms (Budd et al., 2020; Fareed et al., 2021; Lan et al., 2021; Pietz et al., 2020). Further research is necessary to examine the factors that make dashboards more fit for purpose and actionable in the context of COVID-19 and in general.
The flurry of covid-19 dashboards is somewhat a technological response to resilience challenges at a time when we are experiencing that existing social and policy mechanisms are insufficient to deal with new kinds of global change. However, dashboards that communicate only epidemic indicators while being disconnected from social and economic contexts are less likely to influence change in how our communities consume data to prepare for and respond to emerging shocks (Crepaz and Arikan, 2021; Dangermond, 2020). While they might still be useful tools for policymakers, their potential for wider public dissemination to induce behavioral change and situational awareness among communities is limited. This study adopts a novel approach to emphasize the role of social learning (Coe et al., 2001), engaging with socioeconomic aspects of the pandemic to organize and inform communities. Our hypothesis is that disasters may provide windows of opportunity to transform traditional response mechanisms and build new narratives that the enhancement of community-based knowledge networks through tools such as data dashboards will provide pathways to a stronger and more effective model of community resilience (Sharifi et al., 2021; van der Voorn and de Jong, 2021). Our research particularly focuses on providing a socially embedded data and visualization interface for augmenting the technological capacity of communities to comprehend rapidly evolving issues and address them in a timely and efficient manner.
This research advances the ongoing innovations in data analytics and urban science to shape community-focused decision-support systems (Kourtit and Nijkamp, 2018; Kitchin, 2014; Mattern, 2015; Pettit et al., 2017) to enable evidence-based and near-real-time decision-making. This work is grounded in the fact that building community resilience in response to emerging challenges requires a combination of timely data at the local scale (Haraguchi et al., 2022) and easy-to-use decision-support tools (Batty, 2015) that can inspire and organize local communities to initiate local action. Drawing on the lessons learned from the critical literature, we outline a community-driven approach to identify pressing resilience issues and deploy our dashboard tool on targeted areas for maximum community benefit. This approach addresses important gaps highlighted by Pettit et al. (2017) who suggested that dashboards often fail to engage with local issues as they provide high-level executive data and are often designed as a general instrument for conveying a range of discrete information without a clear delineation of expected users. Our research emphasizes a tool that focuses on a single aspect of economic resilience for tangible outcomes while outlining how digital data can be collected, processed, and disseminated through “public dashboards” during the event of a disaster to enable local communities to gain critical knowledge, decipher complex evolving issues, and prepare responses to the current and future crisis.
This article explores 68 publicly available COVID-19 dashboards to identify whether the data presented over these dashboards were socially embedded and actionable to a diverse target audience. The goal was to examine which design elements make some dashboards easier to navigate, more inclusive, and more actionable than others. Based on the lessons from the case study analysis, we developed a dashboard prototype: Economic Resilience Dashboard that brings together multi-agency data to capture the links between the COVID-19 pandemic and the local economic and social dynamics in Arizona, US. The dashboard prototype demonstrates a model that can inform decision-making (beyond basic information sharing) while being accessible and actionable by design. As Mattern (2015) argues, data generated from complex systems cannot be understood without epistemological clarity, and as most of the data on COVID-19 dashboards is sanitized and treated, we investigate the broader knowledge and policy implications of these technological framing of public health crisis (Crepaz and Arikan, 2021). This study’s findings culminate into a set of guidelines and recommendations for dashboard designers to implement robust data visualization tools that are fit for use—meeting the specific information needs of targeted stakeholders.
Critical assessment of the COVID-19 dashboards
We assessed a sample of 68 publicly available web-based COVID-19 dashboards across the States in the US, Canada, and several other countries designed to help people better understand the evolving scenarios of COVID-19. The principal criteria for the selection of the dashboard were the authoritativeness of the projects, the diversity of actors involved in their development, and openly available tools accessible to a wide range of users. The first set of dashboards selected for this assessment is developed and maintained by the US States. These projects provide authoritative information for a broad range of stakeholders, supporting both policy actions and general awareness among communities. The samples also include renowned non-profit sector-generated dashboards (New York Times dashboard (NYTD), COVID Act Now (CAN), and The Atlantic’s COVID Tracking Project) and academic-sponsored projects like the Johns Hopkins Dashboard. These projects are also open and authoritative nonpartisan solutions attracting a diverse audience. Globally, the survey focused on the WHO Coronavirus (COVID-19) Dashboard, and several other state-sponsored projects in Canada and European countries. The list of dashboards assessed is presented in Supplementary Table S1.
We find that 62 out of the 68 dashboards (91.18%) reported data at the county/district level, and all the assessed samples had state-level information. Just over 16% (11/68) of the dashboards showed city-level data, and none had numbers for the metropolitan statistical areas. Our analysis of COVID-19 dashboards further reveals that 9.24% (6/58) of the samples provided data going down to census tracts or postcode level, and none made pandemic statistics accessible for US public use microdata areas (PUMA). These trends highlight that dashboard makers primarily focus on providing macro-level aggregated data with minimal emphasis on granular data dissemination at the scale of local communities and neighborhoods. The findings are important as previous research (Fareed et al., 2021; Ivanković et al., 2021; Pietz et al., 2020) established that local data provision significantly improves public health decision-making abilities and dashboards’ actionability.
Our analysis of the frequency of indicator themes reported by the dashboards presented in Table 1 highlights that COVID-19 dashboards overwhelmingly focused on reporting infection cases and deaths (68/68, 100%), testing rates and positivity (52/68, 76%), and hospitalization status (47/68, 69%). Indicators reported at moderate frequency are hospital bed, ICU, and ventilator capacities (42/68, 62%) and vaccinations (54/68, 79%). While the public health and epidemiological indicators were prominent on these COVID-19 dashboards, the emphasis on social, behavioral, and economic indicators was limited. Except for the state of California, no other dashboard provided data on the social impacts of the pandemic (e.g., measuring health equity, evictions, and food scarcity). A meager 4% (3/68) of dashboards (including the state of Ohio and Pennsylvania) presented data on changing economic and employment scenarios during the pandemic. Just over 10% (7/68) reported data on public adherence to restrictions, self-reporting, and the community’s behavioral response to various emergency orders, public restriction, and actions designed to safeguard people. Eight dashboards out of the 68 samples (California, Pennsylvania, and Michigan) disseminated some form of COVID-19 future projections or risk models). Ivanković et al. (2021), in an assessment of COVID-19 dashboards across 53 countries, found a strikingly similar trend of underreporting of social and economic indicators that limits the user’s ability to explore the links of socioeconomic outcomes inflicted by the pandemic.Table 1. Characteristics of the assessed COVID-19 dashboards (N = 68).
Value (n) %
Frequency of indicator themes reported
COVID-19 cases and deaths 68 100
Testing 52 76
Hospitalization status 47 69
Hospital bed, ICU, ventilator capacities 42 62
Vaccinations 54 79
Future projections/risk models 8 12
Mobility changes 6 9
Impacts on the economy and employment 3 4
Social impacts (evictions, food scarcity, etc.) 2 3
Public adherence to restrictions, self-reporting 7 10
Economic relief and packages 2 3
Data provision by population sub-groups
Age 54 79
Sex 44 65
Pediatric (children) 12 18
Pregnant 3 4
Ethnicity 47 69
Race 49 72
Income or socioeconomic status 0 0
Comorbidities 5 7
Others (schools, correctional facilities, etc.) 11 16
Table 1 summarizes the COVID-19 dashboard assessment of the provision of information by population sub-groups, indicating to what extent these tools offer disaggregation options. We found that the 68 dashboards offered eight types of breakdowns that allow users to investigate data by population sub-groups. Of these, the most common breakdowns included age (54/68, 79%), sex (44/68, 65%), ethnicity (47/68, 69%), and race (49/68, 72%). Although less frequently reported, other breakdowns included pediatric information (12/68, 18%) and schools and correctional facilities (11/68, 16%). Only 4% of dashboards (3/53) provide granular data on pregnant women and comorbidities with preexisting health conditions. None of the dashboards present data breakdowns by income or socioeconomic status. The analysis reveals that many COVID-19 dashboards, including those developed by Connecticut, Illinois, Iowa, Kansas, Alaska, and North Carolina, do not provide disaggregation options for viewing population sub-group level data. Kitchen (2014) argues that dashboard projects of this nature often translate into technological framing of a problem; rather, they should be designed as a humane solution that is cognizant of the context, people, and underlying unequal societies that these tools address.
We assessed the design and functionality of the dashboards (n = 68) and analyzed the data (Supplementary Figure S1) to indicate the commonly used visualization techniques and the features that characterize an effective dashboard. The analysis shows that over one-third (28/68) of dashboards had a single-screen view, where all the data visualization is fitted on a set view with no option for up-and-down navigation or scrolling. The more favorable option was a multiple granular view dashboard interface which allows users to navigate across pages and scroll up and down over the webpage. The second option provides more space to align data visualization and integrate textual narratives alongside visuals. 54% of the dashboards (37/68) used narratives to describe the visualizations. The data download option—an essential dashboard element found missing in over half the dashboards (33/68). Similarly, 60% of the samples (41/68) did not provide metadata that describes critical information, such as the purpose of the data, time and date of creation, data quality, source, and the process used to create and explore the data. Most dashboards (48/68, 71%) included some user interaction element. For example, the New York Times dashboard provides dynamic click-to-filter options, where clicking on any chart or map creates a quick filter that applies to all dashboard data, delivering new insights instantly. Another interesting finding was that a high share of dashboards (36/68, 53%) did not use a color palette that meets accessibility standards. Less than half of these tools (31/68, 46%) used icons and menus that allow users to navigate data seamlessly. These are some of the fundamental elements of inclusive and accessible dashboards (Batty, 2015; Few, 2006). We also learned that 85% (58/68) of the samples presented visualizations in distinct sections to guide users through the data, an increasingly popular storytelling feature (Elias et al., 2013; Knaflic, 2015) in dashboard design practice.
Methods and approach for the Economic Resilience Dashboard development
Building on the lessons learned from the COVID-19 dashboard assessments in the previous section, we categorize the dashboard design observations and development challenges. We further identify approaches to address the issues and challenges scientifically and technically.
The primary challenge with dashboard design we found is that higher-level aggregated data is less compelling and actionable. Broad data may provide interesting insights, but most agencies and groups interacting with such data lack the authority or responsibilities to act at that level. Our goal was hence to provide granular local data to assist local stakeholders in identifying key issues and solutions. As Pettit (2017) argues, integrating micro-level real-time data can assist executives and citizens in responding to disruptions in a timely and efficient manner. We collected economic data across spatial scales, the State, County, metro areas, and cities on several core indicators and sub-measures (Supplementary Table S2) that can quantify and visually capture the immediate economic loss and the policy-induced social processes during the pandemic. We also offer data segregation by population sub-groups (e.g., race, ethnicity, and education) that enlighten users about the unequal effects of the pandemic. Another design issue for at-a-glance dashboards is the lack of ability to explore time-series information (Praharaj and Wentz, 2022) and data segregation to drill into the history of data metrics (Kitchin, 2014). We address this limitation by providing time-series data at various frequencies for users to examine weekly, monthly, and annual trends.
Our dashboard evaluation indicated the issue of limited background information and interpretation of data available over the single-screen dashboards, working as a barrier for many users. We tackle this fundamental design challenge by adopting a multiple-view scrolling webpage dashboard allowing more space to describe the contextual information behind the data and lay visualizations in clearly defined sections that improve users’ understanding and navigability.
The previous section noted an acute lack of socially embedded data provided through the COVID-19 dashboards as they overwhelmingly focused on reporting public health data. This approach limits the authority’s ability to pinpoint disproportionately affected communities and establish connections between the pandemic and social vulnerability. Our dashboard emphasizes novel factors, including changes in earnings, delayed medical care, evictions and foreclosures, and food shortages that help communities visualize the interaction between economic changes and social stress.
We designed a four-layer dashboard technical architecture (Figure 1) seamlessly connected to produce the dashboard's visual analytics interface. The first, database layers act as an edge-level device, providing secure data feeds. We connected with the servers of data providers through APIs to retrieve published historical time-series data in JSON data-interchange format. Specific processing and parsing were applied on machine-readable datasets in the controller logic to extract the relevant variables within the dataset and to render it into the real-time visualizations of the dashboard. In the integration layer, each file was stored locally, new data was refreshed as it became available, and we simultaneously used ArcGIS Pro and Tableau Business Intelligence (BI) software for layer joining and blending to match features and geographies to ensure the consistency of datasets from different sources. The third layer provided the platform for creating various maps, charts, widgets, and navigation menus assembled into the dashboard. We published the dashboards to a server to generate JavaScript codes for deploying the dashboard visualizations to a web page https://resilience.asu.edu/economic-resilience-dashboard. The user interface (fourth layer) provides the output view to navigate different metrics visualizations. We have optimized the dashboard for the visualization to adjust on both computer/laptop and mobile devices, and the interface allows users to enter full-screen view mode for each section for deeper dives into the data.Figure 1. Technical architecture and workflow for the Economic Resilience Dashboard development.
Results
Overview and analysis of the Economic Resilience Dashboard
We present an overview of the different modules, sections, and infographics of the Economic Resilience Dashboard tool in Figure 2. The dashboard tool supports communities, planners, non-profit responding organizations, and policymakers to explore and query data to formulate questions and examine responses. They can evaluate issues like the monthly change in the unemployment rate in a County, or which sector experienced the most job loss in the last quarter. The graphical user interface adheres to the Model-View-Controller (MVC)—a three-part design pattern, dependent and connected, that allows the controller to receive handler inputs. It manages and sends a query to the model, which contains the data and the rules for carrying out a specific task. The interface provides users with a set view highlighting the most important trends, and detailed information can be visualized through drill-down buttons, navigation panels, map-based selection and filtering, and timeline sliders placed in strategic locations across the interface. The dashboard tool gives users the capability to perform historical data analysis in over 15 counties, seven metropolitan statistical areas, and 91 towns in Arizona. The dashboard is hierarchically organized into five different modules (see Supplementary Figures S2–S5 in Supplementary Materials) to enable a connected dashboard environment to be navigated across indicators from summary-to-detail exploration within a single system. The interface ultimately addresses the characteristics of exploratory dashboards (Kitchin, 2014).Figure 2. A screenshot of the Economic Resilience Dashboard. Source: https://resilience.asu.edu/economic-resilience-dashboard.
The analysis presented in the dashboard links the economic trends with demographic information (e.g., race, ethnicity, and education) to examine equity issues around the COVID-19 impact on communities. The tool integrates weekly data from the Census household pulse survey, including households’ perceptions of loss in employment income, difficulty in paying household expenses, housing insecurity and the likelihood of eviction, food scarcity, and delayed medical care. Such data help users understand correlations and interdependencies between COVID-19 and social dynamics. The best practice design approach to information dashboard design (Few, 2006) is used to integrate a range of charts, maps, and big number KPIs to communicate and visualize the data. Line charts in our dashboard analyze indicators with historical time-series information. Donut charts visually capture proportional data such as the share of unemployment insurance claims by different racial/ethnic groups. The butterfly charts capture two-dimensional data (sector-wise total nonfarm employment and annual change in jobs generated by these sectors) and bar charts visualize categorical and continuous datasets (e.g., unemployment rate by cities). The dashboard also has KPI text indicators to highlight key facts and outliers, such as which county shows positive changes in unemployment in the last month or quarterly changes in wages across regions.
The Economic Resilience Dashboard placed significant emphasis on map-based analysis to provide users with a spatial understanding of how the pandemic impacts different regions. We follow the approach of geospatial BI dashboards (Badard and Dubé, 2009; Presthus and Canales, 2015) to integrate spatial querying and filtering tools in our dashboard. The map area selection commands enable users to select different geographic units, dynamically changing the views over charts, graphs, and numbers. An introduction for the overall dashboard was provided first, followed by the background descriptions for each of the five sections to inform users about why we chose those indicator data, how they help us measure what we are trying to measure, and the sources of information. The dashboard provides links to the original data repositories to maintain transparency. The tool supports the download of maps, visualizations, and underlying datasets. Young and Kitchin (2020) found that an explanation of the history and epistemology of data and the provision of data and chart download play a crucial role in engaging users to create a participatory environment over dashboards.
The first section of the dashboard presents the visualization output for the unemployment rate and labor force data (see Supplementary Figure S5). It provides a snapshot of how a combination of maps, various types of charts, and indicator KPIs were used to illustrate the changing state of the Arizonan economy while flagging key issues. Standard navigation tools, including a time slider and dropdown menus, were deployed to show historical unemployment data since 2018 visualized over varying geographic scales ranging from the State and Counties to the level of metropolitan areas and individual cities in Arizona. These tools allow users to select and visualize data for a defined timeframe and geography. KPIs were used strategically to highlight which regions were the worst hit and the ones that showed remarkable resilience to the COVID-19 economic shock. Tooltips were used throughout the visualizations allowing users to view background and contextual information, definitions, and description of the data when hovering over dashboard elements. The line chart reveals the striking impact of the pandemic on the unemployment rate in most places, including in the most populated Phoenix-Mesa-Scottsdale metropolitan area, where the unemployment rate shot up to 13.5% in April 2020 from 4% in February of the same year. The data allows researchers to investigate further questions, such as the linkage of these unemployment trends with government policies, including vaccination, lockdown and reopening, social distancing orders, and the COVID-19 Economic Impact (Stimulus) Payments.
The second section shows data on weekly unemployment insurance claims highlighting which geographic regions, industry sectors, and socioeconomic groups received the most filings, and the changes observed every week (see Supplementary Figure S3). The maps and charts in this section and throughout the dashboard were built with color palettes that conform with accessibility standards (Healy, 2018) to engage with diverse users, including people with color blindness. Interactive features are embedded, which allow dynamic changes to multiple views and indicators when users select a county from the dropdown menu. The map highlights how the unemployment insurance claims in Maricopa County shot up from 1003 claims in February last week to over 48,000 in the first week of April 2020. The line chart establishes an apparent relationship between different waves of infections and related increases in claims by individuals due to job losses. A significant finding is that specific industries have emerged as most vulnerable to the COVID-19 effects. Workers from five industries—accommodations and food services, healthcare and social assistance, retail trade, administration, and manufacturing—received over 250,000 claims in Arizona during the initial COVID-19 outbreak (April to May 2020).
Section 3 of the Economic Resilience Dashboard tool highlights nonfarm employment trends, monthly changes in employment by industry, and weekly average hours and wages/earnings of workers on the payroll (see Supplementary Figure S4). A significant drop in nonfarm employment was found during COVID-19, with a loss of 244,300 jobs between February and April 2020. The butterfly chart showing the employment change by sector points out that the leisure and hospitality sectors lost an overwhelming 138,000 jobs during that period, followed by 34,000 job losses in trade, transportation, and utilities, and 28,000 jobs in education and health services. While jobs were lost across sectors as an effect of COVID-19, the bar chart analysis suggests a trend of increasing wages/earnings during the pandemic. The dashboard also provides BAN (Big Ass Number) indicators to draw attention to the regions showing the most significant gains and losses in employment and earnings. A BAN is a big number meant to attract attention from end-users and is popularly used in business analytics dashboards (Badard and Dubé, 2009). The dashboard interface shows BANs as bold, big, and colored in a way that makes users notice them.
The last two sections of the dashboard (see Supplementary Figure S5) explore the linkage and impact of the COVID-19 pandemic with the socioeconomic profile of communities. We visualize the weekly Census Household Pulse Survey data from the US Census Bureau (Schanzenbach and Pitts, 2020) to show the social and economic impacts of the COVID-19 pandemic on households. The dashboard visualization reveals severe issues; over half of the households surveyed in the Arizona and Phoenix metro area reported some form of loss in employment income by July 2021. Nearly 40% of the households faced a delay in accessing medical care in the State. Over a quarter of the households faced housing insecurity amidst the first wave of the pandemic. This analysis of the Census data also indicates how resilient our communities are and their ability to reconstruct and recover through the economic shocks, as our data suggest consistent improvements in the economic scenario since the relaxation of COVID-19 policies. The analytics on CARES Act fund allocations data, too, is a classic measure of the policy-induced dynamic ability of the economy to recuperate from disruptions through financial stimulus from the governments and donors. This dashboard section provides intuitive icons (Pettit et al., 2017) placed in strategic locations of the web interface for end-users to hover over the icons to view definitions and explanations of keywords (e.g., housing insecurity, food scarcity, and the likelihood of evictions).
Usability assessment of the dashboard
We performed a comprehensive evaluation experiment to understand how the dashboard meets the users’ needs and to determine future research and development priorities. We designed a set of usability metrics for assessing the dashboards based on established literature (Dowding and Merrill, 2018; Forsell and Johansson, 2010; Nielsen and Molich, 1990). User ratings on a five-point scale were obtained on these metrics through an online questionnaire survey (O’Brien and Cairns, 2015) with 30 survey participants. A detailed note on the participant backgrounds and recruitment process is outlined in Supplementary Material, Page 11. The first set of questions focused on assessing the suitability of indicators used in the dashboard and the overall clarity of understanding or ease of grasping the information presented in various sections of the tool. Lan et al. (2021) and Kitchin (2014) in their dashboard studies highlighted that user experience and engagement over visual dashboards are largely influenced by the nature of metrics provided and the clarity of information for a range of users. The second round of questions delved into the navigability and ease of data exploration (Letouzey, 2012) as well as the visual and aesthetic appeal of the various maps, charts, and widgets presented in the dashboard. Based on evidence from earlier studies by Pettit et al. (2017) and Fareed et al. (2021), we included questions to measure the users’ level of trust in the data sources used in the dashboard, the interactivity of different visualization elements, and whether the dashboard offers new knowledge and novel understandings to users. The last set of questions assessed the usefulness of the dashboard for decision-making for various user groups and based on the experience whether the users would recommend the tool within the wider community for extensive dissemination. We included these metrics based on previous studies (Batty, 2015; Kourtit and Nijkamp, 2018) that found dashboards that are actionable and useful for decision-making purposes are more likely to be used by the community.
Figure 3 provides the results from the dashboard usability survey, where experts provided one to five ratings on a set of questions. A low rating of one—two suggests negative sentiment, whereas a higher four—five rating indicates a significantly positive opinion. A rating of three would mean a somewhat neutral response. We find that overall, 88% of respondents rated 4 to 5 indicating very high suitability of the indicators presented over the dashboard and that the metrics/themes adequately capture the critical components of the evolving economic scenario in Arizona during and post-pandemic. In response to Question 2 on the clarity of understanding, just over 70% rated 4–5, signaling that the dashboard provides information that is easy to understand, and digest. A sizeable 23% of respondents assigned a rating of three, taking a neither-agree nor-disagree positioning on this question. 74% believed the dashboard is easy to navigate and over 80% positively opined on the visual and aesthetic appeal of the data representation. 76% of the experts reposed a high level of trust in the data used for dashboard analytics as well as 92% suggested that the dashboard offers new knowledge to advance resilience thinking. Nearly 87% of survey respondents found that the dashboard is highly interactive and engaging, and an equal share would recommend the tool to their colleagues and community. A modest 63% believed that the dashboard is useful for decision-making. However, one-third of the respondents did not strongly agree with the question (providing a 3-point rating) indicating that further work and research are required to make the dashboard actionable. The usability ratings are further analyzed by the job roles and sectors represented by the respondents in Supplementary Figures S6 and S7 (pages 11–12 in Supplementary Material).Figure 3. Overall usability ratings (N = 30) on the Economic Resilience Dashboard.
Discussion and findings
This paper has examined and consolidated how public data can be collected and visualized through information dashboards and novel interaction techniques. We assessed the state of the art of public web-based COVID-19 dashboards in the US and worldwide to identify standard features, indicators, data granularity, and the design and functionality of these tools that make them engaging and actionable. We developed the prototype of the Economic Resilience Dashboard in Arizona, drawing on the lessons learned from the global dashboard review. The findings indicate that the COVID-19 dashboards ultimately share a common aim: to serve as both a communication tool and a decision-support system to respond to the COVID-19 pandemic. However, we find the approach to dashboard development varies from case to case in terms of their indicator selection, the geographical scale of data, granular data provision by population sub-groups, and the functional elements on the interface. We learned the common features that these visualization tools display and the elements that distinguish effective, more actionable, and inclusive dashboards from others. The study highlights that while there is no single approach to dashboard design, several aspects are critical for the successful application of such technologies, explained in the following paragraphs.
Traditional information design textbooks define dashboards as a single-screen representation of various metrics (Few, 2006). While at-a-glance dashboards do offer users a span of control over a large amount of data, our analysis reveals that the COVID-19 dashboards are revolutionizing the design approach. We found that 57% of the dashboards were built using a multiple-view scrolling webpage format (e.g., New York Times and California State COVID-19 dashboards) against 43% of projects sticking to the traditional single-screen layout. At-a-glance dashboards show overall underuse of explanatory narratives (due to limited space on the interface), which are proven techniques for clarifying complex information for end-users, making them motivated and confident in using the data in their decision-making (Ivanković et al., 2021). A more extended viewing area on the multiple-view dashboards, such as the one provided in the Economic Resilience Dashboard, allows laying visualizations in distinct sections, building narratives around the maps, graphs, and charts to engage visitors, and attract them to spend longer on the site (Sarikaya et al., 2018). The custom-made multiple-view dashboard design approach is emerging much more robust than the standard single-screen layout in improving interpretation and storytelling, reducing cluttering of information and widgets, and leading to an overall improvement in delivery and communication.
Existing literature established that data communicated over dashboards are not always neutral and value-free, independent of external influence, and always treated and engineered before sharing (Batty, 2015; Kitchin, 2014; Mattern, 2015). This study found that while COVID-19 dashboards reveal a broad set of epidemiological data (e.g., cases, deaths, hospitalizations, and vaccinations), they also in several cases hide critical data on population sub-groups, including ethnicity, income status, and comorbidities. There is also a severe gap in reporting social, behavioral, and economic impacts and interdependencies with the COVID-19 pandemic. Our findings are consistent with previous research (Lan et al., 2021) that noted without estimating and sharing data about meaningful population sub-groups, communities are at risk of not being educated about these issues. We find that the provision of segregated local data and data breakdown options over public dashboards offers novel opportunities for exploring interrelations between epidemiological trends and social determinants (e.g., economy and unemployment, behavioral responses to government policies, and equitable recovery).
Inclusive design strategies determine the credibility and trustworthiness of dashboards. As Kitchin (2014) suggests, dashboards act as translators rather than mirrors, and it’s the designers who frame how data are visualized and thus what the user can see, what questions can be asked, and how the answers are displayed. We found that inclusive and convincing dashboards explicitly describe what data they choose, justify their selection, and provide a point of truth access to the data sources. Contextual information, metadata attached to visualizations, links to open data repositories, and options for downloading maps and charts over dashboards provide clarity and transparency while encouraging replicability and iterative improvements in outcomes. Our review suggests that more than half of the State dashboards in the US failed to recognize these elements, indicating the need for more transparency in public reporting of pandemic-related data. We demonstrate a range of inclusive design elements in the dashboard tool, including the use of an accessible color palette, info icons and signage, storytelling with clear sections, and data download menus, which improves community engagement, accessibility to a range of users, and serves to leverage the two-way communication potential of dashboards. Further research could explore the development of uniform standards to guide dashboard designers across States and Countries to produce inclusive and trustworthy data tools and bring uniformity to public health data reporting.
The scientific novelty of this work advances the COVID-19 dashboard literature beyond technological or epidemiologic framing of data dissemination (Fareed et al., 2021; Shankar et al., 2021; Solis et al., 2021; Wang et al., 2022) to discourses around socially embedded participatory visualizations (Beheshti, 2020; Hippala, 2020; Lock et al., 2020). From identifying the qualities of actionable dashboards through expert opinion surveys to defining the data metrics and assessing the usability of the tool through the engagement of local leadership, this research explored the boundaries of user community-focused dashboards to meet pressing resilience challenges. The success of our dashboard project is reflected in the fact that leaders cutting across job roles and sectors agreed that the tool provides novel understandings of the evolving economic and social scenario during and post-COVID-19. Furthermore, the high usability ratings on indicators presented over the dashboard and positive experience with the interactivity of visualization elements reinforce the value of engaging local stakeholders in defining the scope of dashboard tools and grounding the metrics selection process within current debates that impacts the communities directly. As Dangermond et al. (2020) suggest the proliferation and growth of GIS technologies provide researchers and policymakers with several options to design dashboard visualization; however, how far they succeed in engaging the wider community and supporting critical social problems will determine their use and uptake in the future. Findings from this study offer new clues for the research and development community who are designing public-facing dashboards to explore further how different user groups perceive visualization tools differently and the ways these data tools can be standardized and co-designed to meet the expectations and needs of a wide range of audiences.
Conclusion
This article contributes to a rapidly growing research domain around dashboards and geospatial data visualization technologies used to monitor, inform, and respond to the ongoing public health crisis. Indicators, data, and software tools are increasingly playing a vital role in the shaping and proliferation of government policies, including resilience planning and disaster response. The ability of our organizations and cities to collect, process, and utilize data to enforce a logic of control enacted through digital technologies shows immense potential to transform how we address and respond to emerging challenges and shocks. While many dashboard project implementations are coming to fruition, there is still considerable research and development potential yet to be explored and exploited. Our global analysis of the 68 COVID-19 dashboards shows the varying contexts, levels of focus, data sources and indicators, and design elements—a testament to the advancements in health informatics and a growing sense of public responsibility to report health and community data. While this research acknowledges that there is no one-size-fits-all model for dashboard design, we encourage authorities to consider the essential features of inclusive and actionable dashboards identified in this study.
This research highlights the existing gaps in COVID-19 dashboards in two dimensions. In terms of data provision, there is a lack of granular information at the local level, missing data by population sub-groups, and severe inadequacy in reporting social and economic indicators. From the perspective of user-interface design, we reveal the challenges of the single-screen dashboard layout adopted by many projects, limited focus on data download and metadata provision, and a general disregard for providing explanatory narratives to describe the background, source, and purpose of the data. Building on these gaps, we illustrate an empirical experiment of building a dashboard prototype—the COVID-19 Economic Resilience Dashboard. We demonstrated the fit-for-purpose dashboard model (Kitchin, 2014) that provides localized data, drill-down options, visualization narratives, open access to data, and accessible features on the interface to facilitate two-way information exchange. We emphasized and exhibited the value of linking pandemic-related information with socioeconomic data to illustrate how intervention policies affect the spread of COVID-19 and vice versa, a feature generally lacking in the existing COVID-19 dashboards (Fareed et al., 2021; Ivanković et al., 2021). The tools we have discussed and introduced here are synergetic to the broader agenda of “dashboard governance” (Few, 2006) where data sharing and visualization interface plays a vital role in everyday decision-making, acting as a potential channel of communication between decision-makers and community stakeholders. The application we provided can easily be replicated for other regions across the world as we consistently used standard technologies and open data. These findings together suggest a clear pathway forward for researchers and governments to incorporate more action-oriented data and easy-to-use interfaces as they refine existing and develop new information systems and data analytics dashboards. Such projects may generate an exciting collection of informed and engaged community development strategies on a systematic comparative basis.
Supplemental Material
Supplemental Material - Deploying geospatial visualization dashboards to combat the socioeconomic impacts of COVID-19
Click here for additional data file.
Supplementary Material for Deploying geospatial visualization dashboards to combat the socioeconomic impacts of COVID-19 by Sarbeswar Praharaj, Patricia Solis, and Elizabeth A Wentz in Environment and Planning B: Urban Analytics and City Science.
Sarbeswar Praharaj, PhD, is the Associate Director (Data and Visualization) and Assistant Research Professor at the Knowledge Exchange for Resilience, School of Geographical Sciences and Urban Planning at Arizona State University. He is a Senior Global Futures Scientist at the Julie Ann Wrigley Global Futures Laboratory. Dr Praharaj leads research on smart cities, critical urban studies, and data visualization and dashboards. He engages in research-led interactive teaching and learning pedagogies in urban planning and geographical science. Before joining ASU, he was a postdoctoral researcher and manager of the City Analytics Lab at the City Futures Research Center, UNSW Sydney, Australia.
Patricia Solís, PhD, is Executive Director of the Knowledge Exchange for Resilience and Associate Research Professor at Arizona State University. She is Co-Founder and Director of YouthMappers, a consortium of student-led chapters on more than 208 university campuses in 48 countries who create and use open spatial data for humanitarian and development needs in collaboration with USAID. Prior to joining ASU, she was Co-Director of the Center for Geospatial Technology at Texas Tech University and a Research Associate Professor of Geography in the Department of Geosciences. She served as Deputy Director and Director of Research at the American Association of Geographers.
Elizabeth Wentz, PhD, is the Vice Provost and Dean of the Graduate College and a Professor in the School of Geographical Sciences and Urban Planning at Arizona State University. She is the Director and PI of the Knowledge Exchange for Resilience, aiming to support knowledge sharing and discovery for community resilience in Maricopa County, Arizona. She is the former PI of ASU ADVANCE, to become a higher education leader in inclusion and diversity. Her research focuses on the design, implementation, and evaluation of geographic technologies with particular emphasis on how such technologies can be used to understand urban environments.
ORCID iDs
Sarbeswar Praharaj https://orcid.org/0000-0002-7383-2751
Patricia Solis https://orcid.org/0000-0003-1374-9400
Elizabeth A Wentz https://orcid.org/0000-0002-2881-0668
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by Virginia G. Piper Charitable Trust.
Supplemental Material: Supplemental material for this article is available online.
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J Gen Physiol
J Gen Physiol
jgp
The Journal of General Physiology
0022-1295
1540-7748
Rockefeller University Press
36595663
jgp.202213224
10.1085/jgp.202213224
Commentary
Excitation-Contraction Coupling
Excitation–Contraction CouplingOrai1 as a potential “fits-all approach” therapeutic target for the treatment of DMD
Orai1 and DMD
https://orcid.org/0000-0003-3862-2967
Cheng Arthur J. 1
von Walden Ferdinand 2
https://orcid.org/0000-0002-1222-9473
Lanner Johanna T. 3
1 School of Kinesiology and Health Sciences, York University , Toronto, ON, Canada
2 Women’s and Children’s Health, Karolinska Institute , Stockholm, Sweden
3 Physiology and Pharmacology, Karolinska Institute , Stockholm, Sweden
Correspondence to Johanna T. Lanner: [email protected]
06 2 2023
03 1 2023
155 2 e202213224© 2023 Cheng et al.
2023
Cheng et al.
https://creativecommons.org/licenses/by-nc-sa/4.0/ http://www.rupress.org/terms/ This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
Obliterating skeletal muscle Orai1 expression improves muscle pathology in the mdx mouse model of DMD—could Orai1 emerge as a potential drug target?
Swedish Research Council http://dx.doi.org/10.13039/501100004359
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pmcDuchenne muscular dystrophy (DMD) is an X-linked disorder caused by loss-of-function mutations in the dystrophin gene. DMD is a progressive disease that affects 1 in 3,500–5,000 male births and results in muscle weakness, respiratory and cardiac failure, and, in due course, premature death commonly in the third decade of life (Mendell and Lloyd-Puryear, 2013). Histopathological hallmarks of DMD muscles include central nucleation, fibrosis, inflammation, and muscle fiber pseudohypertrophy amongst others (Zweyer et al., 2022). The dystrophin gene was discovered in 1987 (Hoffman et al., 1987), and since then there have been numerous efforts to develop therapies to delay disease progression and enhance muscle function in DMD patients.
Present widespread therapeutic recommendations include corticosteroids, cardiac medications, and, eventually, assisted ventilation to slow down disease progression, reduce symptoms, and improve quality of life, but do not target the underlying mechanism of the disease. However, pharmacological advances using oligonucleotide-mediated exon skipping to bypass variant exons and gene editing to provide truncated forms of dystrophin have gained interest in recent years, and several clinical trials are under way to assess their efficacy (Eser and Topaloğlu, 2022). Although these are promising advancements, there are still challenges to overcome and room for improvement in the area. For example, the TREAT-NMD DMD global database contains over 7,000 dystrophin mutations (Bladen et al., 2015), raising significant challenges for the development of gene correction therapies that might be applicable to large cohorts of patients rather than single individuals. Moreover, present exon-skipping therapies focusing on restoring dystrophin at low levels are suggested to have the potential to benefit ∼30% of all patients (Eser and Topaloğlu, 2022) and hence also struggle to treat a large cohort of DMD individuals. However, the largest challenge to the field of therapeutic genome editing might be the high cost of these drugs (Segal, 2022). Nevertheless, there is still a need for curative treatments suitable for a large group of DMD individuals, and pharmacological interventions with a “fits-all approach” are desired. This is one of the reasons the recent publication by García-Castañeda et al. (2022) and colleagues in Dr. Robert T. Dirksen’s lab is attractive with its obvious transferability into drug development.
Chronically elevated levels of myoplasmic free [Ca2+] is an acknowledged explanation for the observed skeletal muscle fiber deterioration and loss-of-function in DMD. Two main pathomechanisms have been proposed to cause sustained abnormalities in myoplasmic Ca2+ in DMD (see Fig. 1); enhanced RyR1-mediated Ca2+ leak from the sarcoplasmic reticulum (SR; Bellinger et al., 2009) and excessive extracellular Ca2+ influx via membrane tears and/or store-operated Ca2+ entry (SOCE; Edwards et al., 2010; Zhao et al., 2012). The Ca2+ sensor stromal interaction molecule-1 (STIM1) in the ER/SR membrane and the highly Ca2+-selective Orai channel in the plasma membrane forms the basis for SOCE (Emrich et al., 2022).
Figure 1. A schematic illustration of the role of Ca2+ in the development of skeletal muscle dysfunction in DMD. The two proposed pathways of Ca2+-induced muscle dysfunction include (A) increased SR Ca2+ leak and extracellular Ca2+ entry via (B) sarcolemmal membrane damage or (C) SOCE. Dihydropyridine receptor (DHPR), ryanodine receptor 1 (RyR1), sarcoplasmic reticulum Ca2+ ATPase (SERCA), mitochondrial reactive oxygen species (ROS), Orai Ca2+ release-activated Ca2+ modulator 1 (Orai1), stromal interaction molecule 1 (STIM1). Figure created with Biorender.com.
Here, García-Castañeda and colleagues used skeletal muscle-specific tamoxifen-inducible Orai1-knockout mice crossed with mdx mice to elucidate the impact of Orai1-dependent Ca2+ entry in the DMD muscle pathology (Fig. 1). This approach allowed them to knockout Orai1 in the skeletal muscle of young, post-developmental mice and thus establish the effects of inducing a change into Ca2+ entry after the disease manifestation. Excitingly, eliminating Orai1-mediated Ca2+ influx in young (2–3-mo-old) mdx mice resulted in considerable functional and morphological improvements. Skeletal muscle from mdx mice lacking Orai1-mediated Ca2+ influx exhibited normalized intracellular Ca2+ homeostasis and improved muscle strength. Obliterating Orai1 expression also had a protective effect on the plasma membrane (sarcolemma) in mdx mice as it protected muscles from eccentric contraction–induced damage. Further, muscles from mdx mice with markedly reduced Orai1 expression displayed fewer histopathological hallmarks, i.e., normalized fiber cross-sectional area (CSA) and reduced muscle fibrosis. However, central nucleation was still present in muscles of mdx mice lacking Orai1 expression, which indicates ongoing regenerative processes in the mdx muscle fibers that are not reversed by restoring the intracellular Ca2+ homeostasis.
As addressed by the authors, many questions remain to be answered including the molecular link explaining how removal of muscle-specific Orai1-mediated Ca2+ influx has these beneficial effects in mdx muscle. Here the nuclear-factor κB (NfκB) and transforming growth factor β (TGF-β) signaling pathways might be of interest, given their known DMD pathomechanistic links, and that their pathway activities have been associated with Orai expression levels (Berry et al., 2018; Kang et al., 2021). Orai1 also plays an important role in maintaining Ca2+ homeostasis in healthy skeletal muscle, and the potential limitations of Orai1-targeted pharmacological therapy for chronic diseases, such as decreased muscle fatigue resistance with Orai1 knockout as shown by the authors need to be investigated further. Also, patients with Orai1 deficiency display muscle hypotonia and severe combined immunodeficiency coupled with autoimmunity (McCarl et al., 2009), highlighting Orai1’s important role for health. Until the physiological function of Orai1 in skeletal muscle and other tissues is better understood, it is still too early to suggest it as a promising drug target in humans. Drug target or not, Orai1 is also expressed in a wide variety of tissues, which stresses that a potential future intervention needs to be tissue specific and carefully dosed to avoid detrimental and dangerous side-effects. Another DMD-related aspect that would be interesting to assess is a potential Orai1 involvement in the DMD-induced diaphragm and cardiac dysfunction. Although DMD clinically manifests as progressive muscle weakness, it is the respiratory failure and heart problems (often develop in the form of dilated cardiomyopathy) that ultimately causes the premature death. The diaphragm is a skeletal muscle with a similar cytoskeleton, extracellular matrix, and excitation–contraction (EC) coupling as the limb muscles examined by García-Castañeda and colleagues, and hence one could speculate that it is likely that the removal of Orai1-mediated Ca2+ influx has beneficial effects in DMD muscle. On the other hand, there is more uncertainty whether eliminating Orai1-mediated Ca2+ influx in cardiac muscle would have any effect given that cardiac muscle is designed to handle Ca2+ influx as its EC coupling, and thus every heartbeat, is initiated by Ca2+ influx via the voltage-gated Ca2+ channel (Cav2.1, L-type Ca2+ channel). Hopefully this and additional aspects of Orai physiology and pathophysiology in striated muscle will be addressed by the Dirksen laboratory or others in the near future. Nevertheless, the results of García-Castañeda et al. (2022) demonstrate an important role of enhanced Orai1-mediated Ca2+ entry in exacerbating the dystrophic phenotype of mdx mice, rendering Orai1 a potential therapeutic target with a “fits-all approach” for the treatment of DMD. On that note, it is promising that there is a continuous development of Orai1 inhibitors (Azimi et al., 2020) for various indications, and some have entered clinical trials with DMD potentially included in the near future.
Acknowledgments
Eduardo Ríos served as editor.
The authors declare no competing financial interests.
J.T. Lanner is supported by the Swedish Research Council.
Author contributions: A.J. Cheng, F. von Walden and J.T. Lanner drafted the manuscript and approved the final draft of the manuscript.
This work is part of a special issue on excitation–contraction coupling.
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References
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Berry, C.T., M.J. May, and B.D. Freedman. 2018. STIM- and Orai-mediated calcium entry controls NF-κB activity and function in lymphocytes. Cell Calcium. 74 :131–143. 10.1016/j.ceca.2018.07.003 30048879
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Eser, G., and H. Topaloğlu. 2022. Current outline of exon skipping trials in Duchenne muscular dystrophy. Genes. 13 :1241. 10.3390/genes13071241 35886024
García-Castañeda, M., A. Michelucci, N. Zhao, S. Malik, and R.T. Dirksen. 2022. Postdevelopmental knockout of Orai1 improves muscle pathology in a mouse model of Duchenne muscular dystrophy. J. Gen. Physiol. 154 :e202213081. 10.1085/jgp.202213081 35939054
Hoffman, E.P., R.H. Brown Jr, and L.M. Kunkel. 1987. Dystrophin: The protein product of the Duchenne muscular dystrophy locus. Cell. 51 :919–928. 10.1016/0092-8674(87)90579-4 3319190
Kang, Q., X. Peng, X. Li, D. Hu, G. Wen, Z. Wei, and B. Yuan. 2021. Calcium channel protein ORAI1 mediates TGF-β induced epithelial-to-mesenchymal transition in colorectal cancer cells. Front. Oncol. 11 :649476. 10.3389/fonc.2021.649476 34055617
McCarl, C.A., C. Picard, S. Khalil, T. Kawasaki, J. Röther, A. Papolos, J. Kutok, C. Hivroz, F. Ledeist, K. Plogmann, . 2009. ORAI1 deficiency and lack of store-operated Ca2+ entry cause immunodeficiency, myopathy, and ectodermal dysplasia. J. Allergy Clin. Immunol. 124 :1311–1318.e7. 10.1016/j.jaci.2009.10.007 20004786
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Segal, D.J. 2022. The promise of gene editing: So close and yet so perilously far. Front. Genome Ed. 4 :974798. 10.3389/fgeed.2022.974798 35910414
Zhao, X., J.G. Moloughney, S. Zhang, S. Komazaki, and N. Weisleder. 2012. Orai1 mediates exacerbated Ca2+ entry in dystrophic skeletal muscle. PLoS One. 7 :e49862. 10.1371/journal.pone.0049862 23185465
Zweyer, M., H. Sabir, P. Dowling, S. Gargan, S. Murphy, D. Swandulla, and K. Ohlendieck. 2022. Histopathology of Duchenne muscular dystrophy in correlation with changes in proteomic biomarkers. Histol. Histopathol. 37 :101–116. 10.14670/hh-18-403 34873679
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PMC009xxxxxx/PMC9900426.txt |
==== Front
NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
36755961
10.1093/narcan/zcad005
zcad005
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
Critical Reviews and Perspectives
Roles of trans-lesion synthesis (TLS) DNA polymerases in tumorigenesis and cancer therapy
https://orcid.org/0000-0003-2071-9891
Anand Jay Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, 614 Brinkhous-Bullitt Building, Chapel Hill, NC 27599, USA
Chiou Lilly Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, 614 Brinkhous-Bullitt Building, Chapel Hill, NC 27599, USA
Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Sciandra Carly Department of Biochemistry, Duke University School of Medicine, Durham, NC 27710, USA
Zhang Xingyuan Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, 3101 McGavran-Greenberg Hall, Chapel Hill, NC 27599, USA
Hong Jiyong Department of Chemistry, Duke University, Durham, NC 27708, USA
https://orcid.org/0000-0001-8331-2357
Wu Di Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, 3101 McGavran-Greenberg Hall, Chapel Hill, NC 27599, USA
Zhou Pei Department of Biochemistry, Duke University School of Medicine, Durham, NC 27710, USA
Vaziri Cyrus Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, 614 Brinkhous-Bullitt Building, Chapel Hill, NC 27599, USA
To whom correspondence should be addressed. Tel: +1 919 843 9639; Fax: +1 919 966 5046; Email: [email protected]
The authors wish it to be known that, in their opinion, the first four authors should be regarded as Joint First Authors.
06 2 2023
3 2023
06 2 2023
5 1 zcad00530 1 2023
10 12 2022
04 10 2022
© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Cancer.
2023
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
DNA damage tolerance and mutagenesis are hallmarks and enabling characteristics of neoplastic cells that drive tumorigenesis and allow cancer cells to resist therapy. The ‘Y-family’ trans-lesion synthesis (TLS) DNA polymerases enable cells to replicate damaged genomes, thereby conferring DNA damage tolerance. Moreover, Y-family DNA polymerases are inherently error-prone and cause mutations. Therefore, TLS DNA polymerases are potential mediators of important tumorigenic phenotypes. The skin cancer-propensity syndrome xeroderma pigmentosum-variant (XPV) results from defects in the Y-family DNA Polymerase Pol eta (Polη) and compensatory deployment of alternative inappropriate DNA polymerases. However, the extent to which dysregulated TLS contributes to the underlying etiology of other human cancers is unclear. Here we consider the broad impact of TLS polymerases on tumorigenesis and cancer therapy. We survey the ways in which TLS DNA polymerases are pathologically altered in cancer. We summarize evidence that TLS polymerases shape cancer genomes, and review studies implicating dysregulated TLS as a driver of carcinogenesis. Because many cancer treatment regimens comprise DNA-damaging agents, pharmacological inhibition of TLS is an attractive strategy for sensitizing tumors to genotoxic therapies. Therefore, we discuss the pharmacological tractability of the TLS pathway and summarize recent progress on development of TLS inhibitors for therapeutic purposes.
National Cancer Institute 10.13039/100000054 R01 CA215347 R01 CA229530 CA191448 National Institute of Environmental Health Sciences 10.13039/100000066 R01 ES029079 Duke Clinical and Translational Science Institute UL1TR002553 Duke Cancer Institute 10.13039/100014228 P30 Cancer Center NIH CA014236 National Institute of General Medical Sciences 10.13039/100000057 5T32GM135128
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pmcINTRODUCTION
Mutability has long been recognized as a key hallmark and enabling characteristic of cancer (1,2). Identification of trans-lesion synthesis (TLS) DNA polymerases that perform error-prone replication of damaged DNA templates was once heralded as a major breakthrough in the field of chemical carcinogenesis: TLS polymerases provided a new molecular mechanism for mutagenesis and thus were hypothesized to be potential drivers of carcinogenesis (3). The extent to which this hypothesis has been validated is discussed here.
Historically, TLS was first viewed largely as a mechanism for replicative bypass of bulky DNA adducts and was presumed to be restricted to the S-phase of the cell cycle. We now know that TLS polymerases perform diverse functions in genome maintenance far beyond mediating replicative bypass of damaged DNA templates. TLS is an important gap-filling process that eliminates single-stranded DNA (ssDNA) discontinuities in the genome. TLS DNA polymerases are not always restricted to S-phase and may also be deployed in G0, G1 and G2 (4,5,6,7,8,9,10). Moreover, TLS is closely integrated with the replicative cell cycle and other genome maintenance mechanisms. It is now appreciated that neoplastic cells experience considerable DNA damage from intrinsic and therapeutic sources. Thus, in addition to promoting mutagenesis during carcinogenesis, the DNA damage-tolerance conferred by TLS DNA polymerases has the potential to sustain proliferation and viability of cancer cells. Accordingly, TLS is a dependency of some cancer cells and represents an appealing target pathway for therapeutic development. Small molecule inhibitors of TLS polymerases sensitize cancer cells to genotoxic therapeutic agents and preferentially inhibit the viability of neoplastic cell lines when compared with untransformed cells.
Here, we review the molecular anatomy of the TLS pathway and describe its proposed roles in genome maintenance. We emphasize potential mechanisms by which the TLS pathway activity may be dysregulated in cancer. We survey the evidence that TLS shapes the genomic landscape of cancer cells and contributes to carcinogenesis. We discuss emerging evidence that TLS is a dependency and vulnerability of cancer cells. Finally, we describe ongoing strategies and efforts to target the TLS pathway for therapeutic purposes.
TLS PATHWAY IMBALANCE DUE TO POLη-DEFICIENCY CAUSES SKIN CANCER
Our understanding of TLS and its relationship with cancer is founded largely on the Hanaoka group's seminal discovery of the TLS DNA polymerase eta (Polη) as the mutated gene product of xeroderma pigmentosum-Variant (XPV) patients (11,12). XPV is a sunlight-sensitivity and skin cancer-propensity syndrome. Solar UVB (290–320 nm) exposure is causally linked to skin cancer (13). Two frequent DNA lesions induced by UVB are cis–syn cyclobutane pyrimidine dimers (CPD) and pyrimidine (4,5,6) pyrimidone photoproducts (14). In XPV patients nucleotide excision repair (NER) of CPD is intact yet post-replicative DNA repair (to be defined later) is compromised. Using biochemical complementation, Masutani et al. identified Polη as the factor that corrects defective replication of CPD-damaged DNA templates by XPV cell extracts (11). Those workers subsequently showed that the POLH gene is mutated in XPV patients and that Polη is the human homologue of yeast Rad30. How then are the clinical features of XPV explained by Polη-deficiency?
Polη has low processivity and low fidelity when replicating undamaged DNA templates. However, this enzyme is specialized to perform efficient and error-free replicative bypass of templates containing helix-distorting CPD (12). Thus, Polη is important for ongoing DNA replication of genomes harboring CPD and confers ‘DNA damage tolerance’. XPV patients lacking Polη cannot replicate their UV-damaged genomes and lose viability, thereby explaining the sunlight-sensitivity phenotype. XPV patients develop carcinoma (both squamous- and basal cell carcinoma) with frequencies that are 10,000- and 2,000-fold higher, respectively, when compared with the general population (15). XPV patients are also prone to melanoma (16,17). The high skin cancer incidence of XPV patients is attributed to increased rates of UV-induced mutagenesis in those individuals. Mechanistically, when Polη is absent, compensatory, yet error-prone TLS of CPD lesions by other ‘inserter’ Y-family DNA polymerases, Pol kappa (Polκ) and Pol iota (Polι) working cooperatively with the B-family ‘extender’ DNA polymerase Polζ leads to hypermutability (18,19) (Figure 1). There is also evidence for compensatory usage of other genome-altering DNA repair pathways in UV-irradiated XPV cells, including Homologous Recombination (HR) which leads to increased sister chromatid exchanges, or SCE (20). Therefore, XPV is a malignancy resulting from deployment of alternative, more error-prone genome maintenance mediators when an individual Y-family TLS polymerase (Polη) is absent (Figure 1). Such ‘imbalance’ and altered DNA repair pathway choice is a general conceptual framework for explaining mechanisms of genome instability in cancer (21). A good example of how imbalanced DNA repair pathway choice may lead to genetic change is provided by BRCA1-deficient ovarian cancers in which HR is defective. D’Andrea and colleagues showed that those HRD tumors rely on compensatory overexpression and deployment of the error-prone DNA polymerase POLQ for DSB repair (22). POLQ mediates error-prone microhomology-mediated end-joining (MMEJ) of DSB which helps explain the genomic landscape of BRCA-mutated cancers (23,24).
Figure 1. Aberrant activation of TLS affects genome stability. (A) In normal cells, Polη performs error-free bypass of CPD. TLS is restrained and used sparingly to minimize error-prone DNA synthesis and mutagenesis. (B) In XPV cells, Polη is absent. Compensatory error-prone TLS of Polη-cognate lesions by alternative Y-family inserter DNA polymerases and the B-family extender DNA polymerase Polζ leads to mutagenesis. (C) HR-deficient cancer (such as BRCA1/2 mutant cells) have increased dependency on TLS for ssDNA gap-filling, which may lead to increased error-prone DNA synthesis and mutagenesis. The mutagenic outcome will depend on the type of DNA damage and the choice of TLS polymerase deployed. (D) Cancer-associated increase (green arrow) or decrease (red arrow) in expression of TLS mediators might affect TLS polymerase selection. The upregulation of RAD18 and the TLS polymerase imbalances illustrated here represent the mRNA expression patterns observed in endometrial cancers when compared with normal adjacent tissues (Figure 3).
Each TLS polymerase is specialized to perform relatively accurate and efficient bypass of a specific type of DNA damage, referred to as their ‘cognate lesion’ (25,26). For example CPD are cognate lesions for Polη. Given the error-propensity of TLS polymerases on undamaged DNA templates or non-cognate lesions, their imbalanced activities in relation to each other might promote mutagenesis. XPV is a situation in which Polη activity is reduced relative to other TLS polymerases. By analogy, there are other possible ways in which imbalance and altered pathway choice may develop between the different TLS DNA polymerases (or between TLS polymerases and other DNA repair mechanisms). Figure 1 illustrates possible ways in which TLS might be altered in cancer. Later, we consider some of these possibilities and review evidence that altered TLS impacts DNA damage-sensitivity and the genomic features of cancer cells. First, we describe mechanisms for activation of the Y-family DNA polymerases and integration of TLS with the cell cycle and other elements of the DNA damage response.
THE TLS POLYMERASE SWITCH: ROLES OF RAD18-MEDIATED UBIQUITIN SIGNALING
To minimize the risk of mutagenesis, TLS DNA polymerases must be tightly regulated and employed only when necessary to replicate damaged templates. During S-phase, encounters between leading strand DNA polymerases and bulky DNA lesions (or other hard-to-replicate helix-distorting structures) lead to DNA replication fork stalling, triggering replacement of replicative DNA polymerases with TLS polymerase(s) at the replisome. This ‘polymerase switch’ is stimulated by ubiquitin modification of Proliferating Cell Nuclear Antigen (PCNA, a DNA polymerase processivity factor). In response to DNA replication fork stalling, PCNA is mono-ubiquitinated at lysine residue 164 by the RAD18-RAD6 complex (comprising two molecules of the E3 ubiquitin ligase RAD18 and one molecule of the E2 ubiquitin-conjugating enzyme RAD6) (27,28).
RAD18 is recruited to sites of DNA damage through direct interactions with RPA-coated single-stranded DNA (29). During S-phase, ssDNA can arise via helicase-polymerase uncoupling at stalled replication forks (30). The kinetics of RAD18-mediated PCNA mono-ubiquitination typically coincide with ATR/CHK1 signaling which is also initiated by accumulation of RPA-ssDNA. ssDNA can also be generated outside of S-phase, (for example as an intermediate during NER or BER), explaining how both PCNA-mono-ubiquitination and CHK1 activation may occur independently of DNA replication (6,7,31,32).
The four Y-family TLS DNA polymerases (Polη, Polκ, Polι, REV1) associate preferentially with the mono-ubiquitinated form of PCNA through their Ubiquitin-Binding Zinc Finger (UBZ) and Ubiquitin-Binding Motif (UBM) domains (33). Polη, Polκ, and Polι also contain PCNA-interacting Peptide (PIP) domains that interact with PCNA directly to facilitate replisome-binding (33,34). REV1 lacks a PIP-box and instead binds PCNA via its N-terminal BRCA1 C-terminus (BRCT) domain or polymerase-associated domain (PAD) (35,36,37). PCNA-association of Polη is also facilitated by a direct interaction with RAD18 which helps to chaperone the polymerase to the vicinity of stalled DNA replication forks (38,39).
Different Y-family TLS polymerases have different preferred ‘cognate lesions’ that are bypassed with relative accuracy and efficiency. For example, Polη performs relatively error-free bypass of UV-damaged DNA templates but is error-prone when replicating Benzo[a]pyrene (B[a]P)-adducted DNA (40,41). Conversely, Polκ can bypass B[a]P adducts in a relatively error-free manner (42,43) yet is mutagenic when replicating DNA harboring CPD lesions. Unlike other Y-family enzymes, REV1 cannot synthesize DNA polymers, but adds single C residues to primer termini (44). The main role of REV1 in TLS is non-catalytic, as discussed below. TLS DNA polymerases all lack proofreading exonuclease activity and are viewed as DNA damage tolerance factors. Collectively, recruitment of Y-family TLS DNA Polymerases to mono-ubiquitinated PCNA helps cells to replicate damaged genomes and tolerate diverse forms of genotoxicity. Depending on the nature of DNA damage and the choice of Y-family DNA polymerase(s) deployed, TLS may be error-free or mutagenic (Figure 1).
PCNA-ubiquitination is a trigger for recruitment of all Y-family DNA polymerases to stalled DNA replication forks. It is unclear how the DNA polymerases are selectively recruited in response to their cognate DNA lesions. One possibility is a ‘trial-and-error’ mechanism whereby all TLS DNA polymerases are recruited to mono-ubiquitinated PCNA but only the relevant polymerase will stably engage the stalled replisome. This model implies that TLS DNA polymerases may compete for mono-ubiquitinated PCNA at stalled replication forks. If Y-family DNA polymerases compete for binding mono-ubiquitinated PCNA, altered expression of any individual DNA polymerase in cancer cells may influence DNA polymerase selection, DNA damage tolerance and mutagenesis.
TLS DNA polymerases also have different affinities for PCNA, a feature that may impact selection of TLS polymerases at stalled DNA replication forks (38,45). For example, the Polη PIP box binds PCNA with much higher affinity than the Polκ PIP box (38). Polη is a versatile enzyme that can bypass diverse species of DNA lesions. The high-affinity PIP box may explain why Polη is generally the default TLS polymerase recruited in response to wide variety of DNA lesions.
For some adducts, a single TLS DNA polymerase can perform both nucleotide incorporation opposite the DNA lesion and the subsequent extension from the inserted nucleotide. For example, Polη alone performs both insertion and extension phases of TLS when replicating UV-induced CPD (46,47). However, for many DNA lesions, particularly those that significantly distort the DNA double helix, TLS is mediated by the sequential actions of an ‘inserter’ and then a separate ‘extender’ DNA polymerase. First, a Y-family TLS enzyme (usually Polη, Polι, or Polκ) inserts a nucleotide across a DNA lesion. In the second step, the 4-subunit B-family DNA polymerase Polζ (comprising Rev3/Rev7/PolD2/PolD3) performs extension from the initial nucleotide when inserted across from a diverse array of DNA lesions (48,49). Sometimes, Polζ can act as both the inserter and extender, for example when replicating over thymine glycol lesions generated by free-radicals (50).
The requirement for dual DNA polymerases to perform TLS necessitates a second switch from inserter to extender. The inserter/extender TLS polymerase switch is mediated by REV1. REV1 has very limited DNA polymerase enzymatic activity. Instead, the main role of REV1 in TLS is non-catalytic. Consistent with a non-enzymatic role for REV1 in TLS, a catalytically-inactive human REV1 mutant rescues DNA damage-sensitivity of Δrev1 DT40 cells (51). Moreover, human full length REV1, catalytically-inactive REV1, and REV1 lacking the BRCT correct replication fork progression defects of NQO-treated Δrev1 cells to wild-type levels (52). However, a REV1 mutant lacking the C-terminal domain is inactive for DNA damage tolerance (52). The major role of REV1 in TLS is to facilitate dynamic exchange of DNA polymerase partners with PCNA. The C-terminal of REV1 (REV1-CT) can simultaneously bind the REV7 subunit of Polζ (the extender) and REV1-interacting regions (RIRs) from Polη, Polκ and Polι (inserters) (53,54,55). The POLD3 subunit of Polζ also contains an RIR that interacts with the REV1-CT (56).
Taken together, analysis of TLS polymerase-PCNA association dynamics and structure/function studies suggest that the default inserter Polη interacts with monoubiquitinated PCNA via its UBZ domain and high-affinity PIP motif. Simultaneously REV1 associates with a different monoubiquitinated subunit of the same PCNA trimer via BRCT (57,58), PAD and UBM domains (33). If Polη fails to perform bypass (e.g. due to a non-cognate DNA lesion), a more appropriate Y-family enzyme may substitute, also associating with mono-ubiquitinated PCNA via UBZ/PIP-box interactions in a manner that is facilitated by RIR-REV1-CT interactions. Following insertion, Polζ displaces the inserter RIR/Rev1-CT interaction with the RIR motif of its POLD3 subunit. The REV1-CT interactions with REV7 and PolD3 subunits of Polζ promote an ‘extender’ Rev1/Polζ assembly that is tethered to mono-ubiquitinated PCNA via the BRCT, PAD and UBM domains of REV1 and the PIP motif of PolD3 (56). According to this model, non-enzymatic scaffold functions of REV1 are critically important for survival after DNA damage. Targeting the non-catalytic domains of REV1 may be an effective approach for sensitizing cancer cells to therapy-induced genotoxicity. In particular, the TLS polymerase-interacting C-terminal region represents an appealing target for small molecules that disrupt protein-protein interactions.
RAD18-INDEPENDENT MECHANISMS OF TLS PATHWAY ACTIVATION
RAD6/RAD18 activity accounts for most of the PCNA K164 mono-ubiquitination in mammalian cells and represents the canonical mechanism of TLS activation. However, PCNA can also be monoubiquitinated at K164 by other E3 ligases that are redundant with RAD18 (59). For example, the E3 ubiquitin ligase cullin-4 in complex with the substrate recognition factor CDT2 (CRL4CDT2, which targets multiple cell cycle-regulated proteins for degradation) mono-ubiquitinates PCNA at K164 to activate mutagenic TLS. Moreover, CDT2-depleted cells are cisplatin-sensitive, consistent with a role for CRL4CDT2-mediated PCNA ubiquitination in DNA damage tolerance (59). In some cell lines, CDT2 is dispensable for PCNA mono-ubiquitination following UV treatment, suggesting a minor role of CDT2 in TLS when compared with RAD18 (59). The RAD5 homologue HLTF also plays a back-up role in mediating PCNA mono-ubiquitination when RAD18 is absent (60).
TLS pathway activation can also occur in the complete absence of PCNA K164 mono-ubiquitination. For example, PCNA ubiquitination does not disrupt Polδ-PCNA interactions or enhance interaction of TLS DNA polymerases with PCNA (61). It has been suggested that K164 mono-ubiquitination displaces putative inhibitors of PCNA–TLS Polymerase interactions (61). A UBZ-deficient Polη mutant retaining the PCNA-interacting Peptide (‘PIP’ domain) corrects UV-sensitivity defects of XPV cells, again suggesting that PCNA mono-ubiquitination is dispensable for TLS polymerase activation (62).
On the other hand, mouse embryonic fibroblasts (MEFs) harboring a PCNAK164R/K164R mutant show reduced colony-forming ability after UV irradiation when compared to wild-type cells, indicating that TLS is defective when PCNA cannot be mono-ubiquitinated (63). However, knockdown of Rev3l, Rev1 and Polh in PcnaK164R/K164R cells results in additive UV-sensitivity, suggesting existence of a secondary PCNA-ubiquitination independent TLS pathway (63). Polκ is the main TLS polymerase responsible for bypassing DNA lesions induced by the alkylating agent methyl methanesulfonate (MMS). Double mutant PcnaK164R Polk−/− MEFs show greater sensitivity to MMS treatment when compared with MEFs harboring individual PcnaK164R or Polk−/− mutations. Therefore, Polκ functions in a PCNA-mono-ubiquitination independent pathway for tolerance of MMS-induced DNA damage (64). Double mutant PcnaK164R Polη+/− mouse pre-B cells show greater sensitivity to UV and cisplatin treatment than either single mutant alone, again suggesting a PCNA-ubiquitination independent role of Polη in TLS (65). Taken together, PCNA mono-ubiquitination-independent mechanisms contribute to TLS under some circumstances.
REGULATED EXPRESSION AND ACTIVITY OF TLS FACTORS
Genome maintenance factors are often regulated at the level of expression, and via post-translational modifications that control subcellular distribution, stability, activity, and interactions with binding partners. Such regulatory mechanisms are often important for integrating genome maintenance with the cell cycle. For example, HR is a relatively accurate DSB repair mechanism when compared with NHEJ. However, HR must be restricted to S-phase and G2 when DNA has been replicated and an intact sister chromatid is available as template for repair. Cyclin-dependent kinases (CDKs) play an important role in the HR versus NHEJ pathway choice, particularly by controlling DSB resection, which is required for HR but not NHEJ. Many of the DNA DSB resection and repair factors involved in HR and NHEJ are subject to phosphorylation by CDKs to restrict error-prone NHEJ to G1 and promote relatively error-free HR in S/G2 (66,67,68,69,70,71,72,73).
Transcriptional regulation of resection factors also coordinates DSB repair pathway choice with the cell cycle (74). Cyclins and CDKs are highly dysregulated in neoplastic cells and likely affect DNA repair pathways that impact tumorigenesis and cancer therapy. By comparison with DSB repair factors, little is known regarding transcriptional and post-translational control of TLS proteins, although existence of such regulatory mechanisms seems likely.
Cell cycle-dependent regulation at the level of mRNA and protein expression is reported for REV1 and RAD18. In fission yeast, Rev1 protein levels peak during G1 and decrease during the G1/S transition in a proteasome- dependent manner (75). In contrast, in budding yeast Saccharomyces cerevisiae, Rev1 protein levels are low in G1 and early S phase, increase in late S-phase and peak during G2/M (76). REV1 mRNA levels only change 3-fold throughout the cell cycle whereas Rev1 protein levels are 50-fold higher in G2/M than in G1, suggesting that Rev1 protein is regulated post-transcriptionally (76). DNA damage by UV exposure does not significantly alter Rev1 protein expression (76). Rev1 protein expression is relatively low in S-phase, perhaps to limit mutagenic TLS during DNA replication. Rev1 protein expression peaking during G2/M suggests that TLS plays a role outside of active DNA replication in S-phase. Expression of RAD18 is also cell cycle regulated. In synchronized and untransformed human fibroblasts, RAD18 protein levels are high in S-phase when compared with G0 (quiescent) and G1 cells (6). RAD18 expression is under transcriptional control of E2F3 (77), possibly explaining the elevated expression of RAD18 in S-phase when RB is phosphorylated and E2F proteins are de-repressed. Loss of RB and excess E2F activity are common in cancer and may lead to elevated RAD18 expression. As discussed later, RAD18 mRNA levels are typically higher in cancer when compared with normal tissue. The RAD18 protein can also be stabilized and overexpressed due to its cancer-specific binding partner, the Cancer/Testes Antigen MAGEA4 (78). Over-production of RAD18 in cultured cells can lead to increased PCNA mono-ubiquitination and DNA damage-independent recruitment of Y-family TLS polymerases to chromatin (79,80). Therefore, high-level expression of RAD18 in cancer (via E2F activity, MAGEA4-binding or other processes) represents a potential mechanism for aberrant TLS activation and mutagenic DNA synthesis. The POLH promoter contains a p53-response element (81) and DNA damage-inducible expression of Polη is p53-mediated in several human cell lines (82). Importantly, p53-inducible Polη expression contributes to accelerated bypass of UV-lesions (83).
Integration of TLS factors with the cell cycle and other branches of the DDR is also achieved via phosphorylation-based mechanisms. For example, basal and DNA damage-inducible interaction between RAD18 and Polη is dependent upon RAD18 phosphorylation in the Polη-interacting domain by S-phase kinase CDC7 (84). CDC7 is both a biomarker and drug target in cancer (85,86). Therefore, it is likely that CDC7-mediated RAD18 phosphorylation contributes to TLS pathway activation in tumor cells.
The Polη-interacting domain of RAD18 is also phosphorylated in a DNA damage-inducible manner by c-Jun N-terminal kinase (JNK) to facilitate TLS (87). JNK controls many adaptive responses to both intracellular stresses and environmental exposures including UV light (88). Therefore, RAD18 phosphorylation by JNK might integrate TLS with other stress response pathways. During an unperturbed cell cycle, the RAD18-associated E2 ubiquitin-conjugating enzyme RAD6 is also phosphorylated by CDKs, leading to an increase in its catalytic activity (89). In synchronized cells, RAD6 phosphorylation peaks in G2/M. The extent to which TLS is affected by CDK-induced RAD6 phosphorylation has not been determined. However, RAD6 phosphorylation is a possible mechanism for promoting TLS after completion of S-phase.
Polη is phosphorylated by ATR and Protein Kinase C in response to UV and therapeutic agents (90). The Polη site targeted by ATR (Ser 601) is necessary for normal recovery of DNA replication and DNA damage tolerance after UV-irradiation (91). Polη is also phosphorylated at Ser 687 by CDK2 in a cell cycle-dependent manner. Ser 687 phosphorylation regulates Polη stability during the cell cycle, allowing accumulation in late S and G2 (92).
Whether Polι and Polκ are regulated via direct phosphorylation has not been determined. Nevertheless, multiple core components of the TLS pathway (RAD18, REV1, RAD6, Polη) are directly phosphorylated by kinases whose activities are altered in cancer (CDKs, CDC7, ATR, JNK). Protein phosphorylation represents a potential mechanism for creating TLS pathway imbalances that contribute to mutability and altered DNA damage tolerance of cancer cells.
TLS OCCURS BOTH AT STALLED LEADING DNA REPLICATION FORK AND POST-REPLICATIVELY
TLS may directly alleviate replication blockades on stalled leading strands (termed TLS ‘on-the-fly’), and may operate post-replicatively (i.e. behind an active leading strand) to eliminate daughter strand discontinuities.
TLS on-the-fly
Seminal work by Lehmann and colleagues revealed that the conversion of the low molecular weight nascent DNAs synthesized in UV-irradiated cells into high-molecular-weight DNA is compromised in XPV cells (93,94). The term post-replication repair (PRR) was used to describe growth of low molecular weight DNA to high molecular weight species. Hanaoka's work eventually revealed that Polη-mediated TLS accounted for PRR (11,12). Subsequent work from many groups has shown that Polη contributes to replication fork progression in cells harboring CPD and other lesions (95,96,97). Polη travels with the replication fork in unperturbed cells (98), is recruited to the vicinity of replication forks in UV-irradiated cells, and resolves helicase/polymerase uncoupling (99). In summary, for UV-induced CPD, on-the-fly direct lesion bypass by Polη is the default response. In the absence of Polη-mediated TLS, other compensatory mechanisms are deployed to sustain DNA synthesis.
TLS at post-replicative ssDNA gaps
It has long been recognized that UV-irradiation leads to ssDNA-gapped daughter strand DNA on both leading and lagging strands (100,101). Potentially such gaps may result from persistence of Okazaki fragments on the discontinuous lagging strand, and/or due to repriming downstream of a blocked leading strand. Two excellent recent reviews provide a historical perspective on mechanisms of ssDNA gap formation and repair (102,103).
TLS is an important mechanism for filling post-replicative ssDNA gaps. Lopes et al. analyzed DNA replication dynamics of TLS-compromised S. cerevisiae mutant strains following UV-irradiation (104). Those studies showed that strains lacking rev1, rev3 and rad30 (yeast genes encoding homologues of human REV1, REV3 and Polη respectively) maintain normal rates of fork progression following UV-treatment. However, TLS mutant strains accumulate more ssDNA gaps along replicated duplexes. It was concluded that post-replicative gaps accumulate along replicated daughter strand DNA due to repriming events downstream of the lesions on both leading and lagging strands. Furthermore, TLS (together with HR) counteract gap accumulation without affecting replication fork progression (104). Karras and Jentsch showed that restricting expression of rev3, rad30 or rad18 to G2/ M-phase is sufficient to confer lesion tolerance, further supporting the notion that TLS acts on ssDNA gaps behind newly re-primed replication forks (9). In a related study, Daigaku et al. directly visualized and quantified PRR tracts after UV-irradiation in TLS-mutant yeast strains. Those workers showed that TLS is temporally and spatially separable from global genomic DNA replication (10). Similar PRR roles have been proposed for TLS in human cells (95,105,106). Notably, UVC-irradiated XPV fibroblasts accumulate longer ssDNA stretches both at the replication fork, and behind the leading strand when compared with isogenic Polη-complemented cells (99). Therefore, Polη resolves helicase/polymerase uncoupling and also prevents post-replicative gap accumulation in cells harboring CPD.
The type of DNA damage might determine whether TLS occurs directly at or behind the stalled replication fork (95). For example, repriming may be favored when TLS is absent (99), when template switching is suppressed (107) or when replication fork obstacles are too bulky to be accommodated by DNA polymerases (103).
Insight into a re-priming/restart process that allows continued elongation of nascent daughter strands copying UV-damaged templates was revealed by the discovery of Primase-Polymerase (PRIMPOL; CCDC111). PRIMPOL is an archaeal-eukaryotic primase (AEP) in eukaryotic cells that performs lesion bypass and additionally re-primes DNA replication downstream of bulky lesions and other barriers to DNA synthesis (108,109,110). PRIMPOL is recruited to ssDNA via its interaction with RPA. PRIMPOL does not interact with PCNA, yet contains two RPA-binding motifs, one of which mediates recruitment to stalled replication forks. RPA also stimulates PRIMPOL primase activity (110,111). Therefore, direct associations of PRIMPOL with RPA-coated ssDNA at stalled DNA replication forks may stimulate restart of DNA synthesis and generate gaps behind the newly-reprimed daughter strand. PRIMPOL-mediated repriming activity provides a possible explanation for the presence of post-replicative ssDNA gaps that are filled by the Y-family TLS polymerases (112). Consistent with this idea, PRIMPOL depletion impedes fork progression to a higher extent in XPV fibroblasts when compared with isogenic POLH-complemented cells, and suppresses formation of post-replicative ssDNA gaps (99). Interestingly, PRIMPOL primase catalytic activity is dispensable for generation of ssDNA gaps in UV-irradiated XPV cells (99). Moreover, PRIMPOL is not the sole mediator of repriming when Polη is absent (99). RAD51 may also contribute to repriming downstream of stalled replication forks, independently of its roles in recombination-based mechanisms (99).
Post-replicative ssDNA gaps can also be filled by an error-free DNA damage-avoidance mechanism termed ‘template switching’ (TS). TS relies on DNA replication fork reversal to generate ‘chicken-foot structures’ and uses the newly synthesized strand as a template to replicate across damaged DNA and suppress discontinuities (113,114,115). TS is initiated by HLTF and SHPRH, mammalian homologues of the yeast E3 ubiquitin ligase and SWI/SNF ATPase family member RAD5 (116). HLTF and SHPRH recruit the ubiquitin-conjugating enzyme UBC13 and a non-canonical UBC variant MMS2 to damaged chromatin, to form a complex that cooperates with RAD6–RAD18 to poly-ubiquitinate PCNA at K164 (27). The two RAD5 homologues poly-ubiquitinate PCNA via distinct mechanisms. SHPRH mediates extension of mono-ubiquitinated K164 (117), while HLTF forms a thiol-linked Ub chain on UBC13 that is transferred to RAD6. RAD18 then transfers the pre-conjugated Ub chain to K164 of unmodified PCNA (118). HLTF and SHPRH promote TS in a DNA damage-specific manner (60). SHPRH mediates responses to alkylating agents (e.g. MMS) whereas HLTF mediates PCNA poly-ubiquitination and confers DNA damage tolerance in response to bulky DNA lesions (including UV, 4-NQO and MMC) (119,120). Poly-ubiquitinated PCNA generated via the concerted actions of RAD18 and RAD5 provides a platform for recruiting ZRANB3 (Zn finger, RAN-binding domain containing 3, also known as Annealing Helicase 2 or AH2) (121). Once recruited to the stalled fork, ZRANB3 facilitates fork regression, replication fork restart and confers DNA damage tolerance (92–94). TS is intimately coordinated with the TLS pathway. For example, in response to UV-irradiation, HLTF promotes PCNA mono-ubiquitination and Polη recruitment, while inhibiting SHPRH function. Conversely, MMS promotes formation of a RAD18-SHPRH complex and induces HLTF degradation (60). It is unclear how cells choose between error-prone TLS and error-free TS. It is possible that TS is deployed for DNA damage that is too severe to be bypassed via TLS and results in persistence of 3′ ends at stalled DNA replication forks (122). Like TLS, the TS pathway in S cerevisiae is also functional when RAD5 is restricted to G2. Therefore, template switching may also play a role in ssDNA gap maintenance in G2 phase (9). Figure 2 illustrates the canonical mechanisms of PRR gap-filling via TS and TLS.
Figure 2. Post-Replication Repair (PRR) is mediated by TS and TLS. (A) Stalling of replicative DNA polymerases leads to recruitment of the primase/polymerase PRIMPOL and re-initiation of DNA synthesis downstream (3′) of the stalled DNA polymerase. PRIMPOL-mediated restart of DNA synthesis generates ssDNA gaps or discontinuities in the daughter strand. (B) TS is activated by PCNA poly-ubiquitination, and is dependent upon RAD5 homologues HLTF and SHPRH. (C) TLS is mediated by PCNA K164 mono-ubiquitination, which recruits Y-family TLS polymerases in a process termed ‘polymerase switching’. The RAD6/RAD18 (E2/E3 ubiquitin ligase) complex accounts for most (∼95%) of the mono-ubiquitinated PCNA in mammalian cells.
Tirman et al. showed that in cisplatin-treated U2OS cells, TLS factors are involved in temporally distinct mechanisms of gap filling: during G2 phase, ssDNA gap filling is dependent upon RAD18-mediated PCNA mono-ubiquitination and subsequent recruitment of REV1-Polζ (8). However, gap-filling during S-phase is RAD18-independent and requires RAD51, UBC13 and REV1-Polζ, (8). In avian DT40 cells, RAD18 and PCNA ubiquitination, but not REV1, are essential for post-replicative gap filling after UV exposure (52). Like cisplatin-treated U2OS cells, REV1 (but not RAD18 or mono-ubiquitinated PCNA) is required to maintain replication fork progression following DNA damage. In DT40 cells, mutation of REV1, but not RAD18 or PCNA, results in more frequent fork stalling after UV or NQO exposure (52), perhaps reflecting a role of REV1 in template-switching during S-phase. Taken together, these studies underscore the complexity of TLS and demonstrate temporally distinct mechanisms of gap-filling involving different mediators.
The specific factors involved in gap filling, and whether gap filling involves canonical TLS or TS (as may be the case for REV1/Polζ) may depend on multiple factors such as cell type, the species of DNA damage, and availability of other DDR pathways (103). In this regard, oncogenes and the intrinsic replication stresses of neoplastic cells are emerging as key causes of ssDNA gaps that require remediation via the TLS pathway. Yang et al. showed that RAD18 and Polκ are important for preventing both accumulation of ssDNA gaps and mitotic catastrophe in primary cells expressing CCNE1 or KRAS (123). In a related study, Nayak et al. showed that pharmacological inhibition of REV1 interferes with DNA replication and induces ssDNA gap formation in cancer cells (124). HR-deficient cancer cells may be particularly dependent on TLS for gap-filling (125,126). For example in BRCA1 mutant breast cancer cells, spontaneous ssDNA gaps arising from PRIMPOL-mediated repriming are filled by REV1 and Polζ-dependent TLS (127). It is important to determine precisely how global DNA repair status as well as other factors impact TLS-dependency of cancer cells. This knowledge will enable precision medicine strategies for targeting cancer cells using TLS inhibitors based on their unique genome maintenance characteristics. A more detailed discussion of mechanisms of gap formation may be found in two excellent reviews by Menck and Vindigni (102,103).
EVIDENCE THAT TLS POLYMERASES CONTRIBUTE TO CANCER
Since mutagenesis is a hallmark of cancer cells and drives carcinogenesis, it is intuitive to hypothesize that error-prone TLS polymerases promote cancer. Since cancer cells rely on TLS for ssDNA gap suppression and tolerance of intrinsic replication stresses (123,124) it is also tempting to hypothesize that TLS sustains carcinogenesis. Here we review evidence that TLS polymerases shape cancer genomes. We describe how TLS polymerases and their regulators are altered in cancer, and we summarize experimental evidence that TLS drives carcinogenesis.
Contributions of TLS to cancer based on analysis of mutation signatures
Each cancer cell harbors a composite ‘mutational portrait’ comprising many individual mutations generated by various error-prone genome maintenance processes (128). The ‘mutation signature’ concept provides a convenient way to analyze large cancer genome sequence data sets and annotate different classes of superimposed mutations in tumors. Studies of multiple cancer types have revealed over 30 single-base substitution (SBS) mutation signatures. Classification of SBS signatures is based upon relative frequencies of the six base substitutions (C>A, C>G, C>T, T>A, T>C and T>G) in tri-nucleotide sequence settings that consider every possible combination of flanking 5′ and 3′ nucleotides. Additionally, methods have been developed for classification of doublet-base substitutions (DBSs) and insertion/deletion mutations (indels): Indels are classified as deletions or insertions. Single-based indels are classified based upon the length of the mononucleotide repeat tract in which they occur. Longer indels are classified based on whether they reside at repeats or with overlapping microhomology at deletion boundaries, and based upon indel-size, repeat and microhomology (128). The etiologies of many mutational signatures have been attributed to specific genotoxic exposures and mutagenic processes. It is proposed that TLS polymerases mediate four of the SBS signatures, namely signatures 2, 5, 13 and 9.
Mutational signature 13, found in breast and bladder cancers, is attributed to the concerted activities of APOBEC (apolipoprotein B mRNA editing enzyme, catalytic) and REV1. APOBEC proteins deaminate Cytosine to Uracil. Subsequent excision of Uracils formed via cytosine deamination generates non-coding AP sites. REV1 is postulated to insert cytosines across the AP sites that lead to C>T or C>G mutations (129,130,131). A recent study experimentally validated the role of REV1 in APOBEC3-mediated mutagenesis. Petljak et al. annotated mutation signatures in human cancer cells that accumulate APOBEC3-associated mutations over time (131). Using REV1-knockout derivative lines, these workers demonstrated that REV1 has a critical role in the generation of signatures SBS2 and SBS13, and might also contribute to a low-fidelity TLS process underlying SBS5 (131). Therefore, there is strong evidence that REV1 contributes to mutagenesis in human tumors.
The other Y-family TLS polymerase strongly implicated in generating mutation signatures in human cancers is Polη. Mutation signature 9, found in Chronic Lymphocytic Leukemias (CLL) and malignant B-cell lymphomas, is attributed to Polη-dependent somatic hypermutation (SHM) activity. Consistent with a role for Polη in mediating signature 9, Polh−/− mice have a GC-biased mutation profile demonstrating that Polη is a major contributor to A/T mutations during SHM (132). Supek and Lehner identified a prevalent mutational signature in solid tumors that is directed toward the 3′ end of active chromatin, associated with carcinogen exposure, and attributed to Polη (133). However, there has been no direct experimental validation that signature 9 is dependent upon Polη. There remains a need for experiments that test how specific TLS polymerase deficiencies impact mutational signatures of cancer genomes.
Altered expression of TLS polymerases and their regulators in cancer
There are several reports that TLS polymerases and their regulators are altered in cancer cells or patient-derived tumors. Polymorphisms in TLS polymerase genes are documented in a small set of studies and these alterations may be associated with cancer predisposition. For example, mutations in REV1 are associated with lung cancer (134), POLK mutations are linked with lung and breast cancer (134,135), and POLI mutations correlate with risk of adenocarcinoma and squamous cell carcinoma (136). Whether such TLS polymerase mutations necessarily affect their biochemical activities and participation in TLS, or threaten genome stability is not clear. However, some-cancer associated REV1 mutants reportedly have altered DNA polymerase and DNA-binding activities that lead to increased mutagenesis (137).
Altered expression of TLS polymerases represents another possible mechanism for TLS imbalances and mutagenesis. Potentially, over-expressed or aberrantly activated TLS polymerases might compete with replicative DNA polymerases for access to the replisome leading to mutagenesis (138). Improperly-expressed (both over-expressed and under-expressed) TLS polymerases may also compete with each other for replisome association via their PIP boxes and ubiquitin-binding motifs. Elevated expression of individual TLS polymerases may increase error-prone DNA synthesis on an undamaged template or might lead to aberrant deployment for bypass of non-cognate DNA lesions, leading to increased mutagenesis. For example, overexpression of POLK is reported to increase N-methyl-N-nitrosurea (MNU)-induced mutagenesis and occurrence of intestinal adenomas (139).
Pathological changes in TLS modulators can also lead to XPV-like imbalances that impact genome stability. The E3 ubiquitin ligase MDM2 which is overexpressed in many cancers degrades Polη and recapitulates hallmark TLS defects of XPV cells (140). The NPM1 gene, which encodes the Polη-binding protein Nucleophosmin, is frequently mutated in Acute myeloid leukemia (AML). A prevalent NPM1 gene mutation in AML patients leads to excessive Polη degradation and reduces error-free TLS activity (141).
The proximal TLS activator RAD18 is often overexpressed in cancer both at the mRNA level (Figure 3) and at the protein level, owing to its stabilization by a cancer selective binding partner Melanoma Antigen A4 (MAGEA4, a Cancer/Testes Antigen or CTA) (78). TLS polymerase activation and mutagenesis are highly sensitive to changes in RAD18 expression levels. Ectopically-expressed RAD18 induces PCNA mono-ubiquitination and recruitment of TLS polymerases to replicating DNA, even in the absence of DNA damage (78,79,80). DNA damage-independent activation of TLS polymerases due to excessive RAD18 could be a source of mutagenesis. Different TLS polymerases have different affinity for PCNA (45) and may be differentially reliant on RAD18-mediated PCNA mono-ubiquitination or chaperone activity for engaging the replisome (38). Excessive RAD18-mediated chaperone activity or PCNA-mono-ubiquitination favoring any individual TLS polymerase could create a selection bias and TLS imbalance that contributes to mutagenesis in cancer cells. The E3 ubiquitin ligase RNF168 is another potential TLS modulator which is often overexpressed in cancer (142). RNF168 increases levels of ubiquitinated H2A in the vicinity of replicating DNA which recruits Polη via direct interactions (143).
Figure 3. Heatmap showing expression of TLS and TS pathway genes in Lung Adenocarcinoma (LUAD). The table shows analysis of TLS pathway genes in representative tumor types from TCGA gene expression datasets. The heatmap illustrates relative expression levels of mRNAs corresponding to TLS and TS pathway genes in LUAD tumors and adjacent normal tissues. Classifiers for tumor stage, histologic subtype, TP53 mutation, Replication Stress (RS), and smoking, are shown for each tumor. Tumors harboring at least one of the following genetic alterations are classified as ‘RS-high’: (i) CCNE1 amplification, RB1 two-copy loss, CDKN2A two-copy loss; (ii) KRAS amplification, NF1 mutations, MYC amplification, MYCL1 amplification, ERBB2 amplification (264). As an example of TLS polymerase imbalance revealed by these analyses, we considered the ratios of POLH:POLK expression in normal and cancer tissues. In UCEC, the average expression of POLH in tumors is 1.14 times the average of POLH in the adjacent normal, and the average expression of POLK in tumors is 0.57 times the average of POLK in adjacent normal. Therefore, the ratio for the fold change for POLH: POLK in UCEC is 1.14: 0.57. For LUAD, LUSC and BLCA, the ratios for fold change in POLH:POLK when comparing tumors with adjacent normal tissues are 1.06: 0.74, 0.90: 0.54 and 0.99: 0.63 respectively.
However, there is no single paradigm for how TLS polymerases (or other TLS pathway components) are altered in cancer and whether such changes correlate with mutation burden or other tumorigenic properties. Therefore, we interrogated Cancer Genome Atlas (TCGA) gene expression datasets and determined the extent to which TLS pathway gene expression levels are altered in representative cancers. Additionally, we asked whether expression levels of TLS pathway genes correlate with mutation load. We analyzed expression levels of RAD18 (upstream activator of Y-family TLS polymerases), POLH, POLK, POLI and REV1 (the four Y-family TLS polymerases), and REV3L and MAD2L2 (encoding the two subunits of Polζ, which mediates the extension phase of TLS).
We examined data from three different cancer types with a smoking-related etiology: Bladder cancer (BLCA), Lung Adenocarcinoma (LUAD), and Lung squamous cell carcinoma (LUSC). We reasoned that tobacco smoke genotoxicity may stimulate mutagenesis and reveal associations with TLS status for these cancers. For comparison, we also examined TCGA data from tumors whose etiology is unrelated to tobacco smoke exposure, namely prostate cancer (PRAD), glioblastoma (GBM), pancreatic cancer (PAAD) and endometrial cancer (UCEC).
Figure 3 and Supplementary Figure S1 summarize expression patterns of TLS genes in tumor samples relative to adjacent normal tissue. Also indicated are correlations between TLS gene expression and total Single Nucleotide Variation (SNV) burden (which is a direct measure of genome-wide point mutations). Several key patterns and trends are revealed by our analyses: Of the TLS genes studied here, only RAD18 is overexpressed in most tumor types. The only tumor not significantly overexpressing RAD18 is pancreatic cancer (for which datasets were available from only 4 normal samples, limiting our ability to accurately determine fold changes in tumor samples). Interestingly, RAD18 expression shows a strong positive correlation with the total SNV numbers in most cancer types, both in smokers and non-smokers. The overexpression of RAD18 in most tumors is consistent with the hypothesis that excessive RAD18 stimulates error-prone TLS and increases mutation burden. MAD2L2 (encoding REV7) is another TLS gene that is also overexpressed in most tumors. However, unlike RAD18, MAD2L2 expression does not show a strong correlation with SNV burden. REV3L is typically expressed at low levels in tumor cells when compared with adjacent normal tissues. Moreover, in some tumors (e.g. PAAD, PRAD), REV3L expression is negatively correlated with SNVs. Rev3l loss in mice leads to spontaneous tumorigenesis (144). Therefore, it is possible that the reduced REV3L expression in human tumors contributes to tumorigenicity.
There are reports that the Y-family polymerases are expressed at higher levels in tumors than normal cells and tissues (145,146,147,148). Surprisingly however, our expression analyses reveal that POLH, POLK, POLI and REV1 expression levels in cancers are typically equivalent to or reduced when compared with adjacent normal tissues. In LUAD, the expression levels of Y-family DNA polymerases are negatively correlated with the total SNV counts in tumors from smokers. In contrast, in PAAD, the general trend is that tumors with reduced expression of POLH, POLK, POLI and REV1 have a high number of SNV counts.
Taken together, these analyses reveal remarkable complexity in expression patterns of the core TLS pathway genes and how they are altered in different cancers. With the important caveat that mRNA expression is an imperfect surrogate for protein level and activity, the expression patterns described in Figure 3 suggest many ways in which imbalance between TLS polymerases and their activators might affect DNA damage tolerance and mutability in different cancers. The impact of such TLS pathway alterations is also likely to depend on the availability of other genome maintenance mechanisms. For example, HR-compromised cells have increased reliance on TLS (127). Accordingly, altered expression of TLS genes might be far more consequential in a BRCA-mutant cancer cell than in a BRCA-sufficient background. There is a need to determine the ways in which TLS and its intersecting DNA repair pathways are rewired in all individual tumor settings, and how those changes impact mutagenesis and DNA damage tolerance. For example, it is reported that HLTF promoter methylation and loss of HLTF expression are observed in some cancers (149). Changes in relative expression or activities of RAD5 homologues could affect the selection of (error-free) TS vs (error-prone) TLS and influence mutagenic outcomes. To investigate potential TS pathway alterations in cancer, we also analyzed expression patterns of HLTF, ZRANB3, SHPRH, MMS2 and UBC13 for all the malignancies described in Figure 3. In contrast with reported findings (149), HLTF expression was not reduced in tumors. Interestingly, we observed several trends: HLTF was overexpressed in tumors and positively correlated with SNVs in most cancer types. However, SHPRH was downregulated in tumors of most cancer types.
It is important to recognize that cancer constitutes many different diseases in which tumor types and subtypes are highly divergent based on proteogenomic characteristics. It is overly-simplistic and imprecise to conclude that any specific TLS factor is universally over- or under-expressed in cancer when compared to normal tissues. The analyses and a corresponding heatmap in Figure 3 illustrate that even despite general trends, every individual tumor is unique and expresses a distinct repertoire of TLS factors (as well as other genome maintenance genes). The goal of targeting TLS for therapy may require a precision medicine approach which is contingent on understanding the unique DNA repair characteristics of every individual patient tumor. The correlations we have identified between TLS factors and SNV burden in cancer provide an essential source of hypothesis. The results of Figure 3 could help prompt future studies to model pathologically-relevant TLS polymerase imbalances and test the impact of those imbalances on mutagenesis.
TLS is a dependency of some cancer cells
During multistep tumorigenesis, neoplastic cells acquire extensive DNA damage from intrinsic sources including oncogenes. Oncogene signaling leads to DNA damage via multiple mechanisms including: (a) dysregulated replication origin licensing and firing leading to under- or over-replicated DNA (b) increased transcription factor activation leading to replication-transcription conflicts (RTCs) (c) altered nucleotide metabolism leading to reduced dNTP pools and (d) increased ROS production causing oxidative DNA damage (150,151,152). Oncogenes may also deregulate R-loop homeostasis which further leads to RTCs (153). Taken together, oncogene signaling impedes replication fork advancement via multiple mechanisms and causes DNA replication stress.
Accumulating evidence suggests that TLS averts formation of lethal DNA damage in cells experiencing various forms of oncogene-induced DNA replication stress. TLS can be error-prone on undamaged DNA or templates containing non-cognate lesions. Therefore, it is possible that error-prone TLS sustains tumorigenesis by promoting both DNA damage tolerance and mutagenesis. Here we discuss key evidence that supports a role of TLS in cancer cell survival.
A cancer-essential gene (also termed a cancer-dependent essential gene) is required for proliferation or survival of cancer cells (but not normal cells) (154). Several reports suggest that TLS factors fulfill criteria for being genetic dependencies of cancer cells: TLS facilitates continued replication in cells challenged by oncogene-induced replication stress (123), prevents accumulation of ssDNA gaps and promotes cell survival (124). Inhibition of TLS by a small molecule, JH-RE-06 (which targets REV1) reduces survival of several cancer cell lines even in absence of an external stress (124). JH-RE-06 also suppresses growth of A549 and H1299 lung cancer cells grown as xenografts in vivo (155). Further consistent with a role for TLS in sustaining tumors, RAD18 overexpression elevates colonization of esophageal cancer cells in the mice lung (156), while RAD18-deficient TNBC xenografts show reduced tumor volume (157). Taken together, these studies suggest that TLS is a cancer-dependency at least under select conditions. TLS limits fork slowing by restricting fork reversal without altering replication restart or dormant origin firing through its gap filling function (124). Recent studies suggest that the cancer cell reliance on TLS for survival is contingent on ssDNA gap suppression (124,158).
Not all cancer cells rely on TLS for viability (124). According to the cancer dependency map (DepMap, a database of loss-of-function CRISPR screens in large number of cancer cell lines), REV1 is a dependency in only few cell lines (CRISPR 16/1086; RNAi 0/597). Similarly, other TLS polymerases are rarely identified as dependencies in DepMap. It remains to be determined what factor(s) determine whether TLS is a cancer dependency. Different oncogenes might induce distinct species and levels of DNA damage that are differentially reliant on TLS for remediation. For example, ectopic expression of CCNE1 in primary untransformed human fibroblasts induces a more robust expression of DDR markers (including mono-ubiquitinated PCNA) when compared with ectopically-expressed oncogenic RAS (123). Moreover, multiple DNA repair pathways (including HR, TMEJ, NHEJ) contribute to tolerance of oncogene-induced replication stress and DNA damage (123,152). Every cancer cell has a distinct DNA repair landscape. Therefore, the repertoire of redundant pathways available to repair intrinsic DNA damage might determine the extent to which any cancer cell depends on TLS.
Defining the factors and biomarkers that accurately predict TLS-dependency of cancer cells is critical if we are to develop precision medicine approaches based upon TLS inhibition. One factor that has been shown to create TLS-dependency in cancer cells is loss of BRCA1. Homologous recombination-deficient BRCA1 mutant breast cancer cells develop spontaneous ssDNA gaps which are repaired via PRIMPOL and REV1-Polζ-dependent gap-filling mechanism required for viability (127). Tutt and colleagues showed that cancer cells mis-expressing the germ cell protein HORMAD1 (a Cancer Testes Antigen or CTA) have increased dependency on several TLS mediators including POLH, POLK, REV1, REV3L and REV7 (159). Functions of HORMAD1 in cancer cells are not well understood. It is reported that HORMAD1 can both inhibit HR (160) and activate HR (161,162) when mis-expressed in cancer cells. Nevertheless, aberrant expression of HORMAD1 could generate species of DNA damage that are repaired via TLS. More work is needed to reveal the mechanistic underpinnings of TLS-dependencies in neoplastic cells. The availability of the REV1 inhibitor JH-RE-06 has been a valuable tool for demonstrating that some cancer cell lines rely on TLS for survival. The use of orthogonal methods such as CRISPR and RNAi to ablate REV1 (and other TLS genes) will help validate the TLS pathway as a cancer dependency. It is also crucial to test the TLS-dependency of cancer cells in pathologically-relevant models (including GEMM, PDX, organoids) that better recapitulate the characteristics and dependencies of tumors in patients.
Contributions of TLS to cancer based on mouse models
Mouse models are important for validating hypothetical roles of oncogenic drivers or tumor suppressors in carcinogenesis in vivo. Here, we consider the evidence that TLS polymerases and RAD18 are physiologically-relevant modifiers of spontaneous or induced carcinogenesis in vivo.
Spontaneous tumorigenesis
Deletion of the TLS activator Rad18 in mice does not affect rates of spontaneous mutagenesis or incidence of spontaneous tumors (163,164). Polk-deficiency elevates mutagenesis at G:C base pairs in the mouse lung, liver and kidney (165), while Rev1-deficient mice have reduced weight and lifespan (166,167). However, loss of Rad18 (164), TLS polymerases Polh (168), Poli (168,169), Polk (165), Rev1 (166)) or Rev7/Mad2l2 (170) does not affect spontaneous tumorigenesis in mice. Intriguingly, Rev3l-deficient mice suffer from embryonic lethality (171,172,173) and conditional deletion of Rev3l in adult epidermal or lymphoid cells leads to spontaneous squamous cell carcinomas in specialized sebaceous glands and skins, and lymphomas, respectively (144,174). Although the cancer-propensity of Rev3L-deficient mice may point to a tumor-suppressive role for Polζ, this phenotype is not seen in Rev7-deficient mice. Therefore, Rev3L may have additional Rev7-independent functions in tumor-suppression, possibly unrelated to TLS. Unfortunately, in vivo experiments assessing the tumorigenic consequences of TLS imbalance due to overexpressed TLS polymerases are scarce. In a one-of-a-kind experiment, Sasatani et al. did not observe spontaneous tumorigenesis in the intestine of Rev1-overexpressing mice (139). Collectively, the studies described above suggest that altered expression of TLS polymerases and Rad18, under unperturbed conditions is not oncogenic in mice.
Oncogene-induced tumorigenesis
Although TLS is implicated in mediating tolerance of oncogene-induced DNA replication stress, there is no published information regarding the requirement for TLS in oncogene-induced tumorigenesis in vivo. In our unpublished study, Rad18-defciency did not affect KrasG12D-driven lung carcinogenesis in either p53+/+ or p53−/− C57BL6 mice. This result could indicate that TLS is dispensable for oncogenic Kras-driven lung carcinogenesis, or that Rad18-independent TLS mechanisms are sufficient to sustain KrasG12D-driven lung tumors. Rad18 loss also did not affect the mutation burden of Kras-driven lung tumors in p53-null mice. RAD18 is typically overexpressed (not ablated) in human lung tumors. It would also be interesting to determine whether Rad18 overexpression in the mouse lung affects rates of oncogene-induced lung carcinogenesis.
Orthotopic tumor models
Implanting cancer cells orthotopically in mice provides a convenient approach to study tumor growth in a physiologically relevant environment. Orthotopic injection of esophageal cancer cells overexpressing RAD18 increases colonization (156), while RAD18-deficient TNBC cell xenografts show reduced tumor growth (157). TLS inhibition by the REV1 inhibitor JH-RE-06 decreases colonization of lung cancer cells in mice (155). Therefore, studies with orthotopically-implanted cells support a role for TLS in tumor growth and as a cancer target.
Carcinogen-induced tumorigenesis
Polh-/- mice fully phenocopy the UV-induced skin cancer-propensity of XP-V patients. Heterozygous Polh+/− mice are also sensitive to UV-induced skin carcinogenesis, but succumb at a lower rate when compared with Polh-/- (175). Although Poli-/- mice are not susceptible to UV-induced skin carcinogenesis; Poli−/− Polh−/− double knockout mice have increased rates of UV radiation-induced skin carcinogenesis, compared with Polh-/- animals (168,169). Therefore, both Polη and Polι guard against UV-induced carcinogenesis. Interestingly though, UVB-treatment of Poli−/− deficient animals induces mesenchymal tumors that are not observed in Polh−/− mice (169). Polk−/− mice are viable, but have a shorter lifespan than Polk+/− and Polk+/+ mice (165). Moreover, Polk−/− mice have a spontaneous mutator phenotype in the kidney, liver and lung, which is attributed to a role of Polκ for accurate lesion bypass of bulky DNA adducts generated endogenously by cholesterol and its metabolites. Therefore, Polκ protects against spontaneous mutagenesis. Rev1−/− mutant mice are viable on a 129/OLA background, but not on a C57BL/6 background. Rev1−/− mice show reduced weight, but no other gross abnormalities (166). Whether Rev1 mutation impacts carcinogenesis has not been reported.
Although Rad18 is proximal to Y-family TLS polymerases, Rad18-deficient mice do not phenocopy the UV-induced skin tumor-susceptibility of XPV (176). One possible explanation is that Polη bypasses UV-induced CPD in a Rad18-independent manner in the mouse skin. Interestingly, Chk2−/−Rad18−/− double knockout mice develop spontaneous lymphomagenesis whereas Chk2−/− or Rad18−/− mice do not. Chk2-deficiency alleviates UV-induced apoptosis of Rad18−/− cells but leads to increased genomic instability (176). Therefore, the genetic interaction of Rad18 and checkpoint genes is important for maintaining genome stability and preventing carcinogenesis.
In a DMBA-ingestion tumorigenesis model, the incidence of leukemia and liver tumors is reduced in Rad18−/− mice when compared with Rad18+/+ littermates (164). DMBA-induced skin tumor incidence is unaffected by Rad18, yet the mutation signatures of Rad18+/+ and Rad18−/− skin tumor genomes are profoundly different: Rad18-deficiency is associated with reduced overall SNV burdens, increased levels of insertion/deletion (indels) and different contributions of COSMIC mutation signatures to the overall tumor mutational portrait (164). Therefore, Rad18 promotes error-prone TLS of DMBA-induced lesions, resulting in mutagenesis (SNV). However, in the absence of Rad18, TLS polymerases may not be recruited to sites of PAH-induced DNA damage efficiently, leading to replication stalling and fork collapse. Error-prone repair of DBSs resulting from collapsed replication forks could explain the insertions and deletions present in Rad18-/- tumors.
In summary, most TLS polymerase- or Rad18-deficiencies do not lead to spontaneous tumorigenesis in mice. However, Rad18 and TLS pol-defects lead to altered mutagenesis and carcinogenesis following genotoxic exposures (UV, DMBA, cholesterol metabolites). It is unknown whether over-production of Rad18 or TLS polymerases impacts mutagenesis and carcinogenesis in vivo. Whether Rad18 and TLS polymerase-deficiencies compromise growth of oncogene-driven tumors in vivo has not been addressed comprehensively. However, accumulating evidence supports roles for TLS in conferring resistance to cancer therapy.
HOW DOES TLS AFFECT RESPONSE TO CANCER THERAPY?
Genotoxic cancer therapies are intended to interfere with DNA-templated processes and ultimately cause permanent replicative arrest and cell death. Many therapeutic genotoxins induce primary lesions that cause DNA replication fork stalling. Processing of stalled DNA replication forks may lead to secondary forms of DNA damage such as ssDNA and DSB (the latter often being viewed as the species of DNA damage responsible for lethality). The integrity of DNA repair and DDR signaling in both cancer cells and normal healthy cells critically impacts the overall response to therapeutic agents and patient outcomes. DDR is a protective mechanism and its deployment in cancer cells will antagonize the desired effects of therapy. Accordingly, inhibiting DNA repair processes and compromising DNA damage tolerance in cancer cells is predicted to sensitize tumors to therapy. We exemplify three classes of therapeutic agents whose mechanisms of action are highly dependent on TLS including platinum drugs, checkpoint protein kinase inhibitors and poly(ADP-ribose) polymerase (PARP) inhibitors, as described below.
Platinum drugs
Platinum compounds such as cisplatin and carboplatin are important genotoxic drugs used for the treatment of many cancers (177). Platinum drugs induce DNA damage (primarily intra-strand bulky DNA adducts and ∼1% inter-strand cross-links or ICL) that interfere with DNA synthesis in proliferating cancer cells. Platinating agents primarily induce formation of DNA mono-adducts that induce replication stress, mitotic catastrophe and cell death via apoptosis.
However, many cancer cells have innate resistance to platinum drugs, or acquire cisplatin-resistance during treatment and become refractory to therapy. Mechanisms of cisplatin-resistance include decreased uptake and increased efflux of the platinating agents, and increased DNA repair and increased DNA damage tolerance (178,179). Cisplatin therapy also leads to serious side effects including ototoxicity, nephrotoxicity and neurotoxicity due to cisplatin-induced ROS that cause apoptosis of neuronal and renal cells (180,181,182). Therefore, it is important to devise means to overcome the current limitations of cisplatin therapy.
TLS can help cancer cells acquire resistance to platinating agents and other genotoxic chemotherapies (183). Polη allows replication of cisplatin-damaged DNA templates and the structural basis for Polη-mediated chemoresistance to cisplatin has been elucidated (184,185,186). TLS-deficient cells lacking Polη (187,188,189) or RAD18 (190,191) fail to replicate cisplatin-damaged genomes and instead accumulate unfilled post-replicative gaps, collapsed replication forks and lethal DNA double stranded breaks (DSB). Moreover, high-level Polη expression is correlated with poor survival of platinum-treated non-small cell lung cancer and gastric adenocarcinoma patients (192,193). Polη expression and activity is elevated in cancer stem cells (CSC), the rare progenitors that both self-renew, repopulate tumors and account for chemoresistance (194). Other TLS polymerases are also implicated in mediating cisplatin-resistance. Overexpression of REV1 is strongly correlated with reduced survival probability of prostate cancer patients (195,196,197). Similarly, expression of REV1 is significantly upregulated in lung tumors compared with matched adjacent tissues, and such upregulation is associated with poor prognosis (155). Suppression of REV1 or REV3L not only sensitizes drug-resistant cancers to cisplatin, but also prevents acquisition of drug resistance in murine tumor models (198,199). Inhibition of TLS through RNAi-mediated depletion of REV1 and REV3L profoundly sensitizes human prostate cancer cells to a cisplatin prodrug and dramatically extends animal survival in vivo (200). Similarly, knockout of Rev7 in KRASG12D, TP53−/− non-small cell lung cancer (NSCLC) renders cancer cells hypersensitive to cisplatin treatment with significant improvement of animal survival over the REV7 proficient tumor (201).
Inhibiting TLS also represents an opportunity for overcoming toxic side effects of cisplatin therapy: high doses of cisplatin induce ROS and cause apoptosis of neuronal and renal cells leading to neurotoxicity and ototoxicity (180,181). Thus, many toxic side effects of cisplatin therapy are mediated via a DNA damage-independent mechanism that is not affected by TLS. Inhibiting TLS could lower the doses required for cisplatin therapy and reduce the toxic side effects associated with high doses of platinating agents. Rad18−/− and Polh−/− mice are viable and display no overt developmental defects. Thus, specific inhibition of TLS is unlikely to cause toxicity or be detrimental to cancer patients. In summary, there is very strong rationale to explore the TLS pathway as a therapeutic target whose inhibition will confer killing of cancer cells by cisplatin and reduce toxic side effects of cisplatin. By analogy, combining other genotoxic therapeutic agents with TLS inhibition will likely be an effective strategy to inhibit DNA damage tolerance in neoplastic cells.
Checkpoint kinase inhibitors
Cell cycle checkpoints integrate DNA repair with cell cycle progression to promote cell viability. The ATM/CHK2 and ATR/CHK1 pathways represent two major branches of the DDR and mediate DNA damage-inducible checkpoints that arrest cells in G1, S and G2 phases (202). Protein kinase inhibitors targeting ATM, ATR, CHK1 and CHK2 provide a strategy for uncoupling DNA repair from cell cycle progression and sensitizing cancer cells to therapy-induced genotoxicity (182,202,203,204,205,206,207).
ATR/CHK1 signaling and RAD18-mediated TLS occur simultaneously in response to replication stalling. ATR and RAD18 pathways are temporally-coincident because both are activated by RPA-coated ssDNA generated via helicase-polymerase uncoupling (205,208). There is also extensive cross-talk between the ATR and TLS pathways. RAD18 and Polη activities are regulated by ATR/CHK1-dependent phosphorylation (84,87,91). Conversely, when TLS is compromised (e.g. in XPV cells), there is a compensatory increase in ATR/CHK1 signaling which helps preserve cell viability (209). The compensatory ATR response of TLS-deficient cells following genotoxin exposure explains early observations that XPV cells are not UV-sensitive unless treated with caffeine (210). It is now appreciated that caffeine inhibits ATR, and that the ATR/CHK1 and TLS pathways are partially-redundant (209). Therefore, synthetic lethality resulting from inhibiting ATR/CHK1 together with TLS might represent a therapeutic strategy for sensitizing cancer cells to intrinsic or therapy-induced DNA damage.
Pharmacological inhibition of the WEE1 protein kinase also leads to bypass of cell cycle checkpoints and lethality. WEE1 performs inhibitory phosphorylation of Y15 on Cyclin-Dependent Kinases 1 and 2, thereby restricting S-phase and G2/M progression (211). WEE1 inhibitors de-repress CDK2 and CDK1 activities and promote bypass of S-phase and G2/M cell cycle checkpoints (212,213). Adavosertib is an inhibitor of WEE1 that is currently in clinical trials to treat multiple cancers and has been shown to have promising results in overcoming the resistance to platinum-based drugs, such as cisplatin (214).
In addition to promoting bypass of S/G2 checkpoints, the aberrant activation of CDK2 due to WEE1 inhibition in cancer cells causes excessive origin firing that triggers a DDR (211). WEE1 inhibitors are pharmacological equivalents of Cyclin E overexpression which is often used to experimentally model oncogene-induced DNA replication stress. In WEE1 inhibitor-treated cells, the combination of S-phase damage (due to excess CDK2 activity) and a compromised G2 checkpoint (due to excess CDK1 activity) allows S-phase cells to enter mitosis inappropriately and succumb to lethal mitotic catastrophe (211). Interestingly, TLS prevents WEE1 inhibitor-induced lethality (123,215). The role of TLS in protecting against the lethal effect of WEE1 inhibition is likely due to post-replicative gap filling since Rad18 and Polk-ablated cells aberrantly accumulate ssDNA in G2 after WEE1 inhibition (123). Therefore, concurrent inhibition of WEE1 and TLS represents a potential strategy for inducing mitotic catastrophe in cancer cells.
Poly(ADP-ribose) polymerase (PARP) inhibitors
PARP inhibition has received extensive attention as a synthetic lethal approach for killing BRCA-mutant HR-compromised tumors. PARP enzymes are DNA damage sensors and transducers that bind DNA at ssDNA breaks and synthesize negatively-charged, branched poly(ADP-ribose) (PAR) chains on target proteins in the vicinity of the damage (216). This PTM (termed PARylation) leads to the recruitment of DNA repair enzymes such as ERCC1 and remodeling of damaged DNA (217). In 2005, two groups reported the seminal observation that BRCA2-deficient cells, because of their deficiency in Homologous Recombination, are acutely sensitive to PARP inhibitors (218,219). Therefore, targeting PARPs is an attractive strategy for killing BRCA2-deficient tumors (220).
The synthetic lethal interaction between PARP inhibition and BRCA-deficiency was originally proposed to be due to persistent SSBs which are repaired by HR during S-phase. Thus, HR was originally proposed to avert fork collapse and lethal DSB. Subsequently it has become evident that PARPi not only block PARP-mediated SSB repair but also trap the PARP enzyme on damaged DNA. Thus, the trapped PARP protein itself is also an obstacle to replisome movement which cannot be resolved in the absence of HR (220,221,222).
Several recent studies suggest that PARPi toxicity in HR-deficient (HRD) cells is caused by DNA replication-associated single-stranded DNA (ssDNA) gaps (125,126,158,223) - a vulnerability which also suggests interesting and potential tractable roles of the TLS pathway as a therapeutic target in HRD tumors. Cantor's excellent recent review describes the various ways in which ssDNA gaps arise in BRCA1 and BRCA2-mutant cells (126). Interestingly, repriming mediated by PRIMPOL is a major source of spontaneous ssDNA gaps when HR is compromised (127). Consistent with TLS as a post-replicative gap-filling process that operates behind newly-reprimed forks, BRCA1/2-deficient cells are dependent upon RAD18 and Polζ-for gap suppression and viability (127). The demonstration that HR-deficiency creates a reliance on TLS suggests that patient stratification based on ‘BRCA-ness’ might reveal clinical contexts in which TLS inhibition has most therapeutic benefit.
ROLE OF TLS IN THERAPY-INDUCED MUTAGENESIS
The action of TLS polymerases on non-cognate lesions could plausibly play a role in acquisition of therapy-induced mutations. Therapy-induced mutations are of great potential clinical significance because they might help drive acquired chemoresistance. A good example of a clinical setting in which therapy-induced genotoxicity is linked to acquired chemoresistance is during Temozolomide (TMZ) treatment of brain cancers.
Temozolomide (TMZ) is a DNA methylating chemotherapeutic agent used in the treatment of Glioblastomas (GBM) and is the only FDA-approved first-line chemotherapeutic drug for this disease (224). All GBM eventually become TMZ-refractory and recur (225). The extent to which TMZ- induced hypermutation causes GBM recurrence is debated, but it has been proposed that therapy-induced mutations account for the adaptations that allow GBM to resist TMZ (226,227,228). Tumors from TMZ-treated GBM patients harbor a specific mutational signature (designated Single Base Substitution 11 or SBS 11) which is characterized by a preponderance of G:C>A:T transitions at non-CpG sites (229). Moreover, the therapy-associated mutational signature of hypermutated gliomas has been recapitulated by TMZ-treatment (230). However, the underlying error-prone DNA repair mechanisms that mediate de novo and therapy-induced mutation patterns and hypermutability in GBM are not fully understood. GBM cells do rely on RAD18 and Polκ to tolerate TMZ treatment (231,232,233). Overexpression of POLK in GBM patients is associated with TMZ-resistance and is a prognostic indicator for shorter survival (234,235). However, the contribution of TLS polymerases to TMZ-induced mutagenesis is unknown. To address the contribution of TLS to TMZ-induced hypermutation, Yang et al. annotated TMZ-induced mutation signatures in genomes from TMZ-treated isogenic RAD18+/+ and RAD18−/− GBM. In those experiments, analysis of mutation signatures from TMZ-treated GBM reveals a role for RAD18 in error-free bypass of O6mG (the most toxic TMZ-induced lesion), and error-prone bypass of other TMZ-induced lesions. However, the RAD18-dependent TLS polymerases responsible for mutagenic bypass of TMZ-induced DNA lesions have not yet been identified.
Cisplatin-induced mutation patterns resemble the cancer mutation signatures attributed to smoking and other sources of guanine-directed base damage. TLS has been implicated in generating cisplatin-induced mutational spectra at sites of guanine-directed base damage (236). Mutagenesis of cisplatin-induced DNA lesions is most likely due to the inserter/extender properties of Polη and Polζ which cooperate to perform bypass of GG cisplatin adducts (237). Further work is needed to elucidate how TLS polymerases impact therapy-induced mutations associated with acquired chemoresistance (238). Platinum-based and other chemotherapies for solid tumors are associated with increased risk of secondary hematological neoplasms (notably myelodysplastic syndrome, MDS and acute nonlymphocytic leukemia, ANLL) (239,240). Presumably these therapy-induced hematological malignancies are caused by genotoxicity-induced oncogenic mutations in hematopoietic stem cells. The extent to which TLS mediates mutagenic events leading to therapy-induced secondary neoplasms has not been determined.
TARGETING TLS FACTORS FOR THERAPY
There have been attempts to develop small molecules that target components of the TLS pathway (Table 1). The REV1 inhibitor JH-RE-06 was discovered by the Zhou and Hong laboratories in a screen for small molecule inhibitors that target the REV7-binding surface of the REV1 CTD and disrupt the REV1–REV7 interaction (241). Binding of JH-RE-06 induces REV1 dimerization, thereby blocking the REV1–REV7 interaction and preventing Polζ recruitment to the replisome. JH-RE-06 treatments induce all the hallmarks of TLS-deficiency including inhibition of mutagenic lesion bypass and enhancement of DNA damage-sensitivity in cultured human and mouse cell lines (241). Co-administration of JH-RE-06 with cisplatin suppresses the growth of human melanoma xenografts in vivo, thereby providing encouraging pre-clinical validation for TLS inhibitors as a novel class of chemosensitizers for cancer therapy. A different small molecule inhibitor of REV1 inhibitor that binds to the REV1-CTD and inhibits interaction with TLS insertion polymerases also sensitizes cultured murine and human cells to genotoxic exposures (242). Therefore, pharmacological disruption of associations between REV1 and its partner DNA polymerases is emerging as a feasible approach for inhibiting TLS and chemosensitizing cancer cells (243).
Table 1. TLS targeting agents
Target Inhibitor Chemical structure Function IC50 or Kd Structure of the Inhibitor-bound complex In vitro & In vivo
REV1 (REV7 interface) JH-RE-06 Blocks the REV1-REV7 interaction IC50 = 0.78 μM (AlphaScreen)
Kd = 0.42 μM (ITC) PDB: 6C8C (241) In vitro:
• Sensitize multiple tumor cell lines to cisplatin (241)
• Selective killing of BRCA1-deficient breast and ovarian cancers (127)
In vivo:
• Synergize with cisplatin to suppress melanoma growth in mice (241);
• Selective suppression of BRCA1-deficient breast tumorigenesis in mice (127);
• Suppress lung tumorigenesis in mice (155).
REV1 (RIR interface) Phenazopyridine (PAP) analog 1 Blocks the REV1-RIR interaction K d = 21 μM (MST) PDB: 6WS5 (265) In vitro & in vivo:
• Synergize with Olaparib/ temozolomide and induces cell death in PDXres 1518–3 SCLC cells (258)
REV1 UBM2 (ubiquitin interface) MLAF50 Blocks the REV1-ubiquitin interaction K d = 37 μM (SPR) ND In vitro:
• Inhibit chromatin co-localization of REV1 with PCNA (259)
REV7 Compound 7 Binds REV7 IC50 = 78 μM (FP) ND In vitro:
• Sensitize HeLa cells to cisplatin (260)
Pol eta/REV1 PNR-7-02 Inhibits the catalytic activity of Pol eta and REV1 with similar efficiency IC50 = 8.0 μM (polymerase activity assay) ND In vitro:
• Sensitize HAP-1 cells to cisplatin (261)
Pol kappa IAG-10 Selectively inhibits human Pol Kappa IC50 = 0.7–7 μM (polymerase activity assay) ND In vitro:
• Sensitize HAP-1 cells to TMZ (232)
PCNA T2AA Binds to PCNA in a bimolecular fashion; inhibits PNCA-interaction with Pol eta or REV1 IC50 = 1 μM (FP) PDB: 3WGW (262) In vitro:
• Sensitize HeLa and U2OS cells to cisplatin treatment (262,263)
MAGEA4 (Rad18 interface) Cyclic and linear MTP peptides
Blocks MAGEA4/RAD18 interaction IC50: <1 nM to 10 μM (TR-FRET) PDB: 7UOA (246) In vitro:
• Selectively pulldown MAGEA4 (246)
Given the central role of PCNA as a hub for inserter and extender polymerase engagement at the replisome, targeting the interaction between PCNA and TLS polymerases may be a feasible strategy for inhibiting TLS. Vanarotti and colleagues identified small molecules that bind to the UBM2 domain of REV1 and prevent its association with monoubiquitinated PCNA (244). The REV1–UBM2-binding compound prevents cisplatin-induced recruitment of REV1 to PCNA on chromatin, prevents UV-induced mutagenesis of the HPRT gene, and sensitizes cells to cyclophosphamide or cisplatin. Another study identified a PCNA-binding small molecule designated PCNA-I1S that stabilizes the PCNA trimer, and reduces levels of chromatin-associated PCNA (245). PCNA-I1S inhibits cell growth and enhances cytotoxic effects of UV radiation and cisplatin. However, it is unclear whether the cytotoxic effects of PCNA-I1S result from inhibition of TLS or other PCNA-mediated genome maintenance processes.
The most ideal therapeutic target pathways in cancer are ones that are critically required for neoplastic cell growth, yet are dispensable for viability of normal healthy cells. The TLS pathway meets this criterion in some contexts. For example, oncogene-expressing cells are more dependent on Rad18 and Polκ when compared with untransformed cells (123). Some cancer cells pathologically stabilize RAD18 via a mis-expressed Cancer/Testes Antigen (CTA), termed MAGEA4 (78). Moreover, MAGEA4-expressing cancer cells become critically dependent on MAGEA4 to maintain RAD18 expression, activate TLS, and tolerate DNA damage (78). Owing to its cancer cell-specific expression and requirement for DNA damage tolerance, the MAGEA4/RAD18 signaling axis is an appealing pathway for therapeutic inhibition. Fleming et al. identified small cyclic peptide inhibitors that bind directly to MAGEA4 and inhibit RAD18 binding with nanomolar potency (246). That study validates the chemical tractability of MAGEA4/RAD18 as a cancer-specific vulnerability and justifies further work to identify more drug-like pharmacological inhibitors of pathological TLS in cancer. RAD18 is also implicated in DSB repair (247,248,249) and ICL repair (250) independently of its roles in TLS. Inhibiting MAGEA4-dependent RAD18 activities in genome maintenance might also sensitize cancer cells to diverse classes of anti-cancer agents that induce replication fork stalling, DSB, and ICL.
CONCLUSIONS AND FUTURE DIRECTIONS
In the past decades our understanding of TLS as a PRR mechanism merely involving a PCNA-ubiquitination-mediated DNA polymerase switch has expanded considerably. We are now aware of multiple ubiquitination-dependent and independent TLS activation mechanisms that may operate both within and outside of S-phase, and that are intimately integrated with other components of the DDR. Future work will likely reveal additional complexity and fine-tuning mechanisms that regulate TLS polymerases and coordinate genome maintenance with other cellular processes. Matsuoka and Elledge showed that substrates of the proximal DDR mediators ATM and ATR are involved in diverse pathways including metabolism, chromatin packing and remodeling, trafficking, transcription, and translation (251). By analogy, integration of TLS with broad regulatory pathways in a cell is likely important for genome stability and DNA damage tolerance.
For many other genome maintenance mechanisms (notably DSB signaling and NER), chromatin architecture and histone modifications are critical determinants of DNA repair activity (252,253). Pharmacological modifiers of chromatin structure impact the nuclear mobility of TLS polymerases (254), yet little else is known regarding links between chromatin and TLS. More connections between chromatin modification and TLS are likely to be discovered. In the case of DSB repair, pathway choice between error-free HR and error-prone NHEJ/MMEJ is enormously consequential for genome stability (255,256). It is likely that mechanisms of pathway choice between error-prone TLS and error-free TS (or other processes) will similarly emerge as important determinants of mutagenesis and DNA damage tolerance.
Many questions remain regarding the relationship between TLS and cancer and the extent to which TLS sustains tumorigenic phenotypes such as mutagenesis and DNA damage tolerance. Whole genome sequencing is the new standard for evaluating roles of candidate genome maintenance pathways in cancer-associated mutation patterns. Genome sequencing approaches have provided evidence that TLS polymerases contribute to some of the cosmic mutation signatures in cancer. There is a need for more studies to model the effects of TLS-deficiencies on genome-wide mutation patterns and to determine whether TLS polymerases contribute to the mutation signatures of unknown etiology. The role of TLS in mediating therapy-induced mutations that drive disease recurrence and secondary malignancies is also unclear and must be addressed. Putative TLS-mediated mutational scars of cancer cells could provide biomarkers that are predictive or prognostic of sensitivity to specific therapeutic agents and that guide clinical decisions.
There is increasing evidence that TLS allows neoplastic cells to tolerate intrinsically-arising DNA damage. Such TLS-dependency could be a useful and actionable vulnerability that presents opportunities to target cancer cells using TLS inhibitors. It is important to define the pathological contexts in which tolerance of various types of intrinsic (and therapy-induced) DNA damage is TLS-dependent. For example ssDNA gap-suppression has been proposed as an important role of TLS in cancer cells (124), but not all cancer cells are sensitive to REV1-inhibition.
It is possible that different cancer cells harbor distinct forms of intrinsic DNA damage and also have differential TLS-dependencies based upon availability of other DDR pathways. It will also be valuable to identify synthetic lethal genetic interactions between TLS and other DNA repair pathways which could be exploited for therapy.
Genetically engineered mouse models (GEMM) have been invaluable for identifying genes that sustain multistep tumorigenesis. There exist sophisticated GEMM that recapitulate the genetics, disease progression and histopathological features of many human cancers. As yet however, there have been few studies to model impact of TLS perturbations on oncogene-driven cancers.
Therefore, GEMM would also be useful for defining the cancer settings in which TLS sustains tumor cells (and in which TLS inhibition would likely provide therapeutic benefit). GEMM would also be useful to determine whether the altered expression patterns of TLS polymerases and regulators observed in cancers (e.g. over-abundance of RAD18 or reduced REV1 expression compared with normal tissues) impact disease progression and shape mutation signatures. GEMM would also be extremely useful in preclinical studies to determine therapeutic efficacies of pharmacological TLS inhibitors.
For effective cancer therapy it is important to selectively discriminate between neoplastic cells and normal healthy cells. Representative untransformed cells and cancer cell lines might have different TLS-dependencies (123,257). However, cancer is a highly heterogeneous disease. Mechanisms of DNA damage tolerance and chemoresistance often differ tremendously between different patients, even those with the same cancers and cancer subtypes. Therefore, it may prove impossible to conclude that all cancers are globally dependent on TLS. Ultimately, precision medicine approaches that target individual tumors based on their unique disease characteristics (including driver oncogenes, intrinsic stresses and repertoire of available DDR pathways) might provide the best vehicle for TLS inhibitors as cancer therapies.
METHODS
Lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), pancreatic adenocarcinoma (PAAD), glioblastoma (GBM), were selected for evaluation of relationships between TLS/TS gene status and genome instability in human tumors. TCGA datasets containing RNA expression, mutation, genomic alteration and clinical information for these tumors were from the TCGA data portal (https://portal.gdc.cancer.gov) and were all downloaded in April 2022. Specific datasets used in this study include: (i) HTSeq-FPKM-UQ gene expression quantification (duplicated samples for the same patients were removed), (ii) somatic mutations aggregated and masked by Mutect2 and organized as MAF files, publicly available, (iii) smoking information for most subjects in LUAD, LUSC and BLCA patients. To avoid zeros when using a log scale, log2(FPKM + 1) was used to display gene expression data obtained by RNA-seq.
Statistical analysis and data presentation
R (version 4.1.0) was used for data analysis and presentation. Wilcoxon Rank Sum Test (wilcox.test, with option alterative = ‘two sided’ and paired = False) was used for comparison of gene expression between tumor and adjacent normal samples as well as the comparison of SNVs. R package ComplexHeatmap (version 2.8.0) was used to generate the heatmap illustrating relative expression levels of TLS and TS pathway genes in LUAD. Z-score transformed FPKM-UQ value was used as the gene expression value in the heatmap. Stage, p53 mutation, Replication stress (RS) and smoke annotations were derived from clinical files, Mutect2 MAF files and GISTIC2 copy number files.
DATA AVAILABILIY
The raw data in this study including RNAexpression, clinical information, mutation, and genomic alterations can be found at TCGA data portal (https://portal.gdc.cancer.gov).
Supplementary Material
zcad005_Supplemental_File Click here for additional data file.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Cancer Online.
FUNDING
National Cancer Institute [R01 CA215347, R01 CA229530 to C.V., CA191448 to P.Z., J.H., in part]; National Institute of Environmental Health Sciences [R01 ES029079 to C.V.]; Duke Clinical and Translational Science Institute (CTSI) [UL1TR002553 to P.Z., J.H.]; developmental funds of the Duke Cancer Institute (to P.Z.) as part of the P30 Cancer Center Support Grant [NIH CA014236]; L.C. was supported in part by a grant from the National Institute of General Medical Sciences [5T32GM135128].
Conflict of interest statement. None declared.
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PMC009xxxxxx/PMC9912191.txt |
==== Front
Diabet Med
Diabet Med
10.1111/(ISSN)1464-5491
DME
Diabetic Medicine
0742-3071
1464-5491
John Wiley and Sons Inc. Hoboken
10.1111/dme.14859
DME14859
Letter
Letters
Diabetic ketoacidosis as a unique initial presentation of cystic fibrosis
Sugrue Michelle 1
Liddy Anne Marie 1
McDermott John H. 1
Sreenan Seamus https://orcid.org/0000-0003-2457-2612
1 [email protected]
1 Department of Diabetes and Endocrinology Royal College of Surgeons in Ireland Connolly Hospital Blanchardstown Ireland
* Correspondence
Seamus Sreenan, Department of Diabetes and Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital, Blanchardstown, Dublin 15, Ireland.
Email: [email protected]
05 5 2022
11 2022
39 11 10.1111/dme.v39.11 e1485923 12 2021
22 4 2022
© 2022 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
cystic fibrosis diagnosis
cystic fibrosis related diabetes
diabetes diagnosis
diabetic ketoacidosis
source-schema-version-number2.0
cover-dateNovember 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
==== Body
pmc1 BACKGROUND
Cystic fibrosis–related diabetes (CFRD) is an increasingly common complication of CF as life expectancy of people with the condition increases. The pathophysiology differs from type 1 and type 2 diabetes. In CFRD, there is a reduced and delayed insulin response to carbohydrates while basal insulin is often spared. Many people with CF have abnormally high glucose levels at intermediate time points of an oral glucose tolerance test despite meeting conventional criteria for normal glucose tolerance. This pattern of transient postprandial hyperglycaemia also means that a normal HbA1c in persons with CF does not reliably exclude CFRD. 1 Aggressive screening and early institution of insulin has been reported to be associated with reduced mortality, but there are differing opinions on whether good glycaemic control does modify the course of CF. For example, the rationale for treatment with insulin in CFRD may include reducing the impact on lung function and infection risk. 1 , 2 However, it is important to differentiate CFRD from other forms of diabetes as the insulin regime and treatment rationale are different.
2 CASE PRESENTATION
A 45‐year‐old man presented to the emergency department with a two‐to‐three‐week history of feeling unwell with nausea and vomiting. He also described unintentional weight loss of 15 kg over the preceding 3 months. He did not complain of polydipsia or polyuria.
He reported a history of depression and anxiety but no known history of diabetes or acute pancreatitis. His father had two brothers who died of pulmonary tuberculosis in childhood. There was no known family history of diabetes or hemochromatosis. The participant is a pharmacist, a non‐smoker and stopped drinking alcohol 5 years previously.
Blood tests on admission are presented in Table 1.
TABLE 1 Laboratory values on admission
Test At presentation Reference range
Arterial pH 6.89 (7.32–7.43)
Glucose 38 mmol/l (3.9–6.1)
PC02 4.7 mmol/l (4.6–6.4)
Lactate 7.0 mmol/l
HCO3‐ 2.8 mmol/l (22–26)
Ketones 6.5 mmol/l
HemoglobinA1c (HbA1c) 13.4% (123 mmol/mol) (4.3–6) (23–42)
C‐reactive protein 267 mg/l (0–5)
Amylase 14 U/l (28–100)
Sodium 131 mmol/l (136–145)
Potassium 4.9 mmol/l (3.5–5.1)
Urea 11.9 mmol/l (2.8–8.1)
Creatinine 127 µmol/l (59–104)
Phosphate 1.52 mmol/l (0.81–1.45)
John Wiley & Sons, Ltd
After initial fluid resuscitation lactate was 1.3; acidosis persisted (pH 7.15) and ketones remained elevated. A chest X‐ray demonstrated a left upper zone patchy opacification, indicative of an infective process, a normal right lung and normal cardiac shadow.
Computed tomography of the thorax, abdomen and pelvis (CT TAP) suggested a left upper lobe pneumonia. Calcification was noted in the pancreas indicative of chronic calcific pancreatitis of uncertain aetiology. The pancreas appeared atrophic with no acute changes identified (Figure 1).
FIGURE 1 CT TAP demonstrating atrophic, extensively calcified pancreas (black arrow)
2.1 Diagnosis
The initial diagnosis was ketoacidosis precipitated by a lower respiratory tract infection in a participant without known diabetes. Based on the clinical presentation and history, it was thought that the participant most likely had autoimmune type 1 diabetes.
2.2 Treatment
The participant was educated on diabetes and insulin administration and discharged on a basal bolus insulin regimen.
2.3 Outcome and follow‐up
At out‐patient follow‐up the participant's Hba1c was 6% (42 mmol/mol), anti‐GAD antibodies and islet cell antibodies were undetectable, insulin was 1.2 µU/ml (2.6–24.9), and C‐peptide levels were 0.38 ng/ml (1.1–4.4), with a glucose of 9.2 mmol/l. He reported frequent hypoglycaemic episodes on insulin glargine 11 units once daily and insulin aspart 2 units three times daily with meals. Even when taking 1–2 units of insulin aspart with a large carbohydrate meal, he was apt to have hypoglycaemia postprandially. Anti‐GAD antibody was repeated and was again undetectable.
It was felt that he may have entered a honeymoon period of type 1 diabetes. However, the participant revealed that his brother had recently been diagnosed with CF in his thirties when he and his wife were being investigated for primary infertility. The participant asked as to whether he too could have CF. He reported that throughout his life he experienced profuse sweating in hot environments and had experienced several near‐syncopal episodes during exercise. He had undergone extensive investigation for these episodes with no cause found. He reported no pulmonary problems prior to his admission with ketoacidosis. He had never attempted to father a child.
Considering this new information in the setting of GAD antibody negative diabetes, a sweat test was performed. The sweat sodium level was 104 mmol/l (<60), supporting a diagnosis of CF. Genetic testing for CF was performed, and this demonstrated that the participant was heterozygous for pPhe508del and pARG117His variants with 5T variant in intron 8 (formally 5T/9T), confirming a diagnosis of CF. The participant reported significant diarrhoea and a faecal elastase was measured at <15 µg/g stool (<100 consistent with severe pancreatic exocrine insufficiency). Commencement of pancreatic enzyme replacement ameliorated his bowel symptoms. A repeat chest X‐ray demonstrated almost complete clearing of the prior infection. Pulmonary function testing demonstrated a mildly obstructive pattern without significant reversibility, mildly increased residual volume and mildly reduced diffusion capacity.
During follow‐up, the participant temporarily discontinued insulin due to tight control with frequent hypoglycaemia. As he moved residence, he has not been seen recently in our clinic but reports being well controlled on basal‐bolus insulin.
3 DISCUSSION
CF is an autosomal recessive disorder caused by a mutation of the CF transmembrane conductance regulator (CFTR) gene. CF is the most common autosomal recessive condition in white populations with an incidence of 1 in 2500 live births. CF is caused by the presence on both alleles of at least one of 2000 known mutations in the CFTR gene. It is characterized by chronic pulmonary infections, pancreatic insufficiency, biliary cirrhosis, low BMI, male infertility and increasingly CFRD. In people with CF, the incidence of CFRD increases with increasing age with up to 50% of adults with CF developing CFRD by adulthood. It is characterized by insulin deficiency and periods of reduced insulin sensitivity. Postprandial hyperglycaemia with normal fasting glucose levels is the most frequently seen abnormality. CFRD has been reported to be associated with accelerated pulmonary function decline, poor nutritional status and excess mortality. The deleterious clinical outcomes associated with CFRD can start prior to the usual thresholds for diabetes being reached. Some guidelines, therefore, now recommend using continuous glucose monitoring to help identify dysglycaemia in patients with CF. 1
The severity of disease in an individual with CF is determined by the least severe CF mutation they carry. In most white populations, F508del is the most common mutation, and patients homozygous for this mutation have severe disease. Our participant had one F508del, a 3 nucleotide deletion that removes the phenylalanine residue at position 508 of CFTR resulting in misfolding of the CFTR protein. The second mutation in our participant, R117H, is a missense mutation that can result in a wide variety of clinical features including pancreatic insufficiency, congenital bilateral absence of the vas deferens (CBAVD) and chronic pulmonary disease, but interestingly some patients have normal lung function and normal pancreatic function. The 5T variant in our participant is usually associated with a high level of non‐functional CFTR, and it is perhaps surprising that he did not have more severe multi‐organ involvement. 3 In recent years, CF screening programs have been developed across Europe; however this does not preclude the diagnosis of milder forms of CF in adulthood or the development of CFRD in cases of unknown CF diagnoses as screening programs can only test for a small subset of the approximately 2000 mutations associated with CF. In addition, there has been debate regarding whether R117H mutations should be included in new‐born screening programs, so cases such as the one described here may not consistently be diagnosed at screening. 4
Ketoacidosis is thought to be rare in people with CFRD, although ketoacidosis has previously been reported as the initial presentation of CFRD in a patient with known CF. 5 Our patient had a low C‐peptide, in keeping with insulin deficiency, but his antibodies were negative suggesting that his diabetes was not autoimmune in origin. Intercurrent infection has been reported to be a cause of ketoacidosis in CFRD, and we believe that the lower respiratory tract infection, which the participant experienced, was the precipitant for his ketoacidosis. To our knowledge, this is the first case of a new diagnosis of CF following a first presentation of diabetes with ketoacidosis.
CONFLICT OF INTEREST
None.
ETHICAL APPROVAL
This study was not related to either human or animal experimentation.
INFORMED CONSENT
Informed consent was obtained from the participant included in the case report.
ACKNOWLEDGEMENTS
Many thanks to our participant for his contribution in the preparation of this manuscript. Open access funding provided by IReL. [Correction added on 5 July 2022, after first online publication: IReL funding statement has been added.].
==== Refs
REFERENCES
1 Kayani K , Mohammed R , Mohiaddin H . Cystic fibrosis‐related diabetes. Front Endocrinol. 2018;9 :20. 10.3389/fendo.2018.00020
2 Adler AI . To treat or not to treat: dysglycaemia in cystic fibrosis. Diabet Med. 2019;36 (11 ):1365‐1366. 10.1111/dme.14112 31429490
3 Kiesewetter S , Macek M , Davis C , et al. A mutation in CFTR produces different phenotypes depending on chromosomal background. Nat Genet. 1993;5 (3 ):274‐278. 10.1038/ng1193-274 7506096
4 Scotet V , Audrézet MP , Roussey M , et al. Immunoreactive trypsin/DNA newborn screening for cystic fibrosis: should the R117H variant be included in CFTR mutation panels? Pediatrics. 2006;118 (5 ):e1523‐e1529. 10.1542/peds.2005-3161 17015492
5 Eenkhoorn V , Van den Driessche A , Van Gaal L , Desager K , De Block C . Diabetic keto‐acidosis as a presentation of cystic fibrosis‐related diabetes: a case report. J Diabet Complicat. 2011;25 (2 ):137‐141. 10.1016/j.jdiacomp.2010.05.003
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PMC009xxxxxx/PMC9912196.txt |
==== Front
Biotechnol Appl Biochem
Biotechnol Appl Biochem
10.1002/(ISSN)1470-8744
BAB
Biotechnology and Applied Biochemistry
0885-4513
1470-8744
John Wiley and Sons Inc. Hoboken
34487563
10.1002/bab.2249
BAB2249
Original Article
Original Articles
Selymatra: A web application for protein‐profiling analysis of mass spectra
Nardone Davide https://orcid.org/0000-0003-0486-1791
1
Ciaramella Angelo https://orcid.org/0000-0001-5592-7995
1 [email protected]
Cerreta Mariangela 2
Pulcrano Salvatore https://orcid.org/0000-0002-5324-8689
3
Bellenchi Gian C. https://orcid.org/0000-0002-9302-0021
3
Leone Linda https://orcid.org/0000-0001-7293-1814
[email protected]
4
Manco Giuseppe https://orcid.org/0000-0001-6191-3952
[email protected]
2 §
Febbraio Ferdinando https://orcid.org/0000-0002-3653-9847
2 § [email protected]
1 Department of Science and Technology University of Naples Parthenope Naples Italy
2 Institute of Biochemistry and Cellular Biology National Research Council Naples 80131 Italy
3 Institute of Genetic and Biophysics “A. Buzzati Traverso” National Research Council Naples Italy
4 Department of Chemical Sciences University of Naples “Federico II” Naples 80126 Italy
* Correspondence
Ferdinando Febbraio, Institute of Biochemistry and Cellular Biology, National Research Council, Naples 80131, Italy.
Email: [email protected];
Angelo Ciaramella, Department of Science and Technology, University of Naples Parthenope, Naples, Italy.
Email: [email protected]
§ these authors contribute equally to the work (co‐last)
20 9 2021
10 2022
69 5 10.1002/bab.v69.5 18211829
06 7 2021
23 8 2021
© 2021 The Authors. Biotechnology and Applied Biochemistry published by Wiley Periodicals LLC on behalf of International Union of Biochemistry and Molecular Biology.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Abstract
Surface enhanced laser desorption/ionization‐time of flight (SELDI‐TOF) mass spectrometry is a variant of the matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) mass spectrometry. It is used in many cases especially for the analysis of protein profiling and for preliminary screening of biomarkers in complex samples. Unfortunately, these analyses are time consuming and protein identification is generally strictly limited. SELDI‐TOF analysis of mass spectra (SELYMATRA) is a web application (WA) developed to reduce these limitations by (i) automating the identification processes and (ii) introducing the possibility to predict proteins in complex mixtures from cells and tissues. The WA architectural pattern is the model‐view‐controller, commonly used in software development. The WA compares the mass value between two mass spectra (sample vs. control) to extract differences, and, according to the set parameters, it queries a local database to predict most likely proteins based on their masses and different expression amplification. The WA was validated in a cellular model overexpressing a tagged NURR1 receptor, being able to recognize the tagged protein in the profiling of transformed cells. A help page, including a description of parameters for WA use, is available on the website.
SELYMATRA is a user‐friendly web application for the analysis of SELDI‐TOF MS data, enabling fast and automated protein identification even in complex samples.
MALDI‐TOF
protein prediction
protein profiling
SELDI‐TOF
SELYMATRA
source-schema-version-number2.0
cover-dateOctober 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
Nardone D , Ciaramella A , Cerreta M , Pulcrano S , Bellenchi GC , Leone L , Manco G , Febbraio F . Selymatra: A web application for protein‐profiling analysis of mass spectra. Biotechnology and Applied Biochemistry. 2022;69 :1821–1829. 10.1002/bab.2249 34487563
==== Body
pmcAbbreviations
Avg average
MALDI matrix‐assisted laser desorption/ionization
MS mass spectrometry
MSMS tandem mass spectrometry
MVC model‐view‐controller
MW molecular weight
NURR1 nuclear receptor related 1
pI isoelectric point
Q quadrupole
SELDI Surface enhanced laser desorption/ionization
TET tetracycline
TOF time of flight
WA web application.
1 INTRODUCTION
Comparisons of the protein profiling incellular systems could give important information about the changes in the protein expression after cell alterations such as under oxidative stress or in cancer, 1 , 2 , 3 , 4 in order to better understand the cell answer to these events, and for the discovery of disease signals to be used as diagnostic markers or targets for drugs. 5 , 6 , 7 In the last decades, several mass spectrometry (MS) instruments and methodologies have been developed in order to improve the results of protein profiling. Surface enhanced laser desorption/ionization‐time of flight (SELDI‐TOF) mass spectrometry is a variant of the matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) mass spectrometry, especially used for the high‐throughput analysis of protein profiles in cell samples 8 , 9 , 10 as well as for the characterization of protein‐based nanodevices. 11 , 12 This technique exploits active matrices to select/sort proteins on the basis of their chemical–physical characteristics. 13 Despite the high potential of this technique, the lack of protein identification and the difficulty to analyze complex spectra reduce the fields of SELDI‐TOF application. 14 In particular, a large amount of data must be accumulated in order to identify putative biomarkers, and often pretreatment steps of samples become necessary to enhance differences and reduce the background noise in the MS spectra. In addition, several other techniques are later necessary for the identification of biomarkers and protein–protein interactions, such as sequencing by tandem quadrupole‐time of flight mass spectrometry (MSMS‐QTOF), immunoprecipitation, and Western blotting. 9 , 15 , 16 , 17 , 18 These limits made it necessary to develop new strategies in order to improve these methodologies.
The SELDI‐TOF MS analysis process can be summarized in the following phases: (i) spectra acquisition; (ii) data preprocessing (baseline subtraction, normalization); (iii) clusterization; (iv) statistical analysis. Bioinformatic tools are crucial in the last phase, for the comparison of protein peak patterns obtained from different samples. One of the most common algorithms used for this purpose is the classification and regression tree. 19 Decision trees are easy to use and interpret, but their application is not suited to complex sets of data, where multidimensional relationships between SELDI peaks are expected. Alternatively, to methods based on decision trees, different software have been employed for biostatistical cluster analysis, based either on the classic linear regression algorithm, such as the ProPeak Software package 20 or the logistic algorithm. 21 Typically, these methods require a deep knowledge of mathematics and statistics to be efficiently used, limiting their accessibility to more experienced users.
In this work, we present a new web application (WA), named SELYMATRA, for the SELDI‐TOF analysis of mass spectra (http://140.164.61.25:5055/SELYMATRA). The WA was developed with the aim of significantly reducing the analysis time on large amounts of MS data, and at the same time to obtain information on proteomic profiles so far not obtainable. This WA is able to predict clusters of proteins starting from the comparison among proteome profiles obtained by SELDI‐TOF MS measurements. The architectural pattern used to develop the WA is the model‐view‐controller (MVC), extremely used in the development of software systems (Figure 1). The WA expects a user to upload a Microsoft Excel spreadsheet file format, which is the only format whereby the application works, usually generated by means of proprietary MS software, such as SELDI‐TOF software. The WA was validated using immortalized neuronal cells overexpressing a tagged NURR1 receptor. Using our WA, we were able to recognize the tagged protein in the profiling of transformed cells with respect to the profiling of neuronal cells not expressing it.
FIGURE 1 Scheme of model‐view‐controller used for WA design. The MVC model describes how the application represents the data, including the database management (model), how the information is displayed at the end‐user (view), and how the interface is managed and controlled by the user (controller)
2 RESULTS AND DISCUSSION
The type of data treated by the application is strictly related to the SELDI‐TOF or MALDI‐TOF MS technology and their own software. Samples from different biological sources can be used, such as plasma, serum, blood, etc. The WA aims to carry out a particular pattern recognition analysis, presently not included in any proprietary SELDI software package. This is accomplished by looking for specific similarities between two mass spectra under different washing and binding conditions, and in doing that, it yields to a deeper data analysis, which significantly reduces the duration of troublesome proteomic experiments (Figure 2).
FIGURE 2 Work‐flow of experimental data production, preprocessing, and data elaboration. The process was described by three phases: an experimental phase for the production of MS spectra; followed by a preprocessing of data for the generation of peak clusters; and a final phase involving the WA, which compares the MS spectra clusters and performs the protein database searching and prediction
Once a file is uploaded, the WA applies a method of data analysis and processing, as follows: Mass spectra comparison process;
Recognition process;
Protein ID matching process.
Through these three actions, the WA elaborates the data to perform a key feature extraction among two mass spectra and give differences as well as up‐ and downregulation in protein expressions, respectively. According to these results and to the characteristics of the experiment, it queries a local database for the prediction of the proteins that are most likely related to the previous results (Figure 2).
2.1 Mass spectra comparison and recognition processes
The mass spectra comparison and recognition processes are fully tunable by the choice and values of some parameters. Based on the user parameter selection, different results may be obtained. Basically, each procedure is separately developed, but the underlying idea is quite similar. That is to say, a math inequality is used for both modeling procedure, by which a result set of values is generated: (1) Am/zavgAm/zavg−σm/z∗δ<Bm/zBm/z<Am/zavgAmzavg+σm/z∗δ,
where Am/z avg and σ m∕z are the mean value and the standard deviation of the sample (A), respectively, δ is a correction factor used for increasing or diminishing the search range, and Bm/z is the current m/z value to find in the control sample (B).
FIGURE 3 WA option panel. This panel contains all the parameters used in SELYMATRA to recognize and predict proteins. Almost all parameters are editable by users (for their complete description, see Supplementary Table 2)
According to the user needs, four types of m/z can be identified for any experiment (Figure 3 point 1): Found: A set of m/z avg satisfying Equation (1);
Missing: A set of m/z avg not satisfying Equation (1) or rather the complement ensemble to the found (A) masses against all the (B) masses, formally expressed as
(2) A=B−A=m/zavg∈B∧m/zavg∉A.
Upgrade or downgrade: A set of m/z avg satisfying Equation (1) and the median intensity of mass variation constraint, expressed as (3) Bm/zBm/z−Am/zavgAm/zavgAm/zavgAm/zavg∗100≤λ,
where Bm/z avg and Am/z avg are the m/z avg of the control sample (B) and the running sample (A), respectively (which both satisfy Equation (1)) and λ is an index of variation of the intensity median for distinguishing the affinity among the relative intensities of two mass peaks.
2.2 Protein ID matching process
The set of the identified masses alone, as obtained by the mass spectra comparison and recognition process, is not sufficient to predict the protein identities. Therefore, we have built up a process to infer more information on such masses, which helps in predicting the most likely protein corresponding to a specific m/z value. In order to make this possible, two further features were considered for each mass of the result set: a dynamic amplification factor (depending on the m/z value), used for the assessment of a molecular mass (MW) error‐range (Figure 3 point 2);
a type of chip and binding/washing condition, for the assessment of an isoelectric point (pI) range (Figure 3 point 3).
Both these features help in defining a confidence interval that limits the protein matching process. ID matching process is possible through the building, maintaining, and tuning of a database of proteins, whose data have been retrieved through java API provided by Uniprot database. 22
A score parameter (Figure 3 point 4) was added in order to set the threshold for the range of confidence used for the identification/prediction of proteins. Lower values correspond to a strict rule for the correspondence of masses to assigned proteins. All these parameters are editable by the user in order to allow the software to adapt to his/her own experiments. The analysis button starts the processing of data resulting in an output listing the predicted proteins. In addition, two other buttons, activating a graphical report of the predicted proteins ordered according to their functions or their biological role, were implemented (Figure 3 point 5). This tool produces a histogram representation of the data, allowing for a fast comparison between different measurements of the same sample (Figure 4).
FIGURE 4 Graphical reports of predicted proteins. The predicted proteins can be grouped on the basis of their molecular function and/or their involvement in the biological processes
2.3 Validation of SELYMATRA
The WA should be able to recognize rough patterns of proteins related to the phenomena represented in the target sample. An example was the identification, in the nuclei of the A1 cell line, of the transcription factor 3 × FLAG‐Nurr1, 23 overexpressed in the expression cassette TET‐ON. We analyzed two different sample conditions for each sample (overexpressing or not the transcription factor 3 × FLAG‐Nurr1). The comparison of clustered files generated by the ProteinChip Data Manager software was only able to indicate a single MS peak in the range of masses (∼71 to 72 KDa) of the transcription factor 3 × FLAG‐Nurr1 (Figure 5).
FIGURE 5 SELYMATRA WA validation using the transcription factor 3 × FLAG‐Nurr1. Representation of cluster of peaks from the preprocessing analysis of A1 cell line expressing the transcription factor 3 × FLAG‐Nurr1 (in gray), compared to a standard A1 cell line (in black). In the insert table, the assignment of the single MS peak at about 72 kD to the 3 × FLAG‐Nurr1 by the SELYMATRA WA is reported
As expected, SELYMATRA associated the single MS peak at about 72 KDa to the 3 × FLAG‐Nurr1 ID, which was present in the local database (Figure 5), demonstrating good confidence in the data analysis.
3 CONCLUSION
SELYMATRA web application provides the scientific community with a new SELDI‐TOF analysis toolset that is able to deliver further insight into complex clustered mass data. Particularly, through ad hoc modeling procedures, it is possible to obtain key features identifying potential outliers or features discerning the similarity between two mass spectra. Based on this result, it has also been possible to predict sets of proteins, simply interfacing the WA to a proteome database. As a consequence, SELYMATRA significantly speeds up the data analysis and the processing of the experimental results of protein expression in the differentiation of dopaminergic cell line A1 driven by 3 × FLAG‐Nurr1 overexpression. In conclusion, this WA looks like a worthy tool of interest for these applications in the biological field.
4 MATERIALS AND METHODS
4.1 Cell culture and NURR1 gene overexpression
The dopaminergic cell line A1 24 was infected with a couple of lentiviruses overexpressing the cDNA for the transcription factor NURR1 (LV‐NURR1) under the control of an inducible (Tet‐On) promoter and the rtTA transactivator (rtTA‐LV). This procedure allows controlling the expression of Nurr1 by the addition of doxycycline (Supplementary Figure 1). Cells expressing NURR1 have been plated in MEMF12 10% FBS and infected the following day with LV‐NURR1 and rtTA‐LV or with rtTA‐LV alone, as a control, and growth in presence of doxycycline for 48 h.
4.2 Protein extraction
After 48 h of induction (approximately 72 h of culture), the cells have been collected in ice‐cold phosphate‐buffered saline in the presence of protease inhibitors, to avoid further protein modification, like dephosphorylation, proteolysis, and so on. The cells used as a control were grown in the absence of doxycycline in the medium. The cells were lysed according to the Andrews et al. protocol in order to separate the cytoplasm and the nuclear fractions for the analysis to obtain separately the cytoplasm and the nuclear extract. 25 The protein concentration of nuclear and cytoplasmic samples was determined by the Bradford method, 26 using bovine serum albumin as standard.
4.3 ProteinChip preparation
The ProteinChip arrays were purchased from Bio‐Rad (BioRad Laboratories, Hercules, CA, USA). We performed our analysis on the anion exchange (Q10) array with 100 mM Tris–HCl buffer pH 9.0 (low stringency condition) and with 100 mM Tris–HCl buffer pH 7.0 (high stringency condition); and on the cation exchange (CM10) array with 100 mM sodium acetate buffer pH 4 (low stringency condition) and Hepes 50 mM pH 7 (high stringency condition). Samples were loaded in quadruplicate onto preequilibrated spot surfaces of arrays; each sample corresponds to 5 μg of total proteins. After 30 min incubation at room temperature, the unbound proteins were washed out in two steps with the corresponding low and high stringency condition buffer, and a final washing step using HPLC‐grade water has been applied once to remove salts. Finally, 1 μL of saturated sinapinic acid (BioRad Laboratories) in 50% acetonitrile (Aldrich, Sigma‐Aldrich) and 0.5% trifluoroacetic acid (Fluka, Sigma‐Aldrich) was added twice to each spot on the chip and allowed to dry.
4.4 ProteinChip acquisition
The ProteinChips were analyzed using a linear TOF mass spectrometer SELDI‐TOF (BioRad Laboratories, USA), with the following protocols. Protocol A: laser power 3000 nJ, matrix attenuation 2700, focus mass 12,500, sample rate 600 and 50% spot surface fired for ion profiling between the mass/charge (m/z) range of 0–25,000. Protocol B: laser power 5000 nJ, matrix attenuation 2500, focus mass 30,000, sample rate 600 and 50% spot surface fired for ion profiling between the mass/charge (m/z) range of 25,000–50,000. We have used the ProteinChip Data Manager 3.5 software (BioRad Laboratories), and the spectra were externally calibrated with ProteinChip all‐in‐one peptide standard (BioRad Laboratories). We have processed the spectra through baseline subtraction and smoothing. The mass has been aligned and normalized using the total ion current within the m/z range of 3–30 kDa for the low mass proteins and 25–50 kDa for the high mass proteins.
4.5 Data preprocessing
The software ProteinChip Data Manager identifies the peaks in the spectra and groups them into clusters. The comparison between the clusters of peaks highlights, by a scatter plot, the differences in intensity between them. Furthermore, it is able to graphically depict any differences only between the cluster of peaks present in the two samples but is not able to classify the cluster of peaks with different intensity values and to detect the presence/absence of peaks between two samples. So cluster data were exported in data‐sheet format for importing in WA.
4.6 WA principles
SELYMATRA has been developed with the purpose of helping researchers in the study of protein profiling in complex mixtures from cells and tissues. Starting from the comparison among two protein profiles, the SELYMATRA WA detects the presence/absence of peaks between the two samples and assigns the m/z values obtained from MS spectra to the most probable corresponding protein. The protein prediction was carried out using the whole integrated database of protein sequences from the mouse. In this way, the WA makes it possible to easily study the changes in protein expression in altered cell and tissue samples. Moreover, it expects that the file given as input is not just a single mass spectrum but that it is generated as an elaborated mass spectrum (got from two or more mass spectra) obtained by a clustering analysis which may be carried out through the ProteinChip Data Manager (BioRad Laboratories) as well or by other alternative MS software. Of course, we strongly suggest not skipping the clustering analysis step because it is done with the purpose to minimize the bias introduced by the experiment steps. If so, for each given sample (more than one mass spectra) it will generate a data sheet representing a spectrum cluster. Regardless of how the clustered file is generated, it must contain some specific fields, such as the type of chip and conditions used for the sample (Supplementary Table 1). It is important to point out that the real numeric fields taken into account by the software are essentially three: m/z average, m/z STD, and intensity median of the spectral clustering. Therefore, theoretically, the application could work also with the lack of the other fields, but, practically, for now, the file format to give in input to the application has to be structured with all fields listed in Supplementary Table 1.
4.7 WA Implementation
The SELYMATRA web application takes the m/z values obtained by SELDI or MALDI‐TOF from two separate experiments as input and returns both the identified (found or missing) m/z value and numeric scores and the list of predicted corresponding proteins (Supplementary Figure 2). The application has been realized to work with the Microsoft Excel spreadsheet file format such as xls, xlsx, etc. We used the MVC design, in which the communications between the client and the database are mediated by the web application server (Figure 1). The server is implemented using a combination of hypertext markup language pages (HTML), and Java Server Pages (JSP) as a rich component‐based user interface. The mySQL software is used for the relational database. Graphical output of predicted proteins based on their function and a biological pathway were also given. The Apache Tomcat, an open‐source implementation of the Java Servlet and JavaServer Pages, is used for reporting the results in graphical output.
CONFLICTS OF INTEREST
The authors declare no competing interests in this article.
AUTHORS’ CONTRIBUTION
DN carried out the WA development and programming, produced the pictures and helped to draft the manuscript. AC participated in the design of the study, in the WA development and helped to draft the manuscript. MC carried out the MS measurements and the sample analysis. SP carried out the cell grow, NURR1 overexpression and protein extraction. GCB supervised the cell biology experiments and NURR1 overexpression. LL participated in the design of the study and helped to draft the manuscript. GM participated in the design of the study and helped to draft the manuscript. FF conceived of the study, and participated in its design and coordination and wrote the manuscript. All authors read and approved the final manuscript
Supporting information
Supplementary Figure 1 Expression cassette of the TET‐ON virus.
Supplementary Figure 2 Example of the output from the WA describing the predicted proteins.
Supplementary Table 1 Fields to be filled out in the WA input file and their description.
Supplementary Table 2 SELYMATRA option parameters and their description.
Click here for additional data file.
5 ACKNOWLEDGMENTS
The research leading to these results has received funding from MIUR‐FIRB: Medical Research in Italy (MERIT). Grant no. 2: ref. 0017153, granted to GM. We thank Mr. Fabio Concilio for the informatics support, and Ms. Liliana Parlato for her technical assistance.
Open Access Funding provided by Consiglio Nazionale delle Ricerche within the CRUI‐CARE Agreement.
[Correction added on May 16, after first online publication: CRUI‐CARE funding statement has been added.]
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PMC009xxxxxx/PMC9912200.txt |
==== Front
New Phytol
New Phytol
10.1111/(ISSN)1469-8137
NPH
The New Phytologist
0028-646X
1469-8137
John Wiley and Sons Inc. Hoboken
36028985
10.1111/nph.18443
NPH18443
2022-40615
Full Paper
Research
Full Papers
Comparative transcriptomics and metabolomics reveal specialized metabolite drought stress responses in switchgrass (Panicum virgatum)
Tiedge Kira https://orcid.org/0000-0002-7239-6767
1 2
Li Xingxing https://orcid.org/0000-0002-7725-0329
3 4
Merrill Amy T. https://orcid.org/0000-0003-4801-1721
5
Davisson Danielle https://orcid.org/0000-0001-5570-0301
1
Chen Yuxuan 1
Yu Ping https://orcid.org/0000-0001-7610-6512
6
Tantillo Dean J. https://orcid.org/0000-0002-2992-8844
5
Last Robert L. https://orcid.org/0000-0001-6974-9587
3 4 7
Zerbe Philipp https://orcid.org/0000-0001-5163-9523
1 [email protected]
1 Department of Plant Biology University of California, Davis Davis CA 95616 USA
2 Groningen Institute for Evolutionary Life Sciences University of Groningen 9747 AG Groningen the Netherlands
3 Department of Biochemistry and Molecular Biology Michigan State University East Lansing MI 48824 USA
4 DOE Great Lakes Bioenergy Research Center Michigan State University East Lansing MI 48824 USA
5 Department of Chemistry University of California, Davis Davis CA 95616 USA
6 NMR Facility University of California, Davis Davis CA 95616 USA
7 Department Plant Biology Michigan State University East Lansing MI 48824 USA
* Author for correspondence:
Philipp Zerbe
Email: [email protected]
17 9 2022
11 2022
236 4 10.1111/nph.v236.4 13931408
01 7 2022
09 8 2022
© 2022 The Authors. New Phytologist © 2022 New Phytologist Foundation
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Summary
Switchgrass (Panicum virgatum) is a bioenergy model crop valued for its energy efficiency and drought tolerance. The related monocot species rice (Oryza sativa) and maize (Zea mays) deploy species‐specific, specialized metabolites as core stress defenses. By contrast, specialized chemical defenses in switchgrass are largely unknown.
To investigate specialized metabolic drought responses in switchgrass, we integrated tissue‐specific transcriptome and metabolite analyses of the genotypes Alamo and Cave‐in‐Rock that feature different drought tolerance.
The more drought‐susceptible Cave‐in‐Rock featured an earlier onset of transcriptomic changes and significantly more differentially expressed genes in response to drought compared to Alamo. Specialized pathways showed moderate differential expression compared to pronounced transcriptomic alterations in carbohydrate and amino acid metabolism. However, diterpenoid‐biosynthetic genes showed drought‐inducible expression in Alamo roots, contrasting largely unaltered triterpenoid and phenylpropanoid pathways. Metabolomic analyses identified common and genotype‐specific flavonoids and terpenoids. Consistent with transcriptomic alterations, several root diterpenoids showed significant drought‐induced accumulation, whereas triterpenoid abundance remained predominantly unchanged. Structural analysis verified select drought‐responsive diterpenoids as oxygenated furanoditerpenoids.
Drought‐dependent transcriptome and metabolite profiles provide the foundation to understand the molecular mechanisms underlying switchgrass drought responses. Accumulation of specialized root diterpenoids and corresponding pathway transcripts supports a role in drought stress tolerance.
bioenergy crops
diterpenoids
drought stress
metabolomics
natural products
Panicum virgatum (switchgrass)
plant specialized metabolism
transcriptomics
Deutsche Forschungsgemeinschaft 10.13039/501100001659 TI 1075/1‐1 U.S. Department of Energy 10.13039/100000015 DE‐AC02‐05CH11231 DE‐SC0018409 DE‐SC0019178 source-schema-version-number2.0
cover-dateNovember 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
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pmcIntroduction
Water scarcity exacerbated by climate change threatens biofuel and food crop production across the world (Challinor et al., 2014; W. Kim et al., 2019; Pokhrel et al., 2021). In the United States, about one‐third of all counties are currently designated as crop loss disaster areas through drought by the US Department of Agriculture (USDA Farm Service Agency, 2021). Crop production is further impacted by climate‐associated increases in pest and pathogen damage (Newbery et al., 2016), calling for new solutions to develop crops that can withstand current and future climate conditions.
The perennial grass switchgrass (Panicum virgatum) is a characteristic species of North American tallgrass prairie land and of agroeconomic value as a C4 lignocellulosic feedstock (McLaughlin et al., 1999). A high net‐energy yield and environmental resilience make switchgrass economically viable for biofuel production on marginal lands with reduced agricultural inputs. Two major switchgrass ecotypes, northern upland and southern lowland, differ in climatic and geographical adaptation, morphological characteristics and genetic architecture (Lowry et al., 2014; Ayyappan et al., 2017). Upland ecotypes are mostly octoploid (2n = 8x = 72), whereas lowland ecotypes are predominantly tetraploid (2n = 4x = 36) and feature taller phenotypes with thicker stems and a later flowering time (Casler et al., 2011). The recent development of genome resources for the allotetraploid lowland ecotype Alamo (c. 1.23 Gb, NCBI:txid38727) (Lovell et al., 2021) now provides the foundation needed to investigate genetic and biochemical mechanisms underlying switchgrass environmental resilience. Indeed, genomic analysis of 732 switchgrass genotypes across 1800 km latitude range revealed an extensive correlation of genomic architecture to climatic adaptation (Lovell et al., 2021). Comparative morphological and physiological analysis of 49 upland and lowland ecotypes showed significant differences in the drought tolerance of different switchgrass ecotypes (Liu et al., 2015). Large‐scale transcriptomic changes were also observed, including a drought‐induced downregulation of photosynthetic genes, consistent with physiological responses such as reduced leaf water potential, reduced chlorophyll, and other photosynthetic metabolites (Meyer et al., 2014; Liu et al., 2015; Lovell et al., 2016). Comparative analysis of rhizosheath metabolites further showed an increase in amino acids, carbohydrates and organic acids in response to drought (Liu et al., 2019).
In contrast, knowledge of the contribution of specialized metabolites to switchgrass stress response mechanisms has remained largely unexplored. For example, drought‐induced alterations in terpenoid and phenylpropanoid metabolism have been reported (Meyer et al., 2014). In addition, a recent study demonstrated ecotype‐specific metabolomes, comprising distinct compositions of specialized steroidal saponins, sesquiterpenoids, diterpenoids, and flavonoids (Li et al., 2022). Our prior work revealed an expansive network of terpenoid‐metabolic terpene synthase (TPS) and cytochrome P450 monooxygenase (P450) genes in P. virgatum var. Alamo, and combined metabolite and transcript profiling illustrated the formation of species‐specific diterpenoids and the corresponding biosynthetic genes in switchgrass leaves and roots exposed to ultraviolet (UV) radiation and oxidative stress (Pelot et al., 2018; Muchlinski et al., 2019; Tiedge et al., 2020). Furthermore, emission of volatile monoterpenoids and sesquiterpenoids was observed from switchgrass leaves and roots upon herbivore stress and treatment with defense‐related plant hormones (Muchlinski et al., 2019). These collective insights support the importance of terpenoids and other specialized metabolite classes for switchgrass abiotic and biotic stress tolerance. Recent maize (Zea mays) and rice (Oryza sativa) studies showing induced diterpenoid formation under UV, oxidative and drought stress and decreased abiotic stress tolerance in diterpenoid‐deficient maize mutants support a broader role of terpenoids in abiotic stress adaptation in monocot crops (Park et al., 2013; Schmelz et al., 2014; Horie et al., 2015; Vaughan et al., 2015; Ding et al., 2021).
A deeper understanding of the biosynthesis, diversity, and relevance of specialized metabolites for drought responses and more broadly climatic adaptation in perennial biofuel crops can provide resources to improve breeding strategies for developing locally and broadly adapted feedstock systems (Morrow et al., 2014). In this study, we integrated tissue‐specific transcriptome and metabolite analyses to investigate specialized metabolism responses to drought in two major switchgrass ecotypes with distinct habitats and contrasting drought tolerance (Liu et al., 2015), namely the lowland Alamo and the upland Cave‐in‐Rock genotypes.
Materials and Methods
Plant material and treatment
Switchgrass (P. virgatum L.), genotypes Alamo AP13 and Cave‐in‐Rock were kindly provided by Dr Malay Saha (Noble Research Institute, Ardmore, OK, USA). Plants were propagated from tillers to maintain low genetic variation and cultivated in glasshouses to the reproductive stage (R1) under ambient photoperiod and c. 27°C : 22°C, day : night temperature prior to drought treatment in a random block design. Following prior drought studies (Liu et al., 2015), drought stress was applied by withholding water consecutively for 4 wk, whereas control plants were watered daily. Volumetric soil water content (SWC) was monitored regularly using a HydroSense II (Campbell Scientific, Logan, UT, USA). Leaf and root tissues of treated and control plants (n = 6 per group) were collected before the start of the treatment (week 0), after 2 wk (week 2), and after 4 wk (week 4) at a consistent time and immediately flash‐frozen in liquid nitrogen (N2). To enable comparative data integration, samples for transcriptome and metabolite analyses originated from the same plant tissue samples, which were split for the different analyses.
RNA isolation, transcriptome sequencing, and differential gene expression analysis
Total RNA was extracted from 100 mg of leaves or roots of Alamo and Cave‐in‐Rock plants (n = 6) either drought‐stressed or well‐watered (control) using a Monarch® Total RNA Miniprep Kit (New England Biolabs, Ipswich, MA, USA) and subsequently treated with DNase I for genomic DNA removal. Following assessment of RNA integrity and quantitation using the Bioanalyzer 2100 RNA Nano 6000 Assay Kit (Agilent Technologies, Santa Clara, CA, USA), four of the six biological replicates with highest RNA quality were selected for sequencing. Preparation of complementary DNA (cDNA) libraries and transcriptome sequencing was performed at Novogene (Novogene Corp. Inc., Sacramento, CA, USA). In brief, following RNA integrity analysis and quantitation, cDNA libraries were generated using a NEBNext® Ultra™ RNA Library Prep Kit (New England Biolabs) and sequenced on an Illumina Novaseq 6000 sequencing platform generating 40–80 million 150 bp paired‐end reads per sample. Filtered, high‐quality reads were aligned to the reference genome (P. virgatum var. Alamo AP13 v.5.1) using Hisat2 (D. Kim et al., 2019). Gene functional annotation was based on best matches to databases from Phytozome v.13 (phytozome‐next.jgi.doe.gov), including Arabidopsis, rice, Gene Ontology (GO), and Panther, as well as in‐house protein databases of biochemically verified terpene‐metabolic enzymes (Pelot et al., 2018; Murphy & Zerbe, 2020). Differentially expressed genes (DEGs) were identified based on adjusted P‐value (P adj) < 0.05 and ¦log2FoldChange (FC)¦ > 1 as selection criteria. Statistical analyses were conducted in R and also plots and heatmaps were created using the ggplot2 and pheatmap packages in R (cran‐project.org, v.3.6.3).
Metabolite extraction
Metabolite analysis followed previously established protocols (Liu et al., 2020). Here, 100 mg tissue were ground to a fine powder in liquid N2 and metabolites were extracted with 1 ml 80% methanol containing 1 μM telmisartan internal standard by vortexing briefly and incubation for 16 h at 200 rpm and 4°C. Samples were centrifuged for 20 min (4000 g , 4°C) to remove solid particles and the supernatants transferred into fresh vials and stored at −80°C prior to liquid chromatography–mass spectrometry (LC–MS) analysis.
UPLC‐ESI‐QToF‐MS analysis
Reversed‐phase ultra‐performance liquid chromatography–electrospray ionization–quadrupole time‐of‐flight–mass spectrometry (UPLC–ESI–QToF–MS) analysis was performed in positive and negative mode following a previously established protocol (Li et al., 2022). In brief, metabolite analyses were performed on a Waters Acquity UPLC system equipped with a Waters Xevo G2‐XS QToF MS (Waters, Milford, MA, USA). Metabolite separation was conducted on a UPLC BEH C18 column (2.1 mm × 150 mm, 1.7 μm; Waters) using 10 mM ammonium formate (NH4HCO2)–water (solvent A) and 100% acetonitrile (solvent B) as mobile phase and the following parameters: flow rate of 0.4 ml min−1; column temperature 40°C; 10 μl injection; method: 0–1 min (99% A : 1% B), 1–15 min linear gradient to 1% A : 99% B, 15–18 min (1% A : 99% B), 18–20 min (99% A : 1% B); QToF parameters: desolvation temperature of 350°C; desolvation gas flow rate at 600 l h−1; capillary voltage of 3 kV; cone voltage of 30 V. Mass spectra were acquired in continuum mode over m/z 50–1500 using data‐independent acquisition (DIA) or data‐dependent MS/MS acquisition (DDA), with collision potential scanned between 20 and 80 V for the higher‐energy function of DIA (and 20–60 V for DDA). For DDA mode, the three most abundant molecular ions were automatically selected to pass through the mass filter for fragmentation analysis at each scan. The MS system was calibrated with sodium formate. Leucine enkephalin served as the lock mass compound without but automated mass correction during DIA data acquisition. Quality control (QC) and reference samples were analyzed every 20 injections to evaluate the stability of the LC–MS system.
Data processing and metabolite mining
The obtained DIA MS data were processed using Progenesis QI (v.3.0; Waters) using retention time (RT) alignment, lock mass correction, peak detection, adduct grouping and deconvolution and the following parameters: sensitivity for peak picking: default; minimum chromatographic peak width: 0.15 min; RT range: 0.3–15.5 min. Annotated metabolites were defined by RT and m/z information (designated as features). Before downstream statistical analyses, ion abundances of all detected features were normalized to the internal standard telmisartan, based on five biological replicates. The normalized data (abundance > 300) were used for statistical analysis using MetaboAnalyst 5.0 (Pang et al., 2021). Kyoto Encyclopedia of Genes and Genomes (KEGG), MassBank, PubChem and MetaboLights databases were used to provide feature annotations based on 10 ppm precursor tolerance, 95% isotope similarity and 10 ppm theoretical fragmentation pattern matches with fragment tolerance. In addition, Canopus was used to predict chemical classes of features of interest based on their MS/MS information (Dührkop et al., 2021) with threshold of confidence level 2 (Sumner et al., 2007). DDA analysis was performed for a pooled sample set to generate positive mode MS/MS spectra for the three most abundant parent ions at each MS survey scan (scan time: 0.2 s). The discovery of specialized metabolites (flavonoid glycosides, sesquiterpenoids, diterpenoids, triterpenoids) was achieved by mining the DDA data with focus on the most abundant metabolites.
NMR analysis of terpenoid metabolites
About 200 g fresh root tissues of Cave‐in‐Rock plants were harvested. Compound purification was performed according to the method described in the Li et al. (2022). The differences are the ethyl acetate and hexane phases (in which the diterpenoids were concentrated) were evaporated to dryness using a SpeedVac vacuum concentrator. The residue was re‐dissolved in 8 ml of 95% methanol. Supernatants were transferred to LC vials. Purification was carried out as previously described using a C18 HPLC column (100 mm × 4.6 mm × 5 μm). For nuclear magnetic resonance (NMR) analysis, c. 0.4–0.8 mg of each high‐performance liquid chromatography (HPLC) purified compounds were dissolved in deuterated chloroform (CDCl3; Sigma‐Aldrich, St Louis, MO, USA) containing 0.03% (v/v) tetramethylsilane (TMS). NMR one‐dimensional (1D) (proton (1H) and carbon‐13 (13C)) and two‐dimensional (2D) (heteronuclear single quantum correlation (HSQC), correlation spectroscopy (COSY), heteronuclear multiple‐bond correlation (HMBC) and nuclear Overhauser effect spectroscopy (NOESY)) spectra were acquired as previously described (Pelot et al., 2018) on a Bruker Avance III 800 MHz spectrometer (Bruker Corp., Billerica, MA, USA) equipped with a 5 mm CPTCI cryoprobe using Bruker TopSpin 3.6.1 software and analyzed with MestReNova 14.1 software. Chemical shifts were calibrated against known TMS signals.
Results
To investigate metabolic drought responses in switchgrass, we selected the lowland genotype Alamo (AP13) and the upland genotype Cave‐in‐Rock which were ranked among the most drought‐tolerant and drought‐susceptible genotypes in a comparative study of 49 switchgrass varieties (Liu et al., 2015). At the beginning of the reproductive stage (R1), plants were exposed to 4 wk of continuous drought treatment, whereby SWC measured as available water capacity (AWC) remained stable at 75% in well‐watered control plants and decreased from 75% to 0% in drought‐stressed plants (Supporting Information Fig. S1). Leaf and root tissue of both genotypes and treatment groups was harvested before the treatment (week 0), after 2 wk (week 2) and after 4 wk (week 4), and samples were subject to transcriptomic and metabolomic analyses. Visual inspection of the plants after 2 wk of drought treatment showed no differences between Alamo and Cave‐in‐Rock and no significant phenotypic drought symptoms. Between 3 and 4 wk of drought treatment Cave‐in‐Rock plants displayed an increasing wilting phenotype, whereas Alamo plants showed no or only minor wilting throughout the 4‐wk drought treatment (Fig. 1).
Fig. 1 Left panel: Photographs of switchgrass (Panicum virgatum) Alamo and Cave‐in‐Rock plants after 4 wk of drought treatment (+d) or normal watering (−d). Right panels: Volcano plots of differentially expressed genes identified after 4 wk of drought in (a) Alamo leaves, (b) Cave‐in‐Rock leaves, (c) Alamo roots and (d) Cave‐in‐Rock roots. Differential expression was performed using the DESeq2 R package. Resulting P‐values were adjusted for controlling the false discovery rate (FDR). Differential expression of genes with an adjusted P‐value (P adj) < 0.05 and a log2FoldChange > 1 was assigned as being significantly different.
Alamo and Cave‐in‐Rock plants show distinct transcriptomic alterations in response to drought
Illumina Novaseq 6000 RNA‐sequencing yielded a total of 2.4 billion and 2.7 billion high‐quality reads for Alamo and Cave‐in‐Rock samples, respectively, representing > 97% of the total reads obtained in both datasets. Alignment of the high‐quality sequences against the switchgrass Alamo AP13 genome (phytozome‐next.jgi.doe.gov/info/Pvirgatum_v5_1) resulted in average mapping rates of 86% for Alamo and 83% for Cave‐in‐Rock, thus providing a comprehensive transcriptomic dataset for gene discovery and gene expression analyses. Using this dataset, differential gene expression analysis was performed for control and drought‐treated plants of both genotypes. A total of 565 DEGs were identified in roots and 204 DEGs in leaves (DEG threshold: P adj < 0.05; ¦log2FC¦ > 1) of Alamo plants after 4 wk of drought treatment compared to well‐watered control plants (Fig. S2). Cave‐in‐Rock plants showed stronger drought‐induced changes with a total of 1198 DEGs in roots and 1120 DEGs in leaves; constituting two‐ and five‐fold more DEGs as compared to Alamo roots and leaves, respectively (Fig. S2). In addition, Cave‐in‐Rock plants showed an earlier onset of transcriptomic changes compared to Alamo. After 2 wk of watering withdrawal, 2951 and 897 genes were differentially expressed in Cave‐in‐Rock leaves and roots, respectively, whereas only 19 and 128 genes were differentially expressed in Alamo leaves and roots (Table S1), concurrent with the earlier onset of phenotypic drought symptoms in Cave‐in‐Rock (Fig. S2). Furthermore, differential gene expression was more pronounced in roots than leaves in both genotypes. Indeed, permutational multivariate analysis of variance (PERMANOVA) illustrated that tissue type (leaves or roots) had the predominant impact on gene expression levels (38.6%, ***, P < 0.001), followed by genotype (8%, **, P < 0.003) and stress treatment (6%, **, P < 0.007) (Table S2).
Major transcriptomic changes include genes of known drought response mechanisms and core general metabolic processes
Consistent with the differences in the number and onset of DEGs between Alamo and Cave‐in‐Rock, the identified genes showing the most significant differential expression differed between the two genotypes and tissues (Fig. 1). Only two genes, a putative circadian clock protein (Pavir.9NG553200) and a predicted lipid transfer protein (Pavir.5NG603383) were differentially expressed in all genotypes and tissues (Fig. S2). The genotype‐ or tissue‐specific DEGs included numerous so far uncharacterized genes as well as genes involved in known drought response mechanisms. For example, in roots of both genotypes dehydrin (log2FC Alamo = 5.2, CiR = 6.24) and other Late Embryogenesis Abundant (LEA) genes (log2FC Alamo = 4.34, CiR = 5.78), as well as several AWPM19‐like plasma‐membrane‐associated abscisic acid (ABA) influx transporters implicated with drought tolerance (log2FC Alamo = 6.25, CiR = 6.04) were highly upregulated (Fig. 1; Table S1). Notably, among the 11 AWPM19‐like genes, Alamo and Cave‐in‐Rock featured distinct genes (Pavir.2NG274300 in Alamo vs Pavir.9NG018700 in Cave‐in‐Rock) as the most differentially expressed AWPM19‐like genes. The increased expression of known drought‐associated genes supports the drought response in both genotypes, despite the visual lack of a wilting phenotype in Alamo plants. Along the lack of a wilting phenotype in Alamo plants, no genes associated with cell death and necrosis processes such as caspases and proteases were present among the differentially upregulated genes (i.e. featured an P adj > 0.05 with a ¦log2FC¦ < 1), supporting that the observed expression patterns represent a drought response rather than being associated with drought‐related plant death.
Next, we investigated the impact of drought on core metabolic pathways independent of known drought response processes. Among 26 GO terms significantly enriched in all samples combined most encoded for biological processes or molecular functions (Fig. S3). In Alamo leaves DNA‐binding and electron carrier processes were most significantly enriched, whereas carbohydrate metabolism was predominant in roots. Interestingly, Cave‐in‐Rock roots featured highly enriched abiotic stress response processes rather than carbohydrate metabolism under drought stress, whereas leaves showed patterns similar to Alamo with DNA‐binding and hydrolase activities being differentially expressed (Fig. S3). Additional pathway enrichment analyses using KEGG terms confirmed a substantially higher number of metabolic pathways enriched in Cave‐in‐Rock as compared to Alamo, with more genes underlying the enriched pathways on average (Figs 2, S4). Plant signal transduction process ranked among the most significantly enriched in Alamo and Cave‐in‐Rock leaves, especially in response to drought stress. By contrast, endoplasmic reticulum protein processing and nitrogen metabolism were most significantly altered in Alamo and, to lesser degree, in Cave‐in‐Rock roots (Fig. 2). Albeit at lower levels, pathway enrichment was also observed for general and specialized metabolism, including carbon and amino acid metabolism, as well as carotenoid, steroid, and phenylpropanoid biosynthesis (Fig. 2; Table S3).
Fig. 2 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes in switchgrass (Panicum virgatum). Circle color denotes the adjusted P‐value (P adj), circle size is proportional to the number of genes involved in the enrichment of the pathway (Count). The clusterprofiler R package was used to test the statistical enrichment of differentially expressed genes in kegg pathways where genes with an P adj < 0.05 were considered significantly enriched.
Switchgrass features tissue‐, genotype‐ and drought‐specific alterations in terpenoid and phenylpropanoid pathways
The enrichment of terpenoid and phenylpropanoid metabolism is consistent with prior studies illustrating ecotype‐specific profiles of steroidal saponins and flavonoids (Li et al., 2022), and the upregulation, albeit at moderate levels, of switchgrass terpenoid‐ and phenylpropanoid‐metabolic pathways in response to drought, UV irradiation and oxidative stress (Meyer et al., 2014; Pelot et al., 2018; Muchlinski et al., 2019; Tiedge et al., 2020). To investigate in more detail the impact of drought on switchgrass specialized metabolism, we compared the transcript abundance of key genes of the terpenoid and phenylpropanoid scaffold‐forming pathways in both genotypes. Due to the lack of a Cave‐in‐Rock genome, gene annotations are based on homology searches against the switchgrass Alamo AP13 genome (phytozome‐next.jgi.doe.gov/info/Pvirgatum_v5_1). Interestingly, the focal genes showed similar tissue‐specific expression profiles in Alamo and Cave‐in‐Rock and no substantial drought‐induced gene expression changes were observed (Fig. 3). For example, of the four annotated 1‐deoxyxylulose 5‐phosphate synthase (DXS) genes of the methylerythritol phosphate (MEP) pathway, two homologs, Pavir.3KG128241 and Pavir.3NG140939, were abundant in leaves but c. 10–60‐fold less in roots of both Alamo and Cave‐in‐Rock (Fig. 3a). Likewise, the predicted squalene synthase, Pavir.4NG340500, displayed high abundance in leaves and low gene expression in roots. However, select genes showed genotype‐specific differences in their expression patterns. This included the predicted geranylgeranyl pyrophosphate synthase (GGPPS), Pavir.6Ng089000, that was expressed in Alamo but not Cave‐in‐Rock roots (Fig. 3a). A similar trend of gene expression was observed for core genes of phenylpropanoid metabolism with several annotated phenylalanine ammonia lyase (PAL), cinnamate‐4‐hydroxylase (C4H), and 4‐coumaroyl‐CoA ligase (4CL) genes showing higher expression in roots as compared to leaves in both Alamo and Cave‐in‐Rock (Fig. 3b).
Fig. 3 Plot of normalized gene expression profiles of genes with predicted functions in (a) terpenoid backbone biosynthesis and (b) flavonoid backbone biosynthesis after 0, 2 or 4 wk in drought‐treated (D) or well‐watered control (C) switchgrass (Panicum virgatum) Alamo and Cave‐in‐Rock (CiR) plants. Gene expression data are based on four biological replicates and gene functional annotations are based on best matches in Blast searches against public and in‐house protein databases. Gene IDs are derived from the P. virgatum genome v.5.1 (phytozome‐next.jgi.doe.gov/info/Pvirgatum_v5_1). DXS, 1‐deoxyxylulose 5‐phosphate synthase; DXR, 1‐deoxyxylulose 5‐phosphate reductase; HMGR, HMG‐CoA reductase; FPPS, farnesyl pyrophosphate synthase; GGPPS, geranylgeranyl pyrophosphate synthase; SQS, squalene synthase; SQE, squalene epoxidase; PAL, phenylalanine ammonia lyase; C4H, cinnamate‐4‐hydroxylase; 4CL, 4‐coumaroyl‐CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase.
Contrasting the largely unaltered and comparable expression of the highly conserved upstream pathway genes, both Alamo and Cave‐in‐Rock plants featured distinct gene expression profiles for downstream pathway branches that generate species‐specific, functionalized metabolites. Following the recent discovery of specialized triterpenoid and steroid saponins in switchgrass (Li et al., 2022), identification and gene expression analysis of predicted cycloartenol synthases (CAS), lanosterol synthases (LAS), β‐amyrin synthases (BAS), as well as members of the CYP71A, CYP90B and CYP94D cytochrome P450 families and sterol 3‐β‐glucosyltransferases with known functions in triterpenoid metabolism revealed distinct expression patterns across tissue type and genotype (Fig. 4). For example, hierarchical gene cluster analysis illustrated predicted triterpenoid synthase (TTS) genes and a putative CYP72A gene with similar inducible expression patterns in Alamo leaves after 2 and 4 wk of drought (Table S4). Likewise, in Alamo roots a different group of TTS, sterol 3‐β‐glucosyltransferase, and putative CYP72A, CYP94D and CYP90B genes, known to function in the biosynthesis of triterpenoid saponins such as diosgenin (Ciura et al., 2017), displayed common inducible expression patterns after 4 wk of drought (Fig. 4, upper panel). By contrast, co‐expression patterns of triterpenoid‐biosynthetic genes were not detectable in Cave‐in‐Rock (Fig. 4, lower panel).
Fig. 4 Hierarchical cluster analysis of select genes with predicted functions in triterpenoid biosynthesis in switchgrass (Panicum virgatum) Alamo and Cave‐in‐Rock plants. Gene functional annotations are based on best matches in Blast searches against in‐house protein databases of known triterpenoid‐metabolic genes. Gene IDs are derived from the P. virgatum genome v.5.1 (phytozome‐next.jgi.doe.gov/info/Pvirgatum_v5_1). Gene expression data are based on four biological replicates. Dashed boxes highlight genes with relevant co‐expression patterns. Right side: C0L, C2L, C4L: leaves of well‐watered control plants after 0, 2 and 4 wk of treatment; C0R, C2R, C4R: roots of well‐watered control plants; D0L, D2L, D4L: leaves of drought‐stressed plants; D0R, D2R, D4R: roots of drought‐stressed plants.
Our prior research identified expansive, species‐specific diterpene synthase (diTPS) and P450 families in switchgrass that form complex metabolic networks toward a range of labdane‐related diterpenoids, including syn‐pimarane and furanoditerpenoid compounds that occur, perhaps uniquely, in switchgrass (Fig. S5) (Pelot et al., 2018; Muchlinski et al., 2021). This pathway knowledge enabled a detailed analysis of transcriptomic alterations related to diterpenoid metabolism. Contrasting the largely similar expression patterns of MEP and mevalonate (MVA) pathway genes (Fig. 3a), hierarchical gene cluster analysis revealed distinct diTPS and P450 gene expression between Alamo and Cave‐in‐Rock (Fig. 5). In Alamo roots, the cis‐trans‐clerodienyl pyrophosphate (CLPP) synthase PvCPS1 and the P450 genes, CYP71Z25, CYP71Z26, and CYP71Z28 shown to form furanoditerpenoids (Muchlinski et al., 2021), showed patterns of co‐expression at 2 and 4 wk of drought. Similarly, the predicted syn‐CPP synthases, PvCPS9 and PvCPS10, as well as two class I diTPS, PvKSL4 and PvKSL5, shown to form syn‐pimaradiene compounds (Pelot et al., 2018), co‐expressed in roots, albeit without significant drought‐inducible transcript changes. In Cave‐in‐Rock, diTPS and P450 genes were expressed mostly in the well‐watered plants and water deficiency did not elicit significant transcript accumulation. Also, contrasting roots, no drought‐elicited changes in the expression of diterpenoid pathway genes was detectable in leaves of either ecotype.
Fig. 5 Hierarchical cluster analysis of select genes with known or predicted functions in diterpenoid biosynthesis in switchgrass (Panicum virgatum) Alamo and Cave‐in‐Rock plants. Gene functional annotations are based on previous biochemical enzyme characterizations or best matches in Blast searches against in‐house protein databases of known diterpenoid‐metabolic genes. Gene IDs are derived from the P. virgatum genome v.5.1 (phytozome‐next.jgi.doe.gov/info/Pvirgatum_v5_1). Gene expression data are based on four biological replicates. Dashed boxes highlight genes with relevant co‐expression patterns. Solid and dashed arrows indicate characterized and predicted pathway steps, respectively. C0L, C2L, C4L: leaves of well‐watered control plants after 0, 2 and 4 wk of treatment; C0R, C2R, C4R: roots of well‐watered control plants; D0L, D2L, D4L: leaves of drought‐stressed plants; D0R, D2R, D4R: roots of drought‐stressed plants.
Switchgrass leaves and roots show drought‐inducible metabolite alterations
To complement transcriptomic studies leaf and root metabolomes of Alamo and Cave‐in‐Rock plants under drought‐stressed and well‐watered conditions were examined using untargeted liquid chromatography–quadrupole time‐of‐flight–mass spectrometry (LC–QToF–MS) analysis. Using accurate mass, RT, and fragmentation patterns, we identified 5181 and 3234 metabolite features in positive and negative ion mode, respectively. To compare metabolite profiles across tissues, genotypes, and treatments, after filtering for ion‐abundance (see the Materials and Methods section for details) 2519 positive mode mass features (identified as RT : m/z ratio pairs) were selected for downstream statistical analysis (Table S5).
Aligned with the transcriptomic changes, biostatistical analysis of the untargeted metabolomic data via PERMANOVA showed that tissue type had the highest impact on metabolite composition (72.4%, ***, P < 0.001), followed by difference in genotype (2.8%, *, P < 0.028) and drought‐treatment vs control (0.6%, P < 0.332) (Table S2). Hence, we further analyzed the metabolite profiles independently within each tissue type. Despite the relatively lower impact of drought treatment on metabolic alterations, under a multivariate dimension‐reduction based on genotype metabolite features clustered together before water deprivation (week 0), but separated in leaves and, to a lesser extent, in roots after 4 wk of drought treatment (Fig. 6). This shift in metabolite composition was driven by several major features (Fig. 7). In leaves, most compounds showing accumulation differences in control and drought‐stressed plants were classified as phospholipids based on database searches for each feature. These compounds increased in abundance in Alamo during drought treatment, whereas a decrease was observed in Cave‐in‐Rock (Fig. 7). In addition to predicted phospholipids, a few features were enriched in Alamo leaves under drought stress, whereas many unidentified compounds were enriched in drought‐treated Cave‐in‐Rock leaves (Table S2). In contrast, several compounds identified as diterpenoids and triterpenoids by comparison of RT and fragmentation patterns to previously identified compounds (Muchlinski et al., 2021; Li et al., 2022) accumulated in roots of both Alamo and Cave‐in‐Rock plants under drought stress, with a stronger increase in Alamo (Fig. 7). Other root metabolites that accumulated differentially under drought conditions either did not score significant database matches or could only be assigned to the general classes of carbohydrates, acids, or alcohols (Fig. 7). Among the few features that were generally enriched in both leaves and roots and in both ecotypes under water deficient conditions was also ABA, which was increased by c. 40–300‐fold under drought (Fig. 8a; Table S6, ABA: 6.08_247.1244 m/z).
Fig. 6 Partial least‐squares discriminant analysis (PLS‐DA) plots of liquid chromatography–mass spectrometry (LC–MS) positive mode metabolome divergence based on five biological replicates. x‐axis, principal component 1; y‐axis, principal component 2.
Fig. 7 Scree plot of metabolite features obtained via liquid chromatography–mass spectrometry (LC–MS) positive mode analysis that show the most significant contribution to changes in metabolite profiles in response to drought stress in switchgrass (Panicum virgatum) plants. A higher coefficient (x‐axis) denotes a higher importance for this feature in the partial least‐squares discriminant analysis (PLS‐DA) shown in Fig. 6. Boxes display the relative abundance of a feature among the different groups as based on five biological replicates. Metabolite annotations are based on matching m/z ratios, retention time and fragmentation patterns against online databases.
Fig. 8 (a) Hierarchical cluster analysis of select specialized metabolite accumulation patterns in switchgrass (Panicum virgatum). Sucrose and abscisic acid (ABA) abundance provide as drought‐related reference metabolites. Metabolite annotations are based on matching m/z ratios, retention time and fragmentation patterns against online databases. (b) Structures of drought‐induced diterpenoids, 15,16‐epoxy‐2‐oxo‐5α8α‐cleroda‐3,13(16),14‐triene (m/z 301, panicoloid A), 15,16‐epoxy‐2‐oxo‐5β8α‐cleroda‐3,13(16),14‐triene (m/z 301, panicoloid B) and 2‐oxo‐5α8α‐cleroda‐3,13‐dien‐16,15‐olide (m/z 317, panicoloid C), isolated from drought‐stressed switchgrass roots. Metabolite abundance is based on five biological replicates.
Drought induces the accumulation of specialized furanoditerpenoids in switchgrass roots
Considering that predicted specialized steroidal and triterpenoid saponins and diterpenoids contribute substantially to the metabolic differences between Alamo and Cave‐in‐Rock roots, we examined these compounds in more detail in leaf and root extracts after 4 wk of drought where the physiological stress symptoms were most pronounced. Several predicted flavonoid glycosides were identified in leaves of Alamo and Cave‐in‐Rock plants but were absent in root tissue. However, these compounds showed no or only minimal patterns of drought‐elicited accumulation (Fig. 8a). In addition, a range of distinct terpenoid metabolites was identified. Among these metabolites, the largest group represented recently identified steroidal or triterpenoid saponins (Li et al., 2022), showing distinct profiles across tissues and genotypes. Triterpenoid saponins occurred predominantly in leaves of both Alamo and Cave‐in‐Rock plants, whereas the larger group of steroidal saponins were present in leaves and/or roots and occurred predominantly in either Alamo or Cave‐in‐Rock plants. Despite the overall abundance of these saponins, the vast majority of the annotated metabolites were not significantly enriched upon drought stress (P ≤ 0.05; Table S6). In addition to the larger group of triterpenoids, 11 compounds predicted as specialized diterpenoids were identified, the majority of which occurred predominantly in Alamo roots and were absent or abundant at only low levels in Cave‐in‐Rock (Fig. 8a). Notably, two pairs of predictably isomeric diterpenoids were detected in Alamo and Cave‐in‐Rock that showed substantial accumulation mostly in drought‐stressed roots. One metabolite pair at RTs of 9.06 min and 9.26 min featured a dominant precursor mass ion of m/z 317 [M + H]+ and one compound pair at RTs of 11.50 min and 11.72 min featured a dominant ion of m/z 317 [M + H]+. Together with the presence of additional mass ions of m/z 257, m/z 189, m/z 177 or m/z 135, these fragmentation patterns suggested that these compounds represent labdane‐related diterpenoids carrying one or more oxygenation functions (Fig. S4). To elucidate the precise structure of these diterpenoids, metabolites were extracted from mature Cave‐in‐Rock roots and purified via liquid–liquid phase partitioning followed by HPLC. Purified samples (0.4–0.8 mg for each compound) of both m/z 301 isomers and both m/z 317 diterpenoid isomers were used for 1D (1H and 13C) and 2D (HSQC, COSY, HMBC and NOESY) NMR analyses. Collectively, the generated data identified the m/z 301 diterpenoids as 15,16‐epoxy‐2‐oxo‐5α8α‐cleroda‐3,13(16),l4‐triene and its C19 enantiomer, while the earlier eluting diterpenoid m/z 317 was identified as 2‐oxo‐5α8α‐cleroda‐3,13‐dien‐16,l5‐olide, together designated as panicoloid A, panicoloid B, and panicoloid C respectively (Figs 8b, S6). Insufficient abundance and purity of the later eluting m/z 317 diterpenoid isomer prevented structural analysis of this metabolite. Based on the similar mass fragmentation pattern and RT compared to the other m/z 317 isomer (Fig. S4), it is plausible that this compound represents its enantiomeric isomer and is tentatively named panicoloid D. The identified diterpenoids represent derivatives of previously identified switchgrass furanoditerpenoids that feature additional carbonyl functions at C2 and/or C14 and are collectively named here as the group of panicoloids (Fig. S4).
Discussion
Knowledge of the gene‐to‐metabolite relationships underlying specialized metabolic pathways that contribute to plant stress resilience can enable new crop optimization strategies for addressing exacerbating environmental pressures and associated harvest loss (Savary et al., 2019). Extensive studies in major food and bioenergy crops such as rice, maize, and sorghum (Sorghum bicolor) have demonstrated that species‐specific blends of specialized terpenoids, phenylpropanoids, oxylipins, and benzoxazinoids mediate complex responses to biotic and abiotic perturbations (Schmelz et al., 2014; Murphy & Zerbe, 2020). By contrast, knowledge of the diversity of specialized metabolites in the perennial bioenergy crop switchgrass and its relevance for drought adaptation in different switchgrass genotypes is incomplete. Combined transcriptome and metabolome analysis of the lowland ecotype Alamo shown to be drought‐tolerant and the upland ecotype Cave‐in‐Rock with low drought tolerance (Liu et al., 2015) revealed common and distinct metabolic alterations and identified specialized diterpenoid metabolites with possible functions in switchgrass drought adaptation.
Consistent with prior studies showing that upland and lowland switchgrass ecotypes have different transcriptomic and metabolite profiles under optimal conditions (Ayyappan et al., 2017; Li et al., 2022), this study demonstrates that metabolic alterations in Alamo and Cave‐in‐Rock are predominantly driven by differences in tissue type and genotype, thus reflecting the different habitat range and climatic adaptation of switchgrass ecotypes. A stronger wilting phenotype after 4 wk of drought treatment, along with more than twice as many DEGs illustrate more pronounced drought‐induced metabolic changes in Cave‐in‐Rock, consistent with prior studies identifying Cave‐in‐Rock as particularly drought‐susceptible among switchgrass varieties (Liu et al., 2015). Presence of known drought‐response genes among the most DEGs in Alamo and/or Cave‐in‐Rock supported substantial drought responses in both genotypes during drought treatment, despite the lack of significant phenotypic changes in Alamo. Known drought‐associated genes included LEA genes, including dehydrin shown to impact drought tolerance in Arabidopsis and cotton (Gossypium spec.) (Olvera‐Carrillo et al., 2010; Magwanga et al., 2018), a NAC transcription factor (Pavir.8KG003520) shown to contribute to drought tolerance in a recent switchgrass genome‐wide association study (GWAS) (Lovell et al., 2021), and AWPM‐19‐like ABA influx transporters (Yao et al., 2018), Differential expression of several AWPM‐19‐like genes in both Alamo and Cave‐in‐Rock is consistent with an increase in ABA observed in both genotypes and tissues in response to drought stress. While prior studies that showed increased ABA and sugar accumulation in drought‐tolerant switchgrass genotypes (Liu et al., 2015), the final concentration of ABA in drought stressed plants was at comparable levels in both genotypes in our study, whereas the concentration in the control plants was lower in Alamo, resulting in a higher fold‐change/increase in Alamo when compared to Cave‐in‐Rock. Notably, the specific genes of the earlier gene families being differentially expressed differed between Alamo and Cave‐in‐Rock, suggesting that switchgrass genotypes recruit specific genes governing stress response mechanisms.
Pathways of general and specialized metabolism showed overall comparatively moderate differential gene expression in response to drought stress with apparent metabolic differences between leaf and root tissue. Accumulation of sucrose and ABA in Alamo and Cave‐in‐Rock tissues is consistent with previously demonstrated switchgrass drought responses (Liu et al., 2015). In addition, the observed major contribution of phospholipids to metabolic alterations in leaves of both genotypes may be related to an upregulation of pathways involved in membrane lipid systems and cell wall biosynthesis as shown in, for example, drought‐resistant maize lines (Zhang et al., 2020). Additional phospholipid roles in drought tolerance may include maintenance of membrane integrity or mitigation of drought‐related cell damage (Hamrouni et al., 2001), as well as signaling processes during water deficiency as reported in selected drought‐resistant species (Quartacci et al., 1995; Moradi et al., 2017). Contrasting drought‐related flavonoid functions in, for example, wheat (Triticum aestivum) and other species (Ma et al., 2014; Gai et al., 2020), leaf flavonoid glycosides did not accumulate in response to drought in either switchgrass genotype, despite a moderate upregulation of select pathway genes such as CHS in drought‐stressed plants. Similarly, steroidal and triterpenoid saponins were identified in switchgrass leaves, and have been shown to increase as part of the leaf cuticular waxes in response to drought (Kim et al., 2007). However, our metabolite analysis did not show significant drought‐elicited triterpenoid accumulation that would support a similar function in switchgrass.
Different from leaves, steroidal and other triterpenoid saponins as well as diterpenoids constituted the major determinants of metabolic differences in drought‐stressed roots. Notably, MEP and MVA pathway genes showed no or very minor patterns of drought‐inducible expression in Alamo or Cave‐in‐Rock, indicating that no major change in the production of terpenoid precursors occurs in response to drought. One exception is the putative GGPPS, Pavir.6NG089000, that is expressed in Alamo but not Cave‐in‐Rock roots and may contribute to differences in terpenoid metabolism between these genotypes. This apparent lack of drought activation of terpenoid backbone pathways suggests that terpenoid accumulation has to derive from existing precursor pools via changes in pathway branches en route to specific terpenoids. Indeed, in leaves and roots of Alamo, but not Cave‐in‐Rock, downstream triterpenoid‐metabolic pathway genes increased under drought conditions. Interestingly, no apparent triterpenoid‐biosynthetic gene clusters were identified in the switchgrass genome, which differentiates switchgrass from other plant species where triterpenoid metabolism is commonly arranged in form of often stress‐inducible biosynthetic clusters (Liu et al., 2020; Bai et al., 2021). Despite the lack of apparent genomic clusters, clear co‐expression patterns were observed for several TTS genes as well as sterol 3‐β‐glucosyltransferases and P450 genes of the CYP71A, CYP94D and CYP90B families, thus supporting the presence of co‐expressed pathways toward specific triterpenoids. The identification of diverse mixtures of diosgenin and closely related triterpenoid saponins in roots of several upland and lowland ecotypes, including Alamo and Cave‐in‐Rock, supports this hypothesis (Lee et al., 2009; Li et al., 2022). However, the annotated triterpenoid metabolites were not significantly enriched in response to drought in either genotype, suggesting that the inducible expression of triterpenoid pathways in Alamo is related to distinct drought‐stress responses. It can be speculated that root triterpenoids serve antioxidant functions to mitigate oxidative damage caused by water deficiency as shown in Arabidopsis and other species (Posé et al., 2009; Nasrollahi et al., 2014; Puente‐Garza et al., 2017). Also, recent studies have demonstrated bioactive triterpenoids in roots exudates of soybean (Glycine max) and tomato (Solanum lycopersicum) that aid the assembly of the root microbiome to confer robustness against environmental stresses (Fujimatsu et al., 2020; Nakayasu et al., 2021). While distinct microbiome responses to drought stress have been reported in upland and lowland switchgrass ecotypes (Liu et al., 2021), the role of specialized metabolites such as saponins in these interactions is yet to be discovered.
Unlike root triterpenoids, significant drought‐induced accumulation of several diterpenoids in roots supports a role in drought response mechanisms. Although requiring further biological studies, the higher abundance of these diterpenoids in Alamo as compared to Cave‐in‐Rock may contribute to the distinct stress resilience in these genotypes. In addition, accumulation in both Alamo and Cave‐in‐Rock support a role of these metabolites in drought responses rather than general stress responses associated with the more pronounced wilting phenotype observed in Cave‐in‐Rock. Structural analysis identified three compounds as oxygenated clerodane furanoditerpenoids, named here panicoloid A–C, which likely represent derivatives of furanoditerpenoid scaffolds recently identified in switchgrass (Pelot et al., 2018; Muchlinski et al., 2021). Notably, the enantiomeric stereochemistry of panicoloid B and panicoloid C is likely derived from the activity of yet unidentified diTPS functionally related to the CLPP synthase PvCPS1. Drought‐induced gene expression increases of characterized pathway genes toward clerodane‐type furanoditerpenoids, including the diTPS PvCPS1 and the P450 genes CYP71Z25, CYP71Z26 and CYP71Z28 supports a role of these pathway genes in panicoloid biosynthesis. Additional co‐expression of select P450s of the CYP99 family and predicted short‐chain alcohol dehydrogenases/reductases, shown to function in specialized diterpenoid metabolism in maize and rice (Swaminathan et al., 2009), suggests possible functions in the position‐specific oxygenation reactions toward panicoloid biosynthesis. Similar to triterpenoid metabolism, the lack of co‐expression of these genes in the drought‐susceptible Cave‐in‐Rock genotype may support a role of panicoloids in switchgrass drought responses. Combined with the abundance of yet unidentified diterpenoids in switchgrass roots, drought‐elicited co‐expression patterns of additional predicted syn‐CPP synthase genes (PvCPS9, PvCPS10) and characterized class I diTPS forming syn‐pimarane diterpenoids (PvKSL4, PvKSL5) suggest the presence of a broader diversity of drought‐induced diterpenoids in switchgrass. Similar clerodane‐type furanoditerpenoids have also been identified in species of Vellozia spec. (Pinto et al., 1994), Solidago spec. (Anthonsen et al., 1973; McCrindle et al., 1976), and Croton campestris (El Babili et al., 1998), where they will have evolved independently given the phylogenetic distance between these plant genera. While the drought‐induced expression of diterpenoid‐metabolic genes and associated accumulation of panicoloids and possibly other diterpenoids supports a role in switchgrass drought tolerance, the underlying mechanisms will require future investigation. However, drought‐related diterpenoid bioactivities have recently been supported in other monocots. For example, maize studies demonstrated the accumulation of specialized kauralexin and dolabralexin diterpenoids in response to oxidative, drought and salinity stress (Christensen et al., 2018; Mafu et al., 2018), and diterpenoid‐deficient maize mutants show decreased resilience to abiotic stress (Vaughan et al., 2015). Additionally, antioxidative functions in relation to drought stress have been shown for select diterpenoids (Munné‐Bosch & Alegre, 2003), and diterpenoid roles in the root microbiome assembly have been suggested based on changes in the microbiome composition in the kauralexin‐ and dolabralexin‐deficient maize an2 mutant (Murphy et al., 2021).
Collectively, these findings exemplify the power of combining transcriptomic and in‐depth metabolite analyses to accelerate the discovery of plant specialized pathways and products and enable a deeper investigation of their relevance and role in plant stress responses. This approach revealed common and distinct drought‐induced metabolic changes in switchgrass genotypes of contrasting drought tolerance. These insights provide the foundation for future targeted genetic studies to investigate the diversity and protective function of terpenoids and other specialized metabolites in switchgrass drought tolerance.
Competing interests
None declared.
Author contributions
PZ, KT and RLL conceived the original research and oversaw data analysis; KT conducted plant drought stress experiments and transcriptome analysis; XL and KT performed metabolite profiling and analysis; ATM, PY, and DJT performed NMR structural analyses; DD and YC assisted with plant harvesting, sampling, and sample processing; KT and PZ wrote the original article draft with editing by all authors. All authors have read and approved the manuscript.
Supporting information
Fig. S1 Available water content in the soil during the treatment.
Fig. S2 Differentially expressed genes between all groups after 4 wk of drought treatment.
Fig. S3 Identification of significantly enriched metabolic pathways at the end of the treatment via Gene Ontology term analysis.
Fig. S4 Liquid chromatography–mass spectrometry chromatograms and spectra of identified panicoloids.
Fig. S5 Diterpenoid network in switchgrass.
Fig. S6 Nuclear magnetic resonance analysis of panicoloids A–C.
Click here for additional data file.
Table S1 Complete list of differentially expressed genes.
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Table S2 Permutational multivariate analysis of variance of gene expression levels and metabolite abundances.
Click here for additional data file.
Table S3 Enrichment of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways in Alamo and Cave‐in‐Rock.
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Table S4 Complete list of the calculated FPKM (fragments per kilobase of transcript per million mapped reads) values for all genes.
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Table S5 List of all mass features from the positive mode liquid chromatography–mass spectrometry dataset that were selected for the downstream statistical analysis.
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Table S6 Statistical analysis for detected liquid chromatography–mass spectrometry features of annotated specialized metabolites.
Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.
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Acknowledgements
Financial support for this work was provided by the US Department of Energy (DOE) Early Career Research Program (DE‐SC0019178, to PZ), the German Research Foundation (DFG) Research Fellowship (TI 1075/1‐1, to KT), and the DOE Joint Genome Institute (JGI) DNA Synthesis Science Program (grant no. 2568, to PZ). The gene synthesis work conducted by the US DOE JGI, a DOE Office of Science User Facility, is supported by the Office of Science of the US DOE under contract no. DE‐AC02‐05CH11231. Work in the Last group was supported by the Great Lakes Bioenergy Research Center, US DOE, Office of Science, Office of Biological and Environmental Research, under award no. DE‐SC0018409. The authors gratefully acknowledge Dr Malay Saha at the Noble Research Institute for providing tillers for cultivation of Alamo AP13 and Cave‐in‐Rock, Dr Daniel A. Jones at Michigan State University for his help with identification of diterpenoid features, and Dr Andrew Muchlinski (Firmenich, San Diego, CA, USA) for helpful discussions on the manuscript.
Data availability
The RNA‐sequencing data were submitted to the Sequence Read Archive (SRA), accession no. PRJNA644234.
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PMC009xxxxxx/PMC9926171.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595693
202207466
10.1073/pnas.2207466120
research-articleResearch ArticleneuroNeuroscience424
Biological Sciences
Neuroscience
Nonquantal transmission at the vestibular hair cell–calyx synapse: KLV currents modulate fast electrical and slow K+ potentials
Govindaraju Aravind Chenrayan a b https://orcid.org/0000-0002-2306-8599
Quraishi Imran H. c https://orcid.org/0000-0003-2859-7446
Lysakowski Anna d https://orcid.org/0000-0001-6259-0294
Eatock Ruth Anne e https://orcid.org/0000-0001-7547-2051
Raphael Robert M. [email protected]
b 1 https://orcid.org/0000-0003-4986-7847
aApplied Physics Graduate Program, Smalley-Curl Institute, Rice University, Houston, TX 77005
bDepartment of Bioengineering, Rice University, Houston, TX 77005
cDepartment of Neurology, Yale University School of Medicine, New Haven, CT 06510
dDepartment of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL 60612
eDepartment of Neurobiology, University of Chicago, Chicago, IL 60637
1To whom correspondence may be addressed. Email: [email protected].
Edited by Robert Fettiplace, University of Wisconsin-Madison, Madison, WI; received May 1, 2022; accepted November 17, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e220746612001 5 2022
17 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
The ability of the vestibular system to drive the fastest reflexes in the nervous system depends on rapid transmission of mechanosensory signals at vestibular hair cell synapses. In mammals and other amniotes, afferent neurons form unusually large calyx terminals on certain hair cells, and communication at these synapses includes nonquantal transmission (NQT), which avoids the synaptic delay of quantal transmission. We present a quantitative model that shows how NQT depends on the extent of the calyx covering the hair cell and attributes the short latency of NQT to changes in synaptic cleft electrical potential caused by current flowing through open potassium channels in the hair cell. This mechanism of electrical transmission between cells may act at other synapses.
Vestibular hair cells transmit information about head position and motion across synapses to primary afferent neurons. At some of these synapses, the afferent neuron envelopes the hair cell, forming an enlarged synaptic terminal called a calyx. The vestibular hair cell–calyx synapse supports a mysterious form of electrical transmission that does not involve gap junctions, termed nonquantal transmission (NQT). The NQT mechanism is thought to involve the flow of ions from the presynaptic hair cell to the postsynaptic calyx through low-voltage-activated channels driven by changes in cleft [K+] as K+ exits the hair cell. However, this hypothesis has not been tested with a quantitative model and the possible role of an electrical potential in the cleft has remained speculative. Here, we present a computational model that captures experimental observations of NQT and identifies features that support the existence of an electrical potential (ϕ) in the synaptic cleft. We show that changes in cleft ϕ reduce transmission latency and illustrate the relative contributions of both cleft [K+] and ϕ to the gain and phase of NQT. We further demonstrate that the magnitude and speed of NQT depend on calyx morphology and that increasing calyx height reduces action potential latency in the calyx afferent. These predictions are consistent with the idea that the calyx evolved to enhance NQT and speed up vestibular signals that drive neural circuits controlling gaze, balance, and orientation.
ephaptic transmission
synapse
vestibular
hair cells
computational neuroscience
HHS | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) 100000055 R01 DC012347 Anna LysakowskiRuth Anne EatockRobert M. Raphael Hearing Health Foundation (HHF) 100002046 863950 Robert M. Raphael HHS | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) 100000055 DC002290 Anna LysakowskiRuth Anne EatockRobert M. Raphael HHS | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) 100000055 NIH R01 DC012347-08S1 Anna LysakowskiRuth Anne EatockRobert M. Raphael
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pmcThe vestibular inner ear transduces head motion and drives the fastest known reflexes in the nervous system. These reflexes support gaze, balance, and orientation (1) and are essential for locomotion. The first synapses in these pathways are from mechanosensory hair cells onto primary afferent nerve terminals. In mammals and other amniotes, many vestibular afferent terminals form prominent cup-like structures, termed calyces, around specialized type I hair cells (2–4). Transmission at these synapses has been inferred to be exceptionally fast (5), prompting speculation that it includes a direct electrical component in addition to glutamate exocytosis from synaptic vesicles [quantal transmission (QT)]. Electrical transmission typically involves gap junctions (6), but gap junctions are not present at vestibular hair cell–calyx (VHCC) synapses (3). Nonetheless, electrophysiological recordings from calyx-bearing afferents established that an additional mode of transmission can occur, either with or without QT (7–11). This mysterious form of transmission came to be called Nonquantal Transmission (NQT).
NQT is thought to be mediated by the flow of ions from the presynaptic hair cell to the postsynaptic calyx through low-voltage-activated K+ (KLV) channels (12, 13), but how presynaptic currents alter the driving forces of postsynaptic currents has not been well understood. A key challenge for understanding NQT at the VHCC synapse is the lack of direct experimental access to the synaptic cleft and pre- and postsynaptic membranes. Early models (14, 15) recognized that the elongated cleft space between the type I hair cell and enveloping calyx structure could provide increased electrical resistance and limit the diffusion of ions. In these formulations, K+ that enters hair cells during mechanotransduction is extruded into the synaptic cleft, leading to K+ accumulation and a change in the K+ chemical potential that drives membrane currents. Changes in cleft electrical potential were not thought to be significant. In support of these models, subsequent experimental results emphasized the role of K+ accumulation (11, 16–19). Other processes were also suggested, including accumulation of glutamate following quantal release (20) and proton extrusion from hair cells (10). However, the existence and role of an electrical potential in the synaptic cleft have remained matters of speculation. A clear biophysical explanation for how NQT occurs and how currents and driving forces interact quantitatively at the VHCC synapse has not yet been presented.
To elucidate the biophysical basis of NQT, we have developed a detailed computational model of the VHCC synapse based on the anatomy of the afferent calyx terminal and the properties of ion channels and transporters expressed in the hair cell, calyx terminal, and afferent fiber. Simulated responses of the VHCC model to hair bundle deflection and voltage-clamp steps accurately predict experimental data. Such validation gives us confidence in the model’s predictions for sites with limited experimental data, notably the inaccessible synaptic cleft. Results show that NQT at the VHCC synapse depends on the geometry of the calyx, involves modulation of both the electrical potential and K+ concentration in the synaptic cleft, and is mediated primarily by currents through KLV conductances that take different forms in the presynaptic and postsynaptic membranes (“gK,L” channels and KV7 channels, respectively). A major outcome of the model is the recognition of features in postsynaptic responses (9, 18) as evidence for changes in electrical potential in the synaptic cleft during NQT.
Changes in extracellular electrical potential have long been known to play a role in ephaptic coupling, a general term for coupling via proximity of neural elements (6, 21). Ephaptic coupling has been implicated in signal transmission in the retina (22, 23), cerebellum (24), hippocampus (25), and heart (26). At the VHCC synapse, however, the importance of changes in cleft electrical potential has gone unrecognized despite its extensive synaptic cleft, which is formed by the close apposition of the presynaptic (hair cell) and postsynaptic (afferent) membranes. The VHCC model we develop is able to decouple electrical and K+ potentials and demonstrate that change in cleft electrical potential is necessary to explain: 1) the short latency of NQT; 2) the experimentally observed phase response of NQT (9); 3) fast retrograde events in the hair cell during postsynaptic action potentials (APs) (11); and 4) the shape of the fast current response of the calyx to voltage changes in the hair cell (18). By showing how the magnitude and speed of NQT depend on the calyx, our model supports the idea that the calyx evolved to support faster transmission in response to increased locomotory challenges presented by the tetrapod transition from water to land (12, 27).
Abbreviations
Electrical potentials (with respect to perilymph):
ϕH electric potential in the hair cell
ϕSC electric potential in the synaptic cleft
ϕC electric potential in calyx
Transmembrane voltages
VH voltage across the basolateral hair cell membrane, ϕH-ϕSC
VCIF voltage across calyx inner face membrane, ϕC-ϕSC
Concentrations and ionic potentials
K+SC K+ concentration in the synaptic cleft
Na+SC Na+ concentration in the synaptic cleft
EK Equilibrium potential for K+ (K+ potential) across hair cell or calyx membranes
General Description of the Model
The model has been developed for an afferent calyx around a single type I hair cell (“simple calyx-only terminal”) based on the geometry of a vestibular hair cell and calyx from the striolar zone of the rat utricle, see Fig. 1 and SI Appendix, Fig. S1. The morphology of vestibular hair cells and calyces is highly conserved within particular zones of vestibular epithelia (28, 29), and thus the model is representative of “simple” VHCC synapses in the striolar zone.
Fig. 1. Illustration of the VHCC synapse depicting the distribution of ion channels and transporters in the hair cell (H), synaptic cleft (SC), calyx (C), and afferent fiber (F). (A) Confocal micrograph of type I hair cells (H, blue myosin immunolabel) within calyces (C, green calretinin immunolabel). Long–Evans adult rat utricle, striolar zone. (B) Geometry of a type I hair cell-calyx synapse from central/striolar zones of rodent vestibular organs. A 2D curve representing the hair cell, synaptic cleft, and calyx was generated (SI Appendix, Fig. S1) based on measurements from Lysakowski et al. (30), their Fig. 2C. Long–Evans adult rat crista, central zone. Dimensions were comparable (within 10%) to those of hair cells and calyces in A. (B1) Cleft and calyx width. (C) The ion channel types at different key locations in the model. See SI Appendix, Tables S6 and S7 for sources of channel kinetics and distributions. ϕSC, electrical potential in the synaptic cleft; K+SC, K+ concentration in the synaptic cleft.
The model incorporates ion channels and transporters identified from experimental studies of type I hair cells and associated afferents to simulate the voltage response of the calyx to mechanical and electrical stimuli: hair bundle deflection and hair cell voltage steps. Most of the parameters in the model are from published literature (SI Appendix, Tables S5, S6, and S7); methods are provided for our experimental data (SI Appendix, Experimental Methods). The model was created to understand how ionic currents and driving forces interact at the VHCC synapse during NQT: a change in the calyx potential in response to a change in hair cell potential that involves neither exocytosis of packets (vesicles or quanta) of neurotransmitter nor gap junctions. As the goal is to understand NQT as an independent phenomenon, we do not consider QT. The predictions of the model are compared with experimental results from synapses that displayed only NQT. Simulations used physiological parameters from experimental data performed at room temperature.
The key parameters of interest are changes that occur in the electrical potential and K+ concentration of the synaptic cleft, which are not directly experimentally measurable. The model incorporates capacitive and resistive currents across both the presynaptic hair cell and postsynaptic calyx membranes and uses continuity and electro-diffusion equations to describe the relationship between currents, electrical potentials, and ion concentrations in the synaptic cleft (31). Using a finite element approach and COMSOL Multiphysics® software, we simulate the effects of changes in electrical potential in each model compartment and how these changes represent the mechanosensory signal as it travels from one compartment to the next. In the synaptic cleft, spatiotemporal changes in both electrical potential and ion concentrations are simulated and show spatially varying driving forces for currents along the basal-to-apical extent of hair cell and calyx inner face membranes. Initial conditions, boundary conditions, and full details on channel kinetics are provided in SI Appendix. The overall geometry, localization of ion channels, and other relevant variables are presented in Fig. 1.
Results
We use the VHCC model to delineate biophysical events and driving forces that underlie NQT and describe how the mechanosensory signal is communicated to the calyx terminal of the afferent neuron. We first describe the resting state of the VHCC compartments, then responses to step bundle deflections. In each condition, we provide the model’s output in terms of: 1) electrical potentials relative to a distant ground for the hair cell (ϕH), synaptic cleft ϕSC, and calyx (ϕC); 2) transmembrane voltages for the hair cell (VH) and the calyx inner face (VCIF) membranes facing the synaptic cleft; 3) K+ and Na+ concentrations in the synaptic cleft ([K+]SC, [Na+]SC); and 4) key ion-channel currents across each membrane. The contributions of ϕSC and K+SC are then analyzed separately to determine their roles in driving pre- and postsynaptic currents and thus NQT. We then compare model results with published data showing NQT during sinusoidal hair bundle displacement and paired voltage-clamp recordings from hair cells and calyces to identify features that corroborate the existence and role of an electrical potential in the cleft. Finally, to understand the extent to which NQT depends on calyx morphology, we analyze the effect of calyx height on the electrical and potassium potentials in the cleft (ϕSC, EK) and the response latency of the calyx.
Resting State of the System.
The model was fully defined with no free parameters that required additional estimation. From the initial values and boundary conditions, we calculated the resting currents, ion concentrations, electrical potentials, and membrane voltages, using the stationary solver in COMSOL (SI Appendix, Computational Methods). At rest, the MET (mechanoelectrical transduction) channels had a 10% open probability, corresponding to −43 pA of current. The model produced hair cell and calyx resting potentials (ϕH: –78 mV, ϕC: –65 mV) that are consistent with reported resting ϕH and ϕC (20, 32–36). Model resting output values for which there are no experimental data for comparison are: ϕSC at the base of the synapse, +2 mV; K+SC and Na+SC at the base of the synapse, 7 mM and 129 mM, respectively. The nonzero value for ϕSC emerges from currents at rest carried by the same ion channels and transporters that participate in stimulus-evoked NQT.
These results were taken as the initial conditions from which to determine, with the time-dependent solver (COMSOL), changes in response to a stimulus. In all figures, the values at t = 0 show conditions at rest.
Synaptic Response to a Step Displacement of the Hair Bundle.
In Fig. 2, we illustrate the simulated response of sequential stages in the VHCC model to a positive (excitatory) step (+1 μm) of hair bundle displacement at 50 ms followed by a negative (inhibitory) displacement (–1 μm). The stimulus (Fig. 2A) is bundle displacement viewed from above relative to resting position, taken mid-way up the bundle and parallel to the plane of the epithelium (Fig. 1B, “stimulus”). The mechanotransduction current (IMET) elicited by bundle stimulation is shown in Fig. 2B. Positive bundle displacement evoked a peak inward IMET of ~400 pA, and the negative step reduced IMET to 0. These changes in IMET depolarized and hyperpolarized ϕH (Fig. 2C, modeled by SI Appendix, Eq. S2), respectively, and altered hair cell basolateral (presynaptic) currents (Fig. 3). This model does not consider transducer adaptation, such that any decay (adaptation) of IMET is driven by changes in ϕH, which changes the driving force on IMET. NQT manifests as a change in calyx potential, ϕC (Fig. 2G), in response to changes in hair cell potential, ϕH. For this bundle deflection, the NQT-induced change in ϕC was large enough to trigger an AP in the calyx, which is in turn transmitted retrogradely by NQT back to the hair cell, showing up as transients at each stage (Fig. 2 B–F, arrowheads and Fig. 3 A–C, vertical dashed line and asterisks).
Fig. 2. Propagation of the mechanosensory signal evoked by hair bundle displacement, illustrating dynamic changes in electrical and K+ potentials at each stage. (A) Displacement (X) of the hair bundle: +1 μm, 250-ms step, beginning at t = 50 ms and followed by negative step to −1 μm (300-ms) step. (B–G) Responses to full stimulus, progressing from (B) the mechanotransduction current IMET to (G) the calyx postsynaptic potential ϕC. Arrowheads point to the retrograde echo of a calyx AP that was stimulated by anterograde transmission. (C–G) Values shown are at the base of the synapse. (C1–G1) Responses in C–G to +step on expanded time scale (between 48 and 55 ms). (C and C1) Hair cell receptor potential (electrical potential re: ground, ϕH) rises rapidly. (D and D1) Voltage across the basolateral hair cell membrane (VH) rises rapidly but is smaller than ϕH by the cleft potential (VH= ϕH-ϕSC). (E) Top row, Synaptic cleft electrical potential relative to ground (ϕSC); Bottom row, ion concentrations [K+]SC (solid line; Left axis) rises while [Na+]SC (dotted line; Right axis) falls. (E1) ϕSC Top row, rises much faster than K+ potential EK for [K+]SC, Bottom row. (F and G) Voltage across calyx inner face membrane (VCIF=ϕC-ϕSC) rapidly hyperpolarizes (F and F1) and then reverses as ϕC depolarizes (G and G1), ultimately producing a calyceal AP (G). Note that after +step onset, fast components (*) of VH (D1) and VCIF (F1) have opposite polarities and same time course as the fast rise (*) in ϕSC (E1).
Fig. 3. Ionic currents during hair bundle stimulation and Retrograde NQT of a calyx AP to the hair cell. During excitatory step bundle displacements (Top), ionic currents (A), primarily through hair cell gK,L and calyx KV7, are affected by and affect cleft properties (ϕSC and [K+]SC in B). Anterograde NQT evokes a single AP in the calyx (gray line, C; peak shown by dashed line). Asterisks in A–C show the retrograde influence of the calyx AP on hair cell and calyx currents (A), hair cell and cleft properties (B), and calyx (C) voltages. (A) Net currents through gK,L (hair cell, blue), KV7 (calyx, gold) and HCN channels (hair cell, brown, and calyx, purple) in response to the first 15 ms of the +1-μm step. (A) inset, expanded view of KV7 current decrease at + step onset. (B) ϕH, VH, [K+]SC, EK and ϕSC at the base of the synapse during excitatory stimulus, showing that fast retrograde transmission to the hair cell occurs through ϕSC and not EK. See asterisks. (C) VCIF (cyan) and ϕC (gray, featuring the calyx AP) at the base of the synapse during excitatory stimuli.
Predicted Changes in Cleft Electrical Potential, Na+ and K+ Concentrations and Transmembrane Voltages.
The model permits dynamic simulation of biophysical properties of the cleft microenvironment as a function of spatial location. During NQT, the synaptic cleft potential at the base, ϕSC, changes as shown in Fig. 2 E and E1. The dynamic changes in ϕSC (modeled by SI Appendix, Eq. S5) allow calculation of presynaptic (VH=ϕH-ϕSC ) and postsynaptic (VCIF=ϕC-ϕSC) transmembrane voltages. VH (Fig. 2D) and VCIF (Fig. 2F) differ by ϕSC from the intracellular potentials with respect to ground (ϕH and ϕC, Fig. 2 C and G).
Fig. 2E shows the predicted changes in [K +]SC and [Na+]SC in the synaptic cleft at its base: K+ accumulation was accompanied by Na+ depletion, as predicted by a previous electro-diffusion model of the type I-calyx that considered the effect of the cleft on the hair cell receptor potential (37). In our VHCC model, changes in ion concentrations in the cleft are caused by: 1) altered ionic currents, mostly through channels in the presynaptic and postsynaptic membranes; and 2) the axial electro-diffusion of ions from the source (ion channels facing the cleft) to the sink (perilymph at the apical edges of the calyx) (Fig. 1 and SI Appendix, Eqs. S3 and S4).
Synaptic Cleft Electrical Potential, ϕSC, Rises Much Faster than the Synaptic K+ Potential.
To more clearly illustrate the variables responsible for the speed of NQT, we show the dynamic changes in ϕH (Fig. 2C1) and ϕC (Fig. 2G1), VH and VCIF (Fig. 2 D1 and F1), and synaptic cleft potential, ϕSC (Fig. 2E1) at the onset of the +1-μm step. We also plot the equilibrium potential of K+, (EK), which reflects changes in synaptic cleft potassium level, K+SC (Fig. 2E1).
Differences in the time courses of stimulus-evoked changes in ϕSC, EK, and VCIF provide key insight into the driving forces for NQT. At the onset of the positive step, the synaptic cleft potential, ϕSC, rises from 1.9 mV to +6 mV in 0.43 ms, eightfold faster than the time for EK to rise the same amount (−79.9 mV to −75.8 mV) (Fig. 2E1). The rise in EK is also slow compared with the change in VCIF (Fig. 2F1). This provides an important insight into the mechanism of NQT that has not been previously recognized: ϕSC provides the initial driving force for change in the postsynaptic membrane current, and only later does change in EK make a significant contribution. This difference arises because fewer ions are needed to change ϕSC than to change K+SC and EK (38); see SI Appendix, Notes. Fig. 2 (C1–F1) highlights how changes in ϕSC (Fig. 2E1) affect the magnitude of VH (Fig. 2D1) and the magnitude and direction of VCIF (Fig. 2F1). At step onset, VCIF hyperpolarizes while ϕC depolarizes (Fig. 2 F1 and G1, asterisks). The hyperpolarization of VCIF occurs because the fast depolarization of the cleft (ϕSC) is larger than the fast depolarization of the intracellular calyx potential (ϕC) making VCIF=(ϕC-ϕSC) negative. The subsequent slow rise in VCIF follows the depolarization of ϕC which results from the slow increase in K+SC and EK. That VCIF and ϕC are different and can change in opposing directions makes it apparent that voltage across the calyx inner and outer face membranes can be different despite the common intracellular potential. While the VHCC model results agree with experiments showing that changes in K+SC affect both hair cell and calyx signals (11, 16, 17, 19), the model further shows a pivotal role for the synaptic cleft potential in reducing transmission latency. The independent contributions of ϕSC and K+SC to NQT are analyzed later.
Contributions of Different Ion Channels to NQT.
The synaptic cleft is a dynamic system where electric potentials, ion concentrations, and ionic currents interact. The changes in cleft electrical potential and ion concentrations shown in Fig. 2 are driven by currents through voltage-sensitive ion channels (see SI Appendix, Table S6 for kinetics) on the hair cell basolateral membrane and on the calyx inner face, and in turn modulate these currents (Fig. 3). NQT is bidirectional: we first describe the roles of key channels during anterograde (hair cell to calyx) NQT and later discuss retrograde (calyx to hair cell) NQT.
Currents through Open gK,L Channels Respond Very Rapidly to Hair Cell Depolarization.
The large low-voltage-activated K+ conductance “gK,L” is a distinctive feature of type I vestibular hair cells (32, 34, 39). At the resting bundle position (t < 50 ms, Figs. 2 and 3) before the step, VH is only slightly positive (~1 mV) of EK (−80 mV; Fig. 2E1). Despite this small driving force, gK,L carries significant outward K+ current, IK,L, into the cleft (Fig. 3A) because it has a large open probability (~50%) due to its very negative activation range and there are a large number of channels (35). At the onset of the positive step, ϕH and VH depolarize faster than EK (Fig. 2 C1–E1). Since gK,L is already significantly open, the increase in driving force (VH-EK) instantaneously increases IK,L (Fig. 3A), altering both ϕSC and, more gradually, [K+]SC. IK,L is largely sustained; a modest decrease (~10%, from +350 pA to +317 pA) reflects the reduction in driving force as [K+]SC increases and EK depolarizes (Fig. 3B).
In Fig. 3 we have presented local potentials at the base of the synapse and whole-cell ionic currents. However, the VHCC model has the ability to calculate the spatiotemporal distributions of these variables. In our model conditions, the net IK,L was never inward (negative). However, using the model to examine membrane currents as functions of location along the cleft, we noticed that inward IK,L can occur locally at the base of the hair cell during a negative step if VH becomes negative to EK (SI Appendix, Fig. S2). These results support the idea that gK,L may under certain conditions carry inward K+ currents into the hair cell and aid in K+ clearance (19).
Fig. 3 shows that the current through gK,L is fast, large, and noninactivating, allowing it to closely follow the hair bundle stimulus. Since gK,L is high in the resting state, the receptor potential-induced change in IK,L is not delayed by ion channel activation and is near-instantaneous. IK,L rapidly changes the cleft electrical potential (Fig. 3B, Bottom trace), which in turn rapidly alters currents through low-voltage-activated potassium (KLV) channels in the calyx inner face membrane.
Excitatory Stimuli Decrease KV7 Current into the Synaptic Cleft from the Calyx.
The postsynaptic KLV conductances include the KV7 family of voltage-gated channels (40). The VHCC model permits quantitative characterization of the role of these channels in modulating [K+]SC and ϕSC during NQT, which has not previously been clear. According to the model, at resting membrane potential (VCIF = –68 mV), outward current through partly open (29%) KV7 channels on the calyx inner face supplies K+ to the synaptic cleft, helping to set resting levels of [K+]SC and ϕSC. During positive bundle displacement, changes in VCIF and EK across the calyx inner face membrane (Fig. 3B) slightly reduce outward KV7 currents into the synaptic cleft (Fig. 3A, IKv7, inset) except during the calyx AP where outward KV7 currents increase (Fig. 3A, IKv7, asterisk).
Synaptic HCN Channels Establish Resting Conditions but Have Minimal Effect on [K+]SC and ϕSC during NQT.
Hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels are found on both sides of the VHCC synapse. Type I hair cells primarily express HCN1 channel subunits (41), and vestibular afferent neurons primarily express HCN2 subunits (42). Model results show that under physiological concentrations and potentials, HCN channels support a net inward current in both cells, mediated by influx of Na+ ions, that is 10 to 100-fold smaller than outward K+ currents through gK,L and KV7 channels (Fig. 3A). In the model, we localized HCN1 to the hair cell and HCN2 to the calyx and found that both HCN channels had smaller open probabilities (HCN1 - 18%; HCN2 - 8%) than KLV channels (gK,L - 50%; KV7 - 29%) at rest (VH = −79 mV; VCIF = –68 mV) and contributed very small currents to either positive (Fig. 3A) or negative (SI Appendix, Fig. S2) bundle step responses. This difference relative to KLV-associated currents is explained by the HCN channels’ smaller whole-cell conductance values (SI Appendix, Table S6), their hyperpolarized voltage range of activation, and their mixed cationic currents (K+ and Na+) moving in opposite directions. In the model, both HCN1 and HCN2 channels, along with the Na+/K+-ATPase, contribute to the regulation of [K+] and [Na+] in the synaptic cleft (Fig. 2E). Blocking HCN channels blocks NQT (11), but only under conditions that artificially favor HCN channels, i.e., when the calyx was held at –100 mV, an unphysiological (substantially negative to EK) condition at which most KV7 channels would be closed and most HCN channels would be open.
Retrograde Transmission from Calyx to Hair Cell.
The VHCC model naturally captures retrograde transmission (Fig. 2 B–F, arrowheads), defined as a change in hair cell potential caused by a change in calyx potential. This is especially pronounced during NQT-induced postsynaptic APs that occur for sufficiently large hair bundle displacements. An AP in the fiber (Figs. 2G and 3C) causes the calyx inner face membrane to depolarize (Fig. 3C, VCIF) and increases outward current, primarily through KV7 channels (Fig. 3A; compare KV7 and HCN2); ϕSC rises (Fig. 3B); VH becomes more negative (Fig. 3B); outward K+ currents from the hair cell, primarily through gK,L channels, decrease (Fig. 3A, see IK,L and HCN1); and the hair cell potential, ϕH, depolarizes (Fig. 2C, arrowhead; Fig. 3B). The fast component of retrograde transmission is mediated by changes in ϕSC and not EK (Fig. 3, asterisks). These results indicate that fast retrograde events seen in electrophysiological recordings of the hair cell and calyx (11) are caused by changes in electrical potential in the synaptic cleft. It has been suggested that the bidirectional nature of NQT, which our VHCC model captures, could be used to modulate the sensitivity of both the calyx and the hair cell (43).
Role of the Synaptic Cleft in NQT: ϕSC and [K +]SC .
The predictions of the VHCC model agree with experimental observations (11, 16–19) that support the hypothesis that K+ currents through the basolateral membrane of the type I hair cell modulate K+SC (14, 15) and change EK to affect pre- and postsynaptic currents. The model further shows that modulation of ϕSC also alters both presynaptic and postsynaptic ionic currents but on a substantially faster time scale. What are the relative contributions of ϕSC and K+SC to NQT? Are both mechanisms necessary? What advantages do they provide? To answer these questions, we use the VHCC model to isolate ϕSC and K+SC during both step and sinusoidal bundle displacements.
Isolation of the Contributions of ϕSC and [K +]SC during Step Bundle Displacements.
We mathematically define two conditions: ϕSC modulation and K+SC modulation. The ϕSC modulation-only condition is set by fixing K+SC and Na+SC at 5 mM and 140 mM, respectively, while ϕSC is allowed to vary as a function of ionic currents (SI Appendix, Eqs. S8–S10). The K+SC modulation-only condition is set by fixing ϕSC at 0 mV and allowing K+SC and Na+SC to vary as functions of the ionic currents (SI Appendix, Eqs. S11–S13). Fig. 4 illustrates how ϕSC and K+SC separately and together shape the calyx potential (Fig. 4 A1 and A2) at the onset of positive bundle displacements of different magnitudes. The ϕSC modulation reduced the latency of the calyx potential by 1.8 ms (0.3-μm step) and 1.5 ms (1-μm step).
Fig. 4. ϕSC and K+SC are responsible for the fast and slow components of NQT, respectively. (A1 and A2) Simulations of calyx potential ϕSC for two bundle displacement (X) steps (0.3 μm, 1 μm) with ϕSC modulation only (blue), with K+SC modulation only (yellow) and with both enabled (dark orange).
Fig. 4. also shows that K+SC modulation is responsible for the slow component of the rise in calyx potential. When ϕSC and K+SC are allowed to vary concomitantly, both fast and slow components are seen. Comparing the rise of ϕC in the K+SC -only and “Both” conditions clearly shows that change in ϕSC confers a ~1 to 2-ms advance to the calyx response. Thus, the modulation of ϕSC enables faster signal transmission between hair cell and calyx. The modulation of K+SC, although slower, results in a larger depolarization of the calyx. For these inputs to the VHCC model, both mechanisms together did not trigger spiking for smaller hair bundle deflections, but both were necessary to elicit spiking for the larger deflection. In the presence of an AP, the response of the model is influenced by currents at the hemi-nodes and nodes of the afferent fiber (SI Appendix, Eqs. S6 and S7 and Table S6). ϕC (Fig. 4A2) closely resembles experimental whole-cell recordings of the voltage response of a rat saccular calyx to positive step displacements (Songer and Eatock (9), their Fig. 5A).
Fig. 5. Changes in ϕSC and K+SC shape the phase and gain of the transmitted signal (ϕC). (A) Modeled ϕC response and whole-cell current-clamp recording of ϕC from rat saccular calyx (Songer and Eatock (9), their Fig. 5B) during ±1 μm sinusoidal bundle displacement. (B–E) Model outputs for ±0.3-μm bundle displacement analyzed into ϕSC modulation-only component (blue), K+SC modulation-only component (yellow), and “Both” together (dark orange). Bode plots showing phase (B, D), normalized gain (C1 and E1) and gain (C2 and E2) of ϕH and ϕC relative to bundle displacement, X. (D) The contribution to the phase of ϕC from the K+SC component (yellow) falls off much more steeply and with lower corner frequency than ϕSC component (blue). Combining them (dark orange) better matches experimental data [green, from Songer and Eatock (9), their Fig. 5C]. (E1 and E2) In the model, K+SC modulation dominates the gain of NQT between 2 and 100 Hz, while ϕSC provides a small constant gain across a wide frequency band (blue, E2). Both components together produce slightly greater gain above 20 Hz (dark orange, E1).
The VHCC Model Captures the Frequency Response and Gain of NQT.
Songer and Eatock (9) recorded responses from calyces in the excised rat saccule to sinusoidal hair bundle stimulation between 2 and 100 Hz in order to capture the frequency filtering characteristics relevant to natural head motions (1). An example of NQT from these experiments is reproduced in Fig. 5A. In addition to measuring the frequency-following capacity of the NQT-induced changes in calyx potential (ϕC), these data showed that for large bundle displacements NQT alone (without QT) can trigger APs in the afferent. Here we show the VHCC model’s ability to reproduce these salient features of the experimental data and use the model to illustrate the roles of K+SC and ϕSC in shaping the calyx response (Fig. 5).
The Model Captures NQT-driven Firing in Response to Sinusoidal Stimulus.
The model produced calyx potentials that are consistent with whole-cell recordings (Fig. 5A), including spikes for large stimuli. In both data and model, the calyx was most likely to spike for stimulus frequencies between 10 and 50 Hz.
In Fig. 5 B–E, a small stimulus (±0.3 μm) was used to avoid triggering APs and better reveal the underlying changes in hair cell current and postsynaptic potential (ϕC). The model predicts that resistive currents (IH,R) are much larger than capacitive currents (IH,C) flowing from the type I hair cell (SI Appendix, Fig. S3). The capacitive current does increase with frequency, as expected, and can noticeably affect NQT during large voltage step stimuli (discussed later in Fig. 6).
Fig. 6. Changes in synaptic cleft potential ϕSC explain the shape of fast postsynaptic currents in paired voltage recordings. (A1 and A2) Voltage protocols and currents from experiment. (A1) Calyx Potential (ϕC) held at −100 mV. Hair cell potential stepped at 50 ms from holding potential of −100 to 20 mV. A2, Calyx Potential (ϕC) held at −70 mV. Hair cell potential stepped at 50 ms from holding potential of −70 to 20 mV. Currents reproduced from Contini et al. [(18), their Fig. 10]. (B–E) Model results. (B1 and B2) Resistive postsynaptic current through the calyx inner face membrane (ICIF,R) as calculated by the model. Note that fast currents (* and **) do not occur in the [K]SC-only case (faded blue). Note similarities in shape (not magnitude) to recorded currents in A1 and A2. (C) Electrical potential in the synaptic cleft (ϕSC) and voltage across the calyx inner face (VCIF) membrane. (C1) The cleft potential is primarily shaped by capacitive currents from the hair cell (IH,CAP, D1). (C2) The cleft potential is primarily shaped by resistive currents from the hair cell (IH,R, D2). (E) Whole-cell currents through individual ion conductances. From the nonphysiological −100-mV holding potential (A1), HCN1 hair-cell current is prominent, fast, and sustained, because HCN channels are open at −100 mV (E1). Note that from −70-mV holding potential (A2), gK,L current is fast and sustained and KV7 current (yellow) is the most prominent calyx current (E2). Currents through gK,L (E2) sustain the step in ϕSC following the voltage step in the hair cell. The slow decay reflects the change in driving force as K+ accumulates in the cleft.
Changes in Both ϕSC and [K +]SC Are Required to Explain the Phase of the ϕC Response to Sinusoidal Stimuli.
We examined the contribution of ϕSC and K+SC to the phase of ϕH (Fig. 5B) and ϕC (Fig. 5D) as a function of frequency. A positive phase indicates that the maximal response of ϕH or ϕC is achieved before the maximal bundle displacement and is indicative of a high-pass filtering process. As shown, ϕSC contributes to a positive phase response in ϕH at frequencies below 32 Hz. For ϕH, at lower frequencies, the positive phase arises from gK,L activation (for kinetics, see SI Appendix, Table S6): during the depolarizing half cycle, an appreciable increase in gK,L (>10% at 2 Hz) produces an increasing outward current that truncates the rise in ϕH before the peak of the bundle displacement. Likewise, for ϕC the positive phase at lower frequencies arises from activation of KLV channels within the calyx and fiber. The ϕC response was nearly flat with frequency for the ϕSC -only condition but showed strong low-pass filtering for the K+SC -only component.
The low-pass behavior of [K+] modulation can be explained by the time course of K+ accumulation or depletion in the cleft (SI Appendix, Eq. S3) and its effect on EK, which is limited by the rate of K+ entry through gK,L, diffusion out of the cleft and flux across the calyx inner face membrane (11, 16). While the modulation of K+SC is slow, the modulation of ϕSC is fast because it requires fewer ions than equivalent changes in EK (SI Appendix, Notes). As a result, ϕSC rapidly changes both pre- and postsynaptic membrane voltages, altering both capacitive currents and resistive (ionic) currents through open channels. This explains the short response latency of ϕC at the onset of a step stimulus (Fig. 4). The ϕSC component is also required to closely approximate the phase of experimental data above 5 Hz (Fig. 5D). This effect supports the evidence from step data (Fig. 4) that a stimulus-modulated electrical potential in the synaptic cleft speeds up NQT.
[K+]SC Dominates the Gain of NQT but ϕSC Slightly Boosts Gain at All Frequencies.
Next, we examined the normalized and actual gain of averaged peak ϕH (Fig. 5 C1 and C2) and ϕC (Fig. 5 E1 and E2) as a function of frequency. For ϕH, the K+SC mechanism contributes to a gain that rolls off above ~20 Hz, while the ϕSC mechanism linearly increases with frequency. Interestingly, the ϕSC mechanism kicks in to noticeably boost the ϕH gain above ~30 Hz. The crossover in ϕH gains resulting from changes in K+SC and ϕSC shows that the impact of ϕSC increases with frequency. For ϕC, the gain at all frequencies is larger for the K+SC component than for the ϕSC component (Fig. 5E2). The model also shows that the K+ modulation mechanism amplifies the calyx potential response at all frequencies but especially below ~20 Hz. This effect is consistent with the increasing size of the K+SC component relative to the ϕSC component with time during a displacement step (Fig. 4 A1 and A2). The ϕSC component is approximately constant with frequency, and the model predicts will boost the high-frequency gain (Fig. 5E1). However, the experimental ϕC gain (green) has a higher corner frequency (66 Hz) than the model output (36 Hz), which may reflect differences in afferent fiber properties between the model and specific experiments.
Paired Voltage Clamp Recordings from Hair Cell and Calyx Provide Evidence of Changes in Synaptic Cleft Potential.
We have previously speculated that NQT involves the flow of K+ through a large number of gK,L channels that are open at typical hair cell resting potentials (12). In this condition, receptor potentials instantaneously change K+ current flowing into the synaptic cleft, bypassing the time—on the order of 1 to 100 ms—required to activate voltage-gated channels from the closed conformation. Experiments involving paired voltage-clamp recordings of whole-cell currents from turtle type I hair cells and postsynaptic calyces (18) address this hypothesis. In these experiments the hair cell was stepped to +20 mV from either –100 mV or –70 mV, while the calyx was held at either –100 mV or –70 mV, and fast inward calyx currents were recorded (Fig. 6 A1 and A2, respectively). At –70 mV (Fig. 6A2), currents were attributed to resistive coupling through presynaptic and postsynaptic ion channels open at the holding potential immediately prior to the voltage step. At –100 mV (Fig. 6A1), where calyceal KLV channels are closed but HCN channels are open, fast currents were also visible.
The VHCC model was applied to analyze the experimental data with either the K+SC -only condition or the K+SC and ϕSC (“Both”) condition, described earlier. The fast postsynaptic currents only occurred in the “Both” condition (Fig. 6 B1 and B2), illustrating that changes in ϕSC were the primary driving force and that the recorded currents cannot be explained solely by changes in EK. Under the “Both” condition, we first examined the model’s output for a calyx and hair cell holding potential of –100 mV (Fig. 6 C1–E1). At this holding potential, gK,L and KV7 channels were mostly closed and carried very little current prior to the voltage step. As ϕH is depolarized to +20 mV, ϕSC (Fig. 6C1) is primarily shaped by capacitive currents across the basolateral hair cell membrane (IH,CAP) which precede gK,L activation (Fig. 6 D1 and E1). This variation in ϕSC alters VCIF, shapes currents through the calyx inner face membrane (Fig. 6B1, asterisk), mostly via HCN2 channels (Fig. 6E1), and explains the recorded current (Fig. 6A1, asterisk). We identify these currents as evidence for changes in ϕSC in response to depolarization of the hair cell.
We then examined the model’s output for calyx and hair cell holding potentials of –70 mV (Fig. 6, Right Column), where gK,L and KV7 channels are significantly open prior to the voltage step. When ϕH is depolarized to +20 mV, ϕSC (Fig. 6C2) is primarily shaped by resistive currents through the basolateral hair cell membrane (IH,R), mostly via gK,L (Fig. 6 D2 and E2). The change in ϕSC hyperpolarizes VCIF (Fig. 6C2) and increases the inward driving force for currents through the calyx inner face, principally through KV7 channels rather than HCN channels (Fig. 6E2), causing a fast current (double asterisks) similar in time course to the experimental record (18) (compare Fig. 6 A2 and B2). The larger predicted amplitude of the fast current may reflect differences in key channel properties between the model parameters based on data from excised mouse and rat sacular epithelia with attached ganglia, and the experimental record, from excised turtle crista with attached brainstem. Based on the literature, key properties that differ across species or organs include the precise voltage dependence and size of gK,L and KV7 and whether BK currents are evident in type I hair cells. For example, BK currents in type I hair cells have been implicated in NQT in the turtle crista (18), but were not detected in mouse utricle (34).
For depolarizing steps from both −70 and −100 mV (Fig. 6 A1 and A2), the postsynaptic currents have a fast component and a slow component. These components are consistent with the existence of two driving forces with different time courses—ϕSC and EK. Note that ϕSC (Fig. 6 C1 and C2) follows the general shape of d(ϕH)dt when capacitive currents dominate (at –100-mV holding potential, Fig. 6D1). In contrast, ϕSC (Fig. 6 C1 and C2) follows the general shape of ϕH when resistive currents from the hair cell dominate (at –70-mV holding potential, Fig. 6D2, similar to reported hair cell resting potentials (~–80 to –60 mV, see SI Appendix, Table S4 for references). Under these physiological conditions, open gK,L channels (Fig. 6E2) are responsible for the changes in cleft potential and postsynaptic currents leading to both fast and sustained transmission of the hair cell receptor potential to the calyx (Fig. 6 B2 and C2). These results show the importance for NQT of the unusual properties of gK,L: together the large conductance and unusually negative half-activation voltage ensure that many hair cell channels are open near resting potential and ready to immediately respond to incoming stimuli. Interestingly, the model predicts that fast transmission can occur when gK,L is closed at −100 mV (Fig. 6B1). In this condition, capacitive coupling between the hair cell and the cleft changes ϕSC and VCIF (Fig. 6C1), providing the driving force for a resistive current flow through HCN2 channels (Fig. 6E1) on the calyx inner face.
The predictions in Fig. 6 assume an ideal voltage clamp; the potential effects of series resistance are analyzed in the SI Appendix. Inclusion of series resistance and a model of gK,L that includes potassium relief of block (18) reduced the discrepancy between model predictions and experimental recordings at −70 mV (SI Appendix, Fig. S5).
NQT Depends on Calyx Morphology.
The simulations we have presented are for a calyx shape typical of many calyces in the central and striolar zones of rodent vestibular epithelia (Fig. 1). However, calyces vary in their shape in different regions of amniote vestibular epithelia (30), and shorter “proto-calyces” are reported to exist in fish and amphibians (4, 44–47). To address the effects of calyx geometry on the two components (ϕSC and K+SC) of NQT, we studied both the steady-state condition and the step response (Fig. 7) for different calyx heights, while retaining the same ion channel and transporter expression per unit area of membrane. As the calyx was truncated (Fig. 7) to one-half or one-quarter of the height of our standard value for a rodent (11.33 μm) (Figs. 1 and 7, “Calyx”), both ϕSC and K+SC were closer to perilymph values (0 mV and 5 mM, respectively) at rest (Fig. 7 A and D), had smaller responses to stimuli (Fig. 7 B and E), and no spikes were initiated in the afferent fiber. However, the model does suggest that the truncated calyces could accumulate enough K+ (Fig. 7F) to facilitate afferent depolarization. The effect on ϕC at the onset of bundle stimulation is small, ~2 mV (Fig. 7C). On the other hand, increasing calyx height by 2 μm (Fig. 7, “Ext”) increased ϕSC and K+SC at rest, increased rate and magnitude of stimulus-evoked changes in ϕC, and reduced transmission latency (Fig. 7C). The greater calyx height is found in “dimorphic” afferents (30), which have both calyx and bouton endings.
Fig. 7. Magnitude and speed of NQT increase with calyx height. (A and D) The 3D visualizations represent a shell the width of the cleft space resting between the hair cell and calyx. Silver edges delineate calyces of different heights. Spatial gradients of ϕSC and K+SC are visible within these regions. The outer surface of the hair cell above the calyx is exposed to perilymph (ϕ = 0 mV, ground and [K+] = 5 mM). (B, C, E, and F) Changes in ϕSC, ϕC, K+SC and EK increase with calyx height. Curve colors correspond to those at the base of each calyx in A and D at steady state. (A) The spatial distribution of ϕSC at rest as a function of calyx height. (B) ϕSC at the base of the synapse over the course of step bundle displacement (same as in Fig. 2A). (C) Changes in ϕC (the transmitted signal). The inset demonstrates the reduction in time to spike as calyx height is increased by 2 μm from 11.3 to 13.3 μm. (D) The spatial distribution of K+SC at rest. (E) K+SC at the base of the synapse over the course of step bundle displacement. (F) EK values corresponding to changes in K+SC. Compare with ϕSC (B).
Fig. 7 shows that increasing calyx height and thereby extending the synaptic cleft increases resting values of ϕSC and K+SC, depolarizes both hair cell and calyx potentials, opens more ion channels at rest, increases stimulus-evoked changes in both ϕSC and K+SC, increases the likelihood of postsynaptic spiking, and reduces first-spike latency (Fig. 7C; a calyx spike is evident in ϕSC traces for the standard and extended calyx). Simulations of calyx heights at finer intervals indicated that NQT-initiated spiking required a calyx height within 95% of our standard value (11.3 μm), suggesting that the central-zone calyx has the minimal height required for NQT-driven spiking. Once this minimal height is achieved, further increases raise the magnitude and rate of NQT and reduce the time-to-spike, reflecting increases in the calyceal membrane area, the extent of the synaptic cleft, axial resistance, and the distance to perilymph at the calyx apex.
Discussion
We have constructed a biophysical model that incorporates ultrastructural data on the geometry of the VHCC synapse, immunocytochemical data on the expression of ion channels and transporters, and electrophysiological data on kinetics of ion channels. Our goal was to understand the mechanism of NQT at the unique VHCC synapse, which is characterized by its extensive and unfenestrated synaptic cleft, and the many low-voltage-activated potassium (KLV) channels that populate the pre- and postsynaptic membranes. The model predicts dynamic changes in the potassium concentration K+SC and electrical potential ϕSC in the synaptic cleft in response to hair bundle deflection or paired voltage-clamp recordings, which explain experimental recordings of both fast (9, 18) and slow (10, 11) NQT. In the process, we were able to identify three salient features in existing experimental recordings that support the existence of an electrical potential in the synaptic cleft and its role in NQT: the frequency dependence of the phase of NQT (Fig. 5D) (9); the fast retrograde events in hair cell potential during retrograde transmission of postsynaptic spikes (Fig. 3, see asterisks) (11); and the shape of fast postsynaptic currents (Fig. 6) (18).
The VHCC model permits a clear description of the sequence of events that constitute NQT, shown in Figs. 2 and 3. Modulation of hair cell potential immediately changes the synaptic cleft potential ϕSC and gradually changes the potassium potential EK. During physiological operation, both are primarily accomplished by change in a large K+ current through the noninactivating, low-voltage-activated potassium conductance, gK,L, on the hair cell basolateral membrane. The properties of gK,L suit its role in NQT. First, gK,L is >50% open at the hair cell resting potential, allowing the receptor potential to immediately alter membrane current and thereby ϕSC. Second, gK,L is noninactivating, ensuring that significant K+ will accumulate in the cleft during sustained or low-frequency hair cell stimulation. We also considered the proposal that cleft K+ modulates gK,L activation by relieving channel block (18). For the physiological voltage changes evoked by a large hair bundle stimulus, our Hodgkin–Huxley modeling of gK,L captured the recorded behavior well (Fig. 5). Implementing K+ relief of block to describe gK,L activation generated comparable results (SI Appendix, Notes and Fig. S4). However, the K+ relief of block model provides more accurate predictions for experiments involving large voltage excursions (SI Appendix, Fig. S5).
The changes in cleft electrical and K+ potentials alter the driving forces of currents across the calyx inner face, which contains a large number of noninactivating, low-voltage-activated channels (KV7) that are significantly open at rest (~30%). Upon stimulation, increases in ϕSC and K+SC reduce the outward KV7 current and depolarize the calyx. In short, the presence of open KLV channels in the pre- and postsynaptic membranes enables NQT as previously proposed (12, 19). The VHCC model clarifies the role of ϕSC and EK as separate driving forces for currents through all channels facing the synaptic cleft. The fast components of NQT are transmitted via the modulation of ϕSC and the slow components via K+SC.
The cleft electrical potential (ϕSC) improves the ability of both the hair cell and calyx to follow sinusoidal stimuli at higher frequencies (Fig. 5 B–E). Consistent with this prediction, fast postsynaptic currents observed following instantaneous voltage steps (Fig. 6) are also driven by changes in ϕSC caused by capacitive and resistive currents from the hair cell (Fig. 6 D and E). The VHCC model identifies the hair cell, cleft, and calyx as three compartments in series. Given that resistive currents between any two compartments (hair cell and cleft; cleft and calyx) are driven by both electrical potential and ion concentration gradients, resistive coupling contributes to both fast and slow components of NQT. Capacitive coupling contributes exclusively to the fast NQT component by rapidly altering the cleft electrical potential. Predictably, capacitive currents from the hair cell increase with frequency during bundle stimulation (SI Appendix, Fig. S3), and are prominent during voltage steps from voltages where gK,L is closed (Fig. 6C1). Both resistive and capacitive coupling are observed in other systems where neural membranes are held in close proximity by specialized structures, such as the Mauthner cell axon cap and the basket cell’s pinceau terminal on a Purkinje cell (6).
At the calyx of Held, a large presynaptic calyx important for auditory processing, analysis of ephaptic coupling (48) suggests that although presynaptic capacitive and resistive currents occur, the fenestrations in the calyx of Held reduce synaptic cleft potentials, ensuring that presynaptic calcium channels sense the full presynaptic AP and rapidly initiate neurotransmitter release. In contrast, the VHCC model shows that the unfenestrated morphology of the calyx increases cleft K+SC and ϕSC, complementary mechanisms that mediate NQT. The slower change in K+SC increases the gain of NQT for slow or sustained hair bundle deflections, while the near-instantaneous change in ϕSC enables the calyx to closely follow dynamic hair cell potentials and explains the calyx-bearing afferent’s short latency and ability to follow relatively high-stimulus frequencies, including sound and vibration (5, 9, 18, 27). The calyx is essential for both fast and slow NQT, and as calyx height increases, the efficacy of both mechanisms increases and transmission latency decreases.
These findings explain the exceptional speed of neurotransmission between type I hair cells and vestibular afferents, which is even faster than the QT between cochlear hair cells and afferents (5). At its fastest, QT imposes a 0.4 to 0.6-ms delay (49), reflecting the time required for multiple steps from the opening of presynaptic voltage-gated calcium channels to the opening of postsynaptic neurotransmitter-gated receptor channels. For fast vestibular reflexes, with latencies as short as 5 ms between head motion and motor response (50), a mechanism that avoids the synaptic delay of quantal release is a significant advantage. Our results show that robust NQT, enabled by the evolution of the full calyx, substantially reduces the latency and advances the phase of vestibular signals that drive neural circuits controlling gaze, balance, and orientation.
While we have focused on NQT, type I vestibular hair cells have ribbon synapses that coordinate vesicle release of glutamate into the synaptic cleft (28) in a process dependent on activation of voltage-gated calcium (CaV1.3) channels. Glutamate transiently activates AMPA receptors on the calyx inner face and may also accumulate in the cleft to influence the slow phase of NQT and to broaden quantal EPSCs (20). In our model, the hair cell membrane depolarizes to -62 mV (at the base) (Fig. 2 D and D1) and -58 mV (toward the cleft opening) during bundle displacement, these voltages correspond to CaV1.3 channel open probabilities of just 4 to 8%. This may not reflect the situation in vivo; e.g., the MET conductance we used in the VHCC model is based on recordings in 1.3 mM external [Ca2+]. However, vestibular endolymph has only 100 to 200 μM Ca2+ (51) and external Ca2+ blocks MET channels (52), such that the MET conductance and therefore both NQT and QT may be larger in vivo. On the other hand, type I vestibular hair cells have relatively small Ca2+ currents and quantal release compared with other hair cells (53). Our results agree with evidence that 1) NQT alone can elicit afferent spikes (9) and 2) NQT reduces the number of quanta necessary to elicit APs in the afferent (11). When QT and NQT occur together, they filter the incoming signal in different ways (9), but how the combined information is handled centrally is not understood.
In conclusion, the VHCC model provides a quantitative description of NQT and describes two parallel paths for NQT across the synaptic cleft: changing the cleft electrical potential and changing the potassium potential. It also delineates the roles of specific ion channels and calyx geometry in NQT. In contrast to experiments that can only calculate the effective K+SC based on whole-cell currents, the model is able to predict the gradients that must occur in both K+SC and ϕSC along the extent of the cleft. This capability offers promise for investigating channel microdomains (30) at the VHCC synapse. Here we have focused on NQT in a simple calyx from central or striolar zones of vestibular epithelia; the model can be extended to explore QT and the functional effects of other afferent terminal forms, such as the complex calyces that enwrap multiple type I hair cells and the elaborate dimorphic terminal arbors that include calyces and boutons (reviewed in refs. 4, 54 and 55).
Methods
Computational Methods.
To investigate NQT, model simulations were performed using a finite element geometry representative of the vestibular hair cell and calyx in COMSOL Multiphysics software. The general setup of the model, the modeling of membrane currents, the governing equations for electrical potentials and ion concentrations, solver conditions, and channel distributions and kinetics are fully described in the SI Appendix.
Experimental Methods.
Whole-cell patch-clamp recordings were performed on mouse vestibular afferent neurons to measure the activation and inactivation time constants of the NaV current. Confocal microscopy and immunocytochemistry were performed to study the localization of KCC, the relative density of Kv7.4, and the morphology of the VHCC synapse. Experimental details are fully described in SI Appendix.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
This study was supported by NIH (R01 DC012347 and DC002290), an Emerging Research Grant from the Hearing Health Foundation and a seed grant from the Rice University ENRICH program. We are grateful to Hannah Martin and Oleg Igoshin for stimulating discussions and Selina Baeza-Loya for providing activation and inactivation time constants of afferent voltage-gated sodium channels.
Author contributions
R.A.E. and R.M.R. designed research; A.C.G., I.H.Q., A.L., R.A.E., and R.M.R. performed research; R.A.E. analyzed data; and A.C.G., A.L., R.A.E., and R.M.R. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
The model was implemented in Comsol Multiphysics 5.6 software. Equations and data sources are available in SI Appendix, Computational Methods and Experimental Methods. The Comsol file (.mph) containing the model, and other supporting files, are publicly publicly available on GitHub: (https://github.com/acgsci/vestibularHairCellCalyxNQT).
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926172.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595694
202211977
10.1073/pnas.2211977120
videoVideoresearch-articleResearch Articlemed-sciMedical Sciences422
Biological Sciences
Medical Sciences
A bioengineered probiotic for the oral delivery of a peptide Kv1.3 channel blocker to treat rheumatoid arthritis
Wang Yuqing a
Zhu Duolong b c https://orcid.org/0000-0002-8048-2171
Ortiz-Velez Laura C. d https://orcid.org/0000-0002-2096-1795
Perry Jacob L. b c
Pennington Michael W. e https://orcid.org/0000-0001-5446-3447
Hyser Joseph M. [email protected]
a b c 1 2
Britton Robert A. [email protected]
b c 1 2
Beeton Christine [email protected]
a f g 1 2 https://orcid.org/0000-0002-2500-5874
aDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030
bDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030
cAlkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX 77030
dPana Bio, Inc., Houston, TX 77051
eAmbiopharm, Inc., North Augusta, SC 29842
fBiology of Inflammation Center, Baylor College of Medicine, Houston, TX 77030
gCenter for Drug Discovery, Baylor College of Medicine, Houston, TX 77030
2To whom correspondence may be addressed. Email: [email protected], [email protected], or [email protected].
Edited by Michael Cahalan, University of California Irvine, Irvine, CA; received July 13, 2022; accepted November 21, 2022
1J.M.H., R.A.B., and C.B. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e221197712013 7 2022
21 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
New therapeutics that combine efficacy with limited side effects and can be delivered noninvasively are needed to adequately treat patients with rheumatoid arthritis (RA) and other autoimmune diseases. Kv1.3 channel-expressing CCR7− effector memory T (TEM) lymphocytes are significant players in the pathogenesis of multiple autoimmune diseases, and blocking Kv1.3 reduces disease severity in rat models of RA and patients with plaque psoriasis. However, peptide therapeutics require repeated injections, reducing patient compliance. We used a bioengineered Lactobacillus reuteri as an oral delivery method of a Kv1.3 blocker for immunomodulation in rat models of atopic dermatitis and RA. This study demonstrates a novel approach for the noninvasive delivery of peptide-based therapeutics for the oral treatment of chronic inflammatory diseases.
Engineered microbes for the delivery of biologics are a promising avenue for the treatment of various conditions such as chronic inflammatory disorders and metabolic disease. In this study, we developed a genetically engineered probiotic delivery system that delivers a peptide to the intestinal tract with high efficacy. We constructed an inducible system in the probiotic Lactobacillus reuteri to secrete the Kv1.3 potassium blocker ShK-235 (LrS235). We show that LrS235 culture supernatants block Kv1.3 currents and preferentially inhibit human T effector memory (TEM) lymphocyte proliferation in vitro. A single oral gavage of healthy rats with LrS235 resulted in sufficient functional ShK-235 in the circulation to reduce inflammation in a delayed-type hypersensitivity model of atopic dermatitis mediated by TEM cells. Furthermore, the daily oral gavage of LrS235 dramatically reduced clinical signs of disease and joint inflammation in rats with a model of rheumatoid arthritis without eliciting immunogenicity against ShK-235. This work demonstrates the efficacy of using the probiotic L. reuteri as a novel oral delivery platform for the peptide ShK-235 and provides an efficacious strategy to deliver other biologics with great translational potential.
synthetic biology
Kv1.3 channel
drug delivery
BCM | Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine (Alkek Center for Metagenomics and Microbiome Research) 100018622 None Joseph M HyserChristine Beeton Baylor College of Medicine (BCM) 100007915 None Christine Beeton Cancer Prevention and Research Institute of Texas (CPRIT) 100004917 RP180672 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton Cancer Prevention and Research Institute of Texas (CPRIT) 100004917 RP150578 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton Cancer Prevention and Research Institute of Texas (CPRIT) 100004917 RP170719 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton Cancer Prevention and Research Institute of Texas (CPRIT) 100004917 RP1806721 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton HHS | NIH | NIDDK | Division of Diabetes, Endocrinology, and Metabolic Diseases (DEM) 100017618 DK56338 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton HHS | NIH | National Cancer Institute (NCI) 100000054 CA125123 Christine Beeton HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) 100000050 HG006348 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton HHS | NIH | National Center for Research Resources (NCRR) 100000097 RR024574 Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton Dan L. Duncan Cancer Center, Baylor College of Medicine (DLDCC) 100008527 None Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton John S. Dunn Gulf Coast Consortium for Chemical Genomics None Yuqing WangDuolong ZhuLaura C. Ortiz-VelezJ. Lance PerryMichael W. PenningtonJoseph M HyserRobert BrittonChristine Beeton
==== Body
pmcBiologics now constitute a significant element of available medical treatments for various conditions such as chronic inflammatory disorders, cancer, and metabolic disease. Nearly 30% of all drugs approved by the U.S. Food and Drug Administration in 2015 to 2018 were biologics (1), yet the majority of biologics are administered via parenteral route because of poor oral bioavailability. Fear of needles, injection-associated infection and pain are responsible for skipping doses by patients, especially for those with chronic inflammatory diseases that often requires lifelong treatment. Rheumatoid arthritis (RA), one of the most common autoimmune diseases, mainly affects synovial joints but is also associated with a higher risk of cardiovascular, skeletal, and psychological disorders and carries a significant socioeconomic burden (2). Although current therapeutics have considerably improved the management of RA in the last decade, RA-induced reduction in lifespan has not improved and maybe even worsened (3). Current RA treatments include nonsteroidal anti-inflammatory drugs, corticosteroids, disease-modifying antirheumatic drugs, and biologic response modifiers such as antibodies targeting cytokines and their receptors (TNF-α or IL-1β, for example). These approaches focus on improving inflammation and pain control; however, they have little effect on the pathogenesis of RA and many increase the probability of infections or cancer (4). Many patients also experience significant side effects such as kidney or liver damage, leading 30 to 50% of patients to alter their treatment regimens (5, 6). Furthermore, the current biologics can be immunogenic and stimulate the generation of neutralizing antibodies after repeated drug administration (7, 8). Thus, new therapeutics that combines efficacy with limited side effects is needed to treat patients with RA adequately.
The pathogenic role of T lymphocytes in RA has been extensively studied. CCR7+ naïve and central memory T (TCM) cells are the predominant T lymphocyte populations in the circulation and lymphoid organs. In contrast, most T cells in the synovium and synovial fluid of patients with RA are CCR7− effector memory T (TEM) cells, making them a desirable therapeutic target for RA (9, 10). At rest, human and rat T lymphocytes express low levels of two K+ channels, Kv1.3 and KCa3.1, that regulate plasma membrane potential and the homeostasis of Ca2+, a crucial second messenger in T cell activation (11–13). Upon activation, CCR7− TEM cells up-regulate Kv1.3, while naïve and TCM cells up-regulate KCa3.1. Thus, TEM cells are exquisitely sensitive to inhibition by Kv1.3 blockers. On the contrary, naïve and TCM cells rely on KCa3.1 and escape Kv1.3 blockers. ShK-186 is a potent and selective peptide blocker of Kv1.3 that has been extensively tested in rats, non-human primates, healthy volunteers, and patients with a TEM cell-mediated autoimmune disease in a Phase 1A/B clinical trial (11, 12, 14, 15). The in vivo safety and efficacy of ShK-186 were demonstrated, like for other biologics, after injections. The delivery of Kv1.3-blocking peptides via the buccal mucosa showed that a transmucosal route of delivery is feasible, albeit with low efficacy (16).
Here, we report the bioengineering of a probiotic for the oral delivery of ShK-235. As ShK-186 cannot be produced recombinantly due to a non-amino acid adduct, we generated ShK-235 that can be produced recombinantly and retains the potency and selectivity of ShK-186 for Kv1.3 channels over related Kv1 channels (17). Lactobacillus reuteri is an indigenous bacteria of the human and vertebrate animal gastrointestinal (GI) tract. It is one of the lactic acid bacteria groups that has long been used as a cell factory in the food industry and is recognized as safe by the U.S. Food and Drug Administration (18). It has an excellent safety profile in infants, children, adults, and even in an immunosuppressed population (19). The strain L. reuteri ATCC PTA 6475 is a well-characterized probiotic that does not colonize but survives transit through the GI tract of humans and rodents (20). It primarily resides in the proximal GI tract but is also found in the urinary tract, skin, and breast milk (19). The benefits of L. reuteri include modulating host immune response, enhancing gut mucosal integrity, inhibiting bacteria translocation, and promoting nutrient absorption (19). With the genetic tools being developed to facilitate the engineering of L. reuteri 6475 genomes, interest in using L. reuteri as a bioengineered tool for the oral delivery of therapeutics has increased.
In this project, we bioengineered L. reuteri to generate LrS235 that secretes ShK-235. The secreted peptide is functional in blocking Kv1.3 channels and suppressing the activation of TEM lymphocytes in vitro. It crosses from the lumen of the GI tract into the circulation after the oral gavage of healthy rats and displays good bioavailability and pharmacokinetics. Treatment with LrS235 effectively reduces disease severity in a rat model of RA, including joint inflammation, cartilage destruction, and bone damage, without immunogenicity.
Results
Bioengineering of L. reuteri LJ01 to Secrete Functional ShK-235.
To generate a L. reuteri strain capable of secreting ShK-235, we codon-optimized the gene for ShK-235 for expression in L. reuteri, fused the modified usp45 signal peptide (21, 22) sequence to the 5′ end, and cloned it into the vector pSIP411 (23) resulting in plasmid pLL01. We have previously shown that the modified usp45, a signal peptide identified from Lactococcus lactis, is capable of high-level secretion of IL-22 from L. reuteri LJ01 (22). pSIP411 allows for the controlled expression of genes by the addition of a peptide pheromone that activates a promoter, based on a two-component regulatory system identified in Lactobacillus sakei (see SI Appendix, Fig. S2 A and B for details). The resulting bacterial strain containing the pLL01 was named LrS235 (L. reuteri ShK-235). As a control we used pSIP411 containing the gusA gene that encodes a β-glucoronidase (bacteria strain referred to as LrGusA thereafter). Induction of neither ShK-235 nor GusA secretion had any measurable effect on cells growth.
We assayed for the presence of active peptide by testing the ability of LrS235 supernatants to block Kv1.3 channels in mouse L929 fibroblast stably expressing mKv1.3 channels using whole-cell patch-clamp. LrS235 and LrGusA were grown to mid-exponential phase and induced for expression of ShK-235 or GusA, and supernatants were processed as described in the Materials and Methods. As an additional control, we used known concentrations of synthetic ShK-235 to plot a dose-response curve of the block of Kv1.3 (IC50 = 79 ± 21 pM; SI Appendix, Fig. S3 A and B). We next tested culture supernatants from LrS235 and LrGusA for Kv1.3 channel block and calculated the concentration of ShK-235 in supernatants using the dose-response curve. Supernatants from LrS235, but not those from LrGusA, blocked Kv1.3 currents (Fig. 1A). We calculated the mean concentration of ShK-235 in the supernatants from LrS235 to be ~450 pM (Fig. 1 A and B and SI Appendix, Fig. S3 A and B), well above the IC50 of ShK-235 for Kv1.3.
Fig. 1. Supernatants from LrS235, but not from LrGusA, block Kv1.3 currents and inhibit the proliferation of human CCR7− TEM cells. (A) Representativewhole-cell recordings of L929 cells stably expressing mKv1.3 before (control) and after the addition of supernatants diluted 1/10 of LrGusA or LrS235. (B) Percentage of remaining mKv1.3 currents after addition of LrGusA () or LrS235 () supernatants diluted 1/10. Mean ± SEM, each data point represents a different measurement. (C) Representative flow cytometry plots of CellTrace Violet dye dilution and CCR7 expression of CD3+ cells from human peripheral blood MNC stimulated for 7 d without any bacterial supernatants (Left) or in the presence of supernatants from LrGusA (Middle) or LrS235 (Right). (D) Percent of divided human CCR7− TEM and CCR7+ naïve/TCM cells in the absence of stimulation () and after anti-CD3 induced stimulation in the presence of Lr medium (■) or supernatants of LrGusA () or LrS235 () diluted 1/100. Mean ± SEM, N = 4 different buffy coat donors. *P < 0.05, **P < 0.01.
To further validate LrS235’s production of active ShK-235, we tested the ability of LrS235 supernatants to inhibit the proliferation of TEM cells. Incubation of supernatants from LrS235, but not from LrGusA, preferentially suppressed human CCR7− TEM cell proliferation by 63%, confirming the presence of biologically active and physiologically relevant ShK-235 peptide (Fig. 1 C and D).
Functional ShK-235 Is Detected in the Circulation of Healthy Rats following the Oral Administration of LrS235.
After validating that LrS235 produced and secreted functional ShK-235, we determined if oral gavage of the probiotic efficiently delivers functional ShK-235 to the circulation of rats. We first tested whether a compound of a molecular weight similar to that of ShK-235 can cross from the lumen of the GI tract into the circulation of healthy rats and rats with the collagen-induced arthritis (CIA) model of RA. The oral gavage of healthy rats or rats at the onset of CIA with 4 kDa dextran labeled with FITC showed that dextran could reach the circulation of both healthy and arthritic rats within 6 h, with a higher permeability in the latter (SI Appendix, Fig. S4).
We orally administered a single dose of 109 colony-forming units (cfu) of LrS235 or LrGusA to healthy rats by oral gavage. Six hours later, we collected sera and tested the ability of serum samples to block Kv1.3 channels by whole-cell patch-clamp. Sera from rats given LrS235, diluted 1/100, inhibited Kv1.3 currents by 24%, whereas sera from rats administered with LrGusA had no effect on Kv1.3 currents (Fig. 2 A and B). Based on the dose-response curve, the calculated concentration of LrS235 in the circulation 6 h after a single oral gavage is 7 nM, significantly higher than the IC50 for Kv1.3 block of 79 ± 21 pM (SI Appendix, Fig. S3 A and B). As controls, we encapsulated 0.3 mg ShK-235 peptide or unflavored gelatin powder into Torpac size 9 h gelatin capsules and enteric-coated these capsules with Acryl-EZE to target content delivery to the small intestine. Kv1.3 block was undetectable in both controls (Fig. 2 A and B).
Fig. 2. LrS235 secretes sufficient ShK-235 in the intestines for detection in the circulation of healthy rats. Healthy rats received an oral bolus of 1 × 109 cfu of LrGusA (▲) or LrS235 (▼), or an enteric-coated capsule filled with ShK-235 (●, 2 mg/kg body weight) or gelatin (o). Blood was drawn at different time points, and a single-cell patch-clamp was used to assess the ability of the serum to block Kv1.3 currents. A, Representative traces before (control) and after addition of serum diluted 1/100 from the 6-h time point. B, Current block of serum samples collected from rats at the 6-h time point. Each data point represents an individual rat. N = 3 to 4 measurements per rat. Serum dilution: 1/100. C, Current block of serum samples collected at the indicated time points. Each data point represents a rat. N = 3 to 4 measurements per rat. Serum dilution: 1/10. D, An active DTH reaction was induced against ovalbumin and rats received a single bolus of the following immediately before ear challenge: 1 × 109 cfu of LrGusA (▲) or LrS235 (▼) orally, an enteric-coated capsule filled with ShK-235 (●, 2 mg/kg body weight) or gelatin (o) orally, 1 mL of LrS235 culture supernatant orally (▽), or subcutaneous injection of 0.1 mg/kg synthetic ShK-235 (■) or vehicle (2). N = 6 rats per group (three males, three females). **P < 0.01, ***P < 0.001.
We next determined the pharmacokinetics of ShK-235 after oral delivery of LrS235 by providing LrS235 by single oral gavage (109 cfu) to healthy rats and measuring blockade of Kv1.3 channels by whole-cell patch-clamp. Serum samples were collected at various time points ranging from 1 to 72 h after the single bolus of LrS235. Peak levels of ShK-235 were detected 6 h after LrS235 delivery, with the range of functional activity from 3 h to 48 h postgavage (Fig. 2C). These results demonstrate bacteria-delivered peptides to the gut can enter the serum and remain functional.
We then assessed if LrS235 gavage could reduce a delayed-type hypersensitivity (DTH) reaction in the ear of rats, a local autoinflammation mediated by antigen-specific TEM lymphocytes (24–26). As expected, the oral gavage of LrS235 and the injection of ShK-235 significantly reduced inflammation by 30% and 58%, respectively, compared with the vehicle control (Fig. 2D). In contrast, treatment with LrGusA, capsules filled with ShK-235 or gelatin, and supernatants from the culture of the LrS235 had no significant effects on inflammation.
LrS235 Administration Stops Disease Progression and Bone and Joint Damage in CIA in Rats.
Since we detected functional ShK-235 in vitro and in the circulation of rats, we sought to assess its efficacy in a model of arthritis in Lewis rats induced by porcine collagen II. Four groups of rats were treated with P6N buffer vehicle or synthetic ShK-235 injections, or LrS235 or LrGusA by oral gavage, starting from the onset of clinical signs. All vehicle-treated animals developed severe arthritis with a mean score of 26 ± 3 (Fig. 3A). The administration of LrGusA did not affect overall disease severity (mean score 25 ± 2). In contrast, the injection of synthetic ShK-235 reduced the mean score by ~60% to 11 ± 3. The administration of LrS235 was even more effective with a mean score of only 4 ± 1, or an 84% reduction when compared to the vehicle control group. Histology performed on joints collected at the end of the in vivo trials showed severe cartilage degradation and erosion, angiogenesis, pannus formation, and synovial hyperplasia and inflammation in the CIA control rats (Fig. 3 B and C). LrGusA had no benefits in any of these parameters, whereas both synthetic ShK-235 and LrS235 significantly reduced all parameters, and no significant differences were seen between healthy controls and ShK-235 or LrS235 treated groups (SI Appendix, Table S1). Micro-CT imaging of hind limbs shows severe bone erosions in the CIA rats treated with vehicle or LrGusA and better-preserved bones in CIA rats treated with synthetic ShK-235 or LrS235 (Fig. 3D). The zoomed, axial, and pseudocoloral images are shown in SI Appendix, Fig. S5 and in Movie S1.
Fig. 3. LrS235 stops disease progression and reduces bone and joint damage and inflammation in rats with CIA. A. Clinical scores of paw inflammation from rats with CIA treated with vehicle (2) or with 100 μg/kg ShK-235 () every other day starting disease onset, 1 × 109 cfu LrGusA () or LrS235 gavage daily (). B. Hematoxylin and eosin (Left) and safranin O/fast green (Right) staining and histology scoring (C) of joints from paws from CIA rats received different treatments. Original magnification, 10×, scale bars, 100 μm. Refer to “Histology and micro-CT” in the Materials and Methods for more details of the scoring system. D. Representative micro-CT of paws from CIA rats treated with vehicle, synthetic ShK-235 every other day, or oral gavage with LrGusA, LrS235 daily. Data presented as mean ± SEM. N = 7 to 10 rats per group. Asterisks indicate areas of cartilage erosions. *P < 0.05; **P < 0.01, ***P < 0.001, and ****P < 0.0001.
ShK-235 Produced by LrS235 Is Not Immunogenic.
To test if ShK-235 produced by LrS235 is immunogenic, we assessed the sera of rats at the end of the 21-d CIA trial shown in Fig. 3 for anti-ShK-235 IgG by ELISA. Plates coated with collagen II and HsTX1[R14A] were used as controls. HsTX1[R14A] is another peptide blocker of Kv1.3 with no sequence or structural homology to ShK-235. As expected, we detected high titers of antibodies against collagen II in rats with CIA, but only low titers of anti-ShK-235 antibodies (Fig. 4A and SI Appendix, Fig. S6). Similar low reactivity was observed against HsTX1[R14A], suggesting that the signal detected against ShK-235 was non-specific. We also performed a neutralization assay with the same CIA rat serum samples, and no neutralization of ShK-235 was detected, showing the absence of ShK-235 neutralizing antibodies (Fig. 4B).
Fig. 4. ShK-235, delivered via injection or LrS235, is not immunogenic A. ELISA plates were coated with 10 μg/mL of either porcine collagen II, ShK-235, or HsTX1[R14A]. Sera from rats with CIA treated with vehicle, ShK-235, LrGusA, or LrS235 were tested at tenfold dilution steps starting from a 1:100 dilution. The serum from non-immunized rats was used as a negative control. Each column represents the mean OD of four individual rat sera ± SEM. B. Data from different PBMC donors were normalized to the unstimulated cells with 100% CellTrace Violet. The higher dilution of CellTrace Violet indicates a higher division rate. Data are present as mean ± SEM.
To further investigate if long-term exposure of rats to LrS235 would elicit antibodies against ShK-235, we treated healthy rats daily with LrS235 or LrGusA for 8 wk, stopped treatment for 12 wk, and followed by additional daily gavages of either LrS235 or LrGusA for 1 wk. ELISA performed on blood samples collected either at the 8 wk or the 21 wk time points showed no IgG with specificity for ShK-235 was produced (SI Appendix, Fig. S7 A and B). This is expected as in the previous phase 1b trial for ShK-186, none of the patients who received ShK-186 by subcutaneous injection developed anti-peptide antibodies (15).
ShK-235 Does Not Block Other Potassium Channels of Importance in CIA Pathogenesis.
We had previously shown that ShK-235 exhibits a 2,250-fold selectivity for Kv1.3 over the closely related Kv1.1 channel and an even higher selectivity against Kv1.4 and Kv1.6 channels (17). T lymphocytes also express the KCa3.1 channel (a.k.a. IKCa), and block of this channel could explain some of the benefits of ShK-235 in DTH and CIA (12). In addition, joint-resident fibroblast-like synoviocytes express the KCa1.1 channel (a.k.a. BK, Maxi-K or Slo1), and blocking this channel stops disease progression in CIA (27). We therefore tested ShK-235 for its ability to block either KCa3.1 or KCa1.1. At a high concentration of 100 nM, ShK-235 inhibited less than 3% of either channels tested, showing a selectivity of ShK-235 of at least 1,000-fold (SI Appendix, Table S2).
Discussion
We have developed a bioengineered probiotic, LrS235, that secretes functional ShK-235 both in vitro and in vivo. Our engineering strategy enabled the production of an inducible peptide secretion system that fused ShK-235 to the modified usp45 secretion peptide, secretion of which can be induced upon the addition of the SppIP peptide pheromone. Oral gavage of the LrS235 daily starting at the onset of CIA in Lewis rats results in a reduction in disease severity. The protective effects of LrS235 in CIA are accompanied by a reduction in bone and joint damage with no immunogenicity.
Most peptide-based drugs require parenteral administration due to poor oral bioavailability (28). Those biologics are usually used for chronic diseases, and repetitive injections during the long-term disease course could reduce patient compliance and lead to poor outcomes. The convenience and widespread use of oral administration makes it the most desirable method in a clinical setting. Engineered probiotics can be used to deliver drugs or enzymes to treat metabolic disorders into the GI tract or tumors (29, 30). A genetically engineered strain of Escherichia coli Nissle was used to deliver the angiogenesis inhibitor tumstatin to treat murine melanoma (31). More recently, the Phe-metabolizing enzyme phenylalanine ammonia lyase and L-amino acid deaminase (LAAD) enzyme expressing Escherichia coli Nissle have completed human phase 1/2a clinical trials and yielded positive results in phenylketonuria patients (32), demonstrating the potential of using bacteria as a promising drug delivery method. However, in those successful experiments, tumstatin was delivered via intrapetitoneal injection of the bioengineered bacteria that were subsequently cleared by the immune system, and LAAD was effective directly in the GI tract. Here, we show that the engineered probiotic LrS235 can be given orally with a good bioavailability of the ShK-235 it produces in the circulation of the rats. In contrast, the oral administration of a high dose of synthetic ShK-235 packaged into enteric-coated capsules yielded no detectable ShK-235 in the circulation of the rats and no benefits in the DTH model and neither did the oral gavage of supernatants from the culture of LrS235 affect DTH. This is consistent with prior work showing low oral bioavailability of peptide and protein therapeutics (28). The bioavailability of venom-derived Kv1.3-blocking peptides through mucosal membranes has been demonstrated after buccal and pulmonary delivery (16, 33), suggesting the feasibility of delivery via the intestinal mucosa. A probiotic-based delivery presents several advantages as the daily ingestion of probiotics by humans is well established, and L. reuteri 6475 has already been tested in humans for research purposes and is also available as a commercially available supplement (Osfortis™). In addition, L. reuteri will continuously deliver ShK-235 throughout the GI tract, whereas delivery via capsule is pH-dependent and thus restricted to a one-time bolus to the distal intestine. Furthermore, L. reuteri has an affinity to mucus and will deliver the peptide in close proximity to the intestinal epithelium. Many bacteria produce extracellular vesicles that deliver bacterial products to the host’s circulation or tissues (34). More work is needed to determine whether such vesicles are involved in the remarkable bioavailability of ShK-235 following the oral delivery of LrS235.
We chose the modified signal peptide of Usp45 from L. lactis for the secretion of ShK-235 because it is cleaved from the peptide during secretion (35). The addition of large moieties to the N-terminus of ShK and its analogs prevents the peptide from accessing its binding site in the pore of the Kv1.3 tetramer and thus prevents channel block (36). The fact that the supernatants from LrS235, but not from LrGusA, block Kv1.3 channels and TEM cell proliferation suggests that the signal peptide is indeed cleaved from ShK-235.
Prior studies with injected blockers of the Kv1.3 channels, including ShK analogs, showed that a high efficacy in reducing the severity of CIA was accompanied by a reduction in DTH severity. Both injected synthetic ShK-235 and ShK-235 delivered orally via LrS235 reduced inflammation in both CIA and DTH, but LrS235 was more effective in CIA, and synthetic ShK-235 was more effective in DTH. Following subcutaneous injection, ShK and its analogs reach the circulation in less than 30 min (25). In contrast, the peak concentration of ShK-235 in the circulation of healthy rats is achieved 6 h after a single oral delivery of LrS235. After the DTH challenge with ovalbumin, ovalbumin-specific TEM cells enter the site of challenge and interact with local antigen-presenting cells within 3 h (24). We administered both synthetic ShK-235 and LrS235 at the time of challenge, giving sufficient time for the synthetic peptide to reach the circulation and the TEM cells before the start of the local inflammatory response but inflammation at the site of challenge had already begun by the time ShK-235 produced by LrS235 reached the circulation thus likely reducing the effectiveness of the treatment in such an acute model. Furthermore, other charged peptides with structures similar to that of ShK-235 accumulate in the joints following systemic administration, therefore increasing their local concentration at the site of inflammation in CIA (37, 38). This unique, and not fully understood, feature of these peptides likely also plays a role in the remarkable efficacy of ShK-235 in reducing disease severity in CIA.
LrS235 had significantly higher efficacy than the injected synthetic ShK-235 in reducing disease severity in CIA rats. At first glance, this could be explained by the administration frequency, LrS235 being given daily, while the synthetic peptide was injected every other day. However, we had tested Kv1.3 blocker administration at frequencies ranging from every 6 h to once a week and found that injections every other day provided a peak efficacy not improved by more frequent injections. This is likely due to the natural depot of the peptide at the site of subcutaneous injection, which leads to a slow release in the circulation (15). Another likely explanation is that the probiotic itself has anti-inflammatory effects. L. reuteri decreases intestinal inflammation in several mouse models (20, 39). In our CIA trials, L. reuteri producing GusA instead of ShK-235 reduced disease severity at the early stage of CIA, although significantly only on days 3 and 5 after the onset of clinical signs. Since RA is a chronic inflammatory disease, the probiotic and ShK-235 may synergistically suppress inflammation and improve the therapeutic effects of LrS235. While Kv1.3 blockers preferentially target TEM lymphocytes, L. reuteri products may target effectors of the innate immune system, such as macrophages and dendritic cells, thus showing a modest protective effect early in CIA progression. Since CIA induction involves incomplete Freund’s adjuvant that induces a strong inflammation, LrGusA may show more anti-inflammatory benefits in milder models of inflammation. In RA and its animal models, joint inflammation disturbs the balance between osteoclasts and osteoblasts by inhibiting osteoblast differentiation and augmenting osteoclast function (40). Indeed, probiotics have been shown to regulate bone health through secretion of peptides or lipids, modulation of the host immune system, modifying the gut microbiome, and influencing local pH (41–43). Finally, the intestinal microbiome is perturbed in RA and its animal models (44), and it is possible that the rescue of dysbiosis by LrS235 treatment accounts for the improved efficacy than synthetic ShK-235 injections. Further work is needed to validate this hypothesis.
We have used mKv1.3 (NCBI accession number NP_032444) to test ShK-235 by patch-clamp, hKv1.3 (NCBI accession number NP_002223) to test ShK-235 in functional assays in vitro, and rKv1.3 (NCBI accession number NP_062143) to test ShK-235 in vivo. All three are regularly used interchangeably when testing pore-blocking peptides as the alignment of all three amino acid sequences show complete homology in the pore region and all transmembrane domains between these species. The only differences between mKv1.3, hKv1.3, and rKv1.3 are in the intracellular N-terminus of the protein that is not implicated in peptide binding. Indeed, ShK has been tested by patch-clamp electrophysiology on mKv1.3, hKv1.3, and rKv1.3 with similar IC50s (45–47).
Many drugs, and especially biologics, are immunogenic, and the resulting neutralizing antibodies can eventually render the medications ineffective. ShK is highly homologous to a domain of MMP-23 (48), and thus resembles a self-peptide, and is likely recognized as such by the immune system. As a result, ShK and its analogs induce little to no immunogenicity in rats or humans (10, 15), and we have found no immunogenicity of ShK-235 delivered via LrS235 in either healthy or CIA rats, regardless of short or long-term treatment. In our immunogenicity ELISA, we used porcine collagen II as a positive control for detecting IgG as the rats had been immunized against this protein to induce CIA. There was no difference in anti-porcine collagen II IgG titers between the rats in the different treatment groups, and this suggests that the B lymphocytes producing these antibodies belong to the CD27−IgD− memory subset that relies on KCa3.1 rather than Kv1.3 channels for their function (49). The frequency of CD27-IgD- B cells is increased in patients with both early and established RA (50) and is likely the subset of B cells producing the IgGs detected in the rats with CIA.
Altogether, we have developed the engineered ShK-235 secreting probiotic LrS235 that treats the animal model of RA efficiently. Our findings provide an alternative delivery strategy for peptide-based drugs and suggest that such techniques and principles can be applied to a broader range of drugs and the treatment of chronic inflammatory diseases.
Materials and Methods
Construct Design for the Inducible Expression of ShK-235.
ShK-235 differs from ShK by a Q16K substitution, an I21M substitution, and the addition of an Ala to the C-terminus (SI Appendix, Fig. S1) (17). Codon optimized ShK-235 with modified signal peptide Usp45 was synthesized and ligated into NcoI-EcoRI digested pSIP411 generated pLL01 (SI Appendix, Fig. S2). The ShK-235 secretion plasmid pLL01 was enlarged in Escherichia coli 1,000 and was then electransformed into L. reuteri 647 competent cell resulting in ShK expression strain LrS235. All LrS235 or LrGusA used in this paper were induced with induction peptide unless otherwise stated.
Synthesis of ShK-235 and HsTX1[R14A].
Both Kv1.3-blocking peptides were previously described (17, 51). ShK-235 and HsTX1[R14A] were synthesized using an Fmoc-tBu solid-phase synthesis strategy. Each coupling was mediated with diisopropyl carbodiimide in the presence of hydroxybenzotriazole. All deprotections were accomplished with 20% piperidine in dimethyl formamide. Following synthesis of the linear chain, each peptide was cleaved and deprotected using trifluoroacetic acid (90%) with carbocation scavengers [triisopropyl silane, H2O, 3,6-dioxa-1,8-octanedithiol, and thioanisole, (2% of each v/v)] for 3 h at ambient temperature. The peptides were each precipitated into methyl t-butyl ether. The linear peptides were purified by reverse phase -high-performance liquid chromatography and subsequently oxidatively folded in the presence of glutathione in ammonium acetate buffered aqueous solution. The cyclized products were isolated by RP-HPLC, and fractions with a purity >95% by analytical high-performance liquid chromatography were subsequently pooled and lyophilized. Each peptide was found to have expected theoretic mass for the formation of three and four disulfide bonds, respectively, for ShK-235 and HsTX1[R14A]. Synthetic ShK-235 and HsTX1[R14A] were dissolved in P6N buffer (10 mM sodium phosphate, 0.8% NaCl, 0.05% polysorbate 20, pH 6.0) to prepare stocks at 1 mg/mL (15, 25).
Animals.
Male and female Lewis rats (7 to 8 wk old; Envigo) were group-housed and provided food and water ad libitum. All animals were housed in a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International-accredited. All experiments involving rats were approved by the Institutional Animal Care and Use Committee at Baylor College of Medicine.
Cells and Cell Lines.
Buffy coats were purchased from the Gulf Coast Regional Blood Center, and MNC were enriched using Histopaque-1077 (Sigma-Aldrich). Mouse L929 fibroblasts stably expressing mKv1.3 channels (52) were a kind gift from Dr. K. George Chandy (University of California, Irvine) and were cultured in Dulbecco's Modified Eagle Medium (DMEM) + 10% fetal bovine serum (FBS) + 0.5 mg/mL G418 (EMD Chemicals). HEK 293 cells stably transfected with the KCa1.1 channel α subunit were a gift from Dr. Heike Wulff (University of California, Davis) and were cultured in DMEM medium + 10% FBS + 0.5 mg/mL G418. HEK 293 cells stably transfected with the KCa3.1 channel were a gift from Dr. Heike Wulff and were cultured in DMEM medium + 10% FBS + 10 µg/mL puromycin.
ShK-235 Production from L. reuteri for In Vitro Assays.
Inducible expression of ShK-235 in L. reuteri was performed based on previous expression conditions (22). Briefly, overnight cultures of LrS235 were grown in buffered de Man, Rogosa & Sharpe (MRS) broth with 8 µg/mL erythromycin at 37°C in anaerobic conditions. The next day, 400 µL of the overnight culture was added to 40 mL of buffered MRS with 8 µg/mL erythromycin in a 50 mL conical tube and incubated in a 37°C water bath. When the cultures reached an optical density at 600 nm (OD600) of 0.6 to 0.8, expression of ShK-235 was induced with 50 ng/mL of the pSIP411 induction peptide, and the culture was incubated for 5 h in a 37°C water bath. Then the cultures were centrifuged for 5 min at 4,800 g to pellet the cells, and the supernatant was removed and stored at −20°C. The supernatant was buffer exchanged from MRS to PBS using dialysis filtration. Briefly, supernatants were centrifuged in an Amicon Ultracel (Millipore) concentrator with a 3 KDa cutoff filter to reduce the volume by 80% and then resuspended to the original volume using sterile 1× PBS. This was repeated 5 to 6 times.
Preparation of Rat Sera for Patch-Clamp Electrophysiology.
Blood was withdrawn from animals by cardiocentesis under anesthesia (53) and allowed to clot at room temperature for 10 min. Serum was extracted by centrifugation at 300 g for 15 min at room temperature. The serum was then aliquoted and stored at −80°C until testing. No further purification was performed.
Patch-Clamp Electrophysiology.
To determine the concentration of ShK-235 in the LrS235 supernatants and rat sera, a dose-response of ShK-235 block of Kv1.3 was determined by adding known concentrations of the peptide to naïve rat serum and then testing by whole-cell patch-clamp of mouse L929 fibroblasts stably expressing mKv1.3 channels (52) using a Port-a-Patch automated patch-clamp system (Nanion), as described (17, 54). Culture supernatants and serum samples were then assessed with the same technique, and the dose-response curve was used to determine peptide concentration. The effects of 100 nM ShK-235 on KCa1.1 and KCa3.1 channels were assessed by manual whole-cell patch-clamp electrophysiology on HEK 293 cells stably expressing either channel using established protocols (55–57).
Human T Lymphocyte Proliferation Assays.
Peripheral blood MNC were loaded with 5 µM CellTrace Violet (Invitrogen) according to the manufacturer’s instructions (58) and incubated for 30 min with sterile-filtered supernatants from LrS235 or LrGusA, buffered to pH 7.4 and diluted 1/10 in tissue culture media, before the addition of anti-CD3 antibodies to stimulate T lymphocytes (clone OKT3, 1 ng/ml, 037-85, Thermo Fisher). Seven days later, cells were stained with anti-CD3 antibodies conjugated to phycoerythrin (BioLegend 300308, lot B209105) and anti-CCR7 antibodies conjugated to fluorescein (R&D Systems, FAB197F, lot LEU1615081), and the dilution of CellTrace Violet in CD3+CCR7− TEM cells and CD3+CCR7+ naive/TCM cells was measured by flow cytometry on a BD FACSCantoII as quantification of cell proliferation. Data were analyzed with FlowJo.
Encapsulation of ShK-235.
Torpac size 9 h gelatin capsules (Fairfield, NJ) were filled with either 0.3 mg synthetic ShK-235 or unflavored gelatin powder (Kraft Heinz) using the Torpac dosing kit. Each capsule was enteric-coated with Acryl-EZE® (Colorcon) prior to delivery via oral gavage to target content delivery to the small intestine.
Bioavailability and Pharmacokinetics of ShK-235 after Oral Delivery.
Male rats received a single oral gavage of either 1 × 109 cfus LrS235 or an enteric-coated capsule filled with ShK-235. Blood was collected via the saphenous vein at the different time points indicated in the figure, the last blood draw being a terminal cardiac puncture (59). Serum samples were assayed for Kv1.3 block by patch-clamp electrophysiology.
Induction and Monitoring of an Active DTH Reaction.
Male and female rats were immunized in the flanks with 200 μL of a 1:1 emulsion of ovalbumin (Sigma) in complete Freund’s adjuvant (Difco/Becton Dickinson, Franklin Lakes, NJ) (26). After 7 d, under isoflurane anesthesia, the rats were challenged with ovalbumin dissolved in saline in the pinna of one ear; the collateral ear was injected with saline (60). Rats received either a single subcutaneous injection of 0.1 mg/kg ShK-235 or P6N vehicle, a single oral gavage of 1 × 109 cfus LrS235 or LrGusA, oral gavage of 1 mL supernatant of LrS235, or a capsule filled with ShK-235 or gelatin immediately before the ear challenge. The DTH reaction was measured 24 h post-challenge as the thickness of the ear using a spring-loaded micrometer (Mitutoyo, Japan) and ear inflammation was determined by comparing the ear thickness of the ovalbumin-challenged with the saline-challenged ear from each rat (60). The animals were euthanized after the ear measurements.
Induction, Monitoring, Randomization, and Treatment of Rat CIA.
CIA was induced as described previously (54, 61). Briefly, female Lewis rats received a subcutaneous injection of 200 μL of a 1:1 emulsion of 2 mg/mL porcine type II collagen (20031, Chondrex, Redmond, WA) with incomplete Freund’s adjuvant at the base of the tail. After 7 d, rats were given a booster of 100 μL of collagen and adjuvant emulsion. Disease onset was defined as the development of at least one swollen or red paw joint. Clinical scores were determined daily by assigning one point for each swollen or red toe joint, two points for mildly swollen wrist or ankle joints, and five points for each severely swollen wrist or ankle, giving each rat a maximum possible score of 60. Upon disease onset, rats were treated every other day by the subcutaneous injection of P6N buffer vehicle or 0.1 mg/kg ShK-235, or the oral gavage of 1 × 109 cfus LrGusA or 1 × 109 cfus LrS235 daily. CIA is more severe in rats with early disease onset. To avoid biasing our results based on disease severity on the day, each rat developed signs of disease and accounting for differences in the time between immunization, and when a rat developed signs of illness, every rat that developed signs of disease on a given day was placed in a different treatment group to fill all groups in parallel.
Histology and Micro-CT.
Healthy rats and rats from the CIA trials were euthanized after 21 d of treatment, and their hind paws were collected and fixed in 10% buffered formalin. One paw from each rat was imaged in the Optical Imaging & Vital Microscopy Core at Baylor College of Medicine by Micro-CT using a Bruker SkyScan 1272 Scanner set at 13 µm resolution with no filtering, no averaging, and a rotation step of 0.3. Raw images were analyzed with CTvox (Bruker, MA, US). The other hind paw was decalcified, embedded in paraffin, and sectioned by the Pathology & Histology Core at Baylor College of Medicine. Slides were stained with either hematoxylin and eosin or safranin O/fast green and imaged at 4× magnification on a Nikon Ci-L bright-field microscope (Nikon Inc.) in the Integrated Microscopy Core at Baylor College of Medicine. Scoring of the slides was completed by an investigator blinded to treatment groups using a comprehensive histological scoring system as described elsewhere (54, 62), in which cartilage degradation, cartilage erosion, angiogenesis, pannus formation, synovial hyperplasia, and synovial inflammation were evaluated by the following criteria: synovial inflammation: five high-power magnification fields (HMF) were scored for the percentage of infiltrating mononuclear cells (MNC) as follows: 0 = absent, 1 = mild (1 to 10%), 2= moderate (11 to 50%), and 3 = severe (51 to 100%); cartilage erosion and degradation: 0 = absent, 1 = mild (1 to 10%), 2= moderate (11 to 50%), and 3 = severe (51 to 100%); synovial hyperplasia: 0 = absent, 1 = mild (5 to 10 layers), 2 = moderate (11 to 20 layers), and 3 = severe (>20 layers); angiogenesis: the number of vessels was counted in five HMF of synovial tissue, and the mean was used for analyses; extension of pannus formation was based on the reader’s impression (62).
Immunogenicity Assays in Rats with CIA.
High-protein binding 96 well microplates (3855, Thermo Fisher) were coated overnight with 10 µg/mL of either porcine collagen II (20031, Chondrex, Woodinville, WI), ShK-235, or HsTX1[R14A], dissolved in PBS. Non-specific binding sites were blocked with PBS + 5% skimmed milk and washed with PBS + 0.05% Tween 20 before adding serum from the CIA rats, diluted in PBS. After washes, anti-rat IgG antibodies conjugated to horseradish peroxidase (1 µg/mL; Pierce catalog 31471, lot UA280036) were added to all the wells. One-step TMB-ELISA (34028, Thermo Fisher) was used to detect absorbance on a plate reader at 650 nm.
Antibody Neutralization Assays.
To assay whether the serum of rats treated with synthetic ShK-235 or LrS235 contains neutralizing antibodies against ShK-235 and thus reduce the ability of ShK-235 from inhibiting the proliferation of CCR7− TEM cells, we pre-incubated synthetic 1 nM ShK-235 for 30 min with media supplemented with 10% of serum from the rats of the CIA trials or healthy, non-immunized and untreated rats. Human MNCs were loaded with CellTrace Violet according to the manufacturer’s instructions (58) and resuspended in the media supplemented with rat serum and synthetic ShK-235. After a 30 min incubation at 37°C, T lymphocytes were activated by the addition of anti-CD3 antibodies (clone OKT3, 1 ng/mL, 037-85, Thermo Fisher). Seven days later, cells were stained with anti-CD3 antibodies conjugated to phycoerythrin and anti-CCR7 antibodies conjugated to FITC, and the dilution of CellTrace Violet in CD3+CCR7− TEM cells was measured by flow cytometry on a BD FACSCantoII as quantification of cell proliferation. Data were analyzed with FlowJo.
Long-Term Immunogenicity Assays in Healthy Rats.
Healthy Lewis rats received a daily oral gavage of 1 × 109 cfus LrS235, 1 × 109 cfus-induced LrGusA, or vehicle for 8 wk. The rats were left untreated for 12 wk and then were again treated daily for 1 wk, reaching a total of 21 wk. Blood was drawn at the 8 wk and the 21 wk time points, and serum was tested for IgG against ShK-235 and HsTX1 [R14A] by ELISA, as described above.
Statistical Analysis.
Student’s t test, one-way ANOVA, and two-way ANOVA without correction were used to determine whether differences among the groups were statistically significant (P < 0.05). The CIA clinical scores analyses were completed using repeated measure one-way analysis of variance with Bonferroni post hoc test. Data were presented as mean ± SEM. All analyses were performed using GraphPad Prism.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. Representative video of pseudocolored micro-CT paws from CIA and control rats. 3D reconstructed and psedocolored paws from fig. S5. Red color corresponds to low density pixels, indicating area of bone erosion and cartilage damage, blue color corresponds to high density pixels, indicating high density bone.
This project was funded in part by a pilot grant from the Alkek Center for Metagenomics and Microbiome Research at Baylor College of Medicine (to C.B. and J.M.H.) and by Bridge Funding from Baylor College of Medicine (to C.B.). The work was supported by the Cytometry & Cell Sorting Core, the Pathology & Histology Core, the Optical Imaging & Vital Microscopy Core, and the Integrated Microscopy Core funded in part by the Cancer Prevention and Research Institute of Texas (RP180672, RP150578, RP1806721, and RP170719), the NIH (DK56338, CA125123, HG006348, and RR024574), the Dan L. Duncan Comprehensive Cancer Center, and the John S. Dunn Gulf Coast Consortium for Chemical Genomics.
Author contributions
Y.W., D.Z., L.C.O.-V., J.L.P., J.M.H., R.A.B., and C.B. designed research; Y.W., D.Z., J.L.P., J.M.H., R.A.B., and C.B. performed research; D.Z., L.C.O.-V., J.L.P., M.W.P., J.M.H., and R.A.B. contributed new reagents/analytic tools; Y.W., R.A.B., and C.B. analyzed data; and Y.W., D.Z., J.L.P., J.M.H., R.A.B., and C.B. wrote the paper.
Competing interest
The authors declare competing interest. The authors have organizational affiliations to disclose. R.A.B. is a cofounder of Mikrovia and PanaBio. The authors have patent filings to disclose. C.B. and M.W.P. are inventors on the patent protecting ShK-186, currently in clinical trials for the treatment of autoimmune diseases. C.B., J.M.H., and R.A.B. are inventors on a patent disclosure protecting LrS235.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926201.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595687
202219691
10.1073/pnas.2219691120
commCommentarymicrobioMicrobiology423
437
Commentary
Biological Sciences
Microbiology
Culture-based approaches to studying “microbial dark matter”
He Xuesong [email protected]
a b 1 https://orcid.org/0000-0002-3333-9188
aDepartment of Microbiology, The Forsyth Institute, Cambridge, MA 02142
bDepartment of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA 02115
1Email: [email protected].
3 1 2023
10 1 2023
3 7 2023
120 2 e2219691120Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR) 100000072 1R01DE023810 Xuesong He HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR) 100000072 1R01DE030943 Xuesong He
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pmcIn the January 2015 issue of PNAS, He et al. reported the isolation and characterization of the first Saccharibacteria (formerly known as TM7) strain from the human oral cavity (1). Named Nanosynbacter lyticus TM7x—this first cultured representative of Saccharibacteria displays an intriguing biology: It has a small cell size (200 to 300 nm in diameter) with a reduced genome and enjoys an obligate epiparasitic lifestyle by living on the surface of its bacterial host. Later work from the Banfield group showed that Saccharibacteria belongs to the candidate phyla radiation (CPR), a large bacterial linage with over 70 phyla accounting for more than a quarter of microbial diversity with predicted symbiotic lifestyle similar to that of TM7x (2–4). The discovery of CPR organisms has greatly expanded the bacterial tree of life (5); however, we still have only a limited number of cultured representatives due to their recalcitrance to conventional cultivation. This severely limits exploration of the biology and ecological roles of these mysterious bacteria. Expanding cultured representatives of CPR organisms for in-depth study is essential for the illumination of this so-called "microbial dark matter."
In the current issue of PNAS, Xie et al. (6) report the isolation of the first insect-associated Saccharibacteria, named Leucosynbacter cicadicola TM7i (TM7i), achieved by capturing in situ Saccharibacteria–host association guided by epicPCR (Emulsion, Paired Isolation, and Concatenation PCR). The study offers a high-throughput method for identifying candidate host bacteria, elevating our ability to cultivate CPR organisms. Furthermore, using high-resolution live cell imaging, the study also provides strong evidence demonstrating the crucial role of the CPR-encoded type IV pili (T4P) in its symbiotic lifestyle.
EpicPCR-Assisted Identification of TM7 Host Bacteria
One of the critical challenges in studying Saccharibacteria and other CPR bacteria is in obtaining stable cultured representatives to allow in-depth physiological and pathological characterization in the laboratory. Culture-independent metagenomic data and imaging analysis of natural samples indicate that Saccharibacteria and other CPR are submicron bacteria with reduced genomes and a likely symbiotic lifestyle (7–9). The symbiotic nature of these nanosized bacteria makes it challenging, if not impossible to isolate them as a pure culture for laboratory study, since so far, the cultivated representatives of Saccharibacteria can only persist and propagate when cocultured with their host bacteria (10). Furthermore, except for Mycolasynbacter amalyticus JR1, which is a cultured representative of environmental Saccharibacteria with lytic ability against its bacterial host (11), all the cultivated Saccharibacteria strains are derived from the human oral cavity.
The most commonly used approach in isolating Saccharibacteria is the “baiting” method (10), which is based on the initial observation that free-floating TM7x cells can attach to and infect naive host bacteria (12). In this method, a panel of potential host bacteria is chosen based on their co-occurrence with Saccharibacteria within the same microbial sample, and infected with Saccharibacteria cells which are collected by filtering the sample through a submicron filter paper. In the presence of suitable hosts, Saccharibacteria will initiate a symbiotic interaction and eventually form stable binary coculture with their host bacteria, or, for virulent TM7 strains with a lytic ability against their host, host bacteria can be periodically added to maintain the coculture (10). Attention has been given to Actinobacteria when choosing putative host bacteria in the baiting method based on the observation that all the isolated Saccharibacteria strains prefer Actinobacteria as hosts (13) and microbial association data from metagenomic analyses (7). However, limiting host bacterial candidates to Actinobacteria is likely biased and could miss the true host bacteria for some of the Saccharibacteria species. Furthermore, without spatial proximity information, the selection of putative host bacteria for baiting is neither targeted nor efficient.
Cross et al. developed a reverse genomics approach that uses genomic information of Saccharibacteria to engineer antibodies to immunolabel and isolate Saccharibacteria together with its physically associated host bacteria via fluorescence-activated cell sorting (14). This approach allows more effective and targeted isolation of Saccharibacteria strains. However, it demands bioinformatics capability and involves multiple steps including antibody synthesis and flow cytometry.
In the article published in this PNAS issue, taking advantage of the close physical interaction between Saccharibacteria and their host bacteria as a result of symbiosis, Xie et al. present a high-throughput approach to identify Saccharibacteria–host bacterial candidates by detecting the presence of Saccharibacteria–host bacterial associations within a community. This was achieved by repurposing epicPCR, which was originally developed for linking functional genes with phylogenetic markers, to capture in situ Saccharibacteria–host physical associations (15). Identifying potential host bacteria with intimate physical interaction with Saccharibacteria revealed by epicPCR further directs the effort toward isolating these candidate hosts, which is less challenging compared with the direct isolation of Saccharibacteria–host symbionts. Using this method, they identify and isolate the candidate host bacterium, Leucobacter aridicollis J1 from Cicadae Periostracum–associated microbiome. By further employing the baiting method using L. aridicollis J1 as “bait,” they successfully isolate the first insect-associated TM7, named L. cicadicola TM7i.
So far, Saccharibacteria is the only phylum among the CPR group with cultivated representatives that can be stably maintained in laboratory conditions. Increasing evidence from culture-independent studies indicates that symbiotic, particularly episymbiotic, lifestyle is likely widespread among CPR bacteria (7). Thus, combined with the established baiting method, the epicPCR-assisted approach for identifying candidate host bacteria for Saccharibacteria in situ will enable the isolation of more Saccharibacteria from diverse environments. It will also help to culture other CPR organisms, particularly those that share a similar episymbiotic lifestyle to TM7x for in-depth study of their physiology, lifestyle, and ecological role, as well as their impact on host health and diseases for those mammalian-associated CPR bacteria.
High-Resolution Live Cell Imaging Reveals T4P-Mediated Symbiotic Association between TM7 and Host Bacteria
One of the fundamental questions related to the episymbiotic nature of Saccharibacteria is how they achieve initial contact with the host bacteria, eventually leading to the establishment of the symbiotic relationship. Direct visualization of the physical associations between CPR organisms and their host bacteria in natural environments using high-resolution microscopy has frequently revealed the presence of pili-like structures on CPR bacterial surface (7, 8). Meanwhile, comparative genomic analysis revealed well-conserved T4P-encoding gene clusters associated with Saccharibacteria and other CPR organisms (9, 16). T4P are bacterial surface-associated dynamic filamentous appendages with diverse functions, including DNA uptake, twitching motility, and cellular adherence among others (17). T4P encoded by CPR have long been suspected of mediating their physical interaction with host bacteria (7, 8). However, due to the symbiotic lifestyle, no effective genetic tools have been developed thus far for CPR, and the role of T4P in symbiosis cannot be confirmed using a conventional mutagenesis-based approach. Meanwhile, snapshots of high-resolution imaging cannot provide convincing evidence of the involvement of T4P in the dynamic physical association process. Thus, the role of T4P in mediating symbiotic interaction remains to be determined.
Xie et al. present a high throughput approach to identify Saccharibacteria host bacterial candidates by detecting the presence of Saccharibacteria-host bacterial associations within a community.
In this article, Xie et al. first reveal the presence of pili-like appendages on the surface of TM7i by transmission electron microscopy. Using high-resolution real-time live cell imaging, they further demonstrate that the presence of host bacteria significantly triggers the movement of TM7i, while the addition of quercetin, a PilB inhibitor that prevents T4P extrusion, greatly inhibits TM7i twitching motility. And the inhibited motility of TM7i was restored upon quercetin removal.
The more convincing data in supporting the T4P-enabled twitching motility in TM7i come from their nanometer-resolution fluorescence imaging of TM7 symbionts using structured illumination microscopy. The imaging directly captures TM7i’s pili in motion, showing the active extrusion and retraction of pili accompanied by the active translocation of TM7i cells. Furthermore, they interrogate the effect of T4P-dependent twitching motility on the episymbiotic lifestyle of TM7i. Using long-term microscopy monitoring, they record the proliferation of TM7i when physically associated with its bacterial host. The addition of quercetin suppresses the motility and inhibits the propagation of TM7i cells as a result of drastically reduced host-associated TM7i. This high-resolution real-time imaging provides compelling data emphasizing the crucial role of TM7i-encoded T4P in mediating its initial physical association with host bacteria and impacting its episymbiotic lifestyle.
While the most definitive evidence needs to come from genetic studies when effective genetic tools are available for Saccharibacteria, the high-resolution live cell imaging data presented by Xie et al. provide thus far the most convincing data on the critical role of T4P in Saccharibacteria’s symbiotic lifestyle. Meanwhile, the finding on the role of Saccharibacteria T4P may well apply to other CPR organisms considering their well-conserved T4P and symbiotic lifestyle similar to that of Saccharibacteria (9, 16).
Dubbed "microbial dark matter", CPR organisms are omnipresent yet remain poorly understood. Effective isolation methods as described by Xie et al. will greatly facilitate the cultivation of CPR organisms from diverse environments for comprehensive study under well-controlled laboratory conditions. Meanwhile, high-resolution imaging techniques, such as electron microscopy and real-time live cell imaging (6), combined with bioinformatics-based metabolic network analysis (18) and multiomics studies (19, 20) will help shed light on the intricate symbiotic relationship between CPR and their hosts. More effort should be made to develop genetic tools that can be applied to CPR for a detailed mechanistic understanding of their unique biology at the gene level (Fig. 1).
Fig. 1. Expanding cultured representatives for studying CPR organisms. Effective isolation methods for targeted cultivation are critical in CPR research. The application of high-resolution microscopies, such as electron microscopy and real-time live cell imaging, combined with bioinformatics-based metabolic network analysis and multiomics, as well as bacterial genetics, will help shed light on the unique biology and the ecological impact of these omnipresent "microbial dark matter".
I would like to express my gratitude to Dr. Susan R. Rittling for her thoughtful editing of the manuscript. This work was supported by the National Institute of Dental and Craniofacial Research of the NIH under awards 1R01DE023810 and 1R01DE030943.
Author contributions
X.H. wrote the paper.
Competing interest
The author declares no competing interest.
See companion article, “Type IV pili trigger episymbiotic association of Saccharibacteria with its bacterial host,” 10.1073/pnas.2215990119.
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PMC009xxxxxx/PMC9926219.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595704
202210181
10.1073/pnas.2210181120
videoVideoresearch-articleResearch ArticleimmunImmunology and Inflammation420
Biological Sciences
Immunology and Inflammation
AIM2 sensors mediate immunity to Plasmodium infection in hepatocytes
Marques-da-Silva Camila a b 1
Poudel Barun c 1
Baptista Rodrigo P. b d
Peissig Kristen a b
Hancox Lisa S. e https://orcid.org/0000-0003-1940-2619
Shiau Justine C. b f https://orcid.org/0000-0002-5125-5156
Pewe Lecia L. e
Shears Melanie J. g h
Kanneganti Thirumala-Devi i
Sinnis Photini g h https://orcid.org/0000-0003-2954-7547
Kyle Dennis E. a b f
Gurung Prajwal c j
Harty John T. e j https://orcid.org/0000-0001-7266-2802
Kurup Samarchith P. [email protected]
a b 2 https://orcid.org/0000-0002-0640-0502
aDepartment of Cellular Biology, University of Georgia, Athens, GA 30605
bCenter for Tropical and Emerging Global Diseases, University of Georgia, Athens, GA 30605
cDepartment of Internal Medicine, University of Iowa, Iowa City, IA 52242
dInstitute of Bioinformatics, University of Georgia, Athens, GA 30605
eDepartment of Pathology, University of Iowa, Iowa City, IA 52242
fDepartment of Infectious Diseases, University of Georgia, Athens, GA 30605
gJohns Hopkins Malaria Research Institute, Johns Hopkins University, Baltimore, MD 21205
hDepartment of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD 21205
iDepartment of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38105
jInterdisciplinary Graduate Program in Immunology, University of Iowa, Iowa City, IA 52242
2To whom correspondence may be addressed. Email: [email protected].
Edited by Katherine Fitzgerald, University of Massachusetts Medical School, Worcester, MA; received June 15, 2022; accepted November 18, 2022
1C.M.-d.-S. and B.P. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e221018112015 6 2022
18 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Clinical malaria caused by Plasmodium parasites is responsible for the death and suffering of millions of people around the world. While humans are known to mount protective innate immune responses against Plasmodium during the course of its initial development in the liver, we understand little about the mechanism of this process. This knowledge gap has hindered our ability to exploit natural immune responses to combat malaria. We show that DNA molecules of Plasmodium parasites are detected by AIM2 receptors present in host hepatocytes, leading to unconventional processing of Caspase-1 and activation of the inflammasome pathway. This results in programmed cell-death of the hepatocytes harboring Plasmodium and early control of the infection in the liver itself, potentially limiting clinical malaria.
Malaria, caused by Plasmodium parasites is a severe disease affecting millions of people around the world. Plasmodium undergoes obligatory development and replication in the hepatocytes, before initiating the life-threatening blood-stage of malaria. Although the natural immune responses impeding Plasmodium infection and development in the liver are key to controlling clinical malaria and transmission, those remain relatively unknown. Here we demonstrate that the DNA of Plasmodium parasites is sensed by cytosolic AIM2 (absent in melanoma 2) receptors in the infected hepatocytes, resulting in Caspase-1 activation. Remarkably, Caspase-1 was observed to undergo unconventional proteolytic processing in hepatocytes, resulting in the activation of the membrane pore-forming protein, Gasdermin D, but not inflammasome-associated proinflammatory cytokines. Nevertheless, this resulted in the elimination of Plasmodium-infected hepatocytes and the control of malaria infection in the liver. Our study uncovers a pathway of natural immunity critical for the control of malaria in the liver.
Malaria
innate immunity
Caspase-1
liver
UGA | University of Georgia Research Foundation (UGARF) 100012483 Kurup Startup Samarchith P Kurup HHS | National Institutes of Health (NIH) 100000002 AI168307 Photini SinnisPrajwal GurungJohn T HartySamarchith P Kurup HHS | National Institutes of Health (NIH) 100000002 AI85515 AI95178 AI100527 Photini SinnisPrajwal GurungJohn T HartySamarchith P Kurup HHS | National Institutes of Health (NIH) 100000002 AI132359 Photini SinnisPrajwal GurungJohn T HartySamarchith P Kurup HHS | National Institutes of Health (NIH) 100000002 K22AI127836 Photini SinnisPrajwal GurungJohn T HartySamarchith P Kurup
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pmcMalaria is a devastating disease that affects over 200 million people each year (1). Plasmodium parasites that cause malaria undergo an asymptomatic phase of obligatory development and replication in the hepatocytes that lasts for approximately 7 days in humans (the preerythrocytic ‘liver-stage’), before advancing to the disease-causing ‘blood-stage’ of the infection, where they infect the red blood cells (2). Considering that the maturation of Plasmodium in the liver is a prerequisite for clinical malaria and transmission, the liver-stage is also the favored target for antimalarial vaccinations and therapies (3–6). Natural immune responses that impede Plasmodium infection in the liver are considered to limit the development and impact of blood-stage malaria (7–12). Therapeutically targeting such host responses, rather than Plasmodium itself which is more genetically flexible, would reduce the likelihood of emergence of new drug resistance, which has been a major challenge in our fight against malaria (6). In addition, P. vivax is known to establish long-term dormant infections in hepatocytes, enabling them to seed sporadic relapse infections for years (13). Enhancing innate immune responses in the liver would eliminate such dormant parasites, preventing relapse infections. Although various innate immune pathways stimulated by Plasmodium in its blood-stage are currently known, such responses are not induced until several days after the initiation of an infection, and would have minimal impact on the early control of natural infections or the resultant clinical disease (14). Thus, filling the knowledge gap in our understanding of the innate immune responses directed at Plasmodium in the liver is a critical step in our fight against malaria.
A defining component of innate immunity in vertebrates is the response to pathogen-associated molecular patterns (PAMPs) that are recognized through germline-encoded pattern recognition receptors (PRRs) expressed in host cells (15, 16). Although host genetics and natural immunity are known to offer resistance to malaria in humans, the immune pathways that hinder Plasmodium infection of the liver, specifically in hepatocytes, remain relatively unknown (14, 17, 18). Currently, the only pathway of natural immunity known to curb malaria infection in the liver is driven by melanoma differentiation-associated protein 5 receptors in the hepatocyte cytosol, which sense Plasmodium RNA to induce type I interferon (IFN) responses (8, 10, 14). Strong type I IFN responses are known to enhance the control of Plasmodium in the liver, resulting in delayed onset of blood-stage malaria, reduced parasitemia levels, and improved survival in mice (7, 12). This suggests that the identification of innate immune responses that control malaria in the liver would be of immense translational value.
It is unlikely that RNA is the only Plasmodium-derived PAMP that gains access to the hepatocyte cytosol, or would be the sole driver of innate immune responses in the liver. Various intracellular bacterial, fungal, and protozoal PAMPs are sensed by PRRs in the cytoplasm of host cells to induce the formation of ‘inflammasomes’ (19–24). Inflammasomes are discrete macromolecular enzymatic complexes formed in the cytosol constituted by PAMP-bound PRRs, procaspase-1, and in some cases, the ASC (apoptosis-associated speck-like protein containing a CARD) adaptor molecules (19, 25). Autoproteolytic cleavage of the procaspase-1 precursor molecule (p46) into its constituent CARD (caspase activation and recruitment domain), p20 and p10 domains occurs at these inflammasome complexes, and results in the catalytically functional hetero-tetrameric Caspase-1, composed of the p20 and p10 subunits (26, 27). Activated Caspase-1 in turn facilitates the elimination of pathogens by inducing programmed death (pyroptosis) in host cells through proteolytic activation of the membrane pore-forming protein, Gasdermin D (GSDMD). Additionally, the inflammasome complex also activates the proinflammatory cytokines interleukin (IL)-1 and IL-18 to alert the immune system more broadly (28–30). Of note, this current model for Caspase-1 activation dynamics has been established based on studies in immune cells such as macrophages or monocytes, or neoplastic cell-lines (19, 31). The mechanisms or consequences of Caspase-1 activation in parenchymal cells or nonimmune cells such as hepatocytes have rarely been investigated, despite their potential relevance to the innate immune-surveillance mechanisms in tissues and the global inflammatory responses of the host (32, 33).
We surmised that a comprehensive screen for transcriptional perturbations in hepatocytes infected with Plasmodium would offer insights into the innate immune responses elicited by Plasmodium infection in the liver. Here, we use single-cell RNA (scRNA) sequencing in primary human hepatocytes infected with the most important human malaria parasite, Plasmodium falciparum, to reveal a concerted upregulation of transcripts associated with the programmed cell-death and inflammasome pathways. Using biochemical approaches and in vivo studies in mice, we demonstrate that AIM2 receptors in Plasmodium-infected hepatocytes detect Plasmodium DNA to induce inflammasome-mediated Caspase-1 activation. Although Caspase-1 was observed to undergo noncanonical proteolytic processing in Plasmodium-infected hepatocytes, it resulted in GSDMD-mediated pyroptotic cell-death and control of malaria in the liver itself.
Results
Inflammasome-Mediated Caspase-1 Activation in Plasmodium-Infected Hepatocytes.
To gain an unbiased and comprehensive understanding of the innate immune responses elicited by Plasmodium parasites in hepatocytes, we determined the total transcriptional changes in primary human hepatocytes infected with P. falciparum (Pf) using scRNA sequencing. Primary human hepatocytes were employed specifically to gain access to the full range of biochemical perturbations in response to Pf infection, considering that the immortalized or neoplastic cell-lines that have been relied on in the past for similar purposes are known to have fundamentally altered immune, cell-death, and metabolic pathways (34–36). Primary human hepatocytes were infected with a field isolate of Pf sporozoites obtained from infected mosquitoes, and scRNA sequencing was performed on infected and uninfected hepatocytes (SI Appendix, Fig. S1A and Fig. 1A). The Pf-infected hepatocytes were identified bioinformatically from the sequence reads using Pf-specific transcripts, and compared with the uninfected hepatocytes derived from the same culture (Fig. 1 B and C). Our experimental approach normalized each cell to environmental exposures such as any mosquito or salivary gland–derived debris, while reliably distinguishing infected and uninfected cells. The Pf-infected and -uninfected hepatocytes clustered separately in principal component analysis, signifying their discrete transcriptional profiles (Fig. 1B). Gene set enrichment analyses indicated that various genes pertinent to cell-injury, cell-invasion, and acute phase responses in the host were transcriptionally perturbed in Pf-infected hepatocytes (SI Appendix, Fig. S1 B–D). Canonical pathway analysis and the enrichment of functional molecular interactions indicated that liver cell-damage responses, programmed cell-death pathways, and inflammatory responses in hepatocytes were the key immune processes impacted, admitting the possibility of the induction of the inflammasome pathway in Plasmodium-infected hepatocytes (Fig. 1D). Corroborating this finding, we observed broad transcriptional changes in a variety of genes of the inflammasome pathway (Fig. 1E). Of note, hepatocytes are known to upregulate the expression of its cytoplasmic PRRs such as NLRP(Nucleotide-binding oligomerization domain) 3, AIM2 etc., as well as the proinflammatory cytokine IL-1β following PAMP stimulation and the induction of the inflammasome pathway (37). In support of the above foundational data, we observed the presence of inflammasome complexes containing Caspase-1 associated with human hepatocytes infected with Pf (Fig. 2A). Of note, above cryosections were prepared from Pf-infected liver-humanized mice, where the infection is limited to the hepatocytes of human origin embedded in the mouse liver. The data also signify the potential existence of the inflammasome pathway in vivo. It is also noteworthy that in many instances, we observed that Caspase-1 containing inflammasomes were generated proximal to the early developmental stages of Plasmodium parasites in both human and mouse hepatocytes (SI Appendix, Fig. S1E), offering stark similarity to the inflammasomes formed in response to the detection of DNA from intracellular bacterial pathogens in myeloid cells (38, 39). Taken together, these data suggested that inflammasome-mediated Caspase-1 activation occurs in hepatocytes in response to Plasmodium infection.
Fig. 1. P. falciparum infection induces inflammatory cell-death pathways in hepatocytes. (A) Representative pseudocolored fluorescent micrographs showing in vitro cultured primary human hepatocytes infected with Pf, 4 d p.i. The individual panels show staining with separate Pf-specific antibodies (GAPDH and merozoite surface protein-1, MSP-1) or DNA (DAPI), and fluorescence overlays. MSP-1 expression represents the liver-stage of Pf. (B and C) Transcriptional differences determined by scRNA sequencing of Pf-infected or uninfected primary human hepatocytes at 4 d p.i. Pf-infected hepatocyte transcripts were plotted for principal component analysis, which corroborated the unsupervised clustering of hepatocytes based on gross transcriptional changes (B) or as heat map to depict gross transcriptional differences (C). (D) Significantly enriched network of functional interactions evaluated by gene-set and biochemical pathway enrichment analysis comparing Pf-infected and uninfected human hepatocytes. The nodes represent the key functional outcomes predicted based on the transcriptional identity (represented by color coded proteins, see legend) of the Pf-infected primary human hepatocytes at 4 d p.i. (E) Heat map depicting transcriptional differences in the genes of the canonical inflammasome pathway determined to be up-regulated by unbiased biochemical pathway enrichment analysis in primary human hepatocytes, maintained ex vivo and coincubated with Pf for 4 d. The bar graph on right shows log fold change ± SD (logFC ± SD) in the differential expression of the indicated genes. Dotted line represents logFc of 1. (C and E) Data obtained from a total of four replicate infections (samples a–d). See SI Appendix, Fig. S1A for experimental details.
Fig. 2. AIM2-Caspase-1 axis drives the control of liver-stage malaria. (A) Representative (of >10 cryosections) pseudocolored confocal image depicting Caspase-1 aggregates (arrows) in human hepatocytes in the liver of a humanized mouse inoculated with Pf (30 h p.i). Stained using DAPI for nucleus, hsp70 for Pf, and anti-hCaspase-1 antibody. Frequency (%) of association of Pf and hCaspase-1: 82.05 ± 2.69. Data presented as mean ± SEM compiled from ≥10 cryosections. Hsp70: heat shock protein 70. (B and C) Scatter plots showing relative liver-parasite burdens in the indicated mice inoculated with Py, at 36 h p.i. (D) Scatter plots showing relative parasite burdens at 36 h p.i in the whole livers of B6 or Casp1/11KO chimeric recipient mice reconstituted with B6 or Casp1/11KO bone-marrow and inoculated with Py i.v. (E) Scatter plots showing relative whole-liver parasite burdens at 36 h p.i. in B6 or AIM2KO chimeric recipient mice reconstituted with B6 or AIM2KO bone-marrow and inoculated with Py i.v. (B–E) The dots in the scatter plots represent individual mice, with the data combined from 3 separate replicate experiments and presented as mean ± SEM and analyzed using ANOVA with Dunnett’s corrections, yielding the indicated P values.
AIM2 Facilitates Caspase-1-Mediated Control of Liver-Stage Malaria.
Although Pf is the deadliest and the most widespread species of Plasmodium affecting humans, they do not productively infect mouse hepatocytes. Therefore, we employed the mouse malaria parasite, Plasmodium yoelii (Py) to mechanistically and feasibly dissect the immunobiology of liver-stage malaria using in vitro or in vivo studies. The pathogenesis of Py in mice resembles that of Pf in humans in many respects (40). To determine the innate immune signaling pathways in hepatocytes responding to Plasmodium infection, we established a murine primary hepatocyte culture model. Here, primary hepatocytes isolated from mice (SI Appendix, Fig. S2A) were coincubated with sporozoites purified from the salivary glands of Py-infected mosquitos (SI Appendix, Fig. S2B). Such sporozoites were also used for intravenous inoculation into mice for in vivo experiments.
To determine how Caspase-1 activation in hepatocytes impacted the control of liver-stage malaria, we infected Caspase-1/11 deficient (Casp1/11KO (41)), or wild-type C57BL/6 (B6) mice with Py sporozoites derived from mosquitoes, and examined the resultant parasite burdens in the liver. Casp1/11KO mice exhibited significantly higher levels of Py 18S rRNA in the liver, suggesting that Caspase-1/11 aided in controlling Plasmodium infection in its preerythrocytic stage (Fig. 2B). In agreement with our finding that Caspase-1 activation in Py-infected hepatocytes was dependent on the adaptor molecule, ASC (PYCARD) (SI Appendix, Fig. S2C), genetic deficiency of ASC resulted in suboptimal control of liver-stage malaria (Fig. 2B). Considering that Casp1/11KO mice are also deficient in Caspase-11 in addition to Caspase-1, we sought to determine the extent to which Caspase-11 impacted the control of Plasmodium in hepatocytes. Caspase-11 was not activated by Py infection in the hepatocytes, nor influenced Py control in the liver (SI Appendix, Fig. S2 D–F).
ASC being a vital component of Py control in the liver indicated that Plasmodium parasites in hepatocytes are likely detected by one or more of the ASC-limited PRRs: NLRP3, NLRP1b, (NLR family CARD domain containing 4) NLRC4, or AIM2 (19). In order to delineate the contributions of these innate sensors in the control of liver-stage malaria, we compared Py infections in B6 or NLRP3-, NLRP1b-, NLRC4-, or AIM2- deficient mice. AIM2KO mice exhibited significantly higher Py burdens in the liver, suggesting that Caspase-1 activation in hepatocytes in response to Plasmodium infection is likely mediated by AIM2 sensors (Fig. 2C).
Though only the hepatocytes are known to support Plasmodium replication and development in the liver, various hematopoietic cells such as the dendritic cells can acquire Plasmodium in the liver, skin, or lymphoid organs (9, 42). It is worth highlighting that inflammasome pathways have been characterized almost exclusively in antigen presenting cells (APCs) (25, 32), with little to nothing known about how this pathway operates in parenchymal cells such as the hepatocytes. To assess the extent to which Caspase-1 or AIM2 in hematopoietic cells might contribute to the control of liver-stage malaria, we generated reciprocal bone-marrow chimeric mice using B6, Casp1/11KO, or AIM2KO mice. Quantitation of Py infections in these mice indicated that the lack of Caspase-1 or AIM2 expressed in hematopoietic cells did not impact the control of liver-stage malaria (Fig. 2 D and E). These data indicated that AIM2-mediated Caspase-1 activation in hepatocytes facilitates the control of Plasmodium in the liver.
AIM2 Sensors Detect Plasmodium DNA in the Infected Hepatocytes.
The only known ligand for AIM2 in host cells is double-stranded (ds) DNA of either the host or pathogen origin (27). We hypothesized that Plasmodium-derived dsDNA is directly sensed by AIM2 receptors in the infected hepatocytes. To objectively test the interaction between AIM2 sensors and Plasmodium dsDNA, as a proof of concept, we first determined whether AIM2 can be coimmunoprecipitated with Bromodeoxyuridine (BrdU)-incorporated DNA that has gained access to hepatocyte cytosol. Anti-BrdU-mediated pulldown of BrdU+ DNA obtained from mammalian cells and transfected into primary hepatocytes reliably coimmunoprecipitated AIM2 (SI Appendix, Fig. S3A). To query the possibility of the direct association of Plasmodium DNA and hepatocyte AIM2, we incorporated BrdU into the DNA of P. berghei (Pb) sporozoites. BrdU is known to integrate into the DNA of in vitro cultured Pf that replicate in an environment replete with BrdU (43, 44). However, to generate sporozoite stages with BrdU-incorporated DNA, we reared Pb-infected Anopheles mosquitoes fed with a high concentration of BrdU in sugar-water. As a result, high frequencies (94 ± 2%) of BrdU+ Pb sporozoites were generated, which retained BrdU through its development to the exoerythrocytic forms (EEF) in hepatocytes (Fig. 3 A–C and SI Appendix, Fig. S3B). Of note, we switched to using Pb in this experiment, because Py-infected mosquitoes reared on BrdU-laced water consistently failed to yield sufficient viable sporozoites. We were able to coimmunoprecipitate BrdU and AIM2 from BrdU+Pb sporozoite-infected primary mouse hepatocytes, indicating that Plasmodium DNA likely directly associated with AIM2 in the infected host cells (Fig. 3D). Hepatocytes coincubated with wild-type Pb (BrdU−) sporozoites or salivary gland debris extracted from uninfected mosquitoes fed BrdU-laced water served as additional controls in this experiment (SI Appendix, Fig. S3A and Fig. 3D). The above finding was further supported by the observation of colocalization of AIM2 and BrdU+Pb EEFs in the infected mouse hepatocytes (Fig. 3E).
Fig. 3. AIM2 receptor binds Plasmodium DNA in the infected hepatocytes. (A) Pseudocolored confocal image of a representative field (>10 fields) of BrdU+ Pb sporozoite stages (stained using anticircumsporozoite protein, CSP) derived from infected mosquitoes. CSP is located on the plasma membrane of the sporozoites. Arrows indicate BrdU incorporation. Frequency (%) of colocalization of CSP (sporozoites) and BrdU: 94 ± 2.0. Data presented as mean ± SEM from ≥10 microscopy fields obtained from at least 3 separate experiments. (B) Representative (>10 fields) pseudocolored confocal image of BrdU+ Pb exoerythrocytic form (EEF, stained using anti-Acyl carrier protein, ACP) in an in vitro cultured primary murine hepatocyte at 24 h p.i. ACP-stained Plasmodium EEF is indicated by the arrow. Frequency (%) of colocalization of ACP and BrdU in such Pb-infected hepatocytes: 91.6 ± 1.42. Data presented as mean ± SEM from ≥10 fields derived from at least 3 separate experiments. (C) ELISA comparing lysates of Pb sporozoites derived from BrdU-fed or control-infected mosquitoes to determine relative BrdU levels. Data presented as mean ± SEM from 3 technical and two biological replicate experiments, compared with t tests to yield the presented P value. (D) Immunoblot analysis for AIM2 after immunoprecipitation of the whole-cell lysates of primary mouse hepatocytes infected with BrdU+ Pb (24 h p.i.), using anti-BrdU antibodies. Hepatocytes were either infected with wild-type Pb (BrdU−), coincubated with salivary gland extracts from BrdU-fed mosquitoes, or remained uninfected/untreated to serve as controls. Data represent 4 separate replicate experiments. (E) Representative pseudocolored confocal image (>10 fields) of BrdU+ Pb exoerythrocytic form in an in vitro cultured primary murine hepatocyte at 24 h p.i. Arrow indicates the developing parasite. Frequency (%) of colocalization of AIM2 and BrdU: 58.50 ± 1.42. Data presented as mean ± SEM from ≥10 fields derived from at least 3 separate experiments.
Although the deficiency of NLRP3 itself did not significantly hamper the control of Plasmodium infection in the liver (Fig. 2C), NLRP3 is known to cooperate with AIM2 in the control of a variety of intracellular pathogens (45–48). To determine the extent to which NLRP3 receptors might facilitate AIM2-mediated control of Plasmodium infection in the liver (or vice versa), we generated a strain of mouse deficient in both AIM2 and NLRP3 (AIM2-NLRP3dKO). Indeed, the deficiency of both receptors impaired the control of Plasmodium infection in the liver, significantly more so than when either receptor was independently absent (SI Appendix, Fig. S3C). It is noteworthy that AIM2, NLRP3, or ASC colocalize with Caspase-1, adjacent to Plasmodium in the infected hepatocytes (SI Appendix, Fig. S3D). This is reminiscent of supramolecular organizing centers formed on membrane-bound organelles within the cytoplasm, which serve as signal amplification platforms to drive Caspase-1-mediated pyroptotic cell-death (49).
While type I IFNs are known to facilitate the control of Plasmodium infection in the liver, the exact mechanism behind this process has remained unresolved (8, 12, 14). We observed that Py infection enhanced the expression of AIM2 in the infected hepatocytes in a type I IFN-dependent manner (SI Appendix, Fig. S4 A and B). This observation offered a potential pathway through which type I IFNs can facilitate Plasmodium control in the liver. Of note, type I IFN stimulation alone was sufficient to enhance AIM2 expression in bone marrow-derived macrophages (BMDMs) as well, strengthening this idea (SI Appendix, Fig. S4C). Type I IFN treatment has been shown to drive AIM2, ASC, and Caspase-1 transcript levels in THP-1 monocytes (50). Taken together, our data illustrate that the AIM2 receptor in hepatocytes senses Plasmodium DNA directly, is induced by type I IFNs, and is instrumental in the innate immune control of malaria in the liver.
Noncanonical Processing of Caspase-1 in Hepatocytes.
In the current model of Caspase-1 activation in cells, autoproteolytic cleavage of the procaspase-1 (p46) molecule into its constituent CARD, p20, and p10 domains is followed by the formation of catalytically functional hetero-tetramers composed of the p20 (~20 kDa) and p10 (~10 kDa) subunits, which carry out the downstream biological functions of Caspase-1 (26, 27) (SI Appendix, Fig. S5A). To confirm Caspase-1 activation in the Plasmodium-infected hepatocytes biochemically, we infected primary mouse hepatocyte cultures with Py sporozoites and examined the presence of Caspase-1 p20 subunit using western blotting. Surprisingly, and in stark contrast to the existing paradigm, we observed that Caspase-1 processing in both murine and human primary hepatocytes infected with Plasmodium generated a Caspase-1 cleavage product of approximately 32 kDa (which we call p32), without any detectable p10 or p20 cleavage forms (Fig. 4 A–C and SI Appendix, Fig. S5 B and C). Treatment of primary hepatocytes with the standard inducers of Caspase-1 activation- LPS and ATP also induced p32, unlike in the case of BMDMs, where the conventionally processed p20 subunit was readily generated (Fig. 4 A–C). This suggested that the generation of p32 is likely an inherent, hitherto unknown characteristic of Caspase-1 processing in hepatocytes.
Fig. 4. Non-canonical processing of Caspase-1 in hepatocytes. (A and B) Immunoblot screen for Caspase-1 cleavage forms in primary mouse hepatocytes infected with Py (A) or treated with LPS+ATP (B) for the indicated time-frames. Murine BMDMs coincubated with LPS+ATP served as control. Caspase-1 p20 subunit-specific antibodies detect uncleaved procaspase-1 (p46) and the cleaved Caspase-1 products, p32 or p20. (C) Immunoblot analysis for cleaved Caspase-1 in human hepatocytes infected with Pf (Left) or treated with LPS+ATP (Right) for the indicated time-frames. (D) Immunoblot analysis confirming the identity of Caspase-1 pulled down from whole-liver lysates of liver-humanized mice infected with Pf (30 h p.i.), using with anti-p20-specific antibody. (E) Immunoblot analysis for Caspase-1 cleavage in primary mouse-hepatocytes infected with Py and probed with Caspase-1 p20 subunit (Left) or Caspase-1 p10 subunit (Right)-specific antibodies. Murine BMDMs coincubated with LPS+ATP served as the standard for conventional Caspase-1 cleavage pattern, indicating p20 and p10. (F) Immunoblot analysis in whole-cell lysates of primary B6 hepatocytes cocultured with Py for 24 h, immunoprecipitated with anti-mCaspase-1p20 and the precipitate probed with anti-mCaspase-1p10. (A–E) LC represents the protein loading controls from the SDS–PAGE, to provide an estimate of the representation of pro/Caspase-1 in the total protein content of the hepatocytes or BMDMs. Note that these are single exposure blots and due to the high signal intensity, the amount of total protein added from the BMDM lysates was insufficient for detection by Coomassie blue staining in the loading control. All data are representative of at least 3 separate experiments.
To test whether the generation of p32 could be an artifact of in vitro culture or stimulation of hepatocytes, we infected liver-humanized chimeric mice with Pf sporozoites in which the Pf infection would be limited to the hepatocytes of human origin (51). Only human Caspase-1 p32 was immunoprecipitated from the whole-liver lysates of these mice (Fig. 4D), indicating that Caspase-1 is noncanonically processed in the human hepatocytes in vivo following Plasmodium infection.
Remarkably, when probed with either p20 or p10 Caspase-1 subunit-specific antibodies separately, only the p32 Caspase-1 was detected in Py-infected hepatocytes. This is unlike in the BMDMs stimulated with LPS and ATP, where distinct p20 and p10 cleavage products were clearly detected (Fig. 4E). This finding suggested that Caspase-1 p32 observed in hepatocytes may be composed of unseparated p20 and p10 domains of Caspase-1. To objectively test this possibility, we employed a p20 subunit-specific antibody to immunoprecipitate (IP) Caspase-1 cleavage products from the whole-cell lysates of Py-infected hepatocytes, resolved it on a denaturing gel, and probed with the p10 subunit-specific antibody. The observation of p32 immunoprecipitate in this experiment confirmed that p32 is indeed composed of unseparated p20 and p10 domains (Fig. 4F).
A short-lived (<30 min after inflammasome formation) intermediate product of conventional Caspase-1 processing, composed of unseparated CARD and p20 domains (p33, SI Appendix, Fig S5A) has been reported in LPS- and ATP-stimulated BMDMs (52). However, Caspase-1 p32 generated in hepatocytes remained consistently detectable for over 24 h post infection unlike the p33 intermediate (SI Appendix, Fig. S5B).
One potential reason for the generation of p32 in hepatocytes is the existence of alternate spliced form of procaspase-1, where the interdomain linker (IDL) sequence connecting p20 and p10 subunits is not amenable to proteolytic processing (SI Appendix, Fig. S5A) (26). Therefore, to further characterize p32 and to determine if p32 may be a product of alternate splicing of procaspase-1 in hepatocytes, we sequenced the hepatocyte procaspase-1 transcripts in Py-infected or LPS+ATP stimulated primary mouse hepatocytes. We found that the Caspase-1 sequence in such hepatocytes did not differ from that of myeloid cells and the IDL sequence in the hepatocyte transcript was identical to that of the reference sequence (SI Appendix, Fig. S5D). In addition, bioinformatic analysis of our (9) and others’ (53–56) published RNAseq data also did not indicate the presence of any procaspase-1 splice variants in the infected or uninfected, human or murine hepatocytes.
In order to rule out the possibility that contaminating mosquito debris is an inadvertent contributor to the Caspase-1 activation observed in the above experiments, we compared uninfected or Py-infected hepatocytes with hepatocytes coincubated with salivary gland extracts from uninfected mosquitoes (mock infection). Only Py-infected hepatocytes exhibited any detectable Caspase-1 processing (SI Appendix, Fig. S5E). In addition, transfection of Plasmodium DNA into primary hepatocytes and BMDMs induced the Caspase-1 activation in an AIM2-dependent manner, corroborating our previous finding that Plasmodium DNA can be directly sensed by AIM2 to induce Caspase-1 activation (SI Appendix, Fig. S5 E and F). Together, these observations challenged the universality of the existing model of Caspase-1 processing dynamics, and suggests that Caspase-1 biochemistry may vary based on the cells or tissues involved.
Caspase-1 Controls Liver-Stage Malaria through GSDMD Activation.
Proteolytically activated Caspase-1 is known to control intracellular pathogens through the induction programmed cell-death in the host cells, in addition to the induction of proinflammatory cytokine responses (19). Caspase-1 proteolytically activates GSDMD, which generates plasma membrane pores and causes osmotic instability in host cells (28, 57). This process results in the elimination of the pathogen along with its host cell. Py infection induced significant cell-death in hepatocytes in an AIM2 and GSDMD-dependent manner (Fig. 5A). We observed that the hepatocytes underwent characteristic vacuolation and rupture following Py infection in a GSDMD-dependent manner, suggesting that Py infection induced pyroptotic cell-death in hepatocytes (Fig. 5B and Movies S1 and S2) (58). Caspase-1-dependent proteolytic cleavage of GSDMD was evident in Py-infected hepatocytes (Fig. 5C). Consistent with these findings, genetic deficiency, or therapeutic inhibition of GSDMD using the drug disulfiram (59) significantly compromised the control of malaria in the liver (Fig. 5 D and E). These results indicated that AIM2-mediated Caspase-1 activation brings about GSDMD-mediated pyroptotic cell-death and elimination of Plasmodium-infected hepatocytes, resulting in a better overall control of malaria in its preerythrocytic stage.
Fig. 5. Caspase-1 activation induces GSDMD-mediated cell-death in hepatocytes. (A) Comparison of cytolysis determined by LDH release assay in ex vivo cultured primary hepatocytes derived from the indicated mice, coincubated with Py for the indicated times. Data presented as mean ± SEM, analyzed using ANOVA with Dunnett’s correction comparing each time point to the corresponding one in B6 mice, to yield the presented P-values. The dotted line indicates median cytolysis levels in B6 hepatocytes treated for 24 h with salivary gland extracts derived from uninfected mosquitoes. (B) Representative confocal time-lapse images showing primary hepatocytes from tdTomato+ B6 mice infected with Py (CellTrace Violet+, indicated by arrows), observed at 24 h and 32 h of coincubation. Hepatocyte #1 and #2 shown on left just prior to undergoing pyroptotic rupture; please see Movie S1 for the full sequence of events. (C) Immunoblot analysis for GSDMD cleavage in primary hepatocytes obtained from the indicated mice coincubated with Py, at 24 h. BMDMs treated with LPS+ATP (4 h) served as the positive control. LC: loading control. Bar graphs on the right depict the relative densities of cleaved GSDMD, presented as the mean ratio of cleaved: uncleaved GSDMD bands calculated from three separate immunoblots. Data presented as mean ± SEM, analyzed using ANOVA comparing each group to the B6 Py group to yield the presented P-values. (A–C): all data shown represent ≥3 separate experiments. (D) Scatter plots showing relative liver-parasite burdens in the indicated mice inoculated with Py, 36 h p.i. (E) Scatter plots showing relative liver-parasite burdens in the vehicle-treated (Rx ctrl) or Disulfiram-treated (−1 d, 0, 1 d p.i) mice inoculated with Py, 42 h p.i. (D and E) Data presented as mean ± SEM, analyzed with 2-tailed t tests to yield the presented P value and combined from three separate replicate experiments.
In addition to GSDMD activation, Caspase-1 is also known to mature proinflammatory cytokines such as IL-1 and IL-18 in host cells. Although these cytokines have the ability to support the recruitment of various antigen presenting and phagocytic cells to the site of infection, they appear to have a limited role in the elimination of the intracellular niche of pathogens, or the pathogen itself (57, 60). Compared with the BMDMs, primary mouse hepatocytes produced undetectable levels of mature IL-1β or IL-18 (SI Appendix, Fig. S6 A and B), although the activated Caspase-1 in hepatocytes was catalytically functional (SI Appendix, Fig. S6C). In support of the above finding, therapeutic blockade of IL-1 signaling did not impact the control of Py infection in mouse livers (SI Appendix, Fig. S6D). These findings further reinforced the premise that programmed cell-death itself may be the principal means of Caspase-1-mediated control of liver-stage malaria. Together, these data suggest that AIM2-mediated Caspase-1 activation in hepatocytes promotes the control of malaria through GSDMD-mediated pyroptotic elimination of infected hepatocytes.
Discussion
Plasmodium parasites have to undergo development in the liver before progressing to the blood-stage of infection (2). Therefore, by arresting Plasmodium life-cycle in the liver, clinical malaria and transmission can be completely prevented (3), making the inhibition of Plasmodium development in the liver a key priority of the current vaccination and therapeutic strategies against malaria (4, 5). Innate immune responses are instrumental in bringing about rapid and early control of infections before the more mechanistically elaborate adaptive responses can be invoked (16). Considering the very short time-frame of Plasmodium development in the liver, innate immune responses elicited in the liver serve a critical function in impeding the progression of Plasmodium infection to the blood-stage (12, 14, 61, 62). Several innate immune pathways stimulated by Plasmodium during its blood-stage have been identified in the past (14, 48, 63–65). However, natural transmission of malaria occurs only through the inoculation of sporozoites, which have to undergo development in the liver in mammalian hosts before advancing to the blood-stage (2). Furthermore, the innate responses generated against blood-stage malaria are limited to the myeloid cells, and may not be particularly effective in limiting the onset of clinical malaria (14). Innate immune responses that impede Plasmodium infection in the liver actively limit the incidence and severity of clinical malaria (7, 12). Therefore, understanding how natural immune responses control Plasmodium infection during its preerythrocytic development is critical. Here, we describe a pathway of innate immunity to malaria in which AIM2 sensors in hepatocytes detect Plasmodium DNA to induce Caspase-1 activation, aiding the control of malaria in the liver. We also show that proteolytic activation of Caspase-1 occurs in an unconventional manner in hepatocytes, and it enables the control of liver-stage malaria primarily through the induction pyroptotic cell-death.
In order to gain a comprehensive and unbiased view of the transcriptional changes produced by Plasmodium infection in hepatocytes, we performed scRNA sequencing of primary human hepatocytes infected with Pf. As discussed in the results section, utilizing primary hepatocytes and a field isolate of Pf in this experiment allowed us to create a valuable resource that, as close as practically possible, would represent the changes induced in the hepatocytes of human malaria patients. The transcriptional upregulation of genes pertinent to hepatocyte injury, inflammation, and cell-death pathways suggested that Plasmodium infection is a potential driver of the inflammasome pathway in hepatocytes. It is known that PAMP stimulation can transcriptionally upregulate certain PRRs and proinflammatory cytokine genes in hepatocytes (37). Our experimental data eventually validated the above speculations with the observation of Caspase-1 activation and pyroptotic cell-death in Plasmodium-infected hepatocytes. Of note, the pyroptotic cell-death responses associated with Plasmodium infection in hepatocytes has been considered a critical driver of the adaptive immune responses generated against liver-stage malaria (9). Our transcriptional data also showed that a variety of metabolic and molecular synthetic pathways are impacted in the hepatocytes following Plasmodium infection. Considering that hepatocytes have a significant role in the breakdown (e.g., toxins) or synthesis (e.g., albumin, cholesterol) of biomolecules, future studies relying on the resource we have generated would provide insights into the biological impact of Plasmodium infection on the hepatocytes and the liver as a whole.
Immune responses to Plasmodium infection in the liver remain relatively understudied despite its immense translational relevance to the treatment and prevention of malaria (4, 5). Recognition of a functional AIM2-Caspase-1 axis in the Plasmodium-infected hepatocytes offers opportunities to therapeutically and immunologically improve antimalarial immunity (66). For instance, enhancing innate immune responses by employing exogenous PAMPs is known to engender better adaptive responses in the context of live-vaccines (67). Although type I IFNs produced by Plasmodium-infected hepatocytes facilitate innate control of liver-stage malaria (8, 10, 12), how type I IFNs exert control of malaria in the liver has not been fully characterized. We show that type I IFNs enhance AIM2 expression in hepatocytes. This would enhance the sensitivity of hepatocytes to Plasmodium DNA. Efficient sensing of Plasmodium DNA by infected hepatocytes would lead to the rapid elimination of such hepatocytes by pyroptosis, thereby limiting Plasmodium infection in the liver. Although we present evidence for the association of Plasmodium DNA with host AIM2 receptors, there is a possibility of host’s own DNA, including mitochondrial DNA triggering the AIM2-mediated inflammasome pathway in hepatocytes (68). Only future studies will shed light into such prospects.
In addition to AIM2 or NLRP3, host or pathogen-derived DNA that gains access to host cell cytosol can be detected by a variety of other sensors such as STING, IFI16/p204, etc. (69). Several factors such as the expression levels of DNA sensors, various partner molecules in the sensing pathway, etc. would impact the relevance of such sensors in mediating protection from intracellular pathogens. For instance, we observed that the cGAS/ STING pathway does not have a direct role in controlling Plasmodium infection in the mouse liver, possibly because hepatocytes do not express significant levels of the STING receptor (12, 70). We have shown previously that type I IFN signaling in hepatocytes help control Plasmodium infection in the liver, possibly through the induction of cell-autonomous immune responses (12). We anticipate such responses to target and lyse the intracellular Plasmodium, potentially exposing Plasmodium nucleic acids to the hepatocyte cytoplasm (8). Only future studies will elucidate the mechanisms by which Plasmodium-derived nucleic acids are exposed to the hepatocyte cytosol, or how the hepatocytes respond to the presence of such molecules in the hepatocyte cytoplasm in the context of liver-stage malaria.
Of note, there are conflicting reports about the ability of Plasmodium to incorporate BrdU into its genome. It has been suggested that BrdU may not be incorporated into the DNA of replicating in vitro cultured schizont stage (i.e., during schizogony) of Pf, perhaps due to the absence of active thymidine kinase in the parasites in that stage (71). BrdU was not detected using ELISA in Pf when cultured in vitro, in the presence of 1 mM BrdU. However, other studies have indicated that Pf does incorporate BrdU when cultured in the presence of BrdU, with this approach being adapted for drug discovery efforts (43, 44). We show that BrdU is incorporated by the mouse malaria parasite, Pb during its in vivo development in mosquitoes (i.e., during oocyst development), by relying on microscopy and ELISA. We presume that thymidine kinase or a functional ortholog may be conserved and active in the oocyst stage of Pb. Only future studies will resolve the differences in BrdU incorporation into various Plasmodium species and stages.
Although various groups have examined Caspase-1 activation in the liver in the past, the majority of such studies were conducted using whole-liver extracts that also contained myeloid cells (72, 73), or in neoplastic cell-lines possessing dysregulated cell-death pathways (74–77). In those instances where primary hepatocytes were discretely examined in the context of infectious or aseptic liver diseases, conventionally cleaved Caspase-1 products have consistently remained elusive in the hepatocytes (78–80). We considered the possibility that procaspase-1 expressed in hepatocytes is a splice variant incapable of cleavage at the IDL sequence (SI Appendix, Fig. S5A). A procaspase-1 variant transgenically generated in in vitro cultured BMDMs through site-directed mutagenesis of the autoproteolytic cleavage site in IDL produced unseparated p20 and p10 fragments, akin to p32, and exhibited detectable biological activity (26). Nevertheless, no alternately spliced variants of procaspase-1 were observed in primary human or mouse hepatocytes upon analysis of published transcripts, or by sequencing murine hepatocyte transcripts, suggesting that Caspase-1 p32 generation in hepatocytes is unlikely to be a consequence of modified IDL sequence of procaspase-1 in hepatocytes. Only future studies will determine why procaspase-1 undergoes incomplete processing in the hepatocytes.
Various unconventionally cleaved Caspase-1 products have been reported in other parenchymal cell lineages such as the epithelial cells and neurons, as well as in certain types of neoplasms (81–87). Despite being genetically conserved, Caspase-1 processing is known to generate versatile cleavage products in lower order animals or birds (60). Although Caspase-1 cleavage patterns have evolved to extend the biological functions of Caspase-1 in higher order animals such as mammals, remarkably, Caspase-1 appears to have conserved its ability to induce host cell-death throughout this evolutionary process (60). This suggests that the primary immunological function of Caspase-1 in higher order mammals may be the induction of host cell-death, with the production of proinflammatory responses being a secondary, specialized function present primarily in the more dedicated immune cells. Of note, when Caspase-1 processing was transgenically limited to the p32 form in BMDMs by mutating the IDL sequence of procaspase-1 as described above, such BMDMs generated significantly reduced levels of mature IL-1β in response to Salmonella infection or LPS+ATP stimulation, but conserved the ability to undergo cell-death (26). Of note, hepatocyte p32 is catalytically active. It has also been shown that Caspase-mediated cell-death in hepatocytes is crucial to deliver Plasmodium antigens to APCs recruited to the infected liver, implying that Caspase-1 function in hepatocytes is vital for the induction of adaptive immune responses against liver-stage malaria (9).
The precedent of undertaking biochemical and functional characterizations of the inflammasome pathway using immune cells and cell-lines may have skewed our understanding of Caspase-1 biology, and may have led to the presumption that Caspase-1 activation and functions are uniform in all cell-types (19, 21, 25, 31). Similarly, usage of cancer cell lines such as hepatoma cells to study Plasmodium infection biology may have allowed at least some innate immune pathways pertinent to liver-stage malaria infections to remain elusive. We believe that our focus on purified primary human and mouse hepatocytes have played a crucial role in uncovering AIM2-mediated innate immune response in Plasmodium infection in the liver, and of unconventional Caspase-1 processing in hepatocytes. In addition to inspiring future studies that investigate potential immune mechanisms that evoke the release of Plasmodium DNA into the host hepatocytes, we believe that this work will invoke fundamental questions about the determinants of unconventional processing of Caspase-1 in hepatocytes, and possibly in other parenchymal cells.
Materials and Methods
Mice and Pathogens.
C57BL/6 (B6) mice were purchased from the National Cancer Institute or Jackson Laboratory and liver-humanized mice were obtained from Yecuris. GSDMDKO mice were provided by Dr. Thirumala-Devi Kanneganti (St Jude’s Children’s Research Hospital), tdTomato expressing mice by Dr. John Englehardt (University of Iowa), ASCKO, NLRP3KO, NLRC4KO, and Casp1/11KO mice by Dr. Fayyaz Sutterwala (Cedar-Sinai), IFNAR1KO, NLRP1bKO, and AIM2KO mice procured from the Jackson laboratory. All mice were housed with appropriate biosafety containment at the animal care units. The animals were treated and handled in accordance with guidelines established by the respective Institutional Animal Care and Use Committees. Anopheles stephensi mosquitos parasitized with P. yoelii 17XNL (Py) and GFP+ P. yoelii 17XNL (Py-GFP) were obtained from New York University. A. stephensi mosquitos infected with Pf NF54 or BD007 isolates were obtained from Johns Hopkins University or the University of Georgia. A. stephensi mosquitos infected with P. berghei ANKA (Pb) was obtained from the University of Georgia.
Primary Hepatocyte Culture, In Vitro Sporozoite Infection and PRR Stimulation.
Primary hepatocytes were isolated from mice as described in detail before (9). Primary human hepatocyte infection in this study followed published methods (88). The methodological details are described in detail in SI Appendix.
scRNAseq and Analysis.
Primary human hepatocytes cultured in vivo in replicates were infected with Pf (BD007 isolate) and the hepatocytes were analyzed using scRNA sequencing. The methodological details of the experiment and data analysis are described in detail in the SI Appendix.
Plasmodium Inoculations into Mice.
For sporozoite challenge experiments to determine liver-parasite loads, salivary glands of parasitized A. stephensi mosquitoes were dissected and sporozoites were isolated, counted, and injected 3 to 5 × 104 in 200 μL RPMI with 1% mouse serum (Innov-research) retroorbitally or into the tail vein of mice (9). For some imaging experiments, parasitized A. stephensi mosquitoes (~100/carton) were allowed to bite the abdomen of ketamine anesthetized mice (5/carton) for 6 separate 5-min intervals.
For infecting of liver-humanized mice, A. stephensi infected with Pf NF54 strain were allowed to bite ketamine-anesthetized liver-humanized mice for 15 min. The remaining sporozoites were subsequently isolated from the same mosquitoes and inoculated intravenously into the tail vein of the mosquito-fed mouse at 7.5 × 105 sporozoites per mouse. The animals were euthanized at 30 h p.i. to collect the liver.
Flow Cytometry.
Hepatocyte fractions collected after prefusion, density gradient separation, and adhesion to culture plates (dislodged by trypsin, 0.25% trypsin-EDTA, 5 min at 37 °C) were stained with 0.5 μg/mL anti-CD45 F4/80, CD11c, CD11b. or CSF1R (Biolegend) (20 min at 4 °C) to determine the presence of hematopoietic or Kupffer cells, or to phenotypically characterize them as presented in detail before (9). Cells were stained for cell surface markers with appropriate antibodies in PBS for 30 min prior to washing and resuspending in PBS to analyze by flow cytometry. To determine Caspase-1 activation, Plasmodium-infected hepatocytes were tested using the FAM-FLICA Caspase-1 assay kit (ImmunoChemistry Technologies) as per the manufacturer’s protocol. Data were acquired on an LSR Fortessa (BD Biosciences) and analyzed with Flowjo (Treestar).
Macrophage Differentiation.
BMDMs were prepared as described previously (89). In short, bone marrow cells were grown in L-cell-conditioned IMDM medium (ThermoFisher) supplemented with 10% FCS, 1% nonessential amino acids and 1% penicillin–streptomycin for 5 d to differentiate into macrophages. On day 5, BMDMs were seeded in 6-well cell culture plates. The next day BMDMs were stimulated with 100 ng/mL LPS (3.5 h) followed by 5 mM ATP or 10 μM Nigericin (0.5 h), and the whole-cell lysates or supernatants were collected as indicated.
Assessment of Liver Parasite Burden.
Liver parasite burden was assessed by quantitative real-time RT-PCR for parasite 18s rRNA in the livers of mice challenged with sporozoites isolated from infected mosquitoes, as described in detail before (9, 90). RNA was extracted at the indicated time points after Plasmodium inoculation using TRIzol, followed by DNase digestion/cleanup using the RNA Clean and Concentrator kit (Zymo Research). 2 µg liver RNA per sample was used for qRT-PCR analysis for Plasmodium 18S rRNA using TaqMan Fast Virus 1-Step Master Mix (Applied Biosystems). Data were normalized for input to the GAPDH control (hepatocytes) for each sample and are presented as ratios of Plasmodium 18s rRNA to GAPDH RNA. The ratios depict relative parasite loads within an experiment and do not represent absolute values.
BrdU Incorporation.
To generate BrdU (5-bromo-2′-deoxyuridine) incorporated sporozoites, female B6 mice were inoculated with Pb-infected RBCs (1.5 to 2 × 106/ mouse), i.p., followed by mosquito feeding at days 4 and 5 postinfection. The infected mice were anesthetized and exposed to cages containing around 200 overnight-fasted female A. stephensi mosquitoes. The mosquitoes were then maintained on 12.5% (w/v) sucrose with 1 mg/mL BrdU, at 19 to 20 °C and 80% relative humidity. The sucrose-BrdU solution was made fresh and replaced every 2 d, until the mosquitoes were dissected to obtain the sporozoites on day 21 after the initial feeding. To incorporate BrdU into DNA of B16 melanoma cells, BrdU (10 µM) was added to the tissue culture media and cultures maintained for 7 d. To prepare salivary gland extracts from BrdU-fed mosquitoes, we maintained the uninfected mosquitoes on sucrose-BrdU solution as described above. Their salivary glands were removed and processed as for sporozoite extraction. The salivary gland extracts from BrdU-fed Pb-infected or uninfected mosquitoes were diluted to equivalent amounts for immunoprecipitation or flow-cytometry assays.
Cell-Death Assay.
Lactate dehydrogenase (LDH) release assay to determine cell lysis: Overnight cultures of 5 × 104 hepatocytes/ well in 48-well plates were inoculated with 2.5 × 104 Py sporozoites. Cell culture supernatants were replaced with fresh media at 4 h incubation after washing the cells twice with media, to remove free sporozoites. Culture supernatant was subsequently collected at various time points and assayed for LDH as a measure of total cytolysis, using the CytoTox 96 Non-Radioactive Cytotoxicity Assay (Promega) according to the manufacturer’s instructions. %Cytolysis calculated as 100× (experimental LDH release signal/ maximum LDH release signal).
Microscopy.
Liver sections (30 µM thickness) collected from infected (5 × 105 Py or 7.25 × 105 Pf sporozoites) mice were fixed, permeabilized with 1% Triton X-100 (Fisher Bioscience) and imaged after staining. In addition, cultured hepatocytes in 10u-slide for chemotaxis (ibidi) were imaged live. Images were acquired on SP8 NLO Microscope (Leica) using a 10×/0.40 dry objective (live) or 25×/0.95 water immersion objective with coverslip correction (fixed), as described in detail previously (91). All images acquired were processed using Imaris software (Bitplane).
Caspase-1 (p10, polyclonal, Bioss Antibodies) was used on human hepatocytes and 1 µg/mL Caspase-1 (p20, Clone Casper-1, Adipogen), 0.5 μg/mL GasderminD (Clone EPR19828, AbCam), 0.5 μg/mL ASC (Clone D2W8U, Cell Signaling), 0.5 μg/mL NLRP3 (Clone 768319, R&D), or 0.5 μg/mL AIM2 (Clone EPR18793, AbCam) on mouse hepatocytes. Pfhsp70 (polyclonal, GenWay) was used to mark Pf. The following were used to identify Py: hep17 (gift from Dr. Scott Lindner, Pennsylvania State University), ACP or CSP (polyclonal, 1:100, gifts from Dr. Stefan Kappe, Seattle Children’s Hospital), along with 0.4 μg/mL anti-BrdU (clone BU1/75, ThermoFisher) or 5 μM CellTrace Violet (CTV, ThermoFisher). The following were used to identify Pb: GFP (Clone FM264G) and UIS4 (polyclonal, 1:100, gift from Dr. Scott Lindner, Pennsylvania State University). For live imaging, hepatocytes were cultured overnight in collagen coated chamber slides (ibidi) and infected with Plasmodium. Cultures were maintained in a climate-controlled chamber during imaging. For determining BrdU incorporation in Plasmodium, air-dried sporozoites (104/well) in “PTFE” printed slides (Electron Microscopy Sciences), or frozen sections from infected livers were used. Samples were fixed with 4% paraformaldehyde/PBS (10 min), permeabilized with Cytofix/Cytoperm buffer (BD Biosciences) for 15 min/4 °C or 1% tritonX100, washed once with Perm/Wash buffer (BD Biosciences), treated with Cytoperm Permeabilization Buffer Plus (BD Biosciences), for 15 min/4 °C, washed again with Perm/Wash buffer, and treated again with Cytofix/Cytoperm (BD Biosciences) for 15 min/4 °C. After washing with Perm/Wash buffer again, the samples were treated with DNAse in PBS/BSA for 90 min at 37 °C. The samples were subsequently washed with Perm/Wash buffer and coincubated with rat monoclonal anti-BrdU antibody (Clone BU-1, Thermofisher) and anti-CSP (sporozoites) or anti-ACP (schizont) antibodies. Subsequently the samples were washed thrice with Perm/Wash buffer, probed with 5 μg/mL fluorophore-conjugated secondary antibodies and 0.1 μg/mL of DAPI, washed thrice with Perm/Wash buffer, and imaged.
The percentage of Plasmodium-infected hepatocytes in which specific subcellular components were detected (e.g., Caspase-1) was represented by its ‘frequency of association’, and the percentage of Plasmodium-infected hepatocytes in which distinct subcellular components colocalized with each other spatially (e.g., Plasmodium and AIM2) was represented by ‘frequency of colocalization’. Data are presented in the corresponding figure legends as mean ± SEM.
Transfection of Cells.
Hepatocytes (7 × 105 cells) were transfected with various nucleic acids (at 6 μM concentration) using the Mouse/ Rat hepatocyte Nucleofector kit (Lonza) following the manufacturer’s protocol. The transfected cells were transferred to collagen-coated wells, 16 h prior to infection or treatments.
Differentiated BMDMs were detached by treatment with trypsin and resuspended at a concentration of 106 cells per 100 μL of nucleofector solution. These cells were transfected with ON-TARGETplus SMARTpool siRNAs (Dharmacon) using the Mouse Macrophage Nucleofector kit (Amaxa) following the manufacturer’s protocol. The transfected cells were transferred to plates and allowed to recover for 24 to 48 h prior to infections or treatments. The DNA extracted from Plasmodium or B16 tumor cells lines using Phenol/chloroform/isoamyl alcohol precipitation was sonicated at 20% power (Qsonica) with 5 s/45 s on/off cycle, 8 times, to shear it to uniform 200-bp fragments. BMDMs in 6-well plates were transfected using lipofectamine2000 as described before (92).
Therapeutic Regimens.
The following treatment regimens were used in this study: siRNA (corresponding Dharmacon siGENOMESMARTpool): 1 nM/mouse, hydrodynamic i.v., −1 or −2 dpi, Poly I:C (Invivogen): 100 µg/mouse, hydrodynamic i.v., Mammalian expression plasmids: 10 µg/ mouse, Anakinra: 10 mg/kg, i.v., −1,0,1 dpi, Disulfiram (Sigma-Aldrich): 50 mg/kg in sesame oil, i.p.
Hydrodynamic Delivery of Nucleic Acids.
Hydrodynamic injections were performed as described in detail before (93). In short, the desired amount of the siRNA was resuspended in PBS (at 10% volume/ body weight of the mouse) and delivered to the tail vein of mice, using constant pressure, within 7 s. The mice were then placed on a warm heating pad and allowed to recover for about 30 min, before being transferred back into their cages.
Inflammasome Activity Assay.
Caspase-1 activity was assayed in cells as described in detail before (94). In short, 8 × 105 hepatocytes from 24 h infected or uninfected cultures were lysed and the supernatants were assayed for residual Caspase-1 activity by its ability to cleave the fluorogenic substrate Z-YVAD-AFC (Enzo) by incubating at 37 °C for 60 min. The fluorescence was evaluated at Ex/Em 400/505 in a microplate reader (SynergyH1, BioTek).
ELISA.
To determine BrdU incorporation in sporozoites, ELISA was performed as described in detail before (71). The details are described in the SI Appendix.
Western Blot and Immunoprecipitation Assays.
Western blots were performed as detailed before (89). Immunoprecipitation was carried out as described before (95). The details of these processes are described in the SI Appendix.
Sequencing.
To determine the sequence of procaspase-1 transcripts in hepatocytes, total RNA was prepared as from the hepatocytes as described above; cDNA was synthesized as described in detail before (96), and the procaspase-1 gene was amplified with the following primers: 5′-atggctgacaagatcctgagggcaaag-3′ (F) and 5′-ttaatgtcccgggaagaggtagaaac-3′ (R). The amplicons were cloned using TOPO TA cloning kit (Invitrogen) following the manufacturer’s protocol and multiple clones (>20) sequenced.
Statistical Analyses.
Data were analyzed using Prism7 software (GraphPad) and as indicated in figure legends. The exact P values are indicated in the figures, except when <0.001. In the case of latter, we show it as p < 0.001.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. Plasmodium-infected hepatocyte undergoes pyroptotic death. Related to Figure 5. Confocal time-lapse live-microscopy images showing Py (CellTrace Violet+, green) infected primary hepatocytes in culture (tdTomato+), imaged from 24-32h of co-culture, at 0.5h intervals. Infected cells indicated by arrowheads.
Movie S2. Pyroptotic death in Plasmodium-infected hepatocytes is dependent on GSDMD. Related to Figure 5. Confocal time-lapse live-microscopy images showing Py (GFP+, green) infected primary, GSDMDKO hepatocyte in culture (CellTrace Violet+, blue), imaged from 8-47h of co-culture, at 0.5h intervals. Infected cell indicated by the arrowhead.
We thank Dr. Rick Tarleton for comments, Gibran Nasir for preparing humanized mice for analyses, Dr. Magdy Alabady for assistance with scRNAseq, Dr. Teneema Kuriakose for helpful discussions, Carson Bowers for help with the mouse colonies, and Dr. Fayyaz Sutterwala for providing valuable knock-out mice. We also express our gratitude to the UGA CTEGD Flow Cytometry Core, UI Central Microscopy Research facility, UGA CTEGD Sporocore, UGA Georgia Genomics and Bioinformatics Core, Iowa Institute of Human Genetics, UGA and UIowa animal research facility staff and the NYU and Johns Hopkins Malaria Institute Insectary Cores. Support for these studies was provided by NIH (AI168307 to S.P.K., AI85515, AI95178, AI100527 to J.T.H., AI132359 to P.S. and K22AI127836 to P.G.) and the UGA Research Foundation (Startup funding to S.P.K.).
Author contributions
C.M.-d.-S., B.P., R.P.B., K.P., J.C.S., P.S., P.G., J.T.H., and S.P.K. designed research; C.M.-d.-S., B.P., K.P., L.S.H., J.C.S., L.L.P., M.J.S., and S.P.K. performed research; T.-D.K. and D.E.K. contributed new reagents/analytic tools; R.P.B. analyzed data; and J.T.H. and S.P.K. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
Primary RNA sequencing data are deposited with NCBI and is available from BioProject PRJNA694978. The other data supporting the findings of this study are available within the article or the SI Appendix files.
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926222.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595706
202208963
10.1073/pnas.2208963120
research-articleResearch ArticleneuroNeuroscience424
Biological Sciences
Neuroscience
Altered integration of excitatory inputs onto the basal dendrites of layer 5 pyramidal neurons in a mouse model of Fragile X syndrome
Mitchell Diana E. a b 1
Miranda-Rottmann Soledad a b 1 https://orcid.org/0000-0001-8384-8661
Blanchard Maxime a
Araya Roberto [email protected]
a b 2 https://orcid.org/0000-0001-6932-874X
aDepartment of Neurosciences, Faculty of Medicine, University of Montreal, Montréal QC H3T 1C5, Canada
bCHU Ste-Justine Research Center, Montréal, QC H3T 1C5, Canada
2To whom correspondence may be addressed. Email: [email protected].
Edited by Mark Nelson, University of Vermont, Burlington, VT; received May 25, 2022; accepted November 11, 2022
1D.E.M. and S.M.-R. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e220896312025 5 2022
11 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Fragile X syndrome (FXS) is the most commonly known cause of autism spectrum disorders (ASD). We found that while control animals integrate subthreshold feedforward excitatory inputs linearly, this integration is sublinear in FXS mice, a defect that was reversed by genetic manipulation of BK channel activity. The sublinear integration contradicts what would be expected of sensory hypersensitivity classically associated with ASD. These findings instead support a model of FXS encompassing sensory hyposensitivity and predictive hypersensitivity at the level of cortical neurons. Moreover, these results highlight the need to study, in FXS and other forms of ASD, channelopathies in cortical feedforward and feedback pathways independently, their associations, and how cortical pyramidal neurons output is affected, which ultimately shapes behavior.
Layer 5 (L5) pyramidal neurons receive predictive and sensory inputs in a compartmentalized manner at their apical and basal dendrites, respectively. To uncover how integration of sensory inputs is affected in autism spectrum disorders (ASD), we used two-photon glutamate uncaging to activate spines in the basal dendrites of L5 pyramidal neurons from a mouse model of Fragile X syndrome (FXS), the most common genetic cause of ASD. While subthreshold excitatory inputs integrate linearly in wild-type animals, surprisingly those with FXS summate sublinearly, contradicting what would be expected of sensory hypersensitivity classically associated with ASD. We next investigated the mechanism underlying this sublinearity by performing knockdown of the regulatory β4 subunit of BK channels, which rescued the synaptic integration, a result that was corroborated with numerical simulations. Taken together, these findings suggest that there is a differential impairment in the integration of feedforward sensory and feedback predictive inputs in L5 pyramidal neurons in FXS and potentially other forms of ASD, as a result of specifically localized subcellular channelopathies. These results challenge the traditional view that FXS and other ASD are characterized by sensory hypersensitivity, proposing instead a hyposensitivity of sensory inputs and hypersensitivity of predictive inputs onto cortical neurons.
layer 5 pyramidal neurons
synaptic integration
autism spectrum disorders
BK channels
dendritic spines
The Canadian Institutes of Health Research (CIHR) MOP-133711 Diana E MitchellMaxime G. BlanchardRoberto Araya The Canadian Institutes of Health Research (CIHR) MOP-377520 Diana E MitchellMaxime G. BlanchardRoberto Araya Canada Foundation for Innovation (CFI) 501100000196 Fonds des leaders 29970 Roberto Araya Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) 501100000038 Discovery grant #418113-2012 Roberto Araya Scottish Rite Charitable Foundation of Canada (SRCFC) 501100000096 Research Grant Roberto Araya Fonds de recherche du Québec Santé (FRQS) postdoctoral fellowship Diana E MitchellMaxime G. BlanchardRoberto Araya Herber Jesper postdoctoral fellowship at Université of Montréal postdoctoral fellowship Diana E MitchellMaxime G. BlanchardRoberto Araya
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pmcFragile-X syndrome (FXS) is the most frequent form of inherited intellectual disability and the commonest known cause of autism (1, 2). FXS and other neurodevelopmental disorders associated with autism spectrum disorders (ASD) have a negative impact in most, if not all, everyday life activities, with a global prevalence that has been growing steeply since the 1990s (3). FXS occurs because of the inactivation of the Fragile X Mental Retardation 1 (FMR1) gene, which encodes the FXS Protein (FMRP), a polyribosome-associated RNA-binding protein that inhibits the translation of bound messenger (m)RNAs, especially at synapses (4, 5). At the cellular level, it has been shown that spines [tiny protrusions covering the dendrites of cortical pyramidal and spiny stellate neurons that constitute the postsynaptic element of ~95% of all the excitatory synapses (6–8)] are abnormally long and dense in mice models of FXS (Fmr1-knockout (KO) mice) (9, 10). These synaptic alterations suggest the presence of defects in the transmission, plasticity, and integration of excitatory inputs in FXS (11, 12). At the circuit level, the neocortex of Fmr1-KO mice has been found to be hyperexcitable (13–17) with pyramidal neurons exhibiting abnormally high and synchronous firing, leading to recurrent bursting (18)—something long assumed to cause sensory hypersensitivity (reviewed in ref. 19).
A key function of the neocortex is to associate external sensory information with an internal representation of the world to make predictions about the future (20–22). Specifically, layer 5 (L5) pyramidal neurons integrate sensory inputs (feedforward) in their basal dendrites with information from other cortical areas (feedback) at the apical dendrites in layer 1 (20). Feedback information provides contextual or internal invariant predictive representation of the world built from previous experiences, while feedforward inputs provide external sensory information. Thus, to make predictions about the world (cognition and conscious perception) the brain has to combine aspects of perceived experience (sensory feedforward information) with an internal representation of the world (predictive feedback information) (23–26). L5 pyramidal neurons, vertically spanning all cortical layers, are the main candidates believed to perform this task (20, 27). Hence, defects in the processing and integration of excitatory inputs at the level of single dendritic spines in the basal and/or distal apical tuft dendrites of L5 pyramidal neurons could contribute to the functional cortical defects associated with neurological disorders such as FXS and ASD. Indeed, previous work has shown during repetitive electrical stimulation of layer 1, an enhanced temporal summation of excitatory inputs in the apical dendrites of Fmr1-KO L5 pyramidal neurons (13). In addition, this work also found an increase in the number of action potentials, evoked during tactile stimulation, in Fmr1-KO L5 pyramidal neurons compared with those in wild-type (WT) neurons (13). It remains unknown, however, how sensory inputs are processed at the level of individual synapses in the basal dendrites of Fmr1-KO L5 pyramidal neurons, and whether the synaptic integration impairments are equally or differentially affected within feedforward and feedback pathways in L5 pyramidal neurons in FXS.
Here, we aim to uncover if the integration of feedforward sensory inputs at the level of single synapses in the basal dendrites of L5 pyramidal neurons is altered in Fmr1-KO mice and, if so, explore the underlying mechanisms responsible for any observed differences. To do so, we first determined how near-simultaneous inputs onto neighboring spines are integrated in L5 pyramidal neuron dendrites from Fmr1-KO versus WT mice. As previously demonstrated (28), we found that the basal dendrites of WT L5 pyramidal neurons integrate inputs linearly before the generation of a dendritic spike, whereas, surprisingly, synaptic inputs in L5 pyramidal neuron basal dendrites summate sublinearly in Fmr1-KO mice. To try to understand the underlying mechanism, we focused on spine channelopathies that have been associated with FXS (29), in particular defects in large-conductance voltage- and calcium-activated potassium (BK) channels that have been described in the apical dendrites of Fmr1-KO L5 pyramidal neurons (13). Previous work has further revealed that FMRP interacts with the β4 regulatory subunit of BK channels (30), which are localized to dendritic spines where they play a role contributing to synaptic efficacy in L5 pyramidal neurons (31). Thus, in WT mice, FMRP can sequester the β4 subunit, as has been shown in the hippocampal pyramidal neurons (30), and prevent it from interacting with BK channels, whereas in Fmr1-KO animals, an increased binding of BK channels and its β4 subunit would occur. The β4 subunit decreases the probability of BK channels opening at low calcium concentrations and increases it at high calcium concentrations while also slowing down activation and deactivation kinetics (32, 33), which would ultimately affect L5 pyramidal neuron integrative properties. Hence, we predicted that the sublinear integration in the basal dendrites of Fmr1-KO L5 pyramidal neurons was due to altered BK channel activity via its interaction with the β4 subunit in the absence of FMRP and subsequent increased dendritic repolarization when two spines are activated, compared with WT neurons. Here, we found that knockdown of the β4 subunit of BK channels, using short hairpin RNA (shRNA), rescues the linear integration of subthreshold inputs in the basal dendrites of Fmr1-KO L5 pyramidal neurons while injection of a scrambled version has no effect. Numerical simulations corroborate these experimental results, showing that synaptic integration occurs linearly only when the kinetics of the α-subunit of spine BK channels are modeled, while the β4 subunit boosted the number of open BK channels per spine, leading to a greater repolarization and sublinear integration of subthreshold excitatory inputs in the basal dendrites of Fmr1-KO L5 pyramidal neurons. Taken together, our findings support a model of FXS that is more intricate than one simply characterized by a global cortical hypersensitivity, and help to uncover the role of ion channels in excitatory input integration to identify localized dendritic targets for the design of specific treatment options to alleviate symptoms associated with FXS.
Results
Sublinear Integration of Synaptic Inputs in the Basal Dendrites of L5 Pyramidal Neurons in Fmr1-KO Mice.
To study synaptic integration in the basal dendrites of L5 pyramidal neurons in acute brain slices of the mouse visual cortex from WT and Fmr1-KO mice, we applied two-photon (2P) uncaging of caged glutamate (4-methoxy-7-nitroindolinyl glutamate (MNI)-glutamate, 2.5 mM; see Methods) at two individual spines separately and then together near-simultaneously (Fig. 1A). Whole-cell patch clamp recordings in current-clamp were performed to measure the uncaging (u)-evoked excitatory postsynaptic potentials (uEPSP) at the soma. This technique allows for the precise activation of individual dendritic spines with responses that are similar to those from physiological activation of single synapses (28, 31, 34–41). As shown previously (28), subthreshold synaptic inputs onto spines in the basal dendrites of WT L5 pyramidal neurons integrate linearly (Fig. 1A). Specifically, we found that activating individual spines triggered uEPSPs (black traces in Upper panel of Fig. 1A), which added linearly to accurately predict the response when two spines were activated near-simultaneously (compare black and blue dashed traces in the Upper Right panel of Fig. 1A). We quantified these results by calculating linearity indices, using either the peak or integral of the uEPSP as well as a gain measure (see Methods). Linearity indices were not significantly different from 100% for WT L5 pyramidal neurons when we considered each experiment individually (100.45 ± 1.74%, P = 0.78, for peak uEPSP, and 102.82 ± 2.15% P = 0.20 for uEPSP integral, Wilcoxon test, n = 40 spine pairs; Fig. 1B) or when we averaged each individual experiment per mouse (98.35 ± 2.82%, P = 0.49, Wilcoxon test, n = 10 mice for peak uEPSP, and 99.90 ± 3.79% P = 0.32, Wilcoxon test, n = 10 mice for uEPSP integral; Fig. 1C). We next computed a gain measure, which compares the time-varying uEPSP in response to the activation of two spines to that predicted from the linear summation of individual responses, with a measure of one being a perfect match. We found that the gain measure was not significantly different from the one for WT L5 pyramidal neurons (individual experiments: 1.03 ± 0.02, P = 0.29, n = 40 spine pairs; average per mouse: 1.00 ± 0.02, P = 0.98, n = 10 mice; Wilcoxon test, Fig. 1 B and C). Surprisingly, we observed uEPSP responses that were smaller in amplitude and integral than expected based on the sum of individual responses in Fmr1-KO mice (compare red and blue dashed traces in the Lower Right panel of Fig. 1A) with linearity indices significantly lower than 100% (individual experiments: 91.27 ± 1.47%, P < 0.0001 for peak uEPSP and 89.58 ± 1.90%, P < 0.0001 for uEPSP integral, Wilcoxon test, n = 74 spine pairs; average per mice: 88.56 ± 1.79%, P < 0.0001 for peak uEPSP and 88.28 ± 1.89%, P < 0.0001 for uEPSP integral, Wilcoxon test, n = 16 mice; Fig. 1 B and C) and the gain measure significantly lower than one (individual experiments: 0.89 ± 0.02, P < 0.0001, n = 74 spine pairs, Wilcoxon test; average per mice: 0.87 ± 0.03, P < 0.001, n = 16 mice, Wilcoxon test; Fig. 1 B and C). These results were surprising, since they contradict what would be expected from a hyperexcitable cortex that has been described in FXS.
Fig. 1. Sublinear summation of excitatory synaptic inputs in basal dendrites of Fmr1-KO L5 pyramidal neurons. (A) Representative basal dendrite selected for 2P glutamate uncaging to activate spines from WT (Top) and Fmr1-KO L5 pyramidal neurons (Bottom). Green dots indicate the site for uncaging. Spines were first activated individually (Spine 1 or Spine 2) and then together (Spine 1 & Spine 2). Blue dashed traces correspond to the linear sum of individual events of each spine. (B) Observed response has smaller amplitude, integral and gain than expected based on the sum of individual responses in Fmr1-KO mice, while in WT mice, the responses summate linearly. (C) As in B, but values were averaged per mouse.
We found no difference in the intrinsic cellular properties between Fmr1-KO versus WT L5 pyramidal neurons (SI Appendix, Fig. S1), except for a significantly longer full width at half maximum (FWHM) of the action potential waveform in Fmr1-KO L5 pyramidal neurons compared to WT (SI Appendix, Fig. S1F). When we activated one (individual) versus two (combined) spines, we found that individual and combined uEPSP responses were smaller in the basal dendrites of Fmr1-KO versus WT L5 pyramidal neurons (SI Appendix, Fig. S2). This effect was washed out, however, when experiments were pooled per mouse (SI Appendix, Fig. S2). Thus, in some animals, there was an overrepresentation of experiments yielding smaller uEPSP sizes. The morphology of activated spines was comparable for Fmr1-KO and WT mice (SI Appendix, Fig. S2) as has been previously reported for this age range (42). Moreover, spine density is also similar in WT and Fmr1-KO L5 pyramidal neurons by age P14 (42). Overall, these data indicate that the sublinear integration in Fmr1-KO L5 pyramidal neurons cannot be explained by changes in intrinsic cellular properties or spine morphology.
Expression of BK Channel and Its β4-Subunit in the Neocortex of WT Versus Fmr1-KO Mice: Neocortical Excitatory Synaptic Enrichment of the β4-Subunit in Fmr1-KO Mice.
We next sought to identify the mechanism for this sublinear integration of spine activation in the basal dendrites of Fmr1-KO L5 pyramidal neurons. Large conductance calcium-activated potassium (BK) channels are homogeneously distributed along the dendritic tree of L5 pyramidal neurons and are believed to play an important role in synaptic transmission and integration (31, 43). BK channels are high-conductance potassium channels formed by a tetramer of α subunits. Membrane depolarization and intracellular calcium activate these channels, and in many tissues auxiliary subunits modulate their kinetics (reviewed in: 44). For example, it has been demonstrated in hippocampal CA3 pyramidal neurons that FMRP modulates BK channel activity by sequestering its regulatory β4 subunit (30). The β4 subunit decreases the probability of BK channel openings at low calcium concentrations but increases the probability of channel openings at high calcium concentrations while also slowing down activation and deactivation kinetics (32, 33). We thus predicted that Fmr1-KO L5 pyramidal neurons exhibit altered BK channel activity in the absence of FMRP, compared with WT neurons, which would ultimately affect L5 pyramidal neuron integrative properties. Using Western blot analysis of total protein (TP) and synaptoneurosomes (SN) prepared from the visual cortex, we show that αBK subunits are enriched in synapses, and there is no expression difference between WT and Fmr1-KO mice (WT: 1.00 ± 0.13 in SN versus 7.31 ± 0.31 in TP; KO: 1.54 ± 0.09 in SN versus 7.31 ± 0.44 in TP, n = 3, P < 0.001, ANOVA) (Fig. 2 A and B). By contrast, β4 subunits are not enriched in synapses in WT mice but are enriched in those of Fmr1-KO mice (WT: 0.90 ± 0.19 in SN versus 0.51 ± 0.09 in TP; Fmr1-KO: 1.04 ± 0.36 in SN versus 2.26 ±0.17 in TP, n = 3, P < 0.05, P < 0.0001, ANOVA) (Fig. 2 C and D). This increase in β4 immunoreactivity in cortical synapses of Fmr1-KO versus WT mice is consistent with FMRP sequestering the β4 regulatory subunit, preventing its interaction with BK channels. To demonstrate that FMRP also interacts with β4 in the visual cortex, we designed an assay of coimmunoprecipitation (co-IP) where FMRP can only be detected if it is pulled down by interaction with β4 (Fig. 2E, diagram). The assay demonstrates a specific interaction of FMRP with β4 in the mouse visual cortex (Fig. 2E). In addition, immunofluorescence data in transgenic mice expressing green fluorescent protein (GFP) in L5 pyramidal neurons show that αBK (Fig. 2F) and β4 (Fig. 2G) subunits are present in basal dendrites and spines from WT and Fmr1-KO L5 pyramidal neurons, consistent with our previous immunoelectron microscopy findings showing that αBK subunits are localized to dendritic spines in the basal dendrites from L5 pyramidal neurons with nanoscale resolution (31). Interestingly, quantitative image analyses of 3D dendrite reconstructions (Fig. 2H) show an increase in the β4 signal intensity in dendritic spines of Fmr1-KO compared with WT L5 pyramidal neurons (WT: 3.22 ± 0.29%, n = 143; KO: 6.05 ± 0.68%, n = 206, P = 0.015. Mann-Whitney test; Fig. 2I) while the total number of spines with detectable levels of β4 immunoreactivity remains unchanged (WT: 47.00 ± 8.44%; KO: 36.00 ± 3.81%, n = 5, P = 0.31. Mann–Whitney test). Taken together, these experiments, along with previous findings, provide evidence that BK channel activity and its interaction and regulation by the β4 subunit in cortical spines from Fmr1-KO L5 pyramidal neurons might be at the core of the altered integration of synaptic inputs in these neurons.
Fig. 2. Expression of αBK and β4 subunits in synapses of Fmr1-KO mice. (A and B) Mouse visual cortex TP and Synaptoneurosome (SN) fractions were analyzed by western blot with specific monoclonal antibodies and normalized by the expression of γ-tubulin. (A) Expression of αBK (20 µg/line). Synaptic enrichment in the SN fraction was confirmed by the expression of the postsynaptic density (PSD) marker PSD95. (B) αBK expression normalized by γ-tubulin and by the expression in WT/TP (n = 3, P < 0.0001, ANOVA). (C) Expression of β4. A mix of three samples for each condition was loaded at decreasing concentrations (24, 12, 6 µg/line). (D) β4 expression normalized by γ-tubulin and by the expression in WT/TP (n = 3, P < 0.05, P < 0.0001, ANOVA). (E) Representation of the experimental workflow (see Methods) and blots corresponding to co-IP of FMRP, immunoprecipitation (IP) of β4 and the loading control γ-tubulin (200 μg/sample input protein). Left side of the bottom panel, co-IP Western blot performed using anti-β4 antibody to precipitate and detect a protein complex containing FMRP bound to β4 subunit in the visual cortex and hippocampus of Fmr1-KO and control mice P25±1 (n = 3, pooled samples). Right side (pre-IP) shows the composition of the original sample (16 μg/line). (F–I) Expression of channel subunits in excitatory synapses was studied in P26±1 Fmr1-KO and control transgenic mice expressing GFP in L5 pyramidal neuron (green) by immunofluorescent detection of αBK (F) and β4 subunits (red) (G) Dendritic spine channel expression is shown in one <1-μm confocal optical slice. Arrow heads indicate spines with detectable expression of the BK subunit. Insets (yellow boxes) and additional examples at higher magnification are shown below each group with clear αBK or β4 subunit spine localization. (Scale bar, 1 μm.) (H) Examples of dendrite 3D reconstructions from the same neurons shown in G), above threshold β4 subunit immunoreactivity and signal overlapping with the spine head are shown as red dots. (I) Mean intensity of the β4 subunit signal in each spine head was compared between Fmr1-KO and control mice. (Fmr1-KO, n = 206 spines, WT, n = 143 spines, P < 0.05, Mann–Whitney test).
Knock-Down of BK Channel β4 Subunit in L5 Pyramidal Neurons of Fmr1-KO Mice Rescues the Subthreshold Linear Integration of Synaptic Inputs.
In order to test the hypothesis that the lack of FMRP in Fmr1-KO mice leads to an elevated binding of the β4 subunit to BK channels in spines and dendrites, thus disrupting the linearity of incoming synaptic inputs, we performed a knockdown of the β4 subunit of BK channels, using shRNA in Fmr1-KO L5 pyramidal neurons (Fig. 3A). Two shRNA sequences recognizing the open reading frame of the mouse kcnmb4 gene encoding the β4 subunit (β4shRNA, see Methods) were inserted into a viral vector to produce adeno associated virus (AAV) (Fig. 3A). The efficiency of the β4shRNA was first validated in mouse brain by IP-western blot showing a ~50% reduction in β4 protein consistent with sparse neuronal expression observed during imaging (SI Appendix, Fig. S3 A–C) and in 293T cells analyzed by qRT-PCR showing a significant β4 mRNA expression inhibition (SI Appendix, Fig. S3 D and E, control: 100.67 ± 9.68%, β4shRNA: 43.33 ± 1.45%, P = 0.0042, unpaired t test) with 31.09 ± 4.29% of β4-expressing cells also expressing β4shRNA, assessed by fluorescent image analysis (SI Appendix, Fig. S3 F–G).
Fig. 3. Knock-down of β4 subunit of BK channels rescues sublinearity of synaptic inputs in the basal dendrites of Fmr1-KO L5 pyramidal neurons. (A) Schematic of the shRNA vector used to knockdown β4. (B and C) shRNA knock-down of the β4 subunit of BK channels rescues the sublinearity in the basal dendrites of Fmr1-KO L5 pyramidal neurons (B) while injection of scrambled shRNA has no effect (C). (D) Observed responses were not significantly different in amplitude, integral, and gain than that expected based on the sum of individual responses in Fmr1-KO mice injected with the β4shRNA, while those injected with a scrambled version, the responses summate sublinearly. (E) As in D, but values were averaged per mouse. (F and G) Spine density (F) and spine morphology (G) are not significantly different in Fmr1-KO L5 pyramidal neurons injected with shRNA targeted to the β4 subunit of BK channels versus nonspecific scrambled shRNA in L5 of the visual cortex.
Mice were injected with β4shRNA-expressing AAVs at postnatal day 18, and 2 wk later we performed 2P uncaging of caged glutamate at two individual spines separately and then together near-simultaneously as described above. Analyzing each experiment individually, we found that expression of β4shRNAs rescued the subthreshold linear integration of synaptic inputs in the basal dendrites of Fmr1-KO L5 pyramidal neurons whereas the expression of a nonspecific scrambled version of the shRNA did not (peak linearity: 106.64 ± 2.62 versus 86.61 ± 4.51%, P = 0.001; integral linearity: 103.37 ± 2.88 versus 85.93 ± 5.17%, P = 0.0082; gain linearity: 1.13 ± 0.06 versus 0.87 ± 0.05, P = 0.0063; Mann–Whitney test; Fig. 3 B–D). Similar results were found when the average per mouse was instead considered (peak linearity: 105.36 ± 4.47 versus 88.02 ± 4.68%, P = 0.03; integral linearity: 106.28 ± 3.66 versus 87.67 ± 4.63%, P = 0.0017; gain linearity: 1.20 ± 0.09 versus 0.89 ± 0.06, P = 0.0017; Mann-Whitney test; Fig. 3E). Moreover, we found no significant difference between linear indices calculated from experiments in L5 pyramidal neurons from Fmr1-KO mice expressing β4 subunit shRNA versus those performed on WT L5 pyramidal neurons (individual experiments: peak linearity: P = 0.18; integral linearity: P = 0.99; gain linearity: P = 0.12; Mann-Whitney test; SI Appendix, Fig. S4 E–G).
Since activity-dependent spine morphological changes [spine head: (45), neck: (36, 38) or both: (46)] have been correlated with synaptic efficacy by altering the biochemical and electrical spine properties (reviewed in: 40), we analyzed spine shape in mice transfected with either the β4 subunit shRNA (β4shRNA) or the scramble shRNA. We found that spine head volume and neck length, as well as spine density remained the same in the basal dendrites of Fmr1-KO L5 pyramidal neurons transduced with shRNA against the β4 subunit of BK channels (β4shRNA) versus the scrambled version (spine density: 5.6 ± 1.4, n = 10 images versus 4.1 ± 0.5 spines per 10 µm, n = 12 images; P = 0.432; spine head volume: 0.18 ± 0.02, n = 30 spines, versus 0.19 ± 0.03 µm3, n = 28 spines, P = 0.932; neck length: 0.50 ± 0.05, n = 33 spines, versus 0.60 ± 0.05 µm, n = 34 spines, P = 0.096, Mann–Whitney test; Fig. 3 F and G). We also found no difference in the intrinsic cellular properties between Fmr1-KO L5 pyramidal neurons transfected with either the β4 subunit shRNA (β4shRNA) or the scramble shRNA (SI Appendix, Fig. S4). These findings indicate that altered synaptic integration in the basal dendrites in Fmr1-KO L5 pyramidal neurons arises from an increased activity of the regulatory β4 subunit. This increase can be explained by the lack of FMRP-mediated β4 sequestering in Fmr1-KO mice (30) leading to an elevated binding of the β4 subunit to BK channels impacting the integration of incoming synaptic inputs in the basal dendrites L5 pyramidal neurons.
Biophysical Simulations of Synaptic Integration in Basal Dendrites of Fmr1-KO and WT L5 Pyramidal Neurons: Increased Number of Synaptically Activated BK Channels upon β4 Subunit Binding in Fmr1-KO Mice.
To corroborate our experimental observations indicating the interaction of the β4 subunit with BK channels in spines impacts the integration of subthreshold excitatory inputs in the basal dendrites of L5 pyramidal neurons, and gain detailed information on the mechanistic underpinnings of this process, we turned to multicompartmental simulations in the NEURON environment (47). Briefly, a morphologically realistic L5 pyramidal neuron was built (adapted from refs. 31 and 48) where two spines were simulated with neck lengths of 1 µm and head volumes of 0.18 µm3, which matched the morphology of spines probed in our 2P experiments (Fig. 4 A and B). The dendritic spines were connected to a randomly selected basal dendrite located 170 µm away from the soma of the modeled L5 pyramidal neuron. α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-receptors(R), N-methyl-D-aspartate (NMDA)-receptors(R), voltage-gated calcium channels, sodium channels, and BK channels were placed in the plasma membrane of each spine head compartment (see Methods; Fig. 4 A and B). In one set of simulations, a WT L5 pyramidal neuron was modeled by incorporating the activation/deactivation parameters and kinetics of the α-subunit of BK channels, while in another group a Fmr1-KO L5 pyramidal neuron was modeled by using those obtained when the β4 subunit is bound to the α-subunit of BK channels (49). We then simulated our 2P experiments by modeling synaptic inputs onto two individual spines separately and then together (Fig. 4 A and B, Right). The simulations show that synaptic integration occurs linearly in the simulated WT neuron, when α-subunit of BK channels in spines are modeled (Fig. 4A), while inputs integrate sublinearly in the simulated Fmr1-KO neuron when the β4 subunit are incorporated in spines (Fig. 4B), emulating our experimental observations. We quantified these results by calculating linearity indices, using either the peak or integral of the EPSP as well as a gain measure (Fig. 4C). To understand the mechanistic underpinnings underlying the sublinear integration in Fmr1-KO L5 pyramidal neurons, we analyzed the number of open BK channels per spine when one versus two spines were activated in WT versus Fmr1-KO L5 pyramidal neurons. Our simulations show that more BK channels are open when two spines are activated, but in the presence of the β4 subunit there is a significantly larger boost in the number of open BK channels per spine (Fig. 4D), leading to the sublinear integration of subthreshold excitatory inputs in the basal dendrites—mimicking our experimental results found in Fmr1-KO L5 pyramidal neurons (Fig. 1A).
Fig. 4. Biophysical modeling shows the effect of the BK channel β4 subunit on synaptic integration in dendritic spines. (A and B) Left panels: Schematic of the model used to assess synaptic integration in the basal dendrites of a WT L5 pyramidal neuron (A) expressing FMRP, which sequesters the β4 subunit so only the α subunit is present in the spine and a Fmr1-KO L5 pyramidal neuron (B), which does not express FMRP so the β4 subunit is free to bind the BK channel. Right panels: Spines were first activated individually (Spine 1 or Spine 2) and then together (Spine 1 & Spine 2), and the EPSP generated at the soma was recorded. Blue dashed traces correspond to the linear sum of individual events of each spine. In the WT neuron, the actual and expected EPSP responses match (compare black and dashed blue traces), whereas in the KO neuron, the actual EPSP response is much less than expected (compare dashed red and blue traces). (C) Observed response has smaller amplitude, integral, and gain than the expected based on the sum of individual responses in Fmr1-KO neuron, while in WT mice, the responses summate linearly. (D) Number of BK channels open during activation of 1 spine or 2 spines in the modeled WT and Fmr1-KO L5 pyramidal neuron.
Discussion
Previous work suggested that FXS and ASD are characterized by a hyperexcitable neocortex (13–17), thought to be the main contributor to hypersensitivity of sensory stimuli observed in autistic individuals (reviewed in ref. 19). Our experimental findings presented here challenge this generalized view that there is a global hypersensitivity in the neocortex associated with FXS, as they show that the integration of subthreshold excitatory inputs onto the basal dendrites, the main recipients of sensory inputs, of L5 pyramidal neurons in Fmr1-KO mouse is sublinear (i.e., combined response smaller than arithmetic sum of individual events). We further studied the mechanism for this sublinear integration. BK channels and their β4 subunit are localized to basal dendritic spines of WT and Fmr1-KO L5 pyramidal neurons (Fig. 2) (31) and FMRP (absent in Fmr1-KO mice) binds the β4 subunit in the neocortex of WT mice (Fig. 2E) thus preventing it from binding to BK channels and altering their activation/deactivation parameters and kinetics (32, 33). We thus investigated how BK channels and their β4 regulatory subunit affect synaptic integration in Fmr1-KO L5 pyramidal neurons. We found that a knockdown of the β4 regulatory subunit in Fmr1-KO L5 pyramidal neurons rescued linearity from the sublinear integration previously observed in their basal dendrites. Finally, our numerical simulations corroborate these findings, as they show that the linear integration of excitatory synaptic inputs occurs only when the kinetics of the α-subunit of BK channels are modeled, while when the β4 subunit is incorporated the number of open BK channels per spine is boosted, leading to an enhanced repolarization when two spines are activated and sublinear integration of subthreshold excitatory inputs.
To make predictions about the future, the brain has to associate feedforward sensory with feedback predictive information (20–22). Hypersensitivity to sensory stimuli is described as one of the classical symptoms experienced by individuals with FXS and other forms of ASD (reviewed in ref. 19). One might think that being hypersensitive to certain stimuli makes you better able to detect it and perform certain behavioral tasks more aptly, which does not actually seem to consistently be the case for autistic individuals or mouse models of ASD. For example, children with ASD exhibit increased detection thresholds during tactile stimulation (50). While it has been shown that Fmr1-KO mice exhibit an enhanced acoustic startle response at low sound intensities; they actually show a decreased response to high-intensity sounds (51, 52). Fmr1-KO mice also showed impaired learning in a visual discrimination task and took longer to reach expert-level performance comparable to WT animals (53). Moreover, this study did not find a significant increase in either spontaneous or visually evoked activity in Layer 2/3 pyramidal neurons of Fmr1-KO mice. Thus, the results of these behavioral studies are in agreement with those we report here, in that they cannot consistently be explained by hyperexcitability of the cortex to sensory stimuli. Our data show that near simultaneous activation of two spines in the basal dendrites generates a subthreshold depolarization that summates sublinearly in Fmr1-KO mice, whereas, in their WT counterparts, this integration occurs in a linear fashion (Fig. 1 and see also ref. 28). The apical dendrites of L5 pyramidal neurons receive contextual information from other brain areas, and exhibit a greater density of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels (54), in contrast to BK channels, which are homogenously distributed across the dendritic tree of L5 pyramidal neurons. The apical dendrites of Fmr1-KO pyramidal neurons are hyperexcitable due to an increased impedance caused by the reduction of HCN channels in apical dendrites, resulting in an enhanced temporal summation of excitatory input (13). Thus, taken together, the hypersensitivity characterized in FXS is not simply due to an upweighting of sensory inputs at the level of L5 pyramidal neurons, but instead by a hyposensitivity of sensory inputs and a hypersensitivity of predictive inputs. Specifically, the overrepresentation of predictive inputs onto the apical dendrites of L5 pyramidal neurons, coinciding with a down-representation of sensory inputs at the level of the basal dendrites of cortical pyramidal neurons, may be at the core of the behavioral phenotypes associated with FXS. These changes would likely have significant effects on the input/output properties of L5 pyramidal neurons, and thus in the ability of the cortex to decipher sensory signals in order to make predictions about the future. Moreover, the results from the present study highlight the need to study, in FXS and other forms of ASD, cortical feedforward and feedback pathways independently, their associations, and how they ultimately affect cortical pyramidal neuron’s output to shape behavioral learning.
The relevance for the subthreshold activation of clustered spines, synaptic microclusters (38) (~2 to 5 spines), in L5 pyramidal neuron dendrites for representing multiple independent tasks and stabilizing learning has been observed by recent computational studies (55, 56), and is supported by anatomical (57–61) and functional (31, 61–63) observations. Hence, the sublinear integration in spine microclusters would likely affect the threshold for the induction of synaptic plasticity, sensory task representations in basal dendrites, and ultimately learning in Fmr1-KO mice.
Although the present study focused on L5 pyramidal neurons in Fmr1-KO mice, there have been many studies on how FXS and ASD in general alters the activity of cortical interneurons. Cortical pyramidal neuron activity is modulated by inhibitory interneurons, which control the firing of their postsynaptic target neurons to maintain network activity within a certain range and prevent epileptic episodes (64). Many mouse models of ASD exhibit altered interneuron function and development (65). Specifically, compared with WT animals, Fmr1-KO mice displayed delayed learning of a visual discrimination task, and reduced activity of parvalbumin (PV) interneurons in the visual cortex. Restoring PV cell activity accelerated the learning in Fmr1-KO mice (53). Moreover, the somatosensory cortex of Fmr1-KO mice has a significant reduction in the density of PV neurons (66). Thus, it is likely that the stereotypical behavior observed in FXS is caused by complex alterations in cortical circuits, where both the computations performed by L5 pyramidal neurons integrating feedforward sensory and feedback predictive inputs in addition to their modulation by inhibitory interneurons are impaired (67).
Studies investigating how the BK channel β4 subunit impacts the activity of hippocampal and neocortical pyramidal neurons have shown that FMRP interacts with the β4 subunit [in the hippocampus (30) and in the cortex (Fig. 2E)] and that the lack of FMRP leads to excessive broadening of pyramidal neuron action potentials, and consequently their inter-spike-interval during repetitive activity (30). Thus, FMRP can regulate neural firing patterns through a BK channel-mediated mechanism by the interaction of FMRP and the BK channel β4 subunits (30). The effect of the β4 subunit on BK channels is complex as it decreases the probability of BK channels opening at low intracellular calcium concentrations but promotes channel opening at high intracellular calcium concentrations while also slowing down activation and deactivation kinetics (32, 33). Thus, how the β4 subunit will affect BK channel opening depends on intracellular calcium levels. In the present study, we focused on how the BK channel β4 subunit influences the integration of subthreshold inputs onto spines (i.e., uEPSPs). Our results showing that knockdown of the β4 subunit of BK channels rescues synaptic integration are in agreement with the activation voltage range of BK channels at intracellular calcium levels reached in the spine upon synaptic activation [V1/2 of ~−50 mV versus ~−20 mV, for β4-bound BK channels versus unbound, respectively, for intracellular calcium concentration of ~20 µM (49)] and the dendritic spine depolarization generated by activating clustered spine pairs (Fig. 4D). How the behavior of these rescued Fmr1-KO mice is affected is a future direction of research. Taken together, our results along with previous studies show that in Fmr1-KO pyramidal neurons, the β4-bound BK channels causes 1) subthreshold inputs to be integrated sublinearly in the basal dendrites due to a greater repolarization when two spines are activated and 2) alterations in the timing of action potentials and information transmission compared with WT mice (30). Both of these defects can severely influence how sensory information is transmitted to downstream neurons from L5 pyramidal neurons.
This is a demonstration of hyposensitivity of sensory inputs at the level of L5 pyramidal neurons in a mouse model of ASD. Specifically, these results reveal a hyposensitivity of basal integration of sensory inputs in Fmr1-KO L5 pyramidal neurons through a mechanism dependent on the interaction between BK channels and β4 subunit. This work adds to our understanding of FXS by demonstrating that it is not solely characterized by a global sensory hypersensitivity at the cellular level and provides instead a more complex picture where the differential integration of feedforward and feedback inputs in L5 pyramidal neurons could be at the core of the cellular basis of FXS.
Materials and Methods
Animals.
C57B/6 (WT, RRID:IMSR_JAX:000664) and Fmr1-KO (B6.129P2-Fmr1tm1Cgr/J, RRID:IMSR_JAX:003025) and Fmr1-KO;thy1GFPM (Fmr1-KO backcrossed to Tg(Thy1-EGFP)MJrs/J, RRID:IMSR_JAX:007788) mice were used in this study and housed on a 12-h light/dark cycle with ambient temperature 20 to 24 °C and 40 to 70% humidity. Since FXS is more prevalent in males than females, only male mice were used in this study.
Brain Slice Preparation and Electrophysiology.
Mice (P14-37), anesthetized with isoflurane, were decapitated, and their brains dissected and placed in cold (4 °C) carbogenated sucrose cutting solution containing (in mM) 27 NaHCO3, 1.5 NaH2PO4, 222 sucrose, 2.6 KCl, 1 CaCl2, and 3 MgSO4. Coronal brain slices (300-μm-thick) of the visual cortex were prepared using a Vibratome (VT1000 S, Leica) and slices were incubated for 30 min at 32 °C in Artificial cerebrospinal fluid(ACSF) (in mM: 126 NaCl, 26 NaHCO3, 10 dextrose, 1.15 NaH2PO4, 3 KCl, 2 CaCl2, 2 MgSO4) and then at room temperature until ready for use. MultiClamp 700 B amplifiers (Molecular Devices) were used for electrophysiological recordings of L5 pyramidal neurons with a patch electrode (4 to 7 MΩ) filled with internal solution containing (in mM): 0.1 Alexa-568, 130 Potassium D-Gluconic Acid (Potassium Gluconate), 2 MgCl2, 5 KCl, 10 HEPES, 2 MgATP, 0.3 NaGTP, pH 7.4, and 0.4% Biocytin. DIC optics were used to clearly visualize and patch the soma of L5 pyramidal neurons.
Two-Photon Imaging and Two-Photon Uncaging of Glutamate.
Two-photon imaging was performed using a custom-built two-photon laser scanning microscope (37, 38), consisting of 1) a Prairie scan head (Bruker) mounted on an Olympus BX51WI microscope with a ×60, 0.9 NA water-immersion objective; 2) a tunable Ti-Sapphire laser (Chameleon Ultra-II, Coherent, >3 W, 140-fs pulses, 80 MHz repetition rate), 3) two photomultiplier tubes (PMTs) for fluorescence detection. Fluorescence images were detected with Prairie View 5.4 software (Bruker).
Fifteen minutes following break-in, two-photon scanning images of basal dendrites were obtained with 720 nm at low-power (<5 mW on sample) excitation light and collected with a PMT. Two-photon uncaging of MNI-caged L-glutamate (2.5 mM; Tocris) was performed using a 4-ms pulse at 720 nm and ~30 mW on sample (28) with the uncaging spot ~0.3 μm away from the upper edge of the selected spine head. The uEPSPs were recorded with the patch pipette at the soma of L5 pyramidal neurons. To assess the integration of individual synaptic inputs onto the basal dendrites of L5 pyramidal neurons, two-photon uncaging of MNI-glutamate was used. Uncaging was performed first at two neighboring spines separately and then together (interstimulus interval of <0.1 ms) with a 2-s delay in between each uncaging event. This sequence was repeated 10 times. Recordings were obtained using a MultiClamp 700B amplifier (Axon Instruments) interfaced to a dedicated computer by a BNC-2090A data acquisition board (National Instruments). The electrophysiological signals were acquired at 10 kHz using the PackIO open-source software package (https://github.com/apacker83/PackIO).
Electrophysiology and Imaging Data Analysis.
Offline data analysis was performed with the MATLAB (Mathworks) EphysViewer package (https://github.com/apacker83/EphysViewer) and custom written algorithms. We first quantified our results by calculating linearity indices, using either the peak or integral of the uEPSP, as described in the following equations:[1] Linearitypeak=max(uEPSPsp1&sp2)maxuEPSPsp1+uEPSPsp2
and
[2] Linearityintegral=∫uEPSPsp1&sp2∫uEPSPsp1+uEPSPsp2,
where uEPSPsp1, uEPSPsp2, and uEPSPsp1&sp2 are the time varying uEPSP responses when we applied 2P uncaging of glutamate at spine 1, spine 2, and then together near-simultaneously, respectively.
Linear optimization techniques were also used to quantify the linearity of synaptic inputs for WT and Fmr1-KO mice. Specifically, the uEPSP response to near-simultaneous uncaging of neighboring spines was modeled using the following equation:[3] uEPSPsp1&sp2(t)=gain×(uEPSPsp1(t)+uEPSPsp2(t)),
where uEPSPsp1t, uEPSPsp2(t), and uEPSPsp1&sp2(t) are the time varying uEPSP responses when we applied 2P uncaging of glutamate at spine 1, spine 2, and then together near-simultaneously, respectively and gain is a measure of how close the expected (based on the arithmetic sum) and actual responses to uncaging spine 1 and spine 2 are to each other—a measure of linearity of the time-varying uEPSP responses. When only older animals (range P22-37) are considered, we obtain the same statistical results for linearity indices and gain measure of WT versus Fmr1-KO L5 pyramidal neurons (peak linearity: 101.7 ± 2.22 versus 91.4 ± 1.67%, P = 0.0004; integral linearity: 103.8 ± 2.77 versus 90.9 ± 2.37%, P = 0.0005; gain linearity: 1.04 ± 0.03 versus 0.88 ± 0.02, P = 0.0002, Mann–Whitney test).
Analysis of the spine morphology was performed in the open source image processing package Fiji (NIH). Specifically, the spine neck was measured as the proximal edge of the spine head to the edge of the dendrite. In cases where the spine topology could not be precisely determined, the spine neck length was estimated as the shortest orthogonal distance between the base of the spine head and the edge of the dendrite. When the spine neck could not be measured, a minimum value of 0.2 µm was used. For the spine head size, the longest and corresponding orthogonal diameter was measured. These measures were computed from a Gaussian curve fit to the fluorescent profile of these axes, generated from a z-stack over the entire spine heads (Δz = 0.4 µm). The corresponding spine head volume (V, in µm3) was estimated using the following formula:[4] V=43πdlong+dorth23,
where dlong and dorth are the FWHM of the spine head along the longest and corresponding orthogonal dimension, respectively. The FWHM was calculated using the SD (σ) of the Gaussian curve fit to the fluorescent profile of the spine heads (FWHM = 2.335 σ).
Synaptoneurosome (SN) Preparation.
An adaptation of a previously described method was used (68, 69) as follows: P14 mouse visual cortex was dissected, flash frozen, and stored at −80 °C. Individual samples (approx. 1 mg) were homogenized in 700 μL SN lysis buffer (10 mM Hepes, pH 7.4, containing proteinase inhibitor) using a 2-mL Kimble–Chase tissue grinder (Thermo Fisher K885300-0002) and applying six loose pestle and 12 tight pestle strokes. A 150 μL aliquot of this TP fraction was boiled in 10% sodium dodecyl sulfate (SDS) for 10 min and stored. The remaining fraction was centrifuged at 2,000×g for 2 min at 4 °C to remove cellular and nuclear debris. The supernatant was loaded into a 1-mL syringe and filtered through three layers of a prewetted 100-μm pore nylon filter (Millipore NY1H04700). The filtrate was directly loaded into 5-μm-pore centrifugal filters (Ultrafree-CL, Millipore UFC40SV25) and centrifuged at 5,000×g for 15 min in a fixed angle rotor at 4 °C. The supernatant was carefully removed, and the loose pellet resuspended in boiling SN buffer (containing 2% SDS), boiled for 10 min and stored at −80 °C (SN protein fraction). Protein concentration was determined with the Bicinchoninic acid(BCA) protein assay kit (Thermo Fisher 23227) using a bovine serum albumin (BSA) standard curve.
Co-IP.
Proteins were prepared adapting a previously described method (70). The flash frozen visual cortex and hippocampus from P25±1 Fmr1-KO and control mice were homogenized over ice using a 2-mL glass/glass tissue homogenizer (Kontes, Fisher K885300-0002) in 400 μL ice-cold lysis buffer containing 20 mM Tris (pH 7.4), 200 mM NaCl, 1% NP-40, and proteinase inhibitors. Proteins were then extracted by incubating the homogenate on ice with occasional agitation for 30 min and then centrifugating at 15,000 g for 20 min. The pellet was resuspended in 200 μL lysis buffer, and the extraction step repeated. Both supernatants were pooled, and protein concentration was determined using the BCA method (Pierce 23227). Dynabeads protein G were used as suggested by the manufacturer (Thermo Fisher 10007D): For each sample 50 μL magnetic beads were cross-linked to 5 μg anti-β4 antibody (Antibodies Inc 75-086) with 230 μL 5 mM BS3 (Thermo Fisher A39266). Samples were incubated with the prepared beads for 16h at 4 °C. The supernatant containing the unbound proteins (post-IP) was stored and the beads washed. Finally, the bound protein (IP) was recovered in 10 μL elution buffer (50 mM Glycine pH 2.8) and used directly for Western blot with the addition of desaturating loading buffer.
Western Blot.
An adaptation of a previously described method was used (71) as follows: protein samples were separated in NuPAGE™ 3 to 8% Tris-Acetate Protein Gels (Thermo Fisher EA03755) in Tris-Acetate-EDTA running buffer (Thermo Fisher BP13354) at 200V for 1 h for αBK and Bis-Tris 4 to 12% polyacrylamide gradient gels (Thermo Fisher NP0323, NW0412B) in MES–SDS running buffer (Thermo Fisher B0002) at 200 V for 30 min for β4 subunit. Separated proteins were transferred into a PVDV membrane using a mini trans-blot cell (BioRad 170-3930) at 200V for 1 h for αBK and at 150 V for 50 min for the β4 subunit. Ponceau red staining was performed after transfer and recorded. Membranes were blocked in 0.1% Tween20 in phosphate–buffered saline (PBS) starting block (Thermo Fisher 37538) and incubated with primary antibodies diluted in the same buffer: anti-BK channel 1:500 (Antibodies Inc 75-022) or 1:100 anti-BKbeta4 potassium channel (Antibodies Inc 75-086) and after striping a mix of anti-γ-tubulin 1:10,000 (Sigma-Aldrich,T5326,) and anti-PSD95 1:5,000 (Antibodies Inc 75-028), in addition, for the Co-IP: 1:1,000 anti-FMRP (Clone 2F5, a gift from the Darnell lab) and 1:500 anti-β4 (KCNMB4) (Alomone APC-061). Signal was developed using ECL substrate (BioRad 170-5060) and recorded on film or using a bio imager. Densitometric analysis was performed with the open-source program FIJI (72), and data were plotted using Prism9 (GraphPad).
Immunofluorescence.
Anesthetized P25-27 Fmr1-KO;thy1GFPM mice were intracardially perfused with 4% PFA in PBS. Postfixing of the brain was done in 4% PFA for 2 h at 4 °C followed by washes in PBS and dehydrated in 30% sucrose at 4 °C and then mounted in OCT by freezing in a bath of 2-methylbutane at −50 °C. The 40-μm cryosections were cut at −18 °C and placed over glass slides treated with tissue capture pen (Electron Microscopy Sciences 71314) and stored at −80 °C. Cryosections were permeabilized and blocked in TSA blocking reagent (Perkin Elmer FP1020). Primary antibodies used were anti-BK 1:100 (Antibodies Inc AB_224953), 1:300-1:100 anti-BKbeta4 potassium channel (Antibodies Inc 75-086) and anti-GFP 1:500 (Rockland 600-101-215). Incubations were performed for 16 h at 4 °C followed by incubation with secondary antibodies: Alexa 568 anti-mouse 1:200 (Thermo Fisher A10037), and Alexa 488 anti-goat 1:200 (Thermo Fisher A11055) for 2 h at room temperature (RT) in TSA blocking reagent. Washes were done in PBS. The samples were mounted in ProLong Diamond Antifade Mountant with DAPI (Thermo Fisher P36971) or Vectashield (Vector Labs H-1200), and the basal dendrites from L5 pyramidal neurons were imaged using a Zeiss LSM700 confocal microscope (IRIC bio-imaging core). Each optical slice was 0.7 to 1.0-μm thick.
Image Processing for Immunofluorescence Experiments.
The Laser Scanning Microscopy (LSM) toolbox Fiji (72) plugin was used to process the confocal images as follows: Raw images saved in LSM format were opened and optical slices of the z stack with good dendritic morphology observed in the green (GFP) channel were duplicated (to extract one slice), rotated, and cropped. Red (αBK or β4 subunit) and green channels were separated with the color split function and contrast and brightness were manually adjusted to achieve clear images of similar intensity in both channels. Gaussian blur set at 1 to 2 (the same for both channels) was then applied to smooth the pixels. Both channels were stacked using the color merge channel function, which generates a yellow color (merge) in overlapping areas of similar intensity. Scale bars were automatically drawn using the LSM bar tool function.
For quantitative analysis of β4 subunit expression in spine heads, Imaris software (Oxford Instruments version 9.8) was used for semi-automatic dendrite and spine reconstruction of the GFP signal, followed by spine head object generation with a MATLAB tracking plugin (total spine heads). Dots generated from the β4 channel signal overlapping with spine heads were selected, and their mean intensity determined. In addition, the data were used to calculate the percentage of spines with detectable signal. We discarded the signal in the spine neck because the confocal images of transgenic GFP did not allow for a good estimation of the real shape and volume.
Virus Injection.
To perform the knockdown of the β4 subunit of BK channels, a mix of two different AAV1 were used, each containing a different shRNA based on kcnmb4 the mouse sequence (NM_021452): shkcnb4-NM_021452.126s1c1: 5′-CCGGCCCGCCUUGCAGGAUCUGCAACUCGAGUUGCAGAUCCUGCAAGGCGGGUUUUUG-3′ (binding nucleotides 126 to 146) and shkcnb4_NM_021452.1-266s1c: 5′-CCGGCGUGAACAACUCCGAGUCCAACUCGAGUUGGACUCGGAGUUGUUCACGUUUUUG-3′ (binding nucleotides 266-189) or a control custom scramble shRNA: 5′-CCGGACCACCGTCGAATCGTGACAACTCGAGTTGTCACGATTCGACGGTGGTTTTTTG-3′ cloned downstream of the U6 promoter in a pAAV-shRNA-tdTomato vector and prepared at a concentration of 10 × 1013 GC/mL (abm, Canada). The morning of the surgery, Fmr1-KO mice (P18) were given sustained-release buprenorphine (1 mg/kg). Mice were then deeply anesthetized with 3% isoflurane, confirmed by the absence of a toe pinch response and placed into a stereotaxic frame (Kopf Instruments). An incision was made into the disinfected bare skin to expose lambda and bregma landmarks. A small hole was drilled into each side of the skull at 3 mm posterior and 2 mm lateral to the bregma. The virus was loaded into a Hamilton syringe and injected 0.5 mm below the pial surface. The virus (450 µL) was injected bilaterally, over the course of 20 min followed by a 5-min wait period before the syringe was slowly and carefully removed. The incision was sutured and mice were returned to their home cage and given 2 wk to recover and obtain a good virus expression. 2P uncaging experiments, as described above, were performed on L5 pyramidal neurons expressing the reporter gene tdTomato.
Modeling.
We used a morphologically realistic L5 pyramidal neuron NEURON model (31, 48) to study the effects of the β4 subunit on synaptic integration. Two spines, with neck lengths of 1 µm and spine head volumes of 0.18 µm3, were inserted 170 µm from the soma on a randomly selected basal dendrite. The neck width was estimated based on the head volume using previously reported linear relationships that found a positive correlation between head size and neck width in distal dendrites (7), also been observed in-vivo using Stimulated emission depletion (STED) nanoscopy (73) and other cortical pyramidal neurons (74). Neck and spine head axial resistances were set to 500 and 150 MΩ, respectively, based on published values (75, 76). High voltage-activated (HVA) calcium channels (https://senselab.med.yale.edu/modeldb/ShowModel?model=135787&file=/ShuEtAl20062007/ca.mod#tabs-2), calcium buffering/removal mechanisms [resting concentration of 1nM (77)], Nav1.6 sodium channels [4 S/cm2 (78)], and BK channels (14 S/cm2) (31, 49) were inserted in the spine heads. In one set of simulations, we incorporated the activation/deactivation parameters and kinetics of the α-subunit of BK channels, while in another set of simulations we used those obtained when the β4 subunit is bound to BK channels (49). To activate individual synapses, we used an ionotropic glutamate receptor stimulation mechanism (79) such that AMPA-R and NMDA-R conductances (10 nS and 0.086 nS, respectively) were calibrated to obtain physiologically realistic EPSPs at the soma (~0.5 to 0.6 mV).
Ethics.
These studies were performed in compliance with experimental protocols (13-185, 15-002, 16-011, 17-012, 18-011, and 19-018) approved by the Comité de déontologie de l’expérimentation sur les animaux of the University of Montreal and protocol 2020-2634 approved by the Comité institutionnel des bonnes pratiques animales en recherche of the Centre de Recherche, CHU Ste-Justine.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank Hélène Klein for excellent technical assistance and are grateful to all other members of Roberto Araya’s laboratory for helpful discussions. We thank E. Cook and G. Di Cristo for insightful comments, critical discussion, and reading of the manuscript. Confocal microscopy experiments were carried out at the IRIC Bio-Imaging Core. This work was funded by the Canadian Institutes of Health Research (CIHR) grants MOP-133711 and MOP-377520 to R.A., a Canada Foundation for Innovation (CFI) equipment grant Fonds des leaders 29970 to R.A., a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant #418113-2012, and a Scottish Rite Charitable Foundation of Canada (SRCFC) research grant to R.A. M.B. was supported by a Herber Jesper postdoctoral fellowship at Université of Montréal. D.E.M. was supported by postdoctoral fellowships from the Fonds de recherche du Québec Santé (FRQS) and the Quebec Autism Research Training (QART) Program.
Author contributions
D.E.M., S.M.-R., and R.A. designed research; D.E.M., S.M.-R., and M.B. performed research; D.E.M., S.M.-R., M.B., and R.A. analyzed data; and D.E.M., S.M.-R., and R.A. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission.
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595705
202212456
10.1073/pnas.2212456120
videoVideoresearch-articleResearch Articlebiophys-physBiophysics and Computational BiologyPhysical Sciences
Biophysics and Computational Biology
Nanoscopy of single antifreeze proteins reveals that reversible ice binding is sufficient for ice recrystallization inhibition but not thermal hysteresis
Tas Roderick P. [email protected]
a b 1 https://orcid.org/0000-0003-3111-8784
Hendrix Marco M. R. M. a b
Voets Ilja K. [email protected]
a b 1 https://orcid.org/0000-0003-3543-4821
aDepartment of Chemical Engineering and Chemistry, Laboratory of Self-Organizing Soft Matter, Eindhoven University of Technology, Eindhoven 5612 AP, the Netherlands
bInstitute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612 AP, the Netherlands
1To whom correspondence may be addressed. Email: [email protected] or [email protected].
Edited by Pablo Debenedetti, Princeton University, Princeton, NJ; received July 27, 2022; accepted November 20, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e221245612027 7 2022
20 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Antifreeze proteins (AFPs) are essential for the survival of many cold-blooded species in extremely cold environments. These highly potent proteins can lower the freezing point and inhibit ice recrystallization at very low concentrations, making them interesting for many applications. However, currently, the mechanisms for their activity remain unclear because the interactions of these proteins with ice could not be visualized. Here, we develop nanoscopy below zero degrees to visualize how AFPs interact with ice at the single-molecule level. We observe that tight binding of AFPs at the interface is required for freezing point depression but not ice recrystallization inhibition. By correlating the single-molecule dynamics to activity, we thus find that distinct binding modes underlie the differential activity of different types of AFPs.
Antifreeze proteins (AFPs) bind ice to reduce freezing temperatures and arrest ice crystal ripening, making AFPs essential for the survival of many organisms in ice-laden environments and attractive as biocompatible antifreezes in many applications. While their activity was identified over 50 years ago, the physical mechanisms through which they function are still debated because experimental insights at the molecular scale remain elusive. Here, we introduce subzero nanoscopy by the design and incorporation of a freezing stage on a commercial super-resolution setup to resolve the interfacial dynamics of single AFPs with ice crystal surfaces. Using this method, we demonstrate irreversible binding and immobilization (i.e., pinning) of individual proteins to the ice/water interface. Surprisingly, pinning is lost and adsorption becomes reversible when freezing point depression activity, but not ice recrystallization inhibition, is eliminated by a single mutation in the ice-binding site of the AFP. Our results provide direct experimental evidence for the adsorption-inhibition paradigm, pivotal to all theoretical descriptions of freezing point depression activity, but also reveal that reversible binding to ice must be accounted for in an all-inclusive model for AFP activity. These mechanistic insights into the relation between interfacial interactions and activity further our understanding and may serve as leading principles in the future design of highly potent, biocompatible antifreezes with tunable affinity.
antifreeze proteins
interfacial interactions
optical nanoscopy
single molecule
ice binding
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) 501100003246 202.220 Roderick P Tas EC | European Research Council (ERC) 501100000781 2020-CoG 101001965 Ilja K Voets
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pmcAvoiding freezing is essential for the survival of many cold-blooded species that live at subzero temperatures, such as fish, plants, and bacteria. To cope, these organisms express a wide variety of structurally diverse and highly potent antifreeze proteins (AFPs) that selectively bind to ice crystal facets. This prevents rapid outgrowth of internal ice, which may damage tissues and block circulation (1–3). AFPs function by modulating the growth of existing ice crystals in various ways. Many AFPs can lower the freezing point (thermal hysteresis, TH activity) below which small ice crystals rapidly increase in size through the so-called burst growth. Additionally, all AFPs inhibit ice recrystallization. This ice recrystallization inhibition (IRI) activity prevents many small, often harmless, crystals from ripening into large crystals that can interfere with cellular and vascular function (2, 4). While conventional antifreezes are only effective in large quantities, AFPs function at nano- to millimolar concentrations, making them highly interesting as biocompatible antifreezes in various applications, ranging from food technology and regenerative medicine, to construction and transportation (1, 5–8).
The central framework on which all (theoretical) descriptions of AFP activity are based, is the adsorption-inhibition paradigm. Pivotal to this framework is the assumption that TH-active AFPs can only lower the freezing point of ice when they are irreversibly adsorbed to the ice/water interface and tightly bound; i.e., immobilized at a fixed position, a state which we denote as “pinned” to the ice/water interface (9–11). So far, computational molecular dynamic studies offer a theoretical description of ice binding, complementary to extensive experimental studies on the ensemble level (12–17), but direct single-molecule studies on the interfacial interactions of AFPs with ice crystals remained out of reach. As a result, the exact nature of the interaction between the various AFPs and ice crystals and its consequences for both TH and IRI activity remain elusive. Here, we present a strategy to bridge this gap and address the central paradigm for AFP activity at the single-molecule level.
To probe experimentally how individual ice-binding proteins interact with ice, we designed and incorporated a freezing stage on a commercial super-resolution setup to adopt subzero nanoscopy through single-molecule localization microscopy (SMLM) (18, 19). This allowed us to directly visualize the dynamics of single AFP at the ice/water interface. With this approach, we were able to demonstrate that individual TH- and IRI-active type III AFPs from ocean pout adsorbed irreversibly to the ice by being pinned in a fixed position. These results provide direct evidence for the adsorption-inhibition theory at the single-molecule level. Surprisingly, the irreversible pinning vanished when TH activity was compromised by a single point mutation in the ice-binding site of the protein, revealing that the IRI activity that remained can depend completely on reversible AFP interactions with ice. This reversible binding was then also observed in winter flounder type I AFP, which has similar IRI but lacks notable TH activity at low concentrations. Our results, therefore, demonstrate the potential of subzero nanoscopy and reveal that the interaction of AFPs with ice dictates their activity. Whereas pinning is consistent with previous theoretical models and diffraction-limited experiments for TH activity, we now show that reversible binding can be sufficient for IRI activity which is not accounted for in current descriptions for AFP activity. These fundamental insights further our understanding in the mechanisms driving AFP activity and may serve as guiding principles for future biomimetic antifreeze analogs.
Results
Establishing Optical Nanoscopy at the Ice–Water Interface.
To directly measure the interactions of AFPs with ice, at the single-molecule level, we set out to apply optical nanoscopy in temperature ranges of a few degrees Celsius below zero. First, an objective cooler and a dedicated sample stage were essential to achieve the precise temperature control required to generate and stabilize polycrystalline and single ice crystals with <0.1 °C accuracy in the presence of fluorescently tagged AFPs (Fig. 1 A and B and SI Appendix, Fig. S1). Our in-house built stage is inspired by a previously reported Peltier-based cooling stage (20) and adapted to be compatible with a Nikon dSTORM super-resolution microscope (SI Appendix, Fig. S1). Next, to achieve SMLM of single AFPs, we generated recombinant fusions to photoswitchable fluorophores (e.g., mEos3.2) to perform single-particle tracking photoactivated localization microscopy (sptPALM) experiments (21). In these experiments, controllable, UV-induced, stochastic photoconversion of the fluorescent protein, fused to the AFPs, allows imaging of single molecules at ice/water interfaces (Fig. 1B). Subsequently, the fluorescent signal from each AFP fusion can be detected over time and localized with nanometric precision. As a consequence, the position and trajectories of single, ice-bound AFPs can be extracted with diffraction-unlimited and high temporal resolution for the first time (Fig. 1C). Additionally, this approach allowed visualization of the entire AFP population by (total internal reflection fluorescence) TIRF prior to sptPALM acquisitions (Fig. 1B and SI Appendix, Fig. S2). The central paradigm, whether or not AFPs are pinned at the ice/water interface, underlying virtually all theoretical models of AFP activity can now be tested experimentally.
Fig. 1. Probing single ice-binding proteins at the ice/water interface by sptPALM. (A) Schematic illustration of the optical setup and generation of polycrystalline ice for sptPALM acquisitions. A stage with two Peltier elements and an objective cooler are added on an inverted TIRF microscope to control sample temperature below zero degree Celsius. (B) Schematic representation of single photoconvertible fluorophore labeled AFP in the sample and the corresponding diffraction-limited TIRF image of mEos3.2-typeIII AFP on parallel crystals of polycrystalline ice. UV illumination induces stochastic photoconversion of mEos3.2 to allow single-molecule detections over time. (C) Single-molecule localizations are used to extract the position (Top) and dynamics (Bottom) of AFPs at the ice/water interface with diffraction-unlimited resolution. (Scale bar: 10 µm (B).)
To interrogate the relation between AFP activity and dynamics at the ice/water interface, we selected the QAE isoform of the type III AFP expressed in the Ocean Pout Zoarces americanus (abbreviated as QAE). This AFP is well characterized, easily expressed, and exhibits both IRI and TH activity (12, 22, 23). Additionally, mutation of threonine 18 to asparagine (T18N) in its ice-binding site results in loss of TH, but not IRI (22–24), making this AFP ideally suited to study the relation between the biophysical behavior and the two modes of activity. An N-terminal mNeonGreen fusion of recombinant QAE was designed for diffraction-limited fluorescent measurements and an mEos3.2 fusion for sptPALM acquisitions, both of which were purified from bacteria (SI Appendix, Fig. S2A). Consistent with previous work (25), activity assays showed that fusion of this small (~7 kDa) AFP to a relatively large fluorescent protein (~25 kDa) did not perturb the TH (0.6 to 0.8 °C at 10 µM) nor IRI (Ci ~ 0.95 µM) activity (SI Appendix, Fig. S3 and Fig. 3A). Fluorescently labeled mNeonGreen-QAE AFP adsorbed and accumulated selectively at primary prism or pyramidal planes, shaping single ice crystals into hexagonal bipyramids consistent with its previously described binding preference (5, 26, 27) (SI Appendix, Fig. S2 C–E).
Tracking of Individual AFPs on Ice by Nanoscopy Reveals Irreversible Binding and Pinning of TH-Active Proteins.
Next, we performed sptPALM experiments on stochastically nucleated polycrystalline ice using the mEos3.2-QAE construct (Fig. 2 A–D). Due to its selectivity for the primary prism and pyramidal planes and the random ice crystal orientation in the sample, QAE did not decorate all ice/water interfaces in the field of view. As a consequence, the ice planes densely covered with AFPs can be either the primary prism or pyramidal crystal plane (Fig. 2B). Subsequently, fast sptPALM reconstructions of these planes, parallel to the imaging plane, revealed that QAE AFP localized densely and specifically at the ice/water interface (Fig. 2 C and D). Interestingly, individual AFPs in the acquisition displayed little motility and single tracks collected for ice-bound and coverslip-immobilized AFPs, adhered through aspecific adsorption, were essentially indistinguishable (Fig. 2 D–H and Movie S1). Mean square displacement (MSD) analysis at 1, 3, and 10 µM QAE showed that the distribution of the diffusion coefficients of individual tracks displayed a single peak at very low diffusion coefficients, roughly four orders of magnitude lower than the predicted values for free diffusion of QAE in solution around 0°C. Additionally, there was no difference between the diffusion coefficient distribution for ice-bound AFPs and coverslip-immobilized AFPs in the control (Fig. 2I and SI Appendix, Fig. S4). Similarly, the average MSD curves of all conditions, regardless of concentration, are characteristic for highly confined dynamics of immobile molecules (Fig. 2J). These results provide direct evidence, at the single-molecule level, for irreversible pinning of individual AFPs at the ice/water interface. Additionally, these findings show that the observed binding behavior of QAE AFP is independent of concentration. The latter suggests that pinning is noncooperative, where single QAE AFPs are not affected by previous ice-binding events nor by neighboring molecules at the lattice.
Fig. 2. QAE AFP pins at the ice/water interface in a concentration-independent manner. (A) Schematic illustration of the sample for sptPALM acquisitions via mEos3.2-QAE on polycrystalline ice. (B) Excitation of the green fluorescent channel to image selective accumulation of mEos3.2-QAE (1 µM) on polycrystalline ice. Plane-selective accumulation at the prism plane (p), soluble fraction (s), and nonabsorbing ice planes (b) are depicted in this image. (C) sptPALM reconstructions of mEos3.2-QAE on the polycrystalline ice in (B). (D) Diffraction-limited localizations (Top) and the corresponding filtered trajectories (Bottom) of individual QAE molecules for the zoom indicated in (C). (E) Stills from two neighboring QAE trajectories that are ice bound. The interval between the depicted frames is 45 ms. The black and red arrows indicate the separate tracks. (F) Diffraction-unlimited reconstruction of the tracks in (E). (G, H) Highly zoomed-in view of the isolated ice-bound tracks in (F) and comparable trajectories for coverslip-immobilized mEos3.2-QAE with 40 ms exposure time. (I) Distribution of the diffusion coefficient of coverslip-immobilized QAE (mean ± SEM = 0.066 ± 0.0008 black) compared to ice-adsorbed QAE at 1 µM (0.063 ± 0.0010, blue), 3 µM (0.069 ± 0.0010, green), and 10 µM (0.53 ± 0.0010, red) (N = 13,851, 11,910, 9,122, and 13,393 trajectories, respectively, from three acquisition of separate polycrystalline ice assemblies). (J) Average MSD curve of trajectories in (I). Error bars (dotted filled lines) represent SD. (K) TIRF acquisition to monitor accumulation of mEos3.2-AFP over time after burst growth induced by lowered temperature. Timepoint zero indicates the moment of ice growth and intensity is background corrected. Stills of the newly formed ice plane position before and after are included. (L) Stills of a sptPALM acquisition before and after burst growth leading to a new ice/water interface. (M) Reconstruction of the QAE AFP localizations at the newly formed interface in (L). (N) Example trajectories of tracks localized within the first minute after interface formation (black), the 3rd (green), and after 5 min (red). (O) Scatter plot of the diffusion coefficients (mean ± SEM) of trajectories observed within the first, third, or fifth minute after new interface formation. (Scale bar: 10 µm (B), 1 µm (D), 2 µm (K), 5 µm (L), 500 nm (M).)
To further investigate whether or not QAE AFPs adsorb to ice crystals in a noncooperative fashion (i.e., in a density-independent manner), QAE trajectories were acquired at variable surface coverages. To achieve this, fresh ice/water interfaces were generated through slow sample cooling, resulting in burst growth of the crystals. Now, interfaces perpendicular to the imaging plane were imaged so that newly formed planes would appear in focus directly after the growth event. Monitoring AFP accumulation showed that AFP saturation did not occur until approximately 7 min after growth (Fig. 2K), which is consistent with previously reported adsorption rates for QAE fusions (13, 25). Consequently, right after formation and before saturation, the interface will be decorated with increasing AFP densities. Subsequent sptPALM acquisitions showed that there were no significant differences in the dynamic behavior of single AFPs within the first minute after burst growth, or after accumulation for 3 min and 5 min before saturation (Fig. 2 L–O). In summary, these results are consistent with noncooperative, irreversible binding and pinning of TH and IRI active QAE AFP to ice.
Reversible Ice Binding Is Sufficient for IRI.
To unravel whether both TH and IRI activities require irreversible adsorption and pinning of AFPs to ice, we introduced the T18N mutation in QAE. Consistent with previous work (22–24), this mutant did not exhibit TH activity whereas IRI activity was only slightly reduced compared to the wild-type QAE (Fig. 3 A–D and SI Appendix, Figs. S3 and S5). Surprisingly, at 10 µM, no distinct accumulation at the ice/water interface was visible for mEos3.2-QAE(T18N) by diffraction-limited fluorescence microscopy. Instead, fluorescent signal of the mutant was only observed diffusively in the soluble fraction. This contrasts sharply with the behavior of wild-type QAE, which adsorbed strongly to the ice at this concentration, suggesting highly increased unbinding rates for the mutant from the ice (Fig. 3 E and F). Moreover, hardly any single molecules could be localized at the interface upon sptPALM acquisition of mEos3.2-QAE(T18N) with 40 ms exposure times (Fig. 3 E and F). These results, therefore, suggest that prolonged adhesion to ice at a fixed position is required for TH activity but not essential for IRI activity. The marked difference in adsorption behavior and TH, yet comparable IRI activity, supports the premise that a weak, reversible interaction with ice is sufficient to inhibit ice recrystallization but inadequate to depress the freezing point significantly in a noncolligative manner.
Fig. 3. Pinning correlates with TH, but not IRI activity. (A) Crystal structure of QAE (pdb: 1HG7) displaying the threonine to asparagine mutation at position 18 in red. This point mutation in the ice-binding site transforms the TH-active QAE into the TH-inactive T18N. (B) Still images of ice crystal recrystallization assays at constant −7 °C in 20 wt% sucrose taken after 20 and 60 min incubation with 10 µM QAE, 10 µM QAE(T18N), or without (CTRL). (C) Thermal hysteresis activity assays of 10 µM QAE and QAE(T18N) in 20 wt% sucrose. ∆T indicates the temperature reduction during undercooling from the −4 °C starting point. Burst growth of crystals in the presence of QAE occurs between 0.6 and 0.8 °C below the melting temperature (i.e., TH = 0.6 to 0.8 °C), while crystals in the presence of 10 µM QAE(T18N) continuously grow (i.e., TH = 0 °C)). (D) Relative crystal area as a function of temperature reduction for the crystals indicated in C. (E, F) Diffraction-limited image (Left), intensity linescan along the yellow line (Middle), and maximum intensity projection of the corresponding sptPALM acquisition (Right) of ice/water interfaces in the presence of mEos3.2-QAE (E) or mEos3.2-T18N (F). The blue box in the Middle panel indicates the position of the interface. (G) Schematic illustration describing labeling of QAE with photoactivatable Janelia fluor646 through a HaloTag fusion. (H) Sum projection for the sptPALM acquisition of Halo-tagged QAE; QAE(T18N) and wfAFP labeled with paJF646. Long-term pinning events are detectable as distinct bright spots (Top), whereas a strong diffuse signal indicates reversible binding (Bottom). (I) Examples of still images from sptPALM acquisitions for the same QAE, QAE(T18N), or wfAFP molecule tagged with paJF646, taken at 0.02- or 1-s intervals around the same frame where it interacts with the interface (*). The arrows indicate individual proteins that move along the interface. The superimposed single-molecule trajectories relative to the ice/water interface are depicted in the last column. Tracks of T18N and wfAFP could be identified for short but not for long time intervals (N.D: Nondetected). (Scale bar: 10 µm (B, C), 2 µm (E, F), 2 µm (H, I).)
To examine this interaction between AFPs and ice and its consequences for activity more closely, three AFPs were fused to a HaloTag (28). This tag allows coupling to the bright, stable organic photoactivatable JF646 fluorophore (29), to acquire single-molecule tracks with sufficient photons for single-molecule localization at shorter exposure times (20 ms) aiming to capture both pinned and more mobile proteins (Fig. 3G). In addition to wild-type QAE and the T18N mutant, we included winter flounder type I AFP (pdb:1wfa). This small alpha helical AFP only binds the pyramidal ice plane and displays similar IRI activity to the QAE(T18N) mutant (Ci ~ 6 µM) with no notable TH activity (<0.1 °C at 10 µM) at low micromolar concentrations (24, 30, 31). Indeed, upon testing the TH activity in the presence of 10 or 20 µM wfAFP, we did not observe clear crystal growth bursts during undercooling, consistent with QAE(T18N) (SI Appendix, Fig S6). Hence, we argued that if the observed TH activity of wild-type QAE AFP at 10 µM concentration does require irreversible binding and pinning, while IRI does not, the single-molecule trajectories of wfAFP would resemble those of T18N and deviate significantly from the pinned QAE molecules. Again, as expected, individual QAE molecules pinned and were immobile without detectable desorption or in-plane diffusion for as long as several seconds (Fig. 3 H and I and Movies S2 and S3). In contrast, both T18N and wfAFP displayed highly diffusive dynamics with interfacial residence times up to 40 ms (i.e., the proteins were detected in one or two consecutive 20 ms frames). Consequentially, subsequent localizations spaced apart by both short (20 ms) and long (1 s) time intervals could be associated with one-and-the-same AFP for the immobile wild-type QAE, but not for the mobile T18N and wfAFP. Their entire single-molecule trajectories and interaction with the interface could only be observed for a few frames spaced apart by 20 ms time intervals (>50-fold shorter than the longest wild-type QAE tracks), but not in subsequent frames when a 1-s interval was analyzed (Fig. 3 H and I and Movies S2 and S3). Furthermore, for these proteins, no apparent pinning events could be observed during the acquisitions. In conclusion, our results show that, reversible AFP/ice interactions with residence times less than tens of milliseconds are sufficiently strong to elicit IRI activity in a noncolligative manner but not TH activity, for which irreversible binding and pinning appear essential.
Conclusion and Discussion
Here, we adopted SMLM at the ice–water interface to visualize individual AFPs with high spatiotemporal resolution. This allowed us to study the interfacial interactions of AFPs with ice at the single-molecule level. By visualizing the IRI- and TH-active QAE isoform of type III AFP, that has previously been shown to bind irreversibly to ice (12, 32), we were able to resolve that it is not merely irreversibly bound but in fact pinned at a fixed position without the ability to “surf” (i.e., in-plane motion) along the surface regardless of concentration. This observation is consistent with the theoretical description of TH activity by the adsorption-inhibition model (9–11) and provides direct evidence for the central assumption that AFPs pin tightly on ice/water interfaces. Surprisingly, upon imaging the QAE(T18N) mutant that only displayed IRI¸ pinning was lost and the molecules interacted in a highly reversible manner with the ice–water interface. Each interaction now lasted no more than a few tens of milliseconds. These results, therefore, show that reversible adsorption is sufficient for IRI activity, but not for the TH activity of QAE AFP, for which irreversible adsorption and/or pinning appears to be an essential condition. To our knowledge, these highly reversible interactions with ice have not been included in current theoretical models for IRI activity. Similar short-lived, reversible binding events may underlie the activity of more ice binders that display potent IRI but little or no TH activity and should therefore be further investigated. For example, our approach might aid to elucidate the interfacial behavior of antifreeze glycoproteins (AFGPs). These have been shown to bind ice irreversibly at very low undercooling (13), whereas other simulation and experimental studies have suggested weaker ice binding by AFGPs than type III AFPs and the ability of the proteins to walk/surf along the surface (15, 16). Additionally, it could now be investigated whether reversible interactions with ice or interfacial surfing might explain why the widely studied, IRI active polymer, polyvinyl alcohol, does not elicit TH activity. Molecular dynamic simulations have already predicted length-dependent, reversible ice-binding kinetics of this polymer (33–35), which can now be tested experimentally at the single-molecule level.
Similar to the ice-binding dynamics of QAE(T18N), our sptPALM experiments showed highly reversible ice binding for HaloTagged-wfAFP. As this AFP did not display any notable TH activity nor crystal growth burst during undercooling at the measured concentrations, these findings are consistent with our hypothesis that reversible interactions are sufficient for IRI. However, interestingly, at higher concentrations, previous studies have found that wfAFP can display TH at about half the activity of wild-type QAE (30). Simultaneously, other studies hypothesized that this TH activity of wfAFP is due to time-dependent protein rearrangement and/or accumulation at the interface (20) and that plane selectivity of the various AFPs affects TH dynamics (25). How concentration- and time-dependent interactions with distinct ice planes vary and underlie different activities, could be an interesting avenue for future studies at the single-molecule level. Additionally, it should be noted that even though we could visualize the highly reversible binding events by wfAFP and the QAE(T18N), which lasted tens of milliseconds, our sptPALM experiments could not resolve the exact dynamics during these events. Resolving the exact moment when these and other IRI-active molecules interact with ice, and upon doing so whether they are shortly pinned or diffuse freely along the surface, could provide further exciting insights to elucidate the underlying mechanisms behind their activity. To achieve this, future work could use even faster sptPALM imaging or the more recently developed MINFLUX technique (36) in order to resolve these interactions at submillisecond timescales.
In summary, here, we establish a nanoscopic strategy to visualize single AFP on ice that offers exciting insights into the interfacial dynamics of AFPs at the single-molecule level. We anticipate that this approach will be widely applicable to better understand how adsorption and interfacial dynamics can vary and underlie the differences in activity of the large variety of structurally diverse natural and synthetic ice binders. This will greatly advance our understanding of the antifreeze mechanisms and will facilitate the development of engineered ice binders with activities tailored for optimal performance in complex environments.
Methods
DNA Constructs.
First, to generate mNeonGreen-QAE (quaternary aminoethyl type III-AFP isoform): mNeonGreen with N-terminal NcoI; C-terminal EcoRI sites and QAE AFP with an N-terminal EcoRI; a small GAG linker; and a C-terminal XhoI site were PCR amplified with ~20 aa overhangs. Subsequently, the PCR fragments were inserted via Gibson assembly into an NcoI/XhoI-restricted pET28a vector. mEos3.2-QAE, HaloTag-QAE, mEos3.2-QAE(T18N), and HaloTag-QAE(T18N) were generated by PCR amplification of the mEos, HaloTag, and AFP fragments and inserted accordingly into NcoI/XhoI-restricted pET28a vector. To generate Halo-wfAFP (pdb:1wfa) spaced by a small GGGGS linker and flanked by NcoI/XhoI, a gblock (IDT) was ordered, restricted, and ligated in the linearized pET28a vector. All sequences were verified by sequencing. Protein sequences are depicted in SI Appendix, Table S1.
Protein Expression and Purification.
The pET28a expression vectors containing the constructs for the recombinant AFPs were transformed into BL21DE3 bacteria and grown in LB containing 50 μg/mL kanamycin. At OD0.6, expression was induced by adding IPTG to 1 mM final concentration and cells were grown for 0.5 h at 37 °C, followed by overnight incubation and expression at 20 °C. The cultures were then pelleted by centrifugation, stored on ice, and the supernatant was discarded. The cells were resuspended in the appropriate buffer (QAE constructs 20 mM Tris pH 7.5; wfAFP construct PBS), supplemented with lysozyme and EDTA-free protease inhibitor cocktail (Sigma), and lysed by sonication on ice. The soluble fraction was separated from the insoluble fraction by centrifugation (10.000 × g, 45 min) and incubated with washed Ni-NTA Agarose precharged resin (Qiagen) for 1.5 h at 4 °C while rotating. Subsequently, the protein-loaded beads were collected in disposable columns (Bio-rad) and washed two times with 15 mL buffer, followed by two washes with buffer + 40 mM imidazole. The protein was then eluted by incubation with buffer + 400 mM imidazole. Finally, buffer exchange to the appropriate buffer for QAE or wfAFP was performed (PD-10 desalting columns, Cytiva) and the protein was supplemented with 10% (w/v) glycerol and flash frozen in liquid nitrogen. Protein concentrations were determined by SDS-page of the purified proteins next to a BSA standard. The final concentrations after purification were 63, 99, 118, 95, 113, and 111 µM for mNeonGreen-QAE, mEos3.2-QAE, mEos3.2-QAE(T18N), HaloTag-QAE, HaloTag-QAE(T18N), and HaloTag-wfAFP, respectively.
AFP Activity Assays.
Microscope Setup.
Activity assays were performed on a Nikon ECLIPSE Ci-Pol optical microscope under wide field illumination. Both IRI and TH activities were imaged using a Nikon L Plan 20× (NA 0.45) objective. The Linkam LTS420 stage, controlled by Linksys32 software, was used to control and stabilize the sample temperature.
Sample Preparation.
QAE or wfAFP was diluted in 10 mM TRIS pH 7.5 or PBS, respectively, and supplemented with 20 w% sucrose final concentration. Prior to sample preparation, coverslips (Thermo Scientific Menzel ×1000, #1.5, 22 × 22 mm) were cleaned by 5 min sonication in MQ followed by 5 min sonication in acetone and drying under a nitrogen gas flow. Then, a 2-µL sandwich sample was prepared between two coverslips by depositing a 2-µL sample on one coverslip and lowering the second coverslip onto it from a parallel position. The latter ensures homogeneous sample spreading.
IRI Assay.
Samples were diluted to the indicated concentrations and frozen to −40 °C with 20 °C/min. The samples were then heated to −10 °C with 20 °C/min followed by 1 °C/min heating to −7 °C upon which the temperature was kept stable during the experiment. Ice recrystallization was then monitored by imaging at 1-min intervals using the 20× objective.
TH Measurement.
TH experiments of QAE- and QAE(T18N) were performed at a final concentration of 10 µM. Samples were frozen to −40 °C with 20 °C/min. After stabilization, the samples were heated to −10 °C with the same rate and then to −4 °C with 1 °C/min. After stabilization of the temperature, the sample was cooled with 0.2 °C/min, and crystal growth and burst growth events were monitored by imaging at 5-s intervals using the 50× long working distance objective. A sudden increase in crystal area marks the burst growth at the limit of the hysteresis gap whereas continuous crystal growth in 10 µM T18N indicates a lack of TH activity. The relative crystal area in Fig. 3D was measured by manual selection of the indicated crystals using the ImageJ polygon tool at the indicated temperatures. Subsequently, using the measure function, the total number of pixels (90.4 * 90.4 nm), in the selected area for each crystal, was extracted. Each crystal was then normalized to the crystal at the beginning of the acquisition to generate the relative crystal area.
SMLM at the Ice/Water Interface.
Optical Setup.
Single-particle tracking PALM (sptPALM) was performed on a Nikon Eclipse Ti-E N-STORM system equipped with a Nikon 100× Apo TIRF oil immersion objective (NA 1.49) and perfect focus system. Photoactivation and excitation were performed with the 405 nm, 488 nm, 561 nm, and 647 nm excitation lasers within the MLC400B laser box (Agilent technologies) under TIRF or HiLo illumination through a quad-band polychroic mirror (Nikon 97335). An Ixon3 EMCCD (Andor) was used for detection, resulting in an effective pixel size of 160 nanometer. The microscope was fitted with a temperature-controlled stage that was adapted to fit the Nikon microscope. A pump was used to continuously flow cold water through the heat sink with 120 mL/min, to buffer the residual heat from the Peltiers. The Peltiers were controlled via Meerstetter Engineering TEC controllers and software. Additionally, the objective was cooled using a fitted copper collar with a fluid channel in the center that allowed a continuous flow of ~100 mL/min of cooled water through a peristaltic pump from a cold water bath of approximately 2.5 °C (see also Fig. 1A and SI Appendix, Fig. S1 for the full experimental setup). This allowed control of the sample temperature with <0.1° centigrade precision.
Sample Preparation.
To perform sptPALM acquisitions, 22 × 50 mm #1.5 coverslips (Thermo Scientific Menzel) were cleaned by 5-min sonication in ultrapure water, followed by sonication in acetone and drying under nitrogen flow. Subsequently, 30 to 40 μL recombinant QAE or type I AFP, diluted to the desired concentrations in 20 mM TRIS pH 7.5 or PBS, respectively (SI Appendix, Table S2), was added to the center of one coverslip and the second coverslip was applied on the top to generate a thin film of sample between the cover glasses. For HaloTagged proteins, 1, 0.5, or 1 µM of HaloLigand-paJF646 was added in the sample containing Halo-tagged QAE, QAE(T18N), and 1WFA, respectively, to achieve sparse labeling of the proteins. The samples were then mounted in the cooling stage which was placed on the Nikon dSTORM stage holder prior to imaging.
Freezing Protocol.
As the lower limit of the Peltiers was −10 °C, the samples were cooled to −9.5 °C at a speed of ~0.5 °C/s upon which single or polycrystalline ice nucleated randomly throughout the sample. The samples were then slowly heated (0.05 °C/s) to a few degrees below zero until clear ice/water interfaces could be detected and the ice volume fraction did not exceed roughly 40%. The samples were then stabilized and the temperature control speed was set to a maximum of 0.02 °C/s to make sure that the sample remained stable. Typical temperatures at which the sample was stabilized and imaged under satisfactory conditions lay between −1 and −4 °C. Here, it should be noted that such a range could be expected because the exact temperatures at the imaged planes may deviate by a small offset due to the observation-based selection of the optimal conditions, the surrounding conditions, and the distance between the imaged region and the thermometer in the Peltier. After stabilization, clear ice/water interfaces parallel or perpendicular to the imaging plane could be identified and imaged a few hundred nanometers above the coverslip. As a result of the difference in depth between the coverslip and the AFP-decorated ice interfaces, tagged AFPs that aspecifically adsorbed at the coverslip did not interfere with the localizations of the AFPs specifically bound at the ice–water interfaces. Conversely, dynamics of the immobilized AFPs, adsorbed to the coverslip through aspecific interaction as shown in Fig. 2 H–J and SI Appendix, Fig. S4, were obtained by focusing and imaging at the coverslip so that only these molecules were detected.
SMLM.
After the temperature and sample were stabilized and interfaces were identified, SMLM was performed. First, in the case of mEos3.2-tagged constructs, a diffraction-limited image was acquired using the 488 lases. Then, rapid continuous acquisitions were performed using the 561-nm or 647-nm excitation lasers to perform sptPALM of mEos3.2- or HaloLigand-paJF646, respectively. Imaging parameters are indicated in SI Appendix, Table S2 under HiLo illumination. During the acquisition, excitation with the 405-nm laser was activated at low-intensity levels to stimulate sparse photoconversion of the fluorescent molecules so that enough single photoactivation events were maintained to detect AFPs throughout the acquisition. The 405-nm laser power was reduced when too many molecules were activated to avoid the detection of multiple overlapping molecules. The emitted point spread function of each single molecule was fitted to a Gaussian function to find the center of the localization emission to determine the exact position of the AFP fusion protein. For further more in-depth reading on SMLM and sptPALM, we refer to the excellent reviews and study in refs. 18, 19, and 21. To image AFP behavior at new ice/water interfaces (Fig. 2 K–O), imaging was performed at identified interfaces perpendicular to the imaging plane and the sample was slowly cooled until a burst growth event occurred upon which the temperature was set to stabilize.
Analysis.
Detection and Localization of sptPALM Acquisitions.
sptPALM acquisitions were imported in ImageJ/Fiji (9–11, 37, 38) and analyzed using the Detection of Molecules (DoM version 1.2.1, https://github.com/ekatrukha/DoM_Utrecht) plugin. To detect single molecules, a signal-to-noise ratio between 3 and 4 was chosen. For single particle tracking, the “link particles to tracks” function was used with a maximum permitted distance of 2 pixels from the next detected position and a maximum linking gap of one frame. This performed a faster variation of a nearest-neighbor determination within consecutive frames. These settings were kept constant for all conditions so that occasionally mislinked tracks would occur consistently and not induce differences in the mean squared displacement (MSD) results. SMLM reconstructions of the AFP localizations were performed in DoM.
Diffusion Analysis.
MSD analysis was performed by importing the DoM linked tracks, highly similar to the approach used by Katrukha et al. (39), into the “msdanalyzer” class (40) for Matlab (R2021a). Tracks consisting of a minimum of 5 localizations and a maximum of 30 localizations were included to filter for individual mEos3.2-AFP trajectories.
Additional Software.
Image processing was performed using functions in ImageJ/Fiji. Open-source PyMol by Schrödinger and GraphPad Prism were used to visualize structures and graphs, respectively. Figures were made in Adobe Illustrator.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. Tracking single QAE AFPs on polycrystalline ice by sptPALM. This movie corresponds to Figure 2A-G. sptPALM acquisition with 15ms interval of mEos3.2-QAE played. Raw acquisition (left) and corresponding tracks (right) are displayed. Total time sec, scale bar 5 μm.
Movie S2. Different dynamics of Halo-tagged QAE, QAE(T18N) and wfAFP at the ice water interface. Movie corresponds to Figure 3H, I. paJF646 labelled AFPs were imaged on interfaces oriented perpendicular to the imaging plane. Total time 10 sec Scale bar 5 μm.
Movie S3. Zoomed in view of individual binding events at interfaces for Halo-tagged QAE, QAE(T18N) and wfAFP. Movie corresponds to Figure 3H,I. Raw acquisition with the superimposed interface (left) and corresponding tracks (right) are displayed. Total time 100 milliseconds, scale bar 2 μm.
We thank A. Aloi for initiating the construction of the cooling stage and objective collar and J. Grimm and L. Lavis at Janelia Research Campus for kindly gifting HaloLigand-paJF646 and E. Katrukha for the script to import DoM data into the msdanalyzer Matlab class. This work is supported by the Dutch Research Council to R.P.T (NWO-VENI: 202.220, understanding ice growth inhibition by AFPs through super-resolution microscopy) and the European Research Council to I.K.V (ERC-2020-CoG 101001965).
Author contributions
R.P.T. and I.K.V. designed research; R.P.T. and M.M.R.M.H. performed experiments; R.P.T. analyzed data; and R.P.T. and I.K.V. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
Data will be made available upon request and can be requested from both corresponding authors (R.P.T. and I.K.V.).
Supporting Information
This article is a PNAS Direct Submission.
==== Refs
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PMC009xxxxxx/PMC9926235.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595669
202204134
10.1073/pnas.2204134120
research-articleResearch Articledev-bioDevelopmental Biology412
Biological Sciences
Developmental Biology
Dry eye disease in mice activates adaptive corneal epithelial regeneration distinct from constitutive renewal in homeostasis
Lin Joseph B. a b https://orcid.org/0000-0001-6667-9018
Shen Xiaolei c d
Pfeifer Charles W. a b https://orcid.org/0000-0002-7579-2984
Shiau Fion a https://orcid.org/0000-0003-0848-6562
Santeford Andrea a https://orcid.org/0000-0002-7691-6213
Ruzycki Philip A. a https://orcid.org/0000-0003-3520-6407
Clark Brian S. a e f https://orcid.org/0000-0002-7291-2055
Liu Qin a c d https://orcid.org/0000-0002-1589-0881
Huang Andrew J. W. a https://orcid.org/0000-0003-1939-9149
Apte Rajendra S. [email protected]
a e f g 1 https://orcid.org/0000-0003-2281-2336
aJohn F. Hardesty, MD Department of Ophthalmology & Visual Sciences, Washington University School of Medicine, St. Louis, MO 63110
bDivision of Biology and Biomedical Sciences Neurosciences Graduate Program, Washington University School of Medicine, St. Louis, MO 63110
cCenter for the Study of Itch and Sensory Disorders, Washington University School of Medicine, St. Louis, MO 63110
dDepartment of Anesthesiology, Washington University School of Medicine, St. Louis, MO 63110
eDepartment of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110
fCenter of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110
gDepartment of Medicine, Washington University School of Medicine, St. Louis, MO 63110
1To whom correspondence may be addressed. Email: [email protected].
Edited by Balamurali K. Ambati, Knight's Campus for Accelerating Scientific Impact, University of Oregon, OR; received March 10, 2022; accepted November 28, 2022 by Editorial Board Member Jeremy Nathans
3 1 2023
10 1 2023
3 7 2023
120 2 e220413412010 3 2022
28 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
We resolve cell type-specific transcriptional signatures of corneal epithelial differentiation in health, injury, and disease; identify unique genes and gene networks that may be amenable to therapy for the treatment of ocular surface diseases; and create a framework for future studies of corneal limbal stem cells and other stem cell compartments.
Many epithelial compartments undergo constitutive renewal in homeostasis but activate unique regenerative responses following injury. The clear corneal epithelium is crucial for vision and is renewed from limbal stem cells (LSCs). Using single-cell RNA sequencing, we profiled the mouse corneal epithelium in homeostasis, aging, diabetes, and dry eye disease (DED), where tear deficiency predisposes the cornea to recurrent injury. In homeostasis, we capture the transcriptional states that accomplish continuous tissue turnover. We leverage our dataset to identify candidate genes and gene networks that characterize key stages across homeostatic renewal, including markers for LSCs. In aging and diabetes, there were only mild changes with <15 dysregulated genes. The constitutive cell types that accomplish homeostatic renewal were conserved in DED but were associated with activation of cell states that comprise “adaptive regeneration.” We provide global markers that distinguish cell types in homeostatic renewal vs. adaptive regeneration and markers that specifically define DED-elicited proliferating and differentiating cell types. We validate that expression of SPARC, a marker of adaptive regeneration, is also induced in corneal epithelial wound healing and accelerates wound closure in a corneal epithelial cell scratch assay. Finally, we propose a classification system for LSC markers based on their expression fidelity in homeostasis and disease. This transcriptional dissection uncovers the dramatically altered transcriptional landscape of the corneal epithelium in DED, providing a framework and atlas for future study of these ocular surface stem cells in health and disease.
stem cell
cornea
limbal
epithelium
dry eye
HHS | NIH (NIH) 100000002 R01 EY019287 Joseph B LinBrian S ClarkQin LiuRajendra S Apte HHS | NIH (NIH) 100000002 R00 EY027844 Joseph B LinBrian S ClarkQin LiuRajendra S Apte HHS | NIH (NIH) 100000002 R01 EY024704 Joseph B LinBrian S ClarkQin LiuRajendra S Apte HHS | NIH (NIH) 100000002 F30 DK130282 Joseph B LinBrian S ClarkQin LiuRajendra S Apte HHS | NIH (NIH) 100000002 P30 EY02687 Joseph B LinBrian S ClarkQin LiuRajendra S Apte Jeffery T. Fort Innovation Fund GF0003776 Rajendra S Apte Centene Corporation Contract P19-00559 Rajendra S Apte Research to Prevent Blindness (RPB) 100001818 GF0004793 Rajendra S Apte HHS | NIH (NIH) 100000002 T32 GM07200 Joseph B LinBrian S ClarkQin LiuRajendra S Apte
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pmcThe transparent cornea is the protective anterior surface of the eye that provides an entry point for and initial focus of photons onto the neurosensory retina. The epithelial layer on the corneal surface is constantly renewed by a population of epithelial stem cells that resides in the basal layers of the limbus at the peripheral rim of the cornea that separates it from the surrounding conjunctiva (Fig. 1A) (1). The importance of this limbal stem cell (LSC) compartment is highlighted by cases of LSC deficiency resulting from trauma or congenital defects, in which case the corneal epithelium exhibits reduced regeneration that can lead to progressive vision loss (2). Additionally, when LSCs are dysfunctional or deficient, the adjacent conjunctiva may invade the cornea causing corneal vascularization and opacification (i.e., conjunctivalization), resulting in blindness (2).
Fig. 1. Single-cell atlas of the homeostatic mouse corneal epithelium. (A) Schematic for canonical model of LSC differentiation (created in BioRender). (B) Summary of experimental strategy (created in BioRender). (C) UMAP plot of scRNAseq atlas of the mouse corneal epithelium with table delineating cell frequencies for each population (n = 3 independent sequencing experiments). LSC, limbal stem cell; TAC, transit amplifying cell; Squam, squamous; Conj, conjunctiva. (D) Stacked bar plot showing relative frequencies for each cell cluster separated by sample. Colors correspond to those in C. (E) Schematic of cell adhesion molecules defining basal, wing, and squamous cell layers (created in BioRender). (F) Dot plot showing marker gene expression patterns. (G) UMAP plot showing cell cycle position assigned for each individual cell.
Despite its unique importance in maintaining vision, the LSC lineage remains poorly characterized. The canonical model of LSC differentiation proposed nearly four decades ago postulates that LSCs give rise to transit amplifying cells (TACs) that extend centripetally toward the central cornea along the basement membrane as they proliferate (Fig. 1A) (3, 4). Differentiating cells exit the cell cycle and migrate apically through the stratified corneal epithelium, which includes layers of suprabasal wing cells and the most superficial squamous cells. Recently, studies have demonstrated that there may be another noncanonical pathway of differentiation that gives rise to differentiated cells that extend circumferentially around the limbus (5–7).
In this work, we utilized single-cell RNA sequencing (scRNAseq) to uncover the transcriptional signatures of LSCs and their differentiation in health and disease. In homeostasis, we resolve at the single-cell level all of the cell types that enable constitutive renewal of the corneal epithelium. We identify potential genes and gene networks delineating key stages of differentiation in resting turnover. In models of diabetes and aging, these constitutive cell types appear to be largely unchanged at the transcriptional level. We also examined a murine model of dry eye disease (DED), a condition that affects tens of millions of individuals around the world (8). In DED, an inadequate tear film leads to recurrent injury to the ocular surface. We uncovered that constitutive cell types are maintained in aqueous tear deficiency but observe activation of a previously undescribed disease-state transcriptional program. This DED-induced response likely reflects activation of repair programs following epithelial insults, such as may arise from desiccative stress. We term this “adaptive regeneration.” We delineate the unique transcriptional signatures of constitutive tissue turnover and adaptive regeneration in DED. One marker of adaptive regeneration is SPARC, which we validate in corneal epithelial wound healing and identify several SPARC target genes. The scRNAseq dataset of the enclosed work is a significant advance in our understanding of understudied but important ocular surface stem cell regenerative pathways. Critically, this study also provides insight into expression fidelity of putative LSC markers in nonhomeostatic conditions and is clinically relevant to the treatment of ocular surface disease in humans. The transcriptional programs described here may also have potential broader applicability to our understanding of other epithelial stem cell populations in diverse tissues that accomplish tissue renewal and regeneration.
Results
scRNAseq Captures the Heterogeneity of Corneal Epithelial Cell States in Homeostatic Turnover.
We prepared single-cell suspensions of the mouse corneal epithelium by dissecting the eye to isolate the cornea followed by two sequential enzymatic digests. First, enzyme G was used to loosen epithelial–stromal adhesions and cleanly separate the epithelium from the other corneal layers (i.e., stroma + endothelium) (SI Appendix, Fig. S1A). Then, trypsin was used to dissociate the epithelial sheets into individual cells (SI Appendix, Fig. S1B), which were then prepared for scRNAseq using the 10x Chromium platform (Fig. 1B). We sequenced single-cell preparations of the homeostatic mouse corneal epithelium in three independent experiments, with each sample containing cells from ≥4 pooled mouse corneas (sample details are provided in SI Appendix, Fig. S1C). In total, after filtering out low-quality cells and integration to control for experiment-specific variation, we obtained transcriptomes of 38,111 cells across three samples (Fig. 1 C and D and SI Appendix, Fig. S1 D and E).
Visualization of single-cell transcriptomes in the Uniform Manifold Approximation and Projection (UMAP) space and clustering by the Louvain algorithm identified eight transcriptionally distinct cell states. These cell states appeared with similar proportions in all three sequencing experiments (Fig. 1 C–E and SI Appendix, Fig. S1D). Six of these eight cell states represent stages across corneal epithelial differentiation: LSCs, TAC1/2, basal cells, wing cells, and squamous cells. LSCs were characterized by high expression of putative stem cell markers (Krt14, Ifitm3, and Gpha2) and low expression of differentiation markers (Krt12 and Slurp1), indicating that these likely represent the undifferentiated LSC population (Fig. 1F) (1, 5, 9–14). Krt14 expression was also detected in TACs and basal cells, although at a lower level as compared with LSCs (Fig. 1F). The remaining five corneal epithelial cell types showed higher expression of the mature corneal epithelium markers Krt12 and/or Slurp1. Their identities were further inferred based on i) expression of adhesion molecules that roughly define their basal–apical position, ii) cell cycle scoring to identify proliferating cells (i.e., TACs), and iii) marker genes identified agnostically in our scRNAseq dataset. Cells in the basal corneal epithelium are anchored to the basement Bowman layer by hemidesmosomes and connected to adjacent basal cells by gap junctions (Fig. 1E) (15). There was high expression of hemidesmosome and gap junction components (Col17a1, Itgb4, and Gja1) in TACs and basal cells (Fig. 1F), consistent with the fact that these reside in the basal layer of the corneal epithelium (Fig. 1A). TACs were distinguished from resting basal cells by cell cycle scoring and were identified as either TAC1 in the S phase or TAC2 in the G2/M phase (Fig. 1G). The distinction between TAC1 and TAC2 may not represent distinct cell populations but likely are proliferating cells at different stages of the cell cycle. Wing cells in suprabasal layers are adjoined by desmosomes with high expression of Dsg1a, Dsp, and Dsc2 (Fig. 1 E and F). Squamous cells in the most superficial layer express tight junction genes such as Cldn23 that form an intercellular seal (Fig. 1 E and F). Squamous cells also expressed Muc4, a mucin secreted by surface epithelial cells, and the cornification protein Lce3a (Fig. 1F). Therefore, we have captured the following corneal epithelial subpopulations: LSCs, TACs, basal cells, wing cells, and squamous cells. These cell clusters in the UMAP space show that LSCs form a continuous trajectory with TACs which connect to basal, wing, and then squamous cells, suggesting that the UMAP embedding faithfully captures canonical corneal epithelial differentiation in homeostasis (Fig. 1C).
We corroborated the existence of these distinct transcriptional cell states and their expected in situ localization by validating markers at the protein level using immunohistochemistry. GJA1 was detected throughout the basal epithelium (Fig. 2 A and B), and DSC2 was detected throughout suprabasal layers (Fig. 2 A and C). MUC4, a squamous cell marker, was localized only to the most superficial layers of the corneal epithelium (Fig. 2 A and C). Our scRNAseq dataset suggests that a fraction of the cells in the basal layer should be proliferating TACs. We used confocal microscopy to assess for the proliferation marker Ki67 in the basal epithelium of whole-mount corneas. Ki67 was detected in a subset of basal epithelial cells in the central cornea (Fig. 2 A and D). The LSC marker IFITM3 was restricted to the basal limbus, the putative stem cell niche, and was absent from the central cornea (Fig. 2 E and F).
Fig. 2. Markers for cell types in constitutive corneal epithelial renewal. (A) Violin plots showing expression of Gja1, Dsc2, Muc4, and Mki67. (B and C) Immunostaining of GJA1, DSC2, and MUC4 in the central cornea. Images reflect maximum projection across the entire thickness of the tissue section. (D) Ki67 immunostaining in the whole-mount cornea. This image was created by maximum projection of a Z-stack including only the basal layer of epithelial cells. (E) Violin plots showing expression of Ifitm3 and Krt19. (F) Immunostaining of the cornea for IFITM3 and K19. (Scale bars are the same for images in B and C.)
In addition to the six corneal epithelial cell states in our scRNAseq dataset, there was also a small population of immune cells (Fig. 1C) likely representing dendritic cells and/or macrophages as indicated by marker genes Cd74, C1qa, and H2-Ab1 (Fig. 1F) (16). As expected, there was also a small population of conjunctival epithelial cells characterized by expression of Krt19, Krt8, and Krt7 (Fig. 1 C and F) (5, 17). These conjunctival cells were localized peripheral to the limbus (Fig. 2 E and F). The six corneal epithelial, one immune, and one conjunctival cell state(s) constitute all of the cell populations expected to be captured in our sample.
We compared our scRNAseq dataset with two existing datasets for the mouse cornea. We examined the markers for the cell populations reported by Kaplan et al. and found there was very good concordance between ours and their dataset (SI Appendix, Fig. S2A) (18). A subsequent scRNAseq study by Altshuler and Amitai-Lange et al. revealed that there may be two transcriptionally distinct LSC compartments: one in the inner limbus (Atf3+/Mt1-2+) and the other in the outer limbus (Ifitm3+/Cd63+/Gpha2+) (5). We examined expression of these and other markers reported by Altshuler and Amitai-Lange et al. (SI Appendix, Fig. S2B). Again, there was very good agreement for the majority of these marker genes (SI Appendix, Fig. S2B). Expression of outer LSC markers Ifitm3, Gpha2, and Cd63 coincided with LSCs in our dataset (SI Appendix, Fig. S2B). However, inner limbal markers Mt1 and Mt2 were detected in LSCs, TACs, and basal cells, whereas Atf3 and Socs3 were not substantially detected in our dataset (SI Appendix, Fig. S2B). Our data suggest that there is a single bona fide population of LSCs, but it is possible that sequencing depth precludes identifying LSC subpopulations.
Our homeostatic dataset provides complementary information to existing scRNAseq studies by independently validating cell populations and their markers as well as providing new marker genes. We leveraged our scRNAseq dataset to identify potential new LSC markers. We identified the top 30 up-regulated genes that distinguished LSCs compared with other cell types (SI Appendix, Fig. S2C). Of these, Krt17, Fabp5, Id1, Cyp2f2, Serpinb3a, Abi3bp, Ptma, Gas5, Txnip, Fxyd3, Rbp1, and Ccnd2 were also expressed substantially in non-LSC cell types (SI Appendix, Fig. S2C). Many of the remaining 18 genes have been previously reported as LSC markers: Gpha2, Krt14, Ifitm3, and Cd63 (5). We also noticed that some of these LSC markers were also expressed in immune cells (Apoe, Tmem176a/b, 2410006H16Rik, and Cd63), consistent with a previous study showing that immune cells regulate the LSC niche (5).
Gene Expression Changes across LSC Differentiation in Homeostasis.
We performed supervised pseudotemporal analysis to characterize the gene expression changes throughout canonical LSC differentiation using psupertime (19). We ordered cell states in the following sequence in concordance with the canonical model of LSC differentiation: i) LSCs, ii) TAC1, iii) TAC2, iv) basal cells, v) wing cells, and then vi) squamous cells (SI Appendix, Fig. S3A). We grouped gene expression patterns into four categories: i) genes that decrease, ii) genes with an early feature (peak or trough), iii) genes with a late peak, and iv) genes that increase (SI Appendix, Fig. S3B). We believe that these four categories of genes broadly capture different stages of LSC differentiation. For instance, Krt14 decreases in expression as LSCs progress to TACs (SI Appendix, Fig. S3C); thus, these reflect markers of stem cell activity, consistent with Krt14 role in maintaining basal layer cell proliferation (20). Genes with early peaks include the hemidesmosome component Col17a1 and proliferative marker Mt2 (SI Appendix, Fig. S3C); the latter likely reflects activation of proliferation programs in TACs. Increased expression of Col17a1 in TACs as compared with LSCs could reflect de novo synthesis of hemidesmosome components as they extend centripetally toward the center of the cornea. Differentiation is achieved as basal cells migrate superficially, and wing cells exhibit peak expression of Hopx (SI Appendix, Fig. S3C), which has been shown to regulate differentiation in other epithelial tissues (21). Last, genes that maintain high expression in squamous cells include Cryab that helps to maintain a transparent ocular surface (22), Muc4 to help provide surface lubrication (23), and Fth1 which protects corneal epithelial DNA from UV damage (24) (SI Appendix, Fig. S3 B and C).
Gene Regulatory Networks (GRNs) across LSC Differentiation in Homeostasis.
GRNs represent transcription factors and their target genes, and we sought to identify the key regulators responsible for coordinating the broad transcriptional changes that occur across corneal epithelial differentiation in homeostasis. We used single-cell regulatory network inference and clustering (SCENIC) to discover the GRNs characterizing cell states in the corneal epithelium (25, 26). Hierarchical clustering of cell populations based on these GRNs indicated that epithelial cells in the basal cell compartment (i.e., LSCs, basal cells, and TAC1/2) have distinct transcriptional regulation from other more differentiated epithelial cells (i.e. squamous and wing) (Fig. 3A). There also appeared to be groups of GRNs that broadly define stemness, proliferation, differentiation, and function of ocular surface squamous cells. Regulons of stem cell activity include Trp63, Sox9, and Cebpd (Fig. 3 B and C), some of which have been described to be transcription factors important for stem activity in corneal and other epithelial tissues (27–29). The putative stem cell marker Trp63 demonstrated activity not only in LSCs but also in TACs and some basal cells (Fig. 3B), consistent with protein localization detected by immunostaining (SI Appendix, Fig. S4). TACs are characterized by regulators of cell cycle entry and DNA repair such as Ezh2, E2f8, and Tfdp1 (Fig. 3 B and C). The factors important in early corneal epithelial differentiation can be identified by those with increased activity in wing cells such as Kdm5b, Xbp1, and Nfe2l3 (Fig. 3 B and C), some of which have been reported to regulate differentiation in corneal or other epithelial tissues (30). Finally, transcription factors important in late-stage differentiation of squamous cells include Prdm1, Irf7, and Srf (Fig. 3 B and C). Prdm1 regulates type III interferon responses (31) shown to be responsible for immune responses to viral pathogens including Zika and herpes simplex 1 viruses (32). Furthermore, Srf is known to be important in forming cell–cell adhesions (33), a critical aspect of protective barrier function by the ocular surface. Therefore, we are able to identify not only transcriptional programs of stemness, proliferation, and differentiation but also those maintaining corneal function as a protective barrier.
Fig. 3. Gene regulatory networks characterizing corneal epithelial cell types in homeostasis. (A) Heat map depicting GRN activity in each cell cluster. Each row is a single GRN, and hierarchical clustering of cell types was performed based off of GRN activity. (B) UMAP plots of GRN activity for transcription factors that may define each stage of differentiation. (C) Heat maps show average expression of transcription factors and their target genes for each cell cluster.
Corneal Epithelial Cell States Are Maintained Transcriptionally in Resting Aged and Diabetic Cornea.
We have delineated the transcriptional hallmarks of LSC differentiation in the resting cornea, but the role, activation, and/or dysfunction of LSCs in nonphysiologic conditions remains poorly understood. Previous studies have demonstrated exhaustion of numerous stem cell populations with age (34). Indeed, LSC dynamics were also found to be altered during aging with streaks of centripetally migrating LSCs/TACs increasing in width and decreasing in number (35, 36). An additional study demonstrated that aging human LSCs exhibit decreased colony-forming efficiency (37). Therefore, to test the hypothesis that LSC differentiation may become perturbed in aged mice, we performed scRNAseq to capture LSC differentiation in the young (11 wk old) vs. aged (≥18 mo old) corneal epithelium. Based on our scRNAseq data, all cell states from homeostatic turnover were maintained in aged mice (SI Appendix, Fig. S5 A–C). Their transcriptomes were also largely preserved with Pearson correlation coefficients ranging from 0.99 to 1.0 (SI Appendix, Fig. S5D). We determined differentially expressed genes (DEGs) in each cell population (i.e., |log2FC| ≥ 1) and found only a few genes to be changed across aging (SI Appendix, Fig. S5E). Therefore, we were not able to capture substantial age-related stem cell exhaustion at the transcriptional level in the resting mouse corneal epithelium, consistent with previous reports that limbal tissues sourced from older donors can be suitable for LSC transplantation (38, 39).
Another context in which corneal epithelial maintenance may be dysfunctional is diabetes. The diabetic corneal epithelium exhibits increased fragility, recurrent ulcers and erosions, edema, keratitis, and delayed healing (40–42). These corneal manifestations of systemic hyperglycemia are also seen in animal models of diabetes, including both streptozotocin (STZ)-induced hyperglycemia and Leprdb/dbdiabetic mice (43, 44). Therefore, we sought to determine whether hyperglycemia perturbs the LSC compartment in mice. We induced hyperglycemia using the pancreatic beta cell toxin STZ and measured blood glucose weekly to certify chronic hyperglycemia was maintained (i.e., >250 mg/dL; SI Appendix, Fig. S6A). We performed scRNAseq of the corneal epithelium after 12 wk of hyperglycemia and compared the corneal epithelium with euglycemic controls. Twelve weeks of hyperglycemia is sufficient to incur ocular surface sequelae of diabetes as described in previous studies (45–53). Even after extended systemic hyperglycemia, the cell heterogeneity of the corneal epithelium was conserved (SI Appendix, Fig. S6 B–D). Similar to aging, transcriptomes of each cell population were still largely maintained in hyperglycemia with correlation coefficients ranging from 0.99 to 1.0 in diabetic corneas (SI Appendix, Fig. S6E). There were only two DEGs (i.e., |log2FC| > 1) (SI Appendix, Fig. S6F). Our data therefore imply that dysfunction of LSC differentiation does not dramatically contribute to diabetic keratopathy in the otherwise uninjured cornea, at least at the transcriptional level.
Aqueous Tear–Deficient DED Activates an Adaptive Regenerative Program.
We have provided evidence that the cell states characterizing the homeostatic corneal epithelium are largely maintained at the transcriptional level even after aging or extended hyperglycemia. We next hypothesized that the corneal epithelium may activate wound healing programs in ocular surface diseases characterized by epithelial defects and injury. DED results from disruptions to the ocular tear film and affects ~20% of individuals impacting their quality of life and can progress to vision loss or even blindness if left untreated (8). The tear film comprises the following three components: i) aqueous secretions from lacrimal glands, ii) lipid component from Meibomian glands, and iii) mucous layer produced by goblet cells. If the stability of the tear film is compromised by deficiency of any of these components, the ocular surface becomes dry and irritated. Patients may have isolated DED, but it can also arise with other multisystem diseases such as in Sjögren’s syndrome or graft-vs.-host disease in which there are lacrimal gland inflammation and dysfunction (8).
To study the effects of DED on the transcriptional heterogeneity of the corneal epithelium, we surgically excised the right extraorbital and intraorbital lacrimal glands in mice (54), with the left side serving as the control eye (Fig. 4A). At 1 wk after excision of lacrimal glands, we confirmed the presence of DED-induced corneal epitheliopathy by fluorescein staining, which revealed patchy areas of ocular surface injury in all eyes used for further analysis (Fig. 4B). Nine days after lacrimal gland excision, we isolated corneal epithelial cells from control and dry eyes pooled from n = 7 mice and performed scRNAseq. We excluded one dry eye sample from study because it suffered collateral injury during excision of the intraorbital lacrimal gland. Our scRNAseq dataset of DED demonstrated that along with immune and conjunctival cells, all of the cell populations of homeostatic renewal were conserved in DED: LSCs, TAC1/2, basal cells, wing cells, and squamous cells (Fig. 4 C–E). However, there was activation of new cell states (Fig. 4C, cell types in bold with asterisk). These DED-elicited cells were very rare in control eyes (total 172/17,465 = 1.0%) but dramatically expanded >10-fold to 11% (1,793/16,420) of the corneal epithelial cell population in DED. Based on moderate Krt12 expression and lower Krt19 expression (Fig. 4D), these DED-elicited cells appear to be corneal epithelial cells. This DED model likely affects predominantly the cornea due to protection of the conjunctiva by the eyelid. We were able to identify five cell states elicited by DED including LSC-like cells (LSC*), TACs (TAC1*/TAC2*), wing cells (wing*), and squamous cells (squamous*) based on the same markers used to identify cell types in homeostasis (Fig. 4 D and E). The heterogeneous composition of these DED-induced cell states seems to roughly mirror the canonical sequence of LSC differentiation in homeostasis. Taken together, these results indicate that although the constitutive transcriptional program seen in homeostatic tissue turnover is maintained in dry eye–induced epitheliopathy, there is also activation of a disease-specific regenerative response. We term this DED-activated program as adaptive regeneration.
Fig. 4. Single-cell atlas of the corneal epithelium in a mouse model of DED. (A) Schematic of experimental approach (created in BioRender). (B) Fluorescein staining of ocular surface in control vs. DED at 7 d after lacrimal gland excision. The magenta dashed outline circumscribes areas of epithelial injury. (C) UMAP plots of corneal epithelial cell populations in control and DED with table delineating cell cluster frequencies. LSC, limbal stem cell; TAC, transit amplifying cell; Squam, squamous; Conj, conjunctiva; * denotes cell types elicited in DED. (D) Dot plot showing marker gene expression patterns. (E) UMAP plot of cell cycle positions assigned to each individual cell.
Constitutively Renewing Corneal Epithelial Cell Types Are Conserved in DED.
To shed further light into the transcriptional changes associated with DED epitheliopathy, we first evaluated the cell types that were conserved from homeostatic turnover (i.e., LSCs, TAC1/2, basal cells, wing cells, and squamous cells). The transcriptional signature of these constitutive cell types in control vs. dry eye is largely unchanged, with Pearson correlation coefficients ranging from 0.98 to 1.0 (Fig. 5A). To more specifically define these changes, we assessed for DEGs (|log2FC| ≥ 1). We found that there were 11 unique genes up-regulated and one gene down-regulated in DED across all of the conserved cell types (Fig. 5 B and C), with many of these having been previously associated with wound healing in other epithelial compartments. While some of these changes were cell type-specific, other changes were conserved responses across multiple cell types (Fig. 5B). For instance, Fabp5 was up-regulated in all six cell types (Fig. 5 B and C), consistent with upregulation that is seen in the psoriatic epidermis (55). The putative LSC marker Krt14 was up-regulated in basal cells, TACs, and wing cells (Fig. 5 B and C), suggesting an increased stem-like transcriptional signature in these cells. The cystatin gene Cstdc5 was up-regulated in basal cells and TACs (Fig. 5 B and C). Krt16 was up-regulated in TACs, wing cells, and squamous cells similar to upregulation that is also seen in the wounded epidermis (Fig. 5 B and C) (56). The keratinocyte envelope protein Sprr1a was up-regulated in wing and squamous cells (Fig. 5 B and C) similar to upregulation that was recently reported in an acute model of intestinal injury (57). The remaining seven genes were dysregulated in a cell type–specific manner. In LSCs, Clu and Ly6a were up-regulated, while Aqp5 was down-regulated (Fig. 5 B and C). Both Clu and Ly6a are up-regulated in acute villus injury (57, 58) (Fig. 5 B and C). In TAC2s, Krt16 binding partner Krt6a was up-regulated (56) (Fig. 5 B and C). In wing cells, Ifitm1 was up-regulated (Fig. 5 B and C). In squamous cells, Cnfn and Cdsn were up-regulated, which are both members of the cornification cell envelope (Fig. 5 B and C). In general, there were no more than five DEGs for each conserved cell type, suggesting that the constitutive renewal program is largely maintained in DED. This is likely due to the fact that ocular surface injury in this DED model is regional with areas of the intact epithelium (Fig. 4B).
Fig. 5. Transcriptional signature of adaptive corneal epithelial regeneration activated in DED. (A) Correlation matrix comparing transcriptomes of cell types in DED that are conserved from homeostasis. (B) Venn diagram showing the overlap of genes dysregulated in DED for cell types conserved from homeostasis. (C) Scatterplots compare the expression of genes in cell populations that are present in both control and DED. Listed genes are significantly dysregulated (i.e., adjusted P value < 0.05; |log2FC| ≥ 1). Genes that are up-regulated are listed in red and down-regulated in blue. (D) Venn diagram showing the overlap of genes that define DED-elicited cell types compared with their constitutive counterparts (i.e., LSC* vs. LSC; TAC1* vs. TAC1; TAC2* vs. TAC2; wing* vs. wing; squamous* vs. squamous). (E) Heat map depicts the average expression of genes that broadly distinguish cell types of constitutive turnover in homeostasis vs. adaptive regeneration activated in DED. (F) Heat map depicting the average expression of genes that distinguish proliferating and differentiated cells in homeostasis vs. DED. (G) Heat map of average expression of LSC markers in constitutive and DED-elicited cell types.
Transcriptional Signature of Adaptive Regeneration Activated by Aqueous Tear–Deficient DED.
We next determined the unique transcriptional signature of the adaptive cell responses activated in DED (i.e., LSC*, TAC1/2*, wing*, and squamous*). We assessed for genes distinguishing DED-elicited cell states from their constitutive counterparts (i.e., we compared LSCs vs. LSCs*; TAC1 vs. TAC1*; TAC2 vs. TAC2*; wing vs. wing*; squamous vs. squamous*) (SI Appendix, Fig. S7A). In total, there were 303 unique dysregulated genes across these five comparisons with |log2FC| > 1 (Fig. 5D).
We first evaluated which, if any, genes could serve as markers that globally distinguish adaptive regeneration in disease from constitutive tissue turnover. Therefore, we identified genes that were dysregulated in the majority of DED-elicited cell types (Fig. 5 D and E). There were 40 unique genes that were identified as DEGs in ≥3/5 comparisons (Fig. 5D and SI Appendix, Fig. S7B). In addition to the 2 potential LSC markers Lgals7 and Krt14, there were 12 genes that appeared to broadly distinguish DED-elicited cell types from cell types conserved from homeostasis. Four of these transcripts displayed reduced expression in DED: Cdo1, Hlf, F3, and Adh7 (Fig. 5E). Hlf, F3, and Adh7 were not expressed in squamous cells but were expressed in all other homeostatic corneal epithelial types. We also identified eight markers that were globally enriched in DED-elicited cell types: Serpinb5, Pkm, Ifngr1, Stom, Urah, Rbp1, Clu, and Fabp5 (Fig. 5E). Therefore, we have identified potential markers that broadly distinguish cell types that contribute to constitutive tissue turnover from cell types that accomplish adaptive regeneration in DED (Table 1).
Table 1. Constitutive corneal epithelial renewal in homeostasis vs. adaptive regeneration in DED
Constitutive tissue renewal Adaptiveregeneration
Cell types LSCs, TAC1/2, basal cells, wing cells, and squamous cells LSCs*, TAC1*/2*, wing* cells, and squamous* cells
Homeostasis +++ -
Aging (uninjured) +++ -
Diabetes (uninjured) +++ -
Aqueous tear–deficient DED ++ +
Global marker(s) Cdo1, Hlf*, F3*, and Adh7* Serpinb5, Pkm, Ifngr1, Stom, Urah, Rbp1, Clu, and Fabp5
Marker specific to proliferating cells (TAC1/2 or TAC1*/2*) ND† Mt1‡, Mt2‡, and Sparc
Marker specific to differentiated cells (wing/squamous or wing*/squamous*) Id2 and Ttc36 Il1f9, Ceacam1, Shroom3, Ecm1, Spink5, Aldh1a3, Sprr1b, Cnfn, Sprr1a, S100a9, and Krt16
*Expressed minimally in squamous cells.
†None determined.
‡Expressed in TAC1/2, although at a lower level than TAC1/2*.
LSC, limbal stem cell; TAC, transit amplifying cell.
Comparison of constitutive tissue renewal vs. adaptive regeneration including cell states involved, presence/absence in homeostasis and disease models, and marker genes. Gene in bold were validated in corneal epithelial wound healing by immunohistochemistry in the current work.
To delineate the unique transcriptional signature of DED-elicited proliferating cell states, we identified the overlap in markers for TAC1* and TAC2*, which were in the S phase and G2/M phase, respectively (Figs. 4E and 5D, and SI Appendix, Fig. S7C). There were 24 overlapping genes identified, with three of these (Mt1, Mt2, and Sparc) appearing to be markers for both TAC1* and TAC2* cells (Fig. 5F). Mt1 and Mt2 were expressed in homeostatic TAC1/2 cells, although at a lower level compared with DED-elicited TAC1/2* cells (Fig. 5F). Thus, Sparc is the most specific transcriptional marker of DED-elicited proliferating cell states and may be a marker of adaptive proliferation activated in DED (Fig. 5F).
To define the unique transcriptional signature of DED-elicited differentiated cell states, we identified the overlap in DEGs for wing* and squamous* cells (Fig. 5D and SI Appendix, Fig. S7D). There were 32 overlapping genes, of which 13 genes appeared to distinguish DED-elicited differentiated cell types from differentiated cells that exist in homeostasis (Fig. 5F). Wing and squamous cells in homeostasis had high expression of Id2 and Ttc36, whereas wing* and squamous* cells induced in DED exhibited high expression of Il1f9, Ceacam1, Shroom3, Ecm1, Spink5, Aldh1a3, Sprr1b, Cnfn, Sprr1a, S100a9, and Krt16 (Fig. 5F). Due to its highest 5.6 to 5.8 log2 (fold change), we chose Krt16 as the defining marker of DED-elicited differentiated cell states. We suggest that Krt16 activation could reflect unique differentiation programs i) to help protect the ocular surface in disease scenarios and/or ii) that are only apparent when the corneal epithelium undergoes accelerated differentiation in the presence of injury.
Finally, we noticed that some putative LSC markers expanded their expression to DED-elicited adaptive cell states (Fig. 5E and SI Appendix, Fig. S7 B–D). Therefore, we assessed which stem cell markers remain restricted to bona fide LSCs in disease and which markers were promiscuous with expression in DED-elicited cell states. The putative LSC markers Krt14, Lgals7, and Ifitm3 demonstrated dramatically expanded expression to several DED-elicited cell types (Fig. 5G). Igfbp2/7 showed expanded expression to the DED-elicited LSC* population but not others. Therefore, our scRNAseq dataset allows for the classification of putative LSC markers as 1) stringent if their expression remains completely restricted to bona fide LSCs in disease, 2) semistringent if their expression is nonspecific to one other cell population, or 3) labile if their expression expands to several other populations in DED (Table 2).
Table 2. LSC marker expression fidelity
Stringent, semistringent, or labile Cell type promiscuity Previous studies
Gpha2 Stringent ND* (5, 9, 59–61)
Id3 Stringent ND* (5, 60, 62, 63)
Epas1 Stringent ND* (62)
Csrp2 Stringent ND*
Golim4 Stringent ND*
Kitl Stringent ND* (64)
Ccdc3 Stringent ND*
Mfge8 Stringent ND*
Apoe Semistringent Immune Up-regulated in cluster 10 of ref. 62
Tmem176a/b Semistringent Immune
2410006H16Rik Semistringent Immune
Cd63 Semistringent Immune (5)
Igfbp2/7 Semistringent LSC*
Ifitm3 Labile LSC*, TAC1/2*, and wing* cells (5)
Lgals7 Labile LSC*, TAC1/2*, and wing* cells
Krt14 Labile LSC*, TAC1/2*, wing* cells, and squamous* cells Leaky expression to other cells in basal layer after wound healing (65)
*None determined.
LSC, limbal stem cell; TAC, transit amplifying cell.
We classified putative LSC markers based on their expression stringency in homeostasis and disease models.
Gene Regulatory Networks Characterizing DED–Elicited Response.
To better define the transcriptional regulation of DED-induced cell states, we also assessed for GRNs whose activity is enriched in these cells. Strikingly, hierarchical clustering based on GRN activity revealed that DED-elicited cell populations were transcriptionally regulated very similarly to their homeostatic counterpart (SI Appendix, Fig. S7E). For example, TAC1* and TAC2* cells were positioned adjacent to TAC1 and TAC2 cells. There also appeared to be GRNs that were enriched in DED-elicited cells, including the FOSB GRN, which has been reported to have a role in epithelial differentiation in other tissues (66), again pointing to activation of epithelial differentiation programs that are uniquely activated in DED.
Role of SPARC in Corneal Epithelial Wound Healing.
Our scRNAseq data indicate that SPARC is a marker of adaptive regeneration that is activated when the corneal epithelium experiences severe desiccative stress. Since there may be conserved transcriptional programs activated in both DED and injury to the corneal epithelium, we hypothesized that SPARC expression may also be induced during wound healing. In mice, we mechanically debrided a 2-mm-diameter circle from the central corneal epithelium with an AlgerBrush II rotating burr. One day after injury, we found that SPARC protein was strongly expressed at the leading edge of the corneal epithelial wound, while it was completely absent in the uninjured corneal epithelium (Fig. 6 A and B). At this early time point following injury, SPARC is localized mostly intracellularly prior to secretion. Therefore, activation of SPARC appears to be a conserved response of corneal epithelial cells in DED, injury, and potentially other perturbations to the ocular surface.
Fig. 6. Role of SPARC in corneal epithelial wound healing. (A) Immunostaining for SPARC and Ki67 at 1 d following mechanical debridement of the central corneal epithelium. These images are of flat-mounted corneas and are maximum projections of the entire corneal epithelium. (B) Quantification of mean SPARC fluorescence intensity. Bars indicate mean ± SEM, and each circle represents an individual eye. Statistical significance was assessed using the Mann–Whitney test. (C) Heat map showing target genes of SPARC in human corneal epithelial cells. (D) Immunostaining for SPARC and FN1 at 1 d following mechanical debridement of the central corneal epithelium. These images are of flat-mounted corneas and are maximum projections of the entire corneal epithelium. (Scale bar same as shown for Fig. 6A.) (E) Representative images of scratch assay performed with human corneal epithelial cells when treated with SPARC. Yellow dashed lines indicate the edge of the cell-free scratch area. (F) Quantification of the remaining cell-free area relative to each treatment group’s initial average wound size. Each bar is colored corresponding to the labels in Fig. 7E and indicates the mean ± SEM calculated for n = 11 to 13 total scratches across three independent experiments. Statistical significance was assessed using the Brown–Forsythe ANOVA test for each time point (24-h omnibus P value < 0.01). For the 24-h time point, we performed a post hoc Dunnett’s T3 multiple comparisons test for all pairwise comparisons. *P < 0.05 and **P < 0.01.
We next sought to elucidate the mechanistic effects of SPARC, a secreted protein, on corneal epithelial cells. We performed RNA sequencing to identify transcriptional changes when human corneal epithelial cells (hTCEpi) are incubated with SPARC protein comparing control cells with cells treated with 0.05, 0.50, or 5.00 µg/mL SPARC for 24 h (n = 5/group). We obtained an average of 36.5 million reads per sample, and of these, an average of 36.0 million reads (98.6%) mapped to the human genome. We performed DEG analyses to compare control vs. 0.05 µg/mL SPARC, control vs. 0.50 µg/mL SPARC, and control vs. 5.00 µg/mL SPARC. We found that there was a dose-dependent effect on the number of DEGs with FDR < 0.05 (110 DEGs for 0.05 µg/mL, 554 DEGs for 0.50 µg/mL, and 2,631 DEGs for 5.00 µg/mL). Of the 2,631 genes dysregulated by treatment with the highest SPARC concentration, 68 had |log2FC| > 1 (Fig. 6C). In order to evaluate for any dose dependency in these 68 genes, we identified which—if any—overlapped with the 554 DEGs for control vs. 0.50 µg/mL SPARC and the 110 DEGs for control vs. 0.05 µg/mL SPARC. Out of the 68 genes dysregulated by the highest SPARC concentration, 19 of these were also dysregulated by the intermediate SPARC concentration, and three were also dysregulated by the lowest SPARC concentration (Fig. 6C). FN1, CCDC80, PMEPA1, FLRT2, AC037198.1, THBS1, ITGB6, SERPINE1, and TGFBI demonstrated a clear dose-dependent effect on gene expression in response to SPARC treatment (Fig. 6C). Many of these genes have been previously reported to become activated during corneal injury (67), and we found that along with SPARC, FN1 was also strongly expressed at the leading edge of corneal epithelial injury in mice (Fig. 6D). Taken together, our results support that SPARC may be a key gene in coordinating the conserved corneal epithelial response under nonhomeostatic conditions.
To test the role of SPARC in epithelial wound healing, we performed a scratch assay with hTCEpi cells comparing untreated cells with SPARC-treated cells. This assay compares how quickly cells reestablish confluency following a mechanical scratch that creates a gap in a monolayer of confluent cells. Compared with untreated cells, cells treated with SPARC showed increased wound closure at 24 h (Fig. 6 E and F). We quantified the percentage of scratch area remaining, and there was a statistically significant difference between the four treatment groups at 24 h after scratch (Brown–Forsythe ANOVA test, omnibus P value < 0.01). Specifically, the cells treated with 5 µg/mL SPARC had less scratch area remaining compared with all other treatment groups (Fig. 6F), and there also seemed to be a trend indicating a dose-dependent effect of SPARC on wound closure. Therefore, SPARC accelerates corneal epithelial scratch closure.
Discussion
Here, this series of scRNAseq experiments captures the full heterogeneity of the corneal epithelial cell types in homeostasis, aging, diabetes, and DED, forwarding our understanding of a poorly defined yet critically important stem cell population and its differentiation. In homeostasis, we robustly capture all of the cell types that contribute to constitutive renewal of the resting cornea. Our dataset suggests that there is a single population of bona fide LSCs that give rise to proliferating TACs, which differentiate into wing and squamous cells. There also seems to be a population of nonstem/proliferating basal cells. We provide independent validation of previously reported LSC markers and provide others to contribute to the long-standing search for LSC markers. We also clearly show that there appears to be 3 transcriptionally distinct populations of cells in the basal layer: TAC1, TAC2, and basal cells. Both TAC populations are proliferating with cell cycle scoring indicating status in the S phase or G2/M phase. Leveraging our scRNAseq dataset of homeostatic differentiation, we are also able to identify genes and gene networks potentially regulating different stages of homeostatic LSC differentiation. Genetic or pharmacologic manipulation of these genes and/or gene networks of interest could shed further light into the role of these in the constitutive renewal of the corneal epithelium.
Furthermore, our scRNAseq data of aged and diabetic mice indicate that even after extended hyperglycemia or aging, if the ocular surface remains uninjured, populations of stem cells through mature corneal epithelial cell types mirror physiologic conditions. Lack of age-related changes in the mouse corneal epithelium is consistent with previous reports indicating that LSC grafts from elderly donors can be suitable for transplantation (38, 39). We found that diabetes does not substantially perturb resting (i.e., noninjured) corneal epithelial cells at the transcriptional level. Instead, diabetic keratopathy may be driven by i) dysfunction of LSC activation when the corneal surface is injured, ii) perturbations to proteins or other non-mRNA molecules, and/or iii) cell types or noncellular components that are not captured well in our scRNAseq workflow (e.g., nerves, extracellular matrix, and immune cells).
More importantly, our dataset reveals the reconfiguration of the corneal epithelial transcriptional landscape that occurs in DED. We show that all cell types that accomplish constitutive renewal in homeostasis are maintained in DED. However, there was also activation of five disease-specific cell responses—LSC*, TAC1*, TAC2*, wing*, and squamous* cells. These appear to be a rough facsimile of the cell types observed in homeostatic turnover, suggesting that this is an adaptive program of regeneration that is activated in DED, which we term adaptive regeneration. The existence of both constitutive renewal in homeostasis and adaptive responses activated by injury is also seen in other tissues that undergo homeostatic turnover (57). It remains unknown whether bona fide LSCs give rise to the entire adaptive regenerative program or if there is a coordinated transcriptional shift of the cells from constitutive tissue renewal. The expression of LSC markers Ifitm3 and Krt14 in DED-elicited cell types is weak circumstantial evidence that these disease-induced cell populations are derived from the bona fide LSC population. Although we did not detect substantial changes in the corneal epithelium in aging or diabetes, these were performed in resting (i.e., noninjured) tissue. It remains possible that the activation of this adaptive program may become dysfunctional in aging and/or diabetes. Further study is needed to determine whether or not adaptive differentiation is perturbed in these or other pathological contexts that involve the ocular surface.
We leveraged our scRNAseq to determine DEGs that broadly distinguish adaptive regeneration from constitutive tissue renewal in the corneal epithelium (Table 1). Globally, constitutive tissue renewal can be characterized by expression of Adh7, Cdo1, F3, and Hlf, whereas adaptive regeneration has high expression of Fabp5, Clu, Rbp1, Urah, Stom, Ifngr1, Pkm, and Serpinb5 (Table 1). Adh7 is a possible target of the LSC marker P63 (68) and has also been previously reported to be dysregulated in patients with aniridic keratopathy from PAX6 haploinsufficiency (69). Of the markers for adaptive corneal epithelial regeneration activated by DED, Fabp5 and Pkm have been shown to be up-regulated in the psoriatic epidermis (70, 71). Clu was up-regulated in intestinal epithelial cells in an acute villus injury model (57). Urah encodes an enzyme involved in urate metabolism. Urate generated from purine metabolism is an alarmin that triggers inflammation and wound healing responses (72). Thus, it is conceivable that purines released from corneal trauma generate urate that contributes to activation of injury response pathways. Rbp1 was up-regulated after epidermal injury in rats (73), and Serpinb5 has been previously reported to regulate corneal stromal wound healing (74). Thus, further investigation is warranted regarding these genes’ roles in constitutive corneal epithelial turnover and wound healing.
We also showed that Sparc is a previously underrecognized marker that distinguishes adaptive proliferation from constitutive proliferation in homeostasis (Table 1). SPARC, or osteonectin, is a secreted protein that has been studied for its role in corneal wound healing (75–79). Our scRNAseq data complement these previous studies by highlighting that Sparc is up-regulated specifically in proliferating cells of adaptive regeneration. We also show that SPARC protein is strongly expressed at the leading edge of wound corneal epithelium and that SPARC treatment accelerates scratch closure in human corneal epithelial cells, consistent with a previous study showing that treatment with exogenous SPARC accelerated corneal wound healing (78). We also identified SPARC target genes, several of which are known to be activated in corneal epithelial wound healing including FN1. Taken together, our data suggest that SPARC may be a key molecule coordinating the conserved ocular surface response to desiccative stress and injury. We also identified Krt16 as a marker that distinguishes differentiated cells in the adaptive program from those in homeostasis (Table 1), consistent with a role for this keratin in wound healing that has been described in other epithelial tissues (80).
Finally, this scRNAseq transcriptional dissection of the corneal epithelium in homeostasis and DED also enabled key findings critically relevant to the long-standing search for LSC markers. We show that LSC markers that remain restricted to LSCs can be classified as stringent, in contrast to labile LSC markers that expand their expression dramatically in DED (Table 2). In our dataset, Krt14, Lgals7, and Ifitm3 expanded expression to several DED-elicited cell types, consistent with previous work that has demonstrated that Krt14 expression expands after corneal injury (65). On the other hand, stringent LSC markers like Gpha2 among others appear to be faithfully restricted to LSCs even in injury. We also demonstrate that some LSC markers are semistringent and have nonspecific expression in either immune cells or DED-elicited LSC-like* cells (Table 2). This classification of LSC markers is of practical significance and may aid in the isolation of LSCs, especially which to use in homeostasis vs. disease contexts involving ocular surface injury. We thereby provide more granular and nuanced insight into the transcriptional hallmarks defining corneal epithelial stem cells in health and disease, which is critically important given the lack of consensus on a widely accepted LSC marker.
Overall, this study is a significant advance in our understanding of the function of a poorly characterized yet critically important ocular stem cell lineage. LSCs are crucial for maintaining visual function by renewing and regenerating a clear ocular surface under physiologic and pathologic conditions. We have resolved transcriptional signatures of corneal epithelial differentiation in health and disease, identified unique genes and gene networks that may be amenable to therapy, and created a framework for future studies of LSCs and other ocular surface stem cell compartments.
Materials and Methods
We generated mouse models of diabetes with streptozotocin, DED by excising lacrimal glands, and corneal epithelial injury using an AlgerBrush II. We performed scRNAseq using the 10× Genomics platform with analysis using the Seurat package (81). We validated scRNAseq data with immunostaining of mouse tissue along with RNA sequencing and scratch wound healing assay of a human corneal epithelial cell line. Experimental details are described in SI Appendix, Materials and Methods.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
This work was supported by the NIH grants R01 EY019287 (R.S.A.), R00 EY027844 (B.S.C.), R01 EY024704 (Q.L.), and P30 EY02687 (Vision Core Grant); Jeffery T. Fort Innovation Fund (R.S.A.); Centene Corporation contract (P19-00559) for the Washington University-Centene ARCH Personalized Medicine Initiative; Siteman Research Fund; and an unrestricted grant from the Research to Prevent Blindness to the John F. Hardesty, MD Department of Ophthalmology and Visual Sciences at the Washington University School of Medicine in St. Louis. J.B.L. was supported by the NIH grant F30 DK130282 and the Washington University in St. Louis Medical Scientist Training Program (NIH grant T32 GM07200). We also thank Washington University’s Genome Technology Access Center for help with scRNAseq studies, the Morphology & Imaging Core for assistance with paraffin sectioning and staining (Washington University Department of Ophthalmology & Visual Sciences), and Dr. Todd Margolis and Nicolas Ledru for helpful discussions.
Author contributions
J.B.L., X.S., P.A.R., Q.L., A.J.W.H., and R.S.A. designed research; J.B.L., X.S., C.W.P., A.S., and A.J.W.H. performed research; J.B.L., F.S., P.A.R., B.S.C., A.J.W.H., and R.S.A. analyzed data; and J.B.L. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
Genomic data have been deposited in Gene Expression Omnibus (GSE182419; GSE182477; GSE182583; GSE182582; GSE215149) (82–86).
Supporting Information
This article is a PNAS Direct Submission. B.K.A. is a guest editor invited by the Editorial Board.
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PMC009xxxxxx/PMC9926236.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595690
202210467
10.1073/pnas.2210467120
research-articleResearch Articlesoc-sciSocial Sciences432
Social Sciences
Social Sciences
Longer trips to court cause evictions
A. Hoffman David [email protected]
a 1 https://orcid.org/0000-0001-5550-313X
Strezhnev Anton b https://orcid.org/0000-0001-6847-8340
aWilliam A. Schnader Professor of Law, University of Pennsylvania Carey School of Law, Philadelphia, PA 19104
bDepartment of Political Science, University of Chicago, Chicago, IL 60637
1To whom correspondence may be addressed. Email: [email protected].
Edited by Susan Hanson, Clark University, Worcester, MA; received June 17, 2022; accepted November 8, 2022.
3 1 2023
10 1 2023
3 7 2023
120 2 e221046712017 6 2022
08 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND)..
Significance
Recent disruptions have brought a surge of interest in the determinants of access to justice as the downstream effects of legal outcomes like eviction and criminal adjudication become more apparent. We demonstrate the connection between physical barriers to justice and individual case outcomes in a 15-y sample of Philadelphia eviction outcomes. Tenants who must travel longer to get to court are more likely to fail to show up and contest their evictions—they take default judgments. By contrast, during the COVID-19 pandemic, when tenants had an option to Zoom, and in public housing evictions, where tenants have more robust procedural protections, this transit effect disappears. We recommend that courts permit more flexible and remote scheduling to achieve justice.
Studying ∼200,000 evictions filed against ∼300,000 Philadelphians from 2005 to 2021, we focus on the role of transit to court in preventing tenants from asserting their rights. In this period, nearly 40% of tenants facing eviction were ordered to leave their residences because they did not show up to contest cases against them and received a default judgment. Controlling for a variety of potential confounds at the tenant and landlord level, we find that residents of private tenancies with longer transit travel time to the courthouse were more likely to default. A 1-h increase in estimated travel time increases the probability of default by between 3.8% and 8.6% points across different model specifications. The effect holds after adjusting for direct distance to the court, unobserved landlord characteristics, and even baseline weekend travel time. However, it is absent in public housing evictions, where timing rules are significantly laxer, and during the COVID-19 pandemic, when tenants had the opportunity to be present virtually. We estimate that had all tenants been equally able to get to the court in 10 min, there would have been 4,000 to 9,000 fewer default evictions over the sample period. We replicate this commuting effect in another dataset of over 800,000 evictions from Harris County, Texas. These results open up a new way to study the physical determinants of access to justice, illustrating that the location and accessibility of a courthouse can affect individual case outcomes. We suggest that increased use of video technology in court may reduce barriers to justice.
eviction
access to justice
housing
transit
mapping
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pmcAs the first paragraph of Philadelphia’s official tenant guidance document warns, those subject to eviction must appear at the Landlord Tenant Court downtown by 8:45 AM. sharp on their hearing day. Or else:
If you are late or fail to appear, a default judgment will be entered against you. The court will send you a notice that a default judgment has been entered against you. You may file a petition to open the default judgment at 1339 Chestnut Street, Room 1000. You must have a good reason for missing or being late for the trial, must file the petition promptly after learning of the default judgment, and must have a valid, meritorious claim or defense.*
The default judgments that result from failing to show up—or just as often, appearing late—to eviction court in Philadelphia and elsewhere should matter to policymakers. Scholars increasingly have come to conclude that evictions not only reflect but in fact cause social pathologies, making understanding one of their major drivers an urgent concern. Defaults are notoriously difficult to reopen, even if the tenant simply missed her bus and arrived late. And, though policymakers have poured resources into eviction diversion programs, tenants who do not show up cannot take advantage of the newly robust “Civil Gideon” protections offered in major urban areas.
It is thus surprising that although policymakers describe defaults as a part of the eviction crisis (2), we lack information about their incidence across jurisdictions. Many localities do not gather default data or categorize it in inconsistent ways. Reported default rates vary enormously between cities, with some places (Chicago) having few defaults and others (as in Texas) reporting defaults in about half of all filed cases. † Defaults also vary over time. In Maricopa County, Arizona, authorities reported that default rates (normally between 20% and 30%) have recently declined to just 12%. As the Clerk of Court wrote us: “During the pandemic we’re holding almost all cases remotely and scheduling only 25 cases per hour. That allows people to simply call in rather than take a whole day off work and have to wait for their case to be called. It’s a simple matter now to call in and have the case be done in less than an hour.”
But these anecdotes shed dim light. Basic questions remain, including the following: How many defaults are there, really? Are the tenants who fail to show up worse off? Why do tenants fail to show up? And, given the shock of Zoom justice wrought by COVID-19 in eviction court, did making justice remotely accessible matter to outcomes?
Studying 223,862 eviction proceedings from 2005 to 2021 from Philadelphia’s landlord tenant court, we provide a large-scale account of default rates in eviction court across time in a large urban center. We find that defaults are common though in decline, from almost half of unsubsidized housing cases in 2005 to a bit over 30% today. Missed opportunities to contest evictions are sticky as petitions to reopen defaults are rarely filed and infrequently granted.
Controlling for census tract characteristics and residence type, we find that excess public transit commuting time increases default rates. This effect holds when comparing properties owned by the same landlord, when controlling for direct distance to the courthouse, and even when controlling for commuting time as measured on a weekend. As a placebo test, when tenants are offered Zoom hearings during the COVID-19 pandemic, the commuting time effect disappears. The result is also absent for tenants in public housing, whose eviction processes and substantive rules are more tenant-protective. Finally, we replicate the transit effect in a large dataset of cases from Harris County, Texas, where the presence of multiple courthouses allows us to directly compare default rates among tenants of the same building that happen to be assigned to different court locations.
We estimate that for every 10 min in additional transit commuting time, tenants are between .65% and 1.4% points more likely to default depending on model specification. This small percentage has a cumulative impact. In our sample, had tenants been afforded an equally short trip of a maximum of 10 min to the courthouse, Philadelphians would have suffered between 4,125 and 9,246 fewer default evictions. In our supplementary analysis of data from Harris County, Texas (discussed in SI Appendix, section 9), the effect of a 10-min increase in driving commute time is estimated to be about three times as large.
These results contribute to four related literatures.
First, many scholars have focused on the structural legal barriers that prevent disadvantaged people from flourishing. Redlining, undersupply of polling places, pretextual traffic stops, excessive bail, and extractive fines are but a few examples of many that mark the legal system’s subordination of the poor, particularly members of minority groups (4). To date, that work has not empirically demonstrated that structural factors—like where the courtroom is located in a city and the efficiency of the transit network—can directly impact individual legal case outcomes.‡ Our contribution here is to identify a way through which transit affects legal outcomes: by causing citizens to be evicted. That said, there is work on distributive effects of transit time on residents outside of the legal context. Previous research has shown adverse effects of transportation challenges on a variety of outcomes related to access to health care (6–8), employment (9), and voter turnout (10).
Second, scholars have asked how justice operates in lawyerless courts. A particular concern is citizens who do not show up to the court (11, 12). For some types of judgments, like debt collection, more than 90% of defendants do not show up to contest claims, even though many may have had meritorious defenses (13). We are aware of only one large-N study of defaults in eviction court, a paper from 2006 studying around 700 cases, finding (primarily) that defaults were more likely in cases alleging a failure to pay rent (14). Other papers have noted the absence of large scale evidence on the determinants of default (15).
Third, we contribute to the literature on the effects of housing instability on various social ills, including crime (16), preterm birth (17), and other measures of health (18), lack of civic engagement (19), as well as the spread of COVID-19 (20).
Many existing studies in this literature, unfortunately, are difficult to interpret as causal since the determinants of eviction (e.g., poverty, personal instability, and loss of a job) are socially pathologic. That is, crime may cause evictions (21), even as evictions cause crime. Or, having children may cause evictions but might also affect the downstream outcome of interest either directly or through a mediating variable like a reduction in net income (22). Recent work has attempted to address endogeneity problems through more credible identification strategies, such as leveraging the random assignment of judges with varying levels of “severity” in eviction courts as an instrument for eviction (23). In Philadelphia, unfortunately, judge assignment cannot be reliably observed from docket records.§ Additionally, considering how few cases in eviction court involve actual adjudication, the amount of variation in severity across judges may be minimal for many jurisdictions and only weakly predict eviction outcomes.
We show how eviction can potentially be untangled from its confounds by identifying a mechanism that influences outcomes in eviction court but is only weakly related to many of these common socioeconomic factors: commuting time. We thus illustrate a possible instrument for researchers to use in estimating the downstream effects of eviction. Transit could also be a hidden cause of outcomes in other legal proceedings where showing up at a particular time determines the exercise of substantive rights. This would include criminal and immigration proceedings as well as a wide variety of low-stakes civil claims.
Finally, we add to a nascent line of research on the effect of remote hearings on legal outcomes. Prior to the COVID-19 pandemic, scholars had written widely on the advantages and disadvantages of limited experiments in remote access to justice (24–26). That scholarship has suggested that remote access might make it easier for less-resourced defendants to access court proceedings but that decision-makers tend to be less sympathetic to remote participants (27–30). The recent large-scale adoption of remote hearings heightened the stakes of this trade-off but suggested that the introduction of online access could significantly reduce both default rates and disparities based on race, gender, and income (31–34). If remote justice reduces barriers, it would fit alongside another interventions—like court-prompting text messages, calls, and postcards (35–37)—in the repertoire of policymakers.
Fig. 1. Distribution of outcomes in Philadelphia Landlord-Tenant court: 2005 to 2021. 223,840 eviction proceedings, 283,812 defendants.
Institutional Background
There is a small but growing literature about state courts adjudicating low-value claims, where most defendants lack lawyers (3). Mass adjudication is how most citizens experience civil lawsuits, though it bears little to no relationship to the highly formalized and legalized courtroom culture depicted on TV. One emphasis in that research is the importance of local legal cultures, rules, and procedures in meaningfully evaluating outcomes. As Mona Lynch explained:
[H]ow criminal and penal law as practiced is significantly shaped by the local (and locale) such that, although law on the books might lead us to expect some homogenization of outcomes within state and federal jurisdictions, law in action indicates much more microlevel variation shaped by local norms and culture related to how the business of criminal justice happens in any given place (38).
We focus on Philadelphia’s eviction system.¶ In previous work, we extracted approximately 170,000 leases from eviction proceedings and studied their terms (40). Here, we return to the underlying eviction dockets. Philadelphia’s formal eviction process may begin with a “Notice to Quit”—a letter from the landlord to the tenant directing the latter to vacate. That notice is required unless waived by contract. We previously found that approximately 70% of all unsubsidized leases in Philadelphia waived the notice to quit.
Next, the landlord will file a complaint in municipal court, a lower-level trial court, and receive a date and time for the proposed eviction hearing. Landlords must serve the tenant with notice of the eviction in writing (and the court will also mail the tenant a copy of the eviction complaint). The complaint and its attachments are public documents.
After filing the complaint, the court sets a day and time for the hearing. During the period we studied, there were two primary times during weekdays at which the court calendared each eviction: 8:45 AM. and 12:45 PM. The morning times were far more common. Of the 210,074 pre-COVID nonpublic housing eviction proceedings in our data, 144,849 were scheduled for 8:45 AM, and 60,769 were scheduled for 12:45 PM. The tiny remainders are typically scheduled for 30 min after either of these two times. The assignment time is not random: We find a positive relationship between the total number of cases in which a landlord attorney appears in the data and the share of those cases scheduled for 8:45 AM. For example, although about 69% of cases are scheduled for 8:45 AM on average, the most common landlord attorney in our data has 84% of their cases scheduled for that time. Frequent filers cluster in particular times, perhaps because the court fills up time slots in bins and seeks to maximize the efficiency of the process (so that one attorney can present multiple cases in a row).
If tenants do not arrive by their assigned time, the court staff will mark them as having defaulted. An eviction judgment will then issue unless the tenant successfully files a notice to reopen the default. Notices to reopen require a showing of good cause—both a meritorious defense and a good excuse. The prevailing wisdom among advocates is that default judgments are very hard to reopen (41), and we find that this is indeed the case from the eviction docket data (SI Appendix, section 3). After default, if a tenant does not voluntarily vacate the property, the landlord must undertake two additional legal steps to secure a court order which it can us to enlist enforcement officers to lock the tenant out of the property, resulting in a delay of at least 21 d.
Materials and Methods
Data.
From Philadelphia Legal Assistance (PLA), we obtained 339,172 eviction dockets involving residential properties from January 2005 to July 2021. Each docket entry contains a set of covariates related to the property and the eviction proceedings such as tenant and landlord names and the address of the property. Addresses entered into the court docket website were imperfect, containing typographical errors or ambiguous locations. PLA attempted to geocode as many of these locations as possible given the address data. We filtered this dataset to only those properties where we could unambiguously parse an address number and street name from the text of the listed address and obtain, from the Google Maps API, a correctly matching address with a latitude and longitude. We provide additional detail for this process and discuss the representativeness of our remaining sample in SI Appendix, section 1.
Our primary confounders of concern are the socioeconomic characteristics of different neighborhoods in Philadelphia. If lower-income or predominantly minority segments of the city have longer commute times by virtue of their geographic location and also have higher default rates, we may see a spurious relationship between commuting time and default if we do not account for these factors. We also are concerned about potential unobserved confounding driven by variation in landlords across the city.
For the demographic covariates, we identified the census tract and block for each of our properties. Median income and median contract rent were obtained from the 2015 American Community Survey. Because racial demographic data are available at a finer-grained level, we measure the percentage of Black, White, and Hispanic residents using the 2010 census’ block-level data. To obtain data on landlords, we rely on a database of Philadelphia’s landlords that we obtained via an agreement with the Pew Charitable Trust. Because the docket often lists only the filing LLC as the plaintiff, and since individual landlords may own multiple properties through different LLCs, we would be unable to identify common landlords across eviction proceedings without additional data. Pew Charitable Trust used Philadelphia administrative data to match roughly 55,000 landlords to 136,000 rental properties (42).
To generate our primary independent variable of interest— commuting time to the Municipal Courthouse—we queried the Google Maps Distance Matrix API to determine the estimated distance and travel time between each building in our dataset and the Philadelphia Municipal Court.# We measured the distance and time it would take to reach the courthouse via public transit on a weekday—when hearings are scheduled—and on the weekend.|| Because historical transit data are not available on the Google Maps API, our measure only varies across space and captures the general ease or difficulty of commuting downtown. We queried transit times for a fixed day (Wednesday, May 11 for weekday and Sunday, May 15 for weekend) and time (1:00 PM).
For each case, we extracted the outcome of the proceedings from the docket. Because eviction proceedings are often filed against multiple individuals and result in different outcomes—as is the case where one tenant defaults while the other does not—we generated the case outcome data at the individual level. After all of our preprocessing, our primary dataset consists of a total of 223,840 eviction proceedings across 61,014 unique buildings and 283,812 unique named defendants. A total of 280,143 of our defendants’ filings precede the COVID-19 pandemic, and about 40% of these defendants defaulted. We focus our primary analysis on these pre-COVID cases as eviction proceedings during the pandemic changed significantly, as we describe below.
The plurality of our judgments favor the landlord (plaintiff) and nearly all are defaults. The second most common outcome is a negotiated judgment by agreement—a settlement. Recent research, analyzing a sample of these settlements in Massachusetts, found that 1/3 led to the tenant leaving the property (often after a grace period), and 2/3 led to a set of conditions for repayment of rent due, enforceable by a later summary eviction action (47). The third most common outcome is a withdrawal of the case by the landlord. Fewer than 2% of judgments are tenant wins. Most evictions are filed by a small fraction of landlords. Among private evicting landlords, the median number of filings is just 2, but the most frequent filer brought 3,412 cases. Of 25,855 landlords, the top 10 alone account for 10% of all eviction filings.
Fig. 2 displays the trends in default judgments, plotting separate trends for cases involving public housing and nonpublic housing cases. Default rates vary over time and space, and generally decline between January 1, 2005, and February 28, 2020. It shows that during the early period of our data (around 2005 to 2010), default judgments comprised over 40% of all case outcomes for nonpublic housing cases. That number has dropped steadily over time. This drop coincided with the imposition of new landlord regulations, which increased the costs of filing frivolous evictions.** Notably, only 20 to 25% of public housing evictions are defaults, and there is no similar pattern of steady decline. Because those evictions are so distinctive, for our primary analysis, we focus on non-PHA cases.
Fig. 2. Default rates over time. Eviction cases filed prior to the COVID-19 pandemic. N = 280,143 individuals; 220,964 eviction cases (210,052 nonpublic housing); plots are smooth regression splines. Points denote binned averages.
Empirical Approach.
Our quantity of interest is the average effect of a unit increase in weekday transit commuting time to the Philadelphia Municipal Courthouse on the probability that a tenant defaults. We use a selection-on-observables strategy with an extensive set of controls to identify this effect. We leverage the fact that Philadelphia’s public transit system developed in a haphazard, politically charged, and contingent manner, resulting in a system with only two primary subway lines running east–west and north–south (bisecting near the courthouse at City Hall) operating alongside a variety of bus routes that service the other parts of the city (48). As a consequence, properties located in neighborhoods with similar socioeconomic characteristics may nevertheless have wildly different commuting times due to the peculiar layout of the transit network.
Our first and simplest design assumes that commuting time is as good as randomly assigned conditional on a set of covariates and the month and year of the hearing. The covariates include variables that are likely to be correlated with general proximity to downtown Philadelphia and that would also be predictive of default. Three highly salient covariates—income, racial demographics, and rent—are discussed in greater detail in SI Appendix, section 4.
To estimate the effect of a change in commuting time, we fit the following linear probability model:
E[Yijt]=τDj+Xj′β+γt,
where Yijt is whether we observe a default against defendant i in building j at month-year t, γt is a fixed effect for each unique month-year combination, and Xj is a vector of covariates associated with the building (tract-level, block-level, or building-level characteristics depending on how fine-grained our measure is). Specifically, we control for logged census tract median income (2015 ACS), logged census tract median contract rent (2015 ACS), a quadratic polynomial for the share of White residents in the census block (consistent with the curvilinear form we see in SI Appendix, Fig. S6), a quadratic polynomial for the share of Hispanic residents in the census block, and whether the building is an apartment (provided by the Pew dataset). To address possible within-tract variations in rents, we also adjust for our best estimate of the monthly rent owed by the tenant. Although we cannot observe this directly from each lease, we do have access to a very close proxy—the amount of monthly “ongoing rent” demanded by the landlord in the filing. This should typically be equivalent to the monthly rent in the lease but is not always present in each filing. Also, due to data entry or other errors, we observe in the data a small number of extreme and likely incorrect values in both directions. We recoded as “missing” ongoing rent entries above 10,000 dollars per month. In our main analysis, about 7% of cases have missing or potentially erroneous “ongoing rent” amounts. We therefore use the missing covariate indicator method to adjust for this confounder without further dropping additional observations and reducing statistical power. We recode the missing rents to an arbitrary value (0) and control for two additional quantities: the observed rent interacted with an indicator for nonmissingness and an indicator for whether the rent data are missing.†† We cluster all standard errors at the level of the building as this is the level at which the “treatment” of interest is assigned (two tenants in the same building will, by construction, receive the same value of the treatment) (51).
We augment our simple identification strategy with three additional approaches that expand the conditioning set and weaken the as-good-as-random assumption even further. The first leverages the fact that the distance from the center of the city and the commuting time are not perfectly correlated. Two buildings that may be the same “as-the-crow-flies” distance from the city center can nevertheless have dramatically different commuting times due to the structure of Philadelphia public transit. For example, even when we restrict the sample to just those properties that are between 5 and 10 km from the courthouse, the variation in commuting times is still substantial—from 15 to 65 min. Properties that are not located on the two main north–south or east–west subway lines and which rely on inconsistent bus transport have much higher commuting times even when holding constant the distance from the center of the city. We therefore weaken our conditional ignorability assumption by including the direct distance between the property and the Philadelphia Municipal Courthouse. To avoid imposing strong functional form assumptions on the relationship between distance and the outcome in the regression model, we do this adjustment via a set of “ring fixed effects”: dummy indicators that take on a value of 1 if the property is located within a particular 2-km-wide ring around the courthouse.‡‡ This approach allows us to adjust for all unobserved confounders that are correlated with commuting time only by way of their relationship with the direct distance between the property and the Philadelphia city center. In other words, any remaining unobserved confounder that might threaten identification under this strategy would have to be associated specifically with transit commuting time and not merely the distance from downtown Philadelphia. We also are interested in potential heterogeneity in the treatment effect based on distance. It may be the case that there is very little effect for the areas of the city that are furthest from the center (such as the northeast) because most residents there already primarily commute by car.
We next estimate a model that includes landlord fixed effects. Using the Pew data, we match each property to its ultimate landlord. While some landlords have only a single property, a not-insignificant number have properties in multiple locations across the city. In some cases, the differences in commuting time among common properties can be significant. The landlord with the most unique buildings in our data—517—has properties with commuting times that range from 13.5 to 43 min. The next largest landlord has properties with commuting times that range from 18.5 to 58.7 min. Examining variation in defaults among properties with common owners allows us to rule out unobserved confounders associated with landlord characteristics, such as their overall propensity to evict or the characteristics of their leases (40), that may affect the ability of the tenant to appear in court.
Finally, we consider an identification strategy that uses the idiosyncratic differences in the quality of the transit network between the weekday and the weekend. Since all hearings are held on weekdays, we estimate the effect of transit time as measured on a weekday. However, because of changes in traffic patterns and variations in the bus and subway service schedules, the estimated commuting times for some properties can differ substantially depending on whether they are measured on a weekday or on the weekend, even when arrival time is held constant. SI Appendix, section 5 illustrates the differences between these two measures in our dataset. Even after adjusting for “crow flies” distance or our other covariates, we may be concerned that bad transit is an amenity of housing, so that the value of staying put is lowered in those locations. It is also possible that transit is correlated with other amenities (ability to access check cashing services, access to government support) that we do not easily observe. However, if such an unobserved confounder did exist, it is very unlikely that it would affect weekend commutes differently from weekday commutes. Therefore, our final identification strategy further controls for weekend commuting time in addition to all of our other covariates—essentially adjusting for a “baseline” measure of public transit quality. Under this design, the variation used to identify the treatment effect comes entirely from the gap between weekday and weekend commuting times, which is much more plausibly assigned “as-if-randomly.”
To further evaluate the plausibility of our identification strategies, we estimate our regression models on two “placebo” samples—eviction proceedings for public housing and eviction proceedings conducted after the start of the COVID-19 pandemic—where we anticipate null effects due to the absence of our primary causal mechanism.
Before filing an eviction, Philadelphia public housing tenants are entitled to a protracted grievance and hearing process: almost no public housing tenants are evicted without knowing about its likelihood. A Resident Advisory Board is often in touch with those subject to eviction, providing them with advice and support. In addition, public housing evictions occur in a distinct courtroom and a later time (9:15 AM. instead of 8:45 AM.) They do not initially see a judge in that court but rather a PHA manager or paralegal who attempts to resolve their eviction without recourse to the court. Finally, according to lawyers for tenants, the show-up time is not usually strictly enforced, unlike in private tenant courtrooms. One wrote to us:
“In public housing court, they will work with you when you show up even if you are late … If they can’t resolve the dispute on that day, a new court date will be set for the public housing tenant to go before a judge … The legal representatives of PHA are less bent on obtaining judgments at any cost, unlike the way that the handful of repeat landlord attorneys build defaults into their economic model and depend on them to get through many eviction listings in a court morning.”
We thus anticipate that the effect of commuting time on defaults in public housing evictions should be zero if our selection-on-observables assumptions hold true.
Likewise, changes in both Philadelphia law and the operation of the court during the COVID-19 pandemic made distance less salient for this time period. First, the eviction moratorium altered the pool of cases. At the same time, tenants were given the option to apply for a remote—phone or Zoom—hearing. In our data, only 1,820 evictions were filed from July 2020 (when post-COVID filings resume in our data) through December 2020. This is a substantial drop from the 7,773 filed in the comparable period from July 2019 to December 2019. Of all the post-COVID, nonpublic housing filings in our dataset (through July 2021) about 7% were marked as having a remote request. But the possibility that tenants could file for a remote hearing means that those which were not requested are a selected group. We find that despite the moratorium, about 47% of hearings resulted in defaults during the COVID-19 emergency period.
Results
For our analysis of the relationship between commuting distance and (default) eviction, we focus on cases where the date of the first outcome was the same as the first hearing, removing any cases where the first hearing was continued, deferred, or rescheduled. This leaves us with, in the pre-COVID period, a dataset comprised of 232,709 defendants in 181,958 cases across 53,578 unique buildings (53). Fig. 3 plots the spatial distribution of commuting times and default rates across these evictions.
Fig. 3. Distribution of public transit commuting times to Philadelphia Municipal Courthouse and default eviction rates. January 2005 to February 2020. 53,578 unique buildings, 181,958 eviction proceedings, and 232,709 defendants. Transit times were estimated using Google Maps Distance Matrix API. Hexes with fewer than 25 defendants were treated as blank.
In the commuting time map, we can clearly see the contours of the main public transit arteries—notably the Broad and Market Street subway lines. This transit effect means that properties located at similar distances from the center of the city may have drastically different commuting times. Among eviction cases in our sample, the typical defendant can expect a commuting time between 25 and 50 min, with a handful of defendants facing commuting times of over an hour. The right panel shows the spatial distribution of default judgments. While much noisier than the transit plot, we can nevertheless see similar contours with lower default rates along the two major subway lines.
Fig. 4 plots the unadjusted bivariate relationship between commuting time (in minutes) and the probability of a default judgment. Notably, the relationship appears to be roughly linear with no obvious deviations from the regression fit when we overlay a binned scatterplot. The first plot also gives a sense of the range of the causal variable: most of the observations lie roughly between 25 and 75 min, suggesting that even a 1-h increase in commuting time is within the support of the treatment variable. The second plot shows the bivariate regressions of default on commuting time within each 5-km-wide ring around the Philadelphia Municipal Courthouse. We see that the slopes vary substantially across these rings, with the largest positive relationship between commuting time and default rate appearing among properties between 5 and 10 km from the courthouse. This suggests that both potential confounding correlated with distance and also likely heterogeneity in the treatment effect, which we explore in the subsequent analysis.
Fig. 4. Bivariate regression of default probability on commuting time. 53,578 unique buildings, 181,958 eviction proceedings, and 232,709 defendants. Points denote binned averages of the outcome variable. Robust standard errors are clustered on building.
Fig. 5 presents the estimated average treatment effects of a 1-h increase in commuting time on the probability of default. We report the corresponding regression tables in SI Appendix, section 8. Adjusting for covariates and including month-year fixed effects, we estimate that increasing commuting time by 1 h raises the probability of default by about 8.5% points. In other words, for every 10 additional minutes that we predict a defendant will need to commute to court by public transit, the probability that the tenant fails to appear and the landlord receives a favorable judgment by default increases by about 1.4% points. Incorporating either the 2-km-wide distance ring fixed effects or landlord fixed effects reduces the estimated treatment effect by about half, but the effect remains positive and statistically significant. Moreover, even after adjusting for weekend commuting time, we find a strong positive and statistically significant relationship between weekday commuting time and default across all specifications. While the confidence intervals for these estimates are much larger, the estimated effects are all of a comparable magnitude to what we find in our primary regressions.
Fig. 5. Estimated average effects of a 1-h increase in commuting time on probability of tenant default. Estimates from a linear probability model estimated by ordinary least squares. Covariates include census tract median income (logged), census tract median contract rent (logged), a quadratic polynomial of census block % White, a quadratic polynomial of census block % Hispanic, estimated monthly rent from eviction complaint and whether the building is classified as an apartment. Lines denote cluster-robust 95% confidence intervals.
Taking the most conservative estimate of the treatment effect of a 1-h increase in commuting time from the landlord fixed effects analysis—3.8% points—we evaluate the counterfactual scenario where no tenant in our sample has more than a 10-min commuting time. In the dataset used for analysis, we observe 99,283 default judgments. We predict that the default probability for the average tenant will be reduced by about 1.8% points. Across the entire sample, such a change would have resulted in about 4,125 fewer default judgments against tenants. For our largest estimate with covariate adjustment—8.6% points—we predict that the average tenant’s default probability under the counterfactual would be reduced by about 4% points, resulting in 9,246 fewer defaults.
Our placebo analyses also suggest that there does not appear to be a significant source of unobserved confounding driving our results. As shown in the lower two panels of Fig. 5, we see no statistically significant relationship between commuting time and default rates among either public housing or post-March 2020 cases, particularly when we include relevant covariates. While it is the case that our standard errors are much larger in these analyses due to a much smaller sample of both post-COVID and public housing evictions, all of the point estimates in the placebo analyses where we include covariates are either negative or essentially zero.
We also examine whether the treatment effect varies by distance. Fig. 6 plots the estimated average treatment effects adjusting for covariates and month-year fixed effects within four 5- km-wide strata. The effect of commuting time is positive and statistically significant within all of the strata except for the subset of properties that are further than 15 km from the courthouse. Notably, the presence of a strong positive effect in the 0 to 5 km ring despite the absence of a relationship prior to adjusting for the confounders illustrates how some of the confounding between commuting time and default may be biasing us against discovering a treatment effect. While there does appear to be some effect heterogeneity, with commuting time mattering less for properties that are furthest out from the city center, for the vast majority of our sample, we detect a strong positive relationship between transit time and default judgments. In SI Appendix, section 7 we also investigated whether seasonality moderates the effect of treatment. We find no statistically significant heterogeneity in the treatment effect between the spring, summer, fall, and winter seasons. The treatment effects we identify appear to persist throughout the entire year.
Fig. 6. Estimated average effects of a 1-h increase in commuting time on the probability of tenant default-heterogeneity by distance. Estimates from a linear probability model estimated by ordinary least squares. Covariates include census tract median income (logged), census tract median contract rent (logged), a quadratic polynomial of census block % White, a quadratic polynomial of census block % Hispanic, estimated monthly rent from eviction complaint and whether the building is classified as an apartment. Month-year fixed effects are included in all regressions. Lines denote cluster-robust 95% confidence intervals
Last, we attempted a replication of the travel distance effect in Harris County, Texas, using data accessed through the Eviction Lab (54). We relay the results of that replication in SI Appendix, section 9. Studying over 800,000 evictions from 2000 to 2018, we find results that are extremely similar to Philadelphia, despite the radically different jurisdiction. There, we find that a 10-min increase in driving time—since mass transit is largely unavailable in Harris County—raises the probability of default by about 3% points. Notably, the presence of multiple courthouses allows us to leverage differences in the assigned courthouse at the block and even building level in a way that we could not in Philadelphia. Even when we compare evictions taking place in the same building, in the same month assigned to two different courthouses, defaults are more likely at the courthouse that is further away.
A potential concern is that our observed default effect is offset by a positive effect on reopening petitions. But we find no significant association between commuting time and whether a tenant either files a reopening petition or successfully reopens a default judgment. While a small number of defaults are mitigated via reopening, most judgments are final. We discuss these results more in SI Appendix, section 3.
A second potential concern is that tenants who default may have received only slightly less bad outcomes in court had they shown up. As we explore in SI Appendix, section 6, there is a complicated relationship between defaults, “settlements” at the landlord tenant court and landlord withdrawing their petitions. To the extent that defaults substitute for landlord withdrawal, they are obviously bad outcomes for tenants. But in those cases in which landlords show up too, tenants in our data, like those previously studied, were locked out at a rate only slightly lower, and slightly later, than those who took defaults (55). That is so because such in-court settlements were historically the result of significant differences in bargaining power and sophistication between repeat-player landlords and tenants, at least in many cases. Unobserved differences among landlords, and between tenants, make it difficult to disentangle exactly when withdrawal or settlement is the most likely counterfactual for all of our defaults. For example, landlords who file weaker cases at the outset may prefer to avoid court, but those with stronger ones may prefer to show up, meaning that we would expect in-court settlements to be unusually tilted against tenants.
Nevertheless, there remain good reasons to believe that defaulting in court is a bad outcome, which should be prevented. Tenants who default cannot achieve the good outcome of a landlord withdrawing the case, and they will be deemed to have admitted the complaints’ allegations including the amount due. Those who show up preserve their options for better outcomes. And at least in Philadelphia, they will be increasingly likely to achieve them, considering recent changes in the experience of unrepresented tenants in court. In 2018, the city initiated the Philadelphia Eviction Prevention Project, which included a variety of measures aimed at improving tenants’ ability to navigate the legal system, including the provision of a “courtroom navigator” to provide generalized advice and counseling to tenants facing eviction. The subsequent year, the City Council passed “Right to Counsel” legislation to allow low-income tenants to access free legal representation (56). Despite setbacks due to the pandemic, the program has been expanded and funded such that in 2022, tenants in two Philadelphia zip codes with high eviction rates are now eligible for representation through the program (57).
These changes in the aggregate have resulted in significant differences for those who show up today, versus the past: tenants are now told about their rights explicitly on entry to the court, and all provided with lay, and some legal, assistance in negotiations with landlords. This means that the welfare effects of missing court are now even more significant than they were before since the inequality that generated relatively poor settlement outcomes will be ameliorated. Thus, we have strong reasons to believe that there would be advantages to virtual court proceedings which would reduce the incidence of defaults since only tenants who show up, virtually or in person, can receive assistance with negotiations, and get better outcomes as a result.
Conclusion
Our results indicate that policymakers should consider the distributive effects of rules which forfeit legal rights conditional on showing up to the courthouse by a particular time. Alternatives—from remote hearings, to easy rescheduling, to no-excuse reopening—are available and would reduce the incidence of this pathologic practice. And scholars should inquire as to whether other legal proceedings are similarly affected by transit. Essentially, we highlight the role of physical place in producing access to justice. And our results may offer a better-identified tool to study the downstream effects of evictions.
Philadelphia centralizes its eviction system in one court. And its mass transit system may have unique characteristics: because it (relatively) serves West Philadelphia well, but Northeast Philadelphia poorly, it provides relatively better transit to City Hall for (distant) Black than White citizens. Since non-White citizens face distinct barriers in accessing justice, the role of race in failing to appear to court may be dampened in our results. Regardless, as default evictions are a national phenomenon (as are no-shows in many court proceedings), our results illustrate the crucial importance of transit access in the disposition of legal rights. The fact that the transit effect replicates in Harris County, which operates under different procedures and a different transit network, gives us some confidence that it is a phenomenon that might show up in other kinds of courts as well.
Eviction is a social pathology that the legal system channels into an adversarial process. The purpose of that process is to ensure that the state does not sponsor wrongful deprivations of shelter. And yet eviction courts—like other parts of the legal system that primarily burden the poor (33)—offer one-sided fights. Over the last 20 years, a plurality of all cases in landlord tenant court have resulted in defaults and most evictions have followed from default judgments. In this paper, we have asked about the role of structure, process, and procedure in producing that lamentable, and preventable, status quo.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank J. Anes Sung and Luke McCartney for research assistance, and Andrew Baker, Robin Effron, Arpit Gupta, Paul Heaton, Dan Ho, Jon Klick, Ioana Marinescu, J.J. Prescott, Len Rieser, Lou Rulli, Kathryn Sabbeth, Colleen Shanahan, Evan Starr, Andy Eggers, and Molly Offer-Westort for comments. The analysis contained herein is based in part on data provided under license from The Pew Charitable Trusts. We thank Peter Hepburn and Carl Gershenson of the Eviction Lab for providing access to Harris County data. The views expressed herein are those of the authors and do not necessarily reflect the views of The Pew Charitable Trusts. Jon Pyle of Philadelphia Legal Assistance provided comments and scraped the underlying eviction dockets.
Author contributions
D.A.H. and A.S. designed research; performed research; analyzed data; and wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
The data and code necessary to replicate the analysis are available via the Harvard Dataverse at https://doi.org/10.7910/DVN/WUXLF2 (53).
Supporting Information
This article is a PNAS Direct Submission.
*See ref. 1. As we describe in the text below, almost all show-up times historically were at 8:45, but a distinct minority were at 12:45 PM. During the Covid-19 eviction moratorium, these timing rules changed.
†Despite contacting court clerks and examining public dockets, we could only imprecisely estimate default rates at select large urban jurisdictions: New York (∼25%), Chicago (∼8 to 24%), Houston (∼40%), and San Antonio (∼55%). For others, like Los Angeles, Dallas, and San Diego, no good data exist. Defaults in smaller communities can be significantly lower—well under 10% (3).
‡A recent related field experiment failed to find that transit subsidies decreased the rate at which defendants failed to appear for low-level civil and criminal offenses (5).
§The docket records a judge’s name for each eviction. However, we found that these names do not reliably indicate the actual judge sitting in the courtroom on the day that the case was heard.
¶For previous, though now dated, qualitative research, see ref. 39.
#See ref. 43 for a discussion of the advantages of the Maps API for estimating travel times and refs. 44–46 for other work that uses this methodology.
||We also obtained estimates of travel time by driving. Driving and transit commute times correlate quite highly (r = .749) but diverge significantly for certain regions.
**In 2011, a law (originally passed in 2006 but subject to court challenges) required that eviction filing be accompanied by certain documents, including a “Certificate of Rental Suitability,” to be issued if the landlord has a property clear of code and tax violations, had secured a valid housing inspection license, and averred that the property is fit for habitation. The court was not supposed to issue a default without a CRS certificate, but it was underenforced. Beginning in 2012, landlords who owned properties built before 1978 and who knew that they had tenants with children under the age of 6 y on the property were to obtain certificates certifying that they had made efforts to make the property safe from lead poisoning. Again, enforcement of that rule was weak. Starting in February, 2017, the city required lead safe certification as a condition of obtaining a CRS and thus a default judgment. Compliance until 2020 involved self-reporting.
††As we are not interested in the coefficient on rent itself as it is a nuisance parameter in our analysis and clearly not casual, we are not as concerned about bias in the estimation of that particular parameter as we are in addressing sources of confounding of commuting time and default. Recent work has shown that with small levels of missingness, the missing covariate indicator adjustment method provides for generally valid inferences (49). Intuitively, under correct model specification and the absence of confounding between the “missing” covariates and the treatment/outcome, the method can provide unbiased estimates of the treatment effect (50). In general, the inclusion or exclusion of this covariate does not alter the effect estimates in any appreciable manner.
‡‡The longest distance for any property in our dataset is around 24 km, resulting in 12 such bins. For a different use of “circles” as neighborhood controls, see ref. 52.
==== Refs
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3 L. Sudeall, D. Pasciuti, Praxis and paradox: Inside the black box of eviction court. Vand. L. Rev. 74 , 1365 (2021).
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8 S. T. Syed, B. S. Gerber, L. K. Sharp, Traveling towards disease: Transportation barriers to health care access. J. Community Health 38 , 976–993 (2013).23543372
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13 W. J. Bearden, Employing the prima facie standard in third party debt collection default judgments. Urb. Law. 48 , 365–389 (2016).
14 E. Larson, Case characteristics and defendant tenant default in a housing court. J. Empir. Leg. Stud. 3 , 121–144 (2006).
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17 A. Khadka, G. Fink, A. Gromis, M. McConnell, In utero exposure to threat of evictions and preterm birth: Evidence from the United States. Health Serv. Res. 55 , 823–832 (2020).32976630
18 G. Himmelstein, M. Desmond, Eviction and health: A vicious cycle exacerbated by a pandemic. Health Aff. (2021).
19 G. Slee, M. Desmond, Eviction and voter turnout: The political consequences of housing instability. Polit. Soc., 1–27 (2021).
20 K. M. Leifheit , Expiring eviction moratoriums and Covid-19 incidence and mortality. Am. J. Epidemiol. 190 , 2503–2510 (2021).
21 S. Kroeger, G. La Mattina, Do nuisance ordinances increase eviction risk? AEA Papers Proc. 110, 452–56 (2020).
22 M. Desmond, W. An, R. Winkler, T. Ferriss, Evicting children. Soc. Forces 92 , 303–327 (2013).
23 J. E. Humphries, N. S. Mader, D. I. Tannenbaum, W. L. van Dijk, Does eviction cause poverty? Quasi-experimental evidence from cook county, IL, (National Bureau of Economic Research), Working Paper 26139 (2019).
24 C. Gourdet , Court Appearances in Criminal Proceedings Through Telepresence: Identifying Research and Practice Needs to Preserve Fairness While Leveraging New Technology (RAND Corporation, Santa Monica, CA, 2020).
25 I. Eagly, Remote adjudication in immigration. Northwest. Univ. Law Rev. 109 , 933 (2015).
26 J. Prescott, Improving access to justice in state courts with platform technology. Vanderbilt Law Rev. 70 , 1993–2050 (2016).
27 S. Diamond, L. Bowman, M. Wong, M. Patton, Efficiency and cost: The impact of videoconferenced hearings on bail decisions. J. Crimin. Law Criminol. 100 , 869–902 (2010).
28 M. P. Barak, Can you hear me now? Attorney perceptions of interpretation, technology, and power in immigration court J. Migr. Hum. Secur. 9 , 207–223 (2021).
29 F. M. Walsh, E. M. Walsh, Effective processing or assembly-line justice? The use of teleconferencing in asylum removal hearings Georgetown Immigration Law J. 22 , 259–284 (2008).
30 D. Horton, Forced remote arbitration. Cornell Law Rev. 103 (2022).
31 A. L. Bannon, D. Keith, Remote court: Principles for virtual proceedings during the Covid-19 pandemic and beyond. Northwest. Univ. Law Rev. 115 , 1875–1920 (2021).
32 J. I. Turner, Remote criminal justice. Texas Tech Law 53 , 197–271 (2020).
33 J. Prescott, A. Sanchez, “Platform procedure: Using technology to facilitate (efficient) civil settlement” in Selection and Decision in Judicial Process Around the World: Empirical Inquiries, Y. Chien Chang, Ed. (Cambridge University Press, Cambridge, 2020), pp. 30–72.
34 A. Mentovich, J. Prescott, O. Rabinovich-Einy, Are litigation outcome disparities inevitable? Courts, technology, and the future of impartiality. Alabama Law Rev. 73, 893–979 (2020).
35 A. Fishbane, A. Ouss, A. K. Shah, Behavioral nudges reduce failure to appear for court. Science 370 , eabb6591 (2020).33033154
36 R. Ferri, The benefits of live court date reminder phone calls during pretrial case processing. J. Exp. Criminol. 18 , 149–169 (2022).
37 B. H. Bornstein, A. J. Tomkins, E. M. Neeley, M. N. Herian, J. A. Hamm, Reducing courts’ failure-to-appear rate by written reminders. Psychol. Public Policy Law 19 , 70–80 (2013).
38 M. Lynch, Mass incarceration, legal change, and locale. Criminol. Public Policy 10 , 673–698 (2011).
39 D. L. Eldridge, Ph.D. Thesis (University of Pennsylvania, 2001).
40 D. A. Hoffman, A. Strezhnev, Leases as forms. J. Empirical Legal Stud. 19 (2022).
41 Community legal services the eviction process in Philadelphia (2019).
42 E. Haider, Who are Philadelphia’s landlords? (Tech. Rep., Pew Charitable Trust, 2021).
43 F. Wang, Y. Xu, Estimating O-D travel time matrix by Google maps API: Implementation, advantages, and implications. Annal. GIS 17 , 199–209 (2011).
44 R. Rothfeld, A. Straubinger, A. Paul, C. Antoniou, Analysis of European airports’ access and egress travel times using Google maps. Transp. Policy 81 , 148–162 (2019).
45 C. Costa, J. Ha, S. Lee, Spatial disparity of income-weighted accessibility in Brazilian cities: Application of a Google maps API. J. Transp. Geogr. 90 , 102905 (2021).
46 Z. Chang, J. Chen, W. Li, X. Li, Public transportation and the spatial inequality of urban park accessibility: New evidence from Hong Kong. Transport. Res. Part D: Transp. Environ. 76 , 111–122 (2019).
47 N. Summers, Civil probation. Stanford Law Rev. 75 (2021).
48 S. Wolfinger, Running the rails: Capital and labor in the Philadelphia transit industry. Cornell (2016).
49 M. Song, X. Zhou, M. Pazaris, D. Spiegelman, The missing covariate indicator method is nearly valid almost always. arXiv [Preprint] (2021). http://arxiv.org/abs/2111.00138.
50 H. A. Blake , Estimating treatment effects with partially observed covariates using outcome regression with missing indicators. Biom. J. 62 , 428–443 (2020).31994239
51 A. Abadie, S. Athey, G. W. Imbens, J. M. Wooldridge, Sampling-based versus design-based uncertainty in regression analysis. Econometrica 88 , 265–296 (2020).
52 A. Y. Agan, J. Prescott, Sex offender law and the geography of victimization. J. Empirical Legal Stud. 11 , 786–828 (2014).
53 D. A. Hoffman, A. Strezhnev, Replication data for: Longer trips to court cause evictions. Harvard Dataverse. 10.7910/DVN/WUXLF2. Deposited 28 November 2022.
54 J. Reichman, Data from Harris county justice of the peace courts, collected by January advisers and processed by the eviction lab. Accessed 2022.
55 Reinvestment Fund Resolving landlord-tenant disputes: An analysis of judgments by agreement in Philadelphia’s eviction process. Reinvestment Fund Policy Brief (2020).
56 Reinvestment Fund Words from the field: Practitioner perspectives on eviction process improvements in Philadelphia. Reinvestment Fund Policy Brief (2021).
57 E. Rushing, Low-income tenants facing eviction can now get free legal representation in 2 philly zip codes. Philadelphia Inquirer (2022). https://www.inquirer.com/real-estate/housing/philadelphia-evictions-free-legal-representation-rental-assistance-20220131.html. Accessed 15 June 2022.
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595683
202219060
10.1073/pnas.2219060120
commCommentaryphysioPhysiology427
437
Commentary
Biological Sciences
Physiology
Reassessing assumptions about the evolution of herbivore teeth
Sanson Gordon D. [email protected]
a 1
aSchool of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
1Email: [email protected].
3 1 2023
10 1 2023
3 7 2023
120 2 e2219060120Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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pmcIt is inevitable that we conduct research within the lens of existing paradigms, but Thomas Kuhn argued that reevaluation of assumptions encourages paradigm shifts. For over 150 y, the coevolution of grasses and large mammalian herbivores has interested biologists and has become a classic textbook paradigm of adaptation. Valerio et al.’s (1) contribution prompts a fresh look at the assumptions underlying this paradigm.
Considerable research, particularly on horses and ruminant animals, suggests that the paradigm is robust. There are repeated convergent shifts in tooth morphology in different lineages that appear to correlate with changes from a diet of dicotyledonous forb, shrub, and tree leaves to one dominated by monocotyledonous grasses. That is not to say that other scholars have not made significant contributions, but Valerio et al.’s paper raises questions that are worth unpacking. Those familiar with the field will recognize that the following discussion, necessarily brief, simplifies a very complex set of biological interactions.
The rise and spread of grasslands on different continents during the Tertiary coincided with the appearance of dental characters assumed to be adaptations for eating grass. The dogma was, and largely still is, that grasses are particularly tough and abrasive compared to the ancestral diet of woodland plants. Grasses have long been thought to be particularly abrasive because of their high levels of endogenous silica bodies (phytoliths) although exogenous dust or grit on the surface of grass leaves can also cause tooth wear. Grass-eaters evolved very durable teeth with sacrificial high crowns that could endure high levels of wear (Fig. 1). The teeth also developed highly folded and more complex enamel ridge patterns, assumed to be necessary for chewing a tough fibrous diet. There were other adaptations associated with moving onto grasslands, including changes in locomotory morphology, herd behavior, and body size, but the linkage between tooth form and function and the changing biomechanical properties of the diet are of interest here. It is a particularly rich story because teeth, being so hard and durable, are well preserved in the fossil record.
Fig. 1. Comparison of low-crowned browser teeth with high-crowned grazer teeth. (A) Lower teeth of a browsing giraffe adapted to a diet of leaves of tall shrubs and short trees. The 3rd molar is extracted showing a short crown that is always erupted and once worn is no longer effective. (B) Lower teeth of a grazing pronghorn antelope that consumes grasses at ground level. The jawbone is cut away exposing a very long crown that continually erupts as the surface is worn away.
The two fundamental issues of the relative cause of abrasion and the functional basis of the evolution of complex enamel ridges are relevant in appreciating Valerio et al.’s findings. Valerio et al. provide the first in vivo support for a suggestion that has been around for some time, that in ruminant herbivores (e.g., cattle, antelope, deer) residence of food in the rumen washes dust off the food before it is regurgitated for more intensive chewing as the animal ruminates or “chews the cud.” This, they argue, provides an “inadvertent” advantage over animals that do not ruminate (e.g., horses) in that much if not most of the intensive chewing is on food washed free to some extent of external abrasives. It is true that in many cases, ruminants have lower indices of hypsodonty, a measure of crown height, and maybe this is the explanation.
Stromberg et al. (2) present an elegant study in which they used grass phytoliths in soil assemblages in South America to indicate dominant vegetation. They concluded that hypsodonty in South America developed before the grasslands and therefore was unlikely to be an adaptation to open-grassland vegetation but rather a response to dietary grit from abundant ash in subtropical forests. They do point out that hypsodonty is consistent with an adaptive response to abrasives in grasslands of North America and parts of Eurasia, but their findings show that it does not have to be the case. Given the paleoecological implications, it is not a surprise that the evolution of hypsodonty has attracted so much attention and one might be forgiven for assuming that the basic elements of the story are done and dusted.
Valerio et al. provide the first in vivo support for a suggestion that has been around for some time that in ruminant herbivores (e.g., cattle, antelope, deer) residence of food in the rumen washes dust off the food before it is regurgitated for more intensive chewing.
The putative “inadvertent advantage” of ruminants is likely to be linked to the relative amount of external grit to endogenous silica, the degree of washing in the rumen and the amount of chewing before the food is “washed.” There is some evidence that the amount of chewing relates to the fiber level and toughness of the food. These are interesting issues that invite further analysis, but the inadvertent advantage cannot apply to endogenous silica abrasives, and it is yet to be shown whether, in general, grasses accumulate more grit than other plants. Psammophory, or sand entrapment on plant surfaces, is not restricted to grasses (3).
While the abrasiveness of grasses has long been associated with high levels of silica phytoliths, it was not until 1959 that the hardness of phytoliths was measured (4) and showed to be harder than sheep teeth. However, the methods and conclusions have since been challenged, giving impetus to the emerging suggestion that the exogenous grit and dust load on plant food may be as important, if not a more important, cause of tooth wear. Grasses being low to the ground and in open grasslands may be more likely to trap wind-borne sediments than grasses in woodlands where the wind velocity is abated. However, abrasive grit can be transported into the rainforest canopy even in conditions of low wind velocity (5).
The cause of abrasion has become more contentious since several studies have questioned the hardness of plant phytoliths and consequently their capacity to wear tooth enamel. However, even if plant phytoliths do contribute to enamel wear, it has been estimated that African buffalo may consume between 10 and 28 kg of grit and 300 and 400 kg of endogenous silica per year depending on the soil type (6). A fifteen-year-old buffalo on granite soils might have chewed over 200 million times on a diet containing about 6,000 kg of silica, 14 times the amount of grit on the food. These are formidable quantities and attest to the durability of teeth and the necessity for high crowns. With the potential for such quantities of abrasives in the diet, does any inadvertent advantage become less important in grazers if the wear from endogenous silica swamps the wear from exogenous grit? On the other hand, if browsing ruminants consume lower grit levels but virtually no silica, they may have a relatively higher inadvertent advantage. The relative contribution of endogenous to exogenous abrasives needs to be systematically measured over diets, seasons, and soil types and integrated with studies on chewing behavior.
Valerio et al. bring a knowledge of agricultural animal husbandry to add a new twist to the debate. They suggest that the way the sorting mechanism works that promotes washing might favor the proportion of larger particles that are passed back to the teeth for further chewing. The net effect of the rumen sorting mechanism is not only to reduce the abrasiveness of the diet, but, even more interesting, might increase the effectiveness of the teeth, especially if the teeth are adapted for the kind of particles that are regurgitated. They suggest that this leads to an “unparalleled” chewing efficacy and might have contributed to the success of the ruminant radiation. On the face of it, this suggestion makes a great deal of sense. However, we need to know how a herbivore’s teeth work and the significance of enamel complexity to assess this interesting idea.
There is little evidence to support the fundamental assumption that grass is intrinsically tougher than nongrass diets. The limited data we have suggest that grass toughness falls within and at the lower part of the range of dicotyledonous leaf toughness (7). The complex enamel ridges in opposing upper and lower molars are conventionally thought of as providing more cutting actions when the ridges meet during chewing and are somehow more effective, but as noted, browse can be tougher and more fibrous. Several studies have shown a good correlation between enamel complexity and a grass diet. A high-crowned or hypsodont tooth is more durable because it can withstand more wear before becoming functionally compromised. However, enamel ridge complexity and crown height are linked in that a more complex enamel ridge pattern packs more hard durable enamel into the tooth crown, which adds to the overall durability. What is the driver for the evolution of tooth enamel complexity, durability, or cutting efficiency or effectiveness, or both?
Arguably, Valerio et al. inadvertently highlight just how much we do not know about chewing, which is such a vital part of food mechanical preparation that a large herbivore might invest 8 h a day in the activity. Ruminant teeth must deal with fresh abrasive food on ingestion and softened, washed, and sorted food on rumination, possibly engaging with a wider range of biomechanical properties than a nonruminant. A horse must accommodate unwashed and unsorted food particles. Why then are the teeth so similar in many ways and why does the fundamental paradigm still make sense when the assumptions may not be so robust after all? Are the biomechanical properties relevant? Diet toughness is often considered in terms of the energy required to chew the food, but that may not be a limiting factor. Rather toughness might affect how the food locally resists fracture and flows along the basins between the complex enamel ridges when chewed. Food with different biomechanical properties might respond differently to approach angles of occluding tooth ridges, for which there is some anecdotal evidence. Complex enamel folding and packing inevitably means that intervening basins are shallower and more constrained. Could a more homogeneous diet of grass with a narrower suite of properties allow folding while the “simple” ancestral enamel pattern is required for a more varied browsing diet? We do not really know why folding is important in grass feeders and conversely less so in browsers and, therefore, what to measure to assess the interesting suggestion of Valerio et al. That does not make the suggestion any the less valuable.
Valerio et al.’s paper is also welcome in crossing the “esophageal” barrier and reinforcing so forcefully that teeth are an integral part of the digestive process and must be understood in terms of gut function. While there have been notable contributions linking chewing and digestion, such as Janis’ seminal paper (8), Valerio et al. suggest an added level of complexity. Their perspective, as agricultural and veterinary scientists familiar with the intricate workings of the ruminant’s digestive system, bears on the assumptions made by paleontologists about the coevolution of grasses and grazers. Unraveling the selective forces that have led to the patterns of dental evolution has just become more difficult.
Author contributions
G.D.S. wrote the paper.
Competing interest
The author declares no competing interest.
See companion article, “The Ruminant sorting mechanism protects teeth from abrasives,” 10.1073/pnas.2212447119.
==== Refs
1 S. O. Valerio, J. Hummel, D. Codron, J. M. Hatt, M. Clauss, The ruminant sorting mechanism protects teeth from abrasives. Proc. Natl. Acad. Sci. U.S.A. (2022).
2 C. A. E. Stromberg , Decoupling the spread of grasslands from the evolution of grazer-type herbivores in South America. Nat. Commun. 4 , 1478 (2013), 10.1038/ncomms2508.23403579
3 E. F. LoPresti, R. Karban, Chewing sandpaper: Grit, plant apparency, and plant defense in sand-entrapping plants. Ecology 97 , 826–833 (2016).27220199
4 G. Baker, L. H. P. Jones, I. D. Wardrop, Cause of wear in sheep’s teeth. Nature 184 , 1583–1584 (1959).
5 P. S. Ungar , Dust accumulation in the canopy: A potential cause of dental microwear in primates. Am. J. Phys. Anthropol. 97 , 93–99 (1995).7653510
6 G. D. Sanson, S. Kerr, J. Read, Dietary exogenous and endogenous abrasives and tooth wear in African buffalo. Biosurface Biotribol. 3 , 211–223 (2017), 10.1016/j.bsbt.2017.12.006.
7 G. D. Sanson, Cutting food in terrestrial carnivores and herbivores. Interface Focus 6 , 20150109 (2016), 10.1098/rsfs.2015.0109.27274799
8 C. Janis, The evolutionary strategy of the Equidae and the origins of rumen and cecal digestion. Evolution 30 , 757–774 (1976).28563331
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595686
202213056
10.1073/pnas.2213056120
research-articleResearch ArticleimmunImmunology and Inflammation420
Biological Sciences
Immunology and Inflammation
Sec22b is a critical and nonredundant regulator of plasma cell maintenance
Bonaud Amélie a b c https://orcid.org/0000-0002-4153-9171
Gargowitsch Laetitia d
Gilbert Simon M. e
Rajan Elanchezhian f https://orcid.org/0000-0002-6257-3678
Canales-Herrerias Pablo g https://orcid.org/0000-0002-1865-6476
Stockholm Daniel h i https://orcid.org/0000-0002-5069-5256
Rahman Nabila F. j
Collins Mark O. f https://orcid.org/0000-0002-7656-4975
Taskiran Hakan k l m https://orcid.org/0000-0002-2690-3887
Hill Danika L. n o
Alloatti Andres p q https://orcid.org/0000-0003-0555-0653
Alouche Nagham d
Balor Stéphanie r
Soldan Vanessa r
Gillet Daniel s https://orcid.org/0000-0003-0477-3599
Barbier Julien s
Bachelerie Françoise d
Smith Kenneth G. C. e t
Jellusova Julia k l
Bruhns Pierre g https://orcid.org/0000-0002-4709-8936
Amigorena Sebastian p
Balabanian Karl a b c https://orcid.org/0000-0002-0534-3198
Linterman Michelle A. n https://orcid.org/0000-0001-6047-1996
Peden Andrew A. f https://orcid.org/0000-0003-0144-7712
Espéli Marion [email protected]
a b c 1 https://orcid.org/0000-0001-5005-1664
aUniversité Paris-Cité, Institut de Recherche Saint-Louis, INSERM U1160, F-75010 Paris, France
bCNRS, GDR3697 “Microenvironment of tumor niches,” Micronit, France
cOPALE Carnot Institute, The Organization for Partnerships in Leukemia, Hôpital Saint-Louis, 75010 Paris, France
dUniversité Paris-Saclay, INSERM, Inflammation, Microbiome and Immunosurveillance, 92140 Clamart, France
eDepartment of Medicine, University of Cambridge, Cambridge Biomedical Campus, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK
fSchool of Bioscience, University of Sheffield, Sheffield S102TN, UK
gUnit of Antibodies in Therapy and Pathology, Institut Pasteur, Université de Paris, INSERM UMR1222, F-75015 Paris, France
hPSL Research University, EPHE, 75014 Paris, France
iSorbonne Université, INSERM, Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France
jDementia Research Institute, University of Cardiff, Cardiff CF24 4HQ, UK
kInstitute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich 81675, Germany
lTranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich 81675, Germany
mMax Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg im Breisgau, Germany
nLymphocyte Signalling and Development, Babraham Institute, Cambridge CB22 3AT, UK
oDepartment of Immunology and Pathology, Monash University, Melbourne, VIC 3004, Australia
pPSL Research University, Institut Curie Research Center, INSERM U932, 75005 Paris, France
qFacultad de Ciencias Médicas, Instituto de Inmunología Clínica y Experimental de Rosario (IDICER)-CONICET/Universidad Nacional de Rosario, S2002LRL Rosario, Argentina
rMETi, Centre de Biologie Intégrative, Université de Toulouse, CNRS, UPS, 31062, Toulouse, France
sUniversité Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé, SIMoS, Gif-sur-Yvette 91191, France
tCambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre Cambridge Biomedical, University of Cambridge, Cambridge CB2 0AW, UK
1To whom correspondence may be addressed. Email: [email protected].
Edited by Michael Reth, Albert-Ludwigs-Universitat Freiburg, Freiburg, Germany; received July 29, 2022; accepted November 15, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e221305612029 7 2022
15 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Despite their central role in health and disease, the cellular mechanisms underlying how plasma cells persist while producing large quantities of antibodies are still poorly understood. In this paper, we describe that plasma cell survival is under the strict dependency of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) molecule Sec22b. We showed that this molecule is a key regulator of endoplasmic reticulum and mitochondrial structure in plasma cells, and in its absence, the humoral immune response is abrogated. Our findings have important implications for our understanding of antibody-mediated immunity but also open avenues for targeting plasma cells in pathological contexts.
Despite the essential role of plasma cells in health and disease, the cellular mechanisms controlling their survival and secretory capacity are still poorly understood. Here, we identified the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) Sec22b as a unique and critical regulator of plasma cell maintenance and function. In the absence of Sec22b, plasma cells were hardly detectable and serum antibody titers were dramatically reduced. Accordingly, Sec22b-deficient mice fail to mount a protective immune response. At the mechanistic level, we demonstrated that Sec22b contributes to efficient antibody secretion and is a central regulator of plasma cell maintenance through the regulation of their transcriptional identity and of the morphology of the endoplasmic reticulum and mitochondria. Altogether, our results unveil an essential and nonredundant role for Sec22b as a regulator of plasma cell fitness and of the humoral immune response.
plasma cell
SNARE
antibody
endoplasmic reticulum
mitochondria
Agence Nationale de la Recherche (ANR) 501100001665 ANR-19-CE15-0019-01 Pierre BruhnsKarl BalabanianMarion Espeli Agence Nationale de la Recherche (ANR) 501100001665 ANR-14-ACHN-0008 Pierre BruhnsKarl BalabanianMarion Espeli Agence Nationale de la Recherche (ANR) 501100001665 ANR-18-IDEX-0001 Pierre BruhnsKarl BalabanianMarion Espeli Fondation ARC pour la Recherche sur le Cancer (ARC) 501100004097 PJA20181208173 Marion Espeli Agence Nationale de la Recherche (ANR) 501100001665 ANR-17-CE14-0019 Pierre BruhnsKarl BalabanianMarion Espeli Institut National Du Cancer (INCa) 501100006364 PRT-K 2017 Karl Balabanian Agence Nationale de la Recherche (ANR) 501100001665 ANR-18-CE15-0001 Pierre BruhnsKarl BalabanianMarion Espeli Agence Nationale de la Recherche (ANR) 501100001665 ANR-11-CARN-0017-01 Pierre BruhnsKarl BalabanianMarion Espeli UKRI | Biotechnology and Biological Sciences Research Council (BBSRC) 501100000268 BBS/E/B/000C0427 Michelle A. LintermanAndrew A Peden UKRI | Biotechnology and Biological Sciences Research Council (BBSRC) 501100000268 BS/E/B/000C0428 Michelle A. LintermanAndrew A Peden UKRI | Biotechnology and Biological Sciences Research Council (BBSRC) 501100000268 BB/L022389/1 Michelle A. LintermanAndrew A Peden DHAC | National Health and Medical Research Council (NHMRC) 501100000925 APP1139911 Danika Hill Wellcome Trust (WT) 100010269 083650/Z/07/Z Kenneth Smith Deutsche Forschungsgemeinschaft (DFG) 501100001659 419193696 Julia Jellusova
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pmcPlasma cells (PCs) are the cellular source of humoral immunity via the long-term secretion of large quantities of antibodies that provide protection against reinfection. These cells can also contribute to diseases including plasmacytomas as well as antibody-mediated autoimmune and inflammatory pathologies. However, the therapeutic arsenal to target PCs is still very limited. Despite the essential role of these cells in health and disease, the cellular mechanisms controlling their secretory function and their survival are poorly understood. Closing this knowledge gap is thus of paramount importance for designing approaches to target this cell type.
During the transition from B cell to PC, the cell is reprogrammed to produce and secrete around 102 to 103 antibodies per second (1). To accommodate this large protein load, PCs expand their endoplasmic reticulum (ER) and adapt to tolerate the extra stress induced via upregulation of the Ire1α/Xbp1 branch of the unfolded protein response (UPR) (2–4). Early works from the 70s report that antibody secretion happens via the conventional constitutive exocytosis pathway meaning that they are not prestocked in granules but secreted as they are produced and trafficked from the ER to the Golgi apparatus where they are glycosylated (5–7).
Here, through a proteomics analysis, we identified the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) Sec22b as an interesting molecule up-regulated in mouse and human PCs. Members of the SNARE family are essential for the intracellular transport and fusion of protein cargoes between organelles. They are involved in both regulated and constitutive exocytosis and form a large family composed of different subtypes (Qa-, Qb-, Qc-, and R-SNAREs) that interact to form a ternary SNARE complex allowing membrane fusion between vesicles and organelles (8). Distinct sets of SNAREs are expressed on the different organelles and can also be cell type specific (9). On top of vesicular transport, Sec22b has other noncanonical functions, some of which could be highly relevant for PC biology, including ER branching, nuclear shuttling of transcription factors, and plasma membrane expansion (10–13). Using a conditional knock-out (KO) mouse model, we demonstrated that Sec22b is essential for PC maintenance and effective humoral response after vaccination and infection. At the mechanistic level, we showed that Sec22b governs expression of genes involved in not only cell cycle but also mitochondrial function and ER structure. Accordingly, Sec22b-deficient PCs displayed a dramatically altered ER network, reduced contact between the ER and the mitochondria, and hyperfused mitochondria associated with poor survival. To date, little is known about how organelle homeostasis is controlled in PCs, and our results highlight Sec22b as a critical regulator of this process with clear relevance for normal immune response regulation and for PC-mediated diseases.
Results
The Sec22b SNARE and Its Partner Are Overexpressed in PCs.
To identify molecular actors that contribute to PC biology, we performed a mass spectrometry analysis of sorted splenic B cells and in vitro generated PCs. All the data generated are available through a user-friendly interface (https://plasmacytomics.shinyapps.io/home/). We first confirmed that the expression of well-known factors of B cell and PC differentiation behaved as one would predict. As expected, the B cell transcription factor Pax5 was significantly down-regulated in PCs compared to B cells, while the transcription factor Xbp1 was significantly up-regulated in PCs (Fig. 1A). Molecules involved in ER to Golgi trafficking, including SNAREs [syntaxin 5 (Stx5), Sec22b, and Ykt6], the syntaxin regulator Sec1/Munc-18 protein (Scfd1/Sly1), and tethering factors (Uso1/p115), were also up-regulated at the protein level in PCs compared to B cells (Fig. 1A and SI Appendix, Fig. S1). We confirmed these results by western blotting and observed that the SNAREs Stx5, Sec22b, and Ykt6 were twice more expressed in PCs compared to B cells (Fig. 1B). Published microarray and RNAseq datasets were also included in the PlasmacytOMICs interface, and in support of our proteomic data, we also observed a significant increase in the levels of expression of the genes encoding these three SNAREs in sorted murine PCs compared to B cells (Fig. 1C). Data mining of human RNAseq datasets also showed significantly increased expression of SEC22B and STX5A in human PCs compared to B cells (Fig. 1D), suggesting that this is a conserved feature in PCs across species.
Fig. 1. The syntaxin-5-Sec22b SNARE complex is overexpressed in PC. (A) Changes in protein abundance between murine naive B cells and CD138-enriched PCs generated in vitro were measured using label-free LC-MS/MS analysis and plotted using the PlasmacytOMICcs interface. The log2 fold change between naive B cells and the indicated cell subset is shown for each protein. Error bars show SEM. * indicates false discovery adjusted P-value <0.05. We were unable to consistently measure sufficient peptides for quantification of CD138 in the PC samples possibly due to its high level of glycosylation. The fold change shown for XBP1 is an underestimate, as the ratio plotted is calculated using an imputed value for the B cell samples as the protein was not detected in these samples (SI Appendix, Fig. S1). (B) Representative immunoblots for Xbp1, IgM, CD138, Scfd1, Uso1, Stx5, Ytk6, Sec22b, and beta-actin (Top to Bottom, respectively) from samples prepared from splenic B cells (Left) or CD138 enriched in vitro differentiated PCs (Right). The band shown for CD138 is the nonglycosylated form of the protein. For Stx5, the two bands correspond to the short and the long isoforms of the protein. The fold change between B cells and PCs normalized to actin is indicated on the right for each protein. (C) The PlasmacytOMICs interface was used to perform a meta-analysis of gene expression changes between murine naive B cells and a range of antibody-secreting cell types (mSplPC.Array = microarray/mouse splenic PCs; mBMPC.Array = microarray/mouse BM PCs; mPB.rSeq = RNAseq/mouse plasmablasts generated in vitro; mSplPC.rSeq = RNAseq/mouse splenic PCs; mBMPC.rSeq = RNAseq/mouse BM PCs) for the indicated genes. The log2 fold change between naive B cells and the indicated cell subset is shown for each gene. Error bars show SEM. * indicates false discovery adjusted P-value <0.05. (D) Changes in gene expression between human naive B cells and selection of human antibody-secreting cell types (hPB.rSeq = RNAseq/human blood PC; hBMPC.rSeq = RNAseq/human BM PC) were calculated for the indicated genes using the PlasmacytOMICcs platform. The log2 fold change between naive B cells and the indicated cell subset is shown for each gene. Error bars show SEM. * indicates false discovery adjusted P-value <0.05.
Previous works have shown that the ER-localized SNARE Sec22b forms a complex with the Golgi-localized SNARE Stx5 to allow fusion of transport vesicles between these two organelles (9, 14, 15). In the absence of a suitable animal model, we first tested the functional relevance of Stx5 expression in PC in vitro using an shRNA specific for Stx5 together with green fluorescent protein (GFP) reporter expression (SI Appendix, Fig. S2 A–D). From day 3, the frequency of total PCs in the Stx5 knockdown (KD) samples progressively diminished while it was constant in the control samples (SI Appendix, Fig. S2 C and D, Left), and this decrease was due entirely to the loss of GFP+ PCs in the Stx5 KD samples (SI Appendix, Fig. S2 D, Central). The frequency of GFP+ B cells was not modified by Stx5 KD (SI Appendix, Fig. S2 D, Right), confirming the specific requirement of Stx5 expression for PC persistence in vitro. We next took advantage of a small molecule, Retro-2, reported to block Stx5 function by mislocalizing it and blocking its recycling (16, 17). When in vitro differentiated wild-type (WT) PCs were cultured for 5 h in the presence of nontoxic doses of Retro-2, we observed a significant reduction of antibody secretion (SI Appendix, Fig. S2E). Moreover, injection of a single dose of Retro-2 intraperitoneally (ip) to WT mice led to a reduction of the antibodies secreted by bone marrow (BM) PCs ex vivo (SI Appendix, Fig. S2F), supporting a role for Stx5 in antibody secretion on top of PC survival. Altogether, our data suggest that the machinery required for ER to Golgi transport including Stx5/Sec22b is up-regulated during PC differentiation and that disruption of Stx5 leads to defects in both PC viability and function.
Dramatic Loss of PCs and Circulating Antibodies in the Absence of Sec22b.
Taking into consideration the effect of Stx5 on PC maintenance and antibody secretion, we next explored the implication of its partner Sec22b. We crossed a Sec22b-floxed mouse model (10) with the mb1-cre strain (18) to generate a mouse model lacking Sec22b expression specifically in the B cell lineage (Sec22bflox/flox × mb1-cre, hereafter referred to as Sec22bB-KO) (SI Appendix, Fig. S3 A and B). Data mining of publicly available RNAseq datasets (Immgen) suggests that Sec22b is expressed at a constant low level throughout B cell differentiation (SI Appendix, Fig. S3C), so we investigated whether Sec22b deficiency may alter this process. In the absence of Sec22b, B cell development was roughly normal with only a mild reduction of the number of BM mature B cells and splenic follicular B cells compared to WT mice, whereas BM B cell precursors, splenic immature, marginal zone, and CD93−/CD21−CD23− B cells were unaffected (SI Appendix, Fig. S3 D and E). Despite this roughly normal B cell development, Sec22bB-KO mice had almost no circulating antibody at steady state for all the isotypes tested (Fig. 2A). Igκ representing around 90% of secreted antibodies in the mouse was reduced over 40 times. IgG1 titers were 55 times lower in the absence of Sec22b. IgM, IgA, and IgG3 were 40, 30, and 20 times lower in the absence of Sec22b, respectively (Fig. 2A).
Fig. 2. Strong reduction of PCs and circulating antibodies in the absence of Sec22b. (A) ELISA quantification of Ig titers in sera of unimmunized Sec22bWT or Sec22bB-KO mice. (B and C) Representative dot plots (Top) and quantification (Bottom) of the absolute number of total PCs (CD138+TACI+) and percentage of each PC subsets: PBs (CD138+TACI+B220+CD19+), early PCs (CD138+TACI+B220lowCD19+), and late PCs (CD138+TACI+B220−CD19−) in Sec22bWT and Sec22bB-KO mice in the spleen (B) and BM (C). Cells were first gated on their size and structure, then on their viability. Dead cells and doublets were excluded. (D) Unsupervised clustering based on the relative expression of Prdm1, Xbp1, Irf4, Tnfrsf13b, Cd93, Pax5 and Bach2 of sorted Sec22bWT and Sec22bB-KO PCs determined by Biomark multiplex qPCRs at steady state. The heatmap was generated using the heatmapper.ca website and row Z score based on (2-D Ct) values. n = 7 to 19 mice from 2 to 5 independent experiments. The P-values were determined with the two-tailed Mann–Whitney nonparametric test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. ns, nonsignificant P-value. Sec22bWT controls were mb1cre+ and Sec22bfl/fl / Sec22bfl/+ mice.
This was associated with a 10-fold reduction of the frequency and absolute number of PCs in the spleen and the BM with the mature CD19−B220− PCs being the most affected (Fig. 2 B and C). Accordingly, we observed that the few remaining splenic PCs in Sec22bB-KO mice display a less mature profile than their WT counterparts with reduced expression of the PC markers Cd93 and Tnfrsf13b and of the PC master regulators Prdm1, Xbp1, and Irf4. In contrast, enhanced expression of the B cell master regulators Pax5 and Bach2 was detected at the transcriptional level in Sec22bB-KO PCs compared to the WT (Fig. 2D). Thus, our results demonstrate that Sec22b plays a critical and nonredundant role in PC maintenance and in the control of antibody circulating titers at steady state.
Sec22b Regulates PC Maintenance and Molecular Identity.
To unravel at which step of PC differentiation Sec22b is required, we performed in vitro differentiation assays of splenic B cells. In control cultures, PC frequency doubles between day 2 and day 4, whereas it remains constant between these two time points in Sec22bB-KO cultures (Fig. 3 A and B). This was associated with enhanced apoptosis of in vitro derived PCs lacking Sec22b as detected by the frequency of active Caspase3+ cells (Fig. 3C). In addition, cell cycle was impaired in Sec22bB-KO PCs generated in vitro with a progressive exit from the cell cycle particularly clear on day 4 (Fig. 3D). These results indicate that Sec22b is not required for the initiation of PC differentiation but is important for the maintenance and expansion of this cell subset.
Fig. 3. Sec22b is essential for PC maintenance and molecular identity. (A and B) Representative dot plots (A) and quantification (B) of PCs (CD138+B220+/−) generated in vitro from Sec22bWT and Sec22bB-KO splenocytes after 2 or 4 d of stimulation with LPS. Cells were gated on their size and structure, then on their viability and finally doublets were excluded. (C) Frequency of caspase 3+ PCs on days 2 and 4 after LPS stimulation determined by flow cytometry. (D) Flow cytometry analysis of the cell cycle phases based on DAPI and Ki-67 staining in in vitro generated Sec22bWT and Sec22bB-KO PCs on day 2 (Left) and day 4 (Right) after LPS stimulation. (E–I) RNAseq analysis of Sec22bWT and Sec22bB-KO PCs generated in vitro on day 2 after LPS stimulation. (E) Principal component analysis of the RNAseq data of Sec22bWT and Sec22bB-KO PCs. The proportion of variance is indicated for PC1 and PC2. (F) Unsupervised clustering of Sec22bWT and Sec22bB-KO PCs based on the 500 most differentially expressed genes. (G) Volcano plot showing the differentially expressed genes between Sec22bWT and Sec22bB-KO PCs. Genes significantly down-regulated and up-regulated in Sec22bB-KO PCs are shown in green and red, respectively (FC > 1.2; q value < 0.05). (H) Normalized Enrichment Scores (NES) of representative gene sets significantly enriched in Sec22bB-KO vs. Sec22bWT PCs and characteristic of selected cellular pathways. Gene sets significantly down-regulated and up-regulated in Sec22bB-KO PCs are shown in green and red, respectively. (I) Expression of Ykt6, Gosr1, Gosr2, Stx5, and Bet1 in fragments per kilobase of exon per million reads mapped (FPKM) determined by RNAseq in Sec22bWT and Sec22bB-KO PCs. (J) Quantification of the IgM secretion rate from in vitro generated PCs from Sec22bWT and Sec22bB-KO splenocytes after 2 or 4 d of LPS stimulation analyzed by DropMap. Each point represents one cell. (A and B) n = 12 to 16 mice from at least three pooled independent experiments. (C and D) n = 4 of 1 representative experiment out of 2. (E–I) n = 4 in 1 experiment; (J) n = 4 in 2 independent experiments. The P-values were determined with the two-tailed Mann–Whitney nonparametric test *P < 0.05; **P < 0.01; ***P < 0.001, ns, nonsignificant P-value. Sec22bWT controls were mb1cre+ and Sec22bfl/fl/Sec22bfl/+ mice.
To gain further insight into how Sec22b controls PC fate, we performed RNAseq analysis on in vitro generated PCs. We chose an early time point, 2 d after lipopolysaccharide (LPS) stimulation, to detect defective mechanisms before the loss of PCs observed rapidly thereafter. Despite being numerically normal (Fig. 3 E and F), Sec22b-deficient PCs on day 2 after stimulation were transcriptionally very different from their WT counterparts as shown by unsupervised analyses (Fig. 3 E and F and SI Appendix, Tables S1 and S2). Over 6,000 genes were differentially regulated between both genotypes with 3,388 genes up-regulated and 3,184 genes down-regulated in Sec22bB-KO PCs compared to WT PCs (14,026 genes total, fold change > 1.2 and q-value < 0.05) (Fig. 3G). Although cell purity was equivalent between sorted Sec22bWT and Sec22bB-KO PCs (>80%), we cannot formally exclude that the differences observed result from heterogeneity in the cells generated in vitro at this early step in both conditions. Transcriptional expressions of Ptprc (encoding B220) and Sdc1 (encoding CD138) were equivalent between samples from Sec22bWT and Sec22bB-KO PCs, suggesting that, at least based on these two markers, the sorted cells were homogeneous between the two groups (SI Appendix, Fig. S4 A and B). Sec22b was recently proposed to contribute to transcription factor nuclear shuttling, and this might account for the important transcriptional differences observed in our datasets (13). Gene set enrichment analyses (GSEAs) revealed that several pathways were significantly different between WT and Sec22bB-KO PCs. In line with our experimental results (Fig. 3H), Sec22b seems essential for promoting cell cycle possibly via control of the Myc/E2F signaling axes. Pathways pertaining to “Mitochondria” were also significantly down-regulated in Sec22bB-KO compared to WT PCs, whereas “UPR,” “ER–Golgi transport,” and “protein secretion” pathways were up-regulated (Fig. 3H and SI Appendix, Figs. S5 and S6). We thus explored more precisely how Sec22b deficiency may affect these processes. We first observed that the expression of the second R-SNARE normally involved in ER to Golgi trafficking, Ykt6, was conserved in the absence of Sec22b. Moreover, expression of all the Qa, Qb, and Qc SNAREs with whom Sec22b or Ykt6 interact to form a trans-SNARE complex was significantly increased in Sec22bB-KO PCs, suggesting the induction of compensatory mechanisms to deal with cargo transport in the absence of Sec22b (Fig. 3I).
To unravel whether Sec22b intrinsically regulates antibody secretion, we took advantage of a droplet microfluidic-based technique to assess the secretion rate at the single-cell level (1). Between 6,000 and 10,000 cells were individually encapsulated into droplets and analyzed for their IgM secretion rate after 2 and 4 d of in vitro culture. Up to 12% of encapsulated cells secreted a detectable amount of IgM over the 40 min of imaging with less secreting cells in the Sec22bB-KO condition. We observed that in vitro generated WT PCs secreted on average 12 IgM/s on day 2 and 20 IgM/s on day 4. In contrast, Sec22b-deficient PCs secreted on average 2 to 4 times less IgM/s on days 2 and 4, respectively (Fig. 3J). Hence, Sec22b deficiency leads to a significant reduction, albeit not a total block, in antibody secretion. The massive drop in antibody titer in the serum of Sec22b-deficient mice is thus probably caused by the combination of a reduced secretion rate together with severely altered PC maintenance.
Sec22b Is Essential for ER Expansion and Structure in PCs.
Our transcriptional data indicate that in the absence of Sec22b, all three branches of the UPR (i.e., Ern1, Atf4/Ddit3/Perk, and Atf6) (Fig. 4 A and B) were up-regulated, including the Atf4/Chop pathway normally actively repressed in PCs to protect from ER stress–induced cell death (4). Our results suggest that in the absence of Sec22b, this protective mechanism is deficient and could lead to Chop-mediated cell death and exit from the cell cycle. Exacerbated UPR may be a sign of enhanced ER stress, and we thus assessed whether ER biogenesis might be altered. Using a permeable cell tracker, we showed that the ER membrane expansion normally observed as PC differentiate was significantly reduced in the absence of Sec22b (Fig. 4C). The structure of the ER was also altered in Sec22bB-KO PCs. The perinuclear envelope was roughly normal, whereas peripheral ER appeared poorly branched with dilated cisternae (Fig. 4D), suggestive of accumulation of hyperdilated ER and loss of the parallel rough ER typically seen in WT PCs. Electron microscopy confirmed that the stacked ER sheets characteristic of PCs were lost in the absence of Sec22b with a hyperdilatation of the ER cisternae and a defective stacking that was even more evident on day 4 (Fig. 4E). Dilated ER was observed in all but one PC in Sec22bB-KO PCs, while it was observed in less than 11% of Sec22bWT PCs. Sec22b expression is thus crucial for ER spatial expansion and organization during PC differentiation.
Fig. 4. Sec22b is essential for ER expansion and structure in PCs. (A) Expression of Ern1 (encoding Ire1a), Atf4, Atf6, and Ddit3 (encoding Chop) in fragments per kilobase of exon per million reads mapped (FPKM) determined by RNAseq in Sec22bWT and Sec22bB-KO PCs. (B) Heatmap showing the relative expression of selected ER stress genes from PCs generated from Sec22bWT and Sec22bB-KO splenocytes after 2 d of LPS stimulation, determined by Biomark multiplex qPCRs at steady state. (C) Flow cytometric quantification of ER-Tracker MFI (geometrical mean) on PCs generated from Sec22bWT and Sec22bB-KO splenocytes after 2 or 4 d of LPS stimulation. n = 4, one representative experiment shown out of 3. (D) Confocal microscopy images of Sec22bWT and Sec22bB-KO PCs obtained from splenocytes stimulated with LPS for 2 d. Cells were stained with an anti-IgM antibody to detect intracellular IgM, an anti-calnexin antibody to detect the ER and an anti-IRF4 antibody. Nuclei were counterstained with Hoechst. Images are representative of 60 Sec22bWT PC and 71 Sec22bB-KO PC from two mice per group. (E) Electron microscopy images of Sec22bWT (Left) and Sec22bB-KO (Right) PCs obtained from splenocytes stimulated with LPS for 2 d (Top) or 4 d (Bottom). Red arrowheads indicate ER sheets. (Scale bar, 1 mm or 0.5 mm.) Images are representative of 64 WT PCs (9 on day 2 and 55 on day 4) and 39 KO PCs (21 on day 2 and 18 on day 4). Data are from five mice per genotype out of two experiments. The heatmaps were generated using the heatmapper.ca website, row Z score based on (2-ΔCt) values. The P-values were determined with the two-tailed Mann–Whitney nonparametric test *P < 0.05. Sec22bWT controls were mb1cre+ and Sec22bfl/fl/Sec22bfl/+ mice.
Sec22b Deficiency Affects PC Fitness via Altered Mitochondrial Dynamics.
In addition to protein folding and export, an important function of the ER is the control of organelle dynamics and in particular of mitochondria. Indeed, ER–mitochondria membrane contact sites are pivotal not only for ER–mitochondria exchange but also for mitochondrial fission and thus function (19). Through our RNAseq analysis, we observed a downregulation of several genes involved in mitochondrial function and dynamics (Fig. 3H and SI Appendix, Fig. S5) including Dnm1l, Inf2, and Spire1 that encode proteins involved in mitochondrial fission at the ER–mitochondria contact site (Fig. 5A). We thus quantified ER–mitochondria contact sites by a proximity ligation assay (PLA, measuring the distance between the mitochondrial protein VDAC1 and the ER protein IP3R1) and revealed that they were significantly reduced in Sec22bB-KO PCs (mean 1.233 ± 0.06910 for Sec22bB-WT vs. 0.8571 ± 0.05122 for Sec22bB-KO) (Fig. 5B and SI Appendix, Fig. S7). Moreover, mitochondrial content and potential were increased in PCs after 4 d in culture in Sec22bB-KO PCs compared to WT PCs (Fig. 5C). Accordingly, we detected more mitochondria, increased total mitochondrial area but with fewer fragments in Sec22bB-KO PCs, compared to WT PCs suggesting a hyperfused mitochondrial phenotype (Fig. 5 D and E). We wondered whether this defective mitochondrial conformation may contribute to the PC loss observed in the absence of Sec22b. Treatment of WT PCs with M1, a Drp1 antagonist, together with Mdivi, an agonist of mitochondrial fusion, promoted the generation of a hyperfused mitochondria phenotype comparable to that of Sec22bB-KO PCs (Fig. 5F). Moreover, M1+Mdivi-treated WT PCs numbers were significantly reduced in culture compared to control conditions, confirming that mitochondrial dynamics affect PC fitness (Fig. 5G). In addition to this, hyperfused phenotype Sec22bB-KO PCs presented several abnormalities in mitochondrial gene expression (Fig. 5H) including genes implicated in the respiratory chain (Atp5 family), mitochondrial fission (Mtfp1 and Mtfr2), structure (Timm and Tomm families), and metabolite transport (Slc25 family) (Fig. 5H) suggesting a global alteration of mitochondrial size and function in the absence of Sec22b. Altogether, these results demonstrate that Sec22b expression regulates ER–mitochondria contact sites and consequently mitochondrial dynamics in PCs with important consequences for cell fitness and survival.
Fig. 5. Sec22b deficiency affects PC fitness via altered mitochondrial dynamics. (A) Expression of Dnm1l, Inf2, and Spire1 in FPKM determined by RNAseq in Sec22bWT and Sec22bB-KO PCs obtained from splenocytes stimulated with LPS for 2 d. n = 4 and data are representative of one experiment. (B) PLA performed on splenocytes stimulated with LPS for 2 days. Representative images are shown (Left), PC stained with anti-IgM (green), PLA (red dot), and nuclei counterstained with DAPI. Quantification of dots per PC (Middle) (one representative experiment) and mean of dot number per PC in each experiment (Right) are represented. (Scale bar, 30 µm.) n = 1,271 cells Sec22bWT and 1,893 cells Sec22bB-KO of 3 to 4 mice in three independent experiments. (C) Flow cytometric quantification of MitoTracker (mitoT) green (Left) and mitoT orange CMTMRos (Right) MFI (geometrical mean) on PCs generated from Sec22WT and Sec22bB-KO splenocytes after 2 or 4 d of LPS stimulation. n = 4 mice in one representative experiment out of three. (D and E) Confocal microscopy images of PCs obtained from Sec22bWT and Sec22bB-KO splenocytes stimulated with LPS for 4 d. Cells were stained with an anti-IgM antibody to detect intracellular IgM, an anti-cytochrome c antibody to detect mitochondria, and nuclei were counterstained with Hoechst. (Scale bar, 10 µm.) Representative images (D) and quantification (E) of the mean area (Top), mean intensity (mean per z-stack) (Middle), and number of fragments (over the full cell volume quantified through 10 z-stacks) (Bottom) per cell of the cytochrome c staining in PCs obtained from Sec22WT and Sec22bB-KO splenocytes stimulated with LPS for 4 d. n = 304 for the Sec22bWT and n = 231 for the Sec22bB-KO from three independent mice per genotype. (F and G) Tridimensional projection of confocal microscopy images of PCs obtained from Sec22WT and Sec22bB-KO splenocytes stimulated with LPS for 4 d and after 2 d of treatment with DMSO or M1/Mdivi for Sec22WT. Cells were stained with an anti-IgM antibody, an anti-cytochrome c antibody, and nuclei were counterstained with Hoechst. (Scale bar, 6 mm.) Representative images (F) and quantification (G) of fold change of PC frequency normalized to control Sec22bWT PCs are shown. n = 10 from two pooled experiments. (H) Supervised heatmap based on RNAseq expression data of nine mitochondrial gene families differentially expressed between Sec22bWT and Sec22bB-KO PCs. For flow cytometry experiment, cells were first gated on their size and structure, then on their viability and finally doublets were excluded. The P-values were determined with the two-tailed unpaired Mann–Whitney nonparametric test *P < 0.05; ****P < 0.0001, ns, nonsignificant P-value except for PLA experiment, determined with Welch’s t test or paired t test. Sec22bWT controls were mb1cre+ and Sec22bfl/fl/Sec22bfl/+ mice.
Sec22b Is Indispensable for the Generation of a Protective Humoral Immune Response.
Considering the importance of PCs and circulating antibodies for the humoral immune response, we finally assessed the impact of Sec22b deficiency on these processes. Following T-dependent immunization with sheep red blood cells (SRBCs) (Fig. 6 A–C), the frequency and number of PCs in the spleen remained extremely low in Sec22bB-KO mice, being reduced over 100 times compared to controls (Fig. 6B). A similar observation was made for antibody titers after SRBC immunization (Fig. 6C). We also investigated antigen-specific humoral immune response by immunizing ip with the T-dependent antigen 4-hydroxy-3-nitrophenylaceyl-keyhole limpet hemocyanine (NP-KLH) in alum and boosting with NP-KLH only (Fig. 6D). Seven days after the boost, the frequency and numbers of splenic and BM PCs were again dramatically reduced in the absence of Sec22b (Fig. 6E). Moreover, we barely detected NP-specific IgM and IgG1 antibodies in the serum of Sec22bB-KO mice throughout the immunization, while a potent antibody immune response with the expected kinetics was observed in control animals (Fig. 6F). We next infected WT and Sec22b-deficient mice with influenza A virus (Fig. 6G) and observed a profound defect in the frequency and number of PCs in the draining mediastinal lymph nodes (Fig. 6H). Flu-specific antibodies were undetectable in the serum of Sec22b-deficient mice (Fig. 6I). These defects were associated with exacerbated weight loss in Sec22bB-KO mice compared to their WT littermates, suggesting a poorer control of the infection in the absence of Sec22b (Fig. 6J). Altogether, these results establish that Sec22b is essential and play a nonredundant role for the establishment of a potent and efficient humoral immune response after both vaccination and infection.
Fig. 6. Sec22b is indispensable for the generation of a protective humoral immune response. (A) Schematic representation of the SRBC immunization protocol for Sec22bWT and Sec22bB-KO mice. (B) Representative dot plots (Top), frequency (Bottom Left), and absolute number (Bottom Right) of splenic PCs (CD138+B220+/−) determined by flow cytometry 7 d after SRBC immunization. (C) ELISA quantification of IgM (Top) and IgG1 (Bottom) serum titers 7 d after SRBC immunization. A–C, one representative experiment of two is shown. (D) Schematic representation of the NP-KLH immunization/boost protocol. Sec22bWT or Sec22bB-KO mice were immunized, rechallenged 28 d later, and analyzed 7 d later. Sera were collected on days 7, 14, 21, and 7 after boost. (E) Frequency (Left) and absolute number (Right) of splenic PCs (CD138+B220+/−) (Top) and BM PCs (CD138+B220+/−) (Bottom) determined by flow cytometry 7 d after boost with NP-KLH (n = 9 mice from two pooled independent experiments). (F) ELISA quantification of NP-IgM (Top) and NP-IgG1 (Bottom) serum titers on days 7, 14, and 21 after primary immunization and 7 d after boost (n = 5 mice. One experiment representative of two is shown). (G) Schematic representation of the Flu infection protocol for Sec22bWT and Sec22bB-KO mice. Mice were analyzed 14 d after infection with influenza A virus. (H) Frequency (Left) and absolute number (Right) of mediastinal PCs (CD138+B220+/−) determined by flow cytometry 14 d after infection with influenza A virus. (I) Quantification of HAI titers 14 d after flu infection. (J) Weight change of Sec22bWT or Sec22bB-KO mice over 12 d after flu infection. (H and I; n = 5, one representative experiment of two is shown, J; n = 15 from two pooled experiments). For flow cytometry, experiment cells were first gated on their size and structure, then their viability. Dead cells and doublets were excluded. The P-values were determined with the two-tailed Mann–Whitney nonparametric test *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 or with the two-way ANOVA with Sidak correction for multiple comparisons (££P < 0.01, £££P < 0.001). Sec22bWT controls were mb1cre+ and Sec22bfl/fl/Sec22bfl/+ mice.
Discussion
Despite their essential role in health and disease, how PCs maintain a high rate of antibody secretion while persisting on the long term is unclear. Here, we demonstrate that the SNARE Sec22b is essential for PC maintenance. Accordingly, in the absence of Sec22b expression in the B cell lineage, the humoral immune response was severely impaired after both vaccination and infection.
The reduced antibody secretion rate observed could be caused by a defective ER to Golgi transport due to altered formation of the Stx5/Sec22b SNARE complex. Supporting a role for this complex in this process, PC treatment with Retro-2, a drug known to alter Stx5 cellular localization (16), also leads to decreased antibody secretion. However, after Retro-2 treatment but also in the absence of Sec22b, antibody secretion was reduced but not totally abrogated. Like Sec22b, the R-SNARE Ykt6 has been shown to form a trans-SNARE complex with the Qa, Qb, and Qc SNARES Gosr1/2, Stx5, and Bet1 and could partially compensate for Sec22b loss (20, 21). All these SNAREs are strongly expressed in PCs and the four Q-SNAREs are twice more expressed in Sec22bB-KO PCs, thus suggesting that redundant mechanisms may exist to export antibodies at a suboptimal level via the classical constitutive or via unconventional secretion pathways (22, 23).
Importantly, the altered PC maintenance in the absence of Sec22b cannot uniquely be linked to the defective secretory capacity of the cells as several studies report that PC survival and antibody secretion are uncoupled. For example, mice lacking Xbp1 or the ligase Rctb, important for Xbp1 activation, display reduced antibody secretion but normal PC numbers (24–27). Moreover, PC producing pathogenic antibodies often fail to secrete them while the PC compartment is not altered in these mouse models (28). Another example is PCs generated in the LMP2A model that can survive without secreting any antibody reenforcing the independence between antibody secretion and PC survival (29). Additive defects must thus explain the poor persistence of PCs in the absence of Sec22b. We report here that Sec22b deficiency is associated with a profound alteration of the PC transcriptional profile and with deregulation of the UPR as well as morphological alterations of the ER and mitochondria.
In PCs, the Ire1α/Xbp1 axis is normally highly expressed and favors antibody production and folding without causing cell death (2, 30), whereas Atf6 is weakly expressed and the Perk-dependent branch of the UPR is repressed (4, 31). Activation of the Perk/Atf4/Chop pathway has been associated with increased susceptibility to cell death in PCs and is normally suppressed by the Ire1α/Xbp1-dependent Ufbp1 protein (32). In the absence of Sec22b, the upregulation of Perk may contribute to the inhibition of the cell cycle we observed (33) and eventually to PC cell death (34), hence explaining, in part, the poor PC maintenance observed in vitro and in vivo.
An important observation made in the absence of Sec22b was the disturbed ER structure with poorly stacked and hyperdilated cisternae. This could be the consequence of the reduced transport of antibodies from the ER to the Golgi apparatus but could also be caused directly by the absence of Sec22b as was shown in yeast and drosophila (35–37). In yeast, Sec22p interacts with Sey1p to regulate sterol biosynthesis and with atlastins to support ER homotypic fusion (35, 36). In the drosophila, Sec22 was identified as an important regulator of ER structure with dilatation of the ER when Sec22 was mutated or knocked down (37). More recently, Sec22b knockdown in HUVECs was also associated with dilated ER, but the mechanism at play remains unclear (38). Interestingly, the long form of Stx5 was also shown to be important for ER branching via interaction with Climp63 that links the ER membrane to the microtubules (11). Thus, Sec22b could directly control ER morphology in PCs either by interacting with the long form of Stx5 or via another partner still to identify.
Another striking defect observed in Sec22b-deficient PCs was the accumulation of hyperfused mitochondria and the reduction of ER/mitochondria contact sites that regulate mitochondrial fission (19). Sec22b, through its longin domain, has already been shown to regulate ER/plasma membrane contact sites and consequently plasma membrane expansion in neurites (12, 39). Whether Sec22b contributes to ER/mitochondria contact sites directly or indirectly remains to be established. In addition, we demonstrated that hyperfused mitochondria were associated with poor PC survival, hence suggesting that altered mitochondrial dynamics contribute to the decreased PC number in the absence of Sec22b perhaps through metabolic rewiring.
To conclude, we demonstrated that Sec22b is a crucial regulator of PC survival. Altogether, our results demonstrate that Sec22b-mediated regulation of organelle dynamics is indispensable for PC biology and for the establishment of a protective humoral immune response (SI Appendix, Fig. S8). Discovery of pharmacological regulators of SNAREs is an active field of research notably for neurological disorders and toxin antagonisms (40–42). Specifically targeting this molecule in a tissue and time-controlled manner may thus constitute an attractive avenue for PC-mediated diseases.
Methods
Mouse Model, Immunization, and Infection.
The Sec22bfl/fl mice (C57Bl/6J background) were obtained from Sebastian Amigorena and crossed with the mb1-Cre mice (18) (C57Bl/6J background) purchased from The Jackson Laboratory. All experiments were conducted in compliance with the European Union guide for the care and use of laboratory animals and have been reviewed and approved by appropriate institutional review committees (C2EA-26, Animal Care and Use Committee, Villejuif, France, and Comité d’éthique Paris-Nord N°121, Paris, France). In all experiments, Sec22bWT controls were mb1cre+ or Sec22bfl/fl/Sec22bfl/+ mice in equal proportions. Immunizations/infections were performed ip with 100 µg of NP-KLH (Biosearch Technologies) adjuvanted with alum (Imject Alum, Thermo Fisher Scientific) or with 200 µL of SRBCs (Eurobio) or intranasally with 104 plaque-forming units of influenza A/HK/x31 virus (H3N2) under inhalation anesthesia with isoflurane. Influenza infections were performed on chimeric mice. CD45.1 recipient mice were lethally irradiated with 11gy before reconstitution with BM cells from Sec22bB-KO or Sec22bB-WT mice. Chimera were infected 8 to 12 wk later once reconstitution was complete.
Proteomic Analysis.
Splenic B cells were purified using a mouse B cell isolation kit following the manufacturer’s instructions (Miltenyi Biotec) and cultured for 3 d with 10 μg/mL LPS (Sigma). The differentiated cells were enriched using anti-CD138 antibodies coupled to Phycoerythrin (PE) and anti-PE magnetic beads (Miltenyi Biotec). Purified cells were lysed into Filter Aided Sample Preparation (FASP) buffer. The protein concentration of the samples was normalized before digestion using the FASP method (43) using trypsin, and 100 μg was fractionated into 36 fractions by high-pH reverse-phase chromatography, pooled into 12 fractions, and peptides were analyzed using Liquid Chromatography coupled to tandem Mass Spectrometry (LC-MS/MS) on an Orbitrap Elite (Thermo Fisher Scientific) hybrid mass spectrometer equipped with a nanospray source, coupled with an Ultimate RSLCnano LC System (Dionex). Mass Spectrometry (MS) data were analyzed using MaxQuant (44) version 1.5.8.3. Data were searched against mouse UniProt sequence databases. Full technical details can be found in SI Appendix.
Cell Preparation and Flow Cytometry.
Spleen and BM cells were isolated as previously described (45, 46). Briefly, single-cell suspensions were stained with appropriate antibodies (SI Appendix, Table S3) in Phosphate Buffered Saline (PBS) supplemented with 2% Bovine Serum Albumin (BSA) and 2 mM ethylene-diamine-tetraacetic acid (EDTA) for cell surface staining. Staining with ER tracker (Invitrogen), MitoTracker Green (Invitrogen), and MitoTracker Orange CMTMRos (Invitrogen) were performed as recommended by the supplier. Intracellular staining was performed using the FoxP3/transcription factor staining buffer set (eBioscience) according to the provider recommendation. Flow cytometry analyses were performed on a BD LSR Fortessa cytometer, and cell-sorting experiments for RNAseq and Biomark analysis were performed using a BD FACS AriaIII cell sorter. Data were analyzed with the FlowJo software (TreeStar).
In Vitro Cell Differentiation and Infection of Primary Cells.
A total of 1 × 106 splenocytes were stimulated with 5 µg/mL of LPS (InvivoGen) for 2 or 4 d in complete culture medium composed of Roswell Park Memory Institute medium (RPMI) supplemented with 10% fetal calf serum (Sigma), 0.05 mM 2-mercaptoethanol, 100 U/mL penicillin–streptomycin, 1 mM sodium pyruvate, and 0.1 mM nonessential amino acids (Gibco). Where indicated, cells were treated with 20 μM M1 and 10 μM Mdivi (Sigma) for 2 d from day 2 to day 4.
Stx5a-specific shRNAs were designed thanks to the RNAi consortium (https://portals.broadinstitute.org/gpp/public) and cloned in the pLKO.3G (Addgene #14748) vectors. Lentiviral particles were produced in the Human Embryonic Kidney 293T (HEK293T) cell line with the psPAX2 (Addgene #12260) and pMD2.G (Addgene #12259) vectors. For primary cell transduction, splenocytes were put in culture in complete culture medium in the presence of 80 ng/mL CD40L (Thermo Scientific) and 1 U/mL interleukin-4 (Miltenyi) for 24 h to promote B cell entry into cycle. The cells were then washed and transduced with lentiviral particles together with polybrene. After 24 h, cells were washed and differentiated into PCs by addition of LPS as indicated above.
Western Blot.
Cells were resuspended in Radio Immunoprecipitation Assay buffer (RIPA) lysis buffer supplemented with Protease and Phosphatase Inhibitor (Thermo Fisher Scientific). Total proteins were quantified with Bradford buffer (Thermo Fisher Scientific), and 15 µg of proteins were separated on a NuPAGE™ 4 to 12% Bis-Tris Gel (Invitrogen) and transferred to a Poly VinyliDene Fluoride membrane. Primary antibodies (SI Appendix, Table S3) or β-actin (Cell Signaling) were incubated overnight at 4 °C. Secondary antibodies (SI Appendix, Table S3), specific for the primary antibody species, conjugated to horseradish peroxidase (HRP) were incubated 2 h at room temperature (RT) and detected using Pierce ECL (Thermo Fisher Scientific), and the signal was quantified by the ChemiDoc™ Touch Gel Imaging System (Bio-Rad).
Confocal Microscopy.
Cells were loaded on poly-Lysine (Sigma)-coated slides and fixed in PBS/4% PFA prior to analysis. Cells were permeabilized with PBS/0.3% Triton, washed in PBS and incubated with primary antibodies coupled or not overnight at 4 °C and then with secondary antibodies 1 h at RT, when necessary (SI Appendix, Table S3). Cells were incubated with Hoechst (Thermo Fisher Scientific) for 1 h at RT. Images were acquired using an LSM800 confocal microscope equipped with the Airyscan system (Zeiss) using a 63× objective and z-stacks. Images were analyzed with Fiji and Zen.
Electron Microscopy.
Cells were prefixed with 2% glutaraldehyde/2% PFA in Sorensen phosphate buffer 0.1 M pH 7.2 for 15 min before being fixed with 2.5% glutaraldehyde/2% PFA in Sorensen phosphate buffer 0.1 M pH 7.2 for 2 h at RT. Cells were then washed in Sorensen phosphate buffer, resuspended in PFA 1%, and sent to the METi platform (Toulouse). Cells were then postfixed with 1% OsO4 in Cacodylate buffer (0.1 M, pH 7.2, Electron Microcopy Sciences), rinsed in the same buffer and pelleted, concentrated in agarose, and treated for 1 h with 2% aqueous uranyl acetate. The samples were then dehydrated in a graded ethanol series and embedded in Epon. After 48 h of polymerization at 60 °C, ultrathin sections (80-nm thick) were mounted on 200-mesh Formvar–carbon–coated copper grids. Finally, sections were stained with UranyLess and lead citrate. Grids were examined with a transmission electron microscope (Jeol JEM-1400, JEOL, Inc.) at 80 kV. Images were acquired using a digital camera (Gatan Orius, Gatan, Inc.).
Enzyme Linked Immunosorbent Assay (ELISA).
ELISA assays were performed as previously described (47) for the determination of the different isotype of Ig or NP-specific antibody titers in sera or culture supernatants. Briefly, plates were precoated with goat anti-mouse IgM (Southern Biotech) or with NP(15)-BSA (Biosearch Technologies). After a step of saturation in PBS/2% BSA, diluted sera/supernatant was added before incubation with HRP-conjugated secondary antibody. Enzymatic revelation was performed with the TetraMethylBenzidine substrate reagent set (BD OptEIA). All Antibodies used are indicated in SI Appendix, Table S3.
PLA.
Anti-VDAC1 (20B12AF2) (Abcam) and Anti-IP3R1 (E-8) (Santa Cruz Biotechnology) antibodies were used to generate PLA probes using Duolink In Situ Probemaker (Sigma). B cells were stimulated with LPS (InvivoGen) (5 µg/mL) for 2 d. Stimulated cells were settled on Polytetrafluoroethylene diagnostic slides (Thermo Fisher Scientific) for 30 min at 37 °C, fixed with 4% paraformaldehyde (Thermo Fisher Scientific) in PBS for 15 min, permeabilized with 0.5% saponin (quillaja bark, Sigma) in PBS for 30 min, and blocked with Duolink Blocking Solution (Sigma) for 1 h. Cells were incubated with Anti-VDAC1 (1/100) and Anti-IP3R1 (1/100) PLA probes overnight at 4 °C. PLA was performed according to the manufacturer’s instructions (Duolink, Sigma). Later, cells were further incubated with anti-mouse IgM-Fluorescein Isothiocyanate antibody (1/100) (Jackson Immuno Research) for 30 min at 4 °C. Resulting samples were mounted in Fluoromount-G with DAPI (Invitrogen). Fluorescence was detected by using an inverted confocal microscope, Leica TCS SP8 (Leica). PCs were detected based on their high IgM expression. Quantification of detected signal per nucleus in these cells was performed manually.
Hemagglutination Inhibition (HAI) Assay.
Antibody titers before and after vaccination were determined using the HAI assay using the standard World Health Organization protocol, as previously described (48). Sera were treated overnight with receptor-destroying enzyme (Denka Seiken Co.) and were subsequently tested by standard methods using 4 HA units of virus and a 0.5% suspension of turkey red blood cells. HAI titers were recorded as the reciprocal of the highest dilution of the serum that completely inhibited agglutination of erythrocytes by 4 HA units of the virus.
Biomark-Based Transcriptomic Analysis.
Multiplex qPCR analysis was performed using the Biomark system (Fluidigm). Cells were sorted at 100 cells/well directly into PCR tubes containing 5 µL of reverse transcription/preamplification mix as previously described (45). Briefly, the mix contained 2X Reaction mix and SuperscriptIII (CellDirect One-Step qRT–PCR kit, Invitrogen) and 0,2X Taqman assay (Life Technologies- Thermo Fisher Scientific) (SI Appendix, Table S4). Targeted complementary DNA (cDNA) preamplification was performed for 19 cycles before processing with the Dynamic Array protocol according to the manufacturer’s instructions (Fluidigm). Wells positive for Gapdh, Actb, and control gene (Prdm1, Irf4, and Xpb1 for PCs and Pax5 for B cells) expression and negative for expression of a control gene (Cd3e) were considered for further analysis. Mean expression of Actb and Gapdh was used for normalization. Unsupervised clustering (Spearman rank correlation test) and heatmap representation were generated with (http://www.heatmapper.ca) using the Z scores.
RNAseq and RNAseq-Based Transcriptomic Analysis.
For RNAseq, in vitro generated PCs were sorted on day 2, and RNA were extracted with “RNeasy plus micro kit” (Qiagen) as recommended. RNAs were proceeded by the IntegraGen company for sequencing. Libraries were prepared with the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina protocol according to supplier recommendations. Briefly, the key stages of this protocol were successively the purification of PolyA-containing mRNA molecules using poly-T oligo–attached magnetic beads from 100 ng total RNA (with the Magnetic mRNA Isolation Kit from NEB), a fragmentation using divalent cations under elevated temperature to obtain approximately 300-bp pieces, double-strand cDNA synthesis, and finally Illumina adapters ligation and cDNA library amplification by PCR for sequencing. Sequencing was then carried out on Paired End 100-b reads of Illumina NovaSeq. Image analysis and base calling were performed using Illumina Real Time Analysis (3.4.4) with default parameters.
For analysis, Spatio temporal array (STAR) was used to obtain the number of reads associated with each gene in the Gencode vM24 annotation (restricted to protein-coding genes, antisense, and lincRNAs). Raw counts for each sample were imported into R statistical software. Extracted count matrix was normalized for library size and coding length of genes to compute Fragments Per Kilobase of transcript per Million mapped read (FPKM) expression levels. The Bioconductor edgeR package was used to import raw counts into R statistical software and compute normalized log2 counts per millions of mapped reads using the weighted trimmed mean of M-values as the normalization procedure. The normalized expression matrix from the 500 most variant genes (based on standard deviation) was used to classify the samples according to their gene expression patterns using principal component analysis (PCA), hierarchical clustering, and consensus clustering. PCA was performed by the FactoMineR::PCA function with “ncp = 10, scale.unit = FALSE” parameters. Hierarchical clustering was performed by the stats::hclust function (with euclidean distance and ward.D method). Differential expression analysis was performed using the Bioconductor limma package and the voom transformation. To improve the statistical power of the analysis, only genes expressed in at least one sample (FPKM ≥ 0.1) were considered. A q value threshold of ≤0.05 and a minimum fold change of 1.2 were used to define differentially expressed genes. A gene list from the differential analysis was ordered by decreasing log2 fold change. GSEA was performed by the clusterProfiler::GSEA function using the fgsea algorithm. Gene sets from MSigDB v7.2 database were selected among the C2_curated and Hallmark classes, keeping only gene sets defined by 10 to 500 genes.
DropMap-Based Analysis of IgM Secretion.
DropMap experiment was performed as described (1), modified to detect IgM secretion from a single cell. Cells from in vitro cultures on day 2 or day 4 after LPS stimulation were centrifuged and resuspended in DropMap medium (RPMI without phenol red, supplemented with 0.1% Pluronic F68, 25 mM Hydroxyethyl Piperazine ethane Sulfonic acid buffer (HEPES) pH 7.4, 10% KO serum replacement (all Thermo Fisher Scientific), and 0.5% recombinant human serum albumin (Sigma Aldrich). Microfluidic droplets were generated as water-in-oil emulsions using a coflow of aqueous phases, one containing bioassay reagents (bioassay phase) and the other one containing in vitro generated PCs (cell phase). Bioassay phase: Streptavidin-coated paramagnetic beads (300 nm, Ademtech) were washed with PBS using a magnet and then resuspended in a 1 μM solution of CaptureSelect biotin anti-mouse Igκ conjugate (Thermo Fisher Scientific) and incubated at RT for 20 min. The beads were washed with PBS and resuspended in 5% pluronic F127 (Thermo Fisher Scientific) and incubated for 20 min at RT. Following a PBS wash, the beads were resuspended in DropMap buffer and incubated at RT for 20 min. Beads were washed for the last time with PBS and resuspended in a solution of 150 nM goat anti-mouse IgM (μ chain specific) F(ab’)2 (Alexa647, Jackson ImmunoResearch) in DropMap buffer. Cell phase: To produce single-cell droplets, cell concentration was adjusted to achieve 0.3 cells per droplet in DropMap buffer. For calibration purposes with cells, purified monoclonal IgM was diluted in DropMap buffer.
Droplets were produced by hydrodynamic flow-focusing on a custom-made microfluidic device as in ref. 1, by coflowing the two aqueous phases. Immediately after generation, droplets were injected into the two dimensional (2D) observation chamber (1) until filled, which was then closed for image acquisition. The droplet array in the 2D chamber was imaged using a Nikon Ti-2 Eclipse inverted microscope with a motorized stage and excitation light source (Lumencor Spectra X). Fluorescence was captured using a 10× objective and a Cy5 filter, and images were recorded by a digital Complementary Metal Oxide Semiconductor camera (ORCA-flash 4.0, Hamamatsu). For each time point, an array of 10 × 10 images was acquired, and a total of six acquisitions were recorded through 37.5 min. The images were analyzed with a custom Matlab script as described (1). In brief, the ratio of fluorescent signal between the beadline and the background was estimated for every droplet at every time point. The fluorescence ratio was then used to estimate the concentration of IgM in the droplets by using a calibration curve, which was generated by measuring the fluorescent ratio from different concentrations of purified monoclonal IgM antibody.
Statistical Analysis.
The P-values were determined as indicated in the figure legends using Prism GraphPad software with the two-tailed unpaired Mann–Whitney nonparametric test for the WT vs. Sec22bB-KO comparison (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001, ns, nonsignificant P-value) or with the two-way ANOVA with Sidak correction for multiple comparisons (££P < 0.01, £££P < 0.001).
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank Dr. N. Setterblad, C. Doliger, and S. Duchez (Plateforme technologique IRSL, Paris, France), Dr. V. Parietti (Mouse facility IRSL, Paris, France), S. Guibert (IntegraGen), M. Khamyath, and V. Gourhand for their technical assistance. We are grateful to Pr. A. Toubert for his comments on this manuscript. The study was supported by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LabEx LERMIT) (M.E. and K.B.); an ANR JCJC grant (ANR-19-CE15-0019-01), an ANR @RAction grant (ANR-14-ACHN-0008), a “Fondation ARC pour la recherche sur le cancer” grant (P JA20181208173), and a grant from IdEx Université Paris-Cité (ANR-18-IDEX-0001) to M.E.; and an ANR PRC grant (ANR-17-CE14-0019) and an INCa grant (PRT-K 2017) to K.B. P.B. acknowledges funding from the French National Research Agency grant ANR-18-CE15-0001 project Autoimmuni-B, by the Institut Carnot Pasteur Microbes et Santé grant ANR-11-CARN-0017-01, the Institut Pasteur, and the Institut National de la Santé et de la Recherche Médicale (INSERM). M.A.L. is supported by Biotechnology and Biological Sciences Research Council (BBS/E/B/000C0427, BBS/E/B/000C0428, and the Campus Capability Core Grant to the Babraham Institute). A.A.P. and M.O.C. were supported by a grant from the Biotechnology and Biological Sciences Research Council (BB/L022389/1). D.L.H. is supported by a National Health and Medical Research Council Australia Early-Career Fellowship (APP1139911). N.A. was supported by a PhD fellowship from the French Ministry for education and by a fourth year PhD fellowship from the “Fondation ARC pour la recherche sur le cancer.” P.C.-H. was supported partly by a stipend from the Pasteur-Paris University (PPU) International PhD program, and by a fellowship from the French Fondation pour la Recherche Médicale (FRM). K.G.C.S. was supported by the Wellcome Trust (Programme Grant Number 083650/Z/07/Z). J.J’s research is supported by the German Research Foundation project number: 419193696 and through the CRC1335. H.T. is supported through the graduate school of the Max Planck Institute for Immunobiology and Epigenetics (IMPRS-IE) and through the CRC1335. The "EMiLy" U1160 INSERM unit is a member of the OPALE Carnot institute, The Organization for Partnerships in Leukemia (Institut Carnot OPALE, Institut de Recherche Saint-Louis, Hôpital Saint-Louis, Paris, France. Web: www.opale.org. Email: [email protected]).
Author contributions
A.B., F.B., K.G.C.S., K.B., A.A.P., and M.E. designed research; A.B., L.G., S.M.G., E.R., P.C.-H., N.F.R., M.O.C., H.T., D.L.H., N.A., S.B., V.S., and M.A.L. performed research; A.A., D.G., J.B., J.J., P.B., S.A., and A.A.P. contributed new reagents/analytic tools; A.B., E.R., D.S., N.F.R., M.O.C., H.T., M.A.L., A.A.P., and M.E. analyzed data; and A.B. and M.E. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
RNAseq data have been deposited in GEO (GSE188452) (49).
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926244.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595702
202214912
10.1073/pnas.2214912120
videoVideoresearch-articleResearch Articlemed-sciMedical Sciences422
Biological Sciences
Medical Sciences
A double-switch pHLIP system enables selective enrichment of circulating tumor microenvironment-derived extracellular vesicles
Zong Zhiyou a https://orcid.org/0000-0002-4453-7331
Liu Xinzhuo a
Ye Zhuo a https://orcid.org/0000-0002-8328-9617
Liu Dingbin [email protected]
a 1 https://orcid.org/0000-0003-4153-9822
aState Key Laboratory of Medicinal Chemical Biology, Research Center for Analytical Sciences, and Tianjin Key Laboratory of Molecular Recognition and Biosensing, College of Chemistry, Nankai University, Tianjin 300071, China
1To whom correspondence may be addressed. Email: [email protected].
Edited by Xandra O. Breakefield, Massachusetts General Hospital, Charlestown, MA; received August 31, 2022; accepted December 8, 2022 by Editorial Board Member Rakesh K. Jain
3 1 2023
10 1 2023
3 7 2023
120 2 e221491212031 8 2022
08 12 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Circulating tumor environment-derived extracellular vesicles (cTME-EVs) are a small portion of EVs in blood co-existing with those derived from other tissues; the accurate identification of cTME-EVs could facilitate both fundamental research and clinical liquid biopsy of cancer. In this work, we report the double-switch pH-low insertion peptide (D-S pHLIP) system, which allows selective enrichment of cTME-EVs from blood serum for comprehensive transcriptomic analysis. Plentiful differentially expressed genes and hub genes were identified, demonstrating the high performance of this technology. This work contributes to the design of pHLIP variants for specific functions and the discovery of tumor diagnostic biomarkers and therapeutic targets.
Circulating tumor microenvironment-derived extracellular vesicles (cTME-EVs) are gaining considerable traction in cancer research and liquid biopsy. However, the study of cTME-EVs is largely limited by the dearth of a general isolation technique to selectively enrich cTME-EVs from biological fluids for downstream analysis. In this work, we broke through this dilemma by presenting a double-switch pH-low insertion peptide (D-S pHLIP) system to exclusively harvest cTME-EVs from the blood serum of tumor mouse models. This D-S pHLIP system consists of a highly sensitive pH-driven conformational switch (pKa ≈ 6.8) that allows specific installation of D-S pHLIP on the EV membranes in TME (pH 6.5 to 6.8) and a unique hook-like switch to “lock” the peptide securely on the cTME-EVs during the systemic circulation. The D-S pHLIP-anchored cTME-EVs were magnetically enriched and then analyzed with high-resolution messenger RNA sequencing, by which more than 18 times the number of TME-related differentially expressed genes and 10 times the number of hub genes were identified, compared with those achieved by the gold-standard ultracentrifugation. This work could revolutionize basic TME research as well as clinical liquid biopsy for cancer.
pHLIP
conformational switch
computational design
extracellular vesicle
tumor microenvironment
MOST | National Key Research and Development Program of China (NKPs) 501100012166 2019YFA0210100 Dingbin Liu National Natural Science Foundation of China (NSFC) 501100001809 21775075 Zhiyou ZongDingbin Liu National Natural Science Foundation of China (NSFC) 501100001809 21977053 Zhiyou ZongDingbin Liu National Natural Science Foundation of China (NSFC) 501100001809 22005157 Zhiyou ZongDingbin Liu China Postdoctoral Science Foundation 501100002858 2021M701788 Zhiyou Zong
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pmcExtracellular vesicles (EVs), which carry rich molecular cargos (e.g., proteins, lipids, metabolites, and nucleic acids) of their parental cells, are regarded as the mediators of intercellular communication (1). In tumor microenvironment (TME), bidirectional communication between tumor cells and diverse tumor-associated cells (TACs) via EV trafficking plays a crucial role in the sustained growth, invasion, and metastasis of cancer (2). Therefore, circulating EVs derived from TME (termed cTME-EVs, including both tumor cell- and TAC-derived EVs) have emerged as ideal non-invasive biomarkers, thus prompting the development of basic TME-associated research and clinical diagnosis. However, cTME-EVs have not been fully exploited due to the lack of efficient means to selectively enrich them from bodily fluids for downstream analysis.
Currently available approaches for EV enrichment, such as ultracentrifugation (UC) (3), polymer-based precipitation (4), filtration (5), and size exclusion chromatography (6), are unable to discriminate cTME-EVs from the multitudinous co-existing EV counterparts derived from other tissues, resulting in the cover-up of cancer signatures. At present, cancer-specific molecules such as aptamers (7) and antibodies (8, 9) have been introduced for the immune-enrichment of tumor cell-derived EVs, such as the recently reported lipid patch microarrays (10) and lipid-protein capture systems on herringbone microfluidic chips (11). However, these biomarker-dependent strategies are largely limited by the huge heterogeneity of biomarker expression on the EV surfaces (12, 13). Epithelial cell adhesion molecule (EpCAM), for example, is the most common biomarker used for magnetic enrichment of tumor cell-derived EVs in the commercial EpCAM Exo-Flow Capture Kit; this biomarker is only expressed in less than 50% of these EVs (14). In addition, there are a dozen kinds of TACs (typically immune cells, endothelial cells, and fibroblasts) alongside tumor cells in TME, which also constitute cancer signatures (15, 16). However, TAC-derived EVs cannot be enriched by the cancer biomarker-based kits, resulting in the loss of vast indispensable molecular information reflecting TME. It is therefore highly desirable to develop a general platform for the selective and efficient enrichment of cTME-EVs from biological fluids irrespective of cancer types.
TME is characteristic of weak acidity, which is commonly recognized as one of the significant cancer signatures (17). This property has shed light on the development of a variety of diagnostic and therapeutic strategies for cancer (18). The pH-low insertion peptide (pHLIP) (19) is wildly studied as a TME-targeting agent that can fold by a pH-driven switch upon acidification to insert into membrane-like structures (20). However, wild-type (WT) pHLIP cannot be used as a handle for selective harvesting of cTME-EVs for the following two reasons. First, WT pHLIP (pKa ≈ 6.2) (21) cannot respond optimally to TME (pH 6.5 to 6.8) (22), which means low TME-targeting ability. Second, and more importantly, WT pHLIP will disassociate from the cTME-EV membranes during systemic circulation (pH 7.2 to 7.4), resulting from the pH-driven conformational conversion of the peptide back to its initial unstructured state. This reversible conformational change would be the essential cause, which makes WT pHLIP impossible to capture cTME-EVs in blood samples.
In this work, we present a general platform capable of selectively enriching cTME-EVs from the blood plasma of diverse tumor mouse models. We performed a series of computational designs on pHLIP to optimize its inherent pH-driven switch to efficiently target TME and create a pH-driven hook-like switch to prevent the dissociation of pHLIP from cTME-EVs in systemic circulation. We investigated the performance of this double-switch pHLIP (D-S pHLIP) system by molecular dynamic (MD) simulations, free-energy calculations, in vitro and in vivo experiments, and performed high-resolution transcriptomic sequencing for the selectively enriched cTME-EVs. This D-S pHLIP-based platform shows excellent potential in excavating comprehensive and precise cancer-related information.
Results
Design Strategy for D-S pHLIP.
As depicted in Fig. 1 A and B, D14 and D25 are the two key residues that control the folding of pHLIP (23). The pK (24) values of the two residues were, respectively, calculated to be 3.873 and 4.261 by H++ software. Compared with D14, D25 possesses a higher pK value, indicating that D25 obtains H proton preferably, thereby governing the folding response of the peptide (i.e., pKa). Additionally, the relevant pH sensitivity can be mediated by D14. H++ software is regarded as an accurate and advisable tool to predict the effect of a point mutation on the pK of a nearby site. Therefore, to improve the pKa of pHLIP from the wide-type 6.2 to ideally 6.8 for better targeting TME, a feasible strategy is to increase the pK value of D25 from approximately 4.2 to 4.8. Based on this assumption, we screened the residues that are closely associated with the pK values of D14 (i region, Fig. 1C) and D25 (ii region, Fig. 1D).
Fig. 1. D-S pHLIP design. (A) The unfolding state of WT pHLIP in neutral environment. Two critical residues, D14 and D25 with their calculated pK values. (B) The folding state of WT pHLIP in acidic environment. Design regions for switch I (i and ii) and switch II (iii). (C and D) Residues that influence the pK values of D14 and D25. (E) A double mutation, L28H/D31A for the hook-like switch II. (F and G) Structural snapshots of the two D-S variants, i.e., L28H/D31A/L21F/L22G (D-S v1) and L28H/D31A/L21F/L22G/T18H (D-S v2) generated from the trajectories of their 100 ns MD simulations. Electrostatic and hydrogen bonding interactions are colored by orange and green dotted lines, respectively. (H and I) Calculations of free-energy and interaction-energy for WT pHLIP and two D-S variants. d denotes the center-of-mass distance between the two residues of D33 and E34 in the C-terminal and R11 nearby the N-terminal. In (A–G), nonpolar hydrogens are hidden. N and C in (A) denote the N- and C-terminal of the peptide, respectively, wherein the C-terminal will insert into the phospholipid bilayer structure after folding.
With the aid of H++ software, we designed more than 200 variants by virtual saturation mutation on these sites and further built the relevant three-dimensional structures to calculate the mutation effect on the pK increment of D25 and D14 (SI Appendix, Tables S1 and S2). The optimal pK value of D14 achieves 4.752 (about 1 pK unit improvement) by T18Q mutation, while that of D25 is still below 4.7. Multiple mutations were, therefore, employed to further increase the pK value of D25 (SI Appendix, Table S3). As summarized in SI Appendix, Table S4, the six variants (L21W/L22W, L21W/L26N, L21F/L22W/L26N, L21Y/L22W/L26N, L21F/L22G, and L21W/L22T/L26N) were selected as the promising residues for D25, in which the pK values range from 4.8 to 5.2.
To avoid the dissociation of pHLIP from the captured EVs, we proposed a hook-like switch II, where L28, at the bottom of the alpha-helix nearby the C-terminal, was mutated to His (Fig. 1E). Given the calculated pK value of L28H (6.563, see SI Appendix, Table S5), the nitrogen (Nε) in L28H is anticipated to obtain a proton in or below neutral environments. L28H will, therefore, become a positively-charged residue to attract the negatively-charged residues D31, D33, and E34 in the C-terminal. To form an ideal hook-like structure, D31 was mutated to Ala to facilitate the interactions between L28H and D33/E34. As provided in SI Appendix, Table S5, we integrated the mutation strategy of the hook-like switch II with the promising variants of switch I and calculated the pK values of ionizable residues in the peptides. We finally selected four D-S variants, i.e., L28H/D31A/L21F/L22G (named D-S v1), L28H/D31A/L21F/L22G/T18H (named D-S v2), L28H/D31A/L21F/L22G/T18F (named D-S v3), and L28H/D31A/L21F/L22G/T18Q (named D-S v4), and further investigated the performance of the hook-like switch II in each assembly by MD simulations. In D-S v1 and D-S v2 assemblies, we found that the two negatively-charged residues, D33 and E34, were attracted by L28H via electrostatic interactions at the outset of the simulations, causing the bending of the loop in the C-terminal into a small “hook” (see Movies S1 for D-S v1 and S2 for D-S v2). Interestingly, D33 and E34 further interacted with the positively-charged residue, R11, which was unexpected but more desirable. As depicted in Fig. 1 F and G, the distinctive hook-like structures were formed in these two variants. By contrast, no hook-like structures were formed in the D-S v3 and D-S v4 assemblies.
We next performed free-energy calculations to quantify the barriers against forming hook-like structures in these peptides. There are approximately 6 kcal/mol for WT pHLIP, no more than 1 kcal/mol for the D-S v1, and no free-energy barrier for the D-S v2 to change their “upright” conformations into the hook-like ones (Fig. 1H). These results indicate that it is difficult for WT pHLIP to form a hook-like structure spontaneously, while it is easy for the two variants. Additionally, we calculated the interaction energies between the interactional charged residues to quantify the intensity of the “hooks” in the two variants (Fig. 1I). Given the intense interaction energies, we reasoned that the hook-like structures are very steady. Furthermore, the interaction energy of D-S v2 is much more potent than that of D-S v1, owing to the additional T18H mutation.
Fluorescence and Spectral Studies.
To investigate the actual performance of the D-S variants, we examined the insertion behaviors of these peptides using 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) model membrane by recording the Trp fluorescence (W9 and W15 in pHLIP, excitation at 295 nm). In a neutral environment, the unstructured pHLIP could suspend in solutions or interact with the membrane surface (Fig. 2A). When the pH condition decreases from neutral to acidic, WT pHLIP will fold at its folding-response value and start to insert into the POPC membrane (Fig. 2B), leading to the enhancement of fluorescence intensity. Furthermore, as the acidity rises, the Trp residues will move into the bilayer’s hydrophobic core (Fig. 2C). As a result, vigorous fluorescence intensity and clear blue shift occur owing to the minimal water quenching of Trp emission in the hydrophobic region (23).
Fig. 2. The Trp fluorescence and corresponding CD spectra of WT pHLIP, D-S v1, and D-S v2 during membrane insertion. (A–C) Schematic diagram of the insertion behaviors. W9 and W15 in WT pHLIP are shown in sticks. The C atoms are colored in purple, and all the hydrogen atoms are hidden. Trp fluorescence and corresponding CD results for (D and G) WT pHLIP, (E and H) D-S v1, and (F and I) D-S v2, respectively. WT pHLIP and D-S v1 start to fold and insert into POPC at pH 6.2 and 6.8, respectively. The fluorescence intensity and CD signals in the D-S v2 group did not change under different pH conditions.
As illustrated in Fig. 2D, with the decrease of pH values, the Trp fluorescence intensity of WT pHLIP enhances gradually and reaches the maximum at pH 5.0, along with a clear blue-shift of emission (9 nm). The pKa value of WT pHLIP is calculated to be approximately 6.2 by using the Henderson–Hasselbalch equation (25) via plotting the shift of the maximum emission wavelength versus pH values (SI Appendix, Fig. S1A), which is in accordance with the value reported in previous works (21, 25). When the pH values are raised from 5.0 to 7.4, the fluorescence intensity decreases significantly (purple dotted line), implying that the WT pHLIP moves from the hydrophobic layer back to the membrane surface. This reversibility has also been announced by previous works (21, 23). Additionally, the relevant circular dichroism (CD) spectra verify that WT pHLIP folds at pH 6.2 and unfolds at pH 7.4 and pH 5.0→7.4 (Fig. 2G).
As depicted in Fig. 2E, the fluorescence intensity of D-S v1 starts to elevate visibly by pH 6.8 treatment and reaches the maximum at pH 6.5 to 5.0. However, compared with WT pHLIP, the overall fluorescence intensity of D-S v1 is low, and the degree of blue shift is small (2 nm). As discussed above, both fluorescence intensity and blue shift are positively associated with the depth of peptide insertion into the membrane. Therefore, we analyzed the hydrophobic property of the peptides by ProtScale. The results indicate that the hydrophobicity of D-S v1 is a little lower than that of WT pHLIP (SI Appendix, Fig. S2), which may weaken the interaction degree of the peptide with the membrane and impair the depth of insertion. Furthermore, the hook-like switch II will cause the C-terminal of D-S v1 to fold back toward the N-terminal instead of inserting into the membrane deeper (Fig. 1F), which could be the fundamental reason why the overall fluorescence intensity of D-S v1 is lower than that of WT pHLIP. The slight blue shift makes it impossible to calculate the pKa value for D-S v1 using the current Henderson–Hasselbalch equation. Therefore, we plotted the fluorescence changes of the maximal intensity versus pH, by which the pKa value of D-S v1 was determined to be approximately 6.8 (SI Appendix, Fig. S1B), which agrees well with the pK calculation result. This result was further confirmed by the CD experiment (Fig. 2H).
It should be noted that the fluorescence intensity of D-S v1 at pH 5.0→7.4 is as high as that at pH 5.0 (Fig. 2E, purple dotted line), and this peptide remained folding at the condition of pH 5.0→7.4 (Fig. 2H). These results imply that once D-S v1 has inserted in the membranes at acidic conditions, it can be locked firmly, even when the pH is switched back to neutral. This irreversible insertion, contrary to WT pHLIP, is ascribed to the expected performance of the hook-like switch II. In view of the results of Fig. 1 F and I, we conjecture that the strong electrostatic interactions between the positively-charged R11/L28H and the negatively-charged D33/E34 enable the peptide to be “locked” securely on the membrane. As shown in Fig. 2 F and I, D-S v2 has lost the ability of pH-driven conformational change. According to the results of Fig. 1I, we infer that the electrostatic attraction between the charged residues in D-S v2 may be too strong to separate, thereby hindering the folding process. The fluorescence and CD experiments demonstrated that only D-S v1 meets the requirements of high-sensitivity TME targeting and irreversible installation on cTME-EVs, i.e., i) folding at pH 6.8; and ii) “locking” firmly in the membrane after pH returning to neutral.
D-S pHLIP Insertion in the Membranes of Cells and EVs.
We then investigated the insertion behaviors of WT pHLIP and D-S v1 on human lung tumor cells (A549) and related EVs (the schematic diagram is shown at the top of Fig. 3). The peptides were conjugated with biotin tags (peptides@biotin), which can be recognized by the fluorescein isothiocyanate (FITC)-modified streptavidin (FITC@SA) for membrane labeling. As depicted in Fig. 3 A–D, and 3G, there are no FITC signals (green fluorescence) on the cell surfaces, indicating that WT pHLIP and D-S v1 were unable to insert into tumor cells in a neutral environment (pH 7.4). When the pH conditions changed to acidic, the FITC signals were detected in both groups (Fig. 3 B, E–H). These results suggest that the peptides have folded at their own folding-response values and inserted into the cell membranes. When the pH condition switched back to neutral again (7.4→6.2→7.4), no FITC signals were detected on the cell membranes in the WT pHLIP group (Fig. 3 C and F). However, strong green fluorescence was detected in the D-S v1 group with pH treatment of 7.4→6.8→7.4 (Fig. 3I), indicating that D-S v1 was retained on the cell membranes with the assistance of the hook-like switch II.
Fig. 3. Insertion states of WT pHLIP and D-S v1 in the membranes of tumor cells and corresponding EVs under different pH treatments. (A–C) Schematic diagram of the interactions between the pHLIP peptides and the biological membranes under different pH treatments. The molecular structure of WT pHLIP unfolds at 7.4, folds at pH 7.4→6.2 to insert into membranes, and move out under the treatment of pH 7.4→6.2→7.4; D-S v1 unfolds at 7.4, folds at pH 7.4→6.8 to insert into membranes, and maintains folding after pH 7.4→6.8→7.4 treatment. The biotin-tagged pHLIP is anchored on the membranes and then labeled by the FITC-modified streptavidin (FITC@SA) for fluorescence imaging. (D–I), Cell imaging experiments. Cell nuclei are labelled with DAPI (blue fluorescence). The green fluorescence in cell membranes denotes that the peptides have inserted into cells in the acid environment (E and H), or “locked” on cells after switching the pH back to neutral (I). (J and K) EV imaging experiments. The EV membranes are stained by DiI (red fluorescence). The merged yellow fluorescence denotes that D-S v1 still “locked” on EV membranes after pH 7.4→6.8→7.4 treatment. (L and M) Fluorescence quantification data for the cell and EV results. MFI denotes mean fluorescence intensity. The results are expressed as mean ± SEM (n = 3 biological replicates).
We further investigated whether these peptides can be retained on the A549-derived EV membranes under the pH 7.4→6.2/6.8→7.4 transition. As shown in Fig. 3J, the FITC signals in the EVs of the WT pHLIP group were undetectable, confirming the release of the FITC-tagged WT pHLIP from the EV surfaces at neutral conditions. In contrast, in the D-S v1 group, strong FITC signals were found to merge with the red fluorescence of DiI, which is commonly used for staining EV membranes (Fig. 3K). The quantification data for the cells and EVs are shown in Fig. 3 L and M, respectively. The particle size, concentration, and morphological features of EVs detected by nanoparticle tracking analysis (NTA) and transmission electron microscope are, respectively, shown in SI Appendix, Fig. S3. These results verify the insertion capability of D-S v1 at the cellular and EV levels.
After incubating the cells with D-S v1@biotin under the pH 7.4→6.8→7.4 treatment, the cells kept growing in the fresh culture medium. To investigate the dynamic distribution of D-S v1 inserted into cell membranes, we prolonged the incubation time to 8 h. The FITC signals on the cell surfaces were found to decrease gradually; simultaneously, the FITC signals of the cell-secreted EVs (as validated by the merged fluorescence with DiI) increased with the time course (SI Appendix, Fig. S4). These results imply that the peptides inserted into the cell membranes were taken away by the continuously secreted EVs. Therefore, we reason that there may be two modes of D-S v1 installation on cTME-EVs: i) D-S v1 directly inserts into the cTME-EVs that already exist in TME; and ii) D-S v1 inserts into tumor cells and TACs first and then transfers to their secreted EVs.
To assess the cell viability and cytotoxic effects, various concentrations of D-S v1 (0, 5, 10, 20, 30, and 40 μM) were incubated with RAW264.7 cells at different pH values and then tested by Cell Counting Kit-8 and MTT assay kits, respectively. When the concentrations of D-S v1 were below 10 μM under both pH 6.8 and 7.4 conditions, the cells retained over 95% viability and D-S v1 showed negligible cytotoxicity (<4%) (SI Appendix, Fig. S5). When the D-S v1 concentrations were above 20 μM, the cell viability declined gradually, along with the manifestation of cytotoxicity. There is no significant difference between the two pH conditions. In light of these results, the D-S pHLIP shows trivial cytotoxic effects at concentrations lower than 10 μM.
Selective Enrichment of cTME-EVs from the Tumor Model Mice.
We demonstrated the tumor-targeting capability of the D-S pHLIP system through intraperitoneally (i.p.) injecting Cy5.5-conjugated D-S v1 (D-S [email protected]) into the opposite side of the lung adenocarcinoma in model mice for fluorescence imaging in vivo. At 24 h post-injection, intense D-S [email protected] signals were found at the tumor site while nearly no signals were detected on the muscle of the non-tumor site, and the tumor-to-muscle fluorescence ratios were determined to be 2.2 ± 0.3 (n = 3, SI Appendix, Fig. S6). These in vivo imaging results support the positive tumor-targeting ability of this D-S pHLIP variant.
To verify the availability of this D-S pHLIP system for selective enrichment of cTME-EVs from the serum of animal models, the peptides@biotin were i.p. injected into the tumor model mice. The blood sera of the mice were collected at 6, 16, 24, 48, and 72 h post-injection and the cTME-EVs installed by the peptides@biotin were harvested using streptavidin-modified magnetic beads (namely MB@SA). The schematic diagram of pHLIP installation on cTME-EVs, total RNA analysis of the captured cTME-EVs, confocal fluorescent imaging, flow cytometry (FCM), Western blot (WB), and NTA characterizations are depicted in Fig. 4. From the results of total RNA detection (Fig. 4B), the D-S pHLIP group harvested many more cTME-EVs than the WT pHLIP group, especially at 24 h. At that optimal time point, the fluorescence intensities between the D-S and WT pHLIP groups have no significant difference (P > 0.05, n = 3) in the liver and kidney, suggesting that they show the similar metabolism pathway (SI Appendix, Fig. S7). The results also confirm that the D-S pHLIP group shows higher fluorescence intensity on the tumor than the WT pHLIP group (P < 0.001, n = 3). The higher targeting capability of D-S pHLIP is ascribed to its optimized folding-response value (pKa ≈ 6.8).
Fig. 4. Selective enrichment of cTME-EVs from tumor-bearing mice via the D-S pHLIP system. (A) Schematic diagram of installing biotin-tagged pHLIP on cTME-EVs for magnetic enrichment and transcriptomic analysis. (B–G) Identification of the captured cTME-EVs by (B) RNA detection, (C and D) confocal fluorescent imaging, (E) FCM, (F) WB, and (G) NTA. In (A), Li. and Ki. denote liver and kidney, respectively. In TME, i and ii denote the two modes of D-S v1 installation on cTME-EVs. In (B), WT pHLIP@biotin and D-S v1@biotin were injected into the lung cancer model mice, and the blood was withdrawn after 6, 16, 24, 48, and 72 h. In (C), the EV membranes were dyed by DiI (red fluorescence). In (D), MFI denotes the mean fluorescence intensity. ***P < 0.001. In (E), intense CD63 and EpCAM signals were detected in the D-S v1 group. In (F), clear biomarker bands were detected in the D-S v1 group, while those in the WT pHLIP group were too weak to be detected. In (G), after capture, the sizes of the cTME-EVs at 40 nm and 130 nm are reduced, and those at 66 nm and 96 nm appear to merge into the size at 79 nm before capture. The results are expressed as mean ± SEM (n = 3 biological replicates).
We next investigated the dynamic distribution of the biotin-tagged D-S v1 in the major organs of tumor model mice. The organs were collected at three representative time points (6, 24, and 72 h) and stained with FITC@SA, followed by recording the fluorescent images ex vivo (SI Appendix, Fig. S8). Significant signals were enriched in the liver and kidney at 6 h, suggesting that the peptides were rapidly metabolized by the body after i.p. injection. Comparing the results at 24 h to those at 6 h, the signals on the tumor increased obviously (P = 0.008, n = 3) while those on the other organs continued to decline. At 72 h, the signals on most organs—aside from the kidney—decreased obviously, demonstrating that the peptides were being eliminated from the body via the urinary system. The metabolism of D-S v1 is similar to that of WT pHLIP in terms of tumor-targeting time and clearance rate (26).
Confocal fluorescent imaging was employed to further identify the captured cTME-EVs. To do this, DiI was used to stain the EV membranes. As shown in Fig. 4C, high-density strong fluorescent dots (red) were detected on the MB surfaces in the D-S v1 group, suggesting that the D-S v1@biotin has been installed on the cTME-EVs and collected by MB@SA efficiently. By contrast, a few weak fluorescent dots were observed in the WT pHLIP group. The quantification data show that the fluorescence intensity in the D-S v1 group is approximately ninefold higher than that of the WT pHLIP group (Fig. 4D). We then confirmed the cTME-EVs by measuring the protein biomarkers with FCM and WB experiments. The results in Fig. 4E indicate no fluorescence signals of CD63 (a specific biomarker of EV, Q4 area) in the WT pHLIP group. In contrast, intense CD63 signals (20.7% proportion, Q4 area) were detected in the D-S v1 group. Furthermore, the fluorescence signals of EpCAM (a specific biomarker of cancer) merged with CD63 were also detected in the D-S v1 group (1.4% proportion, Q2 area), suggesting that the D-S pHLIP system has captured the tumor cell-derived EVs, which account for approximately 6.8% of all the captured ones. In addition to CD63 and EpCAM, the bands for the other two EV biomarkers (Alix and CD9) were detected in the D-S v1 group by WB experiments (Fig. 4F). We infer that the weak signals in the WT pHLIP group may result from the minimal retaining of WT pHLIP@biotin on the EV surfaces or slight nonspecific adsorption of EVs on the MBs. Additionally, we demonstrated that i) the MB@SA shows very weak nonspecific adsorption toward the EVs, and ii) D-S v1 can hardly capture EVs from a healthy mouse model, suggesting its tumor specificity (SI Appendix, Fig. S9).
NTA was finally employed to investigate the particle size and concentration of EVs before and after magnetic enrichment from the mouse serum samples (Fig. 4G). The mean concentration of total EVs in the serum samples is 8.47 × 108 particles/mL; after magnetic capturing, the mean concentration of the rest EVs decreases to be 8.14 × 108 particles/mL. The mean concentration of the captured EVs was calculated to be 3.3 × 107 particles/mL, accounting for 3.9% of the total EVs. The sizes of the EVs are distributed from 30 to 200 nm, meaning that majority of the vesicles are exosomes (1). Additionally, given the TME-responsive feature of D-S pHLIP, this technology can be amenable to other cancer types. For instance, the similar capture results were achieved from the mice with breast cancer. The corresponding RNA detection, FCM, and NTA results confirmed the broad applicability of this D-S pHLIP-based platform (SI Appendix, Fig. S10).
Transcriptomic Profiling of the Yielded cTME-EVs.
We then took advantage of transcriptome to further verify the availability of this isolation platform at the genetic level and reveal the potential cellular origin of these captured cTME-EVs. UC, the gold-standard approach for EV isolation in various settings, was employed as a control to isolate EVs from the mice with lung cancer and healthy ones (SI Appendix, Fig. S11). SI Appendix, Fig. S12 indicates that all the samples have good biological repeatability. We then calculated the differentially expressed genes (DEGs) for the two groups of tumor samples. 6,256 DEGs, including 448 upregulated genes and 5,808 downregulated genes, were identified in the D-S v1 group, while only 332 DEGs (243 upregulated and 89 downregulated) were found in the UC group (SI Appendix, Fig. S13). Apart from number, the volcano plots (Fig. 5 A and D) show that the DEGs in the D-S v1 group possess more significant differences than that of the UC group. Among the detected RNAs, the protein-coding messenger RNAs (mRNAs) and long intergenic non-coding RNAs (lincRNAs) were identified as the most abundant biotypes in the D-S v1 group (Fig. 5B), while mRNAs and ribosomal RNAs (rRNAs) are abundant in the UC group (Fig. 5E). To date, the source tracking of EVs is significant but remains challenging (27). Therefore, to trace the potential cell sources of the captured EVs, we matched the top 50 DEGs (SI Appendix, Fig. S14) against the exosomal biomarker database–ExoCarta (http://exocarta.org/index.html). The traceable top DEGs in the D-S v1 group originate from both tumor cells and TACs (SI Appendix, Table S6), wherein the latter mainly includes mast cells, fibroblasts, and macrophages that play important roles in many cancers (16, 28, 29). Additionally, the traceable top DEGs in the UC group are mainly from mast cells and fibroblasts (SI Appendix, Table S7). Obviously, both the quantity and cancer relationship of the traceable genes in the UC group are much lower than that in the D-S v1 group.
Fig. 5. Transcriptomic profiling of the yielded cTME-EVs enriched by D-S v1 and UC. Volcano plots showing DEGs in (A) D-S v1 and (D) UC groups. q < 0.05 and a fold change (FC) >2 are used to define upregulation (red) and downregulation (blue), while the gray ones are not significant. Reads of the distribution of RNA biotypes from DEGs in (B) D-S v1 and (E) UC groups. The differential expression degrees of hub genes in (C) D-S v1 and (F) UC groups.
Gene ontology analysis shows that the DEGs in the D-S v1 group mainly participate in the biological processes of signal transduction and G protein-coupled receptor (GPCR) signaling pathway to perform molecular functions of protein binding, metal ion binding, and GPCR activity (SI Appendix, Fig. S15). Many diseases including cancers are associated with GPCRs, which are targeted by a variety of modern drugs (30, 31). Therefore, this D-S pHLIP system may have the potential to search for TME-associated therapeutic targets of cancer. In the UC group, the DEGs mainly participate in biological processes of oxidation-reduction process and immune system to perform molecular functions of protein binding and metal ion binding. We then analyzed the protein–protein interaction (PPI) network for these DEGs (SI Appendix, Fig. S16), by which the top 10 hub genes were identified (SI Appendix, Fig. S17). In the D-S v1 group, the hub genes are Apoa5, Plg, Apoc3, Apoh, Kng1, Fga, Ahsg, Fgg, Apoa1, and Alb, wherein four of them (Apoa5, Apoc3, Apoh, and Apoa1) belong to the apolipoprotein (Apo) family. Furthermore, Fig. 5C indicates that all the hub genes are significantly upregulated, especially Apos. Previous works have demonstrated that the Apo levels are associated with many cancerous risks and can be used to predict tumor progression (32–34). Furthermore, the level of Plg was found to be upregulated in bladder carcinoma patients (35). Fga, Fgg (36), and Ahsg (37) were employed as biomarkers for prostate cancer and gastric cancer, respectively. However, the association of Kng1 and Alb with cancers has rarely been reported, which may serve as tumor biomarkers or therapeutic targets. Collectively, the significantly upregulated hub genes in the lung cancer models have great potential to be used as the combined diagnostic biomarkers and may further be employed to develop relevant therapeutic targets. In the UC group, all the hub genes are downregulation (Fig. 5F). Furthermore, the differential expression level is much lower than that of the D-S v1 group. The most significant one, Atp5b, was reported to relate to cancers (38), while the other hub gens are all ribosomal RNA.
Discussion
Nowadays, several long-standing issues are difficult to solve in EV-based liquid biopsy and cancer research (15). For instance, how to discriminate the weak cancer-related signatures from the intense background noise of normal human physiology especially at the early stages of cancer? How to decipher the contributions of TME to tumor development and to further develop TME-associated diagnostic and therapeutic strategies? The comprehensive identification of cTME-EVs by developing selective enrichment technology provides unprecedented opportunities to attack the aforesaid issues.
pHLIP is a widely exploited TME-targeting agent for tumor-targeted imaging and drug delivery (39). However, its targeting ability to TME is limited by its relatively low folding-responsive value (pKa ≈ 6.2). The development of engineered pHLIP with higher folding-responsive values (ideally pKa ≈ 6.8) that can be sensitively activated in TME (pH 6.5 to 6.8) is crucial for achieving higher tumor targeting efficiency. To improve its responsiveness to TME, the structure of pHLIP has been engineered using traditional mutation screening (40) and non-canonical amino acids (21), which unfortunately suffer from complexity and time-consuming. Recently, computational means, such as the pharmacokinetic model (20) and kinetics (41), were introduced as practical strategies to advance the development of pHLIP variants.
We herein present a computational tactic to design hundreds of virtual variants via pK calculations on the key regions of pHLIP to efficiently respond TME. This general strategy enables us to evaluate the potential mutation effects in silico in a straightforward, high-efficiency, and time-saving manner. Additionally, to selectively enrich cTME-EVs from bodily fluids, a pH-driven conformational switch should be designed to conjugate with pHLIP to prevent their dissociation in the neutral systemic circulation. Although computational designs have made significant progress in engineering proteins, creating conformational switch with specific functions is generally considered full of challenges (42). Here, the hook-like switch was tied in the C-terminal of D-S pHLIP and its tight binding with cTME-EVs was simulated through computational tests using MD simulations, free energy calculations, and interactional energy calculations.
In our study, cTME-EVs include the EVs derived from tumor cells and TACs in TME, which were confirmed by transcriptome analysis. To date, a variety of tumor biomarkers (e.g., EpCAM) have been identified on the tumor cell-derived EVs. Based on this fact, numerous antibodies or aptamers targeting the tumor biomarkers have been developed to enrich the tumor cell-derived EVs from biofluids for discovering potential cancer biomarkers. For instance, Choi et al. have employed antibodies against CD9, CD63, CD81, and EpCAM to enrich lung tumor cell-derived EVs from serum (43). However, the EVs enriched by these approaches are tumor cell-derived EVs other than TAC-derived ones. The TAC-derived EVs are highly heterogeneous and have rarely been studied due to the lack of effective tools to isolate them from biofluids. The proposed D-S pHLIP system, to our knowledge, represents the approach that is able to selectively enrich both tumor cell-derived EVs and TAC-derived EVs from blood serum. The analysis of cTME-EVs could not only achieve the comprehensive information of the important relationships between tumor and TME, but also uncover the roles of TAC-derived EVs in the tumor development and metastasis. Moreover, the secretion of TAC-derived EVs could be influenced by tumor cell-derived EVs to produce a complex TME. In combination with multiple omics analysis, this D-S pHLIP system would prompt the development of EV-based cancer diagnosis and therapy.
Except for the acidic foci in tumors, pHLIP can target the acidic inflammatory sites as well (26). In many established cancers, inflammation has been demonstrated as a hallmark of cancer and contributes to cancer progression substantially (44–46). Therefore, the EVs derived from cancer inflammation sites can be captured by our system to offer rich cancer signatures, aiding more comprehensive cancer diagnosis and personalized treatment. It is worth noting that the EVs derived from the non-cancer-related inflammatory sites in patients with multiple complications may influence the specificity of cTME-EVs. By coupling EVs with other classical biomarkers, such as free proteins and circulating DNA, these complex diseases could be discriminated accurately.
The proposed D-S pHLIP system breaks through the dilemma of selective enrichment of cTME-EVs from complex bodily fluids in broad cancer types. Given the excellent conformational switches, D-S pHLIP can be further engineered by simply replacing the biotin in the N-terminal with dyes or drugs, which will show great potential in tumor imaging and TME-targeted drug delivery.
Materials and Methods
The D-S pHLIP was computationally designed via H++ software of 3.2 version and SWISS-MODEL and assessed by MD simulations, free-energy calculations, and interaction energy calculations. pHLIP variants were synthesized and purified by GL Biochem Ltd. Liposome preparation. The WT pHLIP and variants were characterized by high-performance liquid chromatography and mass spectrometry. Detailed descriptions of fluorescence and CD measurements, in vitro and in vivo experiments, and transcriptome analysis are presented in SI Appendix.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. Conformational changes of D-S v1 based on its trajectory in 100 ns MD simulation.
Movie S2. Conformational changes of D-S v2 based on its trajectory in 100 ns MD simulation.
This study was supported by the National Key R&D Program of China (2019YFA0210100), the National Natural Science Foundation of China (21775075, 21977053 to D.L., and 22005157 to Z.Z.), China Postdoctoral Science Foundation (2021M701788), and Frontiers Science Center for Cell Responses, Nankai University.
Author contributions
Z.Z. and D.L. designed research; Z.Z., X.L., and Z.Y. performed research; Z.Z., X.L., Z.Y., and D.L. analyzed data; and Z.Z. and D.L. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission. X.O.B. is a guest editor invited by the Editorial Board.
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PMC009xxxxxx/PMC9926247.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595685
202200633
10.1073/pnas.2200633120
research-articleResearch ArticledemoDemographystatStatistics433
Social Sciences
Demography
Physical Sciences
Statistics
Mapping the number of female sex workers in countries across sub-Saharan Africa
Laga Ian a 1
Niu Xiaoyue b 1
Rucinski Katherine c https://orcid.org/0000-0002-9858-5953
Baral Stefan c
Rao Amrita c https://orcid.org/0000-0002-9596-2418
Chen David b
Viswasam Nikita d
Phaswana-Mafuya Nancy Refilwe e
Diouf Daouda f
Sabin Keith g https://orcid.org/0000-0002-2290-8621
Zhao Jinkou h https://orcid.org/0000-0001-9184-3385
Eaton Jeffrey W. i https://orcid.org/0000-0001-7728-728X
Bao Le [email protected]
b 2 https://orcid.org/0000-0003-0191-5918
aMontana State University, Bozeman, MT 59717
bPennsylvania State University, University Park, PA 16802
cJohns Hopkins University, Baltimore, MD 21205
dUniversity of Maryland, College Park, MD 20742
eSouth African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit and Faculty of Health Sciences, University of Johannesburg, Johannesburg 2028, South Africa
fEnda Santé, Dakar 3370, Senegal
gThe Joint United Nations Programme on HIV/AIDS, Geneva 1211, Switzerland
hThe Global Fund to Fight AIDS, Tuberculosis and Malaria, Geneva 1218, Switzerland
iMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London SW7 2AZ, UK
2To whom correspondence may be addressed. Email: [email protected].
Edited by Samuel J. Clark, Ohio State University; received January 12, 2022; accepted November 16, 2022 by Editorial Board Member Ronald D. Lee
1I.L. and X.N. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e220063312012 1 2022
16 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
In 2021, the proportion of sub-national areas in sub-Saharan Africa with FSW size estimates remains low, challenging evidence-based resource allocation for health and HIV services. We produced model-based FSW population size estimates in all areas across sub-Saharan Africa. Ultimately, achieving the vision of no new HIV infections by 2030 necessitates dramatic improvements in our delivery of evidence-based services for sex workers across sub-Saharan Africa.
Female sex workers (FSW) are affected by individual, network, and structural risks, making them vulnerable to poor health and well-being. HIV prevention strategies and local community-based programs can rely on estimates of the number of FSW to plan and implement differentiated HIV prevention and treatment services. However, there are limited systematic assessments of the number of FSW in countries across sub-Saharan Africa to facilitate the identification of prevention and treatment gaps. Here we provide estimated population sizes of FSW and the corresponding uncertainties for almost all sub-national areas in sub-Saharan Africa. We first performed a literature review of FSW size estimates and then developed a Bayesian hierarchical model to synthesize these size estimates, resolving competing size estimates in the same area and producing estimates in areas without any data. We estimated that there are 2.5 million (95% uncertainty interval 1.9 to 3.1) FSW aged 15 to 49 in sub-Saharan Africa. This represents a proportion as percent of all women of childbearing age of 1.1% (95% uncertainty interval 0.8 to 1.3%). The analyses further revealed substantial differences between the proportions of FSW among adult females at the sub-national level and studied the relationship between these heterogeneities and many predictors. Ultimately, achieving the vision of no new HIV infections by 2030 necessitates dramatic improvements in our delivery of evidence-based services for sex workers across sub-Saharan Africa.
key populations
size estimates
Bayesian hierarchical model
HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 100000060 R01AI136664 Ian LagaXiaoyue NiuKatherine RucinskiStefan BaralAmrita RaoDavid ChenNikita ViswasamLe Bao
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pmcFemale sex workers (FSW) are at increased risk of negative health outcomes and often face stigma and discrimination from their communities, clients, partners, and families. FSW are more likely to acquire and experience ongoing transmission of HIV than other reproductive-aged women (1, 2). Global median HIV prevalence among FSW is 3.9% (3, 4), while FSW in sub-Saharan Africa are at much higher risk of HIV with about one in five sex workers estimated to be living with HIV. In addition, FSW have an increased risk of syphilis and herpes simplex virus type 2 (HSV-2) (5). As noted by Strathdee and colleagues, “sex—whether paid for or not—does not cause HIV infection” (6). Rather, a higher prevalence of HIV and other STIs is due to increased exposure to infection coupled with limited access to essential sexual and reproductive health services including HIV prevention and treatment. Recent literature calls attention to FSW’s reproductive health and desire to be mothers (7, 8), need to provide financial support to their family and friends (6, 9), increasing number of human rights violations against sex workers (10, 11), higher vulnerability to homicide (10), sex trafficking (12, 13), and the prevalence of sexually exploited children and adolescents (14, 15). The issues FSW face extend beyond HIV, and it is important to better understand FSW populations to address these problems.
Epidemiologic, health, and social risks among FSW have been well documented across sub-Saharan Africa, but the scarcity of comparable data characterizing FSW within and across countries challenges an empiric and equitable HIV response (16). Given the limitedness of robust surveillance systems, rigorous methodologies have been developed to estimate the size of FSW and other hidden populations, including census and enumeration, capture–recapture, multiplier, network scale-up, and geographic mapping methods (16). However, few real-world examples of these rigorous studies exist (17, 18). Additionally, multiple size estimates for FSW within and across settings, including estimates derived from different approaches, make it challenging to directly compare estimates and to fully understand the true variation in the size of FSW across countries in sub-Saharan Africa. Generating consistent FSW size estimates can help inform equitable responses for national HIV programs to effectively reduce new infections (17, 19–21). These size estimates can also guide other programs such as those focused on protecting FSW from exposure to unsafe working conditions, increasing condom usage, and improving reproductive health for FSW.
Results
Overview.
While there have been multiple studies and recent systematic reviews to estimate HIV prevalence across Africa (22), including among key populations (23–26), there is no published comprehensive effort to estimate population size of sex workers in sub-Saharan Africa at the national or sub-national level. One study presented FSW size estimates for a subset of African countries and additional countries around the world, primarily at the national level (26). We searched for existing FSW population-size estimates in sub-Saharan Africa, geo-located these data, and used a Bayesian hierarchical model to meta-analyze the proportion of adult women who are FSW across sub-Saharan Africa, accounting for different study methodologies.
We modeled the proportions of women of reproductive age, aged 15 to 49 y who are FSW for all mainland sub-Saharan African countries and Madagascar for all sub-national areas (typically the largest geographic units that divide a country, additional details regarding the spatial resolution are found in the Methods section) in the year 2015. Note that sex workers are only recognized as aged 18 and above by the United Nations (27). We present estimates for ages 15 to 49 because this is one of the most common age ranges considered in our literature review and includes FSW of reproductive age. Additionally, we provide national-level estimates by aggregating the sub-national estimates within each country. Our estimates are based on a Bayesian hierarchical model to leverage all existing estimates in the literature and address the heterogeneity in the existing FSW estimates by controlling for the effects that country and estimation method have on the size estimates and including auxiliary spatial covariates that explain much of the variability. While the FSW population sizes are of main interest, we modeled the proportions to better handle the varying population sizes in sub-national areas and cities. Furthermore, we present the results primarily as FSW proportions to directly compare estimates both within and between countries.
Female Sex Worker Sizes.
We performed a literature review and meta-analysis of FSW size estimates in 44 countries in sub-Saharan Africa (SI Appendix, Table S3). For this review, we considered all definitions of FSW that involve the exchange of sex services for money or anything of value. We identified 1,253 unique FSW population size estimates (PSE) across 38 countries in sub-Saharan Africa at various spatial resolutions between 2000 and 2018. Existing FSW size estimates were not uniformly distributed across countries in sub-Saharan Africa. While 38 of the 44 sub-Saharan African countries in our meta-analysis had at least one FSW size estimate, more than 50% of the 1,253 unique FSW PSE belonged to only four countries (Ghana, Malawi, Nigeria, and Uganda). There were fewer than five FSW size estimates in eight of the 38 countries. We classified observations as national (related to the entire country), sub-national (related to an administrative division, either administrative-one or administrative-two level), city, or city area. After stratifying sub-Saharan Africa into sub-national areas for prediction and modeling, we removed PSE if they corresponded to multiple sub-national areas (127 instances; 100 corresponded to regions in Ghana, 14 to states in Nigeria, and 13 to the eight former provinces in Kenya before the provinces were subdivided into 46 districts and Nairobi), related to a sub-city area (58 instances, all of which corresponded to cities with other city-level estimates), or missed key information like the name of the city (55 instances). We also excluded 81 national estimates since many represent scaled-up versions of the sub-national estimates and including the national estimates adversely affected out-of-sample model performance. Finally, we removed one unreliable observation that estimated the FSW proportion of Nairobi, Kenya, to be larger than 60% of the number of women aged 15 to 49 and more than 10 times larger than the next highest FSW proportion estimate of the same area. All other estimates, including some with FSW proportions larger than 60%, were kept due to lack of evidence they were wrong. After removing these PSE, we were left with 932 observations, composed of 616 sub-national area estimates and 316 city-level estimates across 33 countries. PSE availability at the city and sub-national level and the FSW percent of these observations from 2000 to 2018 are shown in Fig. 1. Data availability was generally sparse and varied widely across countries. Very few countries had both sub-national area and city estimates or estimates across multiple years, making longitudinal study of FSW size difficult, if not impossible. We chose 2015 as the representative year for our estimates since most literature estimates were available around 2015. The dataset and analytic files are available at https://github.com/ilaga/Mapping-FSW-SSA. (28).
Fig. 1. Data availability and FSW Percent in sub-Saharan Africa. (A–C) Number of usable city (A) and sub-national area (B) FSW PSE estimates available from the literature in each area across all years. Sub-national areas in sub-Saharan Africa with no estimates are shown in light grey and countries not in sub-Saharan Africa are shown in dark grey with stripes. In (C), the percent of FSW in each country with respect to the number of women aged 15 to 49 are plotted against the country ISO-3 codes (dots are jittered for readability). Sub-national estimates are shown in orange and the city estimates are shown in light blue.
The FSW size estimates in sub-Saharan Africa were of varying quality and exhibited large heterogeneity both across different and within the same geographic areas. Within our sub-national dataset, the proportion of women who were FSW varied widely, ranging from 0.1 to 70%. Estimates were often significantly different for the same sub-national area within a country. For example, one FSW size estimate for Mwanza, Tanzania, was over three times larger than another. The literature estimates also varied widely with respect to the direct size estimation methods. We grouped the many different reported methods into eight categories (capture–recapture, enumeration, expert opinion, mapping, multiple methods, multiplier, miscellaneous, and not reported). Eighteen countries contained sub-national area or city estimates from more than one method. Zambia reported the largest variety of estimates, containing 47 area or city estimates from six different methods. Conversely, Nigeria contained 96 estimates, all associated with a mapping method. Systematically combining existing FSW size estimates and extrapolating to areas with no estimates must handle the large variation in the data and accurately reflect the uncertainty of the resulting estimates.
Uncertainty estimates of the existing FSW size estimates should vary significantly depending on the sample size and method used in the literature. Unfortunately, very few of the existing size estimates were reported with uncertainty intervals (less than 13%). Ideally, any model-based estimator would utilize the uncertainty of the data to produce more accurate estimates and uncertainty intervals. In our case, this was not possible, but we emphasize that future size estimation studies should report uncertainty intervals in addition to the size estimates, and models based on these estimates should incorporate the uncertainty intervals into the model.
We developed a Bayesian hierarchical random effects model to combine the existing studies and FSW PSE. Specifically, we assumed that the logit FSW proportion followed a Student’s t distribution. Heterogeneity was captured using auxiliary variables, independent and identically distributed Gaussian random effects for the estimation method effect, and independent and identically distributed Gaussian random effects for the country effect. Reference population sizes were calculated using citypopulation.de and WorldPop data to convert FSW PSE to FSW proportion with respect to women aged 15 to 49 (SI Appendix). In the case where the reference population size could not be calculated, the reference population size from the literature was used if available. We do not use the reference population from the literature by default because many studies use populations other than women of reproductive age as the reference population.
We conducted all analyses using R (versions 3.6.3–4.0.2) (29).
Modeled FSW Size Estimates.
We fitted a Bayesian linear hierarchical model with spatially varied economic, social, and environmental predictors and effects for country and estimation method. We present here the estimated sub-national FSW proportions and uncertainties (Fig. 2 A and B) and national FSW proportions and uncertainties (Fig. 2 C and D). Overall, we estimated there are 2.5 million (95% uncertainty interval 1.9 to 3.1) FSW aged 15 to 49 in sub-Saharan Africa, corresponding to a proportion as percent of all women of childbearing age of 1.1% (95% uncertainty interval 0.8 to 1.3%). FSW proportions varied widely at both the national and sub-national levels. The estimated national FSW proportions ranged from 0.3% in Malawi to 2.8% in Burundi. Within countries, the range of FSW proportions was also substantial. For example, in Nigeria, the proportion estimates ranged from 1.1% in Jakusko to 2.3% in Ogori-Magongo.
Fig. 2. Estimated FSW percent of women aged 15 to 49 in 2015 at the country and sub-national area level and the relative uncertainty of the area estimates. (A–D) Estimated FSW percent at the sub-national area level (A) and at the country level (C). We do not produce estimates for countries not in sub-Saharan Africa (dark grey with stripes). The relative uncertainty of FSW percent in each sub-national area is shown in (B) and each country in (D). Relative uncertainty is defined by the length of the 95% credible interval divided by the posterior mean. Lighter colors correspond to lower FSW percent and uncertainty, and darker colors correspond to higher FSW percent and uncertainty.
For each country, we examined the contribution from the predictors and the country effects by decomposing the final estimates into the predictor effects and the country effects, reported in SI Appendix, Table S5. This decomposition is useful for better understanding the components that contribute to a country’s estimate. For example, the low estimated FSW proportion in Malawi was primarily due to the country effect. The estimated FSW proportion in Malawi from only the predictors was 0.9% (the 30th smallest among all countries), but the relatively large amount of literature estimates (103 PSE for 27 districts) with small FSW proportion resulted in a large negative country-level effect and consequently a small FSW proportion of 0.3%.
FSW Size Predictors.
To handle poorly behaved predictors (e.g., those that exhibited extreme distributions with little relationship to the response), we screened many covariates based on visual diagnostics of their distribution and their relationship with the response variable. We then performed Bayesian variable selection via a horseshoe prior (SI Appendix) (30). The penalized regression shrunk nonsignificant coefficients toward zero and handled covariates with high collinearity. Our final model contained 69 predictors and an interaction term. Note that all predictors remain in the model for Bayesian penalized regression. We present all estimated coefficients, covariate explanations, and credible intervals in SI Appendix, Table S2 and Fig. S10. In Table 1 here, we present the estimated coefficients and corresponding 95% credible intervals for the ten largest estimated coefficients. These nine covariates (excluding intercept) in the order of absolute value of coefficient estimate are indicator for city, the interaction between city and log number of women of reproductive age, log number of women of reproductive age, population density, percent urban, G6PD (glucose-6-phosphate dehydrogenase) deficiency allele frequency, population-weighted walking time to nearest healthcare, population-weighted sickle hemoglobin allele frequency, and isothermality. For the continuous predictors, the estimated coefficients indicate how much the logit-transformed FSW prevalence changes for one SD increase of the predictor. For interpretation, SI Appendix, Fig. S11 includes a plot which shows how one SD increase of the above predictors affect the estimated FSW percent.
Table 1. Predictor coefficient summaries for centered and scaled predictors
Predictor Estimate (95% credible interval)
Intercept −4.402 (−4.784, −3.992)
City 0.564 (0.000, 0.947)
Reference population (log)* City −0.299 (−0.472, −0.057)
Reference population (log) −0.249 (−0.521, −0.022)
Population density 0.106 (−0.014, 0.448)
Percent Urban 0.060 (−0.007, 0.188)
G6PD Freq 0.038 (−0.009, 0.152)
Walking time to healthcare PW −0.035 (−0.263, 0.021)
HBS Freq PW 0.025 (−0.030, 0.267)
Isothermality −0.010 (−0.099, 0.024)
Fixing all other covariates, for sub-national areas, as the number of women of reproductive age increases, the FSW proportion decreases. In cities, FSW proportion decreases even faster, so larger cities tend to have lower FSW proportion than smaller cities. For an average population size, city estimates are higher than sub-national estimates. Additionally, sub-national areas with higher urban density, higher population density, higher G6PD allele frequency, and higher sickle hemoglobin allele frequency have higher FSW proportions. Conversely, areas where people must walk farther to the nearest healthcare facility and areas with higher isothermality have lower FSW proportions.
Uncertainty in estimates was large for sub-national areas in countries with few or no existing FSW estimates. The relationship between data availability and estimate uncertainty is shown in SI Appendix, Fig. S2. Estimates in countries with few data are drawn towards the average sub-Saharan Africa FSW proportion. While the predictors we included in the model improved predictions, there remains a large country-level effect that is unidentified for countries with no usable estimates. Including even a few estimates (as in Sudan and Republic of the Congo, for example) can drastically improve estimates for the remaining sub-national areas in the country. It is unclear, however, whether the significant differences in FSW proportions between countries reflects the actual proportions in the countries or whether the FSW PSE produced in the studies differ dramatically in implementation, obscuring the true FSW proportions.
Size Estimation Methods.
The measurement methods used for direct size estimation were not strongly associated with the observed population size. All eight 95% credible intervals included zero (full parameter estimates are shown in Table 2). These parameter estimates do not correspond to any methodological biases and are only observations about how different methods compared to each other. One explanation for the lack of significant difference between the estimation methods in our study is that there was not sufficient data within each country to accurately detect systematic variation due to the estimation method. In the literature, countries overwhelmingly used only one or two estimation methods. Notable exceptions included Malawi (29 estimates from capture–recapture, 20 from enumeration, and 56 from multiple methods) and Zambia (9 from enumeration, 8 from expert opinion, 13 from multiple methods, 4 from multiplier, 12 miscellaneous methods, and 1 not reported). The observed FSW proportions for each method varied considerably, suggesting that even after accounting for variability using the auxiliary spatial covariates, the uncertainty associated with the estimation method is too large to quantify significant differences in the method. There were likely significant differences in the implementation of the estimation methods, obscuring any systematic differences in the methods.
Table 2. Method coefficient summaries
Method Estimate (95% credible interval) FSW percent estimate at methods
Capture–Recapture 0.041 (−0.180, 0.304) 1.29
Enumeration 0.209 (−0.013, 0.495) 1.52
Expert opinion −0.212 (−0.599, 0.067) 1.01
Mapping −0.084 (−0.385, 0.188) 1.14
Misc −0.113 (−0.406, 0.142) 1.11
Multiple 0.032 (−0.182, 0.286) 1.27
Multiplier −0.051 (−.301, 0.201) 1.18
NR 0.197 (−0.050, 0.528) 1.50
The baseline FSW percent estimate corresponding to no method used at the average covariate value is 1.23%.
Discussion
This study presents FSW size estimates inclusive of all countries in sub-Saharan Africa. Through an extensive synthesis of existing FSW size estimates from the literature and the use of geospatial auxiliary covariates, we were able to comprehensively generate FSW size estimate proportions for both national and sub-national areas of the entire sub-Saharan African region. Our findings demonstrate substantial variation in estimates within and across countries. Resulting estimates also had limited precision in settings where direct size estimates were largely absent, affirming the importance of continued primary data collection for FSW PSE moving forward.
Consistent with all model-based methods, estimates are reliant on the quality of the data. In our case, this is not only the FSW size estimates from the literature, but also our reference population size calculations as an underwhelming percent of studies identified through the literature reported either the reference population or the corresponding FSW proportion. While our generated reference populations are largely consistent with the more reliable reference populations where available in the literature (SI Appendix, Fig. S12), in general, the true reference population is unknown, making it difficult to verify the populations for all estimates. We posit that our generated reference populations are accurate, and any error introduced is small relative to the variability in the size estimates and the modeling, but we emphasize the importance of reporting reference population sizes alongside FSW size estimates. Along these lines, it is also beneficial to report the definition of FSW used in the study, the year the data were collected, the catchment area, what administrative areas are represented, and whether extrapolation has been used for each size estimate. Additionally, many existing estimates do not include quantitative uncertainty assessments making it difficult to distinguish between reliable estimates based on rich information and ambiguous guestimates. To improve future studies that rely on the synthesis of existing estimates, it is also helpful to report the uncertainty in the form of variance or uncertainty intervals. The above recommendations are also applicable to documenting the size estimates of other hard-to-reach populations.
While this study provides FSW proportion estimates in areas with no previous estimates, the handling of competing estimates in the same area is equally important. Repeated studies for the same sub-national area typically produce widely different FSW size estimates, and our model provides a way to combine these estimates in a straightforward and sensible approach. A key assumption of our model is that averaging across all size estimation methods, FSW PSE are unbiased. Given that no single estimation method clearly outperforms the other methods in practice, this assumption is desirable. If a method emerges as the gold standard, our model can be easily modified to center around that method. We believe that overall, our model-based estimates provide reliable FSW estimates, and all estimates and measures of uncertainty are available as an interactive map at https://github.com/ilaga/Mapping-FSW-SSA/blob/main/Figures/FSW_Map_Interactive.zip. In many cases, the uncertainty is relatively large, and any future analysis which utilizes our FSW size estimates should incorporate the uncertainty and not rely solely on the point estimates.
Our study is also a call to action for more accurate and extensive estimates of FSW. Importantly, model-based methods that rely on existing FSW size estimates are a complement rather than a substitute for primary data collection and direct size estimation studies. Interventions implemented specifically for FSW and clients have a larger impact on HIV prevention than equivalent resources directed more broadly, including toward low-risk activity groups (31). UNAIDS emphasizes that the strategy to end AIDS as a public health threat by 2030 relies on mitigating inequities, both among and within countries (11). In order to meet new 95–95–95 targets, UNAIDS highlighted that countries need to have updated size estimates of all key populations, including FSW, to allow HIV prevention programs and implementation partners to allocate the correct amount of resources for the true population sizes (11). It is not surprising that the sub-national areas and countries with more FSW data correspond to the estimates with the smallest uncertainty. By increasing both the quality and quantity of FSW size estimation studies, not only does resource allocation become easier and more efficient, but these studies shed light on FSW in neighboring areas.
WHO guidelines on combination HIV prevention programs suggest the need for comprehensive and well-specified strategies for sex workers as a means of improving HIV outcomes and quality of life. Historically, the majority of services have focused on individual risks such as condom use (6). While necessary, these programs are not sufficient in terms of overcoming the significant risks for both the acquisition and transmission of STIs and reproductive health services for FSW. Addressing structural risks with community empowerment approaches where sex workers collectively control the HIV programs in their community has been shown to reduce HIV and STI prevalence and increase condom usage (32). Sex work is work, but it is work done often in dangerous conditions, and affected by intersecting stigmas and criminalization of sex workers challenging the reporting of physical and sexual violence (10). In 2020, the world came nowhere close to achieving the goal of 500,000 new HIV infections. Since then, the emergence of COVID-19 has further affected the ability to deliver services. Moving forward necessitates a dramatic shift in how we address the needs of FSW to decrease disease incidence and improve their quality of life. Advocating for services to achieve this and address the structural risks faced by sex workers necessitates improved data on how many sex workers there are and where they live. Our study provides the most comprehensive answer to these questions for all sub-national areas in sub-Saharan Africa available to date.
Materials and Methods
Overview.
This analysis estimates female sex worker (FSW) proportion among women of reproductive age (aged 15 to 49 y) at the sub-national level for most sub-Saharan African countries. The final estimates represent the FSW proportion in 2015 since most literature estimates were available around 2015.
PSE Data.
We compiled a dataset of 1,253 unique estimates across 38 countries in sub-Saharan Africa from public resources. The reference population was women of reproductive age (aged 15 to 49). Reference population sizes were calculated using citypopulation.de and WorldPop data (SI Appendix). In the case where the reference population size could not be calculated, the reference population size from the literature was used if available. We did not use the reference population from the literature by default because many studies used populations other than women of reproductive age as the reference population. Information extracted from the literature included country, spatial region, type of spatial region (e.g., district, region, city, etc.), estimation method (which we later categorized into eight categories: capture-recapture, enumeration, expert opinion, mapping, multiple methods, multiplier, miscellaneous, and not reported) PSE, FSW proportion, publication year, and data year.
Spatial Resolution.
Model fitting and proportion estimates used GADM 3.6 administrative-one level units (33) for all countries except Ghana, Uganda, and Nigeria. For these three countries, literature estimates were available in almost all administrative-two level units. Both Ghana and Uganda added many new districts from the creation of the GADM 3.6 shapefile, so shapefiles better matching the literature estimates were used from the government of Ghana (34) and the government of Uganda (35) statistical agencies’ websites. The GADM 3.6 administrative-two level units in Nigeria matched with the literature estimates, so these were used. For simplicity, we refer to these administrative units as sub-national areas. Because our analysis includes cities and areas of varying size, our covariates are limited to values standardized by area (e.g., population density is acceptable while total population is not). However, the error is introduced both at the response and the covariate level. In the absence of the original spatial units used to estimate the population proportions, the alignment to the sub-national areas was necessary and seemed to be reasonable, and the error introduced was likely small relative to the variation from both the literature estimates and the model.
Literature estimates were classified as either city, sub-national, national, or miscellaneous. For modeling purposes, estimates were classified as a city if they represented a geographic area smaller than the sub-national area and miscellaneous if they represented a geographic area bigger than the sub-national area or corresponded to multiple sub-national areas. Thus, estimates were labeled a sub-national area in cases where estimates corresponded to both a city and a sub-national area (e.g., Brazzaville, Republic of Congo).
Covariates.
Our primary analysis included auxiliary spatial covariates (SI Appendix, Table S6). The auxiliary spatial covariates were all available at a grid cell resolution across sub-Saharan Africa, and the average value for each covariate was calculated for each sub-national area, weighted by WorldPop in each area when relevant. When yearly data was available, estimates were matched with the covariate in their respective years or to the nearest year where data was available. In most cases, only 1 y was available, so the variable was processed identically regardless of the size estimate year. Many of the auxiliary spatial covariates were highly skewed with heavy upper tails. Variables were plotted against the logit of the observed FSW proportions to evaluate whether a log transformation resulted in a better linear relationship. These transforms are specified in Table 1 and SI Appendix, Table S2. Additionally, the detailed estimation methods from the literature were mapped to eight general methods and used as a random effect group in the statistical model.
In a secondary analysis, we considered additional covariates from Demographic and Health Surveys (DHS) (36). The DHS data contain information at household clusters across a subset of the SSA countries and includes thousands of unique survey questions which were narrowed down to several hundred using pairwise correlations with the logit-transformed proportions. We considered only DHS surveys with GPS coordinates of cluster locations. DHS surveys were not available in all administrative units. We attempted to impute DHS values for missing sub-national areas using both multiple imputation chained equations and via a conditionally autoregressive spatial model (CAR). In both cases, the imputed values were not consistent with observed values, and thus we chose to omit DHS variables from our model. We performed a study to compare estimates with and without the DHS variables. Using only data with available DHS observations, we fit and compared three models: 1. the “combined model” which included both DHS and auxiliary spatial covariates, 2. the “auxiliary model” which included only the auxiliary spatial covariates, and 3. the “DHS model” with included only the DHS covariates. These models were compared using leave-one-out cross-validation. The auxiliary model performed marginally better but was similar in performance to the combined model. The DHS model performed the worst. In addition, the fitted and predicted values from the combined model and auxiliary model exhibited no systematic differences and were very similar. The standard errors of the estimates were essentially identical. Based on these results, we concluded that imputing the missing DHS values would introduce more error than the DHS values would explain, and thus we did not lose predictive accuracy by omitting the DHS covariates. Additional discussion and diagnostics regarding this decision are shown in the SI Appendix.
Statistical Model.
We modeled the number of FSW in sub-national area i and replicate j (Yi,j) using a Bayesian hierarchical model to allow sharing in FSW estimates between literature and within a country. We excluded national estimates and sub-city estimates since many national estimates were simply the summation of the sub-national area estimates and all sub-city estimates had a matching city estimate which was the sum of sub-city FSW sizes. Including the national and sub-city estimates would lead to smaller than expected uncertainty intervals. We assumed the Yi,j comes from the following hierarchical model: logit(Yi,jNi,j)=β0+β1Xi,j+αmethod[i,j]+γci+Єi,j,
Єi,j~St(0,σ,ν),
where ~ denotes “distributed as,” St denotes the Student’s t distribution, and all Єi,j are independent. In the above, the logit-transformed FSW proportion in sub-national area i and replicate j came from a Student’s t random variable with location (mean) β0+β1Xi,j+αmethod[i,j]+γci, scale σ, and degrees of freedom ν. The number of women of child-bearing age in area i corresponding to replicate j is denoted by Ni,j. We assumed a linear predictor on the mean, where β0 is an overall intercept, β1Xi,t is the covariate effect corresponding to the set of predictors Xi,t in sub-national area i and replicate j, and αmethod[i,j] was a Gaussian random effect to account for the estimation method associated with the literature PSE. Country-level Gaussian random effects centered around zero were included in γci, where ci represents the country for area i. While area i is nested inside c[i], the areas were uniquely coded. The mean includes the index j to note that logit-transformed FSW proportion in sub-national area i was measured repeatedly across years, by different estimation methods, and possibly from a city estimate.
FSW estimates from the fitted model for sub-national area i in the year 2015 (Y^i) were obtained using standard Bayesian methodology with posterior samples, excluding αmethod[i,j] to ignore any method effect, using estimated γci for countries with available FSW size estimates, and simulating new γci from a normal distribution with estimated country-level variance. Specifically, for sub-national area i, we made an inference about β^0+β^1Xi,j+γ^ci, where Xi,j is the covariate matrix corresponding to the year 2015.
The model was fitted using the RStan (37) and brms (38, 39) packages. Diagnostic plots are found in the SI Appendix.
Code Availability.
All code (in addition to generated figures and results) is available at https://github.com/ilaga/Mapping-FSW-SSA. The nighttime light processing code is available at https://github.com/TheDavidChen/NL_Africa.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Research reported in this work was supported by NIAID of the National Institutes of Health under grant numbers R01AI136664 and R01AI170249. R.P. acknowledges support from the South African Medical Research Council (SAMRC) through its Division of Research Capacity Development under the Mid-Career Scientist Programme through funding received from the South African National Treasury and the South African Medical Research Council, and the Global Excellence Stature (GES) 4.0, University of Johannesburg. The funders of the study had no role in the data collection, data analysis, data interpretation, or writing of the report. The content hereof is the sole responsibility of the authors and does not necessarily represent the official views of the NIH, SAMRC, or GES.
Author contributions
I.L., X.N., K.R., S.B., K.S., J.Z., J.W.E., and L.B. designed research; I.L., X.N., S.B., J.W.E., and L.B. performed research; I.L., K.R., A.R., D.C., N.V., K.S., and J.Z. analyzed data; I.L., D.C., N.V., and K.S. curated datasets; and I.L., X.N., K.R., S.B., N.R.P.-M., D.D., J.W.E., and L.B. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
The authors declare that the literature female sex worker meta-analysis data supporting the findings of this study are available in the SI Appendix and at https://github.com/ilaga/Mapping-FSW-SSA (28). Auxiliary data are subject to third party restrictions, but links to all data used for the current study are provided inthe SI Appendix and can be accessed after successful registration.
Supporting Information
Preprint Servers: SSRN: https://doi.org/10.2139/ssrn.3919697.
This article is a PNAS Direct Submission. S.J.C. is a guest editor invited by the Editorial Board.
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PMC009xxxxxx/PMC9926250.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595680
202218345
10.1073/pnas.2218345120
research-articleResearch ArticleimmunImmunology and Inflammation420
Biological Sciences
Immunology and Inflammation
Thioredoxin-interacting protein is essential for memory T cell formation via the regulation of the redox metabolism
Kokubo Kota a 1 https://orcid.org/0000-0001-6896-1981
Hirahara Kiyoshi [email protected]
a 1 2 https://orcid.org/0000-0002-9128-9449
Kiuchi Masahiro a 1
Tsuji Kaori a
Shimada Yuki a
Sonobe Yuri a
Shinmi Rie a
Hishiya Takahisa a
Iwamura Chiaki a
Onodera Atsushi a b
Nakayama Toshinori [email protected]
a c 2 https://orcid.org/0000-0002-1434-2007
aDepartment of Immunology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba 260-8670, Japan
bInstitute for Advanced Academic Research, Chiba University, Chuo-ku, Chiba 260-8670, Japan
cCore Research for Evolutionary Science and Technology, Japan Agency for Medical Researchand Development, Chuo-ku, Chiba 260-8670, Japan
2To whom correspondence may be addressed. Email: [email protected] or [email protected].
Edited by Jeff Rathmell, Vanderbilt University Medical Center, Nashville, TN; received October 27, 2022; accepted November 30, 2022 by Editorial Board Member Philippa Marrack
1K.K., K.H., and M.K. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e221834512027 10 2022
30 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Metabolic reprogramming is crucial for the generation of memory T cells; however, the mechanisms that control the redox metabolism in allergen-specific memory T cell formation still remain unclear. In this study, we found that thioredoxin-interacting protein (Txnip), a regulator of oxidoreductase, controlled reactive oxygen species (ROS) metabolism via the nuclear factor erythroid 2-related factor 2 (Nrf2)–biliverdin reductase B (Blvrb) pathway, which made memory Th2 cell generation possible and shaped the pathology of allergic airway inflammation. This study will contribute to the development of therapeutic strategies for intractable inflammatory diseases by focusing on the redox metabolism and the formation of CD4+ memory T cells.
CD4+ memory T cells are central to long-lasting protective immunity and are involved in shaping the pathophysiology of chronic inflammation. While metabolic reprogramming is critical for the generation of memory T cells, the mechanisms controlling the redox metabolism in memory T cell formation remain unclear. We found that reactive oxygen species (ROS) metabolism changed dramatically in T helper-2 (Th2) cells during the contraction phase in the process of memory T cell formation. Thioredoxin-interacting protein (Txnip), a regulator of oxidoreductase, regulated apoptosis by scavenging ROS via the nuclear factor erythroid 2-related factor 2 (Nrf2)–biliverdin reductase B (Blvrb) pathway. Txnip regulated the pathology of chronic airway inflammation in the lung by controlling the generation of allergen-specific pathogenic memory Th2 cells in vivo. Thus, the Txnip–Nrf2–Blvrb axis directs ROS metabolic reprogramming in Th2 cells and is a potential therapeutic target for intractable chronic inflammatory diseases.
thioredoxin-interacting protein (Txnip)
reactive oxygen species (ROS)
nuclear factor-erythroid factor 2-related factor 2 (Nrf2)
biliverdin reductase B (Blvrb)
memory Th2 cells
Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 26221305 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP19H05650 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP20H03685 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP17K08876 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP20KK0351 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP21H05120 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP21H05121 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP21K20754 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP22H02885 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Ministry of Education, Culture, Sports, Science and Technology (MEXT) 501100001700 JP22K15484 Kota KokuboKiyoshi HiraharaAtsushi OnoderaToshinori Nakayama Japan Agency for Medical Research and Development (AMED) 100009619 JP19ek0410045 Kiyoshi HiraharaToshinori Nakayama Japan Agency for Medical Research and Development (AMED) 100009619 JP22ek0410092 Kiyoshi HiraharaToshinori Nakayama Japan Agency for Medical Research and Development (AMED) 100009619 JP22gm1210003 Kiyoshi HiraharaToshinori Nakayama MEXT | JST | Fusion Oriented REsearch for disruptive Science and Technology (FOREST) 501100020964 JPMJFR200R Kiyoshi Hirahara
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pmcCD4+ memory T cells are central to adaptive immunity and play a critical role in the host defense against microorganisms (1). CD4+ memory T cells are also involved in shaping the pathology of chronic inflammation (2). Upon antigen recognition by T cell receptor (TCR), naïve CD4+ T cells proliferate and differentiate into effector T cells. After elimination of antigens, most of the effector T cells die by apoptosis in the contraction phase (3), but some survive and become long-lived memory T cells. The population size of the antigen-specific memory T cells is important for ensuring quality adaptive immune responses (4, 5). Metabolic reprogramming, including the upregulation of adenosine monophosphate-activated protein kinase signaling and downregulation of mechanistic target of rapamycin signaling in T cells, is crucial for the formation of antigen-specific memory T cells (6, 7). However, metabolic reprogramming in other pathways, such as reactive oxygen species (ROS) metabolism, which regulates the formation of memory T cells, remains unclear.
Metabolic reprogramming is also known to be essential for the proper activation of naïve T cells and their differentiation into effector T cells (8–10). Antigen stimulation via TCR causes a dramatic increase in aerobic glycolysis, which induces the generation of ROS in effector T cells (11, 12). ROS are also produced in mitochondria during the metabolic process of oxidative phosphorylation or by NADPH oxidase expressed in effector T cells (13–15). ROS are necessary for the transmission of TCR signaling and subsequent proliferation of effector T cells during the expansion phase (14). However, they also induce activation-induced apoptotic cell death of effector T cells via DNA damage and the outflow of cytochrome C via depolarization of the mitochondrial membrane (16, 17). Thus, the appropriate regulation of cellular ROS metabolism is required to ensure proper differentiation of effector T cells. However, the role of ROS metabolism in the formation of memory T cells remains unclear.
In the present study, we investigated the changes in ROS metabolism and its regulation of the formation of CD4+ memory T cells. We found that ROS metabolism dramatically shifts from the production to the scavenging of effector T cells during the contraction phase. The thioredoxin-binding protein Txnip was gradually up-regulated in effector T cells during the contraction phase. It scavenged cellular ROS by activating the Nrf2-Blvrb pathway but not by activating thioredoxin in effector T cells. Furthermore, we found that Txnip regulated the pathology of allergic airway inflammation in the lung by controlling the number of antigen-specific memory T helper-2 (Th2) cells in vivo. Thus, metabolic shift from ROS production to ROS scavenging via the Txnip–Nrf2–Blvrb pathway is essential for the formation of the memory-type pathogenic Th2 cells that shape the pathology of allergic inflammation.
Results
The Metabolic Pathways for Scavenging Oxidative Stress Were Up-Regulated in CD4+ T Cells during the Contraction Phase In Vivo.
To determine the dynamics of cellular ROS in CD4+ T cells, we first examined the intensity of CellROX, an oxidative stress reagent that is nonfluorescent in a reduced state and exhibits intense fluorogenic signal upon oxidation, in naïve, effector, and memory CD4+ T cells (SI Appendix, Fig. S1 A and B). Effector Th2 cells showed a higher fluorescence intensity of CellROX signals than that in naïve CD4+ T or memory Th2 cells (Fig. 1 A and B). A fluorescence-activated cell sorting analysis confirmed the increase in the mean fluorescence intensity (MFI) of CellROX in effector Th2 cells (Fig. 1 C and D and SI Appendix, Fig. S1 C and D).
Fig. 1. Pathways for scavenging oxidative stress are activated in CD4+ T cells during the contraction phase in vivo. (A) Representative confocal micrograph images of naïve CD4+ T cells, effector Th2 cells, and memory Th2 cells stained with DAPI (blue), CellROX (red), Mitotracker (green), and anti-CD4 (white). The white bar in the image is a 1-μm scale bar. Representative findings of two independent experiments are shown. (B) Fluorescence intensities of CellROX in each cell are shown (naïve CD4+ and memory Th2, n=15; effector Th2, n=22). (C and D) A representative histogram plot (C) and the MFI (D) of CellROX from two independent experiments are shown (n=5). (E) The number (Left) and MFI of CellROX (Right) of transferred T cells at each time point after cell transfer (days 1, 5, and 17, n=3; day 7, n=10; day 11, n=11; 1 mo, n=6; 2 mo, n=8) from four independent experiments are shown. (F) A UMAP projection of transferred T cells at days 7 (blue) and 11 (orange) after cell transfer is depicted. (G) A ranking plot of ssGSVA scores is depicted. Red dots indicate the top 10% pathways. (H) ssGSVA scores of oxidation–reduction process (left column) and oxidoreductase activity (right column) are projected on UMAPs (upper row) and ridgeline plots (lower row). (F–H) Cells from two mice were pooled for each group. Data are expressed as the mean ± SD. P-values were calculated by a one-way ANOVA. **P < 0.01, ***P < 0.001, ****P < 0.0001.
To assess the dynamics of cellular ROS in more detail, we performed an in vivo experiment, in which OVA-specific naïve CD4+ T cells were adoptively transferred to syngeneic BALB/c mice that had been administered ovalbumin (OVA) and Alum intraperitoneally on days 1 and 6 (SI Appendix, Fig. S1E). The cell number and MFI of CellROX in antigen-specific CD4+ T cells started to decrease between 7 and 11 d after the initial antigen administration, followed by a gradual decrease up to 2 mo in vivo (SI Appendix, Fig. S1E and Fig. 1E). These results indicate that antigen-reactive CD4+ T cells on days 7 and 11 were in the effector and contraction phases, respectively. In another experimental model in which in vitro-differentiated effector CD4+ T cells were adoptively transferred, a significant decrease in the MFI of CellROX was also observed at 48 h after cell transfer (SI Appendix, Fig. S1 F and G).
We next performed single-cell RNA sequencing (scRNA-Seq) using cells at 7 and 11 d after the initial antigen administration (Fig. 1F). A single-sample Gene set variation analysis (ssGSVA) revealed that the metabolic pathways related to the scavenging oxidative stress, including the “oxidation–reduction process” and “oxidoreductase activity” pathways, were enriched in cells at 11 d compared to those at 7 d after the initial antigen administration (Fig. 1 G and H); furthermore, these pathways were also enriched in transferred Th2 cells that had been collected 48 h after adoptive transfer (SI Appendix, Fig. S1 H–J). Thus, the levels of cellular ROS in CD4+ T cells in the contraction phase were dramatically decreased, accompanied by an increased expression of genes involved in scavenging oxidative stress.
The Thioredoxin-Interacting Protein Txnip Is Up-Regulated in the Contraction Phase.
A pseudo-time trajectory analysis revealed the differentiation trajectories of individual CD4+ T cells recovered from mice 7 and 11 d after the initial antigen administration (Fig. 2A). Based on the differentiation trajectories determined by the pseudo-time, the genes were categorized into three major groups using Pearson’s correlation coefficient (Fig. 2B). Group 1 genes, including Myc and Rtc4, showed an apparent decrease in expression during the contraction phase (Fig. 2C, top rows [blue]). In sharp contrast, Group 3 genes, such as Txnip and Foxp1, showed an apparent increase in expression during the contraction phase (Fig. 2C, bottom rows [red]). Group 2 genes showed a gradual decrease in expression (Fig. 2C, middle rows [orange]). The uniform manifold approximation and projection (UMAP) analysis also revealed a similar expression pattern of representative genes among Groups 1, 2, and 3 (Fig. 2D).
Fig. 2. Thioredoxin-interacting protein, Txnip is up-regulated in transferred T cells. (A–E) Analyses of scRNA-Seq data of transferred T cells at days 7 and 11 after cell transfer. A UMAP projection of transferred T cells at days 7 and 11 with the pseudo-time trajectory analysis is depicted (A). The plotting symbol for each cell is colored based on the pseudo-time. Cells that have infinite pseudo-time are shown in gray. The relative expression of genes (rows) across cells (columns) is shown (B). The cells sorted by pseudo-time are determined in Fig. 2A. The genes are classified into three groups based on the temporal expression patterns. A gene expression heat map of the top five characteristic genes in each group (Group 1, blue; Group 2, orange; and Group 3, red) is depicted (C). Density plots on UMAPs colored by the density of each cell in the expression of top two characteristic genes in each group (Group 1, upper row; Group 2, middle row; and Group 3, lower row) are shown (D). A volcano plot that shows the differential gene expression between transferred T cells at days 7 and 11 is depicted (E). Orange dots indicate the oxidative stress-related genes, which are included in the two pathways in Fig. 1H.
Most of the 102 oxidative stress-related genes involved in the “oxidation–reduction process” or “oxidoreductase activity” pathways shown in Fig. 1G were up-regulated in antigen-reactive CD4+ T cells 11 d after the initial antigen administration (Fig. 2E orange color dots). Txnip, which encodes thioredoxin-interacting protein, showed the highest fold expression among the up-regulated oxidative stress-related genes in antigen-reactive CD4+ T cells 11 d after the initial antigen administration (Fig. 2E and SI Appendix, Fig. S2A). Txnip also showed the highest fold change in expression between transferred Th2 cells that were recovered from mice 48 h after adoptive transfer and effector Th2 cells among all the up-regulated oxidative stress-related genes (SI Appendix, Fig. S2 B–H). Thus, the majority of the oxidative stress-related genes, including Txnip, were up-regulated in the CD4+ T cells during the contraction phase in vivo.
Txnip Deficiency Resulted in Enhanced Apoptosis Accompanied by an Increased MFI of CellROX in Transferred Th2 Cells.
We next investigated the role of Txnip in CD4+ T cells using genetically modified mice. CD4-specific Txnip-deficient mice (referred Txnip KO mice from now on) at 6 to 12 wk old showed a comparable proportion of CD4+ T cells in the thymus and spleen to control wild-type mice, consistent with a previous report (18) (SI Appendix, Fig. S3 A–D). Txnip-deficient effector T cells, including Th1, Th2, and Th0 cells, produced a similar amount of effector cytokines to wild-type effector T cells (SI Appendix, Fig. S3E).
At 48 h after adoptive transfer (SI Appendix, Fig. S3F), Txnip-deficient transferred Th2 cells showed a higher MFI of CellROX than wild-type Th2 cells (Fig. 3 A, Right), although Txnip-deficient effector Th2 cells showed a comparable MFI of CellROX to wild-type effector Th2 cells (Fig. 3 A, Left). In whole cells, Txnip deficiency in transferred Th2 cells resulted in an increased percentage of annexin V-positive cells and a decreased number of cells compared with wild-type cells (Fig. 3 B and C and SI Appendix, Fig. S3 G–J). The administration of MitoTEMPO and Trolox, ROS scavenger reagents that have been reported to quench ROS and oxidative damage in vivo (19, 20), inhibited the elevation of the MFI of CellROX in Txnip-deficient transferred Th2 cells (Fig. 3 D and E, Left). The administration of these ROS scavenger reagents also inhibited the elevation of annexin V-positive cells in Txnip-deficient transferred Th2 cells (Fig. 3 D and E, Middle and Right). Preloading with methyl-GSH, another ROS scavenger reagent (21), resulted in decreased ROS levels in both Txnip-deficient effector and transferred Th2 cells (SI Appendix, Fig. S3 K and L, Left). However, the preloading of methyl-GSH showed relatively little effect on apoptosis in Txnip-deficient transferred Th2 cells (SI Appendix, Fig. S3 L, Middle and Right). Thus, Txnip deficiency resulted in an increased MFI of CellROX and increased apoptosis in transferred Th2 cells.
Fig. 3. Txnip deficiency causes enhanced apoptosis accompanied by increased cellular ROS in transferred Th2 cells. (A) The MFI of CellROX of wild-type or Txnip-deficient effector Th2 cells (Left panel; wild-type: n=5, Txnip-deficient: n=5) and transferred Th2 cells (Right panel; wild-type: n=13, Txnip-deficient: n=8) from more than two independent experiments are shown. (B) Percentages of annexin V+ cells in effector Th2 cells (Left panel; wild-type: n=6, Txnip-deficient: n=6) and transferred Th2 cells (Right panel; wild-type: n=10, Txnip-deficient: n=10) from six independent experiments are shown. (C) The number of annexin V- cells in transferred Th2 cells. (D) The MFI of CellROX (Left panel; MitoTEMPO (−): n=7 in both wild-type and Txnip-deficient transferred Th2 cells, MitoTEMPO (+): n=4 in both wild-type and Txnip-deficient transferred Th2 cells), percentages of annexin V+ cells (Middle panel; n=4 in all indicated groups), and the number of annexin V− cells (Right panel; n=4 in all indicated groups) in transferred Th2 cells with in vivo MitoTEMPO treatment. The data from three independent experiments are shown. (E) The MFI of CellROX (Left), percentages of annexin V+ cells (Center), and the number of annexin V- cells (Right) in transferred Th2 cells with in vivo Trolox treatment (n=4 in all indicated groups). The data from two independent experiments are shown. (F–H) Analyses of scRNA-Seq data of wild-type and Txnip-deficient transferred Th2 cells. A UMAP projection of wild-type (green) and Txnip-deficient (purple) transferred Th2 cells is depicted (F). A ranking plot of ssGSVA scores is depicted. Red dots indicate the top 10% pathways (G). ssGSVA scores of NRF2-ARE PATHWAY (left column) and Nrf2-ARE REGULATION (right column) are projected on UMAPs (upper row) and ridgeline plots (lower row) (H). The dotted line with the red arrow indicates Txnip-deficient Th2 cell-specific cell population. Cells from two mice were pooled for each group (F–H). (I and J) An immunoblot analysis of the Nrf2 expression from two independent experiments. A representative immunoblot is shown in I. β-actin was used as a loading control. Quantified intensities of Nrf2 are shown in J (n=6 in all indicated groups). Data are expressed as the mean ± SD. P-values were calculated by the Mann–Whitney U test (A–C, and J) and a one-way ANOVA (D and E). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Thioredoxin, a ROS scavenging oxidoreductase, is a binding partner of Txnip (22). Txnip restrains the oxidoreductive function of thioredoxin through its binding (23). Unexpectedly, Txnip-deficient transferred Th2 cells showed no marked difference in the protein expression of thioredoxin (SI Appendix, Fig. S3 M and N) or thioredoxin activity (SI Appendix, Fig. S3O) from wild-type effector or transferred Th2 cells. To explore the molecular mechanisms underlying the ROS scavenging by Txnip in transferred Th2 cells, we performed scRNA-Seq analyses using wild-type or Txnip-deficient Th2 cells recovered 48 h after cell transfer. Characteristic clusters of Txnip-deficient Th2 cells were detected among transferred Th2 cells but not among effector Th2 cells (Fig. 3F and SI Appendix, Fig. S3P). Nuclear factor-erythroid factor 2-related factor 2 (Nrf2) is a transcription factor that regulates scavenging oxidative stress (24). The ssGSVA analysis showed that the Nrf2-downstream pathways, including “Nrf2-ARE PATHWAY” and “Nrf2-ARE REGULATION,” were enriched in wild-type compared with Txnip-deficient transferred Th2 cells (Fig. 3G). In both pathways, the GSVA score was decreased in Txnip-deficient transferred Th2 cells compared with wild-type transferred Th2 cells (Fig. 3H). Consistent with these results, the protein expression of Nrf2 was reduced in Txnip-deficient transferred Th2 cells (Fig. 3 I and J, and SI Appendix, Fig. S3Q). These results suggest that Txnip may activate Nrf2-downstream genes and scavenge cellular ROS in transferred Th2 cells via regulation of the protein expression of Nrf2.
Txnip Regulates the Scavenging of Cellular ROS via the Nrf2–Blvrb Pathway.
We next performed conventional RNA-Seq analyses using transferred Th2 cells from wild-type and Txnip KO mice to determine the Nrf2-downstream genes. We noted the decreased expression of Biliverdin reductase B (Blvrb), which is a reductase and a downstream gene of Nrf2, in Txnip-deficient transferred Th2 cells (Fig. 4A and SI Appendix, Fig. S4A). A decreased number of Blvrb-expressing Th2 cells among Txnip-deficient cells was also detected in our scRNA-Seq data set (Fig. 4B). An assay to detect transposase-accessible chromatin using sequencing (ATAC-Seq) with transferred Th2 cells revealed the Txnip-dependent chromatin regulation in the Blvrb gene locus (Fig. 4C and SI Appendix, Fig. S4B). The protein expression of Blvrb was also reduced in Txnip-deficient transferred Th2 cells (Fig. 4 D and E, and SI Appendix, Fig. S4C). Consistent with these results, the reductase activity of Blvrb was attenuated in Txnip-deficient transferred Th2 cells (Fig. 4 F, Right) but not in Txnip-deficient effector Th2 cells (Fig. 4 F, Left).
Fig. 4. Txnip regulates scavenging cellular ROS via the Nrf2–Blrvb pathway. (A) A MA plot for DESeq from the results of bulk RNA-Seq of wild-type and Txnip-deficient transferred Th2 cells is depicted. Colored dots indicate the genes that show fold changes >2 (red) or <1/2 (blue) accompanied by P values < 0.01. Three mice per group were analyzed. (B) Violin plots of Blvrb expression in wild-type and Txnip-deficient transferred Th2 cells are shown. (C) Gene track views of ATAC-Seq signals in wild-type transferred Th2 cells and Txnip-deficient transferred Th2 cells across the Blvrb gene locus and the Actb gene locus are shown. Two mice per group were analyzed. (D and E) An immunoblot analysis of the Blvrb expression from two independent experiments. A representative immunoblot is shown in D. β-actin was used as a loading control. Quantified intensities of Blvrb are shown in E (n=6 in all indicated groups). (F) The Blvrb activity in effector Th2 cells (Left panel; n=4 in both wild-type and Txnip-deficient effector Th2 cells) and transferred Th2 cells (Right panel; n=9 in both wild-type and Txnip-deficient transferred Th2 cells) from two independent experiments is shown. (G) Ratios of the amount of NADPH to the amount of NADP+ in transferred Th2 cells (wild-type: n=8, Txnip-deficient: n=10) from three independent experiments are shown. (H) The relative expression of Blvrb in transferred Th2 cells (mock-controlled wild-type: n=6, Nfe2l2 overexpressing wild-type: n=10, mock-controlled Txnip-deficient: n=8, Nfe2l2 overexpressing Txnip-deficient: n=8) from four independent experiments is shown. (I and J) The MFI of CellROX (I) and percentages of annexin V+ cells (J) in transferred Th2 cells (mock-controlled wild-type: n=10, Nfe2l2 overexpressing wild-type: n=9, mock-controlled Txnip-deficient: n=12, Nfe2l2 overexpressing Txnip-deficient: n=13) from six independent experiments are shown. (K and L) The MFI of CellROX (K) and percentages of annexin V+ cells (L) in transferred Th2 cells (mock-controlled or Blvrb-overexpressing wild-type: n=4, mock-controlled or Blvrb-overexpressing Txnip-deficient: n=8) from two independent experiments are shown. Data are expressed as the mean ± SD. P-values were calculated by Mann–Whitney U test (E–G, K, and L), or a one-way ANOVA (H–J). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Blvrb mediates reduction of biliverdin to bilirubin and functions as a potent antioxidant through scavenging ROS (SI Appendix, Fig. S4D). The Blvrb-dependent reduction is driven by the cofactor nicotinamide adenine dinucleotide phosphate (NADPH) (25) (SI Appendix, Fig. S4D). We wondered whether or not the decreased activity of Blvrb in Txnip-deficient transferred Th2 cells was compensated by an increased production of NADPH. We found that the amount of NADPH was decreased in Txnip-deficient transferred Th2 cells (Fig. 4G and SI Appendix, Fig. S4E). At the same time, Txnip deficiency showed little effect on the uptake of glucose, fatty acids, or glutamine in transferred Th2 cells (SI Appendix, Fig. S4 F–I). Finally, we found that the overexpression of the Nfe2l2 gene, which encodes Nrf2, restored the expression of Blvrb significantly in Txnip-deficient transferred Th2 cells (Fig. 4H). Furthermore, the overexpression of Nfe2l2 resulted in a decreased MFI of CellROX and decreased apoptosis in Txnip-deficient transferred Th2 cells (Fig. 4 I and J). At the same time, the overexpression of Blvrb also resulted in a decreased MFI of CellROX and decreased apoptosis in Txnip-deficient transferred Th2 cells (Fig. 4 K and L and SI Appendix, Fig. S4J). These results indicate that Txnip regulates the Nrf2–Blvrb pathway and controls the levels of cellular ROS and apoptotic cell death in Th2 cells.
Txnip Deficiency Resulted in a Decreased Number of Memory Th2 Cells Accompanied by an Increased MFI of CellROX.
Next, we explored whether or not Txnip was important for the proper formation of memory T cells in vivo. The expression of Txnip in Th2 cells after cell transfer increased over time (Fig. 5A). Even at 4 wk after adoptive transfer, Txnip-deficient cells showed a higher MFI of CellROX than wild-type Th2 cells (Fig. 5B). Furthermore, the numbers of Txnip-deficient transferred Th2 cells in vivo were consistently lower than those in wild-type transferred Th2 cells over 4 wk (Fig. 5 C and D). The significant decrease in the number of Txnip-deficient transferred helper T cells in vivo was observed in not only Th2 cells but also Th1 and Th0 cells (SI Appendix, Fig. S5A). Moreover, 4 wk after adoptive transfer, an increased MFI of CellROX and decreased number of Txnip-deficient memory Th2 cells were observed in the lung, peripheral blood mononuclear cell (PBMC), bone marrow, and lymph nodes (Fig. 5 E and F and SI Appendix, Fig. S5B). As expected, the overexpression of Txnip in effector Th2 cells resulted in an increased number of Th2 cells for at least 4 wk after cell transfer (Fig. 5 G and H). Neither Txnip deficiency nor its overexpression showed any marked effect on the production of Th2 cytokines by memory Th2 cells, indicating that the Th2 cell function is not affected by the expression of Txnip (SI Appendix, Fig. S5 C and D). Thus, Txnip regulates the levels of cellular ROS and the formation of memory Th2 cells in vivo while leaving their function intact.
Fig. 5. Deletion of Txnip causes a decrease in the number of memory Th2 cells accompanied by an increased amount of ROS. (A) The relative expression of Txnip in the Th2 cells at each time point (effector Th2: n=6, 48 h: n=6, 1 wk: n=4, 2 wk: n=4, 3 wk: n=6, 4 wk: n=5) from three independent experiments is shown. (B and C) The relative MFI of CellROX, compared with naïve CD4+ T cells (wild-type Th2 cells: n=6 in the indicated time point, Txnip-deficient Th2 cells; 1 wk: n=6, 2 wk: n=4, 3 wk: n=4, 4 wk: n=6) (B), and the number of transferred Th2 cells (wild-type Th2 cells; 1 wk: n=7, 2 wk: n=5, 3 wk: n=7, 4 wk: n=5, Txnip-deficient Th2 cells; 1 wk: n=11, 2 wk: n=6, 3 wk: n=5, 4 wk: n=5) (C) at each time point after cell transfer from more than three independent experiments are shown. (D) Representative plots of anti-CD4- and anti-OVA-specific TCR (KJ1) staining in spleen at 4 wk after cell transfer are shown. Three independent experiments were performed, with similar results. (E and F) MFI of CellROX of transferred Th2 cells (n=4 in each group) (E) and percentages of KJ1+ cells in CD4+ T cells (n=4 in each group) (F) recovered from each tissue at 4 wk after cell transfer from two independent experiments are shown. (G) The numbers of mock-control or Txnip overexpressing Th2 cells in spleens (mock; 1 wk: n=9, 2 wk: n=6, 3 wk: n=6, 4 wk: n=7, Txnip overexpression; 1 wk: n=9, 2 wk: n=5, 3 wk: n=5, 4 wk: n=5) from four independent experiments are shown. (H) Representative plots of cell surface staining of anti-CD4- and anti-OVA-specific TCR in transferred Th2 cells are shown. Three independent experiments were performed, with similar results. Data are expressed as the mean ± SD. P-values were calculated by a one-way ANOVA (A), two-tailed unpaired t test (B), or Mann–Whitney U test (C, E, F, and G). *P < 0.05, **P < 0.01, ***P < 0.001.
Txnip Regulates the Pathology of Allergic Airway Inflammation In Vivo.
Finally, we investigated the roles of Txnip in the formation of allergen-specific pathogenic memory Th2 cells that induce allergic airway inflammation in vivo. Wild-type mice and Txnip KO mice were administered OVA and HP-β-CD adjuvant intranasally, and 5 wk later, they were challenged intranasally with OVA on days 42 and 43. Assays were performed on day 44 (Fig. 6A). The number of CD4+CD44hi memory T cells in the lung was significantly lower in OVA-challenged Txnip KO mice than those in OVA-challenged wild-type mice (Fig. 6B). Memory responses, such as infiltration of eosinophils in bronchoalveolar lavage fluid (BALF), were significantly decreased in Txnip KO mice along with decreased levels of Th2 cytokines, such as IL-4, IL-5, and IL-13 (Fig. 6 C and D). Infiltration of inflammatory cells in the lung parenchyma was also decreased in Txnip KO mice (Fig. 6E). When Txnip-overexpressing OVA-specific (KJ1+) memory Th2 cells were transferred into recipient mice followed by challenge with OVA, the number of OVA-specific memory Th2 cells in the lung was significantly higher than those in control mice that had received mock-control Th2 cells (Fig. 6 F and G). Txnip overexpression in memory Th2 cells resulted in enhanced memory responses, including increased infiltration of eosinophils (Fig. 6H), increased production of Th2 cytokines in BALF (Fig. 6I), and increased infiltration of inflammatory cells into the lung parenchyma (Fig. 6J). Thus, the pathology of allergic airway inflammation induced by allergen-specific memory Th2 cells was controlled by the expression of Txnip in CD4+ T cells.
Fig. 6. Txnip regulates the pathology of allergic airway inflammation in vivo. (A) A schematic illustration of the intranasal exposure to OVA and HP-β-CD followed by challenge with OVA after a 5-wk rest period is depicted. (B) The numbers of CD4+CD44high T cells recovered from the lungs (wild-type OVA (−): n=9, Txnip KO OVA (−): n=7, wild-type OVA (+): n=9, Txnip KO OVA (+): n=8) from two independent experiments are shown. (C and D) The numbers of macrophages (Mac.), lymphocytes (Lym.), neutrophils (Neut.), eosinophils (Eos.), and total cells (Total) (C) and the concentration of cytokines, including IL-4, IL-5, IL-13, and IFN-γ (D) in BALF (wild-type OVA (−): n=6, Txnip KO OVA (−): n=6, wild-type OVA (+): n=8, Txnip KO OVA (+): n=7), from two independent experiments are shown. (E) Representative histological sections of the lungs stained with hematoxylin and eosin are shown. More than three mice per group were analyzed. Two independent experiments were performed, with similar results. (F) A schematic illustration of the intranasal exposure to OVA after a 4-wk rest period from the adoptive transfer is depicted. (G) The numbers of transferred Th2 cells recovered from the lungs in recipients of mock control or Txnip overexpressing Th2 cells (n=4 in each group) from two independent experiments are shown. (H and I) The numbers of macrophages, lymphocytes, neutrophils, eosinophils, and total cells (H) and the concentration of cytokines, including IL-4, IL-5, IL-13, and IFN-γ (I) in BALF (mice without Th2 cell transfer: n=7, mice transferred with mock control Th2 cells: n=6, mice transferred with Txnip overexpressing Th2 cells: n=6), from two independent experiments are shown. (J) Representative histological sections of the lungs stained with hematoxylin and eosin are shown. More than three mice per group were analyzed. Two independent experiments were performed, with similar results. Data are expressed as the mean ± SD. P-values were calculated by a one-way ANOVA (B and D), two-way ANOVA (C and H), or Mann–Whitney U test (G and I). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Discussion
We identified Txnip as a key molecule in the regulation of ROS metabolism during the formation of allergen-specific memory Th2 cells that induce allergic airway inflammation in vivo. We found that cellular ROS metabolism dramatically shifted from the production process to the scavenging process in the contraction phase of Th2 cells. Txnip, as a ROS metabolism checkpoint molecule, scavenged cellular ROS via the activation of the Nrf2–Blvrb pathway. The genetic deletion of Txnip attenuated allergic airway inflammation as a result of the decreased formation of allergen-specific memory Th2 cells in vivo. In contrast, the overexpression of Txnip in Th2 cells exacerbated the pathology of airway inflammation due to the increased formation of allergen-specific memory Th2 cells. Thus, this study highlights unique molecular mechanisms in redox metabolism in Th2 cells that are crucial for the induction of allergen-specific memory responses in vivo.
Metabolic reprogramming during the activation of T cell is essential for inducing proper immune responses (9, 15). The ROS metabolism in CD4+ T cells shifts to the production process to meet the energy requirements of activated CD4+ T cells after TCR stimulation (14). ROS is required in order for activated CD4+ T cells to produce IL-2 (26). The amount of cellular ROS is dramatically increased in effector T cells (17). We investigated the dynamics of cellular ROS once T cell activation had ceased and found a rapid decrease in cellular ROS in CD4+ T cells during the contraction phase. This rapid decrease was probably induced by the enhanced scavenging pathways of cellular ROS. In CD8+ T cells, CD28 signaling is reported to be critical for the downregulation of Txnip, which regulates mitochondrial fatty acid oxidation (27). Another group showed that TCR stimulation—rather than CD28 costimulation—was crucial for the downregulation of Txnip (18). In the current study, we found that Txnip was rapidly up-regulated once T cell activation ceased in CD4+ T cells and that the upregulation of Txnip was critical for the formation of allergen-specific memory Th2 cells that induce allergic airway inflammation.
The thioredoxin system, which is composed of NADPH, thioredoxin, and thioredoxin reductase, is important for the maintenance of cellular redox metabolism (28). The thioredoxin system scavenges cellular ROS through its disulfide reductase activity. Txnip binds to reduced thioredoxin and restrains the thioredoxin function (23). Importantly, however, we found that the thioredoxin activity was not changed in Txnip-deficient transferred Th2 cells. Instead, our single-cell RNA sequencing analysis revealed that a loss of Txnip resulted in the decreased activity of the Nrf2 pathway in transferred Th2 cells. Consistent with this result, Txnip deficiency resulted in the decreased protein expression of Nrf2. Nrf2, a basic leucine zipper (bZip) transcription factor, controls the cellular antioxidant responses (29). The expression of Blvrb, an Nrf2 target gene, as well as the protein level and activity of Blvrb, was down-regulated in Txnip-deficient transferred Th2 cells. Our ATAC-Seq results suggest that Txnip regulates the expression of Blvrb epigenetically. Thus, our results indicate that Txnip regulates cellular redox metabolism via the epigenetic control of the expression of Blvrb.
In our experimental model of allergic airway inflammation, the expression of Txnip in allergen-specific memory Th2 cells regulated the pathology of allergic airway inflammation. Both Txnip-deficient and Txnip-overexpressing memory Th2 cells showed similar levels of cytokine production to control memory Th2 cells. Furthermore, the loss of Txnip showed little effect on the production of effector cytokines, such as IL-4, IL-5, and IL-13, by effector Th2 cells. However, Txnip deficiency resulted in an increased amount of cellular ROS and decreased formation of memory Th2 cells in vivo. Thus, ROS metabolic reprogramming by Txnip may regulate the quantity—not the quality—of memory Th2 cells. In any event, targeting redox metabolism in allergen-specific Th2 cells may be a novel therapeutic target for allergen-specific airway inflammation.
In summary, we identified a unique metabolic mechanism that controls the formation of allergen-specific memory Th2 cells. We elucidated the critical role of the Txnip–Nrf2–Blvrb axis in the redox metabolism of CD4+ T cells for the formation of allergen-specific memory T cells, which induce the pathology of allergic airway inflammation. Understanding the metabolic mechanisms that govern the formation of CD4+ memory T cells will be crucial for the development of new therapeutic strategies for intractable inflammatory diseases.
Materials and Methods
Detailed descriptions of all materials and methods are provided in SI Appendix, Materials and Methods. In brief, splenic naïve CD4+ T cells (CD44loCD62Lhi) from DO11.10 OVA-specific TCR transgenic mice were transferred intravenously into BALB/c recipient mice. The mice that were transferred naïve CD4+ T cells were immunized with 100 μg OVA (Sigma-Aldrich) and 4 mg Alum (Thermo Fisher Scientific) administered intraperitoneally on 1 and 6 d after cell transfer. Before the first immunization, KJ1+CD62Lhi T cells in the spleen were purified as naïve CD4+ T cells. And, 5, 7, 11, 17, 30, and 60 d after cell transfer, KJ1+CD44hi T cells in the spleen were purified as antigen-recognized T cells. These purified T cells were used to investigate the gene expression and the amount of oxidative stress.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank K. Sugaya for her excellent technical assistance. We thank A. Matsumoto for providing helpful suggestions. This work was supported by the following grants: Ministry of Education, Culture, Sports, Science and Technology (MEXT Japan) Grants-in-Aid for Scientific Research (Nos. 26221305, JP19H05650, JP20H03685, JP17K08876, JP20KK0351, JP21H05120, JP21H05121, JP21K20754, JP22H02885, and JP22K15484); Practical Research Project for Allergic Diseases and Immunology (Research on Allergic Diseases and Immunology) from the Japan Agency for Medical Research and Development, AMED (Nos. JP19ek0410045, and JP22ek0410092); AMED-CREST, AMED (No. JP22gm1210003); JST FOREST Project (No. JPMJFR200R, Japan); Mochida Memorial Foundation for Medical and Pharmaceutical Research, MSD Life Science Foundation, Takeda Science Foundation, and Kowa Life Science Foundation. K. Kokubo was supported by the PhD Scholarship (Kibou Project) from Japanese Society for Immunology.
Author contributions
K.K., K.H., M.K., and C.I. designed research; K.K., K.H., M.K., K.T., Y. Shimada, Y. Sonobe, R.S., and T.H. performed research; K.K., K.H., M.K., A.O., and T.N. analyzed data; and K.K., K.H., and T.N. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
scRNA-Seq, bulk RNA-Seq and ATAC-Seq datasets data have been deposited in [Genome Expression Omnibus (GEO)] [Accession Number GSE221291 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221291)] (30).
Supporting Information
This article is a PNAS Direct Submission. J.R. is a guest editor invited by the Editorial Board.
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PMC009xxxxxx/PMC9926255.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595666
202215882
10.1073/pnas.2215882120
research-articleResearch Articleenv-sci-bioEnvironmental Sciences417
Biological Sciences
Environmental Sciences
Holocene variations in Lake Titicaca water level and their implications for sociopolitical developments in the central Andes
Guédron Stéphane [email protected]
a 1 https://orcid.org/0000-0003-4229-1566
Delaere Christophe b https://orcid.org/0000-0002-6468-7847
Fritz Sherilyn. C. c https://orcid.org/0000-0002-4423-4189
Tolu Julie d e f https://orcid.org/0000-0001-5249-9497
Sabatier Pierre g https://orcid.org/0000-0002-9620-1514
Devel Anne-Lise g
Heredia Carlos h
Vérin Claire a
Alves Eduardo Q. i https://orcid.org/0000-0003-0919-326X
Baker Paul A. j
aUniversité Grenoble Alpes, Institut des Sciences de la Terre, Institut de Recherche pour le Développement, Grenoble 38000, France
bCentre de Recherches en Archéologie et Patrimoine, Université libre de Bruxelles, Brussels, 1050, Belgium
cDepartment of Earth and Atmospheric Sciences and School of Biological Sciences, University of Nebraska – Lincoln, Lincoln, NE 68588
dEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, CH-8600, Switzerland
eETH Zürich, Zürich, CH-8092, Switzerland
fDepartment of Ecology and Environmental Science, Umeå University, 901 87 Sweden
gUniv. Savoie Mont Blanc, Centre National de la Recherche Scientifiquel, Environnements Dynamiques et Territoires de la Montagne, Chambéry, 73000, France
hLaboratorio de Hidroquímica – Instituto de Investigaciones Químicas – Universidad Mayor de San Andres, Campus Universitario de Cota Cota, La Paz, casilla 3161, Bolivia
iRoyal Belgian Institute of Natural Sciences, Bruxelles, 1000, Belgium
jDivision of Earth and Climate Sciences, Duke University, Durham, NC 27708
1To whom correspondence may be addressed. Email: [email protected].
Edited by Cathy Whitlock, Montana State University Bozeman, Bozeman, MT; received September 16, 2022; accepted November 17, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e221588212016 9 2022
17 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
The Lake Titicaca Basin has been inhabited for millennia, and cultural development has been linked to regional climate history. We reevaluated Lake Titicaca’s Holocene water-level history by integrating organic-carbon-stable-isotope measurements from multiple sediment cores with analysis of paleoshoreline indicators. We developed a lake-level model that is compatible with archaeological timescales and shows Late Holocene fluctuations of ~5 m relative to the mean values. The model suggests low stands much shorter than indicated by prior studies and a more gradual lake-level rise, consistent with reconstructions of regional precipitation. Major lake-level rises coincided with periods of cultural emergence, including the Tiwanaku culture. The general trend of increased wetness during the Late Holocene likely promoted agricultural and sociopolitical development and influenced population migration.
Holocene climate in the high tropical Andes was characterized by both gradual and abrupt changes, which disrupted the hydrological cycle and impacted landscapes and societies. High-resolution paleoenvironmental records are essential to contextualize archaeological data and to evaluate the sociopolitical response of ancient societies to environmental variability. Middle-to-Late Holocene water levels in Lake Titicaca were reevaluated through a transfer function model based on measurements of organic carbon stable isotopes, combined with high-resolution profiles of other geochemical variables and paleoshoreline indicators. Our reconstruction indicates that following a prolonged low stand during the Middle Holocene (4000 to 2400 BCE), lake level rose rapidly ~15 m by 1800 BCE, and then increased another 3 to 6 m in a series of steps, attaining the highest values after ~1600 CE. The largest lake-level increases coincided with major sociopolitical changes reported by archaeologists. In particular, at the end of the Formative Period (500 CE), a major lake-level rise inundated large shoreline areas and forced populations to migrate to higher elevation, likely contributing to the emergence of the Tiwanaku culture.
central Andes
Lake Titicaca sediment
biomarkers
carbon isotopes
societies
Agence Nationale de la Recherche (ANR) 501100001665 ANR10 LABX56 Stéphane Guédron National Science Foundation (NSF) 100000001 EAR-1338694 Sherilyn C. FritzPaul A. Baker National Science Foundation (NSF) 100000001 EAR-1812857 Sherilyn C. FritzPaul A. Baker National Science Foundation (NSF) 100000001 EAR-1812681 Sherilyn C. FritzPaul A. Baker National Geographic Society (NGS) 100006363 9299-13 Sherilyn C. FritzPaul A. Baker
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pmcLake Titicaca is a large, deep, high-elevation lake in the central Andes (Fig. 1) that is sensitive to climate variation on interannual to millennial timescales. For millennia, the lake has been an important resource for people living in the region. Many studies by different research groups over the last 50 y used diverse tools to reconstruct the history of the lake and its watershed and to infer variation in climate and its drivers. Those studies include long drill core records that span hundreds of thousands of years (1, 2) and multiple shorter records that extend from the Last Glacial Maximum (3, 4) through parts of the Holocene (5–7).
Fig. 1. (A) Location of Lake Titicaca in the western tropical Andes. (B) General map of Lake Titicaca (C) Ok’e Supu site (OKE) where the 18 underwater archaeological test pits (dark red squares) were excavated in Lago Mayor to the north of the Strait of Tiquina (8). (D) Location of the 13 sediment cores sampled in Lago Menor (W1 to W7 and E1 to E6) referenced based on their location in the eastern (E) or western (W) southern subbasin of Lake Titicaca, and of the archaeological test pits at K'anaskia (KA) and Ojelaya (OJ). All isobaths are in meters below the 3,810 masl average modern lake level.
The Late Holocene (4.2 ka BP to the present) history of the Lake Titicaca Basin has been studied more intensively than have prior periods of time, in part because of its relevance to cultural development in the watershed, particularly around the southern basin of the lake, Lago Menor (also known as Lago de Huiñaymarca). During the generally arid climate of the Middle Holocene (8.2 to 4.2 ka BP), lake level in the large deep basin of Lake Titicaca, Lago Mayor, dropped as much as 85 m (9). As a consequence, most of the relatively shallow Lago Menor was desiccated (6), and there was no hydrologic connection between the northern and southern basins through the Strait of Tiquina. However, approximately 4000 y ago, lake level began to rise as the climate became wetter, Lago Mayor overflowed into Lago Menor, and the sedimentary history of the modern lacustrine phase of Lago Menor began (3).
The Lake Titicaca Basin has a rich Late Holocene archaeological record, including villages and ceremonial sites in the watershed of Lago Menor (10) and in regions of Lago Mayor proximal to the Strait of Tiquina (11). Many previous studies of these sites related changes in cultural history to lake level and climate variability, including the potential influences of this environmental variability on the initial expansion of agriculture in the Early Formative period (ca. 1500 to 800 BCE) and subsequent changes in resource use, such as pastoralism, fishing, hunting, and arable agriculture (12–14). Sociopolitical patterns, including population growth, development of powerful political and religious polities, warfare, and collapse of the Tiwanaku State also have been tied to the dynamics of the lake and climate (15–19).
Holocene lake-level variation in Lake Titicaca has been inferred from multiple types of data, including seismic profiles (9), sedimentology (5, 7), geochemistry (3, 5), diatoms (4, 20), and ostracods (21). Reconstructions based on several of these variables provided physical and quantitative constraints on lake elevation for given time intervals, including seismic and sedimentological data (7, 9, 22), as well as a transfer function based on ostracod data (21). However, the temporal resolution of these prior quantitative reconstructions is low and generally insufficient for comparison with many aspects of change in the archaeological record (10). In addition, the stratigraphic record in some of these earlier studies was incomplete, because of erosional sediment loss during low-stand intervals.
Here, we present a high-resolution quantitative model of water-level variability in Lake Titicaca during the Middle to Late Holocene, inferred from a carbon isotope transfer function developed using 13 cores from the southern basin of the lake. The transfer function was calibrated using short cores whose sediments span the instrumental record of lake-level variation measured at Puno, Peru since 1915. We integrated the model reconstruction with data from 21 underwater archaeological test pits in Lago Menor and in Lago Mayor just north of the Strait of Tiquina (8), which provide sedimentological evidence of discrete intervals of past lake-level lowering. We documented changes in water chemistry (salinity) and biotic components of the aquatic ecosystem (macrophytes, algae) using multiple inorganic and organic geochemical proxies in the sediment cores to help corroborate fluctuations inferred from the transfer function. Finally, we re-evaluated sociopolitical changes inferred from the archaeological record (10) in the context of our reconstructions of water-level change in Lake Titicaca during the Late Holocene.
Settings and Transfer Function Development
Lake Titicaca (16°S, 69°W) is a high-altitude lake on the northern Altiplano (average = 3,810 masl) of Bolivia and Peru (23). The lake consists of two subbasins (Fig. 1), Lago Mayor (7,131 km2, 125 m average depth) and Lago Menor (1,428 km2, 9 m average depth), connected by the Strait of Tiquina (39.5 m depth, SI Appendix, Fig. S1). When the lake level exceeds 3,804 masl, the lake discharges southward via the Río Desaguadero into the southern Altiplano to Lago Poopo.
Lake-level fluctuations are driven by the relative balance between precipitation and riverine inflow, and evaporation (which is closely linked to temperature and solar radiation) and riverine outflow. Variations in lake level can be used to infer fluctuations in this moisture balance. A substantial portion of the moisture variation is controlled by the changing intensity of the South American Summer Monsoon (SASM). During the instrumental period (1915 to 2022), evaporation has accounted for ~90% of water output (23), the remainder being attributed to the highly variable flow of the only outlet, the Rio Desaguadero. Annual lake-level rise (driven by the SASM seasonality) and fall averages ~0.69 m. Variability of lake-level rise is greater than variability of lake-level fall, and interannual fluctuations in lake level are correlated with precipitation variability (3). Over the last century, the total range of lake-level variation was ~6 m (24).
Both sediment cores and underwater archaeological test pits (SI Appendix, Figs. S2 and S3) enabled the identification of intervals of past lake-level lowering, including exposure surfaces (i.e., peat beds and gleyed soil), erosion surfaces or paleoshorelines (i.e., scour marks, abrupt transitions to dense sandy sediments, and highly fragmented shell materials), and non-depositional environments (i.e., soil horizons and hiatuses in sedimentation characterized by abrupt changes in radiocarbon ages). In addition, changes in the source of organic matter, as recorded by its carbon stable isotope signature, can be used to reconstruct historical displacements of the littoral margins (5). Along the gentle slopes of Lago Menor [average slope = 0.04 ± 0.01% (25, 26)], littoral areas <2 m deep are mainly colonized by emergent sedges (Schoenoplectus totora) and to a lesser extent by Hydrocharitaceae (Elodea sp.), Lemnaceae, and Haloragaceae (Myriophyllum sp.). Benthic macrophytes (e.g., Characeae and Potamogetonaceae) are found between 2.5 and 15 m depth, with maximum development of Characeae between 4.5 and 7.5 m (27). Stable isotope values of sediment organic carbon (δ13Corg) in the euphotic zone are more negative at depths less than 2.5 m, with values typical of emergent C3 sedges (av. = −25.6 ± 1.1‰), and higher at water depths between 2.5 and 15 m, with values characteristic of Characeae δ13Corg (av. = −10.2 ± 0.8‰) (SI Appendix, Fig. S4). In the aphotic areas (>15 m deep), the sediment δ13Corg values are dominated by those of pelagic algae (av. δ13Corg = −23 ± 2‰). The imprint of this ecological distribution with contrasting δ13Corg values along the slopes is inherited by the underlying sediments (5, 27–29) (SI Appendix, Table S1 and Fig. S4). Eroded soil organic material (av. δ13Corg = −24.5 ± 0.9‰) can also affect the δ13Corg values of littoral sediments through mixing processes. In deeper areas, atmospheric deposition (i.e., soil dust) during dry periods may reduce carbon isotope values (30, 31).
To reconstruct a continuous history of Middle to Late Holocene water level for Lago Menor, 13 short sediment cores were taken at water depths ranging from 3 to 43 m below the modern lake level (3,810 masl) to ensure good coverage of the amplitude of past lake-level variation (Fig. 1 and SI Appendix, Table S2A). This methodology enables different levels of resolution to be achieved as sedimentation rates and the number of sedimentary discontinuities decrease with depth. In addition, 18 underwater archaeological test pits (SI Appendix, Table S2B) were made between 1 and 15 m deep at Ok’E Supu in Lago Mayor, just north of the Strait of Tiquina (8), and 3 were dug in Lago Menor at K'anaskia and Ojelaya (SI Appendix, Fig. S3E), to refine the spatial resolution of paleoshoreline indicators and provide constraints on connectivity between the northern and southern basins of the lake. These sites were dated by determining the radiocarbon age of charcoal in selected stratigraphic layers. For each core, a transfer function (detailed below) that links the change in sediment δ13Corg to the change in lake level was computed based on a modern maximum interannual amplitude of lake variation. Paleoshoreline data were used to constrain the lowest lake level for each time period covered by the sediment archives (SI Appendix, Table S3 A and B).
Based on the instrumental record of lake level over the last century (24), a depth-δ13Corg relationship was developed and calibrated from two cores that span the last century (Fig. 2), with chronologies established from short-lived radionuclides (SI Appendix, Fig. S1 A and B). The model of lake-level variation for the last century (Fig. 2A) was based on two end-member depths, i.e., the lowest (3,807.7 masl) and highest (3,810.8 masl) average lake levels recorded in 1944 and 1986, respectively, and their associated δ13Corg values corrected for the Suess effect (32–34). The lake level was modeled using a transfer function (TFCorg) following Eq. 1:[1] Δδ13Corg/Δlakelevel=-14.65--16.883810.8-3807.7=0.59‰/m.
Fig. 2. Comparison of A the instrumental record of Lake Titicaca water-level variation over the last 100 y (24), and modeled lake levels based on the TFCorg of cores E5 (red symbols) and E6 (orange symbols). Horizontal error bars represent the period covered by each sediment slice in cores E5 and E6. Vertical error bar shows the average annual lake-level fluctuation (24). (B) Modeled lake levels based on TFCorg for 8 cores from the southern basin collected between 4 and 43 m below modern water depth. The period 1500 CE to modern is calibrated using the last 100 y TFCorg. For each period corresponding to the major stratigraphic units of the cores (SI Appendix, Fig. S3 A–D and Tables S1–S4), the elevations of the lowest and highest lake levels EM obtained using paleosoil and paleoshoreline indicators (SI Appendix, Table S3C) are indicated by blue (EMt) and red (EMb) triangles, respectively. The horizontal dashed lines indicate the period over which these EM are used in the model. a* is the lake level value for 1986 CE (24) and g** for 6000 BCE (3, 35).
Within the sediment cores and archaeological test pits (8), dated paleosoils and paleoshorelines were identified (SI Appendix, Table S3C), which enables us to constrain the elevation of multiple high and low stands (Fig. 2B). The lithologies of the cores were correlated to develop a composite stratigraphy within depth transects (SI Appendix, Fig. S2).
Within each lithological unit, the lowest (highest) δ13Corg value was associated with the highest (lowest) lake-level elevation end-member (i.e., EMt and EMb, with b as the bottom and t as the top), and the lake level of each unit was modeled following Eq. 2:[2] Modeled LL = EMb + TFCorg ∗ Δδ13Corg,
where Δδ13Corg is the difference between the δ13Corg of EMb and that of the depth considered in the profile. For intervals without high-stand end-members (EM), we used the maximum value of lake-level variation from the 100-y instrumental record (24).
Models were developed for each core (Fig. 2B) and subsequently combined into a single curve (Fig. 3) by averaging over 25-y intervals for the period 1913 to 1750 CE, 50-y intervals for the period 1700 CE to 900 BCE, 100-y intervals from 1000 to 2400 BCE, and 500-y intervals from 4000 to 6000 BCE, with the decrease in resolution of the dataset associated with increasing age (Fig. 2). To constrain the timing and amplitude of lake-level variations, we used several complementary methods, including paleobiotic, geochemical, and sedimentological analyses, as well as information on the paleohydrology and paleoprecipitation of the central Altiplano.
Fig. 3. (A) Modeled average lake level obtained from the 8 cores. The gray envelope represents the cumulative error resulting from the combination of the eight models for each time step considered. (B) Algal organic biomarkers (n-alkanes C25-31). (C) Organic carbon stable isotopes (δ13Corg). (D) Benthic macrophyte organic biomarkers (Characeae). (E) Calcium carbonate (CaCO3) concentration. (F) Titanium (Ti) abundance (*normalized independently for each core to provide a common scale). Y-axes for panels C–E are reversed. Shaded blue background lines represent intervals of high or rising lake levels from the averaged modeled lake-level curve. Cores from deep sites (>20 m) are presented in blue to black, and cores from shallow sites in green to red.
The fraction of freshwater algal biomarkers (n-alkane C25-31) provides information about the water depth and salinity of the lake (increasing values with rising lake level and decreasing salinity) (30). Chlorophyll was used as a proxy for algal productivity (36), which was found to reflect primarily the productivity for Characeae in Lake Titicaca, caused by better preservation in encrustations of calcium carbonate (CaCO3) (30). Hence, high concentrations of benthic macrophyte biomarkers (chlorophyll) and CaCO3 in shallow areas reflect higher production of Characeae. Elsewhere in the lake, higher CaCO3 values reflect lower lake level and the resultant higher chemical precipitation of CaCO3 with increasing salinity (30, 37). Finally, because soils are enriched in Ti compared to lake sediments (38), Ti was used as an inorganic tracer of detrital inputs to the lake. Soil erosion from the catchment is mainly recorded at water depths less than ~2.5 m, because the dense totora sedges on the lake’s shore intercept more than 90% of terrestrial eroded particles (39). In deeper areas, the detrital contribution is likely dominated by aeolian inputs, deposited mainly during dry periods (40).
Results and Discussion
Our reconstruction provides continuous quantitative constraints on Titicaca water-level variation during the last 4000 y at multidecadal scale. The pattern of change in the reconstructed lake level (Fig. 2) tracks the measured water levels for the last century (R2 = 0.70 and 0.73, P < 0.01), with maximum errors for modeled lake level of ± 2 m. The modeled error can be attributed to the lower resolution of the sediment records compared with the monitored lake level, coupled with cumulative errors associated with the age model.
This reevaluation of Lake Titicaca water-level variation provides higher temporal resolution than did previous reconstructions (7, 21) and is constrained with sedimentological data that enable the timing and amplitude of variation to be refined, particularly for the Middle and Late Holocene.
In the Middle Holocene, from 6000 to 4000 BCE, a rapid and large-amplitude decline in lake level of ~15 m characterized the southern basin of Lake Titicaca. δ13Corg in deep areas increased from <−22 to approximately −15‰, and n-alkanes C25–31 dropped to the lowest values in the record (Fig. 3), indicating that Lake Titicaca transitioned from a deep, fresh, and overflowing lake to a shallow lake with its sediments colonized by Characeae. Subsequently, between 4000 and 2400 BCE, no sedimentation was evident in the cores collected in the deepest area (Chua trough), suggesting that the southern basin was almost desiccated during that interval. This observation is consistent with the conclusions of prior studies from Lago Menor (6, 21) and the Middle Holocene low stand reported for the Lago Mayor between 4000 and 3000 BCE (3, 6, 35), when lake level dropped ~85 m below the modern level (3, 9). We have reevaluated the depth of the current sill between the two basins and placed it at 39.5 m below modern lake level (SI Appendix, Fig. S1). Hence, during the 4000 to 2500 BCE period Lago Menor was no longer fed by Lago Mayor. This period of long-term falling lake level coincided with low austral summer insolation values and with the Northern Hemisphere (NH) Middle Holocene climate optimum. Both factors (low insolation, warm NH temperature) are known to be associated with reduced intensity of the SASM and lower amounts of precipitation (41).
Increased moisture and lake-level rise are evident in Lago Menor beginning ~2400 BCE, with the flooding of all sites located 25 m or more below modern lake level (i.e., post-hiatus lake sedimentation recovery in cores E2 and E3). Chlorophylls and CaCO3 concentrations are high, and values of δ13Corg are above −15‰ (Fig. 3), consistent with the presence of a shallow Lago Menor colonized by Characeae, a condition likely restricted to the deeper eastern and western troughs. These shallow conditions lasted until at least 2000 BCE, after which the lake rose rapidly during the following 500 y. Consistency between the eastern (E1, E2, and E3) and western (W1) lake-depth records suggests that both the eastern and western basins of Lago Menor were connected by a channel that passed north of Suriqui Island (SI Appendix, Fig. S1).
Inter-basinal flow via this connection probably occurred even when Lago Menor was below its overflow level (3,804 masl). Subsequently, all areas 10 m or more below the modern level were flooded starting around 1500 BCE (cores W2, W3), and those below 7 m were flooded around 1300 BCE (cores W4, W5), as corroborated by successive abrupt drops in Ti at these sites, which characterizes the transition between soil and overlying lake sediment (Fig. 3 and SI Appendix, Fig. S2). At the same time, δ13Corg, CaCO3, and chlorophyll concentrations increased, indicating that sediments were flooded by more than 3 m of water, enabling Characeae to colonize the area.
During the Late Holocene, stepwise increases in average lake level produced three phases, each with lake-level oscillations around a different baseline elevation: i) from 1300 to 700 BCE, when the lake fluctuated above and below ~3,798 masl; ii) from 700 BCE to 500 CE, with fluctuations around ~3801 masl; and iii) from 500 to 1450 CE, with fluctuations around ~3,804 masl. During that period of sequential lake-level rise, CaCO3 decreased gradually, and algal biomarkers (n-alkanes C25–31) increased, both in response to the progressive decrease in lake salinity associated with rising lake level (20). The reconstruction suggests that Lago Menor reached the modern overflow level after 500 CE (flooding of OJ and KA sites). In response, benthic macrophytes disappeared from the deep areas (drop in δ13Corg, CaCO3, and chlorophyll) but likely colonized the newly flooded shallow ones. During the entire Late Holocene, decadal to secular lake-level declines coincided with increases in both Characeae biomarkers (chlorophylls and CaCO3) and Ti values. For the latter proxy, one cannot exclude anthropogenic influence, because major development of agropastoralism on the lake shores occurred during the emergence of the Tiwanaku culture (42), which may have enhanced clastic inputs to the lake.
In the final period of Late Holocene lake-level increase, between 1450 and 1750 CE, lake level rose steadily to reach its modern level (>3,810 masl). The reconstructed period of lake-level rise at the beginning of the Fifteenth century is consistent with reports by Spanish chroniclers for the Inca and Colonial periods of great inundation of nearshore areas (43, 44) and corroborated by increased underwater offerings by the Inca in two ritual sites near the Island of the Sun, which were partially submersed at that time (11, 45). Following a lake level maximum at ca. 1750 CE, two substantive lake declines are reconstructed at ~1850 and 1940 CE. Both are consistent with reports of a course reversal of the Rio Désaguadero into the lake at 1835, 1845, and 1865 CE (46, 47) and the most intense recorded drought in the last century at 1943–1944 CE (21, 24).
Our reevaluation of the Middle to Late Holocene lake-level variation in Titicaca is in relatively good agreement with previous reconstructions of lake elevation from sedimentology, diatoms, and geochemistry (Fig. 4 B–D), notably for the timing of major lake rises (~1600 BCE, 700 BCE, 200 BCE, 500 CE, 800 CE, and 1500 CE) and declines (~1000 BCE, 400 BCE, 100 CE, and 1300 CE) (3, 7, 20). Our record differs in that it does not show abrupt changes in lake level of 10 m or more, or lowstands that persisted for centuries, such as those apparent in the stratigraphic record (7) around 300 BCE, 150 CE, and 1300 CE (Fig. 4B). These persistent intervals of no apparent sediment accumulation likely resulted from erosion of portions of the stratigraphic record during lowstand intervals.
Fig. 4. (A) Averaged curve of Titicaca lake level (this study), (B) lake-level reconstruction based on sedimentological proxies (7), (C) lake-level reconstruction based on diatom record (20), (D) δ13Corg and CaCO3 profiles recorded in Lago Mayor (3), and (E) reconstructed lake surface of Lago Menor for an averaged paleobathymetry of 3,798 masl during the Early Formative, 3,803 masl during Middle Formative, 3,807 masl at the end of the Late Formative, and 3,810 masl for the modern period. The locations of ancient ceremonial centers in the lake area are presented as blue dots, and underwater archaeological sites currently submerged are presented as red dots. Shaded blue background lines represent intervals of high or rising lake levels from the averaged modeled lake-level curve.
The higher spatial and temporal resolution of our record relative to earlier studies yields smoother oscillations (of ~5 m) and a more gradual pattern of lake-level rise over time, which is consistent both with the observed pattern of interannual oscillations in the instrumentation period of the last century and with the evolution of regional precipitation reconstructed from the Quelccaya ice cap (48), Lake Umayo (49), Huagapo Cave (50), Huascaran ice core (51), and Lake Junin (52) (SI Appendix, Fig. S5).
Changes in lake stage inferred from the Middle to Late Holocene portions of our sediment record coincided with many periods of cultural change, which suggests that the inferred changes in the availability of water and arable land had consequences for people who resided around the lake. During (pre)history, the largest lake transgressions all coincided with major sociopolitical changes in the region. They include the shift from mobile hunting-gathering to a more settled agropastoral and fishing lifestyle (18) at the transition from the Late Archaic to the Early Formative period, which occurred during the 2500 to 1700 BCE abrupt lake-level rise. Persistent regional moisture during the Formative period (1500 BCE to 500 CE), which resulted in moderately high lake levels, likely promoted the expansion of agriculture, pastoralism, and the appearance of ranked societies (18, 53). In particular, the first chiefdoms and ceremonial centers, such as Tiwanaku, Chiripa, Lukurmata, and Pajchiri (Fig. 4E), were established around the lake during the Middle Formative period (MF: 800 to 200 BCE). At the time, all these sites were situated above the high-stand lake shoreline and therefore were protected from inundation during lake transgressions, such as those recorded at ~700 BCE and 200 BCE. Rain-fed terrace agriculture also began as early as the MF on the steep shores of Lake Titicaca (18, 54, 55), which provided additional land to meet agricultural needs related to population growth and to compensate for the loss of land covered by the lake during the MF (Fig. 4E).
Early development of complex societies in the Titicaca Basin occurred primarily during the Late Formative (LF: 200 BCE to 500 CE) and involved the control of domestic labor by emergent elites, intensification of agricultural systems, development of raised field agriculture (56), expansion of interregional trade, creation of elite ideologies, and competition with other elites (18, 57). By 400 CE, Tiwanaku stood alone, and other sites, such as Lukurmata, came under its influence by ~600 CE (58). This major change in the political landscape coincided with the largest recorded lake-level rise ~500 CE (Fig. 4A), when the lake reached a level close to that of the present. It is likely that a large number of agricultural and potentially residential areas around ceremonial centers located in the lake basin (e.g., Lukurmata) were impacted by this transgression, with the exception of the Tiwanaku capital (Tiahuanaco), which is located farther inland. Such a transgression could also have shifted the trade routes of the llama caravan to the Yungas along the western shores of Lake Titicaca through Tiahuanaco (59). The coincident major increase in population (60–62), together with the restructuring of Tiahuanaco and the development of the agropastoral landscape, including raised field systems (13, 18, 42, 63), suggests that this event influenced a migration of human populations, from previously occupied flooded regions, to cities located on higher ground. This densification may have contributed to the rise of the Tiwanaku state. The second half of the Tiwanaku state period was drier overall, marked by a gradual decline in lake level from 850 to 1000 CE, followed by a lake level rise around 1150 CE. This latter event coincides with the terminal portions of the sedimentary hiatuses identified in previous lake-level reconstructions (7, 15) and corroborates the hypothesis that portions of those stratigraphic sequences were lost via erosion and thus the duration of dry periods was shorter than those prior reconstructions suggested. Nonetheless, although reduced precipitation during that period likely affected Tiwanaku's residents, it does not imply a drought-induced collapse of the Tiwanaku culture (15, 64), given the evidence that prolonged aridity can be endured without major cultural changes (10, 65, 66).
In general, the trend of increased wetness during the Late Holocene promoted agricultural and social development, although at times, major lake rises may have influenced population migration, because of flooding of nearshore areas. This was certainly the trigger for the emergence of the Tiwanaku culture at the expense of other early sociopolitical formations, whose arable lands were flooded during the 500 CE lake transgression. Other geoarchaeological evidence buried in the sediment of Lake Titicaca will allow us to complete and deepen the chronology and context of this pivotal period in the development of Andean societies.
Materials and Methods
Sediment Core Collection and Underwater Archaeological Prospections.
Thirteen gravity cores (SI Appendix, Table S2A) were collected in Lake Titicaca between 2014 and 2017 using a Uwitec gravity corer with hammer. Detailed information for core sampling and processing is provided elsewhere (27, 67). Eighteen underwater archaeological test pits were excavated and sampled during two field campaigns in 2016 and 2018 in Lago Mayor north of the Strait of Tiquina (SI Appendix, Table S2B). Their stratigraphy and analysis of archaeological artifacts are provided elsewhere (8). Three other underwater archaeological test pits were excavated and sampled in 2017 and 2018 in Lago Menor at K'anaskia (KA) and Ojelaya (OJ) (SI Appendix, Table S3E). All information about sediment facies, unconformities, and lake level EM are provided in SI Appendix, Table S3A for the sediment cores and SI Appendix, Table S3B for the archaeological test pits.
Chronological Framework.
Chronological frameworks were established with short-lived radionuclides (210Pbex and 137Cs), analyzed using well-type germanium detectors at the Laboratoire Souterrain de Modane (LSM) following published procedures (68), and radiocarbon ages obtained from aquatic gastropods and charcoal (SI Appendix, Table S4), using Accelerator Mass Spectrometry (AMS) at Woods Hole Oceanographic Institution’s National Ocean Sciences AMS facility, at the radiocarbon facility of the Belgian Royal Institute for Cultural Heritage (RICH), and at the Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL). Radiocarbon ages were calibrated to calendar years Before Present (cal. yr BP) using the calibration curve for the Southern Hemisphere SHCal20 (69) and a post-bomb curve (70) and a reservoir offset of 250 14C-yr for Lago Menor (7). The age models for cores NE98-10BXA and NE9810BXA (3) used in Fig. 4C were reprocessed following the same procedure. Age-depth models were generated using the Constant Flux Constant Sedimentation model using the serac R package (71) for uppermost core strata dated with short-lived radionuclides (E5 and E6), combined into a general model established with the clam R package (72). All age models are presented in SI Appendix, Fig. S1 A and C.
Chemical Analyses.
Freeze-dried and crushed sediment and aquatic plant samples were used for all chemical analyses. Sediment dry bulk density (DBD) was obtained from the mass of a fixed freeze-dried volume of wet sediment. Total carbon content (TC, %), organic carbon content (Corg, %), and isotopic composition (δ13C and δ13Corg, ‰) were measured by Cavity Ring-Down Spectrometer (Picarro, Inc.®) coupled with a Combustion Module (CM-CRDS, Costech, Inc.®) using previously reported analytical methods (including sample decarbonation), calibration, and sample preparation (73–76). CaCO3 content was calculated from the difference between TC and Corg reported as molar masses. Samples for major element analysis (Ca and Ti) were digested with 10 mL of a mixture of pure acids (HF/HCl/HNO3,1:6:2), sonicated for 2 h, and heated on a hot block (120 °C, 4 h) following a published procedure (30, 77). Major elements were analyzed with an inductively coupled plasma optical spectrometer (ICP-OES, Varian 720-ES) within the analytical chemistry platform of ISTerre (OSUG-France). In all cores, analyses of titanium (Ti) were performed on the fresh surfaces of the split sediment cores every 1 to 5 mm using a nondestructive Avaatech XRF core-scanner at tube settings of 10 kV and 2 mA (78). The measured power spectra were then deconvolved into elemental relative abundances expressed in counts per second. The intensity signals of Ti abundance in Fig. 3 were normalized independently for each core to provide a common scale. Organic biomarkers were determined using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) following the method of Tolu et al. (79) adapted for Lake Titicaca sediment (30). Briefly, the sediment samples were pyrolyzed in a Frontier Labs PY-2020iD oven (450 °C) connected to an Agilent 7890A-5975C GC–MS system. A data processing pipeline was used under the “R” computational environment to automatically detect and integrate the peaks and extract their corresponding mass spectra using the software “NIST MS Search 2” containing the library “NIST/EPA/NIH 2011” and additional spectra from published studies (79). All sediment core depth profiles for CaCO3, Ti, Corg, and δ13Corg are presented in SI Appendix, Fig. S1 A–C.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
This work is a contribution to the PaleoBol project, supported by a grant from IRD (BQR@ISTerre2016), and TRACISOMER supported by a grant from Labex OSUG@2020 (PI: S.G.: [email protected]). S.G. (ISTerre/IRD/UGA) is part of Labex OSUG@2020 (Investissements d'avenir ANR10 LABX56). S.C.F. and P.A.B. were supported by grants from NSF (EAR-1338694, EAR-1812857, and EAR-1812681) and the National Geographic Society (9299-13). The authors thank the LSM facilities for the gamma spectrometry measurements, the EDYTEM for the X-ray fluorescence analyses, and the LSM for the gamma spectrometry facilities. Some of the 14C analyses were acquired thanks to the CNRS-INSU ARTEMIS national radiocarbon AMS measurement program at Laboratoire de Mesure 14C (LMC14) in the CEA Institute at Saclay (French Atomic Energy Commission). We thank M.-A. Vella, E. Brisset, K. Escobar-Torres, and C. Giguet-Covex for their help in fieldwork; Delphine Tisserand, Sarah Bureau, and Sylvain Campillo for their help in chemical analysis (ICP-AES analysis performed within the analytical chemistry platform of ISTerre, OSUG-France); R. Bindler and the Umeå Plant Science Center for the Py-GC/MS analysis; and A. Chevalier for his help and support in radiocarbon dating at the Belgian Royal Institute for Cultural Heritage. We also wish to thank J. Gardon, A. Terrazas, C. Gonzalez, N. Clavijo, L. Salvatierra, R. Rios, J.C. Salinas, A. Castillo, M. Claure (IRD Bolivia), D. Acha (UMSA Bolivia), G. Mollericon, and la Familia Catari (Don Ramon, Don Maximo, Don Eric, Don Ruben, and Donia Maria) for their help and assistance during the field campaigns.
Author contributions
S.G., C.D., S.C.F., and P.A.B. designed research; S.G., C.D., S.C.F., J.T., C.H., and P.A.B. performed research; S.G., J.T., P.S., A.-L.D., C.H., C.V., and E.Q.A. contributed new reagents/analytic tools; S.G., C.D., S.C.F., J.T., and P.S. analyzed data; and S.G., C.D., S.C.F., and P.A.B. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
All complementary information from this study and other studies are included in the article and/or SI Appendix. Lake Titicaca water-level reconstruction data are archived at the Mendeley Digital commons Data repository, doi: 10.17632/9fkdt5j35v.1 (80).
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926256.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595678
202201886
10.1073/pnas.2201886120
research-articleResearch Articleag-sciAgricultural SciencesSustainability-physSustainability Science401
9
Biological Sciences
Agricultural Sciences
Biological Sciences
Sustainability Science
The productive performance of intercropping
Li Chunjie a https://orcid.org/0000-0002-2761-1085
Stomph Tjeerd-Jan b https://orcid.org/0000-0001-5984-1523
Makowski David c https://orcid.org/0000-0001-6385-3703
Li Haigang d
Zhang Chaochun a
Zhang Fusuo [email protected]
a 1 https://orcid.org/0000-0001-8971-0129
van der Werf Wopke [email protected]
b 1 https://orcid.org/0000-0002-5506-4699
aCollege of Resources and Environmental Science, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
bCentre for Crop Systems Analysis, Wageningen University, 6700 AK, Wageningen, The Netherlands
cINRAe, AgroParisTech, Université Paris-Saclay, Unit Applied mathematics and computer science, Palaiseau 91120, France
dInner Mongolia Key Lab. of Soil Quality and Nutrient Resources, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010018, China
1To whom correspondence may be addressed. Email: [email protected] or [email protected].
Edited by David Tilman, University of Minnesota College of Biological Sciences, St. Paul, MN; received February 2, 2022; accepted November 5, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e22018861202 2 2022
5 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Agricultural diversification is useful for agronomic, environmental, and dietary reasons. Here, we confirm, based on a meta-analysis of 226 field experiments, that the simultaneous cultivation of two species in the same plot (intercropping) leads to substantial land savings over single crops when the objective is to produce a diversified set of crop products. While intercropping leads on average to a small yield penalty for grains and calories compared with the most productive single crop species comprised in the mixture, it can provide similar or even higher protein yields, especially with modest N fertilizer application. In addition, it provides further ecological services. Intercropping thus has the potential to diversify crop production and make cropping systems more sustainable.
Crop diversification has been put forward as a way to reduce the environmental impact of agriculture without penalizing its productivity. In this context, intercropping, the planned combination of two or more crop species in one field, is a promising practice. On an average, intercropping saves land compared with the component sole crops, but it remains unclear whether intercropping produces a higher yield than the most productive single crop per unit area, i.e., whether intercropping achieves transgressive overyielding. Here, we quantified the performance of intercropping for the production of grain, calories, and protein in a global meta-analysis of several production indices. The results show that intercrops outperform sole crops when the objective is to achieve a diversity of crop products on a given land area. However, when intercropping is evaluated for its ability to produce raw products without concern for diversity, intercrops on average generate a small loss in grain or calorie yield compared with the most productive sole crop (−4%) but achieve similar or higher protein yield, especially with maize/legume combinations grown at moderate N supply. Overall, although intercropping does not achieve transgressive overyielding on average, our results show that intercropping performs well in producing a diverse set of crop products and performs almost similar to the most productive component sole crop to produce raw products, while improving crop resilience, enhancing ecosystem services, and improving nutrient use efficiency. Our study, therefore, confirms the great interest of intercropping for the development of a more sustainable agricultural production, supporting diversified diets.
intercropping
productivity
land-use efficiency
transgressive overyielding
food security
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pmcHigher agricultural production and lower environmental footprint are required to meet the global demand for food and feed in a sustainable manner (1, 2). Furthermore, the world’s food supply is increasingly homogeneous in composition and less species rich (3). Loss of crop species diversity may make global food production less sustainable and less stable (4, 5) and increase the need for crop protection against pests, diseases, and weeds due to lower resilience (6–9). Intercropping, i.e., the mixed cultivation of two (or more) crop species on the same field (10, 11), is a crop diversification strategy which allows lowering inputs while achieving higher crop yields than expected based on the sole crop yields of the constituent species (12, 13). Due to its contribution to efficient use of resources and diversification of crop species, intercropping provides a compelling opportunity for the sustainable intensification of agriculture. Nevertheless, worldwide adoption of intercropping is lagging, particularly in the global North, while its adoption in the global South could be challenged by the movement of labor to the cities. An in-depth analysis of the productive performance of intercropping is required to assess its potential in modern agriculture and inform policymakers.
Overyielding of intercrops, when compared with sole crop yields, is usually ascribed to resource complementarities between species (14, 15) and may also be due to increased resilience to pests, diseases, and weeds (6–8). Overyielding may be defined in different ways, and there are several metrics to quantify the benefits of growing intercrops instead of sole crops (Table 1). These metrics should be interpreted carefully in line with their definitions (16). The land equivalent ratio (LER) is the most commonly used index to assess the land use of intercrops compared with sole crops (17). It is by definition the same as the relative yield total (18). The LER represents the land area required by sole crops to produce the yields of component species obtained in a unit area of intercrop. An LER larger than one means that intercropping is more efficient in land use than sole cropping. Based on the values of LER estimated from large databases, previous meta-analyses have shown that intercropping saves on average 18 to 23% of the required land compared with production of the same species in sole crops (13, 19–21). That means intercropping allows to obtain the same crop outputs on a smaller land area.
Table 1. Comparison of metrics assessing intercropping productive performance
Metrics LER TOI NER
Definition Sum of the relative yields of intercropped species compared with the respective sole crops. The ratio of total intercrop yield and the highest sole crop yield among the component species. The ratio of the sum of observed yields to the sum of expected yields calculated according to the proportions of each crop in the mixture.
Formula* LER = Y1/M1 + Y2/M2 TOI = (Y1 + Y2)/max(M1, M2) NER = (Y1 + Y2)/(P1M1 + P2M2)
Interpretation The relative land area needed for sole crops to obtain the same crop outputs as obtained in a unit area of intercropping. The relative yield obtained when shifting from the most productive sole crop to the intercrop. Also, the area of land needed for the most productive sole crop to get the same yield as the total yield obtained in intercropping. The relative yield when intercropping two crop species compared with the weighted average of the sole crop yields where the species proportions in the mixture serve as weights.
Mean effect size for grain yield† 1.23 [1.20, 1.27] 0.96 [0.93, 0.98] 1.28 [1.25, 1.32]
Mean effect size for protein yield 1.23 [1.20, 1.27] 1.02 [0.99, 1.06] 1.23 [1.20, 1.26]
*Y1, Y2, M1, and M2 are the intercrop (Y) and monocrop (M) yields of species 1 and 2, respectively. See Materials and Methods, Eqs. 1, 2, and 4, and associated explanation and definitions.
†Mean effect size and 95% CI (in brackets) for grain yield and protein yield were estimated from the dataset considered in this study. A mean effect size higher than one indicates that intercrops perform better than sole crops. For an example calculation of metric values, see Box 1.
Another measure of intercropping performance is provided by the net effect (22). The net effect is defined as the difference between the actual intercrop yield (defined in a common unit for the constituent species, e.g., grain or protein yield) and the expected intercrop yield based on sole crop yields and species proportions in the mixture (22, 23) (Box 1, Figure). Contrary to the LER, which is a sum of dimensionless ratios, the net effect of intercropping is expressed in terms of a yield difference per unit area (16). Using global data on crop yields in intercropping, Li et al. (13) showed that intercropping produces 1.5 t grain yield per hectare more than expected on the basis of the sole crop yields, confirming that on average intercrops outperform the mean of the component sole crops. Here, following Cardinale et al. (19), we will express the net effect as a yield ratio (total yield observed)/(total yield expected) to make it more easily comparable to the LER (Table 1 and Box 1). This net effect ratio (NER) reflects the relative yield when intercropping two crop species, compared with the weighted average of the sole crop yields where the species proportions in the mixture serve as weights.
Box 1 Calculation of productivity metrics in intercropping
Assume an intercrop with 50% maize (P1 = 0.5) and 50% soybean (P2 = 0.5). Sole maize grain yield is M1 = 10 t ha−1, while sole soybean grain yield is M2 = 4 t ha−1. Fertilizer input in maize is Nfert1 = 250 kg ha−1 and in soybean Nfert2 = 50 kg ha−1. NfertIC = 150 kg ha−1 in the intercrop (i.e., the average of the sole crop inputs). Grain yields in the intercrop are Y1 = 8 t ha−1 for maize and Y2 = 2 t ha−1 for soybean. Expected yields (EY) are calculated by multiplying sole crop yields by their corresponding land shares, i.e., EY1 = P1M1 = 5 t ha−1 for maize and EY2 = P2M2 = 2 t ha−1 for soybean.
LERLER=Y1M1+Y2M2=810+24=0.8+0.5=1.3
Transgressive overyielding index (TOI)TOI=(Y1+Y2)max (M1, M2)=8+210=1
NERNER=Y1+Y2EY1+EY2=Y1+Y2P1M1+P2M2=8+20.5×10+0.5×4=107=1.43
TOI for N fertilizer use efficiency (TOIN)TOIN=(Y1+Y2)/NfertICM1/Nfert1=(8+2)/15010/250=53=1.67
In this example, intercropping produces 8 t ha−1 of maize and 2 t ha−1 of soybean with 23% less land than sole crops, produces 43% more yield than expected, is as productive as the most productive sole crop (maize), and is 67% more N use efficient than the most productive species, maize.
Graphical illustration of the calculation of different metrics. The yields in sole crops and intercrop are shown as orange bars (maize) and green bars (soybean).
Producing a diversity of agricultural products is necessary to meet demands from the society and the market, but for an individual farm, it may be more interesting to produce as high a total amount of raw material as possible. In practice, farmers may be inclined to grow only the most productive species in their fields without consideration for crop species diversity, aiming to maximize total yield of grain, calories, or protein per unit of land, regardless of diversity. This aim is particularly relevant if the product is used as bulk, for instance to feed animals or to serve as raw material for the food industry or as feedstock for biofuels. When the production objective is to maximize the total biomass or yield of grain, calories or proteins, growing the most productive single crop species per unit of land would be more efficient than also including a crop species with a lower productivity in the cropping system, unless there is such strong complementarity in mixtures that total intercrop yield would exceed the yield of the highest yielding sole crop. In this context of maximum bulk production, a relevant benchmark for assessing the production efficiency of intercrops would be the yield of the single most productive species. An intercrop shows transgressive overyielding if its total yield is greater than that of the highest yielding species comprised in the mixture (24). Transgressive overyielding is relevant when the objective is to maximize the production of calories, protein, forage, biomass, or bioenergy per unit area (25–28). As a metric for transgressive overyielding, we propose the ratio of total intercrop yield over the highest sole crop yield of the component species (i.e., TOI, Table 1 and Box 1) (29). The TOI is never larger than the LER or NER, i.e., it is at most equal to the smallest of LER and NER (see Materials and Methods) (29).
Previous studies showed that in natural ecosystems, although species mixtures produced more biomass than expected as measured by the net biodiversity effect, transgressive overyielding was often not achieved, i.e., TOI < 1 (30, 31). In only 35% of the observed plots, species mixtures produced greater biomass than that of the single most productive species (30, 32). While the LER and net biodiversity effect of intercropping have been analyzed in several global meta-analyses (9, 14–17), no such analysis has been conducted for transgressive overyielding in mixtures of crop species.
Maize/legume mixtures are characterized by high LER and they allow reducing nitrogen (N) fertilizer input without loss of productivity (13). High N use efficiency is of paramount importance for lowering N fertilizer input and lowering N losses to the environment, to make crop systems more sustainable (33). We, therefore, also analyzed a TOI for N fertilizer use (TOIN). This index measures to what extent intercropping produces greater output per unit of N fertilizer than the most productive sole crop does (Eq. 6, see Materials and Methods). It is unknown what species combinations and which management are able to improve N fertilizer efficiency compared with the most productive sole crop species.
An important quality trait of grain crops is the protein content of the grain. Legumes have high protein contents and are the world’s primary source of plant dietary protein, offering a diversity of amino acids, complementing the profiles of cereals (34). Previous studies have shown enhanced cereal grain protein content in cereal/legume mixtures compared with sole grain cereal crops, especially at low N fertilizer input (35, 36). However, legumes have in general lower grain yields than cereal crops (37), potentially affecting the benefit of cereal/legume intercrops for protein yield as it depends both on total production and the N content of the grain. Cereal/legume mixtures show positive net effects when compared with the expected yield per unit area (13, 23); however, it is not known whether such mixtures achieve transgressive overyielding, i.e., higher grain yield, calorie yield, or protein yield, when compared with the sole cereal or legume with the highest grain, calorie or protein yield. Furthermore, we do not know whether cereal/legume intercrops show contrasting performance when considering grain yields or protein yields. It is thus important to assess overyielding in intercropping not only using metrics for grain yield but using, in addition, metrics for calorie or protein yield.
In this paper, we used a large global dataset comprising results of 226 experiments to assess the different types of productive performance of intercropping, considering land area and N fertilizer input as production resources. After having analyzed grain yield data, we further made use of data on calorie and protein concentrations in grain (38) to assess the performance of intercropping for producing food and feed calories and protein. We explored which kind of species combinations and management achieved transgressive overyielding in the production of grain, calorie, and protein yield.
Results
Intercrops showed 19% higher average land-use efficiency than sole crops for the production of grain with a mean LERgrain of 1.23 (95% CI: [1.20, 1.27], n = 934, land-saving proportion = (1.23-1)/1.23 = 0.19 (39)) and a 28% higher grain yield than expected from single crops, with a mean NERgrain of 1.28 (95% CI: [1.25, 1.32], n = 934), but intercropping produced on average 4% less grain yield per unit area than the most productive sole crop species comprised in the mixture (mean TOIgrain = 0.96, 95% CI: [0.93, 0.98], n = 934) (Fig. 1). The LERgrain and NERgrain were larger than one for 84% and 87% of data records, respectively, but TOIgrain was larger than one for only 36% of the data records. The results were similar for calorie production. For protein production, however, the average intercrop productivity was not significantly different from that of the most protein-productive sole crop (mean TOIprotein= 1.02, 95% CI: [0.99, 1.06], n = 934) (Fig. 1), and TOIprotein was greater than one for nearly half of the data records (47%).
Fig. 1. Values of metrics for assessing the productive performance of intercropping. LER, NER, and TOI based on grain yield (LERgrain, NERgrain, TOIgrain), and TOI based on calorie yield (TOIcalorie) and protein yield (TOIprotein). Histograms show the distribution of the data for each metric. The small black points and error bars represent the mean metric values and their 95% CIs. The vertical dashed line at 1.0 represents the reference value for the index if intercropping is equivalent in production efficiency to sole crops.
We found a higher N use efficiency in intercrops compared with the most productive component sole crop for production of grain or calories (TOINgrain = 1.11, 95% CI: [1.02, 1.20], n = 638, TOINcalorie = 1.11, 95% CI: [1.01, 1.21], n = 638, Fig. 2). However, N use efficiency for protein production was not significantly higher for the intercrop than for the most protein-productive sole crop (mean TOINprotein= 1.06, 95% CI: [0.97, 1.15], n = 638, Fig. 2).
Fig. 2. Values of N fertilizer TOI (i.e., TOIN). N fertilizer TOI for grain yield (TOIgrainN), calorie yield (TOIcalorieN), and protein yield (TOIproteinN). These indices express the extent to which the PFP of N fertilizer on intercrop grain yield, calories, or protein exceeds that of the sole crop species with the highest grain yield, calorie yield, or protein yield, respectively. The small black points and error bars represent the mean metric values and their 95% CIs. The vertical dashed line at 1 represents the reference value for the index if intercropping is equivalent in production efficiency to sole crops.
TOI values are by definition at most equal to the smallest of the LER and the NER (Fig. 3 and SI Appendix, Fig. S1; see also Materials and Methods), because the NER and LER compare the intercrop yield with, respectively, the weighted total or species-specific sole crop yield, while the TOI compares the intercrop yield with the higher of these sole crop yields. However, there were strong correlations between each two of the three metrics (Fig. 3). TOI for grain yield increased with LER and NER for grain yield, suggesting there is no trade-off between producing more bulk commodities and increasing a diversity of crop products. Therefore, the search for transgressive overyielding in intercropping would likely favor similar crop species and management choices as the search for high land-use efficiency or high relative yield gain.
Fig. 3. Bivariate scatter plots illustrating relationships between LER, NER, and TOI (A–C) and proportions of data records with performance metrics larger than one for three types of species combinations in intercropping (D). Metric values are based on grain yield in maize/legume (turquoise), maize/nonlegume (orange), and nonmaize/legume intercrops (purple). A 1:1 line is shown in panels A–C for reference. The horizontal lines represent TOI = 1 or NER = 1, and the vertical lines represent NER = 1 or LER = 1. Blue lines in panels A–C are regressions fitted using linear models based on data of the three types of species combinations. Bars in panel D represent the proportions of data records with metric values larger than one and their approximate 95% CIs, calculated as p±1.96×p(1-p)n, where P is the observed proportion of data records with the metric value greater than one, and n is the number of observations.
The three most frequent species combinations in the data set (i.e., maize/legume, maize/nonlegume, and nonmaize/legume intercrops, SI Appendix, Table S1) showed differences in performance. The proportion of LERgrain values larger than one was higher in maize/legume intercrops (estimated proportion 0.88, 95% CI: [0.85, 0.91], n = 436) and maize/nonlegume intercrops (0.92, 95% CI: [0.79, 0.91], n = 132) than in nonmaize/legume intercrops (0.77, 95% CI: [0.73, 0.81], n = 352). On the contrary, the proportion of TOIgrain larger than one was lower in maize/legume intercrops (estimated proportion 0.29, 95% CI: [0.24, 0.33], n = 436) than in maize/nonlegume (0.41, 95% CI: [0.33, 0.49], n = 132) and nonmaize/legume intercrops (0.43, 95% CI: [0.38, 0.48], n = 352). While TOIgrain was lower than one in most cases for maize/legume intercrops, TOI for protein yield was larger than one in 55% of the maize/legume data with a mean TOIprotein of 1.10 (95% CI: [1.05, 1.15], n = 436, SI Appendix, Fig. S2). Thus, maize/legume intercropping produced on average 10% more protein per ha than the most protein-productive sole crop. Furthermore, the mean TOINprotein was 1.18 (95% CI: [1.07, 1.30], n = 436) in maize/legume intercrops, indicating that maize/legume intercrops are much more N-use efficient than the most protein-productive sole crop (often maize, 253 out of 436 data records of maize/legume intercrops). TOIprotein exceeded one in 65% of maize/nonlegume intercrops and 30% of nonmaize/legume intercrops, but the mean TOI value was not significantly higher than one for these two types of intercrops, whether expressed per unit land or per unit N fertilizer (SI Appendix, Fig. S2). Only few data were available for legume/legume intercrops. Within this group, the TOIs for grain yield and calorie yield were largest for pigeon pea/soybean intercrops (mean TOIgrain = 1.32, 95% CI: [1.11, 1.52], n = 11; mean TOIcalorie = 1.22, 95% CI: [1.02, 1.42], n = 11) (SI Appendix, Fig. S3).
Component species in mixtures may differ substantially in yielding ability. The probability of achieving transgressive overyielding increased as the grain yield of the lower yielding species approached that of the higher yielding species, i.e., when the yields of the two species are similar (Fig. 4A). This trend was found in all the three main species combinations (Fig. 4 A and B). In maize/legume and maize/nonlegume intercrops, there was a greater than 50% chance of transgressive overyielding when the grain yield ratio exceeded 0.5 (i.e., when sole crop yield of the low yielding species exceeded 50% of the sole crop yield of the high yielding species). In nonmaize/legume intercrops, there was a greater than 50% chance of getting transgressive overyielding when the yield ratio exceeded 0.7. The results suggest that similar yield levels in sole crops result in a large TOI (SI Appendix, Figs. S4 and S5) in combination with a small selection effect [i.e., no strong dominance by species with particular traits (22)] (SI Appendix, Fig. S6). Likewise, the probability of obtaining transgressive overyielding for protein production increased with the similarity of the protein yields of the component species (Fig. 4B). The results thus indicate that transgressive overyielding for protein production in cereal/legume mixtures can be obtained by selecting component species with similar protein yields.
Fig. 4. Probability of a TOI larger than one (TOI > 1) as a function of the ratio of the grain yields (A) or protein yields of the sole crops (B) and value of TOI in response to N fertilizer input (C and D). Metric values are based on grain yield in maize/legume (turquoise), maize/nonlegume (orange), and nonmaize/legume intercrops (purple). Yield ratio is defined as the ratio of the sole crop yield of the lowest yielding species to the sole crop yield of the highest yielding species. A yield ratio of one indicates that the two sole crop yields are equal. The dashed lines in panels (A and B) represent a probability of TOI > 1 equal to 0.5, and the dashed lines in panels (C and D) represent TOI = 1.
Maize/legume intercrops provided significant transgressive overyielding in terms of protein yield in the absence of N fertilization (intercept of 1.15, 95% CI: [1.07, 1.23], n = 363) (Fig. 4D), while intercrops with maize but without legumes had higher values of TOI when N fertilizer input was high (Fig. 4 C and D). Both calorie TOI and protein TOI of nonmaize/legume intercrops were independent of N fertilizer input (P = 0.77, Fig. 4D, and P = 0.17, SI Appendix, Fig. S5D, respectively).
Discussion
In this study, we analyzed transgressive overyielding in intercropping and compared it with performance metrics related to land saving (LER) and relative yield gain (NER). We found that intercropping resulted in substantial (19%) land savings compared with sole crops to produce a diverse set of crop outputs. Furthermore, based on an average grand mean NER of 1.28, intercrops had on average 28% greater yield than expected from monocultures. In addition, although our results showed that intercropping did not guarantee transgressive overyielding (TOI) for grain production (on average 4% lower yield than the most productive species), we found that intercropping achieved the same average level of protein production as the most protein-productive single crop. Thus, our results indicate that intercropping is an efficient cropping system to produce diverse crop outputs on a limited area of land (as shown by high LER and NER) while it had higher protein production than the most protein-productive sole crop in 47% of the cases. The positive correlation between the different measures of excess yield suggests that there is no trade-off between producing more bulk products and producing a diversity of crop products.
Although transgressive overyielding for grain production was on average not achieved, maize/legume intercrops produced on average 10% higher protein yield than the most protein-productive sole crop and a 18% higher protein yield than the most protein-productive sole crop per unit N fertilizer, indicating strong species complementarities and options for increased efficiency of fertilizer use for the production of food and feed protein by using maize/legume intercropping.
The absence of systematic transgressive overyielding for grain production must be put into perspective, given the limitations of this criterion. Indeed, societies and markets often (but not always) require a diversity of foods and feeds and do not just require the output of the highest yielding crop species, i.e., populations cannot be fed on maize alone. Moreover, diversification of agriculture may be required to allow crop rotations and diverse landscapes not focusing on a single crop to reduce crop vulnerability to pests, pathogens, and weeds (40). Diversified cropping systems can reduce the risk of crop failure associated with drought or erratic rainfall (9) and show greater yield stability than monocultures (41). Therefore, TOI as a concept has an intrinsic limitation due to its lack of recognition of these benefits of diversity that go beyond single season production efficiency. Also, TOI shares with LER and NER the limitation that it ignores differences in market prices between crop species. Another limitation of TOI is its reliance on a posthoc choice of the high yielding sole crop while, in practice, farmers do not always know in advance which crop species will be the most productive, especially in the case of strong year-to-year variation in the performance of sole crops (32). Nevertheless, maize is usually the most productive crop compared with small grains and legumes across a broad range of conditions, making the use of TOI relevant in this case. Given the results of our multicriteria evaluation and all its additional well-known benefits [pest, disease, and weed control (6, 8, 42), improved drought resistance (9), and soil carbon accumulation (43)], intercropping should be considered a promising alternative to sole cropping.
Our study shows transgressive overyielding in intercropping for 36% of data records and land saving and relative yield gain in intercropping for 84 to 87% of data records, where each record comprises metric values that compare the performance of intercrops and sole crops for a certain species combination and management in an experiment. These results are comparable to the results of ecological studies on grassland species mixtures (30, 31), where 35% of the species mixture plots of natural plant communities produced higher biomass than achieved by the most productive component species when grown alone (30, 31). On the contrary, only 2% of cover crop mixtures achieved transgressive overyielding based on 243 comparisons (44).
A lack of transgressive overyielding is not necessarily in conflict with positive species complementarity and facilitation that enhance resource capture. Species differ intrinsically in productivity due to the length of the growth duration, water use efficiency, harvest index, or the resources necessary for grain production (higher for grains with a high oil or protein content) (45). Interestingly, our analysis reveals that transgressive overyielding (i.e., TOI > 1) is more likely to occur when intercropping combines species with similar yields. In this case, a moderate species complementarity effect is able to increase the yields of the species in the mixture to a level high enough to compensate for the intrinsically lower yield of one of the components. On the contrary, when the two species have very different yield levels, complementarity is generally not high enough to give a productive advantage to intercropping compared with the most productive sole crop. Transgressive overyielding requires strong niche differentiation to make up for replacement of individuals of the highest yielding species with individuals of a lower yielding species (46). A high probability to achieve transgressive overyielding is obtained when species combinations with similar yielding abilities are grown together.
The cultivation of the most productive species as sole crops can be relevant when the production objective is gross energy, and when there is no need for crop rotation or spatial diversity of crop species to control pests and diseases and maintain productivity. Maize production is an example of this production orientation in many parts of the world. However, maize/legume intercrops have the potential to combine high yields (including protein yields) and high production efficiency per unit fertilizer [e.g., Xu et al. (21)], especially when including legumes with high protein yield. Converting existing large areas of corn, wheat, and soybeans, currently grown as single crops, to intercrops could improve land-use and fertilizer-use efficiency, with additional nonproduction benefits, including increased resource use efficiency (21); dietary diversity (47); pest, disease, and weed control (6, 8, 42); and improved organic soil carbon and N content (43). Such a transition to intercropping practices could be made both in systems with a production orientation toward improved sustainability (low input–low output–high efficiency) and a production orientation toward high productivity (moderate inputs–high outputs–high efficiency) (13).
Optimizing plant densities, spatial plant arrangement, and crop varieties could enhance transgressive overyielding in intercrops in the future. We found that TOI increased with relative density total (SI Appendix, Fig. S7). This finding is consistent with the results of grassland biodiversity studies showing that increased plant density contributed to positive effects of biodiversity on plant productivity (48). Such a positive effect of plant density on total intercrop productivity could be the logical consequence of complementary resource use between companion species, allowing for higher densities. However, it may indicate that sole crop densities in experiments were suboptimal (49, 50). It is also possible that monocultures cannot be grown at the optimal density for yield because high density would increase the risks of crop failure. This is for instance the case for maize in China, which reaches highest yield at densities of about nine plants per m2, whereas farmers prefer using lower densities, e.g., six plants per m2, to reduce the risk of stem lodging (51). In strip intercropping, spatial arrangement may be optimized to allow strong maize stems that are not prone to lodging such that additive designs, as compared with the monostands, become possible (52). In China, maize/soybean strip intercropping was found to perform better with an additive intercropping design (53). Breeding research is ongoing to find resource foraging traits of species to maximize niche complementarity and intercrop performance (54), which could be suitable for additive intercropping. Further research is needed to ascertain whether high TOIs in systems with high relative density total reflect strong complementarity, allowing such density increases or improper low densities in the sole crop treatments.
A meta-analysis of published experimental data is necessarily constrained by the densities and configurations tested in experiments, and reported LERs may be inflated due to suboptimal monostands (49, 55) or a suboptimal LER outcome may be obtained due to suboptimal mixture designs. The available field experiments on intercropping cover only a relatively small number of all the possible spatiotemporal crop arrangements. There are many more possible spatiotemporal arrangements than those tested in the field experiments published in the literature, in part because current mechanization techniques do not allow for the implementation of diversified arrangements and because conducting experiments to compare a large number of spatiotemporal crop arrangements is resource intensive. Spatially explicit mechanistic models (56, 57) could facilitate the exploration of alternative crop arrangements in order to identify optimal crop association modalities. Such models may assist in identifying optimal trait complementarities in mixtures, tailored to the growing conditions and management.
Maize/legume intercrops on average produced 10% higher protein yield than the sole crop with the highest protein yield, and they produced 18% higher protein yield per unit of N fertilizer than the species with the highest protein yield (Fig. 2). Maize/legume intercrops gave 15% higher protein yield than the component sole crop with the highest protein yield under no fertilizer input (Fig. 4). However, transgressive overyielding in protein production of maize/legume intercrops decreased with N fertilizer input and the intercrop advantage with respect to protein production disappeared entirely at N fertilizer inputs above 283 kg ha−1 (Fig. 4). This finding is consistent with that of previous studies showing that the yield advantage of cereal/legume intercrops was greatest with no N fertilizer input but was reduced when N fertilizer was applied (25, 58). Thus, maize/legume intercrops offer potential particularly if the objective is the production of grain or protein while environmental impacts from nutrient spillovers need to be mitigated by lowering N fertilizer inputs. The protein production performance of cereal/legume intercrops could be even higher than estimated in the present analysis since we estimated protein yields assuming a constant protein concentration of crops, but a higher cereal grain protein and N concentration has been found when cereals were grown in mixtures with legumes (36, 47).
Greater adoption of intercropping in practical farming needs advances in many domains. Demonstrations of intercropping practices are needed to give farmers a chance to learn and appreciate the opportunities. Ideally, farmers and researchers explore opportunities for intercropping jointly in a cocreation setting (59). The Wageningen University research farm, for example, hosts a large experiment with diversification strategies including strip cropping (with wide strips of 3 m) and, within strips, species mixtures (e.g., wheat/faba bean) or variety mixtures (e.g., in potato) (60). This experiment is visited yearly by hundreds of farmers. While such 3 m strips allow cultivation with standard implements, these do not enable optimal complementarity between mixed crop species (61). Hence, developments in technology are needed to allow cultivation with narrower strips (62). Efforts are ongoing to build implements for cultivation in narrower strips (53), but much greater efforts are needed. Robotization and miniaturization may be enabling technologies for intercropping adoption (53, 63). Likewise, technology for postharvest separation of grains and adaptation of supply chains may greatly favor intercropping adoption (64).
It is also not likely that current varieties are already optimally suited for intercropping (54, 65, 66). Research is needed to elucidate whether and how the performance of intercropping systems may be further optimized by breeding “plant teams” that optimize complementary resource capture (54). The management of intercrops needs to be fine-tuned to local climate and soil conditions, available varieties, and the production orientation, calling for a reorientation of agronomy to embrace diversity. As intercrops consist of multiple species, the challenge of optimizing genotype by environment by management interactions is greatly aggravated for intercropping as compared with that of sole crops (54). Finally, in-depth insight into mechanisms underlying high performance of intercrops is needed to support technology development in breeding and agronomy (67).
Governments can do much to promote intercropping as a diversification strategy for yield increase. The Chinese government has issued a policy in 2022 promoting maize/soybean intercropping to boost national maize and soybean yields and allow a transition toward more efficient use of N in farming (68). The European Union has consistently made resources available for research and cocreation on intercropping through its Horizon 2020 and Horizon Europe research programs to accelerate the development and acceptance of intercropping and fostering both research and cocreation between farmers and researchers. While diversification of agriculture is challenging, both in the global North and the global South, the potential of intercropping to make agriculture more sustainable should provide continued incentive for societies and policymakers to invest in its development and adoption and overcome the lock-in on monostands.
In conclusion, intercropping performs well in producing a diverse set of crop products and performs almost similar to the most productive component sole crop to produce raw products. Furthermore, intercrops provide additional advantages for making agriculture more sustainable by suppressing diseases, pests (67, 69), and weeds (6, 8), and using N more efficiently (21). Intercrops with legumes, especially maize/legume intercrops, showed transgressive overyielding under low N fertilizer input, indicating their potential for developing more sustainable low N input cropping systems, particularly for producing dietary protein. All in all, this analysis therefore supports the great potential of intercropping for diversifying cropping systems to contribute to sustainable intensification of agriculture.
Materials and Methods
Data Collection.
We used the database described in the study by Li et al. (13), which includes field experiments on grain-producing intercrops consisting of two crop species from three crop types: cereals, legumes, and oilseed crops. The data set includes 934 observation records, representing data from 226 field experiments described in 132 publications (SI Appendix, Method S1). Each record contained data on the intercrop and monocrop yields and all associated management in the intercrop and monocrop treatments. “Experiment” was defined as a unique combination of site and year. Within experiments, different data records represent different combinations of species, densities, pattern, N fertilizer, and other agronomic management factors in intercropping and monocropping, whereby the management in intercrops and sole crops was similar or the same such that monocrop treatments provided a valid reference for the intercrop treatment. During data extraction, particular attention was given to data records with large LER in order to check that the monocultures and intercrops were conducted under the same or similar management. It was assumed that monocultures achieved optimal yields. This could not be formally verified because source publications did not report this information, but many publications mention that monostands are grown in accordance with local recommendations or farmer practice. Monocultures and intercrops were always replicated and averages across replicates were extracted for monoculture and intercrop treatments from the selected studies. Metric values (LER, NER, TOI, and TOIN) were calculated from these treatment means for each record. The data set includes several descriptors such as the publication title, year and author, species combination, the yield and N fertilizer rate of both sole crops and intercrops, intercrop design (SI Appendix, Table S3), intercropping pattern (SI Appendix, Table S4), and number of replicates. As most of the studies did not report calorie and protein data, grain calorie content and protein content were calculated for all data records using crop-specific standard values from the United States Department of Agriculture (USDA) Nutrient Database (38).
Conceptualization.
Three metrics were used for comparing the production efficiency of intercrops and sole crops, as follows (Table 1).
LER.
The LER is defined as the sum of the relative yields of intercropped species compared with their respective sole crops (Eq. 1) (17).[1] LER=Y1M1+Y2M2=pLER1+pLER2,
where Y1 and Y2 are the yields (per unit of total area of the intercrop) of species 1 and 2 in an intercrop, M1 and M2 are the yields of species 1 and 2 in the sole crops, and pLER1 and pLER2 are the partial land equivalent ratios (relative yields) of species 1 and 2, respectively. Partial LERs are calculated as the ratio of the yields of a species in the intercrop and the sole crop. The LER indicates the relative land area required under sole crops to obtain the same yield of the component species as a unit area of the intercrop begets under the same or comparable management (17). An LER greater than one indicates that a larger area is needed to produce the output quantities of species 1 and 2 with sole crops than with an intercrop. The partial LERs represent the relative areas of the component crop species required to produce the yield obtained in a unit area of the intercrop.
TOI.
The TOI is defined as the ratio of total intercrop yield over the highest sole crop yield of the component species (Eq. 2) (29).[2] TOI=(Y1+Y2)max (M1, M2)=Y1max (M1, M2)+Y2max (M1, M2).
To calculate TOI, the yields of the species need to be expressed in the same units, e.g., ton grain ha−1, calories yield ha−1, or protein yield ha−1. Depending on the chosen units, different variants of TOI can be defined, e.g., for grain yield, calorie yield, or protein yield. For instance, TOIgrain is calculated using for Y1, Y2, M1, and M2 the grain yields, while TOIprotein is calculated using for Y1, Y2, M1 and M2 the protein yields.
The relationship between the LER and the TOI can be easily expressed mathematically. Without loss of generality, assume that M1 ≥ M2 (i.e., by definition, crop species 1 is taken as the most productive species in sole cropping). Then:[3] TOI=Y1+Y2M1=Y1M1+R×Y2M2,
where R=M2M1 is the sole crop yield of the lower yielding species expressed as a proportion of the sole crop yield of the higher yielding species. Equivalently, TOI=pLER1+R×pLER2. Because R ≤ 1, TOI ≤ pLER1 + pLER2, and thus TOI≤LER. Thus, TOI cannot be larger than LER, and TOI equals LER only if the two sole crops have equal sole crop yields (R = 1). The practical consequence is that the sum of the pLERs should exceed a threshold that depends on the yield ratio R in order to achieve transgressive overyielding, as illustrated in Fig. 5. The lower the yield of the less productive species, the greater the sum of the pLERs should be to achieve transgressive overyielding (Fig. 5).
Fig. 5. Lines indicating TOI = 1 (i.e., on the verge of transgressive overyielding) as a function of pLER1 of a high yielding species (y-axis) and pLER2 of a low yielding species (x-axis). If the sum of pLER1 and pLER2 is greater than the limit indicated by the drawn line, the intercrop will show transgressive overyielding. Different lines are characterized by different values of the yield ratio of species 1 and 2 and greater pLERs are required to reach transgressive overyielding if R =M2M1 is smaller. When the sole crop yields are equal (R = 1), transgressive overyielding is achieved if LER > 1. However, the condition LER > 1 is not sufficient to achieve transgressive overyielding when one of the two sole crop species has a lower yield than the other, which is generally the case.
NER.
The NER is defined as the ratio of the observed yield to the expected yield expressed as a weighted average of the component crop yields according to the proportions of the crop species in the mixture (Eq. 4) (22, 30).
[4] NER=Y1+Y2EY1+EY2= Y1EY1+EY2+Y2EY1+EY2=Y1P1M1+P2M2+Y2P1M1+P2M2.
Here, EY1 and EY2 are the expected intercrop yields of the two species, which are calculated as the product of the respective sole crop yields and the corresponding land shares (23), i.e., EY1=P1M1 and EY2=P2M2, where P1 and P2 are the proportions of species 1 and 2 in the intercrop, respectively. These proportions represent (by approximation) the proportion of the intercrop area covered with both species with P1 + P2 = 1. They are calculated on the basis of the densities of a species in the intercrop and the sole crop [the relative density total was scaled to 1 for additive designs (392 data records)] or on the basis of row or plant arrangement. Detailed procedures are given by Li et al. (23). The NER expresses by which proportion intercrop yields are per unit area different from the expected (i.e., weighted average) yields of the sole crops when mixed in an area ratio or relative density ratio P1:P2 (23). TOI is necessarily smaller than the NER because NER compares the intercrop yields with the weighted sole crops yields, while TOI compares the intercrop yields with the highest sole crop yield.
Whether the NER is larger or smaller than the LER depends on whether the species with the lower or higher sole crop yield has the higher relative yield gain in intercropping compared with sole crops, where relative yield gain is defined as:[5] ΔRYi=RYi-Pi=pLERi-Pi,
where RYi is the relative yield of species i, which is equal to pLERi. The difference between the NER and the LER (i.e., NER–LER) equals the ratio of the selection effect (SE) (22) and the weighted average sole crop yield, P1M1+P2M2 (SI Appendix, Method S2). If the species with the higher sole crop yield has the higher relative yield gain in intercropping compared with sole crops, there will be a positive selection effect and the NER will be greater than the LER.
TOI and NER are most easily interpreted as field-level metrics as they compare yields within given unit areas of different cropping systems (Table 1). A value of TOI > 1 indicates that an intercrop produces a greater yield per unit area than growing the highest yielding species as a sole crop. TOI is useful to compare the production efficiency of intercropping with sole crops if the objective is restricted to the production of a single type of product—quantified by a single outcome (i.e., tons of dry matter, calories, protein, euros, yuan, etc.)—and if obtaining a diversity of crop outputs (potentially with diverse uses or different market value) is not necessary and neither is there a need for crop rotation or landscape diversity to maintain productivity. A value of NER > 1 indicates that intercropping produces greater yield per unit area than would be expected if there were no complementarities between the species, such that the relative yields obtained would be equal to the respective land shares, or the absolute yield gain of one species (Y1-EY1) would exactly cancel out the absolute yield loss of the other species (Y2-EY2) or vice versa. In other words, the NER assesses whether intercropping is a zero-sum game, considering absolute yields. On the contrary, the LER assesses whether intercropping is a zero-sum game, considering relative yields. LER is best interpreted as a measure for land-use efficiency.
TOI for N Fertilizer Use Efficiency.
We define the TOIN as the ratio of the partial factor productivity (PFP) of N fertilizer in an intercrop over the PFP of N fertilizer in the sole crop with the highest yield. PFP is calculated as grain production divided by N fertilizer applied (70).
Assume that M1 ≥ M2 (i.e., species 1 is the most productive species in sole cropping)[6] TOIN=(Y1+Y2)/NfertICM1/Nfert1=Y1+Y2M1×Nfert1NfertIC=TOI×Nfert1NfertIC,
where NfertIC is the N fertilizer input per unit area of the intercrop (kg ha−1) and Nfert1 is the N fertilizer input per unit area of the sole crop 1, which is the crop with the highest sole crop yield. In the calculation of TOIN for grain yield, the yields, Y1, Y2, and M1 are the grain yields per unit area while in the calculation of TOIN for protein yield, Y1, Y2, and M1 are the protein yields per unit area. TOIN quantifies by which factor the output of an intercrop per unit of N fertilizer exceeds that of the most productive sole crop. A value of TOIN greater than one indicates that intercropping is more efficient in N fertilizer use than the most productive sole crop, i.e., the quantity of product (e.g., grain yield, calories, protein) obtained for 1 kg of fertilizer is higher in intercropping than that in the sole crop with the highest grain, protein, or calorie yield. TOIN is 0 when the species with highest grain, protein, or calorie yield is unfertilized in a sole crop but not in the intercrop. Data records with unfertilized intercropping (157 out of 934) were excluded from the calculation of TOIN to avoid an undefined fraction.
Statistical Analysis.
Linear mixed-effects models were fitted using the function lme of the R package nlme to estimate the average values of LER, NER, TOI, and TOIN based on grain yield, calorie yield, and protein yield, and to estimate their relationships with N fertilizer input and with the yield ratio, calorie yield ratio, or protein yield ratio of the sole crops. We used publication and experiment within publications as random effects to account for differences among the studies (publications) and the experiments (sites*years) within studies. Similar to previous meta-analyses on intercropping (19–21), an unweighted meta-analysis was performed in this study because standard errors were not reported and could not be estimated with sufficient confidence for most papers in our data set, due to lack of information. Excluding those papers would be more detrimental to the accuracy of the statistical estimates than the use of unweighted data. Analyses were repeated with the function lmer of the more recent lme4 R package with identical outcomes.
We used funnel plots to assess publication bias in LERgrain, NERgrain, TOIgrain, and TOIprotein (71). For each funnel plot, we plotted average LERgrain, NERgrain, TOIgrain, and TOIprotein in each of the 132 studies against the total number of experimental units (replicates) in each study as a proxy for study accuracy (19). The funnel plots were symmetrical except the funnel plot of the NERgrain (SI Appendix, Fig. S8B), which was very slightly asymmetrical, with missing values in the bottom left corner representing studies with small study size and low effect size. The very slight asymmetry does not critically affect the conclusions of our study.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank the editor and three anonymous reviewers for helpful comments on an earlier version of the manuscript. We acknowledge funding from the Program of Advanced Discipline Construction in Beijing (Agriculture Green Development), the 2115 Talent Development Program of China Agricultural University, the National Natural Science Foundation of China (32002127), the China Scholarship Council (No.201913043) and Hainan University, the European Union’s Horizon 2020 Programme for Research & Innovation under grant agreement no. 727217 (www.remix-intercrops.eu), and the project CLAND from the French National Research Agency (16-CONV-0003).
Author contributions
C.L., T.-J.S., D.M., F.Z., and W.v.d.W. designed research; C.L., H.L., C.Z., and W.v.d.W. performed research; C.L., T.-J.S., D.M., and W.v.d.W. analyzed data; and C.L., T.-J.S., D.M., and W.v.d.W. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
The datasets and R scripts used in the current study have been archived in Figshare, https://doi.org/10.6084/m9.figshare.20217611.v1 (72).
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926262.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595703
202217303
10.1073/pnas.2217303120
datasetDatasetinauguralInaugural Articleresearch-articleResearch ArticleanthroAnthropologyenv-sci-physEnvironmental Sciences1
402
417
Social Sciences
Anthropology
Biological Sciences
Environmental Sciences
Inaugural ArticleBiocultural vulnerability exposes threats of culturally important species
Reyes-García Victoria [email protected]
a b c 1 https://orcid.org/0000-0002-2914-8055
Cámara-Leret Rodrigo d https://orcid.org/0000-0001-7705-0602
Halpern Benjamin S. e f
O’Hara Casey e https://orcid.org/0000-0003-2968-7005
Renard Delphine g
Zafra-Calvo Noelia h
Díaz Sandra i https://orcid.org/0000-0003-0012-4612
aInstitució Catalana de Recerca i Estudis Avançats, Barcelona 08010, Spain
bInstitut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona 08193, Spain
cDepartament d’Antropologia Social i Cultural, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona 08193, Spain
dDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, CH-8057, Zurich, Switzerland
eBren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106
fNational Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, CA 93101
gCentre d'Ecologie Fonctionnelle et Evolutive, Univ. Montpellier, Centre National de la Recherche Scientifique, École Practique des Hautes Études, Institut de Recherche pour le Developpement, Montpellier 34090, France
hBasque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain
iInstituto Multidisciplinario de Biología Vegetal, Consejo Nacional de Investigaciones Científicas y Técnicas and Facultad de Ciencias Exactas, Físicas, y Naturales, Universidad Nacional de Córdoba, Córdoba 5000, Argentina
1To whom correspondence may be addressed. Email: [email protected].
Contributed by Victoria Reyes-Garcia, received October 14, 2022; accepted November 22, 2022; reviewed by Eleanor Sterling and David S. Wilkie
This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2021.
3 1 2023
10 1 2023
3 7 2023
120 2 e221730312014 10 2022
22 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Recognizing the connections between people and other parts of nature and incorporating them into decision-making will enable to operationalize actions simultaneously based on biological conservation priorities and cultural values. Our biocultural framework and metric show that high levels of cultural loss, particularly among Indigenous peoples, swamp the influence of biological status on assessing biocultural status. To sustain culturally important species, we need a more complete list of species that are culturally important for different groups and of their status, as well as larger support to the cultures that value them.
There are growing calls for conservation frameworks that, rather than breaking the relations between people and other parts of nature, capture place-based relationships that have supported social–ecological systems over the long term. Biocultural approaches propose actions based on biological conservation priorities and cultural values aligned with local priorities, but mechanisms that allow their global uptake are missing. We propose a framework to globally assess the biocultural status of specific components of nature that matter to people and apply it to culturally important species (CIS). Drawing on a literature review and a survey, we identified 385 wild species, mostly plants, which are culturally important. CIS predominate among Indigenous peoples (57%) and ethnic groups (21%). CIS have a larger proportion of Data-Deficient species (41%) than the full set of International Union for Conservation of Nature (IUCN) species (12%), underscoring the disregard of cultural considerations in biological research. Combining information on CIS biological conservation status (IUCN threatened status) and cultural status (language vitality), we found that more CIS are culturally Vulnerable or Endangered than they are biologically and that there is a higher share of bioculturally Endangered or Vulnerable CIS than of either biologically or culturally Endangered CIS measured separately. Bioculturally Endangered or Vulnerable CIS are particularly predominant among Indigenous peoples, arguably because of the high levels of cultural loss among them. The deliberate connection between biological and cultural values, as developed in our “biocultural status” metric, provides an actionable way to guide decisions and operationalize global actions oriented to enhance place-based practices with demonstrated long-term sustainability.
biocultural diversity
conservation planning
cultural keystone species
Indigenous languages
EC | European Research Council (ERC) 501100000781 FP7-771056-LICCI Victoria Reyes-Garcia Ministerio de Ciencia e Innovación (MICINN) 501100004837 CEX2019-000940-M Victoria Reyes-Garcia
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pmcBiocultural Approaches to Conservation
At a time of global decline in nature, there are growing efforts to conserve the world’s biodiversity both for nature’s sake and for its contributions to humankind (1). In these efforts, conservation policies based mostly on biological criteria miss the social, cultural, and livelihood needs and aspirations held by local communities (2). They thus risk perpetuating existing inequalities in the distribution of social and ecological burdens and benefits of conservation (3, 4). For example, conservation proposals to safeguard 30% (5, 6) or 50% (7) of the planet face opposition on the grounds that they might increase the negative social impacts of conservation actions and pose immediate risks for people whose livelihoods directly depend on nature, in particular Indigenous peoples and local communities (8, 9). To help address these potential conflicts, researchers and practitioners increasingly emphasize the need for different conservation frameworks that, rather than focusing on breaking the relations between people and other parts of nature, include a broader range of worldviews, knowledge, and values and that capture place-based relationships that have supported social–ecological systems over the long term (10–13).
Biocultural approaches can widen existing conservation frameworks by recognizing and honoring the relationships between people and other parts of nature, proposing actions based on conservation priorities and cultural values aligned with local priorities (3, 14, 15). Examples of biocultural approaches to conservation include initiatives that recognize the spiritual significance of landscapes as manifested in sacred sites (16, 17), the importance of social norms, such as taboos or customary rules in wildlife management (18, 19), or the cultural significance of some species, including them in management strategies (20, 21) or in conservation planning in the face of climate change (22). Despite recent applications of biocultural approaches in specific case studies, we lack mechanisms that allow a global uptake of a biocultural framework (but a proposal is provided in ref. 23). This gap most likely exists because many of the interactions that mediate the relationships between people and nonhuman nature are context specific and difficult to articulate to outsiders (11, 24) resulting in challenges for the transferability, integration, and scalability of local knowledge (25).
Here, we propose a framework and implement a metric to assess the biocultural status of specific components of nature that matter to local communities. The proposed metric, “biocultural status,” allows the combination of information on biological and cultural conservation status of different components of nature and is based on the logic that the disappearance of a culture entails the disappearance of relations between human and nonhuman components of nature (2, 26, 27). Drawing on research on cultural keystone species (e.g., refs. 21 and 28), we apply our framework to assess the biocultural status of “culturally important species” (CIS), here defined as species that have a recognized role in supporting cultural identity, as they are generally the basis for religious, spiritual, and social cohesion and provide a common sense of place, purpose, belonging, or rootedness associated with the living world (Methods).
CIS Characterization
Combining information from previous compilations of CIS and an online survey (see Methods), we identified 385 wild species that are culturally important for at least one sociocultural group. We differentiated between CIS that are important for “Indigenous peoples,” “ethnic groups,” “local communities,” and “other sociocultural groups” (see Methods for definitions). We acknowledge that ours is not a comprehensive list of the total (currently unknown) number of CIS on Earth. We also acknowledge that the inclusion of species in the list is probably biased by researchers’ interpretation of which species are culturally important for a specific sociocultural group as the compilation was not fully informed through diverse knowledge systems. However, the list represents the largest global compilation of wild species identified as culturally important to date. Our list is largely dominated by plants (n = 241; 63%), with a surprisingly low number of mammal (n = 50; 13%), fish (n = 27; 7%), and bird species (n = 16; 4%) (Fig. 1A). CIS were reported in every continent, with more reports in North America (23%) than elsewhere (Fig. 1B). Only four species (all sea turtles, i.e., Caretta caretta, Chelonia mydas, Dermochelys coriacea, and Eretmochelys imbricata) were reported as culturally important by groups in more than one continent. Other reports were continent specific.
Fig. 1. Taxonomic and geographical distribution of culturally important species (n = 385). (A) Taxonomic distribution of CIS. (B) Number of CIS classified by continent and sociocultural group type (i.e., Indigenous people, ethnic group, local community, and other sociocultural group). Each square represents a CIS. In (A) square, color depicts taxonomic distribution and in (B) it depicts the sociocultural group who reported the CIS.
Species in our list are culturally important for a variety of sociocultural groups but mainly for Indigenous peoples (57%) and ethnic groups (21%). Particularly, CIS are documented among Indigenous peoples in the Americas, Oceania, and Asia and among ethnic groups in Africa (Fig. 1B). Some CIS are also documented among local communities (mainly in South America) and other sociocultural groups in Europe (largely referring to CIS for citizens of a given region, such as Extremadura in Spain, Provence in France, or Epirus in Greece).
Biological and Cultural Status
We assessed the biological conservation status of CIS (hereafter biological status) using the International Union for Conservation of Nature Red List of Threatened Species (IUCN) (29) and the “cultural status” of the group(s) for which the species is culturally important using language vitality using the Ethnologue (30). We used language vitality as a proxy for a group’s cultural status because language is the primary means of cultural transmission (31) and the ability of cultures to name, use, and share knowledge about nature might disappear when languages go extinct (27, 32). We assigned a cultural status value to each CIS, combining information from the cultural status of all the groups for which the species is documented as culturally important (Methods).
The distribution of IUCN categories within our list of CIS generally aligns well with the general distribution of the IUCN Red List species [χ2(df = 4, N = 32,713) = 3.23, P = 0.520] except for Data-Deficient species. Our list of CIS has a much higher proportion of Data-Deficient species (41%) than the full set of IUCN species (12%). The high share of Data-Deficient species in our list might result in an underestimation of the biological threat of some CIS as species categorized as Data-Deficient by the IUCN seem to be more threatened than data-sufficient species (33). The disproportionally high amount of missing data on the biological status of CIS also aligns with reports of mismatches in metrics externally defined and those locally considered important (e.g., ref. 34). Importantly, the data gap underscores that cultural considerations remain disregarded in much current biological research (3, 4).
CIS in our list are homogeneously distributed across the selected cultural status categories, with roughly one third of the species falling in the Not Threatened (36%), Vulnerable (28%), and Endangered (34%) categories. Only six CIS have a Data-Deficient cultural status.
Biocultural Status
We combined information on the CIS biological and cultural status to create categories for a new metric of biocultural status (Fig. 2). We acknowledge that there are biases in the datasets used to infer CIS biological (35) and cultural status (36), which preclude the precise assessment of biocultural status. However, using a Pearson’s chi-square test of independence, we found that biological and cultural status of CIS in our sample are independent of one another (χ2(df = 15, N = 382) = 14.95, P = 0.455). Overall, 163 (42%) of the CIS in our list are not of biological concern and a similar number (n = 139, 36%) are not of cultural concern. A much lower share of CIS are not of biocultural concern (n = 62; 16%), with 110 (29%) and 152 (39%) CIS having a Vulnerable and Endangered cultural status, respectively. Overall, then, more CIS in our list are culturally Vulnerable or Endangered than they are biologically Vulnerable or Endangered. Bootstrapped 95% confidence intervals show that the proportion of bioculturally Endangered and Vulnerable CIS is significantly greater than the proportion of Not Threatened and Data-Deficient CIS.
Fig. 2. Biological and cultural status of 385 culturally important species with representative examples. Biological status was assessed from the IUCN Red List of Threatened Species (28), and cultural status was derived from language vitality status from the Ethnologue (29). Colors depict biocultural status, and circle size indicates the number of CIS in each cell. A full list is provided in the SI Appendix.
Many CIS with a biological status other than Data-Deficient are Least Concern or Near Threatened (n = 163, 42%) but span the range of cultural status (Fig. 2). Over one third of the species in the Least Concern and Near-Threatened categories (n = 62; 16% of all CIS) are culturally Not Threatened. Examples of species in this category are Ciconia ciconia (white stork), considered a “national bird” by the Polish (37), Macleania rupestris (uva camarona), whose fruit is widely consumed by high mountain peasants in Colombia (38), and Ptaeroxylon obliquum (sneezewood tree), whose durable wood is used by the Xhosa in West Africa to construct ceremonial houses and represents allegiance to the ancestors (39).
A similar number of species (n = 58; 15% of all CIS) are biologically Least Concern or Near Threatened but are important to culturally Endangered groups. Examples of species in this category include the venomous Naja haje (Egyptian cobra) of North Africa that is sacred to the Ikoma in Tanzania (40) and Echyridella menziesii (New Zealand freshwater mussel), a New Zealand endemic that is culturally keystone to the Maori (41).
Very few CIS are both biologically and culturally Endangered or Critically Endangered (n = 13; 3% of all CIS; Fig. 2). Species in this category include the Endangered Fraxinus nigra (black ash), which plays a central role in the spiritual and material culture (i.e., basketry) of different Native Americans and First Nations people in the Wabanaki Confederacy (42), the Critically Endangered crayfish Cherax tenuimanus in southwestern Australia, culturally important to the Endangered Indigenous Nations in the Murray-Darling River Basin (43), and the Endangered tree Araucaria araucana (pehuén), which plays a key role in the identity and concept of territoriality of the also Endangered Mapuche-Pehuenche people (44).
CIS in our list are not homogenously distributed across continents and sociocultural groups (SI Appendix, Table S1), probably following patterns of the global distribution of these groups, but potentially obscuring patterns for underrepresented cultural groups in a continent (e.g., Indigenous peoples in Europe). An analysis of the geographical distribution of biological, cultural, and biocultural status of CIS (Fig. 3A) shows that North America and Oceania have very high proportions of bioculturally Endangered CIS (71% and 67% of CIS listed in these continents), which is driven almost entirely by Endangered cultural status. Conversely, most species listed in Europe (68%) are not bioculturally threatened. Most of the Data-Deficient biocultural status is driven by lack of data on biological status, particularly prominent in Asia and South America (i.e., Data-Deficient Red List assessments).
Fig. 3. Biological, cultural, and biocultural status of 385 culturally important species. (A) Distribution across continents; (B) Distribution across sociocultural group type. Colors depict status.
Across sociocultural group types, the share of bioculturally Endangered (57%) and Vulnerable (33%) CIS is highest among Indigenous people (Fig. 3B). Conversely, the share of bioculturally Not Threatened CIS is highest among other sociocultural groups (60%). Because Indigenous peoples’ lands show lower declines in nature than other lands (45), the high share of bioculturally Endangered and Vulnerable CIS among Indigenous peoples probably derives from high levels of cultural endangerment of Indigenous peoples (as measured by language endangerment). In fact, a recent global analysis of language endangerment shows that areas with the highest proportion of endangered languages include Australia, North China, Siberia, North Africa and Arabia, North America, and parts of South America (46), which also display high cultural diversity and presence of Indigenous populations (45). That is, the high extinction risk of Indigenous languages may swamp the influence of biological status on biocultural status.
Our approach allows exploration of the biocultural status of species across continents and sociocultural groups, but it does not allow establishment of causal links between biological and cultural threats. One of the predominant conservation approaches is based on the idea of the need to protect a pristine “wilderness” free from the damaging role of humans. According to this approach, separating humans from other parts of nature (which can happen by multiple factors including the loss of cultural identity and traditional livelihoods, migration, or displacement) would lead to the recovery of wild species (47). Even if that is the case (48), breaking the relation between people and other components of nature might eventually lead to the decline of collective attention and memory to a species, or to the “societal extinction of species,” with potential implications for global conservation efforts (26). By contrast, the biocultural approach argues that the removal of the relations between humans and other parts of nature could lead to declines both on the status of nature and on people’s quality of life and ultimately to local extinction of species or habitat loss (2, 49). In part, this might occur because cultural decline entails a loss of culturally unique knowledge and behavior, including forms of nature care and management, which might negatively affect nonhuman parts of nature (27). While further research is needed to understand the causal effects of changing the relations between humans and other parts of nature, particularly in areas where such relations have supported social–ecological systems over the long term, the deliberate connection between cultural and biological values, as developed in our biocultural status metric for CIS, offers a tangible means to advance conservation that meets the needs of both people and nature. Importantly, while the focus of this work has been on CIS, the framework is transferable to species that are valued for their material contributions (e.g., food and regulation of freshwater) or even to other components of nature (e.g., domesticated species and culturally important sites or ecosystems).
As part of the conservation community increasingly seeks to include diverse worldviews, knowledge, and values in nature management and restoration, the framework and metric proposed here offer a concrete mechanism that combines local perspectives on which species are culturally important with scientific assessments of the biological and cultural status of these species. Thus, the framework and metric provide an actionable way to guide decisions and operationalize global actions oriented to enhance place-based practices that have supported the conservation of social–ecological systems over the long term (e.g., Indigenous people practices). In that sense, our results for a subset of the global CIS identify how and where global and local conservation priorities intersect and highlight the predominant biocultural vulnerability of CIS species from loss of culture. We derive two specific recommendations from these main results. First, there is a need for a larger focus to 1) assess a representative list of species with cultural significant relations informed by the concerned communities themselves using culturally appropriated methods and 2) accelerate evaluations on the biological status of CIS as there is a disproportionately high number of CIS with Data-Deficient biological status. Such focus would allow for the planning of actions simultaneously based on conservation priorities and cultural values aligned with local priorities. Second, as cultural endangerment drives the high levels of biocultural endangerment of CIS, there is a need to increase the support to maintaining thriving cultural diversity. In that sense, there are growing calls for the conservation community to actively engage with and support Indigenous rights to land, resources, diverse livelihoods, and lifeways, and particularly claims of Indigenous peoples and local communities for autonomous territorial management (13, 50, 51). By recognizing the connections between people and other parts of nature and directly incorporating them into decision-making, we hope our approach enables more effective action to reach the 2050 Convention of Biological Diversity goal of “living in harmony with nature.”
Methods
Defining and Identifying CIS.
To connect cultural perspectives with environmental conservation and restoration discourses, ethnobiologists have used the concept of “cultural keystone species” (e.g., refs. 28 and 42), proposing a set of criteria for identifying them (28). For the work presented here, we assembled information gathered by two previous compilations of cultural keystone species and an online survey. However, since we could not verify whether all the species in the list actually fit the criteria of cultural keystone species (as defined in ref. 28), here we use the more lax term CIS. The two compilations used include the list available in ref. 21 and an unpublished list provided by Michael Coe elaborated as part of his PhD dissertation and subsequent publications (52, 53). The analysis of the species appearing in the two compilations showed a geographical bias for North America and a likely taxonomic bias for plant species. To enlarge the list and potentially minimize the observed biases, we conducted an online survey (available in a dedicated web page between January and June 2021) and distributed it through social media and distribution lists of targeted networks (e.g., ICCA Consortium, and the list of the Anthropology and Environment Society of the American Anthropological Association). The survey, available in English, Spanish, French, Portuguese, Bahasa Indonesia, German, and Russian, asked for information (i.e., local name(s), scientific name, and uses) about species considered culturally important for any sociocultural group and requested details on the cultural identity of the group (i.e., group name, language, and territory). The survey included 503 entries of CIS. Most respondents only entered information on one CIS/group, although some informants entered as much as 10 CIS/group (average = 1.2). We eliminated incomplete records and records where the species could not be identified by the scientific name. We merged information from the literature and the survey to create our list of CIS (SI Appendix). Because our focus is on wild biodiversity, we excluded 23 domesticated species (i.e., crops or pets) from the analysis.
Plant taxonomic names were standardized using the Plants of the World Online (http://www.plantsoftheworldonline.org, accessed 1 January 2022), and animal names were standardized following the IUCN Red List of Threatened Species (2020). Names of cultural groups were recorded at the most specific level possible (e.g., Cree vs. First Nations), although in reports retrieved from the literature some authors provided only general names (e.g., Aboriginals and Indigenous communities). Cultural groups with internal divisions (i.e., the Cree people are formed by numerous subgroups, such as the Plains Cree, Woods Cree, and James Bay Cree) were aggregated in the denominator that best captures the identification used by the group based on language (e.g., Plains Cree or Oji-Cree).
Assessing Species’ Biological, Cultural, and Biocultural Status.
We assessed the biological and the cultural status of all the wild species in our list. We equated biological status with species’ conservation assessments from the IUCN Red List of species (2020), which includes categories of Least Concern, Near Threatened, Vulnerable, Endangered, and Critically Endangered. We generally defined a cultural group as a community of practice who share a core set of beliefs, patterns of behavior, and values. We identified four types of sociocultural groups: 1) Indigenous peoples, those who belonging to specific nations or ethnic groups, self-identify as “Indigenous” or “Aboriginal” and live in nation-states acknowledging Indigenous peoples’ rights; 2) ethnic groups, ethnically distinctive groups, who do not self-identify as Indigenous or who live in nation-states that do not acknowledge their specific rights as Indigenous peoplesIndigenous people’s rights; 3) local communities, such as caboclo or mestizo riverine dwellers, and forest extractive communities who have long-term relations with the territory); and 4) other sociocultural groups, including citizens of regions in nation-states who are identified by their language and ways of thinking and behaving, including religion (e.g., Catalans in Spain or Epirus in Greece). To assess the cultural status of the group reporting the species, we used language vitality as a proxy as language is the primary means of cultural transmission (31, 32). Specifically, for each cultural group in our database, we collected information on language vitality from the Ethnologue, the most comprehensive and updated inventory of the status of languages in the world (https://www.ethnologue.com/about). While language is not a direct indicator of cultural vitality, it remains an approximate indicator, and a good one especially for aspects related to cultural transmission of knowledge about nature (27, 32). The Ethnologue uses four categories to classify languages: Institutional, Stable, Endangered, and Extinct. In this work, we used the first three categories, and when the categorization of the cultural group was too general to identify the language spoken by the group (e.g., “ethnic community”), we coded language vitality as “Data Deficient” except in cases for which we could assume the status. For example, of the more than 250 known Australian Indigenous languages, only about 145 are still spoken and of those 110 are critically endangered (46), so we assumed that the linguistic vitality of any Australian Indigenous language was Endangered.
As some species were reported as culturally important for more than one cultural group, we followed several steps to create a measure that captures a CIS cultural status. First, if the species was reported as culturally important for one group, or for groups with the same language vitality, we equated the species cultural status to language vitality using the following equivalence: Institutional language = Not Threatened, Stable language = Vulnerable, and Endangered language = Endangered. Second, if a species was reported as culturally important for groups with different levels of language vitality, we considered the species cultural status to be i) Not Threatened if the species was reported only by groups with Institutional only or a combination of Institutional and Stable languages, ii) Vulnerable if the species was reported only by groups with Stable languages or by any combination of groups with Institutional/Stable languages and Endangered/Extinct languages, and iii) Endangered if the species was reported only by groups with Extinct and/or Endangered languages. If the species was reported by several groups and one of the groups lacked information on language vitality, we classified its cultural status as Data Deficient.
To assess CIS biocultural status, we combined information on the species’ biological and cultural status to create categories of biocultural status (Fig. 2). Specifically, we created the categories of Data Deficient, Not Threatened (which includes CIS biologically Least Concern or Near Threatened and culturally Not Threatened), Vulnerable (which includes CIS biologically Vulnerable and all categories of cultural status except Endangered), and Endangered (which includes CIS biologically Critically Endangered or Endangered and CIS culturally Endangered).
We provide descriptive statistics of the biological, cultural, and biocultural status of the 385 species in our list. We compare the share of CIS (n = 385) that falls into four main categories (i.e., Data Deficient, Not Threatened, Vulnerable, and Endangered) according to their i) biological, ii) cultural, and iii) biocultural status, aggregated by continent (excluding four global species) (Fig. 3A) and sociocultural group category (excluding 13 species with insufficient data on cultural group) (Fig. 3B). To calculate 95% confidence intervals around species’ counts within each category, we resampled the dataset 1,000 times with replacement, counted the number of species in each category, and identified the 2.5% and 97.5% quantiles as the CI bounds. To test for independence of values given in the biological and cultural status datasets, we constructed a contingency table of species counts in each combination of biological status and cultural status and then performed a chi-squared test using the chisq.test () function in R. All analyses were performed in R version 4.2.1.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Dataset S01 (XLSX)
Click here for additional data file.
This project received funding from the European Research Council under an ERC Consolidator Grant (FP7-771056-LICCI) and logistical support from the National Center for Ecological Analysis and Synthesis. We thank M. Coe for graciously providing a preliminary compilation of CIS and T. Ibarra, G. Mattalia, and C. Schunko for comments to previous versions and bibliographical leads. This research contributes to the “María de Maeztu Unit of Excellence” (CEX2019-000940-M). We thank our colleagues who assisted us in translating the survey: Pungki Lupiyaningdyah (Bahasa Indonesia), Marilia Palumbo Gaiarsa (Portuguese), and Michael Kessler (German).
Author contributions
V.R.-G., R.C.-L., B.S.H., D.R., N.Z.-C., and S.D. designed research; V.R.-G., R.C.-L., D.R., and N.Z.-C. performed research; V.R.-G., R.C.-L., C.O., D.R., and N.Z.-C. analyzed data; and V.R.-G., R.C.-L., B.S.H., C.O., D.R., N.Z.-C., and S.D. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Supporting Information
Reviewers: E.S., University of Hawai’i Manoa Hawaii Institute of Marine Biology; and D.S.W., Wildlife Conservation Society.
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PMC009xxxxxx/PMC9926264.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595665
202211416
10.1073/pnas.2211416120
videoVideoresearch-articleResearch ArticleengEngineering416
Physical Sciences
Engineering
Plant cell-like tip-growing polymer precipitate with structurally embedded multistimuli sensing ability
Park Chan Jin a https://orcid.org/0000-0003-2839-2360
Ha Jonghyun a b
Lee Hae-Ryung c
Park Keunhwan [email protected]
d 1
Sun Jeong-Yun [email protected]
c e 1 https://orcid.org/0000-0002-7276-1947
Kim Ho-Young [email protected]
a 1 https://orcid.org/0000-0002-6813-2398
aDepartment of Mechanical Engineering, Seoul National University, Seoul 08826, Korea
bDepartment of Mechanical Engineering, Ajou University, Suwon 16499, Korea
cDepartment of Materials Science and Engineering, Seoul National University, Seoul 08826, Korea
dDepartment of Mechanical Engineering, Gachon University, Seongnam 13120, Korea
eResearch Institute of Advanced Materials, Seoul National University, Seoul 08826, Korea
1To whom correspondence may be addressed. Email: [email protected], [email protected], or [email protected].
Edited by Rayne Xiaoyu Zheng, University of California–Los Angeles; received July 3, 2022; accepted November 23, 2022 by Editorial Board Member Yonggang Huang
3 1 2023
10 1 2023
3 7 2023
120 2 e22114161203 7 2022
23 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Here, we report a technology to grow a polymer structure, which can adapt its growing direction in response to environmental stimuli. Inspired by tip-growing cells like pollen tube and fungal hyphae, our artificial system grows from tips as polymer precipitates but requires much weaker pressure than the natural counterparts. We demonstrate various applications, including printing 3D structures in a confined space, autonomously bypassing mechanical obstacles, and transporting liquids underwater in a shielded manner. In addition to suggesting a material system for novel soft robots that can autonomously interact with the surrounding environments, our bioinspired growing system serves as a tool to enhance physical understanding of nature’s tip-growing cells.
Soft systems that respond to external stimuli, such as heat, magnetic field, and light, find applications in a range of fields including soft robotics, energy harvesting, and biomedicine. However, most of the existing systems exhibit nondirectional, nastic movement as they can neither grow nor sense the direction of stimuli. In this regard, artificial systems are outperformed by organisms capable of directional growth in response to the sense of stimuli or tropic growth. Inspired by tropic growth schemes of plant cells and fungal hyphae, here we report an artificial multistimuli-responsive tropic tip-growing system based on nonsolvent-induced phase separation of polymer solution, where polymer precipitates as its solvent dissolves into surrounding nonsolvent. We provide a theoretical framework to predict the size and velocity of growing precipitates and demonstrate its capability of sensing the directions of gravity, mechanical contact, and light and adjusting its growing direction in response. Exploiting the embedded physical intelligence of sensing and responding to external stimuli, our soft material system achieves multiple tasks including printing 3D structures in a confined space, bypassing mechanical obstacles, and shielded transport of liquids within water.
tip growth
polymer precipitation
biomimetics
physical intelligence
National Research Foundation of Korea (NRF) 501100003725 2018052541 Jeong-Yun SunHo-Young Kim National Research Foundation of Korea (NRF) 501100003725 2021M3F7A1017476 Jeong-Yun SunHo-Young Kim National Research Foundation of Korea (NRF) 501100003725 2018M3A7B4089670 Jeong-Yun SunHo-Young Kim
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pmcImplementing the adaptability of living organisms in an artificial system is a topic of significant interest in soft robotics, (1–4) energy harvesting, (5) and biomedicine (2, 4, 6). A range of materials that expand, shrink, stiffen, or soften in response to environmental stimuli like heat, magnetic field, electric field, light, and humidity have been developed to perform programmed tasks (1–5). However, mostly responding in a nondirectional manner as they lack the ability to sense the direction of the stimulus, artificial stimulus-responsive systems are still frequently outperformed by living organisms, which conduct diverse challenging tasks while sensing the directions of various stimuli imposed from the environments. For example, while navigating their environments through growth as shown in Fig. 1A, some plants can sense the direction of gravity and grow along or against the direction, a behavior termed gravitropism (Fig. 1B) (7). Many plants sense and respond to the direction of light or exhibit phototropism, by either growing toward or away from the light (Fig. 1C) (8). Similarly, some plants can sense and respond to the mechanical contact (Fig. 1D), exhibiting thigmotropism (9, 10). Tropic growth, the directional growth in response to the directional stimuli, allows the plants and fungi to adapt to the surrounding environments. Developing artificial systems equipped with this trait will allow us to build innovative robots that can actively adapt their shapes and movements to the environments for medical, industrial, military, and environmental applications. In addition, building and scrutinizing artificial tropic growth systems can enhance our understandings of nature’s tropic growth, in turn.
Fig. 1. Tip-growing polymer precipitate and its tropic characteristics. (A) A pollen tube of lily, Lilium longiflorum, germinated from a pollen grain. A long cylindrical cell grows out of the pollen with the growth limitedly occurring at the tip. Growing direction of the pollen tube is denoted by a black arrow. (Scale bar, 50 μm.) (B) Gravitropism of Aeonium arboreum leaves. The leaves grow in the direction against the gravity. (C) Phototropism of Glycine max sprouts. The sprouts grow toward the light. (D) Mimosa pudica leaves before applying touch stimulus (Left) and after applying the stimulus (Right). The leaves fold when touched. (E) Tip-growing of CA precipitate in water. As the solution is continuously fed, the opaque cylinder elongates while the growing tip remains transparent. t = 0 corresponds to the initiation of solution extrusion. (Scale bar, 3 mm.) (F) Tropic characteristics and functions of our system. Similar to the plant cells, the growing precipitate can sense gravity, light, and physical contact and change growing direction in response. This allows the precipitate to bypass obstacles and grow toward a target (lead of a LED). The size of the precipitate can be precisely controlled, and therefore, it can pass a small gap. The grown precipitate can be used as a fluid conduit to transport fluid in a shielded manner, and by transporting conductive fluid, the precipitate can be used as a growing wire that completes an electronic circuit underwater.
We first consider the fundamental physical mechanism underpinning the tropic tip growth of plants and fungi. Plant cells or fungal hyphae grow through expansion, which is driven by the internal turgor pressure while resisted by cell wall strength (11). Although turgor pressure has no preferential direction as a scalar quantity, some cells can grow in an anisotropic manner. For example, as shown in Fig. 1A, pollen tubes can guide the growth to occur only at its tip by locally reducing the cell wall strength at the tip (12, 13). Such tip growth is adopted in various cells from different kingdoms including protonemata and root hairs of plants and hyphae of fungi. Adopting the identical growth scheme, those tip-growing cells exhibit a similar shape of a long cylindrical body topped with a semi-spheroidal tip. Tip growth offers multiple advantages including rapid and precise direction adjustment in response to external signals and energy efficiency (14). It is widely perceived that the following three conditions are necessary for tip growth to occur: i) cell wall strength gradient in the meridional direction with the lowest strength at the apex, ii) sufficiently large turgor pressure to expand the cell wall, and iii) tip-targeted cytoplasmic streaming which provides expanding cell wall with building blocks and prevents thinning of the cell wall (13).
Inspired by this tip-growing strategy of nature, here, we developed a system that can mimic tip growth by simultaneously satisfying the aforementioned three conditions using nonsolvent-induced phase separation (NIPS) of a cellulose acetate (CA)–acetone solution. Because the CA precipitate grows in an analogous manner to the tip-growing cells, it possesses pollen tube-like morphology—a long cylinder topped with prolate semispheroidal tip as shown in Fig. 1E (Movie S1). Moreover, similar to the plant cells, the growing CA precipitate adjusts its direction in response to the applied external stimuli, i.e., gravity, light, and mechanical contact, thereby exhibiting gravitropism, phototropism, and thigmotropism. Tropic characteristics allow us to steer the growing precipitate using various stimuli as depicted in the first three panels of Fig. 1F. In addition to the growing direction, the size of the precipitate can also be precisely controlled by varying the internal pressure as illustrated in the fourth panel of Fig. 1F. Finally, as shown in the rightmost panel of Fig. 1F, the growing precipitate can be used as a conduit for shielded underwater transport of liquids.
Results
Conditions for Tip Growth.
Tip-growing of CA precipitate is achieved by extruding the CA–acetone solution inside a bath of water as shown in Fig. 1E, the experiment carried out in a setup illustrated in Fig. 2A. The extruded polymer solution rises to form an opaque cylinder with a transparent, prolate semispheroidal tip via NIPS. As shown in Fig. 2A, when the transparent solution contacts water, the solution rapidly transforms to a transparent gel at the interface as significant amount of solvent, acetone, immediately diffuses out into the water (15). Through continuous deprival of acetone and invasion of water, the inner solution near the gel layer eventually turns into an opaque solid of CA when the CA concentration crosses binodal curve giving rise to phase separation (SI Appendix, Supplementary Discussion S1) (16). Therefore, always transparent tip indicates that fresh solution is continuously replenished at the growing tip to lower the CA concentration.
Fig. 2. Tip-growing condition and growth dynamics. (A) Schematic illustration of the experimental setup (Left), solvent–nonsolvent exchange in the tip during growth (Middle), internal flow visualization using fluorescent particles (Top Right), and SEM image of the growing precipitate (Bottom Right). Red arrow in the left schematic denotes the meridional direction. The cylinder in the SEM image was obtained by first replacing the internal fresh solution with liquid metal during growth and then rinsing the liquid metal away after the cylinder is solidified. (Scale bars, 300 μm.) (B) Regime map of tip-growing (Right Inset image) and basal-growing (Left Inset image) conditions of extruded CA solution. Red and black dots denote tip-growing conditions and basal-growing conditions, respectively. (Scale bars, 3 mm.) (C) Experimentally measured radius of the tip-growing precipitate, R versus growth rate, U. Black line stands for y=45x2. Inset: log–log plot of R versus U. Line with a slope of 2 is shown together with the data points in the Inset. (D) Lengthening rate τ-1 of various tip-growing cells in nature versus the pressure that drives the growth. Data and references are provided in SI Appendix, Table S1. Each data point is the average of three measurements, and error bars correspond to the SD (C and D). Error bar is smaller than the size of the symbols in D.
Because acetone and water are gradually exchanged at the solution–water interface, gel’s viscosity at the tip, which provides the wall strength, increases monotonically along the meridional direction (17, 18) and establishes wall strength gradient. The high internal pressure coming from extrusion expands the semispheroidal tip but the expansion is ceased at the equator as the viscosity increases. This is verified by visualizing the internal flow through particle tracking velocimetry, in which fluorescent particles seeded in the solution are tracked during growth (Movie S1). As shown in the top right picture of Fig. 2A, similar to the tip-growing cells that exhibit orthogonal growth at the tip (19), the fresh solution within the tip flows orthogonal to the tip interface, consistent with the trajectory predicted by a mathematical model (SI Appendix, Supplementary Discussion S2).
The outer phase-separated layer at the stalk guides the internal flow toward the continually advancing tip. This is also confirmed by internal flow visualization in Fig. 2A, which clearly shows that the fresh solution flows toward the tip with negligible radial velocity in the cylindrical stalk. The scanning electron microscopy (SEM) image in the bottom right of Fig. 2A shows the cross section of the resulting hollow cylinder of CA. Therefore, the aforementioned three conditions required for tip growth (13) can be met by extruding various polymer solutions undergoing NIPS in a nonsolvent (SI Appendix, Supplementary Discussion S3).
The tip growth is only achieved within limited ranges of CA concentration and extruded flow rate as shown in the regime map of Fig. 2B, where the red dots denote the tip-growing condition. Otherwise, the extruded solution grows from the nozzle (base) with its tip solidifying first (Left Inset), in contrast to the tip-growing solution (Right Inset). This is because the tip growth of polymer solution is only possible when the wall strength at the tip, a function of CA concentration, balances the internal pressure, determined by the flow rate. If the wall is too strong, or the solution turns into solid too rapidly due to high solution concentration (20), the internal pressure cannot deform the wall and therefore basal growth rather than tip growth arises. If the wall is too weak to withstand the internal pressure owing to the high flow rate, the tip bursts as in over-pressurized tip-growing plant cells (SI Appendix, Supplementary Discussion S4) (21). The burst pressure, or maximum flow rate for tip growth, increases with increasing solute concentration because solution’s viscosity increases with solute concentration. Growth is not observed when extruding solutions with low concentrations, below 15 wt%, where no solid structures can be formed (22). Previous studies on extrusion of CA–acetone solution in water using similar experimental setups failed to achieve tip growth because they usually extruded the solution very fast, thereby causing solidification to initially occur at the tip (23, 24).
Growth Dynamics.
The growing precipitate’s diameter is controlled by the extruded flow rate while being independent of the extruding nozzle size (SI Appendix, Supplementary Discussion S5), which allows us to grow structures whose size vary significantly in a single setup. This is in contrast with typical fluid jets whose size is determined by the nozzle—only the jet velocity varies when the flow rate is altered. This is because as mentioned above, tip growth from the solution is achieved by the balance between the wall strength at the tip and internal pressure. With increasing flow rate, the internal pressure increases, which in turn promotes expansion of outer gel layer at the tip, resulting in increased radius. For solutions that contain CA with different average molecular weights as a solute, we varied extrusion flow rates to obtain the relationship between the radius of growing precipitate, R, and the growth rate of cylinder length, U, R∼U2 as shown in Fig. 2C. We found that change in the molecular weight of the solute exerts an insignificant effect on the size of the growing CA precipitate. When two CA precipitates growing from different nozzles come close to each other at their tips, they merge and then regrow from a single tip (SI Appendix, Supplementary Discussion S6) similar to the fusion observed in fungal hyphae (25, 26). The size of the merged tip is also determined by the sum of the two flow rates.
We now rationalize the above empirical relation between R and U. As the pressure governs growth, we first measured the pumping gauge pressure, P, in the syringe pump for different flow rates, to find R∼√P (SI Appendix, Supplementary Discussion S7). As the tip growth of CA shares the similar physical mechanism to that of tip-growing cells in nature, we apply the corresponding biomechanical theory to the tip growth of CA solution (27, 28). Because the tip expands orthogonal to its own interface (SI Appendix, Supplementary Discussion S2), the geometry of the tip represented by the ratio between two principal curvatures can be expressed using a single flow coupling variable, ν(s), such that κs(s)/κθs=1-2ν(s), where s is the meridional position, κs(s) is principal curvature in meridional direction, and κθs is curvature in circumferential direction (27). While the radius of the precipitate increases with the flow rate, the tip is always a prolate semispheroid with the major axis being 1.3 times longer than the minor axis (SI Appendix, Supplementary Discussion S8). This is because flow coupling variable ν(s), analogous to Poisson’s ratio in elasticity, is determined by the polymer solution (27).
As every tip of the growing precipitate assumes a similar shape, they must have identical nondimensional strain rate on the identical position when we nondimensionalize the tip geometry using the radius R. To calculate the strain rate, we consider the constitutive equation of ε˙=σ/μ, where ε˙ is the strain rate, σ is the stress, and μ is the gel viscosity. Although typical plant cell walls behave like Bingham plastic, (29) we use the constitutive equation for viscous cell walls (28) because the yield stress of our growing solution is less than 4 kPa, an insignificant value compared to the extrusion pressure (SI Appendix, Supplementary Discussion S7). From axial force balance, meridional strain rate at the tip ε˙s is written as (27, 28),ε˙s=P2μκθδ,
where δ is the wall thickness that increases proportional to the square root of water contact time because acetone–water exchange is diffusion-driven (SI Appendix, Supplementary Discussion S9) (30).
Here, μ depends on water contact time t because μ increases as acetone–water exchange proceeds. We consider time-varying viscosity μ∼tα, as in thermosetting polymers, where α is a fitting parameter (31, 32). As every tip has a similar shape, the distance the solution travels from the apex of the tip before being deposited at the equator, is proportional to the tip radius R. Therefore, given the self-similar nature of the tip growth, the time required for the fresh solution at the apex of the tip to reach the equator is proportional to the characteristic time τ=R/U, and the nondimensional strain rate is given by ε˙sτ. Substituting P∼R2, μ∼τα, κθ∼1/R, and δ∼τ0.5 into ε˙sτ, we obtain the relation between the cylinder radius R and the growth rate U that the growing precipitate must satisfy to fulfill the identical nondimensional strain rate condition, Rα-3.5∼Uα-0.5. For α=6.5, we get R∼U2, which matches our experimental observation, noting that α ranges typically from 4 to 8 (32). We also provide a theoretical model to predict the length of the transparent region at the tip of the growing precipitate (SI Appendix, Supplementary Discussion S10).
To compare our tip-growing precipitate with tip-growing cells, we consider the lengthening rate defined by τ-1=U/R, and pressure P driving the growth in a given system (33). As shown in Fig. 2D, cells adopt large turgor pressure, at least a few hundred kPa, to grow (34, 35). In contrast, tip growth of precipitate is achieved with far smaller pressure since the outer skin-layer is soft gel with the yield stress σy∼1 kPa, two orders of magnitude lower than plant cell walls with σy∼100 kPa (36). Tip-growing CA precipitate exhibits higher lengthening rate than plant cells even with far smaller pressure, achieving energy efficient growth.
Control of Size and Growth Direction.
Size control of the growing precipitate via flow rate modulation is even possible during the growth and is perfectly reversible with no hysteresis. As shown in Fig. 3A, when the flow rate is altered during extrusion, the precipitate’s soft tip changes in size as the internal pressure varies (Movie S1). However, the size of the opaque cylinder, which has already undergone phase separation, is not affected. These growing characteristics allow us to build a multistepped cylinder with precisely controlled diameters. Using a dumbbell-shaped rod made by controlling the flow rate during extrusion, we conducted a tensile test to characterize the mechanical property of the tip-grown object (SI Appendix, Supplementary Discussion S11).
Fig. 3. Size control and tropism in the tip-growing precipitate. (A) In situ size modulation of a growing cylinder. The thick and thin parts are made by extruding the solution with flow rates of 8 and 2 mL/h, respectively. The black line in the graph denotes the radius of the growing tip, R, and the yellow line denotes flow rate. (B) Gravitropism of growing CA precipitate. Although a nozzle from which the solution is extruded is rotated 90° at 100 s (α=90∘), the tip keeps growing in the direction of buoyancy (θ=90∘). The black line in the graph denotes growing angle, and yellow line denotes the rotated angle of the nozzle. (C) Phototropism of the growing CA precipitate. By partially shedding light on the tip using a laser sheet, the growing precipitate can be remotely guided. Polymerization occurring at the light-shed part of the tip locally increases the viscosity (μ1>μ2), and ceases the growth. Original growing direction is denoted by white dotted line and laser sheet is denoted by yellow arrows. The yellow line in the graph denotes on-off status of the laser, the red line denotes the time when the growing precipitate first encounters the laser sheet, and the black line denotes growing angle. (D) Thigmotropism of growing CA precipitate. When the precipitate contacts an inclined hydrophilic foam, it changes direction to grow along the obstacle. Red dye was added to the solution to enhance contrast. The yellow line in the graph denotes the contact status, and black line denotes growing angle. White arrows in the pictures denote reorientation of growth direction in response to the external stimuli (B, C, and D). (Scale bars, 5 mm (A–D).)
Because the internal pressure drives the tip growth, the weakest part of the tip that expands the most determines the growing direction. Therefore, tip-growing cells subtly regulates the cell wall strength at the tip to change its growing direction (37, 38). Similarly, tropism is observed in the tip-growing precipitate when an applied external stimulus shifts the weakest position, and sensing ability is structurally embedded. Without any artificial intervention, as CA–acetone solution is lighter than the surrounding water, the fresh solution at the tip tends to flow against gravity, implying that the apex of the tip is weakest. This corresponds to the negative gravitropism, and Fig. 3B shows that the tip keeps growing in the direction of buoyancy even after the nozzle extruding CA solution is rotated (Movie S2). Defining the growth angle θ as the angle between the growth direction and the horizontal line, θ remains as 90° even when the nozzle is rotated (α=0∘ to 90∘). When the density of the solution is higher than the surrounding water, the precipitate grows in the direction of gravity, exhibiting positive gravitropism (SI Appendix, Supplementary Discussion S12).
Besides gravity, we can actively steer the growing precipitate using light by adding photoinitiator, camphorquinone (CQ), coinitiator, ethyl-4-(dimethylamino)benzonate (EDMAB), and cross-linker of poly(ethylene glycol) diacrylate (PEGDA), to the solution. These additives cause polymerization of the monomer when irradiated by light with 450 nm wavelength (39). When the tip is subjected to a local photostimulus of laser sheet, generated by splitting laser beam with plano-concave cylindrical lens (LK1336RM, Thorlabs), wall strength is locally modulated as the light-shed part stiffens. With the growth ceased at the stiffened part, the tip changes direction by growing from the shaded region. As shown in Fig. 3C, when the precipitate originally growing against gravitational direction encounters a tilted laser sheet, it grows along the laser sheet (θ=65∘), exhibiting phototropism. When the laser is turned off, the growing precipitate recovers the original negative gravitotropism (θ=90∘) at t = 260 s (Movie S2).
The growing precipitate can passively interact with the surrounding environments through mechanical contact without being damaged thanks to soft gel covering the tip. When the growing tip contacts a hydrophilic porous mechanical obstacle (SI Appendix, Supplementary Discussion S13), although the growth is ceased at the contact spot, neighboring region can still grow leading the precipitate to change direction. Fig. 3D shows precipitate encountering mechanical obstacle, a tilted hydrophilic foam, grows along the foam (θ=30∘) (Movie S2).
Thigmotropism enables the precipitate to autonomously sense and bypass obstacles. As shown in Fig. 4A, when the tip-growing precipitate enters a forest of pillars of hydrophilic foam, the precipitate smoothly makes its way through the obstacles, a result of combination of thigmotropism and gravitropism (Movie S3). However, when a basal-growing polymeric solution, 30 wt% CA–acetone solution, encounters an identical pillar array, it buckles and coils after initial contact rather than bypassing the obstacles as shown in Fig. 4B.
Fig. 4. Autonomous exploration and applications of the tip-growing precipitate. (A) Tip-growing polymeric solution bypassing a pillar array of hydrophilic foam. Negative gravitropism and negative thigmotropism of the tip-growing precipitate enables the precipitate to make its way through the pillar array without external intervention. (Scale bar, 10 mm.) (B) Basal-growing solution (30 wt% CA–acetone solution) encountering a pillar array. Unlike tip-growing solution, basal-growing solution is blocked by the pillars. (Scale bar, 10 mm.) (C) Tip-growing precipitate autonomously solving a maze through growth. Trajectory of the growing solution is denoted by gray lines. The schematic of experimental setup is shown next to the images, where the maze patterned foam is covered with a thin hydrophilic foam. (Scale bar, 10 mm.) (D) A spiral built inside a vial exploiting negative gravitropism. The schematic of the experiment is shown next to the image, where the size of the blue arrows denotes the magnitude of the angular velocity. (Scale bar, 5 mm.) (E) Growing precipitate used as a conduit of liquid metal to connect a battery (outside the picture) and a submerged lead of the LED. The LED bulb is lit only when the transported liquid metal contacts the lead at t = 300 s. The schematic right to each picture illustrates the connection status, and the overall circuit diagrams are shown below the pictures. (Scale bar, 5 mm.) (F) Shaped growth of the precipitate for transport of water-miscible liquids in water. The water-miscible acetone–ethanol mixture is collected at the container above the water bath at t = 37 min.
Thigmotropism also enables the growing precipitate to solve mazes autonomously. Fig. 4C shows the precipitate solving a maze comprising dead ends and an exit after corners. Although the precipitate entering the maze at t = 0 may first enter a dead end at t = 8 min, it can sense the dead end, change direction, return to the crossroad (t = 14 min), and eventually solve the maze (t = 20 min). This ability is owing to the precipitate’s tendency to seek a path of minimal resistance to its growth. Similarly, when the tip of a growing precipitate encounters the edge of an obstacle, it can split into two branches. However, growth is soon ceased at the smaller branch and only the larger one keeps growing since the internal solution tends to flow toward the larger branch where it will experience less resistance.
We finally demonstrate the possible applications of our growing polymeric precipitation system. First, exploiting its capability to sense the direction of external force, the precipitate can print a 3D structure inside a confined space. Conventional extrusion-based 3D printing techniques, such as fused deposition modeling (40) or direct ink writing (41), require adequate space for the printing nozzle to freely move. On the contrary, we can make the precipitate to grow into a specific shape from a fixed nozzle, by rotating the system. For example, Fig. 4D shows a spiral of CA printed inside a vial, which is built by rotating the axially tilted vial during extrusion, utilizing the negative gravitropism of the growing precipitate. We can control the diameter of the spiral by changing the angular velocity, such that for solution extruded with flow rate of 8 mL/h, large angular velocity (0.93 rpm) results in small diameter (6 mm) and small angular velocity (0.54 rpm) results in large diameter (10 mm) as illustrated in Fig. 4D.
Typical tip-growing plant cells act as a channel, through which materials are transported, e.g., pollen tubes transport sperm cells from the pollen to the ovule, and root hairs uptake nutrition and water from the soil. Inspired by these biological functions, we suggest our growing precipitate tube as a material transporting channel. The significantly low permeability of the precipitate wall is discussed in SI Appendix, Supplementary Discussion S14. Just by replacing the fresh CA–acetone solution with another fluid, the tube can serve as the fluid conduit. As shown in Fig. 4E, when the internal fluid is switched to liquid metal (eutectic Ga-In alloy), the CA tube guides a growing conductive wire that can complete an electric circuit from a battery to an LED (light emitting diode) bulb (Movie S3). To do so, the precipitate first grows from the nozzle toward the lead (t = 80 s). Once the precipitate touches the lead, liquid metal, instead of CA–acetone solution, is fed through the nozzle. Since the precipitate is nonconductive, the LED bulb is not lit when only the precipitate contacts the lead (t = 270 s). However, when the transported liquid metal touches the lead, the bulb is lit (t = 300 s). In this manner, materials heavier than its surrounding media can be transported against gravity while electrically insulated by the polymeric sheath.
The solidified wall of CA is capable of transporting water-miscible liquid in water (Movie S3). In Fig. 4F, the growing precipitate is guided using a laser sheet toward a target (t = 4 min), an open end of a glass tube situated just below the free surface of water in the bath. By modulating the flow rate, the diameter of the tube was reduced from 3.8 mm in the beginning to 2.0 mm before entering the glass tube and then increased again to 5.0 mm to enable tight fitting with the glass tube (t = 15 min). Then, water-miscible acetone–ethanol mixture (colored with red dye) is extruded into the CA tube and finally collected at a container above the water bath (t = 37 min).
Conclusions
We have reported tropic tip growth of polymeric precipitate from CA–acetone solution extruded in water, which is able to sense multiple stimuli and change its growing direction accordingly. Sharing identical underlying physical mechanism in growth, the shape of growing polymeric precipitate resembled that of tip-growing cells in nature while the artificial system grew in a highly energy efficient manner compared to the cells. With the position of weakest part at the tip determining the growing direction, the precipitating system exhibited negative gravitropism and thigmotropism. By adding photosensitive additives to the solution, we imposed negative phototropism on the growing solution. These tropic characteristics enabled the growing polymer precipitate to perform various tasks including printing 3D structures in a confined space, sensing and bypassing mechanical obstacles or mazes, and transporting another liquid through water.
Stimuli-responsive systems that can grow in a tropic manner can pave a pathway to artificially realizing various biological functions observed in plants and fungi. In addition, because the system is based on a polymer solution, various materials can be mixed or dissolved to enhance the responsivity or functionality of the system. For instance, phase separation front can be remotely guided using magnetic field by adding ferromagnetic particles to the solution.
Unlike rigid robots that require both sensors and actuators to interact with their surrounding environments, our system is capable of perceiving environmental changes without sensory parts thanks to its pressure-driven growth mechanism that structurally embeds the sensing ability. Soft mechanical systems capable of versatile adaption to the surrounding environments as demonstrated in this work will enrich soft robotic technologies armored with physical intelligence (42).
Materials and Methods
Materials.
CA–acetone solution was prepared by dissolving CA (Sigma-Aldrich) in acetone. The concentration of CA in the solution varied from 10 wt% to 35 wt%, and the polymeric solution was extruded using a syringe pump (LSP04-1A, Longer Precision Pump). When visualizing the internal flow, 0.5 wt% fluorescent particles (PS-FluoRed-10.0, microparticles GmbH) were added to the solution. In some experiments, Oil Red O (Sigma-Aldrich) was added to the polymer solution for visualization purposes. In phototropism experiments, CQ (Sigma-Aldrich), EDMAB (Sigma-Aldrich), and PEGDA (Sigma-Aldrich, average Mn ~ 700) were added to the solution. The composition of the photo-responsive solution was 16 wt% CA, 56 wt% acetone, 2 wt% CQ, 2 wt% EDMAB, and 24 wt% PEGDA, and when using the photo-responsive solution, 20 wt% acetone–water solution was used as a surrounding liquid. EGaIn used in liquid transport experiment was purchased from Thermo Fisher Scientific.
Gravitropism Experiment.
For the experiment shown in Fig. 3B, the nozzle was attached to a rotary stage. The rotary stage was then submerged in a bath of water, and CA–acetone solution was extruded through the nozzle. During the extrusion, the stage was rotated 90° counterclockwise. After the rotation, the precipitate started to grow in the direction of buoyancy at the tip.
For the experiment shown in Fig. 4D, first the nozzle was inserted and fixed on the bottom of the vial before water was filled in the vial. CA–acetone solution was then extruded through the nozzle. During the extrusion, the vial was rotated to guide the growing precipitate.
Phototropism Experiment.
For the experiments shown in Fig. 3C, a tilted laser sheet was generated on the path of growing precipitate using a 450-nm laser with power of 1 W and a plano-concave cylindrical lens. When the precipitate originally growing in the direction of buoyancy entered the laser sheet, it started to grow along the sheet. The tip-growing precipitate recovered its original growing direction when the laser was turned off. The growing direction of the precipitate was guided by changing the tilted angle of the laser sheet.
Thigmotropism Experiment.
For the experiments shown in Figs. 3D and 4 A, B, and C, hydrophilic melamine foam was patterned using a laser cutter. Patterns of inclined plate (Fig. 3D), pillar arrays (Fig. 4 A and B), and maze (Fig. 4C) were engraved in the foam, respectively. The patterned foam was placed on the path of the growing precipitate, and when the tip of the growing precipitate contacted the foam, it changed the growing direction.
Liquid Transport Experiment.
For the experiments shown in Fig. 4E, the cathode lead of the LED was connected to a 220 Ω resistor and cathode of 3 V battery. The anode lead of the LED was placed on the water bath above the nozzle. The nozzle was connected to a T-shaped fitting, whose other two branches were connected to a syringe filled with polymer solution and EGaIn, respectively. The syringe containing EGaIn was connected to the anode of the battery using a jumper wire. Through the nozzle, first the CA–acetone solution was extruded to connect the nozzle and the anode lead, then EGaIn was extruded to replace the fresh CA–acetone solution inside the grown tube. Because CA is an insulator with the dielectric constant of about 5, LED was not turned on when the growing precipitate contacted the lead, but when conductive EGaIn contacted the lead, the electric circuit was completed and LED was turned on.
For the experiments shown in Fig. 4F, the nozzle was attached to the open end of the 90° bent glass tube with inner diameter of 5 mm. The glass tube was fixed on the plate that covers the liquid bath, and the nozzle connected to the tube was placed above the petri dish on the plate. The other end of the glass tube was placed inside the bath of acetone–water solution. Using a T-shaped fitting, the nozzle placed on the bottom of the bath, from which the polymer was extruded, was connected to two syringes filled with photo-responsive polymer solution and 20 wt% acetone–ethanol solution, respectively. Photo-responsive polymer solution was initially extruded from the nozzle, and during the growth, the precipitate was remotely guided toward the glass tube using a laser sheet. To enter the tube, the diameter of the precipitate was reduced by reducing the flow rate. After the entrance, the diameter was increased to tightly fit in the tube and extruded liquid was switched to acetone–ethanol solution. Through continuous extrusion of the acetone–ethanol solution, this water-miscible liquid was transported through the grown tube and was collected on the petri dish.
Tip-Growing Cell Data.
Tip-growing cell data were obtained from the literature. The turgor pressure that drives the growth P and lengthening rate τ-1 of the data presented in Fig. 2D are provided in SI Appendix, Table S1.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. Characteristics of tip-growing polymer precipitate When CA-acetone solution is extruded in the bath of water, the precipitate grows from its tip, which can be noted by the tip that always remains transparent. Internal flow visualization verifies that the tip growth is internal pressure-driven, and that the fresh polymer solution is replenished at the tip. The size of the growing precipitate can be precisely modulated by controlling the flow rate.
Movie S2. Tropic characteristics of tip-growing precipitate When the nozzle in which the polymer solution is extruded is rotated during the extrusion, the precipitate reorients the growing direction and continues to grow in the direction of the buoyancy (negative gravitropism). When light is partially shed at the tip of the photo-responsive solution using laser sheet, the precipitate grows along the laser sheet (negative phototropism). When the growing precipitate contacts inclined plate of melamine foam, the solution changes direction and grows along the obstacle (negative thigmotropism).
Movie S3. Applications of tip-growing precipitate Negative gravitropism and thigmotropism enables the growing precipitate to bypass array of pillars of melamine foam without any external intervention. The grown precipitate can be used as a fluid conduit, while the solidified wall enables shielded transport. The grown precipitate can be used to transport conductive liquid metal in water and complete an electric circuit. Water-miscible liquid can also be transported in water through a non-straight path using remotely steered CA tube. Water-miscible acetone-ethanol solution is transported through water and collected in the petri dish above the water bath.
We acknowledge Dongjo Kim for his help in acquiring plant images. This work was supported by National Research Foundation of Korea (Grant nos. 2018052541, 2021M3F7A1017476, 2021R1A4A3027074, and 2018M3A7B4089670). H.-Y.K. acknowledges administrative support from SNU-IAMD.
Author contributions
K.P., J.-Y.S. and H.-Y.K. designed research; C.J.P., J.H., and H.-R.L. performed research; C.J.P., J.H., H.-R.L., K.P., J.-Y.S., and H.-Y.K. analyzed data; and C.J.P., J.H., K.P., J.-Y.S., and H.-Y.K. wrote the paper.
Competing interest
The authors declare a competing interest. The authors have patent filings to disclose. H.-Y.K. and C.J.P. are the inventors on the patent applications (#10-2020-0142306, Republic of Korea) submitted by SNU R&DB Foundation that covers manufacturing method of growing body and its applications.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission. R.X.Z. is a guest editor invited by the Editorial Board.
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PMC009xxxxxx/PMC9926266.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595677
202206480
10.1073/pnas.2206480120
research-articleResearch ArticleimmunImmunology and Inflammation420
Biological Sciences
Immunology and Inflammation
The Maresin 1–LGR6 axis decreases respiratory syncytial virus-induced lung inflammation
Krishnamoorthy Nandini [email protected]
a 1
Walker Katherine H. a 2 https://orcid.org/0000-0003-1198-8125
Brüggemann Thayse R. a 2 https://orcid.org/0000-0003-3961-618X
Tavares Luciana P. a
Smith Ethan W. a https://orcid.org/0000-0001-8235-3632
Nijmeh Julie a https://orcid.org/0000-0002-4392-6263
Bai Yan b
Ai Xingbin b
Cagnina R. Elaine a https://orcid.org/0000-0002-4210-7928
Duvall Melody G. a https://orcid.org/0000-0002-4173-3628
Lehoczky Jessica A. c https://orcid.org/0000-0002-9210-2922
Levy Bruce D. [email protected]
a 1
aPulmonary and Critical Care Medicine Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115
bNeonatology Division, Mass General Hospital for Children, Boston, MA 02114
cDepartment Of Orthopedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115
1To whom correspondence may be addressed. Email: [email protected] or [email protected].
Edited by Harvey Cantor, Dana-Farber Cancer Institute, Boston, MA; received April 27, 2022; accepted November 29, 2022
2K.H.W. and T.R.B. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e220648012027 4 2022
29 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Respiratory syncytial virus (RSV) is a leading cause of severe viral bronchiolitis among children, the elderly, and immunocompromised patients. Unresolved and prolonged immune responses to RSV can lead to respiratory failure. Regulatory T cells (Tregs) work to subvert inflammation; however, RSV hijacks the Treg machinery to imprint a prophlogistic phenotype. Here, we have determined that the specialized pro-resolving mediator Maresin 1 (MaR1) signaling via its receptor Leucine-Rich Repeat-Containing G Protein-Coupled Receptor 6 (LGR6) can reverse the RSV-induced maladaptive Treg programming and decrease inflammation. These findings have uncovered a molecular counter-regulatory mechanism that limits pathogen-induced lung immunopathology and hastens the resolution of an important respiratory viral infection.
The resolution of infection is an active process with specific molecular and cellular mechanisms that temper inflammation and enhance pathogen clearance. Here, the specialized pro-resolving mediator (SPM) Maresin 1 (MaR1) inhibited respiratory syncytial virus (RSV)-induced inflammation. inlerleukin-13 production from type 2 innate lymphoid cells (ILC) and CD4 T helper type 2 cells was decreased by exogenous MaR1. In addition, MaR1 increased amphiregulin production and decreased RSV viral transcripts to promote resolution. MaR1 also promoted interferon-β production in mouse lung tissues and also in pediatric lung slices. MaR1 significantly inhibited the RSV-triggered aberrant inflammatory phenotype in FoxP3-expressing Tregs. The receptor for MaR1, leucine-rich repeat-containing G protein-coupled receptor 6 (LGR6), was constitutively expressed on Tregs. Following RSV infection, mice lacking Lgr6 had exacerbated type 2 immune responses with an increased viral burden and blunted responses to MaR1. Together, these findings have uncovered a multi-pronged protective signaling axis for MaR1–Lgr6, improving Tregs’s suppressive function and upregulating host antiviral genes resulting in decreased viral burden and pathogen-mediated inflammation, ultimately promoting restoration of airway mucosal homeostasis.
resolution of inflammation
specialized pro-resolving mediators
regulatory T cells
innate lymphoid cells
CD4 T helper type 2 cells
National Institute of Health P01GM095467 Bruce D. Levy National Institute of Health R01HL122531 Bruce D. Levy
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pmcRespiratory syncytial virus (RSV) is a major cause of lower tract respiratory infections in children and is a leading cause of infant hospitalization in the United States (1). RSV infection is also seen in adults and is the second leading cause of death associated with viral infections in the elderly (2, 3). Chronic Obstructive Pulmonary Disorder (COPD) patients are also susceptible to RSV infection resulting in exacerbations (4, 5). RSV infection typically presents as a mild and self-resolving infection; however, in some children, particularly those less than 2 y old, an overexuberant immune response to the virus results in severe bronchiolitis and even respiratory failure evidenced pathologically by occlusion of the airways with cellular debris and mucus (6). Potential mechanisms to curtail the pathologic unwarranted airway inflammation and injury with RSV would provide needed therapeutic relief for vulnerable patients.
Regulatory T cells (Tregs) express the canonical transcription factor FoxP3 and subvert inflammation to maintain immunological homeostasis (7). RSV infection disables Tregs and programs them to become prophlogistic cells (8). These RSV Tregs retain FoxP3-expression but also express the type 2 associated cytokine inlerleukin-13 (IL-13) and transcription factor GATA-3 that exacerbate virus-induced immunopathology. Broadly, the acquisition of a prophlogistic phenotype by Tregs is linked to the pathophysiology of several common inflammatory diseases. Mechanisms to restore these pathologic Tregs to their protective phenotype and suppressive function under inflammatory conditions remain to be determined.
Counter-regulatory cell mechanisms are generally responsive to environmental cues. The resolution of inflammation is mediated in part by specialized pro-resolving mediators (SPMs), a family of endogenous molecules that can limit pathogen-induced inflammation and accelerate pathogen clearance (9, 10). These mediators are produced from essential fatty acids during tissue inflammation and carry potent anti-inflammatory and pro-resolving functions (11). SPMs can exert cell-specific actions via their receptors and deficiency in receptor expression in mouse and human studies correlate with delayed resolution (9). Dietary docosahexaenoic acid (DHA) supplementation during pregnancy in mothers with lower endogenous levels of this omega-3 fatty acid can lead to a lower risk of wheezing and respiratory infections in infants, highlighting the potential immunoregulatory effects of essential omega-3 fatty acid-derived mediators in human health (12).
Here, in a self-limited mouse model of RSV infection, the administration of the DHA-derived SPM Maresin 1 (MaR1; 7R,14S-dihydroxy-docosa-4Z,8E,10E,12Z,16Z,19Z-hexaenoic acid) decreased pulmonary inflammation and enhanced viral clearance. MaR1 reduced IL-13 production from type 2 innate lymphoid cells (ILC2s) and CD4 T helper type 2 (CD4 TH2) cells and increased lung amphiregulin (AREG) levels. MaR1’s protective actions were mediated by Leucine-Rich Repeat-Containing G Protein Coupled Receptor 6 (LGR6) signaling with a prominent effector role for Tregs. These mechanistic findings translated to a model of ex vivo RSV infection of precision cut human pediatric lung slices. Taken together, these data have uncovered multipronged actions for MaR1 signaling via LGR6 to accelerate the resolution of inflammation in mouse and human experimental models of RSV infection.
Results
Maresin 1 Enhances Viral Clearance and Decreases Type 2 Inflammation after RSV Infection.
To determine whether MaR1 could decrease the pathogen-evoked immune responses triggered by RSV and accelerate resolution, we first defined the inflammatory and resolution time course for mouse infection with a human clinical strain RSV line 19 (105 PFU) that is known to trigger IL-13 and mucous production (13). The pathologic consequences of infection of 3-wk-old mice were analyzed by histology and flow cytometry (SI Appendix, Fig. S1 A and B). On day 3 post-infection (pi), there were significant increases in IL-13 levels and viral titers (SI Appendix, Fig. S1 C and D). With increasing viral replication and inflammatory responses, this time point (Day 3) was chosen to administer MaR1 [10 ng; intranasal route (i.n.)] (Fig. 1A). This dose of MaR1 was chosen based on prior publications wherein exogenous administration of MaR1 in preclinical models of inflammation promoted resolution (14, 15). At day 6 pi, animals that received MaR1 had significantly decreased airway epithelial mucous cell metaplasia and reduced leukocyte infiltrates around the airways as determined by histopathological examination after Periodic Acid–Schiff (PAS) staining (Fig. 1B). We also looked at lung inflammation by H&E staining and found a significant increase in cellular infiltrate around the airways following RSV infection which was reduced with MaR1 administration (SI Appendix, Fig. S2 A and B). Given the role of Gob5 in driving mucus production, we analyzed mouse lungs following MaR1 administration. MaR1-treated mice had lower levels of gob5 gene expression compared to the vehicle group (Fig. 1C). With the RSV-induced mucous cell metaplasia, IL-13 production from ILC2 (IL13+ Lineage-) and CD4 TH2 (CD4+IL-13+) cells peaked at day 6 pi (SI Appendix, Fig. S3). Compared with vehicle control, at day 6 pi, MaR1 administration significantly lowered IL-13 tissue levels (Fig. 1D) and decreased IL-13 production by ILC2 and CD4 TH2 cells (Fig. 1E).
Fig. 1. Maresin 1 enhances viral clearance and decreases type 2 inflammation after RSV infection. (A) Schema of the mouse model of RSV infection with clinical strain RSV line 19 (105 PFU/mouse, intranasal route, i.n.) in 3-wk old Balb/c ByJ mice. Vehicle or MaR1 (10 ng/mouse) administered i.n. on day 3 pi. Inflammatory and virological parameters analyzed on day 6 pi. (B) Representative lung tissue sections from mice 6 d post-mock infection (mock) or RSV infection with either i.n. vehicle (Veh) or MaR1 administered on day 3 pi (MaR1). Sections were stained with PAS. Original magnification (×10). Inset magnification (×20). Arrows indicate areas of high mucous production. (C) gob5 expression analysis by qRT-PCR. Fold increase was calculated over mock infected mice. (D) IL-13 levels in lung tissue measured by Enzyme-linked immunosorbent assay (ELISA). (E) Number of IL-13+ Lineage− ILC2 and IL-13+ CD4+ TH2 cells in the lungs with or without MaR1 exposure after RSV as measured by flow cytometry. (F) AREG levels in lung tissue with or without MaR1 exposure after RSV as measured by ELISA. (G) RSV L Gene expression analysis by qRT-PCR. Fold increase was calculated over mock infected mice. Values represent the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001 and data were analyzed using Student’s t test for all the panels shown here. This experiment was performed more than twice and the results from two experiments pooled. Each experiment used 3 to 4 mice per group.
Since MaR1 inhibited RSV-induced type 2 immune responses, we next evaluated whether it also promoted resolution of inflammation. MaR1 significantly increased levels of AREG in lung tissue compared to vehicle control at day 6 pi (Fig. 1F). Of note, RSV-infected lungs had barely detectable levels of this cytokine at this time point. In addition, MaR1 decreased RSV polymerase L gene expression at day 6 pi indicative of decreased amounts of actively replicating virus (Fig. 1G).
Maresin 1 Promotes Resolution of RSV Infection with Increased AREG Production and Decreased Type 2 Inflammation.
To determine whether the actions of MaR1 were transient, inflammatory responses at day 10 pi were next analyzed in the RSV mouse model (Fig. 2A). PAS staining of lung sections at day 10 pi revealed lower levels of mucous cell metaplasia compared to day 6 pi. Even with waning airway inflammation day 10 pi, relative to vehicle control, MaR1-exposed mice had less mucous cell metaplasia and cellular infiltrates (Fig. 2B). gob5 gene transcripts showed decreased expression with MaR1 (Fig. 2C). Tissue levels of IL-13 decreased from day 6 to day 10 pi, and they were further lowered with MaR1 (Fig. 2D). ILC2 and CD4 TH2 cells were also decreased with MaR1 compared to vehicle at day 10 pi (Fig. 2E). AREG levels continued to show significantly increased levels at day 10 pi with MaR1 (Fig. 2F), and RSV L gene expression was further reduced on day 10 pi with MaR1 compared to vehicle (Fig. 2G). Taken together, these data indicate that MaR1 promoted resolution as a potent regulator of RSV-triggered inflammatory responses and viral transcripts, and a selective inducer of tissue AREG.
Fig. 2. Maresin 1 promotes resolution of RSV infection with increased AREG production and decreased type 2 inflammation. (A) Schema of the mouse model of RSV infection as in Fig. 1A. Inflammatory and virological parameters analyzed on day 10 pi. (B) Representative lung tissue sections from mice 10 d post-mock infection (mock) or RSV infection with either i.n. vehicle (Veh) or MaR1 administered on day 3 pi (MaR1). Sections were stained with PAS. Original magnification (×10). Inset magnification (×20). Arrows indicate areas of high mucous production. (C) gob5 expression analysis by qRT-PCR Fold increase was calculated over mock infected mice. (D) IL-13 levels in lung tissue measured by ELISA. (E) Number of IL-13+ Lineage− ILC2 and IL-13+ CD4+ TH2 cells in the lungs with or without MaR1 exposure after RSV as measured by flow cytometry. (F) AREG levels in lung tissue with or without MaR1 exposure after RSV as measured by ELISA. (G) RSV L gene expression analysis by qRT-PCR. Fold increase was calculated over mock infected mice. n≥3 mice per group. Values represent the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001 and data were analyzed using Student’s t test for all the panels shown here. This experiment was performed more than twice and the results from two experiments pooled. Each experiment used 3 to 4 mice per group.
Unique Anti-viral Lung Gene Transcripts and Increased Interferon-β Production Following MaR1 Administration.
In human and mouse models of RSV infection, IL-13 and type 2 immune responses to RSV have strongly correlated with disease severity (16). The ability of MaR1 to blunt viral gene transcripts could potentially be linked to reduced IL-13 levels seen in the mice. To identify this unique gene signature, we isolated RNA from mouse lungs 24 h following MaR1 administration (day 4 pi) and on day 6 pi. There were several time-dependent changes in gene expression belonging to the Toll-like receptor (TLR)-signaling pathway, Nucleotide-binding oligomerization domain (NOD)-like receptor pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling, and type I interferon signaling and response on both day 4 pi and day 6 pi following MaR1 treatment. Importantly, some of the genes associated with innate immune responses (e.g., Aim2, CARD9) that were upregulated on day 4 pi with MaR1 either returned to baseline or were downregulated by day 6 pi. Similarly, members of the mitogen-activated protein (MAP) kinase family were activated on day 4 pi but showed decreased expression on day 6 pi (Fig. 3A). As shown on the heat map, we found interferon-β expression to be upregulated with MaR1 expression on day 4 pi with RSV, and this expression was sustained also on day 6 pi with RSV (Fig. 3A). RSV in known to inhibit interferon-β making the host vulnerable to reinfection (17). To validate induction of interferon-β transcript levels by MaR1, mouse lung tissues from day 4 pi and day 6 pi were analyzed for protein levels. On day 4 pi, levels of interferon-β showed a small trend toward increased expression with MaR1 treatment (Fig. 3B), and levels were significantly upregulated on day 6 pi compared to control group (Fig. 3C). Importantly, RSV-infected lungs on day 4 pi and day 6 pi showed little change compared to the mock-infected group.
Fig. 3. Unique anti-viral lung gene transcripts and increased Interferon-β production following MaR1 administration. (A) Heat map showing fold change of gene transcripts in animals after MaR1 administration over Vehicle control. The gene expression was normalized to β-actin. The Left panel represents mice at day 4 pi and the Right panel represents mice at day 6 pi. For each time point 2 mice were used per group on two individual plates. The RNA was not pooled. The numbers in the heat map indicate the fold change of MaR-1-treated animals over Vehicle control. (B) Interferon-β concentration measured in ling tissue at day 4 pi as measured by legend plex ELISA. (C) Interferon-β concentration in ling tissue at day 6 pi as measured by legend plex ELISA. Values represent the mean ± SD and the data was analyzed using one-way ANOVA with Tukey's multiple comparisons test. *P < 0.05. For each group 3 to 4 mice were used per group per experiment. The experiments were performed twice.
Alarmins and specific cytokines released from epithelial cells early in inflammation are known to trigger IL-13 production and activate type 2 immune response highlighting a potential role for MaR1 in inhibiting early cytokine release and thereby reducing overall type 2 response (18). Toward this goal, we analyzed mouse lung tissue at day 4 pi for levels of cytokines IL-33, TSLP, and IL-25, all of which have been shown to play a critical role in modulating the outcome of RSV infection (19–22). IL-33 and TSLP were slightly increased with RSV infection, while MaR1 administration had no effect on their levels. IL-25 was also unperturbed with MaR1 administration (SI Appendix, Fig. S4). These data show a unique gene signature triggered by MaR1 in conjunction with increased interferon-β production, highlighting a type 1 interferon-dependent mechanism in reduction of viral transcripts.
Regulatory T Cells Express LGR6 and MaR1 Regulates Their Phenotype and Function During RSV Infection.
Given the potent regulatory effect of MaR1 on virus-triggered type 2 inflammation, we profiled the expression of the recently discovered MaR1 receptor LGR6 (23) on inflammatory cell types by flow cytometry. Mouse macrophages expressed high levels of Lgr6 at baseline, which did not change significantly 6 d following RSV infection (SI Appendix, Fig. S5). B cells, ILCs (Lineageneg), neutrophils, and eosinophils showed low levels of receptor expression at baseline and at day 6 pi (SI Appendix, Fig. S5). Mock-infected FoxP3eGFP mice showed constitutive expression of Lgr6 on lung CD4+ T cells, particularly Tregs (CD4+FoxP3+), and CD8+ T cells expressed the receptor, but to a lower extent than Tregs (Fig. 4 A, Left). Of note, Lgr6 expression on Tregs was reduced on day 6 pi. Lgr6 expression increased back to near baseline levels on day 10 pi (Fig. 4 A, Right). To determine whether the change of Lgr6 expression on Tregs with RSV infection was associated with other phenotypic or functional changes, Tregs from mice on day 6 pi were probed for the type 2 master regulator, GATA-3, along with FoxP3 (i.e., type 2 Tregs) by flow cytometry. RSV infection led to higher numbers of GATA-3+ CD4 TH2 cells (Fig. 4B). Type 2 Tregs expressing the dual transcription factors FoxP3, and GATA-3 were increased on day 6 pi and MaR1 significantly reduced the numbers of these pathologic RSV Tregs (Fig. 4B). A similar trend was seen by analyzing IL-13 cytokine production by flow cytometry (Fig. 4C). MaR1 significantly reduced the numbers of pathologic RSV Tregs expressing both FoxP3 and IL-13.
Fig. 4. Regulatory T cells express LGR6 and MaR1 regulates their phenotype and function during RSV infection. (A) Lgr6 expression on T cell subsets from mock infected FoxP3eGFP mice. The Left panel shows the histogram, and the Right panel compares MFI of Lrg6 expression following Mock or RSV infection days 6 and 10 pi. (B) The expression of FoxP3 and GATA-3 within the CD4 population in lungs of RSV infected mice with or without MaR1 at day 6 pi as analyzed by flow cytometry and the quantification of cells. Values represent the mean ± SD. The data was analyzed using two-way ANOVA with Sidak's multiple comparisons test. *P < 0.05, ***P < 0.001. (C) The expression of FoxP3 and IL-13 within the CD4 population in lungs of RSV infected mice with or without MaR1 at day 6 pi as analyzed by flow cytometry and the quantification of cells. Values represent the mean ± SD. The data was analyzed using two-way ANOVA with Sidak's multiple comparisons test. *P < 0.05. (D) ILC2-Treg suppression was set from the different FoxP3eGFP mice cohorts. ILC population were sorted from RSV-infected lungs day 6 pi and Tregs from mock and RSV-infected lungs day 6 pi ± MaR1 exposure were added to ILC2 (1:10 ratio). (E) IL-13 production was measured by ELISA 72 h after co-culture. Values represent the mean ± SD and the data was analyzed using one-way ANOVA with Tukey's multiple comparisons test. *P < 0.05, **P < 0.01, ***P < 0.001. The mouse experiments were performed twice and the data from the two experiments pooled. Each experiment used 3 to 4 mice per group. (F) Tregs were profiled from three healthy donors and the expression of LGR6 was analyzed by flow cytometry and expressed as MFI. The data was analyzed using Student’s t test and NS.
Pulmonary Tregs serve a critical role in restricting type 2 inflammation (24). Because Tregs express Lgr6 (Fig. 4A), we next determined whether MaR1 had direct actions on Tregs. Lung Tregs were isolated from mock- and RSV-infected FoxP3eGFP mice that were exposed in vivo to MaR1 or a vehicle control (Fig. 4D). The ability of the isolated Tregs to suppress IL-13 production from ILC2 (IL-13+Lineage−) was determined using our recently developed assay of cell–cell interactions (15). ILCs were isolated from mice on day 6 pi with RSV and added to Tregs (ratio of 1:10; Treg/ILC). Supernatants from Treg/ILC co-cultures were harvested 72 h later and IL-13 levels were measured by ELISA. Tregs isolated from mock-infected mouse lungs inhibited IL-13 production by ILCs (Fig. 4E). Tregs from RSV-infected lungs were defective in their suppressive actions, failing to substantially inhibit IL-13 production by the ILCs (Fig. 4E). In contrast, Tregs from RSV-infected mice exposed to MaR1 showed significantly improved suppressive function, inhibiting IL-13 by approximately 40% (Fig. 4D). Treg-mediated suppression was dependent on transforming growth factor - β (TGF-β) signaling since a Smad3 inhibitor (SIS3) prevented their inhibition of IL-13 production, with IL-13 levels even higher than Tregs without SIS3 (Fig. 4E).
To translate these mechanistic insights from MaR1’s actions on mouse Tregs, we next ascertained whether human Tregs expressed LGR6 and whether RSV infection had an impact on receptor expression. PBMCs from healthy donors were isolated and infected ex vivo with RSV (Multiplicity of infection (MOI) = 0.1), and 48 h pi, the cells were harvested and analyzed for LGR6 expression by flow cytometry. Human Tregs (CD3+CD4+CD45RA−CD25+CD127lowFoxP3+) showed constitutive expression of LGR6, and its mean fluorescent Intensity (MFI) was significantly downregulated by RSV infection (Fig. 4F). These data demonstrate the translational potential for MaR1 regulation of Treg suppressive function and phenotype after RSV infection.
Lgr6-Deficient Mice Have Exacerbated Type 2 Immune Responses and Higher Viral Load.
To assess the impact of MaR1–LGR6 signaling on inflammatory responses to RSV infection, we used Lgr6 knockout mice. Lgr6+/+ and Lgr6−/− mice were infected with RSV Line 19 (105 PFU, i.n.), and at day 6 pi, several parameters of pulmonary inflammation and viral burden were analyzed. Lgr6−/− mice showed more mucous cell metaplasia as seen by PAS staining of lung sections compared to Lgr6+/+ mice (Fig. 5A). gob5 transcripts levels and IL-13 protein levels in lung tissue were higher in Lgr6−/− mice compared to Lgr6+/+ mice (Fig. 5 B and C). To determine whether Lgr6 was necessary for MaR1-mediated protective actions, cells were isolated from Lgr6+/+ and Lgr6−/− lungs at day 6 pi and exposed to MaR1 (10ng/mL) ex vivo. Forty-eight hours post-MaR1 exposure, the cells were stimulated with phorbol ester, phorbol-12-myristate-13-acetate (PMA)/ionomycin in the presence of protein transport inhibitors and assessed by flow cytometry. Lungs from Lgr6−/− mice had higher IL-13-expressing CD4 T cells and ILC cells compared to lungs from Lgr6+/+ mice (Fig. 5D). MaR1 exposure reduced the number of IL-13-expressing CD4 T cells and ILC cells in Lgr6+/+ lungs; however, this reduction was lost in Lgr6−/− lungs (Fig. 5D).
Fig. 5. Lgr6 deficient mice have exacerbated type 2 immune responses and higher viral load. 3-wk-old Lgr6+/+ and Lgr6−/− mice were infected with RSV line 19 and euthanized on day 6 pi. (A) PAS staining of lung sections. (B) gob5 expression analysis by qRT-PCR. Fold increase was calculated over naïve Lgr6+/+ and Lgr6−/− mice. Values represent the mean ± SD. *P < 0.05 using Student’s t test. (C) IL-13 levels in lung tissue of Lgr6+/+ and Lgr6−/− measured by ELISA. Values represent the mean ± SD. *P < 0.05 using Student’s t test. (D) Number of IL-13+ Lineage− ILC2 and IL-13+ CD4+ TH2 cells in the lungs of Lgr6+/+ and Lgr6−/- mice with or without MaR1 ex vivo exposure as measured by flow cytometry. One-way ANOVA with Tukey's multiple comparisons test was performed. Values represent the mean ± SD. *P < 0.05, **P < 0.01. (E) Number of CD4 TH2 cells (FoxP3− GATA-3+) and type 2 Tregs (FoxP3+ GATA-3+) in Lgr6+/+ and Lgr6−/− mice as measured by flow cytometry. Error bars indicate mean ± SD. The data were analyzed by two-way ANOVA with Sidak’s multiple comparison test. *P < 0.05. (F) RSV L gene expression analysis by qRT-PCR. Fold increase was calculated over naïve Lgr6+/+ and Lgr6−/− mice. The data was analyzed using Student’s t test and was NS. (G) In vitro generation of Tregs from naïve CD4 T cells isolated from spleens of Lgr6+/+ and Lgr6−/− mice. The cells were analyzed for FoxP3 induction by flow cytometry after 4 d in culture with the addition of TGF-β, MaR1, or in combination. (H) The production of AREG from cell culture supernatants from Lgr6+/+ and Lgr6−/− mice was measured by ELISA after the addition of TGF-β, MaR1, or in combination. This experiment was performed twice. Error bar indicate mean ± SD. The data for panels G and H were analyzed by two-way ANOVA with Sidak’s multiple comparison test. *P < 0.05, ***P<0.001.
Because MaR1 decreased the RSV-induced conversion of Tregs into GATA-3+-expressing cells (Fig. 4B), we next evaluated lung Tregs day 6 pi in Lgr6+/+ and Lgr6−/− mice. Flow cytometric analysis showed Lgr6−/− mice had higher numbers of pathologic type 2 Tregs co-expressing FoxP3+ and GATA-3+ in the lungs compared to Lgr6+/+ mice (Fig. 5E). The increased type 2 expression profile was consistent with the increased L gene transcript seen in the Lgr6−/− mice, indicating reduced ability to restrain RSV viral replication (Fig. 5F). Given the increase in pathologic type 2 Tregs in Lgr6−/− mice after RSV infection, we next determined the capacity for MaR1–LGR6 interactions to augment de novo generation of Tregs. Naive T cells were polarized in vitro with TGF-β and MaR1 (see Materials and Methods). TGF-β alone robustly promoted the generation of Tregs from naïve CD4 T cells in the Lgr6+/+ and Lgr6−/− mice (Fig. 5G). Co-incubation with TGF-β and MaR1 led to additive increases in FoxP3-expressing Tregs in the WT mice, but not in Lgr6−/− mice (Fig. 5G). Of interest, AREG levels in the supernatants of these cell incubations were synergistically increased with dual administration of TGF-β and MaR1 in Lgr6+/+ mice, while this enhancing effect for Mar1 was lost in Lgr6−/− mice, with AREG barely detectable (Fig. 5H). Taken together, this data indicates a critical role for MaR1 signaling via LGR6 on Treg number and functional suppression of type 2 immune responses after RSV infection.
MaR1 Regulates RSV-Induced Inflammatory Responses in Human Precision-Cut Lung Slices.
To investigate whether the lung tissue mechanisms uncovered in mouse RSV infection translate to humans, we exposed human precision-cut lung slices (hPCLSs) from cryopreserved lungs of infants to RSV line 19. These hPCLSs retain airway epithelial function and have viable immune cell populations (25). The hPCLS were exposed to MaR1 (10 ng/mL) or vehicle and 6 h later were infected with RSV line 19 (MOI 0.5). RSV infection induced significant cellular changes to the lung tissue 72 h pi as shown by phase-contrast microscopic images. These RSV-induced changes were substantially decreased with MaR1 pre-exposure (Fig. 6A). Similar to MaR1 counter-regulating mouse airway immune responses, MaR1 significantly decreased the RSV-triggered increases in IL-13 levels (Fig. 6B) and significantly increased AREG levels after RSV infection (Fig. 6B). We confirmed that the inhibition was not mediated by a decrease in IL-25, IL-33, or TSLP (SI Appendix, Fig. S6). Given the anti-viral role of MaR1 in promoting interferon-β in mouse lung tissues, the supernatant samples were analyzed for cytokine levels and MaR1 showed a trend towards increased production of interferon-β (Fig. 6C).
Fig. 6. MaR1 regulates RSV-induced inflammatory responses in hPCLSs. Human pediatric lung slices were infected with RSV ± MaR1. MaR1 was added to lung slices 24 h prior to infection. Seventy-two hours after the treatment, the following analyses were performed: (A) Phase contrast microscopy (airways indicated by red asterisk). (B) IL-13 and AREG protein levels in cell culture supernatant were measured by ELISA. The data were analyzed by two-way ANOVA with Sidak’s multiple comparison test. ***P<0.001, ****P<0.0001. (C) Interferon-β in cell culture supernatant were measured by legend plex ELISA. This experiment was performed using three pediatric donors. The data were analyzed by one-way ANOVA with Tukey’s multiple comparison test. *P<0.05.
Discussion
The immunopathogenesis of RSV can produce substantial morbidity and mortality in vulnerable populations (1). Here, lung immune responses and their resolution after infection with a clinical strain of RSV were mapped out. RSV line 19 evoked type 2 innate and adaptive immune responses that were associated with viral evasion of host control mechanisms and structural changes to the airways in infected mice. The virus crippled the immune-suppressive function of Tregs, converting them to prophlogistic effector cells. In this study, we have identified a pro-resolving signaling pathway with potent regulatory actions on RSV-induced airway inflammation involving the DHA-derived SPM MaR1 and its recently identified receptor LGR6 in orchestrating resolution of inflammation by controlling type 2 immune responses, countering RSV’s subversion of Treg cell function, and increasing production of AREG. Together, these actions promoted lung catabasis, including enhanced viral clearance in a mouse model of RSV infection.
IL-13 is a potent inducer of mucin genes (16). At day 6 pi, the lung levels of this cytokine significantly increased and mucous cell metaplasia was present. IL-13 levels and gob5 transcript expression in the mouse lung mirrored the viral burden as monitored by RSV L gene expression. The deficiency in IL-13 reduces mucous cell metaplasia following RSV infection and decreases viral burden (13, 16). Human volunteers infected with RSV display viral loads that correlate with mucus levels in nasal aspirates (26). Because IL-13 is a pivotal cytokine for airway type 2 immune responses and mucous cell metaplasia and exhibits a strong correlation to viral burden, we focused on RSV-induced IL-13. RSV triggered IL-13 production by both innate (ILCs) and adaptive (CD4 TH2 cells) lymphocytes. Six days pi, MaR1 reduced IL-13 production from these two cell types and reduced mucous cell metaplasia as evident from reduced gob5 gene transcript levels. It is important to note that MaR1-mediated effects in reducing type 2 response were not mediated by targeting alarmin IL-33, TSLP, or IL-25.
The reduction in IL-13 levels with MaR1 was accompanied by decreased RSV L gene expression, suggesting that MaR1 disrupted the pathologic environment that had been conducive to viral replication after infection. The ability of MaR1 to upregulate gene transcripts of several pathways associated with anti-viral responses highlights the potency of SPMs in this viral model. There is also specificity of anti-viral action exhibited by SPMs with Protectin D1 promoting interferon-λ production in mouse lung tissues and primary human epithelial cells (27). These host protective actions for MaR1 may have clinical implications given that current anti-inflammatory treatments for RSV, including steroid administration, are associated with immunosuppression and increased susceptibility to other infections (1, 2).
Epithelial cell restitution is an important aspect of the resolution of airway inflammation, and AREG, an epithelial cell mitogen, aids in this process (28). The receptor for AREG is the epidermal growth factor receptor (EGFR). The fusion protein of RSV line 19 interacts with EGFR to activate prophlogistic signaling (29). Sustained EGFR signaling potentiates the survival of RSV-infected epithelial cells, and thus an alternate ligand such as AREG could potentially decrease RSV pathogenesis (29, 30) and restore immune homeostasis. MaR1-mediated reduction in viral burden also correlated with increased AREG and decreased airway epithelial mucous cell metaplasia. In addition to the MaR1-induced AREG production by Tregs shown here, direct targeting of airway epithelium by the related DHA-derived SPMs protectin D1 and protectin conjugate of tissue regeneration 1 increased interferon-λ production to enhance viral host defense and promote the resolution of mouse RSV infection (31), suggesting that DHA metabolism to SPMs in response to viral respiratory tract infection leads to cell-type-specific responses that can work together to promote resolution of the pathogen-evoked airway inflammation. Of interest, compared to placebo control, omega-3 fatty acid supplementation of pregnant mothers with low endogenous DHA levels significantly reduces the risk for persistent wheeze and respiratory viral infections in their infants (12). There is evidence to suggest that SPM production might also be decreased in aging muscles of mice indicating that neonates and elderly might not have sufficient levels of SPMs for protection against viral infection (32). Our findings here suggest that DHA-derived SPMs could be important in protection against viral infections.
Immune responses to RSV are critical to promoting viral clearance, yet the type 2 immune responses in RSV drive pathology rather than protection (33). Tregs can exert their suppressive effect by curtailing pathologic cytokine production from ILC2 and CD4 TH2 cells, the major drivers of type 2 immunopathology in RSV infection (20, 34); however, pathologic responses to RSV are surprisingly exacerbated by Tregs after an RSV-induced switch in Tregs to an IL-13 producing prophlogistic cell phenotype (8). It is notable that MaR1 restored the suppressive function of the RSV type 2 Tregs as it reduced pathogen-elicited lung inflammation and mucous cell metaplasia. The MaR1 receptor LGR6 (23) was constitutively expressed at a high level on Tregs and macrophages with Treg LGR6 expression markedly changing during RSV infection. Macrophages are a critical source for MaR1 (14), and the sustained expression of LGR6 on macrophages enables autacoid regulation of macrophage responses for resolution (23). Lgr6-deficient mice were more susceptible to RSV-induced inflammation and had decreased Treg responses ex vivo to MaR1, highlighting the potent roles for MaR1 signaling and its receptor in host defense against RSV infections. In addition to important pro-resolving actions for MaR1–LGR6 signaling on macrophages, the increased pathologic type 2 Tregs in RSV-infected Lgr6−/− mice was consistent with Treg targeting by MaR1 in this experimental model.
SPMs are present in human maternal milk, suggesting roles for SPMs in priming the immature neonate immune system (35). Neonates, specifically preterm infants, are at risk for an omega-3 fatty acid deficit, and severe RSV infection (36). In addition to our findings here with mice, human Tregs also constitutively expressed LGR6. Exposing human pediatric lung slices to RSV infection decreased LGR6 expression on Treg. MaR1 also limited IL-13 levels and pathologic tissue responses when the human lung tissues were infected with RSV, consistent with translationally relevant protective actions for MaR1–LGR6 signaling in human tissues.
In summary, MaR1–LGR6 signaling regulated pathologic type 2 immune responses to RSV infection in mouse and human lungs. IL-13-producing innate and adaptive lymphocytes were integral to RSV pathogenesis, in part, because Treg-mediated control was disabled with RSV pathologic Tregs converted into prophlogistic effector lymphocytes. Tregs expressed LGR6 that could be targeted by the SPM MaR1, which restored Treg suppressive function, decreasing IL-13 and increasing AREG production in conjunction with accelerated viral clearance. Together, these findings point to a new pro-resolving function for SPMs in licensing Treg suppressive function that in RSV infection was amplified by MaR1–LGR6 signaling, suggesting a putative therapeutic target that is distinct from the current clinical approach emphasizing broad immunosuppression with corticosteroids.
Materials and Methods
Animals.
Balb/cByJ (Stock Number: 001026) and FoxP3eGFP (Stock Number: 006769) mice were purchased from The Jackson Laboratory. Mice were used at 3 w of age. Breeding pair of Lgr6-deficient mice (Lgr6−/−) were obtained from Lehoczky and were originally from The Jackson Laboratory (Stock number 016934). All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) at the Brigham and Women’s Hospital. The protocol was approved by Harvard Medical Area (protocol number: 05115) and Brigham and Women’s Hospital (protocol number: 2016N000357).
Virus Propagation, Quantification, and Mouse Model of Infection.
RSV line 19 is a clinical strain isolated originally from a sick infant in University of Michigan Health System that mimics human infection (37). The virus was propagated as previously described (31). Virus was propagated using Hep-2 cell line (American Tissue Culture Collection, ATCC) and quantified by visible plaque formation in STAT-1 deficient cell line (31). Mice were briefly anesthetized with isoflurane and infected intranasally with 1 × 105 pfu (plaque-forming units) of RSV line 19. MaR1 (10 ng/mouse) was administered via i.n. route on day 3 pi.
RSV Infection of hPCLSs.
The hPCLS from young infants were generated as described in prior study (25). Lungs were purchased from International Institute for the Advancement of Medicine, a nonprofit institute that provides non-transplantable human tissues for medical research. These donor lungs were de-identified and were from children (0 to 5 y of age). The frozen lung slices were thawed at 37 °C and rested overnight in complete RPMI media. Two lung slices were plated per well in a 12-well plate with 1 mL of media. These slices were exposed to MaR1 (10 ng/mL; 6 h prior to infection) or vehicle and then infected with RSV line 19 (MOI 0.5). Seventy-two hours after infection, the cell culture supernatants were harvested and analyzed by ELISA for cytokine production.
RSV Infection of Human Peripheral Blood Mononuclear Cells (PBMCs).
Human PBMCs were isolated from de-identified blood donors, as previously described (38). The frozen PBMCs were thawed at 37 °C plated (1 million/mL) in complete RPMI media. One hour following the plating, the cells were infected with RSV line 19 (MOI 0.1). Forty-eight hours after the infection, the cells were harvested and analyzed by flow cytometry.
Antibody and Flow Cytometry.
The following antibodies were used in flow cytometry with the indicated antibody clones and manufacturers for the mouse experiments: Anti-CD3 (17A2; BD Biosciences), anti-CD19 (6D5; BioLegend), anti-CD11c (clone: N418; Biolegend), anti-CD11b (M1/70; BioLegend), anti-CD49b (DX5; eBioscience), anti-CD25 (PC61; BD Biosciences), anti-CD90.2 (anti-Thy-1.2; 53 2.1; eBioscience), anti-CD11c (N418; eBioscience), anti-ST2 (DIH9; BioLegend), anti-Siglec-F (clone: E50-2440; Biolegend) anti-CD4 (RM4-5; BD Biosciences), anti-IL-13 (ebio13A, eBioscience), anti-Foxp3 (FJK-16S; eBioscience), anti-CD62L(MEL-14; eBioscience), LGR6 (17658-1-AP; Proteintech). Flow cytometric data acquisition was performed on an LSRFortessa flow cytometer (BD Biosciences), and the data analyzed using FlowJo software version 10.3 (Tree Star). The gating strategy is provided in SI Appendix, Fig. S7 for populations analyzed.
Mouse Single Cell Lung Preparation.
The lungs were perfused with sterile PBS, removed, and digested as described previously (8, 15). Lungs were then dissociated with a gentle MACS Dissociator (Miltenyi Biotech) according to the manufacturer’s protocol. Single-cell suspensions were obtained by passing the dissociated tissue through a 70 µm cell strainer (BD Falcon).
ILC/Treg Co-culture.
ILCs were isolated from RSV-infected mice and seeded at 50,000 cells/well in cRPMI. Tregs were isolated from FoxP3eGFP and the ratio of ILC/Tregs was at 10:1. To activate ILC2 in the pool of ILC, IL-7 (10 ng/mL; Biolegend) was added. Tregs were activated with plate-bound anti-CD3 and soluble CD28 (2 μg/mL). Seventy-two hours post-stimulation, cell culture supernatants were collected and analyzed for IL-13 levels by ELISA.
Airway Inflammation.
Lungs were perfused and fixed with zinc fixative (BD Pharmingen) at 20 cm H2O, and histology performed as previously described (15). Tissues were embedded, sectioned, and stained with hematoxylin and eosin or PAS staining by the Dana Farber/Harvard Cancer, Center Rodent Histopathology Core, or the Brigham and Women's Pulmonary and Critical Care Medicine Histology Core. The samples for H&E shown in SI Appendix, Fig. S2 were scored in a blinded manner by author Dr. Cagnina who was not involved in the samples collection or processing or any other aspect of the experiment.
ELISA.
Mouse IL-13, AREG, and human IL-25 ELISA kits were purchased from R&D Systems and used according to the manufacturer’s instructions. Mouse legend plex was used for IL-33, IL-25, TSLP, and interferon-β were purchased from BioLegend.
RNA Isolation and Quantitative PCR.
Messenger RNA (mRNA) was isolated using TRIzol as previously described (39), and complementary DNA (cDNA) was generated from DNase-treated mRNA using a reverse transcription kit (Life Technologies). Quantitative PCR was performed using EvaGreen master mix (BioRad) or TaqMan primers and universal master mix (Life Technologies) on the Mx3005P system (Agilent Technologies). The primers for il13, gob5, and RSV Large polymerase (L) gene (31) were purchased from IDT and relative gene expression was determined as 2−ΔΔCT (39).
PCR Array.
The PCR array was performed according to the manufacturer’s instructions. mRNA was isolated as stated above. cDNA was generated from DNase-treated mRNA using a reverse transcription kit (Life Technologies). The amplified cDNA was diluted with nuclease-free water 1.5 and added to the RT2 qPCR array (Qiagen, MD). The PCR array was for mouse anti-viral response. cDNA from each mouse per group per time point was coated on an individual plate. The genes were normalized to house-keeping gene β-actin.
Statistics.
Student's unpaired two-tailed t test was used for all statistical analyses comparing two groups. For comparisons with more than two groups, one-way ANOVA with Tukey’s for multiple comparisons test or two-way ANOVA with Sidak’s multiple comparison test was used. All statistical analyses were performed with GraphPad Prism. Results are expressed as mean ± SD. Differences between the groups were considered significant if P<0.05.
*P<0.05, **P<0.01, ***P<0.001. All data are representative of at least two independent experiments with a minimum of three mice per group in each experiment.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank Dr. Nicholas Lukacs (University of Michigan, Ann Arbor MI) for providing the RSV clinical isolate strain Line 19, and Dr. Joan Durbin (Rutgers, New Jersey Medical School) for providing the STAT1−/− cell line for RSV plaque assay. Funding:This work was supported by NIH P01GM095467, R01HL122531(to B.D.L.) and R56HL155917 (to N.K.). N.K. was also supported by a Fund to Sustain Research Excellence Award from BWH.
Author contributions
N.K. and B.D.L. designed research; N.K., K.H.W., T.R.B., L.P.T., E.W.S., Y.B., X.A., R.E.C., M.G.D., and J.A.L. performed research; N.K., K.H.W., T.R.B., L.P.T., E.W.S., Y.B., X.A., R.E.C., M.G.D., J.A.L., and B.D.L. analyzed data; J.N. editing and submission; and N.K., J.N., and B.D.L. wrote the paper.
Competing interest
The authors declare competing interest. The authors have patent filings to disclose. B.D.L. is an inventor on patents (resolvins) assigned to Brigham and Women's Hospital (BWH). His interests were reviewed and are managed by the BWH according to conflict of interest policies. The other authors have no conflicts of interest to declare.
Data, Materials, and Software Availability
No new transgenic mice or cell lines were developed in this part of study. The data are provided in the main text and SI Appendix. All other data are included in the article and/or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926268.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595700
202213528
10.1073/pnas.2213528120
videoVideoresearch-articleResearch ArticleengEngineering416
Physical Sciences
Engineering
A submillimeter bundled microtubular flow battery cell with ultrahigh volumetric power density
Wu Yutong a 1 https://orcid.org/0000-0003-1214-9147
Zhang Fengyi a 1
Wang Ting b 1 https://orcid.org/0000-0002-4658-7789
Huang Po-Wei a
Filippas Alexandros a
Yang Haochen a https://orcid.org/0000-0002-8367-9711
Huang Yanghang a
Wang Chao a https://orcid.org/0000-0001-6418-0504
Liu Huitian a
Xie Xing [email protected]
b 2 https://orcid.org/0000-0002-2253-0964
Lively Ryan P. [email protected]
a 2 https://orcid.org/0000-0002-8039-4008
Liu Nian [email protected]
a 2 https://orcid.org/0000-0002-5966-0244
aSchool of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332
bSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332
2To whom correspondence may be addressed. Email: [email protected], [email protected], or [email protected].
Edited by Yi Cui, Stanford University, Stanford, CA; received August 6, 2022; accepted November 18, 2022
1Y.W., F.Z., and T.W. contributed equally to this work.
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Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Due to an uneven renewable energy resource distribution and time-dependent fluctuation in energy generation, the harvest, storage, and delivery of renewable energy are crucial to match a rapidly growing demand. Flow batteries are a promising technology for large-scale energy storage and exhibit unparalleled advantages in scalability and design flexibility because of the spatially decoupled energy storage and power conversion units. However, commercial flow batteries are still based on cell configurations developed decades ago. The planar configuration exhibits a large cell size with bulky components for high performance, which results in low volumetric power density, high footprint and capital cost. Step-change decreases in cell size will enable reductions in flow battery capital costs and increase their competitiveness ingrid-level applications.
Flow batteries are a promising energy storage solution. However, the footprint and capital cost need further reduction for flow batteries to be commercially viable. The flow cell, where electron exchange takes place, is a central component of flow batteries. Improving the volumetric power density of the flow cell (W/Lcell) can reduce the size and cost of flow batteries. While significant progress has been made on flow battery redox, electrode, and membrane materials to improve energy density and durability, conventional flow batteries based on the planar cell configuration exhibit a large cell size with multiple bulky accessories such as flow distributors, resulting in low volumetric power density. Here, we introduce a submillimeter bundled microtubular (SBMT) flow battery cell configuration that significantly improves volumetric power density by reducing the membrane-to-membrane distance by almost 100 times and eliminating the bulky flow distributors completely. Using zinc–iodide chemistry as a demonstration, our SBMT cell shows peak charge and discharge power densities of 1,322 W/Lcell and 306.1 W/Lcell, respectively, compared with average charge and discharge power densities of <60 W/Lcell and 45 W/Lcell, respectively, of conventional planar flow battery cells. The battery cycled for more than 220 h corresponding to >2,500 cycles at off-peak conditions. Furthermore, the SBMT cell has been demonstrated to be compatible with zinc–bromide, quinone–bromide, and all-vanadium chemistries. The SBMT flow cell represents a device-level innovation to enhance the volumetric power of flow batteries and potentially reduce the size and cost of the cells and the entire flow battery.
flow battery
energy storage
high power
tubular reactor
hollow fiber membrane
National Science Foundation (NSF) 100000001 ECCS-2025462 Nian Liu Georgia Institute of Technology (GIT) 100006778 faculty startup funds Nian Liu
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pmcRenewable energy sources are driving a global energy transition toward a zero-emission society (1–3). Cost-effective grid-scale energy storage technologies that are not constrained by geography are in urgent need to address mismatched renewable energy supply and demand in the time and spatial domains (4, 5). Unlike secondary battery systems using solid active materials, flow batteries decouple energy storage (i.e., the concentration of electrolyte and storage container size) and power conversion (i.e., the central electrochemical reaction energy conversion device), thus enabling relatively safe energy storage and long battery life (4, 6–8). While progress has been made in developing state-of-the-art electrode and electrolyte materials to improve the flow battery areal current density, there is still plenty of room for performance improvement and cost reduction. The capital cost of flow batteries (~US$800/kWh) (9) is still significantly higher than that of Li-ion batteries (<US$300/kWh) (10) and far from the US$125/kWh goal set by the US Department of Energy (1). The power module composes ~40% of the cost of flow batteries (11). This number will be even higher when the recycling of electrolytes is considered (12). Although volume and weight are less of a constraint for stationary energy storage as compared with electric vehicle batteries, increasing the volumetric and gravimetric power of the power module can potentially reduce the footprint and capital cost, which can accelerate the wide adoption of energy storage technologies for the grid.
The power module (or cell stack) where redox active species receive or lose electrons typically consists of electrodes, planar ion-exchange membranes, bipolar plates, gaskets, and frames. Fig. 1A represents a single cell, which can be stacked to form a cell stack. Efforts have been made to optimize the cell design and flow patterns based on the conventional planar cell geometry (8, 13–15). However, bipolar plates and other inactive components are costly and occupy most of the cell volume, which limits the volumetric power density (11). Individual tubular cells in the centimeter and millimeter scale have been reported to eliminate the bipolar plates and reduce capital costs for vanadium (16) and aqueous organic flow batteries (17). Individual, millimeter-scale tubular electrochemical reactors have also been developed for slurry electrode flow batteries (18), solid oxide fuel cells (19) and CO2 reduction (20). Further decreasing the diameter of tubular cells to submillimeter could leverage the scaling effect and boost volumetric power density, but it has not been successful. Furthermore, how to move beyond individual tubular cells is critical for practical application but is also yet to be addressed.
Fig. 1. Comparison between the conventional flow battery cell with a planar membrane and the microtubular (SBMT) flow battery cell design. (A) Conventional planar cell design. (B) SBMT cell design.
In the chemical separation industry, submillimeter bundled microtubular (SBMT) membranes are known as the most space-efficient configuration, which provides around 10,000 m2 surface area per 1-m3 module, an order of magnitude higher than conventional modules made of planar membranes (21) (e.g., spiral wound). The microtubular membrane itself withholds the transmembrane pressure, without the need for supporting components such as spacers and fabric backing. This highly scalable configuration has been proven effective at reducing the footprint of separation systems (via increases in volumetric throughput, i.e., moles of product per second per cubic meter of device) in various applications such as gas separation, liquid phase separation, and organic solvent separations (21, 22).
Herein, we report a SBMT flow battery cell design to achieve an order of magnitude increase in volumetric power density for flow batteries. We explain why decreasing the tubular cell size to submillimeter is critical for a leap in the scaling effect and provide simulation/experimental evidence that the bundle geometry boosts volumetric power density. We also provide a step-to-step protocol for the SBMT module assembly and scaling-up processes. As a result, the membrane-to-membrane distance was reduced by almost 100 times, and the bulky flow distributors were completely eliminated in the SBMT. Using zinc–iodide chemistry as a demonstration, peak charge and discharge power densities of 1,322 W/Lcell and 306.1 W/Lcell were achieved, respectively, compared with average charge and discharge power densities of <60 W/Lcell and 45 W/Lcell, respectively, of conventional planar flow battery cells. Besides, the SBMT flow battery cycled for more than 220 h corresponding to >2,500 cycles at off-peak conditions. Furthermore, the SBMT cell has been demonstrated to be compatible with zinc–bromide, quinone–bromide, and all-vanadium chemistries as well as directly scaled up following industrial strategies.
Results and Discussion
In the SBMT cell, electrolytes pass through the device from the bore and shell sides and are separated by submillimeter-sized microtubular ion-exchange membranes, while the electrodes extend out from the center (Fig. 1B). The numbers of positive and negative electrodes in a cell can be adjusted to balance the reaction kinetics of two half reactions and the electrolytes flow in the same direction during operation. All the components used to fabricate the SBMT cell in this work are off the shelf, and the scaling-up can follow the hollow fiber modules in the chemical separation industry (23).
The zinc–iodide redox system (reactions shown in Fig. 2A) was chosen to demonstrate the concept of the SBMT flow battery because of its high theoretical energy density of up to 322 Wh/Lelectrolyte and ability to operate in ambient air (7, 24, 25). Carbon fiber electrodes were inserted into the bore sides of 4 Nafion microtubular membranes (0.65 mm OD) to serve as the cathode for the iodide half reaction. A zinc wire (0.25 mm OD) was placed on the shell side outside the membranes to serve as the anode for the zinc half reaction (Fig. 2 A, Bottom). The effective working length of the SBMT cell, where the reaction occurred, was 5 cm. The length was increased to 8 cm in Fig. 2A for demonstration purposes and can potentially be further increased. The detailed SBMT cell assembly and gluing processes are described in the SI Appendix, Fig. S1–S6 and Movies S1 and S2. Fig. 2B and SI Appendix, Fig. S7 show the cross-sectional scanning electron microscopy (SEM) images of the microtubular cell, revealing the membranes in submillimeter size and the packing pattern. SI Appendix, Fig. S8A compares the SBMT module with a typical laboratory-scale planar cell module. SI Appendix, Fig. S8B and Movie S3 show an operating SBMT, eliminating the thick battery frame for pressing the cell components together and flow distributors. As a result, the overall cell size is drastically decreased because of the small-sized components. The distance between electrodes and membranes is much smaller, and the volumetric power density can thus be greatly increased. The microtube itself serves as the electrolyte distributor without any other supporting materials, eliminating extra flow distributors that decrease volumetric power density. And the closer packed components decrease impedance and enhance the ion transfer for the flow battery cell, further promoting volumetric power density.
Fig. 2. SBMT flow battery using zinc–iodide redox as a demonstration. (A) On the top is a picture of a zinc–iodide SBMT module with four positive electrodes and one negative electrode; on the bottom is an illustration of its unit cell, detailed structure, and the electrochemical reactions during charging. (B) A cross-sectional SEM image of the zinc–iodide SBMT module. (C) A simplified unit cell model for tubular cell, where D represents the distance between membranes, and R is the radius of the microtubular membrane. (D) A simplified unit cell model for the planar cell, where D is the distance between membranes, and B is the membrane height. (E) Comparison between the D values of the SBMT and literature reported values in SI Appendix, Table S1.
To independently investigate the scaling effect from the tubular membrane size and packing density on the volumetric power densities, we calculated the membrane surface area in unit volume of tubular and planar configurations. The simplified tubular unit cell is shown in Fig. 2C, where D is the distance between membranes, and R is the tubular membrane radius. The ratio of membrane surface area to cell volume for the tubular cell can be calculated using Eq. 1, and we assign factor f through Eq. 2 as follows:[1] 1D×2π4RD+4+DR,
[2] 2π4RD+4+DR=f.
The planar configuration of the unit cell is shown as Fig. 2D, where D is the distance between membranes, and B is the membrane height. The surface-area-to-volume ratio for tubular cell can be calculated using Eq. 3 as follows:[3] a^=1D.
The derivation for Eqs. 1 and 3 is listed as SI Appendix, Eqs. S1 and S2. With the same D value, whether tubular or planar cell has a larger surface-area-to-volume ratio relies on f. The factor f is always smaller than 1 when plotted against D/R (SI Appendix, Fig. S9), meaning that when having the same D value, a tubular cell always has a smaller surface-area-to-volume ratio than a planar cell. In other words, the mere adoption of a tubular geometry (i.e., rolling the electrodes with the membranes) does not grant a higher volumetric power density.
However, the spacing between membranes, D, is drastically smaller in SBMT than in planar cells. We compared 39 literature values and calculated the corresponding D values in SI Appendix, Table S1, and the D values are in the range >0.15 cm, while the D value for SBMT is <0.012 cm (Fig. 2E). The difference in the D values overcomes the difference in factor f in reality, and in our specific case, f for SBMT is ~0.3, meaning that the SBMT surface-area-to-volume ratio is at least >3.75 folds compared with the planar cell. Note that most of the literature does not report all the parameters (typically flow distributors, gasket, electrode, membrane, and current collectors), so the D values are underestimated and could be higher.
The effect of the bundled configuration on the overall kinetics and electrochemical performance was evaluated using multiphysics finite element simulation. We proposed a performance evaluation basis named active volume (electrode area multiplied by the necessary thickness of the cell, unit: per Lcell) as an alternative power density comparison instead of the conventional method based on electrode surface area or electrolyte volume. Active volume is the effective volume necessary for a flow battery cell to achieve its ascribed performance; cell parts such as flow distributors are accounted for since they are indispensable for the flow battery to overcome the pressure and transport issues and achieve high performance. Using electrode surface area as a reference overestimates power density while using electrolyte volume also leaves uncertainty because the volume used depends on the battery geometry and can vary in a wide range based on different experimental purposes. The dimensions of microtubular and planar cell geometries were chosen to reflect realistic cell volumes (Fig. 3A and SI Appendix, Table S2).
Fig. 3. Simulated and experimental power density of zinc–iodide microtubular cell. (A) Finite element simulation models of a closely packed SBMT cell stack and a planar cell stack with the same volume. (B) Simulated current density of SBMT vs. planar cells at a series of cell voltages. (C) Simulated current density of SBMT vs. planar cells at a series of redox reaction rate constants. (D) Comparison of the electrochemical impedance among SBMT cells with i) one and ii) four Nafion microtubes, iii) H cells without a membrane, iv) with a planar Nafion 117 membrane, v) a Nafion microtubular membrane enclosing one of the electrodes, and vi) a planar cell with 5-cm2 carbon felt electrode and a Nafion 117 membrane. (E) Comparison of the OCV (Inset) and cell voltage vs. volumetric current density at five different SOCs between the SBMT and planar cells; the electrolyte was 2 M ZnI2, and the higher and lower cutoff voltages were 2.1 and 0.25 V, respectively. (F) Comparison of maximum charge power density vs. maximum discharge power density of literature values from SI Appendix, Table S8. For the literature that did not report volumetric power, their areal power was converted to volumetric one based on the active volume of their cells. Volumetric power density is recalculated based solely on the information provided in the references. The thickness of end plates in planar cells and wall thickness of the outer PTFE tube in SBMT cells are not considered.
Within the same volume of the cells, the surface area of the membrane in the microtubular cell was 20 times larger than the one in the planar cell (a detailed calculation is described in the SI Appendix, Eqs. S3–S5 and Fig. S10 compares the two models and specific parameters). The current densities at a series of states of charges (SOCs) and cell voltages were calculated and compared. At 75% SOC, for example, the current densities of the microtubular cell were >7 times higher than the ones of the planar cell at all cell voltages (Fig. 3B and SI Appendix, Table S3). The results are consistent with the improvements in the mass transfer area in the geometry calculation mentioned above. A comparison of the total overpotential between the SBMT and planar modules was conducted using a stand-alone limiting case method (26), and the overpotential of SBMT module was significantly reduced compared with that of the planar module at each current density (SI Appendix, Fig. S11A). The overpotential breakdown for the two systems (SI Appendix, Eq. S6, Fig. S11 B and C, and Tables S4 and S5) indicated that the percentage of membrane ohmic to the overall overpotential decreased significantly. Note that compared with the planar module, the anode side overpotential contribution for the SBMT module was higher; it was reasonable since that nonporous Zn anode was used for the SBMT module, while carbon porous anode was used for the planar module; the overpotential from the anode can be further reduced with packing optimization and enhanced materials. It was also found that the current densities will in general be enhanced by the increase of redox reaction rate constant. At the same reaction rate constant, the current density of the microtubular geometry was always higher than that of the planar geometry because of the decreased membrane spacing and enhanced membrane surface area (Fig. 3C and SI Appendix, Table S6).
Next, to prove that the closer packed components decrease impedance and promote electrochemical properties compared with the planar configuration, electrochemical impedance spectroscopy was conducted. First, carbon fiber and Zn wire electrodes of the same total length as those used in the microtubular cell were inserted into two 2 M ZnI2 electrolytes containing H-shaped electrolytic cells (H cells). For one H cell, a planar Nafion 117 membrane was sandwiched in the middle, and for the other H cell, a Nafion microtubular membrane was enclosing one of the carbon fiber electrodes, and the two electrodes were 4 cm effective in length and 5 cm apart in both H cells. Fig. 3D shows that the impedance of the Nafion microtubular cell and Nafion 117 membrane cell was of the same magnitude, indicating that the microtube was capable of being used as an ion-exchange membrane for the cell. Note that we also compared the impedance to an H cell without a membrane, and the impedance was also close to the two samples with a membrane, indicating that the electrode-to-electrode distance is significant for a cell to have low impedance. On the other hand, two microtubular cell modules with one and four Nafion microtubes inside were compared with the H cell results. The impedance for the microtubular cell with one microtube was significantly lower than the H cell results because of close packing and decreased electrode-to-electrode distance. The impedance was further reduced for the microtubular battery with four microtubes because of the higher electrode area. For the planar module, the electrode area was 5 cm2, and a Nafion 117 membrane was sandwiched in between with a clamping force of 30 lbf-in. The impedance was similar to that of the SBMT cell, note that the electrode and membrane area of the planar cell was significantly higher than that of the SBMT cell, and the result suggests that close packing is critical for a low impedance.
The experimental electrochemical performance of the zinc–iodide flow battery with the SBMT cell also supported that a SBMT cell design significantly enhances the volumetric power density. A comparison of the cell voltage vs. volumetric current density at five different SOCs between the SBMT and planar cell is shown in Fig. 3E. The SBMT device was able to sustain current density as high as 632.4 A/Lcell during charging and discharging. At 632.4 A/Lcell, the volumetric power density reached 1,322 W/Lcell, in charging and 306.1 W/Lcell in discharging. The maximum charge/discharge volumetric current density of the planar cell was around 30 W/Lcell, significantly lower than that of the SBMT cell module. SI Appendix, Fig. S12 shows the detailed microtubular cell voltage vs. volumetric current density at different SOCs. As the SOC increased, the open circuit voltage increased from 1.21 V to 1.27 V in the range of a typical zinc–iodide redox couple. SI Appendix, Fig. S13 shows the current density vs. power density at different SOCs. Based on the total inner surface area of microtubular membranes, the corresponding areal current density was 24.49 mA/cm2, and the areal power density was 51.2 mW/cm2 in charging and 10.8 mW/cm2 in discharging.
When comparing the volume-normalized maximum charge and discharge power density to 47 research articles and 3 commercial flow batteries references using various redox couples (Fig. 3F and SI Appendix, Table S8), the performance was significantly higher than most of the references with charge/discharge volumetric power density lower than 60/45 W/Lcell. For the literature that did not directly report volumetric power, their areal power was converted to volumetric one based on the active volume of their cells. The above experimental results confirm the hypothesis and numerical simulation that the microtubular cell with increased membrane surface-area-to-volume ratio and packing efficiency increase the volumetric power density compared with the planar cell.
The zinc–iodide SBMT flow battery was deeply charged to 50% total capacity and fully discharged using current density of 253 A/Lcell (13.1 mA/cm2) as shown in Fig. 4A, achieving a coulombic efficiency (CE) of 95.8%, voltage efficiency (VE) of 65.0%, and energy efficiency (EE) of 62.2%. The deep cycling performance proved that the SBMT flow battery can discharge at an ultrahigh volumetric power density in a standard commercial flow battery operation time range (4 to 12 h) (27). Maintaining a long and stable battery life during the off-peak period is another significant characteristic of flow batteries. A long-duration battery test was performed using 1.25% zinc wire capacity, and the battery was able to run for >2,500 cycles corresponding to more than 220 h at 31.6 A/Lcell (1.63 mA/cm2; Fig. 4B); the CE was 100%, and VE and EE were kept ~70% for ~2,000 cycles, indicating a steady cycling performance. SI Appendix, Fig. S14 shows the zoomed-in view of the voltage profile after 1,664 cycles. The voltage profile was still steady at the late cycling stage, indicating that the microtubular flow battery can operate stably and safely during off-peak hours. Note that the EE dropped at ~2,000 cycles with a noticeable cycling voltage change, while the CE maintained 100%; itF could be attributed to minor dead Zn accumulated in the narrow tubing. We believe that using advanced materials and chemistry adapted from conventional flow battery research on electrodes, electrolytes and the membrane will further increase the volumetric power density. The packing efficiency and patterns can also be optimized to enhance the volumetric power density through a balance between module design, flow characteristics, and electrochemical performance with the assistance of advanced simulation and analytical technologies. The axial pressure drop of the 4-membrane SBMT device was estimated by the Hagen–Poiseuille equation; the modeled data were ~30% lower than the experimental data (SI Appendix, Fig. S15), and a detailed calculation is provided in SI Appendix. The difference between the pressure drop can be attributed to the dead volume between carbon fibers and the diameter difference in the microtubular membranes supplied by the manufacturer. The pressure drop can be decreased with optimized microtubular membrane size, carbon fiber filling, and packing patterns.
Fig. 4. Zinc–iodide flow battery cycling performance and SBMT cell configuration tested with other redox couples. (A) Charging and discharging voltage profile of the zinc–iodide flow battery cycled between 0% and 50% SOCs at 253 A/Lcell (13.1 mA/cm2) with 2 M ZnI2 as the initial catholyte and anolyte, with the higher cutoff voltage being 1.65 V. (B) Long-term cycling of the zinc–iodide microtubular cell using 1.25% zinc wire capacity at 31.6 A/Lcell (1.63 mA/cm2), with the higher and lower cutoff voltages being 1.5 and 0.5 V. (C) Cycling of the zinc–bromide redox couple, with the higher cutoff voltage being 2 V. (D) Cycling of organic quinone electrolyte vs. Br2, with the higher cutoff voltage being 1.5 V. (E) Cycling of the vanadium redox couple, with the higher cutoff voltage being 1.6 V. The three redox couples were cycled at 6.32 A/Lcell (0.33 mA/cm2) for 100 times. (F) The CE, VE, and EE of the batteries in C–E.
Importantly, the SBMT cell configuration is universal and can be applied to other redox couples, especially to the more representative redox couples with dissolved active materials on both sides. Besides zinc–iodide (Movie S4), the SBMT cell configuration has been tested with other prominent redox couples. As shown in Fig. 4C, the zinc–bromide electrolyte was cycled steadily for 100 cycles at 6.32 A/Lcell (0.33 mA/cm2) in the SBMT cell. The voltage was steady around the 1.8 V thermodynamic voltage. Quinone is a popular chemical for organic flow batteries because of its high solubility and ability to be modified by functional groups. As shown in Fig. 4D, 9,10-anthraquinone-2,7-disulphonic acid was selected as the anolyte and paired with bromine catholyte and cycled 100 times in the SBMT cell. The zinc wire electrode was switched to carbon fiber to avoid side reactions. The voltage fell in the typical cycling range (less than 1.2 V). As shown in Fig. 4E, vanadium redox was also cycled in the SBMT cell 100 times as the other two redox couples and showed a steady cycling performance. Fig. 4F shows the CE/EE/VE for the three redox couples, while the CE stayed at 100%, the VE and EE varied because of the different redox couples and can be improved with advanced materials and system optimization. The SBMT flow cell is compatible with various flow and hybrid flow battery chemistries. The cell components can be modified to adapt to other redox couples such as size-exclusion–based polymer electrolytes (23).
The SBMT cells in this work were all assembled from off-the-shelf components. It is difficult to compare the cost of different cell configurations at the laboratory scale, so we simply list the cost of our flow battery cell module for reference. The material cost of building our laboratory-scale microtubular cell was only $41.85 (SI Appendix, Table S9), and the cost can be further reduced for the other three redox systems since the Zn wire will be replaced by carbon fiber. The carbon fiber electrode in our module is the same one widely used for pultrusion and enforcement purposes, and thus, the cost is only $0.02 for a single SBMT module containing four fibers. It is important to note that the SBMT flow battery enclosure and ion-exchange membrane are the most expensive components, which is also the case in a planar flow battery cell. The cost of Nafion microtubes can potentially be reduced with process innovation and scale-up.
The components of SBMT module enclosures can be easily tailored to work with various redox couples and battery sizes. The mature hollow fiber design and fabrication techniques can shape both melt-processable materials and solution-processable materials into hollow fibers, which allows the utilization of a wide range of membrane materials including anion exchange membranes. The modular design of the hollow fiber membrane contactor allows easy scale-up and customization for energy storage applications at different scales (23, 28). SI Appendix, Fig. S16 shows a scaled-up 16:4 SBMT module assembled using the same method, proving the simplicity of scaling-up. Note that the tubular membrane should be scaled into short parallel bundles instead of long single tubes to avoid a high overpotential (SI Appendix, Fig. S17).
Conclusion
In summary, we developed a microtubular flow battery cell realizing an ultrahigh volumetric power density. There are several advantages of this design, enabling an order of magnitude increase in volumetric power density compared with the conventional planar cell configuration. First, all the flow battery parts are in a submillimeter size, which is significantly smaller than those used in a planar or the centimeter- and millimeter-scale tubular cells. Second, the bundled microtubular membranes greatly increased the membrane surface area compared with a planar module within the same volume. Using zinc–iodide chemistry as a demonstration, simulation results indicated a >sevenfold improvement in current density in SBMT compared with the planar configuration for the same redox chemistry, which was consistent with the improvements in the mass transfer area. Experimentally, our SBMT battery showed peak charge and discharge power densities of 1,322 W/Lcell and 306.1 W/Lcell, respectively, compared with average charge and discharge power densities of <60 W/Lcell and 45 W/Lcell, respectively, of conventional planar flow batteries. In addition, the battery was able to cycle for more than 220 h corresponding to >2,500 cycles during the off-peak period. This SBMT cell configuration is also broadly applicable to all flow or hybrid flow battery redox couples. We have demonstrated steady long-cycle performance for zinc–bromide, quinone–bromide, and all-vanadium redox couples. The SBMT cell configuration can also be applied to other energy storage systems such as fuel cells and electrolysis and is expected to be highly scalable following successful examples in the separation industry; its electrochemical performance can be further improved with advanced materials and chemistry used in planar modules. The SBMT design can potentially enhance the performance and reduce the footprint of flow batteries, accelerating their grid-level adoption.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. SBMT assembly and gluing part 1.
Movie S2. SBMT assembly and gluing part 2.
Movie S3. SBMT flow battery during operation.
Movie S4. Cycling of zinc-iodide redox couple to 50% total capacity at 253 A/Lcell, corresponding to Fig. 4a.
We thank Prof. William J. Koros for recommending commercial Nafion hollow fiber for battery assembly and acknowledge Bryce Smith, Panyiming Liu, Dr. Chihyun Hwang, and Maxim Zhelyabovskiy for assisting the experiments. Y.W. thanks Kaiyue Yang for figure designing. Funding: N.L. acknowledges support from faculty start-up funds from the Georgia Institute of Technology. Characterizations were conducted in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure supported by the NSF (grant ECCS-2025462).
Author contributions
N.L. and Y.W. formed the initial concept of SBMT flow battery cell. Y.W. and N.L. designed research; Y.W., F.Z., T.W., P.-W.H., A.F., H.Y., Y.H., C.W., and H.L. performed research; Y.W., F.Z., T.W., P.-W.H., A.F., H.Y., Y.H., C.W., H.L., X.X., R.P.L., and N.L. analyzed data; and Y.W., X.X., R.P.L., and N.L. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
All study data are available in the article or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926269.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595698
202214492
10.1073/pnas.2214492120
research-articleResearch ArticleinauguralInaugural ArticleevolutionEvolution1
418
Biological Sciences
Evolution
Inaugural ArticleDiverse mating consequences of the evolutionary breakdown of the sexual polymorphism heterostyly
Yuan Shuai a b 1 https://orcid.org/0000-0002-9851-6384
Zeng Gui a b 1
Zhang Kai c
Wu Mingsong a
Zhang Dianxiang [email protected].
a b 2 https://orcid.org/0000-0001-6549-8872
Harder Lawrence D. d
Barrett Spencer C. H. [email protected]
e 2 https://orcid.org/0000-0002-7762-3455
aKey Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
bSouth China National Botanical Garden, Guangzhou 510650, China
cMinistry of Education Key Laboratory for Ecology of Tropical Islands, College of Life Sciences, Hainan Normal University, Haikou 571158, China
dDepartment of Biological Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada
eDepartment of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3B2, Canada
2To whom correspondence may be addressed. Email: [email protected] or [email protected].
Contributed by Spencer C. H. Barrett, received August 24, 2022; accepted November 28, 2022; reviewed by Deborah Charlesworth and Jeffrey Karron
This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2020.
1S.Y. and G.Z. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e221449212024 8 2022
28 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Reproductive systems of flowering plants often evolve in response to differences among populations in pollinator faunas. In addition to affecting fecundity, these differences should influence mating, with multigeneration consequences for genetic and phenotypic diversity in populations and speciation. We investigated mating in a primrose species undergoing the breakdown of distyly, a sexual polymorphism that usually enforces mating between floral morphs. This mating pattern was evident in populations with abundant pollinators, but not in less visited populations which contained a third floral morph capable of moderate to high rates of self-mating and fewer outcrossed mates. This study highlights the key role that the ecology of plant mating can play in floral divergence and potentially the evolution of reproductive isolation.
Reproductive systems of flowering plants are evolutionarily fluid, with mating patterns changing in response to shifts in abiotic conditions, pollination systems, and population characteristics. Changes in mating should be particularly evident in species with sexual polymorphisms that become ecologically destabilized, promoting transitions to alternative reproductive systems. Here, we decompose female mating portfolios (incidence of selfing, outcross mate number, and intermorph mating) in eight populations of Primula oreodoxa, a self-compatible insect-pollinated herb. This species is ancestrally distylous, with populations subdivided into two floral morphs that usually mate with each other (disassortative mating). Stages in the breakdown of polymorphism also occur, including “mixed” populations of distylous and homostylous (self-pollinating) morphs and purely homostylous populations. Population morph ratios vary with elevation in association with differences in pollinator availability, providing an unusual opportunity to investigate changes in mating patterns accompanying transitions in reproductive systems. Unexpectedly, individuals mostly outcrossed randomly, with substantial disassortative mating in at most two distylous populations. As predicted, mixed populations had higher selfing rates than distylous populations, within mixed populations, homostyles selfed almost twice as much as the distylous morphs, and homostylous populations exhibited the highest selfing rates. Populations with homostyles outcrossed with fewer mates and mate number varied negatively with population selfing rates. These differences indicate maintenance of distyly at low elevation, transition to monomorphic selfing at high elevation, and uncertain, possibly variable fates at intermediate elevation. By quantifying the earliest changes in mating that initiate reproductive transitions, our study highlights the key role of mating in promoting evolutionary divergence.
evolution
mating
pollination
sexual polymorphism
heterostyly
Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) 501100000038 RGPIN/06442-2017 Lawrence D. HarderSpencer C. H. Barrett National Natural Science Foundation of China (NSFC) 501100001809 31800314 Shuai Yuan NSFC | National Natural Science Foundation of China-Xinjiang Joint Fund (NSFC-Xinjiang Joint Fund) 100017053 U160323 Dianxiang Zhang Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) 501100000038 RGPIN/03907-2018 Lawrence D. HarderSpencer C. H. Barrett
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pmcThe unmatched diversity of reproductive systems among angiosperms reflects frequent evolutionary transitions within and among lineages as species adapt to altered pollination and mating environments (1). These changes can affect floral and inflorescence traits (2, 3), gender allocation (4, 5), and mating system characteristics (6, 7), and often occur in association with shifts in the presence and relative abundance of pollinators (8–10). Reproductive transitions have attracted considerable attention in flowering plants (11), but quantification of the spectrum of changes in mating patterns involved in specific transitions has been investigated less often (12, 13).
Heterostylous floral polymorphisms offer particularly revealing cases of the breadth of changes involved in reproductive transitions because the unique morphological and physiological features of heterostyly result in distinctive mating patterns within populations. This reproductive system typically involves two (distyly) or three (tristyly) floral morphs in a population which differ reciprocally from each other in stigma and anther heights within flowers (14, 15; Fig. 1, Classical heterostyly). The large physical separation of stigmas and anthers within flowers (herkogamy) limits self-pollination, and the reciprocal positioning of sex organs (reciprocal herkogamy) promotes pollinator-mediated cross-pollination between morphs. As detailed by Darwin (14), classical heterostyly couples this morphological polymorphism with a physiological incompatibility system (heteromorphic incompatibility) that prevents self and intramorph mating, thus enforcing strict disassortative (intermorph) mating (16, 17). Thus, heterostylous populations should exhibit distinctive mating portfolios (7), with male and female mates belonging to other floral morphs, but little to no selfing or outcrossing within morphs. Marker gene studies have confirmed disassortative mating in a few heterostylous species (18–20); however, mate number has not been investigated.
Fig. 1. Possible evolutionary trajectories if pollinators capable of effectively mediating disassortative mating in a heterostylous species become chronically infrequent visitors. Floral sketches (pistils green, stamens yellow) illustrate the morphological differences between major stages and the associated mating patterns (dashed lines). Letters on the ovaries identify floral morphs, including L, long-styled and S, short-styled morphs in distylous populations; H, smaller-flowered homostyles in selfing populations; h, plants with reduced herkogamy in monomorphic mixed mating populations.
The distinctive disassortative mating of heterostylous species imposes negative frequency-dependent selection, which should adjust morph frequencies within populations over generations so that all morphs realize, on average, equal reproductive success (21, 22). Given these expected outcomes, Darwin (p. 138 in ref. 14) recognized heterostyly as “a most complex marriage-arrangement.” The polymorphism has evolved independently multiple times, occurring in at least 28 animal-pollinated angiosperm families, and is a striking example of convergent evolution in floral form and function (16, 23, 24).
Classical heterostyly can be maintained only if its mating benefits outweigh the costs; otherwise, it breaks down resulting in a range of derived reproductive systems (Fig.1 in ref. 24). Reciprocal herkogamy is beneficial as it promotes disassortative pollination and, by limiting self-pollination, increases pollen available for export. However, reciprocal herkogamy also involves costs, as it inhibits intramorph cross-pollination, thereby limiting the number of potential outcross mates in proportion to the fraction of the population represented by other plants of the same morph. From the female perspective, which is the focus of this study, the variance in genetic offspring diversity caused by mating with multiple partners can increase opportunities for zygote choice during seed development and the chance that some offspring will succeed, given variable establishment environments (25). Similarly, heteromorphic incompatibility is beneficial because by eliminating self-fertilization, it reduces inbreeding depression (26) among offspring and associated seed discounting (27). However, this type of incompatibility system precludes cross-fertilization by intramorph pollen, even though the resulting progeny may be of similar genetic quality to those resulting from intermorph mating. This feature of heterostyly perplexed Darwin (pp. 263 to 265 in ref. 14) who regarded heteromorphic incompatibility as an “incidental and purposeless” obstacle to outcrossing as any given plant in a population is unable to mate with “half its brethren.”
Given these benefits and costs, classical heterostyly should provide net mating benefits when pollinators capable of effectively mediating intermorph pollen transfer visit frequently and morphs are equally frequent within populations. In most heterostylous species, this process involves long-tongued pollinators (mostly bees and butterflies) capable of contacting stigmas and anthers in all floral morphs (Fig. 1 in ref. 28). These pollinators generally probe flowers for nectar in a stereotypical position and pick up pollen from the different anther levels, which becomes segregated on different parts of their bodies (29). Pollen is then deposited on stigmas that correspond in position to the compatible anther levels of the opposite morph (Fig. 1 in ref. 22). However, if pollinators capable of mediating disassortative pollen transfer visit populations infrequently, classical heterostyly may be difficult to maintain, favoring loss of heteromorphic incompatibility (Fig. 1). This change should have two significant mating consequences: it would expand the pool of potential outcross mates to all conspecifics, rather than just those of another morph; and it would allow a plant’s own pollen to self-fertilize its ovules. Given this increased variety of mating options, three different evolutionary transitions from classical heterostyly may occur as depicted in Fig. 1.
Once plants can potentially mate with all conspecifics, two alternative outcrossing paths become feasible if, despite reduced visitation by long-tongued pollinators, other pollinators that promote intermorph pollination visit frequently (Fig. 1). Reciprocal herkogamy may persist if the frequency of disassortative mating exceeds that of assortative mating (20, 22). This form of heterostyly (“compatible heterostyly”) occurs in various heterostylous lineages, including those in which most species possess heteromorphic incompatibility (17, 24).
However, if instead, pollinators causing intramorph pollination visit frequently and assortative mating equals or exceeds disassortative mating, the limitation of the mating pool caused by reciprocal herkogamy renders it disadvantageous (22). This situation should promote the breakdown of heterostyly to monomorphic outcrossing (including mixed mating), like that of numerous nonheterostylous animal-pollinated angiosperms (6). Such species commonly exhibit more limited herkogamy than is typical of heterostylous species, which provides a compromise between promoting outcrossing and restricting self-pollination (30). Examples of this path of breakdown in heterostyly include some populations of Turnera ulmifolia var. angustifolia (12), and three independent transitions in the largely distylous genus Primula responsible for the evolution of mixed mating in P. halleri (31) and of two derived monomorphic, outcrossing clades recognized as section Dodecatheon and subsection Cortusa (32).
In contrast to the paths from heterostyly to alternative outcrossing and mixed mating systems, an evolutionary transition to predominant selfing (autogamy) is most likely if pollinators of any quality visit only rarely so that seed production within populations becomes pollen limited (Fig. 1). In such circumstances, plants confront the general costs of outcrossing, rather than just the costs of reciprocal herkogamy. As is generally the case when outcrossing fails and transitions to predominant selfing occur (33), this situation would favor floral variants with stigmas and anthers positioned close enough within flowers to facilitate autonomous self-pollination, a condition in heterostylous groups referred to as homostyly (14). The breakdown of heterostyly to selfing monomorphism via homostylous evolution is the most commonly studied mating-system transition and occurs widely among heterostylous families (16, 21, 24), including at least nine independent events in Primula (34).
The preceding scenarios imply that the loss of heteromorphic incompatibility in a classical heterostylous species initiates further mating-system changes, as populations transition to compatible heterostyly, monomorphic mixed mating, or predominant selfing (Fig. 1). For simplicity, we have outlined the possible expectations in the context of transitions from classical distyly, although they apply similarly to the breakdown of tristyly (35, 36). During these transitions, populations could differ in the proportions of the ancestral, highly herkogamous, heterostylous morphs as well as phenotypes with reduced herkogamy, as observed in some self-compatible Primula species (37), depending on their pollination environments. We predict that populations visited frequently by long-tongued pollinators should lack individuals with reduced herkogamy and predominately outcross with more disassortative than assortative mating and relatively high outcross-mate diversity. In these populations, the long- and short-styled morphs (hereafter L- and S-morphs) should be equally abundant owing to frequency-dependent selection caused by the prevalence of disassortative mating.
In contrast, transitional populations with abundant pollinators incapable of promoting disassortative mating should include mixtures of the mostly outcrossing distylous morphs and individuals with reduced herkogamy that may outcross less effectively. In such populations, disassortative mating should not exceed assortative mating. In addition, plants with reduced herkogamy should be able to self-fertilize more than the distylous morphs but could still outcross, as is the case in many monomorphic species (38, 39), although probably with fewer mates than the distylous morphs (40). Morph frequencies could vary extensively among these populations, given limited disassortative mating. Finally, in populations with few pollinators and hence few outcrossing opportunities, autonomously self-pollinating homostyles should become fixed in populations and have correspondingly limited outcross-mate diversity. Thus, the combination of floral forms in populations undergoing breakdown to monomorphic mixed mating versus homostylous selfing should exhibit complex but different mating patterns that signal the type of transition in which they are engaged.
Here, we infer the transition paths followed by populations of Primula oreodoxa Franchet based on these expectations. This species is well suited to such analysis for three reasons. First, unlike most distylous Primula species which possess heteromorphic incompatibility (41), P. oreodoxa is self-compatible and the highly herkogamous L- and S-morphs are intramorph compatible (42). Thus, the gamut of mating options (i.e. self, intramorph, and intermorph) is available, which should enable the reproductive transitions summarized in Fig. 1. Second, P. oreodoxa exhibits an unusual diversity of reproductive systems, suggesting that transitions are currently underway in contemporary populations. Specifically, populations range from dimorphic, with reciprocal herkogamy (i.e., L- and S-morphs: SI Appendix, Fig. S1 A–C); through mixed populations, with L- and S-morphs and a third morph (h-morph) with reduced herkogamy (SI Appendix, Fig. S1 D–F); to monomorphic populations with only homostylous plants (H-morph) with smaller flowers (42), a common feature of transitions to high selfing rates (2). These known features reflect the physiological and morphological shifts expected during transitions from classical distyly. Third, previous work (42) revealed the ecological context underlying the breakdown of distyly and spread of phenotypes with reduced herkogamy. Specifically, population type varies strongly with elevation, with a rapid shift from low-elevation dimorphic populations to high-elevation monomorphic populations between 1,600 and 2,000 m, in association with declines in pollinator abundance, especially that of long-tongued pollinators (Fig. 2). Therefore, the outstanding questions addressed here are whether mating patterns, inferred from parental and offspring microsatellite genotypes, differ among P. oreodoxa populations with contrasting morph structure, and which, if any, of the three pathways summarized in Fig. 1 are populations following? Our analyses of these questions considered three dimorphic populations, three mixed populations, and two monomorphic populations containing exclusively homostylous plants. The goal of our study was to characterize mating patterns during early stages of the breakdown of sexual polymorphism from classical distyly to mixed mating and/or predominant selfing.
Fig. 2. Elevational variation among diploid populations of Primula oreodoxa in the abundances of (A) the proportion of “homostylous” plants, (B) flower visitation by long- and short-tongued insects, (C) flower visitation by all visitors, and (D) the association between the proportion of homostylous (reduced-herkogamy) plants with visitation by all visitors, as reported in SI Appendix, Table S5 for the labeled study populations and by Yuan et al. (42) for the remaining populations. Fewer populations were sampled for flower visitors than for the frequency of plants with reduced herkogamy, including homostyles in monomorphic populations. The observations were recorded in 2013 to 2015, and flower visits were counted from 900 to 1,700 h (a “day”) in 3 × 5-m plots. The regression lines in (B and C) illustrate the results of generalized linear mixed models that accommodated negative binomial variation in visit number (ln link function) and included year and the ln(number of observed flowers) as additional independent variables (details are provided in SI Appendix A1). Visitation was recorded for only one day for the sites at 1,045, 1,224, and 1,779 m above sea level, for which SEs are not provided. In (A) and (D), the labeled symbols indicate populations considered in the present study.
Results
Self-Mating.
Variation in the proportion of selfed seeds as a maternal parent (female selfing rate) was largely consistent with our expectations. Based on a six-population analysis of distylous and mixed populations, L- and S-morph plants primarily outcrossed, although the overall mean female selfing rate in mixed populations was twice that in dimorphic populations and mean selfing rates varied among populations of both types (Fig. 3A and SI Appendix, Table S1). Population ELS was noteworthy as the only polymorphic population in which more than 50% of progeny were self-fertilized. A separate analysis of the two monomorphic homostylous populations detected higher selfing overall than in the polymorphic populations (Fig. 3A) but no difference between the populations (X21 = 0.95, P > 0.3). In contrast to the population differences, female selfing rates did not differ between the L- and S-morphs, and this equivalence did not vary between dimorphic and mixed populations or among populations within types (SI Appendix, Table S1).
Fig. 3. Variation in mean (±SE) female selfing rates (A) among three dimorphic, three mixed populations (L- and S-morph only) and two monomorphic populations of Primula oreodoxa, and (B) among the three morphs in mixed populations (averaged over populations). Means associated with different letters differ statistically (A, Dunn–Šidák procedure; B, Tukey’s test). In (A), means were compared within each population type, as indicated by the lowercase and uppercase letters.
Analyses of the mixed populations detected similar population differences to those of the six-population analyses but found large differences between morphs in their patterns of mating (SI Appendix, Table S2). Specifically, although plants with reduced herkogamy (h-morph) outcrossed to some extent, more than half of their offspring were self-fertilized (Fig. 3B). In addition, the mean female selfing rate of h-morph plants was twice that of the largely outcrossing herkogamous L- and S-morphs, which did not differ statistically (Fig. 3B).
Outcross Mate Number.
As illustrated in SI Appendix, Fig. S2, seeds with known fathers often represented a minority of recognized outcrossed seeds in maternal families, especially for dimorphic populations. Recognition of known fathers generally varied positively with the proportion of plants of all morphs that had been genotyped, especially for mixed populations (SI Appendix, Fig. S2B and Table S3 for statistical details).
The mean number of other plants with which maternal plants were detected to have outcrossed varied statistically with per-plant genotyping effort and among populations, but mostly not among morphs (SI Appendix, Tables S1 and S2). Overall, male-mate number increased in a positive, decelerating manner with the number of genotyped outcrossed seeds per plant (e.g., Fig. 4A and SI Appendix, Tables S1 and S2 for partial regression coefficients). Holding this effect constant revealed that, on average, maternal plants in dimorphic populations had more male mates than those in mixed populations (Fig. 4B: only L- and S-morph plants included). Mean male-mate number also varied among populations within population type, being comparatively low in JCC among dimorphic populations and high in HZG among mixed populations. Based on a separate analysis, mean male-mate number was lowest in the two monomorphic populations, but it did not differ statistically among them (X21 = 0.12, P > 0.7: Fig. 4B). In addition, male-mate number varied negatively with selfing rates among populations (test of linear trend for polymorphic populations; t112 = 2.41, P < 0.025), although mixed population QLP had low mean mate number compared to other polymorphic populations despite being primarily outcrossing (Fig. 4C). In the six-population analysis for the L- and S-morphs, male-mate number did not differ statistically among floral morphs, overall, between population types or among populations within types (SI Appendix, Table S1). For the mixed populations, male-mate number did not differ statistically between h-morph plants and either of the distylous morphs (SI Appendix, Fig. S3). However, this analysis revealed more male mates for maternal S-morph plants than L-morph plants in population HZG (SI Appendix, Fig. S3).
Fig. 4. Aspects of variation in the number of male mates among the outcrossed seeds genotyped for maternal Primula oreodoxa plants in three dimorphic, three mixed (L- and S-morph only), and two monomorphic populations, including the influences of (A) the number of genotyped seeds, (B) population, and (C) the association with the population mean female selfing rate. In (A), symbol size varies positively with the number of plants represented by a specific combination of seed sampling and mate number. (B) and (C) depict means ± SE that have been adjusted for among-plant variation in seed sampling. In (B), population means within a specific population type with different letters differ statistically (Dunn–Šidák procedure).
Relative Intramorph and Intermorph Outcrossing.
The genotyped families of 112 L- and S-morph maternal plants from the six polymorphic populations included 516 outcrossed offspring sired by a father with known morph (median 4 offspring per mother; see SI Appendix, Fig. S2A). Based on this sample, individual maternal plants commonly outcrossed with either plants of a different morph (including h-morph individuals: intermorph mating) or of the same morph (intramorph mating), rather than with a mixture of plants of the same and different morphs (Fig. 5A). Indeed, 43.8% of the 89 maternal plants from which we collected multiple seeds with known fathers mated exclusively within or among morphs. Thus, the mean proportions of intermorph mating for individual populations or morphs obscure considerable heterogeneity in mating patterns among maternal plants.
Fig. 5. (A) The association of the numbers of seeds sampled from individual maternal Primula oreodoxa plants that resulted from intermorph or intramorph mating (including homostyles) and (B–E) effects on variation in the mean (±SE) proportion of seeds resulting from intermorph mating. Illustrated influences on proportional intermorph mating include (B) an interaction of maternal morph (L- and S-morph only) among dimorphic and mixed populations; the relations of population mean intermorph mating to population mean (±SE) (C) female selfing rate and (D) male-mate number in all six populations (L- and S-morph only); and (E) overall morph differences for the three mixed populations. The dashed line in each panel indicates equivalent intramorph and intermorph mating. In (B–E), symbols with a central dot indicate cases in which intermorph mating exceeded intramorph mating statistically (i.e., proportion of intermorph mating > 0.5). Means associated with different letters differ statistically (B, Dunn–Šidák procedure; E, Tukey’s test).
The mean proportion of outcrossed progeny in maternal families resulting from intermorph mating generally varied among populations and morphs but not overall between dimorphic and mixed populations (SI Appendix, Tables S1, S4). In a six-population analysis (L- and S-morphs only), differences were evident only among dimorphic populations (specifically, DWS > WWS; Fig. 5B). Intermorph mating statistically exceeded intramorph mating (i.e., proportion > 0.5) for both the L and S morphs only in population DWS, whereas in populations JCC (dimorphic) and ELS (mixed), only L-morph plants engaged in excess intermorph mating (Fig. 5B). Proportional intermorph mating by the L-morph exceeded that by the S-morph only in the mixed population ELS (morph x population within type: Fig. 5B). Comparison of all morphs for the mixed populations detected more intermorph than intramorph mating only in h-morph plants, which also engaged in more intermorph mating than S-morph plants (Fig. 5E). Overall, the mean proportion of intermorph mating did not vary statistically with the mean female selfing rate among populations (linear trend; t99 = 0.65, P > 0.5); however, when dimorphic population DWS was excluded, an unexpected positive relation was evident (t99 = 2.10, P < 0.05: Fig. 5C). In contrast, the relative incidence of intermorph mating did not vary consistently with male-mate number among populations (t99 = 1.26, P > 0.2: Fig. 5D).
Consideration of the extent to which the incidence of intermorph mating differed from the relative frequency of other morphs in populations (Eq. 1) indicated that, on average, plants generally outcrossed randomly with their own and other morphs, with a few exceptions (Fig. 6). In dimorphic population DWS, and perhaps JCC, plants mostly mated disassortatively, as predicted by the Darwinian hypothesis. In contrast, in the mixed population, QLP plants of the distylous, morphs mostly mated assortatively, whereas h-morph plants mated randomly.
Fig. 6. Variation in the mean (±95% CI) incidence of disassortative mating (Eq. 1) among six populations and floral morphs of Primula oreodoxa. The dashed line indicates no excess intermorph mating compared to the expectation for random mating based on the relative morph frequencies in a population. Cases with CIs completely above the dashed line involved excess disassortative mating, whereas those completely below the dashed line involved excess assortative mating.
Discussion
The mating characteristics of P. oreodoxa populations revealed by our analyses contrast with the classic textbook depiction of heterostylous species engaging in symmetrical disassortative mating between morphs (e.g., figure 21.16A in ref. 43, figure 2.8B in ref. 44). Individuals in most populations mated randomly among morphs and the expected pattern of significant disassortative mating for a distylous species was evident in at most two dimorphic populations (Fig. 6). Overall selfing rates in mixed populations were significantly higher than in dimorphic populations (Fig. 3A), and within mixed populations, homostylous individuals selfed almost twice as much as the distylous morphs (Fig. 3B). In monomorphic populations, homostylous plants were predominantly but not exclusively selfing. In addition, except for population HZG which contained a low proportion (0.14) of h-morph individuals, plants in mixed and monomorphic populations generally outcrossed with fewer mates, even after accounting for variation in the number of outcrossed seeds (Fig. 4B). We interpret these results as evidence of the evolutionary breakdown of distyly, primarily in populations above 1,600 masl and that initial stages of breakdown are characterized by increased mating complexity as floral morphs are released from strict intermorph mating.
Fig. 1 illustrates possible evolutionary trajectories that classical heterostylous populations might follow when long-tongued pollinators capable of mediating high rates of disassortative mating become infrequent visitors. The paths involve two distinct routes to floral monomorphism with contrasting mating systems: either maintenance of mixed mating in populations visited by short-tongued pollinators, with assortative mating equalling or exceeding disassortative mating, or the transition to autogamy when pollinators visit rarely and selection for reproductive assurance favors homostyles capable of autonomous self-pollination. Below, we evaluate the extent to which data and observations from P. oreodoxa populations are consistent with these contrasting scenarios.
Reproductive Mechanisms Governing Mating.
Physiological incompatibility limits the range of mating opportunities in most heterostylous species by preventing selfing and intramorph mating (16, 17). In contrast, in self-compatible heterostylous species deviations from strict disassortative outcrossing may occur when ecological conditions limit intermorph pollen transfer. Our findings for P. oreodoxa are consistent with this expectation, as plants in all populations selfed to some extent (Fig. 3) and at least one morph mated randomly or assortatively (Fig. 6). Heteromorphic incompatibility occurs widely in Primula (41) and self-compatibility in P. oreodoxa is likely a derived condition, perhaps in response to limited pollinator service, as depicted in Fig. 1.
A second key reproductive trait that directly influences mating in self-compatible populations is the extent of herkogamy (30). Selfing rates generally increase as the distance between stigmas and anthers diminishes, in part because of greater opportunities for autonomous self-pollination (45–47). Similarly, P. oreodoxa plants in mixed populations with reduced herkogamy selfed almost twice as much as the distylous morphs (Fig. 3B). Nevertheless, h-morph plants commonly also outcrossed, especially in HZG and QLP. This mixture of selfing and outcrossing suggests that pollinators mediated self-pollination to some extent, rather than selfing occurring only autonomously. Such mixed mating differs from the predominant selfing that characterizes homostyles in the well-studied P. vulgaris (21, 48, 49) and other heterostylous lineages (13, 36, 50). In the two monomorphic P. oreodoxa populations, most offspring from H plants resulted from self-mating, but with appreciable (30 to 37%) outcrossing.
Ecological Mechanisms Governing Mating.
In Primula, ecological conditions unfavorable for reliable cross-pollination are commonly associated with the breakdown of distyly to selfing monomorphism (51–54). The striking transition in P. oreodoxa from dimorphic populations at low elevations to monomorphic populations at high elevations with mixed populations occurring only between 1,600 and 2,000 masl (Fig. 2A) in association with declines in pollinator abundance indicates such ecological influences on the morph structure of populations and their mating patterns. If insufficient or inferior pollinator service persists or becomes exacerbated, plants with reduced herkogamy should experience an overall fertility advantage, leading to in situ replacement of the distylous morphs by homostyles (21), although this may not be an inevitable outcome (48). The incidence of h-morph plants in P. oreodoxa populations is consistent with this scenario. Significantly, pollinators, especially long-tongued insects, visited substantially less frequently in populations with h-plants than in distylous populations (Fig. 2 B and C). Such reduced visitation coupled with the functional subdivision of a population by distyly should favor reduced herkogamy, allowing plants to mate more generally (Fig. 1). Replacement of distylous morphs by homostyles likely explains much of the prevalence of purely monomorphic populations of P. oreodoxa at high elevation, where pollinators are infrequent (Fig. 2B). An additional relevant feature that may contribute to the predominance of monomorphic populations at high elevation involves the capacity of a single, autonomously selfing plant to establish a new population (Baker’s Law: ref. 55). Indeed, haplotype network analysis indicates that two homostylous populations (CSQ and XXC) are likely derived from mixed population QLP, probably as the result of separate colonizing events, as they do not share haplotypes (42).
Ecological factors associated with pollinator abundance and quality probably also played an important role in the relaxation of disassortative mating. Darwin (14) proposed that reciprocal positioning of sexual organs in heterostylous populations promotes intermorph cross-pollination, resulting in disassortative mating. In contrast to demonstrations of disassortative pollen transfer in several heteromorphic species (22, 56, 57), including Primula (58), our findings do not support Darwin’s cross-pollen transfer hypothesis for most P. oreodoxa populations. After adjusting for morph frequencies, individuals of the distylous morphs mostly mated randomly, with few exceptions (e.g., DWS, mostly disassortative mating; QLP, mostly assortative mating: Fig. 6). Furthermore, among all six polymorphic populations, almost half of maternal plants with known male mates mated exclusively either within or among morphs, rather than outcrossing with a mixture of morphs. Particularly puzzling is the dominance of assortative, rather than disassortative, outcrossing in QLP and WWS despite their low selfing rates and presence of distylous morphs (Figs. 5C and 6). The pollinator conditions at QLP are unknown, but plants at WWS were visited frequently, mostly by long-tongued pollinators several years before the current study (Fig. 2 B and C, population at 1,224 m). These results indicate broader influences on mating outcomes than floral morphology and pollinator abundance, such as the possible spatial aggregation of morphs, which await future exploration.
Mate Number.
Mate diversity and specifically mate number is an important, if poorly studied (7, 59), component of a plant’s mating portfolio (but see refs. 60 and 61). In particular, a plant’s male-mate number affects the genetic relatedness of siblings (62) and their competition for maternal resources (63), bet-hedging (64), and opportunities for female mate choice (65). In P. oreodoxa, male-mate number differed among populations and between population types. As mate number varies positively with a plant’s number of outcrossed seeds (Fig. 4A), it should correspondingly vary negatively with the fraction of seeds that are self-fertilized. This latter expectation was evident among populations, with one exception (Fig. 4C). The cause of the low mate number in population QLP, despite is low selfing rate is unclear, although it was unusual for its low density, which could reduce pollinator visitation but lengthen visits by attracted pollinators and their pollen deposition (66, 67).
In contrast to the differences among populations, male-mate number did not differ statistically between morphs. This equivalence suggests that the breakdown of distyly in P. oreodoxa does not involve gender differentiation, with one morph increasingly specialized for male function and the other morph for female function. Such specialization has been implicated for transitions from heterostyly to alternative dimorphic, outcrossing sexual systems, such as gynodioecy and dioecy (68–70).
Stages and Mating Consequences of the Dissolution of Heterostyly.
In Primula, distyly has repeatedly become destabilized causing breakdown of the floral polymorphism (21, 34, 37, 48, 71–73). In this genus, breakdown events are frequently associated with speciation and the origin of selfing homostylous taxa (34, 72). The most important functional changes associated with this process are the loss of self-incompatibility followed by the origin and spread of variants with reduced herkogamy, both of which have important mating consequences (Fig. 1).
The presence of distylous morphs and those with reduced herkogamy within mixed populations allowed comparison of the influence of contrasting floral phenotypes on mating under similar ecological conditions. Among the three dimorphic populations, plants in DWS and JCC outcrossed disassortatively (Fig. 6), indicating that they exhibit compatible heterostyly (Fig. 1). By comparison, the persistence of dimorphism in the third population, WWS, is enigmatic as its plants outcross randomly (Fig. 6). Perhaps variants with reduced herkogamy have yet to arise in WWS. At the other extreme, the higher-elevation small-flowered homostylous populations (LWP, XXC) also studied by Yuan et al. (42) are cases of the breakdown of distyly to monomorphism and high selfing rates (Fig. 1). Molecular evidence indicates that the very small-flowered, highly selfing homostylous species P. dumicola, which occurs from 2,400 to 3,000 masl (74), is likely derived from P. oreodoxa, or at least the two sister taxa share a recent distylous ancestor (72), thus potentially implicating the transition to selfing via homostyle evolution in speciation.
The outstanding question is what is the fate of mixed populations of P. oreodoxa? One option is that they will follow the same pathway to monomorphic selfing as occurs in high-elevation populations and that likely occurred in P dumicola. But the transition to autogamy may be slower for these mixed populations because of weaker selection owing to somewhat more frequent pollinator visitation. The alternative option is transition to mixed mating in monomorphic populations promoted by larger flowers and some herkogamy. Given that mating in mixed populations is not disassortative (Fig. 6), the distylous morphs may be disadvantaged compared to plants with reduced herkogamy as they both self-fertilize more readily (Fig. 3B) and outcross more with other morphs (Fig. 5E). Time will tell whether mixed mating is maintained as a stable strategy in mixed populations, and in monomorphic populations with some degree of herkogamy. However, if sufficient pollinator service to maintain fertility characterizes these populations, this scenario seems plausible, at least for monomorphic populations, and appears to have occurred in some Primula species (31, 37) and other heterostylous taxa (47, 75).
Morph ratios were highly heterogeneous in mixed populations of P. oreodoxa, with h-morph frequencies ranging from 0.14 to 0.94 (SI Appendix, Table S5; Table 1 in ref. 42). This variation probably reflects, in part, differences among morphs in mating patterns, relative gamete transmission, and progeny fitness associated with the characteristics of the local pollinator fauna, including its temporal variation. Our analysis of mating patterns revealed that the h-morph sired an appreciable fraction of seeds with known fathers produced by distylous morphs. The most extreme case occurred in population ELS, where h-morph plants sired 46% of outcrossed seeds on S-morph plants and 90% of those on L-morph plants. Because h-morph plants also self-fertilize more of their own seeds than the distylous morphs (Fig. 3B), they should realize a genetic-transmission advantage favoring their spread and fixation in populations. This advantage would be amplified by the higher fruit set of h-morph plants than by the distylous morphs in mixed populations, as expected given their ability to self-pollinate autonomously when seed production is pollinator limited (42). We have no experimental data on the severity of inbreeding depression in progeny of the floral morphs to assess the likelihood that homostyles will replace the distylous morphs in mixed populations (also see ref. 48).
In conclusion, analysis of mating patterns in eight populations of P. oreodoxa uncovered mating complexity not previously observed in heterostylous populations and certainly more complex than the marriage arrangements envisioned by Darwin (14). Mating data on variation among individuals provide opportunities to expose the proximate mechanisms governing reproductive success, and sexual polymorphisms such as heterostyly are especially informative in this regard. Unlike monomorphic species, polymorphic populations are reproductively subdivided into morphologically discrete phenotypes maintained by negative frequency-dependent selection. Intrinsic and extrinsic ecological factors that disturb morph-ratio equilibrium can cause deviations from disassortative mating and frequently set in train processes leading to the breakdown of floral polymorphism and the evolution of alternative floral strategies. By quantifying the earliest changes in mating behavior accompanying these transitions, our study highlights the key role that the mating biology of populations can play in floral divergence and the evolution of reproductive isolation.
Materials and Methods
Study System.
Primula oreodoxa is an insect-pollinated, herbaceous perennial restricted to western Sichuan province, China (102 to 104°E, 28 to 31°N). Populations grow along streams and margins of woodlands between 1,050 and 2,450 m and flower from March to April, with fruits maturing from late May to June. Our studies of mating patterns involved three distylous (DWS, JCC, and WWS; hereafter “dimorphic”), three mixed populations (ELS, HZG, and QLP), and two monomorphic, homostylous populations (LWP and XXC). All studied populations were diploid (2n = 24 chromosomes), although occasional tetraploid populations occur (42). Detailed information on the reproductive biology of P. oreodoxa and the characteristics of each population, including their morph frequencies, is presented in ref. 42 and SI Appendix, Table S5 and Methods.
Sampling of the polymorphic populations occurred during March and April 2016, whereas that of the monomorphic populations occurred during spring 2020. Within each population, we searched for flowering plants, identified their floral morph, and marked them for later sampling of open-pollinated seed families. We collected fresh leaf material from these plants, which was dried in silica gel for subsequent genotyping. During late May, we collected two or three mature open-pollinated fruits from each marked plant. Seeds from two fruits per plant were mixed and sown on soil-filled flats at the Biological Resources Research Station at E’mei Mountain (altitude 800 m), Sichuan Province. The resulting seedlings were cultivated in a glasshouse for about two months before being moved outside to a common garden under seminatural conditions. During the February after seed collection, we randomly selected 8 to 12 seedlings (average 11.9) from each family and collected three to five fresh leaves from each seedling, which were dried in silica gel. In total, we sampled 64/762, 66/790, and 28/326 families/progeny from the L-, S-, and h-morphs, respectively, from the polymorphic populations and 36/426 families/progeny from the monomorphic populations (details are provided in SI Appendix, Table S5).
DNA Extraction and Genotyping.
We used standard procedures to characterize microsatellite variation. DNA extraction from dried leaf tissue for all parental plants and progeny involved a modified cetyl trimethyl ammonium bromide protocol (76). Multiplex PCR was used to amplify eleven SSR markers per individual. Because of contrasting amplification between dimorphic and mixed populations, the marker sets used for the two population types included only three markers in common (SI Appendix, Methods). SSR markers were divided into two (mixed populations) or three groups (dimorphic populations) for PCR amplification. We assayed PCR products using an ABI PRISM 3100 Genetic Analyser (Invitrogen) with an internal size standard (GeneScan™ 500 LIZ). Allele binning and calling involved GeneMarker version 2.4.0 (SoftGenetics LLC, State College).
Paternity Assignment and Estimation of Mating-System Components.
We used COLONY 2.0.6.5 (77, 78) to identify maximum-likelihood two-generation pedigrees for each population sample given the multilocus genotypes of the sampled maternal plants and seedlings. Based on the inferred pedigree for a sample, COLONY estimates the probabilities of whether each offspring is selfed (same maternal and paternal parent) or outcrossed (different parents) and of the most likely paternal parent. Identified seed fathers could be included in the genotyped sample of paternal plants (“known” fathers) or be unsampled plants for which the genotype was inferred (“unknown” fathers). COLONY analyses involved the following conditions: polygamy for both female and male parents with inbreeding, no clonality, full-likelihood method, medium length run, medium precision, and allele frequencies were not updated, as recommended in Wang et al. (78). We allowed locus-specific error rates per population from the genotypic data and used 0.01 as the average genotype error rate.
Subsequent statistical analyses involved various subsets of the 2304 genotyped seeds. The analysis of the female selfing rate considered the 99.0% of genotyped seeds for which the probability of selfing was either <0.05 (i.e., outcrossed) or >0.95 (i.e., selfed). Similarly, analysis of the number of outcross male mates for maternal plants involved the 96.1% of the 1473 outcrossed seeds for which the genotype of the known or unknown father was inferred with probability >0.95. We did not assess the corresponding male parameters as recognized male outcrossing was underestimated to the extent that seeds sired on unknown maternal plants were unidentified. Estimation of the incidence of intramorph versus intermorph mating required identification of the floral morphs of a seed’s maternal and paternal parents and so involved only the 44.6% of outcrossed seeds sired by known fathers. As this subsample of seeds was further divided into intramorph versus intermorph classes, we consider these mating outcomes only from the perspective of maternal plants.
Statistical Methods.
Most statistical analyses involved generalized linear models (79) of mating outcomes for maternal families. These analyses considered distributions and link functions appropriate for the characteristics of the dependent variables (SI Appendix, Tables S1 and S2) and were conducted with R version 4.1.1 (https://cran.rproject.org/) using the glmmTMB package version 1.1.2.2 (80). Distribution assumptions were checked with the testDispersion function of the DHARMa package version 0.4.3. Because dimorphic populations differed from mixed populations by lacking homostylous plants, we conducted two sets of analyses. One set involved only the L- and S-morphs for all six populations and included population type (dimorphic, mixed) and morph as crossed factors, as well as population nested within population type and its interaction with morph. The other set involved the L-, S-, and h-morphs for the three mixed populations and included morph and population as crossed factors. Some analyses also considered continuous covariates to account for sampling variation (e.g., number of genotyped seeds in analyses of mate number; a discussion of the merits of this approach and implications for study design are provided in SI Appendix, Methods). Comparisons among marginal factor means involved Tukey’s test for main effects and the Dunn–Šidák procedure for nested effects and interactions (81) and were conducted using the R emmeans package version 1.6.3. We also assessed whether population mean male-mate number and the relative frequency of inter-morph mating varied with mean female outcrossing rates using contrasts to test linear trends (81). To illustrate statistical results, we present back-transformed marginal means and their (usually asymmetrical) SEs or 95% CIs.
The analysis of intermorph mating required slight modifications of the data to overcome fitting problems associated with invariant mating outcomes among L-morph plants in population ELS. Specifically, the numbers of intramorph and intermorph matings for the plant with the most (11) seeds with known fathers were respectively increased and decreased by 0.1.
Heterostyly is expected to promote disassortative mating (14), so that the frequency of intermorph mating should exceed that expected if plants mated randomly. Estimation of the extent of disassortative mating by known maternal plants of morph j involved the morph’s estimated (mean and CI) proportion of intermorph mating (Ij) and their relative frequency in the population (fj). For a plant of morph j, the expected frequency of random intermorph mating should vary directly with the relative frequency of plants of all other morphs, i.e., Ej = 1 – fj. Therefore, the observed extent of nonrandom disassortative mating is Ij – Ej. In general, the maximum possible extent of disassortative mating is 1 – Ej, so a standardized measure that facilitates comparison among morphs or populations is (18):Dj=Ij-Ej1-Ej.
Estimates of fj, mean I¯j, and its (perhaps asymmetrical) CI, allow calculation of[1] D¯j=I¯j-Ej1-Ej,
lowerCI=D¯j-I¯j-CI-Ej1-Ej,
upperCI=I¯j+CI-Ej1-Ej-D¯j.
We conducted these calculations using the population morph frequencies presented in SI Appendix, Table S5 and the marginal morph or population means estimated from analyses of the proportion of intermorph matings. As the analyses of the proportion of intermorph mating involved the logit link function, means and their confidence intervals were back-transformed to proportions before conducting these calculations. The population marginal mean is a simple average of the morph means, so we used the simple average of morph-specific Ej to calculate the population-specific Ej.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank Cehong Li and Xiaojie Li for assistance with field work and Wei Zhou for providing information on SSR markers. The research was funded by grants (31800314, 31970206, U160323) from the National Natural Science Foundation of China to S.Y. and D.Z. and by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada to S.C.H.B (RGPIN/06442-2017) and L.D.H. (RGPIN/03907-2018).
Author contributions
S.Y., D.Z., and S.C.H.B. designed research; S.Y., G.Z., K.Z., and M.W. performed research; S.Y., G.Z., and L.D.H. analyzed data; and S.Y., L.D.H., and S.C.H.B. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
SSR marker data have been deposited in Dryad (https://doi.org/10.5061/dryad.fj6q573zn). All study data are included in the article and/or SI Appendix.
Supporting Information
Reviewers: D.C., The University of Edinburgh School of Biological Sciences; and J.K., University of Wisconsin.
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==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595679
202214634
10.1073/pnas.2214634120
research-articleResearch ArticleneuroNeuroscience424
Biological Sciences
Neuroscience
Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
Yin Chenzhong a 1 https://orcid.org/0000-0001-6411-7441
Imms Phoebe b 1 https://orcid.org/0000-0002-7205-4177
Cheng Mingxi a 1 https://orcid.org/0000-0002-8070-6665
Amgalan Anar b 1 https://orcid.org/0000-0001-8210-5030
Chowdhury Nahian F. b 1 https://orcid.org/0000-0003-3535-7280
Massett Roy J. b
Chaudhari Nikhil N. b c https://orcid.org/0000-0003-3048-6710
Chen Xinghe a https://orcid.org/0000-0002-8686-359X
Thompson Paul M. c d e f g h i j https://orcid.org/0000-0002-4720-8867
Bogdan Paul a 1 https://orcid.org/0000-0003-2118-0816
Irimia Andrei [email protected]
b c e 1 2https://orcid.org/0000-0002-9254-9388
the Alzheimer’s Disease Neuroimaging Initiative 3
a Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
b Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089
c Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
d Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90033
e Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA 90089
f Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
g Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
h Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
i Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
j Department of Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
Weiner Michael W.
Aisen Paul
Petersen Ronald
Weiner Michael W.
Aisen Paul
Petersen Ronald
Jack Clifford R. Jr.
Jagust William
Trojanowki John Q.
Toga Arthur W.
Beckett Laurel
Green Robert C.
Saykin Andrew J.
Morris John C.
Perrin Richard J.
Shaw Leslie M.
Khachaturian Zaven
Carrillo Maria
Potter William
Barnes Lisa
Bernard Marie
González Hector
Ho Carole
Hsiao John K.
Jackson Jonathan
Masliah Eliezer
Masterman Donna
Okonkwo Ozioma
Perrin Richard
Ryan Laurie
Silverberg Nina
Fleisher Adam
Lilly Eli
Weiner Michael W.
Truran Sacrey Diana
Fockler Juliet
Conti Cat
Veitch Dallas
Neuhaus John
Jin Chengshi
Nosheny Rachel
Ashford Miriam
Flenniken Derek
Kormos Adrienne
Green Robert C.
Montine Tom
Conti Cat
Petersen Ronald
Aisen Paul
Rafii Michael
Raman Rema
Jimenez Gustavo
Donohue Michael
Gessert Devon
Salazar Jennifer
Zimmerman Caileigh
Cabrera Yuliana
Walter Sarah
Miller Garrett
Coker Godfrey
Clanton Taylor
Hergesheimer Lindsey
Smith Stephanie
Adegoke Olusegun
Mahboubi Payam
Moore Shelley
Pizzola Jeremy
Shaffer Elizabeth
Sloan Brittany
Beckett Laurel
Harvey Danielle
Donohue Michael
Jack Clifford R. Jr.
Forghanian-Arani Arvin
Borowski Bret
Ward Chad
Schwarz Christopher
Jones David
Gunter Jeff
Kantarci Kejal
Senjem Matthew
Vemuri Prashanthi
Reid Robert
Fox Nick C.
Malone Ian
Thompson Paul
Thomopoulos Sophia I.
Nir Talia M.
Jahanshad Neda
DeCarli Charles
Knaack Alexander
Fletcher Evan
Harvey Danielle
Tosun-Turgut Duygu
Chen Stephanie Rossi
Choe Mark
Crawford Karen
Yushkevich Paul A.
Das Sandhitsu
Jagust William
Koeppe Robert A.
Reiman Eric M.
Chen Kewei
Mathis Chet
Landau Susan
Morris John C.
Perrin Richard
Cairns Nigel J.
Householder Erin
Franklin Erin
Bernhardt Haley
Taylor-Reinwald Lisa
Shaw Leslie M.
Trojanowki John Q.
Korecka Magdalena
Figurski Michal
Toga Arthur W.
Crawford Karen
Neu Scott
Saykin Andrew J.
Nho Kwangsik
Risacher Shannon L.
Apostolova Liana G.
Shen Li
Foroud Tatiana M.
Nudelman Kelly
Faber Kelley
Wilmes Kristi
Weiner Michael W.
Thal Leon
Khachaturian Zaven
Hsiao John K.
Silbert Lisa C.
Lind Betty
Crissey Rachel
Kaye Jeffrey A.
Carter Raina
Dolen Sara
Quinn Joseph
Schneider Lon S.
Pawluczyk Sonia
Becerra Mauricio
Teodoro Liberty
Dagerman Karen
Spann Bryan M.
Brewer James
Vanderswag Helen
Fleisher Adam
Ziolkowski Jaimie
Heidebrink Judith L.
Zbizek-Nulph Lisa
Lord Joanne L.
Zbizek-Nulph Lisa
Petersen Ronald
Mason Sara S.
Albers Colleen S.
Knopman David
Johnson Kris
Villanueva-Meyer Javier
Pavlik Valory
Pacini Nathaniel
Lamb Ashley
Kass Joseph S.
Doody Rachelle S.
Shibley Victoria
Chowdhury Munir
Rountree Susan
Dang Mimi
Stern Yaakov
Honig Lawrence S.
Mintz Akiva
Ances Beau
Morris John C.
Winkfield David
Carroll Maria
Stobbs-Cucchi Georgia
Oliver Angela
Creech Mary L.
Mintun Mark A.
Schneider Stacy
Geldmacher David
Natelson Love Marissa
Griffith Randall
Clark David
Brockington John
Marson Daniel
Grossman Hillel
Goldstein Martin A.
Greenberg Jonathan
Mitsis Effie
Shah Raj C.
Lamar Melissa
Samuels Patricia
Duara Ranjan
Greig-Custo Maria T.
Rodriguez Rosemarie
Albert Marilyn
Onyike Chiadi
Farrington Leonie
Rudow Scott
Brichko Rottislav
Kielb Stephanie
Smith Amanda
Raj Balebail Ashok
Fargher Kristin
Sadowski Martin
Wisniewski Thomas
Shulman Melanie
Faustin Arline
Rao Julia
Castro Karen M.
Ulysse Anaztasia
Chen Shannon
Sheikh Mohammed O.
Singleton-Garvin Jamika
Doraiswamy P. Murali
Petrella Jeffrey R.
James Olga
Wong Terence Z.
Borges-Neto Salvador
Karlawish Jason H.
Wolk David A.
Vaishnavi Sanjeev
Clark Christopher M.
Arnold Steven E.
Smith Charles D.
Jicha Gregory A.
El Khouli Riham
Raslau Flavius D.
Lopez Oscar L.
Oakley MaryAnn
Simpson Donna M.
Porsteinsson Anton P.
Martin Kim
Kowalski Nancy
Keltz Melanie
Goldstein Bonnie S.
Makino Kelly M.
Ismail M. Saleem
Brand Connie
Thai Gaby
Pierce Aimee
Yanez Beatriz
Sosa Elizabeth
Witbracht Megan
Kelley Brendan
Nguyen Trung
Womack Kyle
Mathews Dana
Quiceno Mary
Levey Allan I.
Lah James J.
Hajjar Ihab
Cellar Janet S.
Burns Jeffrey M.
Swerdlow Russell H.
Brooks William M.
Silverman Daniel H.S.
Kremen Sarah
Apostolova Liana
Tingus Kathleen
Lu Po H.
Bartzokis George
Woo Ellen
Teng Edmond
Graff-Radford Neill R.
Parfitt Francine
Poki-Walker Kim
Farlow Martin R.
Hake Ann Marie
Matthews Brandy R.
Brosch Jared R.
Herring Scott
van Dyck Christopher H.
Mecca Adam P.
MacAvoy Martha G.
Carson Richard E.
Varma Pradeep
Chertkow Howard
Vaitekunis Susan
Hosein Chris
Black Sandra
Stefanovic Bojana
Heyn Chris (Chinthaka)
Hsiung Ging-Yuek Robin
Kim Ellen
Mudge Benita
Sossi Vesna
Feldman Howard
Assaly Michele
Finger Elizabeth
Pasternak Stephen
Rachinsky Irina
Kertesz Andrew
Drost Dick
Rogers John
Grant Ian
Muse Brittanie
Rogalski Emily
Robson Jordan
Mesulam M.-Marsel
Kerwin Diana
Wu Chuang-Kuo
Johnson Nancy
Lipowski Kristine
Weintraub Sandra
Bonakdarpour Borna
Pomara Nunzio
Hernando Raymundo
Sarrael Antero
Rosen Howard J.
Miller Bruce L.
Perry David
Turner Raymond Scott
Johnson Kathleen
Reynolds Brigid
MCCann Kelly
Poe Jessica
Sperling Reisa A.
Johnson Keith A.
Marshall Gad A.
Yesavage Jerome
Taylor Joy L.
Chao Steven
Coleman Jaila
White Jessica D.
Lane Barton
Rosen Allyson
Tinklenberg Jared
Belden Christine M.
Spann Bryan M.
Clark Kelly A.
Zamrini Edward
Sabbagh Marwan
Killiany Ronald
Stern Robert
Mez Jesse
Kowall Neil
Budson Andrew E.
Obisesan Thomas O.
Ntekim Oyonumo E.
Wolday Saba
Khan Javed I.
Nwulia Evaristus
Nadarajah Sheeba
Lerner Alan
Ogrocki Paula
Tatsuoka Curtis
Fatica Parianne
Fletcher Evan
Maillard Pauline
Olichney John
DeCarli Charles
Carmichael Owen
Bates Vernice
Capote Horacio
Rainka Michelle
Borrie Michael
Lee T-Y
Bartha Dr Rob
Johnson Sterling
Asthana Sanjay
Carlsson Cynthia M.
Perrin Allison
Burke Anna
Scharre Douglas W.
Kataki Maria
Tarawneh Rawan
Kelley Brendan
Hart David
Zimmerman Earl A.
Celmins Dzintra
Miller Delwyn D.
Boles Ponto Laura L.
Smith Karen Ekstam
Koleva Hristina
Shim Hyungsub
Nam Ki Won
Schultz Susan K.
Williamson Jeff D.
Craft Suzanne
Cleveland Jo
Yang Mia
Sink Kaycee M.
Ott Brian R.
Drake Jonathan
Tremont Geoffrey
Daiello Lori A.
Drake Jonathan D.
Sabbagh Marwan
Ritter Aaron
Bernick Charles
Munic Donna
Mintz Akiva
O’Connelll Abigail
Mintzer Jacobo
Wiliams Arthur
Masdeu Joseph
Shi Jiong
Garcia Angelica
Sabbagh Marwan
Newhouse Paul
Potkin Steven
Salloway Stephen
Malloy Paul
Correia Stephen
Kittur Smita
Pearlson Godfrey D.
Blank Karen
Anderson Karen
Flashman Laura A.
Seltzer Marc
Hynes Mary L.
Santulli Robert B.
Relkin Norman
Chiang Gloria
Lee Athena
Lin Michael
Ravdin Lisa
Weiner Michael W.
Aisen Paul
Petersen Ron
Weiner Michael W.
Aisen Paul
Petersen Ronald
Green Robert C.
Harvey Danielle
Jack, Jr. Clifford R.
Jagust William
Morris John C.
Saykin Andrew J.
Shaw Leslie M.
Toga Arthur W.
Trojanowki John Q.
Neylan Thomas
Grafman Jordan
Green Robert C.
Montine Tom
Aisen Paul
Jimenez Gustavo
Donohue Michael
Gessert Devon
Salazar Jennifer
Zimmerman Caileigh
Walter Sarah
Adegoke Olusegun
Mahboubi Payam
Danowski Sarah
Coker Godfrey
Clanton Taylor
Pizzola Jeremy
Shaffer Elizabeth
Nguyen-Barrera Catherine
Neylan Thomas
Hayes Jacqueline
Finley Shannon
Harvey Danielle
Donohue Michael
Jack, Jr. Clifford R.
Bernstein Matthew
Borowski Bret
Gunter Jeff
Senjem Matt
Kantarci Kejal
Ward Chad
Tosun-Turgut Duygu
Rossi Chen Stephanie
Landau Susan
Koeppe Robert A.
Foster Norm
Reiman Eric M.
Chen Kewei
Morris John C.
Perrin Richard J.
Franklin Erin
Shaw Leslie M.
Trojanowki John Q.
Korecka Magdalena
Figurski Michal
Toga Arthur W.
Crawford Karen
Neu Scott
Saykin Andrew J.
Foroud Tatiana M.
Potkin Steven
Shen Li
Faber Kelley
Kim Sungeun
Nho Kwangsik
Wilmes Kristi
Schneider Lon S.
Pawluczyk Sonia
Becerra Mauricio
Teodoro Liberty
Dagerman Karen
Spann Bryan M.
Brewer James
Vanderswag Helen
Fleisher Adam
Stern Yaakov
Honig Lawrence S.
Mintz Akiva
Shah Raj C.
Sood Ajay
Blanchard Kimberly S.
Fleischman Debra
Arfanakis Konstantinos
Duara Dr. Ranjan
Varon Dr. Daniel
Greig Maria T
Doraiswamy P. Murali
Petrella Jeffrey R.
James Olga
Borges-Neto Salvador
Wong Terence Z.
Porsteinsson Anton P.
Martin Kimberly S.
Thai Gaby
Pierce Aimee
Reist Christopher
Yanez Beatriz
Sosa Elizabeth
Witbracht Megan
Sadowsky Carl
Martinez Walter
Villena Teresa
Rosen Howard
Perry David
Turner Raymond Scott
Johnson Kathleen
Reynolds Brigid
MCCann Kelly
Poe Jessica
Sperling Reisa A.
Johnson Keith A.
Marshall Gad
Belden Christine M.
Spann Bryan M.
Clark Kelly A.
Zamrini Edward
Sabbagh Marwan
Obisesan Thomas O.
Ntekim Oyonumo E.
Nwulia Evaristus
Nadarajah Sheeba
Asthana Sanjay
Carlsson Cynthia M.
Peskind Elaine R.
Petrie Eric C.
Li Gail
Yesavage Jerome
Taylor Joy L.
Chao Steven
Coleman Jaila
White Jessica D.
Lane Barton
Rosen Allyson
Tinklenberg Jared
Lin Michael
Chiang Gloria
Ravdin Lisa
Relkin Norman
O’Connelll Abigail
Mintzer Jacobo
Wiliams Arthur
Mackin Scott
Aisen Paul
Raman Rema
Jimenez-Maggiora Gustavo
Donohue Michael
Gessert Devon
Salazar Jennifer
Zimmerman Caileigh
Walter Sarah
Adegoke Olusegun
Mahboubi Payam
Mackin Scott
Weiner Michael W.
Aisen Paul
Raman Rema
Jack, Jr. Clifford R.
Landau Susan
Saykin Andrew J.
Toga Arthur W.
DeCarli Charles
Koeppe Robert A.
Green Robert C.
Drake Erin
Weiner Michael W.
Aisen Paul
Raman Rema
Donohue Mike
Mackin Scott
Nelson Craig
Bickford David
Butters Meryl
Zmuda Michelle
Jack, Jr. Clifford R.
Bernstein Matthew
Borowski Bret
Gunter Jeff
Senjem Matt
Kantarci Kejal
Ward Chad
Reyes Denise
Koeppe Robert A.
Landau Susan
Toga Arthur W.
Crawford Karen
Neu Scott
Saykin Andrew J.
Foroud Tatiana M.
Faber Kelley M.
Nho Kwangsik
Nudelman Kelly N.
Mackin Scott
Rosen Howard
Nelson Craig
Bickford David
Au Yiu Ho
Scherer Kelly
Catalinotto Daniel
Stark Samuel
Ong Elise
Fernandez Dariella
Butters Meryl
Zmuda Michelle
Lopez Oscar L.
Oakley MaryAnn
Simpson Donna M.
2To whom correspondence may be addressed. Email: [email protected].
Edited by Brian Wandell, Stanford University, Stanford, CA; received August 26, 2022; accepted November 10, 2022.
1C.Y., P.I., M.C., A.A., N.F.C., P.B., and A.I. contributed equally to this work.
3A complete list of the Alzheimer’s Disease Neuroimaging Initiative can be found in the SI Appendix.
3 1 2023
10 1 2023
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120 2 e221463412026 8 2022
10 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
The phenotypic age of the human brain, as revealed via deep learning of anatomic magnetic resonance images, reflects patterns of structural change related to cognitive decline. Our interpretable deep learning estimates that the brain ages more accurately than any other approaches to date. Furthermore, compared to chronological age, our inferred brain ages are significantly more strongly associated with early signs of Alzheimer’s disease. Maps conveying the importance of each brain region for estimating brain age reveal differences in patterns of neurological aging between males and females and between persons with and without cognitive impairment. These findings provide insight into early identification of persons at high risk of Alzheimer’s disease.
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
brain age
cognitive impairment
Alzheimer’s disease
deep learning
HHS | National Institutes of Health (NIH) 100000002 R01 NS 100973 Phoebe ImmsAnar AmgalanNahian F ChowdhuryRoy J MassettNikhil N ChaudhariAndrei Irimia U.S. Department of Defense (DOD) 100000005 W81-XWH-1810413 Phoebe ImmsAnar AmgalanNahian F ChowdhuryRoy J MassettNikhil N ChaudhariAndrei Irimia James J. & Sue Femino Foundation NA Phoebe ImmsAnar AmgalanNahian F ChowdhuryRoy J MassettNikhil N ChaudhariAndrei Irimia Hanson-Thorell Research Scholarship NA Phoebe ImmsAnar AmgalanNahian F ChowdhuryRoy J MassettNikhil N ChaudhariAndrei Irimia URAP at the University of Southern California NA Phoebe ImmsAnar AmgalanNahian F ChowdhuryRoy J MassettNikhil N ChaudhariAndrei Irimia CURVE at the University of Southern California NA Phoebe ImmsAnar AmgalanNahian F ChowdhuryRoy J MassettNikhil N ChaudhariAndrei Irimia National Science Foundation (NSF) 100000001 CPS/CNS-1453860 Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan National Science Foundation (NSF) 100000001 CCF-1837131 Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan National Science Foundation (NSF) 100000001 MCB-1936775 Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan National Science Foundation (NSF) 100000001 CNS-1932620 Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan National Science Foundation (NSF) 100000001 CMMI-1936624 Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan Okawa Foundation for Information and Telecommunications (The Okawa Foundation) 501100004399 NA Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan DOD | Defense Advanced Research Projects Agency (DARPA) 100000185 N66001-17-1-4044 Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan USC | Stevens Center for Innovation, University of Southern California (USC Stevens) 100018305 NA Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan Intel Corporation (Intel) 100002418 NA Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan Northrop Grumman 100005014 NA Chenzhong YinMingxi ChengXinghe ChenPaul Bogdan
==== Body
pmcAlthough chronological age (CA) reflects disease risk, the rate of aging varies across individuals, organs, tissues, and clinical conditions (1). Because CA does not capture this variation well, there is interest in estimating biological age to predict morbidity (2, 3). Among typically aging adults, in the absence of any clinical indications, biological age is expected to equal CA, on average (4). Neuroanatomic biological age inferred from MRI, henceforth referred to as brain age (BA), can quantify disease-related changes in aging and associated increases in mortality risk (5, 6). Thus, reliable BA estimators can help to stratify individuals according to disease risk (7, 8). The difference between BA and CA, known as age gap (AG), conveys whether aging is faster or slower than expected (9, 10). In clinical cohorts, improving BA estimates can translate into better estimates of participants’ deviations from typical aging (11, 12). For example, BA has the potential to become an affordable and noninvasive preclinical indicator of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) (13) due to the strong association between BA and dementia risk (14, 15).
Deep learning (DL) methods can estimate BA by learning to estimate cognitively normal (CN) subjects’ CAs from MRIs of their brain, while minimizing the mean absolute error (MAE) between BA and CA (16). Compared to other approaches, DL typically yields better BA estimates (17). However, its inherent black-box nature hinders the interpretability of its feature attribution (18), since the relative utility of regional brain features for BA estimation by DL methods is unknown. Furthermore, many DL estimators of BA are inaccurate and lack generalizability to cohorts not encountered during DL training. To address these shortcomings, we introduce an interpretable three-dimensional (3D) convolutional neural network (CNN) to estimate BA from T 1-weighted brain MRIs. To provide neuroanatomic interpretability, MRI feature attribution is achieved through saliency maps. These allow one to identify structural brain patterns of CN aging that reflect regional and sex-specific variations in neuroanatomic features reflecting BA. 3D-CNN generalizability to new cohorts is also illustrated. The translational potential of this study is reflected in the associations between estimated BAs and neurocognitive measures of CI.
Results
Neuroanatomic Patterns of Aging.
We use an interpretable 3D-CNN framework to estimate the BAs of 650 CN adults (age range: 18 to 88 y; 325 males) from the Cambridge Centre for Aging and Neuroscience (CamCAN, Fig. 1 A and C ). BAs were also estimated in 359 participants with AD dementia (age range: 55 to 92 y; 198 males) and in 351 participants with MCI due to AD (age range 55 to 89; 230 males) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, Fig. 1A ). Among participants with MCI, 54% were diagnosed with dementia within 11 y from the acquisition of MRIs analyzed in this study. We generated 3D-CNN saliency maps of each participant’s brain to determine how the 3D-CNN weighs each MRI voxel (Fig. 1 B and D ). Saliency maps can help to identify brain locations whose MRI features are weighted more heavily during age estimation (Fig. 2 and SI Appendix, Figs. S1–S12). Using this strategy, we mapped CI-related aging patterns and studied their variation across sexes, brain regions, and subjects, as well as their association with neurocognitive outcome (Fig. 1E ).
Fig. 1. Overview of BA estimation by an interpretable 3D-CNN. (A) Proportions of participants in the aggregate dataset (ADNI, UKBB, CamCAN, and HCP), where each human symbol represents ∼300 participants. (B) T1 -weighted MRIs were skull-stripped and 3D saliency probability maps were generated from 3D-CNN output for each subject. (C) Prior to BA estimation using the 3D-CNN, participants were split by sex and assigned randomly into training and test sets. MAE was used to evaluate 3D-CNN performance from BA estimation results for test sets. The test set’s CA histogram is displayed in an inset. (D) The 3D-CNN’s input consists of T1 -weighted MRIs, and its output are BA estimates. Saliency maps are extracted from 3D-CNN output after training. A dropout rate of 0.3 is used in all dropout layers, and a ReLU activation function is used in all convolutional and dense layers. x i is the feature map for input i and w i is its weight. (E) Sample sizes for participants with neurocognitive measures.
Fig. 2. Comparison of brain saliency maps across sexes and diagnoses. (A) Sex-specific mean saliency maps (P M , P F ) and the sex dimorphism map Δ P = (P M − P F )/[(P F + P M )/2] of CN participants. In all cases, canonical cortical views (sagittal, axial, and coronal) are displayed in radiological convention. Higher saliencies (brighter regions) indicate neuroanatomic locations whose voxels contribute more to BA estimation. Regions drawn in red have higher saliencies in males (P M > P F ); the reverse (P F > P M ) is true for the regions drawn in blue. (B) Canonical views of the sex dimorphism map Δ P for CN participants. Sex-specific deviations of Δ P from its mean across sexes are expressed as percentages of the mean. Red indicates that Δ P M > Δ P F , i.e., males have higher saliency; blue indicates the reverse (Δ P F > Δ P M ), i.e., females have higher saliency. (C) Like (A), for the comparison between CN participants and participants with CI, where Δ P = (P C I − P C N )/P C N ; red indicates P C I > P C N , blue indicates P C N > P C I . (D) Like (B), for the saliency difference Δ P between CN and CI participants. Images are displayed in radiological orientation convention (the right hand side of the reader is the left hand side of the participant and vice versa).
Our results in CN participants (Fig. 2 A and B ) reveal typical neuroanatomic patterns of aging, including ventricular enlargement, atrophy of frontal, temporal, and hippocampal cortices, and cortical thinning. Cortical features are weighted differently across sexes (Fig. 2 A and B ), which suggests that males’ BA estimation is particularly reliant upon Sylvian fissure widening, ventricular enlargement, and cingulate cortex atrophy. Males’ BA estimation is also weighted more heavily by features of the lateral temporal lobe and dorsolateral frontal lobe in the right hemisphere, a notable lateralization effect. By contrast, females’ saliencies are higher in posterior and medial occipital regions (except the left calcarine sulcus), in the inferior and medial aspects of the parietal lobes, in the supramarginal gyrus and adjacent parietal structures, in the callosal sulcus, in the pars triangularis of the right inferior frontal gyrus, and in posterior insular regions (Fig. 2 A and B ). In females, on average, white matter is weighted more heavily than gray matter when estimating BA.
Fig. 2 C and D compares subject-wise average saliency maps according to the cognitive status (CN vs. CI). This comparison reveals brain features upon which the 3D-CNN relies more when estimating age according to cognitive status. For this reason, such features may reflect how CI modifies regional brain aging. Many structures salient in CN aging are in the cortical gray matter and include the dorsolateral aspect of the right frontal lobe, the lateral aspect of the right temporal lobe, the posterolateral aspect of the right occipital lobe, as well as pericallosal regions in both hemispheres (Fig. 2 C and D ). Cerebral white matter is more salient in aging with CI than in CN aging (Fig. 2C ), as is the brainstem, medial aspects of the temporal lobes (including parahippocampal and fusiform gyri), and the caudal portions of the anterior cingulate gyri (Fig. 2D ). Appreciable lateralization of saliencies is noted when comparing CN participants to participants with CI, and the lateralization pattern is similar to that obviated by the sex comparison (Fig. 2B ). Involved are lateral temporal areas, the angular and supramarginal gyri, middle cingulate cortex, parahippocampal areas, and both medial and dorsolateral prefrontal cortices.
Associations with Neurocognitive Endophenotypes.
The ability of estimated BA to capture neurocognitive endophenotypes was contrasted to that of CA. This was achieved by comparing Spearman’s correlations r S between each age (BA and CA) and every neurocognitive measure of CN aging (Fig. 3 and SI Appendix, Tables S1–S5). For all neurocognitive measures, significant r S values reflect typical aging effects on neurocognitive function (worse performance is correlated with older age). As expected, among CN participants, BA and CA reflect cognition to similar extents. For example, among CamCAN CN participants (Fig. 3A and SI Appendix, Table S1), older BA and CA are correlated with worse performance on word finding (picture priming), motor learning (force matching), motor response time [choice and simple response time (RT) tasks], face recognition (Benton’s unfamiliar face recognition, famous faces test), Cattell’s fluid intelligence, emotional memory, and visual short-term memory (VSTM) measures. In the ADNI CN cohort (Fig. 3B and SI Appendix, Table S2), no neurocognitive measure examined is significantly more correlated with BA than with CA.
Fig. 3. Correlations between neurocognitive measures and both estimated BA and CA. Results are depicted for two independent test sets: CamCAN and ADNI. (A) displays CN participants from CamCAN, (B) displays CN participants from ADNI, (C) displays results only for participants with MCI, and (D) displays results for participants with either MCI or AD. For each independent test set, the sample size for each neurocognitive measure is listed below the measure name. Bar charts depict Spearman’s correlations r S (along x) between BA (green) or CA (red) and each neurocognitive measure (along y). Bars are contoured in black if r S is significant. Error bar widths equate to one SE of the mean. For each neurocognitive measure, the corresponding bar pair is annotated with Fisher’s z statistic. Asterisks indicate neurocognitive measures for which the difference in Spearman’s correlations r S (B A)−r S (C A) is significant.
Across participants with CI, BA is significantly more correlated than CA with neurocognitive measures. In participants with MCI (Fig. 3C and SI Appendix, Table S3), older BA (but not CA) is significantly correlated with worse scores on all measures of neurocognitive function examined, except 1) delayed verbal recall and learning on the Rey auditory verbal learning test (RAVLT), 2) delayed word recall measured by the AD assessment scale question 4 (ADAS Q4), and 3) logical memory. For the clinical dementia rating sum of boxes (CDR-SB) and the functional abilities questionnaire (FAQ), the difference in correlations between BA and CA is significant and BA outperforms CA in its ability to reflect neurocognitive function. In participants with AD, no significant correlations exist between BA and any neurocognitive measure apart from FAQ scores ( SI Appendix, Table S4). Nevertheless, older CA is correlated with poorer delayed verbal memory (RAVLT forgetting). By contrast, among all participants with any type of CI (whether MCI or AD), BA (but not CA) is significantly correlated with all measures except delayed verbal recall (RAVLT, ADAS Q4) and logical memory (Fig. 3D and SI Appendix, Table S5). The difference in correlations between BA and CA is significant for the CDR-SB, mini-mental state exam (MMSE), RAVLT immediate recall (IR), and FAQ. When separating participants with CI by apolipoprotein E4 (APOE4) status, BA is not more correlated with any neurocognitive measure in carriers compared to noncarriers. The omnibus effect of a logistic regression accounting for all interactions between AG, CA, and sex is significant (χ 343 2 = 29.500, P < 0.001). AGs are significantly and positively associated with MCI participants’ probability of conversion to AD (β = 1.417, t 343 = 2.240, P = 0.025). The only significant interaction is between AG and sex (β = −1.121, t 343 = −2.129, P = 0.033), i.e., MCI females with more negative AGs and MCI males with more positive AGs are significantly more likely to convert to AD. When including all interactions, the omnibus effect of the regression that predicts time to conversion is significant if AG is included as the predictor (R 2 = 0.065, F 8, 181 = 2.880, P = 0.007) but not if AG is excluded (R 2 = 0.012, F 4, 185 = 1.790, P = 0.151).
3D-CNN Benchmarking and Evaluation.
We compare our 3D-CNN to an award-winning (19) state-of-the-art model, the simple fully convolutional network (SFCN) of Gong et al. (20, 21), by replicating its training, validation, and benchmarking. The SFCN was pretrained on 5,698 UK Biobank (UKBB) subjects, whereas our 3D-CNN was trained on 4,681 participants (2,513 females; age range: 22 to 95 y) aggregated across the UKBB, Human Connectome Project-Aging (HCP-A), Human Connectome Project-Young Adult (HCP-YA), and ADNI. In the testing set, our model’s MAE between BA and CA is 2.41 y for males and 2.23 for females ( SI Appendix, Fig. S13 A and B ). The coefficient of determination R 2 is 0.96; the correlation coefficient r is 0.98. Across all external testing sets (UKBB, CamCAN, AD, and MCI), our model has a higher R 2 than the SFCN ( SI Appendix, Table S6).
On identical UKBB data (N = 518), the 3D-CNN achieves MAEs of 2.27 y (males) and 2.31 y (females) ( SI Appendix, Fig. S14 A and E ), while the SFCN achieves an MAE of 2.14 y across both sexes ( SI Appendix, Fig. S14 Q and R ). In the independent CamCAN CN cohort, the SFCN’s MAEs are 9.90 y (males) and 9.17 y (females) ( SI Appendix, Fig. S14 Q and R ). By contrast, our 3D-CNN achieves MAEs of 4.71 y (males) and 3.01 y (females) ( SI Appendix, Fig. S14 B and F ). During pretraining, the SFCN yields an MAE within 2% of the 3D-CNN’s. However, in the independent test cohort of CN participants, our MAEs are 42% lower than the SFCN’s in the same cohort. The SFCN yields MAEs of 7.72 y (males) and 7.50 y (females) for participants with MCI and 8.24 y (males) and 8.65 y (females) for participants with AD ( SI Appendix, Fig. S14 Q and R ). By contrast, our 3D-CNN model achieves an MAE of 5.26 y (males) and 4.33 y (females) for participants with MCI ( SI Appendix, Fig. S14 C and G ) and 6.48 y (males) and 5.98 y (females) for participants with AD ( SI Appendix, Fig. S14 D and H ). Compared to the SFCN ( SI Appendix, Fig. S14 Q and R ), the 3D-CNN yields significantly larger mean AGs for A) females with MCI (t 144 = 6.595, P < 0.001), B) males with AD (t 195 = 4.710, P < 0.001) and C) females with AD (t 162 = 6.200, P < 0.001). The 3D-CNN also yields significantly larger AG variances in participants with AD (males: F 197, 197 = 1.857, Pitman’s t 196 = 4.440, P < 0.001; females: F 162, 162 = 2.493, Pitman’s t 161 = 6.006, P < 0.001). Compared to the 3D-CNN, the SFCN yields significantly larger AG variances for the following CN groups: A) UKBB males (F 796, 796 = 1.137, Pitman’s t 795 = 12.967, P < 0.001); B) UKBB females (F 796, 796 = 1.097, Pitman’s t 795 = 9.034, P < 0.001); C) CamCAN females (F 309, 309 = 7.576, Pitman’s t 308 = 21.082, P < 0.001). As expected, the 3D-CNN’s mean AG is ∼75% larger in participants with CI than in CN participants ( SI Appendix, Fig. S14 I–P ), possibly reflecting faster brain aging in the former. The BA estimation parameters of the 3D-CNN and SFCN, evaluated without fine-tuning, are compared in Fig. 4 and SI Appendix, Table S6. The 3D-CNN has shorter execution times (ETs) and fewer trainable parameters, reflecting lower complexity ( SI Appendix, Table S6 and Fig. 4). As SI Appendix, Table S6 and Fig. S14 suggest for participants with CI, our CNN yields higher R 2 and lower MAEs than the SFCN.
Fig. 4. Radar plots of sex-specific MAEs and performance parameters. Radar plots of MAE, R 2, and performance parameters (average ET and the number of trainable parameters) according to sex and diagnostic status (CN: UKBB, CamCAN; MCI or AD: ADNI). The SFCN of Gong et al. (20, 21) (purple) is compared to our 3D-CNN (blue). To facilitate simultaneous comparison, all values are normalized to range from 0 to 1, where the maximum value in each measurement was rescaled as 1 and 0 remained as 0.
Discussion
Significance.
While biological age can be computed for many phenotypic traits, BA summarizes MRI-derived neuroanatomic profiles using one number. This highlights both the appeal and caveats of this measure. Although straightforward to grasp, BA (as defined here) does not capture the nuances and complexity of brain aging. Nevertheless, with cautious interpretation, BA could assist the diagnosis and prognosis despite its limitations. Early screening for CI can help to monitor and improve the welfare of aging adults (22). Although positron emission tomography (PET) can aid the diagnosis of AD at the preclinical and prodromal stages (23), this technique is expensive, involves specialized tracers, and exposes participants to radiation (24). By contrast, MRI is noninvasive, more affordable, and safer. Thus, MRI-derived BAs that capture neurocognitive decline (25) could become affordable and noninvasive preclinical measures of CI risk (26).
The correlations of neurocognitive measures with our estimated BAs are, in many cases, significantly stronger than with CA, suggesting that our BAs better reflect neurocognitive functioning. These correlations are critical because one potential utility of BA estimation is to facilitate the early identification of persons at high risk of MCI and AD. AGs are predictive of AD conversion risk, as others reported (27 –29). BA is not correlated with neurocognitive function in participants with AD, with the exception of informant-rated functional ability. One possible reason is that the 3D-CNN was trained on CN adults rather than participants with CI. Another reason could be that correlations are more difficult to detect due to lower statistical power (smaller sample size) in participants with AD compared to CI participants. Across persons with CI of any severity, BA (but not CA) is significantly correlated with measures used routinely (30) to screen for (or to diagnose) CI, including MCI (Fig. 3 and SI Appendix, Tables S4 and S5). Thus, our contributions can help to understand how CI-related neurocognitive changes within specific functional domains reflect neuroanatomic features that modify regional BAs.
Sex Differences in Anatomic Brain Aging.
Of note, for patient-tailored profiling, our approach can generate subject-specific brain saliency maps reflecting individual neuroanatomic patterns of brain aging. Anatomic interpretability of BA is important because 1) brain regions age differently, 2) neuroanatomic alterations with age may reflect distinct disease processes paralleled by BA (31), and 3) individual neuroanatomic deviations may parallel neurocognitive endophenotypes. Sex differences in saliency confirm findings on the contributions of age to sex dimorphism in the pre- and post-central gyri (32, 33) and the pars triangularis of the left inferior frontal gyrus (34). Males, who are at higher risk of motor impairment due to Parkinson’s disease (35), exhibit greater saliency in the primary motor cortex. Males’ BA estimation relies more on the crowns of gyri on the lateral aspects of the frontal lobes, whereas females’ BA estimation relies more on the troughs of sulci. These findings confirm prior reports on sex differences in older adults’ cortical gyrification (36). Males’ saliencies are higher along ventricular boundaries, indicating that BAs are disproportionately predicated upon ventricular enlargement in men, as reported elsewhere (37). The right hemisphere’s higher saliency in males is consistent with their lateralization of language function (38) and with lateralization trends in old age (39). Thus, in females, typical cortical aging may be relatively slower in the right hemisphere. By contrast, on average, most occipital and medial parietal areas exhibit age-related neuroanatomic patterns that are more salient in males. Males also have higher saliency in superior parietal and frontal regions, reflecting smaller gray matter volumes (40). By contrast, females have higher saliency at the occipital poles and in occipitoparietal regions, reflecting smaller gray matter volumes in these regions (40). Females’ saliencies are higher across inferior parietal regions, where the cortex is thicker than in males (41). Thus, our approach to neuroanatomic saliency mapping can identify sex differences in the neuroanatomy of cortical aging.
Anatomy Changes According to Neurocognitive Status.
Our interpretable 3D-CNN framework captures neuroanatomy changes related to both CN aging and aging with CI. In the case of CN aging, the estimated BAs of CN participants in our two independent samples (CamCAN and ADNI) are correlated with neurocognitive measures reflecting typical aging (e.g., motor learning, multitasking, and word finding). In ADNI CN participants, no significant associations were found between neurocognitive measures and either CA or BA. This was expected, as ADNI cognitive measures are sensitive to CI rather than to CN aging (42). In the case of CI, SI Appendix, Fig. S14 I–P confirms that participants with either MCI or AD have AGs considerably larger than those of sex- and age-matched CN adults (43), mostly due to older-than-expected brains (BA > CA). Atrophy of the parahippocampal gyrus is a strong structural correlate of MCI and AD (44); our 3D-CNN’s greater reliance on this structure during BA estimation reflects this (Fig. 2C ). Similarly, saliency differences between CN and CI participants are greater in parietal, occipital, and temporal cortices (Fig. 2D ), whose atrophy is greater in participants with CI (45) and whose burdens of amyloid β plaques and τ neurofibrillary tangles are typically higher in AD (46). The brainstem, which is affected by amyloid deposition early during AD, is more salient in participants with CI than in CN adults (47). Comparison of the cortical patterns in Fig. 2 B and D indicates that saliency differences between sexes are largely paralleled by saliency differences across cognitive statuses (CN vs. CI). This may reflect females’ higher risk for AD and supports the hypothesis according to which their higher risk is paralleled by faster cortical aging. Comparison of CN and CI cohorts suggests that the SFCN underestimates mean AG in the latter group and that the expected accuracy of BA estimation is lower for participants with CI. These findings highlight the importance of an accurate BA estimator when studying diseased populations. Some cortical structures that atrophy far more in CI than in CN aging are more salient in the latter (blue regions in Fig. 2D ). This may reflect the fact that the 3D-CNN was trained on a CN adult cohort. During training on this cohort, our 3D-CNN likely relies on features whose variance is moderate in CN aging. When estimating the BAs of participants with CI, however, these features exhibit far greater variability. This may cause their relative saliency to decrease, such that the saliency difference Δ P between CN and CI aging is negative in such regions. Thus, although features with negative Δ P values can be useful for understanding how BA estimation relies on CI-related neuroanatomy features, the negative sign of Δ P must be interpreted cautiously.
Comparison to Other Methods.
Our 3D-CNN alleviates major limitations of other approaches. The quantitative comparison below focuses on the SFCN because this open-source approach performed best in a competition (19) for which both training and testing data are available.
Accuracy.
Our 3D-CNN estimates BA more accurately than the state of the art regardless of whether accuracy is quantified using MAE or R 2. In the test set, our model yields an MAE of ∼2.3 y; this is ∼1 y less than the SFCN, which is the second best. Other (published) BA estimators have MAEs that are even higher than that of the SFCN on their testing data. Presumably, since our MAE is ∼2.3 y, these estimators also perform more poorly than ours. However, we could not ascertain this because we did not have access to the testing sets on which others estimators were benchmarked. These estimators include a best linear unbiased predictor (MAE ≃ 3.3 y) (48), a 3D residual neural network (3D-RNN, MAE ≃ 3.3 y) (49), a graph CNN (MAE ≃ 4.6 y) (50), Gaussian process regression (MAE ≃ 4.1 y) (16), support vector regression synergized with a random forest classifier (MAE ≃ 3.5 y) (51), and a 3D-DenseNet (MAE ≃ 3.3 y) (52).
On the testing set, our model yields R 2 ≃ 0.96 and r ≃ 0.98. By contrast, the SFCN model yields R 2 ≃ 0.92 and r ≃ 0.96 (16, 20, 21). During testing, other (published) BA estimators achieve even lower R 2 than the SFCN. These include Gaussian process regression (R 2 ≃ 0.91) (16), a 3D-RNN (R 2 ≃ 0.90) (49), a graph CNN (R 2 ≃ 0.87) (50), and a 3D-DenseNet (R 2 ≃ 0.85) (52). Our R 2 is also higher than that of a BA estimator that used an optimized SFCN (53) with R 2 = 0.94. These comparisons suggest that even high R 2 can involve undesirably large MAE, such that it can be useful to consider both measures when evaluating accuracy.
In all females with CI and in AD males, our AGs are significantly larger than those estimated by the SFCN. As expected, the 3D-CNN’s estimates of these subjects’ CAs are consistently larger than their true CAs. Because CI involves more brain aging, this suggests that the 3D-CNN captures CI better than the SFCN. Females are at higher risk for AD and exhibit faster decline than males (54). Females already have a larger mean AG in the MCI stage, whereas this is not the case for males until the AD stage. Thus, our model captures known sex differences in AD risk.
Variances in AG between the 3D-CNN and SFCN are significantly different for the UKBB cohort even though F = σ S F C N 2/σ C N N 2 ≃ 1, which usually implies lack of significant differences in variance. This finding can be explained by our use of Pitman’s variance ratio test, which is justified here because the variances being compared pertain to correlated samples (CNN- and SFCN-computed AGs measured for the same cohort). Because the 3D-CNN and SFCN were both trained on UKBB CN participants, the abilities of these methods to estimate BA for new UKBB participants are likely better (and therefore more similar) than their ability to estimate BA for participants from altogether new cohorts. This similarity may explain the strong correlation r of UKBB BAs across the two methods (females: r = 0.989; males: r = 0.990). The dependence of Pitman’s t on r (see Methods) satisfies t ∼ (1 − r 2)−1/2. A Maclaurin series expansion indicates that t → ∞ as r → 1. Thus, Pitman’s t is large when r ≃ 1 even when σ S F C N 2/σ C N N 2 ≃ 1. This explains our power to detect even moderate differences between σ S F C N 2 and σ C N N 2 in UKBB CN participants.
Complexity.
Model complexity was quantified using mean ET and the number of trainable parameters in the model. By both measures, our 3D-CNN’s execution complexity is lower than that of previous approaches. For example, the 3D-CNN features a ∼10 times shorter ET compared to the SFCN (20, 21) and ∼4 times fewer trainable parameters. The model of Leonardsen et al. (53), which is based on the SFCN (20, 21), has more trainable parameters and is more challenging to fine-tune. The 3D-DenseNet (53) has ∼7 million trainable parameters (compared to our 682,881) and requires extensive fine-tuning on new validation datasets via grid searches for optimal hyperparameters.
Interpretability.
Lee et al. (52) use a 3D-DenseNet to compute saliency by covering the brain with occlusion masks of size 113 mm3 = 1, 331 mm3. According to these authors, their saliencies correlate with PET-mapped amyloid β and τ burdens. However, for participants with CI, the anatomic patterns of brain aging mapped by Lee et al. are broadly similar to ours (Fig. 2 C and D ) across similar age ranges. This suggests the hypothesis that BA saliencies like ours can reflect dAD-related clinical PET findings. He et al. (55) used two-dimensional (2D) occlusions (box size: 322 mm2 = 1, 024 mm2) to map saliency, whereas Wood et al. (56) monitored performance and saliency by occluding 3D masks (size: 53 mm3 = 125 mm3). Our study advances the state of the art by 1) providing voxelwise saliency maps to reveal detailed spatial variability at native MRI resolution (1 mm3), 2) reporting comparisons by sex and cognitive status, and 3) conveying how cognitive status relates to neurocognitive function.
Generalizability.
Most BA estimators are not typically tested across domain-specific neurocognitive measures, whereas our 3D-CNN features unique generalizability to independent cohorts in its ability to capture neurocognitive endophenotypes. Since the R 2 values achieved on independent and test data are similar, we surmise that overfitting was largely avoided. Compared to CA, the BA of participants with any type of CI is significantly more correlated with measures of neurocognitive function routinely used as clinical indicators of CI. Other published approaches have rarely been evaluated according to this critical performance benchmark. Because the 3D-CNN was trained on subjects aged 22 to 95 y, its utility extends across the age range of adulthood.
Limitations.
Although we validated the 3D-CNN in cohorts independent from those used for its training, differences in acquisition sequences and scanners across MRIs can affect results (57). Like other dementia diagnosis criteria, ADNI’s have limitations [e.g., a risk of false positive diagnoses (42) that may affect the findings of studies like ours. Additionally, floor effects may affect cognitive measures in participants with AD by attenuating their correlation. Conceivably, our failure to find significant correlations between BA and neurocognitive measures in participants with AD could be due to our lower power to detect small effects in the AD sample, which is smaller (N ≤ 172) compared to the MCI (N ≤ 347) and combined CI (i.e., MCI or AD, N ≤ 519) samples. These nonsignificant correlations, however, are not typically relevant for early CI screening because most participants with severe CI have been already diagnosed by the time brain MRIs are typically acquired. The nonuniform distribution of CAs in our aggregate sample translates into potential training data imbalance and inaccuracy in BA estimates. Nevertheless, our approach is more accurate than others currently available, as reflected by our test set’s MAE and R 2, which are the lowest reported to date. Due to the lack of ground truth, there is no consensus on how the interpretability of approaches like ours ought to be evaluated (52, 55, 56, 58).
Methods
Participants and Neuroimaging.
This study was undertaken in adherence with the US Code of Federal Regulations (45 C.F.R. 46) and the Declaration of Helsinki. MRIs analyzed in this study were acquired as part of other studies, with approval from the institutional review boards or similar ethical monitoring bodies at the respective institutions where data had been acquired for ADNI (30) and HCP (59). UKBB efforts were undertaken with ethical approval from the North West Multi-Centre Research Ethics Committee of the United Kingdom. Ethical approval for CamCAN was obtained by the Cambridgeshire 2 (now East of England—Cambridge Central) Research Ethics Committee. Informed written consent was obtained from all participants.
The aggregate dataset consists of 5,851 CN individuals (3,142 females) aged 22 to 95 yr sampled from ADNI (N = 510), HCP-A (N = 508), HCP-YA (N = 1, 112), and UKBB (N = 3, 721; Table 1). The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. MRI acquisition protocols for HCP-A and HCP-YA are described elsewhere (60). For UKBB data, we used preprocessed images generated by a UKBB pipeline whose output included FreeSurfer reconstructions (61).
Table 1. Participant demographics
CA (y) Ratio FreeSurfer version
Repository Status N Min Max μ σ M:F 4.3.0 5.3.0 6.0.0 7.1.1
ADNI CN 510 56 95 75.1 7.2 1:1.17 0 0 260 250
HCP-A CN 508 36 80 55.8 12.0 1:1.38 0 0 309 199
HCP-YA CN 1,112 22 37 28.8 3.7 1:1.17 0 1,112 0 0
UKBB CN 3,721 45 83 62.7 10.1 1:1.13 0 0 3,721 0
CamCAN CN 650 23 88 54.2 18.6 1:1.00 0 0 0 650
ADNI AD 359 56 95 75.9 8.0 1:0.83 359 0 0 0
ADNI MCI 351 55 89 75.2 7.3 1:0.53 351 0 0 0
All all 7,211 23 95 58.4 10.3 1:1.09 710 1,112 4,290 1,099
Sample size, descriptive statistics (minimum, maximum, mean μ, and SD σ), the male-to-female (M:F) sex ratio, and breakdown by FreeSurfer version used for preprocessing. Demographics are listed for each repository and neurological/cognitive status.
An independent test set of 650 CN participants aged 18 to 88 yr was obtained from CamCAN. Additionally, 408 participants with MCI and 359 participants with AD were obtained from ADNI (Table 1). CamCAN inclusion/exclusion criteria (62) and ADNI eligibility/diagnosis criteria are described elsewhere (30). N = 75 participants with MCI were excluded due to MCI diagnosis being unrelated to AD, leaving 351 participants with MCI (190 converted to AD, 161 did not). Of 524 CI (MCI or AD) participants whose correlations between BA and neurocognitive scores were computed, 307 participants were APOE4 carriers.
Neurocognitive Measures.
We used neurocognitive measures available in CamCAN and ADNI to evaluate the utility of our estimated BAs to capture neurocognitive phenotypes (see SI Appendix, Methods for detailed task descriptions). Thirteen cognitive measures that assess emotional processing, executive function, memory, and motor function were obtained from the CamCAN repository (62). Emotional processing was measured via 1) Ekman’s emotion expression recognition test, 2) the emotional memory test, and 3) the emotional regulation test. Executive function was measured using 1) Cattell’s fluid intelligence test, 2) the hotel test, and 3) a proverb comprehension task. Memory was measured using 1) Benton’s face recognition test, 2) the famous faces test, 3) a picture priming task, 4) the tip of the tongue (ToT) test, and 5) a VSTM task. Motor function was assessed via 1) a force matching task, 2) a motor learning task, 3) a reaction time (RT) “choice” task, and 4) a RT “simple” task.
Nine cognitive measures that assess neural function, cognitive performance, and functional impairment were obtained from the ADNI repository (63). To eliminate systematic variability in FreeSurfer software versions, we limited correlation analysis for the CI cohort to subjects from ADNI1 only (FreeSurfer v4.3). For neural function, four established dementia rating scales were obtained, including 1) the clinical dementia rating scale—sum of boxes (CDR-SB), 2) the diagnostic ADAS versions 11 and 13, and 3) the MMSE. Cognitive performance was measured via four neuropsychological measures: 1) the RAVLT, 2) delayed recall on the logical memory test, 3) the digit symbol substitution test, and 4) the trail-making test. Functional impairment was measured by the FAQ.
MRI Preprocessing.
Freesurfer’s recon-all function was used to reconstruct and segment T1 -weighted MRIs. This process includes skull-stripping, motion correction, normalization of nonuniform signal intensities, Talairach space transformation, removal of nonbrain tissues, and registration of all subjects’ brains into a common coordinate space (64). FreeSurfer (FS) was used for three reasons: 1) UKBB makes FS reconstructions available; 2) the FS workflow is fully automated and thus convenient; 3) our study involves surface analyses and registrations across native and atlas spaces, which FS facilitates. During FS preprocessing using recon-all, all MRIs were affinely registered to the MNI305 atlas. Due to sourcing from several MRI repositories, enhancement of segmentation accuracy differed slightly between cohorts (Table 1). UKBB and HCP-YA reconstructions were enhanced using T 2-weighted MRIs, while ADNI, HCP-A, and CamCAN were enhanced using fluid-attenuated inversion recovery MRIs.
3D-CNN Architecture.
We constructed a DL regression model using a 3D-CNN whose inputs are FS brain.mgz output files and whose outputs are estimated BAs. The DL architecture was implemented in Python 3.6 using TensorFlow 2.7.0 and executed on a computer with an Intel Core i7 processor (2.2 GHz clock speed) with 16 GB of RAM and a 12 GB NVIDIA Tesla K80 graphical processing unit. The 3D-CNN consists of three convolutional blocks followed by two dense layers. The input matrix size is 82 × 86 × 100. Each convolutional (conv) block has a 3D conv layer, a batch normalization layer, a max-pooling layer, and an optional dropout layer. The filter sizes of the first three (conv) blocks are 64, 128, and 128, respectively. Conv block filter size determines the dimensionality of the output space. The rectified linear unit (ReLU) activation function is applied to all conv and dense layers. The ReLU activation function is defined as g(x)=m a x(0, x) for input x. g(x) can efficiently reduce the likelihood of a vanishing gradient and makes the output more sparse. After the conv blocks, the fourth block consists of one global average pooling layer (used for global average pooling of 3D data), one dense layer, and one dropout layer (dropout rate = 0.3). The resulting feature map, of size 18 × 18 × 18 × 128, is pooled to 128 × 1 and then projected onto the output dense layer, which has one output neuron to estimate BA using regression.
We choose MSE as loss function and use an Adam optimizer (learning rate = 0.001) (65). The advantage of outputting BAs as real numbers rather than assigning them to discrete age bins (20, 21) is that, in the former case, BA outputs are assigned within a continuous domain and range. Due to regression to the mean (66), estimated BAs exhibit a previously documented CA-dependent bias. To alleviate this effect, we use the zero correlation constraint method of Treder et al. (67) to regress out the bias from the BAs of testing set participants. This is done separately for each cohort. Bias-corrected BAs are used for all analyses. CN participants are aggregated from UKBB, HCP-A, HCP-YA, and ADNI. Participants were randomly assigned into training and test sets of sizes equal to 20% of the total sample size (N= 5,851).
3D-CNN Training and Testing.
We optimized the CNN architecture and fine-tuned hyperparameters. 2D-CNNs use 2D kernels to estimate sliding windows across single slices, such that leveraging information from adjacent slices is challenging (68). We therefore chose a 3D-CNN that overcomes this deficit by using 3D kernels to estimate sliding windows for volumetric patches. The latter captures interslice image context and improves the model performance 68. We also included dropout and batch normalization layers because these help to alleviate overfitting (69). Grid and random searches determined suitable hyperparameter values (e.g., batch size, kernel size, weight decay). An n-dimensional grid was defined to map the n hyperparameters and to identify their ranges. We examined all possible 3D-CNN configurations to identify optimal values for each hyperparameter. Since we used MSE as a loss function, we selected a configuration with the lowest loss value (error).
We tested the 3D-CNN on independent cohorts to refine the 3D-CNN architecture, illustrate model generalizability, alleviate data overfitting, and compare the 3D-CNN to other approaches. The testing set was designed to include a random selection of participants from the same cohorts as the training set. To avoid overfitting the 3D-CNN to the training set, we monitored its performance on the testing set. To avoid overfitting on both training and testing sets, we tested our model on two independent cohorts (CamCAN and ADNI) that had not been used for the 3D-CNN design. The latter of these cohorts includes participants with a range of cognitive statuses (CN, MCI, or AD).
After computing AGs for identical samples using both our 3D-CNN and the SFCN, we performed Welch’s t-tests for paired samples with unequal variances to compare the mean AGs obtained using the two methods. AG variances were compared using Pitman’s variance ratio test for correlated samples, whereby F = σ 1 2/σ 2 2, Pitman’s tN−2=[(F−1)N−2]/[2F(1−r2)] , and r is the correlation of AG S F C N with AG C N N . The AG variances are σ 1 and σ 2, whose subscripts {1, 2} denote the SFCN or CNN, as needed, to satisfy the inequality σ 1 > σ 2.
BA Associations with Sex and Neurocognition.
Each neurocognitive measure m was obtained from CN participiants (ADNI and CamCAN) and from participants with MCI or AD (ADNI). These measures were not normally distributed (Anderson–Darling goodness-of-fit test, SI Appendix, Tables S1–S5), so their Spearman rank correlations r S (m, B A) and r S (m, C A) were computed. These correlations were compared using Fisher’s two-sided z-test after multiple comparison correction using the Benjamini–Hochberg procedure (70) (false discovery rate = 0.05). |r S | and |z| were also calculated for measures whose lower scores indicate better performance. Test statistics and their degrees of freedom, confidence intervals, and effect sizes were tabulated ( SI Appendix, Tables S1–S5). A logistic regression examined whether AG, CA, sex, and their interactions predicted the probability of c o n v e r s i o n from MCI to AD. Another linear regression (independent variables: CA, sex, and their interactions) evaluated the ability of AG to predict the i n t e r v a l between MRI acquisition and AD conversion. AG and its interactions were added to this (reduced) model to examine how AG altered the significance of the regression.
Saliency Maps.
A saliency map is a topographically organized depiction of the visual saliency in an MRI volume V 0. Here, we extend a saliency approach for 2D-CNNs (71) to the 3D case. For an MRI brain volume V 0 and a 3D-CNN model with score function S(V), we rank voxels in V 0 based on their importance to S(V). We consider the linear score model S(V)=w T V + b, where the volume V, weight w, and bias b are in one-dimensional (vectorized) forms. Since the 3D-CNN and score function are highly nonlinear functions of V, the linear score model cannot be applied directly. We approximate S(V) at V 0 using the first-order Taylor series S(V 0)≃w 0 T V 0 + b 0, where w 0 = ∂S/∂V∣ V 0 is the partial derivative of S(V) at V 0 and b 0 = b∣ V 0 is the bias b at V 0. The spatial and temporal distributions of saliencies contain unique patterns conveying information about BA.
Saliency Associations with Sex and Neurocognition.
Two distinct workflows were used for volume- and surface-level transforms, respectively, to remove the confounding effects of subject differences in brain shape and size. For v o l u m e-level analysis, each saliency map was nonlinearly registered to the FS fsaverage atlas. To this end, T1 -weighted brain volumes were first registered to the atlas in MATLAB using the imregister function, which applied the transformation from native space to the atlas, as provided by FS. MATLAB’s imregdemons function was used to deform nonlinearly and to map T1 -weighted scans onto the atlas. The transformations above were applied to each subject’s saliency map, resulting in its registration to the atlas. For s u r f a c e-level analysis, saliencies were projected to the native cortical surface. To achieve this, each subject’s saliency was projected onto the cortical mantle as a cortical overlay using a customized algorithm for volume-to-surface mapping (72, 73). Briefly, voxels assigned to the gray matter ribbon by FS were considered. At each vertex of the native mesh for the mid-thickness surface, ribbon voxels were selected within a cylinder that lay orthogonally with respect to the local surface. The cylinder was centered on the vertex; its height and radius were equal to the local cortical thickness. The saliency of ribbon voxels within the cylinder was averaged according to a Gaussian weighted function (full width at half maximum = 4 mm, σ = 5/3 mm) to compute a mean saliency value at the surface vertex in question. After cortical surface projection, each subject’s saliency overlay was registered from native space onto the atlas. Subjects’ saliency probability overlays were averaged into a cortical map of mean saliency.
For both volume- and surface-level analyses, each saliency map M was operationalized into a saliency probability map P by dividing saliency at each brain location by the sum of all brain saliencies. An average saliency probability map was computed for each sex and cognitive status, yielding P M for males, P F for females, P C N for CN adults, and P C I for participants with any form of CI. Both P C N and P C I were computed after averaging across sex effects. Relative sex differences in P were computed as (P M − P F )/[(P F + P M )/2], i.e., as sex-specific deviations from the average across sexes. The relative deviation of participants with CI from CN participants was computed as (P C I − P C N )/P C N . Relative saliency differences between sexes or diagnostic statuses were mapped after thresholding to include only statistically significant values. For each salience value considered, significance was evaluated using a paired-sample t-test (α = 0.05). Results were corrected for multiple comparisons using the Benjamini–Hochberg procedure (false discovery rate =0.05).
Visualization.
For v o l u m e-level visualization, CN participants’ mean saliency maps were plotted for each sex along the coronal (x), sagittal (y), and axial (z) planes. For each coordinate, maps were generated along planes whose equations were specified by coordinate values of −28 mm, 0 mm, and 28 mm, respectively. In CN participants and participants with CI, the procedure was repeated after averaging across sexes. For s u r f a c e-level visualization, gray matter saliencies were mapped onto the cortex to compare different cortical locations’ relative importance to the 3D-CNN when estimating BA.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Dataset S01 (DOCX)
Click here for additional data file.
A.I. is grateful to Kenneth H. Buetow, Caleb E. Finch, Margaret Gatz, and Mara Mather for useful discussions. The authors acknowledge Alexander S. Maher for assistance with data retrieval. P.E., A.A., N.F.C., R.J.M., N.N.C., and A.I. gratefully acknowledge the support from the (NIH) under grant R01 NS 100973, the US Department of Defense (DoD) under award W81-XWH-1810413, the James J. & Sue Femino Foundation, a Hanson-Thorell Research Scholarship, the Undergraduate Research Associate Program (URAP), and the Center for Undergraduate Research in Viterbi Engineering (CURVE) at the University of Southern California. C.Y., M.C., X.C., and P.B. gratefully acknowledge the support from the NSF under CAREER award CPS/CNS-1453860 and grants CCF-1837131, MCB-1936775, CNS-1932620, and CMMI-1936624, from the Okawa Foundation, from the Defense Advanced Research Projects Agency (DARPA) under a Young Faculty Award and a Director Award under grant N66001-17-1-4044, from a 2021 USC Stevens Center Technology Advancement Grant (TAG) award, from an Intel Faculty Award and from a Northrop Grumman grant. P.M.T. gratefully acknowledges the support from the NIH under grants R01 AG 058854, U01 AG 068057, R01 AG 060610, and P41 EB 015922. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) through NIH grant U01 AG 024904 and DoD ADNI through DoD award number W81-XWH-1220012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development, LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH (http://www.fnih.org). The ADNI grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data for this study were provided, in part, by the Human Connectome Project, WUMinn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1 U54 MH 091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. Research reported in this publication was also supported by the National Institute on Aging of the NIH under award U01 AG 052564. CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and the University of Cambridge, UK. This research has been conducted using the UK Biobank Resource, under application numbers 11559 and 47656. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied by the NIH, DARPA, DoD, NSF, or any other entity acknowledged here. The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Author contributions
P.B. and A.I. designed research; C.Y. and A.A. performed research; C.Y., M.C., and A.A. contributed new reagents/analytic tools; C.Y., P.I., M.C., A.A., N.F.C., R.J.M., N.N.C., and X.C. analyzed data; A.N. data used in research was sourced from the ADNI repository; and C.Y., P.I., A.A., N.F.C., P.M.T., P.B., and A.I. wrote the paper.
Competing interest
The authors have research support to disclose, P.M.T. discloses research grant support from Biogen, Inc. for work unrelated to this study.
Data, Materials, and Software Availability
3D-CNN software is available from https://github.com/irimia-laboratory/USC_BA_estimator MRI data are publicly available from ADNI (https://adni.loni.usc.edu/), UKBB (https://www.ukbiobank.ac.uk/), CamCAN (https://www.cam-can.org/), and HCP (https://www.humanconnectome.org/). There are no relevant accession codes required to access these data, and the authors had no special access privileges that others would not have to the data obtained from any of these databases. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD).
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926275.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595681
202211189
10.1073/pnas.2211189119
research-articleResearch Articledev-bioDevelopmental Biology412
Biological Sciences
Developmental Biology
Human ERG oncoprotein represses a Drosophila LIM domain binding protein–coding gene Chip
Bharti Mahima a https://orcid.org/0000-0002-6328-2127
Bajpai Anjali a b
Rautela Umanshi a c
Manzar Nishat a https://orcid.org/0000-0003-3628-6832
Ateeq Bushra [email protected]
a b 1 https://orcid.org/0000-0003-4682-9773
Sinha Pradip [email protected]
a b 1 https://orcid.org/0000-0002-9202-6050
aBiological Sciences and Bioengineering, Indian Institute of Technology Kanpur, India, 208016
bMehta Family Center for Engineering in Medicine, Indian Institute of Technology Kanpur, India, 208016
cNational Institute of Immunology, India, 110067
1To whom correspondence may be addressed. Email: [email protected] or [email protected].
Edited by Hugo Bellen, Baylor College of Medicine, Houston, TX; received June 29, 2022; accepted October 24, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e221118911929 6 2022
24 10 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
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Significance
Nuclear targets of oncoproteins help decipher cancer mechanisms. Targets of human ERG oncoprotein identified so far have not helped unravel its lineage-specific carcinogenesis. Here, we reveal an alternative strategy to discover ERG targets via its heterologous gain in Drosophila and display Chip, a LIM-domain-coding gene, as its target. ERG-mediated Chip repression induces out-of-context Wg signaling in the notum (thorax) primordium leading to notum-to-wing transdetermination. Further, ERG induces carcinogenesis in the notum in combination with the loss of Lgl tumor suppressor. Remarkably, ERG-positive prostate cancer cells, too, show repression of LDB1, a human homolog of Drosophila Chip. Our results present a strategy to discover functionally relevant oncoprotein targets and unravel essential cancer mechanisms in Drosophila.
Human ETS Related Gene, ERG, a master transcription factor, turns oncogenic upon its out-of-context activation in diverse developmental lineages. However, the mechanism underlying its lineage-specific activation of Notch (N), Wnt, or EZH2—three well-characterized oncogenic targets of ERG—remains elusive. We reasoned that deep homology in genetic tool kits might help uncover such elusive cancer mechanisms in Drosophila. By heterologous gain of human ERG in Drosophila, here we reveal Chip, which codes for a transcriptional coactivator, LIM-domain-binding (LDB) protein, as its novel target. ERG represses Drosophila Chip via its direct binding and, indirectly, via E(z)-mediated silencing of its promoter. Downregulation of Chip disrupts LIM–HD complex formed between Chip and Tailup (Tup)—a LIM–HD transcription factor—in the developing notum. A consequent activation of N-driven Wg signaling leads to notum-to-wing transdetermination. These fallouts of ERG gain are arrested upon a simultaneous gain of Chip, sequestration of Wg ligand, and, alternatively, loss of N signaling or E(z) activity. Finally, we show that the human LDB1, a homolog of Drosophila Chip, is repressed in ERG-positive prostate cancer cells. Besides identifying an elusive target of human ERG, our study unravels an underpinning of its lineage-specific carcinogenesis.
ERG
Chip
LDB1
Cancer
Wnt
DST | Science and Engineering Research Board (SERB) 501100001843 EMR/2016/006723 Bushra AteeqPradip Sinha DST | Science and Engineering Research Board (SERB) 501100001843 EMR/2016/006723 Bushra AteeqPradip Sinha The Wellcome Trust DBT India Alliance (India Alliance) 501100009053 IA/E/13/1/501271 Anjali BajpaiBushra Ateeq The Wellcome Trust DBT India Alliance (India Alliance) 501100009053 IA/S/19/2/504659 Anjali BajpaiBushra Ateeq
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pmcEvolutionary conservation of genetic tool kits such as cellular signaling pathways and homeotic selectors regulating cell lineages, fates, and pattern formations in animals from distant phylogenies reveals deep homology (1). Apart from these well-known genetic tool kits, LIM–homeodomain (LIM–HD) transcription factors are also conserved across the animal kingdom (2). Examples of this class of LIM–HD transcription factors include Tailup (Tup) and Apterous (Ap) in Drosophila; their mammalian homologs being Islet and LIM–homeobox, respectively [for reviews, see ref. 3]. LIM–HD transcription factors are activated by forming tetrameric complexes with a transcription cofactor, LIM-domain-binding (LDB) protein: Chip, in Drosophila (4, 5) and its homolog, LIM Domain Binding, LDB, in mammals [reviewed in ref. 6]. Conservation of LIM–HD complexes across phylogeny (2) also underscores the pervasive nature of deep homology in developmental lineage specification and pattern formation across insects (7) to mammals (8, 9).
During mammalian development, expression of ERG (ETS-Related Gene) master transcription factor is seen in endothelial cells and organs of mesodermal lineage: for instance, developing kidney, urogenital tract, hematopoietic cells, cartilage, and neural crest cells (10, 11). In adults, ERG expression is seen in the cells of endothelial (10, 11) but not in those of epithelial lineages, including the prostatic epithelium (12). Out-of-context ERG activations via chromosomal translocations and fusion with promoters of active genes in select cell lineages trigger carcinogenesis. Prostate cancer (13, 14), Ewing sarcoma (15), or acute myeloid leukemia (16) are some of the exemplars of the extreme diversity of ERG-induced lineage-specific cancers. ERG targets have so far been identified by genome-wide screening of its binding. In prostate cancer, for instance, activated ERG (13) partners with HOXB13 and FOXA1 for binding to its targets; subsets of these ERG targets show enrichment of Notch (N) signaling pathway members (17). ERG-induced prostate cancer also display upregulation of Wnt ligands (18) and EZH2, a member of the Polycomb group complex (19), besides DLX1, a homeobox transcription factor (20). A major hallmark of ERG-induced cancers is phenotypic plasticity, suggesting their cell fate reversals en route to their metastatic progression [for review, see ref. 21]. However, despite identifying these ERG targets in diverse cancers (14), underpinnings of its lineage-specific carcinogeneses remain elusive.
It has long been recognized that heterologous expression of mammalian transcription factors in Drosophila identifies targets that display deep homology. Seminal reports on homeotic transformations in Drosophila following heterologous expression of mammalian Hox-2.2 (22) or Pax6 master transcription factors (23) revealed conserved downstream targets. Like Hox-2.2 or Pax6, the ERG master transcription factor displays an ancient metazoan origin (24), and its evolution is marked by the conservation of its DNA-binding ETS and PNT domains [for review, see ref. 25]. We thus hypothesized that a gain of human ERG master transcription factor in Drosophila would uncover its yet elusive targets, particularly those which underpin its capacities to induce lineage-specific carcinogenesis.
Here, we have expressed ERG oncoprotein in Drosophila developing appendages and revealed the repression of its novel target, Chip. Further, reminiscent of that seen in prostate cancer (19), heterologous ERG binds to E(z), the Drosophila homolog of mammalian EZH2. ERG-induced E(z) epigenetically silences Chip transcription, disrupting the Chip–Tup, LIM–HD complex. A consequent reactivation of N-triggered Wg synthesis in the posterior notum culminates in notum-to-wing transdetermination. Further, upon losing a tumor suppressor, like Lgl, ERG-expressing notal cells display cooperative carcinogenesis via N-Wg signaling. Finally, we show that ERG-positive prostate cancer cells display downregulation of LDB1, a human homolog of Drosophila Chip. These results present a strategy to identify human oncoprotein targets and decipher cancer mechanisms in Drosophila.
Results
Heterologous ERG Induces Notum-to-Wing Transdetermination by Triggering Ectopic N-Wg Signaling.
The proximal and distal domains of the developing wing imaginal disc of Drosophila give rise to the adult thorax (notum) and wing proper, which are held together by a hinge domain. Ap, LIM–HD, and Engrailed (En), homeobox, transcription factors, respectively, specify the dorsal (D) and posterior (P) compartments of the wing primordium [Fig. 1A, for review, see ref. 26]. DV and AP boundaries of the developing wing double up as signaling centers regulating its anisotropic growth by sending out Wg (a Wnt) and Dpp (a BMP) morphogens, respectively [for recent articles, see refs. 27–29]. The presumptive adult wing is marked by a POU-domain protein, Nubbin [Nub, Fig. 1B; (30)], a target of Wg [Fig. 1C; (30–33)]. Nub also marks the inner of the two epithelial folds of the presumptive wing hinge region [blue arrowhead, Fig. 1B, also see refs. 31].
Fig. 1. Human ERG oncoprotein induces notum-to-wing transdetermination in Drosophila. (A–C) Apterous (red, Ap, A) and en>GFP (green, A), respectively, mark the dorsal (D) and posterior (P) compartments of the wing imaginal disc. Yellow and white broken lines mark the AP and DV compartment boundaries, respectively (A). Nub (red, nub>mCherry) expression marks the presumptive adult wing pouch (broken line, B) and the inner (blue arrowhead, B), but not the outer epithelial fold (orange arrowhead, B) of the hinge, and notum (open arrowhead). Wg expression (green) is seen in the DV boundary (yellow arrowhead, C) in both the inner and outer rings of the hinge domain (orange and blue arrowheads, C) and the notum (open arrowhead). Distal (wing pouch) is up, and proximal (notum) is down in this and all subsequent images. (D) Domain of expression of the vg-Gal4 driver (vg>GFP, green, D) on the DV boundary (yellow arrowhead), hinge (blue arrowheads), and in a trail of cells till the edge of the posterior notum (star). (E and F) ERG expression under vg-Gal4 (vg>ERG, GFP, green, E) or en-Gal4 (en>ERG, GFP, green, F) drivers induce ectopic Nub in the notum; yellow star here (stars, E and F) and elsewhere marks the notum-to-wing transdetermination. (G–I) Examples of en-Gal4>ERG wing imaginal disc displaying one (star, G), two (stars, H) or three (stars, I) transdetermined wing primordia in the notum (Nub, red, G–I). (J and K) Frequency of notum-to-wing transdetermination upon the gain of ERG under en-Gal4, vg-Gal4, dpp-Gal4, or ci-Gal4 driver (J). Schematic display of domains of these Gal4 expressions revealing their overlap on a hot spot (yellow stippled dots in a red zone) of wing transdetermination in the posterior notum (K). (L) Constitutive gain of N, (vg>Nintra, GFP, green). The boxed area in (L) is shown at higher magnification in (L’–L'"). XZ optical section further reveals autonomous (orange arrowheads) and non-autonomous (red arrowheads) gain of Nub (red, L'–L'’’). (M–P) Co-expression of ERG with a dominant-negative form of N (blue star, en>ERG; NDN, M) or its downstream effector Mam (blue star, en>ERG; mamDN, N) arrest wing transdetermination in the notum (blue star). Coexpression of a membrane-tethered receptor, GPI-dFz2 with ERG (en>ERG; GPI-dFz2) sequesters the Wg ligand in the DV signaling center (yellow arrowhead, O) and the posterior notum (blue star, O), with accompanying loss of notum-to-wing transdetermination (loss of Nub, blue star, P). (Q–S) Haltere imaginal disc displaying metanotum-to-capitellum transdetermination (Nub, star, en>ERG, Q). Also, note the proximal (Hth, R) and distal patterning in the transdetermined haltere (star, Wg, S). The boxed area in (S) is displayed at higher magnification in the right panel to reveal the absence of Wg in the posterior DV margin of the transdetermined capitellum, a characteristic of endogenous haltere primordium [orange arrowhead, S’, (34)]. Scale bars, 50 µm; N=number of transdetermined wing primordia/total number.
To examine the fallout of a heterologous gain of ERG oncoprotein, we drove its expression in the larval wing imaginal discs under four different Gal4 drivers individually. These were en-Gal4 [en>GFP, Fig. 1A, (35)], vg-Gal [boundary enhancer-BE, vg>GFP Fig. 1D, (36)], ci-Gal4 [ci>GFP, SI Appendix, Fig. S1A, (37)], and dpp-Gal4 [dpp>GFP, SI Appendix, Fig S1C, (38)]. Gain of ERG under vg-Gal4 (star, Fig. 1E) or en-Gal4 driver (stars, Fig. 1 F–I) induced a striking fallout, namely, ectopic expression of Nub in the presumptive notum, revealing a notum-to-wing cell fate switch or transdetermination [for review, see ref. 39]. In extreme scenarios, the transdetermined wing primordium (star, Fig. 1G) outgrew its endogenous counterpart. More than one transdetermined wing primordia were infrequently seen in en>ERG notum (stars, Fig. 1 H and I). Frequencies of transdetermined wings progressively declined in the order of the Gal4 used: namely, en-Gal4, vg-Gal4, dpp-Gal4, and ci-Gal4 (Fig. 1J). Notably, plotting the domains of these Gal4 drivers revealed a shared feature: their expression within or abutting the presumptive posterior notum of the wing imaginal disc wherein out-of-context Nub was induced (yellow stippled dots, Fig. 1K). Thus, ERG-gain under these Gal4 drivers induced ectopic Nub in only the posterior notum, even while their expressions extended far beyond this domain. The posterior notum, therefore, displayed hallmarks of a hotspot: that is, a sensitive zone for cell fate switch as seen during regeneration of the leg (40) or upon gain of Eyeless (Ey) master transcription factor in the wing, leg, eye, and haltere imaginal discs (23, 41).
Wg expression is seen in an anterior ventral wedge (42) in the distal wing imaginal disc of the early second larval instar (33). These Wg-expressing cells represent the progenitors of the future adult wing (42) and are marked by expression of Nub and, Vestigial, Vg (33), the latter being a wing cell fate selector (43). Subsequently, during third larval instar, growth and patterning of the wing primordium are driven by Wg morphogen synthesized and secreted from its DV signaling center (28, 29, 33, 42, 44). -The third larval instar wing imaginal disc, therefore, Wg displays its characteristic, spatially restricted morphogen signaling from the DV boundary, which drives wing growth and patterning (27–29, 33).
N regulates Wg during second phase of its wing growth-promoting role (27–29, 44–46). We noted that the gain of a constitutively active N receptor, Nintra (47), too, induced a notum-to-wing cell fate switch (star in Fig. 1L), reminiscent of that seen upon the gain of ERG (Fig. 1 E–I). Conversely, coexpression of ERG and a dominant-negative form of N, namely, NDN (48), or that of its downstream target, Mastermind, MamDN (49), extinguished wing transdetermination in the posterior notum (blue star, Fig. 1 M and N). Further, coexpression of ERG and a membrane-anchored Frizzled (Fz) receptor, GPI-dFz2—which tethers Wg ligand to the membrane arresting its signal transduction (50)—suppressed wing transdetermination (blue star, Fig. 1 O and P). Finally, ERG gain in the haltere induced metanotum-to-capitellum transdetermination (Fig. 1 Q–S), which is anticipated given that these two dorsal appendages, the wing and haltere, share common developmental ground plan and genetic tool kits (34).
ERG-Induced Wg in the Notum Displays Both Its Early Wing-Specifying and Late Growth-Promoting Roles.
ERG-expressing second instar larval wing imaginal disc, however, did not display notum-to-wing transdetermination, as revealed by the absence of Nub expression (blue star, en>ERG, Fig. 2A), while at this stage, the endogenous wing primordium displayed its characteristic expression (arrow, en>ERG, Fig. 2A). Subsequently, in a mid-third instar vg>ERG wing imaginal disc, we noticed induction of Nub (31–33) at a far posterior margin of its notum (star, Fig. 2 B and B’). Induction of Nub expression in these cells of notum by ERG was cell-autonomous (yellow arrowheads in Fig. 2 B’ and B” and XZ optical section in Fig. 2B’) as well as non-cell autonomous (red arrowhead in Fig. 2 B’ and B” and XZ section in Fig. 2B’). These characteristics were further quantified by their fluorescence intensities (Fig. 2C) and colocalization (Fig. 2D, see SI Appendix, Methods). These results suggest that Wg induced in ERG-expressing cells of the posterior notum is secreted. Indeed, ERG-expressing somatic clones in the posterior notum displayed cell-autonomous (yellow arrowhead, broken line, Fig. 2 E and E”) and extensive non-cell-autonomous notum-to-wing cell fate switches (red arrowhead, Nub, Fig. 2 E and E” ), as can be anticipated from their secretion of the long-range Wg morphogen (51). Moreover, we also noticed cell-autonomous and non-cell-autonomous Wg expressions, respectively, within (broken line, Fig. 2 F’ and F” ) and around (arrow, Fig. 2 F’ and F”) the ERG-expressing clone in the posterior notum.
Fig. 2. ERG-induced Wg specifies wing cell fate and promotes wing growth in the posterior notum. (A) An ERG-expressing, second instar wing imaginal disc (en>ERG; GFP, green): Nub (arrow) marks the presumptive wing; Nub expression is not seen in the developing notum (blue star), despite ERG gain at this stage (for comparison, see Fig. 1 F and G). (B–D) Mid-third instar vg>ERG wing imaginal disc displaying an early sign of notum-to-wing transdetermination in the posterior notum (star, Nub, 2B). The boxed area in (B) is shown at higher magnification in (B'). An XZ optical section along the dotted line in B' is shown in B”. Note the cell-autonomous (yellow arrowheads B', B”) notum-to-wing cell fate switch as seen from expression of both the ERG (GFP, green) and Nub (red). Also, note the non-cell autonomous Nub expression (red arrowhead, B', B”) in a cell neighboring the ERG-expressing cells (yellow arrowhead, orange fluorescence B’, B”). Fluorescence intensities in (B') is also shown in a scatter plot (C). A substantial fraction cells display non-cell autonomous gain of Nub in this region of interest (dot plot, red, D). (E and F) Mosaic wing epithelium with ERG-expressing clones (MARCM>ERG, GFP, green, E and F). A broken line outlines the clonal area in both. A magnified view of the boxed area from (E) is shown in (E') to reveal its cell-autonomous (yellow arrowhead, E') and largely non-cell-autonomous (red arrowhead, E') notum-to-wing transdetermination (red, Nub, E'). The red channel (Nub, E’) is shown separately in (E’’). A magnified view of the boxed area of (F), is shown in (F’). Note the non-cell-autonomous (arrow, F’, F’’) and cell-autonomous (within the broken line, F', F") expression of Wg (red) in fixed preparation of mosaic wing imaginal disc. Clones formed outside the posterior notum do not induce Nub (white arrowheads, E and F). (G–J) Cell-autonomous and non-cell-autonomous induction of Wg (red) in ERG-expressing nota of en>ERG wing imaginal discs. Boxed areas of 3 to 6-d-old, third instar larval wing imaginal discs (en>ERG; G–J) are shown at higher magnification in (G’–J’). XZ and XY (G) or only XY (H and I) optical sections are also displayed. Open arrowhead (J') marks the endogenous notum-specific Wg expression. (K–N) Adults eclosed from the indicated genotypes display a range of notum-to-wing transdetermination ranging from amorphous wing growth (K and L) to near completely patterned wing (M and N). Higher magnifications are shown on the right (K’–M'). (O) The number of vg>ERG adults with notum-to-wing transdetermination (TD) or without (No TD). (P and Q) Schematic interpretation of ERG-induced notum-to-wing transdetermination (TD) in the notum of wing imaginal disc (P) and adult mesothorax (Q). Color scheme of different cell types and Wg gradient are shown at the bottom. Scale bar, 50 µm.
Starting mid-third larval instar stage, en>ERG wing imaginal discs displayed Wg expression in the notum, straddling the anterior (A) and posterior (P) compartment boundary (broken line, Fig. 2G). In older third larval wing imaginal discs, cell-autonomous and non-cell-autonomous Wg expressions in transdetermined wing primordia were more pronounced (Fig. 2 H and I). Finally, we also noticed instances where Wg expression in the transdetermined wing matched its endogenous counterpart (Fig. 2 J and J' ). In ERG-expressing adult thorax, we noticed amorphic wing tissue growth (stars, Fig. 2 K and L) to transdetermination into wing proper that appeared patterned (stars, Fig. 2 M and N), like their endogenous counterparts. Overall, about two-thirds of the eclosed vg>ERG adults displayed notum-to-wing transdetermination (Fig. 2O). Further, transdetermined wing primordia showed growth along their AP, DV, and PD axes (SI Appendix, Fig. S1 E–H), suggesting their anisotropic growth via AP and DV morphogen-signaling centers, much like their endogenous counterpart (27, 29).
Our results reveal that ERG-triggered Wg induces both cell-autonomous and non-cell-autonomous notum-to-wing cell fate switches (Fig. 2P). A near-perfect patterning of the transdetermined wing, although infrequent, suggests the development of DV and AP signaling centers in these transdetermined wings by mechanism(s) that can only be speculated based on the emergent understanding of the growth and patterning in the endogenous wing primordium [(27–29, 33, 42, 44), see Discussion].
Heterologous ERG and Its Drosophila Ortholog, Ets21C, Both Target Chip for Repression at Conserved ERG-Binding Sites (EBS).
LIM–HD protein complexes maintain developmental domain-specific cellular signaling and cell fate during the development of Drosophila appendages (5, 52, 53). For instance, Chip–Tup (52, 54) and Chip–Ap (4, 5) are, respectively, active in the proximal (notum) and distal (wing) domains of the wing imaginal disc, maintaining their respective cell fates. Therefore, it is plausible that the loss of Chip, Tup, or both underlies the notum-to-wing cell fate switch seen upon ERG gain.
We noticed a transcriptional downregulation of Chip in en>ERG wing imaginal discs (Fig. 3 A–C). Therefore, ERG-induced notum-to-wing transdetermination is likely linked to Chip loss. In agreement, we noticed that compromising Chip activity by expressing a dominant-negative form of Chip, ChipΔoid—which partially lacks its other interacting domain, OID (55)—in somatic clones (Fig. 3D) or under the en-Gal4 driver (Fig. 3E and SI Appendix, Fig. S4C)induced ectopic Nub expression in the notum. We also recovered vg-Gal4>ChipΔoid adults, which showed de novo wing development in the thorax (Fig. 3F). Further, knockdown of Chip (en>Chip-RNAi, Fig. 3G), too, phenocopied ERG gain (see Figs. 1 and 2). Conversely, a simultaneous gain of Chip and ERG abrogated de novo Nub (Fig. 3H) or Wg (Fig. 3I) expressions in the notum. These results reveal that ERG-induced notum-to-wing transdetermination entails a downregulation of Chip.
Fig. 3. Both ERG and its Drosophila ortholog, Ets21C, target Chip. (A–C) Quantification of Chip RNA (RT-qPCR) from wing epithelia expressing ERG (en>ERG, A), Chip (en>Chip, B) or displaying knockdown of Chip (en>Chip-RNAi, C); fold changes in Chip RNA are displayed as ratios to that of internal control, GAPDH. **P<0.005. (D–I) Clonal expression of a dominant-negative form of Chip (ChipΔoid, GFP, D) or its expression under an en-Gal4 driver (en>ChipΔoid, GFP, E) displayed notum-to-wing transdetermination (star, Nub, D and E; Insets display only the Nub channel). vg>ChipΔoid adult displaying ectopic but amorphous wing development in the adult thorax (star, F). en>Chip-RNAi displaying ectopic Nub in notum (star, Nub, G). Coexpression ERG and Chip extinguished notum-to-wing transdetermination as seen from the absence of Nub (blue star, en>Chip, ERG, H). These wing imaginal discs also failed to display ectopic Wg in notum (blue star, en>Chip, ERG, I). (J–M) Gain of Ets21C, a Drosophila ortholog of mammalian ERG (en>Ets21C, GFP, J–L). Magnified boxed area of (J and K) in the posterior notum reveals its incipient gain of Nub (star, J') and elevated Wg (K'); open arrowhead marks the endogenous notum-specific strip of Wg (K and K', also see Fig. 1C). These Ets21C-expressing wing imaginal discs display suppression of an N target, Cut, in the posterior DV margin (L and L', blue arrowhead in L'). Most vg>Ets21C animals eclosed as adults with a characteristic notched-wing phenotype (arrowheads, M). (N–P) Consensus DNA-binding sequences of ERG (N) and Ets21C (O) as predicted by JASPAR (56). A schematic representation of evolutionary conservation of −3.0kb cis-regulatory module (CRM) upstream of Chip transcription start site (TSS) across 27 insect species. Approximate positions of the predicted ERG/Ets21C-binding sites (EBSs) are marked by nine red vertical bars, while the bottom brown horizontal bars displayed highly conserved elements calculated by phastCons. Two horizontal green lines mark the regions, which were further tested for physical binding of ERG by performing ChIP-qPCR. The inset table displays individual conservation scores of these nine EBSs, as generated by JASPAR. (Q and R) Enrichment of ERG binding to EBS1 and EBS6/7 assayed by ChIP-qPCR from en>ERG wing imaginal discs using an anti-ERG antibody. **P < 0.001. Scale bar, 50µm, N= number of transdetermined wing primordia/total number.
ERG-mediated repression of Chip is also likely to affect Chip–Ap tetramers formed in the dorsal wing pouch (4, 5). We noted that the ap expression revealed by its ap-lacZ reporter in the dorsal wing primordium was not perturbed by ERG gain (SI Appendix, Fig. S2A). In the notum, ap displays a non-uniform pattern of expression (ap-lacZ, SI Appendix, Fig. S2 B–D). We reasoned that ERG-induced Chip downregulation—and consequently, loss of Chip–Ap tetramers in the dorsal wing—might compromise Ap activity, which likely culminates in the development of de novo Ap+/Ap− boundary: that is, DV signaling center (57, 58). Surprisingly, a ubiquitous gain of ERG (Fig. 1) or loss of Chip (Fig. 3) under different Gal4 drivers did not display signs of ectopic DV signaling center development in the endogenous wing primordium. By contrast, ERG-expressing somatic clones induced ectopic DV signaling centers in only select spatial domain of dorsal wing primordium (SI Appendix, Fig. S2 E–F) while Chip loss-of-function clones invariably induce ectopic DV boundaries in the dorsal wing pouch (59). Thus, ERG-expressing clones might cause a partial and incomplete depletion of Chip levels. Alternatively, it is also plausible that the Chip–Tup complex in the notum is more sensitive to ERG gain than Chip–Ap.
Ets21C [synonym Ets6, (60)] is a Drosophila ortholog of ERG that displays 86% and 26% identity of its ETS- and PNT-domains with those of its human counterpart, respectively (60–63). Ets21C expression is triggered during regeneration and tumorigenesis (64, 65), while its gain in oncogenically targeted cells induces cooperative carcinogenesis in Drosophila (62, 63, 65, 66). We noted that ectopic gain of Ets21C induced Nub and Wg expressions in the posterior notum (Fig. 3 J–K), albeit far less strikingly than that seen upon ERG gain (Figs. 1 and 2) or Chip loss (Fig. 3 A–F). Moreover, a gain of Ets21C under en-Gal4 driver displayed suppression of N target, Cut (Fig. 3L) at the wing primordium's DV boundary, culminating in characteristic notched wing phenotypes in adult flies (Fig. 3M).
We further noted that the DNA-binding sequence of ERG (Fig. 3N) and Ets21C (Fig. 3O) are highly conserved [CCGGAA (Fig. 3 N and O)]. To examine if Chip is a transcriptional target of ERG and Ets21C, we examined a −3.0kb region upstream of its TSS (transcription start site) for putative Ets21C/ERG binding site (EBS). Such an upstream region of Chip may serve as its CRM (cis-regulatory module). We found nine putative Ets21C-binding sites in this −3.0-kb CRM of Chip; eight of these were putative ERG binding sites, (SI Appendix, Fig. S2C). Next, we looked for conservation of the EBS containing CRM of Chip across different insect species. Two standard bioinformatics analysis methods were used for the estimation of evolutionary conservation: phyloP (44, 67) and phastCons [(68), see SI Appendix, Methods] across 27 insect species available at the UCSC genome browser portal. We noticed positive phyloP scores for most nucleotides in this CRM of Chip (SI Appendix, Fig. S3A). Likewise, phastCons analysis, too, revealed stretches of nucleotide runs with a positive score (SI Appendix, Fig. S3A), indicating their conservation across all 27 insect species. We noted that six of the predicted EBSs were located within “Conserved Elements” based on phastCons scores (Fig. 3P and SI Appendix, Fig. S3B). Finally, chromatin immunoprecipitation (ChIP) of en>ERG imaginal discs confirmed the binding of ERG on three (EBS1, EBS6, and EBS7) of the nine EBSs in the CRM of Chip (Fig. 3 Q and R).
The Spatial Domain of Tup Expression in the Posterior Notum Underpins Lineage-Specific Fallouts of ERG Gain.
While the expression of Chip is ubiquitous throughout the wing imaginal discs (69), its LIM–HD-binding partner, Tup, is selectively expressed in the notum of the wing primordium [Fig. 4A, see refs. 54], that largely overlaps with the domain of expression of the posterior cell fate-specifying selector, En (also see Fig. 1A). Consequently, the Chip–Tup tetrameric complex remains restricted to the Tup-expressing cell of the notum. Not surprisingly, tup knockdown induced selective notum-to-wing transdetermination in the Tup-expressing cells of the notum (Nub, Fig. 4 B and C; Cut, SI Appendix, Fig. S4D, also see Fig. 1 E and F”), reminiscent of that seen upon ERG-induced Chip loss. Loss of Tup in the distal wing did not down-regulate N-Wg signaling (SI Appendix, Fig. S4 D and D”)—unlike that seen upon loss of Chip (Fig. 3 D–G)—which is consistent with the fact that the Chip–Tup complex is formed only in the posterior notum. Further, neither Chip loss (Fig. 4D) nor ERG gain (Fig. 4E) repressed Tup expression. Finally, tup gain—unlike that of Chip (see Fig. 3 H and I )—failed to arrest ERG-induced notum-to-wing transdetermination (Fig. 4F). These results reveal that spatial regulation of Tup (Fig. 4G) dictates the fallout of heterologous ERG, although it is not a direct target of repression by the latter.
Fig. 4. Knockdown of Tup, a LIM–HD binding partner of Chip, induces notum-to-wing transdetermination. (A–A") Domain of expression of Tup (magenta) in the notum of the third instar wing imaginal disc (A); broken line marks the AP compartment boundary (en>GFP; green, A). Boxed area of (A) is shown at higher magnification in (A' and A"). (B and C) Knockdown of tup in wing imaginal disc induced notum-to-wing transdetermination (star, Nub, en>tup-RNAi; B and B'), while the adult mesothorax displayed amorphous wing growth (star, a vg>tup-RNAi, C). (D–F) Loss of Chip (green, en>ChipΔoid, GFP, D) or gain of ERG (green, en>ERG, GFP, E) fails to suppress Tup in the transdetermined wing (star, D' and E'). A simultaneous gain of tup and ERG, too, fails to suppress notum-to-wing transdetermination (star, en>tup; ERG, F and F'; for comparison, see Fig. 3 H and I). (G) Schematic representation of Chip–Tup complex-mediated repression of wing cell fate in the posterior notum. Scale bar, 50 µm, N= number of transdetermined wing primordia/total number.
ERG-Induced E(z) Epigenetically Silences Chip.
By a direct binding on EZH2 (19), ERG upregulates its expression in different cancers [for review, see ref. 70]. In ERG-expressing wing epithelium, E(z) (71), a Drosophila homolog of mammalian EZH2, was seen upregulated (en>ERG, Fig. 5A). Further, in a −3.0kb CRM upstream of E(z) TSS, we notice three putative Ets21C-binding sites, two of which were also putative ERG binding sites (Fig. 5B and SI Appendix, Fig. S5C). Notably, this E(z) CRM was conserved across 27 insect species (SI Appendix, Fig. S5 A and B). Finally, we confirmed the binding of ERG on EBS1 through ChIP from en>ERG imaginal discs using ERG antibody (Fig. 5C).
Fig. 5. ERG-up-regulated E(z) epigenetically represses Chip. (A) Quantification E(z) RNA (RT-qPCR) in en>ERG wing imaginal discs; RNA fold changes are expressed relative to that of its internal control, GAPDH. **P < 0.005. (B) A schematic representation of evolutionarily conserved −3.0kb sequence upstream of E(z) TSS across 27 insect species using phyloP (see Fig. 3). Approximate positions of the predicted ERG/Ets21C-binding sites (EBSs) are marked by three vertical bars (red), while the bottom horizontal bars (brown) displayed highly conserved elements calculated by phastCons. The horizontal green line marks the region, which was further tested for physical binding of ERG by ChIP-qPCR. Inset table displays individual conservation scores of these three EBSs, as generated by JASPAR. (C) Enrichment of ERG binding to EBS1 assayed by ChIP-qPCR from en>ERG wing imaginal discs using an anti-ERG antibody. **P < 0.001. (D–G) Consensus Zeste-binding sequence (Top, D) within a −3.0kb Chip TSS displays three Polycomb response elements, PREs (PRE1, PRE2, and PRE3, Bottom, D). Enrichment of these PREs in ChIP-qPCR from E(z)-GFP; en>ERG wing imaginal discs following pulled down with anti-GFP (E), mammalian anti-EZH2 (F), or anti-H3K27me3 (G). *P < 0.01, **P < 0.001, ***P < 0.0001. (H) Larvae (vg>ERG) fed on increasing concentrations of EZH2 inhibitor, GSK126, progressively displayed suppression of ERG-induced notum-to-wing transdetermination (TD) in eclosed adults. (I and J) Knockdown of E(z) in ERG-expressing wing imaginal discs (green, vg>E(z) RNAi; ERG, GFP, I) or in somatic clones (green, hs flp; act>E(z) RNAi; ERG, GFP, J) suppressed notum-to-wing transdetermination (blue star, I' and J'). (K and L) Schema displaying ERG-mediated suppression of Chip by its direct binding (K) and indirectly via E(z) (L). Scale bar, 50 µm; N= number of clones with the desired genotype scored in the notum /total number of wing discs observed.)
E(z) is a transcriptional repressor, a core component of the Polycomb repressive complex 2, PRC2, which binds to the PREs of its targets [see refs. 72 and 73 and SI Appendix, Methods]. ERG-induced E(z) upregulation may thus contribute to the repression of ERG targets, such as Chip. Indeed, we noticed three PREs within the -3.0 kb CRM of Chip (Fig. 5D) that also harbored the EBSs (see Fig. 3P). These PREs were enriched in the ChIP of ERG-expressing wing imaginal discs [E(z)-GFP, en>ERG, Fig. 5 E and F] and displayed the trimethylation of lysine 27 on histone H3, H3K27me3 (Fig. 5G), a hallmark of E(z)-mediated epigenetic silencing (73).
The preceding observations thus suggest an E(z)-mediated silencing of Chip in ERG-expressing imaginal discs. To further test this interpretation, we fed vg>ERG larvae on food supplemented with a well-characterized inhibitor of EZH2, GSK126 (74). These animals display a progressive, concentration-dependent suppression of notum-to-wing transdetermination in vg>ERG animals (Fig. 5H). Likewise, a knockdown of E(z) in ERG-expressing notal epithelium (Fig. 5 I and J) extinguished notum-to-wing transdetermination. Thus, ERG directly represses Chip (Fig. 5K) on the one hand, and on the other, its upregulation of E(z) leads to epigenetic silencing of the former (Fig. 5L).
ERG-Induced Disruption of the Chip–Tup Complex Underpins Its Lineage-Specific Cooperative Carcinogenesis.
Drosophila displays a well-known two-hit (75, 76) paradigm of cooperative carcinogenesis (76) in select developmental lineages (77–79). In cells displaying loss of a tumor suppressor, like Lgl, tumor progression is often driven via the recruitment of endogenously active signaling pathways (78). Given these developmental underpinnings of cooperative carcinogenesis, we reasoned that ERG gain might display lineage-restricted tumor cooperation in the posterior notum. Indeed, in the posterior notum, lgl-; ERG+ clones displayed Nub expression (star, Fig. 6A)—reminiscent of that seen upon ERG gain in the posterior notum (see Fig. 2 B and B’). Further, lgl-; ERG+ somatic clones displayed synthesis and secretion of Wg (Fig. 6 B and B’ )—reminiscent of morphogen-sending neoplastic clones (80, 81)— besides inducing non-cell-autonomous hyperproliferation in the tumor microenvironment (arrow, PH3, red, Fig. 6 C and D). In the rest of the mosaic wing imaginal disc, lgl-; ERG+ clones largely failed to display neoplastic transformation (white arrowheads, Fig. 6 A–C). Notably, a gain of N signaling in lgl clones, too, induced notum-to-wing cell fate switch and neoplastic transformation in the posterior notum (lgl-; N intra, SI Appendix, Fig. S6 A and B) phenocopying their lgl-; ERG+ counterpart (Fig. 6A). Finally, we note that Chip gain (Fig. 6 E and E'), E(z) knockdown (Fig. 6 F and F’), or sequestration of the Wg ligand (Fig. 6 G and G' ) in lgl-; ERG+ clones arrested their neoplastic transformation (white arrowheads, Fig. 6 E'–G' ). Comparable results were also obtained by feeding host larvae with lgl-; ERG+ mosaic discs on food supplemented with an E(z) inhibitor, GSK126 (white arrowheads, Fig. 6 H and H').
Fig. 6. ERG selectively cooperates for lgl tumorigenesis in the posterior notum. (A–A") A mosaic wing imaginal disc displaying ERG-expressing lgl- somatic clones (lgl-, ERG+; GFP, green, A). Box 1 and box 2, respectively, mark a large and a small lgl-, ERG+ clone, which are displayed at higher magnifications in the right panels in (A') and (A"). Endogenous wing (pouch) primordium is marked by broken blue line (A). Note the induction of non-cell autonomous notum-to-wing transdetermination in cells (only Nub-expressing, red, star, A’) overlapping with the clones (yellow, A’). The larger neoplastic transformation lgl-, ERG+ clonal area is marked (actin, broken yellow line, A and A’). Non-cell-autonomous growth and notum-to-wing transdetermination are marked by excessive epithelial folds and Nub expression (star, A and A'). The small clone in box 2 reveals, too, displays these essential hallmarks lgl-, ERG+ clone (A and A”). (B–B'") An unfixed mosaic wing imaginal disc epithelium displaying lgl-, ERG+ clones (GFP, green) displaying secreted Wg (red). Boxed areas in (B and B') are shown at higher magnification in their respective right panels (B” and B”'). Note the autonomous neoplasia within the clone (broken line, actin) while the surrounding epithelium display secreted Wg (arrow, B') and hyperplasia, the latter marked by excessive epithelial foldings (arrow, B–B”'). lgl-, ERG+ clones induced in domains other than posterior notum did not display neoplastic transformation (white arrowheads, B). (C and C') A mosaic wing imaginal disc epithelium with lgl-; ERG+clones (green), stained for a cell proliferation marker phospho-histone, PH3 (red). Boxed area in (C) is shown at higher magnification in (C'). Note the exaggerated PH3 uptake (arrow) around this transformed lgl-; ERG+clone in the posterior notum (arrow, green, C') unlike their counterparts elsewhere (white arrowhead, C). (D) Quantification of cell-autonomous versus non-cell-autonomous PH3 uptake in lgl-; ERG+ clones as compared with their ERG-expressing control (see SI Appendix, Methods). *P < 0.01. (E–H) lgl-; ERG+ clones in mosaic wing imaginal disc epithelia displaying simultaneous gain of Chip (E), knockdown of E(z) (F), expression of GPI-dFz2, membrane-tethered Wg receptor (G), or those from larvae fed on EZH2 inhibitor, GSK126 (25 mg/ml) (H). These lgl-; ERG+ clones neither induced Nub expression in posterior notum (E–H) nor displayed neoplasia (actin, white arrowheads, E’–H'). (I) Cartoon representation of selective carcinogenesis of lgl-; ERG+ somatic clones in the posterior notum (I). Scale bar, 50 µm. N= number of clones with the desired genotype scored in the notum /total number of wing discs observed.
Together, these results reveal that an ERG-induced, lineage-specific, cooperative carcinogenesis stems from its downregulation of Chip, leading to loss of Chip–Tup, LIM–HD complex, triggering an out-of-context N-mediated Wg signaling (Fig. 6I).
Prostate Cancer Cells Display ERG-Induced LDB1 Repression.
We noticed that a protein–protein interaction map centered on the Chip–Tup complex of Drosophila was comparable with its mammalian counterparts (SI Appendix, Fig. S7A and also see ref. 82, (83), suggesting its ancient origin. We thus further asked if ERG targets the LDB genes, the mammalian homologs of Drosophila Chip; namely, LDB1 and LDB2, its two isoforms (84). ERG transcription and protein levels are minimal or absent in the healthy prostatic epithelium, whereas LDB1 and LDB2 show robust levels (SI Appendix, Fig. S7 B–G). Thus, we reasoned that ERG gain might target the repression of these LDBs. We chose to test this hypothesis in ERG-positive or -negative PCa cell lines. ChIP-Seq data of ERG-positive VCaP cell line [GSE28950, (84)] revealed binding peaks of ERG on the LDB1 promoter (Fig. 7A) but not on that of LDB2 (Fig. 7B). This binding peak was no longer seen in ChIP-Seq data of the VCaP cell line that displays knockdown of ERG [Fig. 7C, (GSE110655)]. We further noted the presence of a putative ERG-binding site (EBS) on the LDB1 promoter (Fig. 7D), reminiscent of that seen in its Drosophila counterpart, Chip (see Fig. 3P). In agreement, in ChIP-qPCR using ERG-positive VCaP cells, we noticed the binding of ERG on the LDB1 promoter (ERG_LDB1 Fig. 7 E and F), suggesting a possible causal underpinning of transcriptional downregulation of the latter in this cell line. Conversely, a knockdown of ERG in the VCaP cells up-regulated LDB1 expression (Fig. 7G). Gene expression data of an ERG-negative benign RWPE-1 prostate cell line revealed LDB1 downregulation upon the gain of ERG (Fig. 7H). In contrast, gene expression data of an ERG-positive VCaP cells, ERG-knockdown up-regulated LDB1 expression (Fig. 7I), further reconfirming an inverse association between ERG and LDB1. Finally, we also noticed the downregulation of EZH2 upon ERG-knockdown in ERG-positive VCaP cells (Fig. 7J). Thus, EZH2 is a transcriptional target of ERG in mammalian cancers (19, 84) reminiscent of that seen in Drosophila epithelium (Fig. 4). Together, these results reveal that ERG targets identified from Drosophila are conserved and functionally relevant for ERG-driven carcinogenesis in human.
Fig. 7. ERG-positive VCaP PCa cells displays suppression of LDB1, a homolog of the Drosophila Chip. (A and B) ChIP-seq dataset of ERG-positive VCaP (GSE28950) reveals strong binding peaks (blue arrowhead MACS, Model-based Analysis of ChIP-seq) of ERG on LDB1 (A), which are missing in LDB2 (B). Below in black is the representation of LDB1 and LDB2 genes, wherein each vertical bar represents an exon. (C) ChIP-seq dataset of an ERG-positive VCaP (GSE110655) displays loss of ERG-binding peaks on LDB1 upon knockdown of ERG (blue, Bottom) compared with control (orange, Top). Note orange arrowheads mark the ERG-binding MACS peak in control while reduced blue arrowheads display loss of ERG occupancy on LDB1 upon ERG knockdown. (D) Coordinates of an ERG-binding site, EBS, on the LDB1 locus, upstream of the TSS. (E and F) Enrichment of ERG at the predicted EBS in ChIP-qPCR of LDB1 promoter (E); a known ERG target, PLAU (Plasminogen activator urokinase), served as an internal control (F). (G) Quantification of LDB1 mRNA by qPCR following knockdown of ERG in VCaP cells. (H) Gene expression analysis in a benign prostate cell line (RWPE-1) displaying ERG over-expression (GSE86232) reveals downregulation of LDB1. (I and J) Gene expression analysis in ERG-positive VCaP displaying ERG knockdown (GSE110656). Note the upregulation of LDB1 (*P < 0.05, I) while EZH2 is down-regulated (**P < 0.005, J).
Discussion
Identification of Human Oncoprotein Targets in Drosophila and Unraveling of Essential Cancer Mechanisms.
Our results show that heterologous ERG oncoprotein targets repression of Drosophila Chip. One of the most striking fallouts of ERG gain is registered in the developing posterior notum of the wing imaginal disc, wherein disruption of Chip–Tup, LIM–HD complex leads to out-of-context N-Wg signaling. That ERG could be a transcriptional repressor of Chip/LDB1 was not predictable from the large body of literature directed at identifying ERG targets in diverse cancers [for reviews, see refs. 25 and 85]. Moreover, although we could identify EBS on human LDB1 promoter from ChIP-seq data (Fig. 7) published previously (84), its relevance in cancer progression was not evident in the absence of a display of causal association. Finally, the shared consensus binding sequence of human ERG and fly Ets21C on the CRMs of Chip and E(z) reveal a rationale for discovering functionally relevant human oncoprotein targets in Drosophila. Given the deep homology of essential genetic tool kits of development, it is also not surprising that ERG-positive prostate cancer cells display suppression of LDB1, an ERG target revealed in Drosophila.
Our results further show that the spatial limits of expression of the LIM–HD complexes underlie lineage-specific ERG-induced carcinogenesis. By extension, the developmental history of an oncoprotein-targeted cell prefigures its propensities to become cancer cells-of-origin (see refs. 78 and 86). This essential principle of lineage-specific carcinogenesis may hold for cancers that entail disruption of the LIM–HD complexes. For instance, LDB1-mediated Wnt signaling appears to play a more significant role in proximal colorectal cancer than in distal (87). Likewise, different LIM–HD complex-dependent regulations of N and Wnt signaling could underpin cell-type specificity of ERG-induced cancers (16–18).
ERG Oncoprotein-Induced Pattern Formation in Transdetermined Drosophila Appendage.
Our results show that ERG-induced Wg synthesis initially specifies wing cell fate-specification in the notum and subsequently drives its growth (33, 42, 44). Notably, de novo Wg (this study) or Dpp (81, 88) morphogen signaling centers thus drive tumor progression in cooperation with an oncogenic lesion. An ERG-induced Wg-signaling underlies its diverse, context-specific fallouts: cell fate switch alone or cell fate switch-linked tumor development in the posterior notum (Fig. 8). These findings reaffirm the maxim that carcinogenesis is essentially development gone awry (89, 90).
Fig. 8. ERG-induced disruption of Chip–Tup complex triggering Wg signaling display lineage-specific developmental and oncogenic fallouts. Schema of repression of Wg signaling by Chip–Tup, LIM–HD complex in the posterior notum (Left). ERG-induced suppression of Chip disrupts the Chip–Tup complex, activating Wg signaling that culminates in notum-to-wing transdetermination or, alternatively, neoplasia in collaboration with loss of Lgl tumor suppressor (Right) in a two-hit paradigm of carcinogenesis.
While the ERG-induced Wg in the posterior notum leads to amorphous wing tissue growth from the adult thorax (see Fig. 2), the shapes and sizes of some of these transdetermined wing primordia (Fig. 1) or the adult wings also suggest their acquisition of a near-perfect orthogonal positioning of AP (Dpp morphogen) and DV (Wg morphogen) signaling centers [Fig. 1 and see ref. 27]. We speculate that an initial non-cell-autonomous wing cell fate specification via secreted Wg from ERG-expressing cells of the posterior notum may underlie this phenomenon of near-perfect, albeit infrequent, positioning of morphogen-signaling centers. For instance, when a transdetermined wing primordium straddles an AP boundary (see Fig. 2B), the latter could provide the source of Dpp morphogen for a comprehensive wing patterning. A far more complex scenario may underlie the development of a Wg-morphogen-sending DV signaling center in a transdetermined wing primordia. For instance, since Ap expression is non-uniform in the notum (SI Appendix, Fig. S2), a non-cell-autonomous wing cell fate specification via secreted Wg may juxtapose domains of high and low Ap, reminiscent of that seen in the DV boundary of the endogenous wing (57). Alternatively, Wg signaling from a spatially aligned center in the posterior notum may be prefigured by an initial ERG-induced activation along polar coordinates of the transdetermined wing (42, 91). Further, Wg expression in the transdetermined wing may also display a feed-forward propagation of signals for its growth and patterning (29, 92).
A Future Strategy to Discover Oncogenic Networks in Drosophila.
Drosophila provides unparalleled advantages in the genetic identification of tumor suppressors and oncogenes that control cellular functions ranging from maintenance of apicobasal polarity, chromatin architecture, and vesicular trafficking, to name a few (93). The genetic tractability of Drosophila has helped unravel elusive cancer mechanisms (78, 81, 94). Drosophila also displays the two-hit model of carcinogenesis seen in mammals (75, 76, 79). Further, modeling of human cancer in Drosophila is often based on a heterologous gain of an activated oncogene (95). Results presented here reveal an approach based on the deep homology of essential genetic tool kits and their crosstalks aided by the conservation of transcription factor-binding sites, like EBS shown here, on the oncoprotein targets. In turn, phenotypic fallouts of heterologous gain of an oncoprotein offer clues to its crosstalk with a diverse set of functionally relevant pathways that could double up as oncogenic signaling nodes. For instance, activation of Wg signaling in the notum was also reported earlier upon loss of Osa (32) or subunits of BAP (Brm-associated protein) (96), which are members of a highly conserved chromatin remodeling complex. Osa/BAP, therefore, could be part of the ERG signaling network. Indeed, the binding of ERG to the BAP/BAF chromatin remodeling complex has been reported in prostate cancer (97).
Materials and Methods
Drosophila Stocks, Transgenic Line, and Genetic Mosaic Studies.
Drosophila stocks and method for generation of transgenic fly line and clones (78) are described in SI Appendix, Table S1.
Immunohistochemistry.
Method and antibodies are described in SI Appendix.
Chromatin Immunoprecipitaion and -qRT-PCR.
ChIP was performed using LowCell# ChIP kit protein A (Diagenode, C01010072). For details, see SI Appendix. RNA was extracted using TRIZOL followed by cDNA synthesis (Invitrogen). Fold changes for individual genes was quantified using ΔΔCt method, with GAPDH as internal control. Primer details in SI Appendix, Table S2.
ChIP-seq analysis.
Publicly available ChIP-Seq data (GSE28950, GSE116055) were used to determine the recruitment of ERG on LDB1 and LDB2 promoter. For details, see SI Appendix.
Prediction of Transcription Factor binding and evolutionary conservation.
JASPAR was used to look for putative Ets21C/ERG binding sites (EBSs) in E(z) and Chip promoter, and PREs, in the Chip promoter (for further details, see SI Appendix). Evolutionary conservation of Ets21C/ERG binding on Chip and E(z) upstream regulatory sequence was done using PHAST package at the USCS genome browser. Further details are presented in SI Appendix.
Gene Expression Analysis.
Datasets (GSE86232, GSE110656) were analyzed for LDB1 expression. For detail, see SI Appendix.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
We thank Martin Müller of the University of Zurich for the gift of Ap antibody. This work was supported by Science & Engineering Research Board (SERB), Department of Science and Technology (New Delhi) research grant no. EMR/2016/006723 to P.S. and B.A. M.B and N.M. were supported by University Grants Commission (New Delhi). A.B. was supported by an Early career fellowship by DBT Wellcome Trust India Alliance (IA/E/13/1/501271). B.A. is a Senior Fellow of the DBT/ Wellcome Trust India Alliance and acknowledges financial support from the DBT/Wellcome Trust India Alliance (Grant Number: IA/S/19/2/504659); SERB-POWER (Grant Number: SPG/2021/000851) and S. Ramachandran-National Bioscience Award for Career Development (Grant Number: BT/HRD/NBA/NWB/39/2020–21) from the Department of Biotechnology. B. A and P. S. are respectively, co- and lead-corresponding authors of this paper.
Author contributions
M.B., A.B., B.A., and P.S. designed research; M.B. and N.M. performed research; M.B. and U.R. contributed new reagents/analytic tools; M.B., A.B., and N.M. analyzed data; B.A. and P.S. conceptualization; and M.B., A.B., B.A., and P.S. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
Contact Pradip Sinha ([email protected]) for transgenic fly line generated in this study. We have used published data (GSE28950, GSE110655, GSE86232) and did not generate any shared data.
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9926278.txt |
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595699
202204750
10.1073/pnas.2204750120
datasetDatasetresearch-articleResearch ArticlegeneticsGenetics419
Biological Sciences
Genetics
Exercise preserves physical fitness during aging through AMPK and mitochondrial dynamics
Campos Juliane Cruz a b c 1
Marchesi Bozi Luiz Henrique a d e 1
Krum Barbara a https://orcid.org/0000-0002-3364-1977
Grassmann Bechara Luiz Roberto a https://orcid.org/0000-0002-1642-1008
Ferreira Nikolas Dresch a https://orcid.org/0000-0002-9513-433X
Arini Gabriel Santos a https://orcid.org/0000-0001-9948-2545
Albuquerque Rudá Prestes a
Traa Annika f g
Ogawa Takafumi b c h i https://orcid.org/0000-0001-8673-8804
van der Bliek Alexander M. j k
Beheshti Afshin l m
Chouchani Edward T. d e
Van Raamsdonk Jeremy M. c f g n https://orcid.org/0000-0001-8376-9605
Blackwell T. Keith [email protected]
b c 2 https://orcid.org/0000-0003-3763-9134
Ferreira Julio Cesar Batista [email protected]
a 2 https://orcid.org/0000-0003-2694-239X
aInstitute of Biomedical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
bResearch Division, Joslin Diabetes Center, Boston, MA 02215
cDepartment of Genetics, Harvard Medical School, Boston, MA 02215
dDepartment of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215
eDepartment of Cell Biology, Harvard Medical School, Boston, MA 02215
fDepartment of Neurology and Neurosurgery, McGill University, Montreal H3A 2B4, Canada
gMetabolic Disorders and Complications Program, and Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal H4A 3J1, Canada
hUnit of Biotechnology, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima 739-8530, Japan
iHiroshima Research Center for Healthy Aging, Hiroshima University, Higashi-Hiroshima 739-8530, Japan
jMolecular Biology Institute at University of California, Los Angeles, CA 90095-1570
kDepartment of Biological Chemistry, David Geffen School of Medicine at University of California, Los Angeles, CA 90095-1737
lKBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94035
mStanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
nDivision of Experimental Medicine, Department of Medicine, McGill University, Montreal H4A 3J1, Canada
2To whom correspondence may be addressed. Email: [email protected] or [email protected].
Edited by Paul Sternberg, California Institute of Technology, Pasadena; received March 21, 2022; accepted December 7, 2022
1J.C.C. and L.H.M.B. contributed equally to this work.
3 1 2023
10 1 2023
3 7 2023
120 2 e220475012021 3 2022
7 12 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Exercise is a powerful anti-aging intervention. In muscle, exercise remodels mitochondrial metabolism and connectiveness, but the role of dynamic mitochondrial remodeling in exercise remains unknown. Using Caenorhabditis elegans, we find that the dynamic cycle of mitochondrial fission–fusion is critical for physical fitness. Exercise induces remodeling of the proteome that depends upon mitochondrial dynamics and delays an aging-associated decline in mitochondrial connectiveness and physical fitness. AMPK, a metabolic regulator that senses low energy availability and controls mitochondrial dynamics, is needed for exercise to maintain physical fitness with age and can recapitulate this exercise benefit. Our data identify the mitochondrial dynamics cycle as an essential mediator of exercise responsiveness and an entry point for interventions to maintain muscle function during aging.
Exercise is a nonpharmacological intervention that improves health during aging and a valuable tool in the diagnostics of aging-related diseases. In muscle, exercise transiently alters mitochondrial functionality and metabolism. Mitochondrial fission and fusion are critical effectors of mitochondrial plasticity, which allows a fine-tuned regulation of organelle connectiveness, size, and function. Here we have investigated the role of mitochondrial dynamics during exercise in the model organism Caenorhabditis elegans. We show that in body-wall muscle, a single exercise session induces a cycle of mitochondrial fragmentation followed by fusion after a recovery period, and that daily exercise sessions delay the mitochondrial fragmentation and physical fitness decline that occur with aging. Maintenance of proper mitochondrial dynamics is essential for physical fitness, its enhancement by exercise training, and exercise-induced remodeling of the proteome. Surprisingly, among the long-lived genotypes we analyzed (isp-1,nuo-6, daf-2, eat-2, and CA-AAK-2), constitutive activation of AMP-activated protein kinase (AMPK) uniquely preserves physical fitness during aging, a benefit that is abolished by impairment of mitochondrial fission or fusion. AMPK is also required for physical fitness to be enhanced by exercise, with our findings together suggesting that exercise may enhance muscle function through AMPK regulation of mitochondrial dynamics. Our results indicate that mitochondrial connectivity and the mitochondrial dynamics cycle are essential for maintaining physical fitness and exercise responsiveness during aging and suggest that AMPK activation may recapitulate some exercise benefits. Targeting mechanisms to optimize mitochondrial fission and fusion, as well as AMPK activation, may represent promising strategies for promoting muscle function during aging.
exercise
aging
mitochondrial fusion
mitochondrial fission
C. elegans
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 501100001807 2013/07937-8 2015/22814-5 2017/16694-2 and 2019/25049-9 Julio Cesar Batista Ferreira HHS | National Institutes of Health (NIH) 100000002 R35 GM122610 R01 AG054215 T. Keith Blackwell
==== Body
pmcAge-related disorders are a major and growing public health problem, and an epidemic of aging-related diseases has followed increases in both unhealthy living habits and life expectancy (1). This has made it critical to develop interventions that promote healthy aging, including improvements in healthspan—the period of life in which an individual maintains good health.
Impairment of mitochondrial quality control and the consequent accumulation of fragmented and dysfunctional mitochondria are pivotal in the establishment and progression of chronic degenerative diseases (2–4). Mitochondrial fission and fusion, commonly referred as mitochondrial dynamics, are essential for maintaining mitochondrial function. While fission segregates mitochondria into spherical organelles (5, 6), fusion allows mitochondrial components to be reconstituted within a new mitochondrial network (7), thereby facilitating mitochondrial metabolic remodeling and quality control. By functioning in an integrated continuous cycle, these processes allow mitochondrial physiology to be maintained and fine-tuned (8). Indeed, genetic disruption of mitochondrial dynamics that impairs mitochondrial functions ultimately results in age-related disorders (9–12).
Exercise has been widely employed to improve quality of life and protect against degenerative diseases. In humans, a long-term exercise regimen reduces overall mortality (13), and physical fitness can be a valuable parameter for diagnostics of age-related diseases that include sarcopenia, osteoporosis, and cardiovascular and neurodegenerative diseases (14). Physical fitness is also a valuable physiological marker of healthy aging (15), and its impairment is associated with poor disease prognosis and reductions in quality of life and survival across species (16–18). The various benefits of exercise are conferred in part through transient increases in energy expenditure that affect mitochondrial metabolism and network morphology (19). In rodents, exercise enhances cardiac function in heart failure by favoring mitochondrial fusion (20). Disruption of mitochondrial fusion reduces exercise performance (21). Similarly, mitochondrial fission is necessary to increase bioenergetic flux and meet the cardiac and skeletal muscle energetic demand of exercise (22, 23). However, the contribution of integrated mitochondrial dynamics to physical fitness, exercise responsiveness, and maintenance of physical fitness during aging remains largely to be determined. The dynamic nature of mitochondria may have contributed to this gap of knowledge, because addressing these questions requires an experimental model in which these questions can be explored in real time in the setting of exercise and over the lifespan of an organism.
The nematode Caenorhabditis elegans (C. elegans) is a powerful model for aging research because of its short lifespan and amenability to genetic analysis and microscopy of living tissue (24). Notably, C. elegans also exhibits key features of the mammalian response to exercise (25–30). In these animals, a single session of swimming leads to increased oxygen consumption, fatigue, and transcriptional changes toward mitochondrial oxidative metabolism (28), and daily exercise sessions improve health parameters across multiple tissues and can increase stress resistance and lifespan (25–27, 29, 30). Together with similar findings in D. melanogaster (31, 32), these studies suggest that fundamental exercise adaptations are conserved from invertebrates to humans. Here, we investigated the role of mitochondrial dynamics in physical fitness and the benefits of exercise in young and aging C. elegans. We also explored how exercise capacity and benefits are affected by established anti-aging interventions, including activation of the energy sensor AMP-activated protein kinase (AMPK).
Results
Effects of Aging and Exercise on Mitochondrial Connectivity and Physical Fitness.
We first investigated how aging affects physical fitness and mitochondrial dynamics in wild-type (WT) worms. When we examined physical fitness by recording animals during a swimming session, we observed a progressive decline in the number of body bends per second at 5, 10, and 15 d of adulthood compared to day 1, demonstrating that physical fitness declines during aging (Fig. 1A). A similar age-dependent decline was observed when recording animals during crawling on agar plates (SI Appendix, Fig. S1A). We also scored mitochondrial connectiveness in body-wall muscle, which exhibits many similarities to mammalian skeletal muscle (28), based upon five classes that reflect a gradual increase in fragmentation and disorganization (Fig. 1B). At day 1, most muscle cells (76%) exhibited connected mitochondria (class 1), while at days 5 and 10 there was a significant and progressive shift toward fragmented/disorganized mitochondria (classes 3 to 5) (Fig. 1B). These results align with previous studies showing reduced physical fitness and increased mitochondrial fragmentation during aging across species (17, 33–36).
Fig. 1. Effects of exercise and aging on mitochondrial dynamics. (A) Physical fitness decay and (B) mitochondrial morphology in body-wall muscle cells of WT worms during aging, along with representative images of mitochondrial network organization classes (29). (Scale bar, 5 μm.) (C) Acute exercise protocol: worms maintained at 20 °C were subjected to 4 h of swimming followed by 24 h of recovery on agar plates. (D) Mitochondrial morphology in body-wall muscle cells, (E) physical fitness, and (F) recovery rate of WT worms that were subjected to acute exercise on days 1, 5, and 10 of adulthood. Recovery rate refers to the difference in body bends per second between 24 h and the start of exercise (0 h). (G) Correlation between physical fitness and mitochondrial morphology of WT worms subjected to acute exercise, including data from d1, d5, and d10 of adulthood. Data are presented as mean ± SEM. *P < 0.05 and **P < 0.001 vs. WT (d1 or 0 h); #P < 0.05 and ##P < 0.001 vs. 4 h. Detailed statistical analyses, number of biological replicates, and sample size are described in SI Appendix, Table S3.
Considering that exercise immediately triggers muscle energy imbalance (37), a major regulator of mitochondrial dynamics (38, 39), we investigated how the mitochondrial network is affected by a single cycle of exercise and recovery (Fig. 1C). Acute exercise, but not starvation, induced a progressive increase in muscle mitochondrial fragmentation in young adult worms (day 1, Fig. 1D and SI Appendix, Fig. S1B) and fatigue at the last hour of exercise (4 h, Fig. 1E). Importantly, a 24-h recovery period was sufficient to reestablish mitochondrial connectiveness (toward tubular and interconnected) (Fig. 1D) and mitigate the exercise-induced reduction in swimming capacity (Fig. 1 E and F). Thus, our exercise regimen was within a physiological range that did not cause irreversible harm. Our findings also revealed that the mitochondrial network is remodeled during a cycle of exercise and recovery, with initial fragmentation followed by fusion and network reorganization that parallels the recovery in exercise performance.
We also investigated how short-term exercise affects other parameters of health. Acute exercise was sufficient to increase pharyngeal pumping and protect WT worms against heat stress, measured after a 4-h recovery period (SI Appendix, Fig. S1 C and D). By contrast, this exercise regimen did not induce expression of the mitochondrial unfolded protein response reporters hsp-6p::GFP and hsp-60p::GFP (SI Appendix, Fig. S1 E and F) or significantly affect the lifespan of WT worms (SI Appendix, Fig. S1G). Thus, the benefits of acute exercise for physical fitness do not derive from this mitochondrial stress response (40) or a general anti-aging effect.
Considering that autophagy plays important roles in mitochondrial metabolism (41) and exercise physiology (42), we decided to assess autophagy in body-wall muscle during acute exercise using the reporter Pmyo-3::LGG-1::CFP. We found that in young WT animals, short-term exercise increases autophagic flux (SI Appendix, Fig. S1 H and I), similar to our previous findings in rodents (42). However, these autophagosomes did not co-localize with mitochondria under exercise conditions (SI Appendix, Fig. S1H), suggesting that mitophagy did not increase (SI Appendix, Fig. S1H). Importantly, neither pdr-1 nor pink-1 mutants, in which mitophagy is defective (43), exhibited impaired physical fitness compared with WT animals (SI Appendix, Fig. S1J). These findings suggest that PINK-1/Parkin-mediated mitophagy is not required for acute exercise to enhance physical fitness in young animals.
We also investigated how aging affects these responses to exercise. Compared to day 1 of adulthood, at days 5 and 10 worms displayed a decline in performance throughout the swimming period, including more rapid fatigue, but partially recovered to their respective 0 h baselines (Fig. 1 E and F). These older animals also underwent a cycle of mitochondrial fragmentation and fusion, although the extent to which this remodeling occurred during the cycle was reduced as the network fragmentation increased with age (Fig. 1D). Thus, aging animals also responded to exercise with parallel cycles of performance decline and recovery, and mitochondrial fragmentation and network reorganization, even as physical fitness and network integrity each declined (Fig. 1G).
Mitochondrial Fission and Fusion Are Required for Physical Fitness and Exercise Benefits.
Our findings suggested that mitochondrial fission and fusion might be important for maintaining physical fitness and possibly for physical fitness to be enhanced by a bout of exercise. Mitochondrial fission depends upon dynamin-related protein 1 (DRP-1), and fusion upon FZO-1 (ortholog of mitofusins MFN1 and MFN2, which are responsible for fusing the outer membrane) and EAT-3 (ortholog of optical atrophy 1, which fuses the inner membrane) (Fig. 2A) (44, 45). Throughout their lifespan, worms in which either fission (drp-1) or fusion (fzo-1) is impaired displayed a substantial and sustained decline in physical fitness compared to WT (Fig. 2B and SI Appendix, Fig. S2A). Interestingly, overexpression of these regulators did not affect mitochondrial network morphology in aging animals and impaired overall physical fitness (SI Appendix, Fig. S2 B and C). Moreover, loss of their function abolished mitochondrial network remodeling during a cycle of exercise and recovery (SI Appendix, Fig. S2D) and prevented recovery of physical fitness during a cycle of exercise followed by rest (day 1, Fig. 2C and SI Appendix, Fig. S2 E and F). Together, this suggests that physical fitness depends upon optimal mitochondrial dynamics.
Fig. 2. Mitochondrial fission and fusion are required for exercise-induced benefits during aging. (A) Simplified model for mitochondrial fusion and fission: mitochondrial fragmentation requires recruitment of cytosolic DRP-1 to the organelle, which oligomerizes and constricts the mitochondria into two daughters. The outer mitochondrial membrane fuses through interaction between FZO-1 of two opposing mitochondria, while EAT-3 drives inner mitochondrial membrane fusion. (B) Physical fitness decay (Left) and overall physical fitness (Right, average of d1, d5, d10, and d15) of WT compared to mitochondrial dynamics mutants drp-1(tm1108), fzo-1(tm1133), fzo-1(tm1133);drp-1(tm1108) and eat-3(ad426);drp-1(tm1108) during aging. (C) Physical fitness of WT and mitochondrial dynamics mutants drp-1(tm1108), fzo-1(tm1133), fzo-1(tm1133);drp-1(tm1108) and eat-3(ad426);drp-1(tm1108) submitted to acute exercise on day 1 of adulthood. (D) Long-term exercise: Worms maintained at 20 °C were submitted to 1 h of exercise per day for 10 d, starting at the onset of adulthood (day 1). (E) Physical fitness and (F) mitochondrial morphology in body-wall muscle cells of WT worms submitted to long-term exercise. (G) Correlation between physical fitness and mitochondrial morphology of control and long-term exercise-trained worms. (H) Physical fitness and (I) mitochondrial morphology in body-wall muscle cells of mitochondrial dynamics mutants drp-1(tm1108), fzo-1(tm1133), fzo-1(tm1133);drp-1(tm1108) and eat-3(ad426);drp-1(tm1108) submitted to long-term exercise. (J) Physical fitness of the muscle-specific RNAi strain sid-1(qt-9);myo-3p::sid-1 fed EV control, drp-1, fzo-1 and eat-3 RNAi from the L4 stage and submitted to long-term exercise. Data are presented as mean ± SEM. *P < 0.05 and **P < 0.001 vs. WT or Ctr. Detailed statistical analyses, numbers of biological replicates, and sample size are described in SI Appendix, Table S3.
In C. elegans, genetic inhibition of mitochondrial fission or fusion prevents some interventions from extending lifespan (12, 27). Remarkably, however, simultaneous disruption of fission and fusion can rescue these defects in some cases and even extend lifespan on its own, by preventing the mitochondrial fragmentation and some associated metabolic effects that occur during aging (12). These last findings made it important to investigate how simultaneous prevention of fission and fusion affects physical fitness. In striking contrast to its effects on lifespan, preventing mitochondrial fission and fusion (fzo-1;drp-1 and eat-3;drp-1) failed to restore physical fitness to WT levels, although ablation of fission mitigated some effects of fusion loss (fzo-1;drp-1 vs. fzo-1) (Fig. 2 B and C and SI Appendix, Fig. S2 E and F). We conclude that maintenance of dynamic mitochondrial network remodeling per se is critical for physical fitness.
Next, we investigated whether long-term exercise training might counteract the progressive loss of physical fitness and mitochondrial connectiveness that occurs during aging and whether mitochondrial dynamics is involved. We allowed WT animals to swim for 1 h per day for 10 consecutive days, starting at the onset of adulthood (experimental day 1, Fig. 2D). This exercise regimen significantly improved physical fitness at day 10 (Fig. 2E and SI Appendix, Fig. S2G), a benefit that was maintained for at least 5 d after exercise ceased (Fig. 2E) and mitigated the muscle mitochondrial fragmentation/disorganization seen during aging (Fig. 2F). Under these conditions, mitochondrial fragmentation was tightly and inversely correlated with physical fitness (Fig. 2G). Disruption of mitochondrial fission (drp-1) or fusion (fzo-1) abrogated the benefits of long-term exercise on mitochondrial remodeling (Fig. 2I) and physical fitness (Fig. 2H and SI Appendix, Fig. S2G). Moreover, long-term exercise impaired physical fitness when both fission and fusion were disrupted simultaneously (fzo-1;drp-1 and eat-3;drp-1, Fig. 2H). Muscle-specific knockdown of these mitochondrial dynamics genes also abrogated the benefit of exercise (Fig. 2J and SI Appendix, Fig. S2 H and I). Taken together, our results indicate that exercise training delays the aging-associated decline in physical fitness, dependent upon maintenance of mitochondrial fission and fusion in body-wall muscle.
To understand the molecular consequences of long-term exercise training as well as the lack of exercise benefits in animals with defective mitochondrial dynamics, we examined the proteomes of WT and fzo-1 animals. First, the overall number of proteins that increased in abundance upon exercise was higher in WT worms compared with animals with disrupted mitochondrial fusion (fzo-1) (SI Appendix, Fig. S3 A–C). Moreover, in WT but not fzo-1 animals exercise increased the abundance of proteins associated with major pathways involved in muscle adaptation to exercise, including mitochondrial fission and fusion, the tricarboxilic acid (TCA) cycle, electron transport chain (ETC), oxidative phosphorylation, lipid metabolism, and redox balance (Fig. 3 A–G and SI Appendix, Fig. S4 and Table S1). Importantly, exercised WT animals also exhibited increased abundance of several other key proteins involved in mitochondrial proteostasis, mitochondrial calcium handling, and mitochondrial protein synthesis (Fig. 3 A, B, and G and SI Appendix, Fig. S4), but not cytosolic protein synthesis (SI Appendix, Fig. S5A). Notably, the overall lower levels of the mitochondrial proteome as well as its lack of response to exercise in fzo-1 animals were accompanied by increased protein abundance of cytosolic compensatory pathways including glycolysis, heat shock response, ubiquitin-proteasome system, and autophagy (Fig. 3D and SI Appendix, Fig. S5 B–F). Thus, whereas exercise increased the levels of protein machinery involved in muscle and mitochondrial function, as well as energy metabolism, prevention of mitochondrial fusion globally impaired these adaptations.
Fig. 3. Changes in protein abundance of major pathways involved in muscle adaptation to exercise. Percentage difference in abundance of proteins involved in: (A) mitochondrial fission and fusion, and calcium handling, (B) protein synthesis, (C) Krebs cycle, ETC and oxidative phosphorylation, (D) glycolysis, (E) lipid metabolism, (F) redox balance, and (G) mitochondrial import machinery and proteostasis, determined by comparison between WT and fzo-1(tm1133) worms that were or were not submitted to long-term exercise [starting at the onset of adulthood (day 1), according to the protocol described in Fig. 2D]. Proteomics was performed at day 10. Data are presented as mean ± SEM. *P < 0.05 and **P < 0.001 vs. WT Ctr. Detailed statistical analyses, number of biological replicates, and sample size are described in SI Appendix, Table S3.
AMPK Enhances Physical Fitness through Mitochondrial Dynamics.
Given that increases in mitochondrial fusion and density are associated with increased lifespan in C. elegans (12, 27, 46), we tested whether interventions that extend lifespan might in general improve exercise capacity during aging. We assessed physical fitness in long-lived animals that are subject to mild mitochondrial dysfunction (isp-1 and nuo-6) (47), reduced insulin/IGF-1 signaling (rIIS)(daf-2) (24), a dietary restriction (DR)-like state (eat-2) (48), or increased AMPK activity (49, 50). In the last case, we examined animals in which the AMPKα2 catalytic subunit carrying a constitutively activating mutation is overexpressed transgenically [CA-AAK-2 (50)]. rIIS and DR extend lifespan across metazoans and have been shown to improve various healthspan parameters (24, 51), and AMPK is of particular interest here because it is a master regulator of energy homeostasis during exercise (52) and promotes remodeling of mitochondrial morphology and metabolism (12, 53).
Despite increasing lifespan, the isp-1, nuo-6, daf-2, and eat-2 mutations impaired physical fitness and performance in an exercise-recovery cycle (Fig. 4A and SI Appendix, Fig. S6 A–D and Table S4). Compared to WT, isp-1, nuo-6, and daf-2 mutants also displayed reduced physical fitness with aging (day 10, Fig. 4A and SI Appendix, Table S4). A long-term exercise protocol did not slow the aging-related decline in physical fitness in isp-1, nuo-6, daf-2, or eat-2 animals (SI Appendix, Fig. S6 E and F), in contrast to its beneficial effect on WT worms (Fig. 2E). Together, the data suggest that mechanisms that extend lifespan are not necessarily sufficient to confer exercise-associated benefits.
Fig. 4. AMPK activation preserves physical fitness with aging. (A) Physical fitness decay of WT and the long-lived worms isp-1(qm150), nuo-6(qm200), daf-2(e1370), and eat-2(ad1116) with aging. (B) Physical fitness decay and average physical fitness (average of d1, d5, d10, and d15) of WT and long-lived worms expressing constitutively active AMPK (CA-AAK-2) during aging. (C) Physical fitness of WT and CA-AAK-2 worms submitted to acute exercise on days 1, 5, and 10 of adulthood. (D) Physical fitness and (E) mitochondrial morphology in body-wall muscle cells of CA-AAK-2 worms submitted to long-term exercise. Data are presented as mean ± SEM. *P < 0.05 and **P < 0.001 vs. WT or Ctr. Detailed statistical analyses, number of biological replicates, and sample size are described in SI Appendix, Table S3.
In striking contrast, animals expressing CA-AAK-2 exhibited improved physical fitness and maintenance of exercise performance during aging (Fig. 4 B and C and SI Appendix, Fig. S6 G and H). Surprisingly, however, in CA-AAK-2 animals long-term exercise not only failed to further enhance physical fitness during aging but was slightly detrimental and was associated with a lack of exercise-induced mitochondrial network remodeling (Fig. 4 D and E). Thus, constitutive activation of AMPK dramatically improved physical fitness with age but did not allow further benefit from exercise (SI Appendix, Fig. S6I). Given that none of the other anti-aging interventions we examined improved physical fitness, we conclude that this benefit of constitutively active AMPK derives from functions that are particular to AMPK, not a general anti-aging effect.
We investigated whether AMPK might be required for physical fitness and exercise benefits. Compared to WT, AMPK-deficient mutants (aak-2) exhibited a sustained reduction in physical fitness during aging that was lost at day 15 (Fig. 5A), as well as impairment of the exercise-recovery cycle (at day 1, Fig. 5B and SI Appendix, Fig. S7 A and B). Finally, AMPK-deficient mutants did not benefit from long-term exercise over the course of their lifespan and even exhibited reduced exercise capacity compared with non-exercised animals (Fig. 5C). Thus, not only does AMPK enhance physical fitness during aging when constitutively activated (Fig. 4B), it is also required for normal exercise responsiveness and benefits throughout life.
Fig. 5. AMPK-induced improvement in physical fitness requires mitochondrial dynamics. (A) Physical fitness decay and average physical fitness (average of d1, d5, d10, and d15) of WT and AMPK-deficient worms [aak-2(gt33)] during aging. (B) Physical fitness during 4 h of acute exercise (average of 0 h, 1 h, 2 h, and 4 h) on day 1 of adulthood of WT and aak-2(gt33). (C) Physical fitness of aak-2(gt33) worms subjected to long-term exercise. (D) Physical fitness decay and average physical fitness (average of d1, d5, d10, and d15) of animals carrying the indicated mitochondrial dynamics mutations [drp-1(tm1108), fzo-1(tm1133), and fzo-1(tm1133);drp-1(tm1108)] in either the WT or CA-AAK-2 background during aging. CA-AAK-2 in the WT background is presented in blue. (E) Physical fitness decay of the indicated strains during aging. Data are presented as mean ± SEM. *P < 0.05 and **P < 0.001 vs. WT or Ctr. #P < 0.05 and ##P < 0.001 vs. CA-AAK-2. (F) Working model for maintenance of exercise responsiveness and physical fitness during aging: The beneficial effects of exercise are mediated through AMPK and mitochondrial dynamics and proteome remodeling. Detailed statistical analyses, number of biological replicates, and sample size are described in SI Appendix, Table S3.
Our findings raise the question of whether the salutary effects of increased AMPK activity on exercise performance during aging might depend upon mitochondrial dynamics. Supporting this idea, impairment of mitochondrial fission (drp-1), fusion (fzo-1), or both processes (fzo-1;drp-1) largely abrogated the CA-AAK2-induced improvements in physical fitness during aging (Fig. 5D). In each case, the negative effects of fission or fusion loss on swimming performance were similar in CA-AAK-2-expressing, dietary-restricted [a condition that involves AMPK activity (12, 54) and WT animals] (Fig. 5E and SI Appendix, Fig. S7 C and D). The exception to this trend was that fusion impairment (fzo-1) did not abrogate the positive effect of AMPK at day 1 of adulthood (Fig. 5E), perhaps suggesting that the metabolic effects of mitochondrial fusion are not essential at this early stage and that other AMPK effectors may be involved. It is possible that impairment of mitochondrial dynamics might interfere induction of AMPK phosphorylation by exercise. However, at day 1 adults WT and fzo-1 mutant worms displayed similar increases in exercise-induced AMPK phosphorylation at Thr172 (SI Appendix, Fig. S7E), which is associated with increased AMPK activity (54). Note that basal AMPK phosphorylation was compromised in WT at day 10 of adulthood (SI Appendix, Fig. S7F), as expected (55), whereas long-tern exercise mitigated this aging-associated decline in AMPK phosphorylation (SI Appendix, Fig. S7F). These findings suggest that aging, but not impaired mitochondrial fusion, impairs activation of AMPK, which is partially prevented by long-term exercise. Together, these findings indicate that mitochondrial dynamics is an essential downstream effector of AMPK activity in promoting physical fitness.
Discussion
Despite extensive evidence that impairment of mitochondrial fission or fusion contributes to age-related diseases (56, 57), the role of mitochondrial dynamics in anti-aging interventions such as exercise has remained uncertain. Here, by taking advantage of tools available for C. elegans we demonstrated that mitochondrial fission and fusion are each required for both physical fitness and its improvement by exercise training, and that mitochondrial network plasticity, the capacity for reshaping the mitochondrial network between fused and fragmented states, is also crucial (Fig. 5F). Moreover, the only anti-aging intervention we identified that enhances physical fitness during aging, AMPK activation, depends upon mitochondrial dynamics and plasticity for this benefit. Given that the mechanisms that mediate mitochondrial dynamics are evolutionarily conserved, it seems likely that the generally critical role for mitochondrial dynamics and plasticity we uncovered may be broadly applicable.
We determined that a single exercise session induces a cycle of fatigue and physical fitness recovery that is paralleled by a cycle of mitochondrial fission and network rebuilding in body-wall muscle. Aging dampened the extent to which these parameters change during the exercise/recovery cycle and induced a parallel decline in physical fitness. By contrast, a regular exercise training regimen enhanced physical fitness, delaying its inevitable decline during aging. Mitochondrial dynamics was critical for physical fitness under each of these conditions and its disruption by impairment of fission or fusion dramatically impaired physical fitness and ablated the benefits of exercise. We also determined that exercise induces a dramatic remodeling of the proteome that is dependent upon mitochondrial fusion. This fusion-dependent remodeling involves increases in many proteins associated with muscle and mitochondrial function and energy metabolism, suggesting that the functional effects of mitochondrial dynamics on adaptations to exercise are profound.
Notably, in our analyses of physical fitness it was consistently more deleterious to impair mitochondrial fusion than fission. This finding is consistent with our previous results showing that favoring accumulation of elongated mitochondria (instead of fragmented) either by inhibiting fission (58) or boosting fusion (4) has a positive impact on mitochondrial metabolism under conditions of pathology or physiological stress. Indeed, fragmented mitochondria are more prone to cause oxidative stress (59), reduce adenosine triphosphate (ATP) synthesis (58), and trigger apoptosis through the mitochondrial permeability transition (60). Consistent with the idea that excessive mitochondrial fragmentation is detrimental to muscle physiology (61), impairing mitochondrial fission (fzo-1;drp-1 double mutant) mitigated the deleterious effects of disrupted mitochondrial fusion, and consequent accumulation of fragmented/dysfunctional mitochondria, on physical fitness. It is intriguing that we did not observe a major role for PINK-1/Parkin-mediated mitophagy in the setting of acute exercise, in striking contrast to the critical importance of mitochondrial dynamics, although it is possible that mitochondrial turnover mechanisms are more important in the setting of long-term exertion or in older animals, in which fragmented mitochondria accumulate (62).
Critically, however, “freezing” the mitochondrial network by simultaneous disruption of both fission and fusion still impaired physical fitness significantly compared to WT, demonstrating that network plasticity is essential during the exercise/recovery cycle. This contrasts sharply to one effect of mitochondrial dynamics on aging, because simultaneous ablation of mitochondrial fission and fusion can extend lifespan, apparently by delaying the aging-induced degradation of the mitochondrial network and accompanying metabolic perturbations (12). Thus, maintaining the capacity for network remodeling in muscle seems to be critical for meeting the metabolic demands of exercise. Perhaps analogously, in a previous study fusion deficiency greatly sensitized worms to arsenite exposure, fission deficiency had a small effect, and the abrogation of both did not rescue to WT levels (63). This need for plasticity may explain why most anti-aging interventions we tested impaired physical fitness during aging, because rIIS and DR maintain network structure (12, 27). Also consistent with our findings, simultaneous ablation of fission and fusion blocks lifespan extension from intermittent fasting, which like exercise induces cycles of mitochondrial network fission and recovery (12).
It is striking that constitutive AMPK activation uniquely enhanced physical fitness during aging, benefitting the animal comparably to an exercise regimen. In mammals, AMPK acts as a key signaling molecule in regulating mitochondrial homeostasis during exercise (63–65), although its role in mediating exercise benefits has remained inconclusive (66). Here we demonstrated in C. elegans that AMPK is required for both baseline physical fitness and exercise-induced benefits and that AMPK activation enhances physical fitness dependent upon mitochondrial dynamics. The simplest interpretation of our results that exercise benefits may be conferred through a linear pathway involving AMPK regulation of mitochondrial dynamics (Fig. 5F), although it is certainly possible that other AMPK functions and regulators of mitochondrial dynamics are also critical. Interestingly, exercise training did not improve physical fitness in the setting of constitutive AMPK activation, which modestly impaired the response to long-term exercise (Fig. 4D). This might reflect a maximum limit in physical fitness or an effect of constant AMPK activation on mitochondrial dynamics or another parameter that results in impaired exercise adaptation. However, it is also consistent with the idea that AMPK activation might have acted as a surrogate for exercise training in conferring these benefits. The anti-diabetic drug metformin has many effects on metabolism, including stimulating AMPK indirectly (67). It may be consistent with our results that metformin not only extends C. elegans lifespan but also delays the decline in swimming capacity with aging (68).
An important goal of the aging field is to identify interventions that not only extend lifespan but also enhance important parameters of health (1). Our approach suggests that a multipronged strategy may be necessary, given the unique demands of physical fitness and its maintenance through exercise, and our finding that anti-aging interventions do not, as a rule, enhance exercise responsiveness and physical fitness. In aging humans, a decline in muscle function and exercise tolerance is a major concern that leads to substantial morbidity (69). Our data point toward AMPK and mitochondrial dynamics as potentially fruitful intervention points for forestalling this decline, most likely along with other aspects of aging. Considering the clinical implications of coordinating lifespan with healthspan, it will be of great interest to determine how mitochondrial network plasticity influences physical fitness, along with longevity and aging-associated disease, in humans.
Materials and Methods
Strains and Maintenance of C. elegans.
C. elegans strains used in this study are listed in SI Appendix, Table S2. Nematodes were grown and maintained at 20 °C on standard nematode growth media (NGM) agar plates seeded with live Escherichia coli. E. coli (OP50-1) was cultured overnight in Luria broth (LB) medium containing 10 mg/L streptomycin at 37 °C. RNA interference (RNAi) experiments were performed by using tetracycline-resistant E. coli (HT115) carrying double-stranded RNA (dsRNA) against the genes drp-1, fzo-1 and eat-3, or an empty vector (EV) control (pL4440). RNAi cultures were grown overnight at 37 °C in LB medium containing 50 mg/mL carbenicillin, and dsRNA expression was induced by the addition of 0.2 g/L Isopropyl β-D-1-thiogalactopyranoside (IPTG) prior to seed onto NGM agar plates containing 50 mg/mL carbenicillin and 0.2 g/L IPTG. Synchronized populations of L1 animals were obtained by hypochlorite treatment (70), then allowed to develop at 20 °C on seeded NGM agar plates. Whenever experiments were conducted over the course of a lifespan, 20 mg/L 5-Fluoro-2'-deoxyuridine (FUdR) was added at the L4 stage to prevent hatching. Note that FUdR at this concentration did not affect mitochondrial bioenergetics (SI Appendix, Fig. S7G).
Physical Fitness (Swimming).
Physical fitness was measured by calculating the body bends per second of worms in liquid as previously described (17) with minor modifications. Briefly, we transferred worms to 96-well plates containing M9 buffer (one worm per well) and immediately recorded a 30-s video at a rate of 15 frames per second using a stereomicroscope (Optika SMZ-4) coupled with a device camera. Recorded images were analyzed using the ImageJ plugin Worm-tracker (wrMTrck) (71). At least 15 animals were recorded per biological replicate.
Mitochondrial Morphology.
Mitochondrial network morphology was detected using strains expressing green fluorescent protein (GFP, zcIs14[myo-3::GFP(mito)]) targeted to the mitochondrial matrix and red fluorescent protein (RFP, syIs268[myo-3p::tomm20::mRFP]) targeted to the outer mitochondrial membrane specifically in the body-wall muscle. Worms were anesthetized in Tetramisole 0.2 mg mL−1, mounted on 2% agarose pads on glass slides, and subsequently imaged on Zeiss Axio Imager M2 fluorescence microscope with Axiocam HRC camera. Muscle mitochondria were analyzed in cells midway between the pharynx and vulva, or vulva and tail. Qualitative assessment of mitochondrial morphology was made by scoring cells based on five classes as previously described by Laranjeiro et al. (29). These categories reflect a progressive increase in fragmentation and disorganization from class 1 (tubular and interconnected mitochondrial network) to class 5 (reduced number of fragmented mitochondria) (Fig. 1B). Images were taken of >35 muscle cells from at least 15 worms per biological replicate. All analyses were conducted by a single observer, blinded to the animals’ identity. Note that Tetramisole at this concentration did not affect mitochondrial network (SI Appendix, Fig. S7H).
Exercise Protocols.
Acute exercise.
Acute exercise was performed by allowing worms to swim for 4 h. Briefly, seeded NGM agar plates were flooded with M9 buffer. After 4 h, worms were washed off with M9 buffer into 15 mL conical tubes, centrifuged at 700 rpm for 1 min, transferred to seeded NGM agar plates, and allowed to rest for 24 h at 20 °C (recovery period) (Fig. 1C). Because some swimming benefits have been recently attributed to transient food deprivation (30), exercise sessions were performed in the presence of food by adding M9 buffer to NGM agar plates containing bacteria. Seeded NGM agar plates contained 4.33 × 109 colony-forming units (CFUs). M9 solution after flooding the seeded NGM agar plates displayed 2 × 108 CFUs/mL (SI Appendix, Fig. S7I).
Long-term exercise.
Long-term exercise was performed by allowing worms to swim 1 h per day for 10 d, starting at the onset of adulthood (day 1) (Fig. 2D). Briefly, worm strains were divided into control and exercise groups and maintained at 20 °C on separated 100 mm seeded NGM agar plates. Exercise group plates were flooded with 10 mL of M9 buffer. After 1 h, worms were washed off with M9 buffer into 15 mL conical tubes, centrifuged at 700 rpm for 1 min, and transferred to seeded NGM agar plates using a glass Pasteur pipette to minimize nematode loss. Control animals were also transferred to seeded NGM agar plates using the same method. This procedure was repeated daily for the next 10 d.
Proteomics.
Proteomics were performed using an adapted protocol (72). C. elegans (~1,000 worms) were homogenized in lysis buffer [100 mM 4-(2-Hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) pH 8.5, 8 M urea, 2% Sodium dodecyl sulfate (SDS), and protease and phosphatase inhibitors (Thermo Fisher Scientific)]. Samples were centrifuged (12,000 G), and the supernatant was subjected to bicinchoninic acid assay (BCA) protein concentration measurement and reduced with 5 mM tris(2-carboxyethyl) phosphine and alkylated with 15 mM iodoacetamide. Next, proteins were purified using the methanol-chloroform precipitation method. Samples were reconstituted in 200 mM (3-[4-(2-Hydroxyethyl)piperazin-1-yl]propane-1-sulfonic acid (EPPS) at pH 8.5 and digested by Lys-C and trypsin (1:100 enzyme-to-substrate ratio for 12 h). Peptides were then labeled by tandem mass tag (TMT)-pro 16plex (Thermo Fisher Scientific) following the streamlined TMT (SL-TMT) protocol (73) and quench by adding 5% hydroxylamine. A ratio-check was performed by mixing 1% sample from each TMT channel, and the TMT-labeled peptides were evenly mixed based on the ratio-check. The mixture was then desalted using Sep-pak cartridges (waters), dried, and fractionated in six fractions using high pH reversed-phase peptide fractionation kit (Thermo Fisher Scientific). Samples were dried and desalted via StageTip and reconstituted in 5% formic acid and 5% acetonitrile (ACN) for liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. The proteomics samples were analyzed using an Orbitrap Eclipse mass spectrometer (Thermo Fisher Scientific) coupled with an Easy-nLC 1,200 (Thermo Fisher Scientific) ultra-high pressure liquid chromatography (LC) pump. The columns were packed in house with 100 mm inner diameter microcapillary column packed with 40 cm of GP-18 resin (2.6 mm, 200 A, Sepax). A field asymmetric Ion mobility spectrometry (FAIMSPro, Thermo Fisher Scientific) device for FAIMS separation of precursors was operated with default settings and multiple compensation voltages (40 V/−60 V/−80 V). All samples were analyzed with a 180 min gradient consisting of 2 to 23% ACN, 0.125% formic acid (FA) at 500 nL/min flow rate. Under each FAIMS voltage, peptide ions were collected in data-dependent mode using a mass range of m/z 400 to 1,600 using 2 s cycles. Resolution for MS1 was set at 120,000, with standard automatic gain control target. Multiply charged ions were selected and subjected to fragmentation at 35% normalized collisional energy for MS2 with a dynamic exclusion of 120 s. A multinotch synchronous precursor selection (SPS)-MS3 was used for quantification as described before (74). Peptide ions were collected in data-dependent mode using a mass range of m/z 400 to 1,600 using 2 s cycles. Each ion was selected for further fragmentation in MS2. Maximum injection time was set to 50 ms, and dynamic exclusion was 120 s.
We used the Comet algorithm (75) to search all MS/MS spectra against a database containing sequences of C. elegans proteins downloaded from UniProt (https://www.uniprot.org/). Reversed sequences were appended as decoys for FDR filtering, and common contaminant proteins (e.g., human keratins, trypsin) were included. Parameters used to search peptides were 25 ppm precursor mass tolerance; 1.0 Da product ion mass tolerance; fully tryptic digestion; up to three missed cleavages; variable modification: oxidation of methionine (+15.9949); static modifications: TMT (+304.2071) on lysine and peptide N terminus, carboxyamidomethylation (+57.0214637236) on cysteines. The target-decoy method was employed to control the false discovery rate (FDR) (76). To distinguish correct and incorrect peptide identifications, linear discriminant analysis was used to control peptide-level FDR to less than 1%. Peptides shorter than six amino acids were discarded. Protein-level FDR was also controlled to 1%, and as a result, the number of peptide reverse hits was further decreased. Peptides were matched to the least number of proteins. TMT reporter ion signal-to-noise ratios for all quantified peptides matched to the same protein were summed up to report protein abundance.
Statistics.
Data obtained in this study are presented as mean ± SEM and are compiled from multiple trials. Chi-square test was used to compare the distribution of muscle mitochondrial morphology into multiple categories (Figs. 1 B and D, 2 F and I, and 4F). Linear regression was used to assess the association between variables in Figs. 1G and 2G. Two-way ANOVA, followed by Bonferroni post hoc was used to analyze data depicted in Figs. 2 B, E, H, and J, 3, 4 A, B, and D, and 5 A and C–E. For all other assays, two-tailed Student’s t test was used for comparison between two groups. GraphPad Prism was used for statistical analyses, and statistical significance was considered achieved when the value of P was <0.05. Detailed statistical analyses, biological replicates (independent population of worms tested on a different day), and sample size are described in SI Appendix, Table S3.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Dataset S01 (XLSX)
Click here for additional data file.
This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 2013/07937-8, 2015/22814-5, 2017/16694-2, and 2019/25049-9; Conselho Nacional de Pesquisa e Desenvolvimento—Brasil (CNPq) 303281/2015-4 and 407306/2013-7; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) Finance Code 001 and Instituto Nacional de Ciência e Tecnologia and Centro de Pesquisa e Desenvolvimento de Processos Redox em Biomedicina to J.C.B.F.; National Institutes of Health (NIH) R35 GM122610, R01 AG054215 to T.K.B., the Joslin Diabetes Center P30 DK036836, and R01 GM121756 to J.M.V.R.; FAPESP postdoctoral fellowships 2017/16540-5 and 2019/18444-9 to J.C.C., and 2016/09611-0 and 2019/07221-9 to L.H.M.B; and American Heart Association Career Development Award (2022/926512) to L.H.M.B.; E.T.C is supported by the Claudia Adams Barr Program, the Lavine Family Fund, the Pew Charitable Trust, NIH DK123095, NIH AG071966. We thank William B. Mair (Harvard T.H. Chan School of Public Health) and Malene Hansen (Sanford Burnham Prebys Medical Discovery Institute) for providing some of the worm strains used in this study. Other strains were provided by the Caenorhabditis Genetics Center (CGC), which is funded by the NIH (P40 OD010440).
Author contributions
J.C.C., L.H.M.B., B.K., L.R.G.B., R.P.A., A.M.v., T.K.B., and J.C.B.F. designed research; J.C.C., L.H.M.B., B.K., L.R.G.B., N.D.F., G.S.A., R.P.A., A.T., and T.O. performed research; A.M.v., A.B., E.T.C., J.M.V.R., T.K.B., and J.C.B.F. contributed new reagents/analytic tools; J.C.C., L.H.M.B., B.K., L.R.G.B., N.D.F., G.S.A., R.P.A., A.T., T.O., A.B., and J.C.B.F. analyzed data; and J.C.C., L.H.M.B., T.K.B., and J.C.B.F. wrote the paper.
Competing interest
The authors have stock ownership to disclose, E.T.C. is a founder and equity holder in Matchpoint Therapeutics.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix. Any additional information is available from the corresponding author upon reasonable request.
Supporting Information
This article is a PNAS Direct Submission.
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PMC009xxxxxx/PMC9934005.txt |
==== Front
JDS Commun
JDS Commun
JDS Communications
2666-9102
Elsevier
S2666-9102(23)00019-4
10.3168/jdsc.2022-0253
Animal Nutrition and Farm Systems
Short Communication
Short Communication
Perspectives of dairy employees at the beginning of the COVID-19 pandemic: A survey of health risks and educational needs
Ferreira F.C. [email protected]
12*
Rovai M. 3
Chahine M. 4
de Haro Marti M. 5
Wenz J. 6
Dalton J. 47
Silva-del-Río N. 12
1 Veterinary Teaching and Research Center, University of California, Davis, Tulare 93274
2 Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis 95616
3 Department of Dairy and Food Science, South Dakota State University, Brookings 57007
4 Twin Falls Research and Extension Center, University of Idaho, Twin Falls 83301
5 University of Idaho Extension, Gooding County, Gooding 83330
6 Department of Veterinary Clinical Sciences, Washington State University, Pullman 99164
7 Caldwell Research and Extension Center, University of Idaho, Caldwell 83605
* Corresponding author [email protected]
16 2 2023
5 2023
16 2 2023
4 3 186190
9 5 2022
24 10 2022
© 2023.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Graphical Abstract
Summary: We surveyed dairy employees to understand their perceptions and educational needs at the onset of the COVID-19 pandemic. Survey results highlighted that most respondents were somewhat concerned or very concerned about the COVID-19 pandemic. Social media was the main source of information for farm workers. A greater percentage of farm managers received formal training on COVID-19 mitigation than entry-level workers, and training was mostly limited to information on posters. However, the preferred form of training delivery was in-person meetings, and in the absence of these, video training (YouTube and break-room videos).
Highlights
• At the onset of the COVID-19 pandemic, most dairy workers were concerned about their health.
• COVID-19 safety trainings were mostly limited to posters on walls.
• Dairy managers received more COVID-19 safety training than did entry-level workers.
• Social media was dairy employees' main source of COVID-19 information.
• Successful emergency plans on dairies should consider dairy workers' learning preferences.
Our objective was to understand dairy employees' perceptions and educational needs at the onset of the COVID-19 pandemic. A bilingual (English and Spanish), anonymous survey targeted at dairy employees was circulated nationwide via university and allied industry media outlets. Responses (n = 63) from 11 states were received (May–Sep. 2020). Respondents worked in herds ranging from 50 to 40,000 animals in size. Dairy managers (33%) responded mostly to the English survey (52%), whereas entry-level workers (67%) chose the Spanish format (76%). Survey results highlighted different perspectives, educational needs, and preferred sources of information between English- and Spanish-speaking dairy workers. Overall, 83% of the respondents were somewhat concerned or very concerned about the COVID-19 pandemic. Respondents (51%) indicated that their main concern was “to bring the virus from work to home and make my family sick.” Most dairy employees (83%) perceived that their employers were somewhat or very concerned about the pandemic. Respondents (65%) indicated that COVID-19 informative training was provided at the workplace, but training was more frequently undertaken among dairy managers (86%) than entry-level workers (53%). Most trainings (72%) were limited to posters on walls. The preferred means of information delivery was through in-person meetings at work (35%), with YouTube (29%) and on-demand videos (27%) as second and third options. The main source of information regarding the pandemic was social media (52%). Frequent handwashing (81%), limiting on-farm visits (70%), limiting agglomeration in break rooms (65%), hand sanitizer use (60%), and social distancing (60%) were the most common safety measures implemented at the workplace among the options given to respondents. Few respondents (38%) indicated that face-covering was required at work. Successful emergency plans on dairies should consider the outreach needs and preferences of dairy workers.
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pmcIn December 2019, the world watched the emergence of a pandemic caused by the SARS-CoV-2 virus (COVID-19). Worldwide, agricultural operations struggled to navigate the COVID-19 health and economic crisis (Wang et al., 2020; Weersink et al., 2021; Hambardzumyan and Gevorgyan, 2022). In March 2020, the US administration declared nationwide emergency (CDC, 2022). During the first months of the pandemic, significant disruptions in food supply chain and international market led to an almost 50% drop in milk prices (CME Group, 2020). In the midst of this crisis, dairy farmers were responsible for ensuring the safety and health of over 150,000 essential dairy workers, half of them of Latino ethnicity (Adcock et al., 2015; Malone et al., 2021).
The high risk of occupational hazards among agricultural workers has been a long-standing issue and has disproportionally affected immigrant workers (Harrison and Getz, 2015; Reyes et al., 2016; Liebman et al., 2018; Panikkar and Barrett, 2021). Several factors explain the vulnerability of dairy workers to occupational hazards, such as language and literacy barriers, poverty, undesirable housing conditions, and limited access to health care (Schenker and Gunderson, 2013; Panikkar and Barrett, 2021). It is known that training improves the health and safety of dairy workers and benefits dairy producers by enhancing employee retention and farm profitability (Billikopf and González, 2012; Durst et al., 2018). However, occupational health and safety risk training for dairy workers remains inadequate or insufficient (Hadley et al., 2002; Kolstrup and Hultgren, 2011; Liebman et al., 2016; Durst et al., 2018; Panikkar and Barrett, 2021). Some challenges identified when training dairy employees include lack of basic education, low English proficiency, and cultural and socioeconomic factors (Sanders-Smith, 2007; Erskine et al., 2015). In addition, farmers often lack the formal training or time to properly train workers (Bitsch et al., 2006; NCFH, 2014), and very few dairies have an ongoing training program at the workplace (Benson and Rollin, 2008; Mills et al., 2021).
Understanding dairy workers' concerns regarding the health risks and business impacts of the pandemic, the adequacy of the training received, their preferred sources of information, and their perception on the safety measures adopted at the farm will inform and guide not only COVID-19 outreach efforts for an underserved and vulnerable sector, but will also influence preparedness and response if future hazards arise. Thus, the objective of the present study was to document dairy workers' (managers and entry-level employees) health concerns, safety training received, preferences in training methods, and perceptions of safety measures adopted at their workplaces at the onset of the COVID-19 pandemic.
An exemption was acquired from the Washington State University (Pullman, WA), Institutional Review Board before conducting the survey. A bilingual (English and Spanish) survey questionnaire was designed to collect information on employee perceptions about the risk that the COVID-19 pandemic posed to their health and the farm business, as well as information about the training received and preferences for training on COVID-19 at the beginning of the pandemic (available upon request). The survey questionnaire included a total of 26 questions designed as closed- (multiple choice) and open-ended questions using Qualtrics XM (Qualtrics International Inc.). Whenever Likert scales were used, to improve interpretation of the options, we opted for a 3-point Likert scale (Lehmann and Hulbert, 1972). The survey was advertised nationwide via university and allied industry media outlets in April and May 2020. To increase responses from dairy farm workers, online links to surveys were distributed via dairy workers Facebook groups, by radio stations, and through direct contact of investigators with dairy farm workers. Respondents had the choice of language (English or Spanish) and were allowed to skip questions. Spanish answers were translated by a bilingual coauthor to English before data analysis. Data were transferred into a spreadsheet (Excel 2010, Microsoft Corp.) for analysis.
Reported percentages were rounded to the nearest decimal point. Percentages for responses conditional to previous questions were calculated based on the answer to the previous question. In some cases, fewer answers were provided to the conditional compared with the initial question; in those cases, percentages do not add up to 100. For questions with the option of multiple responses, percentages do not add up to 100% as well. The language chosen by respondents (English vs. Spanish) and their job title (manager vs. entry-level worker) were considered variables of interest. Descriptive statistics were generated with SAS (version 9.4, SAS Institute Inc.) using the MEANS procedure. When comparisons were made between groups, differences were evaluated with inference statistics using the chi-squared statistics test of the FREQ procedure of SAS, which does not require equality of variance among groups or even groups. Only significant (P ≤ 0.05) differences are reported.
A total of 105 respondents started the survey, but 42 (40%) never passed the language choice question, and their responses were excluded. A total of 63 dairy employees completed the survey from May to September 2020 (first wave of COVID-19), and most responses were received in May 2020 (92%). The median time taken to complete the survey was 12 min (English respondents median = 9 min; Spanish respondents = 13 min). Dairy employees' responses were obtained from 11 states (California = 4, Georgia = 1, Idaho = 17, Indiana = 1, Iowa = 1, Minnesota = 6, Ohio = 1, Pennsylvania = 1, South Dakota = 26, Washington = 3, Wisconsin = 1, not reported = 1), but the majority of respondents were from 2 states: South Dakota (41.9%) and Idaho (27.4%). Respondents were farm managers (n = 21; English survey: 52.0%, Spanish survey: 24.0%) or entry-level workers (n = 42; English survey: 48.0%, Spanish survey: 76.0%). Their work responsibilities involved the entire herd (17.4%, n = 11), the milking parlor (27.0%, n = 17), the fresh cow or maternity or hospital pens (19.0%, n = 12), the calf or heifer areas (14.3%, n = 9), and the cow feeding area (7.9%, n = 5). Other work responsibilities included maintenance (6.4%, n = 4), reproduction (3.2%, n = 2), milk hauling (1.6%, n = 1), cafeteria (1.6%, n = 1), and personnel (1.6%, n = 1). Respondents worked in herd sizes varying from 50 to 40,000 cows [16.7% small, <500 cows (n = 7/42); 7.1% medium farms, 500–1,000 cows (n = 3/42); 76.2% large, >1,000 cows (n = 32/42)]. Regarding access to healthcare, overall, dairy employees reported a visit to a doctor <1 yr ago (36.5%, n = 19/52), 1 to 3 yr ago (30.8%; n = 16/52), 3 to 6 yr ago (19.2%; n = 10/52), and >6 yr ago (13.5%; 7/52).
Survey respondents were very concerned (33.3%; n = 21), somewhat concerned (49.2%; n = 31), or not concerned at all (17.5%; n = 11) about the health implications of COVID-19. A greater percentage of respondents using the Spanish versus the English version reported being very concerned about the COVID-19 pandemic (43.0% vs. 14.3%, P = 0.017). Overall, the respondents' main concerns were bringing the virus from work to home and getting their families sick (51.3%, n = 20/39) and bringing the virus from their communities to work (38.5%, n = 15/39).
Most respondents were very concerned (51.0%, n = 26/51) or somewhat concerned (41.2%, n = 21/51) about the negative consequences of the pandemic on the dairy farm where they worked. Respondents perceived that the owners of the dairies where they worked had to face problems related to the risk of employees getting sick (22.2%, n = 14/63), milk having to be dumped (19.0%, n = 12/63), or availability of supplies (15.9%, n = 10/63). However, 28.6% of respondents were unaware of major problems in their dairies (n = 18/63). Respondents reported that they perceived their employer as very concerned (46.6%; n = 27/58), somewhat concerned (36.2%; n = 31/58), or not concerned at all (17.2%; n = 10/58).
The main sources of information for respondents were social media (52.4%, n = 33/63), television (39.7%, n = 25/63), the internet (30.0%, n = 21/63), family and friends (27.0%, n = 17/63), and radio (19.0%, n = 12/63), and 28.6% (n = 18/63) said they got overwhelmed and stopped looking for information (Figure 1). Among the respondents who reported obtaining information from other sources (19.0%, n = 12/63), posters from private companies in the workplace were the most cited source of information (83.3%, n = 5/6). Most respondents reported being familiar with COVID-19 symptoms (76.9%; n = 40/52), whereas some reported being somewhat familiar but unsure (19.2%; n = 10/52) or not clear (3.8%; n = 2/52). Respondents receiving training at work tended to be more familiar with COVID-19 symptoms (85.3%, n = 29/34) than those who had not received training yet (67%, n = 12/18; P = 0.063).Figure 1 Sources of COVID-19 information for US dairy employees (n = 63).
Most survey respondents (64.9%; n = 37/57) reported having received training on the COVID-19 pandemic at work. Among our responses, the most common tool used for training was posters on walls (71.9%, n = 23/32), followed by fact sheets given to employees (37.5%, n = 12/32), and meetings at work by someone from the dairy (37.5%, n = 12/32). All respondents who chose the Spanish version reported receiving the training in Spanish (100%, n = 36/36). However, a greater proportion of managers reported having received training (85.7%, n = 18/21) than entry-level workers (52.7%, n = 19/36; P = 0.011).
Survey respondents indicated that the most urgent questions were who to ask for help if they get sick [Spanish survey: 54.5% (n = 23/42); English survey: 28.6% (n = 6/21)], how to stay safe at work [Spanish survey: 33.3% (n = 14/42); English survey: 38.1% (n = 8/21)], basic information about the COVID-19 pandemic [Spanish survey: 38.1% (n = 16/42); English survey 33.3% (n = 7/21)], and government guidelines about the pandemic [Spanish survey: 38.1% (n = 16/42); English survey: 28.6% (n = 6/21); Figure 2]. Overall, the preferred venues to receive training were in-person meetings at work (34.9%, n = 22/63), written information (31.7%, n = 20/63), YouTube videos (28.6%, n = 18/63), or on-demand videos to watch in their break room (employees would have a dedicated moment to watch informative videos during their breaks; 27.0%, n = 17/63; Figure 3).Figure 2 Most urgent questions regarding the COVID-19 pandemic for respondents who took the English and Spanish versions of the survey.
Figure 3 Preferred ways to receive COVID-19 information among US dairy employees (n = 63).
Among the measures adopted by farmers to control the spread of COVID-19, use of hand sanitizer (60.0%; n = 30/50), increasing the frequency of handwashing (80.8%, n = 38/47), restricting the number of visitors to the farm (70.0%, n = 31/47), limiting agglomeration in break rooms (64.6%, n = 31/48), and practicing social distancing (59.6%, n = 28/47) were the most frequent measures reported by respondents. Changes in workers' schedules (27.9%; n = 12/43) and use of masks during the workday (37.5%; n = 18/48) were the least adopted measures to control the spread of the disease among the options presented.
The results presented so far have supported the research team's effort to document the concerns and preparedness of dairy producers and dairy workers during the COVID-19 health crisis. Along with the present study, this research team conducted a similar effort targeting dairy producers; results can be found at Valldecabres et al. (2022). The present study documents dairy workers' concerns and preparedness at the onset of the COVID-19 pandemic as well as their preferences for information sources and mitigation measures adopted at their workplace. This study is relevant, as occupational hazards and safety issues have been disproportionally prominent among agricultural workers. Furthermore, dairy workers were considered essential, and dairy producers were urgently tasked with implementing strategies to ensure the health and safety of their employees while navigating economic uncertainties.
Most respondents indicated that they had received training at the workplace, but half of the dairy employees using the Spanish version reported that they had received no training on the health risks of COVID-19 at work. However, most dairy producers reported that they had provided training or they were planning to train employees during the early months of the pandemic (78% and 4%, respectively; Valldecabres et al., 2022). It is unknown whether dairy producers were focused on providing training to upper management personnel or to all dairy employees. Training was mainly limited to posters, but entry-level employees would have preferred in-person meetings and fact sheets. Videos (YouTube or to watch in the break room) were also popular, especially among Spanish respondents. Previous studies have also reported a preference to receive training through videos (Friedman et al., 2007; Rodriguez et al., 2018; Panikkar and Barrett, 2021). In our study, all respondents who used the Spanish version received COVID-19 training in Spanish. This represents a commendable effort, especially considering that language has been a consistent barrier when training non-English-speaking dairy employees (Baker and Chappelle, 2012; Hagevoort et al., 2013; Rovai et al., 2016; Durst et al., 2018; Panikkar and Barrett, 2021).
Social media was a popular source of information during the COVID-19 pandemic, especially for Spanish respondents. Some of the reasons that may make social media appealing include the opportunity to follow accounts in Spanish and its easy access through mobile phones. Accordingly, a review study indicated that social media was the platform of choice for health-related COVID-19 information dissemination and consumption (Tsao et al., 2021). However, dairy farmers seemed to prefer more traditional communication channels, such as television, and online resources (Valldecabres et al., 2022).
According to CDC recommendations at the beginning of the pandemic, the most common measures adopted at the workplace to contain the spread of the disease were hand sanitizer and increasing the frequency of washing hands. This agrees with the results of our survey and reports from dairy producers (Valldecabres et al., 2022; Yung et al., 2021). Face masks were reported as being required at work by only 18% of respondents. This contradicts reports from dairy farmers (49%) who required the use of face masks in their farms (Valldecabres et al., 2022). Likely, the use of face masks was not being enforced in dairy farms, as also observed by Yung et al. (2021) in Wisconsin and Minnesota.
The concern of dairy workers about the negative health implications of COVID-19 might be partially explained by the higher incidence of COVID-19 among agricultural labor (Lusk and Chandra, 2021) and the challenges faced by this demographic to access health care. Agricultural workers, especially undocumented immigrants of Latino ethnicity, often lack health insurance or Medicaid, and their fear of immigration law enforcement and language barriers deter them from seeking health care (Liebman et al., 2016, 2018; Migrant Justice, 2019; Panikkar and Barrett, 2021). Instead, they often rely on self-medication or depend on their employer for care (Wolcott-MacCausland, 2014; Panikkar and Barrett, 2021). Furthermore, during the pandemic, dairy workers may have found it difficult to isolate at home if needed, as living in overcrowded homes with inadequate personal space is an issue previously reported (Migrant Justice, 2019).
Concerns with COVID-19 might have extended beyond health care; missing wages due to sick leave or losing their jobs were likely issues of apprehension (Schenker and Gunderson, 2013). Some comments captured in our survey, especially among respondents using the Spanish version, included the fear of getting sick and losing their jobs, lack of income while in quarantine, and inability to provide for their family. The health implications of the pandemic also concerned dairy producers, especially the large dairy farms that relied on hired labor (Valldecabres et al., 2022; Yung et al., 2021). However, most dairy producers perceived that their employees had minimal concerns about the health implications of COVID-19 (Valldecabres et al., 2022). Dairy workers in management positions reported being less concerned about the health implications of COVID-19 than entry-level employees; likely cultural differences, communication gaps, or access to trainings explain the differences observed among job titles.
Our study used a nonrandom sample of respondents, and results should be interpreted carefully. Although the survey was advertised across the US in local and national media outlets, reaching out to dairy workers was a challenge. We relied on personal relationships and on acquaintances from previous collaborations to disseminate our survey. Researchers from Idaho and South Dakota were more successful at recruiting survey participants, reinforcing the importance of personal relationships. We acknowledge that the main dairy states in the US are underrepresented. Moreover, we did not have information regarding participants' age, gender, and scholarly level, which could help reveal further bias in our sample. Nonetheless, the issues raised by our study have been reported elsewhere, such as the need to provide training programs to dairy employees that consider their learning preferences (Rovai et al., 2016; Valldecabres et al., 2022). The survey was available both in English and Spanish, but most respondents used the Spanish version. This was not surprising as, in US dairies, half of hired labor are immigrants of Latino origin (Adcock et al., 2015).
Our survey was simple and designed to be short (Revilla and Hohne, 2020). Respondents took approximately 12 min to complete it (median), but it is possible that, if using their phones and having to go through multiple pages, respondents may have felt discouraged from completing it. We noted that 40% of respondents quit after the language question was asked; this could be due to lack of clarity in how the survey was presented, as they had to click an arrow at the bottom of the page to continue with the survey. We also noticed that questions with long answers as options were more frequently skipped. The online format was chosen because it could be easily and quickly disseminated across the US, but we acknowledge that a written format mailed to dairies may have increased the response rate. Additionally, the survey was conducted during the early months of the COVID-19 pandemic. Given the trajectory of the global pandemic, reassessment of health concerns and the adequacy of training is warranted to inform agencies involved in response to a future pandemic.
Notes
This study received no external funding.
The authors thank the US dairy employees who responded to our survey. We also thank all the media outlets who kindly shared our survey.
The authors have not stated any conflicts of interest.
==== Refs
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PMC009xxxxxx/PMC9976601.txt |
==== Front
Int J Methods Psychiatr Res
Int J Methods Psychiatr Res
10.1002/(ISSN)1557-0657
MPR
International Journal of Methods in Psychiatric Research
1049-8931
1557-0657
John Wiley and Sons Inc. Hoboken
35976617
10.1002/mpr.1937
MPR1937
Original Article
Original Articles
Mental health service areas in Switzerland
Stulz Niklaus https://orcid.org/0000-0001-6805-6631
1
Jörg Reto 2
Reim‐Gautier Constanze 1
Bonsack Charles 3
Conus Philippe 3
Evans‐Lacko Sara https://orcid.org/0000-0003-4691-2630
4
Gabriel‐Felleiter Kerstin 5
Heim Eva 6
Jäger Matthias 7
Knapp Martin 4
Richter Dirk 8
Schneeberger Andres 9
Thornicroft Graham 10
Traber Rafael 11
Wieser Simon 12
Tuch Alexandre 2
Hepp Urs 13 14 [email protected]
1 Integrated Psychiatric Services Winterthur—Zurcher Unterland Winterthur Switzerland
2 Swiss Health Observatory Neuchatel Switzerland
3 Department of Psychiatry Lausanne University Hospital Lausanne Switzerland
4 Care Policy and Evaluation Centre London School of Economics and Political Science London UK
5 Psychiatric Services Lucerne Lucerne Switzerland
6 Institute of Psychology University of Lausanne Lausanne Switzerland
7 Psychiatric Services Baselland Liestal Switzerland
8 Department of Health Professions Bern University of Applied Sciences Bern Switzerland
9 Department of Psychiatry University of California San Diego La Jolla California USA
10 Centre for Global Mental Health and Center for Implementation Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London London UK
11 Organizzazione Sociopsichiatrica Cantonale Mendrisio Switzerland
12 Winterthur Institute of Health Economics ZHAW Zurich University of Applied Sciences Winterthur Switzerland
13 Department of Consultation‐Liaison Psychiatry and Psychosomatic Medicine University Hospital Zurich University of Zurich Zurich Switzerland
14 Meilener Institute Zurich Zurich Switzerland
* Correspondence
Urs Hepp, Department of Consultation‐Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, Culmannstrasse 8, Zurich CH‐8091, Switzerland.
Email: [email protected]
17 8 2022
3 2023
32 1 10.1002/mpr.v32.1 e193722 7 2022
14 6 2022
01 8 2022
© 2022 The Authors. International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Abstract
Objectives
Small area analysis is a health services research technique that facilitates geographical comparison of services supply and utilization rates between health service areas (HSAs). HSAs are functionally relevant regions around medical facilities within which most residents undergo treatment. We aimed to identify HSAs for psychiatric outpatient care (HSA‐PSY) in Switzerland.
Methods
We used HSAr, a new and automated methodological approach, and comprehensive psychiatric service use data from insurances to identify HSA‐PSY based on travel patterns between patients' residences and service sites. Resulting HSA‐PSY were compared geographically, demographically and regarding the use of inpatient and outpatient psychiatric services.
Results
We identified 68 HSA‐PSY, which were reviewed and validated by local mental health services experts. The population‐based rate of inpatient and outpatient service utilization varied considerably between HSA‐PSY. Utilization of inpatient and outpatient services tended to be positively associated across HSA‐PSY.
Conclusions
Wide variation of service use between HSA‐PSY can hardly be fully explained by underlying differences in the prevalence or incidence of disorders. Whether other factors such as the amount of services supply did add to the high variation should be addressed in further studies, for which our functional mapping on a small‐scale regional level provides a good analytical framework.
geography
psychiatry
service use
small area analysis
source-schema-version-number2.0
cover-dateMarch 2023
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
Stulz, N. , Jörg, R. , Reim‐Gautier, C. , Bonsack, C. , Conus, P. , Evans‐Lacko, S. , Gabriel‐Felleiter, K. , Heim, E. , Jäger, M. , Knapp, M. , Richter, D. , Schneeberger, A. , Thornicroft, G. , Traber, R. , Wieser, S. , Tuch, A. , & Hepp, U. (2023). Mental health service areas in Switzerland. International Journal of Methods in Psychiatric Research, 32 (1 ), e1937. 10.1002/mpr.1937 35976617
==== Body
pmc1 INTRODUCTION
Mental health services are highly complex systems with different treatment settings (e.g., inpatient and outpatient), and they often have geographically varying structures and configurations of psychiatric care (hospitals, outpatient clinics, practitioners in private practice, home treatment, etc.). This complexity makes the analysis and comparison of mental health service systems highly demanding. In Switzerland, for instance, the responsibility of 26 Cantons (Federal States) for local health services planning and a dual remuneration system with a strict distinction between outpatient and inpatient services lead to regionally diverse psychiatric care systems. Data on service use are stored in various sources (databases) and on various levels of aggregation at public federal and cantonal institutions and at private insurance companies.
Small area analysis (SAA) is a health services research technique that facilitates geographical comparison of population‐based health services expenditures and utilization rates between hospital service areas or health service areas (HSAs) (Goodman & Green, 1996; Paul‐Shaheen et al., 1987; Wennberg & Gittelsohn, 1973). HSAs are geographical regions around medical facilities (e.g., hospitals, outpatient clinics or physicians in private practice) within which most residents undergo treatment. In contrast to administrative units, which are typically bound to geopolitical entities (Kilaru et al., 2015), HSAs reflect the actual travel patterns of patients (patient‐flows) between residence and service sites (Klauss et al., 2005) and therefore represent spatial units that are more relevant with respect to health care use and planning (functional relevance).
Comparisons of small areas have repeatedly demonstrated wide variations in health care expenditures and utilization rates—both in medical and surgical (Ashton et al., 1999; Birkmeyer et al., 1998; Chassin et al., 1986; Coyte et al., 1996; Gittelsohn & Powe, 1995; Keller et al., 1990; Knickman & Foltz, 1984; Lewis, 1969; McPherson et al., 1982; Wennberg & Gittelsohn, 1973) as well as in psychiatric services (Almog et al., 2004; Ashton et al., 1999; Curtis et al., 2009; Gottlieb et al., 2021; Hendryx & Rohland, 1994; Hendryx et al., 1995; Okumura et al., 2019; Watts et al., 2011). Such‐wide variations cannot be fully explained by underlying differences in the prevalence or incidence of conditions. Whereas some areas may suffer from insufficient supply of medical care, research using SAA has repeatedly suggested the existence of supplier‐induced demand for health services (Watts et al., 2011; Wennberg, 1985, 1993; Wennberg et al., 1982). Rates of medical procedures (Wennberg & Gittelsohn, 1973) and of hospitalizations for general medical illnesses (Wennberg et al., 1987) have been shown to be related more strongly to the supply of a service than to the need for that service. This raises concerns that, once the true need for a health service has been served, excess supply must be consumed by those who do not actually need the services and are therefore exposed to risks without the potential for benefit (Fisher et al., 2003; Thornicroft & Tansella, 2009). Even though the concept of supplier‐induced effects was critically discussed in more recent literature (Leonard et al., 2009; Mercuri & Gafni, 2011, 2018), there is little debate that a better understanding of (geographical) variation in medical care and of its drivers may help to improve health care systems and their cost‐effectiveness (Drake et al., 2008; Harrison et al., 2019).
The techniques of SAA have been underused to comprehend psychiatric services provision and use (Drake et al., 2008; Watts et al., 2011). Previous attempts to map mental health services predominately stem from the USA (Almog et al., 2004; Ashton et al., 1999; Curtis et al., 2009; Gottlieb et al., 2021; Hendryx et al., 1995; Hendryx & Rohland, 1994; Watts et al., 2011), they were often limited to specific subpopulations (e.g., insurees of the Department of Veterans Affairs) (Ashton et al., 1999; Gottlieb et al., 2021) or they predominately focused on inpatient care (Almog et al., 2004; Hendryx et al., 1995; Stocker et al., 2016; Watts et al., 2011). However, if the inpatient and the outpatient mental health care settings are considered complementary, the comprehensive study of both treatment settings is necessary to better understand mental health services systems. In many high‐income countries there has been a shift from inpatient to community and outpatient care over recent decades (Thornicroft et al., 2016). Outpatient care is usually organized on a much smaller spatial scale than inpatient care (Gottlieb et al., 2021) and traveling times to service sites seems to be much more important in outpatient care (Stulz et al., 2018), making the relatively large hospital referral areas too crude a measure to capture the fine‐grained regional differences in outpatient service supply and use (Eveland et al., 1998).
In this study, we therefore aimed at using SAA and health insurance data on use of outpatient services to identify HSAs across Switzerland. In a subsequent step, we combined data on inpatient service use from a different source (databases) to better understand the relationship between utilization of inpatient and outpatient services in Switzerland. The aim of this paper was to describe the application of a new and automated methodological approach (Haynes et al., 2020) to identify psychiatric HSAs for outpatient treatment (HSA‐PSY) in Switzerland based on detailed and comprehensive psychiatric service use data. The resulting HSA‐PSY were described and compared geographically, demographically and with respect to the use of inpatient and outpatient psychiatric services. These HSA‐PSY, and forthcoming analyses based on them, may serve as a health care planning tool for regional authorities and foster supra‐regional coordination regarding health care planning activities.
2 METHOD
2.1 Data sources
To delineate HSA for psychiatric outpatient services (HSA‐PSY) in Switzerland, we used the pooled data of all health insurance companies in Switzerland for the most recently available years 2014–2017. Swiss health insurance data is gathered at the SASIS AG (SASIS AG, 2021) and includes information on all health services (diagnostics, therapies, and other procedures) covered by mandatory basic health insurance. For the current study, we used the following information on all outpatient mental health services recorded in the central database of the SASIS AG: Community of residence of the patient, postcode of the service provider, number of outpatient visits, year of outpatient visit, and number of service providers per postcode. The number of psychiatric outpatient visits is defined as the number of calendar days with utilization of ambulatory psychiatric services covered basic health insurance. Basic health insurance is compulsory for all residents in Switzerland and covers the costs for inpatient and outpatient medical care detailed in the Swiss Federal Law on Health Insurance (Federal Office of Public Health, 2021). Health insurers in Switzerland must ensure equal enrolment, range of services and treatment for everyone, making no distinctions on the basis of health status or other indicators. The health insurance companies in Switzerland provide detailed data on health services covered by basic insurance to the SASIS database (coverage almost 100% in 2014–2017; Schuler et al., 2020).
Whereas the SASIS database was used to delineate the HSA‐PSY and to calculate outpatient service use within these HSA‐PSY, the medical statistics dataset of the Federal Statistical Office (Bundesamt für Statistik, 2021a) was used to calculate hospital use (e.g., bed days per capita) per HSA‐PSY. This national database includes socio‐demographic, administrative and medical information (e.g., diagnoses) on all inpatient treatments provided by Swiss hospitals.
To calculate utilization rates, number of residents living in the identified area was obtained from the Federal Statistical Office for the census year 2017 (Bundesamt für Statistik, 2020).
2.2 Delineation of HSA for psychiatric outpatient services (HSA‐PSY) in Switzerland
There were two conflicting requirements when defining the HSA‐PSY: On the one hand, the HSA‐PSY should be as small as possible (SAA) because smaller regions tend to be more homogeneous and enable identification of differences, which are not recognizable on a more aggregated level (Tobler's first law of geography; Tobler, 1970). On the other hand, the resulting HSA‐PSY should be as self‐containing as possible; that is, most residents of an HSA‐PSY should undergo outpatient treatment at a service site in the HSA‐PSY where they live to enable comparisons between largely independent neighbouring HSA‐PSY.
We delineated HSA‐PSY based on outpatient data in this study. Outpatient services are usually organized on a smaller spatial scale than inpatient services and the resulting HSA‐PSY therefore provide a better basis for future analyses of health services provision and use in the tradition of SAA, which calls for small and homogeneous regions. We delineated HSA‐PSY based on travel flows between patients' residences and outpatient service sites using the R‐Package by Haynes et al. (2020) based on the methodological approach developed by Wennberg and Gittelsohn (1973). Each patient‐flow was defined by an origin (the residential political community of the patient), a destination (the postcode area of the service provider), and the size of the flow (the number of outpatient visits). To delineate HSA‐PSY, we considered all psychiatric services recorded in the SASIS database, which had been provided in an outpatient setting (either in private practices or by institutional outpatient service providers).
To enable comprehensive analyses of in‐ and outpatient services in a later stage, we started delineation of HSA‐PSY with the 706 Swiss MedStat regions, which are used within the context of governmental health data collection and which are the smallest common regional level on which Swiss inpatient data are recorded. The Swiss MedStat regions are based on aggregated postcodes and are of sufficient size to warrant anonymity of patients but they are still small enough to provide an appropriate starting point for HSA delineation (Bundesamt für Statistik, 2021b). In a first step of analysis, outpatient‐flows were aggregated on the level of these MedStat regions. While service providers (destinations of patient flows) could be easily allocated to these MedStat regions based on their postcode, for patients the community of residence (but no postcode) was recorded in the SASIS database. Therefore, patients living in larger communities (i.e., cities), which comprised multiple MedStat regions, could not be unequivocally allocated to one specific MedStat region. We therefore merged MedStat regions within the same community into one overarching, synthetic MedStat region (MedStatSyn regions). Like‐wise, in cases where the boarders of the communities were not congruent with the boarders of postcode areas (i.e., MedStat regions), neighbouring MedStat regions were merged into overarching MedStatSyn regions if such merging increased the “congruency of the external boarder” (the fraction of buildings in a community, which could be allocated to one MedStat or MedStatSyn region, respectively, was used as measure to judge the “congruence of the external boarder”). This‐step of pre‐processing resulted in 563 MedStatSyn regions.
To delineate population‐based HSA‐PSY from patient‐flows between these MedStatSyn regions, we then used the automated, reproducible and objective method developed by Haynes et al. (2020). In contrast to other approaches to delineate HSAs (Delamater et al., 2013; Franconi et al., 2017; Hu et al., 2018; Jia, 2016; Jia et al., 2017; Wang et al., 2020), this approach can also deal with regions where no interventions were performed, it allows for spatial contiguity of HSAs, and is implemented in a readily available open access program, the R package HSAr (Haynes, 2020). The iterative process to delineate HSA‐PSY for Swiss outpatient psychiatric care providers with HSAr encompassed the following steps:Step 1: Identify destinations of patient flows, that is, MedStatSyn regions with service sites (outpatient clinics or physicians in private practice).
Step 2: For each of these destinations (representing a potential HSA), identify all neighbouring MedStatSyn regions, which are sharing a common border with the destination region.
Step 3: Merge the neighbouring MedStatSyn regions with the destination region, if most patients living in the neighbouring (origin) MedStatSyn region travel to service providers in the destination region.
Step 4: Iterate steps 2 and 3 until all patient origin MedStatSyn regions are allocated to the neighbouring destination region (HSA) with most significant flow.
Step 5: Check if all MedStatSyn regions are allocated to a destination. For unallocated regions (either regions without any interventions or regions with primarily long distance flows), identify the neighbouring regions and allocate the unassigned origin region to the neighbouring HSA‐PSY receiving most inflow, that is, outpatient visits (information borrowed from neighbours where necessary).
Step 6: Check if all HSA‐PSY have a localization index (LI) greater than 50%. The LI is the fraction of all outpatient visits which the patients living in an HSA‐PSY use at local psychiatric outpatient services; thus a LI > 50% reflects that patients use the majority of their psychiatric consultations in the HSA‐PSY where they live. Merge HSA‐PSY with an LI below this pre‐defined threshold with the neighbouring HSA‐PSY with the highest patient flow.
Step 7: Check if all HSA‐PSY contain at least 6 service providers to fulfil the data owner's data protection terms. Merge HSA‐PSY with less than 6 service providers with the neighbouring HSA‐PSY receiving most flow.
Step 8: Repeat steps 5–7 until no new changes are made to the HSA‐PSY allocations.
2.3 Validation of HSA‐PSY by experts
These automatically and empirically derived HSA‐PSY were reviewed by a board of local mental health services experts from various parts of Switzerland. Each of these experts (including medical directors of large psychiatric institutions and other experts in mental health services research) reviewed the validity of the HSA‐PSY in his or her part of Switzerland qualitatively based on the following criteria, which are not or only partly covered by the HSAr method:‐ Functional relevance: Patient‐flows between HSA‐PSY should be minimized. While setting the LI > 0.5 in HSAr warranted that most patients undergo treatment in the HSA‐PSY where they live, experts were asked to suggest merging MedStatSyn regions if, due to their experience, the automatically delineated HSA‐PSY were not sufficiently self‐containing (cf. criterion “relevance for health services planning").
‐ Small‐scale areas: The HSA‐PSY should be small and homogeneous to identify differences in health utilization, which are not identifiable on a more aggregated level.
‐ Congruence with mobility spaces: If the allocation of MedStatSyn regions to HSA‐PSY was ambiguous, the resulting HSA‐PSY should be in accordance with the general mobility patterns of the population as represented by the Swiss Labour Market regions (Bundesamt für Statistik, 2018).
‐ Relevance for health services planning: If the allocation of MedStatSyn regions to HSA‐PSY was ambiguous, the HSA‐PSY should facilitate effective health care planning.
The authors then optimized the empirically derived HSA‐PSY manually in an iterative process with the expert panel based on the aforementioned criteria until all experts approved the resulting HSA‐PSY. When manually optimizing the HSY‐PSY, the data protection requirement of at least 6 service providers per HSA‐PSY had to be taken into account. While this requirement was promoted by setting the number of outpatient visits per HSA‐PSY in HSAr to be at least 10,000, in some cases the automatically derived HSA‐PSY had nevertheless to be manually merged into overarching HSA‐PSY to be in accordance with the data owner's data protection terms. Like‐wise, some suggestions of experts to further split‐up HSA‐PSY could not be realized because resulting HSA‐PSY would then have included less than 6 service providers (e.g., HSA‐PSY 25 or 68 in Figure 1).
FIGURE 1 Swiss map with the 68 health service areas for psychiatric outpatient care (HSA‐PSY)
2.4 Service use in the HSA‐PSY
Finally, we calculated utilization rates of inpatient and outpatient services per 1000 population in the empirically derived HAS‐PSY for the year 2017. The population‐based rate of outpatient visits was then correlated with the rate of hospitalizations and with the rate of hospital bed days, respectively.
3 RESULTS
In total, there were 5,568,261 outpatient visits in the final dataset. These outpatients visits were provided at 3871 different service sites (there was at least one outpatient service provider in 382 out of the totally 563 MedStatSyn regions). On the aggregated level of these 563 MedStatSyn regions, there were 23,947 distinct flow patterns between patients' residences and outpatient service providers' sites. The size of these patient flows was indicated by the number of outpatient visits per flow. In MedStatSyn regions with less than 1000 outpatient visits in 2017, data were enriched with the number of outpatient visits in the years 2014–2016, resulting in the aforementioned 5,568,261 outpatient visits. This data enrichment step aimed at getting more reliable estimates of the magnitude of patient flows and hence spatial patterns.
3.1 Psychiatric health services areas in Switzerland
Figure 1 depicts the 68 HSA‐PSY for outpatient treatment in Switzerland (see also Table 1 for further details). These 68 HSA‐PSY resulted after the 61 regions, which had been automatically delineated by HSAr, were manually modified in a post‐processing step based on the inputs of the expert panel and under consideration of data protection requirements (at least 6 service providers per HSA‐PSY).
TABLE 1 Description of the 68 health service areas for psychiatric outpatient care (HSA‐PSY) in Switzerland
No Name Area (km2) Population (2017) Hospitalizations (2017) Hospital bed days (2017) Outpatient visits (2017)
N Rate N Rate N Rate
1 Genève 246 496,229 3874 7.8 127,921 257.8 601,372 1212
2 Nyon 424 114,989 805 7.0 20,367 177.1 98,471 856
3 Morges 217 48,183 359 7.5 8909 184.9 38,592 801
4 Yverdon 615 82,726 721 8.7 17,591 212.6 60,315 729
5 Lausanne 692 386,061 2974 7.7 84,049 217.7 392,885 1018
6 La Chaux‐de‐Fonds 246 54,253 465 8.6 13,887 256.0 29,007 535
7 Neuchâtel 471 123,768 995 8.0 30,780 248.7 68,582 554
8 Payerne 367 64,576 399 6.2 10,418 161.3 35,639 552
9 Montreux 646 135,001 1251 9.3 28,259 209.3 131,202 972
10 Bulle 668 81,127 759 9.4 17,967 221.5 43,948 542
11 Monthey—Martigny 1483 131,488 1022 7.8 28,869 219.6 62,268 474
12 Jura 839 73,351 665 9.1 19,215 262.0 29,405 401
13 Murten 163 35,927 221 6.2 5968 166.1 13,347 372
14 Fribourg 557 156,372 1293 8.3 33,935 217.0 99,745 638
15 Biel 912 217,694 2051 9.4 67,526 310.2 136,549 627
16 Bern 281 272,476 2986 11.0 98,664 362.1 191,091 701
17 Münchenbuchsee 171 58,255 547 9.4 17,600 302.1 27,303 469
18 Münsingen 464 83,219 779 9.4 28,944 347.8 35,257 424
19 Thun 1702 169,907 1444 8.5 49,812 293.1 70,020 412
20 Binningen 374 227,825 2214 9.7 81,923 359.6 175,386 770
21 Solothurn 445 150,083 1774 11.8 50,412 335.9 67,926 453
22 Basel 37 193,921 3273 16.9 108,876 561.4 233,536 1204
23 Burgdorf 682 93,035 965 10.4 27,376 294.3 41,710 448
24 Langenthal 434 89,108 844 9.5 30,732 344.9 41,388 464
25 Sion—Sierre—Brig 3851 218,403 1520 7.0 46,339 212.2 62,613 287
26 Liestal 320 94,225 1059 11.2 38,935 413.2 69,257 735
27 Olten 122 66,854 712 10.7 23,187 346.8 32,354 484
28 Zofingen 89 55,797 492 8.8 17,807 319.1 22,791 408
29 Rheinfelden 283 81,638 643 7.9 22,967 281.3 49,549 607
30 Aarau 269 131,059 1244 9.5 46,372 353.8 73,492 561
31 Sursee 380 98,463 623 6.3 27,941 283.8 26,574 270
32 Interlaken 1229 47,542 436 9.2 16,516 347.4 23,966 504
33 Luzern—Entlebuch 1010 332,985 2727 8.2 109,587 329.1 119,224 358
34 Brugg 126 49,359 586 11.9 32,768 663.9 27,329 554
35 Lenzburg 122 71,437 572 8.0 20,456 286.4 34,982 490
36 Baden 285 168,401 1442 8.6 50,438 299.5 82,896 492
37 Ob‐ und Nidwalden 722 80,498 458 5.7 17,794 221.0 23,665 294
38 Wohlen‐Bremgarten 154 76,564 581 7.6 21,229 277.3 33,056 432
39 Spreitenbach‐Schlieren 66 102,564 897 8.7 28,161 274.6 55,689 543
40 Affoltern am Albis 135 65,775 601 9.1 22,109 336.1 37,834 575
41 Bülach 242 134,289 1039 7.7 34,589 257.6 73,511 547
42 Zürich 90 411,356 4692 11.4 156,728 381.0 373,404 908
43 Wallisellen 86 122,152 1134 9.3 37,283 305.2 67,398 552
44 Schaffhausen 342 95,810 928 9.7 34,452 359.6 53,510 559
45 Thalwil 104 124,118 1064 8.6 33,870 272.9 74,237 598
46 Zug—Schwyz—Altdorf 1708 225,914 1578 7.0 59,622 263.9 67,488 299
47 Uster 63 78,821 858 10.9 29,042 368.5 50,975 647
48 Locarno 1118 69,076 649 9.4 19,160 277.4 46,282 670
49 Meilen 170 159,289 1359 8.5 48,716 305.8 106,395 668
50 Winterthur 502 234,192 2930 12.5 96,931 413.9 175,824 751
51 Wetzikon 233 89,009 873 9.8 29,237 328.5 49,561 557
52 Frauenfeld 175 61,211 672 11.0 22,324 364.7 40,700 665
53 Lugano 291 146,651 1049 7.2 36,330 247.7 92,995 634
54 Lachen—Einsiedeln—Glarus 1127 133,779 987 7.4 34,646 259.9 32,419 242
55 Bellinzona—Misox 1699 90,014 752 8.4 26,230 291.4 55,268 614
56 Rapperswil‐Jona 199 63,470 413 6.5 14,658 229.3 26,913 424
57 Mendrisio 131 56,503 483 8.5 14,550 257.5 36,946 654
58 Kreuzlingen 252 65,117 717 11.0 22,222 341.3 43,965 675
59 Weinfelden 217 52,103 536 10.3 18,027 346.0 32,484 623
60 Wil 679 142,123 1387 9.7 49,960 348.6 73,811 519
61 Münsterlingen 112 65,356 908 13.9 29,843 456.6 44,392 679
62 Herisau 294 39,092 466 12.3 13,427 354.4 19,566 501
63 Sargans 628 53,988 399 7.4 13,809 255.8 21,567 399
64 St. Gallen 305 163,184 1713 10.5 62,869 385.3 98,273 602
65 Chur—Surselva—Prättigau 3038 144,916 1452 10.0 48,754 336.4 68,164 470
66 Rheintal—Werdenberg 275 92,199 693 7.5 26,422 286.6 47,009 510
67 Rorschach 48 44,824 530 11.8 19,380 432.4 26,686 595
68 Engadin—Bergell—Puschlav—Münstertal 3571 44,436 293 6.6 10,618 239.0 19,107 430
Note: Figures relate to the year 2017 and rates are per 1000 inhabitants.
In the densely populated Swiss midlands including most of the urban areas, the area of the HSA‐PSY tended to be smaller, particularly in the metropolitan area around Zurich, Switzerland's biggest city (HSA‐PSY 42 in Figure 1). In the sparsely populated mountainous regions around the massive Alps, the HSA‐PSY were much larger‐scaled (e.g., HSA‐PSY 11, 19, 25, 32, 65 and 68).
3.2 Outpatient service use
The rate of outpatient visits varied between 242.3 and 1211.9 per 1000 population in the 68 HSA‐PSY (Table 1 and Figure 2a). The median rate of the 68 HSA‐PSY was 552.8 visits per 1000 population (IQR = 746.1–935.5).
FIGURE 2 Number of (a) outpatient visits, (b) hospitalizations, and (c) hospital bed days per 1000 inhabitants and year in the 68 health service areas for psychiatric outpatient care (HSA‐PSY). CH, Switzerland
3.3 Inpatient service use
The rates of hospitalizations (Figure 2b) and hospital bed days (Figure 2c) per 1000 population again varied massively between HSA‐PSY. The median rates of the 68 HAS‐PSY were 8.9 (IQR = 10.0–12.2) for hospitalizations and 293.7 (IQR = 352.6–444.0) for hospital bed days.
3.4 Association between inpatient and outpatient service use
The population‐based utilization rate of outpatient services by the residents of an HSA‐PSY was positively associated with the rate of hospitalizations per capita in that HSY‐PSY (p < 0.001) and it tended to be positively associated with the utilization of hospital bed days per capita (p = 0.119). However, despite these overall trends, some HSA‐PSY were characterized by high outpatient and low inpatient utilization or vice versa (Figures 3a and 3b).
FIGURE 3 (a) Rate of hospitalizations and rate of outpatient visits per 1000 inhabitants and year in the 68 health service areas for psychiatric outpatient care (HSA‐PSY). (b) Rate of hospital bed days and rate of outpatient visits per 1000 inhabitants and year in the 68 health service areas for psychiatric outpatient care (HSA‐PSY)
4 DISCUSSION
We used SAA to delineate HSA‐PSY based on flows between patients' residences and outpatient service sites in Switzerland. The resulting 68 HSA‐PSY vary considerably regarding their size (area and population) but they represent functionally relevant spatial units for the comprehensive analysis of supply and use of inpatient and outpatient mental health services and for future service planning. So far, empirically derived and therefore functionally relevant HSA have been rarely used internationally (and never in Switzerland) to comprehensively study both inpatient and outpatient mental health services (Gottlieb et al., 2021).
Our analyses of data compiled from various sources showed a significant variation between HSA‐PSY with regard to the population‐based use of both psychiatric inpatient and outpatient services. It is unlikely that such wide variation in (inpatient and outpatient) service use is solely caused by different prevalence rates of mental disorders in the HSA‐PSY. Whether other factors such as the amount of services supply, the population structure (e.g. age and sex) or the degree of urbanization may add to the explanation of the high variation in the population‐based service use rates between the HSA‐PSY should be addressed in further studies, for which our functional mapping provides a good basis.
If one considers inpatient and outpatient mental health care to be substitutable to some degree, our findings of no or even positive associations between utilization rates of inpatient and of outpatient services appears rather counterintuitive. That is, one might expect lower use of inpatient services in HSA‐PSY with higher use of outpatient services and vice versa. Even though Switzerland has one of the highest numbers of psychiatrists and one of the highest numbers of psychiatric hospital beds per capita when compared to other OECD countries (OECD, 2014), there seems still to be a gap in mental health care—particularly those patients with the most urgent need for treatment and patients in rural areas often lack access to mental health care (Stocker et al., 2016). Such treatment gap despite relatively high resources may raise the question whether psychiatric resources are appropriately allocated to patients and regions in Switzerland. While our findings suggest that there might be an under‐supply in some regions, psychiatric services might be sufficient or even overfunded in other regions. Even though the concept of supplier‐induced effects was more critically discussed in more recent literature (Leonard et al., 2009; Mercuri & Gafni, 2011, 2018) and quantifying the objective need for mental health care supply remains difficult, our findings may foster further research and the discussion for optimal allocation of resources.
As in many high income countries, a shift from inpatient to outpatient mental health care was also proclaimed by policy makers in Switzerland (Schweizerische Gesundheitsdirektorenkonferenz, 2008). However, the dual Swiss remuneration system with a strict discrimination between outpatient and inpatient health care hinders the enforcement of outpatient and intermediate (e.g., day hospital or home treatment) services. Health insurances are currently obliged to fully refund outpatient treatments whereas inpatient treatment is partly financed by both Cantons (55%) and health insurances (45%). Obviously, health insurances are therefore sparsely motivated in the strengthening of outpatient services. In addition, the current financial incentives in Switzerland often make the provision of inpatient mental health care more attractive for psychiatric institutions from an economically point of view because the actual costs of more intensive institutional outpatient services are usually not fully covered by current tariffs (which were developed to cover costs of physicians in private practice). The qualitative and/or quantitative in‐depth study of those HSA‐PSY with a strong focus of outpatient care (while providing relatively little inpatient care) might be an interesting endeavour for future research to learn more about the drivers and outcomes of deinstitutionalization.
4.1 Limitations
Some limitations of the study warrant attention. In Switzerland, treatment for mental health issues is covered by basic health insurance if it is provided by psychiatric hospitals, outpatient clinics, psychiatrists in private practice or general practitioners. If costs for health services were payed out of pocket (e.g. due to deductibles) or by private health insurance models (e.g. treatment at psychologists in private practice), they were not recorded in the SASIS database and hence omitted in our analyses. Psychotherapy by psychologist was only reimbursed by basic health insurances if they were employed at psychiatric hospitals, outpatient clinics or at psychiatrists in private practice. However, out of pocket payments for psychological psychotherapy were estimated to account for less than 10% of the total costs in outpatient psychiatry (Kaiser et al., 2019) and treatment of more severe mental health issues might be covered by basic health insurance in the vast majority of cases. In addition, the data protection requirement of at least 6 service providers per HSA‐PSY did sometimes prohibit delineation of small‐scaled functional areas. However, this merely applied to some sparsely populated mountainous regions.
5 CONCLUSIONS
In this study, we applied a new and automated methodological approach (HSAr) to identify HSA‐PSY in Switzerland based on detailed and comprehensive psychiatric service use data from health insurance claims. From a policy making perspective, this new approach provides an analytical framework to examine the effects of financial incentives and of regional (e.g. cantonal) mental health programs on service use by enabling comprehensive analysis of inpatient and outpatient service use on a small‐scale regional level. As of midyear, psychotherapy by psychologists in private practice will also be covered by basic health insurance in Switzerland. Our HSA‐PSY provide a basis for future examination of the local effects of this new national legislation. As aforementioned, further research should examine service use in patient subgroups (e.g., diagnostic and age groups) and at identifying factors, which are associated with the substantially varying amount of mental health care use between the HSA‐PSY (such as services supply, degree of urbanization, socioeconomic indicators or the role of stigmatization).
AUTHOR CONTRIBUTIONS
All authors made substantial contributions to the conception or design of the research or the acquisition, analysis, or interpretation of the data for the work; to drafting the work; to revising it critically with respect to important intellectual content; and to approving the version to be published.
CONFLICT OF INTEREST
All other authors received no specific grant related to this study from any funding agency, commercial or not‐for‐profit sectors.
ACKNOWLEDGEMENTS
GT is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration South London at King's College London NHS Foundation Trust, and by the NIHR Asset Global Health Unit award. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. GT is also supported by the Guy's and St Thomas' Charity for the On Trac project (EFT151101), and by the UK Medical Research Council (UKRI) in relation to the Emilia (MR/S001255/1) and Indigo Partnership (MR/R023697/1) awards.
DATA AVAILABILITY STATEMENT
Research data are not shared.
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PMC009xxxxxx/PMC9986828.txt |
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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences
36595668
202216338
10.1073/pnas.2216338120
videoVideoresearch-articleResearch Articlebiophys-bioBiophysics and Computational Biology408
Biological Sciences
Biophysics and Computational Biology
Heterotypic electrostatic interactions control complex phase separation of tau and prion into multiphasic condensates and co-aggregates
Rai Sandeep K. a b
Khanna Roopali a c
Avni Anamika a b
Mukhopadhyay Samrat [email protected]
a b c 1 https://orcid.org/0000-0003-1242-9958
aCentre for Protein Science, Design and Engineering, Indian Institute of Science Education and Research Mohali, Mohali, Punjab 140306, India
bDepartment of Chemical Sciences, Indian Institute of Science Education and Research Mohali, Mohali, Punjab 140306, India
cDepartment of Biological Sciences, Indian Institute of Science Education and Research Mohali, Mohali, Punjab 140306, India
1To whom correspondence may be addressed. Email: [email protected].
Edited by H. Jane Dyson, Scripps Research Institute Department of Integrative Structural and Computational Biology, La Jolla, CA; received September 24, 2022; accepted November 22, 2022
3 1 2023
10 1 2023
3 7 2023
120 2 e221633812024 9 2022
22 11 2022
Copyright © 2023 the Author(s). Published by PNAS.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Significance
Biological phase separation stands at the crossroads between physiology and disease. Proteins and nucleic acids undergo intracellular phase separation to form dynamic, liquid-like, multicomponent, membraneless compartments that offer spatiotemporal control of cellular functions. However, aberrant phase transitions are associated with deadly human diseases. Here we show that two neuronal proteins, namely tau and prion proteins, can commingle into multicomponent liquid-like condensates via electrostatic crosslinks. Properties of these complex condensates can be modulated by RNA leading to a diverse range of morphologies. We also demonstrate that liquid-like condensates of tau and prion can gradually convert into solid-like amyloid species reminiscent of pathological aggregates. Our findings provide unique mechanistic insights into multicomponent macromolecular phase separation associated with physiology and overlapping neuropathological features.
Biomolecular condensates formed via phase separation of proteins and nucleic acids are thought to perform a wide range of critical cellular functions by maintaining spatiotemporal regulation and organizing intracellular biochemistry. However, aberrant phase transitions are implicated in a multitude of human diseases. Here, we demonstrate that two neuronal proteins, namely tau and prion, undergo complex coacervation driven by domain-specific electrostatic interactions to yield highly dynamic, mesoscopic liquid-like droplets. The acidic N-terminal segment of tau interacts electrostatically with the polybasic N-terminal intrinsically disordered segment of the prion protein (PrP). We employed a unique combination of time-resolved tools that encompass several orders of magnitude of timescales ranging from nanoseconds to seconds. These studies unveil an intriguing symphony of molecular events associated with the formation of heterotypic condensates comprising ephemeral, domain-specific, short-range electrostatic nanoclusters. Our results reveal that these heterotypic condensates can be tuned by RNA in a stoichiometry-dependent manner resulting in reversible, multiphasic, immiscible, and ternary condensates of different morphologies ranging from core-shell to nested droplets. This ternary system exhibits a typical three-regime phase behavior reminiscent of other membraneless organelles including nucleolar condensates. We also show that upon aging, tau:PrP droplets gradually convert into solid-like co-assemblies by sequestration of persistent intermolecular interactions. Our vibrational Raman results in conjunction with atomic force microscopy and multi-color fluorescence imaging reveal the presence of amorphous and amyloid-like co-aggregates upon maturation. Our findings provide mechanistic underpinnings of overlapping neuropathology involving tau and PrP and highlight a broader biological role of complex phase transitions in physiology and disease.
biological phase transitions
complex coacervation
intrinsically disordered proteins
membraneless organelles
protein aggregation
DST | Science and Engineering Research Board (SERB) 501100001843 SPR/2020/000333 Samrat Mukhopadhyay DST | Science and Engineering Research Board (SERB) 501100001843 CRG/2021/002314 Samrat Mukhopadhyay Department of Science and Technology, Ministry of Science and Technology, India (DST) 501100001409 SR/FST/LS-II/ 2017/97 Samrat Mukhopadhyay Ministry of Education, India (MoE) 501100004541 Centre of Excellence Samrat Mukhopadhyay Council of Scientific and Industrial Research, India (CSIR) 501100001412 Fellowship Sandeep K. Rai
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pmcBiomolecular condensates formed via liquid–liquid phase separation of proteins and nucleic acids serve as on-demand membraneless organelles that are involved in the spatiotemporally controlled organization, compartmentalization, and regulation inside living cells. Owing to their ability to form and dissipate in response to cellular cues, regulatability, permeability, and their ability to selectively concentrate the biomolecules, these noncanonical, liquid-like, membraneless compartments are emerging as central players at all levels of essential cellular activities ranging from gene expression and regulation to modulation of intricate signaling pathways (1–11). An emerging body of work has revealed that intrinsically disordered proteins/intrinsically disordered regions (IDPs/IDRs) often comprising polypeptide repeat units, low sequence complexity, and prion-like domains are ideal candidates for biological phase separation. Such sequence features offer a fuzzy network of weak, multivalent, non-covalent, and transient contacts that govern the relay of making and breaking of weak interactions and promote phase separation into liquid-like condensates (12–19). Material properties of these biomolecular condensates can be tuned by other proteins and nucleic acids such as RNA. Heterotypic interactions between molecular entities involving a multitude of proteins and nucleic acids can often lead to the formation of multicomponent, multiphasic, and mutually immiscible compartments such as nucleolar condensates (20–25). In vitro, many of the essential biophysical features of liquid-like intracellular membraneless organelles can be recapitulated by using purified proteins with or without nucleic acids that can spontaneously demix from a mixed homogeneous phase into two coexisting phases, namely the dense phase and the light phase (11, 26, 27). Macromolecular phase separation is proposed to involve a density transition coupled to percolation that results in the dense phase comprising a viscoelastic network fluid (4). Such physical microgels can undergo maturation into solid-like aggregates via time-dependent changes in the material properties. These protein aggregates formed via aberrant phase transitions are thought to be involved in a range of deadly neurodegenerative diseases such as Alzheimer’s disease, amyotrophic lateral sclerosis, frontotemporal lobar degeneration (FTLD), and so forth (28–32). Therefore, the understanding of the precise molecular determinants of biological phase separation is of great importance in both physiology and pathology. Here, we describe an intriguing interplay of two neuronal proteins such as tau and prion proteins (PrPs) that undergo complex coacervation resulting in the formation of heterotypic condensates. These condensates transform into multiphasic condensates in the presence of RNA and exhibit a time-dependent maturation into solid-like aggregates.
Tau is a microtubule-associated neuronal IDP that is expressed in the human brain as six different spliced variants (33, 34). The longest isoform, full-length tau, harbors two N-terminal inserts and a proline-rich domain followed by four repeat regions, one pseudo repeat region, and a C-terminal domain (Fig. 1A). Under normal conditions, tau interacts with other microtubular proteins mediated by its proline-rich and repeat domains. These domains are prone to modifications in the form of posttranslational hyperphosphorylation under disease conditions (35, 36). Additionally, tau contains a heterogeneous cluster of charged residues, oppositely charged domains, and polar residues with a high proline and glycine content throughout the sequence making it a highly dynamic and amphipathic polypeptide. Recent reports have shown that under physiological conditions, tau can undergo a liquid-to-solid transition via phase separation driven by homotypic as well as heterotypic interactions in the absence or presence of a crowding agent (37–40). Such condensates have been proposed to act as reaction crucibles that can promote aberrant phase transitions involved in disease progression. Besides, tau pathology extends to many other neuronal and RNA-binding proteins (RBPs). The interactions of tau and its hyperphosphorylated variants with several RBPs including Musashi and T cell intracellular antigen one have been proposed to result in heterotypic inclusions that might be responsible for the exacerbation of overlapping neuropathological features and diseases (41). Moreover, the accumulation of neurofibrillary tangles (NFTs) of tau with another IDP, α-synuclein (α-Syn), in Parkinson’s disease hints toward the synergistic interactions between tau and α-Syn (42). Tau protein deposits have also been found in the brains of patients affected by familial cerebral amyloid angiopathy associated with the misfolding and aggregation of the human prion protein. These patients displayed a nonsense stop codon mutation (Q160Stop) in PrP resulting in a highly unstructured truncated variant of PrP. Additionally, Gerstmann–Sträussler–Scheinker syndrome associated with a missense mutation in PrP (F198S) is attributed partly to tau deposits (43–45). These findings revealed a complex interplay of tau and PrP in a range of pathological manifestations. The cellular form of PrP (PrPC) is a GPI-anchored protein consisting of an N-terminal signal peptide (residues 1 to 22), a highly positively charged, intrinsically disordered, N-terminal tail containing oligopeptide repeats (residues 23 to 120), a globular C-terminal domain (residues 121 to 230), and a GPI-anchor signal (residues 231 to 253) (46–48) (Fig. 1B). It also contains a putative RNA-binding site and is often classified as an RBP. In order to elucidate the molecular basis of tau–PrP interactions associated with overlapping neuropathological features, we set out to investigate the role of PrP in regulating the phase behavior of tau and discovered an intriguing interplay of molecular drivers in modulating their complex phase transitions.
Fig. 1. Prion protein potentiates phase separation of tau. (A) Domain architecture and amino acid sequence of full-length tau. A null-cysteine variant of full-length tau (C291S, C322S) was used for our studies. (B) Domain architecture and amino acid sequence of full-length PrP. (C) The propensity of phase separation predicted for tau and (D) PrP using FuzDrop. (E) The turbidity at 350 nm is plotted for tau (10 µM) in the presence of an increasing concentration of PrP (14 mM HEPES, pH 6.8, 1.5 mM NaCl). (F) The saturation concentration (Csat) of tau for tau-only and tau:PrP condensates estimated by ultracentrifugation. (G) A high-resolution two-color Airyscan confocal image of colocalized tau (tau-T17C-Alexa Fluor 594, red) and PrP (PrP-W99C-Alexa Fluor 488, green) in tau:PrP coacervates (Scale bar, 10 µm.) Tau and PrP concentrations were 10 and 20 µM, respectively. The imaging was performed at least thrice with similar observations. (H) FRAP kinetics of tau:PrP droplets. Alexa Fluor 488-labeled proteins were used for both tau and PrP for independent FRAP studies. The data represent mean ± SD; n = 5. (I) The normalized autocorrelation plot obtained from FCS measurements performed for five different droplets. The unnormalized autocorrelation plots are shown in SI Appendix, Fig. S1. (J) Diffusion coefficients (plotted on a log scale) of tau in monomeric dispersed form, tau-only droplets, and tau:PrP droplets obtained from FCS measurements (n = 5). For all FCS measurements, 5 nM of labeled protein (tau-T17C-Alexa Fluor 488) was used. Tau and PrP concentrations were 10 and 20 µM, respectively. See SI Appendix, Methods for details.
Results
PrP potentiates Spontaneous Phase Separation of Tau through Heterotypic Interactions.
Sequence composition governs the phase behavior of a protein by tuning its multivalent interactions. Clustered negatively and positively charged residues, weakly hydrophobic segments, and cation-π modulators such as arginine and aromatic amino acids are important for regulating these interactions. First, we analyzed the amino acid sequence of full-length tau protein. It has a negatively charged N-terminal and positively charged proline-rich domain, in addition to a hydrophobic repeat domain and a mildly negatively charged C-terminal end, making it a highly unstructured amphipathic polymer and hence an ideal candidate for phase separation (Fig. 1A). This was further supported by sequence-based phase-separation predictors such as FuzDrop (49) and catGRANULE (50), which predicted a high phase-separation propensity for the tau protein (Fig. 1C and SI Appendix, Fig. S1A). We began with turbidity measurements and confocal microscopy to complement our bioinformatic analyses with experimental proof and established the in vitro phase separation of tau at a near-physiological condition. We observed that under our condition (14 mM HEPES, pH 6.8), tau remains in a monomeric dispersed form even at very high concentrations (~1 mM) in the presence of 50 mM NaCl. Upon lowering the salt concentration (<10 mM NaCl), the turbidity of the protein solution rose even at protein concentrations as low as ~6 μM, which is close to the physiological concentration of tau (2 μM). To further verify the increase in turbidity with observable evidence, we used an Alexa Fluor 488-maleimide-labeled single-Cys variant of the protein (tau-T17C) to perform confocal fluorescence imaging. Tau protein doped with the labeled variant (~1%) was used for imaging, which reveals the formation of tau protein droplets under this condition (SI Appendix, Fig. S1B). These droplets underwent characteristic fusions and surface-wetting (SI Appendix, Fig. S1D and Movie S1) and displayed a rapid fluorescence recovery after photobleaching (FRAP) indicating their liquid-like behavior. These findings demonstrate the coacervation of tau via homotypic interactions and are in accordance with previous findings on the phase separation of tau (37, 38). Previous results have indicated the ability of RNA to act as an inducer for protein phase separation by engaging in heterotypic interactions with polypeptide chains. Under our conditions, a low concentration of RNA (~5 ng/μL) was able to enhance the phase-separation ability of tau as evidenced by increased turbidity and confocal imaging (SI Appendix, Fig. S1B). These tau:RNA complex condensates behave similarly to tau-only droplets exhibiting liquid-like behavior and rapid FRAP (SI Appendix, Fig. S1 D and E).
We next investigated the effect of PrP on the phase behavior of tau. The N-terminal IDR of PrP has a high propensity to undergo phase separation (Fig. 1D); however, under our solution condition at pH 6.8, full-length PrP does not undergo spontaneous phase separation (SI Appendix, Fig. S1C). We then measured the turbidity of tau solutions (10 µM) in the presence of an increasing concentration of PrP. Our data showed an increase in turbidity of the solution upon the addition of PrP suggesting an increase in the phase-separation propensity of tau in the presence of PrP (Fig. 1E). To quantify this observation, we performed ultracentrifugation to estimate and compare the saturation concentration (Csat) of tau for tau-only and tau:PrP condensates. These results indicated the lowering of Csat values of tau in the presence of PrP (Fig. 1F). Next, to check whether PrP is recruited inside the droplet and undergoing complex phase separation with tau, we performed two-color Airyscan confocal imaging to visualize the heterotypic coacervation of tau and PrP. We labeled single-Cys variants of tau (T17C) and PrP (W99C) using Alexa Fluor 594-maleimide and Alexa Fluor 488-maleimide, respectively. We carried out phase-separation assays with tau and PrP in the presence of ~1% labeled proteins and imaged them under a confocal microscope. Two-color imaging revealed the colocalization of tau and PrP within these liquid condensates (Fig. 1G). Heterotypic coacervation of tau and PrP yielded a much larger number of droplets that were smaller and more spherical compared to tau-only droplets (SI Appendix, Fig. S1F and Movie S2). Next, in order to study the internal material properties of these droplets, we performed FRAP experiments that revealed rapid near-complete recoveries for both proteins indicating their mobility inside the droplets (Fig. 1H). Further, to probe the diffusional properties of the dense phase on a microsecond to sub-millisecond timescale, we monitored the diffusion time of Alexa Fluor 488-labeled tau inside tau-only and tau:PrP droplets by performing fluorescence correlation spectroscopy (FCS) within individual droplets. The diffusion time extracted from our correlation measurements suggested the presence of slower diffusion of tau in tau:PrP droplets compared to tau-only droplets indicating heterotypic interactions of tau and PrP resulting in slower diffusion of tau (Fig. 1I and SI Appendix, Fig. S1 G–I). Taken together, our observations suggest that PrP potentiates the phase separation of tau and is recruited within the droplets resulting in stronger physical crosslinks comprising an intermolecular network of tau and PrP. Next, to further unmask the nature of intermolecular interactions driving tau:PrP complex coacervation, we investigated the sequence features of both proteins. Given the clustering of opposite charges in both proteins, we postulated that they could potentially interact electrostatically in a manner similar to what has been reported before for the complex coacervation of IDPs (51, 52). To investigate the effect of electrostatic interactions, we set out to study the influence of salt on tau:PrP complex coacervation.
Electrostatic Interactions are the Principal Driver of tau:PrP Heterotypic Coacervation.
Tau and PrP possess similar net charges and have isoelectric points (pI) of 8.24 and 9.44, respectively. Although based on their overall charge, one would expect repulsion between these two polypeptide chains, a closer inspection of the amino acid sequence and charge distribution throughout both protein chains (53) revealed that tau and PrP are oppositely charged at the N-terminal ends which can drive their complex coacervation (SI Appendix, Fig. S2 A and B). To test our hypothesis, we performed salt-dependent turbidity assays and confocal imaging of the tau:PrP condensates (Fig. 2A and SI Appendix, Fig. S2C). With an increasing salt concentration, the turbidity of the tau:PrP mixture dropped sharply with a dissolution of droplets at 50 mM NaCl highlighting the role of intermolecular electrostatic interactions in driving their complex coacervation. With increasing PrP concentration, these heterotypic assemblies were able to sustain higher salt concentrations (Fig. 2B and SI Appendix, Fig. S2 C and D). At a physiological salt concentration (~150 mM NaCl), higher protein concentrations (tau 30 µM and PrP 60 µM) were needed to drive complex phase separation (SI Appendix, Fig. S2E). We also observed no significant changes in the phase-separation propensity upon the addition of increasing amounts of 1,6-hexanediol possibly indicating the presence of more pronounced electrostatic interactions within condensates compared to hydrophobic effects (SI Appendix, Fig. S2F). Posttranslational modifications of a protein often play a crucial role in modulating its phase-separation ability and interactions with other proteins or biomolecules primarily by changing its charge distribution (54, 55). We next probed whether the frequently occurring phosphorylation of tau, which adds negative charges to the protein, would increase the phase-separation propensity of tau:PrP. To test this, we created a triple phosphomimetic variant of tau (tau 3P) by selectively mutating three residues to glutamate (S202E, S205E, T208E). This variant of tau (tau 3P) exhibited a higher propensity for phase separation with PrP as observed by our turbidity assays (Fig. 2C). Further, even a much lower concentration of PrP (~2 µM) promoted the phase separation of tau 3P compared to wild-type tau. Two-color imaging also corroborated our turbidity measurements (Fig. 2D). Imaging and FRAP experiments validated the liquid-like nature of tau 3P:PrP droplets (SI Appendix, Fig. S2G). These tau 3P:PrP droplets were larger and more mobile compared to tau:PrP droplets (SI Appendix, Fig. S2H). These results together suggested that electrostatic interactions modulate the complex coacervation of tau and PrP. We next set out to elucidate the roles of specific protein domains in driving the complex coacervation of tau and PrP.
Fig. 2. Electrostatic interactions govern tau:PrP phase separation. (A) Salt-induced dissolution of tau:PrP droplets (tau 10 µM, PrP 20 µM) in the presence of NaCl (Scale bar, 10 µm.) (B) Tau (10 µM, red) and PrP (50 µM, green) complex coacervates in the presence of an increasing concentration of salt (Scale bar, 10 µm.) (C) Turbidity plot of tau (10 µM) and triple phosphomimetic mutant of tau (tau 3P, 10 µM) as a function of the PrP concentration. The data represent mean ± SD; n = 3. (D) Two-color Airyscan confocal images of tau 3P (10 µM, red) and PrP (10 µM, green) droplets (Lower) compared with tau (Upper) (Scale bar, 10 µm.) See SI Appendix, Methods for details.
Domain-Specific Interactions Drive the Co-condensation of Tau and PrP.
In order to obtain domain-specific insights into the electrostatic interactions between tau and PrP, we created several truncated variants of both proteins (Fig. 3 A and B). First, we aimed at characterizing the role of the negatively charged N-terminal fragment and positively charged P-rich region of tau and created two naturally occurring truncations, namely Nh2-tau (aa 26-230, pI = 5.32) and tau 0N4R (aa 151-391, pI = 10.23). These variants of tau are found in tau deposits in the Alzheimer's disease brain and are thought to play a vital role in tau pathogenesis (56, 57). Based on the high net negative charge, we posited that Nh2-tau (predicted net charge ~ −5) could undergo phase separation with PrP (predicted net charge ~ +10) by a charge-neutralization mechanism that is reminiscent of RNA-driven reentrant phase transitions. Indeed, our turbidity measurements showed a reverse bell-shaped profile for Nh2-tau:PrP at a fixed concentration of PrP (10 µM) (Fig. 3C). The turbidity value peaked at a 2:1 stoichiometric ratio of Nh2-tau:PrP where the charge neutralization was expected. Our two-color fluorescence microscopy also corroborated this observation (Fig. 3D). These Nh2-tau:PrP droplets showed liquid-like characteristics, grew with time, and after 1 h these droplets appeared to completely coalesce (SI Appendix, Fig. S3A). Further, as expected, the other positively charged fragment of tau, tau 0N4R, did not phase separate either alone or in the presence of PrP (Fig. 3E). These results indicated that the negatively charged N-terminal domain of tau is important for the heterotypic condensation of tau and PrP.
Fig. 3. N-terminal domains of tau and PrP drive complex coacervation. (A and B) The depiction of tau and PrP truncations used. (C) Turbidity plots of Nh2-tau:PrP (PrP, 10 µM) and Nh2-tau:Y145Stop (Y145Stop, 10 µM). The data represent mean ± SD; n = 3. (D) Two-color Airyscan confocal images of Nh2-tau:PrP (Nh2-tau 20 µM, PrP 10 µM, Upper) and Nh2-tau:Y145Stop (Nh2-tau 20 µM, Y145Stop 10 µM, Lower) droplets (Scale bar, 10 µm.) (E) Images of the mixed homogeneous phase of 0N4R tau (Upper, green), 0N4R with PrP (second panel, green), 0N4R with Y145Stop (third panel, green), and Nh2-tau (Bottom, red) (Scale bar, 10 µm.) Same concentration of each protein (20 µM) was used. (F) Effect of PrP truncations (20 µM) on tau (10 µM) turbidity. The data represent mean ± SD; n = 3. (G) Confocal images of droplets of tau (10 µM with 1% tau-T17C-Alexa Fluor 488) in the presence of N- and C-terminal truncations of PrP (20 µM each, Scale bar, 10 µm.) (H) Steady-state fluorescence anisotropy measurements of F-5-M labeled single-Cys mutants of tau spanning the sequence; in the dispersed monomeric (wine), tau-only droplets (cyan), and tau:PrP droplets (blue). The data represent mean ± SD; n = 3. (I) Steady-state fluorescence anisotropy measurements of F-5-M labeled single-Cys mutants of PrP in the dispersed monomeric (red) and tau:PrP droplets (cyan). The data represent mean ± SD; n = 3. (J) Time-resolved fluorescence anisotropy decays of IAEDANS labeled at S56C of tau in monomer (red), tau-only droplets (blue), and tau:PrP droplets (olive). The solid lines are fits obtained using decay analysis. Similar anisotropy decay profiles were obtained for the N-terminal segment of PrP (SI Appendix, Fig. S3H). Tau and PrP concentrations were 10 and 20 µM, respectively. See SI Appendix, Methods for details of measurements and analysis and SI Appendix, Table S2 for recovered parameters.
In order to investigate the role of different regions of PrP in tau:PrP co-condensation, we used a naturally occurring, pathological C-terminally truncated variant of PrP, namely Y145Stop (PrP 23-144) (Fig. 3B) (58). Y145Stop exhibited a phase-separation behavior with tau similar to full-length PrP, as evident from the turbidity measurements and imaging (Fig. 3 F and G and SI Appendix, Fig. S3C). Moreover, the turbidity and droplet profiles of Nh2-tau:Y145Stop were similar to that obtained with full-length PrP (Fig. 3 C and D). Similarly, as in the case of full-length PrP, with the tau3P variant, Y145Stop showed an increase in turbidity in comparison with the unmodified tau, highlighting the role of electrostatic interactions (SI Appendix, Fig. S3D). However, the C-terminal globular domain of PrP (PrP 112-231) neither enhanced the phase-separation propensity nor changed the morphological appearance of tau droplets (Fig. 3 F and G and SI Appendix, Fig. S3E). These findings highlight that the N-terminal intrinsically disordered segment of PrP is the key modulator for complex coacervation of tau and PrP primarily via electrostatic interactions between oppositely charged disordered domains. Previous studies have shown that such domain-specific electrostatic interactions yield nano-complexes that can serve as primary units for such heterotypic complex coacervates (52). These primary units can offer a dynamic network of physical crosslinks resulting in a dense phase that exhibits liquid-like properties at the mesoscopic length scale but contains short-range ordering and dynamic heterogeneity at the nanoscopic level. We thus hypothesized that region-specific electrostatic interactions could potentially induce temporal molecular ordering within the condensed phase. Next, we used site-specific picosecond time-resolved fluorescence anisotropy measurements to discern short-range dynamic heterogeneity.
Electrostatic Nanoclusters in Heterotypic Condensates.
In order to investigate the region-specific structural ordering in tau:PrP condensates, we performed fluorescence anisotropy measurements that report the local rotational flexibility. To record the site-specific anisotropy, we used a thiol-active fluorescent dye, Fluorescein-5-maleimide (F-5-M) to label single-Cys variants of tau at residue locations 17, 56, 158, 291, 322, 400, and 433 spanning the entire protein chain. Any rise in the steady-state anisotropy is interpreted as the loss of conformational flexibility. The steady-state fluorescence anisotropy exhibited an increase at all positions compared to monomeric dispersed tau (Fig. 3H). Upon the formation of tau:PrP complex condensates, we observed a rise in the anisotropy at locations 56, 158, 400, and 433 indicating these regions of tau possessing negatively charged residues are involved in heterotypic interactions with the positively charged N-terminal domain of PrP (Fig. 3H). In contrast, residues 291 and 322 located near the basic region of tau did not exhibit an increase in the anisotropy presumably due to electrostatic repulsions with the positively charged N-terminal domain of PrP. Notably, the anisotropy value at residue 56 located at the negatively charged N-terminal domain of tau showed the most significant increase followed by residue positions 158 and 400 (C-terminal end). Additionally, we measured the changes in the anisotropy at various residue positions in PrP by using respective cysteine variants (Fig. 3I). A significant increase in the anisotropy at residue position 99 located at the positively charged intrinsically disordered N-terminal domain of PrP indicated the role of this segment in promoting the electrostatically driven complex coacervation of tau and PrP. Moreover, our single-droplet anisotropy measurements for tau and PrP also corroborated our ensemble measurements (SI Appendix, Fig. S3 F and G). These observations are in line with our results on domain-specific interactions described in the previous section. Together our steady-state fluorescence anisotropy measurements highlighted the central role of the acidic N-terminal segment of tau and the basic N-terminal domain of PrP in forming heterotypic tau:PrP con]densates.
Steady-state fluorescence measurements provide time-averaged information and thus cannot distinguish between the different modes of rotational dynamics experienced by polypeptide chains. In order to discern the different modes of chain dynamics as well as to estimate the hydrodynamic sizes of the primary units formed via electrostatic interactions, we performed picosecond time-resolved fluorescence anisotropy measurements using fluorescently labeled tau at the 56th position which showed the most significant increase in the steady-state anisotropy upon complex phase separation of tau and PrP. Monomeric dispersed tau exhibited fast depolarization kinetics that is typical for an expanded polypeptide chain (Fig. 3J). A biexponential decay model resolved a fast (sub-nanosecond) rotational correlation time corresponding to the local motion of the fluorophore and a characteristic slower (nanosecond) rotational correlation time that is attributed to collective backbone dihedral rotations and long-range conformational fluctuations. Upon homotypic phase separation of tau, the depolarization kinetics became slower indicating the chain–chain interactions within condensates. Upon heterotypic phase separation of tau and PrP, the depolarization kinetics exhibited an additional slower rotational correlation time (~43 ns) suggesting the formation of heterotypic clusters. Assuming these clusters are spherical and there is no significant change in the viscosity within droplets, the estimated hydrodynamic radius of these tau:PrP heterotypic electrostatic clusters is ~3.6 nm. We would like to point out that this is an approximate estimate of the dimensions of the nanoclusters within tau:PrP condensates. Such nanoclusters were detected in other electrostatically driven complex coacervates (32, 52). Taken together, these findings show that domain-specific electrostatic interactions between the acidic N-terminal domain of tau and the basic N-terminal domain of PrP drive the complex phase separation of tau and PrP. Additionally, since nucleic acids are known to alter the protein phase behavior, we next set out to examine the effect of RNA on the complex phase separation of tau and PrP.
RNA drives Tau:PrP Heterotypic Assemblies into Multiphasic Condensates.
The interior of biomolecular condensates is thought to be a dense, yet dynamic, tangled-mesh-like organization of proteins and nucleic acids. RNA, because of its shape, charge, sequence, and conformational plasticity, acts as a scaffold for the proteins and introduces multivalency into a multicomponent system (59, 60). Because of these properties, RNA can modulate the phase behavior and tune the partitioning and material properties of condensates. Typically, RNA-controlled electrostatically driven condensates exhibit a distinct three-regime phase behavior (61, 62). To recapitulate this behavior in the tau:PrP system, we started with individual tau and PrP droplet formation. Tau and PrP demonstrate a reentrant phase behavior, with PrP phase separating over a wider regime than tau for approximately the same protein mass (SI Appendix, Fig. S4A). This difference in the profile can be attributed to the sequence composition of both proteins; tau is K/G-rich, whereas the N-terminal disordered domain of PrP is enriched with R/G/Y residues. Compared to a K-rich polypeptide, an R-rich polypeptide interacts more strongly with RNA because of its potential to engage with RNA via cation-π interactions in addition to the electrostatic interactions (63, 64). Next, we set out to study the effect of polyU RNA on preformed tau:PrP heterotypic condensates. For this ternary system (tau:PrP:RNA), the phase-separation regime gets further broadened and taller in comparison with the individual protein-RNA systems (Fig. 4A and SI Appendix, Fig. S4A). At low RNA concentrations, tau:PrP droplets remain miscible and colocalize within the droplets (type I) (Fig. 4 B, first panel and Fig. 4C). With increasing concentrations of RNA, however, these assemblies acquire a wide range of immiscible multiphasic morphologies from a core-shell structure to a nested-droplet organization and then an inverse core-shell structure (Fig. 4B). This type of architecture is a result of differing interfacial tension, viscosity, and density among the interacting coacervates (65–68). In our system, this behavior might be a result of the preferential binding of PrP with RNA through a distinct RNA-binding site at the N-terminal domain of PrP that is absent in tau. In the core-shell morphology (type II) (Fig. 4B, second panel and Fig. 4C), toward the left side of the RNA-dependent phase diagram, PrP forms the core of the droplet (green), whereas tau distributes itself around the core. With increasing RNA concentrations, a higher amount of PrP gets recruited inside tau droplets resulting in nested droplets (type III) (Fig. 4 B, third panel and Fig. 4C). Interestingly, tau and PrP remain immiscible at this stoichiometric ratio and retain their individual droplet identity as depicted by the FRAP profile for the tau:PrP:RNA ternary mixture (Fig. 4D). The formation of these multiphasic nested condensates remained relatively unaffected by changing the order in which the components were added to the reaction mixture suggesting its preference for the tau:PrP:RNA ternary-phase system (SI Appendix, Fig. S4B). On moving toward the right side of the turbidity curve, we again observed a core-shell morphology (type IV), however, with an inverse distribution profile for tau and PrP (Fig. 4 B, Bottom and Fig.4C). This observation is further corroborated by our steady-state anisotropy measurements performed for tau:PrP with varying amounts of RNA in the ternary mixture (SI Appendix, Fig. S4 C and D). This organization can be attributed to the different dissolution concentrations of RNA required for each component because of their net charge difference (63). Inverting the charge on tau:RNA complexes enables them to interact non-specifically with PrP:RNA complexes. The addition of RNA to the preformed tau:PrP heterotypic droplets, therefore, results in the switching of coacervate morphology and composition. A further increase in RNA concentration results in the complete dissolution of assemblies. The morphological transitions of these multicomponent condensates appear to be reversible as indicated by the RNA hydrolysis using ribonuclease A (RNase A) (Fig. 4 E and F and Movies S3 and S4). We observed the reappearance of tau:PrP mixed droplets upon the addition of RNase A after an RNA-mediated complete dissolution of tau:PrP condensates. Taken together, our results indicate an RNA-induced tuning of these multicomponent condensates in a context-dependent manner (Fig. 4G). Such an interplay can potentially introduce an additional level of spatiotemporal regulation in molecular enrichment in condensates. However, the increased enrichment of biomolecules within these condensates makes them vulnerable to aberrant phase transitions into pathological aggregates. Therefore, we next set out to elucidate the effect of tau:PrP complex coacervation on the aggregation propensity of these heterotypic condensates.
Fig. 4. Tau:PrP coacervates form multiphasic condensates in the presence of RNA. (A) The tau:PrP binary system shows a reentrant phase behavior in the presence of polyU RNA. (B) Two-color Airyscan confocal imaging of tau:PrP:RNA (tau-red; PrP-green) ternary system as the function of increasing RNA concentration. With increasing concentration of RNA, tau:PrP coacervates form immiscible multiphasic condensates that transition into a core-shell structure followed by nested droplets. In these multiphasic condensates, PrP is concentrated in the core and tau is in the periphery. A further increase in RNA results in an inverse core-shell structure (tau and PrP form the core and the shell, respectively) and subsequently leads to droplet dissolution (Scale bar, 10 µm.) (C) Select single condensates to clearly show mixed, core-shell, nested, and inverse core-shell morphologies obtained in the presence of an increasing concentration of RNA (scale bar of 5 µm is shown in yellow). (D) FRAP kinetics of tau (red) and PrP (olive) in the tau:PrP:RNA ternary system. Alexa Fluor 488-labled proteins were used for both tau and PrP in independent FRAP experiments. The data represent mean ± SD for n = 3 independent experiments. The white arrows in the tau FRAP images show the bleaching spot. (E) The transition of multiphasic condensates to colocalized mixed droplets upon RNA hydrolysis using RNase A (1.5 µM) showing the reversibility in morphologies (Scale bar, 10 µm.) (F) The reappearance of tau:PrP mixed droplets upon the addition of RNase A after an RNA-mediated (400 ng/µL) complete dissolution of condensates (Scale bar, 10 µm.) Tau and PrP concentrations were 10 and 20 µM, respectively. See SI Appendix, Methods for details. (G) Schematic illustration of morphological transformations during complex phase separation of tau, PrP, and RNA.
Complex Coacervates of Tau and PrP Convert into Solid-Like Co-Aggregates.
We observed that upon longer incubation, heterotypic condensates of tau and PrP, in the absence of RNA, undergo maturation into gel-like and solid-like aggregates as evident by the time-dependent FRAP kinetics over a period of 48 h (Fig. 5 A and B). Our FRAP data revealed that tau:PrP droplets undergo a faster transition into a solid-like state compared to tau-only droplets (Fig. 5 A and B and SI Appendix, Fig. S5A). Next, we asked if these aggregates were amyloid-like. We monitored the conversion kinetics of tau:PrP condensates using a well-known amyloid marker, namely thioflavin-T (ThT) that exhibits a characteristic emission band at ~483 nm. When compared to tau-only droplets, tau:PrP condensates exhibited a significant increase in the ThT fluorescence after 48 h indicating the formation of ThT-positive aggregates upon the liquid-to-solid phase transition of these heterotypic condensates (Fig. 5C and SI Appendix, Fig. S5B). However, the emission maxima at ~487 nm indicated the presence of amorphous aggregates along with amyloid-like aggregates. We next structurally characterized these phase-separation-mediated heterotypic aggregates using vibrational Raman spectroscopy, which allowed us to identify the secondary structural components that were present in these aggregates. For Raman experiments, we used the dense phase of the tau:PrP reaction mixture and monitored the time-dependent changes in the amide I peak (1,630 to 1,720 cm−1) that primarily arise due to the C=O stretching of the backbone. A broad peak spanning from 1,660 cm−1 to 1,675 cm−1 indicated the presence of the hydrogen-bonded cross-β amyloid organization as well as amorphous aggregates supporting our ThT-binding results (Fig. 5 D and E). Additionally, we used atomic force microscopy (AFM) to visualize these heterotypic aggregates. AFM images of 48-h-old mixtures revealed the presence of amyloid-like fibrillar morphologies together with amorphous aggregates (Fig. 5F). Further, our two-color high-resolution Airyscan imaging of incubated samples indicated the presence of colocalized tau:PrP into rod-like fibrillar structures (Fig. 5G and SI Appendix, Fig. S5C) indicating heterotypic fibrillation as observed for other neuronal proteins (52, 69). Taken together, our findings showed that tau and PrP together undergo phase separation into complex coacervates that gradually transition into intermixed aggregates comprising both amorphous and amyloid-like species.
Fig. 5. Maturation of tau:PrP droplets into solid-like aggregates. (A) Time-dependent FRAP kinetics of tau and (B) PrP inside tau:PrP condensates. Alexa Fluor 488-labeled proteins (~1%) were used for FRAP in both cases. The data represent mean ± SD; n = 3. (C) Time-dependent ThT fluorescence spectra of the dense phase of tau-only and tau:PrP reaction mixtures. (D) Vibrational Raman spectra of tau:PrP dense phase over time. (E) Amide I vibrational Raman band of the dense phase of tau:PrP reaction mixture recorded over time. (F) Two-color high-resolution Airyscan confocal image of ~48-h-old tau:PrP reaction mixture showing the heterotypic association of tau (red) and PrP (green) in fibrils (Scale bar, 10 µm.) (G) AFM images with height profiles of 48 h tau:PrP reaction mixture showing the presence of amyloid fibrils along with amorphous species. Tau and PrP concentrations were 10 and 20 µM, respectively. See SI Appendix, Methods for details.
Discussion
In this work, we showed that tau and PrP undergo spontaneous complex phase separation that is primarily driven by domain-specific electrostatic interactions. Such a complex coacervation gives rise to highly dynamic, two-component, liquid-like droplets. The slower fusion, smaller size, slower internal diffusion, and increased robustness of these droplets formed by the complex phase separation of the two proteins suggested that tau forms condensates comprising a highly networked viscoelastic fluid in the presence of PrP. Moreover, as suggested by our estimated Csat values, the phase-separation propensity of tau increases in the presence of PrP (Fig. 1). Tau and PrP interact electrostatically in a domain-specific manner, and these heterotypic interactions are further strengthened in the case of the phosphomimetic mutant of tau since glutamic acid residues increase the net negative charge of tau (Fig. 2). By using naturally occurring truncated variants of tau and PrP, we elucidate the importance of their N-terminal domains in driving tau:PrP complex coacervation. Our results provide mechanistic support for such a heterotypic interaction between tau and PrP (70, 71). Our site-specific picosecond time-resolved fluorescence anisotropy data revealed the formation of relatively ordered, short-range, electrostatic nanoclusters of tau and PrP (Fig. 3). Such electrostatic nanoclusters have been shown to act as the primary units of heterotypic condensates (52). These clusters are stable on the nanosecond timescale but can potentially undergo making and breaking on a slower timescale. Such a relay of making and breaking of interactions can make the assembly highly dynamic, mobile, and liquid-like as indicated by our FRAP studies. On a slower timescale of FCS and FRAP (milliseconds-to-seconds), this complex coacervate possessing a mobile interior can exhibit a typical liquid-like behavior. In such a liquid-like two-component assembly, a protein can also undergo oligomerization. Our work is in line with the RBP-induced phase separation and vitrification of tau (41). Additionally, our findings also indicate the buffering capacity of RNA (72) in the context of tau:PrP interactions. Tau:PrP heterotypic condensates that remain colocalized and miscible at lower concentrations of RNA assume multiphasic morphologies upon the increase in the RNA concentration. The resulting immiscible multiphasic condensates of differing architecture comprise core-shell and nested droplets reminiscent of nucleolar condensates. These coexisting, immiscible, and nested condensates in which the core of the large droplets is constituted by smaller PrP-rich droplets with tau occupying the peripheral regions, arise as a result of differing interfacial tensions between individual condensates formed by the two proteins in the presence of RNA (Fig. 4). This morphology undergoes a transformation into an inverse core-shell and mixed hollow droplets upon the addition of higher amounts of RNA. These morphological transitions of tau:PrP condensates are reversible as evident by RNA hydrolysis by the addition of RNase A. Further, our aging experiments demonstrated that liquid-like tau:PrP condensates gradually mature into solid-like aggregates comprising both amorphous and amyloid-like species. Taken together, our study unveils an intriguing interplay of molecular determinants that promote and regulate the heterotypic phase transition, multiphasic coacervation, and maturation into intermixed ordered aggregates highlighting the molecular basis of overlapping neurodegenerative diseases involving tau and PrP (Fig. 6).
Fig. 6. Schematic illustration of complex phase separation of tau and PrP. The N-terminal segments of tau and PrP interact electrostatically to potentiate tau phase separation into heterotypic condensates that mature into intermixed aggregates. Upon the addition of RNA, tau:PrP mixed droplets convert into multiphasic, immiscible condensates and are dissolved at a much higher concentration of RNA.
The inherent sequence attributes of tau, which are also typical of many other phase-separating proteins, regulate its ability to undergo phase separation. Tau, therefore, assembles into membraneless compartments, which sequester tubulin dimer units into well-defined foci, where it is proposed to regulate microtubule polymerization (73). Moreover, tau is also known to localize in other membraneless bodies, including the nucleolus (74) and stress granules (75), where it may contribute to various physiological and pathological roles. Such liquid-like biomolecular condensates of tau can mature into an aggregated form that is a hallmark of neurodegenerative diseases like Alzheimer’s disease and FTLD-associated tauopathies (76, 77). The pathology of tau is not only limited to Alzheimer’s disease and tauopathies but also extends to various forms of prion diseases and synucleinopathies (42–44, 78). The colocalization of tau and PrP as cytoplasmic inclusion bodies and the accumulation of tau NFTs in the brain of prion disease patients have been observed (71, 79). Interestingly, like Aβ oligomers and α-Syn, various forms of tau have been thought to interact with PrP resulting in dysfunction of the synaptic plasticity (78, 80). Although PrP is a GPI-anchored protein, a sparse level of neurotoxic intracellular PrP exists during ER stress. Additionally, in some instances, PrP may only partially cross the ER membrane and adopt one of two transmembrane topologies because of its core hydrophobic region and ineffective translocation (46, 47, 81). In healthy brains of different species, these forms make up no more than 10% of the total PrP molecules, while in transmissible spongiform encephalopathies, they can comprise up to 30%. Two different mechanisms have been proposed for the PrPC-mediated neurotoxicity caused by several protein aggregates. In one case, PrPC acts as a receptor and facilitates the internalization of specific extracellular proteins. In another, it activates metabotropic glutamate receptors by functioning as a transducer to elicit the detrimental effects of certain protein deposits (80, 82, 83). Moreover, PrPC-Aβ-oligomer-mediated Fyn kinase activation results in tau hyperphosphorylation (81). Inclusions of hyperphosphorylated tau have also been observed in various acquired and familial forms of prion diseases. Thus, tau and prion show a spectrum of overlapping pathologies following the heterogeneity of these tau:PrP co-deposits. Our results emphasize the phase-separation-mediated heterotypic clustering of tau and PrP that can potentially mature into mixed inclusions found in the pathophysiology of several neurodegenerative diseases. Given that variants of tau and PrP are also known to localize in the stress granules (75, 84, 85) as well as in the nucleus (86–89), more precisely in the nucleolar region in the case of tau, our current findings can potentially explain the role of multicomponent tau:PrP assemblies, especially considering the pertinent role of tau, in rRNA-coding, DNA transcription, stabilization, and rRNA processing (90). These types of multicomponent, multiphasic, and anisotropic condensates may be common for many other intracellular nucleoprotein bodies (20, 21, 25). We propose that such interactions might be present in the cellular milieu depending on the subcellular locations. In addition to the direct secretion and absorption of soluble tau isoforms from the membrane-bound receptors, tau, similar to Aβ oligomers and α-syn, is also transmitted among neuronal bodies via extracellular vesicles and exosomes (90). These compartments are rich in RNA (91–93) and can therefore provide sites for the formation of tau:PrP:RNA ternary complexes.
In summary, our study relates to both the functional and pathological aspects of complex phase separation of tau and PrP with or without nucleic acids. Heterotypic phase separation of α-Syn and PrP has been shown to drive the formation of intermixed α-Syn:PrP amyloids (52). Therefore, PrP can potentially play a central role in recruiting other neuronal IDPs into multicomponent condensates via electrostatic coacervation. Aberrant phase transitions mediated by complex phase separation can potentially serve as a common mechanism for the development and progression of late-life neurodegenerative diseases having overlapping neuropathological features. Targeting such complex phase-separated condensates using small molecules might serve as a potent therapeutic strategy against these debilitating human diseases.
Materials and Methods
Detailed materials and methods are included in SI Appendix. The truncations of tau protein and human PrP (23-231) were created using full-length variants of the respective constructs. All mutations including single-Cys of tau and PrP and triple phosphomimetic mutant of tau were created using a QuikChange site-directed mutagenesis kit (Stratagene). Tau protein was purified using a cation-exchange column followed by gel filtration, whereas cleavable N-terminal His-tagged PrP was purified using a Ni-NTA column and His-tag was removed using thrombin. Phase-separation assays were performed using turbidity measurements and confocal microscopy. All the FRAP experiments were performed using ~1% Alexa Fluor 488-C5-maleimide-labeled protein on a Zeiss LSM 980 super-resolution microscope coupled with an Elyra 7. Single-droplet anisotropy measurements and FCS experiments were performed on a MT200 time-resolved fluorescence confocal microscope (PicoQuant). F-5-M labeled and Alexa Fluor 488-C5-maleimide-labeled tau proteins were used in the anisotropy and FCS measurements, respectively. Both data acquisition and analysis were performed using the commercially available software SymphoTime64 version 2.7 (PicoQuant). All the steady-state fluorescence experiments were performed on a FluoroMax-4 spectrofluorometer (Horiba Scientific) using a 1-mm path length quartz cuvette. Time-resolved anisotropy measurements were taken on a time-correlated single-photon counting setup (Horiba Jobin Yvon, NJ). Vibrational Raman spectroscopic studies were performed on an inVia laser Raman microscope (Renishaw) using a 100× objective lens (Nikon) and a 785-nm near-infrared laser. AFM images were acquired using an Innova atomic force microscope (Bruker) operating in tapping mode.
Supplementary Material
Appendix 01 (PDF)
Click here for additional data file.
Movie S1. Tau-only droplets.
Movie S2. Tau:PrP droplets.
Movie S3. Multiphasic to mixed tau:PrP droplets in the presence of RNase A.
Movie S4. Dispersed phase to mixed tau:PrP droplets in the presence of RNase A.
We thank IISER Mohali, Science and Engineering Research Board, Department of Science and Technology (SPR/2020/000333 and CRG/2021/002314 to S.M. and FIST grant # SR/FST/LS-II/2017/97 to the Department of Biological Sciences, IISER Mohali), Council of Scientific and Industrial Research (fellowship to S.K.R.), Ministry of Education, Govt. of India (Centre of Excellence grant to S.M.) for financial support, Prof. E. Rhoades (University of Pennsylvania, USA) and Prof. W. Surewicz (Case Western Reserve University, USA) for the kind gift of the DNA plasmids for full-length tau and PrP, respectively, Prof. N. Periasamy (Retd. TIFR Mumbai) for providing us with the fluorescence decay analysis program, Ms. L. Arora for helping with the fluorescence decay analysis, and the current members and former members (Dr. A. Agarwal, Dr. M. Bhattacharya, and Dr. P. Dogra) of the Mukhopadhyay lab for critically reading this manuscript.
Author contributions
S.K.R. and S.M. designed research; S.K.R., R.K., and A.A. performed research; S.K.R., R.K., and A.A. analyzed data; and S.K.R., R.K., and S.M. wrote the paper.
Competing interest
The authors declare no competing interest.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Supporting Information
This article is a PNAS Direct Submission.
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91 M. Li , Analysis of the RNA content of the exosomes derived from blood serum and urine and its potential as biomarkers. Philosop. Trans. R. Soc. Lond. B. Biol. Sci. 369 , 20130502 (2014).
92 A. Hartmann, C. Muth, O. Dabrowski, S. Krasemann, M. Glatzel, Exosomes and the prion protein: More than one truth. Front. Neurosci. 11 , 194 (2017).28469550
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==== Front
Br J Dermatol
Br J Dermatol
10.1111/(ISSN)1365-2133
BJD
The British Journal of Dermatology
0007-0963
1365-2133
John Wiley and Sons Inc. Hoboken
35996840
10.1111/bjd.21813
BJD21813
BJD-2022-1582
Commentary
Commentaries
Anti‐interleukin‐17A for pityriasis rubra pilaris: catching the psoriasis biologic wave
Anti‐interleukin‐17A for pityriasis rubra pilaris: catching the psoriasis biologic wave
Balak Deepak M.W. https://orcid.org/0000-0002-0755-3770
1 [email protected]
Rissmann Robert https://orcid.org/0000-0002-5867-9090
2 3
1 Department of Dermatology Leiden University Medical Center Leiden the Netherlands
2 Centre for Human Drug Research Leiden the Netherlands
3 Leiden Academic Centre for Drug Research Leiden University Leiden the Netherlands
* Correspondence: Email: [email protected]
22 8 2022
11 2022
187 5 10.1111/bjd.v187.5 633634
23 7 2022
26 7 2022
© 2022 The Authors. British Journal of Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Linked Article: Boudreaux et al. Br J Dermatol 2022; 187:650–658.
source-schema-version-number2.0
cover-dateNovember 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
Conflicts of interest: D.B. serves as a consultant and/or speaker for AbbVie, Almirall, Celgene, Eli Lilly, Janssen, LEO Pharma, Novartis, Sanofi Genzyme and UCB. R.R. has no conflicts of interest to declare.
==== Body
pmcPityriasis rubra pilaris (PRP) is a rare skin disease belonging to the spectrum of psoriasiform dermatoses. 1 Initially, PRP was considered a psoriasis variant until it achieved status as a distinct disease by the end of the 19th century. 2 However, PRP is now considered a group of related diseases known for their extensive phenotypic heterogeneity – classified into six subtypes – as well as being notoriously recalcitrant to therapy. 2 , 3 In the absence of approved therapies and high‐level evidence, patients with PRP are trialled with conventional treatments. 3 Considering the clinical–histopathological overlap with psoriasis and the efficacious targeted therapies available for psoriasis, off‐label treatment with psoriasis‐approved biologics has been applied for challenging cases of PRP. 4 These include biologics targeting tumour necrosis factor‐α and the interleukin (IL)‐23–helper T cell 17 pathway. 4 , 5 , 6
In this issue of the BJD, Boudreaux and colleagues report findings from a single‐centre, open‐label clinical trial that assessed the anti‐IL‐17A biologic secukinumab in PRP. 7 The study included 12 patients with PRP who had not responded to prior topical corticosteroids and at least one systemic agent – most often systemic corticosteroids (67%), acitretin (42%) and methotrexate (33%). Patients received secukinumab according to the psoriasis dosing regimen: 300 mg once weekly for the first 4 weeks, followed by once every 4 weeks. The primary endpoint of ≥ 75% reduction in Psoriasis Area and Severity Index (PASI 75) at week 28 was met by six of 11 patients (55%). Three patients (27%) achieved PASI 90, and there was a statistically significant reduction in mean Dermatology Life Quality Index. Another important finding was a 97% improvement in overall gene expression profiles in lesional skin after 2 weeks of treatment. Significant enrichment of innate immune pathways was noted in patients who did not respond to secukinumab.
This trial is relevant and timely given the need for effective therapies for PRP. It adds to mounting evidence in support of targeting IL‐17 in PRP. 8 However, several limitations need consideration. The trial’s lack of a placebo arm may have introduced bias, given that PRP is associated with spontaneous remission. Inclusion of a control arm would have enabled a double‐blind design, increasing the strength of evidence tremendously. Dose ranging and refinement of the dosing regimen should be investigated as these might not be directly translatable from one dermatological disease to another.
Furthermore, the primary outcome measure PASI, borrowed from the ‘cousin’ psoriasis, is not validated for PRP and might not fully capture PRP manifestations. Development and agreement on PRP‐specific outcome measures in a minimal clinical dataset – ideally a core outcome set – would allow harmonization of data collection across centres. 9 Multicentre collaboration is crucial given the rarity of PRP. 6
Finally, the baseline transcriptomic analyses demonstrated extensive heterogeneity, which could explain the variable clinical responses to targeted therapy in PRP. 9 The way forward would be to utilize multi‐omics technology to profile patients on a genetic, transcriptomic or proteomic level and to identify biomarkers to guide optimal drug selection.
In conclusion, building on the lessons learned from the ‘tsunami’ in psoriasis‐targeted therapies, this trial is an important step forward to improving outcomes for a rare, high‐burden skin disease. Further steps would benefit from dermatological drug development approaches, which also apply for drug repurposing. 10
Author contributions
Deepak Mukesh Wieshwaykumar Balak: Conceptualization (lead); writing – original draft (lead); writing – review and editing (equal). Robert Rissmann: Conceptualization (equal); writing – original draft (supporting); writing – review and editing (equal).
==== Refs
References
1 Iznardo H , Puig L . Beyond plaque psoriasis – pathogenesis and treatment of other psoriasis phenotypes. Curr Opin Rheumatol 2022; 34 :225–34.35699337
2 Wang D , Chong VC , Chong WS , Oon HH . A review on pityriasis rubra pilaris. Am J Clin Dermatol 2018; 19 :377–90.29302927
3 Roenneberg S , Biedermann T . Pityriasis rubra pilaris: algorithms for diagnosis and treatment. J Eur Acad Dermatol Venereol 2018; 32 :889–98.29247481
4 Speeckaert R , Lambert J , van Geel N . Learning from success and failure: biologics for non‐approved skin diseases. Front Immunol 2019; 10 :1918.31440261
5 Feldmeyer L , Mylonas A , Demaria O et al. Interleukin 23‐helper T cell 17 axis as a treatment target for pityriasis rubra pilaris. JAMA Dermatol 2017; 153 :304–8.
6 Duarte B , Paiva Lopes MJ . Response to: ‘Successful treatment of refractory extensive pityriasis rubra pilaris with risankizumab’. Br J Dermatol 2021; 185 :235–6.33730424
7 Boudreaux BW , Pincelli TP , Bhullar PK et al. Secukinumab for the treatment of adult‐onset pityriasis rubra pilaris: a single‐arm clinical trial with transcriptomic analysis. Br J Dermatol 2022; 187 :650–58.35701384
8 Haynes D , Strunck JL , Topham CA et al. Evaluation of ixekizumab treatment for patients with pityriasis rubra pilaris: a single‐arm trial. JAMA Dermatol 2020; 156 :668–75.32293641
9 Wain T , Choy B , Satchell AC et al. Secukinumab in pityriasis rubra pilaris: a case series demonstrating variable response and the need for minimal clinical datasets. JAAD Case Rep 2018; 4 :500–5.29984296
10 Rissmann R , Moerland M , van Doorn MBA . Blueprint for mechanistic, data‐rich early phase clinical pharmacology studies in dermatology. Br J Clin Pharmacol 2020; 86 :1011–14.32253783
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==== Front
Environ Plan B Urban Anal City Sci
Environ Plan B Urban Anal City Sci
spepb
EPB
Environment and Planning. B, Urban Analytics and City Science
2399-8083
2399-8091
SAGE Publications Sage UK: London, England
10.1177_23998083231164398
10.1177/23998083231164398
Special Issue Articles
Exploring changes in residential preference during COVID-19: Implications to contemporary urban planning
Yao X. Angela
Crooks Andrew
Jiang Bin
Krisp Jukka
Liu Xintao
Huang Haosheng
https://orcid.org/0000-0003-1080-1920
Wolday Fitwi
Department of Mobility, 72994 Institute of Transport Economics , Oslo, Norway
https://orcid.org/0000-0001-8631-8391
Böcker Lars
72994 Institute of Transport Economics , Oslo, Norway
Fitwi Wolday, Department of Mobility, Institute of Transport Economics, Gaustadalléen 21, Oslo 0349, Norway. Email: [email protected]
23 3 2023
6 2023
23 3 2023
50 5 Special issue: Urban Analytical Approaches to Combatting Covid-19 12801297
© The Author(s) 2023
2023
SAGE Publications
https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
The advent of the COVID-19 pandemic and the subsequent lockdown has reshuffled our daily routines and activity spaces. The home and its immediate environment have attained a critical role in coping with the confinement both as living, working and recreational space. Drawing on a longitudinal survey from greater Oslo, we analyze shifts in residential preferences amidst COVID-19. Given the pandemic induced movement restrictions, we pay special attention to the mediating role of perceived accessibility on the link between several sociodemographic/locational/housing characteristics on preference shifts. Amidst a drop in perceived accessibility, outdoor assets like gardens or balconies, and motility by car and public transport are amongst the residential and locational amenities that have gained most in people’s considerations for a new dwelling peri-pandemically. Dwelling satisfaction and preferences with regard to neighborhood density, dwelling size, and work-proximity, have remained largely unchanged despite a clear trend towards more teleworking. Our results can be understood as a nod of approval to contemporary compact urban planning. Nonetheless challenges as to bolstering its resilience still linger and more needs to be done to mitigate the inequalities in (perceived) access to in-home, near-home, and mobility assets that we have also observed to have (re)-immerged in the wake of the pandemic.
Residential preference
residential satisfaction
residential mobility
residential amenities
perceived accessibility
compact urban development
COVID-19
The Norwegian Research Council (NFR) https://doi.org/10.13039/501100005416 316126 316126 typesetterts10
==== Body
pmcIntroduction
The health threat posed by the COVID-19 pandemic and the efforts to contain it, have strongly enhanced the significance of the home zone (the residence and the immediate environment) in everyday life for healthy and safe living (Zarrabi et al., 2021). This paper explores the potential impacts of the pandemic and the subsequent restrictive measures imposed on mobility and daily activity participation on people’s preferences for residential and neighborhood amenities.
People’s choice of both the unit of residence and the neighborhood where the residential unit is located are a result of a multi-criteria (socio-economic, cultural, demographic, idiosyncratic factors such as taste, etc.) trade-off and selection process. This selection process often involves evaluating contrasting and mutually exclusive alternatives (Cao X., 2008; Filion et al., 1999; Schwanen and Mokhtarian, 2004; Wolday et al., 2018). Residential location choices are largely long-term decisions, with enduring influences on daily habit formations and the consolidation of those habits into time-bound routines. These habits define to a large extent how people travel, do-shopping, recreate, and generally live about their daily life. As such, residential relocation is costly to the individuals involved, and therefore usually occurs infrequently. Yet, when there is cause for residential mobility, for example, due to major life course events or other unforeseen disruptions, it carries a potential to reconfigure established daily routines and travel habits.
The contemporary urban planning paradigm of dense, mixed-use, transit-oriented, and human scale cities and neighborhoods is widely promoted to combat car dependency, CO2-emissions, congestion, noise and air pollution, obesity and other automobility-related negative externalities. Meanwhile, the intended environmental sustainability objectives of compact cities can only be viable as long as these compact urban designs are perceived as attractive living and working environment. Disruptions in residential preference that could lead to residential relocation patterns away from dense and transit-oriented neighborhoods may thus cause a significant shock to the widely held and practiced contemporary urban planning ideal.
How big and lasting a nudge the COVID-19 experience will elicit in terms of current and future relocation decisions is yet to be empirically established. However, the agility of the virus as evidenced by the recurring waves of infections suggests greater likelihood of a prolonged stay under the shadow of COVID-19. With a growing number of scientists converging on the proposition of COVID-19 becoming endemic (Kissler et al., 2020; Shaman and Galanti, 2020), the protracted stay of the pandemic and the prolonged restrictions imposed as a public safety measure may likely consolidate nascent coping mechanisms into long-term decision criteria on issues about urban living, working, and mobility conditions that may in turn trickle down to residential mobility. Yet, perhaps regardless of endemicity, the genie may already be out of the bottle when it comes to the reshuffling of face-to-face working practices and mobility with the now increasingly institutionalized practices of teleworking likely to retain a firmer presence. Such consolidated coping mechanisms carry a significant potential for major paradigm shifts in established urban planning practices regarding transportation, urban design, and housing. The wider urban planning community is therefore better served by proactive efforts to understanding residential coping in times of COVID-19 and guides the transition towards resilient, safe, livable, and climate friendly urban spaces. This study contributes towards such a proactive effort by providing timely analysis on the dynamics of residential preference during the pandemic.
Our understanding of how attitudes are formed and how they may potentially change (Ajzen, 1991; De Vos et al., 2020; Gärling et al., 1998), and more specifically the intersection between built environment, travel attitudes, and travel behavior (Cao X. & Chatman, 2015; De Vos et al., 2020; De Vos et al., 2018; Schwanen and Mokhtarian, 2005), enlightens us that people’s coping mechanisms during COVID-19 outbreak are likely to have reverberating influences on how we use space, travel and also probably strengthen entrenched attitudes regarding residential location choice.
This paper has the overall objective to examine potential residential and locational preference shifts during the pandemic in a comprehensive way, by analyzing the multi-dimensionality of such shifts, its underlying inequalities in access to in-home, near-home, and mobility resources, and its mechanisms of mediation through perceived accessibility 1 and residential satisfaction. The paper draws on a longitudinal survey from greater Oslo, Norway, analyzed in Structural Equation Models (SEM). The remainder of the paper is organized in four additional sections. The next section presents the nascent literature on residential mobility during COVID-19 and contextualizes it on the broader residential preference and mobility discourse. Data and methods are introduced following the literature review, followed by the discussion and results section. The final section covers concluding remarks and reflects on the significance of the findings for contemporary urban policy and planning, and for future research.
Literature review
The debate on whether high density megacities exacerbate infection rate and hamper pandemic control (Hang, 2020) or, whether diligent management in disease control combined with proximity accorded by dense cities can in fact contribute to better overall disease control (Badger, 2020) and reduce confinement-related mental health impacts, may not settle any time soon. On the positive perks of density, Badger (2020) asserts that proximity makes diverse facilities accessible, enables alternative delivery ways when people cannot leave homes for essentials and allows for shared individual mobility devices. Nonetheless, whichever direction the pointer tilts in this debate, one thing is certain, that the pandemic is poised to have its footprint on urban planning discourse and practice. As Klaus (2020) states “part of the history of urbanization is building and managing your way out of infectious diseases.”
There is little dispute that continued high density urbanization is here for the longer haul. Even so, the prolonged lockdown following COVID-19 is nonetheless destined to have certain attitudinal and behavioral implications relating to residential choice, telework and travel practices (Jones and Grigsby-Toussaint, 2020; Salama, 2020). As an additional affirmation of the potential long-term effects of the pandemic on travel behavior and activity participation, Van Wee and Witlox (2021) undertook a multi-perspective review based on theoretical concepts from various disciplines. Based on their review, they anticipate the likelihood of COVID-19 leading to lasting behavioral change. Nonetheless, the extent and severity of impact from this extraordinary situation in altering ingrained attitudes and practices will largely depend on the longevity of the pandemic and planed responses from urban planners in fostering livable urban development and abating fear driven negative spillovers.
Residential mobility decision is a long-term and binding undertaking, not least due to the monetary costs involved and the lasting implications to social networks. Testament to this, residential mobility decisions are considered in response to major transition in one’s life such as changes in life course (Coulter et al., 2015; Feijten and Van Ham, 2007; Michielin and Mulder, 2008), work life (Dieleman and Mulder, 2002; Mulder and Hooimeijer, 1999), income differentials (Galster and Turner, 2017) and lifestyle choices (Ærø, 2006). Once location decision is settled, the location of residence vis-à-vis other destinations attains immense significance in how travel patterns are shaped and daily life organized (Ellegård and Vilhelmson, 2004).
Above and beyond these ordinary triggers of residential mobility, the extraordinary shock induced by COVID-19 is akin to a lifestyle shock (like childbirth) that could influence residential preferences which may potentially trigger residential mobility. Since the outbreak of the pandemic, research uncovered certain factors as main triggers of residential mobility associated with the COVID-19 situation. The dominant among them include unsuitability of the home as a work and residence space (Duque-Calvache et al., 2021; Rogers and Power, 2020), economic hardships (Jones and Grigsby-Toussaint, 2020), fear of contracting the virus (Kang et al., 2021), and psychological distress (Devaraj and Patel, 2021).
Whether the COVID-19 induced triggers lead to the actualization of residential mobility as a coping response and whether the coping mechanisms are of a temporary or long-term nature depends on various factors such as, sociodemographic and economic conditions, access (or lack thereof) to housing and neighborhood amenities, availability of alternative mobility options, possibility for teleworking to name but a few. Admittedly, the quarantine intensity enacted to curb COVID-19 and the resulting prolonged homestay is likely to influence the in-home and around-home (neighborhood) experiences of residents. However, whether these developments change residential preferences in a manner that is profound enough to trigger residential mobility is an outstanding empirical question.
In a recent study conducted April–May 2020 in Spain, respondents were asked about reasons for residential moving during COVID-19 and the most common reply (46.7%) was the search for greater comfort (Duque-Calvache et al., 2021). The study also gave some interesting perspectives on residential coping and the disparity among various groups. Residential movers during COVID-19 tended to be young (a high mobility group also prior to the pandemic), but the effect of age was not linear as older respondents were more likely to move than middle-aged individuals. Respondents living alone and in non-family households were more prone to have moved. Conversely, children anchored families to their homes. Besides, students and respondents who telework had higher likelihood of residential mobility. In this study, gender did not significantly contribute to moving. In another study on resilience during COVID-19-induced lockdown, women were found to be significantly resilient compared to men (Fernández-Prados et al., 2021).
Coping mechanism may also reflect, permeate, and sustain pre-existing inequalities. Access to private cottage was found to contribute to greater comfort and reduced distress resulting from the ability to evade the negative impacts of the lockdown without having to permanently relocate (Duque-Calvache et al., 2021). Access to private outdoor amenities were also associated with higher residential wellbeing during the pandemic (Lehberger et al., 2021; Poortinga et al., 2021). For poorer individuals and families with limited access to these amenities, COVID-19-related residential mobility ends up being an obligation in response to the economic, health or, at times, psychological problems arising from a situation (Duque-Calvache et al., 2021).
Another structural factor whose relevance was significantly highlighted during COVID-19 has been the access to parks and urban greenery near the residence. A recent study in Oslo investigated a five-year (2016–2020) tracking data and found that outdoor recreational activity increased by 291% during lockdown relative to a 3-year average for the same days. Also, that recreational use of neighborhood green areas and city parks intensified nearly as much as in the forested zone. The study also highlights areas with lower population densities as having greater pedestrian and biking activity counts (Venter et al., 2020).
Residential location and preferences influencing location choices are among the frequently investigated issues in travel behavior studies because home is where daily life is planned, travel scheduled, family life organized, and not least it is the origin and ultimate destination of most of daily travel activities. The focus of this research is inspired by the same sense of purpose and enhances the existing literature in several novel ways: first, it incorporates perceived accessibility during the pandemic as an indicator of residents’ vulnerability following the physical confinement. To our knowledge, this has not been attempted before. Second, the study addresses pandemic induced potential shifts in dwelling and locational preferences in a comprehensive way by analyzing the multi-dimensionality of such shifts, its underlying inequalities in access to in-home, near-home and mobility resources, and its mechanisms of mediation through perceived accessibility 2 and dwelling satisfaction in a structural equation modeling (SEM) framework. Third, this article provides empirical basis in addressing widespread concerns that the pandemic might compromise compact urban development paradigm. Earlier attempts to address this last issue were limited scenario analysis based on theoretical reviews (Van Wee and Witlox, 2021). To our knowledge, this paper is the first to empirically explore the mediating effect of perceived accessibility and dwelling satisfaction during COVID-19 to untangle pandemic induces residential preference change in SEM framework.
Data and method
Study area and data
This study is situated in the Norwegian capital Oslo and its surrounding municipalities. The region is the largest urban conurbation in Norway with over a million residents in its continuous built-up area. Residential conditions in Oslo are characterized by high property value, greater incidence of living in crowded 3 dwelling relative to the adjacent municipalities. Statistics Norway’s data show that a little over 13% of households which amounts to about 21% of Oslo municipality’s residents are categorized as living in crowded dwellings (Statistics Norway, 2020). Moreover, low-income families, and families with small children have a greater incidence of residing in crowded dwellings. Comparing Oslo municipality with the adjacent municipalities, the number of persons, households, and families with children living in crowded dwellings is twice as high in Oslo.
The data utilized in this study were obtained via a self-administered longitudinal survey conducted in June 2020 and in June 2021. The study was tailored to examine general socio-economic, health, wellbeing, and mobility impacts of COVID-19. Questionnaires were sent to a sample of respondents above the age of 17, stratified by geography 4 and mobility 5 characteristics, but otherwise randomly recruited from a questionnaire agency’s internet-based panel 6 . A total of 1,232 respondents participated in the survey in 2020, with a response rate of 63%. Of them, 916 also participated in 2021. The survey yielded a net response of 858 from respondents that were present in both waves of the survey and who had valid answers on all variables in this study.
Besides socio-demographic and socio-economic background information, the survey collected data on residential and neighborhood attributes, access to mobility resources, teleworking frequency, dwelling satisfaction during the pandemic, a four-period longitudinal measurement of perceived accessibility, as well as stated housing preference changes compared to before COVID-19. The latter three comprise the key mediating and dependent variables in this study, which will be introduced and discussed in more detailed in sections 3.2 and 4.1, respectively.
The survey data were complemented with urban form attributes linked to respondents’ geolocated residential addresses. Using geographic information systems (GIS), distances are calculated between residential locations and the nearest local- as well as Oslo-center zones, which serve as indicators of the proximity to shops, services and amenities around the home or lack thereof. In addition, population density is summarized over 250 × 250 m grid cells (2019 data, supplied by Statistics Norway) that intersect with an 800 m buffer around each respondent’s residential location. Alternative 400 and 1200 m buffers have also been tested, but the 800 m buffer was ultimately selected as it best comprises the walkable home zone of significance in pandemic times, which is attested by a superior model fit. Other urban form attributes calculated similarly for the same 800 m buffer around the place of residence, such as employment density, business establishment density, building use diversity and share of green space have also been tested. These have ultimately been excluded for multicollinearity reasons, for not yielding significant effects on any of our dependent variables, and/or for resulting into poorer overall model fit.
Descriptive statistics and modelling techniques
Table 1 presents the descriptive statistics of all independent variables in our study, as well as comparative statistics for the general population in the study area. The sample is reasonably representative of the general population in the study area with regard to key characteristics of interest in this study, such as car ownership, residential environment, and sociodemographic factors, such as age, gender, and income (see Table 1). Respondents in our survey live in denser populated residential environments than featured in our study area, but this comes as no surprise as the majority of respondents come from main built-up areas, while large parts of the study area are forested or otherwise sparsely populated. On education level, our sample shows a significant underrepresentation of lower-educated. While this is also the case for the internet panel from which we have recruited, as well as being quite common in web-based housing and transport-related surveys generally (e.g., Arentze et al., 2005), it is something that requires caution when interpreting and generalizing our results. We have decided against applying sample weights regarding education level or any other variables, in order to not introduce additional bias, because we do not know to what extent the lower-educated respondents in our sample, who are familiar with being questioned for research, are indicative for those in the population generally.Table 1. Independent-variable descriptives and study area population statistics.
Sample statistics (N = 858) Population statistics study areaa
percentage(s) or min-max/mean (SE.) percentage(s) or mean
Age 19-88/56.78 (0.537) 47.5
Gender: Female 50.6% 50.2%
Household type: single/couple/family/other 26.4%/16.8%/52.3%/4.4% 19.4%/24.9%/46.0%/9.7%
Education: Lower/middle/higher 25.8%/36.5%/37.8% 56,2%/16.0%/27.8%
Income: <400K/400K-1 M/≥1M NOK/unknown 7.0%/37.9%/39.0%/16.1% 722 000 NOK
Frequent home worker in Mar/Apr 2021 (≥4 days a week) 33.6% n/a
car owning household 77.2% 71.5% b
Periodic public transport card 32.4% n/a
dwelling: Detached/terraced/apartm. w. balcony/without 31.5%/12.8%/49.3%/6.4% 35.4%/12.7%/51.9%c
Common garden 21.6% n/a
Population density (in 1000 inh./km2) 0–19.5/5.02 (0.161) 2.38
Distance to local center (in km) 0–11.3/0.75 (0.040) n/a
Distance to Oslo center (in km) 0–98.2/8.20 (0.331) n/a
aBased on 2019 Statistics Norway data for Oslo and surrounding former Akershus county, unless mentioned otherwise.
bBased on 2014 National Travel Survey data for Oslo and surrounding former Akershus county.
cThe 51.9% is for apartments with and without balconies combined.
The analysis is carried out using structural equation modeling (SEM) in Stata (a software package). SEM, besides its reputed ability to be comprehensive, mitigates for statistical blind spots/fallacies when theoretical foundation for causal flow between investigated variables is shaky. More specifically, SEM allows us to: a) estimate the simultaneous effects on multiple correlated dependent variables; b) directly integrate a latent construct factor model (for PAC) with the rest of our structural regression model; and c) distinguish between direct effects, indirect effects via mediating variables and non-causal statistical correlations between the residuals of non-independent items or variables. SEM is thus ideally suited for the analysis carried out in this paper both for its comprehensiveness and to reduce misspecification bias. Figure 1(d) visualizes our final structural equation model structure (Figures 1(a), (b) and (c) are discussed in more detail in section 4.1). Besides the causal relationships displayed with arrows and correlations displayed with double arrowed curves, the model also includes correlations between the residuals of our six final housing preference change variables, as well as between the residuals of PAC items 3 and 4.Figure 1. Descriptive statistics of mediators and the SEM model structure.
The study analyzes the simultaneous effects of all independent variables listed in Table 1, on six dependent variables concerning housing preference changes. We also estimate two factors that could mediate these relationships. The six dependent housing preference change variables were obtained in the last survey round in June 2021 by asking respondents to respond to the question “If you were to choose a new home and residential area now, would the pandemic have changed anything in your dwelling and locational preferences?” Respondents were asked to answer this questions by attesting to what extent they agreed on a five-point Likert scale with the following statements: 1) “Living in low density areas is more important now compared to before the pandemic”; 2) “A larger dwelling is more important now compared to before the pandemic”; 3) “Outdoor assets (balcony, terrace, garden) are more important now than before the pandemic”; 4) “Proximity to the workplace is more important now than before the pandemic”; 5) “Easy access by car and good parking opportunities are more important now than before the pandemic”; and 6) “Good public transport access is more important now than before the pandemic.”
The first of the two mediators, pandemic dwelling satisfaction, is measured on a ten-point scale ranging from “very dissatisfied” (1) to “very satisfied” (10)—a subjective satisfaction assessment scale often used in housing and neighborhood satisfaction studies (e.g., Permentier et al., 2011; Brazil, 2019). Respondents stated their satisfaction in response to the question “how satisfied have you been with your dwelling in the last year during the pandemic?”
The conspicuous reality under COVID-19 had been the profound constraint it imposed on accessibility to various daily life activities. Under this new reality, people are expected to experience varying degrees of access to daily facilities depending on residential and neighborhood amenities, urban structural factors, mobility resources, and so on. To capture this, a second mediator was constructed to measure respondents perceived accessibility during COVID-19. We use a scale for perceived accessibility (PAC) developed by (Lättman et al., 2018; Pot et al., 2021). The scale is estimated as a latent construct based on four items PAC1-“ease to conduct travels”; PAC2-“ability to live ones desired life”; PAC3-“ability to partake in desired activities”; and PAC4-“access to all ones needs.” Respondents repeatedly reported their subjective assessments on these items when asked to “consider how [they] travelled in” four time periods: pre-pandemic February 2020; first-wave March-April 2020; post-first-wave June 2020; and second-wave June 2021. Answers considering the first two time periods were asked retrospectively along with answers to the third time period at the first time of survey in June 2020, while the fourth time period was asked at the time of survey in June 2021. It is therefore important to note that items reported for the first two time periods, albeit relatively fresh in memory, may have been subjected to some recall bias. Although we report on the development of the PAC indicator through all four time periods in §4.1, our final multivariate analysis in §4.2 only includes PAC during the first wave in March–April 2020. After testing multiple alternative model configurations with PAC values and value changes measured at and between different time periods, PAC during the first wave period was ultimately selected as it provides the best model fit, but also because it marks the most memorable initial shock of when COVID-19 first hit. Before adding the latent first-wave-PAC construct into our analysis, we first established through factor analysis that all four items load well on one single construct and that internal consistency is good with a Cronbach’s alpha of 0.861.
Result and discussion
Descriptive findings
The fear of contracting the virus coupled with the lockdown measures have extended the functions of the dwelling and its emediate private, shared or public environs beyond the ordinary. The dwelling and dwelling surroundings’ amenities had to accommodate all aspects of daily life as a working and living environment, in many instances for all household members simultaneously. For many households, a mismatch between what was demanded of the dwelling during lockdown and what it actually was able to offer may have been expected. Nevertheless, our survey results point out that satisfaction with the dwelling during the last pandemic year (asked in June 2021) is quite high (mean of 8.367 on a 1–10 scale) and actually not much lower compared to respondents satisfaction with their dwelling generally (mean of 8.429). One thing to note here though, the dispersion of dwelling satisfaction ratings has increased mildly during the pandemic, with ratings at both the highest and the lower ends of the spectrum being somewhat more prevalent (Figure 1(b)).
Another important aspect of daily life which is expected to have instantaneously been altered due to COVID-19 is perceived accessibility (PAC): one’s subjective assessment of being able to conduct travels, partake in activities, access ones needs and live a desired life considering the way one travels on a daily basis (Lättman et al., 2018). During periods of confinement and lockdown, PAC may have been suppressed as a consequence of the avoidance of nearby attractive public spaces, neighborhood green pockets and facilities, whether as a result of fear of contracting the virus, as obligated by the authorities, and/or as a form of voluntary societal contribution to the containment of the spread of the virus. Figure 1(a) shows how PAC, calculated as a mean score across the four PAC items, has evolved from early 2020 through the summer of 2021. PAC scores for February 2020, retrospectively reported in June 2020, are used as a pre-pandemic benchmark.
As can be seen from the box plots, the distributional pattern and magnitude of PAC varies markedly between pre- and post-lockdown periods. Perceived accessibility took a significant decline immediately after the first lockdown, but somewhat recovered in the succeeding months. By June 2021 the PAC score of the 75th percentile (Q3) corresponded with the pre lockdown median score. Moreover, interquartile and minimum-maximum ranges for PAC are substantially larger during different stages of the pandemic (especially during its first wave) than for the pre-pandemic benchmark. This shows that there are larger variations in how respondents have perceived peri-pandemic PAC, which may indicate that the pandemic has hit people (and/or places) unevenly in terms of everyday activity and travel opportunities, especially in the earliest stages of the pandemic.
To shed light on whether and how the pandemic may potentially trigger shifts in relocation behaviors with urban planning ramifications, this study has developed a multidimensional measurement instrument to capture shifting dwelling and locational preferences. Figure 1(c) shows the degree to which different dwelling and locational amenities are considered more or less important in a (post-)pandemic world. It is not so much a larger dwelling, but especially the presence and quality of private outdoor assets, such as a garden, terrace, or balcony, that have become of higher significance. This is in line with earlier findings by Lehberger et al (2021) and Poortinga et al (2021). With everyday life evolving more in and around the home, this preference shift could indicate a way for people to secure access to nature and fresh air directly at the place of residence, as an alternative for their reduced out-of-home (travel) activities.
Tightly following is a stronger consideration for residential environment that offers easier parking and accessibility by car. This aligns with evidence from around the world that people resort to private automobility as their main alternative to public transport in a (post-)pandemic world, at least for longer distance trips (e.g., Thombre and Agarwal, 2021; Harrington and Hadjiconstantinou, 2021). There is also a motility aspect to this locational preference shift. Motility is a concept that relates to the value that people attached to the potentiality of travel options (Kaufmann, 2002). People in a (post-)pandemic world may attach higher value to the resilience of having the possibility and flexibility to park and drive a car as an alternative to public transport (PT), without necessarily committing to do so on a regular basis. Following this line of reasoning, it appears that people want options when faced with pandemic uncertainty. This turns out to include also PT. Rather than having given up on PT, people actually value PT accessibility higher (albeit not as high as they do for car accessibility), possibly seeking residential environments that offer them shorter, more seamless PT options than they may have today.
There has been ample discussion on whether people still want to live in compact cities. With a mean of 3.008 indicating near-absolute neutrality, we do not find pandemic induced significant shift towards lower density areas (versus more central locations). Also, despite the prospect of enhanced teleworking in a (post-)pandemic society, proximity to work seems to neither have lost much of its significance. However, this is only indicative (not conclusive) as Figure 1(c) does not account for respondents’ residential neighborhood when reporting.
It is important to note that the average scores for dwelling satisfaction, PAC, and housing preference shifts discussed above hide a substantial amount of variation between respondents in our sample as indicated by the class distributions in Figure 1. A subsequent multivariate analysis will be deployed next to examine how peri-pandemic PAC, dwelling satisfaction, and housing preference shifts vary across social and spatial categories. The mediating effect of peri-pandemic PAC and dwelling satisfaction on the potential shifts in dwelling and locational preferences will also be investigated by means of SEM analysis.
Multivariate SEM analysis
Table 2 presents our SEM model outcomes. The model is well-fitted with an overall model R2 of 0.504, and RMSEA and CFI values well below and above their respective critical 0.05 and 0.95 levels. As stipulated in Figure 1, the effect of the background attributes on housing preference is estimated to be partially mediated by peri-pandemic PAC and dwelling satisfaction. The first rows in Table 2 present the pandemic PAC factor model and its factor loadings. The remaining rows present the regression coefficients and flagged significance levels of all predictors, across eight columns for the two mediators and the six dependent residential preference shift variables in our model.Table 2. SEM model outputs.
Mediatorsa Residential preference: the following is now more important than before COVID-19:b
PAC during Mar-Apr ‘20 Pandemic dwe. sat. Larger dwelling Outdoor assets Lower density Good PT Workplace prox. Access by car
Factor model loadingsc
PAC2: “live the life I desire” 1 | | | | | | |
PAC3: “do all activities I desire” 0.811*** | | | | | | |
PAC4: “access to all I need” 0.719*** | | | | | | |
PAC1: “easy to conduct my travels” 0.472*** | | | | | | |
Structural model
PAC during March–April 2020 | | −0.025 −0.081*** 0.011 −0.025 −0.012 −0.032*
Pandemic dwelling satisfaction | | −0.086*** −0.031* −0.052*** | | |
age −0.046 0.065** −0.028 −0.009 −0.001 0.028* 0.025* −0.031**
age squared 0.000* 0.000 0.000 0.000 0.000 0.000 0.000 0.000**
female (ref = male) −0.565*** 0.215* 0.079 0.147** 0.059 0.144** 0.169*** 0.266***
single hh. (ref = hh. withkid/s) 0.008 −0.194 −0.140 0.003 0.143 −0.126 −0.008 0.020
couple no kids (ref = hh.with kid/s) −0.048 0.057 0.150 −0.143 0.046 −0.083 −0.002 −0.035
other hh. (ref = hh. withkid/s) −0.210 0.276 0.103 0.203 0.136 0.354** −0.002 −0.232
educ. higher (ref = middle/lower) −0.129 −0.018 0.130* 0.068 −0.025 −0.057 −0.196*** 0.049
income low (ref = middle/higher) −0.116 −0.436* 0.423*** 0.169 0.130 0.064 0.103 −0.015
home office ≥4 days/week (ref = no) 0.018 −0.187 0.139* −0.003 0.037 −0.252*** −0.332*** −0.038
owns a car (ref = no) 0.177 0.166 0.133 0.076 −0.003 −0.160* 0.161* 0.038
periodic PT car (ref = no) −0.174 0.140 0.039 0.003 −0.112 0.197*** 0.092 −0.048
terraced (ref = detached) 0.009 −0.247 −0.306*** −0.245** −0.228** 0.144 0.045 −0.017
apartment, balcony (ref = detached) −0.026 −0.132 −0.407*** −0.287*** −0.419*** 0.074 0.063 −0.145*
apartment, no balc. (ref = detached) −0.400 −0.803*** −0.172 −0.182 −0.157 −0.090 −0.027 −0.182
shared garden 0.172 0.029 0.061 0.126 0.015 0.120 0.120 0.041
pop. density (in 1000 inh./km2) 0.210 0.020 −0.030** −0.022* −0.039*** −0.014 −0.008 −0.029***
distance to Oslod −0.139 0.022 −0.139** −0.108* 0.002 −0.054 −0.050 −0.002
distance to local centerd 0.357** 0.244 −0.163 0.076 0.018 −0.072 −0.249*** −0.008
Constant | 5.450*** 5.244*** 4.454*** 3.865*** 2.630*** 2.391*** 4.438***
Model fit
Individual dependent variable R2 0.057 0.139 0.163 0.072 0.105 0.076 0.079 0.062
Overall model fit Overall model R2: 0.504 model chi2(df): 180.90(101)*** RMSEA: 0.030 CFI: 0.977
Notes: The signs *, ** and *** signify that a statistical relationship is significant with 90%, 95% or 99% confidence.
aCorrelation is estimated between the residuals of the two mediators. This correlation is 0.43 and is significant with 99% confidence.
bCorrelation is estimated between the residuals of all dependent housing preference variables. These correlations are all significant with 99% confidence and range from 0.11 between a larger dwelling and good PT to 0.52 between a larger dwelling and lower density.
cCorrelation is estimated between the residuals of PAC item 3 and 4. This correlation is 0.84 and is significant with 99% confidence.
dThe effects of distance to Oslo and distance to local center are log-transformed on all but two outcomes, to account for the diminishing effects of distance and improve model fit. Exceptions are lower density and access by car, where a linear effect yields better model fit.
Looking at the effects on PAC during the first wave of the pandemic in March–April 2020, it appears that women experienced a sizable decline in PAC when the pandemic first hit. This is in line with a host of studies indicating the uneven gendered impacts of this pandemic on mobility and mobility experiences (e.g., Caselli, et al., 2021; De Paz, et al., 2020). In addition, those with a location of residence in closer proximity to local centers appear to experience lower peri-pandemic PAC than those who live further away from local centers. While this may seem counter intuitive as it counters the accessible opportunities afforded by proximity to local centers, the effect could possibly be explained from the likelihood that those who live more peripherally relative to local centers are perhaps those who are most car dependent in their everyday mobilities, hence less impacted by the pandemic than others.
With regard to pandemic dwelling satisfaction (pandemic dwe-sat), our results indicate that older adults were better satisfied with their dwelling during the pandemic than younger adults. A sensitivity analysis with age categories using linear regression, not presented in this paper, confirmed the same finding. Moreover, apartment dwellers without a balcony experience significantly lower pandemic dwelling satisfaction relative to residents in all other housing categories, but especially compared to those residing in detached houses. This emphasizes the importance of residential amenities generally, and outdoor assets in particular, during lockdown.
Variation amongst dwelling and locational preferences is explained as follows. Respondents who experienced lower dwelling satisfaction during the pandemic and those in low-income households would now in (post-)pandemic times more-strongly consider a larger dwelling. On the contrary, residing in high-density neighborhoods and in areas that are closer to central Oslo appear to reduce the desire to move to larger dwelling. Also, residents in terraced housing and apartments with balcony tend to exhibit reduced preference for larger dwellings. Looking at total effects (not presented in Table 2), residing in an apartment without a balcony significantly raises the desire for larger dwelling relative to those in detached houses. This effect is, however, entirely mediated by the earlier finding that those without a balcony have substantially lower dwelling satisfaction.
Preference for outdoor assets is higher among women and individuals who reported low perceived accessibility when COVID-19 first hit. Residents in terraced housing and apartments with balcony have a lower preference for outdoor assets relative to those in detached houses. Private outdoor assets such as balcony, terrace, and garden are associated with enhanced residential wellbeing both prior to and during the pandemic (De Bell et al., 2020; Lehberger et al., 2021; Poortinga et al., 2021). Townhouses and apartments with balcony are urban dwelling types with that little extra in the domain of private outdoor assets. The fact that these dwelling types display lower preference to outdoor assets compared to detached houses highlights the contribution of minor outdoor assets such as balcony and small backyards associated with terraced houses in enhancing the resilience of urban dwellings during the pandemic. It can also be seen as a proxy for the type of neighborhoods these types of houses are more prevalent. Apartments and terraced houses are more prevalent in urban than suburban areas, while the opposite is true with detached houses.
Respondents who reported dissatisfaction with their dwelling during COVID-19 have a higher preference for dwellings in lower density neighborhoods as compared to more centrally located areas. Conversely, residents in high-density neighborhoods, as well as those residing in terraced houses and apartments (with a balcony) relative to those residing in detached housing, display less of a need to trade their location in preference for a lower density neighborhood.
Residential preferences reflecting good public transport services, proximity to workplace or easy access by car address various dimensions of accessibility. As expected, women, households without children and those that are frequent public transport users have a greater tendency to indicate higher preference for good public transport service during the pandemic. These are among groups with traditionally good public transport patronage (Grimsrud and El-Geneidy, 2014; Levin, 2019). Besides, frequent teleworking reduces the relevance of good public transport accessibility for a potential (post-)pandemic residential choice. Preference for improved accessibility by car declines with age and higher population density. Women on the other hand deem easy access by car and good parking opportunities as more preferable during COVID-19 than previously. This is likely associated with the constraining effect of the pandemic that appears to have been felt by women as indicated by the significant decline in perceived accessibility.
Proximity to the workplace is closely associated with available mobility options, and the possibility of teleworking as expected. The highly educated and those that are frequently teleworking have a lower preference for residential proximity to the workplace. For women, proximity to the place of work during COVID-19 is more important than for men. This is in line with their significantly reduced perceptions of accessibility when the lockdown was imposed. Moreover, it may be related to pre-existing gendered employment divisions (e.g., Begeny et al., 2020), including an overrepresentation of women who work in critical sectors like health that were never locked down, or in employments that are less easy to do on a teleworking basis.
Concluding remarks
The advent of COVID-19 and the subsequent restrictions have the potential to compromise, at least in theory, compact urban development as a planning ideal by partially nullifying the desirable qualities of dense, transit-rich, and walkable neighborhoods. One way to understand the resilience of compact urban development in the face of COVID-19 and its aftermath is to investigate or debunk potential shifts in residential preference trade-offs that the pandemic may have triggered. This study analyzes (post-)pandemic preference shifts with regard to an array of dwelling and locational attributes, its underlying inequalities, and mechanisms of mediation via peri-pandemic perceived accessibility (Lättman et al., 2018) and dwelling satisfaction, drawing on longitudinal survey data analyzed in a structural equation model (SEM). Our results show that outdoor assets (e.g., gardens and balconies) and motility (not only by car but also by PT), are amongst the residential and locational amenities that have gained most in significance peri-pandemically. Preferences with regard to neighborhood density, dwelling size, and work-proximity, have remained largely unchanged, despite a clear trend towards more teleworking.
Behind these averages lay substantial heterogeneities and inequalities between spatial and social categories. Our analysis shows a clear gender divide on the constraining effect of the lockdown and residential qualities that are deemed more attractive during COVID-19. Women have experienced a greater decline in perceived accessibility during the pandemic. This is consistently reflected in women’s tendency to value access to outdoor assets, good public transport, better accessibility by car, and proximity to workplace as more important during COVID-19 as compared to men. It is also noteworthy that younger adults and residents in apartments without a balcony experienced lower dwelling satisfaction during the pandemic. And those with lower satisfaction during the pandemic tended to prefer larger dwellings and lower density residential areas. Moreover, low-income households would more than higher income groups consider an increased need for larger dwellings. This may correspond with property values in the study area that lay more than 50 per cent above the Norwegian average (Krogsveen, 2021), which has culminated into a prevalence of dwelling crowding among low-income households that is equally high and above the nation’s average (Statistics Norway, 2021).
Residents in high density neighborhoods display significantly lower preferences for larger dwelling, lower density neighborhoods and access by car, which all attribute to predominantly exurban neighborhoods. Moreover, dwellers in terraced houses and apartments with balcony have a significantly lower preference for low-density neighborhoods and private outdoor assets such as private garden compared to those in detached houses. This could be understood as an affirmation that the pandemic has not (yet) eroded the attractiveness of living in a compact city, at least not amongst those who do so today. Ironically, it is amongst those living in detached housing and lower-density neighborhoods where the desires for better private outdoor assets and lower-density living are most prevalent. If anything, the pandemic appears to have reaffirmed rather than shifted existing variations in preferences for compact city living.
Overall, the empirical findings show that so far urban dwellers are content with their residential conditions despite the restrictive pandemic measures that had the potential to change how the residence is used and perceived. This can be understood as a nod of approval to the resilience of the contemporary urban planning paradigm. Rather than abandoning their sustainability ambitions, urban and transport planners, designers and property developers may want to bolster the resilience of compact urban living by ensuring that residential environments have access to mobility resources and residential qualities, such as fresh air, nature and outdoor amenities, and that affordable housing is in the mix of urban housing stock.
COVID-19 is still raging. How attitudes and coping mechanism evolve with time is thus still a developing story. Therefore, it is important to take the findings of this paper as a suggestive but not conclusive appraisal of the high density, transit-rich, and compact urban development paradigm. Besides, studies that focus on residential preference are indicative but not conclusive as they tell only part of the story. Comprehensive future analyses should track the development of residential preference shifts towards a firmer established new normal over time, and also examine whether such preference shifts are strong enough to trigger actual structural changes in urban residential mobility patterns.
Author Biographies
Dr. Fitwi Wolday is a senior researcher at the Institute of Transport Economics (Norway), with a background in Development Economics and a Ph.D. in Urban and Regional Planning. Dr. Wolday regularly publishes in highly ranked peer-reviewed scientific journals on topics related to the intersection between urban structure and travel behavior, sustainable mobility, accessibility, residential preferences, and sustainable urban planning. He also lectures and supervises on topics related to these issues.
Dr. Lars Böcker is a senior researcher at the Institute of Transport Economics (Norway) and the University of Oslo, with a Ph.D. in Human Geography from Utrecht University. He publishes regularly in highly ranked peer-reviewed scientific journals on topics related to travel, health and sustainability behaviors, mobility justice, accessibility, smart cities, and sustainable urban planning. He also lectures, supervises, and coordinates research projects on these issues.
ORCID iDs
Fitwi Wolday https://orcid.org/0000-0003-1080-1920
Lars Böcker https://orcid.org/0000-0001-8631-8391
Notes
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Norwegian Research Council (grant no. 316126) funded the project on which this study is based.
1 Drawing on a measurement instrument developed and tested by Lättman et al. (2018)
2 Drawing on a measurement instrument developed and tested by Lättman et al. (2018)
3 Crowded dwelling as used here follows Statistics Norway’s (SSB) definition of the term. As per SSB’s definition, Households are considered as living crowded if: 1. The number of rooms is lower than the number of residents or one resident lives in one room, and 2. Floor area (P-area) is below 25 sq. m. per person.
4 Stratification by municipality was performed to secure that the sample represents sufficient amounts of respondents from all diverse urban, suburban, peri-urban, and ex-urban residential built environments present in our study area, ultimately yielding a 59:41 split of residents from Oslo versus that of surrounding municipalities.
5 The majority of respondents in the survey (74%) were recruited from an earlier pre-COVID study from 2019, recruited via the same Internet panel and with the same stratification scheme, but with one additional recruitment requirement: the use of public transport once-weekly or more.
6 The questionnaire agency’s Internet panel consists of 38,000 panelists in Norway, recruited (not self-recruited) by telephone calls based on probability sampling, supplemented with Internet-based recruitments. The panel is actively monitored and managed and earmarked with the ISO standard 26362:2009 indicative of rigorous quality.
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Chemistry
Chemistry
10.1002/(ISSN)1521-3765
CHEM
Chemistry (Weinheim an Der Bergstrasse, Germany)
0947-6539
1521-3765
John Wiley and Sons Inc. Hoboken
36111677
10.1002/chem.202202267
CHEM202202267
Research Article
Research Articles
Hot Paper
Brønsted Acid versus Phase‐Transfer Catalysis in the Enantioselective Transannular Aminohalogenation of Enesultams
Luis‐Barrera Javier Dr. 1
Rodriguez Sandra Dr. 1
Uria Uxue Dr. http://orcid.org/0000-0003-0372-7005
1 [email protected]
Reyes Efraim Dr. http://orcid.org/0000-0003-2038-9925
1
Prieto Liher Dr. http://orcid.org/0000-0001-7965-7168
1
Carrillo Luisa Prof. Dr. http://orcid.org/0000-0001-5208-6780
1
Pedrón Manuel http://orcid.org/0000-0003-4249-6748
2
Tejero Tomás Prof. Dr. http://orcid.org/0000-0003-3433-6701
3
Merino Pedro Prof. Dr. http://orcid.org/0000-0002-2202-3460
2 [email protected]
Vicario Jose L. Prof. Dr. http://orcid.org/0000-0001-6557-1777
1 [email protected]
1 Department of Organic and Inorganic Chemistry University of the Basque Country (UPV/EHU) P.O. Box 644 48080 Bilbao Spain
2 Instituto de Biocomputación y Fisica de Sistemas Complejos (BIFI) Universidad de Zaragoza 50009 Zaragoza Spain
3 Instituto de Síntesis Química y Catálisis Homogénea (ISQCH) Universidad de Zaragoza-CSIC 50009 Zaragoza Spain
17 10 2022
07 11 2022
28 62 10.1002/chem.v28.62 e20220226719 7 2022
© 2022 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Abstract
We have studied the enantioselective transannular aminohalogenation reaction of unsaturated medium‐sized cyclic benzosulfonamides by using both chiral Brønsted acid and phase‐transfer catalysis. Under optimized conditions, a variety of bicyclic adducts can be obtained with good yields and high enantioselectivities. The mechanism of the reaction was also studied by using computational tools; we observed that the reaction involves the participation of a conformer of the nine‐membered cyclic substrate with planar chirality in which the stereochemical outcome is controlled by the relative reactivity of the two pseudorotational enantiomers when interacting with the chiral catalyst.
Stereocontrol: We have studied the enantioselective transannular aminohalogenation of unsaturated medium‐sized cyclic benzosulfonamides by using both chiral Brønsted acid and phase‐transfer catalysis. The mechanism of the reaction was also studied by using computational tools; we observed that the key step for stereocontrol involves the participation of an interconverting planar chiral conformer of the nine‐membered cyclic starting material
asymmetric catalysis
computational chemistry
electrophilic addition
ELF
medium-ring compounds
transannular reactions
Ministerio de Ciencia e Innovación 10.13039/501100004837 FEDER PID2020-118422GB-I00 FEDER PID2019-104090RB-100 Eusko Jaurlaritza 10.13039/501100003086 IT1558-22 postdoctoral contract to J. L-B. Gobierno de Aragón 10.13039/501100010067 Grupos 17R-34 Predoctoral grant to M. P. Ministerio de Educación, Cultura y Deporte 10.13039/501100003176 FPU fellowship to S. R. source-schema-version-number2.0
cover-dateNovember 7, 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
J. Luis-Barrera , S. Rodriguez , U. Uria , E. Reyes , L. Prieto , L. Carrillo , M. Pedrón , T. Tejero , P. Merino , J. L. Vicario , Chem. Eur. J. 2022, 28 , e202202267.36111677
==== Body
pmcIntroduction
Transannular reactions constitute a powerful yet significantly underdeveloped approach for the synthesis of relatively complex polycyclic scaffolds. [1] This type of reaction, in which the two reacting points are incorporated at nonadjacent positions within the structure of a medium or large sized cyclic compound, shows up as an unconventional method for the construction of a given target compound that typically benefit from the low degree of conformational freedom of the cyclic starting material. This situation very often conditions the structure of the final product, in many cases providing reactivity profiles that differ from the ones observed corresponding intermolecular reaction. Moreover, this fixed preferred geometry on the starting material also calls for a very favorable situation for transannular reactions when stereocontrol is desired. In this context, the literature is rich in examples of highly diastereoselective transannular reactions that take place under substrate control when a chiral starting material is employed. [2] In contrast, the number of examples of enantioselective reactions that convert an achiral cyclic starting material into an enantioenriched polycyclic product is very scarce. [3] All these transformations consist on either pericyclic processes or make use of activated alkenes as the electrophile undergoing transannular reaction with an internal nucleophile incorporated at the starting material.
In a different approach, we wish to present herein our recent studies towards the development of a catalytic and enantioselective version of a transannular aminohalogenation process employing enebenzosultams as substrates in which a nonactivated alkene is employed as the internal electrophile undergoing the transannular process after activation by an electrophilic halogen source (Scheme 1).
Scheme 1 Stereocontrolled transannular aminohalogenation reactions.
It should be pointed out that there are several examples of transannular aminohalogenation reactions described in the literature, [4] although, as mentioned earlier, all of them relying on the use of an enantioenriched chiral starting material and in which typically high diastereoselectivity is obtained by means of the asymmetric induction exerted by the stereogenic centers present at the substrates. In this sense, a catalytic and enantioselective version of a transannular aminohalogenation process is still elusive.
As is also shown in Scheme 1, the reaction design would involve the initial activation of the alkene moiety with a selected electrophilic halogen source in the presence of a chiral catalyst. Subsequently, the transannular process would undergo, being the stereochemistry of the overall process controlled by the presence of the chiral catalyst. [5] In this context, and among the different strategies described for catalytic enantioselective aminohalogenation reactions, we turned our attention to the possibility of using BINOL‐based phosphoric acids as Brønsted acid catalyst for both the activation of the electrophilic halogen source and for achieving stereocontrol. Under this organocatalytic activation manifold, two different concepts have been applied to induce enantiocontrol: On one hand, a soluble halogenating reagent (such as NBS or related N‐haloimide reagents) undergoes activation by the chiral Brønsted acid catalyst itself [6] or, on the other, an insoluble electrophilic halogen source is employed in the reaction, being the BINOL‐based phosphoric acid involved as a chiral phase‐transfer catalyst. [7] Both approaches will be evaluated in this reaction.
Results and Discussion
We started by searching for the optimal reaction conditions that provide the compound 2 a with the best yield and enantioselectivity using enesultam 1 a as model system (Table 1). The initial reaction using NBS as halogenating agent and in the presence of the archetypical chiral phosphoric acid (R)‐TRIP (3 a) as catalyst provided the desired transannular aminobromination product 2 a in a promising 65 % ee after 15 minutes (Table 1, entry 1). Remarkably, the reaction in the absence of acid catalyst under the same conditions was also effective, although it required a significantly longer reaction time (3 h. Table 1, entry 2). Other phosphoric acid catalysts 3 b–f were also evaluated but, even in all cases the reaction proceeded fast and furnished a high yield of 2 a, in all cases enantioselectivities were significantly lower to those provided by 3 a (Table 1, entries 3–7). Solvent effect was also evaluated, observing that moving to chloroform resulted into an important decrease in enantioselectivity (Table 1, entry 8), while the reaction performed similarly when moving from toluene to chlorobenzene (Table 1, entry 9 vs. 1). On the other hand, a slight improvement in the enantiocontrol was found working in m‐xylene and in mesitylene (Table 1, entry 10 and 11). Other halogenating agents were tested (Table 1, entries 12–15) using mesitylene but none of them provided improved performance with respect to the results obtained with NBS, which was therefore selected as the optimal electrophilic bromine source. [8] We next carried out the reaction at lower temperature (0 °C) but this did not result in any significant improvement, only observing a slightly slower reaction (Table 1, entry 16). Finally, the enantioselectivity of the process was increased to 84 % ee by incorporating 5 mol% of succinimide as additive (Table 1, entry 17), which is supposed to facilitate the proton transfer or activate the halogenating agent in the initial stages of the acid‐catalyzed reaction. [9] At this point, we also tested the reaction on larger scale achieving the desired aminohalogenated adduct with similar results. [10] Moreover, the absolute configuration of the adduct 2 a was established by single‐crystal X‐ray analysis of an enantiopure sample, for which a monocrystal could be obtained. [11]
Table 1 Screening for the best reaction conditions.[a]
Cat.
Halogen source
Solvent
Yield [%][b]
ee [%][c]
1
3 a
NBS (4 a)
toluene
89
65
2[d]
none
NBS (4 a)
toluene
70
–
3
3 b
NBS (4 a)
toluene
92
49
4
3 c
NBS (4 a)
toluene
91
40
5
3 d
NBS (4 a)
toluene
91
15
6
3 e
NBS (4 a)
toluene
89
28
7
3 f
NBS (4 a)
toluene
99
17
8
3 a
NBS (4 a)
CHCl3
91
28
9
3 a
NBS (4 a)
PhCl
95
59
10
3 a
NBS (4 a)
m‐xylene
95
73
11
3 a
NBS (4 a)
mesitylene
92
74
12
3 a
4 b
mesitylene
70
7
13
3 a
4 c
mesitylene
97
70
14
3 a
4 d
mesitylene
91
57
15
3 a
4 e
mesitylene
65
5
16[e]
3 a
NBS (4 a)
mesitylene
95
61
17[f]
3 a
NBS (4 a)
mesitylene
92
84
[a] Reactions were performed with 0.072 mmol of 1 a, halogenating agent (0.072 mmol), catalyst (5 mol %) and the corresponding solvent (0.5 M) at RT for 15 min. [b] Isolated yield after flash column chromatography purification. [c] Calculated by HPLC on chiral stationary phase (see the Supporting Information). [d] Reaction required for 180 min to reach to full conversion. [e] Reaction carried out at 0 °C for 30 min. [f] 5 mol % of succinimide was incorporated as additive.
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Once the experimental protocol had been optimized, we next focused on studying the scope and limitations of the reaction to be applied to enesulfonamides incorporating different substituents at the aryl moiety (Table 2). [12] As it can be seen in this table, the reaction proceeded with very high yields in all cases regardless the substitution pattern at the substrate. However, the enantioselectivity of the process was strongly affected by both the nature and the position of such substituents. In particular, the reaction performed well when an electron‐withdrawing substituent was placed at the para‐position with respect to the sulfonamide moiety (compounds 2 b and 2 c) but provided very poor enantioselectivity when an electron‐donating group was placed at the same position (compounds 2 d and 2 e). The opposite tendency was observed when the substituent was placed at the meta‐position with respect to the sulfonamide group, obtaining good er when an electron‐donating group was incorporated (compound 2 f), although the reaction performed much better in the absence of succinimide as additive. In contrast, very poor enantioselectivity was observed when this substituent was replaced by an electron‐withdrawing group (compound 2 g). Finally, 2‐substituted substrate 1 h also failed to provide the corresponding adduct 2 h with high er and, furthermore, in this case the reaction was not completely regioselective, isolating 18 % of the isomer.
Table 2 Use of differently substituted enesulfonamides.[a]
[a] Reactions were performed with 0.072 mmol of 1 a–h, NBS (0.072 mmol), succinimide (5 mol%), catalyst 3 a (5 mol %) in mesitylene (0.5 M) at RT until complete consumption of the starting material (TLC monitoring). Isolated yield after flash column chromatography purification are given and er was calculated by HPLC on chiral stationary phase (see the Supporting Information). [b] Reaction carried out in the absence of succinimide. [c] 18 % of the corresponding regioisomer was isolated as well (see the Supporting Information).
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This observed strong dependence of the enantioselectivity with the substitution pattern was in general attributed to the variable reaction rate of the reaction for each of the substrates tested, which provided an opportunity for the uncatalyzed background reaction to be operative. [13] Moreover, it should be highlighted that in the NMR spectra of precursors 1 a–h conformational rigidity could be appreciated, since there are magnetically non‐equivalent geminal methylene protons. [14]
Taking into account these results, we decided to continue the study by evaluating the effect of modifying the size of the cyclic starting material, concluding that it turns out to be a crucial factor (Table 3). When a smaller ring was used (1 i), the reaction did not provide the aminobrominating adduct 2 i and when employing substrate 1 j with one more CH2, the desired adduct 2 j was isolated in a moderate yield although as a racemic mixture. It should be highlighted that the reaction was completely diastereospecific, observing that 2 j (derived from enesulfonamide 1 j with E configuration) presented the protons of the stereogenic centers in a trans arrangement.
Table 3 Use of enesulfonamides with different ring sizes.[a]
[a] Reactions were performed with 0.072 mmol of 1 a,i–j, NBS (0.072 mmol), succinimide (5 mol%), catalyst 3 a (5 mol%) in mesitylene (0.5 M) at RT until complete consumption of the starting material (TLC monitoring). Isolated yield after flash column chromatography purification are given and er was calculated by HPLC on chiral stationary phase (see the Supporting Information). [b] n.d.: not determined. [c] The substrate 1 j was employed as a mixture of E : Z (10 : 1) diastereoisomers.
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In view of these results, we turned our attention to evaluate the use of phase‐transfer catalysis conditions that, as mentioned, have also provided good results in other reported examples of intramolecular enantioselective aminohalogenation reactions. We started selecting the best halogen source 4 f–k for carrying out the reaction in the presence of the archetypical chiral phosphoric acid (R)‐TRIP (3 a) and excess of neat sodium carbonate in mesitylene at room temperature (Table 4, entries 1–6). Under these conditions, the model substrate 1 a was satisfactorily transformed into adduct 2 a regardless the substitution pattern of the halogen source employed, obtaining in all cases the desired product with high yield and promising ee. Taking these results into account, together with the difficulty of preparing the halogenating agents, the optimization process was continued with salt 4 f which provided the compound 2 a with 92 % yield and 79 % ee (Table 4, entry 1). Temperature effect was also evaluated (Table 4, entries 7 and 8) and a slight improvement in the enantiocontrol was found working at 0 °C, although longer reaction times were needed in order to reach full conversions (3 h vs. 4.5 h). However, working at lower temperatures (−15 °C) was not beneficial due to the slowing down of the reaction (7 h) and decrease in the yield (Table 4, entry 8). Finally, different solvents mixtures were tested (Table 4, entries 9–11), observing that the model substrate 1 a was converted into transannular aminohalogenation product 2 a in excellent yield and ee after 23 h when a mixture of mesitylene/hexane (1 : 1) was used (Table 4, entry 9).
Table 4 Optimization of the reaction employing phase‐transfer catalysis.[a]
Halogen source
Solvent
T [°C]
Yield [%][b]
ee. [%][c]
1
4 f
mesitylene
RT
92
79
2
4 g
mesitylene
RT
75
78
3
4 h
mesitylene
RT
92
78
4
4 i
mesitylene
RT
85
79
5
4 j
mesitylene
RT
73
75
6
4 k
mesitylene
RT
92
80
7
4 f
mesitylene
0
86
86
8
4 f
mesitylene
−15
45
87
9[d]
4 f
mesitylene/hexane (1 : 1)
0
95
91
10[d]
4 f
m‐xylene/hexane (1 : 1)
0
95
87
11[d]
4 f
toluene/hexane (1 : 1)
0
90
84
[a] Reactions were performed with 0.05 mmol of 1 a, brominating agent 4 (0.065 mmol), catalyst 3 a (10 mol%) and Na2CO3 (0.20 mmol) until complete consumption of the starting material (TLC monitoring). [b] Isolated yield after flash column chromatography purification. [c] Calculated by HPLC on chiral stationary phase (see the Supporting Information). [d] Reaction required for 23 h to reach to full conversion.
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After the optimization process, the best conditions involved the use of bromine/ammonium salt adduct 4 f as the most effective halogenating agent in the presence of an excess of neat sodium carbonate, catalyst 3 a and in a binary mesitylene/hexane (1 : 1) solvent system (Table 4). [15] These conditions were subsequently applied to some of the substrates that had previously provided poor stereocontrol in the conditions shown in Table 2. As it can be seen in Table 5, a significant improvement on the enantioselectivity was observed in all cases tested, keeping the excellent yield for all examples.
Table 5 Enantioselective transannular aminohalogenation under PTC.[a]
[a] Reactions were performed with 0.05 mmol of 1 a–h, brominating agent 4 f (0.065 mmol), catalyst 3 a (10 mol %) and Na2CO3 (0.20 mmol) in a 1 : 1 mixture of mesitylene and n‐hexane at 0 °C until complete consumption of the starting material (TLC monitoring). Isolated yields after flash column chromatography purification are given and er was calculated by HPLC on chiral stationary phase (see the Supporting Information). [b] 8 % of the regioisomer was also isolated (see the Supporting Information).
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In order to provide a plausible rationale for the observed reactivity in the Brønsted acid‐catalyzed reaction, we decided to study the reaction between 1 a and 2 a catalyzed by 3 a computationally. The conformational flexibility of compound 1 a generates additional issues for the study of the reaction mechanism. Although it is known that nine‐membered carbocycles present several conformations of similar energies easily interconverted by pseudorotation, [16] the fused aromatic ring, the endocyclic double bond and the sulfonamide group in 1 a introduce nonsymmetric structures and contribute significantly to the ring strain increasing the interconversion barriers. Because of that, it is not possible to carry out a systematic conformational analysis of the sixteen symmetrical configurations of a nine‐membered ring. [17] Moreover, the presence of the double bond provides planar chirality to 1 a and constrain the interconversion of enantiomers. To overcome these issues, we performed a conformational analysis of 1 a. A first analysis using molecular dynamics (MD) simulations showed one predominant conformer A and small amounts of a second one as the preferred situation at ambient temperature in chloroform as a solvent (Figure 1).
Figure 1 Conformational analysis of 1 a by using MD simulations. Population analysis was carried out with 250 000 snapshots. The representation was made on the basis of the more flexible dihedral angles α and β.
Conformer A showed the phenyl group almost perpendicular to the double bond, composing a concave area containing the phenyl group and the three methylenes. All these methylenes are oriented staggered each other and the pyramidalization of the nitrogen pointing out the lone pairs outside the ring.
We carried out, in parallel, a conformational search using Macromodel software [18] from which we constructed a pseudorotational itinerary of lower energy (for other alternatives and conformations see the Supporting Information; Figure 2). All the transition structures between conformers were located and characterized at wb97xd/def2svp/pcm=toluene level of theory. These transition structures were verified by the corresponding IRC analyses; the corresponding energy profile of optimized points is also given in Figure 2.
Figure 2 Top: Pseudorotational itinerary of lower energy for 1 a. The enantiomeric part is given at the bottom of the catalytic cycle (relative energies [kcal mol−1] are given in brackets for one enantiomeric part). Bottom: Energy profile of a half‐cycle corresponding to one enantiomeric part.
The pseudorotational itinerary of 1 a consists of two enantiomeric sections in which conformers A and D, and the corresponding enantiomers, account for 94 and 6 %, respectively, according to a Boltzmann distribution. These results confirmed the MD analysis that predicted the same predominant conformers. The geometrical difference between conformers A and D resides in the relative orientation of the methylenes. In fact, all the conformers present geometries in which the aromatic ring is out of the plane of the double bond preventing conjugation. Consequently, one enantioface of the double bond points towards the inside region of the molecule (nitrogen atom) and the other one to the outside region. The highest barrier in the conformational itinerary for the interconversion of A into ent‐A, is 17.1 kcal mol−1 and corresponds to the loss of the planar chiral information. [19] The kinetic constant calculated for that barrier is 1.78 s−1 (0.12 s−1 at 0 °C) and the half‐life t 1/2 is 0.390 s (6 s at 0 °C). These values agree with a relatively slow conformational racemization at ambient temperature.
When considering the cyclization reaction, that is, the attack of the nitrogen atom to the activated double bound, conformers A/D, and ent‐A/ent‐ D lead to attacks by the 6Re and 6Si faces of the double bond, respectively. Since a conformational equilibrium connects both enantiomers we could see the whole process as a dynamic kinetic resolution (DKR) when a chiral catalyst is used. According to the Curting‐Hammettt principle, [20] such a DKR‐like scenario would require a faster reaction for one conformer, a slow reaction for the other and a much faster equilibration between them. Consequently, we next studied the cyclization reaction.
Denmark and Burk reported [9] that, in the presence of a Lewis base, NBS is cooperatively activated by a phosphoric acid to form a phosphate hypobromite which transfer bromine to the substrate. Accordingly, we can propose the catalytic cycle illustrated in Figure 3. Once formed the reactive halogenating reagent C1, an encounter pair EP is formed with the reactant (1 a) leading to a bromiranium intermediate IN which evolves to PR through TS2. Product PR, by the action of the Lewis base and NBS, yields the product 2 a and regenerates the active catalytic species C1, starting the cycle again. For the calculations, we fixed our attention in the transformation of EP into PR. Any alternative bifunctional interaction between the catalyst and the sulfonamide group of the substrate can be discarded because it would prevent the anti‐type intramolecular attack of the nitrogen atom.
Figure 3 Catalytic cycle for the enantioselective transannular reaction of 1 a.
We could assume that the reaction takes place with the racemic conformers A and ent‐ A. However, on the basis of Curtin‐Hammett's principle [20] the reaction might also take place with racemic D and ent‐D which are only 1.7 kcal mol−1 above the former. Other conformers are too high in energy to be considered as candidates to be the most reactive ones. We carried out the whole study of the process with (R)‐BINOL‐phosphoric acid as a reduced model of the real catalyst. This model, being also chiral, presented a simpler structure which reduces computational cost. Consequently, we approached chiral phosphate hypobromite C1 to A, D and their enantiomers by the corresponding enantiotopic faces (6Si for A and D, and 6Re for ent‐A and ent‐D). After an exhaustive exploration of the potential energy surface (PES), we located the most stable transition structures corresponding to TS1a and TS1b formed from A and ent‐A, respectively. The IRC's for these transition structures indicated that they connect the corresponding encounter pairs EPa and EPb with bromiranium intermediates which, after optimization yield INa (1.09 kcal mol−1 lower than TS1a) and INb (2.1 kcal mol−1 lower than TS1b) as stable stationary points.
It has been reported that in the case of double bonds conjugated with aromatic rings (styrenes and stilbenes), in which there is a quite different electronic distribution due to the substituents, the intermediate formed after bromination features a stabilized carbocation rather than a bromiranium ring. [21] An alternate situation between carbocations have also been suggested based on NMR experiments. [22] However, as it has been mentioned above, the orientation of the aromatic ring out of the plane of the double bond prevents conjugation and justifies the absence of a benzylic carbocationic intermediate. In our case, it can be observed for the transition structures that C7 is more pyramidalized than C6, the C7−Br distance (2.39 and 2.40 Å for TS1a and TS1b, respectively) being shorter than the C6−Br distance (2.51 and 2.52 Å for TS1a and TS1b, respectively). This is in agreement with the development of an incipient positive charge at homobenzylic position, which, in turn, it should be stabilized by the nitrogen lone pairs. NCI calculations [23] corroborated this stabilizing interaction (Figure 4; only the most stable b series is showed; for the whole analysis see the Supporting Information). The above situation observed for TS1a,b is maintained in the corresponding intermediates INa,b which resulted enough stable to be located and characterized as minima. Again, NCI calculations corroborated the stabilizing interaction of the nitrogen lone pairs. Moreover, the observed distances between P−O and the bromine atom (2.35 Å for both INa and INb) are more compatible with an electrostatic interaction proper of an ionic pair. Both NCI and ELF [24] analyses confirmed the ion‐pair character of intermediates, the former revealing a strong electrostatic interaction and the latter demonstrating the absence of electron density between the oxygen and bromine atoms. Those analysis also confirm the nonsymmetric cyclic structure of the bromiranium ring in which the C7⋅⋅⋅Br distance is shorter than the C6⋅⋅⋅Br distance as a consequence of a higher partial positive charge of homobenzylic C6. Furthermore, the ELF analysis confirms the presence of some electron density between the bromine atom and both C6 and C7 demonstrating the formation of the three‐membered ring.
Figure 4 Optimized geometries (wb97xd/def2svp/smd=toluene) of a) TS1b, b) INb and c) TS2b. d) NCI calculations for TS1b. The blue arrow indicates a prebonding interaction, and the green arrow points to a stabilizing van der Waals interaction. e) NCI calculations for INb. The blue arrow indicates a strong electrostatic interaction, and the green delocalized surface indicates van der Waals interactions. Small, lenticular, bluish surfaces indicate strong interactions. f) ELF of INb. The blue arrow indicates the presence of electron density between bromine and carbon atoms. The red arrow confirms the electrostatic interaction by the absence of electron density between bromine and oxygen atoms as expected for an ion‐pair.
Bromiranium intermediates INa,b evolve through a second transition structure TS2a,b to give the products. These transition structures, according to Hammond's postulate [25] are rather similar to the precursor intermediates and represent the intramolecular attack of the nitrogen to the homobenzylic position completing the transannular cyclization. The distances in INa,b are almost the same that in TS2a,b with the exception of the breaking bond C6−Br which is longer in the latter, as expected due to the formation of the C6−N bond.
In terms of energy barriers of the reaction, transition structures TS1a and TS1b showed barriers with respect to the reagents of 29.4 and 29.0 kcal mol−1, and constitute the stereodifferentiating rate‐limiting step of the reaction. The formation of the reactive reagent C1 requires, at least, 23.5 kcal mol−1 (the energy of the most stable encounter pair EPa). This is also compatible with the observed DKR‐type process having a barrier of 17.1 kcal mol−1 for the interconversion of enantiomers (Figure 5). On the other hand, the potential energy surface corresponding to the preferred route (b series) is close to flat with a maximum difference between points of 1.9 kcal mol−1.
Figure 5 Energy profiles (wb97xd/def2tzvp/smd=toluene//wb97xd/def2svp/smd=toluene) for the transformation of 1 a into PR catalyzed by BINOL phosphoric acid.
Next, we grew up the model to the real catalyst 3 a. We located the most stable TS1a‐r and TS1b‐r after introducing the bulky groups into the BINOL system and evaluating the conformational variability of the resulting transition structures. Both transition structures showed barriers referred to starting materials of 23.4 and 24.0 kcal mol−1 for TS1a‐r and TS1b‐r, respectively.
The NCI analysis (see the Supporting Information) reveals stabilizing van der Waals interactions between the catalyst and the substrates that justify the lower barrier calculated for the real model (23.4 vs. 29.0 kcal mol−1 for the BINOL model). Interactions between the isopropyl groups of the catalyst and the substrate, an aromatic ring in TS1b‐r, and methylenes in TS1a‐r are the responsible of the small difference in energy (only 0.6 kcal mol−1) observed for the transition structures. This small difference correctly predicts the modest enantiomeric ratio observed experimentally in several cases. The total energy barrier is compatible with the observed interconversion of conformers leading to a DKR‐type process. So, these results fully agree with the experimental results featuring a not very enantioselective reaction requiring, in some cases, hours for completion at ambient temperature.
Any attempt of locating a bromiranium intermediate or a second transition structure for the real model failed. Moreover, it was not possible to calculate the IRC of the transition structure due to the flatness of the PES. Because of that, we performed relaxed scans in reverse and forward directions. In all cases downhill pathways were obtained, no minima being present between the corresponding transition structure and the product (PR; for details see the Supporting Information). So, it is possible to assume that this part of the reaction‐the intramolecular attack of the nitrogen atom to the bromiranium intermediate‐is, in this case, almost barrierless.
Calculations of noncovalent interactions of TS1a,b‐r corroborated the still present interaction between oxygen and bromine atoms as well as that between the nitrogen and the carbon atom at homobenzylic position, confirming that they correspond to the first transition structures. These findings suggest that we are moving through a stepwise process crossing a flat PES in which the second step requires very low energy. The comparison of the observed distances in TS1a,b of the BINOL and real models indicates that the bulkiness of the catalyst promotes a transition structure in which the bromine atom approximates the double bond in a more symmetrical way, that is, the C−Br distances are more similar between them than in the BINOL‐derived transition structures (C6−Br 2.44 Å and C7−Br 2.45 Å in TS1b‐r vs. C6−Br 2.52 Å and C7−Br 2.40 Å in TS1b). In consonance with such a more symmetrical approach there is a lower stabilization by the nitrogen atom as indicated by a longer C6‐N distance in TS1b‐r (2.81 Å) than for TS1b (2.76 Å, Figure 6). Actually, both are stepwise processes but the presence of bulky groups renders more favored a barrierless second step. This situation is substantially different than that reported previously [26] in the course of a semipinacolinic rearrangement initiated by an alkene halofunctionalization, in which the second part of the reaction took place in a concomitant way with the bromine attack through an only transition structure (Figure 7). The real difference between the two processes is given by the topological NCI and ELF analyses, which in the case of the stepwise process described here show an interruption of the electron density when the intermediate is formed. In contrast, in our previously reported halofunctionalization of 1‐alkenecyclobutanols, there is a continuous electronic interaction along the whole concerted highly asynchronous reaction.
Figure 6 Optimized geometries (wb97xd/def2svp/smd=toluene) of the most favored transition structures TS1a,b‐r for the real catalyst 3 a. Relative energies (ΔΔG, calculated at wb97xd/def2tzvp/ smd=toluene//wb97xd/def2svp/smd=toluene level) are given in brackets in kcal mol−1. Bottom: NCI calculations. The blue arrow indicates a prebonding interaction. The ochre arrows indicate stabilizing interactions of the nitrogen atom. The green arrows indicate stabilizing interactions between the catalyst isopropyl group and the aromatic ring of the substrate in TS1b‐r and the methylenes of the substrate in TS1a‐r.
Figure 7 Comparison of halofunctionalization of alkenes taking place through stepwise (this work, top) and concerted asynchronous (ref. [26], bottom) mechanisms.
Conclusion
In summary, we have shown that chiral phosphoric acids are suitable catalysts for the enantioselective transannular aminohalogenation reaction of enesultams leading to the formation of compounds 2 a–h in excellent yield, diastereoselectivity, and regioselectivity. In order to reach useful enantioselectivities, two complementary methodologies were optimized, one of them based on Brønsted acid catalysis and the other one on PTC. Moreover, enesultam precursors exhibit planar chirality; however, the fast racemization between the most reactive rigid conformers at room temperature allows a DKR process.
Computational studies predict the formation of a bromiranium intermediate with a simple unsubstituted phosphoric acid derived from BINOL. Although the PES is close to planar, with differences of less than 3 kcal mol−1, stabilizing interactions of the endocyclic nitrogen atom allow location and characterization as a stationary point, of a nonsymmetrical bromiranium intermediate in which a higher positive charge is partially developed at the homobenzylic position. The absence of conjugation between the aromatic ring and the double bond resulting in planar chirality prevents the formation of a benzylic carbocation. On the other hand, favorable steric van der Waals interactions formed between the substituted BINOL derivative (the real catalyst 3 a) and the substrate causes a decrease in the stabilizing interactions of the nitrogen that promotes the formation of the product through a barrierless second step. The energetics of the process were also predicted correctly as the barrier of the process (availability of the reactive species C1) is compatible with the reaction conditions and with the observed equilibration of the reactive conformers (visible by NMR). The small difference between the most favored transition structures for each enantiotopic face is in agreement with the modest values of ee observed in several cases. Globally, substitution in the BINOL favored the reaction lowering the energy barrier by 5–6 kcal mol−1 with respect to the unsubstituted derivative.
Experimental Section
Synthesis of 2 a, using Brønsted acid catalysis: A reaction vial was equipped with a magnetic stirring bar, and charged with the substrate 1 a (16.1 mg, 0.072 mmol) and succinimide (0.4 mg, 0.0036 mmol). To the mixture, under argon atmosphere, a solution of (R)‐TRIP hydrogen phosphate (3 a; 2.7 mg, 0.0036 mmol) was added, followed by dry mesitylene (0.15 mL). After thermostatting the mixture at 25 °C, N‐bromosuccinimide (12.8 mg, 0.072 mmol) was added. When the reaction was judged complete (15 min), a saturated aqueous solution of NaHCO3 (1 mL) was added and the mixture was extracted with CH2Cl2 (3×10 mL). The combined organic layers were dried over Na2SO4, filtered and concentrated in vacuo. Purification of the crude by flash column chromatography on silica gel (petroleum ether/EtOAc, 7 : 3) gave the corresponding product 2 a (20.0 mg, 92 %) as a white solid. [α]20D =+49.2 (c=1.0, CH2Cl2); The er was determined by HPLC using a Chiralpak AS−H column [n‐hexane/iPrOH (70 : 30)]; flow rate 1.0 mL min−1; τ 1=22.0 min, τ 2=53.2 min (92 : 8 er).
Synthesis of 2 a, using phase‐transfer catalysis: A reaction vial was equipped with a magnetic stirring bar, and charged with the substrate 1 a (11.2 mg, 0.05 mmol), Na2CO3 (21.2 mg, 0.2 mmol) and (R)‐TRIP hydrogen phosphate (3 a; 3.8 mg, 0.005 mmol). To this vial, under argon atmosphere, 0.5 mL of dry mesitylene and 0.5 mL of dry hexane were added. After thermostatting the mixture at 0 °C, the cationic brominating reagent (4 f; 23.2 mg, 0.065 mmol) was added. When the reaction was judged complete monitored by TLC (48 h), a saturated aqueous solution of Na2S2O3 (1 mL) was added and, after disappearance of the yellow color, the mixture was extracted with CH2Cl2 (3×10 mL). The combined organic layers were dried over Na2SO4, filtered and concentrated in vacuo. Purification of the crude by column chromatography on silica gel gave the corresponding product 2 a (14.8 mg, 98 %) as a white solid. [α]20D : +50.7 (c=1.0, CH2Cl2); The er was determined by HPLC using a Chiralpak OD‐3 column [n‐hexane/iPrOH (70 : 30)]; flow rate 1.0 mL min−1; τ 1=8.7 min, τ 2=14.6 min (96 : 4 er).
Spectral data of compound 2 a: m.p. (petroleum ether/EtOAc): 145–148 °C; 1H NMR (300 MHz, CDCl3): δ=7.88 (dd, J=7.5, 1.0 Hz, 1H, Carom−H), 7.62–7.46 (m, 3H, Carom−H), 5.51 (d, J=3.3 Hz, 1H, C10−H), 4.12 (ddd, J=7.6, 6.3, 3.3 Hz, 1H, C10a−H), 3.63 (ddd, J=9.1, 6.7, 5.1 Hz, 1H, C3−Ha Hb), 3.53–3.32 (m, 1H, C3−Ha Hb ), 2.42–2.19 (m, 1H, C1−Ha Hb), 2.19–1.88 (m, 3H, C1−Ha Hb , C2−H); 13C NMR (75 MHz, CDCl3): δ=136.8 (Carom−S), 135.2 (Carom −C10), 132.7 (Carom−H), 130.6 (Carom−H), 129.7 (Carom−H), 124.4 (Carom−H), 62.1 (C10a), 50.7 (C10), 46.7 (C3), 31.5 (C1), 23.7 (C2); IR (ATR): 1303 (SO2 st as), 1260 (C−N st), 1155 cm−1 (SO2 st sym); MS (EI) m/z (%): 221 ([M‐HBr]+, 97), 156 (100), 130 (19), 129 (33), 128 (30), 115 (20), 102 (19), 89 (20), 63 (20); HRMS (UPLC MS ESI+): Calculated for [C11H13NO2SBr]+: 301.9845 [M+H]+; found: 301.9848.
Conflict of interest
The authors declare no conflict of interest.
1 Supporting information
As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.
Supporting Information
Click here for additional data file.
Acknowledgments
The authors thank the Spanish Ministerio de Ciencia, Innovación y Universidades (FEDER PID2020‐118422GB−I00 and FEDER PID2019‐104090RB‐100), the Basque Government (Grupos IT1558‐22 and postdoctoral contract to J.L.‐B.) and the Regional Government of Aragon (Grupos 17R‐34) for financial support. Predoctoral FPU fellowship to S.R. from the Spanish Ministry of Education and to M.P. from Government of Aragon are also acknowledged. The authors thankfully acknowledge the resources from the super‐computers “Memento” and “Cierzo”, technical expertise and assistance provided by BIFI‐ZCAM (Universidad de Zaragoza, Spain).
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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==== Front
Eur Heart J Open
Eur Heart J Open
ehjopen
European Heart Journal Open
2752-4191
Oxford University Press US
37006408
10.1093/ehjopen/oead024
oead024
Original Article
Coronary Artery Disease
AcademicSubjects/MED00200
Editor's Choice
Eurheartj/30
Eurheartj/45
Eurheartj/47
Eurheartj/15
Eurheartj/18
The association between sodium intake and coronary and carotid atherosclerosis in the general Swedish population
https://orcid.org/0000-0001-5813-5926
Wuopio Jonas Department of Neurobiology, Care Sciences and Society (NVS), Division of Family Medicine and Primary Care, Karolinska Institute, Alfred Nobels Allé 23, 141 52, Huddinge, Sweden
Center for Clinical Research Dalarna, Nissers väg 3, 791 82 Falun, Uppsala University, Sweden
https://orcid.org/0000-0002-5833-0040
Lin Yi-Ting Department of Neurobiology, Care Sciences and Society (NVS), Division of Family Medicine and Primary Care, Karolinska Institute, Alfred Nobels Allé 23, 141 52, Huddinge, Sweden
Department of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No 100, Tzou 1st Road Kaohsiung, Sanmin District, Kaoshiung City, 807, Taiwan
https://orcid.org/0000-0002-3578-2503
Orho-Melander Marju Department of Clinical Sciences, Lund University, Clinical Research Centre, Box 50332, 202 13 Malmö, Sweden
https://orcid.org/0000-0002-8618-9152
Engström Gunnar Department of Clinical Sciences, Lund University, Clinical Research Centre, Box 50332, 202 13 Malmö, Sweden
https://orcid.org/0000-0002-6933-4637
Ärnlöv Johan Department of Neurobiology, Care Sciences and Society (NVS), Division of Family Medicine and Primary Care, Karolinska Institute, Alfred Nobels Allé 23, 141 52, Huddinge, Sweden
Center for Clinical Research Dalarna, Nissers väg 3, 791 82 Falun, Uppsala University, Sweden
School of Health and Social Studies, Dalarna University, 79188 Falun, Sweden
Banach Maciej Handling Editor
The work was performed at the Department of Neurobiology, Care Sciences and Society (NVS), Family Medicine and Primary Care Unit, Karolinska Institute, Huddinge, Sweden And Center for Clinical Research Dalarna, Uppsala University, Sweden
Editorial for this article: Eur Heart J Open 2023; https://doi.org/10.1093/ehjopen/oead025
Conflict of interest: J.Ä. declares that he has received lecturing fees from AstraZeneca and Novartis and has served on advisory boards for AstraZeneca and Boehringer Ingelheim, unrelated to the present study. None of the other authors have any conflicts of interest to declare.
Corresponding author. Tel: +46739322560, Email: [email protected]
3 2023
30 3 2023
30 3 2023
3 2 oead02403 9 2022
11 2 2023
08 3 2023
30 3 2023
© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.
2023
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Aims
A high intake of salt raises blood pressure and the risk of cardiovascular disease. Previous studies have reported on the association between salt intake and carotid stenosis, but the association with coronary atherosclerosis has not been reported. Therefore, this project aimed at studying the association between salt intake and both carotid and coronary atherosclerosis in a contemporary community-based cohort.
Methods and results
Estimated 24-h sodium excretion (est24hNa) was calculated by the Kawasaki formula for participants of two sites (Uppsala and Malmö) of the Swedish Cardiopulmonary bioImage Study, who underwent a coronary computed tomography (n = 9623) and measurement of coronary artery calcium score (CACS, n = 10 289). Carotid ultrasound was used to detect carotid plaques (n = 10 700). Ordered logistic regression was used to calculate odds ratios (OR) per 1000 mg increase in est24hNa. We also investigated potential J-formed associations using quintiles of est24hNa. Increased est24hNa was associated with increased occurrence of carotid plaques [OR: 1.09, P < 0.001, confidence interval (CI): 1.06–1.12], higher CACS (OR: 1.16, P < 0.001, CI: 1.12–1.19), and coronary artery stenosis (OR: 1.17, P < 0.001, CI: 1.13–1.20) in minimal adjusted models. Associations were abolished when adjusting for blood pressure. When adjusting for established cardiovascular risk factors (not including blood pressure), associations remained for carotid plaques but not for coronary atherosclerosis. There was no evidence of J-formed associations.
Conclusion
Higher est24hNa was associated with both coronary and carotid atherosclerosis in minimal adjusted models. The association seemed mainly mediated by blood pressure but to some degree also influenced by other established cardiovascular risk factors.
Graphical Abstract
Graphical Abstract
Sodium
Salt
Hypertension
Cardiovascular disease
Atherosclerosis
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pmcIntroduction
Previous studies have concluded that a diet with a high intake of salt raises blood pressure1–5 and the risk for cardiovascular disease (CVD)6,7 and mortality.8,9 Efforts to favour behavioural changes to reduce salt intake are effective10 and have been shown to reduce blood pressure.11 The underlying pathological processes that cause CVD starts decades prior to clinically overt disease,12 and a better understanding of these processes in order to improve disease prevention could have a major public health impact. Several previous studies have reported association between salt intake with various markers for vascular damage such as arterial stiffness, endothelial dysfunction, and inflammation.13,14 Some have investigated associations with manifest peripheral atherosclerosis,15–17 but whether salt intake associates with coronary atherosclerosis has not been reported previously. The Swedish Cardiopulmonary bioImage Study (SCAPIS) is a unique cohort from the general population in Sweden with detailed characterization of atherosclerosis in the coronary arteries as well as in the carotid arteries.18 We therefore aimed at studying the association between estimated sodium intake and both carotid and coronary atherosclerosis in this cohort. As a second step, we also investigated these associations in individuals with normal blood pressure or without known atherosclerotic CVD.
Method
We used the Swedish cohort SCAPIS,18 a population-based study of 30 154 participants in the age range of 50–64 years. The participants were recruited through mail invitations sent out to a random sample within the target population. Each participant visited the screening centre during 2–3 days, and the examinations included blood and urine samples, questionnaires, clinical examinations, and computed tomography imaging. The biological samples were collected at the time of examination and were frozen in −80°C until analysis. The study is being conducted at six university hospitals with approximately 5000 participants in each node. There was a possibility for each node to add on investigations to the core program, making use of the available extensive infrastructure. The baseline investigations were completed in 2018. We used the Uppsala (n = 5033) and Malmö (n = 6140) nodes for this study where we had analysed urinary sodium and creatinine in addition to the core protocol (Figure 1).
Figure 1 CONSORT flow chart of the included and excluded participants from Swedish Cardiopulmonary bioImage Study.
The SCAPIS-multicentre study was approved by the ethical review board at Umeå University, Sweden (number 2010–228–31 M), and the analysis of urine samples was approved by the ethical boards at Uppsala University and Lund University, Sweden (number EPN Uppsala University 2016/387 and 2018/315; Lund university 2016/1031).
Coronary atherosclerosis
The coronary atherosclerotic burden was measured with coronary computed tomography angiography (CCTA) and coronary artery calcium score (CACS) according to Agatston (Somatom Definition Flash, Siemens Medical Solutions). Details regarding cardiac imaging have previously been described.18
Individual readers assessed 18 segments of coronary arteries for stenosis. The degree of luminal obstruction in the artery was judged visually as above or below 50%. We defined the finding of a stent in a vessel or coronary artery bypass graft (CABG) as a stenosis significant stenosis >50%. ‘Calcium blooming’, where dense calcifications made it impossible for the reader to evaluate, was defined as a non-significant stenosis <50%. Missing values, for example, due to technical problems, were excluded. The outcome was defined as categorical ordinal values of ‘no stenosis’, ‘non-significant stenosis (<50%)’, and ‘significant stenosis (>50%)’.
Non-contrast enhanced images were used to measure the total amount of calcifications in each artery and were summed to a total CACS according to international standards.19 We divided the sum from CACS into five categories usually used in clinical practise (0, 1–9, 10–99, 100–399, and >399).
Carotid atherosclerosis
The carotid arteries of the participants were examined with ultrasound (Siemens Acuson S2000 ultrasound scanner equipped with a 9L4 linear transducer) at inclusion, and the images were examined online. Several different operators performed the examination at each site. A significant plaque was defined as a focal protrusion20 of >50% or 0.5 mm of the surrounding intima media thickness or a thickness >1.5 mm measured from the intima–lumen interface to the media–adventitia interface. The result was provided as categorical ordinal values of ‘no plaque’, ‘plaque in one vessel’, and ‘plaque in both vessels’. Participants with missing values were excluded from this part of the study.
Blood pressure
Systolic and diastolic brachial artery blood pressure was obtained by automatic measurements in the supine position after a 5-min rest. The average value of two measurements from the arm with the highest mean blood pressure was used. Use of antihypertensive medication during the last 2 weeks was self-reported in questionnaires from the examination.
Estimation of sodium intake
The urinary samples were collected as a second morning void at the time of examination. Sodium intake was estimated by calculating the estimated 24-h sodium excretion (est24hNa) through the Kawasaki formula.19
est24hNa(mg/day)=22.99×16.3XNa
where
XNa=SMUNaSMUCr×(PreCr−excretion)
SMUNa (mmol/L),
SMUCr (mg/L),
Male: PreCr-excretion (mg/day) = −12.63 × age + 15.12 × weight + 7.39 × height (cm) − 79.9,
Female: PreCr-excretion (mg/day) = −4.72 × age + 8.58 × weight + 5.09 × height (cm) − 74.5.
To minimize the impact of outliers, we excluded participants with an est24hNa deviating by more than five standard deviations from the mean.
As a validation of the result from the Kawasaki formula, we compared quintiles of est24hNa with sodium calculated from a food frequency questionnaire.21
Statistical analysis
Participants with missing values for urinary analyses (sodium and/or creatinine) were excluded (n = 395) (Figure 1). One participant had missing value for weight that was imputed using the median value prior to calculating est24hNa with the Kawasaki formula. Missing values for covariates were assumed to be missing at random and imputed using multiple imputation with 20 imputations and the result pooled according to ‘Rubin’s rule’.22 For the categorical data (e.g. smoking, diabetes mellitus, and ‘on antihypertensive medication’), we used multiple imputation with a ordered logistic regression model for smoking and a multinomial logistic regression model for diabetes mellitus an antihypertensive medication. For the continuous data, we used a linear regression model. For participants with missing values for diabetes (from the questionnaire), a valid measurement of haemoglobin A1c (HbA1c) ≥ 48 mmol/L or fasting p-glucose ≥ 7 mmol/L was classified as having diabetes mellitus (n = 14).
In our primary analyses, we modelled est24hNa as a continuous variable (expressed per 1000 mg increase). However, since some previous studies have detected a J-formed curve, showing an association with increasing risk for CVD in both high and low intake of salt, we also divided the sample into quintiles of est24hNa and compared the atherosclerosis measures in the lowest quintile to the others.
We used regression analyses to study the association between systolic and diastolic blood pressure and est24hNa. Since the data for the categories of CACS (cCACS), carotid plaque, and CCTA are ordinal, we used ordered logistic regression analysis to analyse association between est24hNa and the different levels of manifest atherosclerosis. Multivariable adjustments were made in three models:
A. Site (node where participants were investigated, Uppsala or Malmö).
B. Site + age + sex.
C. Site + age + sex + (resting systolic blood pressure + resting diastolic blood pressure + self-reported hypertension medication)—as we made the a priori assumption that blood pressure levels would be the most important factor mediating associations between salt intake and atherosclerosis.
As an extra analysis, we added a multivariable adjustment (Model D) to Model B to investigate the impact from potential confounders/mediators other than blood pressure.
D. Body mass index (BMI = weight/length2), total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), diabetes mellitus (doctor diagnosed or self-reported), smoking (current, ex-smoker, and never), and estimated glomerular filtration rate (eGFR calculated from the Lund–Malmö formula).
We performed interaction analyses for site, age, and sex and also stratified for sex since previous studies have reported sex-specific differences in salt intake and cardiovascular risk.23–25
To further study the effect of pre-existing diseases and medications, we did three subgroup analyses. First, we excluded participants with hypertension (systolic blood pressure >140 mmHg, diastolic blood pressure >90 mmHg, self-reported antihypertensive medication, and self-reported diagnosis of hypertension). Second, we excluded participants with known atherosclerosis [self-reported CABG or percutaneous coronary intervention (PCI), myocardial infarction, peripheral artery disease, and stroke]. Third, we excluded participants with antihypertensive medications and medications for heart failure to rule out possible effect of diuretics on the results.
STATA (StataCorp LLC) version 17 was used to perform the analyses.
Results
A total of 10 778 participants with valid results for the urinary data were included in the study. Fourteen participants deviated more than five SD from the mean of est24hNa and were excluded, leaving a total of 10 764 participants for further analysis (Figure 1). Baseline characteristics of the participants are presented in Table 1 and Table 2. Women had a lower est24hNa compared to men (mean value 2925 mg vs. 3642 mg; P < 0.001). Systolic and diastolic blood pressure, BMI, prevalence of diabetes mellitus, HDL, and eGFR all increased, and cholesterol decreased with increasing quintiles of est24hNa (Table 1). There was a positive, significant association between est24hNa and systolic blood pressure (Model A, coefficient 1.74, P < 0.001) and diastolic blood pressure (Model A, coefficient 0.92, P < 0.001).
Table 1 Baseline characteristics of the participants in quintiles (Q1–Q5) of increasing estimated 24-h sodium excretion with standard deviations (n = 10 764)
Total Q1 Q2 Q3 Q4 Q5
est24hNa (mg/24 h)
3267 (1314) 1640 (425) 2507 (185) 3131 (185) 3822 (232) 5235 (951)
Women (n = 5627) 2925 (1175) 1631 (426) 2496 (185) 3120 (187) 3804 (229) 5130 (850)
Men (n = 5137) 3642 (1355) 1663 (422) 2524 (185) 3145 (183) 3837 (233) 5284 (990)
Age (years) 57.6 57.6 57.6 57.6 57.5 57.5
Systolic blood pressure (mmHg) 124 (16) 122 (16) 123 (16) 124 (16) 126 (16) 128 (16)
Diastolic blood pressure (mmHg) 76 (10) 75 (10) 75 (10) 76 (10) 77 (10) 78 (10)
Hypertension % 30.6 28.5 28.5 27.3 30.9 37.9
BMI (kg/m2) 27.2 (4.5) 26.0 (4.3) 26.6 (4.4) 26.9 (4.3) 27.6 (4.3) 28.6 (4.8)
Cholesterol (mmol/L) 5.6 (1.1) 5.7 (1.1) 5.6 (1.1) 5.6 (1.0) 5.5 (1.0) 5.4 (1.1)
LDL (mmol/L) 3.6 (0.9) 3.6 (1.0) 3.6 (0.9) 3.6 (1.0) 3.6 (0.9) 3.5 (0.9)
HDL (mmol/L) 1.6 (0.5) 1.7 (0.5) 1.6 (0.5) 1.6 (0.5) 1.5 (0.4) 1.5 (0.4)
eGFR (mL/min/1.73 m2) 78 (10) 76 (10) 77 (10) 78 (10) 78 (10) 80 (10)
Diabetes mellitus (yes %) 5.1 4.3 4.9 5.2 4.5 6.7
Smoking status (%)
Never 48 52 56 54 52
Former 37 34 32 34 35
Current 15 14 12 12 13
Table 2 Estimated 24-h sodium excretion in the different groups of atherosclerosis [mean (standard deviation)]
est24hNa (mg/24 h)
Carotid plaques
None 3178 (1340)
One 3292 (1340)
Two 3365 (1362)
CACS
0 3167 (1229)
1–9 3341 (1352)
10–99 3355 (1351)
100–399 3527 (1502)
>399 3566 (1533)
Coronary stenosis
None 3164 (1220)
<50% 3380 (1383)
>50% 3574 (1450)
Carotid plaques
A total of 10 700 participants had valid results for the carotid ultrasound (64 participants excluded due to missing values) (Figure 1). The odds ratio (OR) increased with increasing quintile of est24hNa with no sign of a J-formed association (see Supplementary material online, Table S1). There was a significant association between higher est24hNa and burden of carotid plaques in the minimal adjusted Model A (site) (OR: 1.09, P < 0.001) and Model B (site, age, and sex) (OR: 1.03, P < 0.001). When further adjusting for blood pressure (Model C), the significant association was abolished (Table 3).
Table 3 The odds ratio for the association between estimated 24-h sodium excretion (per 1000 mg) and finding of atherosclerosis (95% confidence intervals and P-values)
Study Model A P Model B P Model C P Model D to Model B P
Carotid plaquesa 1.09 (1.06–1.12) <0.001 1.03 (1.00–1.06) 0.028 1.00 (0.98–1.03) 0.815 1.04 (1.01–1.07) 0.017
CACSb 1.16 (1.12–1.19) <0.001 1.04 (1.01–1.07) 0.016 1.01 (0.98–1.04) 0.663 1.01 (0.98–1.04) 0.643
Coronary artery stenosisc 1.17 (1.13–1.20) <0.001 1.04 (1.01–1.07) 0.020 1.01 (0.97–1.04) 0.750 1.01 (0.98–1.04) 0.643
a Findings of carotid plaques with ultrasound in ‘none’, ‘one’, or ‘two’ carotid vessels.
b Coronary Artery Calcification Score divided into groups 0, 1–9, 10–99, 100–399, and >399.
c Findings of coronary artery stenosis (lumen obstruction >50%, <50%, no stenosis) with coronary computed tomography angiography.
When adding established cardiovascular risk factor (without blood pressure) to Model B (Model D), the association between carotid plaques and est24hNa remained statistically significant (OR: 1.04; CI: 1.01–1.07; P = 0.017).
Coronary artery calcium score
A total of 10 289 participants had valid results for measurement of CACS (n = 475 excluded) (Figure 1). There was no sign of a J-formed association (see Supplementary material online, Table S1) between the est24hNa and cCACS. A significant association between est24hNa and cCACS was seen in the minimal adjusted Model A (OR: 1.16, P < 0.001) as well as Model B (OR: 1.038, P = 0.016). When adjusting for blood pressure (Model C), the significant association was abolished (Table 3).
When adding established cardiovascular risk factor (without blood pressure) to Model B (Model D), the association between est24hNa and cCACS was lost (OR: 1.01; CI: 0.98–1.04; P = 0.643).
Coronary artery stenosis
A total of 1141 participants were excluded due to missing values in the readings of the CCTA images (included n = 9623) (Figure 1). The OR in the quintiles of est24hNa showed a linear pattern with no sign of a J-formed association (see Supplementary material online, Table S1). There was a significant association between higher est24hNa and risk of coronary artery stenosis in the minimal adjusted Model A (OR: 1.17, P < 0.001) and Model B (OR: 1.04, P = 0.020). After adjusting for blood pressure (Model C), the association was abolished (Table 3).
When adding established cardiovascular risk factor (without blood pressure) to Model B (Model D), there was no longer an association between est24hNa and coronary artery stenosis (OR: 1.01; CI: 0.98–1.04; P = 0.643).
Stratified and subgroup analyses
Associations between est24hNa and carotid plaques, CACS, and coronary artery stenosis were similar as the original result when excluding participants with antihypertensive medication or medication for heart failure (n = 2106; OR: 1.08, 1.15, and 1.15, respectively; P < 0.001), hypertension (n = 3404; OR: 1.07, 1.16, and 1.16, respectively; P < 0.001) or known pre-existing atherosclerotic disease (n = 356; OR: 1.09, 1.16, and 1.17, respectively; P < 0.001).
When the analyses were stratified with respect to sex, the significant associations between est24hNa and carotid plaques, cCACS, and coronary artery stenosis were only seen among men in Models A and B (see Supplementary material online, Table S2), but there was no statistically significant effect modification by sex (P > 0.13 for all outcomes).
The association between est24hNa and the different indices of carotid and coronary atherosclerosis was similar when only including participants that had data on all three indices (n = 9623, data not shown).
We noticed minor violations against the proportional odds assumption for a few covariates in the separate studies. We did a sensitivity analysis using a generalized ordered logistic regression model that did not alter the results.
Discussion
Main findings
The main finding from this study is that increased sodium excretion had a significant association with carotid atherosclerosis as well as atherosclerotic stenosis in the coronary arteries and overall coronary artery calcification reflected by CACS in minimal adjusted models (site, age, and sex). As the association was abolished when adjusting for blood pressure, our interpretation is that the increase in blood pressure from sodium intake, even below the level that currently defines arterial hypertension, is an important factor that mediates the interplay between salt intake and the atherosclerotic process. As we observed an association in individuals with normal blood pressure, one possible explanation for these findings is that the detrimental pathological processes begin already prior to the development of hypertension. However, due to the observational, cross-sectional design of the study, no causal relationships can be established. Adjusting the result for several other well-known risk factors for arteriosclerosis (adding Model D to Model B) also abolished the results for CACS and coronary artery stenosis, indicating confounding or other pathways salt mediates its harmful effects. Interestingly, this was not the case for carotid plaques, which still yielded a significant result after multifactor adjustment. Stratifying for sex showed higher est24hNa among men, and the associations were only seen among men (although there was no significant interaction by sex).
Comparison with previous studies
Previous epidemiological studies have led to the consensus that a high intake of salt increases the risk for atherosclerosis,26 which is in line with the results from this study. Yet, as far as we know, this is the first study reporting the association between sodium excretion and sub-clinical coronary atherosclerosis measured with computed tomography in a general population. Since some previous epidemiological studies and meta-analyses have suggested a J-formed curve,8,25,27–34 where the cardiovascular risk also increases with a low salt intake, a heated debate has taken place in the last decade.35–37 However, we did not find evidence of a J-formed association in this study. The lowest occurrence of atherosclerosis, both carotid and coronary, was found in the lowest quintiles of sodium excretion (Q1) and then increasing in a linear fashion.
Three previous studies have addressed the question of carotid atherosclerosis and salt intake or sodium excretion. Dai et al.38 found a positive association between findings of carotid stenosis and increasing urinary–sodium–creatinine ratio in 3290 healthy adults. Mazza et al.17 found a positive association between sodium intake (combination of 7-day register and 24-h recall) and carotid atherosclerosis in 108 elderly women. Both these studies are in line with our results. In a study by Tsirimiagkou et al. in 2021,16 the probability of finding femoral plaques showed a reverse association to salt intake (24-h recall) among women but not in men. Similar results were found for carotid plaques, although not statistically significant. This contrasts with our results where we found a positive association to carotid plaques, particularly in men. In the same study, measured arterial stiffness seemed positively associated with salt intake. The authors argue that there may be different pathological processes for low and high salt intake, explaining their apparently divergent findings.
Biological explanations
There is a consensus that a high salt intake raises the blood pressure.36 The reason behind this is not fully known in its details, but theories include altered salt handling by the kidneys, vascular dysfunction, sympathetic nervous dysfunction, and immune system involvement.39 The association between high blood pressure and the atherosclerotic process is well-established, and high blood pressure is considered the leading cause of the development of CVD.40 Salt’s ability to raise blood pressure is the most commonly used explanation of how salt exerts its pathological processes on our vessels, and in this study, the blood pressure seemed to be the most important mediating factor to the association between salt intake and atherosclerosis. But other studies have concluded that salt may have a direct blood pressure-independent detrimental effect on our vessels, leading to increased arterial stiffness and endothelial dysfunction.14,41 These findings could explain the results from other epidemiological studies where the association between salt intake and CVD remains significant after careful adjustments for blood pressure. The reason for this is not fully understood, but reduced availability to nitric oxide through the production of reactive oxygen species as a response to salt intake could explain endothelial dysfunction induced by salt loading.41 Independent of blood pressure, salt intake can induce cascades of molecular signalling, ending up in the production of transforming growth factor β (TGF-β) that promotes reduced vascular compliance. Nitric oxide also serves as negative feedback of TGF-β.42 Evidence also suggests damaging of the glycocalyx of extracellular matrix, initiating cell stiffening.14 Residual confounding due to the inherent variability of blood pressure levels over time may also be an explanation.
When we included several known risk factors for CVD in the multivariable model (but not blood pressure), there was still a significant association between salt intake and carotid, but not coronary arteriosclerosis. The explanation for these divergent findings could be that the female participants have a higher prevalence of arteriosclerosis in the carotids than in the coronary arteries, more comparable to men, so that adjustment for sex does not have such a pronounced effect in the analyses using carotid plaques as the outcome. The reason for this is not clear since the coronary and carotid atherosclerotic processes share the same pathological processes and risk factors,43 and as far as we know, this should not differ between sexes. One speculation could be of technical reason since the carotids were investigated by ultrasound.
There are, however, possible explanations for the overall sex difference. For example, evidence suggests that women handle sodium load differently from men by a more active renal endothelin-1 natriuretic system,44 which gives a greater ability to maintain sodium homeostasis. There are also well-known sex differences in atherosclerotic burden, where women have a known lower prevalence, possibly due to protective effect of oestrogen before menopause.45 This has also previously been reported in SCAPIS.46
Strengths and limitations
The greatest strength of this study is the contemporary cohort with detailed characterization of the study participants. The measurements of coronary atherosclerosis and carotid plaques are state-of-the-art, and considering this, the number of participants is high.
A limitation is that we used the Kawasaki formula to estimate 24-h sodium excretion from spot urinary sodium samples. The Kawasaki formula has been not only validated47,48 but also criticized.37,49 In the debate on whether a J-formed curve between sodium intake and cardiovascular risk truly exists, the Kawasaki formula has often been blamed for creating biased results.36,50,51 One popular theory has been that the constituent variables in the formulae by itself have associations to the cardiovascular outcome and therefore create the J-curve.36 It is, however, hardly proved. When the participants in this study are divided into quintiles according to est24hNa, the blood pressure increases with increasing salt intake (Table 1). The same goes for BMI, which has an established positive association with salt intake52–54 (Table 1). We also used dietary sodium data from food frequency questionnaires to plot against est24hNa. The result confirmed that reported sodium intake was increasingly higher from the lowest to the highest quintile of est24hNa (see Supplementary material online, Figure S1). Also, we did not find any J-formed curve in this material rendering it unlikely that the formula itself has caused the J-formed associations in previous studies. We, therefore, believe that the Kawasaki formula is good enough to estimate salt intake at a population level. However, we do not think the Kawasaki formula is accurate enough to conclude ‘safe levels’ of salt intake in milligrams on an individual level.
Other limitations include unknown generalizability to other populations, residual confounding, and the observational, cross-sectional design of the study where causal relationships cannot be established.
Conclusion
In this study from the general Swedish population, est24hNa was associated to both cardiac and carotid atherosclerosis in minimal adjusted models, even in participants with normal blood pressure and without known CVD. The association seemed mainly mediated by blood pressure (even in the normal range) but to some degree also influenced by other established risk factors.
Ethical statement
The SCAPIS-multicentre study was approved by the ethical review board at Umeå University, Sweden (number 2010–228–31 M), and the add-on collection of urine was approved by the ethical board at Uppsala University and Lund University, Sweden (number EPN Uppsala University 2016/387 and 2018/315; Lund University 2016/1031).
Supplementary Material
oead024_Supplementary_Data Click here for additional data file.
Acknowledgements
The main funding body of The Swedish CArdioPulmonary bioImage Study (SCAPIS) is the Swedish Heart-Lung Foundation. The study is also funded by the Knut and Alice Wallenberg Foundation, the Swedish Research Council and VINNOVA (Sweden’s Innovation agency), the University of Gothenburg and Sahlgrenska University Hospital, Karolinska Institutet and Stockholm County Council, Linköping University and University Hospital, Lund University and Skåne University Hospital, Umeå University and University Hospital, and Uppsala University and University Hospital.
Lead author biography
Jonas Wuopio is a specialist in internal medicine, Mora county hospital, Sweden, and a doctoral researcher at Karolinska Institute, Department of Neurobiology and Care Sciences and Clinical Research Center, Falun, Sweden, Uppsala University. Research interest includes molecular epidemiology, markers for predicting future disease, and the relationship between sodium and the atherosclerotic process.
Data availability
The personal data in SCAPIS are of sensitive nature and therefore cannot be made freely available. However, by contacting the corresponding author or study organization (www.scapis.org), sharing of data can be arranged for reproducing study results and procedures.
Supplementary material
Supplementary material is available at European Heart Journal Open online.
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23 Mill JG , BaldoMP, MolinaM, SchmidtMI, BarretoSM, ChorD, GriepRH, MatosSM, RibeiroALP, DuncanBB, AquinoEM, LotufoPA, BensenorI. Sex-specific patterns in the association between salt intake and blood pressure: the ELSA-Brasil study. J Clin Hypertens (Greenwich) 2019;21 :502–509.30861624
24 Mozaffarian D , FahimiS, SinghGM, MichaR, KhatibzadehS, EngellRE, LimS, DanaeiG, EzzatiM, PowlesJ. Global sodium consumption and death from cardiovascular causes. N Engl J Med 2014;371 :624–634.25119608
25 Wuopio J , Orho-MelanderM, ÄrnlövJ, NowakC. Estimated salt intake and risk of atrial fibrillation in a prospective community-based cohort. J Intern Med 2021;289 :700–708.33210391
26 Cook NR , HeFJ, MacGregorGA, GraudalN. Sodium and health-concordance and controversy. Bmj 2020;369 :m2440.
27 O'Donnell M , MenteA, RangarajanS, McQueenMJ, WangX, LiuL, YanH, LeeSF, MonyP, DevanathA, RosengrenA, Lopez-JaramilloP, DiazR, AvezumA, LanasF, YusoffK, IqbalR, IlowR, MohammadifardN, GulecS, YusufaliAH, KrugerL, YusufR, ChifambaJ, KabaliC, DagenaisG, LearSA, TeoK, YusufS. Urinary sodium and potassium excretion, mortality, and cardiovascular events. N Engl J Med 2014;371 :612–623.
28 Stolarz-Skrzypek K , KuznetsovaT, ThijsL, TikhonoffV, SeidlerováJ, RichartT, JinY, OlszaneckaA, MalyutinaS, CasigliaE, FilipovskýJ, Kawecka-JaszczK, NikitinY, StaessenJA. Fatal and nonfatal outcomes, incidence of hypertension, and blood pressure changes in relation to urinary sodium excretion. Jama 2011;305 :1777–1785.21540421
29 Ekinci EI , ClarkeS, ThomasMC, MoranJL, CheongK, MacIsaacRJ, JerumsG. Dietary salt intake and mortality in patients with type 2 diabetes. Diabetes Care 2011;34 :703–709.21289228
30 Thomas MC , MoranJ, ForsblomC, HarjutsaloV, ThornL, AholaA, WadénJ, TolonenN, SaraheimoM, GordinD, GroopPH. The association between dietary sodium intake, ESRD, and all-cause mortality in patients with type 1 diabetes. Diabetes Care 2011;34 :861–866.21307382
31 O'Donnell MJ , YusufS, MenteA, GaoP, MannJF, TeoK, McQueenM, SleightP, SharmaAM, DansA, ProbstfieldJ, SchmiederetRE. Urinary sodium and potassium excretion and risk of cardiovascular events. Jama 2011;306 :2229–2238.22110105
32 Judge C , O'DonnellMJ, HankeyGJ, RangarajanS, ChinSL, Rao-MelaciniP, FergusonJ, SmythA, XavierD, LishengL, ZhangH, Lopez-JaramilloP, DamascenoA, LanghorneP, RosengrenA, DansAL, ElsayedA, AvezumA, MondoC, RyglewiczD, CzlonkowskaA, PogosovaN, WeimarC, DiazR, YusoffK, YusufaliA, OguzA, WangX, LanasF, OgahOS, OgunniyiA, IversenHK, MalagaG, RumboldtZ, OveisgharanS, HussainFA, YusufS. Urinary sodium and potassium, and risk of ischemic and hemorrhagic stroke (INTERSTROKE): a case-control study. Am J Hypertens 2021;34 :414–425.33197265
33 Mente A , O'DonnellM, RangarajanS, DagenaisG, LearS, McQueenM, DiazR, AvezumA, Lopez-JaramilloA, LanasF, LiW, LuY, YiS, RenshengL, IqbalR, MonyP, YusufR, YusoffK, SzubaA, OguzA, RosengrenA, BahonarA, YusufaliA, SchutteAE, ChifambaJ, MannJFE, AnandSS, TeoK, YusufS. Associations of urinary sodium excretion with cardiovascular events in individuals with and without hypertension: a pooled analysis of data from four studies. Lancet 2016;388 :465–475.27216139
34 Groenland EH , VendevilleJP, BotsML, de BorstGJ, NathoeHM, RuigrokYM, BlankestijnPJ, VisserenFLJ, SpieringW. The relation between urinary sodium and potassium excretion and risk of cardiovascular events and mortality in patients with cardiovascular disease. Plos One 2022;17 .
35 O'Donnell M , MenteA, AldermanMH, BradyAJB, DiazR, GuptaR, López-JaramilloP, LuftFC, LüscherTF, ManciaG, MannJFE, McCarronD, McKeeM, MesserliFH, MooreLL, NarulaJ, OparilS, PackerM, PrabhakaranD, SchutteA, SliwaK, StaessenJA, YancyC, YusufS. Salt and cardiovascular disease: insufficient evidence to recommend low sodium intake. Eur Heart J 2020;41 :3363–3373.33011774
36 Cappuccio FP , CampbellNRC, HeFJ, JacobsonMF, MacGregorGA, AntmanE, AppelLJ, ArcandJ, Blanco-MetzlerA, CookNR, GuichonJR, L'AbbèMR, LacklandDT, LangT, McLeanRM, MiglinasM, MitchellI, SacksFM, SeverPS, StampferM, StrazzulloP, SunmanW, WebsterJ, WheltonPK, WillettW. Sodium and health: old myths and a controversy based on denial. Curr Nutr Rep 2022.
37 Cappuccio FP , BeerM, StrazzulloP. Population dietary salt reduction and the risk of cardiovascular disease. A scientific statement from the European Salt Action Network. Nutr Metab Cardiovasc Dis 2018;29 :107–114.30583888
38 Dai XW , WangC, XuY, GuanK, SuYX, ChenYM. Urinary sodium and potassium excretion and carotid atherosclerosis in Chinese men and women. Nutrients 2016;8 .
39 Ellison DH , WellingP. Insights into salt handling and blood pressure. N Engl J Med 2021;385 :1981–1993.34788509
40 Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease study 2019. Lancet 2020;396 :1223–1249.33069327
41 Farquhar WB , EdwardsDG, JurkovitzCT, WeintraubWS. Dietary sodium and health: more than just blood pressure. J Am Coll Cardiol 2015;65 :1042–1050.25766952
42 Sanders PW . Vascular consequences of dietary salt intake. Am J Physiol Renal Physiol 2009;297 :F237–F243.19339634
43 Jashari F , IbrahimiP, NicollR, BajraktariG, WesterP, HeneinMY. Coronary and carotid atherosclerosis: similarities and differences. Atherosclerosis 2013;227 :193–200.23218802
44 Gohar EY , De MiguelC, ObiIE, DaughertyEM, HyndmanKA, BeckerBK, JinC, SedakaR, JohnstonJG, LiuP, SpeedJS, MitchellT, KriegelAJ, PollockJS, PollockDM. Acclimation to a high-salt diet is sex dependent. J Am Heart Assoc 2022;11 :e020450.
45 Trivedi B , DesaiR, MishraK, HechanovaLA, AbolbashariM. Role of sex in atherosclerosis: does sex matter? Curr Cardiol Rep 2022.
46 Bergström G , PerssonM, AdielsM, BjörnsonE, BonanderC, AhlströmH, AlfredssonJ, AngeråsO, BerglundG, BlombergA, BrandbergJ, BörjessonM, CederlundK, FairedF, DuvernoyO, EkblomÖ, EngströmG, EngvallJE, FagmanE, ErikssonM, ErlingeD, Björn FagerbergB, Agneta FlinckA, GonçalvesI, HagströmE, HjelmgrenO, LindL, LindbergE, LindqvistP, LjungbergJ, MagnussonM, MannilaM, MarkstadH, MohammadMA, NystromFH, OstenfeldE, PerssonA, RosengrenA, SandströmA, SjälanderA, SköldMC, SundströmJ, SwahnE, SöderbergS, TorénK, ÖstgrenCJ, JernbergT. Prevalence of subclinical coronary artery atherosclerosis in the general population. Circulation 2021;144 :916–929.34543072
47 Mente A , O'DonnellMJ, DagenaisG, WielgoszA, LearSA, McQueenMJ, JiangY, XingyuW, JianB, CalikKBT, AkalinAA, MonyP, DevanathA, YusufaliAH, Lopez-JaramilloP, Avezum JrA, YusoffK, RosengrenA, KrugerL, OrlandiniA, RangarajanA, TeoK, YusufetS. Validation and comparison of three formulae to estimate sodium and potassium excretion from a single morning fasting urine compared to 24-h measures in 11 countries. J Hypertens 2014;32 :1005–1014. discussion 15.24569420
48 Kawasaki T , ItohK, UezonoK, SasakiH. A simple method for estimating 24 h urinary sodium and potassium excretion from second morning voiding urine specimen in adults. Clin Exp Pharmacol Physiol 1993;20 :7–14.8432042
49 Tan M , HeFJ, MacGregorGA. Salt and cardiovascular disease in PURE: a large sample size cannot make up for erroneous estimations. J Renin Angiotensin Aldosterone Syst 2018;19 :1470320318810015.
50 Campbell NRC . Estimated salt intake and risk of atrial fibrillation in a prospective community-based cohort. J Intern Med 2021;289 :591–592.33277715
51 He FJ , CampbellNRC, MaY, MacGregorGA, CogswellME, CookNR. Errors in estimating usual sodium intake by the Kawasaki formula alter its relationship with mortality: implications for public health. Int J Epidemiol 2018;47 :1784–1795.30517688
52 Sallehuddin SM , AmbakR, OthmanF, AzizNSA, PalanivelooL, NorNSM, JamaluddinR, BaharudinA, ZakiNAM, AhmadetMH. Sodium intake assessed by 24-h urine excretion and its relationship with anthropometric measurements in Malaysian adults. J Health Popul Nutr 2021;40 :9.34059153
53 Crouch SH , WareLJ, Gafane-MatemaneLF, KrugerHS, Van ZylT, Van der WesthuizenB, SchutteAE. Dietary sodium intake and its relationship to adiposity in young black and white adults: the African-PREDICT study. J Clin Hypertens (Greenwich) 2018;20 :1193–1202.29961983
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PMC010xxxxxx/PMC10082235.txt |
==== Front
Cardiovasc Endocrinol Metab
Cardiovasc Endocrinol Metab
CAEN
Cardiovascular Endocrinology & Metabolism
2574-0954
Wolters Kluwer Health
00001
10.1097/XCE.0000000000000283
3
Correspondence
Enhancing type 2 diabetes treatment through digital plans of care. Patterns of access to a care-planning app over the first 3 months of a digital health intervention
Heald Adrian H. ab
Roberts Sarah c
Gimeno Lucia Albeda c
Gibson John Martin ab
Saboo Anuj c
Abraham Jonathan c
a Department of Diabetes and Endocrinology, Salford Royal Hospital, Salford
b The School of Medicine and Manchester Academic Health Sciences Centre, University of Manchester, Manchester
c Healum, London, UK
Correspondence to Adrian H. Heald, DM, Department of Diabetes and Endocrinology, Salford Royal Hospital, Salford, M6 8HD UK, e-mail: [email protected]
05 4 2023
6 2023
12 2 e028323 11 2022
16 3 2023
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
OPEN-ACCESSTRUE
==== Body
pmcThe increasing digitalization of healthcare brings opportunities to enable much greater patient access to evidence-based interventions [1,2]. A key question is the degree to which patients with long-term conditions such as type 2 diabetes (T2DM) will access an app that supports them in day-to-day management.
We recently investigated how a personalized care-planning software and patient-facing mobile app may aid people to manage their diabetes more effectively [3].
People with T2DM with glycosylated haemoglobin greater than 58 mmol/mol (7.5%) were randomized (randomized controlled trial) to either the active intervention group (usual care + app) or the control group (usual care). The intervention group received a co-created personalized care plan involving daily lifestyle prompts and access to a range of resources. Randomization did not influence other decisions about diabetes management [3,4].
The participant age range was 19×85 years. The mean age of the T2DM participants was 63.2 years. Out of a total of 203 participants, 118 (58%) were male, 68 (33.5%) were female and 17 (8.5%) did not report their sex. The treatment group (app + usual care) and control (usual care) groups constituted 114 and 89 participants respectively.
Analysis of access to the app indicated that 30% of users used the app at least 10 times in the first month of app access, dropping to 20% in the second month. Of those accessing ≥10 times in the first month, one-third of them also used it ≥10 times in the following month and 81% used it more than twice; 84% of participants accessed the app at least twice in the first month after enrolment in the study.
In the first month, the average total number of sessions was 8.06 sessions, and the average total time spent in the app was 36.60 min. App usage in the first 3 months is shown in Fig. 1a: average time spent in the app/month over the 3 months following activation (point of recruitment) and Fig. 1b shows the average app usage/ month in the 3 months after activation in terms of session number and average duration of sessions.
Fig. 1 (a) Average time spent in the app/month over the 3 months following activation (point of recruitment). (b) Average app usage/month in the 3 months after activation in terms of session number and average duration of session.
The length of time patients within the trial had been living with T2DM was between 1 and 42 years. Usage was highest in the group of app users who had been diagnosed with T2DM 11–20 years previously, spending an average total time of 54.5 min in the app in month 1 after download. There was no significant variation in app usage by sex. All users used the app in the first month following enrolment in the study (if in the intervention group arm); 47.6% used it in the second month and 31.4% used it in the third month.
Engagement with different functions within the app led to higher usage, including resources/tracking. Users who viewed between 51 and 60 resources spent 80.9 min within the app in the first month after activation, whereas users who only viewed 0–10 resources spent 19.2 min. This was also the case for app users using the tracking function within the app: specifically, the more times a user tracked something, the higher the total time they spent in the app in the first month (r2 = 0.85).
The age group who used the app the most were those aged 61–70 years old, (average total number of app sessions for these individuals over the 3-month trial = 15), the average total time spent within the app in the first month being 47.7 min. Although the average time spent within the app in the first month was lower for those patients aged 41–50 years old (15.3 min), their app usage diminished less over time.
In relation to the fact that users accessed the app less over time, it has been shown that medical apps have a 90-day retention of 34% and annual retention of 16% [5].
It was found in 2020 that 65% of those over 65 are using smartphones, an increase of 26% since 2016. While more people are using smartphones, those over 65 are still the least likely age group to have a smartphone [6]. It is also relevant to state that retrospective studies in T1DM have identified increasing age as a potential correlate of poorer engagement with diabetes technologies and worse outcomes [7].
The relation between app usage and age/duration of diabetes provides essential insights to improve content [4], so as to enhance the usage of digital support technology for diabetes/other long-term conditions, as well detailed evaluation of the patient experience. Any improvement in blood, glucose control, if sustained will have the potential to reduce cardiovascular event rate and cardiovascular mortality rate in the long term in people with T2DM [8].
Acknowledgements
This research project was funded by Innovate UK.
Conflicts of interest
There are no conflicts of interest.
==== Refs
References
1 Velardo C Shah SA Gibson O Clifford G Heneghan C Rutter H .; EDGE COPD Team. Digital health system for personalised COPD long-term management. BMC Med Inform Decis Mak 2017; 17 :19.28219430
2 Foley P Steinberg D Levine E Askew S Batch BC Puleo EM . Track: a randomized controlled trial of a digital health obesity treatment intervention for medically vulnerable primary care patients. Contemp Clin Trials 2016; 48 :12–20.26995281
3 Heald AH Gimeno LA Gilingham E Hudson L Price L Saboo A . Enhancing type 2 diabetes treatment through digital plans of care. First results from the East Cheshire Study of an app to support people in the management of type 2 diabetes. Cardiovasc Endocrinol Metab 2022; 11 :e0268.35923172
4 Zimmermann G Venkatesan A Rawlings K Scahill MD . Improved glycemic control with a digital health intervention in adults with type 2 diabetes: retrospective study. JMIR Diabetes 2021; 6 :e28033.34075880
5 https://www.apptentive.com/blog/healthcare-apps-2021-engagement-benchmarks/. [Accessed 24 February 2023].
6 https://www.ageco.co.uk/useful-articles/money/the-best-mobile-phones-for-the-elderly/#:~:text=It%20was%20found%20in%202020,have%20grown%20up%20using%20them. [Accessed 1 March 2023].
7 McCarthy MM Grey M . Type 1 diabetes self-management from emerging adulthood through older adulthood. Diabetes Care 2018; 41 :1608–1614.29802144
8 Laiteerapong N Ham SA Gao Y Moffet HH Liu JY Huang ES . The legacy effect in type 2 diabetes: impact of early glycemic control on future complications (the diabetes & aging study). Diabetes Care 2019; 42 :416–426.30104301
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PMC010xxxxxx/PMC10108565.txt |
==== Front
Int Forum Allergy Rhinol
Int Forum Allergy Rhinol
10.1002/(ISSN)2042-6984
ALR
International Forum of Allergy & Rhinology
2042-6976
2042-6984
John Wiley and Sons Inc. Hoboken
35730163
10.1002/alr.23044
ALR23044
Viewpoint
Viewpoint
Indications for absorbable steroid‐eluting sinus implants: Viewpoint via the Delphi method
LEE et al.
Viewpoint on Steroid‐Eluting Sinus Implants
Lee Victoria S. MD https://orcid.org/0000-0003-3221-126X
1 [email protected]
Patel Prayag MD 2
O'Brien Daniel MD https://orcid.org/0000-0002-8394-9902
3
Scangas George A. MD 4 5
Campbell Raewyn G. MD 6 7
Chandra Rakesh MD, MMHC 8
Davis Greg E. MD, MPH 9
Han Joseph K MD 10
Le Christopher H. MD 11
Lee Jivianne MD 12
Luong Amber U. MDPhD https://orcid.org/0000-0001-6078-8010
13
Poetker David M. MDMA 14
Ramadan Hassan MDMSc 15
Setzen Michael MD 16
Smith Kristine MD https://orcid.org/0000-0002-7026-5819
17
Wise Sarah MD, MSCR 18
Villwock Jennifer MD https://orcid.org/0000-0001-5645-4210
19
Ference Elisabeth MD, MPH https://orcid.org/0000-0002-4177-6716
20
1 Department of Otolaryngology – Head and Neck Surgery University of Illinois Chicago Chicago Illinois USA
2 Department of Otolaryngology – Head and Neck Surgery Rutgers New Jersey Medical School Newark New Jersey USA
3 Division of Otolaryngology – Head and Neck Surgery University of Alberta Edmonton Alberta Canada
4 Department of Otolaryngology‐Head and Neck Surgery Harvard Medical School Boston Massachusetts USA
5 Department of Otolaryngology Massachusetts Eye and Ear Boston Massachusetts USA
6 Department of Otolaryngology Head and Neck Surgery Macquarie University Sydney Australia
7 Department of Otolaryngology Head and Neck Surgery Royal Prince Alfred Hospital Sydney Australia
8 Department of Otolaryngology – Head and Neck Surgery Vanderbilt University Medical Center Nashville Tennessee USA
9 Proliance Surgeons Seattle and Puyallup WA Seattle Washington USA
10 Department of Otolaryngology Head and Neck Surgery Eastern Virginia Medical School Norfolk Virginia USA
11 Department of Otolaryngology – Head and Neck Surgery University of Arizona College of Medicine Tucson Arizona USA
12 Department of Head and Neck Surgery David Geffen School of Medicine at the University of California Los Angeles Los Angeles California USA
13 Department of Otorhinolaryngology – Head and Neck Surgery McGovern Medical School of the University of Texas Health Science Center at Houston Houston Texas USA
14 Department of Otolaryngology and Communication Sciences Medical College of Wisconsin Milwaukee Wisconsin USA
15 Department of Otolaryngology – Head & Neck Surgery West Virginia University Morgantown West Virginia USA
16 Michael Setzen Otolaryngology, PC, Great Neck, NY Weill Cornell Medical College New York New York USA
17 Department of Otolaryngology – Head & Neck Surgery University of Manitoba Winnipeg Manitoba Canada
18 Department of Otolaryngology‐Head and Neck Surgery Emory University Atlanta Georgia USA
19 Department of Otolaryngology‐Head and Neck Surgery University of Kansas Medical Center Kansas City Kansas USA
20 Caruso Department of Otolaryngology‐Head and Neck Surgery Keck School of Medicine of the University of Southern California Los Angeles California USA
* Correspondence
Victoria S. Lee, MD, Department of Otolaryngology – Head and Neck Surgery, University of Illinois Chicago, 1855 West Taylor Street, Room 2.42 (MC 648), Chicago, IL 60612, USA.
Email: [email protected]
05 7 2022
10 2022
12 10 10.1002/alr.v12.10 12251231
31 5 2022
17 1 2022
06 6 2022
© 2022 The Authors. International Forum of Allergy & Rhinology published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngic Allergy and American Rhinologic Society
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Abstract
Absorbable steroid‐eluting sinus implants provide targeted corticosteroid release over a sustained period and are designed to prevent both undesirable adhesion formation and sinus ostia restenosis. Here, we highlight the key evidence of these implants to date and query a group of experts via a Delphi process on the indications and optimal timing for intraoperative or in‐office placement of these implants. Six of a total of 12 statements reached consensus and were accepted. Overall, experts largely agree that intraoperative or in‐office use of steroid‐eluting stents could be considered for patients: (1) who are diabetic or intolerant of oral steroids, (2) undergoing extended frontal sinus surgery, and (3) with recurrent stenosis. Given the lack of expert consensus on other key statements, clinicians should carefully consider these treatment options on a case‐by‐case basis after shared decision‐making.
endoscopic sinus surgery
PROPEL
SINUVA
steroid‐eluting implants
source-schema-version-number2.0
cover-dateOctober 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
How to Cite this Article: Lee VS , Patel P , O'Brien D , et al. Indications for absorbable steroid‐eluting sinus implants: Viewpoint via the Delphi method. Int Forum Allergy Rhinol. 2022;12 :1225–1231. 10.1002/alr.23044 35730163
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pmc1 INTRAOPERATIVE ETHMOID SINUS USE
The first implant to reach the market was the PROPEL implant (Intersect ENT, now acquired by Medtronic, Ireland), which was approved by the US Food and Drug Administration (FDA) in 2011 for patients aged ≥18 years with chronic rhinosinusitis with nasal polyps (CRSwNPs) and chronic rhinosinusitis without nasal polyps (CRSsNPs) following endoscopic sinus surgery (ESS). 1 The implant continuously releases a total of 370 μg of mometasone furoate over 30 days with the goals of maintaining the patency of the ethmoid cavity and reducing the need for postoperative interventions. 2 Approval of this device was based on two prospective double‐blind placebo‐controlled, randomized trials using an intrapatient control design. The randomized controlled trials found a significant decrease in polyp recurrence and decreased rate of adhesions on the implant side for up to 90 days postoperatively. 3 , 4
2 INTRAOPERATIVE FRONTAL SINUS USE
Other steroid‐eluting stents include the PROPEL Mini, intended for use in the ethmoid and frontal sinuses, and the PROPEL Contour (both Intersect ENT, now acquired by Medtronic, Ireland), intended for use in the maxillary and frontal sinuses. Similar to the original implant, both the Mini and Contour stents contain 370 μg of mometasone released over 30 days and are designed for insertion in the frontal sinus opening.
The efficacy of the Mini implant placement and the PROPEL Contour in the frontal sinus opening have been assessed in a prospective double‐blind placebo‐controlled randomized trial also using an intrapatient control design. 5 , 6 A meta‐analysis of these two studies showed decreased restenosis or occlusion rates for the implant side up to 90 days postoperatively, irrespective of asthma status, previous endoscopic sinus surgery, extent of surgery, or extent of polyps. 7
3 POSTOPERATIVE USE
SINUVA (Intersect ENT, now acquired by Medtronic, Ireland) is the most recently approved steroid‐eluting sinus implant, obtaining initial approval in late 2017. 8 SINUVA, unlike the other stents, is approved in the United States as a drug rather than a device and is intended for patients with recurrent CRSwNP after prior ethmoid sinus surgery. The SINUVA implant contains 1350 μg of mometasone furoate released over 90 days and can be placed in the outpatient setting. 2 , 9 Two studies examined candidates for revision ESS who were randomized to bilateral implant placement or a sham procedure (the device was inserted without deployment). 10 , 11 Both found significant decreases in both nasal obstruction/congestion score and bilateral polyp grade and decreased indication for revision ESS in the treatment compared with the sham group.
4 COMPLICATIONS
In the abovementioned clinical trials, there were no serious, life‐threatening adverse events that were reported. Common adverse events reported in these studies included nasal discomfort, sinusitis, adhesions, epistaxis, and nasopharyngitis, all of which resolved without any sequelae. The US FDA's Manufacturer and User Facility Device Experience (MAUDE) database was recently queried for any reported adverse events related to the current commercially available implants and a total of 28 adverse events were reported. Of all adverse events, 39% were attributable to postoperative infections, while migration of the stent was the second most common complication. Eight patients in the cohort required reintervention to remove the implant. 12
5 ALTERNATIVES TO STEROID‐ELUTING STENTS
The commercially available steroid‐eluting stents are not available in all countries. Alternatives include synthetic, bioresorbable products, which can be manually steroid‐impregnated before placement into the middle meatus or ethmoid bed; however, adding steroids to a bioabsorbable implant is not FDA‐approved. 13 , 14 , 15 , 16 , 17 Most implant products currently on the market require prior surgical dissection of the ethmoid area although stents that do not require prior dissection are currently undergoing clinical trials. Further clinical trials are warranted to evaluate both the efficacy and cost‐effectiveness of steroid‐impregnated alternatives to the current stents.
6 DELPHI PROCESS
To collect the individual opinions of rhinology experts on the intraoperative and in‐office use of absorbable steroid‐eluting stents, a Delphi process was performed. Institutional review board exemption was obtained from the University of Southern California Keck School of Medicine. All experts had prior experience with intraoperative stent placement; however, not all experts had placed stents specifically designed for office use. A group of five authors (V.S.L., P.P., D.O., G.A.S., and E.F.), who were members of the American Academy of Otolaryngology Outcomes and Evidence Based Medicine Committee and who had published an ENT Bulletin article on the evidence regarding steroid‐eluting sinus stents, determined the wording of the original questions through a series of discussions. 18 Experts were then asked to anonymously answer 12 questions (Q1–Q12) by responding whether they strongly agree/agree/neutral/disagree/strongly disagree. A cutoff of 80% was used for consensus, with pooling of the categories agree and strongly agree and the categories disagree and strongly disagree. Two iterations of the Delphi survey were performed. The initial group of authors extensively discussed the results of each item after the first Delphi survey. Items that reached consensus were accepted. Items that did not meet consensus after the first survey were discussed to determine whether: (1) the wording or specific language was pivotal in the item not reaching consensus, or (2) not meeting consensus was instead caused by a true lack of consensus. The second survey was used to reassess items for which there was near consensus or for items in which there was suggestion of significant alterations in wording that could have affected survey results. All items reaching consensus after the second round were accepted.
7 DELPHI RESULTS
The survey results are displayed in Tables 1 and 2 with statements ultimately reaching consensus highlighted. Fourteen experts answered the first survey and 12 experts answered the second; survey results were anonymous. Of the 12 experts, seven reported having used SINUVA in the past, with five of them continuing its use at the time of the survey. Similarly, all 12 experts reported having used PROPEL in the past, with 10 of them continuing its use at the time of the survey.
TABLE 1 Delphi process results for the original survey*
Consensus (all others voted neutral)
Question Intraoperative/In‐office use Question/statement Resurveyed (yes/no) Agree Disagree Accepted (yes/no)
1 Intraoperative Steroid‐eluting stent placement should only be considered in sinus surgery for chronic rhinosinusitis with nasal polyps Yes 8% 67% No
2 Intraoperative Steroid‐eluting stent placement could be considered in sinus surgery for chronic rhinosinusitis without nasal polyps Yes 92% 8% Yes
3 Intraoperative Steroid‐eluting stent placement should be considered in sinus surgery for patients intolerant of oral steroids No 92% 8% Yes
4 Intraoperative If a patient has diabetes, then steroid‐eluting stents could be considered instead of oral steroids after endoscopic sinus surgery No 92% 0% Yes
5 Intraoperative For extended frontal sinus approaches/surgeries, steroid‐eluting stents could be considered No 100% 0% Yes
6 Intraoperative No more than 2 steroid‐eluting stents should be placed in each sinonasal cavity Yes 50% 0% No
7 Intraoperative For patients with poor compliance with postoperative rinses, steroid‐eluting stent placement could be considered in primary sinus surgery No 58% 17% No
8 Intraoperative Propel should never be placed in an acutely infected field Yes 42% 17% No
9 In‐office SINUVA placement could be considered for ethmoid or frontal recess recurrent polyps after surgery as an alternative to biologic therapy No 75% 0% No
10 In‐office If a patient has recurrent stenosis, then a steroid‐eluting stent could be used in the office No 92% 0% Yes
11 In‐office SINUVA is most optimally positioned only if total ethmoidectomy has been performed Yes 83 % 17% Yes
12 In‐office PROPEL should be removed within 21 days of surgery No 50% 17% No
*Shaded statements reached consensus and were accepted (n = 14). Note: Question 2 was resurveyed despite reaching agreement as it was directly correlated with question 1.
John Wiley & Sons, Ltd.
TABLE 2 Delphi process results for statements rewritten and resurveyed (n = 12)
Question Round Question/statement Consensus (% agree, disagree [all others neutral]) Accepted (yes/no)
1 1 Steroid‐eluting stent placement should only be considered in sinus surgery for chronic rhinosinusitis with nasal polyps 14%, 57%
2 Steroid‐eluting stent placement should primarily be considered in sinus surgery for chronic rhinosinusitis with nasal polyps 67%, 16% No
2 1 Steroid‐eluting stent placement could be considered in sinus surgery for chronic rhinosinusitis without nasal polyps 92%, 8%
2 Steroid‐eluting stent placement could be considered in sinus surgery for chronic rhinosinusitis without nasal polyps 92%, 0% Yes
6 1 No more than 2 steroid‐eluting stents should be placed in each sinonasal cavity 50%, 0%
2 Only one steroid‐eluting stent should generally be used in each sinonasal cavity 58%, 25% No
8 1 Propel should never be placed in an acutely infected field 25%, 21%
2 PROPEL can be considered for placement in an acutely infected field if the surgeon believes it is in the best interest of the patient 58%, 25% No
11 1 SINUVA should only be placed if a total ethmoidectomy has been performed 71%, 14%
2 SINUVA is most optimally positioned only if total ethmoidectomy has been performed 83%, 17% Yes
Note: Question 2 was resurveyed despite reaching agreement as it was directly correlated with question 1.
John Wiley & Sons, Ltd.
An overwhelming majority of experts find a potential, beneficial role for implants after ESS for CRSsNPs (Q2). Conversely, experts did not reach consensus on whether implants should be primarily used for patients with CRSwNPs (Q1). The majority of experts agreed that implants can be used in patients who are diabetic (Q3) or have intolerance to oral steroids (Q4). All of the experts agreed that implants could be considered for extended frontal sinus surgeries (Q5). No consensus was reached by the experts on the optimal number of steroid‐eluting stents to be placed in each sinonasal cavity (Q6) or whether steroid‐eluting implants should be considered in patients who are poorly compliant with postoperative rinses after ESS (Q7).
When questioned about the in‐office use of steroid‐eluting implants, the majority of the experts agreed that implants could be used for patients who have recurrent stenosis (Q10) and that SINUVA is most optimally positioned only if a total ethmoidectomy has been performed (Q11). No consensus was reached when asked whether SINUVA could be an alternative to biologics for recurrent CRSwNP (Q9) or regarding whether PROPEL should be removed within 21 days of surgery (Q12).
8 DISCUSSION
Currently available evidence, although largely funded by industry, has consistently demonstrated a high level of evidence that absorbable steroid‐eluting implants can be beneficial, particularly in the frontal sinus. Accordingly, experts agree that steroid‐eluting stents can be considered intraoperatively for patients with CRSwNPs or CRSsNPs and if the patient is intolerant of oral steroids. In the office, experts agree that stents can be considered for patients with stenosis and they recommend that SINUVA is optimally placed if a total ethmoidectomy has previously been performed.
No consensus was reached by the experts when asked whether steroid‐eluting implants should be considered in patients who are poorly compliant with postoperative rinses after ESS (Q7). This may be caused by the perceived tendency of steroid‐eluting implants to crust and the clinical benefit of daily postoperative rinses over these implants in managing crusts and surgical debris to promote healing and prevent infection during the postoperative period. Additionally, when asked about the optimal number of steroid‐eluting stents to be placed in each sinonasal cavity (Q6), no consensus was reached. This may reflect a more patient‐tailored approach in which the optimal number of steroid‐eluting implants to be placed is determined by anatomy, severity of the patient's sinonasal disease, and the experience of the surgeon. It could also represent different opinions by various experts on the cost versus benefit of additional stent placement in the operated cavity.
To obtain a broad survey of rhinology experts, we included surgeons who practice outside of the United States who might not have routine access to these implants in their health systems. We expected that access to implants in practice and varying personal experience would affect responses to the statements but we wanted to include a range of opinions. All experts had used the products. We did not specifically query why experts who had used the products previously no longer used them at the time of the survey. We also did not query why experts who had access to office‐based stents had not used them. Further studies are necessary to determine decision‐making patterns regarding availability and utilization. Our experts are primarily affiliated with academic medical centers, although two were community‐based and therefore may utilize steroid‐eluting stents differently than general otolaryngologists or surgeons who practice in different types of health systems. Two experts did not respond to the second survey round and therefore specific practice patterns may be differently represented in the five follow‐up questions, which failed to reach initial agreement.
No consensus was reached when asked whether SINUVA could be an alternative to biologics for recurrent CRSwNP (Q9). While biologics have provided clinicians with a promising new option in the treatment of CRSwNP, several cost‐utility analyses have demonstrated unfavorable cost‐effectiveness compared with surgery. 19 Similarly, newer treatment options such as SINUVA should be compared with other postsurgical treatment options such as biologics to determine appropriate patient selection in the treatment of recurrent CRSwNP. The reported change in nasal polyp score for SINUVA is smaller than the reported change in nasal polyp score for dupilumab at comparable time points, and a direct comparison study would be illuminating. 10 , 11 , 20 As for the duration that these implants should remain in the sinonasal cavities, no consensus was reached by the experts when asked whether PROPEL should be removed within 21 days of surgery (Q12). The PROPEL stents are designed to dissolve between 30 and 45 days. Some panelists report removing the stents early to prevent postoperative crusting as the stents break apart.
9 CONCLUSION
A panel of experts agreed that steroid‐eluting stents can be considered intraoperatively for patients with CRSwNPs or CRSsNPs, especially in cases when patients are intolerant of oral steroids or when an extended frontal dissection has been performed. Steroid‐eluting stents can be considered in the office for stenosis. However, newer more rigid office stents are best placed when a total ethmoidectomy has been performed. The impact on patient‐reported outcomes in these contexts and the role of these implants in the wake of newer therapies, such as biologics, need to be investigated to better determine the role of steroid‐eluting stents.
CONFLICTS OF INTEREST
Greg Davis: Intersect ENT (consultant). Amber U. Luong: Aerin Medical (Austin, TX, USA), Glaxo‐SmithKline (Brentford, UK), Lyra Therapeutics (Watertown, MA, USA), Sanofi (Paris, France), Stryker (Kalamazoo, MI, USA), ENTvantage Dx (Austin, TX, USA), Third Wave Therapeutics (San Francisco, CA, USA), and AstraZeneca (Cambridge, UK). Prior agreement with IntersectENT and Acclarent. Robert Kern: Lyra Therapeutics (Chief Medical Officer). Raewyn G. Campbell: Acclarent, prior agreement; Medtronic (Jacksonville FL, USA), Seqirus (Maidenhead, UK). Rakesh Chandra: Acclarent, prior agreement; Joseph Han: Acclarent, prior agreement; Jivianne Lee: Acclarent, prior agreement; David Poetker: Acclarent, prior agreement. Sarah Wise: consultant NeuroENT, Chitogel; Advisory board Optinose, Genentech. None of these companies were involved in the research in the present study.
ACKNOWLEDGMENTS
Dr Robert Kern participated in the expert surveys.
==== Refs
REFERENCES
1 United States Food and Drug Administration . US FDA Premarket Approval Medical Devices Database. Available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pma.cfm. Accessed March 1, 2021.
2 Intersect ENT. Available at https://www.intersectent.com. Accessed March 1, 2021
3 Murr AH , Smith TL , Hwang PH , et al. Safety and efficacy of a novel bioabsorbable, steroid‐eluting sinus stent. Int Forum Allergy Rhinol. 2011;1 (1 ):23–32.22287304
4 Marple BF , Smith TL , Han JK , et al. Advance II: a prospective, randomized study assessing safety and efficacy of bioabsorbable steroid‐releasing sinus implants. Otolaryngol Head Neck Surg. 2012;146 (6 ):1004–1011.22301107
5 Smith TL , Singh A , Luong A , et al. Randomized controlled trial of a bioabsorbable steroid‐releasing implant in the frontal sinus opening. Laryngoscope. 2016;126 (12 ):2659–2664.27363723
6 Luong A , Ow RA , Singh A , et al. Safety and effectiveness of a bioabsorbable steroid‐releasing implant for the paranasal sinus ostia: a randomized clinical trial. JAMA Otolaryngol Head Neck Surg. 2018;144 (1 ):28–35.29098299
7 Singh A , Luong AU , Fong KJ , et al. Bioabsorbable steroid‐releasing implants in the frontal sinus ostia: a pooled analysis. Int Forum Allergy Rhinol. 2019;9 (2 ):131–139.30431709
8 United States Food and Drug Administration . US FDA Drug Approvals and Databases. Available at https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/209310Orig1s000SumR.pdf. Accessed on March 1, 2021
9 Han JK , Forwith KD , Smith TL , et al. RESOLVE: a randomized, controlled, blinded study of bioabsorbable steroid‐eluting sinus implants for in‐office treatment of recurrent sinonasal polyposis. Int Forum Allergy Rhinol. 2014;4 (11 ):861–870.25266981
10 Forwith KD , Han JK , Stolovitzky JP , et al. RESOLVE: bioabsorbable steroid‐eluting sinus implants for in‐office treatment of recurrent sinonasal polyposis after sinus surgery: 6‐month outcomes from a randomized, controlled, blinded study. Int Forum Allergy Rhinol. 2016;6 (6 ):573–581.26992115
11 Kern RC , Stolovitzky JP , Silvers SL , et al. A phase 3 trial of mometasone furoate sinus implants for chronic sinusitis with recurrent nasal polyps. Int Forum Allergy Rhinol. 2018;8 (4 ):471–481.29350840
12 Narwani V , Torabi SJ , DA Kasle , et al. Adverse events associated with corticosteroid‐eluting sinus stents: a MAUDE database analysis. Otolaryngol Head Neck Surg. 2021;166 (1 ):179–182.33848437
13 Adriaensen GFJPM , Lim KH , Fokkens WJ . Safety and efficacy of a bioabsorbable fluticasone propionate‐eluting sinus dressing in postoperative management of endoscopic inus surgery: a randomized clinical trial. Int Forum Allergy Rhinol. 2017;7 (8 ):813–820.28558147
14 Xu J , Park SJ , Park HS , et al. Effects of triamcinolone‐impregnated nasal dressing on subjective and objective outcomes following endoscopic sinus surgery. Eur Arch Otorhinolaryngol. 2016;273 (12 ):4351–4357.27363407
15 Zhao KQ , Yu YQ , Yu HM . Effects of mometasone furoate‐impregnated biodegradable nasal dressing on endoscopic appearance in healing process following endoscopic sinus surgery: a randomized, double‐blind, placebo‐controlled study. Int Forum Allergy Rhinol. 2018;8 (11 ):1233–1241.30281941
16 Côté DW , Wright ED . Triamcinolone‐impregnated nasal dressing following endoscopic sinus surgery: a randomized, double‐blind, placebo‐controlled study. Laryngoscope. 2010;120 (6 ):1269–1273.20513050
17 Li W , Lu H , Wang H , et al. Efficacy and safety of steroid‐impregnated implants following sinus surgery: a meta‐analysis. Laryngoscope. 2020;130 (12 ):2754–2759.31755990
18 Out of Committee: Outcomes Research and Evidence‐Based Medicine | Absorbable Steroid‐Eluting Sinus Implants: What We Do and Don't Know | AAO‐HNSF Bulletin. https://bulletin.entnet.org/aao‐hnsf‐2021/article/21295901/out‐of‐committee‐outcomes‐research‐and‐evidencebased‐medicine‐absorbable‐steroideluting‐sinus‐implants‐what‐we‐do‐and‐dont‐know. Accessed March 19, 2022.
19 Scangas GA , Wu AW , Ting JY , et al. Cost utility analysis of dupilumab versus endoscopic sinus surgery for chronic rhinosinusitis with nasal polyps. Laryngoscope. 2021;131 (1 ):E26–E33.32243622
20 Bachert C , Han J , Desrosiers M , et al. Efficacy and safety of dupilumab in patients with severe chronic rhinosinusitis with nasal polyps (LIBERTY NP SINUS‐24 and LIBERTY NP SINUS‐52): results from two multicenter, randomized, double‐blind, placebo‐controlled, parallel‐group phase 3 trials. The Lancet. 2019;394 (10209 ):1638–1650.
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PMC010xxxxxx/PMC10108567.txt |
==== Front
Pacing Clin Electrophysiol
Pacing Clin Electrophysiol
10.1111/(ISSN)1540-8159
PACE
Pacing and Clinical Electrophysiology
0147-8389
1540-8159
John Wiley and Sons Inc. Hoboken
35983947
10.1111/pace.14584
PACE14584
Review
Reviews
Inappropriate shocks due to air entrapment in patients with subcutaneous implantable cardioverter‐defibrillator: A meta‐summary of case reports
IAVARONE et al.
Iavarone Michele MD https://orcid.org/0000-0003-4298-6165
1
Rago Anna MD 1
Nigro Gerardo MD, PhD 1
Golino Paolo MD, PhD 1
Russo Vincenzo MD, PhD 1 [email protected]
1 Cardiology Unit Department of Translational Medical Sciences University of Campania “Luigi Vanvitelli” – Monaldi Hospital Naples Italy
* Correspondence
Vincenzo Russo MD, PhD, Cardiology Unit, Department of Translational Medical Sciences, University of Campania “Luigi Vanvitelli”, Via L. Bianchi 1 c/o Monaldi Hospital, AORN Colli, Naples, Italy.
Email: [email protected]
30 8 2022
10 2022
45 10 10.1111/pace.v45.10 12101215
12 7 2022
22 4 2022
20 7 2022
© 2022 The Authors. Pacing and Clinical Electrophysiology published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Abstract
Background
Air entrapment has been recently described as a cause of inappropriate shock (IAS) among patients who underwent subcutaneous implantable cardioverter‐defibrillator (S‐ICD) implantation. Data about this complication are lacking in the literature.
Methods
In this meta‐summary 14 case reports describing IAS due to air entrapment were included. Patients' characteristics, S‐ICD implant technique and air entrapment properties were collected.
Results
All patients experienced IAS within 4 days following S‐ICD implant. The subcutaneous air was demonstrated by chest x‐ray in 11 cases (73.3%). The sensing vector was reprogrammed in 11 cases (73.3%), and ICD was switched off in 2 cases (1.3%).
Conclusions
IAS due to air entrapment is an early complication of S‐ICD implant. No association was observed between the implant technique and air retention. Diagnosis is confirmed by chest x‐ray and device interrogation. Management includes switching off the device or, preferably, changing the sensing vector.
air entrapment
complications
inappropriate shock
oversensing
S‐ICD
source-schema-version-number2.0
cover-dateOctober 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
Iavarone M , Rago A , Nigro G , Golino P , Russo V . Inappropriate shocks due to air entrapment in patients with subcutaneous implantable cardioverter‐defibrillator: A meta‐summary of case reports. Pacing Clin Electrophysiol. 2022;45 :1210–1215. 10.1111/pace.14584 35983947
==== Body
pmcList of Abbreviations
CXR Chest X‐ray
IAS Inappropriate shock
ICD Implantable cardioverter‐defibrillator
IQR Interquartile range
PM Pacemaker
SCD Sudden cardiac death
S‐ICD Subcutaneous Implantable cardioverter‐defibrillator
T‐ICD Transvenous Implantable cardioverter‐defibrillator
TWO T wave oversensing
VT/VF Ventricular tachycardia/ Ventricular fibrillation
1 INTRODUCTION
The S‐ICD is an established therapy for the prevention of sudden cardiac death (SCD). 1 and an alternative to a transvenous implantable cardioverter‐defibrillator (T‐ICD) system in selected patients. 2 S‐ICD is non‐inferior to T‐ICD concerning device‐related complications or IAS in patients with an indication for defibrillator therapy and not in need of pacing. 3 , 4 , 5 , 6 , 7 , 8 The most common causes of IAS among S‐ICD recipients are T wave oversensing (TWO), oversensing of ventricular tachycardia/ventricular fibrillation (VT/VF) below the therapy zone, low amplitude signals (defined as the loss of QRS/T ratio) and myopotentials. 4 , 8 , 9 , 10 Subcutaneous air entrapment has been recently considered an underdiagnosed cause of early postimplant IAS, accounting for a part of oversensing of low amplitude signals. 11 , 12 This study aimed to perform a meta‐summary of case reports to characterize patients who experienced IAS due to air entrapment by investigating the possible causes, the time of onset, the management, and the outcomes of this complication.
2 METHODS
We searched for case reports published in Pubmed, Google Scholar, Scopus, and EMBASE from 2012 to December 2021 using the following keywords: “Subcutaneous ICD” “S‐ICD”, “inappropriate shock”, “air entrapment”, “subcutaneous air”. Case reports of IAS due to subcutaneous air entrapment were included. Only English original reports were considered, and double papers were ruled out. Titles and abstracts were independently screened by two researchers (V.R. and M.I.), and discrepancies were resolved by discussion. Finally, 14 eligible case reports/case series were included in our meta‐summary. The diagram of study selection was shown in Figure 1.
FIGURE 1 Flow diagram of study selection [Colour figure can be viewed at wileyonlinelibrary.com]
3 RESULTS
A total of 15 S‐ICD patients who experienced at least one IAS due to air entrapment were examined. The median age was 55 years (IQR 17–75) and 66.6% were males. The indication for S‐ICD implantation was primary prevention in six patients (40.0%) and secondary prevention in nine patients (60%). The implantation technique involves three incisions in seven patients and two incisions in six patients (46.6% vs. 40%; P = 0.72); in two patients the implantation technique was not described. Table 1 shows the patients’ clinical features and the S‐ICD implantation characteristics. Regarding the time to event, all patients experienced IAS within 4 days following S‐ICD implantation, in 66.6% of cases on the same day of the procedure and in 33.3% after discharging. Among the case reports included in the present analysis, no information about the possible causes of air entrapment was provided. Moreover, at the end of S‐ICD implantation, no specific tests or manoeuvres oriented to the early diagnosis of this complication were performed. The intraprocedural chest X‐Ray (CXR) for checking the correct S‐ICD system positioning was performed in 40% of cases; in none of them, the presence of air entrapment has been reported. The subcutaneous air entrapment was diagnosed by post‐operative CXR in 11 cases (73.3%), whereas the diagnosis was based on device interrogation and reproduction of the electrogram alterations by manipulation in two cases, respectively (13.3%). The most common localization of air entrapment was around the proximal electrode (46.6%, n = 7), followed by the distal electrode (26.6%, n = 4) and ICD generator (20.0%, n = 3); whereas in one case, in which the oversensing involved the alternate vector, the precise localization was not investigated. The management of IAS due to air entrapment was more likely the sensing vector reprogramming (73.3%, n = 11) and waiting the time necessary for oversensing resolution and air reabsorption; it usually occurred after 2 weeks in 73.3% of patients, who did not complain of further shocks and in which CXR did not show residual air. The Table 2 resumes subcutaneous air entrapment characteristics, diagnosis, and management.
TABLE 1 Patients’ characteristics and S‐ICD implantation technique
Year Authors Age (years) Gender Underlying disease Indication Implantation technique S‐ICD generation
2014 Gamble J.H.P. et al. 18 22 Female Idiopathic PVT Secondary Two‐incision First generation
2014 Zipse M.M. et al. 16 56 Male IHD Secondary Three‐incision First generation
2015 Yap S‐C. et al. 21 37 Male BrS Secondary Three‐incision Second generation
2016 Chinitz J.S. et al. 11 53 Male IHD Primary Two‐incision Second generation
2016 Chinitz J.S. et al. 11 73 Male DCM Secondary Two‐incision Second generation
2017 Lee S. et al. 20 55 Male HCM Primary Three‐incision First generation
2018 Crea P. et al. 23 75 Male DCM Primary Two‐incision Third generation
2018 Linhart M. et al. 24 22 Male BrS Secondary Three‐incision Third generation
2018 Taguchi Y. et al. 25 17 Female LQTS Secondary Three‐incision Third generation
2019 Nishinarita R. et al 12 57 Male BrS Primary Three‐incision Third generation
2019 Sandhu H. et al 26 60 Female NA Secondary NA Third generation
2019 Yang Y.C. et al. 22 58 Female DCM Primary Two‐incision Third generation
2020 Kanwal A. et al. 27 72 Female HFrEF Primary NA Third generation
2021 Iqbal M. et al. 28 29 Male Pulmonary sarcoidosis Secondary Two‐incision Third generation
2021 Seganfreddo F. et al. 29 43 Male DCM Secondary Three‐incision Third generation
Abbreviations: BrS, Brugada Syndrome; DCM, dilatative cardiomyopathy; HFrEF, heart failure with reduced ejection fraction; IHD, ischemic heart disease; LQTS, long QT syndrome; NA, not available; PVT, polymorphic ventricular tachycardia.
John Wiley & Sons, Ltd.
TABLE 2 Subcutaneous air entrapment characteristics, diagnosis, and management
Year Authors Time to IAS Diagnosis Air entrapped localization Time to resolution Management
2014 Gamble et al. 18 2 h CXR Proximal electrode NA Waiting
2014 Zipse et al. 16 3–4 days CXR, PMs Distal electrode 1 month Sensing vector changing
2015 Yap et al. 21 The evening of the implant CXR, PMs Proximal electrode 10 days Sensing vector changing
2016 Chinitz et al. 11 Several hours Electrogram analysis Proximal or distal electrode 1 day Waiting
2016 Chinitz et al. 11 The evening of the implant Electrogram analysis Distal electrode 1 day Sensing vector changing
2017 Lee S. et al. 20 6 h CXR Proximal electrode 14 days Sensing vector changing
2018 Crea P. et al 23 12 h PMs
negative CXR
Proximal electrode 2 days ICD switched off
2018 Linhart M. et al. 24 1 day CXR ICD generator 2 days Sensing vector changing
2018 Taguchi Y. et al. 25 4 days CXR Distal electrode 13 days Sensing vector changing
2019 Nishinarita R. et al 12 2 h CXR Proximal electrode 7 days Sensing vector changing
2019 Sandhu H. et al. 26 72 h CXR ICD generator 6 days ICD switched off
2019 Yang Y.C. et al. 22 Few hours CXR, PMs Proximal electrode 14 days Sensing vector changing
2020 Kanwal A. et al. 27 The evening of the implant CXR Proximal electrode The following morning Sensing vector changing
2021 Iqbal M. et al. 28 2 days CXR, PMs ICD generator NA Sensing vector changing
2021 Seganfreddo F. et al. 29 3 days PMs
negative CXR
Distal NA Sensing vector changing
Abbreviations: CXR, chest X ray; IAS, inappropriate shock; ICD, implantable cardioverter defibrillator; NA, not available; PMs, physical manoeuvres.
John Wiley & Sons, Ltd.
4 DISCUSSION
In the early investigation studies, evaluating the first‐generation models of S‐ICD, the 1‐year IAS incidence ranged from 6.8% 9 to 13%. 13 , 14 More recent data, including the third generation of S‐ICD with a novel sensing methodology SMART Pass (SP; Boston Scientific Corporation, Natick, MA), showed to significantly reduce oversensing accounting for the 1‐year IAS rate of 2.4–3.1%. 10 In the Prospective Randomized Comparison of Subcutaneous and Transvenous Implantable Cardioverter Defibrillator Therapy (PRETORIAN) Trial, S‐ICD patients showed a higher cumulative incidence of IAS compared to those with transvenous ICD (9.7% and 7.3%, respectively; hazard ratio, 1.43; 95% CI, 0.89–2.30) at a median follow‐up of 49.1 months. 8 The cardiac oversensing, accounting for 58.5% of cases, was the most common cause of IAS among S‐ICD patients; in contrast, supraventricular tachycardia was the main trigger of IAS (93.1%) among TV‐ICD patients.
Data from the Subcutaneous Implantable Cardioverter‐Defibrillator Post Approval Study (S‐ICD Pass trial) revealed a 1‐year IAS rate of 6.8% among 1637 de novo patients and the low amplitude signals accounted for 23% of total IAS episodes.
In the Understanding Outcomes With the S‐ICD in Primary Prevention Patients With Low Ejection Fraction (UNTOUCHED) trial. 10 the IAS rate was lower (2.4–3.1%) than in any previously reported prospective multicenter trials including S‐ICD patients, despite the study population included more likely patients with left ventricular dysfunction and heart failure. However, an association of the two‐incision technique with a higher rate of early post‐implantation IAS has been shown, presumably because of air along the electrode tract or lead displacement. 10
Air entrapment as a cause of cardiac device malfunction was firstly described in 1979 by Kreis et al., who reported the pacing defect of a unipolar cardiac pacemaker (PM) in a patient with air entrapment within PM pocket. 15 Thirty‐five years later, Zipse et al. described entrapped subcutaneous air surrounding the distal electrode as the cause of oversensing leading to early (< 48 h) post‐S‐ICD implantation IAS. 16
From the results of our analysis, the entrapped subcutaneous air leading to IAS occurred in 66.6% in the early post‐implant period (< 24 h) and always within the following 4 days; therefore, it should be considered an early complication of S‐ICD implantation. The relationship between air entrapment and the S‐ICD implantation technique has not been studied yet. According to Gamble et al. 18 the two‐incision technique, by placing the distal electrode without the superior parasternal incision, is less likely to result in the air around the distal electrode. In contrast, Chinitz et al. 11 considered this technique at increased risk of air trapping in a closed space around the sensing electrodes, due to the use of a tear‐away sheath to pass the lead up subcutaneous tract in the parasternal space. In the UNTOUCHED trial, the 2‐incision technique seems to be associated with a higher rate of early post‐implantation IAS, presumably because of air along the electrode track. 10 In our meta‐summary, we did not show a significantly increased incidence of IAS due to air entrapment among patients treated with 2‐incision versus 3‐incision technique.
Even if the case reports included in our meta‐summary did not describe the causes of air entrapment, the presence of death space due to the intra‐operative digital dissection of subcutaneous tissues, the inadequate fixation of electrodes, or the inadequate closure of surgical margins seems to be the more likely causal hypothesis. Moreover, not performing the manoeuvres to expel any residual air may have contributed to air entrapment.
According to the user manual for Boston Scientific EMBLEM S‐ICD. 19 implanting a replacement device in a subcutaneous pocket that previously housed a larger device may result in pocket air entrapment. Several authors. 11 , 12 , 18 , 20 , 21 , 22 , 23 following the instructions of the S‐ICD manufacturer. 19 suggest the subcutaneous injection of normal saline solution and the massage of skin to expel air from the subcutaneous space to prevent aerial entrapment during S‐ICD implantation.
We endorse this intra‐operative approach and suggest a careful searching of entrapped subcutaneous air in the post‐procedure period before switching on the S‐ICD system, since the intra‐procedural CXR may be negative. In particular, we propose to perform the device interrogation during the circular skin massage over the electrodes and ICD generator in both supine and orthostatic positions, considering the natural air rising.
When an early post‐procedural IAS occurs, air entrapment should be considered as a possible cause. The air interposition between sensing electrodes and subcutaneous tissue intermittently interferes with adequate sensing and causes noise characterized by high‐frequency noncyclic signals that replace the isoelectric baseline. 17 The air entrapment diagnosis is based on the baseline shift from the isoelectric line and/or low‐amplitude signals at S‐ICD interrogation; the reproduction of electrogram abnormalities by provocative manoeuvres; and on the air evidence at CXR focused on proximal and distal electrodes or generator. Figure 2 These diagnostic methods should be integrated into a comprehensive evaluation of early IAS, since, alone, have a lower negative predictive value. Reprogramming a different sensing vector to exclude the electrode rounded by the entrapped air or, if unfeasible, temporarily switching off the S‐ICD system, until the air reabsorption, are good practices in complication management.
FIGURE 2 Antero‐posterior (panel A) and lateral (panel B) chest X radiography showing air entrapment around the proximal electrode
Finally, although interruption of the oversensing mechanism probably occurs before complete air disappearance, we assume that after 2 weeks in most patients is no longer possible to demonstrate residual subcutaneous air with CXR and/or provocative manoeuvres.
5 LIMITATIONS
The low number of included cases is certainly a limitation; however, the present is the first meta‐summary regarding this topic. The generation of S‐ICD devices and the experience of implantation centers were different across case reports. The implantation procedures, preventive measures, and timing of follow‐up were not standardized, limiting further analysis.
6 CONCLUSIONS
Air entrapment is an uncommon cause of IAS among S‐ICD recipients, even if its epidemiology may be biased by misdiagnosing and underreporting. It exclusively occurs in the early postprocedural period, generally within 4 days. The subcutaneous injection of normal saline solution and the massage of skin to expel air from the subcutaneous space may prevent the aerial entrapment during S‐ICD implantation; moreover, we suggest a careful searching of entrapped subcutaneous air in the post‐procedure period before switching on the S‐ICD system. The diagnosis should be achieved by performing a systematic approach including device interrogation, provocative manoeuvres, and CXR. Reprogramming a different sensing vector or temporally switching off the S‐ICD system, until the air reabsorption, are part of the acute complications’ management. Reassessing the patient at 2 weeks with further device interrogation during provocative manoeuvres may be useful. Larger studies are needed to increase the knowledge and improve the prevention and management of IAS due to air entrapment in patients with S‐ICD.
CONFLICT OF INTERESTS
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
ACKNOWLEDGMENT
The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not‐for‐profit sectors.
Open access funding provided by Universita degli Studi della Campania Luigi Vanvitelli within the CRUI‐CARE Agreement.
[Correction added on 25 November 2022, after first online publication: CRUI funding statement has been added.]
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
==== Refs
REFERENCES
1 Bardy GH , Smith WM , Hood MA , et al. An entirely subcutaneous implantable cardioverter–defibrillator. N Engl J Med. 2010;363 :36‐44.20463331
2 Kirkfeldt RE , Johansen JB , Nohr EA , Jorgensen OD , Nielsen JC . Complications after cardiac implantable electronic device implantations: an analysis of a complete, nationwide cohort in Denmark. Eur Heart J. 2014;35 :1186‐1194.24347317
3 Brouwer TF , Yilmaz D , Lindeboom R , et al. Long‐term clinical outcomes of subcutaneous versus transvenous implantable defibrillator therapy. J Am Coll Cardiol. 2016;68 :2047‐2055.27810043
4 Boersma L , Barr C , Knops R , et al. Implant and midterm outcomes of the subcutaneous implantable cardioverter‐defibrillator registry: the EFFORTLESS study. J Am Coll Cardiol. 2017;70 :830‐841.28797351
5 Mithani AA , Kath H , Hunter K , et al. Characteristics and early clinical outcomes of patients undergoing totally subcutaneous vs. transvenous single chamber implantable cardioverter defibrillator placement. EP Europace. 2018;20 :308‐314.28383717
6 Rordorf R , Casula M , Pezza L , et al. Subcutaneous versus transvenous implantable defibrillator: an updated meta‐analysis. Heart Rhythm. 2021;18 :382‐391.33212250
7 Boersma LV , El‐Chami MF , Bongiorni MG , et al. Understanding Outcomes with the EMBLEM S‐ICD in Primary Prevention Patients with Low EF Study (UNTOUCHED): clinical characteristics and perioperative results. Heart Rhythm. 2019;16 :1636‐1644.31082539
8 Knops RE , Olde Nordkamp LRA , Delnoy PPHM , et al. Subcutaneous or transvenous defibrillator therapy. N Engl J Med. 2020;383 :526‐536.32757521
9 Burke MC , Aasbo JD , El‐Chami MF , et al. 1‐year prospective evaluation of clinical outcomes and shocks. JACC: Clin Electrophysiol. 2020;6 :1537‐1550.33213814
10 Gold MR , Lambiase PD , El‐Chami MF , et al. primary results from the understanding outcomes with the S‐ICD in primary prevention patients with low ejection fraction (UNTOUCHED) trial. Circulation. 2021;143 :7‐17.33073614
11 Chinitz J . Inappropriate shocks within 24 hours after implantation of a subcutaneous defibrillator with a two‐incision technique. J Innov Card Rhythm Manag. 2016;7 :2295‐2298.
12 Nishinarita R , Kishihara J , Matsuura G , et al. Early inappropriate shock in a subcutaneous cardiac defibrillator due to subcutaneous air. J Arrhythmia. 2019;35 :682‐684.
13 Burke MC , Gold MR , Knight BP , et al. Safety and efficacy of the totally subcutaneous implantable defibrillator. J Am Coll Cardiol. 2015;65 :1605‐1615.25908064
14 Lambiase PD , Barr C , Theuns DA , et al. Worldwide experience with a totally subcutaneous implantable defibrillator: early results from the EFFORTLESS S‐ICD Registry. Eur Heart J. 2014;35 :1657‐1665.24670710
15 Kreis DJ , licalzi L , Shaw RK . Air entrapment as a cause of transient cardiac pacemaker malfunction. Pacing Clin Electrophysiol. 1979;2 :641‐644.95227
16 Zipse MM , Sauer WH , Varosy PD , Aleong RG , Nguyen DT . Inappropriate shocks due to subcutaneous air in a patient with a subcutaneous cardiac defibrillator. Circ Arrhythm Electrophysiol. 2014;7 :768‐770.25140025
17 Swerdlow CD , Asirvatham SJ , Ellenbogen KA , Friedman PA . Troubleshooting implanted cardioverter defibrillator sensing problems I. Circ Arrhythm Electrophysiol. 2014;7 :1237‐1261.25516582
18 Gamble JHP , Grogono J , Rajappan K , Betts TR , Bashir Y , Khiani R , et al. Letter by gamble et al regarding article, “inappropriate shocks due to subcutaneous air in a patient with a subcutaneous cardiac defibrillator.”. Circ Arrhythm Electrophysiol. 2014;7 :1281‐1281.25516592
19 EMBLEM TM S‐ICD, EMBLEM TM MRI S‐ICD SUBCUTANEOUS IMPLANTABLE CARDIOVERTER DEFIBRILLATOR [Internet].
20 Lee S , Souvaliotis N , Mehta D , Suri R . Inappropriate shock in a subcutaneous cardiac defibrillator due to residual air. Clinical Case Reports. 2017;5 :1203‐1206.28781823
21 Yap SC , Bhagwandien RE , Szili‐Torok T , Theuns DAMJ . Air entrapment causing early inappropriate shocks in a patient with a subcutaneous cardioverter‐defibrillator. HeartRhythm Case Rep. 2015;1 :156‐158.28491536
22 Yang YC , Aung TT , Bailin SJ , Rhodes TE . Air entrapment causing inappropriate shock from a subcutaneous implantable cardioverter defibrillator. Cardiol Res. 2019;10 :128‐130.31019644
23 Crea P , Nicotera A , Crea B , Taormina A , Picciolo G . An anomalous case of S‐ICD malfunctioning: a big trouble or a soap bubble? Cor et Vasa. 2018;60 :e472‐4.
24 Linhart M , Tolosana JM , Chipa F , Trucco E , Mont L . Inappropriate shock due to air entrapment in the pocket of a subcutaneous implantable cardioverter‐defibrillator. Revista Española de Cardiología. 2019;72 :79‐81.29246807
25 Taguchi Y , Ishikawa T , Matsumoto K , et al. An inappropriate shock case early after implantation of a subcutaneous cardiac defibrillator by subcutaneous entrapped air. Int Heart J. 2018;59 :417‐419.29445055
26 S‐AB01‐01 to S‐AB36‐06 . Heart Rhythm. 2019 May;16 :S1‐92.
27 Kanwal A , Kaplan J , Saini A . Massage that pocket: a case of inappropriate shocks after subcutaneous ICD placement. J Am Coll Cardiol. 2020;75 :2688.
28 Iqbal M , Younes A , Naniwadekar A . B‐PO05‐218 subcutaneous implantable cardioverter‐defibrillator delivering inappropriate therapy due to subcutaneous air. Heart Rhythm. 2021;18 :S461.
29 Seganfreddo F , Golino M , Ceriani F , et al. 537Oversensing due to micro‐air bubbles leading to inappropriate shocks in patient with subcutaneous implantable cardioverter defibrillator. Eur Heart J Suppl. 2021;23 .
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PMC010xxxxxx/PMC10138106.txt |
==== Front
Am J Transplant
Am J Transplant
10.1111/(ISSN)1600-6143
AJT
American Journal of Transplantation
1600-6135
1600-6143
John Wiley and Sons Inc. Hoboken
35510751
10.1111/ajt.17083
AJT17083
Viewpoint
Personal Viewpoint
cDCDD‐NRP is consistent with US legal standards for determining death
ADAMS et al.
Adams Bradley L. 1 [email protected]
Brenner Lauren 2
Levan Macey https://orcid.org/0000-0002-4239-1252
3
Parent Brendan https://orcid.org/0000-0003-3057-5684
4
1 91242 Southwest Transplant Alliance Dallas Texas USA
2 Mid‐America Transplant St. Louis Missouri USA
3 Department of Surgery NYU Grossman School of Medicine NYU Langone Health New York New York USA
4 12297 Department of Population Health Division of Medical Ethics NYU Langone Health New York New York USA
* Correspondence
Bradley L. Adams, Southwest Transplant Alliance, 8190 Manderville Lane, Dallas, TX 75231, USA.
Email: [email protected]
18 5 2022
10 2022
22 10 10.1111/ajt.v22.10 23022305
07 4 2022
27 2 2022
25 4 2022
© 2022 The Authors. American Journal of Transplantation published by Wiley Periodicals LLC on behalf of The American Society of Transplantation and the American Society of Transplant Surgeons
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Donation after circulatory determination of death (DCDD) has increased organ donation rates in the US over the past decade within an established legal framework, which is consistent with and supports individual and family decisions regarding organ donation in the context of end‐of‐life care. A new application, controlled DCDD donation utilizing thoracoabdominal normothermic regional perfusion (NRP) protocols (cDCDD‐NRP), provides the opportunity to maximize a donation decision by recovering additional organs for transplant, including the heart, and to limit the detrimental impact of warm ischemic time by perfusing organs in situ following the declaration of circulatory death. In this viewpoint, we narrate our rationale for why cDCDD‐NRP is consistent within the existing legal framework for organ donation in the United States and recommend no changes to the Uniform Determination of Death Act.
Since a determination of death under the Uniform Determination of Death Act (UDDA) cannot legally be reversed or affected by donor families or organ recovery professionals through the donation process, inclusive of the use of normothermic regional perfusion, revising the UDDA is unnecessary and ill‐advised.
controlled donation after circulatory determination of death
donation after circulatory determination of death
normothermic regional perfusion
Uniform Anatomical Gift Act
Uniform Determination of Death Act
National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062 5K01DK114388‐05 source-schema-version-number2.0
cover-dateOctober 2022
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:22.06.2023
Adams BL , Brenner L , Levan M , Parent B . cDCDD‐NRP is consistent with US legal standards for determining death. Am J Transplant. 2022;22 :2302–2305. doi:10.1111/ajt.17083 35510751
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pmcAbbreviations
CPR cardiopulmonary resuscitation
DCDD donation after circulatory determination of death
ECMO extracorporeal membrane oxygenation
NRP normothermic regional perfusion
UAG Uniform Anatomical Gift Act
UDDA Uniform Determination of Death Act
1 INTRODUCTION
Organ donation saves lives, although not always in obvious or expected ways. The grieving family members of a potential organ donor, whose life has been unexpectedly and tragically cut short, often find hope and solace—and the will to live—in the fact that their loved one can save the lives of others. To allow for lifesaving impact in every possible instance, including in the context of donation after circulatory determination of death (DCDD), the opportunity for organ donation should be maximized consistent with the potential donor's expressed desire to be a donor, the donor family's willingness to proceed with donation following their loved one's death, and the legal framework for donation established in the United States that regulates the practice. Those of us who work in and with organ procurement organizations in the United States see this impact on organ donor families every day, particularly in the DCDD context where a dying patient's family must first decide to withdraw life‐sustaining therapies, a difficult decision they reach in consultation with medical professionals and premised on their acceptance of their loved one's inability to continue to live.
Without question, DCDD is responsible for a dramatic increase in organ donation over the past 10 years. 1 The ethics of DCDD donation have been considered for some time, and the basic framework for acceptability is settled within the medical community. 2 As a new application of the DCDD donation context, controlled DCDD donation utilizing thoracoabdominal normothermic regional perfusion (hereafter NRP) protocols (cDCDD‐NRP) integrates effective and resource‐efficient technology to maximize the opportunity for donation and to minimize organ injury from warm ischemia after circulatory arrest. After intentional withdraw of life support allows death to occur, NRP provides in situ tissue perfusion following declaration of circulatory death, limiting the detrimental impact of warm ischemic time and allowing the heart and other abdominal organs to be recovered for transplantation with improved outcomes. 3 , 4
Recent publications have considered ethical issues surrounding cDCDD‐NRP. 3 , 4 , 5 , 6 , 7 The thoughtful dialogue has been necessary and welcomed. Going beyond previously addressed ethical considerations, the editorial by Glazier and Capron raises concerns about whether cDCDD‐NRP protocols are in compliance with existing law. 6 They allege, “Under NRP protocols, after death has been declared, circulation is restored,” which they claim “is consequential legally because it contradicts the legal requirement that death depends on circulation having permanently ceased.” 7 Citing the Uniform Determination of Death Act (UDDA), they further suggest that “a patient is dead when circulation neither can nor will resume,” that the intent of the patient and the patient's family leading to the point of donation is without consequence, and that because the patient could be determined to have returned to life, criminal liability could attach to those following NRP protocols. 8 We posit that Glazier and Capron forgo several legal considerations we feel are necessary to explore before deciding whether cDCDD‐NRP protocols are consistent with circulatory and respiratory determination of death.
2 THE UNIFORM DETERMINATION OF DEATH ACT
According to the UDDA, a determination of death is to be made in accordance with “accepted medical standards.” 9 What constitutes accepted medical standards under the UDDA “was left to be determined by the medical community.” 10 This includes what constitutes the “irreversible cessation” of a function and how medical professionals come to its determination. 11 This also includes a medical professional's consideration of the intentions of the patient and the patient's family regarding life‐sustaining efforts or procedures, and the effectiveness or futility of such efforts, which Glazier and Capron object to as being absent from the legal construct. 8
These are not questions of law, though. In drafting the UDDA, the Commission intentionally chose to limit the scope of the proposed statute to identifying the general physiologic standards for recognizing that death has occurred, omitting the methods and information used by medical professionals to diagnose it. 12 The Commission also recognized that the UDDA would “not remove the need for reasoned interpretation—first by physicians and perhaps then by judges.” 12 Furthermore, as provided in the Prefatory Note to the UDDA, the statutory framework itself intentionally defers to the evolving practice of medicine, informed by the principles of medical ethics, to determine death. 13 Any analysis of what constitutes a determination of death must therefore first be rooted in accepted medical standards, including considerations of intent and effectiveness of interventions after circulatory and respiratory functions have ceased, and not simply on a presumed judicial reading or interpretation of select words and phrases in the statute.
Second, regardless of whether “irreversible cessation” means “permanent cessation,” 11 which Glazier and Capron assume to be the case but which the American College of Physicians admits is a subject of continued debate, 6 in either case, the cessation of functions under the UDDA can only rationally be considered in relation to spontaneous cardiorespiratory function. All 50 states recognize the concept of brain death as a matter of law, 9 , 12 although the statutes governing the determination of death in some states specifically reference “spontaneous” functions. 14 Furthermore, the common law standard for determining death has consistently been “the cessation of all vital functions, traditionally demonstrated by an ‘absence of spontaneous respiratory and cardiac functions.’” 13 The UDDA, like all uniform acts, aims to extend the common law to include new techniques for determining death. 13 It does not intend to subvert it.
As a result, in any context, including cDCDD‐NRP, an individual determined to be dead by a medical professional due to irreversible circulatory and respiratory function, which is further confirmed when the individual does not spontaneously resuscitate for a set period of time after declaration of death and before organ procurement begins, is by all definitions and measures dead. NRP does not change that fact. NRP cannot resuscitate the deceased because the capacity for spontaneous function remains absent and because interventions were determined medically ineffective in accordance with accepted medical standards. Instead, NRP perfuses tissues in situ. Organ procurement pursuant to NRP, which proceeds well after death has been determined, and regardless of the way it is performed, cannot be an inducement of death and is consistent in all manners with the dead donor rule.
3 THE UDDA, RESUSCITATION, AND MECHANICAL SUPPORT
A reading of the UDDA as suggested by Glazier and Capron would render it impossible for a medical professional ever to determine death based on an irreversible cessation of respiratory and circulatory function—ironically, the only way to recognize death before the recognition of brain death under the UDDA. Virtually any person whose circulation has stopped could have it restarted with cardiopulmonary resuscitation (CPR) techniques, including extracorporeal membrane oxygenation (ECMO), bag‐mask ventilation, or chest compressions. 15 Such a reading or interpretation of the UDDA would render it meaningless, violating fundamental and universally accepted principles of statutory construction. “A statute ought, upon the whole, to be so construed that, if it can be prevented, no clause, sentence, or word shall be superfluous, void, or insignificant.” 16 Because of that, to be meaningful, “irreversible” or “permanent” inherently must mean “spontaneous” in situations where the patient or their surrogates have requested the allowance of natural death. Otherwise, following do‐not‐resuscitate orders or allowing patients to die after the withdrawal of life‐sustaining therapies would be suspect.
Given these considerations, the mechanism to determine death under the UDDA is legally sufficient as it exists today for a determination of death in all contexts, including cDCDD‐NRP. Across all DCDD protocols, including those accepted by Glazier and Capron, irreversibility is confirmed following a set period of observation during which function does not spontaneously return. Before addressing issues that very well may not exist by engaging in unnecessary revisions to the UDDA, which the 1981 UDDA Commission itself discouraged, other public disruptions to the current determination of death framework as it exists today should be considered. Some states already face unreasonable attempts to revise their determination of death statutes. Revising those statutes unnecessarily could invite other unwanted and ill‐advised revisions.
4 CRIMINAL PROSECUTION FOR CDCDD‐NRP IS INCONSISTENT WITH US ORGAN DONATION LAWS
Glazier and Capron indicate that those following NRP protocols in the organ recovery process could face criminal prosecution by an “ambitious district attorney.” 8 This dramatic assertion ignores protections afforded to those engaged in this work. “No person authorized by law to determine death, who makes such a determination in accordance with the [UDDA], should, or will be, liable for damages in any civil action or subject to prosecution in any criminal proceeding for his acts or the acts of others based on that determination.” 13 Those relying on that determination are also not liable for civil damages or subject to criminal prosecution for their good faith reliance. 17 Further, under the 2006 Revised Uniform Anatomical Gift Act (UAGA), “a person that acts in accordance with this [act] or with the applicable anatomical gift law of another state or attempts in good faith to do so is not liable for the act in a civil action, criminal prosecution, or administrative proceeding.” 18 It is also important to note that, under the UAGA, “[n]either the physician who attends the decedent at death nor the physician who determines the time of the decedent's death may participate in the procedures for removing or transplanting a part from the decedent.” 18 Finally, we as organ procurement professionals take no part in the independent medical professional's determination of a potential donor's death.
All of this offers the appropriate safeguards for the potential donor and his or her family members when considering the withdrawal of life supporting therapies and the potential for organ donation. It also protects the public's trust in the donation system. Prosecutorial efforts in these situations, if attempted at all, would be an affront to the donor and the donor's family and their ability to make autonomous medical decisions in consultation with their medical provider, would be easily overcome in a motion to dismiss presented to a criminal court judge, and would be a complete waste of taxpayer dollars.
5 CONCLUSIONS
Glazier and Capron offer a handful of ways to avoid the issues they posit exist. In our analysis, some of them are legally impossible, others are clinically impossible, and even others, including the use of ex situ support for organs, would unnecessarily and significantly complicate the process, increasing the risk of poor outcomes and health disparities while potentially limiting assessment and optimal recovery of multiple organs from one donor. We maintain that the potential donor and his or her family, including their intentions, remain at the core of this discussion. Contrary to what Glazier and Capron have espoused, the intentions of the donor and his or her family are appropriately and fundamentally part of the medical team's consideration when counseling them on the withdrawal of life sustaining therapies and when determining death according to accepted medical standards pursuant to the legal structure set forth under the UDDA. US laws as they exist today recognize that fact and should stand without revision. Our laws support the potential donor, the donor family, and the medical professionals navigating difficult conversations through the end‐of‐life care of their patients. Critically, existing laws support organ recovery professionals engaged in the lifesaving work of organ donation that occur today, including cDCDD‐NRP.
DISCLOSURE
The authors of thismanuscript have conflicts of interest to disclose as described by the American Journal of Transplantation. BP directs Transplant Ethics and Policy Research at NYU Grossman School of Medicine, a program supported by a gift from United Therapeutics. ML is supported by training grant number K01 DK114388 from the National Institute of Diabetes and Digestive and Kidney Diseases. All other authors of this article have no conflicts of interest to disclose as described by the American Journal of Transplantation.
ACKNOWLEDGMENTS
We would like to thank Seth Karp, MD, Anji E. Wall, MD, PhD, and Stephen P. Wall, MD, MSc, MAEd, for their review of the final draft of this article.
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REFERENCES
1 Organ Procurement and Transplantation Network . 30 OPOs participating in national project to increase procurement of DCD donors. Published January 20, 2022. Accessed February 25, 2022 at https://optn.transplant.hrsa.gov/news/30‐opos‐participating‐in‐national‐project‐to‐increase‐procurement‐of‐dcd‐donors/
2 Gries CJ , White DB , Truog RD , et al. An official American Thoracic Society/International Society for Heart and Lung Transplantation/Society of Critical Care Medicine/Association of Organ Procurement Organizations/United Network of Organ Sharing Statement: ethical and policy considerations in organ donation after circulatory determination of death. Am J Respir Crit Care Med. 2013;188 (1 ):103‐109.23815722
3 Hessheimer AJ , Coll E , Torres F , et al. Normothermic regional perfusion vs. super‐rapid recovery in controlled donation after circulatory death liver transplantation. J Hepatol. 2019;70 :658‐665.30582980
4 Ruiz P , Gastaca M , Bustamante FJ , et al. Favorable outcomes after liver transplantation with Normothermic regional perfusion from donors after circulatory death. Transplantation. 2019;103 :938‐943.30063694
5 Parent B , Moazami N , Wall S , et al. Ethical and logistical concerns for establishing NRP‐cDCD heart transplantation in the United States. Am J Transplant. 2020;20 (6 ):1508‐1512.31913567
6 American College of Physicians, Ethics, Determination of Death, and Organ Transplantation in Normothermic Regional Perfusion (NRP) with Controlled Donation after Circulatory Death (cDCD): American College of Physicians Statement of Concern. Accessed February 25, 2022 at https://www.acponline.org/acp‐newsroom/the‐american‐college‐of‐physicians‐says‐organ‐procurement‐method‐raises‐significant‐ethical‐concerns
7 Wall AE , Fiedler A , Karp S , et al. Applying the ethical framework for donation after circulatory death to thoracic normothermic regional perfusion procedures. Am J Transplant. 2022.
8 Glazier A , Capron A . NRP and US legal standards for determining death are not aligned. Am J Transplant. 2022.
9 Nikas N , Bordlee D , Moreira M . Determination of death and the dead donor rule: a survey of the current law on brain death. J Med Philos. 2016;41 (3 ):237‐256.27097648
10 Barnes E , Greer D . Inconsistency in brain death determination should not be tolerated. AMA J Ethics. 2020;22 (12 ):E1027‐1032.33419503
11 Bernat J . How the distinction between “irreversible” and “permanent” illuminates circulatory‐respiratory death determinations. J Med Philos. 2010;35 (3 ):242‐255.20439357
12 Defining Death: A Report on the Medical, Legal and Ethical Issues in the Determination of Death. President's Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research. July 1981.
13 National Conference of Commissioners on Uniform State Laws. Uniform Determination of Death Act. 1980.
14 Death, legal definition, § 194.005 R.S.Mo. Accessed February 25, 2022 at https://advance‐lexis‐com.ezproxy.med.nyu.edu/api/document?collection=statutes‐legislation&id=urn:contentItem:8N59‐WC82‐8T6X‐736B‐00000‐00&context=1516831
15 Parent B , Caplan A , Moazami N , Montgomery RA . Response to American College of Physician's statement on the ethics of transplant after normothermic regional perfusion. Am J Transplant. 2022;22 (5 ):1307‐1310. 10.1111/ajt.16947 35072337
16 Duncan v. Walker, 533 U.S. 167, 2001 (internal quotation marks omitted); see United States v. Menasche, 348 U. S. 528, 538‐539 (1955) (“It is our duty ‘to give effect, if possible, to every clause and word of a statute.’” (quoting Montclair v. Ramsdell, 107 U. S. 147, 152 (1883))). Accessed February 25, 2022 at https://advance‐lexis‐com.ezproxy.med.nyu.edu/api/document?collection=cases&id=urn:contentItem:439D‐6JH0‐004C‐0034‐00000‐00&context=1516831
17 Limitation of Liability. Tex. Health & Safety Code § 671.002 (This document is current through the 2021 Regular Session of the 87th legislature, 2021 1st Called Session, 2021 2nd Called Session, 2021 3rd Called Session, 2021 ballot propositions, and the 2022 ballot proposition contingencies). Accessed February 25, 2022 at https://advance‐lexis‐com.ezproxy.med.nyu.edu/api/document?collection=statutes‐legislation&id=urn:contentItem:8N59‐WC82‐8T6X‐736B‐00000‐00&context=1516831
18 National Conference of Commissioners on Uniform State Laws. Revised Uniform Anatomical Gift Act (2006). §18.
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PMC010xxxxxx/PMC10163290.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37148463
805
10.1007/s40121-023-00805-1
Original Research
Diagnosis of Respiratory Syncytial Virus in Adults Substantially Increases When Adding Sputum, Saliva, and Serology Testing to Nasopharyngeal Swab RT–PCR
http://orcid.org/0000-0001-7801-5374
Ramirez Julio [email protected]
1
Carrico Ruth 1
Wilde Ashley 1
Junkins Alan 1
Furmanek Stephen 1
Chandler Thomas 1
Schulz Paul 1
Hubler Robin 2
Peyrani Paula 2
Liu Qing 2
Trivedi Sonali 2
Uppal Sonal 2
Kalina Warren V. 2
Falsey Ann R. 3
Walsh Edward E. 3
Yacisin Kari 2
Jodar Luis 2
Gessner Bradford D. 2
Begier Elizabeth 2
1 grid.420119.f 0000 0001 1532 0013 Norton Infectious Diseases Institute, Norton Healthcare, 601 S Floyd St, Louisville, KY 40202 USA
2 grid.410513.2 0000 0000 8800 7493 Pfizer Inc, Collegeville, PA USA
3 grid.412750.5 0000 0004 1936 9166 Department of Medicine, Infectious Diseases Division, University of Rochester Medical Center, Rochester, NY 14642 USA
6 5 2023
6 5 2023
6 2023
12 6 15931603
18 1 2023
6 4 2023
© Pfizer Inc 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Introduction
Nearly all existing respiratory syncytial virus (RSV) incidence estimates are based on real-time polymerase chain reaction (RT–PCR) testing of nasal or nasopharyngeal (NP) swabs. Adding testing of additional specimen types to NP swab RT–PCR increases RSV detection. However, prior studies only made pairwise comparisons and the synergistic effect of adding multiple specimen types has not been quantified. We compared RSV diagnosis by NP swab RT–PCR alone versus NP swab plus saliva, sputum, and serology.
Methods
This was a prospective cohort study over two study periods (27 December 2021 to 1 April 2022 and 22 August 2022 to 11 November 2022) of patients aged ≥ 40 years hospitalized for acute respiratory illness (ARI) in Louisville, KY. NP swab, saliva, and sputum specimens were collected at enrollment and PCR tested (Luminex ARIES platform). Serology specimens were obtained at acute and convalescent timepoints (enrollment and 30–60-day visit). RSV detection rate was calculated for NP swab alone and for NP swab plus all other specimen type/test.
Results
Among 1766 patients enrolled, 100% had NP swab, 99% saliva, 34% sputum, and 21% paired serology specimens. RSV was diagnosed in 56 (3.2%) patients by NP swab alone, and in 109 (6.2%) patients by NP swab plus additional specimens, corresponding to a 1.95 times higher rate [95% confidence interval (CI) 1.62, 2.34]. Limiting the comparison to the 150 subjects with all four specimen types available (i.e., NP swab, saliva, sputum, and serology), there was a 2.60-fold increase (95% CI 1.31, 5.17) compared to NP swab alone (3.3% versus 8.7%). Sensitivities by specimen type were: NP swab 51%, saliva 70%, sputum 72%, and serology 79%.
Conclusions
Diagnosis of RSV in adults was several-fold greater when additional specimen types were added to NP swab, even with a relatively low percentage of subjects with sputum and serology results available. Hospitalized RSV ARI burden estimates in adults based solely on NP swab RT–PCR should be adjusted for underestimation.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40121-023-00805-1.
Keywords
Respiratory syncytial virus
Acute respiratory illness
Polymerase chain reaction
Disease diagnosis
Pfizer IncNorton Healthcareissue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
==== Body
pmcKey Summary Points
Why carry out this study?
Adding the collection and testing of additional specimen types to NP swab RT–PCR increases RSV detection, but prior studies only made pairwise comparisons and the synergistic effect of adding multiple specimen types has not yet been quantified.
We sought to compare RSV diagnosis by PCR testing of NP swab alone versus NP swab plus saliva, sputum, and serology.
What was learned from the study?
RSV was diagnosed in 56 (3.2%) patients by NP swab alone, and in 109 (6.2%) patients by NP swab plus additional specimens, corresponding to a 1.95 times higher diagnosis rate (95% CI 1.62, 2.34).
Approximately half of identified positives were missed by NP swab testing, even with a relatively low percentage of subjects with sputum and serology results available.
Hospitalized RSV ARI burden estimates in adults based solely on NP swab RT–PCR should be adjusted for underestimation.
Introduction
Respiratory Syncytial Virus (RSV) is a leading cause of respiratory illness in adults, with older adults and those with compromised cardiac, pulmonary, or immune systems most at risk of severe disease [1–5]. Although underrecognized, estimated RSV disease burden is comparable to the burden of influenza in older adults, with both viruses contributing to a similar number of symptomatic illnesses, hospitalizations, and death overall, despite substantial variability in the relative burden of the two viruses from year to year [6]. Due to the nonspecific clinical manifestations of RSV, which often overlap with those of other viral and bacterial causes of acute respiratory illness (ARI), and can contribute to exacerbations of common illnesses such as COPD or CHF, laboratory testing is required for confirmation of RSV infection [7].
Published incidence estimates of RSV disease in adult patients hospitalized with ARI have primarily relied on reverse transcription polymerase chain reaction (RT–PCR) testing of NP swabs [8–10]. However, the results of upper respiratory tract testing using NP swabs in adults may be discordant with positive lower respiratory tract (LRT) testing [11]. Possible explanations for this finding include: (1) a decreased viral concentration in the nasopharynx due to sampling late in the infection at a time when virus may still be present at higher concentrations in the lower respiratory tract [12], (2) lower viral concentrations in adult nasal secretions when compared with children [12], and (3) inadequate NP swab samples due to dry nasal mucosa and operational reasons.
Adding the collection and testing of an additional specimen type to NP/nasal swab RT–PCR has been documented to increase RSV detection in pairwise comparisons. A recent metaanalysis quantified the percent increase in RSV diagnosis by specimen type added: 52% increase for sputum RT–PCR, 44% for paired serology testing, and 28% for oropharyngeal swab RT–PCR [11]. However, the synergistic effect of adding multiple specimen types to NP swab testing has not yet been quantified. Furthermore, saliva has recently been shown to be a high yield specimen for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT–PCR testing [13], but it has not been evaluated directly head-to-head against NP or nasal swab RT–PCR for RSV testing.
The quantification of RSV underestimation associated with sole use of NP swab for diagnosis will allow for adjustment of published RSV incidence rates to estimate the true burden of RSV disease. These more accurate burden of disease estimates will facilitate appropriate decision making regarding the use of RSV disease preventive interventions, such as vaccination. The objective of this study was to define the underestimation in RSV diagnosis by comparing RSV diagnosis rates with NP swab RT–PCR alone to RSV diagnosis rates with the addition of saliva, sputum, and/or serology testing.
Methods
Study Design, Setting, and Patients
This was a prospective cohort study of patients hospitalized with ARI in four adult acute care hospitals in Louisville, KY during two study periods from 27 December 2021 to 1 April 2022, and 22 August 2022 to 11 November 2022. Study periods were chosen to align with real-time RSV activity in Louisville, KY. Patients were eligible for inclusion if they (1) were aged 40 years or older, (2) were hospitalized with an ARI, defined as the presence of at least one of the following: (a) new onset or increase from baseline in any of following nine signs and symptoms—nasal congestion, rhinorrhea, sore throat, hoarseness, cough, sputum production, dyspnea, wheezing, hypoxemia, or (b) admitting diagnosis suggestive of ARI or (c) exacerbation of underlying cardiopulmonary disease involving acute respiratory symptoms, and (3) consented to have NP swab plus at least one other specimen obtained. These criteria for ARI are consistent with previous prospective incidence studies [2, 5].
The age cutoff was selected to include older adults, as well as some middle-aged adults, with a higher likelihood of having underlying conditions such as cardiopulmonary disease, whose prevalence increases with advancing age. Patients were excluded from the study if they developed signs and symptoms of ARI after being hospitalized for 48 h or more, had onset of symptoms more than 21 days before hospital admission, or were previously enrolled in this study within the 45 days prior of their current admission.
This study was approved (#21-N0325) by WCG IRB. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. After informed consent was obtained, we sought to collect NP swab, saliva, sputum, and an acute blood specimen on all subjects. Respiratory samples on any given subject were collected on the day of enrollment. Patients were scheduled for a follow up between 30 and 60 days to collect convalescent blood specimens. In subjects that were unable to produce saliva, a saline mouth wash was obtained.
Study Definitions
RSV Diagnosis
RSV detection by RT–PCR from NP swab, saliva, and sputum specimens was defined as a positive RSV case. Evidence of concurrent RSV infection was defined as a four-fold increase between acute and convalescent paired blood specimens in antibodies to any of four RSV antigens tested, consistent with previously published RSV serology studies [9].
RSV Diagnosis Rates
RSV diagnosis rate from NP swab alone was calculated as the number of subjects with RSV detected from NP swab specimens divided by the number of subjects in the study. RSV diagnosis rate from NP swab plus other specimens was calculated as the number of subjects with RSV diagnosed by any specimen divided by the number of patients in the study.
RSV diagnosis rate increased using additional specimens
The ratio of RSV diagnosis was calculated by dividing the proportion of RSV diagnosis from NP swab plus other specimens by the proportion of RSV diagnosis from NP swab alone. The inverse of this ratio was used to determine the underestimation of RSV diagnosis. Additionally, the ratio of RSV diagnosis was calculated for each combination of sample types in addition to NP swab. This was calculated for the study population overall, regardless of specific results available, and for each subset of the study population with specific results available to illustrate what the increase in diagnosis might be in ideal conditions with high levels of sample collection.
Study Variables
Data on demographics, social, and medical history, vaccination status, standard of care SARS-CoV-2 testing, clinical diagnosis, as well as hospital course, including length of stay and patient mortality, were collected from patient questionnaires and from electronic medical records.
Specimen Processing and Testing
NP swab
NP swab specimens were tested using the ARIES Luminex FluA/B/RSV panel and processed in accordance with standard operating procedures [14].
Sputum
Sputum specimens were processed and tested using the ARIES Luminex FluA/B/RSV platform. Briefly, sputum specimens were diluted to 50% water solution, and mixed by vortex. A swab of this solution was mixed into 700 uL of sterile water and mixed by vortex a second time. From this mixed solution, 200 uL was pipetted into the Luminex cartridge vial.
Saliva
Saliva specimens were processed and tested using the ARIES Luminex FluA/B/RSV platform. Briefly, saliva was handled separately depending on viscosity. For normal saliva, the specimen was mixed by vortex and 200 uL was pipetted into the Luminex cartridge vial. Saliva that was too thick or viscous to pipette was processed the same way as sputum (above).
Serology
Serology was performed by Pfizer central laboratory using Luminex-based total antibody RSV assays. Briefly, the 4-plex assay included spectrally distinct magnetic microspheres coated with recombinant matrix protein, nucleoprotein, and peptide sequences unique to the G protein for RSVA and RSVB (Cambridge Research Biochemicals) [15]. Antigen specific antibodies were detected with a goat-anti-human total Ig Phycoerythrin labeled antibody (Southern Biotech) [16]. Fluorescence was expressed as median fluorescence intensity and results are calculated using a serum reference standard. Persons who received intravenous immunoglobulin (IVIG) treatment with positive serology results were considered false positives and removed from analysis.
Statistical Analysis
Only patients with an NP swab and at least one other specimen result were included in this analysis. Patient characteristics were reported as medians and interquartile ranges (for continuous data) and frequencies and percentages (for categorical data). Venn and Euler diagrams were produced to show RSV diagnosis by specimen type. RSV diagnosis rates were calculated and reported as percentage positive. The RSV detection rate by NP swab alone was used as the baseline RSV rate for comparison, and RSV diagnosis ratios were calculated as percent increase from baseline, with 95% confidence intervals (CIs) calculated. The sensitivity of each assay for diagnosing RSV was calculated for each sample type and combination, with 95% confidence intervals calculated as well. P-values less than 0.05 were considered statistically significant. Sensitivity calculations for each specimen type were limited to subjects that had results for that specimen type. The proportion of all positives detected by a given specimen type was also calculated to capture the real-world benefit of adding a given specimen type, given its operational feasibility and sensitivity. All analysis was performed using R version 4.1.2 [17] and SAS version 9.4 (SAS 9.4, Cary, NC. SAS Institute Inc.).
Results
Patient Population
A total of 1766 participants provided informed consent and enrolled in the study. Figure 1 depicts the study flowchart. Among enrolled participants hospitalized for ARI, most were female (55%), white (70%), and were community-dwelling (95%). Diabetes mellitus and chronic obstructive pulmonary disease were the most frequent comorbidities (39% and 38% of subjects, respectively), and 28% (n = 494) of participants were immunocompromised. Table 1 depicts patient characteristics for study participants.Fig. 1 Study flowchart
Table 1 Study specimen collection, RSV prevalence results, and sensitivity by specimen type
Study specimens N Percentage with
this specimen type n positive
by specimen % positive
overall Percentage of all positives
identified by this specimen type 95% CI RSV positives from
any source among patients
with results by that specimen type Sensitivity* 95% CI
Overall 1766 100.0% 109 6.2% N/A N/A 109 N/A N/A
NP swab 1766 100.0% 56 3.2% 51.4% 42.0–60.8% 109 51.4% 42.0–60.8%
Saliva** 1740 98.5% 75 4.3% 68.8% 60.1–77.5% 107 70.1% 61.4–78.8%
Sputum 606 34.3% 41 6.8% 37.6% 28.5–46.7% 57 71.9% 60.3–83.6%
Serology 367 20.8% 23 6.3% 21.1% 13.4%-28.8% 29 79.3% 64.6–94.1%
*Sensitivity is limited to those with results with that specimen type, i.e., count of positives by that specimen type divided by count of all positives from any source among those with results by that specimen type
**In subjects that were unable to produce saliva, a saline mouth wash was obtained
RSV Diagnosis
Among 1766 participants enrolled, 100% had NP swab (n = 1766), 99% saliva (n = 1740), 34% sputum (n = 606), and 21% (n = 367) paired serology specimens tested. Overall, RSV was diagnosed in 109 participants using any/all specimens. Figure 2 depicts RSV detection by sample type for the entire study population for respiratory specimens alone (panel A) and all four specimen types (panel B). While there was some overlap in positives for each specimen type, all specimens contributed unique positives, with the greatest number of unique positives contributed by saliva and serology. A total of 56 among 109 participants (51%) had RSV detected from NP swabs, while 53 participants had RSV diagnosed only by other specimen types (i.e., negative by NP swab), corresponding to 49% of positives missed by NP swab testing alone. Table 1 depicts specimen collection frequency, percent RSV diagnosis by sample type for the entire study population, as well as test sensitivity estimates. Although sputum and serology were not available in all participants, these samples yielded the highest percent of RSV diagnoses when a sample was available (sputum n = 41 of 606 participants, 6.8%, and serology n = 23 of 367 specimens, 6.3%). The percent detection of saliva and NP swabs was less, but the overall number of positives greater: saliva (n = 75 of 1740 specimens, 4.3%), and NP swabs (n = 56 of 1766, 3.2%). In the overall study population, saliva detected the most positives (75/109, 69%), followed by NP swab (56/109, 51%), sputum (41/109, 38%), and serology (23/109, 21%). We examined each test’s sensitivity by limiting to those with that result type, and sensitivity ranged from 51% for NP swab to 79% for serology.Fig. 2 Respiratory syncytial virus (RSV) diagnosis by specimen type: all specimen types contribute unique positives. A (left) Venn diagram of nasopharyngeal (NP) swab, saliva, and sputum specimens detecting RSV from RT–PCR diagnostic testing. B (right) Euler diagram of NP swab, saliva, sputum, and serology specimens diagnosing RSV
RSV Diagnosis Rates by Sample Type Combinations
For the entire study population, regardless of specific results available, the percent increase in RSV diagnosis when adding results from additional sample types, relative to using NP swab alone (reference value), is depicted in Fig. 3 for each combination of sample types. As noted, NP swab alone diagnosed RSV in 56 (3.2% of participants). Adding results from other specimen types, RSV diagnoses increased to 109 (6.2% of participants), corresponding to a 1.95-fold increase (95% CI 1.62, 2.34-fold increase) in detection, over NP swab results alone. The number of participants with each of the various combinations of specimen types tested and the percent increases in RSV diagnosis for each combination of sample types, relative to using NP swab alone (reference value), limiting to those participants with the specific results available in the comparison, are depicted in Table 2. When limiting analysis to the 150 participants with all four specimen types available, RSV was diagnosed in 5 participants (3.3%) by NP swab alone and in 13 participants (8.67%) when all results were used. A 3.20-fold increase in detection (95% CI 1.83, 5.78) was seen adding saliva and serum results to NP among 363 participants with those three specimen types tested. For the 580 subjects with available NP swab, saliva, and sputum samples, there was a 1.63-fold increase in detection (95% CI 1.31, 2.04) when adding the saliva and sputum results.Fig. 3 The percent increase in respiratory syncytial virus (RSV) diagnosis when adding additional specimen types in the analysis, over using nasopharyngeal (NP) specimens alone
Table 2 Increase in RSV detection associated with testing additional specimen types, beyond NP swab, for populations with specific sample results available
Groups by available specimens N Count of patients by
NP swab positive Detection rate with NP
swab positive
(per 100 patients) Count of patients by
any listed
specimen positive Detection rate with any
listed specimen positive
(per 100 patients) Detection rate ratio
(any listed/NP swab) 95% CI of
detection rate ratio
All subjects
All four specimens 150 5 3.33 13 8.67 2.60 1.31, 5.17
Three specimens
NP swab/saliva/sputum 580 30 5.17 49 8.45 1.63 1.31, 2.04
NP swab/saliva/serum 363 8 2.20 26 7.16 3.25 1.83, 5.78
NP swab/sputum/serum 154 5 3.25 14 9.09 2.80 1.39, 5.65
Two specimens
NP swab/saliva 1740 55 3.16 85 4.89 1.55 1.32, 1.81
NP swab/sputum 606 31 5.12 43 7.10 1.39 1.15, 1.67
NP swab/serum 367 8 2.18 24 6.54 3.00 1.70, 5.28
Participant Characteristics by Specimen Type Positive
Characteristics for RSV positive participants by specimen type positive are depicted in Table 2. Overall, participants with RSV identified by NP swab (regardless of other results) had a similar time from symptom onset to specimen collection than subjects exclusively positive by other non-NP respiratory specimen types (median of 4 days versus 3 days, respectively), but subjects that were NP negative and serology positive had a longer median duration of 6 days for symptom onset. Thirty percent of RSV-diagnosed subjects were immunocompromised, 23% of participants with RSV identified by NP swab were immunocompromised compared to 38% of subjects detected by non-NP swab specimen types.
Discussion
Our study indicates that the inclusion of saliva, sputum, and serology to RT–PCR of NP swab increased the diagnostic yield for RSV by two-fold or more in adult patients hospitalized with ARI. Even though RT–PCR of NP swab is the most commonly used test to detect RSV in hospitalized patients, our study indicates that it will miss a substantial percentage of patients hospitalized with RSV-associated ARI. Prior literature has reported increased detection associated with adding sputum or serology to NP swab [9], but this is the first study utilizing a wide variety of specimen types, including saliva, and assessing their synergistic effects for RSV diagnosis.
Our data indicate that a more accurate burden of RSV disease in future studies can be achieved by testing multiple specimen types, or adjusting for underestimation associated with use of limited specimen types. Furthermore, our study suggests that vaccine studies evaluating efficacy or effectiveness of an RSV vaccine should include multiple specimens for diagnosis of disease. In a Centers for Disease Control and Prevention (CDC) meeting of experts for the purpose of identifying gaps in the epidemiology of RSV, the experts noted a need to document potential underestimation of disease burden due to testing behaviors [18]. In a recent metaanalysis of RSV incidence among older adults in the USA, an adjustment factor of 1.5 was included to account for diagnostic testing under-ascertainment when only RT–PCR was used [9]. This correction factor was based on pairwise comparisons of different specimen type results from the literature that did not account for synergistic effects of multiple specimen use. Our results indicate that a correction factor greater than 2 may be more appropriate, as indicated by the 2.6-fold increase in yield among those with all four specimen types.
Notably, saliva specimens yielded the highest number of RSV detections among respiratory specimens if used alone (n = 75) and added an additional 30 unique RSV cases to the 56 diagnosed with NP samples. Since saliva is readily obtained from most subjects, as shown in our data, the simple addition of this sample to NP swabs may provide much more accurate estimates of RSV incidence in this population. There are several potential reasons for this finding. RSV may replicate in the primary salivary glands such as parotid, submandibular, and sublingual glands, producing a constant flow of the virus or viral genetic material into the saliva. In addition, saliva may also serve as reservoir of pooled secretions from the nasopharynx. Saliva has emerged as a sensitive and reliable specimen type for SARS-CoV-2 testing, with one study finding that saliva has higher viral titers than NP swab and is a more consistent specimen, such that no instances were seen of a negative result followed by a positive result [13]. We did not attempt to measure viral load in the saliva in comparison to NP swabs. In our study, it is notable that saliva (or normal saline mouth wash) was available in nearly all study subjects. It is possible that saliva may be a more desirable diagnostic sample for the diagnosis of respiratory viruses, both for better yield and tolerability to patients.
Among hospitalized adults, material captured with NP or nasal swabs can be limited by the difficulty of taking a sufficient sample and nasal dryness, potentially from nasal oxygen use [7], diuretic administration, or dry indoor air. The lower positivity rate of NP swab testing may also be due in part to a prolonged time from symptom onset to hospitalization and swab collection, such that at the time of hospitalization, the viral titers in nasal secretion may have dropped and RSV may no longer be detectable in the nasopharynx [19]. Nasal swabs are more likely to be positive in persons that still have upper respiratory symptoms [19]. Patients with RSV detected by serology specimens only, had a longer duration of symptoms at the time of sampling (median 6 days versus 4 days), but further study is needed to better characterize the differences in the cases detected by each specimen type.
In patients with a productive cough, sputum was a useful specimen for RSV identification. Sputum has been shown to have higher RSV titers than nasal swabs [12], allowing for increased detection of RSV when this specimen is available [11], which is consistent with results from other respiratory viruses such as influenza and SARS-CoV-2 [13, 19–21]. In our study, we found seven patients with RSV that were NP swab negative and diagnosed by sputum alone (Fig. 1), all but one of whom had lower respiratory tract illness diagnosis. This corresponds to a 39% increase in RSV detection over NP swab alone among subjects with both specimen types, comparable to published paired assessments of adding sputum to NP swab testing [pooled percent increase from recent metaanalysis: 52% (95% CI 15, 101)] [11].
Serology testing does not impact the clinical management of a hospitalized patient; however, it represents an important epidemiological tool to define the burden of disease and can be used in vaccine efficacy studies to augment RSV diagnosis end points when feasible. A recent metaanalysis reported a 42% increase in detection (95% CI 19, 70) over NP/nasal RT–PCR swab alone. Analyses limited to older adults (more comparable to our study population) reported a detection increase of 50% to 64% [11]. This higher detection rate by serology among older adults may be due to their higher serum IgG responses following RSV infection compared to younger adults, possibly related to their higher RSV nasal titers and longer viral shedding [12]. This longer viral shedding correlates with persistent secretion of antibody by plasma cells and is presumed due to diminished cellular immunity associated with immune-senescence [22]. One potential limitation of serology is being certain that the rise in IgG clearly brackets an identifiable illness. It is possible that a rise is RSV specific IgG could be related to an illness that occurred after hospital discharge. To mitigate this, we collected NP swabs at convalescent visits from anyone with intercurrent ARI symptoms; we did not have any positive results suggesting intercurrent RSV was not an important contributor to infections, identified by a four-fold rise in serology.
One strength of our study was that all respiratory specimens were collected on the same day, at time of enrollment. Additionally, all collected specimens had RT–PCR tests performed on the same platform. Furthermore, we collected sputum in 95% of the 646 patients producing sputum in our study population.
The primary limitation of our study was the low number of subjects with serology results available, thus diminishing the number of subjects with all 4 sample types for analysis. Consequently, our estimate of the increase in detection of RSV may be too conservative. Another limitation of our study is that with only 109 patients with RSV detected, we were unable to perform analysis in subgroups such as the immunocompromised patients. Further study is required to improve precision regarding the level of RSV underestimation within specific subgroups. Lastly, another potential limitation is that we did not assess if nasal swab or oropharyngeal swab may increase RSV detection when compared with RT–PCR of NP swab alone, because of its dominance in RSV incidence studies [9, 13]. Nasal and oropharyngeal swab likely have substantial overlap regarding material collected with other specimens included in the study, namely NP swab and saliva, respectively. Finally, RT–PCR positive results may uncommonly reflect a prior infection with residual viral RNA in the nasopharynx, particularly among immunocompromised individuals.
In conclusion, our study found that RSV detection increases several-fold with the addition of testing from other specimen types besides NP swab, especially saliva. Future studies assessing the RSV burden should consider additional testing of saliva, sputum, and serology to adequately detect RSV-positive patients. Burden of disease estimates based solely on NP swab RT–PCR should be adjusted for underestimation, as should metaanalyses of existing RSV incidence estimates [23].
Acknowledgements
We would like to thank the NIDI RSV study group (see supplementary materials) for performing the study. Additionally, we would like to thank the nurses at Norton Healthcare for their collaboration with the study.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file 1 (PDF 525 KB)
Funding
This work was supported by Pfizer Inc., including supporting the journal’s rapid service fee. This study was conducted as a collaboration between Norton Infectious Diseases Institute, Norton Healthcare, and Pfizer. Norton Infectious Diseases Institute, Norton Healthcare is the study sponsor.
Author Contributions
Study concept and design was carried out by Elizabeth Begier, Robin Hubler, Julio Ramirez, Ruth Carrico, Ashley Wilde, Paula Peyrani, Ann R. Falsey, Edward Walsh, Luis Jodar, and Bradford D. Gessner. Data acquisition was performed by Julio Ramirez, Ruth Carrico, Ashley Wilde, Alan Junkins, Stephen Furmanek, Thomas Chandler, and Warren V Kalina. Data interpretation and analysis was performed by all authors. Writing the first draft was performed by Julio Ramirez and Stephen Furmanek. Participation in drafting or revision involved all authors.
Disclosures
Julio Ramirez, Ruth Carrico, Ashley Wilde, Alan Junkins, Stephen Furmanek, Thomas Chandler, and Paul Schulz are employees of Norton, which received fees from Pfizer in relation to this study. Robin Hubler, Paula Peyrani, Qing Liu, Sonali Trivedi, Sonal Uppal, Warren V Kalina, Kari Yacisin, Luis Jodar, Bradford D. Gessner, and Elizabeth Begier are employees of Pfizer and may hold Pfizer stock and/or stock options. Ann R. Falsey has research grants from Pfizer, Janssen, CyanVac, and BioFire Diagnostics, served on the Data and Safety Monitoring Board for Novavax, and consulted for Arrowhead Pharmaceuticals. Edward E. Walsh has research grants from Merck and Pfizer and non paid consulting for Moderna and Pfizer.
Compliance with Ethics Guidelines
This study was approved (#21-N0325) by WCG IRB. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. All specimen and data collection were performed after informed consent was obtained.
Data Availability
Data access may be requested from Pfizer data request portal: https://www.pfizer.com/science/clinical-trials/trial-data-and-results/data-requests
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC010xxxxxx/PMC10167631.txt |
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NAR Cancer
NAR Cancer
narcancer
NAR Cancer
2632-8674
Oxford University Press
37180029
10.1093/narcan/zcad019
zcad019
AcademicSubjects/SCI00030
AcademicSubjects/SCI00980
AcademicSubjects/SCI01060
AcademicSubjects/SCI01140
AcademicSubjects/SCI01180
DNA Repair and Nucleic Acids Therapeutics in Cancer
Molecular mechanisms protecting centromeres from self-sabotage and implications for cancer therapy
Nassar Rim UPMC Hillman Cancer Center, Department of Pharmacology and Chemical Biology, University of Pittsburgh Cancer Institute, Pittsburgh, PA 15232, USA
Thompson Lily UPMC Hillman Cancer Center, Department of Pharmacology and Chemical Biology, University of Pittsburgh Cancer Institute, Pittsburgh, PA 15232, USA
Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
https://orcid.org/0000-0002-2542-2563
Fouquerel Elise UPMC Hillman Cancer Center, Department of Pharmacology and Chemical Biology, University of Pittsburgh Cancer Institute, Pittsburgh, PA 15232, USA
To whom correspondence should be addressed. Tel: +1 412 623 3237; Email: [email protected]
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
09 5 2023
6 2023
09 5 2023
5 2 zcad01920 4 2023
27 3 2023
27 1 2023
© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Cancer.
2023
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Abstract
Centromeres play a crucial role in DNA segregation by mediating the cohesion and separation of sister chromatids during cell division. Centromere dysfunction, breakage or compromised centromeric integrity can generate aneuploidies and chromosomal instability, which are cellular features associated with cancer initiation and progression. Maintaining centromere integrity is thus essential for genome stability. However, the centromere itself is prone to DNA breaks, likely due to its intrinsically fragile nature. Centromeres are complex genomic loci that are composed of highly repetitive DNA sequences and secondary structures and require the recruitment and homeostasis of a centromere-associated protein network. The molecular mechanisms engaged to preserve centromere inherent structure and respond to centromeric damage are not fully understood and remain a subject of ongoing research. In this article, we provide a review of the currently known factors that contribute to centromeric dysfunction and the molecular mechanisms that mitigate the impact of centromere damage on genome stability. Finally, we discuss the potential therapeutic strategies that could arise from a deeper understanding of the mechanisms preserving centromere integrity.
Graphical Abstract
Graphical Abstract Centromeres are intrinsically fragile due to their repetitive sequence and secondary structures. Increasing evidence point toward the existence of specific mechanisms to prevent centromere-associated genomic instability.
National Institutes of Health 10.13039/100000002 R35GM142982 UPMC 10.13039/501100005737
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pmcINTRODUCTION: CENTROMERE STRUCTURE AND FUNCTION
The centromere is a specialized region of the chromosome at which the kinetochore assembles. It is essential for faithful chromosomal segregation in both mitotic and meiotic cells (1,2). In humans, centromeric regions contain a highly repetitive DNA sequence composed of 171-bp alpha-satellite monomers that are arranged head-to-tail, forming homogeneous higher order repeat (HOR) arrays that can span several megabases of DNA (3) (Figure 1A). However, DNA sequence is neither necessary nor sufficient for kinetochore assembly and chromosome segregation. Rather, the centromere is defined by both genomic and epigenetic mechanisms. A specialized centromeric histone termed centromeric protein A (CENP-A) replaces the canonical histone H3 and characterizes the centromere (1,2). In addition to CENP-A, the centromere is surrounded by the constitutive centromere-associated network (CCAN), which is a subcomplex of the kinetochore that localizes to the centromere throughout the cell cycle and functions as the base linking the centromere to microtubules (Figure 1B). The CENP-A containing centromere core is flanked by inactive pericentromeric domains. Pericentromeric domains contain repeats that are less ordered, which include smaller arrays of diverged alpha-satellite monomers, transposable elements and non-alpha-satellite repeat families (4,5). A fraction of the alpha-satellite monomers contain a 17-bp motif termed a CENP-B box that is recognized and bound by the centromere binding protein B (CENP-B), which is the only known sequence-specific centromeric binding protein and is present at all centromeres except that of the Y chromosome (6). CENP-B is primarily known to stabilize the nucleator of kinetochore assembly CENP-C at the centromere (6). CENP-B can also shape centromere structure by promoting larger DNA loop formation between repeats (7). Upon CENP-B dimerization, two different CENP-B boxes are brought together, leading to compaction of the centromere (Figure 1C). CENP-B-mediated DNA looping and subsequent compaction promotes recruitment of CENP-C and deposition of CENP-A (7). In addition, non-B-form DNA structures form at centromeres, such as cruciforms, Z-DNA hairpins or i-motifs (8–10). The function of these non-B-form DNA structures at centromeres remains debated, yet some speculate a direct role in specifying centromere identity. It is possible that they mediate the deposition of CENP-A at the centromere. For instance, the CENP-A chaperone HJURP, which can recognize Holliday junctions, could bind the four-way junctions of a cruciform (11–13). i-Motifs have been identified to form in the CENP-B box and in alpha satellites (10,14). Although no studies have yet shed light on their function, it is most likely that they play a role in the structural organization of centromeres. These structures are therefore believed to be essential for the maintenance of centromere structure and function.
Figure 1. Human centromere structure and kinetochore organization. (A) Human centromeres contain arrays of HORs made of ∼171-bp alpha-satellite repeat monomers that are primarily but not completely identical as illustrated by the different color boxes (light pink, brown, purple and gray) and arranged in a head-to-tail manner. Some of these alpha satellites harbor a CENP-B protein binding motif called the CENP-B box (17-bp motif) (yellow box). Pericentromeric regions (dark green) that flank the centromere region (dark blue) are enriched in heterochromatin and contain alpha satellites in a less ordered fashion. (B) The CCAN complex creates a linkage between the centromere and microtubules in the kinetochore. The CCAN comprises five subcomplexes CENP-C, CENP-L/N, CENP-H/I/K/M/L/N, CENP-T/W/S/X and CENP-O/P/Q/R. CENP-B stabilizes the interaction between the chromatin and the kinetochore, via a direct interaction with CENP-C. The CCAN functions as a platform to recruit the KMN network (mis12/NDC80/KNL1) that directly connects the kinetochores. (C) Non-B-form DNA secondary structures formed at alpha satellites, such as cruciforms, Z-DNA hairpins or i-motifs. CENP-B also mediates the formation of DNA secondary structures.
CENTROMERES ARE SOURCES OF GENOME INSTABILITY AND TUMORIGENESIS
Genome instability refers to a range of alterations spanning from DNA base mutations and breaks to chromosome rearrangements and chromosome instability (CIN), which is itself defined by the gain or loss of chromosomes during each cell division (15). More than 95% of cancers display genome instability and CIN, which results in aneuploidy or polyploidy (16,17). Mathematical models indicate that CIN can initiate tumorigenesis prior to tumor suppressor gene inactivation (18), underlying the importance of understanding the mechanisms leading to CIN. Years of research have led to the conclusion that defects in centromere assembly or maintenance are a cause of the numerical aberrations observed in cancer cells (1,19,20). Indeed, these studies have identified centromeres as the location of breaks in multiple cancer types as evidenced by high percentages of whole-chromosome arm gain, loss or translocation (colorectal, oral and squamous cell carcinomas) (1,19,20). These studies demonstrate that centromeric breakage can directly contribute to CIN and further exacerbate cellular transformation.
Although the impact of centromere dysfunctions on genome stability and tumorigenesis is now well established, the molecular mechanisms driving them are not fully uncovered and remain a topic of investigation. Such studies will reveal important insights into the roles of these genomic loci in cancer progression. Here, we review the current knowledge related to the inherent factors contributing to centromeric dysfunction and the molecular mechanisms mitigating its impact on genome stability. Lastly, we discuss how centromere dysfunction and protection mechanisms can be leveraged for the development of promising anticancer therapeutic strategies.
Secondary DNA structures at the centromere
Secondary structures are sources of centromere fragility during replication
Centromeres are intrinsically fragile, which means that they are prone to breakage especially during replication. Centromere breakage can directly promote chromosomal rearrangements, lagging chromosomes, chromosomal bridges, aneuploidy, formation of micronuclei and chromothripsis, all hallmarks of cancer cells (21–27). This fragility is proposed to be due to their highly repetitive sequences and the presence of secondary DNA structures and loops that can make their replication challenging. However, the contribution of these structures to centromere fragility is not fully understood. Given that they promote DNA compaction and centromeric epigenetic identity (7,11,13), it is most likely that they are crucial to ensure proper cell division but need to be resolved during the replication process. Indeed, preventing DNA looping through CENP-B depletion triggers DNA decondensation and chromosome breakage during mitosis, as evidenced by γH2Ax and 53BP1 recruitment (7). Thus, during mitosis, centromeric loops seem to favor genome stability (Figure 1B). Yet, studies performed in Xenopus laevis egg extracts using bacterial artificial chromosomes (BACs) containing human alpha-satellite DNA showed that the secondary structures can slow down replication when compared to BACs harboring repeat-free DNA with similar GC base content (28). Slowing of replication machinery can contribute to fork stalling and collapse and result in replication stress-mediated double-strand break (DSB) formation, thus highlighting the need for the recruitment of specialized enzymes involved in the resolution of these structures.
Mechanisms preventing replication-associated centromere fragility
Centromeres appear to surround themselves with several DNA repair factors during replication (28). For instance, proteomic analysis of centromeric chromatin revealed the presence of the mismatch repair factors MSH2–6, which could promote the resolution of hairpins and other secondary structures potentially formed behind the fork on single-stranded DNA (ssDNA). Another study also found the presence of the nuclease and helicase DNA2. Because loss of DNA2 was found to activate ATR, it was hypothesized to resolve secondary structures ahead of the fork, preventing RPA recruitment and subsequent ATR response signaling (Figure 2A) (28,29). Interestingly, triggering replication stress using the replication inhibitor aphidicolin does not elicit RPA loading and subsequent TopBP1/ATR signaling activation, contrary to what is observed in other regions of the genome (28,29). This unexpected response is attributed to higher order structures embodied by large double-stranded DNA loops formed during replication. These loops are promoted by topoisomerase-mediated positive supercoiling and stabilized by the condensin complex subunits SMC2–4 (28) (Figure 2A). Because treatment with the topoisomerase inhibitor topotecan restores RPA loading, one role of centromere loop formation may be to prevent activation of ATR signaling behind the replication fork (28,29), thereby facilitating the replication process (Figure 2A). Additional studies will be necessary to further uncover the precise timing and dynamics involved in the formation and resolution of centromeric DNA loops, but their formation upon centromeric DNA replication may be an initial step for the chromatin condensation that occurs during the early stages of mitosis. Because these DNA loops are also mediated by CENP-B and are involved in CENP-A deposition (7), their function expands beyond the G2/M phases of the cell cycle, suggesting that they are a stable component of centromere organization. Thus, it implies that their resolution prior to replication is necessary to not impede DNA synthesis. A rapid response could be ensured by the ADP-ribose transferase PARP1, which is also enriched at centromeres (28,30). PARP1 is a DNA binding protein whose poly(ADP-ribosyl)ation (PARylation) activity is triggered upon the recognition of DNA breaks, stalled replication forks and also several non-canonical DNA structures, including hairpins (31,32). At centromeres, PARP1 activity may be directly elicited by secondary structures and DNA loops as well as by the stalled replication forks. Intriguingly, PARP1 interacts with and PARylates both CENP-A and CENP-B (33,34). During single-strand break (SSB) repair, PARylation of histones allows for their local release and subsequent chromatin relaxation (35). Similarly, the negatively charged PAR could affect the binding of the centromeric proteins to promote a local unlooping of centromeric DNA. A study on the replication of alpha-satellite DNA has revealed that depletion of CENP-B triggers an enrichment of proteins of the pre-replication complex (36). Because slowdown of replication forks by exogenous or endogenous impediments elicits the activation of dormant replication origins, one can speculate that removal of CENP-B from the chromatin may ensure the completion of replication in a timely manner. Finally, MSH2–6 and DNA2 are known PAR binders (37,38). These interactions could therefore orchestrate their recruitment at centromeres. The enrichment of proteins implicated in DNA repair and replication stress pathways during centromeric replication highlights the fragility of these genomic loci. It also demonstrates the extent of the mechanisms that have evolved to mediate this fragility. Gaining a deeper appreciation of the challenges caused by inherent centromere structure and their resolutions will certainly advance our understanding of CIN-mediated genome instability.
Figure 2. Structural challenges and their resolution. (A) Alpha-satellite DNA loops are predicted to be stabilized by CENP-B binding and promote CENP-A deposition by the CENP-A chaperone HJURP. These loops are promoted by topoisomerase and stabilized by the condensin complex subunits SMC2–4. The helicase DNA2 may resolve secondary structures ahead of the fork and prevent RPA recruitment and ATR response signaling, while the mismatch repair factors MSH2–6 may promote the resolution of secondary structures formed behind the fork. Slowdown of replication machinery can contribute to fork stalling and collapse and result in replication stress. (B) Non-coding centromeric RNAs (cenRNAs) are transcribed by RNA polymerase II and can form R-loops. R-loop accumulation during replication triggers the formation of DSBs, followed by recombination and translocation events at centromeres. During mitosis, R-loops are stabilized by the ATR kinase and its downstream effector Chk1, promoting the recruitment of the kinase Aurora B and contributing to kinetochore integrity. BRCA1 recruitment prevents R-loop-induced DSBs at centromeres, while the nucleotide excision repair (NER) endonucleases XPG and XPF generate DSBs to trigger removal of centromeric R-loops. Finally, the RNA helicases (DDX5, DDX1), as well as the splicing factor SRSF1 (ASF/SF2), prevent R-loop formation and ensure genome stability. CENP-A is suggested to mediate the recruitment of R-loop resolution factors. (C) While centromeric DSBs can recruit both homologous recombination (HR) and non-homologous end joining (NHEJ) repair factors throughout the cell cycle, DSBs at pericentric heterochromatin recruit HR factors only during S/G2. Failure to repair centromeric DSBs can directly promote chromosomal rearrangements, lagging chromosomes, chromosomal bridges, aneuploidy, micronuclei formation and chromothripsis, all hallmarks of cancer cells.
Centromeric R-loops
Beneficial and harmful roles of centromeric R-loops
R-loops are nucleic acid structures consisting of a DNA–RNA hybrid and a displaced ssDNA. Centromeric repeats are actively transcribed by RNA polymerase II into non-coding cenRNAs that are part of the centromeric chromatin and participate in kinetochore assembly (39–42). Several studies have shown that cenRNAs associate with centromeric DNA in cis and form R-loops at centromeres of yeast, worms and synchronized or unsynchronized mammalian cells (43–46). A recent study also highlights a role for a non-centromeric long non-coding RNA (lnRNA) containing the oncogene c-Myc in forming R-loops and promoting CENP-A recruitment at an ectopic locus in colon cancer (47). These studies imply that R-loops, centromeric or not, may have an active role in centromere specification. Like in the rest of the genome, centromeric R-loops have beneficial roles in the maintenance of centromere functions but can also be detrimental depending on the phase of the cell cycle. Their accumulation impairs kinetochore bi-orientation and CENP-A localization, as well as increases chromosomal breaks and micronuclei formation, likely by acting as physical barriers to the progression of replication forks (45,46). Indeed, during replication, R-loop persistence triggers the formation of DSBs, followed by recombination and translocation events at centromeres (48). On the other hand, during early mitosis, R-loops promote the recruitment of the kinase Aurora B and the chromosome passenger complex (43,49). Aurora B orchestrates major steps of mitosis such as spindle assembly checkpoint, sister chromatid cohesion and attachment of microtubules to the kinetochores. Aurora B activation is dependent on the stabilization of R-loops by the ATR kinase and its downstream effector Chk1 (43). These mitotic R-loops accumulate during prophase but need to be resolved during mitotic progression (49). Collectively, perturbation of R-loop homeostasis causes defects in cohesion, kinetochore integrity and overall mitotic fidelity (43,49).
Mechanisms of resolution
Cells have developed several strategies to prevent or resolve R-loops, including factors involved in RNA biogenesis and stability and DNA–RNA helicases as well as RNase H that digests the RNA strand of the RNA–DNA hybrid (50). Studies addressing the functionality of known factors involved in R-loop metabolism within centromeric chromatin specifically are still in their initial stages. A recent study highlighted a role for the DSB repair protein BRCA1, which was previously described to process R-loops at transcription pausing sites (51,52). BRCA1 recruitment at centromeres of undamaged cells in interphase is indeed R-loop-dependent and protects centromeres from R-loop-induced DSBs, possibly by mediating the recruitment of the RNA–DNA helicase senataxin (Figure 2B). Importantly, the absence of BRCA1 unleashes Rad52-dependent recombination between satellite repeats leading to subsequent chromosome missegregation and micronuclei formation (46). The NER endonucleases XPG and XPF also promote the removal of centromeric R-loops (Figure 2B). However, their activity causes the formation of DSBs, which are at the origin of centromere-related aberrations in patients with immunodeficiency–centromeric region instability–facial anomalies (ICF) syndrome (53). Another study showed that the deletion of Hpr1 in budding yeast, a component of the RNA biogenesis and processing factor THO/TREX complex, leads to an accumulation of centromeric R-loops that can be resolved by exogenous expression of RNase H1 (45). Finally, the centromeric protein interactome includes several DEAD-box RNA helicases, some of them shown to unwind RNA–DNA hybrids (DDX5, DDX1), as well as the splicing factor SRSF1 (ASF/SF2) whose recruitment on nascent RNA transcripts prevents R-loop formation and ensures genome stability (28,54).
Outstanding questions
These studies suggest that centromeric R-loops are most likely processed through canonical pathways. Nonetheless, it cannot be excluded that the unique chromatin environment of centromeres necessitates the recruitment of specific factors. Outstanding questions also remain regarding the relationship of these factors with centromeric proteins and the dynamics of these protein interactions during the different phases of the cell cycle. For instance, because the removal of CENP-A during S phase is followed by an accumulation of R-loops in late S phase, CENP-A is suggested to mediate the recruitment of R-loop resolution factors during DNA synthesis to prevent genomic instability during mitosis (48). Future studies analyzing the CENP-A protein interactome during S phase may enable confirmation of this hypothesis. Moreover, we have recently obtained evidence that the DNA repair enzyme PARP1 can associate with R-loops in vitro and in cells and that this binding triggers its PARylation activity. Additionally, PARP1 activity promotes the association of the RNA–DNA helicase DDX18 with R-loops (55). Because PARP1 is enriched at centromeres during DNA synthesis (2), it is possible that it also orchestrates R-loop resolution at centromeres during S phase by ensuring the recruitment of resolution factors.
R-loops have recently emerged as important genome stability regulators. The duality of their function at centromeres underscores the importance of the tight regulation of their formation and resolution. This invites questions regarding the existence of mechanisms that readily prevent R-loop accumulation during DNA synthesis while allowing their formation during early mitosis and under conditions of DNA damage induction. Uncovering these mechanisms may offer exciting new research directions in the field.
DNA damage induction and repair at centromeres
Sources of DNA damage and their impact on centromeric DNA
Sources of DNA damage are pervasive and include endogenously and exogenously produced reactive oxygen species, UV and ionizing radiations, or various chemicals. These give rise to DNA base lesions, SSBs and DSBs, the latter being the most deleterious form of DNA damage as their unfaithful repair can lead to chromosomal fusions, translocations or deletions. There is a large gap of knowledge regarding the impact of DNA damaging agents on centromeres. Moreover, the common use of irradiations or oxidizing and alkylating chemicals that broadly impact the genome prevents the attribution of cellular and molecular responses to centromeric DNA damage specifically. This is particularly relevant if the centromeric chromatin environment offers protection to the centromeric DNA. Indeed, centromeres are composed of heterochromatin and this compaction could help protect the DNA from damage. Remarkably, telomeres, which are also composed of compact chromatin (56,57), exhibit 2-fold fewer UV photoproducts than the rest of the genome, which was attributed to a shielding of the DNA by telomeric DNA binding proteins (58). Centromeric proteins could therefore play a similar role in protecting DNA, but whether centromeric DNA is less vulnerable to genotoxic insults has not yet been investigated.
Spontaneous DNA breaks
Spontaneous DNA breaks within the centromere have been mainly attributed to active DNA replication. However, a new study has recently highlighted that they can also be induced de novo during quiescence (59). These breaks were initiated by topoisomerase IIβ activity and resolved by RAD51 recombinase. This study provides insights into the centromere paradox that stems from the observation that centromere DNA sequences evolve rapidly and are prone to recombination but can maintain their primary functionality. Moreover, because DSB enrichment was associated with CENP-A occupancy, it also brings evidence that innate centromere fragility may be involved in the epigenetic centromere identity. Therefore, despite the risks of inducing CIN, centromere fragility seems to have beneficial roles at centromeres.
Mechanisms of centromeric DNA DSB repair in the context of chromatin
Although centromeres are intrinsically fragile and represent hotspots for chromosomal breaks, the molecular mechanisms engaged to repair DSBs are not yet fully understood. Like its impact on damage occurrence, the specific centromeric chromatin environment can influence the repair mechanisms. Indeed, while chromatin compaction can help protect DNA from extensive damage, when damage does occur, it could limit the access of repair proteins to the lesion. For example, the repair kinetics of heterochromatin DSBs is slower than that of euchromatin DSBs (60). Although the impact of PARP activities on centromeric proteins and repair rates remains to be tested, one can speculate that the PARylation of CENP-A, CENP-B and BUB3 by PARP1 and PARP2 reported upon induction of DNA strand breaks by γ-irradiation could possibly be one way to promote their release from DNA and subsequent local chromatin relaxation as well as to the recruitment of DNA repair factors (33,34).
Recently, using CRISPR/Cas9 and guide RNA targeting the minor satellite repeats, the Soutoglou laboratory has overcome the issues that arise when using broad DNA damaging agents and shed some light on the centromeric DSB repair mechanisms in the context of chromatin. With this precision tool, they highlighted differences in the mode of repair between DSBs arising within the centromeric and the pericentric heterochromatin. While centromeric DSBs can recruit both HR and NHEJ repair factors throughout the cell cycle and even in G1 phase, DSBs at pericentric heterochromatin recruit HR factors only during S/G2 phases (61) (Figure 2C). Additionally, the presence of the active chromatin marks H3K4me2 at centromeres promotes transcription upon DSB induction and the formation of R-loops, which facilitates DNA end resection (62). Strikingly, the centromere histone variant CENP-A enables the activation of HR in G1 by mediating the recruitment of the deubiquitinase USP11, which subsequently enables the formation of the RAD51–BRCA1–BRCA2 HR complex by deubiquitinating PALB2 (62) (Figure 2C). Interestingly, while NHEJ repair during G1 is positionally stable, repair by HR requires end resection and DSB relocation to the periphery of heterochromatin where RAD51 can operate. Failure to relocate engages repair of the breaks through RAD52-dependent single-strand annealing, which uses homologous repeats to bridge DSB ends but causes deletions and rearrangements between the repeats (61,62). Along with the observation by the Esashi group that RAD51 depletion leads to a loss of CENP-A in both quiescent and cycling cells (59), this work demonstrates that recombination events can be beneficial for centromere stability. It also brings evidence that centromeric heterochromatin is not refractory to repair when it comes to DSB induction. Finally, it highlights that the chromatin environment, which differs between pericentromeres and centromeres, can dictate the repair pathway choice. Whether these observations can be made for other types of DNA damage remains to be tested. Future research efforts on the use and development of tools that can target DNA damage to the centromeres specifically will prove crucial to answering these outstanding questions.
Centromeres are promising therapeutic targets against tumorigenesis
Current anticancer drugs targeting centromere function are mostly antimitotic compounds that destabilize or stabilize microtubules to prevent cell proliferation and promote cell death. However, these drugs are also associated with cytotoxicity of non-cancer cells and a possibility for multiple resistance mechanisms to arise (63), further underscoring the need for new strategies. The recent knowledge acquired on the roles of R-loops and centromeric and DNA repair proteins in preserving centromeric DNA structure, as well as the impact of the centromeric chromatin environment on DNA repair pathway choice, offers potentially promising pathways to target.
Targeting centromeric R-loops
A growing interest in R-loops as targets in cancer therapy has recently emerged, owing to several studies demonstrating their dual role in genome stability. Dysregulated transcription programs in cancer cells have indeed been correlated to an increase in R-loops and subsequent replication stress. Whether centromeric R-loops are particularly more abundant in some cancer cells remains to be established. However, the role of centromeric R-loops in ensuring proper mitotic division is a unique aspect of R-loop biology that could be exploited in cancer therapy to counteract cell proliferation. While strategies that directly perturb R-loop formation and/or resolution may be useful to increase the replication stress burden to deadly levels in cancer cells, targeting centromeric R-loops specifically could therefore confer an additional advantage. Existing drugs such as inhibitors targeting the de novo DNA methyltransferase 3b (DNMT3b), which was shown to protect centromeres against the deleterious effects of R-loops (53), could be exploited in this context. DNMT3b is a DNA methyltransferase that is constitutively present at centromeres to modulate methylation in this region (64). DNMT3b loss-of-function mutation is specifically found in patients suffering from the ICF syndrome, whose cells harbor a high level of centromeric R-loops and DNA breaks. Accordingly, DNMT3b deletion in human carcinoma cells HCT116 leads to reduced level of centromeric R-loop and subsequent DSBs at pericentromeric regions (53). Because DNMT3b is also the most commonly overexpressed DNA methyltransferase in cancer cells, its inhibition may primarily affect diseased cells and is therefore a promising therapeutic avenue to investigate. Alternatively, slowing down cancer cell proliferation by blocking centromeric R-loop formation during mitosis could be another interesting approach to examine. This could be achieved by preventing cenRNA stabilization by inhibiting ATR or by using antisense oligonucleotides (ASOs) that have already been successfully used to target lnRNAs in some human diseases (65). Finally, the recent data describing a role for a non-centromeric lnRNA in promoting CENP-A deposition at an ectopic locus in cancer cells further highlight the importance of focusing on the development of sequence-specific ASOs or small-molecule inhibitors disrupting this interaction (47).
Targeting centromeric proteins
Targeting centromeric proteins is also a promising avenue from which novel treatments may emerge. In 2016, a group developed the centromere and kinetochore gene expression score (CES) and demonstrated that high CES often correlates with increased levels of genomic instability and poor prognosis for several types of cancers (66). CENP-A, in particular, is overexpressed in ∼20 different cancer types in which ectopic deposition is increased and contributes to CIN (67). Additionally, both HJURP and CENP-A are upregulated in p53-null human tumors (68). Thus, elevated centromeric protein expression is proposed to be a biomarker for disease progression and patient outcome. Targeting levels of highly expressed centromeric proteins could therefore be a tactic that could confer a therapeutic advantage. Interestingly, high CES also correlates with improved response to radiotherapy, cisplatin and topoisomerase inhibitors, due to reduced tolerance of these high CES cells to additional genotoxic stress (66). Thus, determining the centromeric protein expression profile in patients could help in deciding whether traditional treatments should be encouraged to improve personalized treatments. The manipulation of centromeric proteins, especially those involved in shaping the centromeric chromatin structures, could also offer a way to modulate the sensitivity of DNA to DNA damaging agents. Chromatin remodeling influences various cell functions and, when dysfunctional, can promote tumorigenesis. Whether centromeric chromatin undergoes drastic remodeling in cancer is not reported. Yet, recent observation on the importance of chromatin state in the choice of DNA repair pathway offers the possibility of exploring the impact of chromatin remodeling-related drugs in combination with DNA damaging agents.
CONCLUDING REMARKS
The molecular mechanisms of centromeric instability represent a relatively recent area of research that has gained momentum in the past decade. The dual nature of centromeres as essential mediators of cell division but also fragile regions prone to breaks and fork stalling is an intriguing dichotomy that highlights the importance of protective mechanisms at the centromere. Because centromeres are vital for faithful transmission of the genome, compromised centromeric integrity can be especially deleterious as evidenced by the prevalence of cancers presenting with aneuploidies that originate from breaks at centromeric regions. Thus, understanding and further characterizing the molecular mechanisms of genomic instability at centromere loci is imperative to informing and identifying novel therapeutic approaches that can curb cancer occurrence and improve disease outcome.
DATA AVAILABILITY
All data generated and presented in this review are available from the corresponding author upon request.
ACKNOWLEDGEMENTS
We thank Natalie Laspata for helpful edits on the manuscript.
FUNDING
National Institutes of Health [R35GM142982]; Start-up fundings from UPMC Hillman Cancer Center (to E.F.).
Conflict of interest statement. None declared.
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PMC010xxxxxx/PMC10173211.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37166567
806
10.1007/s40121-023-00806-0
Original Research
Safety and Immunogenicity of the ID93 + GLA-SE Tuberculosis Vaccine in BCG-Vaccinated Healthy Adults: A Randomized, Double-Blind, Placebo-Controlled Phase 2 Trial
Choi Yu Hwa 1
Kang Young Ae 29
Park Kwang Joo 3
Choi Jae Chol 4
Cho Kwan Goo 1
Ko Da Yeon 1
Ahn Jun Ho 1
Lee Boram 1
Ahn Eunsol 1
Woo Yun Ju 1
Jung Kwangsoo 1
Kim Nan Yul 1
Reese Valerie A. 5
Larsen Sasha E. 5
Baldwin Susan L. 5
Reed Steven G. 6
Coler Rhea N. [email protected]
578
http://orcid.org/0000-0001-8207-537X
Lee Hyejon [email protected]
1910
Cho Sang-Nae [email protected]
19
1 Quratis Inc., Seoul, Republic of Korea
2 grid.415562.1 0000 0004 0636 3064 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
3 grid.251916.8 0000 0004 0532 3933 Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon, Republic of Korea
4 grid.411651.6 0000 0004 0647 4960 Pulmonology Department, Chung-Ang University Hospital, Seoul, Republic of Korea
5 grid.240741.4 0000 0000 9026 4165 Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA USA
6 HDT Bio, Seattle, WA USA
7 grid.34477.33 0000000122986657 Department of Global Health, University of Washington, Seattle, WA USA
8 grid.34477.33 0000000122986657 Department of Pediatrics, University of Washington School of Medicine, Seattle, WA USA
9 grid.15444.30 0000 0004 0470 5454 Institute of Immunology and Immunological Disease, Yonsei University College of Medicine, Seoul, Republic of Korea
10 grid.495992.a 0000 0004 6405 9319 Division of Vaccine Research, International Tuberculosis Research Center, Seoul, Republic of Korea
11 5 2023
11 5 2023
6 2023
12 6 16051624
7 2 2023
11 4 2023
© The Author(s) 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Introduction
This randomized, double-blind, placebo-controlled, phase 2a trial was conducted to evaluate the safety and immunogenicity of the ID93 + glucopyranosyl lipid adjuvant (GLA)-stable emulsion (SE) vaccine in human immunodeficiency virus (HIV)-negative, previously Bacillus Calmette–Guérin (BCG)-vaccinated, and QuantiFERON-TB-negative healthy adults in South Korea.
Methods
Adults (n = 107) with no signs or symptoms of tuberculosis were randomly assigned to receive three intramuscular injections of 2 μg ID93 + 5 μg GLA-SE, 10 μg ID93 + 5 μg GLA-SE, or 0.9% normal saline placebo on days 0, 28, and 56. For safety assessment, data on solicited adverse events (AEs), unsolicited AEs, serious AEs (SAEs), and special interest AEs were collected. Antigen-specific antibody responses were measured using serum enzyme-linked immunosorbent assay. T-cell immune responses were measured using enzyme-linked immunospot and intracellular cytokine staining.
Results
No SAEs, deaths, or AEs leading to treatment discontinuation were found. The solicited local and systemic AEs observed were consistent with those previously reported. Compared with adults administered with the placebo, those administered with three intramuscular vaccine injections exhibited significantly higher antigen-specific antibody levels and Type 1 T-helper cellular immune responses.
Conclusion
The ID93 + GLA-SE vaccine induced antigen-specific cellular and humoral immune responses, with an acceptable safety profile in previously healthy, BCG-vaccinated, Mycobacterium tuberculosis-uninfected adult healthcare workers.
Trial Registration
This clinical trial was retrospectively registered on 16 January 2019 at Clinicaltrials.gov (NCT03806686).
Supplementary Information
The online version contains supplementary material available at 10.1007/s40121-023-00806-0.
Keywords
Tuberculosis
Subunit vaccine
GLA-SE
Safety
Immunogenicity
Quratis Inc. and supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of KoreaHI14C1324 issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
==== Body
pmcKey Summary Points
Why carry out this study?
Tuberculosis (TB) is a major infectious disease inflicting more than 10 million patients and 1.5 million deaths worldwide each year. Given the inconsistent effectiveness of Bacillus Calmette–Guérin (BCG) vaccination against adult pulmonary TB, it is imperative to develop new safe and effective TB vaccine candidates capable of inducing robust and long-term protection against all forms of TB in diverse populations and geographic regions.
Although the ID93 + GLA-SE TB vaccine candidate was reported safe and immunogenic in people in the USA and South Africa, no report is available yet in Asian countries.
This study aimed to evaluate the safety and immunogenicity of the ID93 + GLA-SE TB vaccine candidate among the BCG-vaccinated healthy adults in South Korea.
What was learned from the study?
The study showed the ID93 + GLA-SE vaccine candidate was safe among the BCG-vaccinated healthy adults of South Korea and induced the antigen-specific antibody and type 1 T-helper cellular immune responses.
This is the first report on the safety and immunogenicity of ID93 + GLA-SE in an Asian population, positively supporting the future efficacy trials of ID93 + GLA-SE (QTP101) in human immunodeficiency virus (HIV)-negative, previously BCG-vaccinated, healthy adults.
INTRODUCTION
Both severe acute respiratory syndrome coronavirus 2, the causative agent of coronavirus disease 2019, and Mycobacterium tuberculosis (Mtb), which causes chronic tuberculosis (TB), are the leading infectious agents worldwide, ranking above human immunodeficiency virus (HIV) infection [1]. A recent meta-analysis of interferon-γ (IFN-γ) release assays and tuberculin skin test surveys showed that one-fourth of the world’s population is infected with Mtb [2]. Individuals who come in contact with patients with active TB are at risk of developing latent TB infection (LTBI), whereas 5–10% of those with LTBI develop active TB during their lifetime [1]. Furthermore, nearly 50% of individuals with active TB progression from a recent infection occurs within the first 2 years [2].
In South Korea, TB is a major public health concern, despite high infant Bacillus Calmette–Guérin (BCG) immunization coverage and improvement in controlling TB under the National Tuberculosis Program (NTP). A recent annual report from the surveillance system indicated a TB incidence rate of 46.4 per 100,000 people (approximately 23,821 new cases), with 1610 cases of TB-related deaths [3]. To achieve their goal of reducing TB, the Korean NTP has strengthened a latent TB management program since 2016 with various health policies, such as “mandatory LTBI diagnosis” and “full-subsidy for LTBI treatment costs” for specific groups of individuals, including those eligible for conscription, kindergarten teachers, inmates, and healthcare workers. However, as individuals with LTBI do not feel sick and appear healthy, they often hesitate to start or complete preventive treatment, which requires steady medication for 3–6 months with possible side-effects. To complement such limitations of current NTP policies to control TB infection, initiatives through innovative strategies in diagnosis, treatments, and vaccine development remain essential. The development of effective vaccines against infectious TB is the most successful approach to protect against TB infection and transmission globally. However, the only commercially available vaccine against TB is the live attenuated BCG vaccine, which has some disadvantages, such as variable and partial effectiveness for pulmonary TB in adolescents and adults (0–80%), and is influenced by population, region, and exposure to environmental factors [4–7]. The benefits and pitfalls of the BCG vaccine remain debatable, while international efforts continue to develop a novel vaccine effective against all forms of TB and in all age groups, regardless of environmental factors [8]. In countries where BCG prevents childhood TB for up to a minimum of 10 years, the most efficient strategy to control TB might be the development of booster vaccines to repair vaccine-induced immune failure of BCG instead of developing a better neonatal TB vaccine to replace BCG [9]. Encouraging outcomes from recent clinical trials also support a strategy to combine BCG vaccination with subunit vaccine candidates, designed almost exclusively as BCG boosters to enhance vaccine activity against TB [8]. These subunit vaccine candidates are composed of recombinant protein components with adjuvants, generally requiring multiple doses to induce an effective immune response.
ID93 is composed of four Mtb antigens (Rv2608, Rv3619, Rv3620, and Rv1813) associated with virulence or latency; glucopyranosyl lipid adjuvant (GLA)-stable emulsion (SE) is a synthetic Toll-like receptor 4 agonist developed as an adjuvant formulated with an oil-in-water emulsion [10, 11]. This adjuvant system has been successfully combined with recombinant protein antigens to yield high antibody titers [12, 13] and induce T-helper type 1 (Th1) cellular immune responses associated with Mtb and Leishmania infection models [12–14]. The safety, immunogenicity, and pharmacodynamics of the ID93 + GLA-SE vaccine have been previously demonstrated in various animal models [15, 16]. Previous clinical studies have also demonstrated that the ID93 + GLA-SE vaccine is safe and immunogenic in different populations, including HIV-negative healthy adults, adults with LTBI, and treated patients with TB in the USA and South Africa [17–19]. In addition to its use with the ID93 vaccine antigen, GLA-SE was used in clinical trials for vaccines against schistosomiasis, malaria, leishmaniasis, and influenza [20–24]. Injections of vaccines containing GLA-SE have generally been well tolerated, and adverse events (AEs) have been mostly mild, with no treatment-related serious AEs (SAEs) reported [17–19]. Along with the previous studies that evaluated adults with LTBI or treated patients with TB, this trial focuses on healthy adults not previously infected with Mtb. We evaluated the safety and immunogenicity of two dosages of the ID93 + GLA-SE vaccine compared with those of placebo after three intramuscular (IM) injections in HIV-negative, previously BCG-vaccinated QuantiFERON-TB (QFT)-negative, healthy healthcare workers who are considered at higher risk of TB exposure than the general population in South Korea [25–27].
METHODS
Study Design and Participants
In this randomized, double-blind, placebo-controlled, parallel phase 2a trial, we enrolled HIV-negative, previously BCG-vaccinated, QFT-negative healthy adults (age 19–65 years), with no evidence of historical or current TB, among healthcare workers currently employed in three hospitals located in Seoul and Suwon: Yonsei University Severance Hospital, Ajou University Hospital, and Chung-Ang University Hospital. The study procedures, including inclusion and exclusion criteria, are described in detail in Supplementary Material Appendix 1.1 and 1.2. This study was approved by the Institutional Review Board and Ethical Committee of Yonsei University Severance Hospital (IRB# 4-2018-0230), Ajou University Hospital (IRB# AJIRB-MED-CT2-18-078), and Chung-Ang University Hospital (1833-001-320). Written informed consent was obtained from all study participants. This study was conducted in accordance with the Declaration of Helsinki.
Randomization and Masking
Each participant was sequentially assigned a unique randomization number generated by a randomization manager using the SAS program (SAS v9.4 or higher). Randomization of participants was performed in a 1:1:1 ratio to the three treatment cohorts. Participants who were QFT-negative and BCG-vaccinated were sorted into cohort 1, 2, or 3 and received injections on days 0, 28, and 56. The investigational product (IP: ID93 + GLA-SE) was administered by an unblinded pharmacist, according to the participant’s randomization number. The syringes used for injection were blinded. Unblinded study personnel were not involved in any other duties that could have broken the double-blind setting. Double blinding of the study was maintained until database lock and data analysis were completed at the end of the study. The treatment assignments were disclosed to the participants and the investigator at the end of the study.
Procedures
The IP was purchased from the Infectious Disease Research Institute/Access to Advanced Health Institute (IDRI/AAHI; Seattle, WA, USA) and supplied to pharmacists at the study sites. Study participants were randomized to receive a 0.5 ml IM injection of 2 μg ID93 + 5 μg GLA-SE (cohort 1), 10 μg ID93 + 5 μg GLA-SE (cohort 2), or 0.9% normal saline placebo (cohort 3) on days 0, 28, and 56. All participants were then followed up for 12 months after the final vaccination. Acute AEs were assessed 30 min after each vaccination. Solicited AEs (local and systemic) were assessed 7 days after each vaccination, and unsolicited AEs were assessed 28 days after each vaccination on the basis of their severity, causality, and seriousness. Data on AEs were collected from participant voluntary reporting, monitoring, and interviews at site visits, phone call follow-ups, and participant diaries. For long-term safety assessment, SAEs and adverse events of special interest (AESIs) were monitored for up to 12 months after the final vaccination. Further definitions of AEs are provided in Supplementary Material Appendix 1.3.
Other safety assessments included clinical laboratory tests (clinical chemistry and hematology), collection of vital signs (blood pressure, pulse, and body temperature), and physical examination, including height and weight measurements, chest radiography, pregnancy testing, and virus screening tests that included HIV, hepatitis B, hepatitis C, and sputum culture tests (only if clinically indicated).
Immunogenicity assessment was performed using blood samples collected from the study participants on days 0 (baseline), 28, 56, 84, and 421. Immunogenicity analysis was conducted at central laboratories [Seegene Medical Foundation, Seoul, South Korea; IDRI/AAHI, Seattle, WA, USA; and the Center for Global Infectious Disease Research (CGIDR) of Seattle Children’s Research Institute, Seattle, WA, USA]. To measure humoral immune responses, serum IgG enzyme-linked immunosorbent assay (ELISA) was performed with recombinant ID93 antigen as described in Supplementary Material Appendix 1.4.1. To measure cell-mediated immune responses induced by vaccination, peripheral blood mononuclear cell (PBMC)-based enzyme-linked immunospot (ELISpot) and intracellular cytokine staining were performed according to the method described in Supplementary Material Appendix 1.4.2 and 1.4.3. The overall study schedule for follow-up and blood collection is shown in Fig. 1a.Fig. 1 Study design and trial profile. a Study design and scheduled follow-ups and blood collections. Safety assessment of ID93 + GLA-SE recipients (cohorts 1 and 2) compared with placebo recipients (cohort 3) was conducted following three administrations at days 0, 28, and 56, and follow-ups for 12 months (421 days) from the final vaccination. Blood samples for immunogenicity assessment of ID93 + GLA-SE recipients compared with placebo recipients were collected on days 0 (baseline), 28, 56, 84, and 421. b Eligible participants were randomly assigned in a 1:1:1 ratio to one of three treatment groups to receive either 2 μg ID93 + 5 μg GLA-SE (cohort 1), 10 μg ID93 + 5 μg GLA-SE (cohort 2), or 0.9% normal saline placebo (cohort 3) on days 0, 28, and 56
Outcomes
The safety of ID93 + GLA-SE as primary outcomes included solicited AEs up to 7 days and unsolicited AEs up to 28 days after each vaccination, which were based on their severity, causality, and seriousness. SAEs and AESIs were followed for up to 12 months. AEs were classified according to severity (i.e., mild, moderate, or severe) and causality (i.e., related or unrelated). The immunogenicity of ID93 + GLA-SE as secondary outcomes were identified by the antigen-specific total IgG antibody titer, IFN-γ secreting cells, and CD4+ T cells expressing any Th1 cytokine(s). A safety analysis was performed using a safety set, while immunogenicity analysis was performed using a per-protocol set. This trial was retrospectively registered at ClinicalTrials.gov (NCT03806686).
Statistical Analysis
On the basis of previous phase 1 and phase 2 studies [17–19], the sample size was determined to be 35 participants per cohort, with an estimated dropout rate of 15%. The number of events along with the frequency, incidence rate, and 95% confidence intervals (CIs) were calculated for local and systemic solicited AEs observed for 7 days following each vaccination, unsolicited AEs observed 28 days following each vaccination, and SAEs/AESIs observed up to day 421. The AEs were classified according to their severity and causal relationship to the vaccine, and the frequency of events, number of people, and incidence rate were presented. For severity analysis, the severity of local and systemic solicited AEs was graded according to the upper limit of their severity. All AEs other than local and systemic solicited AEs were classified following the Medical Dictionary for Regulatory Activities (MedDRA) System Organ Class (SOC) and Preferred Terms (PT).
Differences in immune responses between study groups were compared using the Mann–Whitney test (two groups), the Kruskal–Wallis test (three or more groups), and Fisher’s exact test (two nominal variables). In addition, differences in paired immune responses that occurred between baseline and post-vaccination visits within the study group were compared using Friedman’s test (three or more groups) or the Wilcoxon matched-pair signed-rank test. A p value < 0.05 between the compared study groups was considered statistically different. Statistical analyses and graphical representation were performed with SAS version 9.4 and GraphPad Prism version 9.4.1.
RESULTS
Study Participants
As shown in Fig. 1, 107 of the 123 participants screened were eligible. Of these, 16 participants were excluded on the basis of screening criteria: 4 refused before randomization, 9 had QFT-positive results, 1 had an influenza vaccination before study participation, 1 visited outside the window period for randomization, and 1 had a fever > 38 °C. All eligible participants were randomly allocated to three study cohorts: cohort 1 received 2 μg ID93 + 5 μg GLA-SE (n = 35), cohort 2 received 10 μg ID93 + 5 μg GLA-SE (n = 36), and cohort 3 received 0.9% normal saline placebo (n = 36) (Fig. 1b). The median age of study participants was 34 years (23–58 years). The proportion of female participants (73.8%) was higher than that of male participants (26.2%), as there are more female healthcare workers than male. Most participants (63.9–72.2%) worked in a department of the hospital with medium risk of TB exposure, followed by departments of high risk (19.4–22.9%) and low risk of TB exposure (5.7–13.9%) (Table 1). Overall, demographics and baseline characteristics were comparable across the study cohorts. Table 1 Summary of baseline demographics including full analysis of the population
Cohort 1 Cohort 2 Cohort 3 Total
(N = 107)
(2 μg ID93 + 5 μg GLA-SE,
N = 35) (10 μg ID93 + 5 μg GLA-SE,
N = 36) (0.9% normal saline placebo,
N = 36)
Age (years) 35.0 (24, 54) 32.0 (23, 58) 35.5 (23, 47) 34.0 (23, 58)
Sex, n (%)
Female 28 (80.0%) 24 (66.7%) 27 (75.0%) 79 (73.8%)
Male 7 (20.0%) 12 (33.3%) 9 (25.0%) 28 (26.2%)
Baseline BMI (kg/m2) 23.0 (19, 28) 22.5 (19, 32) 22.0 (19, 32) 22.0 (19, 32)
*TB exposure risk, n (%)
High [1] 8 (22.9%) 7 (19.4%) 8 (22.2%) 23 (21.5%)
Medium [2] 25 (71.4%) 26 (72.2%) 23 (63.9%) 74 (69.2%)
Low [3] 2 (5.7%) 3 (8.3%) 5 (13.9%) 10 (9.3%)
Working period (years) 2.0 (0, 27) 1.0 (0, 24) 2.0 (0, 24) 2.0 (0, 27)
Data are presented as n (%) or median (min, max)
*TB exposure risk at the time of the study was defined as “high-risk” if the medical staff had frequent contact with patients with TB or TB specimens, such as in TB clinics, respiratory wards, emergency departments, microbiology laboratories handling TB specimens with bronchoscopy or sputum induction, post-mortem examinations, and lung function testing. “Medium risk” includes medical staff with occasional contacts with patients with TB or TB specimens. “Low risk” includes the medical staff without casual contacts with patients with active TB, such as staff in clerical areas, administrative departments, and non-microbiological laboratories
All eligible participants who received at least one study injection were included in the safety analysis. Of these participants, 28 (80.0%), 32 (88.9%), and 31 (86.1%) participants in cohorts 1, 2, and 3, respectively, completed the study as per the protocol. Sixteen (14.9%) participants discontinued during the study, mostly because they did not consent to continue study follow-up (withdrawal of consent). No participants were withdrawn due to any treatment-related SAEs. In cohort 1, seven participants were excluded owing to the withdrawal of consent, major protocol deviation, and QFT-positive conversion at day 421. In cohort 2, four participants discontinued the study owing to noncompliance with the protocol, withdrawal of consent, loss to follow-up, and QFT-positive conversion at day 84. Five participants in cohort 3 were withdrawn from the study owing to noncompliance with the protocol, withdrawal of consent, loss to follow-up, and QFT-positive conversion on day 421 (Fig. 1b).
Safety Evaluation
Safety analysis indicated that ID93 + GLA-SE was safe and well tolerated at the two dosages assessed. The most frequently reported solicited local and systemic AEs were pain, tenderness at the injection site, headache, fatigue, and myalgia in all three cohorts (Table 2). In the vaccine groups (cohorts 1 and 2), 83.7% participants reported local AEs and 56.9% participants reported systemic AEs after all three injections. In the placebo group, 23.3% participants reported local AEs and 19.4% participants reported systemic AEs during the study. Most solicited local and systemic AEs were mild in severity in all treatment groups. The overall frequencies of participants (%) with solicited local and systemic AEs after each dose of IP, according to symptoms and severity, are presented in Supplementary Material Tables S1 and S2. The most frequently reported (≥ 3.0% of total participants) AEs sorted by PT were nasopharyngitis (6.5%), dyspepsia (4.7%), and headache (3.7%). Of these, the most frequently reported vaccine-related AE was dyspepsia (3.7%) (Table 3); however, most of the unsolicited AEs were mild in severity in the vaccine groups. In total, 24.3% of the participants reported at least one AE that was related to the vaccine (Supplemental Table S3). The most frequently reported (≥ 3.0% of total participants) unsolicited AEs sorted via SOC and PT are presented with their severity and causality in Supplementary Material Table S3.Table 2 Most frequently reported solicited local and systemic AEs listed by symptom, timepoint, severity, and causality assessment
Solicited AEs Symptoms Severity/
Causality Cohort 1 Cohort 2 Cohort 3
(2 μg ID93 + 5 μg GLA-SE, N = 35) (10 μg ID93 + 5 μg GLA-SE, N = 36) (0.9% normal saline placebo, N = 36)
1st dose
(n = 35) 2nd dose
(n = 34) 3rd dose
(n = 33) 1st dose
(n = 36) 2nd dose
(n = 36) 3rd dose
(n = 35) 1st dose
(n = 36) 2nd dose
(n = 34) 3rd dose
(n = 33)
Local AEs Pain,
n (%)
Total 25 (71.4) 28 (82.4) 20 (60.6) 27 (75.0) 32 (88.9) 25 (71.4) 7 (19.4) 3 (8.8) 5 (15.2)
Mild 20 (57.1) 23 (67.6) 17 (51.5) 23 (63.9) 28 (77.8) 23 (65.7) 7 (19.4) 2 (5.9) 4 (12.1)
Moderate 4 (11.4) 3 (8.8) 3 (9.1) 3 (8.3) 4 (11.1) 2 (5.7) 0 1 (2.9) 1 (3.0)
Severe 1 (2.9) 2 (5.9) 0 1 (2.8) 0 0 0 0 0
Related 25 (71.4) 28 (82.4) 20 (60.6) 27 (75.0) 32 (88.9) 25 (71.4) 7 (19.4) 3 (8.8) 5 (15.2)
Tenderness, n (%) Total 25 (71.4) 28 (82.4) 25 (75.8) 31 (86.1) 33 (91.7) 26 (74.3) 9 (25.0) 4 (11.8) 6 (18.2)
Mild 11(31.4) 20 (58.8) 20 (60.6) 18 (50.0) 17 (47.2) 15 (42.9) 8 (22.2) 3 (8.8) 5 (15.2)
Moderate 10 (28.6) 2 (5.9) 4 (12.1) 10 (27.8) 12 (33.3) 10 (28.6) 1 (2.8) 0 0
Severe 4 (11.4) 6 (17.6) 1 (3.0) 3 (8.3) 4 (11.1) 1 (2.9) 0 1 (2.9) 1 (3.0)
Related 25 (71.4) 28 (82.4) 25 (75.8) 31 (86.1) 33 (91.7) 26 (74.3) 9 (25.0) 4 (11.8) 6 (16.7)
Systemic AEs Headache,
n (%)
Total 12 (34.3) 9 (26.5) 6 (18.2) 7 (19.4) 8 (22.2) 7(20.0) 2 (5.6) 3 (8.8) 2 (6.1)
Mild 8 (22.9) 5 (14.7) 3 (9.1) 4 (11.1) 6 (16.7) 6 (17.1) 2 (5.6) 3 (8.8) 1 (3.0)
Moderate 3 (8.6) 4 (11.8) 3 (9.1) 3 (8.3) 2 (5.6) 1 (2.9) 0 0 1 (3.0)
Severe 1 (2.9) 0 0 0 0 0 0 0 0
Related 12 (34.3) 9 (26.5) 6 (18.2) 7 (19.4) 8 (22.2) 7 (20.0) 2 (5.6) 3 (8.8) 2 (6.1)
Fatigue,
n (%)
Total 14 (40.0) 8 (23.5) 5 (15.2) 17 (47.2) 17 (47.2) 15 (42.9) 9 (25.0) 4 (11.8) 3 (9.1)
Mild 11 (31.4) 7 (20.6) 4 (12.1) 16 (44.4) 12 (33.3) 13 (37.1) 8 (22.2) 4 (11.8) 2 (6.1)
Moderate 2 (5.7) 1 (2.9) 1 (3.0) 1 (2.8) 5 (13.9) 2 (5.7) 1 (2.8) 0 1 (3.0)
Severe 1 (2.9) 0 0 0 0 0 0 0 0
Related 14 (40.0) 8 (23.5) 5 (15.2) 17 (47.2) 17 (47.2) 15 (42.9) 9 (25.0) 4 (11.8) 3 (9.1)
Myalgia,
n (%)
Total 20 (57.1) 15 (44.1) 14 (42.4) 12 (33.3) 18 (50.0) 14 (40.0) 3 (8.3) 2 (5.9) 2 (6.1)
Mild 13 (37.1) 11 (32.4) 9 (27.3) 10 (27.8) 12 (33.3) 10 (28.6) 3 (8.3) 2 (5.9) 1 (3.0)
Moderate 4 (11.4) 1 (2.9) 5 (15.2) 2 (5.6) 6 (16.7) 3 (8.6) 0 0 1 (3.0)
Severe 3 (8.6) 3 (8.8) 0 0 0 1 (2.9) 0 0 0
Related 20 (57.1) 15 (44.1) 14 (42.4) 12 (33.3) 18 (50.0) 14 (40.0) 3 (8.3) 2 (5.9) 2 (6.1)
Participants could be counted as having more than one symptom. The percentage of participants was calculated using the number of participants in each group as the denominator. Severities/grades with zero reported events across the treatment groups are not shown
AE adverse event, IP investigational product, N number of participants in the population, n number of participants with events
Table 3 Most frequently reported adverse events by severity and causality assessment
AEs Severity/causality Cohort 1 Cohort 2 Cohort 3 Total
(2 μg ID93 + 5 μg GLA-SE,
N = 35) (10 μg ID93 + 5 μg GLA-SE,
N = 36) (0.9% normal saline placebo,
N = 36) (N = 107)
E n (%) E n (%) E n (%) E n (%)
Nasopharyngitis Total 3 3 (8.6) 3 2 (5.6) 2 2 (5.6) 8 7 (6.5)
Mild 2 2 (5.7) 3 2 (5.6) 1 1 (2.8) 6 5 (4.7)
Moderate 1 1 (2.9) 0 0 1 1 (2.8) 2 2 (1.9)
Severe 0 0 0 0 0 0 0 0
Related 2 2 (5.7) 1 1 (2.8) 0 0 3 3 (2.8)
Not related 1 1 (2.9) 2 1 (2.8) 2 2 (5.6) 5 4 (3.7)
Dyspepsia Total 2 2 (5.7) 3 3 (8.3) 0 0 5 5 (4.7)
Mild 1 1 (2.9) 3 3 (8.3) 0 0 4 4 (3.7)
Moderate 1 1 (2.9) 0 0 0 0 1 1 (0.9)
Severe 0 0 0 0 0 0 0 0
Related 2 2 (5.7) 2 2 (5.6) 0 0 4 4 (3.7)
Not related 0 0 1 1 (2.8) 0 0 1 1 (0.9)
Headache Total 1 1 (2.9) 1 1 (2.8) 2 2 (5.6) 4 4 (3.7)
Mild 0 0 0 0 2 2 (5.6) 2 2 (1.9)
Moderate 1 1 (2.9) 1 1 (2.8) 0 0 2 2 (1.9)
Severe 0 0 0 0 0 0 0 0
Related 0 0 1 1 (2.8) 1 (2.8) 1 (2.8) 2 2 (1.9)
Not related 1 1 (2.9) 0 0 1 (2.8) 1 (2.8) 2 2 (1.9)
AEs sorted using Preferred Terms in MedDRA. AEs in more than 3.0% of total participants are presented. Some participants were included in more than one category
N number of participants in the population; n number of participants with symptoms; IP investigational product, % percentage of participants, calculated using the total number in each cohort as the denominator
During this study, eight participants (7.5%) reported SAEs unrelated to IP, as determined by study clinicians. One (2.9%) participant in cohort 1 reported an ovarian cyst, four (11.1%) participants in cohort 2 reported SAEs: tooth impaction, ankle fracture, colon cancer, and facial paralysis, and three (8.3%) participants in the placebo group reported SAEs: facial bone fracture, spinal column stenosis, and spontaneous abortion. One AESI unrelated to IP was reported in cohort 2 (2.8%), which was also assessed as an SAE (facial paralysis). In summary, 51 (47.7%) participants reported 91 AEs during the study. Cohort 2 (63.9%) had the highest proportion of participants who reported any AEs compared with cohort 1 (40.0%) and cohort 3 (placebo) (38.9%). Approximately half of the reported AEs were related to the vaccine [43 AEs reported in 26 participants (24.3%)], but the most frequently occurring AEs were local injection reactions. One participant in each group reported an AE of grade 3 (severe); however, none of these SAEs were related to the vaccine (Table 4). Moreover, none of the SAEs or AESIs were related to the vaccine and no deaths or AEs associated with vaccine discontinuation were reported.Table 4 Summary of adverse events by study groups
Cohort 1 Cohort 2 Cohort 3 Total
(2 μg ID93 + 5 μg GLA-SE,
N = 35) (10 μg ID93 + 5 μg GLA-SE,
N = 36) (0.9% normal saline placebo,
N = 36) (N = 107)
E n (%) E n (%) E n (%) E n (%)
AEs 25 14 (40.0) 38 23 (63.9) 28 14 (38.9) 91 51 (47.7)
Acute AEs 0 0 0 0 0 0 0 0
Related AEs 16 11 (31.4) 16 10 (27.8) 11 5 (13.9) 43 26 (24.3)
SAEs 1 1 (2.9) 4 4 (11.1) 3 3 (8.3) 8 8 (7.5)
Related SAEs 0 0 0 0 0 0 0 0
Severe AEs 1 1 (2.9) 1 1 (2.8) 1 1 (2.8) 3 3 (2.8)
Related severe AEs 0 0 0 0 0 0 0 0
AESI 0 0 1 1 (2.8) 0 0 1 1 (0.9)
Deaths 0 0 0 0 0 0 0 0
AEs leading to drug discontinuation 0 0 0 0 0 0 0 0
Some participants were counted in more than one category
AE adverse event, AESI adverse events of special interest, E number of events, IP investigational product, N number of participants in the population, SAE serious adverse events. % percentage of participants, calculated using the total number in each cohort as the denominator
Immunogenicity Evaluation
Humoral Immune Response: Antigen-Specific IgG Antibody Titer
To determine the vaccine-mediated humoral immune response, serum total IgG antibody levels specific to the ID93 fusion protein as detected using ELISA with geometric mean titer (GMT), geometric mean fold rise (GMFR), and seroresponse rates (SRR) at post-vaccination timepoints (days 28, 56, 84, and 421) were compared between the three study cohorts. As shown in Table 5, the GMT in cohorts 1 and 2 increased consistently from day 28 and peaked at day 84 (4 weeks after the third IP dose), before decreasing at day 421. At day 421 (12 months after the third IP dose), the GMT in both cohorts decreased to levels close to those at day 28, but remained higher than the baseline. Across the three cohorts, baseline antigen-specific IgG responses were low and equivalent. The increase in GMT over time was reflected in the GMFR, which increased from 5.59 at day 28 to 143.07 at day 84 in cohort 1 and from 8.06 at day 28 to 183.15 at day 84 in cohort 2. The GMFR over time in cohort 2 was slightly higher than that in cohort 1; however, this difference was not significant. From day 84 to day 421, the GMFR decreased just as GMT decreased in cohorts 1 and 2. The GMFR over time in cohort 3 (placebo group) remained unchanged or was lower than 1.00. After the first IP dose (day 28), the SRRs of cohorts 1 and 2 were 57.14% (95% CI 37.18–75.54) and 68.75% (95% CI 49.99–83.88), respectively, with a higher but statistically insignificant SRR in cohort 2. After the second and third IP doses (days 56 and 84), the SRR increased to 100% equally in both vaccine groups. However, after 12 months from the third IP dose, the SRR decreased to 42.86% (95% CI 2.46–62.82) in cohort 1 and 59.38% (95% CI 40.64–76.30) in cohort 2, remaining slightly, but insignificantly, higher in cohort 2. However, all antigen-specific IgG antibody responses in terms of GMT, GMFR, and SRR in vaccine cohorts were significantly higher than those in cohort 3 (placebo) after the first dose and lasted up to 12 months after the third dose (Table 5).Table 5 Serum total IgG antibody immune responses by study groups and visits
Timepoint Statistics Cohort 1
(2 μg ID93 + 5 μg GLA-SE) Cohort 2
(10 μg ID93 + 5 μg GLA-SE) Cohort 3
(0.9% normal saline placebo) Comparison (p value)
Cohort 1 versus
cohort 3 Cohort 2 versus
cohort 3 Cohort 1 versus
cohort 2
Day 0 n 28 32 31
Baseline GMT (anti-log10)
(95% CI)
757.27
(660.78–867.85)
677.69
(585.16–784.85)
893.37
(713.46–1118.66)
ns ns ns
Day 28 n 28 32 31
GMT (anti-log10)
(95% CI)
4234.34
(2757.27–6502.68)
5464.65
(3831.47–7793.98)
898.30
(707.47–1,140.59)
< 0.0001 < 0.0001 ns
GMFR referencing day 0
(95% CI)
5.59
(3.50–8.93)
8.06
(5.55–11.72)
1.01
(0.93–1.08)
< 0.0001 < 0.0001 ns
SRR (%) ≥ fourfold increase (95% CI) 57.14
(37.18–75.54)
68.75
(49.99–83.88)
0.00
(0.00–11.22)
< 0.0001 < 0.0001 ns
Day 56 n 28 32 31
GMT (anti-log10)
(95% CI)
57,890.48
(37,925.08–88,366.53)
72,788.45
(55,289.87–95,825.14)
947.08
(756.47–1,185.72)
< 0.0001 < 0.0001 ns
GMFR referencing day 0
(95% CI)
76.45
(47.36–123.39)
107.41
(80.89–142.62)
1.06
(0.95–1.19)
< 0.0001 < 0.0001 ns
SRR (%) ≥ fourfold increase (95% CI) 100.00
(87.66–100.00)
100.00
(89.11–100.00)
0.00
(0.00–11.22)
< 0.0001 < 0.0001 ns
Day 84 n 28 32 31
GMT (anti-log10)
(95% CI)
108,339.22
(75,498.62–155,464.92)
124,120.57
(99,951.92–154,133.26)
985.47
(784.83–1237.41)
< 0.0001 < 0.0001 ns
GMFR referencing day 0
(95% CI)
143.07
(95.07–215.29)
183.15
(150.57–222.79)
1.10
(1.00–1.22)
< 0.0001 < 0.0001 ns
SRR (%) ≥ fourfold increase (95% CI) 100.00
(87.66–100.00)
100.00
(89.11–100.00)
0.00
(0.00–11.22)
< 0.0001 < 0.0001 ns
Day 421 n 28 32 31
GMT (anti-log10)
(95% CI)
2221.69
(1299.74–3797.63)
2951.19
(2,182.86–3,989.95)
64.29
(53.14–77.78)
< 0.0001 < 0.0001 ns
GMFR referencing day 0
(95% CI)
2.93
(1.64–5.26)
4.35
(3.14–6.04)
0.07
(0.06–0.09)
< 0.0001 < 0.0001 ns
SRR (%) ≥ fourfold increase (95% CI) 42.86
(24.46–62.82)
59.38
(40.64–76.30)
0.00
(0.00–11.22)
< 0.0001 < 0.0001 ns
The Mann–Whitney test was used to compare GMT and GMFR between cohorts, while Fisher’s exact test was used to compare SRRs between cohorts
p value < 0.05 was considered significantly different
n number of subjects, CI confidence interval, LLOQ lower limit of quantification, GMT geometric mean titer, if LLOQ of IgG < 2.50 then 1.75 was substituted, GMFR geometric mean fold rise defined as the GMT of the ratios of post-dose antibody to the pre-dose baseline antibody, SRR seroresponse rate, defined as the proportion of subjects with a fourfold or more increase in rise compared to the baseline GMT after the first vaccination dose, ns not significant
Antigen-Specific IFN-γ-Secreting Cells
The ELISpot assay was used to measure the level of antigen-specific IFN-γ-secreting cells in blood samples compared with PBS and phytohemagglutinin as negative and positive controls, respectively. The antigen-specific IFN-γ-secreting T cells were comparable at baseline in all three cohorts, whose mean values were under the limit of detection [< 25 spot-forming cells (SFCs)]. Both cohorts 1 and 2 showed increased antigen-specific IFN-γ-secreting cells on days 28, 56, and 84, with cohort 2 showing slightly greater responses than cohort 1 (Fig. 2). The peaks of IFN-γ-secreting cells in cohort 1 (mean 151.10, 95% CI 209.40–92.8) and cohort 2 (mean 194.10, 95% CI 263.60–124.60) were observed on day 84. The IFN-γ-secreting cells in both vaccine groups dropped to an equivalent level at 12 months after the third IP dose (cohort 1: 62.80 SFC per 106 PBMCs, cohort 2: 91.15 SFC per 106 PBMCs). The mean SFCs in the placebo group remained low (< 25 SFCs) throughout the study period (Fig. 2a). For cohorts 1 and 2, the level of antigen-specific IFN-γ-secreting cells gradually increased from the first to third IP doses with significant changes from the baseline. From 12 months after the third IP dose, the level of SFCs decreased, but was significantly higher than the baseline. For cohort 3, there was no change in the level of SFCs between baseline and each post-vaccination timepoint (Fig. 2b).Fig. 2 Immunogenicity assessment of antigen-specific interferon-γ (IFN-γ)-secreting cells. ID93-specific IFN-γ producing T cells using ELISpot. PBMCs from blood samples obtained before vaccination (day 0) and on days 28, 56, 84, and 421 after vaccination were stimulated with ID93 fusion protein in vitro. Results are shown for subjects vaccinated with 2 μg ID93 + 5 μg GLA-SE (cohort 1), 10 μg ID93 + 5 μg GLA-SE (cohort 2), or a saline placebo (cohort 3). a Data are represented as the mean and standard deviation and b each box extends from the 25th to 75th percentile, and the line in the middle of the box represents the median value. The whiskers go down to the minimum value and extend to the maximum value. Values were considered significantly different if p < 0.05 within the group, as indicated by *p < 0.05, **p < 0.01, or ****p < 0.0001
Antigen-Specific Th1 CD4+ Cell-Mediated Immune Response
T-cell immune responses induced by ID93 + GLA-SE were determined via PBMC-based intracellular cytokine staining (ICS) analysis using flow cytometry. The gating strategy and representative scatterplots for the ID93 stimulation are shown in Supplementary Material Figure S1. As shown in Fig. 3a, the percentage of ID93 antigen-specific CD4+ T cells that produced at least one, two, or three cytokine(s) [IFN-γ, tumor necrosis factor-alpha (TNF-α), and/or interleukin-2 (IL-2)] were measured on days 0 (baseline), 28, 56, 84, and 421. ID93 antigen-specific CD4+ T-cell responses were low before vaccination, but significantly increased in both vaccine groups after vaccination at all visits. Both cohort 1 and cohort 2 showed increased frequencies of cytokine-secreting CD4+ T-cell responses on days 28, 56, and 84. On day 421, significantly greater cytokine-producing CD4+ T-cell frequencies were observed with cohort 2 than cohort 1 (p = 0.0116), showing a higher magnitude of ID93-specific CD4+ T cells with 10 μg ID93 + 5 μg GLA-SE. Medians for cohort 2 are slightly higher but statistically insignificant at days 56 and 84. Cytokine-secreting CD4+ T-cell responses in the placebo group remained low (< 0.1%) throughout the study. Meanwhile, there were no differences in antigen-specific CD8+ T-cell responses among the three cohorts or timepoints, indicating no CD8+ T-cell responses were observed in this study (Fig. 3b). Similar frequencies of polyfunctional CD4+ T-cell responses on days 56 and 84 were observed in cohorts 1 and 2 (Fig. 3c and d, respectively). Pie charts representing day 84 versus day 421 samples show fewer 4+ and 3+ cytokine-producing CD4+ T cells at day 421 compared with day 84 samples, demonstrating the contraction of the ID93-specific population. Cohort 2 had proportionately more 1+ and fewer 3+ than cohort 1; we speculate that the statistical significance for day 421 (see above) was because of the enhanced retention of single cytokine producers.Fig. 3 Antigen-specific cytokine(s) positive CD4+ T cells from stimulated cryopreserved peripheral blood mononuclear cells. Blood samples were obtained before each vaccination (days 0, 28, and 56) and at 4 weeks and 12 months after the final vaccination (days 84 and 421). a The percentages of ID93-specific CD4+ T cells producing any of the three T-helper type 1 (Th1) cytokines, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and interleukin-2 (IL-2) (single producer, double producers, and triple producers) were measured in PBMC stimulated with ID93 antigen using intracellular cytokine staining and flow cytometry from each study participant. b The percentages of ID93 specific CD8+ T-cell responses. Values were considered significantly different if p < 0.05, as indicated by *p < 0.05 or ****p < 0.0001. For distribution of multifunctional CD4+ T cells by visits, the data is displayed from the two cohorts administered ID93 + GLA-SE; c cohort 1 and d cohort 2. Data are presented as the percentage frequency of ID93-specific CD4+ T cells expressing either 4, 3, 2, or 1 immune marker combination(s) including IFN-γ, IL-2, TNF-α, and CD40L at days 0, 28, 56, 84, and 421 for each cohort. Pie charts represent the mean proportions of cells expressing (after in vitro stimulation) any single marker and combination of IFN-γ, IL-2, TNF-α, and CD40L marker-positive CD4+ T cells of the total immune marker-expressing CD4+ T-cell response, at days 84 and 421 after vaccination. Paired responses of immune marker-positive CD4+ T cells on days 0, 28, 56, 84, and 421 after vaccination are shown in c cohort 1 and d cohort 2
DISCUSSION
This study provides the first data to evaluate the safety and immunogenicity of the ID93 + GLA-SE vaccine on over 70 previously BCG-vaccinated and QFT-negative individuals of an Asian population. Previously, a phase 1b study in South Africa was conducted in 12 QFT-negative adults who were less likely to be latently infected with Mtb, using a dose reported to be safe by the US phase 1 study [17, 18]. Similar to the phase 1 and phase 1b clinical studies conducted previously, no safety issues were reported in the present study, which selected ID93 (antigen) and GLA-SE (adjuvant) doses and an administration route that were based on the initial safety data from clinical studies in South Africa and the USA [17–19], and clinical trials on other vaccines with GLA-SE as adjuvants [23, 24, 28]. In the US phase 1, vaccination of BCG-naive, QuantiFERON-negative, healthy adults with 2 μg ID93 plus 2 μg GLA-SE, 2 μg ID93 plus 5 μg GLA-SE, 10 μg ID93 plus 2 μg GLA-SE, or 10 μg ID93 plus 5 μg GLA-SE induced a significantly higher antibody response than ID93 alone, which peaked after two injections in 100% of recipients and did not differ between varying antigen and adjuvant doses [17]. A preferential increase in IgG1 and IgG3 subclasses was observed, along with a multifaceted Fc-mediated effector function response, and enhanced magnitude and polyfunctional cytokine profile of CD4+ T cells. In the phase 1b in South Africa, vaccination of BCG-immunized, QuantiFERON-negative and positive, healthy adults showed that vaccine dose (10 μg ID93 plus 2 μg GLA-SE, 2 μg ID93 plus 2 μg GLA-SE, and 10 μg ID93 plus 5 μg GLA-SE) did not affect frequency or severity of adverse events and vaccination induced durable antigen-specific IgG and Th1 cellular responses, which peaked after two administrations. Vaccine dose also did not affect magnitude, kinetics, or profile of antibody and cellular responses in these participants [18]. With an acceptable safety profile, three injections of ID93 + GLA-SE induced strong ID93-specific antibody and cellular immune responses in both vaccine cohorts that were significantly higher than those with placebo, and the responses were maintained for a 12 month duration. This result was consistent with a previous study showing an antigen-specific antibody response predominantly composed of IgG1 and IgG3 subclasses, suggestive of strong major histocompatibility complex class II T-cell activity, and CD4+ T-cell responses elicited from all four ID93 antigen components that persisted for a 6 month study period [19].
In this study, there were peaks in both antibody and T-cell responses to the ID93 antigens 1 month after the third doses, which were somewhat different from those in the previous studies. In the phase 1 US study, a linear dose–response relationship was not observed and differences in CD4+ T-cell responses in the whole blood ICS assay were not statistically significant [17]. While responses peaked after two injections in 100% of recipients, responses did not decrease after the third vaccine administration. Vaccine dose or dosage did not affect magnitude, kinetics, or profile of antibody and cellular responses in the phase 1 or phase 1b participants [17, 18]. One of the reasons for differences in a peak time of immune responses to the ID93 antigen may be the BCG vaccination and latent TB infection histories of participants and vaccination intervals among the clinical studies. Further studies are required to select vaccination frequencies and intervals to reach the maximum efficacy in the target populations.
While there was no difference in immune responses and safety between vaccine doses and both ID93 and GLA-SE concentrations in the previous clinical studies [17–19], a fixed dose of 5 μg GLA-SE was chosen in this study on the basis of the adjuvant formulation results [10]. Antigen doses of 2 μg ID93 (cohort 1) and 10 μg ID93 (cohort 2), which were used in the previous studies, were also compared to select a dose in this study. Although there was a tendency of higher antibody and IFN-γ ELISpot responses in cohort 2, there was no statistical significant difference in immune responses to the ID93 antigen, except in higher ID93-specific cytokine(s) positive CD4+ T cells 1 year after the last vaccination in cohort 2 compared with those in cohort 1. Therefore, three intramuscular injections of a dose of 10 μg ID93 + 5 μg GLA-SE are desirable for further studies including efficacy evaluation in the future.
Healthcare workers were selected as the study population since the public health service has the highest distribution of occupational infectious diseases among all industries, with approximately one-third of the diseases being LTBI [29]. According to the Rules for the Prevention of TB, in ordinance of the Ministry of Health and Welfare of the Republic of Korea, the head of a medical institution should periodically conduct screenings for active and latent TB infections in healthcare workers to detect early-stage infection. As per these rules, TB screening is carried out at least once a year for healthcare providers who examine and treat patients with TB, for medical technicians who have diagnosed patients with TB, and for any other healthcare workers who are at high risk of respiratory infection [30]. The results of this study are from a population with a higher risk of Mtb infection and who would be expected to generate data that allows for subsequent studies to evaluate the safety and exploratory outcomes of this vaccine in the wider public, non-healthcare workers who are BCG-vaccinated, and QFT-negative or QFT-positive adults and adolescents.
Despite a confirmation of safety and immunogenicity of the ID93 + GLA-SE TB vaccine candidate in the BCG-vaccinated healthy adults in South Korea, this study had limitations such as more than two-thirds of enrolled participants in all three cohorts with female. Considering that more than 60% of TB patients reported in the country in 2019 were male, the gender ratio appears to be significantly skewed in this study [3]. In addition, this study employed a voluntary reporting procedure of AEs by filling up the daily diary between study visits, which may vary depending on the participants’ understanding of the characteristics and severity of AEs. While their impacts are not known yet, these two limitations need to be addressed in the upcoming phase 2b study by balancing sexes of participants and by employing information technology tools for monitoring AEs between study visits, respectively.
For further applications of ID93 + GLA-SE, we intend to characterize the preexisting and underlying non-vaccine Mtb-specific responses and compare them with the profile of ID93 + GLA-SE-induced and non-vaccine Mtb-specific responses during immunization using an MTB300 megapool without ID93 antigens in future studies. Combinatorial Polyfunctionality Analysis of Antigen-Specific T-cell Subsets (COMPASS) can also be used to evaluate vaccine-induced CD4+ T-cell subsets with unique and/or overlapping profiles compared with preexisting Mtb-specific responses [19].
CONCLUSIONS
This study showed the selected ID93 + GLA-SE regimens had an acceptable safety profile and were clinically well tolerated after three IM injections in BCG-vaccinated, QFT-negative, healthy healthcare workers. This study showed that a higher proportion of participants administered active IP doses reported AEs than those administered placebo. The solicited local and systemic AEs observed in the current study were consistent with those reported in previous studies, with no reported SAEs related to IP or death, or AEs that caused treatment discontinuation. The immunogenicity data in the current study showed an immunogenic profile comparable to those of the previous studies for ID93 + GLA-SE. Participants administered ID93 + GLA-SE showed higher humoral and Th1 cellular immune responses, which persisted throughout the 12 month study period, compared with those who received placebo.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (PDF 777 KB)
ACKNOWLEDGEMENTS
We would like to thank the volunteers who participated in this study. We also thank Dr. Tracey A. Day, who provided us with the ID93 protein for the immunogenicity assessment. Dr. Tracey A. Day’s support was not funded.
Funding
This study was funded by Quratis Inc. and supported by a grant from the Korea Health Technology R and D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI14C1324). Quratis Inc. financially supported the study and was involved in the study design, data interpretation, data analysis, review of the report, and the journal’s rapid service fee. The corresponding authors had full access to all data in the study and had final responsibility for the decision to submit for publication.
Author Contributions
Yu Hwa Choi and Kwan Goo Cho designed the study. Young Ae Kang, Kwang Joo Park, and Jae Chol Choi developed and recruited the cohorts that were used in the study and provided clinical help. Yu Hwa Choi, Kwan Goo Cho, Young Ae Kang, Kwang Joo Park, Jae Chol Choi, Da Yeon Ko, Jun Ho Ahn, Yun Ju Woo, Kwangsoo Jung, Steven G. Reed, Boram Lee, Eunsol Ahn, Nan Yul Kim, Valerie A. Reese, Sasha E. Larsen, Susan L. Baldwin, Rhea N. Coler, Hyejon Lee, and Sang-Nae Cho analyzed the data and interpreted the results. Da Yeon Ko, Jun Ho Ahn, and Yun Ju Woo provided clinical and regulatory support. Da Yeon Ko, Jun Ho Ahn, and Yun Ju Woo, Nan Yul Kim, and Kwangsoo Jung conducted study oversight, study management activities, and study monitoring. Hyejon Lee, Sang-Nae Cho, Rhea N. Coler, Yu Hwa Choi, Young Ae Kang, Kwang Joo Park, Jae Chol Choi, Kwan Goo Cho, Jun Ho Ahn, Kwangsoo Jung, Boram Lee, Eunsol Ahn, Yun Ju Woo, Nan Yul Kim, Valerie A. Reese, Sasha E. Larsen, Susan L. Baldwin, Steven G. Reed, and Da Yeon Ko wrote the manuscript. All authors fully reviewed and revised the manuscript before submission. All authors read and approved the final manuscript.
Disclosures
Yu Hwa Choi, Kwan Goo Cho, Da Yeon Ko, Jun Ho Ahn, Yun Ju Woo, Kwangsoo Jung, Eunsol Ahn, Boram Lee, Nan Yul Kim, Hyejon Lee, and Sang-Nae Cho are employees of Quratis at the time of the study or analysis. Yu Hwa Choi, Kwan Goo Cho, Sang-Nae Cho, Da Yeon Ko, and Jun Ho Ahn own shares or options to shares in Quratis. Valerie A. Reese, Sasha E. Larsen, Susan L. Baldwin, and Rhea N. Coler received grants from Quratis for the immunoassays of the study. All other authors report no competing interests.
Compliance with Ethics Guidelines
This study was approved by the Institutional Review Board and Ethical Committee of Yonsei University Severance Hospital (IRB# 4-2018-0230), Ajou University Hospital (IRB# AJIRB-MED-CT2-18-078), and Chung-Ang University Hospital (1833-001-320). The study was performed in line with the principles of the Declaration of Helsinki. The trial was retrospectively registered at clinicaltrials gov (NCT03806686). All study participants gave written informed consent to participate in the study and to publish the results of the clinical trial.
Data Availability
The data supporting the findings in this study are included in this manuscript and its electronic supplementary material.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yu Hwa Choi and Young Ae Kang contributed equally to this work.
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2. Cohen A Mathiasen VD Schön T Wejse C The global prevalence of latent tuberculosis: a systematic review and meta-analysis Eur Respir J 2019 54 1900655 10.1183/13993003.00655-2019 31221810
3. Korea Disease Control and Prevention Agency. Annual report on the notified tuberculosis patients in Korea 2019. 2020. http://www.kdca.go.kr/npt/biz/npp/portal/nppPblctDtaView.do?pblctDtaSeAt=1&pblctDtaSn=2088. Accessed 30 Nov 2022.
4. Clemens JD Chuong JJ Feinstein AR The BCG controversy. A methodological and statistical reappraisal JAMA 1983 249 2362 2369 10.1001/jama.1983.03330410048027 6834635
5. Menzies R Vissandjee B Effect of bacille Calmette-Guérin vaccination on tuberculin reactivity Am Rev Respir Dis 1992 145 621 625 10.1164/ajrccm/145.3.621 1546843
6. Colditz GA Berkey CS Mosteller F Brewer TF Wilson ME Burdick E The efficacy of bacillus Calmette-Guérin vaccination of newborns and infants in the prevention of tuberculosis: meta-analyses of the published literature Pediatrics 1995 96 29 35 10.1542/peds.96.1.29 7596718
7. Fine PE Variation in protection by BCG: implications of and for heterologous immunity Lancet 1995 346 1339 1345 10.1016/s0140-6736(95)92348-9 7475776
8. Woodworth JS Clemmensen HS Battey H Dijkman K Lindenstrøm T Laureano RS A Mycobacterium tuberculosis-specific subunit vaccine that provides synergistic immunity upon co-administration with Bacillus Calmette-Guérin Nat Commun 2021 12 6658 10.1038/s41467-021-26934-0 34795205
9. Andersen P Doherty TM The success and failure of BCG—implications for a novel tuberculosis vaccine Nat Rev Microbiol 2005 3 656 662 10.1038/nrmicro1211 16012514
10. Anderson RC Fox CB Dutill TS Shaverdian N Evers TL Poshusta GR Physicochemical characterization and biological activity of synthetic TLR4 agonist formulations Colloids Surf B Biointerfaces 2010 75 123 132 10.1016/j.colsurfb.2009.08.022 19748238
11. Coler RN Bertholet S Moutaftsi M Guderian JA Windish HP Baldwin SL Development and characterization of synthetic glucopyranosyl lipid adjuvant system as a vaccine adjuvant PLoS One 2011 6 e16333 10.1371/journal.pone.0016333 21298114
12. Baldwin SL Shaverdian N Goto Y Duthie MS Raman VS Evers T Enhanced humoral and Type 1 cellular immune responses with Fluzone adjuvanted with a synthetic TLR4 agonist formulated in an emulsion Vaccine 2009 27 5956 5963 10.1016/j.vaccine.2009.07.081 19679214
13. Lousada-Dietrich S Jogdand PS Jepsen S Pinto VV Ditlev SB Christiansen M A synthetic TLR4 agonist formulated in an emulsion enhances humoral and Type 1 cellular immune responses against GMZ2–a GLURP-MSP3 fusion protein malaria vaccine candidate Vaccine 2011 29 3284 3292 10.1016/j.vaccine.2011.02.022 21349366
14. Bertholet S Goto Y Carter L Bhatia A Howard RF Carter D Optimized subunit vaccine protects against experimental leishmaniasis Vaccine 2009 27 7036 7045 10.1016/j.vaccine.2009.09.066 19786136
15. Bertholet S Ireton GC Ordway DJ Windish HP Pine SO Kahn M A defined tuberculosis vaccine candidate boosts BCG and protects against multidrug-resistant Mycobacterium tuberculosis Sci Transl Med 2010 2 53ra74 10.1126/scitranslmed.3001094 20944089
16. Baldwin SL Reese VA Larsen SE Beebe E Guderian J Orr MT Prophylactic efficacy against Mycobacterium tuberculosis using ID93 and lipid-based adjuvant formulations in the mouse model PLoS One 2021 16 e0247990 10.1371/journal.pone.0247990 33705411
17. Coler RN Day TA Ellis R Piazza FM Beckmann AM Vergara J The TLR-4 agonist adjuvant, GLA-SE, improves magnitude and quality of immune responses elicited by the ID93 tuberculosis vaccine: first-in-human trial NPJ Vaccines 2018 3 34 10.1038/s41541-018-0057-5 30210819
18. Penn-Nicholson A Tameris M Smit E Day TA Musvosvi M Jayashankar L Safety and immunogenicity of the novel tuberculosis vaccine ID93 + GLA-SE in BCG-vaccinated healthy adults in South Africa: a randomised, double-blind, placebo-controlled phase 1 trial Lancet Respir Med 2018 6 287 298 10.1016/S2213-2600(18)30077-8 29595510
19. Day TA Penn-Nicholson A Luabeya AKK Fiore-Gartland A Du Plessis N Loxton AG Safety and immunogenicity of the adjunct therapeutic vaccine ID93 + GLA-SE in adults who have completed treatment for tuberculosis: a randomised, double-blind, placebo-controlled, phase 2a trial Lancet Respir Med 2021 9 373 386 10.1016/S2213-2600(20)30319-2 33306991
20. Coler RN Baldwin SL Shaverdian N Bertholet S Reed SJ Raman VS A synthetic adjuvant to enhance and expand immune responses to influenza vaccines PLoS One 2010 5 e13677 10.1371/journal.pone.0013677 21060869
21. Coler RN Duthie MS Hofmeyer KA Guderian J Jayashankar L Vergara J From mouse to man: safety, immunogenicity and efficacy of a candidate leishmaniasis vaccine LEISH-F3+GLA-SE Clin Transl Immunol 2015 4 e35 10.1038/cti.2015.6
22. Singh K Mukherjee P Shakri AR Singh A Pandey G Bakshi M Malaria vaccine candidate based on Duffy-binding protein elicits strain transcending functional antibodies in a Phase I trial NPJ Vaccines. 2018 3 48 10.1038/s41541-018-0083-3 30302285
23. Santini-Oliveira M Coler RN Parra J Veloso V Jayashankar L Pinto PM Schistosomiasis vaccine candidate Sm14/GLA-SE: Phase 1 safety and immunogenicity clinical trial in healthy, male adults Vaccine 2016 34 586 594 10.1016/j.vaccine.2015.10.027 26571311
24. Sirima SB Richert L Chêne A Konate AT Campion C Dechavanne S PRIMVAC vaccine adjuvanted with alhydrogel or GLA-SE to prevent placental malaria: a first-in-human, randomised, double-blind, placebo-controlled study Lancet Infect Dis 2020 20 585 597 10.1016/S1473-3099(19)30739-X 32032566
25. Field MJ Tuberculosis in the workplace 2001 Washington (DC) National Academies Press
26. Jung DH Jo KW Shim TS Prevalence of latent tuberculosis infection among medical students in South Korea Tuberc Respir Dis (Seoul) 2012 73 219 223 10.4046/trd.2012.73.4.219 23166557
27. Youakim S The occupational risk of tuberculosis in a low-prevalence population Occup Med (Lond) 2016 66 466 470 10.1093/occmed/kqw040 27036151
28. Tendler M Almeida MS Vilar MM Pinto PM Limaverde-Sousa G Current status of the Sm14/GLA-SE schistosomiasis vaccine: overcoming barriers and paradigms towards the first anti-parasitic human(itarian) vaccine Trop Med Infect Dis 2018 3 121 10.3390/tropicalmed3040121 30469320
29. Chung YK Ahn YS Jeong JS Occupational infection in Korea J Korean Med Sci 2010 25 S53 61 10.3346/jkms.2010.25.S.S53 21258592
30. Joint committee for the revision of Korean guidelines for tuberculosis. Korean guidelines for tuberculosis. 2nd ed. Cheongju: Korea Centers for Disease Control & Prevention; 2016
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PMC010xxxxxx/PMC10188543.txt |
==== Front
Aten Primaria
Aten Primaria
Atencion Primaria
0212-6567
1578-1275
Elsevier
S0212-6567(23)00081-1
10.1016/j.aprim.2023.102648
102648
Special Article
New paradigms on antibiotic recommendations for community-acquired infections in Spain
Nuevos paradigmas en las recomendaciones antibióticas en las infecciones comunitarias en EspañaLlor Carl [email protected]
abc⁎
Hoyos Mallecot Yannick d
Moragas Ana abe
Troncoso-Mariño Amelia f
Bjerrum Lars g
Villmones Heidi C. h
a Primary Care Research Institute Jordi Gol (IDIAPJGol), Barcelona, Spain
b CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Spain
c Department of Public Health, General Practice, University of Southern Denmark, Odense, Denmark
d Department of Microbiology, Vall d’Hebron University Hospital, Catalonian Institute of Health, Barcelona, Spain
e University Rovira i Virgili, Jaume I Health Centre, Tarragona, Spain
f Primary Care Pharmacy Unit, Catalonian Institute of Health, Barcelona, Spain
g Research Unit for General Practice, Department of Public Health, University of Copenhagen, Denmark
h Department of Microbiology, Vestfold Hospital Trust, Tønsberg, Norway
⁎ Corresponding author. [email protected]
09 5 2023
8 2023
09 5 2023
55 8 1026486 4 2023
12 4 2023
© 2023 The Author(s)
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Over the last years, the susceptibility activity of the most common microorganisms causing community-acquired infections has significantly changed in Spain. Based on the susceptibility rates of Streptococcus pyogenes, Streptococcus pneumoniae, Haemophilus influenzae, Escherichia coli, and Klebsiella pneumoniae collected from outpatients aged 15 or older with symptoms of respiratory or urinary tract infections in several Microbiology Departments in Catalonia in 2021, penicillin V should be first choice for most respiratory tract infections, amoxicillin and clavulanate for chronic obstructive pulmonary disease exacerbations and a single dose of fosfomycin or a short-course nitrofurantoin should remain first-line treatments for uncomplicated urinary tract infections. Updated information on antimicrobial resistance for general practitioners is crucial for achieving appropriate empirical management of the most common infections by promoting more rational antibiotic use.
En los últimos años han cambiado significativamente los porcentajes de sensibilidad de los microorganismos más comunes que causan infecciones adquiridas en la comunidad en España. A partir de los porcentajes de sensibilidad de Streptococcus pyogenes, Streptococcus pneumoniae, Haemophilus influenzae, Escherichia coli y Klebsiella pneumoniae, recogidas de aislados de pacientes ambulatorios de 15 años o más, con síntomas de infecciones respiratorias o urinarias en servicios de microbiología de Cataluña en 2021, fenoximetilpenicilina debería ser la primera opción en la mayoría de los infecciones respiratorias, amoxicilina y ácido clavulánico en las exacerbaciones de la enfermedad pulmonar obstructiva crónica y la monodosis de fosfomicina o la pauta corta de nitrofurantoína como tratamiento de primera línea en las infecciones urinarias no complicadas. Es importante que los médicos de familia dispongan de información actualizada sobre la resistencia a los antimicrobianos para lograr un manejo empírico adecuado de las infecciones más frecuentes al promover un uso más racional de los antibióticos.
Keywords
Drug resistance, microbial
Anti-bacterial agents
Primary health care
Penicillin V
Antimicrobial stewardship
Community-acquired infections
Palabras clave
Resistencia a los medicamentos, microbiana
Agentes antibacterianos
Primeros auxilios
Penicilina V
Administración antimicrobiana
Infecciones adquiridas en la comunidad
==== Body
pmcBackground
Antimicrobial resistance (AMR) is one of the main public health challenges worldwide.1 Ensuring prudent antimicrobial utilisation is key to an effective response to this huge problem, mainly in primary care, in which nearly 80% of all antibiotics are issued.2 Reducing the emergence and spread of antibiotic resistance, i.e. prioritising antibiotics that are less likely to lead to antibiotic resistance at the societal and the individual level, is a task that all clinicians should and must implement. The main driver for resistance is antibiotic overprescribing.3 Antimicrobial stewardship programmes in primary care should primarily be focused on prudent use of antibiotics and only when the suspicion of a bacterial aetiology is very likely.4 Most of the respiratory tract infections in the community are viral and most bacterial infections are mild and self-limiting. According to the Centers for Disease Control and Prevention about one half of all antibiotics prescribed for community-acquired respiratory tract infections are unnecessary.5
When antibiotic therapy is warranted, the World Health Organization recommends making use of the WHO Access, Watch, Reserve (AWaRe) classification and selecting an antimicrobial drug with a low impact on AMR (ref www.who.int/publications/i/item/2021-aware-classification). AWaRe encourages the so-called ‘Access antibiotics’ as empirical treatment in primary care because they have a narrow antimicrobial spectrum, low cost, good safety profile and are less likely to select for AMR.6 The category ‘Watch and Reserve Antibiotics’ are broader-spectrum antibiotics and more likely to select for antimicrobial resistance. They are more expensive and should primarily be reserved for patients with severe clinical infections and infections in which the causative pathogens are more likely to be resistant.7 Continuing surveillance for the antibiotic resistance of common pathogens is therefore a recognised public health need, particularly in countries with long-standing high resistance rates, such as Spain, where initial antimicrobial treatment of assumed bacterial respiratory and urinary tract infections is always selected empirically. Antimicrobials should therefore provide appropriate coverage against the most common causative microorganisms.8, 9 Despite this, updated information about susceptibility patterns have been a matter of course at the hospital level and general practitioners have rarely had updated information about the susceptibility patterns of isolates collected in the community so far.10 Based on the Infection Control and Antimicrobial Stewardship Catalonian Program (VINCat), antimicrobial susceptibilities of common bacterial pathogens causing respiratory and urinary tract infections collected in 2021 from outpatients aged 15 or more (summarised in Table 1),11 a new paradigm in the use of first-line antibiotic therapies should be considered.Table 1 In vitro antibiotic susceptibility of common antibiotics against microorganisms causing community-acquired infections in adults in the community collected in 2021 in Catalonia (taken from Ref. 11).
Table 1Type of infections Microorganism Number of isolates Antibiotics tested Average susceptibility (%)
Respiratory tract infections Streptococcus pyogenes 199 Penicillin 100.0
198 Erythromycin 70.7
199 Clindamycin 76.3
Streptococcus pneumoniae 972 Penicillin 97.8
804 Amoxicillin 91.5
986 Erythromycin 75.1
886 Levofloxacin 97.2
Haemophilus influenzae 1,149 Ampicillin 78.0
1,148 Amoxicillin and clavulanate 93.9
1,142 Quinolones 95.0
Urinary tract infections Escherichia coli 68,907 Fosfomycin 96.3
67,676 Nitrofurantoin 98.8
68,881 Amoxicillin and clavulanate 78.0
68,874 Cefuroxime 86.9
68,626 Third generation cephalosporins 90.8
68,940 Quinolones 74.3
68,923 Trimethoprim and sulfamethoxazole 75.7
69,409 ESBL producers 8.6
Klebsiella pneumoniae 14,250 Fosfomycin 70.8
14,465 Amoxicillin and clavulanate 83.6
14,450 Cefuroxime 87.1
14,444 Third generation cephalosporins 89.3
14,472 Quinolones 83.5
14,443 ESBL producers 9.8
ESBL: extended spectrum beta-lactamase.
Streptococcal pharyngitis: Streptococcus pyogenes is totally susceptible to phenoxymethylpenicillin
The most common bacterium causing pharyngotonsillitis in Western countries is group A β-haemolytic Streptococcus. According to the microbiological analysis of strains collected from throat swabs from patients with acute pharyngitis, in 2021 S. pyogenes continues being 100% susceptible towards penicillin – resistant strains have never been isolated against this antibiotic –, indicating that phenoxymethylpenicillin should remain as the first choice for patients with a positive rapid antigen detection test. Despite being recommended for patients with streptococcal pharyngitis for more than one decade, the use of penicillin V continues to be scarce in Spain.12
Hence, given its narrow spectrum of activity, low cost, efficacy in preventing strep throat complications, and benign side-effect profile, high-dose phenoxymethylpenicillin should be the drug of choice for treating streptococcal pharyngitis, with amoxicillin, an antibiotic with a broader spectrum compared to phenoxymethylpenicillin, being reserved as second choice alternative. Although macrolides and lincosamides are recommended as alternative antibiotics in streptococcal infected patients who are allergic to β-lactams, approximately 20% of the strains of S. pyogenes are now resistant.
Germs causing respiratory tract infections: the dramatic reduction of resistance of Streptococcus pneumoniae to penicillin allows clinicians to consider high-dose penicillin as first-line antibiotic for pneumococcal infections
Streptococcus pneumoniae and Haemophilus influenzae are the most common bacteria causing respiratory tract infections. S. pneumoniae has traditionally been a pathogen that is highly susceptible to a large number of antimicrobials, with penicillin being its first-choice antibiotic. Infections due to this microorganism were, in general, easily treatable and presented few management difficulties. Although the resistance of S. pneumoniae to penicillin has been known since the 1960s, the extreme rarity of these strains did not present any clinical problems in the past. However, in 1977 an epidemic outbreak caused by S. pneumoniae resistant to penicillin occurred in South Africa.13 Since then there has been a continuous increase in the number of strains of S. pneumoniae resistant to penicillin. In 1979, the first isolates of S. pneumoniae with decreased sensitivity to penicillin were reported in Spain.14 Since the mid-1980s the resistance rate of pneumococci to penicillin has significantly increased, jeopardising the selection of an effective antibiotic therapy.15 At the beginning of this century the percentage of resistance was as high as 40%, as also reported in Spanish news media.16, 17
However, during the last two decades the resistance rate of S. pneumoniae towards penicillin has markedly changed.18 Based on isolates from ear and sputum samples in patients with upper respiratory tract infections, the current resistance rate of S. pneumoniae towards penicillin is 2.4%. This finding confirms a rapid decline of the resistance rates to penicillin and other β-lactam antibiotics initiated since the beginning of this century.
The VINCat antimicrobial susceptibility results also confirm the steady resistance pattern of the streptococcal strains to macrolides, which is of concern. Unlike penicillin resistance in S. pneumoniae, there has not been a temporal decreasing trend for macrolides over the last two decades in Spain.18 Awareness of this problem should be considered by clinicians when patients are suspected to have an allergy to β-lactams and, thus, fluoroquinolones must be considered as first choice in this situation.
According to this recent microbiological analysis, the resistance rate of H. influenzae towards ampicillin is also of concern. The prevalence of β-lactamase-producing microorganisms slightly exceeds 20% and, in some areas, it may be even higher. β-Lactamase production is a well-known predictor of treatment failure in respiratory tract infections in hospitalised patients.19 In patients with acute exacerbations of severe chronic obstructive pulmonary disease accompanied by purulent sputum or increased C-reactive protein (≥40 mg/l), antibiotic therapy that covers H. influenzae is warranted.20 The Global initiative for Chronic Lung Disease recommends that the choice of antibiotic be based on local resistance patterns, and accordingly, the use of high doses of the association of amoxicillin and clavulanate (875–125 mg/8 h for 5 days) should, therefore, be recommended in these pacients.21
The current susceptibility pattern of pneumococcal strains suggests penicillin as the first choice agent for the majority of respiratory tract infections. The majority of infections caused by pneumococcal strains are fully susceptible to penicillin. There is a small percentage of pneumococci with intermediate susceptibility (minimum inhibitory concentration between 0.12 and 2 μg/l),22 against which a high dose of antibiotic might be required. To encompass the few cases of resistance we suggest high doses of penicillin, either G or V, to be used as first-line therapy for patients with community-acquired pneumonia. Caution is mandatory regarding this infection as this is the leading cause of death due to infectious diseases in adults worldwide. Classic bacteriologic techniques have generally identified pneumococcus as the overwhelmingly common cause, but there has been a great deal of discussion about the role of other microbes in the aetiology of pneumonia, as only half of the episodes of pneumonia show positive cultures in hospitalised patients. With the use of high-quality sputum specimens and modern microbiological techniques, we are able to identify more bacteria and viruses as possible aetiological agents, but there is debate about the true finding when nucleic acid amplification tests detect bacteria, mainly H. influenzae, Staphylococcus aureus, Pseudomonas aeruginosa or Moraxella catarrhalis, which are not later found by culture. This probably reflects bacterial presence in the microbiome and clinicians should not, therefore, treat such cases.23 As Gadbsy et al.24 put it recently, basing antibiotic selection on results of these modern techniques may potentially lead to unnecessary escalation of antibiotic use in some patients, thereby hampering efforts at antibiotic stewardship. The usage of microbiological tests in primary care are not recommended, and although the sensitivity of the pneumococcal antigen in urine ranges from 69% to 81%, with a specificity of 85–98%, the yield of this test in primary care is low as the incidence of pneumonia in the community is much lower than that observed in a hospital. Thus, treatment in primary care must be empirical as soon as severity criteria have been discarded. Doctors are requested to carefully assess if a patient with pneumonia must be referred to the hospital by considering the CRB-65/75 hospitalisation score (confusion, respiratory rate ≥30 breaths/min, blood pressure <90/60 mmHg, confusion, or older age) and the quick Sequential Organ Failure Assessment (SOFA) sepsis criteria (respiratory rate ≥22 breaths/min, systolic blood pressure <100 mmHg or altered mental status with a Glasgow score <15).25
Scandinavian and Dutch guidelines recommend narrow spectrum penicillin G/V in monotherapy as first-line empirical treatment in patients with non-severe CAP with no routine empirical coverage for H. influenzae or atypical pathogens. An example is the Danish or the Norwegian guideline that recommends a dose of 1,000,000 IU of penicillin V every 6 h for five days.26, 27 Two different Scandinavian studies have recently found that penicillin therapy for pneumonia is as safe as other treatments and is not associated with a greater mortality in mild to moderate CAP.28, 29 In addition, the Norwegian study showed that penicillin in monotherapy was associated with a reduced risk of 30-day readmission compared to other antibiotic treatments.29 Based on the results of the current susceptibility patterns of pneumococcal strains in Spain, high-dose penicillin V should be the drug of choice for community-acquired pneumonia. The use of phenoxymethylpenicillin is now only recommended for streptococcal pharyngitis in our country and the recommendation to use this antibiotic for other respiratory tract infections, such as pneumonia, acute bacterial rhinosinusitis, and acute otitis media in children under 2 years of age, will entail an off-label prescription. However, off-label prescription of antibiotics is common in Western countries and should not hinder rational antibiotic usage.30
We are aware of the lack of presentations of phenoxymethylpenicillin in our country, but this should not represent a limitation for prescribing this narrow spectrum antibiotic in Spain. This lack of presentations is now compounded by the fact that there is a shortage of some first-line antibiotics since autumn 2022. The European Medicine Agency, the European Commission and the Heads of Medicines Agencies, through the Executive Steering Group on Shortages and Safety of Medicinal Products are closely monitoring the current shortages of antibiotics affecting Europe.31 This shortage of antibiotics, which has been an ongoing public health concern and the situation in the European Union has been exacerbated by geopolitical events or trends such as the war in Ukraine, the energy crisis, and high inflation rates, mainly affects first-choice antibiotics, and an example is the current lack of oral presentations of 500 mg of penicillin V in our country.32
Microorganisms causing urinary tract infections: either the 3 g single dose of fosfomycin or the short-course of nitrofurantoin should be prioritised
Resistance of uropathogens, mainly Escherichia coli and Klebsiella pneumoniae, to the classical antibiotics has significantly increased in the last years in Spain, mainly due to the high use of antibiotics.33 The resistance of enterobacteria to third generation cephalosporins, mediated by the production of extended-spectrum β-lactamases is a growing problem, with approximately 10% of these strains being producers, reducing the empirical therapeutic options in the community.34 As shown in Table 1, the most active antimicrobials against E. coli are carbapenem, nitrofurantoin and fosfomycin, with susceptibility rates exceeding 95% of the isolates. On the contrary, amoxicillin and clavulanate, quinolones, trimethoprim, and sulfamethoxazole are the least effective antibiotics, with less than 80% of isolates being susceptible. According to the recommendations of the Infectious Diseases Society of America, empirical antibiotic therapy should be substituted when the rates of resistance surpass 20%.35 In our country, current guidelines no longer recommend the former antibiotics for empirical use. Therefore, a single 3 g dose of fosfomycin or a short course of nitrofurantoin should be recommended for the treatment of uncomplicated urinary tract infections, as these shorter lengths are equally effective and show fewer adverse reactions compared to antibiotics used for longer durations.36
Conclusions
Appropriate antibiotic prescribing is essential not only for patient safety and outcome, but also for reducing the emergence of antimicrobial resistance. There is a need to avoid the use of broad-spectrum antibiotics to reduce the risk of selecting resistant strains and to cause the least possible side effects and dysregulation in the patient's microbiome. The current susceptibility rates to common antibiotics are closer to the reality of other northern European countries compared to the rates observed in our country two and three decades ago. A ‘scandinavisation’ of first-line antibiotic therapy for common community-acquired infections has been discussed in this special paper, representing a new paradigm that should be endorsed by family doctors and paediatricians in our country and updated in our local clinical guidelines, and could also be considered in other neighbouring countries that are experiencing similar changes in susceptibility rates.
Funding
Not funded.
Conflict of interest
CL has received research grants from Abbott Diagnostics. The other authors declare no conflicts of interest.
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PMC010xxxxxx/PMC10189572.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206586
50545
10.5114/ceji.2023.126650
Review Paper
The role of monocytes in malaria infection
Xuan Keh Min 1
Bakar Nurhidanatasha Abu 1
Fadzli Mustaffa Khairul Mohd 2
Azlan Maryam 1
1 School of Health Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
2 Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
Maryam Azlan, PhD, School of Health Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia, e-mail: [email protected]
13 4 2023
2023
48 1 5462
24 4 2022
11 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Malaria remains one of the most common human infections worldwide. In endemic areas, malaria is a leading cause of morbidity and mortality and it imposes significant socioeconomic burdens on the people affected. Monocytes are part of the immune system controlling parasite burden and protecting the host against malaria infection. Monocytes play their protective roles against malaria via phagocytosis, cytokine production and antigen presentation. Though monocytes are crucial for clearance of malaria infection, they have also been shown to cause adverse clinical outcomes. In this review, we discuss recent findings regarding the role of monocytes in malaria via mechanisms such as parasite detection and clearance, pro-inflammatory activities, and activation of other immune components. We also highlight the role of different monocyte subsets, and other myeloid cells that are involved in malaria infection. However, more investigations are required in order to explore the exact roles of these monocytes in malaria infection.
malaria
monocytes
Plasmodium
immunity
==== Body
pmcIntroduction
Malaria is one of the most common mosquito-borne disease worldwide. In 2020, an estimated 241 million cases of malaria were reported worldwide with the number of deaths being approximately 627,000 people [1]. Almost 60% of malaria deaths worldwide occur in the poorest 20% of the population. It also imposes a major financial and social burden on many regions of the world. Monocytes are part of the immune system controlling parasite burden and protecting the host against malaria infection [2]. They play their protective roles against malaria via phagocytosis, cytokine production and antigen presentation [3].
The function of monocytes
Monocytes are a type of white blood cell derived from myeloid progenitors which play a crucial role in the innate immune system. Monocytes have two important roles in the immune system. First, they regenerate resident macrophages and dendritic cells under normal conditions, and secondly, they travel to infection sites in the tissues and differentiate into macrophages and dendritic cells to induce an immune response to inflammation signals. Monocytes kill pathogens as well as facilitating the healing and repair process [4]. They are a key component of the innate immune system serving three main immunological functions: phagocytosis, antigen presentation and inflammatory cytokine production [5].
Monocytes can phagocytose pathogens by binding to them directly through pattern-recognition receptors, or by using intermediary opsonizing proteins such as antibodies or complement, which coat the pathogen. Monocytes may also use antibody-dependent cell-mediated cytotoxicity to destroy infected host cells. Monocytes function as phagocytes and antigen-presenting cells in the peripheral blood to ingest and remove microorganisms, foreign material, and dead or damaged cells.
Microbial pattern-recognition receptors recognize pathogen-associated molecular patterns (PAMP) and activate monocytes to kill invading parasites. An example of pattern-recognition receptors is the mammalian Toll-like receptor (TLR), which recognizes a variety of microbial pathogens and their products [6]. Monocytes express more TLRs than neutrophils. When specific ligands bind, TLRs activate nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and stimulate the production of proinflammatory cytokine from monocytes through a pathway involving the adaptor protein MyD88 [7].
Monocytes produce cytokines, which attract more cells and proteins to the infected region, resulting in an activated immune response. In response to parasite ingestion, monocytes secrete both pro-inflammatory and anti-inflammatory cytokines as well as growth factors, which results in parasite removal and minimization of inflammation [8]. Monocytes secrete pro-inflammatory cytokines such as interleukin 1 (IL-1) and tumor necrosis factor α (TNF-α), which can activate other leukocytes and endothelial cells to a pro-adhesion, pro-migratory condition and induce secretion of vasoactive substances [9].
Lymph node-trafficking monocytes are weak antigen presenters and instead serve as antigen transporters, delivering antigens to draining lymph nodes. Other studies, on the other hand, suggest that monocytes process and present antigen similarly to classical dendritic cells (cDCs) [10]. It is likely that the methodological differences are the reason for the variation of results. Both in vivo and ex vivo studies have demonstrated that monocytes play a significant role in the presentation of antigens to T cells and the induction of particular T cell subsets.
Monocyte subsets
Monocytes in humans are heterogeneous. They consist of three subsets based on expression of CD14 and CD16. Circulating human monocytes consist of the CD14++CD16– classical monocytes, CD14++CD16+ intermediate monocytes and CD14+CD16++ non-classical monocytes. Classical monocytes are the most predominant subset, accounting for around 80% of the total circulating monocyte population [11]. The remaining 20% of monocytes are the non-classical monocytes and intermediate monocytes. These subsets vary in their differentiation properties, migratory capabilities, and cytokine production. However, some factors such as gender, ethnicity, age and diet may alter the proportion of monocyte subsets in individuals.
Classical monocytes
The classical monocyte is characterized by high expression of CD14. Classical monocytes were found to be primed for phagocytosis, innate sensing and migration. Classical monocytes, also known as inflammatory monocytes, have a more pro-inflammatory nature, with the ability to infiltrate tissues and produce soluble inflammatory cytokines and differentiate into DCs and inflammatory macrophages, linking between the innate and adaptive immune responses. Classical monocytes express many pattern recognition receptors (PRRs) and are involved in removing microorganisms and dying cells through phagocytosis [12].
Classical monocytes react strongly to bacterial products through TLR4 and infiltrate inflammatory sites in response to the chemokine CCL2. These monocytes proliferate in the bone marrow in response to infection or injury. For example, during bacterial infection, these monocytes migrate to the infection site, phagocytose pathogens, and produce a variety of chemokines that attract other immune cells, and present antigens through MHC class II. These monocytes may leave the blood vessels and survey the tissue microenvironment without further differentiation before exiting through the lymphatics [13]. CD14+ classical monocytes express high levels of chemokine receptors such as CCR1, CCR2, CCR5, CXCR1, and CXCR2, indicating their ability to migrate to signals arising from injured or inflamed tissues [14], but they are also distinguished by their ability to secrete pro-inflammatory molecules such as IL-6, IL-8, CCL2, CCL3, and CCL5 [15]. Classical monocytes are able to differentiate into monocyte-derived macrophages and DCs and they play an important role in regulating inflammation and tissue recovery [16].
Non-classical monocytes
Non-classical endothelial patrolling monocytes are CD14+CD16++ in humans. The non-classical monocyte shows low expression of CD14 and additional co-expression of CD16. They have high fractalkine receptor (CX3CR1) expression but they also migrate in response to a variety of chemokines [17]. Non-classical monocytes are involved in endothelium intraluminal monitoring, complement and Fc γ-mediated phagocytosis of damaged endothelium, and neutrophil recruitment to the injury site. These monocytes are able to detect and respond to circulating nucleic acids and viruses via TLR7 signaling, and trigger an innate immune response by secreting cytokines and chemokines [18]. However, the exact role of the non-classical monocytes is still debatable. They have antigen-processing abilities [19], but they differ from classical monocytes in that they are involved in wound healing proces- ses [20]. Moreover, they have antagonizing functions towards classical monocytes and promote neutrophil adhesion at the endothelial interface via the secretion of TNF-α [21] but they do not produce pro-inflammatory cytokines at the same levels as the classical monocytes.
Intermediate monocytes
Intermediate monocytes CD14+CD16+ in humans have also been identified, but their specific function is still unknown. The intermediate monocyte has high expression of CD14 and low expression of CD16. These intermediate monocytes were found to have similar ROS production and phagocytosis ability as the classical monocytes, but have lower cell surface adhesion and express a higher level of class II molecule and IL-12 [22]. Intermediate monocytes may be a transitional stage of maturation from classical to non-classical monocytes, and they react strongly to viral and bacterial ligands. Intermediate monocytes were the only subset of monocytes that expressed CCR5, making them suitable for antigen presentation, cytokine secretion, apoptosis regulation, and differentiation [23]. Their ability to present antigens and activate T cells is indicated by their gene expression signature. Based on transcriptome studies, intermediate monocytes express more MHC II and are more similar to classical monocytes than non-classical monocytes. Intermediate monocytes also have proinflammatory functions by secreting large quantities of IL-1β, IL-6, IL-12, TNF-α and CCL3 when stimulated by TLR [24]. However, their exact function in immunity is still unknown, as another study revealed that they are the primary producers of IL-10 in response to TLR stimulation [25].
The monocyte subsets show significant variability in surface marker expression and functions; however, the exact role of different monocyte subsets in malaria infection is still unclear. The intermediate and non-classical monocytes have previously been shown to play a significant role in parasitic infection. Classical monocytes have a more pro-inflammatory physiology due to their ability to produce soluble mediators and differentiate into monocyte-derived DCs to link innate and adaptive immune responses. Meanwhile, intermediate monocytes are involved in antigen presentation, whereas non-classical monocytes play a crucial role in anti-viral reactions [10].
Based on their maturation status, monocytes can be differentiated into different subsets which vary in their cytokine secretion and ability to phagocytose merozoites, or infected RBCs (iRBCs). For example, the intermediate monocyte subset appeared to be the monocyte subtype best suited for phagocytosis of P. vivax-infected cells in vitro when compared to the more mature non-classical subset. In P. falciparum infection [26], the expression of CD16/FcγRIIIa on these two subsets is associated with TNF-α production. In severe malarial anemia (SMA) in children, circulating red blood cells are known to be IgG-coated. IgG-coated red cells formed during P. falciparum infection might engage CD16/FcγRIIIa on monocytes, accelerating the destruction by erythrophagocytosis. Thus a negative correlation of the CD16/FcγRIIIa expression level with hemoglobin levels was observed in the more mature non-classical subset, indicating its role in erythrophagocytosis [27]. While there are still large gaps in our understanding of the roles of these monocyte subsets in malaria, they may have enhanced antiparasitic activity.
In regards to the secretion of inflammatory cytokines, the classical, intermediate, and non-classical monocyte subsets differ in their production of IL-1β, IL-6, and TNF-α (Fig. 1). Boyette et al. reported that the classical monocytes are the best cytokine producers and non-classical monocytes are poor cytokine producers. The level of TNF-α produced by intermediate monocytes was equivalent to that produced by classical monocytes. The classical monocyte subset secreted more IL-1β than the other subsets. Accordingly, the classical subset secreted more IL-6 than the non-classical and intermediate subsets [28].
Fig. 1 The role of monocytes in malaria infection. A) Phagocytosis. Monocytes phagocytose iRBCs and merozoites through opsonic or non-opsonic phagocytosis. B) Antibody-dependent cellular inhibition (ADCI). Monocytes interact with antibody-opsonized merozoites through FcγRs. C) Cytokine production. Parasite-associated components activate monocytes via TLRs
The role of monocytes in malaria
The malaria parasite interacts with the innate immune system during its life cycle, with the monocytes and macrophages playing major roles in tissue-specific inflammatory responses. During an acute malaria infection, monocytes are recruited in large numbers from the bone marrow. When monocytes migrate into tissues, these monocytes differentiate into macrophages or DCs, which improve the phagocytic and antigen presentation capabilities. Monocytes migrate through the bloodstream between the bone marrow and the target organs. Once activated, monocytes help to decrease the parasite burden by phagocytosis, cytokine production, and antigen presentation [29]. Table 1 summarizes the role of monocytes in different malaria cases.
Table 1 The role of monocytes in different malaria cases
Malaria condition The role of monocytes References
Acute P. falciparum malaria Monocytes increase secretion of the proinflammatory cytokines TNF-α, IP-10 (CXCL10), IFN-γ, and decrease phagocytosis of iRBCs [11]
Acute P. vivax infection Inflammatory and classical monocytes secrete inflammatory mediators, TNF-α, IL-6, and IL-8 [32]
Severe malaria Inflammatory monocyte subset increases with higher levels of proinflammatory cytokines (IFN-α, IFN-γ, TNF-α) and chemokines (CCL2, CCL3, CCL4, CXCL10) [32]
Placental malaria Opsonic phagocytosis of iRBCs by monocytes helps to eliminate iRBCs. Production of proinflammatory mediators (e.g. IL-1β) by monocytes can destroy the structure and impair the functions of the syncytiotrophoblast. Monocyte accumulation in placenta malaria causes reduced levels of insulin-like growth factor-1, a positive regulator of fetal growth.
In addition, excessive monocyte activation may disrupt placental angiogenesis [11, 32, 47]
Severe malarial anemia Monocytes increased phagocytic activity, leading to accelerated destruction of infected and uninfected erythrocytes. Activation of monocytes by iRBCs led to excessive production of proinflammatory mediators in response to the uptake of erythropoietin, such as TNF-α and nitric oxide, which are associated with suppression of erythropoiesis in the bone marrow. Monocytes loaded with hemozoin suppress erythropoiesis in the bone marrow by inducing apoptosis of the erythroid progenitors via IFN-γ [32, 47, 54, 55]
Cerebral malaria Monocytes can be activated by platelet factor-4 (PF4) to produce reactive oxygen species that subsequently promote endothelial cell apoptosis. Monocytes promote a procoagulant cascade, resulting in increased expression of adhesion molecules and secretion of cytokines such as IL-2, IL-6, IL-8, IL-10, TNF-α, and IFN-γ, which initiate dysregulated hemostasis in cerebral malaria [47, 54]
Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) Activation of monocytes results in the secretion of pro-inflammatory cytokines such as IL-1β and IL-18. These cytokines upregulate adhesion molecules in lung endothelial cells to promote both iRBCs and monocyte sequestration, which lead to increased vascular permeability and cause local injury via inflammatory mediators [47]
Monocytes can be activated directly or indirectly when various parasite components activate other host immune factors. When merozoites invade RBCs, significant changes occur to the surface of the iRBC, including the addition of parasite proteins such as Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) [30] that interact with monocyte surface receptors [31]. iRBCs also result in complement activation, leading to the deposition of C3b fragments on the iRBC surface and communication with monocyte/macrophage complement receptors. Antibodies specific for iRBC surface antigens can be used to recognize iRBCs through monocyte and macrophages Fcγ receptors (FcγRs). Merozoites are released from the iRBCs when iRBCs rupture; they are then attacked by antibodies and activate monocytes/macrophages through FcγR interactions [32]. Monocytes also have surface and intracellular Toll-like receptors (TLRs), which help recognize pathogen- associated molecular patterns (PAMPs). PAMPs that can directly activate TLRs expressed by monocytes/macrophages including glycosylphosphatidylinositol (GPI) anchors, Plasmodium DNA, RNA and hemozoin crystals [33].
Phagocytosis
Monocytes are unable to phagocytose RBCs infected with mature gametocytes [34], but their ability to phagocytose merozoites and asexual iRBCs is crucial for parasitemia regulation. A previous phagocytic assay performed in vitro revealed that intermediate CD14++ CD16+ monocytes were the most effective subset for phagocytosis of Plasmodium vivax iRBCs and complement opsonized P. falciparum iRBCs. In malaria-infected individuals, monocytes are recruited for merozoite phagocytosis by opsonizing antibodies against MSP1 [35], while cytophilic immunoglobulins (IgG1 and IgG3) against MSP2 and MSP3 activate monocytes [36].
Monocytes play an important role in the host’s protection against P. falciparum infection, including non-opsonic and opsonic phagocytosis of iRBC (Fig. 1). During the early phase of malaria infection, monocytes bind and engulf iRBC via the CD36 receptor by non-opsonic phagocytosis. The adherence of iRBCs to CD36 is facilitated by PfEMP-1. Proteolytic degradation of PfEMP-1 from iRBCs decreased the phagocytic activity, suggesting the key role of CD36-mediated adhesion in non-opsonic phagocytosis of iRBCs whereby pharmacological inhibition of CD36-dependent signaling reduces iRBCs uptake, though there is a possibility that additional receptors are also involved. Monocytic CD36 uses both the ERK and p38 MAPK signaling cascades to actively participate in the non-opsonic phagocytosis of iRBCs. However, opsonic phagocytosis leads to more iRBC uptake than the non-opsonic process. Opsonic phagocytosis involves the interaction between opsonin and specific receptors. When the complement system is activated, C3b and C4b act as opsonin which target antigens for phagocytosis. The complement-bound parasite antigens bind with complement receptors on monocytes, and they are subsequently phagocytosed by monocytes [37]. Furthermore, specific antibodies which function as opsonins are produced to facilitate phagocytosis by binding to FcγRs such as FcγRI (CD64), FcγRII (CD32), and FcγRIIIa (CD16). Binding of antibodies to pathogen results in complement deposition and Fc-receptor-mediated phagocytosis. Since various immune cells express different subsets of FcγRs, the efficacy of antibody binding to FcγRs varies depending on the isotype and the immune cell to which the antibody binds. The high-affinity receptor FcγRI can bind monomeric forms of IgG1, IgG3, and IgG4. FcγRII and FcγRIIIa are low-affinity receptors that only interact with IgG in complexed or aggregated form. The FcγRIIa subtype is expressed on neutrophils and monocytes and it initiates phagocytosis, ADCC and cellular activation [38]. Monocyte phagocytic activity is increased by Plasmodium-specific IgGs, which corresponds with protection and lowers the chance of developing symptomatic malaria. Monocytes could be recruited by opsonizing antibodies for merozoite phagocytosis against merozoite surface proteins (MSP)-1, whereas cytophilic immunoglobulins (IgG1 and IgG3) against MSP2 and MSP3 effectively activate monocytes [39]. Human antimalarial antibodies are naturally acquired in the body to prevent merozoite invasion of red blood cells during blood-stage malaria infections through a process known as antibody-mediated complement-dependent inhibition (ADCI). C1q fixation has been found to be the main mediator of ADCI inhibition, and the main targets are MSP-1 and MSP-2 [40]. When the parasites bind to monocytes, the receptor–parasite complex is phagocytosed by an actin-dependent mechanism. The phagosome fuses with lysosomes to form phagolysosome and the parasite components are degraded by acidic proteases.
Hemozoin is released during schizont rupture and immediately phagocytosed by monocytes, macrophages, and DCs [41]. The phagocytosis of hemozoin induces monocytes to undergo oxidative burst and downregulation of MHC class II, intercellular adhesion molecule 1 (ICAM-1), and CD11c expression [42]. Additionally, in co-culture experiments by Kumsiri et al., iRBC or hemozoin has been reported to induce production of B cell activation factor (BAFF) by monocytes, which may promote antibody production [43]. According to Bobade et al., in vitro phagocytosis of hemozoin by monocytes increases the production of IL-10, chemokine ligand 1 (CCL1), CCL17 and the expression of mannose binding lectin receptor (CD206) [41].
Antibody-dependent cellular inhibition
Merozoite surface antigens which are released during schizont rupture induce monocyte-mediated antibody-dependent cellular inhibition (ADCI) of Plasmodium falciparum provided merozoites are opsonized with cytophilic antibodies (IgG1 and IgG3) subtypes [44]. After being exposed to opsonized merozoites in vitro, monocytes release soluble mediators that inhibit parasite growth in iRBCs [45]. The interaction of IgG-merozoite complexes with monocyte FcγRs is needed for ADCI as shown in an in vitro functional assay. An earlier finding was that blocking FcγRI does not affect ADCI, but blocking either FcγRII or FcγRIII could terminate ADCI [46]. The minor CD16+ monocyte subset is needed to enhance ADCI. More research is needed to determine how monocytes can be differentially activated from macrophages in their ability to initiate ADCI and which inflammatory mediators released by monocytes suppress the growth of the parasite [47].
Cytokine production
In response to Plasmodium infection, monocytes secrete pro-inflammatory cytokines, which helps to inhibit parasite growth and infection clearance, but excessive production contributes to pathogenesis. Glycosylphosphatidylinositols (GPIs) are abundantly expressed on the parasite surface monocytes. These malarial GPIs stimulate a pro-inflammatory response by increasing the secretion of TNF-α, IL-1β, IL-6, IL-12, and nitric oxide (NO). Monocytes have been shown to produce large amounts of IL-12 and IL-18 that prevent progression to severe malaria during early infection. IL-18 and IL-12 work together to activate interferon γ (IFN-γ), which is necessary for the activation of monocyte proinflammatory function in order to facilitate parasite clearance [48]. Upon in vitro exposure to P. falciparum, production of GM-CSF, MIP-1β, or IL-34 cytokines initiates the immunity mechanism with lower parasite loads (premunition) by opsonic phagocytosis and cytokine secretions by monocytes [49]. Exposure of these cytokines, specifically TNF-α and IFN-γ, regulates iRBC uptake and endothelial cell activation by increasing the expression of ICAM-1 and other adhesion molecules by endothelial cells [50].
During blood-stage infection with P. falciparum, the inflammatory monocytes increase the expression of activation markers HLA-DR and CD86, which are involved in T cell priming [51]. Stanisic et al. demonstrated that monocytes in children with severe malaria secreted more TNF-α, MIP-1β, and MIP-1α, which are involved in monocyte activation and recruitment than healthy children or children with uncomplicated malaria [52]. The severity of the infection is thought to be influenced by the balance of pro-inflammatory and anti-inflammatory cytokines, chemokines, growth factors, and effector molecules (Table 2). It has been shown that several cytokines such as IL-1β, IL-6, IL-8, and TNF-α are increased in late-onset severe infection [53]. However, the exact function of these cytokines remains unclear and further investigation is required.
Table 2 Characteristics of monocyte subsets. The table shows comparisons between monocyte subsets in terms of their frequency in blood, surface markers, their roles and cytokine production
Parameter Classical monocytes (CD14++CD16–) Intermediate monocytes (CD14++CD16+) Non-classical monocytes (CD14+CD16++)
Frequency ~85% 5% 10%
Surface markers CCR2 CCR2, CX3CR1 CX3CR1
Roles Infection control and inflammatory regulation Pro-inflammatory activity Tissue repair and removal of damaged cells
Cytokine production Pro-inflammatory
(IL-1, IL-10) Anti- and pro-inflammatory (TNF-α, IL-10) Pro-inflammatory
(TNF-α, IL-1β)
Other myeloid cells involved during malaria infection
Macrophages
Mature macrophages are derived from monocytes, stem cells, or from the cell division of pre-existing macrophages [56]. Macrophages lack granules but are densely packed with lysosomes. Macrophages are involved in the clearance of iRBC and control of parasitemia, whereby deficiency of monocytes and macrophages has been shown to accelerate parasite growth and anemia [57]. Infected RBCs can be phagocytosed by macrophages via two different mechanisms. The first mechanism does not involve opsonizing antibodies. Macrophages bind to antigens of the parasites that are expressed on iRBCs through surface receptors after being activated by proinflammatory cytokines such as TNF-α and IFN-γ. For instance, in human malaria, the scavenger receptor CD36 binds to the P. falciparum erythrocyte membrane protein-1 (PfEMP-1). Macrophages are also necessary for parasite clearance during adaptive immunity when the second mechanism, antibody-dependent phagocytosis, takes over [58]. In malaria-immune individuals, antibody-opsonized iRBCs and merozoites are phagocytosed by macrophages via FcγRs [59]. Other than spleen resident macrophages, CD11bhighLy6C+ monocytes have been reported to actively participate in the control of acute parasitemia in murine.
Dendritic cells
Dendritic cells are antigen-presenting cells that engulf pathogens, playing a crucial role in both innate and adaptive immune responses. This is mainly attributable to their presence at pathogen entrance points, their specific ability to sample, uptake, process, and present antigens, and their ability to integrate and react to signals from microbial and other immune cells. Dendritic cells consist of plasmacytoid [CD123+ CD11c–, (pDC)] and myeloid/conventional [CD123– CD11c+, (cDC)] [60]. Though pDCs are most often associated with viral infection defense, they express high levels of TLR7 and TLR9 and can be a major source of type 1 IFNs in other infections [61]. On the other hand, following parasite infection, monocytes are infiltrated and differentiate into MoDC [62].
Dendritic cells express a wide range of PRR on their surface, such as TLRs, which enables them to communicate with diverse microbial molecules. During blood stage malaria, DCs in the spleen monitor the blood flowing through the marginal sinus, and once activated, they can migrate to the white pulp, where they initiate acquired immune responses. A previous study demonstrated that monocyte-derived DCs could communicate with P. falciparum iRBCs by binding of CD36 to PfEMP-1 [63].
After being stimulated, DCs migrate to draining lymph nodes, where they present antigen via MHC class I or II complexes, followed by costimulatory signals such as CD40, CD80, and CD86 to CD4+ and CD8+ T cells. The antigen-MHC I or MHC II complexes are recognized by naïve CD4+ and CD8+ T cells, which proliferate and differentiate into effector cells. Human DCs effectively phagocytose P. falciparum-infected RBCs in vitro; however, the activation depends on the dose. A high iRBC : dendritic cell (iRBC : DC) ratio suppresses maturation by inducing apoptosis and blocking LPS stimulation [64]. At a 3 : 1 iRBC : DC ratio, there is an increase in the expression level of maturation markers such as HLA-DR, CD80, CD86 and CD40, as well as chemokines including CCL2, CXCL9 and CXCL10 [65]. At a 1 : 3 ratio of iRBCs, DCs induce an antigen specific T helper 1 (Th1) cell response, leading to T cell proliferation and secretion of IFN-γ, IL-10 and TNF-α. This also increases the production of type 1 IFN, CXCL9 and CXCL10, which play a role in the induction of type I regulatory T cells (Tr1). Type 1 IFNs have been shown to suppress the production of parasitic-specific CD4+ T cell IFN-γ and enhance Th1 or Tr1 cells, despite the fact that they induce an antiviral immune response. Type 1 IFNs also inhibit the production of IL-6, which impairs the ability of blood monocytes to induce inflammatory reactions [66].
During early malaria infection, parasites induce DCs to produce TNF-α and IL-12, which then activate IFN-γ. As the disease progresses, DCs produce less IL-12 and start to produce IL-10. At the later stage of infection, activated DCs are resistant to TLR stimulation, reducing their ability to phagocytose antigens and priming T cells [67]. A previous study reported that a high dose of P. falciparum iRBCs induces apoptosis in monocyte-derived human DCs while low doses trigger them to induce CD4+ T cell proliferation. Therefore it has been suggested that CD8+ cDCs, which are the major producers of IL-12, may be vital in early infection to activate Th1 responses, whereas CD8-cDCs may play a major role during the acute phase to switch from Th1 to Th2 immune responses [68].
Importance of trained immunity in malaria
The “trained immunity” phenotype relates to a prime condition that increases reactivity of monocyte and macrophages to a secondary challenge after the first stimulus. This phenotype involves epigenetic modifications, metabolic remodeling or cytokine production. TLRs and other pattern recognition receptors are believed to help monocytes in developing a Plasmodium-specific memory.
In humans, exposure of radiation-attenuated sporozoite elicits both antibody and T cell responses against sporozoite and blood-stage antigens and this protection has been associated with pluripotent effector memory T cells that produce IFN-γ, TNF-α and IL-2 that are targeted against liver rather than blood-stage antigens [69]. Jacob and colleagues found that following a secondary TLR ligand exposure, the malarial parasite and its crystal hemozoin may evoke trained immunity as indicated by inflammatory gene expression. Despite the fact that these two stimuli have comparable effects on the inflammatory transcriptome, the differential regulation of iRBC- and hemozoin-induced training by known trained immunity inhibitors suggests that the two stimuli have different training mechanisms.
Monocytes that have been “trained” or primed produce more proinflammatory cytokines when they are challenged to a secondary stimulus [70]. Recent studies have demonstrated a priming effect during P. falciparum infection on the innate immune system. McCall et al. reported stimulated TLR4 and TLR2/TLR1 responses during sub-patent blood-stage infection, which later normalized following curative treatment using an experimental human malaria model [71]. Furthermore, Franklin et al. showed that PBMCs from Brazilian adults with uncomplicated falciparum malaria had primed innate responses to numerous TLR ligands, including TLR4 and TLR2, and stronger proinflammatory cytokine responses than those seen in the experimental malaria volunteers who experienced reversed TLR responses after treatment [72]. Repeated clinical malaria infections may have a training effect on monocytes that lasts for several weeks. Increased extracellular heme and proinflammatory cytokines, such as IP-10, associated with mouse malaria models and human P. falciparum infections have also been reported to increase expression of TLRs, such as TLR4, on the surface of monocyte and macrophages and circulating endothelial progenitor cells [73].
Concluding remarks
Malaria remains a significant global health concern. Monocytes respond to malaria in many different ways, such as by phagocytosis, cytokine secretions and antigen presentation, which can be either protective or pathologic. With substantial phenotypic and functional variations among monocyte subsets, the mechanism of immune responses induced by monocytes remains complicated, and the interaction of the different possible responses is a key factor in deciding disease outcome. In future, it is necessary to focus on investigating how these monocytes are regulated differently depending on the disease severity and different Plasmodium species, the mechanisms involved in recognition of malaria and effects of disease on monocyte function. The knowledge obtained can be useful in the development of antimalarial therapy by modulating the function of monocytes in malaria, giving a new opportunity to develop novel therapeutic strategies.
Acknowledgments
This work has been supported by Research Uni-versity Individual (RUI) Grant (Grant number: 1001/ PPSK/8012309), Universiti Sains Malaysia.
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10189573.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206591
50171
10.5114/ceji.2023.125238
Review Paper
Molecular regulatory mechanism of LILRB4 in the immune response
LIU Haiyin 123
Yang Jun 12
Zhang Jing 23
Zhang Peiyue 123
Zhang Mengting 123
Yang Chaojun 23
Liu Li 23
Huang Cuiyuan 3
Wang Wei 23
Zhai Yuhong 123
Yang Jian 12
1 Department of Cardiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
2 Institute of Cardiovascular Diseases, China Three Gorges University, Yichang, China
3 HuBei Clinical Research Center for Ischemic Cardiovascular Disease, Yichang, China
Jun Yang, PhD, Department of Cardiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang 443000, China, e-mail: [email protected]
23 2 2023
2023
48 1 4347
21 4 2022
23 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Immune diseases are caused by the imbalance of immune regulation. This imbalance is regulated by many factors, both negative and positive. Leukocyte immunoglobulin-like receptor B4 (LILRB4) is a member of leukocyte immunoglobulin-like receptors (LILRs). LILRs are expressed constitutively on the surface of multiple immune cells which associate with membrane adaptors to signal through multi- ple cytoplasmic immunoreceptor tyrosine-based inhibitory motifs (ITIMs) or immunoreceptor tyro-sine-based activation motifs (ITAMs). Through ITIM, LILRB4 could recruit the src homology domain type-2-containing tyrosine phosphatase 1 or 2 (SHP-1 or SHP-2) into the cell membrane. In addition, many factors can induce the expression of LILRB4, such as vitamin D, interferon and so on. Studies have demonstrated that LILRB4 had a negative regulatory role in various of immune diseases. The present review intends to expound the structure and function of LILRB4, as well as its regulators and receptors in the immune cells, so as to provide a theoretical basis for immune disease therapy.
LILRB4
SHP-1
immune disease
molecular mechanism
==== Body
pmcIntroduction
Immune response (IR) refers to the process whereby immune cells recognize, activate, proliferate, differentiate antigen molecules, produce immune substances and generate specific immune effects under stimulation of antigens. The immune response can be activated or inhibited in the body, which are balanced by substances ensuring immune responses to pathogens in the immune system, such as leukocyte immunoglobulin-like receptors (LILRs). Leukocyte immunoglobulin-like receptor B4 (LILRB4) belongs to the LILR family, which is widely distributed on immune cell membranes. LILRB4 is a kind of inhibitory receptor that plays a key role in immune checkpoint pathways and participates in regulating multiple immune diseases [1].
The structure of LILRB4
LILRB4 is a member of the leukocyte immunoglobulin-like receptor (LIR) family, which is found in a gene cluster in chromosomal region 19q13.4 [2, 3]. According to the role of intracellular motifs, immunoglobulin-like transcripts (ILTs) were divided into the activated receptor LILRA and the inhibitory receptor LILRB. The activating receptor LILRA includes six receptors, LILRA1-LILRA6, while the inhibitory receptor includes LILRB1-LILRB5 [4, 5]. Inhibitory LILR members have a long cytoplasmic tail containing different sets of immunoreceptor tyrosine-based inhibitory motifs that recruit phosphatases and thus contribute to downstream inhibitory signaling pathways [6, 7]. Activating members on the other hand have truncated cytoplasmic regions and are thought to associate with activating adaptor proteins via a positively charged amino acid in the transmembrane domain (e.g. LILRA2 and LILRA4 both associate with the γ chain of FcεRI) [8, 9]. LILRBs contain two or four extracellular immunoglobulin domains, a transmembrane domain, and two to four cytoplasmic immunoreceptor tyrosine-based inhibitory motifs (ITIMs) [10]. Interestingly, LILRB4 is somewhat unusual as whereas most family members contain four immunoglobulin-like domains in their extracellular region (designated D1, D2, D3, and D4) LILRB4 is one of two members that only possess two immunoglobulin-like domains (the other one is the closely related activating receptor LILRA5) [11].
The function of LILRB4
The immune system is regulated by LILRB4, which is produced on immune cells and may bind to MHC class I molecules on antigen-presenting cells as well as other ligands such as integrins αVβ3, apolipoprotein E, and fibronectin [12-15]. Furthermore, it can also function in antigen capture and presentation. Previous studies have indicated that ILT3-Fc acts through BCL6 and is a potent immunosuppressive agent for reversing the onset of allo- or possibly autoimmune attacks against pancreatic islets [16]. It is thought to control inflammatory responses and cytotoxicity, which is helpful to focus the immune response and limit autoreactivity. Chang et al. found that ILT3-Fc inhibits T cell activation and induces the generation of Ts targeting multiple inflammatory miRNA pathways [17]. Simultaneously, LILRB4 can regulate marginal zone B cells and antibody production [18]. In cardiomyocytes, LILRB4 can regulate apoptosis and mediate myocardial hypertrophy [19].
Regulators of LILRB4 expression
LILRB4 has been proven to play an important role in the immune system. There are three main substances that can regulate LILRB4 expression: immunosuppressants, immunomodulators and anti-inflammatory drugs (Fig. 1). Penna et al. detected a significant increase in the expression of ILT3 after treatment with 1,25(OH)2D3, and proved that 1,25(OH)2D3 can induce the production of regulatory T cells through ILT3-dependent and non-ILT3-dependent pathways, while ILT3-dependent pathways are necessary for the induction of CD4+Foxp3+ regulatory T cells [20] (Table 1). Rochat et al. found that vitamin D supplementation during pregnancy was associated with increased ILT3 gene expression in a prospective trial [21]. However, Waschbisch et al. found that interferon-β alone or in combination with vitamin D could induce upregulation of ILT3 in vitro [22]. Interferon treatment led to a significant increase in monocyte ILT3 in vitro, and dihydroxyvitamin D3 also enhanced ILT3 expression. Švajger et al. found similar synergies between interferon (IFN)-γ and 1,25(OH)2D3 [23]. These results suggest that vitamin D receptor (VDR) agonists, alone or in combination with other immunomodulators, could up-regulate LILRB4 and contribute to DC tolerance. Some members of the interferon family also upregulate LILRB4 expression. Inui et al. found interleukin (IL)-2 to be an effective inducer of B4+CD38+ cells, and IFN-α is the main inducer of B4+CD38+ cells. The IFN-α pathway is involved in the pathogenesis of systemic lupus erythematosus (SLE) and IFN-α induces LILRB4 expression through plasmacytoid dendritic cells (PDC) and monocytoid dendritic cells (MDCs) [24]. After interferon treatment for 48 h, ILT3 expression was increased on freshly isolated purified monocytes and on immature myeloid dendritic cells derived from blood monocytes of multiple sclerosis (MS) patients and controls [25]. When ILT3 was blocked by anti- ILT3 antibodies, mitogen-driven proliferation of CD4+ T cells was increased several times in IFN-β treated relapsing-remitting MS (RRMS) patients and healthy controls, which may be involved in the inhibition of T cell activation in vivo. Immunosuppressants can also up-regulate LILRB4 expression in kidney transplant patients with chronic allogeneic nephropathy [26]. The infiltration of renal tubule interstitium and the level of ILT3 in endothelial cells were significantly increased, leading to reduction of CD40 in BDCA2+ cells by rapamycin and increase of Treg number, as well as expansion of the CD8+CD28+ T cell population. It was found that the RNA and protein levels of ILT3 and ILT4 in NKL cells increased 12, 24 and 36 h after cyclosporin A (CsA) treatment, and the killing activity of NKL cells towards tumor cells decreased significantly after CsA injection (15 mg/l) for 36 h. Thus, CsA up-regulated the expression of ILT3 and ILT4 in NKL cells, and affected its killing effect on tumor cells with different human leukocyte antigen G (HLA-G) expression and NKL cell proliferation [27]. Svajger et al. found that resveratrol could induce ILT3 expression, resulting in differentiation of dendritic cells (DCs) derived from human peripheral blood mononuclear cells (PBMCs) [28]. HLA-G exerts its inhibitory functions via interaction with ILT2, ILT4, and KIR2DL4, which are differentially expressed by NK, T, and antigen-presenting cells. ILT2, ILT3, ILT4, and KIR2DL4 expression is up-regulated by HLA-G in antigen-presenting cells, NK cells, and T cells, which may not need antigenic costimulation, possibly before the immune response [29]. In addition to immunosuppressants, some anti-inflammatory drugs can also regulate LILRB4 expression. Buckland et al. found that aspirin can induce the production of tolerant DCs, which may be related to the significant expression of ILT-3. Aspirin inhibits nuclear factor κB (NF-κB) translocation to the nucleus and induces antigen-specific Foxp3 positive regulatory T cells. Another nonsteroidal anti-inflammatory drug, niflumic acid (NFA), also upregulates ILT3 expression and participates in the development of immune tolerance in human monocyte derived DCs [30]. Brenk et al. [31] isocytes from peripheral blood of healthy subjects and cultured them under normal (30 µM) and low (5 µM) Trp (DCs+Trp and DCslow-TRP) conditions. The expression of ILT3 and ILT4 increased significantly in the DCslow-TRP group. The addition of anti-ILT3 mab partially restored the stimulation activity of DCslow-Trp on T cells, but had no effect on the stimulation of DC+Trp on T cells. These findings suggest that DCs with low tryptophan can induce high expression of inhibitory receptors ILT3 and ILT4, and increase inhibitory CD4, CD25 and Foxp3T cells in an ILT3-dependent manner, and weaken the stimulation ability of CD4+ T cells, thus leading to the immune tolerance of DCs. Interleukin 10 could inhibit endothelial dependent T cell costimulation by up-regulating ILT3/4 in human vascular endothelial cells [32]. In summary, LILRB4 has a variety of ligands, the discovery of which will provide a new therapeutic target for LILRB4 related diseases.
Table 1 Regulators of LILRB4 expression
Regulators Cell distribution Effect
1,25(OH)2D3 [20] Regulatory T cells Upregulation
Interferon β [25] Monocyte Upregulation
Interleukin 2 [24] B4+CD38+ cells Upregulation
Interferon α [24] B4+CD38+ cells Upregulation
Rapamycin [26] CD8+CD28+ T cell Upregulation
Cyclosporin A [27] NKL cell Upregulation
Resveratrol [28] Dendritic cells Upregulation
Human leukocyte antigen G [29] NK cells, T cells Upregulation
Aspirin [30] Dendritic cells Upregulation
Tryptophan [31] Dendritic cells Downregulation
Interleukin 10 [32] Human vascular endothelial cell Upregulation
Fig. 1 Upstream and downstream structure diagram of LILRB4
LILRB4/SHP-1 or SHP-2
LILRB4 has three ITIMs. The ITIMs of LILRB4 can recruit src homology 2 (SH2) domain-containing phosphatase 1 (SHP-1) and SHP-2 from the cytosol, leading to the activation of SHPs and the subsequent inhibition of various downstream signaling pathways [33]. Li et al. reported that ILT3 promotes tumor motility and angiogenesis via recruitment of SHP2/SHIP1 and subsequent activation of the ERK1/2 signaling pathway [34]. By interaction with its ligand ApoE, ILT3 induced tumor cell migration and invasion as well as tumor angiogenesis via activation of the SHP-2/SHIP1-ERK1/2 signaling pathway, which subsequently promoted EMT and the expression of VEGF-A, leading to non-small cell lung cancer (NSCLC) metastasis. Truong et al. demonstrated that the chicken LILRB4-5 genes activate the JAK/STAT signaling pathway, which plays a key role in multiple cytokine-induced responses [35]. In addition, LILRB4 can recruit SHP1 to inhibit TRAF6 ubiquitination and subsequently inactivate NF-κB and MAPK cascades [36]. Blocking TRAF6 ubiquitination to inactivate downstream MAPK and NF-κB signaling largely explained the inhibitory effect of LILRB4 on nonalcoholic fatty liver disease (NAFLD) progression. NF-κB plays a key role in the cellular inflammatory response and immune response. LILRB4 regulated NF-κB by recruiting SHP-1 or SHP-2 in turn affects atherosclerosis and myocardial hypertrophy [37, 38]. In leukemia, inhibition of NF-κB signaling reversed T cell suppression and reduced AML cell infiltration in a LILRB4-dependent manner. Urokinase-type plasminogen activator receptor (UPAR), an NF-κB target, is known to promote cancer invasion, metastasis, survival and angiogenesis [39]. The urokinase-type plasminogen activator (uPA) system is a biomarker and therapeutic target in human malignancies. It was found that ARG1 is a key downstream effector of LILRB4/NF-κB/uPAR signaling. LILRB4/SHP-2/NF-κB/uPAR-ARG1 could suppress immune activity and supports leukemia migration [40].
Relationship with disease
As an immune checkpoint, LILRB4 is of great significance in the treatment of autoimmune diseases [41]. Jensen et al. revealed that ILT3 loss of function polymorphism was associated with the increase of inflammatory cytokine level in SLE [42]. In addition to SLE, LILRB4 is also associated with a variety of immune diseases, such as Kawasaki disease [43] and allergic diseases [18]. LILRB4 is also closely related to diseases of other systems. By inhibiting NF-κB signaling pathway, LILRB4 can attenuate cardiac hypertrophy and atherosclerosis [44-46]. The maturation and migration of pulmonary DCs to lymph nodes in response to Ag and the innate immune stimulus is associated with upregulation of LILRB4. LILRB4 could attenuate the ability of DCs to elicit pathologic Th2 pulmonary inflammation [47]. Myeloid-derived suppressor cells (MDSC) accumulate in the tumor microenvironment (TME); they are engaged in tumor-associated immunosuppression, and govern tumor growth and metastasis. It was shown that LILRB4 can facilitate tumor invasion and migration by controlling MDSC and preventing the release of miRNAs from the miR-1 family, hence facilitating MDSC-mediated tumor metastasis [48]. ILT3 expressed on M-MDSCs can induce immunosuppression in cancer, and antagonism of ILT3 may be useful to reverse the immunosuppressive function of M-MDSCs and enhance the efficacy of immune checkpoint inhibitors [49]. Sharma et al. also revealed that LILRB4 strongly suppresses tumor immunity in TME [50]. The above evidence indicates that LILRB4 may be an effective target for cancer treatment. LILRB4 may also be a potential drug for the treatment of autoimmunity and graft rejection [51]. For example, LILRB4 is an effective immunomodulator for inhibiting allograft rejection in islet allograft [52]. Furthermore, it is presumed that LILRB4 also participates in regulating central nervous system immune surveillance. Recombinant human ILT3.Fc protein can bind to murine immune cells and further inhibit the release of proinflammatory cytokines, which suggests the feasibility of ILT3 in treatment of multiple sclerosis [53].
Conclusion and perspectives
LILRB4 was first found on the immune cell membrane and gradually came into people’s vision. Current studies prove that LILRB4 not only acts on tumors and immune diseases, but also diseases of multiple systems, which indicates that LILRB4 has great potential in disease treatment. The understanding of the LILRB4 extracellular ligand and downstream signal pathway can deepen the understanding of LILRB4 in physiology and pathology, and help LILRB4 become a new biomarker of disease. However, it is still necessary to study LILRB4 further, especially at the cellular and molecular levels.
Funding
This work was supported by National Natural Science Foundation of China (82070372, 82170418, 82271618), Hubei Province’s Outstanding Medical Academic Leader Program; Natural Science Foundation of Hubei Province (2022CFA015), Educational Commission of Hubei Province (D20221205); Science and Technology Innovation Platform of Hubei Province (2022DCC014); Key Research and Development Program of Hubei Province (2022BCE001).
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10189574.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206588
50183
10.5114/ceji.2023.125309
Case Report
Trigeminal neuralgia occurring after the third dose of Pfizer BioNTech COVID-19 vaccine. Complication or coincidence? An illustrative case report and literature review
Chrostowski Kacper 1
Piasecki Michał 1
Bielewicz Joanna 2
1 Students’ Research Group, Department of Neurology, Medical University of Lublin, Poland
2 Department of Neurology, Medical University of Lublin, Poland
Kacper Chrostowski, Department of Neurology, Medical University of Lublin, Students’ Research Group, Poland, e-mail: [email protected]
27 2 2023
2023
48 1 7580
24 11 2022
03 2 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
The coronavirus disease 2019 pandemic is an ongoing concern for medical care worldwide. Since its emergence, multiple COVID-19 vaccines have been designed, allowing for more effective control of the pandemic. COVID-19 vaccines, like any other form of medical intervention, may cause adverse and unforeseen side effects, varying in frequency and severity. Determining a correlation between the occurring symptoms and the vaccination is often a challenging task, requiring multiple data sources and reported cases. So far, there have been multiple reports of trigeminal neuralgia developing after COVID-19 vaccination. A 36-year-old woman was admitted to the Emergency Ward due to chronic pain attacks in the left side of her face. The pain appeared two months ago, on the day following the vaccination using the third dose of the Pfizer BioNTech COVID-19 vaccine. At the Neurology Department she was diagnosed with trigeminal neuralgia. Based on the lack of any obvious causes, relation to the vaccination, and other similar reports, we assumed that the trigeminal neuralgia was a complication of the vaccination. Hospital treatment consisted of oxcarbazepine, dexamethasone and pregabalin. Treatment was successful, with transient episodes of exacerbation. Six months after the onset of the disorder the patient remains without pain. We believe that the presented case supports the possibility of trigeminal neuralgia occurring in relation to the Pfizer BioNTech COVID-19 vaccine administration. Additional reports may further contribute to establishing a certain link.
adverse effects
case report
SARS-CoV-2
trigeminal neuralgia
COVID-19 vaccine
==== Body
pmcIntroduction
Trigeminal neuralgia (TN) is a relatively uncommon chronic condition, affecting less than 0.5% of the general population [1]. It manifests itself as episodic attacks of sharp, electric, shock-like pain, usually unilateral, in the regions of the face subject to the fifth cranial nerve (CN V). Attacks are triggered by movements of the facial muscles, cold temperature, touch or are spontaneous in nature. TN is included in the 13th chapter in the International Headache Society classification [2]. Based on the aetiology, it is systematized there as classical (due to vascular nerve compression), secondary (evidence of clear cause) or idiopathic (no cause is apparent). Recently, TN has been noted as one of many possible neurological complications of coronavirus disease 2019 (COVID-19). Since December 2020, when COVID-19 vaccines became the primary form of pandemic control, about 13 billion doses of vaccines have been administered worldwide. Having done a literature review, we came across only a few cases of TN in total that were postulated to have developed after COVID-19 vaccination [3-6]. Herein we present a case of TN, which originated shortly after the 3rd dose Pfizer BioNTech COVID-19 vaccine, discussion on its differential diagnosis and suggested effective treatment, based on our observations.
Patient case presentation
A 36-year-old woman was admitted to the Department of Neurology due to persistent pain in the left side of her face. The pain had first appeared two months ago, on the day following the vaccination using the third dose of the Pfizer BioNTech COVID-19 vaccine. Initially, the entirety of the left side of her face had been affected, but a few days later the pain became localized in the region of the second and third branch of the left trigeminal nerve. The pain was paroxysmal, presenting itself as attacks lasting 4-5 seconds each, triggered by movements of the mouth and jaw during activities such as eating or brushing the teeth. Attacks had been excruciatingly strong, with a score of 10 according to the numerical rating scale (NRS), and as such prevented the patient from participating in everyday functions, finally causing her to seek help at the Emergency Ward. Allodynia with painful sensation after application of cold air and hypersensitivity to stimuli were prominent during preliminary examination. Patient reported subjective, distorted sensations in the region of the second branch of the left CN V during exacerbations of pain, but no objective changes in the sense of touch examination were found. No other complaints or changes were observed in neurological evaluation. No relevant history of previous or concomitant diseases was reported. No familial history of neuralgia or similar conditions was reported by the patient. The previous two doses of the same type of vaccine were taken without any complications or adverse effects.
Standard laboratory tests showed no abnormal results. Concurrent COVID (the real-time quantitative polymerase chain reaction [RT-qPCR] test displayed negative results), as well as other potential ongoing infections, were excluded. There was no elevation in D-dimer levels, strongly suggesting a lack of pathological thrombotic processes. Magnetic resonance imaging (MRI) of the head with contrast did not show any significant pathologies that could contribute to the development of symptoms.
Prehospital, initial treatment consisted of carbamazepine in the dose of 200 mg taken twice a day, and Lignocainum hydrochloridum 5 mg per kg of body mass, used to alleviate stronger attacks. This management strategy was only partially effective and caused a decrease in frequency and intensity of pain paroxysms, but resulted in multiple side effects, mainly somnolence, which significantly impacted the daily living of the patient.
Sudden exacerbation of pain was observed after about eight weeks of the treatment described above and was associated by the patient with the onset of concomitant viral infection of the upper respiratory tract. After admission to the Neurological Department, a second MRI with angio- graphy (MRA) was performed (according the European Academy of Neurology guidelines 2019) [7], which revealed no neurovascular conflict.
The new treatment was instituted with replacement of carbamazepine by oxcarbazepine 600 mg twice a day and introducing steroids based on a previous scientific report [3] encountered during our research following the patient’s interview. Our case matched one description very well and, as such, with the patient’s cooperation, therapy based on the reported findings commenced. Steroids were given in the form of dexamethasone 12 mg per day for two weeks and titrating doses during the following two weeks. After combined therapy employing steroids with oxcarbazepine, a reduction both in pain intensity and in frequency of attacks was observed within five days. Because the pain was still present, the treatment was further supplemented with pregabalin in the dose of 150 mg per day for two weeks and was continued for the next two months. Gradually, but with transient periods of weak exacerbation, pain alleviation was achieved and the patient made a full recovery. Treatment with oxcarbazepine and pregabalin was continued for two further weeks. Six months after the onset of the disorder the patient remains without pain. Treatment was officially declared complete in May 2022 (Fig. 1).
Fig. 1 Timeline depicting the continuity of the case described in this article
Discussion of differential diagnoses
Differential diagnosis of TN includes possible presence of other similar conditions, such as glossopharyngeal neuralgia, cluster headaches, painful post traumatic trigeminal neuropathy, persistent idiopathic facial pain, herpes zoster related neuropathy and dental pathologies. Diagnosis relies on highly specific clinical features, allowing it to be easily distinguished from aforementioned ailments. Despite superficial similarities, such as with cluster headache symptoms overlapping somewhat with those of TN, based on different localizations, duration of the attacks and accompanying autonomic signs, a physician should be able to easily provide a proper diagnosis. What is more, in cluster headaches pain tends to migrate from one side of the face to the other, while it consistently remains limited to a single side in TN, usually in the 2nd or 3rd branch of CN V. Triggering factors other than those typically found in TN and different pain quality may indicate persistent idiopathic facial pain. Painful post-traumatic trigeminal neuropathy may resemble TN, but it is always preceded by a major traumatic injury and displays clear neurological abnormalities visible on neuroimaging. Herpes zoster neuropathy shares many common features with TN, but it is differentiated by the presence of highly distinctive skin lesions, usually in the 1st branch division of CN V. Altogether, using judicious observation and exclusions, a physician familiar with the basics of TN should be able to recognize it without much of a struggle.
Discussion of the final diagnosis
The patient described in this case report displayed a multitude of clinical features prompting us to diagnose her with a case of TN. These included specific triggering stimuli, that is cold, touch or movements of the jaw, characteristics of the pain, which was sharp, stabbing, and electric-like, affected area, limited to the 2nd branch of CN V unilaterally, and duration of the attacks, shorter than two minutes.
Differential diagnosis requires a thorough examination to classify TN according to specific subtype. We excluded the possibility of it being a classical TN based on the lack of evidence of neurovascular conflict in MRA. Secondary TN was excluded based on absence of space-occupying tumours, a demyelinating process or other disorders. These results leave us with the possibility of either TN idiopathic or TN attributed to other causes. However, the patient did not suffer from any other conditions predisposing her to develop TN.
There have been many case reports in the literature which concerned TN as a complication of COVID-19 infection [8, 9] and a few reports on TN developing after COVID-19 vaccination [3-6] as well. Neurological complications during the course of COVID-19 infection, apart from TN [8, 9], include the neuropathies facial nerve palsy [10], sixth cranial nerve palsy [11] and Guillain-Barré syndrome [12]. So far, four cases of TN after COVID-19 vaccine have been described, all of which occurred after the Pfizer-BioNtech vaccine, after either the first [3, 5, 6] or the second dose [4]. Our case is the first case of similar symptoms developing after the third dose of the vaccine.
The pain was variously accompanied by a multitude of other neurological symptoms, including numbness in the V1, V2 or V3 branches of CN V, cervical radiculitis [5] and numbness of the left upper limb [4]. Neuroimaging studies are usually recommended to distinguish classic TN from secondary TN [13], and such studies were performed in each case we have encountered. Changes in MRI were detected in two cases as abnormal asymmetric thickening and robust perineural sheath enhancement of the V3 segment of the left CN V [5] or as hyperintensity in the right lateral dorsal pons, at a level above the CN V origin [4]. However, in the remaining cases, including the one described here, neuroimaging showed no significant changes.
Treatment of TN depends on a number of factors, such as age, general health, severity of symptoms and the cause of the condition. The first-line treatment of idiopathic TN usually is restricted to pharmacotherapy [13]. In patients who developed TN after a vaccine, administration of steroids significantly reduced the pain frequency and intensity and improved patients’ condition [3-5]. Steroids were effective when administered both intravenously [3, 4] and orally [4, 5]. In one case combination of pregabalin and carbamazepine alone reduced pain and ameliorated facial numbness [6]. Pregabalin alone was insufficient to control pain attacks in all reviewed cases.
The immune-related reaction is suspected to be the underpinning pathomechanism in the described cases. Such a pathomechanism is proposed as a cause of neurological complications after defective immunization, with demyelination of the central nervous system (CNS) reported [4]. One of the currently suggested pathomechanisms of TN is local demyelination within the CN V root [14]. This process is usually triggered by compression of the root of the nerve, such as in the case of neurovascular conflict. Both peripheral and central demyelination was reported as a rare complication after COVID-19 vaccination [15].
In the cases presented so far in the literature and in our case, neuralgia symptoms developed within a few days, or sometimes even hours, after the vaccination [3-6]. The process that led to demyelination would have to develop rapidly. In addition, in the literature we can find dozens of cases of demyelination of the CNS, other than TN occurring after vaccination. It has been speculated that the mechanism for the development of this demyelination may be bystander activation [16]. Single-stranded mRNAs are able to activate TLR-7 and TLR-8 receptors, causing an increase in secretion of proinflammatory cytokines and a strong response from T and B lymphocytes, which results in activation of existing self-reactive T and B lymphocytes and development of inflammation [17]. The occurrence of this mechanism in the cases of TN discussed here is supported by the short intermediary period from the vaccination to the onset of symptoms. It is possible that the occurrence of such adverse reactions only in a relatively small number of vaccinated individuals is due to a genetic predisposition. Certain polymorphic variants of the pattern recognition receptor (PRR) may induce a stronger immune response [18]. Other mechanisms that can cause CNS demyelination include molecular mimicry and epitope spreading [16].
Our case is the first, as far as we know, occurrence of TN possibly occurring in relation to the 3rd dose of the vaccine. However, based on the data provided by the Centers for Disease Control and Prevention (CDC) [19, 20] we can conclude that the incidence rates of both local and systemic side effects after the 2nd and 3rd doses were comparable and only slightly higher than those occurring after the 1st dose. Remaining on the subject of vaccinations, there is some speculation that the stronger immune response after the non-first doses may be due to the differences in the immune environment encountered by those doses. In the case of non-first doses, the vaccine affects not only naïve cells, but also primary specific antibodies and memory T and B cells formed after the first dose. Additionally, prime-induced resting trained innate cells can respond better than naïve cells to restimulation [21]. The exact molecular mechanisms of immune memory formation and maintenance after vaccination are not fully elucidated, but circulating antibody levels provided by Pfizer- BioNTech COVID-19 vaccination are greatly reduced at 6-8 months after vaccination [22]. Since our patient was vaccinated each time using the Pfizer-BioNTech COVID-19 vaccine, at the constant dose, maintaining the typical interval and employing the same route of administration, we may speculate that the above-described phenomenon might have influenced the occurrence of the adverse reaction after the booster. It still remains unclear why TN developed after the 3rd rather than the 2nd dose, but we can suppose it might have been influenced by other independent factors, which could have caused exaggeration of immunological response such as a subclinical infection the patient might have been suffering from, in the period when the vaccination took place. It is also worth remembering that peak antibody levels are typically reached after three vaccine doses [22]. The exacerbation of pain which was observed later during the course of TN was triggered by viral pharyngitis, an event with immunological implications. The beneficial effect after steroid treatment may indicate, again, the excessive immunological response as a cause. The improvement could not be entirely due to simultaneously introduced oxcarbazepine, because previously management with carbamazepine was partially effective. Carbamazepine and oxcarbazepine share approximately the same mechanisms and clinical efficacy, but in our case, radical improvement after switching from the former to latter, with the addition of steroids, was not only due to the alleviation of adverse effects, but also better pain control. Possibly, administration of steroids could act causally.
It is important to note that the nerve damage would derive not from the direct actions of the virus, but rather from the exaggerated and disproportionate immunological response of the organism to it. As such, it follows that the virus itself is not necessary for the nerve damage to occur, with only improper reaction to it being indispensable. Thus, the COVID-19 mRNA vaccine, which does not contain the virus proper, but generates, by its design, a response resembling that to the virus, could possibly cause similar symptoms to present themselves if this response is similarly distorted.
As was mentioned before, diagnosis of TN relies almost exclusively on the patient’s history and symptoms reported and observed. Based on the clinical diagnosis, we cannot ascertain that there was an undisputed correlation between the occurrence of TN and vaccination. Neurovascular conflict was excluded, and so were the secondary causes, but it is exceedingly difficult to ensure that no idiopathic capacity was present. The question remains whether the origin of the disease was entirely spontaneous or the occurrence just coincided with the vaccination. The small number of similar cases does not allow us to confirm any categorical associations between the vaccine itself and the observed symptoms due to the lack of factual evidence supporting them, but, on the other hand, the infection itself has been definitively linked to multiple symptoms affecting the nervous system [23].
As can be seen in the Table 1, delineating the so-far reported cases of TN disorders possibly related to the COVID-19 vaccine, a number of those cases [4-6] presented themselves with additional sensory disturbances in the trigeminal nerve territory, absent in the other cases [3], including ours. Numbness and other sensory dysfunctions are not typically seen in TN, but rather in painful trigeminal neuropathy [2]. Painful trigeminal neuropathy is recognized as a separate condition, involving damage to the trigeminal nerve causing the loss of sensations. In TN proper, the nerve damage is less pronounced and causes increased function, rather than its loss [24]. We can assume that in those cases the sensory disturbances and the sensation of numbness could have been caused by the greater degree of damage done to the trigeminal nerve. It is also worth noting that those clinical features tended to be the most persistent ones, outlasting pain.
Table 1 Table comparing cases of trigeminal neuralgia with possible connection to COVID-19 vaccination reported to date
Time of onset Clinical presentation Interventions used Clinical outcome
Case 1
Kaya and Kaya [3] After 1st dose
of vaccine Typical trigeminal neuralgia, affecting the right side of the face, without any clear underlying pathologies Initially pregabalin,
later incidentally steroids All symptoms subsided after the introduction of steroids
Case 2
Onoda et al. [6] After 1st dose of vaccine; a month prior the patient had undergone surgery
to alleviate already
existing TN An occurrence of trigeminal neuropathy of the right side of the face in a patient with prior trigeminal neuralgia treated surgically Carbamazepine
and pregabalin Partial recovery,
with persistent numbness
in the affected area
Case 3
Lee et al. [4] After 2nd dose
of vaccine Trigeminal neuralgia, with additional sense of numbness in the region of the left upper limb.
MRI revealed lesions in the pons, in the surroundings of the entry
of trigeminal nerve Carbamazepine, concurrently with methylprednisolone,
later prednisolone Full recovery, patient forwent carbamazepine
due to the efficacy of steroids
Case 4
Narasimhalu et al. [5] After 1st dose
of vaccine Classified as trigeminal neuritis with radiculitis.
Oedema with increase in the diameter of the perineural sheath
was observed in the MRI Pregabalin, later supplemented
with methylprednisolone Majority of the symptoms were alleviated after the use of prednisolone,
with numbness persisting
Case 5 October 2022, after 3rd dose of vaccine Typical trigeminal neuralgia affecting the left side of the face, without any clear underlying pathologies Initially carbamazepine and lignocaine, later oxcarbazepine, dexamethasone
and pregabalin Successful recovery of the patient after switching to the second line of therapy
Conclusions
The temporal relationship, history and exclusion of other causes suggest that TN can occur in patients after the third dose of the Pfizer BioNTech COVID-19 vaccine. Therapy with steroids, oxcarbazepine and pregabalin may reduce the frequency and intensity of pain attacks of TN of such origin. Recurrences and exacerbations of pain are possible during treatment, as seen in our case. It cannot be ruled out that the association between the vaccine and TN was a coincidence and not a causal relationship, so further observation of subjects vaccinated against COVID-19 and investigation of the causes of TN must continue.
Take-away points
Information about COVID vaccinations should be included in the history of patients developing TN and should be considered as a possible cause of TN.
TN can occur as a complication even after subsequent doses of the COVID vaccine when other possible causes are ruled out.
Steroid therapy may be effective as a treatment in TN as a possible complication of COVID vaccination.
Patients with TN as a possible complication of COVID vaccination should preserve vigilance to avoid concomitant infections which can exacerbate symptoms.
The authors declare no conflict of interest.
==== Refs
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==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206590
50137
10.5114/ceji.2023.125043
Review Paper
Interleukin 35: protective role and mechanism in type 1 diabetes
Zhang Si-Ming 1*
Liang Jun 2*
Xia Ji-Ping 1
Li Li 3
Zheng Li 3
Wang Ya-Lan 3
Li Yan-Hong 3
Li Yan 3
Lu Yu 3
1 Wuhan University of Science and Technology, Wuhan, China
2 Wuhan Pulmonary Hospital, Wuhan Institute for Tuberculosis Control, Wuhan, China
3 Wuchang Hospital affiliated to Wuhan University of Science and Technology, Wuhan, China
Yan Li and Yu Lu, Wuchang Hospital affiliated to Wuhan University of Science and Technology, Wuhan 430063, China, e-mails: [email protected] (Yan Li), [email protected] (Yu Lu)
* Si-ming Zhang and Jun Liang equally contributed to this work.
14 2 2023
2023
48 1 4853
20 7 2022
17 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Interleukin 35 (IL-35), a cytokine secreted by regulatory T (Treg) cells from the differentiation of conventional CD4+ T cells, is a member of the IL-12 family. The IL-12 family of cytokines exhibits an anti-inflammatory property. IL-35 has recently been shown to influence the immune modulation in various diseases, including inflammatory bowel disease, Graves’ disease, rheumatoid arthritis, colitis, psoriasis, and type 1 diabetes (T1D). T1D is an immune-related disease caused by destruction of pancreatic β cells, characterized by an absolute lack of insulin. Recently, studies have suggested that protective effects of IL-35 work by improving blood glucose levels and preventing an attack of inflammatory factors on the islets. The protective mechanism may be closely related to the anti-inflammatory properties of IL-35, which include regulating macrophage phenotype, suppressing T cell proliferation, decreasing the differentiation of Th17 cells, increasing the Treg cell population, and inducing IL-35-producing regulatory T cells (iTr35). Here, we review the protective effects and mechanisms of action of IL-35 in T1D.
cytokines
interleukin-35
anti-inflammatory
type 1 diabetes
regulatory T cells
IL-35-inducible Treg
macrophage
==== Body
pmcIntroduction
Diabetes mellitus is a chronic metabolic disorder characterized by persistent hyperglycemia. Diabetes can be caused by impaired insulin secretion, resistance to the peripheral actions of insulin, or both [1]. It is classified as type 1 diabetes (T1D), type 2 diabetes, gestational diabetes mellitus (GDM), and a special type of diabetes caused by other factors [2]. This discussion focuses on T1D, which is characterized by an absolute lack of insulin due to the immune destruction of the islets [3].
In recent decades, the incidence of T1D has been growing globally [4]. Furthermore, at present, there is no cure for T1D; the cornerstone of therapy is diet and exercise, and the established treatment protocol involves controlling blood sugar levels with either daily injections of insulin or continuous oral hypoglycemic drugs [5]. Various factors contribute to the development of T1D, including genetic, environmental, and autoimmune factors. Accordingly, the mechanism of T1D is complex and remains to be fully elucidated.
Type 1 diabetes is associated with autoimmunity, which is characterized by the immune-mediated breakdown of tolerance to the islet antigens, resulting in uncontrolled T cell-mediated autoimmune destruction of pancreatic β cells [2]. The most important of these immune cells are macrophages and T cells, which both play an important role in T1D [1, 6, 7]. Recent studies have shown that interleukin 35 (IL-35) is involved in the pathogenesis of diabetes by regulating the polarization of macrophages, thus improving blood glucose.
Interleukin 35, a recently discovered member of the IL-12 family, is a heterodimeric cytokine composed of Epstein- Barr virus-induced gene 3 (EBI3) and IL-12p35 [8]. It is secreted primarily by regulatory T (Treg) cells (CD4+CD25+) [9] from the differentiation of conventional CD4+ T cells that have a pivotal role in immune homeostasis, regulatory B cells, dendritic cells, and a small amount of activity in endothelial and muscle cells, monocytes, and cancer cells. IL-35 converts Treg cells into induced regulatory T cells (iTr35) and mediates their suppression exclusively. Therefore, IL-35 has a crucial role in maximizing the inhibitory effect of Treg cells [10]. IL-35 exhibits only immune-suppressive properties and has been shown to convey protection in immune-related diseases. Collison et al. [11] first demonstrated, in an inflammatory bowel disease (IBD) model, that the inhibitory effect of Treg cells on effector T cells (Teff cells) is greatly reduced by the deletion of the two subunits, EBI3 and P35. Researchers also observed that CD4+CD25 iTr35 cells stimulated by exogenous IL-35 could have an immuno-suppressive effect in an IBD mouse model [9]. In Graves’ disease (GD), the serum level of IL-35 was reported to be reduced in a patient with GD compared to the controls, as determined by the ELISA method, which suggested that IL-35 may play an anti-inflammatory role in GD [12]. In recent years, IL-35 has also been found to have a protective effect in diabetes. By searching the keywords IL-35 and diabetes through the PubMed medical literature retrieval website, 32 papers describing the relationship between IL-35 and diabetes were found. In this review, we focus on how IL-35 impacts the development of diabetes in various ways, highlighting both the protective effect and the mechanism in both human and animal studies.
Protection against diabetes conferred by interleukin 35
The study of IL-35-induced immunomodulatory protection with regards to autoimmune and anti-inflammatory interactions may contribute to the development of new therapeutic approaches to inflammatory diseases. IL-35 therapy can ameliorate or prevent some autoimmune diseases, such as IBD, rheumatoid arthritis, colitis, psoriasis, and asthma. This is consistent with an analysis of the efficacy of pentosan polysulfate sodium (PPS) on a dextran sulfate sodium (DSS)-induced colitis model; PPS was reported to promote mucosal proliferation and repair by increasing the protein level and gene expression of IL-35 [13]. Furthermore, Xin et al. found that IL-35 can mitigate the progression of rheumatoid arthritis, most notably through maintaining the balance of Treg cells and Th17 cells [14]. Peng et al. reported that recombinant IL-35-BCG can regulate the Treg/Th17 cell imbalance and inflammatory response in asthmatic newborn mice induced by respiratory syncytial virus (RSV) infection through the c-Jun N-terminal kinase (JNK) signaling pathway, suggesting a new pathway to target in neonatal asthma treatment [15]. Recently, IL-35 has been tested in vitro and in vivo to explore its potential application in the treatment of T1D.
Several animal models of T1D, including a non-obese diabetes (NOD) mouse model, a multiple low dose streptozotocin (MLDSTZ)-induced T1D model, and a genetically engineered mouse model of T1D, and analyses of human peripheral blood obtained from patients with T1D and healthy controls are currently available. The mechanism of IL-35 in protecting and preventing T1D may be through inhibiting the proliferation and infiltration of immune cell in T1D models and patients. Bettini et al. [16] demonstrated that IL-35 protects NOD mice from autoimmune attack through the establishment of a NOD RIP-IL35 transgenic mouse model, with wild-type littermates as a control. They found that the number of CD4 and CD8 T cells infiltrating the islet cells in NOD RIP-IL35 mice decreased significantly, along with the frequency of islet antigen-specific T cells, which ultimately played a role in limiting the development of autoimmune diabetes. Further, the ectopic expression of IL-35 in β cells may protect against autoimmune diabetes, and the continued expression of IL-35 in β cells may be necessary to inhibit immune cell infiltration and prevent complications of diabetes. This transgenic technology of transplantation of islets or β cells in patients with diabetes protects inhibitors from autoimmune damage [16]. Mondanelli et al. observed that [17], around the eighth week, NOD mice in the dendritic cell group that received pulse IGRP peptide (a major diabetic autoantigen in NOD mice) had a higher incidence of diabetes than those receiving IGRP-pulsed dendritic cells (DC35). In contrast, the IGRP-pulsed DC35 cell group maintained normal blood sugar until the 10th week, after which the incidence of diabetes increased but did not exceed 25%. At the same time, the study showed that the ectopic expression of IL-35 by dendritic cells increased the number of Treg cells in NOD mice on the 21st day after treatment. Thus, DC cell therapy based on IL-35 Ig-producing and IGRP-presenting DCs may efficiently protect prediabetic NOD mice from developing hyperglycemia and overt diabetes. Subsequently, the same conclusion was obtained in 12-week-old NOD female mice injected with AAV8mIP-IL-35, which had the potential to inhibit β cell autoimmunity, hence suppressing the progression of overt diabetes; this provided evidence for the injection of exogenous IL-35 in a T1D mouse model as a novel anti-diabetes therapy [18]. These data were consistent with Singh et al., who found that transgenic IL-35 expression targeted against β-cells in NOD mice improved inflammation around the islets, preventing the occurrence of primary and secondary T1D, which inhibited the proliferation and infiltration of CD4+ T cells, CD8+ T cells, Foxp3 Treg cells, and regulatory B cells (Bregs) around the islets of NOD mice in the MLDSTZ-induced T1D model [19]. After systemic administration of recombinant IL-35, compared to treatment with phosphate buffer saline (PBS), the former remained normoglycemic with a lower insulitis status while the latter mice became hyperglycemic from day 10 and developed moderate insulitis. However, once IL-35 therapy was limited, diabetes developed. Furthermore, they drew the same conclusion from the NOD mouse model as that from the MLDSTZ T1D model, such that compared with age-matched wild-type CD-1 mice, the proportion of Foxp3 Treg cells was higher in pancreatic draining lymph nodes (PDLNs) of NOD female mice with prediabetes (13-15 weeks of age). Luo et al. [20] observed that IL-35 treatment prevented a decrease in Breg and IL-35+ Breg cells in STZ-induced diabetic mice, which effectively maintained an anti-inflammation phenotype in the T1D mouse model. These data showed that the IL-35 response in the MLDSTZ mouse is in line with the NOD model. In an analysis of human peripheral blood obtained from patients with T1D and healthy controls, insufficient IL-35 levels were reported, and IL-35 expression was significantly decreased in the serum of C-peptide negative patients with T1D in contrast with C-peptide positive patients with T1D [21]. Singh et al. noted that IL-35+ regulatory T cell frequency and plasma IL-35 concentration in patients with late autoimmune diabetes in adults (LADA) were lower than those in healthy controls [22]. Ouyang et al. reported that serum levels of IL-35 in patients with T1D were lower than in healthy controls [23]. These results indicate that IL-35, expressed in local islets, can regulate the immune response of T1D by controlling the immune response of CD4+ and CD8+ T cells. Moreover, these results provide a theoretical basis for the injection of exogenous IL-35 into patients with T1D as a novel anti-diabetes therapy. The protective role of IL-35 in T1D is shown in Table 1.
Table 1 Protective role of IL-35 in type 1 diabetes (T1D) in NOD mice, STZ mice, and T1D patients
Group Animal model
or patient Type Findings Reference
Group 1 NOD mice T1D Nonobese diabetic RIP-IL35 mouse model showed less infiltration of CD4 and CD8 T cells compared with wild-type littermates (Bettini et al., 2012)
NOD mice Autoimmune diabetes Administration of IL-35Ig-producing and IGRP-presenting DCs can efficiently protect prediabetic NOD mice from developing hyperglycemia and overt diabetes (Mondanelli et al., 2015)
NOD mice T1D Injection with AAV8mIP-IL-35 inhibited β cell autoimmunity and suppresses the development of overt diabetes. (Manzoor et al., 2017)
NOD mice T1D Transgenic IL-35 expression targeted against β-cells in NOD mice inhibited the proliferation and infiltration of T cells, Tregs, and Bregs around the islets, which protected the islets (Zhang et al., 2019)
Group 2 STZ-induced mice T1D In contrast with injection of phosphate buffer saline, the systemic administration of recombinant IL-35 revealed normoglycemia with lower insulitis status (Singh et al., 2015)
STZ mice T1D IL-35 treatment prevented the decrease of Breg and IL-35+ Breg cells in diabetic mice, which effectively maintained the anti-inflammatory phenotype of Breg in the T1D mouse model (Luo et al., 2021)
Group 3 Patient T1D Levels of IL-35 in serum of patients with C-peptide negative T1D was decreased in contrast with patients with C-peptide positive T1D (Espes et al., 2017)
Patient LADA Plasma concentrations of IL-35 in LADA patients were lower than those in healthy controls (Singh et al., 2019)
Patient T1D Serum IL-35 levels in T1D patients were lower than in healthy controls (Ouyang et al., 2021)
NOD – non-obese diabetes, STZ – streptozotocin, T1D – type 1 diabetes, LADA – late autoimmune diabetes in adults, IL-35 – interleukin 35
Mechanisms of interleukin 35-dependent protection against diabetes
While the pathogenesis of T1D is unclear, we know that the immune system is involved [24]. The characteristic immune response of IL-35 that protects against diabetes includes the macrophages and the adaptive immune response, such as tumor necrosis factor α (TNF-α), Th1, Th17, Treg, iTr35, and Breg. Th1 cells are involved in the destruction of islet β cells, and polarization of Th1 immunity is a prerequisite for T1D development. According to one study, therapeutic agents targeting CD26 by suppressing T-cell proliferation and Th1 cytokine production regulate autoimmunity in T1D [25]. TNF-α participates in the development of impaired islets in the pathogenesis of T1D, which is one of the most important pro-inflammatory mediators. A raised level of TNF-α induces inflammation around islets in adipocytes and peripheral tissues and can accelerate the course of T1D by impairing insulin signaling through serine phosphorylation [26]. IL-35 is involved in many mechanisms to protect against diabetes, among which is macrophage polarization where Treg and Th1 are the most important.
Polarization of macrophages
Macrophages can be affected by various factors, which can alter the phenotype and thus function of macrophages; activated macrophages are usually divided into two categories, namely, M1-like macrophages and M2-like macrophages, which are closely related to pro-inflammatory and anti-inflammatory responses, respectively [27]. Compared with control animals [28], STZ-induced macrophages in T1D mice showed higher levels of inflammatory cytokines/chemokines, nitric oxide (NO) secretion, NLRP3 and iNOS protein levels, and enhanced glycolysis activity. Improving the inflammatory environment by modulating the activation state of macrophages is an effective method for the treatment of diseases [29]. TNF-α and IL-10 are stimulated after T1D islet transplantation and are expressed in the blood, causing macrophages to change phenotype and content to M2. This then improves immune tolerance, thus promoting islet neovascularization after transplantation; this eliminates apoptosis in pancreatic islet transplantation [30]. Some of the regulatory immune responses induced by IL-35 are dependent on macrophages. Zhang et al. [6] found that IL-35 plays an immune-suppressive role in psoriasis by decreasing the total number of macrophages and the ratio of M1/M2 macrophages. In 2019, Jiang et al. demonstrated that IL-35 is involved in the protection of diabetic neuropathic pain (DNP); they found that the mechanism underlying the therapeutic effect of IL-35 on DNP relates to the promotion of microglial polarization toward the M2 phenotype by inhibiting JNK signaling and Janus kinase-2/Signal transducer and activator transcription 6 (JAK2/STAT6) signaling [31]. All studies emphasized that modulating the macrophage phenotype might be useful to ameliorate immune-related diseases including diabetes. However, the molecular pathway of IL-35 in the protection of T1D through regulation of the macrophage phenotype remains to be explored.
Treg cells
Treg cells are essential for immune homeostasis. Tang et al. [32] reported that the mechanism by which the parasite ameliorates or prevents the development of T1D is particularly related to the activity of Treg cells. Treg cells are characterized by high expression of the transcription factor Foxp3, which releases inhibitory cytokines such as IL-10, TGF-β, and IL-35, as major regulators of Treg cell phenotype and function [33, 34]. Treg cells suppress autoimmunity, which is achieved to a large extent by blocking the T cell-mediated cycle and blocking Th1 and Th17 cell differentiation [18, 29]. Moreover, IL-35 promoted CD4+ T cell differentiation into iTr35 cells, which still possessed the ability to secrete IL-35 [10]. Over the past decade, a growing number of studies have indicated that IL-35 plays a crucial role in regulating Treg cells in immune-related disorders, including autoimmune diseases, infectious diseases, and cancer. In an asthma mouse model [16], IL-35 improved asthma symptoms by promoting the proliferation of Treg cells and inhibiting the differentiation of Th17 cells. In diabetic rats with impaired renal function, increased levels of IL-17A and decreased levels of IL-35 and IL-10 were observed; exogenous resveratrol therapy increased the expression of IL-35 secreted by Treg cells and improved renal function [35]. Moreover, Cao et al. [36] reported that IL-35 is vital for regulating Treg/Breg cell responses during the development and progression of GDM. In a model of islet transplantation [37], Treg cells inhibited Th17 proliferation in islet cells, which was enhanced by IL-35; IL-35 induced an increase in Treg cells, which may further contribute to the protective effect in transplanted islets. While IL-35 can promote differentiation of conventional CD4+ T cells into iTr35 cells [11, 38], IL-35 played a central role in inhibiting the proliferation and expansion of CD4+ and CD8+ T cells by partly regulating Foxp3+ and Treg cells, which are essential in the onset of diabetes. These results suggest that IL-35 may protect against the development of T1D through control of inflammation in circulatory and local metabolic tissues by Treg cells. Moreover, Treg cells may participate in the protective effect of IL-35 on diabetes, as shown in Figure 1.
Fig. 1 Possible mechanism of IL-35 protection against type 1 diabetes
IL-35 – interleukin 35, M1 – classically activated macrophage, M2 – alternatively activated macrophage, Foxp3 – Forkhead box protein P3, Treg – regulatory T cells, Bregs – regulatory B cells, Th1 – T helper 1, STAT6 – signal transducer and activator transcription, JAK2 – Janus kinase-2, IR – insulin resistance, T1D – type 1 diabetes, DNP – diabetic neuropathic pain
Th1
There is growing evidence to suggest that the Th1 immune response and related cytokines such as IFN-γ play an important role in the pathogenesis of T1D [39]. Kathamuthu et al. reported that when latent tuberculosis antigen stimulated diabetes mellitus (DM) and prediabetes (PDM), the levels of Th1-associated cytokines IFN-γ and TNF-α in cultured serum increased significantly in T cells [40]. Importantly, Treg cells can convert Th1 into Th2, secreting the cytokines IL-35, IL-10, and TGF-β to generate immune tolerance, ultimately treating and preventing the occurrence of T1D [32]. Chen et al. pointed out that the injection of exogenous IL-35 can stimulate an increase in Breg cells and can inhibit the proportion of Th1 [41]. In addition, Jiang et al. showed that IL-35 can also inhibit CD8+ T cell activity by inhibiting the expression of Th1 cytokines, hence serving as protection against T1D [42]. All of these contribute to preventing and treating T1D.
Conclusions
In this review, we have summarized the latest evidence on the involvement of IL-35 in protection against T1D and the mechanism behind it. IL-35, as an anti-inflammatory cytokine, improves T1D by regulating the polarization of macrophages and the proportion of T cell-related cytokines. This opens a new avenue to immunotherapeutic strategies for T1D. Despite compelling evidence from animal models supporting the potential role of IL-35 in modulating the course of T1D, the clinical translation of these findings is slow, and many obstacles persist. The ultimate goal is to identify a balanced, non-invasive, and effective means of decreasing the incidence of diabetes without affecting the fitness of an individual.
Acknowledgements
I am incredibly grateful to my supervisor Dr. Chunlian Tang and master’s supervisor Yan Li, who have been willing to patiently teach knowledge and give professional advice and moral encouragement whenever it was required. Moreover, they not only sorted out the ideas in the writing process, but more importantly, after completing the first draft, helped me correct many mistakes in writing and thinking again and again. I sincerely thank my mentors for patiently answering my questions under the pressure of their busy work.
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10189576.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206593
50550
10.5114/ceji.2023.126672
Experimental Immunology
Immunotherapy of B cell lymphoma with CD22-redirected CAR NK-92 cells
Tian Xiaopeng 1*
Zhang Ruixi 2*
Qin Huimin 1
Shi Xiangru 1
Qi Wenhui 1
Jiang Dongpeng 1
Zhu Tingting 1
Sun Aining 1
1 National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University, Institute of Blood and Marrow Transplantation, Medical College of Soochow University, Collaborative Innovation Center of Hematology, Jiangsu Institute of Hematology, Soochow University, China
2 Henan Key Laboratory of Coronary Heart Disease Control, Central China Fuwai Hospital, China
Dongpeng Jiang, Tingting Zhu, Aining Sun, National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Institute of Blood and Marrow Transplantation, Medical College of Soochow University, Collaborative Innovation Center of Hematology, Jiangsu Institute of Hematology, Soochow University, China, e-mails: [email protected], [email protected], [email protected]
* Xiaopeng Tian and Ruixi Zhang equally contributed to this work.
14 4 2023
2023
48 1 113
01 8 2022
11 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Introduction: Chimeric antigen receptor (CAR)-NK cells are considered safer than CAR-T cells due to their short lifetime and production of lower toxicity cytokines. By virtue of unlimited proliferative ability in vitro, NK-92 cells could be utilized as the source for CAR-engineered NK cells. CD22 is highly expressed in B cell lymphoma. The goal of our study was to determine whether CD22 could become an alternative target for CAR-NK-92 therapy against B cell lymphoma.
Material and methods: We first generated m971-BBZ NK-92 that expressed a CAR for binding CD22 in vitro. The expression of CAR was assessed by flow cytometric analysis as well as immunoblotting. The cytotoxicity of the m971-BBZ NK-92 cells towards target lymphoma cells was determined by the luciferase-based cytolysis assay. The production of cytokines in CAR NK-92 cells in response to target cells was evaluated by ELISA assay. Lastly, the cytolytic effect was evaluated by the cytolysis assay mentioned above following irradiation. The level of inhibitory receptor of CAR-expressing cells was assessed by flow cytometry.
Results: CD22-specific CAR was expressed on m971-BBZ NK-92 cells successfully. m971-BBZ NK-92 cells efficiently lysed CD22-expressing lymphoma cells and produced large amounts of cytokines after coculture with target cells. Meanwhile, irradiation did not apparently influence the cytotoxicity of m971-BBZ NK-92 cells. Inhibitory receptor detection exhibited a lower level of PD-1 in m971-BBZ NK-92 cells than FMC-63 BBZ T cells after repeated antigen stimulation.
Conclusions: Our data show that adoptive transfer of m971-BBZ NK-92 could serve as a promising strategy for immunotherapy of B cell lymphoma.
CAR-NK
CD22
NK-92
B cell lymphoma
immunotherapy
==== Body
pmcIntroduction
Chimeric antigen receptors (CARs) have been used to redirect the specificity of autologous T-cells against leukemia and lymphoma with promising clinical results [1-3]. Thus, CAR-T has been become a major breakthrough in cancer therapy, especially in treatment of hematological malignancies. However, therapy based on CAR-T has drawbacks including high manufacturing costs, a significant risk of graft-versus-host disease (GVHD) and potentially fatal toxicities such as cytokine release syndrome (CRS) [4]. Theoretically, NK cells, which represent another important cell type for adoptive immunotherapy, may be an alternative driver for CARs. CAR-NK cells are considered safer than CAR-T cells due to their short lifetime and production of lower levels of toxic cytokines, including interferon γ (IFN-γ) and granulocyte-macrophage colony-stimulating factor (GM-CSF), than those produced by T cells [5]. Additionally, as activating NK cells do not require strict HLA matching, the CAR-NK therapy does not carry a risk of GVHD.
Unlike primary NK cells, which are difficult to isolate, purify and transduce, the human NK cell line NK-92 can be continuously expanded in the presence of interleukin (IL)-2 in a Good Manufacturing Practice-compliant process [6]. The cytolytic effects and safety of NK-92 have been confirmed in preclinical studies and clinical studies [6, 7]. Presently both primary NK cells and the NK-92 cell line are used to generate CAR-NK cells targeting several different antigens, including CD19, CD20, CD33, CD138, CD3, CD5, CD123 and SLAMF7 [8]. CD19-directed CAR-NK-92 showed increased cytotoxic activity against leukemia cells expressing CD19 [9]. Apart from NK-92, there are six other known NK cell lines, while none of these showed consistently and reproducibly high antitumor cytotoxicity. Additionally, only NK-92 cells can easily be genetically manipulated to recognize specific tumor antigens and be infused to cancer patients with clinical benefit and minimal side effects [10].
B cell lymphoma is a heterogeneous group of diseases that includes non-Hodgkin (90%) lymphoma and Hodgkin lymphoma (10%) [11]. Though advances in therapy have substantially increased the likelihood of cure for patients with B cell lymphoma, outcomes have been poor for patients with relapsed/refractory disease. After 2 or more lines of therapy, CAR-T cell therapy offers a potentially curative therapy to previously incurable patients. At present, the most popular target of CAR-T therapy for B cell lymphoma is CD19. CD19 CAR-T is widely used to treat B-cell-derived malignancies, including B-acute lymphatic leukemia (B-ALL), B-chronic lymphatic leukemia (CLL) and B-non-Hodgkin’s lymphoma (B-NHL). Though an encouraging outcome emerged when anti-CD19 CAR-T products were used in several subtypes of B cell lymphoma, progression or relapse appeared eventually in the majority of patients after anti-CD19 CAR T-cell therapy [12, 13]. One main factor leading to anti-CD19 CAR-T failure is that tumor cells can evade the recognition and attack by CD19 CAR-T through downregulating or even losing surface CD19 expression. A study showed that about 10% of B-ALL patients relapsed after CD19-targeted CAR-T therapy due to antigen loss [14]. Thus, there is a great need to seek alternative targets for immunotherapy of B cell lymphoma in case antigen loss occurs.
CD22 is a sialic acid-binding adhesion molecule expressed on most B cell lymphoma patients [15, 16], while its expression on normal tissue is restricted to the B cell lineage. It is reported that monoclonal antibody (mAb)-based therapeutics targeting CD22 have attained promising results in clinical trials [17-19]. There was a study showing that CD22-CAR expressing T cells can mediate similarly potent antineoplastic effects as CD19 CAR-T [20]. In the present study, we accomplished generation as well as molecular and functional characterization of a clonal CD22-redirected CAR-NK-92 cell line. We also detected whether the cytolytic potency could be influenced after irradiation and the expression level of inhibitory receptors after repeated antigen stimulation. In summary, our results suggested that adoptive transfer of CD22-redirected CAR-NK-92 cells may serve as a promising strategy for treatment of B cell lymphoma.
Material and methods
Cells and culture conditions
The three lymphoma cell lines Raji, TMD8 and Daudi, which were all CD22 and CD19 positive, were cultured in RPMI 1640 medium (Gibco). 293T cells were used for the lentivirus and retrovirus vector packaging cell line (ATCC) and cultured in DMEM medium (Gibco). All the above cell culture medium was supplied with 10% heat-inactivated fetal bovine serum (FBS; Biological Industries), 100 U/ml penicillin, 100 µg/ml streptomycin, and 2 mM L-glutamine (Invitrogen). The NK-92 cell line was cultured in α-MEM medium (Gibco) supplemented with 12.5% heat-inactivated FBS (Biological Industries) and 12.5% heat-inactivated Horse Serum (Gibco) in the presence of recombinant human IL-2 (teceleukin, rhIL-2; Roche) at the final concentration of 500 IU/ml.
Construction of CAR NK-92 cells
The single chain variable fragment (scFv) m971 to target CD22 originated from a fully humanized anti-CD22 antibody [21]. To generate anti-CD22 CAR expression constructs, the fragments encoding the CD8 leading peptide, anti-CD22 scFv (m971), CD8 hinge and 41BB-CD3ζ were synthesized by Genewiz (Suzhou) and were amplified by PCR. Then these fragments were cloned into the pCDH-SFFV-IRES.EGFP lentiviral plasmid using EcoR I and Not I sites. The resultant plasmid was designated pCDH-SFFV-m971-41BB-CD3ζ-IRES.EGFP. The methods for production of lentivirus particles encoding CAR and transduction of cells have been described in our previous study [22]. The expression plasmids together with the packaging plasmids including psPax2 and PMD2.G were cotransfected into 293T cells at a ratio of 20 µg : 15 µg : 6 µg, and 48 hours after transfection, the collected supernatants were filtered. NK-92 cells were resuspended in the virus solution in the presence of 500 IU/ml IL-2 and 4 µg/ml polybrene (Sigma) and centrifuged at 600 g for 45 minutes, and then transferred into a CO2 incubator. 8 hours later, the virus solution was replaced with fresh NK-92 medium (mentioned above). After culture for 72 hours, EGFP-positive cells were sorted as CAR-expressing cells by flow cytometric cell sorting with a FACS Melody cell sorter (BD Biosciences).
Flow cytometry analysis of CAR expression
The surface expression of CAR on NK-92 cells was measured by flow cytometry. CAR-transduced NK-92 cells were resuspended with wash buffer (1 × PBS + 1% BSA), and incubated with 1 µg/ml biotinylated protein L (Thermo Scientific) or normal polyclonal goat immunoglobulin G (IgG) as an isotype control. The cells were then incubated with biotin-streptomycin-APC (BD Bioscience) and analyzed by flow cytometry. Data analysis was carried out using FlowJo software (Tree Star Inc.).
Immunoblotting
The sorted NK-92 cells after lysis were separated by SDS-PAGE gel and then transferred to the PVDF membrane (Millipore). The membranes were incubated with anti- human CD247 (CD3ζ) primary antibody (BD Pharmingen). Then membranes were washed and developed using a secondary anti-mouse IgG-HRP antibody (Biworld). Finally, the expression of total CAR protein in NK-92 cells was detected by an enhanced chemiluminescence reagent (GE Healthcare Biosciences).
Generation of lymphoma cell lines stably expressing firefly luciferase
Lymphoma cell lines stably expressing firefly luciferase were generated through transduction with lentiviruses encoding firefly luciferase. EGFP positive cells were sorted as the firefly luciferase expressing cells.
Cytotoxicity assay
Mock NK-92 cells or CAR NK-92 cells were cocultured with three lymphoma cell lines (2 × 104) expressing firefly luciferase at the indicated effector/target ratios (E/T) in the 96-well U-bottom plate at 37oC for 6 hours. Target cells alone were also cultured as the control. At the end of the co-culture, the Bright-Glo luciferase reagent (Promega) was added into the wells directly, and the luminescence value was recorded using a luminescence microplate reader (BioTek). Then the bioluminescence value was detected with the substrate of firefly luciferase. The percentage of specific lysis was calculated as: (the luminescence values of target cells alone – the luminescence values of target cell cocultured with T cells)/the luminescence values of target cells alone × 100%.
For the detection of NK cell-mediated lysis of patient lymphoma cells, primary lymphoma cells prelabeled with eFluor 670 (eBioscience) were cultured alone or cocultured with NK cells for the indicated time period. 123 count beads (eBioscience) were added and the absolute number of eFluor 670-labeled target cells was determined by flow cytometric analysis according to the manufacturer’s protocol. The percentage of specific lysis was calculated as: (the absolute number of target cells when cultured alone – the absolute number of target cells when cocultured with T cells)/the absolute number of target cells when cultured alone × 100%.
Cytokine release assays
Effector cells (2.5 × 105) were cocultured with target cells at an E/T ratio of 1 : 1 in 96-well U-bottom plates at 37oC for 24 hours, and then the cell-free supernatants were collected for the IFN-γ and tumor necrosis factor α (TNF-α) secretion detected by the method of ELISA kits (R&D system) according to the manufacturer’s protocol.
CD107a degranulation assay in the NK-92 cells
Effector cells (3 × 105) were cocultured with the same number of target cells in 96-well U-bottom plates for 4 hours at 37oC. In addition, mock NK-92 cells or CAR NK-92 cells were incubated without target cells for the negative control. After 2 hours, monensin and APC conjugated anti-CD107a antibody (BD Biosciences) or IgG1 isotype antibody (BD Biosciences) were added. The cells were further stained with PE-Cy7-conjugated anti-CD56 antibody (BD Biosciences) and analyzed with flow cytometry.
Irradiation of NK-92 cells
m971 BBZ and mock NK-92 cells were collected by centrifugation, counted, washed, resuspended in fresh medium and irradiated with 5 or 10 Gy using a X-RAD320ix device (PXI Systems, USA). For in vitro cytotoxicity and cytokine release assay, irradiated cells were washed, resuspended in fresh growth medium and cultured for up to 8 days, then used for the subsequent assays.
Repeated stimulation of effector cells
Effector cells including mock, m971-BBZ NK-92 and FMC63 BBZ T cells were co-incubated with target cells (TMD8) for 3 days at an initial E/T ratio of 1 : 2 in 24-well plates. Fresh target cells were repeatedly added after 24 and 48 h. Surface expression of the inhibitory molecules PD-1 and TIM3 on effector cells was assessed by flow cytometry.
Statistical analysis
The data were expressed as the mean ± standard deviation (SD) and analyzed with software Prism software v9.0. The differences between two independent groups were compared using Student’s t test. One-way ANOVA was used when three or more independent groups were compared. P < 0.05 was set as the significant difference level.
Results
Generation of anti-CD22 CAR NK cells
First, we designed a second-generation CD22-specific chimeric antigen receptor which consists of anti-CD22 scFv and transmembrane regions of the CD8 molecule, the 41BB costimulatory signaling moiety, and the cytoplasmic component of the CD3ζ molecule. In addition, we incorporated the EGFP reporter gene to evaluate the efficiency of transduction (Fig. 1A). To confirm whether the m971-41BB-CAR was transduced successfully, the EGFP positive NK-92 cells were sorted and expanded, and then they were subjected to western blotting with anti-CD3ζ mAb (Fig. 1B). In contrast with mock NK-92 cells, which only expressed endogenous CD3ζ protein (16 kDa), m971-BBZ NK-92 cells not only expressed native CD3ζ at the predicted site, but also obviously expressed the chimeric m971-scFv-41BB-CD3ζ fusion protein (55 kDa). Finally, we detected the expression of m971-BBZ on the surface of NK-92 cells by staining with biotin-labeled protein L, which could recognize the scFv portion of anti- CD22. The results of flow cytometry analysis revealed that 82.1% of m971-41BB-CAR was expressed on surface of NK-92 cells, while the CAR expression remained almost undetected on the mock NK-92 cells (Fig. 1C). Taken together, the observations indicate that the CD22-specific chimeric antigen receptor was expressed on the surface of m971-BBZ NK-92 cells successfully.
Fig. 1 Generation of m971-specific CAR NK-92 cells. A) Schematic representation of m971-specific CAR lentiviral vector. B) Immunoblotting analysis of CAR expression. The cell lysates of mock or m971-CAR infected NK-92 cells were subjected to Western blot analysis with anti-human CD3ζ primary antibody. C) Flow cytometric analysis of CAR expression. Mock or m971-BBZ NK-92 cells were stained with biotin-labeled protein L or isotype-matched control antibody, followed by APC-labeled anti-streptavidin antibody staining to define the expression of CAR
In vitro cytolytic potency against lymphoma cell lines triggered by CD22-specific CAR
We detected the expression of CD22 on the surface of the three common lymphoma cell lines Daudi, Raji and TMD8 by flow cytometry. Obviously, the three lymphoma cell lines all highly expressed CD22 protein (Fig. 2A). To define the capacity of m971-BBZ NK-92 cells for recognition of lymphoma cells that express endogenous CD22, we measured the secretion of IFN-γ and TNF-α by the method of ELISA (Fig. 2B). In contrast with mock NK-92 cells, m971-BBZ NK-92 cells stimulated with lymphoma cell lines secreted greater amounts of IFN-γ and TNF-α. The results suggest that m971-BBZ NK-92 cells can more specifically recognize lymphoma cell lines with high levels of endogenous CD22 than mock NK-92 cells.
Subsequently, to define whether the m971-BBZ NK-92 cells can lead to more efficient lymphoma cell line lysis, a bioluminescence assay was performed. Mock NK-92 cells and m971-BBZ NK-92 cells were co-cultured with three lymphoma cell lines, which express high levels of CD22 and firefly luciferase. After 6 hours the bioluminescence values were detected. We found that m971-BBZ NK-92 cells can trigger higher specific killing than mock NK-92 cells at all E : T ratios tested (Fig. 2C). Next, we further measured degranulation of NK-92 cells by detecting the expression of CD107a in mock NK-92 cells and m971-BBZ NK-92 cells incubated with lymphoma cell lines. In agreement with the cytotoxicity result, m971-BBZ NK-92 cells displayed higher CD107a expression than NK-92 cells in the presence of CD22 positive lymphoma cells, suggestive of augmented degranulation by virtue of CAR expression (Fig. 2D, E). In line with that, m971-BBZ NK-92 cells could not induce CD22-independent degranulation against 293 T cells which were CD22 negative (Fig. 3).
Fig. 2 Specific cytotoxicity and cytokine production of m971-BBZ NK-92 cells against lymphoma cell lines. A) Flow cytometric analysis of CD22 expression on the surface of lymphoma cell lines. The three lymphoma cell lines indicated were stained with APC-conjugated anti-CD22 mAb antibody (solid line) or isotype-matched control antibody (dotted line). B) Secretion of cytokines by CAR NK-92 cells activated by CD22-expressing lymphoma cells. Mock or m971-BBZ NK-92 cells were co-cultured with lymphoma cells or cultured alone (no target) for 24 hours, NK cells kept without target cells were included as controls. The supernatants were collected to measure IFN-γ and TNF-α secretion using the method of ELISA. Mean values ±SD are shown; n = 3; ***p < 0.001, **p < 0.01, *p < 0.05, ns, p ≥ 0.05. C) Cytotoxic activity of CAR NK-92 cells against lymphoma cell lines. Three lymphoma cell lines infected with firefly luciferase co-cultured with mock NK-92 cells or m971-BBZ NK-92 cells respectively for 6 hours. The cytotoxicity was determined as the bioluminescence values which were measured with substrate of firefly luciferase. Mean values ± SD are shown; n = 3, ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05, ns, p ≥ 0.05.
Fig. 2 Cont. D) Degranulation of CAR NK-92 cells upon activation by CD22-positive lymphoma cells. Mock or m971-BBZ NK-92 cells were stimulated with the same number of 3 lymphoma cell lines respectively, and flow cytometric analysis of CD107a (a marker of degranulation) expression on the surface of efficient cells (mock or m971-BBZ NK-92 cells) was performed. NK cells kept without target cells were included as controls. Data shown are representative for three independent experiments
Fig. 2 Cont. E) Statistical analysis of expression level of CD107a of activated CAR NK-92 cells. Mean values ±SD are shown; n = 3; ****p < 0.0001, *p < 0.05
Fig. 3 Degranulation of m971-BBZ NK-92 cells against 293T cells. A) Flow cytometric analysis of CD22 expression on the surface of 293T cells. B) Degranulation of CAR NK-92 cells against 293T cells. Flow cytometric analysis of CD107a expression on the surface of effector cells (mock or m971-BBZ NK-92 cells) was performed after being cocultured with 293T cells for 4 hours. Data shown are representative for three independent experiments. C) Statistical analysis of expression level of CD107a of CAR NK-92 cells against 293T cells. Mean values ±SD are shown; n = 3; ns, p ≥ 0.05
Cytotoxic activity of CD22-CAR NK-92 cells against primary lymphoma cells
We harvested lymph node biopsies from two patients with B cell lymphoma and detected the expression of CD22 in lymphoma cells by flow cytometry analysis, and found that CD22 was highly expressed in primary lymphoma cells from both patients (Fig. 4A). Then, to define the capacity of m971-BBZ NK-92 cells for recognition of CD22+ cells isolated from the patients with B cell lymphoma, we measured the secretion of IFN-γ by the method of ELISA (Fig. 4B), with a higher level of IFN-γ detected in the m971-BBZ NK-92 group. Moreover, we measured the cytotoxic activity of anti-CD22 CAR NK-92 cells against those primary lymphoma cells, as shown in Fig. 4C. m971-BBZ NK-92 cells displayed more potent cell killing activity at the E : T ratios of 10 : 1, 5 : 1 and 2.5 : 1. The results revealed that m971-BBZ NK-92 cells could efficiently recognize and lyse primary lymphoma cells.
Fig. 4 m971-BBZ NK-92 cells co-cultured with CD19+ cells isolated from patients with B cell lymphoma respectively achieved remarkably higher cytotoxic activity than mock NK-92 cells. A) Flow cytometric analysis of CD22 expression on the surface of CD19+ cells from patients with B cell lymphoma. The CD19+ cells were stained with APC-conjugated anti-CD22 mAb antibody (solid line) or isotype-matched control antibody (dotted line). B) Secretion of cytokines by CAR NK-92 cells activated by primary lymphoma cells. CD19+ cells derived from two patients were co-cultured with mock NK-92 cells or m971-CAR NK-92 cells, and the secretion of IFN-γ was measured by ELISA. Mean values ±SD are shown; **p < 0.01, ns, p ≥ 0.05. C) Cytotoxic activity of CAR NK-92 cells against primary lymphoma cell lines. CD19+ cells derived from two patients were stained with eFluor 670 and co-cultured with mock NK-92 cells or m971-BBZ NK-92 cells for 6 hours, and then the specific lysis was detected with the method of absolute counting. Mean values ±SD are shown, **p < 0.01, *p < 0.05, ns, p ≥ 0.05
The impact of irradiation on the cytolytic effect and repeated stimulation on the persistence of CD22-CAR NK-92 cells
To determine whether different dosages of X-rays can influence the cytolytic potency of m971-BBZ NK-92 cells, we treated m971-BBZ NK-92 cells with two different dosages of X-rays, and then m971-BBZ cells were co-cultured with Daudi cells. After 6 hours, we detected the specific killing and found that the specific killing efficiency of m971-BBZ NK-92 cells was not influenced by the two different irradiation dosages (Fig. 5A). In addition, we assessed the effect of repeated stimulation on CAR- and mock-transduced NK-92. Here, we included the commonly used CD19-targeting FMC63-BBZ T cells as the control, which have been reported in our previous study [22]. After repeated stimulation with TMD8 cells, the surface expression of inhibitory receptors PD-1 on m971-BBZ NK-92 cells exhibited a lower level than that on FMC63-BBZ T cells (Fig. 5B), while the expression level of TIM3 was increased both in CAR NK-92 and FMC63-BBZ T groups (Fig. 5C).
Fig. 5 Effects of irradiation on the cytotoxicity and antigen repeated stimulation on the persistence of CAR NK-92 cells. A) Cytotoxic activity of CAR NK-92 cells after irradiation. m971-BBZ NK-92 cells were irradiated with different doses of X-rays and co-cultured with Daudi cells for 6 hours which were infected with firefly luciferase, and the cytotoxicity was measured. B) Effect of repeated stimulation on CAR NK-92 cells. Inhibitory surface receptor expression of PD-1 and TIM3 on m971-BBZ, mock-transduced NK-92 cells and FMC63 BBZ CAR-T cells repeatedly stimulated (solid lines) was analyzed. Cells stained with isotype antibody served as controls (dashed lines). Data shown are representative for three independent experiments. C) Statistical analysis of inhibitory receptor expression of PD-1 and TIM3 on effector cells. Mean values ±SD are shown; n = 3; *p < 0.05
Discussion
Compared to T cells, NK cells provide a CAR-engineering platform that is safer and more advantageous [23]. As a source of NK cells, NK-92 cells can be manipulated more easily to express receptors or ligands than peripheral blood NK cells and demonstrated consistent cytotoxic activity against cancer targets [10]. In this study, we constructed a second generation CD22-specific CAR harboring scFv antibody fragment and CD8α hinge region, linked to human composite 41BB (CD137)-CD3ζ signaling domains. To explain our choice of CAR construction, a study proved that the second-generation CAR-NK constructs displayed increased cytotoxicity compared to the first generation [24]. The costimulatory protein domains exhibited different properties: while CD28-CD3ζ CARs led to stronger T-cell activation, 41BB-CD3ζ CARs prolonged in vivo T-cell persistence and reduced exhaustion, which seemed to be more suitable for clinical application [25, 26].
CD22-directed CAR-NK-92 cells (m971-BBZ NK-92) obtained via lentiviral gene transfer displayed homogeneous and stable CAR surface expression, and showed high and specific cytotoxicity against CD22-expressing lymphoma cell lines and primary patient cells even at low E/T. Compared with mock NK-92 cells, m971-BBZ NK-92 cells produced a significantly increased amount of IFN-γ and TNF-α, which were typical for activated NK cells and supported the CAR NK-92-induced activation of endogenous antitumor immunity. Consistent with the data regarding cytolytic activity and cytokine release, a greater extent of degranulation was observed in m971-BBZ NK-92 cells responding to CD22-positive target cells than mock NK-92 cells. The CD22-dependent degranulation of m971-BBZ NK-92 cells was further supported by the evidence that 293 T cells negative for CD22 expression only triggered negligible NK-cell degranulation. More importantly, in a more clinically relevant context, we demonstrated that m971-BBZ NK-92 cells efficiently recognized and eradicated primary lymphoma cells derived from two patients ex vivo. It should be admitted here that the fact that the experiment was performed on only two patient biopsies was the limitation of this study, although the effect of CAR-NK92 cells was really impressive. Even though we have previously tried our best to obtain more patient samples, unfortunately only two patient samples have been available for our analysis so far for many reasons, and we would like to expand the research group to consolidate this conclusion in the future.
As the NK-92 cells which used in the clinical trials are aneuploid, they must be irradiated before being administered to patients [10]. In phase I clinical trials irradiation of untargeted NK-92 cells with 10 Gy prior to infusion had been included as a safety measure to prevent permanent engraftment [7]. This raises the concern that irradiation may dampen the cytotoxicity of m971-BBZ NK-92 cells. Fortunately, based on our data, irradiation at the tested dosages (5 Gy and 10 Gy) did not apparently impair the cytotoxicity of m971-BBZ NK-92 cells. Meanwhile, irradiated NK-92 cells have a limited lifespan in vivo and do not develop memory, minimizing the risk of these side effects [27].
Persistence of the CAR-expressing cells is another aspect for further clinical application. The elevated surface expression of inhibitory molecules such as PD-1 and TIM3 has been associated with exhaustion in CAR-expressing NK cells and other lymphocytes [28]. The flow cytometry results indicated that repeated antigen stimulation with CD22-positive target cells affected the expression of PD-1 and TIM3 differently in our CAR-NK92 cells and CAR-T cells. Compared to FMC-BBZ T cells, repeated antigen stimulation had no major effect on expression of PD-1. Importantly, a recent study revealed that PD-1 expression level remained low after continuous activation of CAR-NK-92 cells with 41BB-CD3ζ signaling domains [29], which was in line with our results of the detection of the inhibitory receptors and indicated an advantage over CAR-T therapy that can be inhibited by PD-L1 from target cells. Conversely, TIM3 expression increased in both m971-BBZ NK-92 and FMC63-BBZ T cells following repeated stimulation with target cells. Nowadays, one of the limitations of CAR-T therapies applied in chronic lymphocytic leukemia and solid tumors is inhibitory activity or a state of exhaustion of T cells [30]. The CAR-expressing cells exposed to chronic antigen stimulation tend to become exhausted, as characterized by impaired expansion, increased expression of inhibitory receptors and lower cytokine production [31]. It is of great importance to uncover the molecular mechanism affecting the persistence of CAR-expressing cells in further research, which could augment the efficiency of CAR-based immunotherapy.
Recently, abundant clinical data have demonstrated that existing targets for CAR engineering cannot meet the requirements, as cunning tumor cells acquired the ability to lose or diminish surface expression of the specific antigen. Our team has also made great effort to explore alternative targets for cancer retargeting in previous research [22, 32]. This study demonstrated that CD22 is a promising antigen for targeting by CAR-NK-92 cells in the treatment of B cell lymphoma. This may contribute to developing CAR-NK immunotherapy toward clinical applications.
Acknowledgments
All the samples were from Jiangsu Biobank of Clinical Resources.
Funding
This work was supported by the National Natural Science Foundation of China (grant numbers 81873449, 81730003), Suzhou Medical Key Discipline Funding Project (grant number SZXK202118), National Science and Technology Major Project (grant number 2017ZX09304021), National Key R&D Program of China (grant numbers 2019YFC0840604, 2017YFA0104502), Key R&D Program of Jiangsu Province (grant number BE2019798), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Medical Outstanding Talents Project (grant number JCRCA2016002), Jiangsu Provincial Key Medical Center (grant number YXZXA2016002), the Natural Science Foundation of Jiangsu Province (grant number BK20220248), Suzhou Science and Technology Program Project (grant number SLT201911) and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (grant number 22KJB310018).
Supplementary material is available on journal’s website
The authors report no conflict of interest.
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PMC010xxxxxx/PMC10189577.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206592
50170
10.5114/ceji.2023.125237
Clinical Immunology
Presence of tubuloreticular inclusions in ultrastructural studies of renal biopsies in children with lupus nephropathy – one-center preliminary study
Koszutski Tomasz 1
Mielańczyk Łukasz 2
Kucharska Grażyna 3
Matysiak Natalia 2
Wiernik Agnieszka 1
Kudela Grzegorz 1
Hyla-Klekot Lidia 1
1 Department of Pediatric Surgery and Urology, Medical University of Silesia, Katowice, Poland
2 Department of Histology and Cell Pathology, FMS in Zabrze, Medical University of Silesia, Katowice, Poland
3 Department of Pediatric Nephrology, Team of Municipal Hospitals in Chorzów, Poland
Agnieszka Wiernik, MD, Department of Pediatric Surgery and Urology, Medical University of Silesia, Katowice, Poland, e-mail: [email protected]
23 2 2023
2023
48 1 2634
17 1 2022
09 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Introduction
Juvenile systemic lupus erythematosus (jSLE) is an autoimmune disease that develops as a result of multi-level immune dysregulation, including the interferon pathway. Nephropathy develops at an early stage and eventually affects 90% of patients. A renal biopsy allows one to classify lupus nephritis and determine the proper treatment. Biopsy assessment should be done not only in a light microscope but also in a transmission electron microscope (TEM). Its usage may reveal the presence of intracellular tubuloreticular inclusions (TRIs), considered as a morphological marker of interferon hyperactivity.
Material and methods
Renal biopsies of 10 children with jSLE and nephropathy were analyzed in TEM. The location, structure, and size of TRIs were assessed. Demographic data, nephropathy manifestation, non-renal symptoms, and serological activity of lupus were analyzed.
Results
All the patients were female with an average onset at 12.7 years of age and met SLE criteria. Nephropathy manifested with proteinuria (n = 10) and hematuria (n = 6). Glomerular filtration rate (GFR) was normal in all patients. In three children with early disease onset, it manifested with hematological disorders. TRIs were revealed in 7 biopsies, with the highest expression in the youngest children, with peripheral cytopenia, membranous glomerulonephritis, and lupus nephritis.
Conclusions
Demonstration of TRIs in renal biopsies of children with juvenile systemic lupus may confirm the diagnosis of lupus nephritis and is a sign of involvement of the interferon pathway at the early stage of the disease.
juvenile-onset systemic lupus erythematosus
lupus nephropathy
renal biopsy
tubuloreticular inclusions
TRIs
==== Body
pmcIntroduction
Juvenile systemic lupus erythematosus (jSLE) is an auto- immune disease characterized by its onset in childhood or adolescence, high immune activity, and a wide spectrum of clinical symptoms [1-3]. Its etiopathogenesis involves many mechanisms, such as dysregulation of apoptosis, production of autoantibodies, formation of immune complexes, and activation of the complement system [4, 5]. The heterogeneity of these mechanisms explains the variety of the clinical symptoms as well as the spectrum of the responses to jSLE treatment [6]. In recent years, activation of the interferon (IFN) pathway has gained interest, as it causes chronic, uncontrolled stimulation of the immune response [5, 7, 8], which is also observed in nearly 50% of adults and the vast majority of children diagnosed with SLE [8]. One of the early and severe manifestations of jSLE is lupus nephropathy (LN), which eventually develops in about 90% of cases [9]. Evidence shows that the IFN pathway is involved in the early, preclinical phase of LN development [5].
Nephropathy is the first manifestation of systemic lupus in about 50-60% of pediatric patients. However, approximately 25% of children do not meet the diagnostic criteria for lupus at the time of the first symptoms of nephropathy [1]. Early diagnosis of LN is crucial for further disease management, as kidney dysfunction contributes to the increase in lupus mortality and, in about 19% of cases, to the development of end-stage renal disease [1, 5]. The clinical manifestation of nephropathy varies from asymptomatic hematuria, mild proteinuria, nephrotic syndrome, nephritic syndrome, thrombotic microangiopathy, rapidly progressive glomerulonephritis, to acute or chronic renal failure [1, 9]. The clinical picture of nephropathy does not correlate with the type and intensity of histopathological changes and does not predict its course. The basis for LN diagnosis is a renal biopsy, which allows one to categorize and assess the level of activity and chronicity of the disease process [1, 5, 9]. Characteristic histopathological changes in biopsy may considerably anticipate abnormalities in urine tests. According to the American College of Rheumatology (ACR) recommendation from 2012, biopsy should be performed in patients with active SLE when kidney involvement is suspected, as well as in children with isolated proteinuria and the presence of ANA and dsDNA antinuclear antibodies [9, 10]. However, approximately 10% of pediatric LN patients do not have auto- antibodies, which does not rule out LN development [11].
The biopsy evaluation should include light microscopy, immunofluorescence, and transmission electron microscopy (TEM) [10]. TEM examination is used to assess the basement membrane, the morphology of pod- ocytes, and the presence and location of deposits [12]. It also makes it possible to reveal the presence of specific intracellular structures such as tubuloreticular inclusions (TRIs) [13, 14].
Tubuloreticular inclusions are visible as delicate reticular aggregates of branching membranous tubules located near or within the endoplasmic reticulum cisterns [15-17]. TRIs were first detected in endothelial cells, but they also have been observed in monocytes, lymphocytes, Schwann cells, and fibroblasts [13]. In samples collected from SLE patients, TRIs were observed first 50 years ago in the glomerular endothelium and were originally termed lupus inclusions [13]. So far, their meaning has not been fully defined. The ultra-cytochemical studies by Rich et al. have shown that TRIs consist of proteins, lipids, and RNA [14, 18]. They have been observed in cases of viral infections, especially human immunodeficiency virus (HIV), cytomegalovirus (CMV), hepatitis B virus (HBV), and Epstein -Barr virus (EBV) infections, as well as after IFN treatment and renal transplants [13, 16, 19, 20]. Recently, numerous TRIs have been reported in endothelial cells of the glomerular capillaries in the course of COVID-19 infection [21]. Viral infections are the second most common group of diseases in which TRIs are commonly seen. The common link between nephropathy induced by a viral infection and LN is IFN-α [19].
Nephropathy and TRIs’ presence often precede other symptoms of SLE many months earlier, which is why TRIs have become a recognized and useful pathomorphological marker of LN in cases of any diagnostic doubts [10]. Such a situation occurs mainly in pediatric cases in which the symptoms are often uncharacteristic and the final diagnosis is hard to make. Unfortunately, there are not many studies focusing on TRIs in jSLE. This study aims to evaluate renal biopsies of 10 patients with jSLE for presence of tubuloreticular inclusions. It also aims to assess TRIs’ occurrence in relation to the International Society of Nephrology/Renal Pathology Society (ISN/RPS) lupus nephropathy classification, SLE clinical manifestation, and serological test results.
Material and methods
It is a preliminary study based on a small number of patients. The study was conducted in compliance with the Declaration of Helsinki. The Ethics Committee of the Medical University of Silesia approved (PCN/0022/KB/46/21, 19th February 2021) of the use of the retrospective data from patients’ files. Signed informed consent forms were obtained from all ten patients and their parents.
Analysis of clinical data
A retrospective analysis of the medical records of 10 pediatric patients diagnosed with jSLE and LN was performed. The following data were analyzed: the clinical course of the disease, the manifestation of nephropathy, the profile of non-renal symptoms (including hematological disorders), and the immune activity of lupus. Lupus activity was assessed according to the ACR (American College of Rheumatology) and EULAR (European League Against Rheumatism) scale.
Renal biopsy samples’ analysis
Renal tissue material obtained by needle biopsy of the studied patients was analyzed. Routine histopathological examination of collected samples was performed and based on the results of the examination they were assigned to the LN class according to the ISN/RPS nephropathy classification.
For light microscopy, semi-thin sections (1 µm) were cut on a glass knife and stained with toluidine blue to find a region of interest.
For TEM analysis, renal biopsy samples were prepared according to the conventional procedure. Ultrathin sections (70 nm) were cut from representative samples with a diamond knife (45°; RMC, Tucson, USA) using a POWER TOMO PC ultramicrotome (RMC Boeckeler, Tuscon, USA), mounted on 300-mesh copper grids, and stained with 0.5% aqueous uranyl acetate and lead citrate (LAURYLAB, Saint-Fons, France) using a Leica EM AC 20 stainer (Leica Microsystems, Vienna, Austria). The grids were air-dried and examined later in a TECNAI G2 12 Spirit BioTWIN transmission electron microscope (FEI, Eindhoven, The Netherlands) at 120 kV.
The images from representative regions were taken with a Morada CCD camera (Olympus Soft Imaging System Solutions GMBH, Münster, Germany) and the ultra- structure of the glomeruli was assessed. Obtained images were analyzed for the location, structure, and size of the deposits, changes in podocytes and the basement membrane, and the presence or absence of TRIs in the analyzed material. Other pathological changes at the ultrastructural level were also examined. The results of the assessment were compared with the clinical presentation, laboratory test results, and the ISN/RPS nephropathy classification.
Literature review
PubMed, Scopus, and Embase were used to search relevant articles published from 1980 to 2020. The search terms used were ‘pediatric’ AND ‘lupus’ AND ‘tubuloreticular inclusions’. Any original research articles, written in English and published in peer-reviewed journals were eligible for initial literature review. All articles included in the evaluation needed to have used keywords in their abstract or title. The search identified 7 studies with 5 articles meeting the above-mentioned inclusion criteria.
Results
All ten patients enrolled in the study group were female. In three cases, the onset of the disease occurred before the age of ten, in seven cases in adolescence. All patients enrolled in the study group met the criteria for SLE with clinically developed nephropathy. According to the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), they received from 16 to 47 points (Table 1). Lupus nephropathy was assessed using the ISN/RPS classification.
Table 1 Variables describing patients with juvenile systemic lupus erythematosus (jSLE) enrolled in the study (n = 10)
Variable Patient no.
1 2 3 4 5 6 7 8 9 10
Disease onset age (years) 16 10 7 16 16 7 15 14 12 14
Sex (F – female, M – male) F F F F F F F F F F
Clinical
manifestation Renal Hematuria + + + – + – + – – +
Leukocyturia + – – + + – + + – –
Proteinuria (g/l) > 1 > 1 0-0.5 0-0.5 > 1 0-0.5 0.5-10 0-0.5 > 1 > 1
Urea (mg/dl) 37 36 23 37 39 17 25 32 20 39
Creatinine (mg/dl) 0.92 0.61 0.51 0.66 0.72 0.57 0.76 0.73 0.61 0.9
Non-renal Hematological symptoms WBC (103/ml) 5.6 3.4 2.2 2.5 3.3 2.6 4.1 4.1 6.6 4.5
Hb (g/dl) 12.7 11.4 11.9 12.2 9.8 11.2 10 11.6 11 11.1
PLT (103/ml) 217 174 12 276 195 63 249 208 131 287
General symptoms – – + + – – + – – +
Joint symptoms – + + + + + + + + +
Dermatological symptoms – + – – + – – + – +
Neuropsychiatric symptoms + – – – + – – – – –
SLEDAI 16 31 37 32 37 27 36 47 38 32
Biopsy results (ISN/RPS classification) II III II III C/A IV V III C/A III II III
Hb – hemoglobin, ISN/RPS – International Society of Nephrology and the Renal Pathology Society, PLT – platelets, SLEDAI – Systemic Lupus Erythematosus Disease Activity Index, WBC – white blood cell.
Joint pain and swelling in nine cases and maculo- papular rash (including butterfly-shaped erythema) in four cases were the first clinical signs. Also, low-grade fever and neuropsychiatric symptoms were observed. In three patients, all of them being under the age of 10, the disease started with hematological disorders such as leukopenia, thrombocytopenia, and anemia (Table 1). In one case, the diagnosis of lupus was made in the coexistence of severe paranoid schizophrenia symptoms. Symptoms of nephropathy were observed in all patients starting from the onset of the disease. All ten patients developed proteinuria, some of them hematuria (n = 6), and sterile leukocyturia (n = 3) (Table 1). However, all patients presented a normal glomerular filtration rate (GFR). All patients showed high immunological activity of SLE – the ANA antinuclear antibody titer exceeded 1 : 640. We also noted a high anti-dsDNA titer and low values of the complement components (Table 2). In our study group, all children have been receiving hydroxychloroquine (200 mg/day) since the lupus diagnosis.
Table 2 Results of immunological tests of patients with juvenile systemic lupus erythematosus (jSLE) enrolled in the study (n = 10)
Immunological
tests Patient no.
1 2 3 4 5 6 7 8 9 10 Norm range
ANA (dilution) 1 : 10240 1 : 1280 1 : 5120 1 : 5120 1 : 10240 1 : 3200 1 : 2560 1 : 2560 1 : 3200 1 : 3200 –
Anti-dsDNA (U/ml) 0.16 6 8 10 9.9 5.2 7.1 2.5 9.4 9.6 < 1
C3 (g/l) 1.28 0.4 1.28 0.36 0.42 0.9 0.5 0.8 0.8 0.5 0.9-1.8
C4 (g/l) 0.15 0.02 0.3 0.04 0.02 0.13 0.04 0.07 0.08 0.04 0.1-0.4
IgG (g/l) 11 8.2 13.7 14.6 20.7 8.7 16 13.1 13.6 25 6.38-17.0
IgM (g/l) 0.84 0.8 0.8 1 0.86 0.36 1.3 2.3 1.03 1.09 0.41-2.03
IgA (g/l) 2.4 2.5 2.12 2.4 2.5 1.9 1.9 13.1 1.4 1.4 0.67-3.67
Anti-cardiolipin IgM (U/ml) 7.5 5.2 2 16.9 2 1.8 6.9 2.2 48.4 9.9 < 12
Anti-cardiolipin IgG (U/ml) 8.2 7.9 2 5.02 10 1.8 18.9 10.5 4.6 4.7 < 12
The evaluation of the biopsy samples in a light microscope revealed class III LN in five cases (50%), class II in three cases (30%), and class IV and class V in two individual cases (Table 1). Ultrastructural analysis of the biopsy material showed the presence of TRIs in seven patients (Table 3). These structures were most often found in endothelial cells of both the vascular bundle and the peritubular capillaries. They were occasionally observed in monocytes within capillaries and mesangial fields (Fig. 1). TRIs occurred within the irregularly widened cisterns of the endoplasmic reticulum. Some of them presented signs of pronounced swelling, which led to disruption of the surrounding membranes. In some inclusions, the tubular structures came into close contact with the membranes of the endoplasmic reticulum (Fig. 2). Both the size of the inclusions and the frequency of their occurrence varied among specimens (Fig. 3). The size of the TRIs varied in the range 300-1600 nm. Numerous TRIs (several inclusions in one glomerulus) were found in the biopsy of a patient with class V LN. All patients were tested for the most common viral infections connected with presence of TRIs (HIV, HBV, HCV, EBV, CMV) and all those disorders were excluded by performing simple blood tests. Only in one patient, after three years of treatment, was the re-biopsy performed. TRIs were still present in the specimen, but they were much less abundant (Fig. 4). In three cases with the highest presence of TRIs, the disease began before puberty (at 8-10 years of age), and in all these cases hematological disorders such as leukopenia, anemia, and thrombocytopenia preceded the LN diagnosis. Inclusions in a typical location, near subendothelial deposits, were observed in only one patient (Fig. 5). Biopsies of three patients with disease onset in adolescence (around 14 years of age) revealed no TRIs in the sampled material. In these patients, arthritis and nephrotic proteinuria dominated the clinical picture.
Table 3 Results of transmission electron microscope (TEM) evaluation of renal biopsy samples of patients with juvenile systemic lupus erythematosus (jSLE) enrolled in the study (n = 10)
Patient no. Basement membrane Deposits Podocytes Tubuloreticular inclusions (TRIs) Mesangium ISN/RPS classification
1 Inhomogeneous, thinning Granular in BM Vacuolization Endothelial cells Hypercellularity II
2 Irregular, double contours Varied in density, SEp, SEd Vacuolization Endothelial cells Hypercellularity III
3 Periodically discontinuous contours Abundant granular electron-dense, SEd, Mes – Endothelial cells Hypercellularity II
4 Thickening SEd, SEp, in Mes BM Swollen cells Endothelial cells Hypercellularity III C/A
5 Thickening Granular electron-dense, SEd, SEp, Mes Vacuolization Endothelial cells, monocytes, macrophages Hypercellularity IV
6 Different thickness Electron-dense, SEp in BM and Mes Vacuolization Endothelial cells, monocyte Hypercellularity V
7 Thickening, discontinuous contours Electron-dense, SEd, SEp, Mes Vacuolization Endothelial cells Hypercellularity III C/A
8 Thickening, discontinuous contours Granular, SEd, SEp, Mes – – Hypercellularity III
9 Irregular, discontinuous contours SEd, SEp, Mes Vacuolization – Hypercellularity II
10 Irregular thickening SEd, SEp Vacuolization – Hypercellularity III
BM – basement membrane, ISN/RPS – International Society of Nephrology and the Renal Pathology Society, Mes – mesangial, SEd – subendothelial, SEp – subepithelial
Fig. 1 Tubuloreticular inclusions (TRIs) observed in monocytes (A) and macrophages (B) in renal biopsy samples collected from patients with juvenile systemic lupus erythematosus (jSLE). TRIs are indicated with arrows
Fig. 2 A, B) Evident tubuloreticular inclusions (TRIs) visible within the degranulated rough endoplasmic reticulum in renal biopsy samples collected from patients with juvenile systemic lupus erythematosus (jSLE). Full arrowheads indicate loss of the reticular membrane continuity. Open arrowheads indicate adherence of individual tubules to the membranes of the reticulum
Fig. 3 Fragment of a capillary with a highly swollen endothelial appendix containing three tubuloreticular inclusions (TRIs) (arrows) in renal biopsy samples collected from patients with juvenile systemic lupus erythematosus (jSLE)
Fig. 4 The figure shows two biopsies of Patient 6. The first biopsy was performed in 2012: A) TRI structures (arrows) are present in a monocyte, B) endothelial cells and within mesangial area; A1) Enlargement of the photo (A) revealing details of the ultrastructure of TRI structures. The second biopsy (C) performed 3 years after initiation of treatment revealed absence of TRIs
Fig. 5 Fragments of capillaries with tubuloreticular inclusions (TRIs) present near subendothelial (A) and mesangial (B) deposits (arrowheads) in renal biopsy samples collected from patients with juvenile systemic lupus erythematosus (jSLE)
Discussion
The attempt to explain the importance of TRIs in LN has a long history, but their diagnostic, pathophysiological, and prognostic role has not been clearly defined so far [13, 17]. The presence of TRIs in the endothelial cells of the glomerular capillaries is considered a very important morphological marker of LN, especially in pediatric cases [11, 16, 22]. TRIs are sometimes called ‘interferon footprints’ because they are considered markers of the IFN pathway [13]. The amount of TRIs is linearly dependent on IFN production [7, 17].
Our ultrastructural studies of renal biopsies in children with jSLE and LN showed TRIs’ presence in 7 out of 10 patients, which is consistent with the observations of other authors in the adult population [14]. Unfortunately, there are not many studies concerning TRIs in children suffering from jSLE. In the pediatric population, the first report of TRIs’ presence in an ultrastructural study of renal biopsies concerned non-LN-related cases. The report described children with severe nephrotic syndrome and rapid progression to end-stage renal disease (ESRD) in whom HIV, CMV, and HCV infections were excluded. The authors suggested that the formation of TRIs resulted from the IFN pathway activation signaling the distorted immune response to common viral infections (rhinoviruses, enteroviruses, coronaviruses) [23].
Interferon is a common pathophysiological link between LN and viral nephropathy [23, 24]. IFN mediates most of the symptoms and pathophysiological processes underlying lupus. IFN also has a suppressive effect on the bone marrow which may result in anemia, leukopenia, and thrombocytopenia [4, 7, 8, 17]. We observed numerous TRIs in endothelial cells in renal biopsies of the youngest patients with prepubertal onset of the disease. These patients developed severe hematological disorders such as leukopenia, thrombocytopenia, and anemia in the early phase of the disease. Blocking the biological activity of IFN-α can stop the inflammatory process, alleviate autoimmune activity, and protect against onset of flares [4, 5]. Anifrolumab, a monoclonal antibody against the IFN-α type 1 receptor, has been under clinical trials. The research has not covered the cases of LN, only the cases of SLE with non-renal manifestation. The authors announced the continuation of the study in LN, which raises new hopes for early, targeted therapy in children with jSLE with kidney involvement [5].
In their work, Tranesh [25] suggested a relationship between the number of TRIs and the nature of histopathological changes in glomeruli, which are the basis for the LN classification. The author showed that the number of TRIs correlated with the ISN/RPS LN class: the increased number of TRIs correlated with class III, IV (proliferative forms) and V (membranous glomerulonephritis – MGN). In cases of MGN, the formation of TRIs was found in 100% of cases [24]. In our study, class V LN was diagnosed only in one patient. We found numerous clusters of TRIs in the ultrastructural image of this patient’s renal biopsy. In the re-biopsy performed after 3-year treatment resulting in remission, these inclusions were still present, but less abundant. In our study group, the proliferative forms of LN dominated, which is typical of jSLE. Due to the small size of the study group, it was impossible to correlate the amount of TRIs in the biopsy with the LN class.
Testing for TRIs is a valuable clue in cases where the diagnosis of SLE is not fully established and the clinical manifestation of lupus is nonspecific, which is often seen in pediatric cases [11, 16, 22]. An interesting example of the role of TRIs in making a final diagnosis is the case of a patient whose first and only manifestation of developing lupus was a severe drug-resistant depressive disorder. In this patient, additional assessment of the biopsy in TEM confirmed the diagnosis of lupus and made it possible to implement an effective treatment [26]. Bromfield et al. presented a case of an infant with severe nephrotic syndrome, anemia, and thrombocytopenia. The patient’s biopsy image indicated class V LN with the presence of numerous TRIs. The image was interpreted as a genetically determined disturbance in the IFN pathway [13]. According to the Mayo Clinic recommendations, TEM examination should be permanently included in the generally applicable guidelines for assessment of renal biopsies in LN [12].
A significant limitation of this study is the small number of pediatric LN patients and the biopsy samples we assessed in TEM. The small number of samples limits the possibility to accurately assess the relationship between presence of TRIs, symptoms, and serological indicators of the disease. That is why it is a preliminary study based on a small number of patients and we are going to continue our research. Moreover, the ultrastructural assessment is limited to a small number of glomeruli present in the samples. Since the formation of TRIs does not affect all endothelial cells, the number of inclusions in the image of a single glomerulus can vary greatly. They can be absent in the image of one glomerulus, while being present in the image of different glomeruli of the same patient. Furthermore, the number of visualized TRIs closely depends on the cross-section made during the preparation of the tissue material. All of these factors make it necessary to proceed with great caution when assessing the intensity of TRIs’ formation, their number, and size. Another limitation of this study is that the biopsies have been performed at a different time since the onset of the disease and we know that the formation of inclusions varies in different phases of the disease.
Nevertheless, TEM images of renal biopsies in patients with jSLE are sources of inspiring information on the pathophysiological processes in renal cells. Based on available reports and our study, it can be concluded that the presence of TRIs in ultrastructural images of renal biopsies is a valuable pathophysiological clue and a sign of IFN pathway involvement in the autoimmune process at the earliest stage of the disease. The evaluation of renal biopsies in TEM should be an obligatory element of the diagnostic process in children with jSLE. It facilitates confirmation of the diagnosis, especially in the youngest patients and cases that do not meet SLE criteria.
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10189578.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206585
50567
10.5114/ceji.2023.126773
Experimental Immunology
Prognostic values of regulatory T cells (Tregs) and Treg-related genes in gastric cancer
Zheng Liang
Lin Luping
Song Jintian
Huang Sha
Chen Lizhu
Li Hui
Ma Ning
Chen Qingyue
Chen Yigui
Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, China
Yigui Chen, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, China, e-mail: [email protected]
20 4 2023
2023
48 1 1425
09 10 2022
19 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Introduction
This study attempted to investigate the potential of a risk model constructed for regulatory T cells (Tregs) and their related genes in predicting gastric cancer (GC) prognosis.
Material and methods
We used flow cytometry to detect the content of CD4+CD25+ Tregs. After detecting expression of five Treg-related genes by quantitative real-time polymerase chain reaction (qRT-PCR), Pearson analysis was employed to analyze the correlation between Tregs and related gene expression. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), colony formation and transwell assays were used to detect the effects of a disintegrin and metalloproteinase with thrombospondin motifs 12 (ADAMTS12) on cell functions. A prognostic risk model was built after Cox regression analysis. The Kaplan-Meier method was employed to assess how Tregs, 5-gene risk scores and expression of 5 genes were correlated with the survival time.
Results
A significantly increased content of Tregs was found in GC tissues (p < 0.05). 5 Treg- related genes were significantly up-regulated in GC with a positive correlation with the content of Tregs (p < 0.05). Overexpression of ADAMTS12 significantly enhanced the viability, proliferation, migration and invasion of tumor cells. Kaplan-Meier analysis demonstrated poor overall survival and disease-free survival in the high-risk group. The results of survival analysis of Treg content and related gene expression were consistent with those of Cox analysis.
Conclusions
The risk model constructed based on five Treg-related genes can enable effective prediction in the prognosis of GC patients.
Tregs
feature gene
gastric cancer
prognosis
==== Body
pmcIntroduction
The fourth leading cause of cancer deaths, gastric cancer (GC) is also the fifth most common malignancy globally [1]. It is worth noting that over 50% of cases occur in developing countries, among which China has the highest GC incidence rate [2]. The diagnosis is always made in GC patients at the advanced stage when the 5-year survival rate stands at around 20% [3, 4]. Though prolonged progression-free survival (PFS) and overall survival (OS) have been partly achieved in advanced GC patients due to chemotherapy, the median survival rate is still below 1 year [4]. Hence, GC remains a global health care issue. Prognostic prediction of GC patients can benefit both disease management and subsequent treatment. However, current disease assessments based on tumor aggressiveness, lymph node involvement, distant metastasis (TNM staging system), and histological grading are not enough to generate prediction for survival rate of GC patients. As one of the most efficient tools in prognostic prediction, novel biomarkers are needed for better predictive accuracy [5]. Biomarkers not only facilitate the evaluation of tumor progression and prediction of OS but also contribute to treatment effect enhancement. This study started with regulatory T cells (Tregs) to seek prognostic biomarkers for GC to assist the disease assessment for GC patients.
Tregs are the CD4+ and CD25+ subsets of T cells [5]. By inhibiting effector cell function or secreting immunosuppressive cytokines, Tregs inhibit anti-tumor immune responses, and are crucial in maintaining immune tolerance and reducing immune responses [6]. Previous studies have found that the number of CD4(+) CD25(+) CD127(low/–) Treg cells in different clinical stages of GC patients was significantly different, and the level of Treg cells was significantly reduced after surgical resection [7]; Ma et al. also found that the level of CD4+FoxP3+ Treg cells was different in distinct clinical stages of GC patients [8]. In addition, the link between Treg infiltration and the poor prognoses of most solid malignant tumors has been suggested in many studies. For instance, Ye et al. [9] found the infiltration of Tregs to be an independent risk factor of colorectal carcinoma. A higher infiltration rate indicates shorter OS of patients. In clear cell renal cell carcinoma, a higher proportion of Tregs is found to be related to poor prognosis as well [10]. Similarly, this study attempted to further explore the effect of Tregs on predicting GC progression and prognosis.
In recent years, advances in cancer genomics have led to a new understanding of the molecular pathogenesis of GC, and the potential markers and therapeutic targets can be explored based on the genomic characteristics of tumors. Furthermore, oncogenes and anti-cancer genes have been reported in previous studies. Ding et al. [11] demonstrated that GSE1 can promote cancer development via regulating the expression of SLC7A. UFM1 was also found to be involved in tumor regulation by inhibiting invasion and metastasis of tumor cells via negatively regulating the PI3K/AKT signaling pathway to increase ubiquitination of PDK1 in GC [12]. Although a pool of genes has been found for GC diagnosis and prognosis, the polygenic signature used to predict prognosis remains to be explored.
Biomarkers are critical factors in marking and predicting a patient’s prognosis [13]. Leucine rich repeat and fibronectin type III domain containing 4 (LRFN4) is a neuronal transmembrane protein that mainly plays a role in the human immune system. Hu et al. [14] demonstrated that the Treg-related gene LRFN4 is associated with GC prognosis. A disintegrin and metalloproteinase with thrombospondin motifs 12 (ADAMTS12) plays a role in the tumor process mainly through its proteolytic enzyme activity. Hou et al. [15] found that highly expressed ADAMTS12 is mainly enriched in cancer- and immune-related signaling pathways. Mast cell-expressed membrane protein 1 (MCEMP1) is a transmembrane protein expressed by immune-related cells such as mast cells and macrophages. The expression of MCEMP1 is related to the immune infiltration of various immune cells and can be used as a potential prognostic marker for GC [16]. Haptoglobin (HP) is a glycoprotein [17] that affects the innate immune response [18]. It has been found that HP can be used as a marker of glioblastoma [17]. Mucin-15 (MUC15) as an important member of the mucin family belongs to high molecular weight glycoprotein, and it has been demonstrated that MUC15-related genes are enriched in immune-related pathways and can be used as prognostic markers for GC [19]. Treg-related genes (LRFN4, ADAMTS12, MCEMP1, HP, MUC15) are found to be closely related to the prognosis of GC through the TCGA database [14]. The evidence suggests that these five genes exert functions in tumor immunity.
Therefore, this study attempted to explore the association between five Treg-related genes and the prognosis of GC patients. We used Tregs and their related genes to construct a risk model to explore their potential in predicting the survival status of patients with GC. This study might offer theoretical support that will help to tailor precise treatment strategies for GC patients.
Material and methods
Clinical sample collection
We collected tumor tissue and adjacent non-cancerous tissue samples from 48 GC patients diagnosed between 2016 and 2019. The collected para-carcinoma tissue and tumor tissue samples were subjected to a flow cytometry experiment. The remaining samples were immediately frozen at –80oC until being analyzed. The study was approved by the Ethics Committee of Fujian Cancer Hospital and samples were obtained with signed informed consent. The whole procedures were performed according to relevant guidelines and regulations.
Cell culture and transfection
Human GC cell lines SGC7901 and AGS were purchased from BeNa Technology (China). Cell culture was mainly according to the methods used in previous research [20]. In brief, all cell lines were kept in RPMI 1640 medium (Thermo Fisher Scientific, USA) containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin in an incubator at 37oC with 5% CO2.
The si-ADAMTS12, oe-ADAMTS12, and negative controls si-NC and oe-NC were synthesized by GenePharma, China. After transfecting the vectors into cells using Lipofectamine 3000 (Thermo Fisher Scientific, USA) following its manual, transfected cells were cultured and then harvested for subsequent experiments.
Flow cytometry
Under sterile conditions, the tumor tissue was first placed in a pre-prepared container, with fat and connective tissues and blood removed. Then, the tissue was cut into 1 mm3 small pieces and transferred to a 10 ml centrifuge tube, and 5 times the volume of 0.25% pancreatic enzyme at 37oC was added for digestion. Digestion was terminated 30 min later, and the tissue masses that were not completely digested were removed. Then the above solution was centrifuged at 1500 r/min for 5 min, and the supernatant was discarded. Finally, the tumor cell suspension was obtained after rinsing and suspension with phosphate-buffered saline (PBS). The cell suspension was incubated with anti-FITC-CD4 (300505, BioLegend, USA) and anti-APC-CD25 (302609, BioLegend, USA) at 4oC in the dark for 45 min. After that, we used the Cell Fixation and Permeation Kit (eBioscience, USA) following the instructions to perform intracellular staining. Analysis was then conducted with a BD FACSAria Fusion flow cytometer (BD Biosciences, USA).
Quantitative real-time polymerase chain reaction
We followed the experimental procedures described by Han et al. [21] in performing quantitative real-time polymerase chain reaction (qRT-PCR) assays. The specific ope- ration was conducted as follows: TRIzol reagent (Thermo Fisher Scientific, USA) was used for the extraction of total RNA, whose concentration was then determined using a NanoDrop ND-1000 instrument (NanoDrop, USA). The mRNA was reverse-transcribed into cDNA using the specific primer (Ribobio, China) and prime script RT kit (Takara, Japan) as per the manufacturer’s instructions. Next, the SYBR Premix Ex Taq II kit (Takara, Japan) was used for quantification of mRNA expression. Finally, q-PCR was performed on an ABI Prism 7500 rapid real- time PCR system (Applied Biosystems, USA). GAPDH was used as the internal reference. The 2-ΔΔCt method was adopted for gene expression quantification. Primer sequences used in PCR are detailed in Table 1.
Table 1 Primers for qRT-PCR
Gene Primer sequence
LRFN4 Forward: 5'-ACAACTTCATCCAGGCCCTG-3'
Reverse: 5'-AGGATGAGGTGCTGCAGATT-3'
ADAMTS12 Forward: 5'-GCCATGGACTGACTGGATTT-3'
Reverse: 5'-TGCCTCCTGTAAACGATGTG-3'
MCEMP1 Forward: 5'-CTCGTCTTTTTATCCTGCATCGT-3'
Reverse: 5'-CCACACCGTGTCTGAAACATTC-3'
HP Forward: 5'-CAGCCAGAAACATAACCC-3'
Reverse: 5'-TCTACACCCTAACTACTCCC-3'
MUC15 Forward:
5'-CTCAAATCTCAAGGCGAGTCATTC-3'
Reverse:
5'-GCAGGTGTAGCATTGGGATGTGC-3'
GAPDH Forward: 5'-TGCATCC-TGCACCACCAACT-3'
Reverse: 5'-AACACGGAAGGC-CATGCCAG-3'
3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
Cells of the exponential growth phase were trypsinized, followed by seeding into 96-well plates (2000 cells/well). After adding 20 µl of 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) solution (5 mg/ml, GenView, USA) at 24, 48, 72, and 96 h, cells were incubated for another 4 h and then treated with 100 µl of DMSO solution. Cell survival was calculated by measuring optical density (OD) at 490 nm with a microplate reader (Tecan, Switzerland).
Colony formation assay
After cell digestion and adjusting the density of transfected GC cells to 400 cells/ml, 2 ml of cell suspension was seeded into 6-well plates and maintained for 10 days to form colonies, with medium replaced every 3 days. Subsequently, cells were subjected to fixation with 4% paraformaldehyde and 5 min of crystal violet (Sigma, USA) staining. With images projected with a fluorescence microscope (Olympus, Japan), the count of colonies (> 50 cells/colony) was recorded.
Transwell migration assays
The cell migration assays were conducted by referring to previous studies, and the specific operation was as follows [22, 23]. The upper chamber of the 24-well transwell plate (Corning, USA) was precoated with Matrigel (BD Biosciences, USA) for invasion assays, and inserts without Matrigel were used for migration assays. Approximately 3 × 104 cells were seeded in the upper chamber and treated with FBS-free medium. Medium with 10% FBS was added to the lower chamber. After 48 h of incubation at 37oC, non-migrating cells were removed gently, and the rest were fixed in 90% ethanol and stained with 1% crystal violet. Cells were counted from five random fields under the inverted microscope.
Statistical analysis
Statistical analysis was carried out on GraphPad Prism Version 6.0 (GraphPad Software, USA). The cut-off value that indicates significance was set at 0.05. For correlation analysis, Pearson’s correlation was used to calculate the r2 value and p value. Prognostic models were built by Cox regression analysis of Tregs, and survival analysis was done using Kaplan-Meier curves.
Results
Increased Tregs in gastric cancer
With surgically resected tumor tissues and adjacent non-cancerous tissues obtained from 12 GC patients, flow cytometry results revealed that the proportion of CD4+CD25+ Tregs was evidently higher in GC tissues (Fig. 1A). Previous studies have found that five Treg-related genes, namely LRFN4, ADAMTS12, MCEMP1, HP, and MUC15, are strongly associated with GC prognosis [14]. Hence, we employed qRT-PCR to assess their expression in GC tissues. The experimental results showed notably increased expression of five Treg-related genes in GC tissues (Fig. 1B). All clinical statistics are shown in Table S1.
Fig. 1 Increased Tregs in gastric cancer (GC). A) Proportion of CD4+CD25+ Tregs in GC tissues and adjacent non-cancerous tissues; B) Expression of Treg-related genes in GC tissues. *p < 0.05
Effect of Treg-related genes on gastric cancer progression
Based on the experimental results in the previous section, the correlation analysis between Treg proportion and the expression of related genes (LRFN4, MCEMP1, MUC15, HP and ADAMTS12) was conducted using Pearson correlation. As shown in Figure 2A, the Treg proportion was found to negatively correlate with LRFN4 expression (r2 = 0.3526, p = 0.0418) and to positively correlate with the other four (p < 0.05). To unveil the role of ADAMTS12 (r2 = 0.4158, p = 0.0236) in GC cells, oe-NC/oe-ADAMTS12 and si-NC/si-ADAMTS12 were transfected into GC cell lines AGS and SGC7901, to construct high-ADAMTS12 and low-ADAMTS12 expression cell lines. By analyzing the transfection efficiency via qRT-PCR, significantly upregulated expression of ADAMTS12 could be observed in the oe-ADAMTS12 group, whereas the si-ADAMTS12 group showed significantly decreased expression of ADAMTS12 (Fig. 2B). According to the results from MTT and colony formation assay, compared with the control group, the oe-ADAMTS12 group presented increased viability and proliferation ability of GC cells, which were inhibited in the si-ADAMTS12 group (Fig. 2C, D). The effect of ADAMTS12 expression on the migration and invasion of GC cells was confirmed by the transwell assay. According to the results, the oe-ADAMTS12 group had enhanced migratory and invasive capabilities of GC cells, while the si-ADAMTS12 group was observed with the opposite results (Fig. 2E, F). The evidence added weight to the observation that, in GC, the proportion of Tregs was positively linked with the expression of ADAMTS12, whose overexpression in promoted the malignant progression of GC.
Fig. 2 Effect of Treg-related genes on gastric cancer (GC) progression. A) Correlation analysis between Tregs and expression of Treg-related genes, namely LRFN4, ADAMTS12, MCEMP1, HP and MUC15 by using Pearson correlation; B) Transfection efficiency of oe-NC/oe-ADAMTS12 and si-NC/si-ADAMTS12 assessed by qRT-PCR; *p < 0.05
Fig. 2 Cont. C) Effect of ADAMTS12 on cell viability detected by MTT; D) Effect of ADAMTS12 on cell proliferation detected by colony formation assay; E) Effect of ADAMTS12 on cell migration and invasion detected by transwell assay. *p < 0.05
Fig. 2 Cont. F) Effect of ADAMTS12 on cell migration and invasion detected by transwell assay. *p < 0.05
Tregs and related genes’ expression can be employed for gastric cancer prognostic prediction
To profile the link between Tregs, Treg-related genes and GC prognosis, we employed Cox regression analysis to build a prognostic model with five Treg-related genes confirmed as prognostic factors (Fig. 3A). Among them, the hazard ratio of LRFN4 stayed below 1, while that of the other four genes was higher than 1. Then, a risk score formula based on the five feature genes was constructed to evaluate the survival risk of the samples according to the expression levels of the five genes: risk score = (1.445558 × LRFN4 expression level) + (0.380839 × ADAMTS12 expression level) + (0.275471 × MCEMP1 expression level) + (0.256952 × HP expression level) + (0.045509 × MUC15 expression level). Based on this established formula, we calculated the score for tumor samples collected from 36 patients. After that, patients were split into high-/low-risk groups by the overall median risk score and followed up for 3 years. As shown in Figure 3B, the low-risk group was found to have notably higher OS and disease-free survival (DFS) (p < 0.05). To analyze the model comprehensively, patients were classified into subgroups by the proportion of Tregs and the median expression level of the five genes. Corresponding survival analyses were plotted by Kaplan-Meier curves (Fig. 3C). According to the results, DFS in the high Tregs group was substantially shorter than that in the low Tregs group. Compared with the corresponding low expression group, the high expression group of LRFN4 had longer DFS, but no significant variation in OS. ADAMTS12 and HP low expression groups had longer DFS. No significant variations were observed in OS and DFS between high and low MCEMP1 and MUC15 expression groups. Although there was no significant correlation between single genes and patients’ OS, the risk score obtained based on these 5 Treg-related genes was significantly correlated with patients’ prognosis (OS and DFS), suggesting that the 5-gene signature might be a prognostic marker for GC patients.
Fig. 3 Prognostic value of Tregs and related genes in gastric cancer (GC). A) Forest plot of Cox regression analysis of Treg-related genes; B) Kaplan-Meier survival curves of patients in different risk groups
Fig. 3 Cont. C) Kaplan-Meier curves analyzing the effect of Treg proportion and expression of five related genes on overall survival (OS) and disease-free survival (DFS) of GC patients
Discussion
Gastric cancer remains a global health problem, with over 1 million new diagnoses worldwide each year [24]. As its early-stage diagnosis remains tricky, the therapeutic effect and OS of GC patients are far from expected. Therefore, genes related to GC prognosis might be used as biomarkers to provide insights for the development of novel targeted therapies. Accurate prognostic prediction and risk stratification can help assign patients to the treatment that they can benefit most from. For example, before immunotherapy, patients receive relevant companion diagnostics (PD-L1 expression assessment) to confirm their treatment response, assisting clinicians to specify the appropriate treatment strategy. Tregs were found to bear important prognostic value in different cancers as effective prognostic biomarkers. Koung et al. found that in gastric cancer patients, patients with low peripheral ImmunoCRIT (cellular ratio of immune tolerance) had longer DFS and OS, and peripheral ImmunoCRIT was related to immune cell infiltration in gastric cancer patients, while ImmunoCRIT was used to assess the status of peripheral regulatory T cells [25]. Because the tumor infiltrating regulatory (TITreg) cells were related to the prognosis of GC patients, Gao et al. found that the ability of TITreg cells in gastric cancer patients can be well predicted through the radiomics signature by using bioinformatics methods such as Cox combined with LASSO regression analysis, thereby predicting the prognosis of gastric cancer patients [26]. Since the correlation between Tregs and GC development and prognosis prediction needs to be further elucidated, this paper investigated the prognostic potential of Tregs and their related genes.
The current assessment that used TNM staging for GC prognostic prediction is limited since the conditions vary greatly among patients in the same tumor progression stage [27]. Research advances have confirmed that both treatment response and survival of GC patients are closely related to the molecular typing of GC [28]. In this case, biomarkers, particularly gene expression in tumor tissues, can be used to distinguish molecular typing in different patients. This study confirmed that the five Treg-related genes were significantly highly expressed in GC, ADAMTS12, MCEMP1, HP, and MUC15 positively correlated with CD4+CD25+ Treg proportion in tumor tissues (p < 0.05), among which the correlation coefficient of ADAMTS12 was 0.4158 (p = 0.0236). Subsequent cell function assays results confirmed that up-regulation of ADAMTS12 significantly promoted migration, proliferation and invasion of GC cells, which was consistent with the findings of Hou et al. [15]. This study confirmed that up-regulation of LRFN4 in GC patients indicated better prognosis. Highly expressed LRFN4 in colorectal cancer is also found to be linked with a low risk of death in patients [29]. However, high LRFN4 expression threatens patient survival in ovarian cancer [30]. Given that there are differences in the effects of LRFN4 on different cancers, we need to further explore its specific role in GC patients’ survival, which may be a new potential therapeutic target. In addition, we found that ADAMTS12, MCEMP1, HP, and MUC15 were risk genes for GC. Previous studies have confirmed that their high expression is associated with the malignant progression of GC or poor prognosis of GC patients. ADAMTS12 acts as a tumor promoter in GC that is responsible for tumor microenvironment status and tumor energy metabolism transition, which plays a cancer-promoting role and is associated with poor OS [15]. Additionally, a link between MCEMP1 and significantly enhanced immune cell infiltration in GC has been reported. Closely related to poor patient’s prognosis, MCEMP1 can affect the invasion and metastasis of GC cells by regulating epithelial-mesenchymal transition [31]. Jeong et al. [32] found that glycosylated HP protein in serum is closely related to GC and can be used as a biomarker for the clinical diagnosis of GC. Additionally, the combination of MUC15 and EMCN can effectively predict the prognosis of patients with gastric adenocarcinoma, and its high expression is associated with low survival rate [33]. However, a thorough investigation of molecular mechanisms has not been achieved for all relevant genes, and hence further experiments are required to specify the role of each gene in GC development. According to our results, the number of Tregs and expression of LRFN4, ADAMTS12, and HP could be used for DFS prediction of GC patients, while the rest were poorly correlated with DFS and showed no significant correlations with OS in patients. Therefore, the value of Tregs and their related genes alone in prognostic prediction in GC is limited, while the risk model we established compensates for the lack of univariate prediction and can better predict DFS and OS in the high/low-risk groups.
In summary, this study constructed a prognostic risk score model for GC based on Treg-related genes by Cox regression analysis, which has good predictive performance and can provide a reference for individualized diagnosis and treatment of GC patients. Compared with similar studies, the prognostic model of GC in this study took immunity as the background to construct a Treg-related polygenic risk model to predict GC patients’ prognosis. At the same time, the risk genes used for modeling can also be used as basic research and potential therapeutic targets for GC. Nonetheless, this study has some limitations. First, the sample used for validation is of relatively small size, and subsequent large-scale clinical trials are still required to assess its predictive accuracy. Second, the risk genes used for modeling in this study have not been further validated by in vivo experiments. Hopefully, the risk model developed in this study can prolong DFS and OS in GC patients and contribute to the development of individualized treatment based on targeted drugs of Treg-related genes.
Funding
This study was supported in part by grants from Fujian Medical University Sailing Project (2020QH1220). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Ethics approval and consent to participate
The study was performed according to the international, national and institutional rules considering clinical studies and biodiversity rights. The study protocol was approved by the Ethics Committee of Fujian Cancer Hospital (SQ2021-024-01).
Supplementary material is available on journal’s website.
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PMC010xxxxxx/PMC10189579.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206589
50172
10.5114/ceji.2023.125239
Clinical Immunology
The comparison of SARS-CoV-2 antibody levels in medical personnel induced by different types of vaccines compared to the natural infection
Božić Ljiljana 12
Knežević Darija 12
Travar Maja 2
Miljuš Nataša 1
Petković Miroslav 2
Aćimović Jela 3
Dević Jelena Djaković 3
Stojiljković Miloš P. 1
Bokonjić Dejan 4
Škrbić Ranko 1
1 Centre for Biomedical Research, Faculty of Medicine, University of Banja Luka, Banja Luka, The Republic of Srpska, Bosnia and Herzegovina
2 Department of Microbiology, Faculty of Medicine, University of Banja Luka, Banja Luka, The Republic of Srpska, Bosnia and Herzegovina
3 Public Health Institute of the Republic of Srpska, Banja Luka, The Republic of Srpska, Bosnia and Herzegovina
4 Faculty of Medicine Foča, University of East Sarajevo, East Sarajevo, The Republic of Srpska, Bosnia and Herzegovina
Ljiljana Božić, PhD, Centre for Biomedical Research, Faculty of Medicine, University of Banja Luka, Banja Luka, The Republic of Srpska, Bosnia and Herzegovina, e-mail: [email protected]
23 2 2023
2023
48 1 3542
18 11 2022
19 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Introduction: During the last two and a half years, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread around the world. Most of the SARS-CoV-2 vaccines are designed to produce anti-SARS-CoV-2 immunoglobulin G (IgG) against the viral S-glycoprotein. The aim of this study was to measure the anti-S antibody titres among the medical personnel who had been fully vaccinated with different types of vaccines, and to compare them with those who were COVID-19 convalescents.
Material and methods: In this study serum was collected from 261 healthcare workers, of whom 227 were vaccinated, while 34 were recovered participants who were not immunised. Serum samples were collected 21 days after the first dose and 60 and 180 days after the second dose of the vaccines and tested with a commercial ELISA kit.
Results: The highest antibody level (12 AU/ml) was measured in the Pfizer-BioNTech group, followed by Sinopharm (9.3 AU/ml), Sputnik V (5.9 AU/ml), Sinovac (4.6 AU/ml) and Oxford/Astra- Zeneca vaccine (2.5 AU/ml) 60 days after the second dose of the vaccines (90 days after the first dose). The seropositivity rate for mRNA vaccine was 88.5%, for vector vaccines 86.2% and for inactivated vaccines 71.4%. When comparing these antibody levels with COVID-19 convalescents, higher antibody titres were found in vaccinated participants (5.76 AU/ml vs 7.06 AU/ml), but the difference was not significant (p = 0.08).
Conclusions: Individuals vaccinated with mRNA and vector vaccines had a higher seroconversion rate compared to the group vaccinated with inactivated vaccines, or convalescents.
COVID-19
IgG antibody
immunity
SARS-CoV-2
vaccines
==== Body
pmcIntroduction
During the past two and a half years, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread around the world, affecting the immunologically naïve population, and causing notable morbidity and mortality [1]. SARS-CoV-2 is classed in the subfamily Coronavirinae in the family of Coronaviridae. The trans-membrane spike (S) glycoprotein that makes up the spikes on the viral surface is responsible for penetration of the virus into the host cells. The S1 and S2 subunits represent a part of the S-glycoprotein, where S1 contains the receptor-binding domain (RBD) that binds to the cell’s receptor, while S2 contains the fusion peptide. S-glycoprotein is the most important focus of neutralising antibodies upon infection and a target of treatment and vaccine design [2].
SARS-CoV-2 infection or vaccination against COVID-19 provides the immune response and protection against infection or reinfection and reduces the risk of clinically significant outcomes. According to Lumley et al. [3], seropositive individuals have 89% protection from reinfection, while the estimated efficacies of different types of vaccines are 50% to 92% [4]. However, the period of immunity conferred by vaccination is still unknown. New virus variants that emerge may escape vaccines or convalescent immune responses [5]. The vaccines are important for raising the immunity, avoiding severe disorders caused by SARS-CoV-2, reducing the burden on healthcare systems, and decreasing the economic cost [6]. Vaccines developed by Pfizer-BioNTech (BNT162b2/Comirnaty) and Moderna (mRNA-1273/Spikevax) with > 90% efficacy against symptomatic infection, and J&J/Janssen (Ad26.COV2. S) with efficacy of 60-70% against symptomatic infection, have been approved in the United States by the Food and Drug Administration (FDA), while the European Medicines Agency (EMA) has additionally permitted a fourth vaccine from Oxford/AstraZeneca (AZD1222/Vaxzevria) (efficacy of 60-70% against symptomatic infection) [7]. Sinopharm COVID-19 vaccine or BBIBP-CorV, as an inactivated vaccine, was the first Chinese vaccine authorised by the World Health Organisation (WHO) for emergency use with efficacy of 79% [8]. Sputnik V vaccine is a recombinant vaccine developed by Gamaleya Research Institute, Russia, and its effectiveness against COVID-19 has been shown to be 91.6% [8].
The real-world praxis with SARS-CoV-2 vaccination at health facilities has shown a marked decrease in the incidence of infections among medical staff members [9]. Because of their engagement in response to the pandemic and of the higher risk of being infected due to occupational exposure, healthcare employees are considered as a key target group for COVID-19 vaccination in most countries, including the Republic of Srpska. Our recently published study shows that the medical personnel in primary healthcare had a high titre of SARS-CoV-2 antibodies after receiving one or two doses of vaccines [10].
The nature and durability of the protective immunity is important in the context of risk evaluation for reinfection and vaccine development. The vaccines are reported to show a high level of effectiveness in preventing the symptoms and spread of the SARS-CoV-2 infection, even after the first dose [11]. The SARS-CoV-2 infection, as well as the vaccines, induce the development of specific antibodies, but their sustainability is still an object of investigation [12].
In contrast to other countries, we have been able to obtain five different types of vaccines for our population. This gave us the opportunity to compare the immune response to different vaccines. The aim of this study was to examine the titres of anti-spike immunoglobulin (IgG) antibody among medical personnel who were fully vaccinated with one of the five COVID-19 vaccines (BNT162b2-Pfizer- BioNTech vaccine, BBIBP-CorV-Sinopharm vaccine, CoronaVac-Sinovac vaccine, Gam-COVID-Vac-Sputnik V vaccine, and Oxford/AstraZeneca vaccine), and to compare them with antibody titres of COVID-19 convalescents.
Material and methods
Study design and participants
This study cohort included healthy medical personnel from the University Clinical Centre (UCC) of the Republic of Srpska, Banja Luka, the Republic of Srpska, Bosnia and Herzegovina who were vaccinated with at least two doses. Participation in the study was offered to all UCC employees including physicians, nurses, laboratory technicians and administration staff. A total of 261 participants were enrolled in this study between February and September 2021. The anti-SARS-CoV-2 IgG antibody responses following immunisation with one of the five different types of COVID-19 vaccines were compared, as well as with antibody titres of participants with previous natural COVID-19 infection. Five groups of participants who were vaccinated (37 individuals vaccinated with BNT162b2, 17 with BBIBP-CorV, 154 with Gam-COVID-Vac, 11 with Sinovac and 8 with Oxford/AstraZeneca) were matched by sex and age categories.
Patients who suffered COVID-19 infection had mild clinical symptoms such as: fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhoea, loss of taste and smell, but with no clinical signs of pneumonia. Among the vaccinated participants, the anti-SARS-CoV-2 IgG antibody titres were measured 21 days after the first dose and 60 and 180 days after the second dose of the vaccine. All COVID-19 unvaccinated participants enrolled in the study had a prior real-time polymerase chain reaction (RT-PCR)-confirmed SARS-CoV-2 infection. The antibody titres measured 60 days after the second dose of the vaccine, i.e., 90 days following the first dose, were compared with the antibody titres which were measured 90 days after the confirmed COVID-19 infection of the recovered patients. These two groups were comparable regarding the dynamics of antibody production. Immunocompromised patients and patients with chronic diseases were excluded from the study, due to the possible influence on clinical outcomes. The immunised participants were vaccinated to induce synthesis of neutralising antibodies against the viral S glycoprotein. Also, our participants were vaccinated with inactivated vaccines (BBIBP-CorV-Sinopharm vaccine, CoronaVac-Sinovac vaccine) that induce the production of antibodies to other relevant virus antigens.
All study participants completed the questionnaire designed for this study related to their demographic and epidemiological characteristics. Informed consent was obtained from all subjects involved in the study. The laboratory technicians were responsible for collecting the 5 ml samples of venous blood for serological testing. After coagulation, the samples were centrifuged at 3,000 rpm for 5 minutes and the sera were transported to the Centre for Biomedical Research, Faculty of Medicine, University of Banja Luka and stored at –20°C until further serological analysis. The study was done in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Medicine Foča, University of East Sarajevo (Decision number: 01-2-8, dated 6 November 2020).
Detection of SARS-CoV-2 antibodies
Anti-SARS-CoV-2 IgG antibodies were identified using the automated enzyme-linked immunosorbent assay (ELISA) method with a Euroimmun ELISA Analyser I-2P (EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany). Serum samples were analysed with a commercial anti-SARS-CoV-2 ELISA (IgG) kit (code EI 2606-9601 G) according to the producer’s instructions. The microplate wells were covered with an S1 domain of the spike protein of SARS-CoV-2. The results were assessed as a ratio of the extinction of the sample over the extinction of the calibrator. According to the manufacturer’s performance characteristics, the test sensitivity and specificity were 90% and 100%, respectively.
Statistical analysis
Descriptive statistics were used to analyse and describe participants’ characteristics. Categorical variables are presented as absolute frequencies and percentages (%) and continuous data as median with interquartile range (IQR). For the analysis of numerical data, the skewness and kurtosis values as well as a visual inspection of the histogram were used. In the event of deviations from the normal data distribution, the Kruskal-Wallis test was used. The Friedman rank-sum test was used for analysing the seroconversion rate for each group of vaccinated individuals. The Pearson χ2 test was used to compare the frequency of occurrence of the analysed categorical variables (male, female, age group) when observing one or more independent samples. Statistical hypotheses were tested at the significance level (α) of p < 0.05 and p < 0.001. Statistical analysis was done using EZR for Windows XP (Version 2.3-0).
Results
There were 261 participants in this study and 227 (86.9%) of them received at least two doses of any of the five types of COVID-19 vaccines, while the remaining 34 participants were those who recovered from COVID-19 and did not receive any vaccine at that time. However, the age-related information for two unvaccinated patients was missing, and the total number of patients defined by age in this group was 32. Of the 227 vaccinated participants, 37 (16.3%) were vaccinated with the Pfizer-BioNTech vaccine, 17 (7.5%) with Sinopharm, 154 (67.8%) with Sputnik V, 11 (4.9%) with Sinovac and 8 (3.5%) with the Oxford/AstraZeneca vaccine. There were no statistically significant differences in gender ratios between the vaccinated and unvaccinated participants, but differences were noted in distribution of age groups (Table 1).
Table 1 Demographic features of studied participants and COVID-19 seroprevalence
Characteristic Participants P-value
Vaccinated Unvaccinated
Gender, n (%)
Male 56 (24.7) 13 (58.3) 0.1
Female 171 (75.3) 21 (36.1)
Age, n (%)
20-40 91 (40.1) 6 (17.7) 0.02*
41-60 114 (50.2) 19 (55.9)
≥ 61 22 (9.7) 7 (20.6)
* Statistical significance
Evaluation of antibody titres among five vaccinated groups showed statistically significant differences in antibody titres 21 days after the first dose. The median antibody titre of 10.0 AU/ml was found in the group vaccinated with the Pfizer-BioNTech vaccine, while in participants vaccinated with Sputnik V, Sinopharm, Sinovac and Oxford/AstraZeneca vaccines the median antibody titres were 9, 7.2, 6.8 and 1.4 AU/ml, respectively (p = 0.01). Sixty days after the second dose of the vaccine, the median antibody titres in participants vaccinated with Pfizer-Bio- NTech, Sinopharm, Sputnik V, Sinovac and Oxford/Astra- Zeneca vaccines were 12, 9.3, 5.9, 4.6 and 2.5 AU/ml, respectively (p < 0.001) (Fig. 1).
Fig. 1 Antibody immunoglobulin G levels of different vaccines measured 60 days after the second dose. The box plot of the median antibody titres after 60 days of the second dose of the vaccines showed statistical significance between them (p < 0.001)
In addition, the comparison of antibody levels among the vaccinated participants 180 days after the second dose of vaccines showed that the Pfizer-BioNTech vaccine induced the highest antibody titre (median = 9.0), followed by those vaccinated with Oxford/AstraZeneca (median = 6.4), Sinopharm (median = 5.6), and Sputnik V (median = 4.7), respectively (p = 0.01).
To analyse the changes in antibody titres at different time-points for each group of vaccinated individuals the Friedman test was used. A statistically significant difference was found between the antibody levels in patients vaccinated with Sputnik V at defined points of time (p < 0.001). Post hoc testing showed that 21 days after the second dose of vaccine, the median antibody titre in participants vaccinated with Sputnik V was significantly higher than the median antibodies titre after 60 days and 180 days after the second dose of the Sputnik V vaccine (p = 0.02, p = 0.0001, respectively). No such differences were found in the groups vaccinated with Pfizer-BioNTech (p > 0.05), Sinopharm (p > 0.05) or Sinovac (p > 0.05) (Table 2).
Table 2 Immunoglobulin G antibody levels of four different vaccines measured at defined points of time after vaccination
Post vaccination time (days) Pfizer-BioNTech Sputnik V Sinopharm Sinovac
Median
(AU/ml) p Median
(AU/ml) p Median
(AU/ml) p Median
(AU/ml) p
21 10.0 0.6 9.0 < 0.001* 7.2 0.6 6.8 0.07
60 12.0 5.9 9.3 4.6
180 9.0 4.7 5.6 0
* Statistical significance
Significant differences in antibody levels 60 days after the second dose of vaccines were not observed between gender and age groups in the same Pfizer-BioNTech and Sputnik V vaccine populations, but a significant difference was noted in the Sinopharm vaccine group (Table 3). Due to the small number of participants vaccinated with Sinovac and Oxford/AstraZeneca, these data were not included in further analyses.
Table 3 Immunoglobulin G antibody levels of three most used vaccines 60 days after administration of the second dose stratified by gender and age
Variables Pfizer-BioNTech, n = 37 Sinopharm, n = 17 Sputnik V, n = 154
Median (IQR) p Median (IQR) p Median (IQR) p
Gender
Male 10.0 (0.5) 0.7 1.9 (1.9) 0.02* 6.2 (3.6) 0.8
Female 12.7 (8.9) 9.7 (4.1) 6.0 (6.2)
Age (years)
20-40 9.5 (1.4) 0.1 2.1 (6.7) 0.5 7.3 (3.6) 0.1
41-60 13.8 (8.7) 9.3 (1.1) 5.4 (6.4)
≥ 61 22.0 (22) 12.2 (12.2) 9.8 (10.3)
* Statistical significance, IQR – interquartile range
In order to analyse the antibody titre levels between the different vaccine types, the participants were clustered in the group that received the mRNA vaccine (Pfizer- BioNTech), the group that received one of the vector vaccines (Sputnik V or Oxford/AstraZeneca), and the group that received one of the inactivated vaccines (Sinovac or Sinopharm vaccines). Detailed data on antibody titres for each of these three vaccine types are presented in Table 4. The data showed significantly higher antibody titres in individuals vaccinated with the mRNA vaccine compared to those vaccinated with inactivated and vector vaccines (p < 0.05). However, when the participants were stratified by gender and age, there were no differences in antibody levels related to the vaccine type (Table 4).
Table 4 Immunoglobulin G antibody levels 60 days after the second dose according to vaccine type stratified by gender and age
Variables Inactivated vaccines (n = 28) Vector vaccines (n = 162) mRNA vaccine (n = 37) P-value
Median (IQR) Median (IQR) Median (IQR)
IgG levels 4.9 (7.3) 5.8 (5.9) 10.2 (7.9) 0.0002*
Gender
Males 9.5 (6.4) 5.4 (5.9) 13.3 (10.7) 0.6
Females 2.3 (5.7) 6.0 (5.6) 9.5 (2.1)
Age
20-40 9.0 (0) 5.5 (5.5) 12.7 (6.7) 0.9
41-60 5.4 (5.7) 7.1 (6.3) 9.1 (6.6)
≥ 61 12.2 (0) 4.2 (5.8) 10.0 (0)
* Statistical significance, IQR – interquartile range
Regarding the vaccine type, it was noted that 88.5% of individuals were seropositive after the mRNA vaccine, 86.2% were seropositive after vector vaccines and 71.4% were seropositive after inactivated vaccines 60 days after the second dose. There were no differences in the efficacy between these three vaccine types (p = 0.1).
Out of the total number of participants, only 109 of them (48%) received the third dose of the vaccine. The COVID-19 infection was confirmed in just 7 participants in this group (6.4%). Out of a total of 227 individuals who received the second dose of the vaccine, in 26 of them (11.5%) the COVID-19 infection after complete vaccination was confirmed. There was no difference between the participants who received the third dose of the vaccine and the participants who received the second dose of the vaccine in relation to the occurrence of COVID-19 infection (p = 0.2).
The analysis showed that the median antibody titre of vaccinated participants 60 days after the second dose of vaccine, which is 90 days after the first dose, was 7.06 AU/ml, and the median antibody level of convalescents 90 days after the positive RT-PCR was 5.76 AU/ml. There was no statistically significant difference between these two groups (p = 0.8).
Discussion
In Western European countries the percentage of the vaccinated adult population is higher than that in Eastern European countries [13]. However, the majority of Western countries were vaccinated either with the mRNA type (Pfizer/Moderna) or with the vector type (AstraZeneca) of vaccine. To the best of our knowledge, this study is the first to analyse the immune response related to five different types of COVID-19 vaccines that were available in the Republic of Srpska in comparison to the immune response of the natural SARS-CoV-2 infection. Immunoassays mostly measure antibodies to the S-glycoprotein or its fragment such as the receptor binding domain (RBD). The ELISA method determines the total sum of antibodies that attach to virus proteins including neutralising antibodies. Tests for detection of neutralising antibodies are also in use, but these tests are not standardised. The neutralising antibody levels as calculated by neutralisation tests are related to antibody titres that are determined by “binding” assays [14]. In the present study the ELISA method, which calculates the quantity of anti-SARS-CoV-2 spike IgG, was used.
In the case of COVID-19 infection, it was shown that only antibodies targeted at the S protein can neutralise the virus and avoid further infection [15]. The SARS-CoV-2 vaccines include at least part of the S-glycoprotein such as the S1 domain or the RBD. It has been shown that the immune response against vaccinal S-glycoprotein significantly differs according to the vaccine type, showing higher seroconversion in mRNA-vaccinated individuals in comparison to other types of vaccines [16].
The results of our study showed the highest antibody levels measured in the group vaccinated with Pfizer-BioNTech. Petrović et al. noted maximum titres in participants vaccinated with Pfizer-BioNTech, followed by Sputnik V and Sinopharm [14]. In the present study, the highest anti- body titres were found in those vaccinated with Pfizer- BioNTech, followed by Sputnik V and Sinopharm 21 days after the first dose of the vaccine, and 60 days after the second dose of the vaccine according to the antibody levels between the groups vaccinated with different vaccine platforms. After 180 days of the second dose of the vaccine the antibody titre was highest in the participants vaccinated with Pfizer-BioNTech, followed by Oxford/AstraZeneca and Sinopharm, and Sputnik V. Comparable differences in antibody titres related to different vaccine types have been reported [14].
The reported divergences in immunogenicity can be explained by the different platforms used to make vaccines. Pfizer-BioNTech vaccine includes mRNA encoding the S-glycoprotein, which is highly immunogenic, wherein the mRNA is stored in a lipid nanoparticle that ensures that the mRNA penetrates more easily into the cytoplasm of the host cells without being degraded by enzymes present in tissues. Adjobimey et al. [16] reported that the vectored vaccines such as Sputnik V and Oxford/AstraZeneca provoked a comparable titre of anti-SARS-CoV-2 specific IgG antibodies. Our results showed weaker efficacy of Oxford/AstraZeneca compared to Sputnik V after the first dose of these vaccines. However, 180 days after the second dose of the vaccine, participants who received Oxford/AstraZeneca or Sinopharm vaccines had higher anti- body titres compared to the individuals vaccinated with Sputnik V. The Sinopharm vaccine may cause production of antibodies to proteins of SARS-CoV-2 other than those against S-glycoprotein. It could be of significance for protection against variants with mutations in the spike region. Hence, induction of the immune response by vaccines depends on host factors and vaccine components. The selection of the vaccine type defines the immunogenic effectiveness of the viral proteins that represent the constituent part of the vaccine. Also, the selection of the appropriate vaccine platform determines that an immune adjuvant is required [17].
When comparing antibody levels measured 21 days after the first dose of vaccine and 60 days after the second dose of vaccine, antibody levels following Sputnik V decreased at a faster rate than the other vaccines. It was also noted that the antibody levels in the group vaccinated with Oxford/Astra Zeneca was higher 180 days after the second dose of the vaccine than 60 days after the second dose of the vaccine. According to the results, Sinopharm and Oxford/Astra Zeneca vaccines are more effective than Sputnik V. This may be due to variations in the platform of these vaccines. Since the COVID-19 infection was new to humanity, it was not clear which vaccine strategies would be the most successful [18]. Studies on vaccinated participants at later time-points are necessary to identify the vaccine that produces the longest-lasting antibody titre.
The Pfizer-BioNTech, Sputnik V, and Sinopharm vaccines induced higher levels of antibody titres in the older population and in females but not at significant levels in groups vaccinated with Pfizer-BioNTech and Sputnik V. Negative correlations between age and antibody expression in Sputnik V, and Sinopharm vaccinated individuals were found by Adjobimey and co-workers [16]. Immunosenescence is a process of immune dysfunction that happens with age and includes changes in the lymphatic organs, leading to a decreased number of naïve T cells, as well as the production of antibodies, which is related to the development of infections, autoimmune diseases, and malignant tumours, as well as lower efficacy of vaccines [19]. The higher level of antibodies in older vaccinated participants in our study seems to refute this statement. The reason for this result may be the sample size of participants in the age group ≥ 61. Besides, Petrović et al. [20] stated that Pfizer-BioNTech, Sputnik V, Sinopharm, and Oxford/Astra Zeneca vaccines had high effectiveness in preventing SARS-CoV-2 infection in the elderly shortly after their administration.
The anti-S antibody levels in participants of this study increased more quickly after vaccination with Pfizer than after Oxford/Astra Zeneca. This was also shown in the study of Iacobucci [21]. In the present study, participants vaccinated with Sputnik V also experienced a decrease in antibody levels, but they were also seropositive 180 days after the second dose of vaccine (6 months from the first dose of the vaccine). In participants vaccinated with Pfizer-BioNTech antibody levels decreased over a period of 6 months, but seropositivity remained. A longitudinal study from Argentina showed that the 118 volunteers vaccinated with two doses of the Sputnik V vaccine had a significant decrease in IgG antibody levels over a period of 6 months, but all subjects remained seropositive [22].
Sinopharm and Sinovac vaccines have been the most widely used vaccines, being registered in 88 and 53 countries, respectively [23]. According to literature data, efficacies of inactivated vaccines Sinopharm and Sinovac increases with the number of vaccine doses [24]. This is contradictory to the results of the present study, since no significant difference was observed in the increase in anti- body levels after the administration of the second dose of the vaccine. Non-viral vaccine platforms require multiple vaccinations to trigger protective immunity, while one dose of live virus vaccines has the capability to provoke ‘one-shot’ immunity. Inactivated virus vaccines must be sometimes inoculated with suitable adjuvants and by repetitive vaccination in order to reach full usefulness [17].
In the present study, participants who were vaccinated with the mRNA vaccine had significantly higher antibody levels compared to participants vaccinated with inactivated and vector vaccines. Polack et al. reported that the effectiveness of the mRNA vaccine was 95% [25]. Also, the data of Adjobimey et al. [16] showed that the mRNA vaccinated groups exhibited the highest neutralisation potential among all the groups. Voysey et al. [26] reported that the vector vaccine achieved an effectiveness rate of 70.4%. Also, we analysed the seroconversion rates in vaccinated persons with three types of vaccine (mRNA, vector, inactivated vaccines). The percentage of participants vaccinated with inactivated vaccines was 71.4%, with vector vaccines 86.2%, and with the mRNA vaccine 88.5%. For the mRNA vaccine, our results are similar to the results obtained in the initial trial [27]. According to the data from the literature the effectiveness of inactivated vaccines ranges between 72% and 78% [28].
Almost half of the participants in the present study received the third dose of the vaccine. The analysis showed that there was no difference between the participants who received the second and the participants who received the third dose of the vaccine in relation to COVID-19 infection. Immune protection against COVID-19 infection depends on the quality, quantity, and duration of antibody levels throughout the disease.
It was noted that the antibody titres in this study were higher in vaccinated groups compared to the convalescents, but without statistical significance, which can be explained by the fact that the patients included in the present study had mild clinical symptoms of COVID-19 infection. This is consistent with other studies that demonstrated higher antibody titres in vaccinated groups compared to convalescents [29]. Israel et al. observed higher titres in the first month following the second vaccination dose than in convalescent patients after SARS-CoV-2 infection [30]. They also found that BNT162b2 vaccine provoked much higher antibody titres three months after vaccination compared to the titres from convalescents, but with a faster fall of antibody titres in vaccinated patients compared to those infected with the SARS-CoV-2 virus [30]. Studies of L’Huillier et al. [31], and Cho et al. [32] also showed that the antibody titre was higher after vaccination compared to the antibody titre after a previous infection.
It is known that IgM and IgG are not predictors of disease severity, but the peak antibody titres are positively associated with the COVID-19 disease severity [33]. Choteau et al. [34] found in their research that a higher titre of virus-specific antibodies was associated with severe COVID-19 infection compared to asymptomatic patients. They also found that severe infection is not associated with a defective immune response, and that in recovered patients antibodies were still detected more than three months after the COVID-19 infection. According to reports in the literature, antibodies in recovered patients persist for 3 to 6 months after infection, and in most patients, they can be detected even 8 to 9 months after infection [35]. It was observed that vaccination of convalescents led to 2-10-fold higher neutralisation titres than those present in naïve individuals after primary vaccination. Also, vaccination of convalescents resulted in a higher neutralisation titre than that present in early convalescents [36]. The present study showed that the COVID-19 convalescents maintained detectable anti-SARS-CoV-2 IgG antibodies 90 days after the beginning of infection. It is confirmed that vaccination of previously recovered patients will provide more permanent protection against variants of SARS-CoV-2 [36]. A question which was not answered is the time interval of these immune responses. The results of some authors showed that there is no significant difference in the decline of antibody levels in the first months between vaccinated persons and those who have recovered from COVID-19 infection [37]. However, this issue has to be evaluated in further studies.
A limitation of this study is the small and different sample sizes between the groups of vaccinated and unvaccinated participants. This might be the reason that some results obtained in this study are not fully comparable with other studies.
Conclusions
The present study compared the efficacy of the five types of anti-SARS-CoV-2 vaccines currently available in the Republic of Srpska, Bosnia and Herzegovina: BNT162b2-Pfizer-BioNTech vaccine, BBIBP-CorV-Sino- pharm vaccine, CoronaVac-Sinovac vaccine, Gam-COVID-Vac-Sputnik V vaccine, and Oxford/AstraZeneca vaccine. Our findings indicated that the Pfizer-BioNTech vaccine caused the highest titres of specific IgG antibodies, followed by Sputnik V and Sinopharm. The results also confirmed that vaccination with all five types of vaccines provided a significant immune response in the majority of vaccinated participants. High rates of seropositivity were detected after vaccination with the mRNA and vector vaccines, while the percentage of seropositive participants vaccinated with inactivated vaccines was lower. The results of this study showed that the antibody levels were higher in vaccinated groups compared to the convalescents but without statistical significance.
Acknowledgments
We would like to thank the laboratory technicians for participation in data collection and blood sampling.
Funding
The study was co-funded by the Ministry of Health and Social Welfare of the Government of the Republic of Srpska and the WHO Country office for Bosnia and Herzegovina (WHO Purchase order No. 202604001).
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10189580.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206584
50185
10.5114/ceji.2023.125312
Case Report
Manifestation of severe congenital neutropenia in the oral cavity. Case report
Krasuska-Sławińska Ewa 1
Klaudel-Dreszler Maja 23
Minota Marta 1
Pożyczka-Fedor Magdalena 4
Olczak-Kowalczyk Dorota 5
Minko-Chojnowska Izabela 4
1 Department of Dental Surgery for Children and Adults, The Children’s Memorial Health Institute, Warsaw, Poland
2 Department of Gastroenterology, Hepatology, Nutritional Disorders and Paediatrics, Children’s Memorial Health Institute, Warsaw, Poland
3 Local Liaison Physician in Poland on behalf of SCNIR (Severe Chronic Neutropenia International Registry)
4 Department of Dental Surgery for Children, The Children’s Memorial Health Institute, Warsaw, Poland
5 Department of Paediatric Dentistry, Warsaw Medical University, Warsaw, Poland
Ewa Krasuska-Sławińska, MD, Department of Dental Surgery for Children and Adults, The Children’s Memorial Health Institute, Warsaw, Poland, e-mail: [email protected]
24 2 2023
2023
48 1 7074
05 5 2022
30 1 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Severe congenital neutropenia (SCN) comprises a heterogenous group of disorders characterized by a constantly low absolute neutrophil count (ANC) below 0.5 × 109/l in the peripheral blood and maturation arrest of the myelopoiesis in the bone marrow at the promyelocyte/myelocyte stage that lead to early onset of severe bacterial infections in affected patients. Clinical symptoms of congenital neutropenia include sepsis, recurrent respiratory tract infections, mouth ulceration, chronic gingivitis, bacterial skin infections, and urinary tract infections. Patients with SCN often develop periodontitis despite standard medical and dental care.
We present oral symptoms in our patient afflicted with SCN due to homozygous mutations in the JAGN1 gene, based on 16 years of observation and treatment at the Paediatric Dentistry Clinic of Children’s Memorial Health Institute.
In our patient, oral cavity changes typical for SCN – in the form of gingivitis and bleeding from periodontal tissues – appeared around the age of 2 and led to the premature loss of primary teeth. The patient also developed an advanced periodontal disease in the permanent dentition, resulting in the loss of 21 teeth at 15 years of age.
Dental care of patients with SCN should be carried out in close cooperation with an immunologist, and dental procedures associated with the risk of bacteremia require antibiotic prophylaxis.
periodontal disease
severe congenital neutropenia
oral ulceration
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pmcIntroduction
Severe congenital neutropenia (SCN) is a heterogeneous group of primary errors of immunity which share a common feature of a persistent decrease in neutrophil counts in peripheral blood, usually below 500/µl (0.5 × 109/l) and a maturation arrest of the myelopoiesis in the bone marrow at the promyelocyte/myelocyte stage [1-3]. Patients with SCN are constantly at risk of life-threatening bacterial infections, and occasionally of fungal infections [2-5]. As the disease progresses, the risk of developing the myelodysplastic syndrome and acute myeloid leukemia increases [1-3, 5]. The prevalence of SCN is estimated at 1-2 : 1,000,000 live births [1, 5].
In 1950, the Swedish physician Rolf Kostmann described a family suffering from an autosomal recessively inherited disease with severe neutropenia and recurrent bacterial infections since early childhood [6]. Since then, genetic studies have identified several gene defects in patients with SCN including the neutrophil elastase (ELANE) gene, HCLS1 associated protein X-1 (HAX1) gene, glucose-6-phosphatase catalytic subunit 3 (G6PC3), growth factor independent protein 1 (GFI1), and vacuolar protein sorting-associated protein 45 (VPS45) [2]. These genetic defects are characterized by impaired maturation of neutrophil granules [2]. In 2014 Boztug et al. published the first description of jagunal homolog 1 (JAGN1) mutations in patients affected with SCN [7]. JAGN1 is required for granulocyte colony-stimulating factor receptor-mediated signaling and is necessary in the differentiation and survival of neutrophils [7, 8].
In the 1950s, the mortality rate from infections in severe congenital neutropenia was 90% [2]. The 1980s brought a breakthrough in the treatment of chronic neutropenia due to the introduction of recombinant granulocyte colony-stimulating factor (rHuG-CSF) for clinical use [2, 9, 10]. For patients with SCN not responding to treatment with subcutaneous G-CSF, the only currently available therapeutic option is hematopoietic stem cell transplantation (HSCT) [3, 5, 11, 12].
The clinical signs of congenital neutropenia include delayed separation of the umbilical cord stump, inflammation of the navel, recurrent upper and lower respiratory tract infections, oral ulcers or mucositis, chronic gingivitis, skin infections such as abscesses or bacterial cellulitis, bacterial lymphadenitis, bacterial septicemia, and urinary tract infections [1-3, 13-16].
The most common microorganisms causing infections in patients with SCN are Gram-positive bacteria – Staphylococcus aureus, Streptococcus pneumoniae and Streptococcus pyogenes – and the second most frequent are Gram-negative bacilli Escherichia coli, Pseudomonas aeruginosa and Klebsiella pneumoniae [1, 4, 5, 13]. Anaerobes constituting the flora of the oral cavity – Peptococcus, Peptostreptococcus, Bacteroides fragilis, and Fusobacterium spp. – can also become a causative factor of severe infection in patients with SCN [15].
Case description
A boy of Roma origin, the son of consanguineous parents, was admitted at the Children’s Memorial Health Institute in Warsaw at 2 years of age due to chronic neutropenia.
His absolute neutrophil count (ANC) varied from 0.06 to 0.6 × 109/l and he presented with high monocytosis in peripheral blood. He suffered from recurrent, severe bacterial infections, chronic gingivitis and painful mouth ulcers. Bone marrow examination showed, typical for SCN, maturation arrest at the myelocyte stage. Because of low height (below 10 percentiles for age) and failure to thrive, Shwachman-Diamond syndrome was considered, but molecular diagnostics did not reveal any SBDS gene mutations. G-CSF treatment was initiated, and some improvement was observed, but his ANC increased to 1.0 × 109/l only after high doses of G-CSF (approx. 40 µg/kg/d). After a few months, G-CSF-treatment had to be stopped due to poor tolerance (bone and muscle pain). Instead, the child was put on antibiotic prophylaxis and vaccinated against encapsulated bacteria. Eventually, HSCT was considered, but his parents did not approve such a treatment. With time, his height and weight increased over 25 percentiles. At first, molecular studies excluded defects in the ELANE and HAX1 genes, but finally homozygous mutations in JAGN1 were identified. Currently the boy is doing well on antibiotic prophylaxis. Despite profound neutropenia he had no severe infections for many years.
When he was 5 years of age the intraoral examination revealed incomplete primary dentition with five missing teeth, marginal gingivitis, interdental papilla hypertrophy, carious lesions in seven primary teeth, and one permanent tooth of various degrees of advancement for conservative treatment (Fig. 1). The patient was qualified for simultaneous dental treatment under general anesthesia. A panoramic radiograph picture was taken showing the advanced destruction of the alveolar bone in the maxilla and the alveolar part of the mandible. Three primary teeth were completely devoid of bone base. The roots of the lower incisors were only half of their normal length (Fig. 2). Under antibiotic protection (amoxicillin with clavulanic acid at a dose of 30 mg/kg), professional removal of dental plaque and conservative treatment of teeth extraction of four primary teeth with complicated caries and periodontitis were performed. The patient was provided with permanent dental care.
Fig. 1 Oral cavity maxilla, June 2008
Fig. 2 Panoramic radiograph, November 2008
During follow-up visits symptoms of gingivitis and periodontitis were observed.
Every time oral hygiene instruction was provided and professional oral cavity hygiene and conservative treatment of cavities were performed under antibiotic prophylaxis. The patient was recommended to use topical chlorhexidine gel 2-3 times a day for 10 days and to rinse the mouth with a preparation containing octenidine twice a day for 10-14 days.
When the patient was 10, the exposure of two-thirds of palatal roots and third-degree loosening of teeth were observed (Fig. 3). A panoramic radiograph picture was taken (Fig. 4), which revealed complete absence of the bone base in all permanent first molars. Teeth 16 and 26 were qualified for extraction, which was performed in an outpatient setting under local anesthesia and antibiotic treatment (clindamycin, 10 mg/kg/dose). The obtained material from granulomatous lesions was submitted for histopathological examination, the results of which indicated non-specific inflammatory granulation tissue.
Fig. 3 Maxilla, right side, December 2013
Fig. 4 Panoramic radiograph, December 2013
The next year, tartar deposits in the incisors of the mandible and gingivitis were discovered, and the mandibular first molars and central incisors showed signs of second-degree loosening. The patient was recommended to continue conservative treatment and undergo removal of the tartar deposits under antibiotic treatment (amoxicillin with clavulanic acid, 2 doses of 30 mg/kg with an interval of 12 hours, given at home). A few months later (Fig. 5), examination revealed intensified inflammation around tooth 36 with its roots halfway exposed, as well as looseness of teeth 32-42. Extraction of tooth 36 was performed under local anesthesia in an outpatient setting and covered with intravenous antibiotic (amoxicillin with clavulanic acid 30 mg/kg/dose).
Fig. 5 Oral cavity, June 2015
At the age of 15 (Fig. 6), the patient came to the Children’s Dentistry Clinic of the Children’s Memorial Health Institute due to an exophytic lesion near teeth 37 and 38 on the lingual side (Fig. 7). During the intraoral examination, a lesion measuring 25 mm was found; it had a wide base and was elastic and hard, covered with a smooth mucosa with a vivid red color, and was not painful. Submandibular lymph nodes on the left side were enlarged, albeit not painful; they were movable against the skin and the base. All teeth were also found to be loose, with deep periodontal pockets and inflammation of the marginal periodontium. A control panoramic radiograph picture was taken (Fig. 8). The patient was qualified for surgery to remove the lesion and extract twenty-one teeth. Despite agranulocytosis, after an immunologist’s consultation, the patient underwent the procedure under general anesthesia and antibiotic treatment (clindamycin intravenously, 10 mg/kg/dose). An exophytic lesion was removed from the alveolar part of the mandible. The sockets and the tumor bed were secured with a collagen sponge and approach sutures. The histopathologic examination of the collected material revealed giant cell epithelium; as for the material taken from the alveoli, its fragments were covered with partially dilated multilayer squamous epithelium without any signs of atypia.
Fig. 6 Oral cavity, July 2018
Fig. 7 Mandible, left side, September 2018
Fig. 8 Panoramic radiograph, September 2018
The numerous extractions and damage to the alveolar process after the removal of the lesion had a negative impact on the emotional state of the patient and required treatment to restore the aesthetics of the dentition. The pa- tient was referred to the Department of Prosthodontics of the Medical University of Warsaw to have his lost chewing function restored and pronunciation and dental aesthetics improved. Due to the condition of the prosthetic base, the patient was offered removable dental prostheses. At the time of the prosthetic procedure at the Department of Prosthodontics, Medical University of Warsaw, the patient came to the Children’s Dentistry Clinic of the Institute of Pediatric Medicine due to a painful traumatic lesion – a pressure ulcer – on the lower lip mucosa around teeth 33-34. Topical application of orthodontic wax on teeth 33 and 34 was recommended. Biostimulation laser therapy with a wavelength of 635 nm and at a dose of 4 J/cm/20 s was performed as supportive treatment. The biostimulation laser therapy significantly reduced pain and shortened the lesion healing time. The patient remains under the constant care of the Dental Surgery Clinic of the Institute of Pediatric Medicine and the Prosthetics Department of the Medical University of Warsaw.
Discussion
Neutropenic patients with ANC decreased below 500 cells/µl are vulnerable to bacterial infections, which may be life-threatening but usually respond well to modern antibiotics. Another common problem of SCN patients who have not undergone HSCT is periodontitis, which may lead to both infections and permanent teeth loss. A recent study of Rotulo et al. showed that approx. 30% of adult patients with ELANE deficiency in the French Severe Chronic Neutropenia Registry present with edentulism [17]. The pathogenesis of gingivitis and periodontitis is multifactorial; it includes interactions between oral microbiota and host defense [18]. Neutrophils are the key cells for oral cavity health and neutrophil deficiency or dysfunction usually results in periodontal disorder [18, 19]. The literature review points to the conclusion that pathological symptoms in the oral cavity in patients with SCN usually start at 2 years of age and become a serious, life-long problem despite rHuG-CSF treatment [13-17, 19]. The literature data indicate that in 20% of patients with congenital neutropenia, ulceration lesions located in various parts of the oral cavity may be symptoms of the disease [16]. Advanced periodontitis, severe gingivitis, and significant alveolar bone loss resulting in the premature loss of teeth – both in the case of primary and permanent dentition – may indicate a severe disturbance in the production and release of neutrophils from the marrow into the peripheral blood [5, 15-17]. In our patient, oral cavity changes typical for SCN – in the form of gingivitis, i.e. swelling, redness, and bleeding from periodontal tissues – appeared when he was 2 years and led to the premature loss of several primary teeth.
Dental management in such patients consists in regular dental visits combined with oral hygiene instruction, health education, professional dental cleaning procedures, and the use of antibacterial gels and rinses. In cases of advanced periodontal disease, it is necessary to extract teeth under antibiotic treatment. The patient described in this case report suffered from significant hygiene negligence caused by pain and bleeding of the gums during hygienic procedures which, coupled with the lack of regular follow-up visits and severe congenital neutropenia, led to the premature loss of dentition. Despite being 15 years old, he had only 5 teeth left, all of them affected by significant progression of periodontal disease. As such, it was necessary to provide him with a removable dental prosthesis.
According to Segel and Halterman and Olczak-Kowalczyk et al., the most effective way to prevent oral inflammation and premature tooth loss is to motivate the patient to maintain proper oral hygiene [1, 15]. Regular and frequent dental checkups and professional prevention of caries and periodontal disease reduce the severity of chronic periodontitis and the risk of caries [15].
Interestingly, besides neutropenia, patients affected with SCN also present with deficiency of neutrophil granule-associated proteins such as antimicrobial peptides cathelicidin LL-37 and human neutrophil peptides 1-3 [19, 20]. The concentration of these anti-inflammatory peptides in patients’ saliva does not increase even during successful treatment with G-CSF preparations [19]. Therefore, chronic periodontitis occurs in patients with SCN who have not undergone HSCT. Though an attempt was made to treat our patient with G-CSF, the high doses that he required to raise his ANC above 1000/µl contributed to the occurrence of side effects and resulted in treatment cessation. After the diagnosis of SCN type 6 (caused by the JAGN1 gene mutations) was confirmed by molecular examination, the boy was offered HSCT treatment, which was not implemented due to the parents’ objection.
Conclusions
The presented literature and the described case report confirm that oral mucosa lesions in patients with SCN are often infectious and may lead to systemic infection and life-threatening conditions. They manifest as erosions, ulce- rations, and hyperplastic changes in the mucosa. Their most characteristic symptoms, however, are gingivitis and rapid periodontal destruction not amenable to local treatment. Attention to oral hygiene, the use of local antibacterial agents, and regular visits to the dentist’s office can alleviate the course of the disease process in the periodontal tissues and prevent the formation of odontogenic infection foci. In the case of procedures related to the disruption of tissue continuity and bacteremia, such as scaling or tooth extraction, the following antibiotic prophylaxis is recommended: amoxicillin with clavulanic acid administered in a single dose of 50 mg/kg, and in children allergic to penicillin, clindamycin in a single dose of 20 mg/kg.
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10189581.txt |
==== Front
Cent Eur J Immunol
Cent Eur J Immunol
CEJI
Central-European Journal of Immunology
1426-3912
1644-4124
Termedia Publishing House
37206587
50543
10.5114/ceji.2023.126615
Review Paper
HMBOX1, a member of the homeobox family: current research progress
Jiang Yu
Mu Hongli
Zhao Hengli
Department of Clinical Research Center, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
Dr. Hengli Zhao, Department of Clinical Research Center, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, China, phone: +86-531-5586-5008, fax: +86-531-5586-5028, e-mail: [email protected]
12 4 2023
2023
48 1 6369
07 11 2022
22 2 2023
Copyright © 2023 Termedia
2023
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
Homeobox containing 1 (HMBOX1) is a transcription factor that was identified in 2006 from a cDNA library of the human pancreas. It belongs to the HNF gene class of the homeobox family. HMBOX1 is widely expressed in normal human tissues; however, its expression level is rather uneven. Homeobox members have been widely reported to participate in embryonic development and differentiation as well as in pathological and physiological processes. Although research on the role of HMBOX1 is still in its infancy, many reports have revealed its regulatory role in cell differentiation, immune regulation, inflammation, and tumor progression. HMBOX1 plays an important role in promoting the differentiation of bone marrow stromal stem cells (BMSCs) into endothelial cells and contributes to their physiological functions. As an immunoregulatory factor, HMBOX1 can significantly inhibit the inflammatory response in hepatocytes and NK cells and impede the infiltration of peripheral immune cells to the liver. In tumor development, HMBOX1 exerts diametrically opposite biological functions, inhibiting or promoting the process. HMBOX1 possesses complex and diverse biological functions. In this review, we provide a brief overview of the research on HMBOX1.
homeobox containing 1
cell differentiation
tumor progression
immune regulation
==== Body
pmcIntroduction
Homeobox genes are a group of genes containing the homeobox DNA sequences that encode homeodomains [1]. The homeodomain consists of four α-helices, a helix-turn-helix motif, and a flexible N-terminal arm that regulate the expression of target genes through the interaction between homologous molecules. This is achieved through the binding of the helix-turn-helix structure to target genes [2]. In the human genome, there are 300 homeobox loci, including 235 functional genes and 65 pseudogenes. These genes are divided into 102 gene families that are divided into 11 gene classes, widely recognized as ANTP, PRD, LIM, POU, HNF, SINE, TALE, CUT, PROS, ZF, and CERS [3]. Among them, ANTP is the largest gene class, hosting the widely known HOX genes [4]. The homeobox gene family has highly conserved DNA-binding domains and plays an important role in embryonic development, including the morphogenesis and differentiation of various tissues and organs [5].
Homeobox containing 1 (HMBOX1), a transcription factor with transcriptional inhibitory activity, was isolated from a cDNA library of the human pancreas in 2006 and belongs to the HNF gene class of the homeobox gene family. The full length of the HMBOX1 gene is 1263 bp and it encodes a protein containing 420 amino acid residues. It contains a highly atypical homeo domain at the N-terminus and is homologous to hepatocyte nuclear factor 1 (HNF1). HMBOX1b, a novel HMBOX1 splicing variant located in both the nucleus and cytoplasm, might function differently from HMBOX1 [6].
A database search revealed that HMBOX1 was widely expressed in various human tissues, but its expression level was uneven. The protein expression level was relatively high in the brain, adrenal gland, testis, and skin. It is expressed with low specificity in both tumor and immune cells and is mainly localized to the nucleoplasm (these data come from Human Protein Atlas). HMBOX1 also acts as a direct telomere repeat-binding protein, known as homeobox telomere binding protein 1 (HOT1). In recent years, in addition to regulation of differentiation and development, its role in tumors, inflammation and other diseases has gradually attracted attention. In the current review, we would like to provide the emerging evidence for the relevance of HMBOX1 in cell biological function and the immunoregulation process and discuss the potential of HMBOX1 as a target for immune-related diseases.
HMBOX1 and cell differentiation
The homeobox gene has been shown to be involved in regulating the differentiation of various tissues and cells [7-11], including HOXC8 suppression in osteo-/dentinogenic differentiation [7], NKX2-3 and NKX2-4 in acute myeloid leukemia subtype formation [8], and Six3 in medium spiny neuron differentiation [10] and caudal repression of intestinal stem cell differentiation [11]. As a member of the homeobox family, HMBOX1 also plays an important role in cell differentiation and functional maintenance. Previous research confirmed that HMBOX1 plays an important role in regulating the differentiation of embryonic and bone marrow stromal stem cells (BMSCs) into vascular endothelial cells (VECs) [12, 13]. By exploring this mechanism, we found that activation of the HMBOX1/CD163/FGF-2 signaling pathway is involved in regulating the differentiation of BMSCs into vascular endothelial-like cells [14]. Suppressing the expression of IP-10 and increasing that of Ets1 were also considered as other mechanisms for the differentiation of BMSCs into VECs [12, 13]. Moreover, HMBOX1 is highly expressed in normal human VECs and is essential for their survival [15]. By interacting with metallothionein 2A, it can increase the level of free Zn2+ in VECs [16], further promoting human VEC autophagy by inhibiting mTOR signaling and cell apoptosis by affecting the level of cleaved caspase-3 [17, 18]. Furthermore, it was proven that heterogeneous nuclear ribonucleoprotein E1 (hnRNP-E1) could directly bind to the 5-UTR region of HMBOX1 to promote its expression, which is essential for maintaining endothelial function [18]. In summary, HMBOX1 plays an important role in the differentiation and function of endothelial cells; however, most of the current studies lack direct evidence on the downstream genes regulated by HMBOX1, and more research is needed to discover the further mechanisms of its regulating effect on endothelial cell behavior.
HMBOX1 and immune regulation
The homeobox gene, especially the NKL gene subclass, has been widely confirmed to regulate the differentiation and development of immune cells including T cells, B cells, natural killer (NK) cells, and dendritic cells, and an imbalance in its regulatory function can induce hematological malignancies [19-22]. However, the role of homeobox genes in the activation and function of the immune system and immune cells remains unclear.
Recent studies have shown that HMBOX1 expression is negatively correlated with NK cell activation and function. HMBOX1 is highly expressed in resting NK cells, inhibits CD107a and cytolysin expression levels and suppresses the NKG2D/DAP10 signaling pathway, thus significantly inhibiting NK cell activity [23]. Activated NK cells show a significant decrease in HMBOX1 levels, accompanied by increased interferon γ (IFN-γ) levels. Further research confirmed that HMBOX1 can negatively regulate IFN-γ expression by inhibiting the transcriptional activity of the IFN-γ promoter [24]. However, these studies lack the support of in vivo experimental data and are limited to in vitro cellular level studies. HMBOX1 can affect tumor progression by regulating NK cell function. miR-30c-1 has been reported to increase the killing ability of NK cells towards human hepatoma cells by targeting the expression of HMBOX1 in NK cells [25]. However, most current studies focus on NK cells, and these studies are relatively limited. The immune regulatory role of HMBOX1 after it has altered the activation and function of NK cells is still little studied. In view of the effect of HMBOX1 on cell differentiation and development, more studies are needed to evaluate both the differentiation and development of immune cells and the maintenance of immune cell function.
HMBOX1 and inflammation
Homeobox genes are involved in the regulation of various inflammatory responses, including inhibition and facilitation [26-29]. For instance, activation of endothelial BMP4-HOXB9-TNF signaling is involved in the formation of arterial inflammation [30]. SIX1 reduces inflammation and rheumatoid arthritis symptoms by silencing the Myd88-dependent TLR1/2 signaling pathway in synovial fibroblasts [31].
At present, HMBOX1 has been confirmed to inhibit inflammation by targeting NF-κB, MAPK, and other forms of inflammatory signaling. HMBOX1 mitigates lipopolysaccharide (LPS)-induced human periodontal ligament stem cell injury by reducing CXCL10 expression through impeding the activation of the NF-κB signaling pathway [32]. In an endothelial model of atherosclerosis, HMBOX1 expression inhibited the endothelial inflammatory response induced by MAPK, the NF-κB pathway, and ROS activation [33]. HMBOX1 is involved in reversing miR-885-5p-induced elevation of interleukin (IL)-1β, IL-18, NLRP3, caspase-1 and GSDMD-N in human myocardial AC16 cells treated with sepsis-exos through the mechanism of NF-κB dependence [34]. These results suggest that HMBOX1 is a new diagnostic marker and therapeutic target for atherosclerosis. Currently, in the reports regarding inhibition of the inflammatory response, HMBOX1 is mainly believed to play a role by inhibiting the NF-κB signaling pathway, but whether HMBOX1 can target the key factors in the NF-κB signaling pathway has not been proven. Studies on the regulatory mechanisms between HMBOX1 and other pathways (such as MAPK) are still insufficient; the previous studies were limited to the observation of changes in the expression of other molecules after the intervention altering HMBOX1 expression.
Immune cells play an important role in the occurrence and progression of inflammation; however, there are few studies on whether HMBOX1 participates in the regulation of inflammation by regulating the activation of immune cells and the release of inflammatory factors. Our previous study demonstrated that HMBOX1 can negatively regulate the NF-κB signaling pathway to weaken the inflammatory response in hepatocytes. Meanwhile, HMBOX1 inhibited macrophage and neutrophil infiltration into liver tissue by downregulating NF-κB/CCL2 signaling. HMBOX1 also affects macrophage activation and cytokine release. All of them synergistically alleviated LPS/D-GalN-induced liver injury [35]. The effect of hepatocyte HMBOX1 on macrophage function is currently under investigation, and further research in this area is needed.
HMBOX1 and tumor progression
An increasing number of studies have shown that homeobox genes are abnormally expressed in the process of tumorigenesis. This suggests that the homeobox gene family not only regulates cell differentiation and development, but also plays an important role in tumor progression [5, 36]. For example, overexpression of HOXC6 enhances BCL-2 mediated anti-apoptosis and promotes cervical cancer cell proliferation [37]. In colon cancer, HOXB13 can inhibit the proliferation of tumor cells by inhibiting the expression of c-Myc and inducing apoptosis of tumor cells, thus inhibiting tumor progression [38]. Interestingly, in gastric cancer, HOXB13 can promote the proliferation, invasion, and migration of tumor cells by activating PI3K/AKT/mTOR signaling [39]. Different homeobox genes can, therefore, regulate tumor progression through multiple mechanisms, and the same gene may play opposing regulatory roles in different tumors, suggesting that homeobox genes are functionally diverse in tumors.
As a member of the homeobox family, HMBOX1 is abnormally expressed in tumors, but its expression is inconsistent. For example, compared with that of adjacent normal tissues, HMBOX1 expression is low in liver cancer tissues, high in renal clear cell carcinoma tissues, and high in both pancreatic cancer and adjacent normal tissues [40].The expression and function of HMBOX1 in tumors depend on the corresponding tumor microenvironment. The difference in the expression levels also implies the diversity of the functions. In Table 1, we summarized relevant literature reports on the behavior of HMBOX1 in tumors.
Table 1 Summary of the behavior of HMBOX1 in tumors
Author Year Tissue or cell type Notes Reference
Zhang P, et al. 2017 Glioma Significantly correlated with WHO grade for glioma disease [47]
Yu YL, et al. 2018 High-grade serous
Ovarian cancer Inhibits cell proliferation by promoting cell apoptosis [41]
Zhao H, et al. 2018 Liver cancer Promoting autophagy, inhibiting stemness and immune escape [42]
Zhang M, et al. 2019 High-grade serous
Ovarian cancer Inhibits the proliferation, migration and invasion of ovarian cancer cell [43]
Diao N, et al. 2019 Gastric cancer Promotes cell proliferation and migration [46]
Chen S, et al. 2020 Osteosarcoma Inhibits osteosarcoma tumorigenesis [45]
Zhou C, et al. 2021 Early-stage cervical cancer Inhibits tumor immune escape [44]
Dermawan JK, et al. 2021 Myxoid spindle cell neoplasm ALK-HMBOX1-fusion occurred in myxoid spindle cell neoplasm [50]
Dermawan JK, et al. 2022 Sarcoma Neuregulin 1-HMBOX1-fusion occurred in high-grade spindle cell sarcomas [49]
HMBOX1 inhibits tumor progression
On one hand, the expression of HMBOX1 in tumor cells can directly inhibit the tumor through regulating cell apoptosis and autophagy. Compared with normal tissues and cells, the expression of HMBOX1 is decreased in ovarian cancer tissues and cell lines. Overexpression of HMBOX1 in ovarian cancer cell lines can downregulate the levels of anti-apoptotic proteins (Bcl-2 and Bcl-xL) and promote the expression of apoptotic regulatory proteins (Bad and Bax), caspase 3, and p53. All of them synergistically promote cell apoptosis and inhibit cell proliferation [41]. Our previous study found that the expression of HMBOX1 is decreased in hepatocellular carcinoma (HCC) and negatively correlated with the clinical staging. Consistent results were observed in HCC animal models and related cell lines. HMBOX1 expression in HCC cells has an inhibitory effect on tumors by promoting autophagy of tumor cells, inhibiting stemness formation, and increasing the sensitivity of tumor cells to NK cell killing [42]. In high-grade serous ovarian cancer patients, tRNA-derived fragments promote ovarian cancer cell proliferation, migration, and invasion by modulating HMBOX1 [43].
On the other hand, HMBOX1 can play an indirect tumor suppressive role by regulating the function of tumor-associated stromal cells and the tumor microenvironment. Recent studies have shown that the expression of HMBOX1-SOCS1 in tumor-associated lympho-endothelial cells can be downregulated by miRNA-1468-5p encapsulated in cervical cancer cell exosomes, thereby activating JAK2/STAT3 signaling, which upregulates the expression of lymphatic PD-L1 to inhibit the function of CD8+ T cells and help tumor immune escape [44]. These results suggest that HMBOX1 expression could inhibit tumors by inhibiting STAT3 signaling activation in lympho-endothelial cells. In addition, HMBOX1 plays a tumor-suppressing role by regulating the activation of tumor-related signaling pathways. As the downstream target of Wilms’ tumor 1-associated protein, HMBOX1 can inhibit the progression of osteosarcoma by inhibiting activation of the PI3K/AKT signaling pathway [45]. However, there are few studies on the regulatory function of HMBOX on tumor stromal cells, especially tumor-related immune cells, so further studies are required.
HMBOX1 promotes tumor progression
Similar to homeobox genes, such as HOXB13, HMBOX1 plays a double-sided role in tumors. However, the expression of HMBOX1 was significantly upregulated in gastric cancer and glioma and was correlated with tumor stage, lymph node metastasis and tumor microvasculogenesis [46, 47]. Zhou et al. confirmed that the c-Fos/miR-18 feedback loop promotes glioma proliferation via acting on HMBOX1 [48].
As a partner gene, HMBOX1 has also been observed to fuse with several key genes in tumor development. Neuregulin 1-HMBOX1 fusion has been reported in high-grade spindle cell sarcomas [49], as well as an aplastic lymphoma kinase–HMBOX1 fusion in myxoid spindle cell neoplasm [50]. However, limited details regarding these cases were obtained, and future studies are needed to better characterize the role of HMBOX1 fusion in tumorigenesis and development.
Although HMBOX1 has been observed to play different regulatory roles in different tumors, as a transcription suppressor, most studies contend that it mainly plays an inhibitory role in tumors. However, there are few relevant studies and the mechanism is not fully understood. Regardless, there is a need for the study of its role in various aspects, including tumor epigenetics, metabolism, and tumor microenvironment regulation.
HMBOX1 and telomere function
Some studies have shown that HMBOX1 expression is related to the maintenance of telomere function [51]. As a direct telomere repeat-binding protein, HMBOX1 is a positive regulator of telomerase recruitment and telomere elongation and acts via association with the active telomerase complex and promoting chromatin association with telomerase [52-54]. Knockdown of HMBOX1 reduces the expression of telomerase in cervical cancer cells, shortens the length of telomerase, reduces the expression of proteins related to DNA damage repair, promotes the sensitivity of tumor cells to radiotherapy, and increases radiation-induced apoptosis [55]. It is known that N6-adenosine (m6A) methylation modification regulates mRNA stability, splicing, transportation, and translation. Recent studies have shown that HMBOX1 is a target mRNA for m6A modification and contributes to maintaining telomere homeostasis [45, 56, 57]. Particularly in tumors, the m6A modification of HMBOX1 mRNA causes progressive telomere shortening and eventually generates genomic instability in cancer cells, which drives malignant progression [45, 56]. However, in chemical-induced male reproductive injury, chemicals enhanced the stability of HMBOX1 by inducing m6A modification and inhibited chemical-induced telomere dysfunction [57]. This is inconsistent with what is observed in tumors. This discrepancy may be caused by the different contexts. The function of m6A is context dependent; that is, the biological outcome is related to the cell types, external stimuli, subcellular localization of effectors and locations of m6A sites on mRNA. Therefore, when observing the regulatory effect of HMBOX1 on telomeres, it needs to be determined according to specific circumstances.
Conclusions and outlook
As a transcriptional suppressor, HMBOX1 has been studied less than other homeobox genes. Currently, research is mainly focused on its regulatory role in cell differentiation and development, inflammation, tumor development, and telomerase regulation (Fig. 1). The regulated molecules or pathways are summarized in Table 2. However, the specific mechanisms are still not fully understood and need to be explored in further studies. In addition, most of the current studies on the regulatory role of HMBOX1 as a transcription factor lack direct evidence and only observe the expression changes of downstream genes on the basis of the intervention on HMBOX1 expression. The research on targeting needs to be rich in research data. With the progress in experimental technology and techniques, higher requirements have been put forward for related research.
Table 2 Summary of HMBOX1 regulated molecules or pathways
Author Year Tissue or cell type Regulated molecules or pathways HMBOX1 Function Reference
Su L, et al. 2010 BMSCs Downstream: IP-10, Ets-1 Promote BMSCs’ differentiation to VECs [12]
Han L, et al. 2012 ESCs Downstream: FGF-2 Promote ESCs’ differentiation to VECs [13]
Lu W, et al. 2016 BMSCs Downstream: CD163/FGF-2 Promote BMSCs’ differentiation to vascular endothelial-like cells [14]
Ma H , et al. 2015 HUVECs Combine with: MT2A
Downstream: mTOR, caspase-3 Inhibit apoptosis and promote autophagy [16]
Ma H, et al. 2016 HUVECs Upstream: ANXA7/TGFB2-OT1/LARP1 Essential for the survival of HUVECs [15]
Meng N, et al. 2019 VECs Upstream: hnRNP E1
Downstream: mTOR, caspase-3 Inhibit apoptosis and promote autophagy [18]
Wu L, et al. 2011 NK cells Downstream: NKG2D/DAP10 Negatively regulates NK cell functions [23]
Wu L, et al. 2011 NK cells Downstream: IFN-γ Negatively regulates interferon γ production in NK cells [24]
Gong J, et al. 2012 NK cells Upstream: miR-30c-1* Negatively regulates NK cell cytotoxicity [25]
Zhao H, et al. 2018 Hepatocytes Downstream: NF-κB/CCL2 Negatively regulates infiltration of macrophages and neutrophils [35]
Yuan HX, et al. 2018 EA.hy926 cells Downstream: NF-κB and MAPK pathway Inhibits LPS-induced inflammation [33]
Tu GW, et al. 2022 Human myocardial cells (AC16) Upstream: miR-885-5p Inhibits the elevation of IL-1β, IL-18, NLRP3, caspase-1, and GSDMD-N in AC16 cells in an NF-κB dependent way [34]
Nie M, et al. 2022 Human periodontal ligament stem cells Downstream: NF-kB/CXCL10 Attenuates LPS-induced periodontal ligament stem cell injury [32]
BMSCs – bone marrow stromal cells, VECs – vascular endothelial cells, ESCs – embryonic stem cells, MT2A – metallothionein 2A, HUVECs – human umbilical vein endothelial cells, ANXA7 – annexin A7, TGFB2-OT1 – TGFB2 overlapping transcript 1, LARP1 – La-related protein 1, hnRNP E1 – heterogeneous nuclear ribonucleoprotein E1, IFN-γ – interferon γ
Fig. 1 HMBOX2 function summary diagram. Lines ending with arrows or bars indicate activating or inhibitory effects, respectively
MT2A – metallothionein 2A, hnRNP E1 – heterogeneous nuclear ribonucleoprotein E1, IFN-γ – interferon γ, TNF-α – tumor necrosis factor α, m6A – methylation on N6-adenosine, FGF-2 – fibroblast growth factor 2, Ets1 – ETS proto-oncogene 1
At present, HMBOX1 is believed to mainly play an anti-inflammatory role in inflammatory responses. However, whether, like other homeobox genes, it can function as a pro-inflammatory protein remains to be explored. Currently, HMBOX1 is considered to be a double-edged sword in tumor progression and may have different functions in different tumor cells, suggesting that tumor cell types should be considered when designing treatment regimens targeting HMBOX1. In addition, there are no small molecule inhibitors targeting HMBOX1, and the inhibition of HMBOX1 expression is mainly through gene level intervention. The tumor microenvironment, especially the immune-related microenvironment, plays an important role in the occurrence and development of malignant tumors. However, whether HMBOX1 is involved in regulating the formation of the microenvironment and the mechanism of this remain to be explored.
In general, the current research on the function of HMBOX1 is still in its infancy, and research on its role and mechanisms in various physiological and pathological conditions is insufficient and in need of further study. This, of course, suggests that there is still plenty of scope for researchers to study this molecule and that more attention should be paid to molecules in their infancy than to those that have been extensively studied.
Acknowledgements
We would like to thank Editage (www.editage.cn) for English language editing.
Funding statement
This work was supported by the Natural Science Foundation of China (No. 81901610), the Jinan Technology Development Program (202019118) and Medical and Health Science and Technology Development Program of Shandong Province (202102070368).
The authors declare no conflict of interest.
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PMC010xxxxxx/PMC10191087.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37198387
813
10.1007/s40121-023-00813-1
Original Research
Dynamic NLR and PLR in Predicting COVID-19 Severity: A Retrospective Cohort Study
http://orcid.org/0000-0002-1618-8867
Asperges Erika [email protected]
1
Albi Giuseppe 2
Zuccaro Valentina 1
Sambo Margherita 13
Pieri Teresa C. 13
Calia Matteo 13
Colaneri Marta 1
Maiocchi Laura 1
Melazzini Federica 4
Lasagna Angioletta 5
Peri Andrea 6
Mojoli Francesco 7
Sacchi Paolo 1
Bruno Raffaele [email protected]
13
1 grid.419425.f 0000 0004 1760 3027 U.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
2 grid.8982.b 0000 0004 1762 5736 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
3 grid.8982.b 0000 0004 1762 5736 Dipartimento di Scienze Clinico-Chirurgiche, Diagnostiche e Pediatriche-Università di Pavia, Pavia, Italy
4 grid.419425.f 0000 0004 1760 3027 U.O.C. Medicina Interna Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
5 grid.419425.f 0000 0004 1760 3027 U.O.C. Oncologia Medica Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
6 grid.419425.f 0000 0004 1760 3027 Dipartimento di Chirurgia Fondazione, IRCCS Policlinico San Matteo, Pavia, Italy
7 grid.419425.f 0000 0004 1760 3027 U.O.C. Anestesia e Rianimazione Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
17 5 2023
17 5 2023
6 2023
12 6 16251640
9 3 2023
18 4 2023
© The Author(s) 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Introduction
The hyperinflammation phase of severe SARS-CoV-2 is characterised by complete blood count alterations. In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) can be used as prognostic factors. We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict four outcomes: use of continuous positive airways pressure (CPAP), intensive care unit (ICU) admission, invasive ventilation and death.
Methods
We retrospectively included all adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia admitted from 23 January 2020 to 18 May 2021. Analyses included non-parametric tests to study the ability of NLR and PLR to distinguish the patients’ outcomes at each timepoint. Receiver operating characteristic (ROC) curves were built for NLR and PLR at each timepoint (minus discharge) to identify cutoffs to distinguish severe and non-severe disease. Their statistical significance was assessed with the chi-square test. Collection of data under the SMACORE database was approved with protocol number 20200046877.
Results
We included 2169 patients. NLR and PLR were higher in severe coronavirus disease 2019 (COVID-19). Both ratios were able to distinguish the outcomes at each timepoint. For NLR, the areas under the receiver operating characteristic curve (AUROC) ranged between 0.59 and 0.81, and for PLR between 0.53 and 0.67. From each ROC curve we computed an optimal cutoff value.
Conclusion
NLR and PLR cutoffs are able to distinguish severity grades and mortality at different timepoints during the course of disease, and, as such, they allow a tailored approach. Future prospects include validating our cutoffs in a prospective cohort and comparing their performance against other COVID-19 scores.
Keywords
COVID-19
Cutoff
ICU
Mortality
NLR
PLR
Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics101016233 101016233 101016233 Albi Giuseppe Sacchi Paolo Bruno Raffaele issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
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pmcKey Summary Points
The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are markers of inflammatory status that can be used as prognostic factors in COVID-19.
We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict use of continuous positive airways pressure (CPAP), ICU admission, invasive ventilation and death.
NLR and PLR were higher in severe COVID-19 and were able to distinguish the outcomes at each timepoint.
An optimal cutoff value was computed for NLR and PLR to predict severity outcomes.
Introduction
Early in the SARS-CoV-2 pandemic, Siddiqi et al. proposed a model on the natural history of SARS-CoV-2 infection that comprises a viral and an inflammatory phase and three stages: early infection, which presents with systemic, mild symptoms, a pulmonary stage, when pneumonia and local inflammation develops, and systemic hyperinflammation that involves only the minority of patients who progress to severe illness with multiorgan involvement [1].
Several markers have been proposed to identify the progression towards this last stage, and C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), troponin I, leucocyte alterations and thrombocytopenia have already been confirmed to be of prognostic significance [2, 3]. Leucocyte alterations involve both the lymphocyte and the neutrophil populations. Neutrophils, in fact, possess several inflammatory and pro-thrombotic properties such as the production of neutrophil extracellular traps (NETs) and reactive oxygen species (ROS), which can lead to sustained inflammation during SARS-CoV-2 infection [4–6]. Lymphocytes, on the other hand, mainly exhibit the hallmarks of immune exhaustion with CD4 and CD8 T-cell loss, which has been linked to an amplified inflammatory response due to the persistent viral load and the consequent neutrophil stimulation [4, 5].
In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) could be used as prognostic factors. Thanks to their availability, low cost and reproducibility, they already have been proposed as inflammatory and prognostic markers in a variety of specialties (traumatic brain injury, cardiovascular diseases, hepatic and pancreatic diseases, solid cancer, rheumatic diseases and chronic obstructive pulmonary diseases) [7–12].
Several studies have shown a similar trend in COVID-19. Three small Chinese studies performed early in the pandemic demonstrated that a high NLR correlates with severity and risk of progression [13–15]. Subsequent studies confirmed this finding, adding that an upward trend correlates with mortality and that peak NLR is associated with mechanical ventilation [16–18]. A large meta-analysis including 30 studies and 5570 patients confirmed that NLR accurately determines COVID-19 severity [19].
Similarly, several studies have determined that a baseline low platelet count and a downward trend are associated with severe COVID-19 [20, 21]. Studies that focus specifically on PLR are scant, but demonstrate that a high PLR and a big PLR increase during treatment is associated with a longer hospital stay and more severe disease [22, 23].
Our aims are:To describe NLR and PLR trends at different timepoints during the hospital stay of patients with COVID-19;
To compute for each timepoint an optimal cutoff that predicts the risk of four outcomes: use of mechanical non-invasive ventilation (in the form of continuous positive airways pressure—CPAP), ICU admission, invasive ventilation and death. For each cutoff, we will also produce a receiver operating characteristic (ROC) curve and we will compute the area under the receiver operating characteristic curve (AUROC) as well as the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV);
To compare NLR and PLR performance as predictors.
Methods
Design and Setting
This is a cohort retrospective study, conducted at the Fondazione IRCCS Policlinico San Matteo (Pavia, Italy), an academic hospital. All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Eligibility
All adult patients (older than 18 years) with pneumonia and a positive RT-PCR SARS-CoV-2 nasopharyngeal swab who were admitted to the hospital from 21 February 2020 to 18 May 2021 were included in the study. Patients without at least one value of neutrophils, lymphocytes and platelets were excluded. No patients were excluded on the basis of comorbidities that could impact their complete blood count, in an effort to improve generalisation of our results.
Ethics Compliance
All patients provided informed consent for the use of clinical data for scientific purpose according to hospital policy. Administrative data about our hospital’s patients with COVID-19 are collected in a registry (SMACORE), approved by the Fondazione IRCCS Policlinico San Matteo’s ethics committee with protocol number 20200046877, within the framework of the PERISCOPE (Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics) European project, funded by Horizon 2020 (grant agreement 101016233).
Data and Variables
Data extracted from the SMACORE registry include epidemiological data (age and gender), duration of hospital stay and wards of stay. Comorbidities data were extracted using ICD-9-CM codes. We selected diabetes mellitus (250), chronic obstructive pulmonary disease (from 490 to 496), hypertension (from 401 to 405) and ischaemic heart disease (from 410 to 414).
Outcome data include ICU stay, death, use of intubation/tracheostomy/invasive ventilation and non-invasive mechanical ventilation (CPAP). ICD-9-PCS codes were used to identify patients who underwent intubation (96.04, 96.05), tracheostomy (31.1), invasive ventilation (96.70, 96.71, 96.72) and non-invasive mechanical ventilation (93.90). In the context of this study, intubation, tracheostomy and invasive ventilation are considered under a single outcome called “invasive ventilation”. No missing data were detected in all the extracted variables.
Laboratory data, extracted from the patients’ records, include neutrophils, lymphocytes and platelet count. Laboratory data were extracted at four timepoints: admission (0–24 h), 48 h (24–72 h), 7 days (120–216 h) and discharge (within the last 48 h before discharge). If more than one value of neutrophils, lymphocytes and platelets was available at each timepoint, the mean value was used to compute NLR and PLR.
Data were anonymised before use.
Statistical Analyses
Categorical data are presented as absolute frequencies and proportions. Continuous data are presented by mean and standard deviation if normally distributed or by median and interquartile range if not (Shapiro test is used to assess the normality hypothesis).
To study the ability of NLR and PLR to distinguish the patients’ outcomes at each timepoint, a non-parametric (Mann–Whitney U) test was used (univariate analysis). We performed a subset of univariate analyses to assess the statistical difference in NLR and PLR values according to the comorbidities.
ROC curves were built for NLR and PLR at each timepoint (minus discharge) for males and females in order to identify a cutoff to distinguish severe and non-severe disease. We defined our optimal cutoff as the NLR or PLR value with the highest sensibility and a minimum specificity of 0.5, to have the smallest possible number of false negatives. The statistical significance of the cutoff values was assessed with the chi-square test, applied to 2 × 2 adjacency tables from which classification metrics, such as sensitivity and specificity, were extracted.
We also compared NLR and PLR ROC curves with DeLon’s test to define which is the best predictor of each outcome at the four timepoints.
All statistical tests were two-sided, and a p value of 0.05 was used.
Data pre-processing, descriptive analysis and univariate analyses were handled with Python 3.7.
ROC curves and cutoff analyses were performed on R 4.0.5.
Results
Population
We found 2204 eligible patients. Readmissions were excluded, generating a total of 2169 patients. The mean age at admission was 68 ± 16 years; 1317 were males (60.7%). Figure 1 correlates the number of admitted patients with age and gender. Hypertension was the most common comorbidity, presenting in 33.5% of patients. The clinical characteristics of the patients population are reported in Table 1.Fig. 1 Pyramid plot of age and gender of the study population. At the extremes (≤ 39 and ≥ 85 years) patients are prevalently females, while in the middle, which constitutes the majority of admissions, they are prevalently male
Table 1 Main clinical characteristics of the populations, extracted from the SMACORE registry
Clinical variable Total population
N = 2169
Age, years 68 ± 16
Sex
Male 1317 (60.7%)
Female 852 (39.3%)
LOS, days 12 ± 13
Diabetes mellitus 364 (16.8%)
COPD 151 (6.9%)
Hypertension 727 (33.5%)
Ischaemic disease 261 (12%)
Numerical variables are presented as mean and standard deviation, while categorical variables with count and percentage
Distribution of length of stay (LOS) is shown in Fig. 2. The mean LOS was 12 ± 13 days; the maximum was 141.Fig. 2 Histogram showing the distribution of patients’ length of stay (LOS), reported in days. The mean LOS was 12 ± 13 days, and the maximum was 141 days
Patients were treated according to the latest evidence available at the time of their admission (e.g. lopinavir/ritonavir and remdesivir early in the pandemic, and remdesivir and steroids later).
Outcomes
The prevalence of each outcome (CPAP, ICU admission, invasive ventilation and death) in the population is presented in Table 2.Table 2 Prevalence of the four outcomes in the overall population, in males and in females
Outcome Total population Males Females
CPAP or NIV 0.285 (0.266, 0.304) 0.334 (0.308, 0.359) 0.21 (0.183, 0.237)
ICU admission 0.167 (0.151, 0.183) 0.22 (0.198, 0.242) 0.084 (0.065, 0.103)
Invasive ventilation 0.104 (0.091, 0.117) 0.14 (0.121, 0.159) 0.048 (0.034, 0.062)
Death 0.253 (0.235, 0.271) 0.264 (0.240, 0.288) 0.234 (0.205, 0.262)
Prevalences are presented as proportion and 95% confidence interval
Neutrophil, Lymphocyte, Platelet, NLR and PLR Trend
In Fig. 3 we report the mean trend at the four timepoints of neutrophils, lymphocytes and platelets, while Fig. 4 shows the mean trend of NLR and PLR. Females experience lower mean levels of neutrophils and higher mean levels of lymphocytes. Both NLR and PLR are higher in males than in females, but while NLR decreases during hospitalisation in both genders, PLR decreases in males but remains stable in females.Fig. 3 Neutrophil (A), platelet (B) and lymphocyte (C) mean trends (with 95% confidence interval) from the total population are presented in grey with a dashed line; the blue and red lines represent the mean trend for male and female patients, respectively
Fig. 4 Ratio trends at the four timepoints. NLR (A) and PLR (B) mean trends (with 95% confidence intervals) from the total population are presented in grey with a dashed line; the blue and red lines represent the mean trend for male and female patients, respectively. NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio
The trends at the four timepoints for each outcome are shown in Fig. 5. The orange lines show the trend of patients who had severe COVID-19 (patients who underwent invasive or non-invasive ventilation, patients who were admitted to the ICU and patients who died), while the blue lines show the trend of patients who had non-severe COVID-19.Fig. 5 Ratio trends for each outcome at the four timepoints. NLR (A) and PLR (B) mean trends (with 95% confidence intervals) are in orange for the patients with the outcome and in blue for the patients without the outcome. P values are reported for each outcome and express the significance of the difference in the NLR or PLR values for each timepoint (Mann–Whitney test). NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio
Both NLR and PLR are always higher in severe COVID-19 at all the timepoints, with a gap that gets bigger with more severe disease. From admission, both NLR and PLR show a progressive increase reaching a peak at 7 days. This trend is steeper for more severe outcomes. There are two exceptions to this: NLR and PLR trends for the CPAP outcome, which increase from admission to 48 h, but decrease slightly after the 48 h timepoint and plummet at discharge. This could be explained by the fact that CPAP therapy is usually implemented in the first hours after admission but rarely as late as the seventh day of hospitalisation. The second exception is the NLR trend for the death outcome: the orange line continually increases, diverging from the blue line that shows the normalisation of NLR of patients who survived.
Univariate Analyses
In Table 3 we report the number of patients who had at least one available measure of NLR and PLR at each timepoint and the results of the first univariate analyses. NLR and PLR values for the first three timepoints were able to distinguish between the outcomes, with the exception of PLR at admission that cannot predict ICU admission. NLR and PLR at discharge distinguish between patients who died and patients who survived.Table 3 Univariate analysis (Mann–Whitney test) on NLR and PLR values for each outcome at the four timepoints
Timepoints Available patients, n (%) Outcome P value (NLR) P value (PLR)
Admission 1948 (89.8%) CPAP or NIV < 0.001 < 0.001
ICU admission 0.021 0.081
Invasive ventilation < 0.001 0.032
Death < 0.001 < 0.001
48 h 1981 (91.3%) CPAP or NIV < 0.001 < 0.001
ICU admission < 0.001 < 0.001
Invasive ventilation < 0.001 < 0.001
Death < 0.001 < 0.001
7 days 1623 (74.8%) CPAP or NIV < 0.001 < 0.001
ICU admission < 0.001 < 0.001
Invasive ventilation < 0.001 < 0.001
Death < 0.001 < 0.001
Discharge 1326 (61.1%) CPAP or NIV 0.35 < 0.001
ICU admission 0.95 0.071
Invasive ventilation < 0.001 0.23
Death < 0.001 < 0.001
Number and percentage of patients with at least one available NLR and PLR value at each timepoint is reported in the second column. P value is considered significant if < 0.05. Results are truncated to < 0.001 if lower than this value
In addition, Table 4 presents the results of the univariate analysis conducted to test the difference in NLR and PLR values between patients with and without the considered comorbidity.Table 4 Univariate analysis (Mann–Whitney test) on NLR and PLR values for each of the considered comorbidities at the four timepoints
Timepoints Outcome P value (NLR) P value (PLR)
Admission Diabetes mellitus 0.756 0.518
COPD 0.913 0.804
Hypertension 0.653 0.621
Ischaemic disease 0.763 0.326
48 h Diabetes mellitus 0.416 0.302
COPD 0.967 0.773
Hypertension 0.599 0.022
Ischaemic disease 0.037 0.098
7 days Diabetes mellitus 0.901 0.957
COPD 0.048 0.385
Hypertension 0.357 0.021
Ischaemic disease 0.029 0.335
Discharge Diabetes mellitus 0.428 0.633
COPD 0.003 0.038
Hypertension 0.662 0.638
Ischaemic disease 0.005 0.016
P value is considered significant if < 0.05. Results are truncated to < 0.001 if lower than this value
Cutoff Analyses (ROC Curves)
The results of ROC curve analyses for NLR and PLR values for males and females are presented in Tables 5 and 6. From each ROC curve we computed an optimal cutoff value, which is presented with sensitivity, specificity, accuracy, and positive and negative predictive values (PPV and NPV respectively). For example, the NLR cutoff for CPAP use at admission is 7.00 for males and 6.36 for females, and 239.22 for males and 233.00 for females for PLR. The cutoffs have been plotted in Figs. 6 and 7 in order of timepoints and outcome severity.Table 5 Results of the ROC curve analyses for NLR for males and females
Timepoint Outcome Cutoff Sensitivity Specificity Accuracy AUROC PPV NPV
Males
Admission CPAP or NIV 7.00 0.65 0.51 0.55 0.59 0.41 0.73
ICU admission 7.2 0.64 0.50 0.53 0.59 0.27 0.83
Invasive ventilation 7.29 0.67 0.50 0.53 0.62 0.19 0.90
Death 7.00 0.66 0.50 0.55 0.60 0.33 0.80
48 h CPAP or NIV 7.33 0.69 0.50 0.57 0.63 0.42 0.75
ICU admission 7.47 0.77 0.51 0.57 0.68 0.33 0.88
Invasive ventilation 7.66 0.84 0.50 0.55 0.71 0.23 0.95
Death 7.27 0.77 0.50 0.57 0.70 0.35 0.87
7 days CPAP or NIV 6.17 0.67 0.50 0.56 0.61 0.42 0.75
ICU admission 5.67 0.85 0.50 0.59 0.73 0.38 0.90
Invasive ventilation 6.03 0.91 0.50 0.57 0.78 0.28 0.96
Death 6.03 0.88 0.51 0.59 0.81 0.33 0.94
Females
Admission CPAP or NIV 6.36 0.62 0.51 0.53 0.60 0.27 0.82
ICU admission 6.61 0.61 0.50 0.51 0.60 0.11 0.93
Invasive ventilation 7.00 0.67 0.55 0.55 0.63 0.07 0.97
Death 6.28 0.66 0.51 0.55 0.62 0.31 0.82
48 h CPAP or NIV 5.57 0.72 0.50 0.55 0.66 0.30 0.86
ICU admission 5.87 0.82 0.50 0.53 0.68 0.14 0.97
Invasive ventilation 6.05 0.76 0.51 0.52 0.69 0.08 0.98
Death 5.35 0.77 0.50 0.56 0.71 0.31 0.88
7 days CPAP or NIV 4.82 0.72 0.50 0.56 0.65 0.31 0.85
ICU admission 5.15 0.81 0.53 0.56 0.71 0.19 0.95
Invasive ventilation 5.38 0.87 0.54 0.56 0.75 0.12 0.98
Death 4.61 0.92 0.50 0.57 0.81 0.29 0.96
For each timepoint and for each outcome the table reports the NLR cutoff values used for the prediction and their sensitivity, specificity, accuracy, AUROC, PPV and NPV
Table 6 Results of the ROC curve analyses for PLR for males and females
Timestep Outcome Cutoff Sensitivity Specificity Accuracy AUROC PPV NPV
Males
Admission CPAP or NIV 239.22 0.61 0.50 0.54 0.58 0.39 0.71
ICU admission 245.86 0.61 0.50 0.53 0.56 0.26 0.82
Invasive ventilation 248.00 0.61 0.50 0.52 0.57 0.17 0.88
Death 250.39 0.55 0.51 0.52 0.53 0.30 0.75
48 h CPAP or NIV 250.05 0.72 0.50 0.58 0.64 0.43 0.78
ICU admission 281.24 0.70 0.50 0.55 0.62 0.30 0.85
Invasive ventilation 288.62 0.72 0.50 0.54 0.63 0.20 0.91
Death 292.85 0.59 0.50 0.52 0.58 0.29 0.78
7 days CPAP or NIV 261.35 0.73 0.50 0.58 0.63 0.44 0.78
ICU admission 268.72 0.73 0.50 0.56 0.65 0.35 0.83
Invasive ventilation 278.74 0.75 0.50 0.55 0.67 0.24 0.91
Death 276.00 0.72 0.50 0.55 0.65 0.28 0.87
Females
Admission CPAP or NIV 233.00 0.65 0.50 0.53 0.60 0.27 0.83
ICU admission 245.90 0.56 0.51 0.52 0.58 0.10 0.92
Invasive ventilation 246.45 0.56 0.51 0.51 0.58 0.06 0.96
Death 241.54 0.56 0.51 0.53 0.55 0.27 0.78
48 h CPAP or NIV 231.53 0.75 0.50 0.56 0.67 0.30 0.87
ICU admission 248.73 0.70 0.51 0.53 0.64 0.12 0.95
Invasive ventilation 250.27 0.68 0.51 0.52 0.62 0.07 0.97
Death 246.19 0.59 0.51 0.53 0.58 0.26 0.81
7 days CPAP or NIV 241.00 0.69 0.50 0.55 0.62 0.31 0.83
ICU admission 257.59 0.61 0.53 0.54 0.61 0.15 0.91
Invasive ventilation 258.23 0.62 0.52 0.53 0.58 0.08 0.95
Death 247.05 0.66 0.50 0.53 0.59 0.23 0.86
For each timepoint and for each outcome the table reports the PLR cutoff values used for the prediction and their sensitivity, specificity, accuracy, AUROC, PPV and NPV
Fig. 6 NLR cutoff values for males and females, plotted in order of timepoint and severity. NLR, neutrophil-to-lymphocyte ratio
Fig. 7 PLR cutoff values for males and females, plotted in order of timepoint and severity. PLR, platelet-to-lymphocyte ratio
Figure 8 shows the composite ROC curves with area under the receiver operating curve (AUROC) values (a higher number indicates a better predictive performance). NLR performed better than PLR, and males performed better than females. For NLR, the lowest AUROC is 0.59 for CPAP and ICU admission at the admission timepoint for males, while the highest is 0.81 for both genders for death at 7 days. For PLR, the lowest AUROC is 0.53 for death at admission for males, while the highest is 0.67 for CPAP at 48 h for females and for invasive ventilation at 7 days for males.Fig. 8 ROC curves for NLR (A) and PLR (B) cutoffs’ ability at predicting each outcome at admission, 48 h and 7 days. AUROCs are reported in the legends
NLR and PLR Comparison
The comparison of NLR and PLR ROC curves (Fig. 9) showed that NLR generally performs better, with comparable curves only for the CPAP outcome.Fig. 9 Comparison of NLR (blue) and PLR (orange) ROC curves for each outcome at each timepoint performed with DeLong’s test. P value is considered significant if < 0.05. Results are truncated to < 0.001 if lower than this value. NLR: Neutrophil to Lymphocyte Ratio; PLR: Platelet to Lymphocyte Ratio
Discussion
We showed that NLR and PLR are able to distinguish between patients with mild/moderate COVID-19 and patients with severe disease (with need of invasive/non-invasive ventilation or ICU admission and patients who died). In fact, both ratio show higher values in the population with severe disease. An NLR that is continually increasing at successive timepoints, in particular, is able to predict death.
We also computed cutoffs for males and females for the four outcomes at each timepoint. Males’ NLR cutoffs are higher than females’ of at least 1 point. Cutoffs’ sensitivity is also higher in males. At each timepoint an “easy to remember” value (e.g. 6.3 and 7.0 at admission for females and males, respectively, or 5.3 and 7.3 at 48 h for males and females, respectively) could be quite good at predicting all the outcomes, with a sensitivity that increases progressively at successive timepoints (the minimum is 0.61 for females at admission for ICU admission; the maximum 0.91 for males at 7 days for invasive ventilation). The positive predictive values are low, but the negative predictive values range from 0.73 to 0.98, making our cutoffs efficacious in excluding patients at risk if their NLR is lower. The same can be said for PLR cutoffs, even though, differently from NLR, they start out similar for males and females. Moreover, at a single timepoint, PLR cutoffs can be quite different depending on the outcome (e.g. 250 versus 293 for males at 48 h for CPAP and death, respectively), possibly making it easier to distinguish different grades of severity.
Finally, we showed that NLR is generally better than PLR at predicting severity.
A Chinese study aimed at finding NLR and PLR reference values for healthy subjects showed that both are sex and age dependent [24]; successive studies confirmed that this distinction is maintained in disease [25], in line with our findings.
Four Chinese studies plotted NLR and PLR from admission to post-discharge and obtained trends similar to ours, with stable and lower values for patients with non-severe disease and higher and increasing values for patients with severe disease [16, 22, 26, 27].
Regarding the predictive value of the ratios and their cutoffs, several studies plotted ROC curves for NLR and PLR, finding that they perform well in predicting severity and death, with NLR performing better than PLR in those studies that compared them [14, 16, 17, 23, 27–29]. A Turkish study on 306 patients found that at admission NLR (but not PLR) significantly predicted COVID-19 pneumonia, and they computed a cutoff of 1.73 [28]. A similar study on 69 patients found a cutoff for severe disease of 3.3 for NLR and 180 for PLR, and another on 61 patients used 3.13 for NLR [13, 23]. All of these studies, however, did not aim at distinguishing patients with severe disease from those with non-severe disease, but patients with or without pneumonia, which could explain the difference between their cutoffs and ours, which are much higher. In fact, a study which tried to distinguish different grades of severity (common cases, severe-non-ICU patients and severe-ICU patients) found values of NLR and PLR that increase with severity, although they did not compute any cutoffs [27]. A cutoff was actually computed by Ye et al., who found a NLR cutoff of 7.13 for death and 7.28 for invasive ventilation, both values that are very similar to our own [16]. An Indian study limited to patients with severe disease found a cutoff of 5.1 for mortality, but it did not specify at which point during hospitalisation the NLR was extracted, and two Iranian ones have the same limitation and, moreover, are focused only on patients with specific characteristics [17, 30, 31]. Finally, Yldiz et al. computed a 5.94 NLR cutoff for mortality on a derivation cohort and subsequently validated it in a successive cohort [29].
All these studies have the limitation of being retrospective and limited to a single centre with no more than a few hundred patients. Our study presents the same limitations, but dealt with more than 2000 patients and avoided stringent exclusion criteria, thus limiting the confounding factors and outliers and expanding the generalisation of our results as much as possible. We also divided our results for males and females, which we consider a strength as it takes into consideration the physiologic differences of NLR and PLR and adds a further level of stratification. Moreover, our cutoffs not only deal with severity and mortality but allow for a more tailored approach, dividing patients with severe disease in those who can still be treated in an ordinary ward, possibly with the implementation of CPAP, and those who need early referral to the ICU.
Another limitation is selection bias, since we selected our patients and stratified them in severity outcomes based on ICD9 codes, which can be inaccurate.
Finally, another confounder could be the use of anti-inflammatory or myelotoxic drugs, but this effect should be corrected by the number and the periodicity of observations. In fact, the greatest usefulness of the cutoffs lies in the admission and 48 h timepoints, where the steroid-induced neutrophilia is not yet present.
Conclusion
Our study shows that NLR and PLR can distinguish mortality, need for ventilation (non-invasive and invasive), and ICU admission, and their trend correlates with disease severity. We also computed male and female cutoffs (with good sensitivity and negative predictive values) for the various stages of severity at different timepoints during hospitalisation. Our score is extremely handy and easy to compute, while many other proposed tools are difficult to measure or vary at different timepoints, which might explain their conflicting results. In fact, future prospects include validating our cutoffs in a prospective, possibly multi-centric cohort and comparing their performance against scores such as the Quick COVID-19 Severity Index (qCSI), and the 4C Mortality Score.
Acknowledgements
We thank the participants of the study.
Funding
This research was granted by the Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics (PERISCOPE) H2020-SC1-PHE-CORONAVIRUS-2020-2 PERISCOPE Project (grant agreement 101016233). The rapid service publication fee will be paid by the Fondazione IRCCS Policlinico San Matteo’s Scientific Direction.
Author Contributions
Conceptualisation: Valentina Zuccaro, Federica Melazzini, Angioletta Lasagna, Andrea Peri and Paolo Sacchi; Methodology, Valentina Zuccaro, Paolo Sacchi, Giuseppe Albi and Erika Asperges; Formal Analysis, Giuseppe Albi; investigation: Margherita Sambo, Teresa C. Pieri, Matteo Calia, Marta Colaneri and Laura Maiocchi; Resources, Raffaele Bruno; data curation: Giuseppe Albi; writing – original draft preparation: Giuseppe Albi and Erika Asperges; writing – review and editing: Valentina Zuccaro, Paolo Sacchi, Federica Melazzini, Francesco Mojoli and Raffaele Bruno; visualisation: Giuseppe Albi; Supervision, Paolo Sacchi, Francesco Mojoli and Raffaele Bruno. All authors read and approved the final manuscript.
Prior Presentation
An abstract based on this study was presented at ECCMID 2022 in Lisbon (23–26 April 2022).
Disclosures
The authors Erika Asperges, Giuseppe Albi, Valentina Zuccaro, Margherita Sambo, Teresa C Pieri, Matteo Calia, Marta Colaneri, Laura Maiocchi, Federica Melazzini, Angioletta Lasagna, Andrea Peri, Francesco Mojoli, Paolo Sacchi and Raffaele Bruno have nothing to disclose.
Compliance with Ethics Guidelines
All patients provided informed consent for the use of clinical data for scientific purpose according to hospital policy. Collection of data under the SMACORE database was approved by the Fondazione IRCCS Policlinico San Matteo’s ethics committee (protocol number 20200046877). The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki.
Data Availability
The published article includes all datasets generated or analysed during the study. Data sharing is available from the corresponding author upon reasonable request.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Erika Asperges and Giuseppe Albi share first authorship.
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PMC010xxxxxx/PMC10193331.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37199881
818
10.1007/s40121-023-00818-w
Original Research
A Comparison of Diagnostic Criteria for Invasive Pulmonary Aspergillosis in Critically Ill Patients
Liu Rui-ting
Chen Yan
Li Shan
Wan Xi-xi
Weng Li
Peng Jin-min [email protected]
Du Bin [email protected]
grid.413106.1 0000 0000 9889 6335 Medical ICU, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, 1 Shuai Fu Yuan, Beijing, 100730 People’s Republic of China
18 5 2023
18 5 2023
6 2023
12 6 16411653
5 4 2023
2 5 2023
© The Author(s) 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Introduction
Invasive pulmonary aspergillosis (IPA) is a common infection in intensive care units (ICUs). There are no consensus criteria for defining IPA in the ICU. We aimed to compare the diagnosis and prognosis performances of three criteria (the 2020 EORTC/MSG criteria, the 2021 EORTC/MSG ICU criteria, the modified AspICU criteria (M-AspICU)) for IPA in the ICU.
Methods
In this retrospective study from our single center, we applied the three different criteria for IPA in patients with suspected pneumonia and undergoing at least one mycological test between November 10, 2016 and November 10, 2021. We compared the diagnosis agreement and prognosis performances of these three criteria in the ICU.
Results
Overall, 2403 patients were included. The rates of IPA according to the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU were 3.37%, 6.53%, and 23.10%, respectively. Diagnostic agreement among these criteria was poor (Cohen’s kappa 0.208–0.666). IPA diagnosed by either the 2020 EORTC/MSG (odds ratio = 2.709, P < 0.001) or the 2021 EORTC/MSG ICU (odds ratio = 2.086, P = 0.001) criteria was independently associated with 28-day mortality. IPA diagnosed by M-AspICU is an independent risk factor of 28-day mortality (odds ratio = 1.431, P = 0.031) when excluding patients who fulfilled neither host criteria nor radiological factors of 2021 EORTC/MSG ICU.
Conclusions
Although M-AspICU criteria have the highest “sensitivity”, IPA diagnosed by M-AspICU was not an independent risk factor of 28-day mortality. Caution is required when using the M-AspICU criteria in ICU, especially in patients with non-specific infiltration and non-classical host factors.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40121-023-00818-w.
Keywords
Invasive pulmonary aspergillosis
Intensive care unit
EORTC/MSG criteria
Modified AspICU criteria
Mortality
Diagnostic
http://dx.doi.org/10.13039/501100012166 National Key Research and Development Program of China 2021YFC2500804 issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
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pmcKey Summary Points
Why carry out this study?
Invasive pulmonary aspergillosis is a common infection in intensive care units. There are no consensus criteria for defining IPA in the ICU.
The study evaluated the diagnostic agreement and performance of latest three criteria (the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU) for diagnosing IPA in critically ill patients.
What was learned from the study?
The three criteria (the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU) for diagnosing IPA showed very poor diagnostic agreement.
IPA diagnosed by either the 2020 EORTC/MSG or the 2021 EORTC/MSG ICU criteria was independently associated with 28-day mortality. Although the M-AspICU criteria have the highest “sensitivity”, IPA diagnosed by M-AspICU was not an independent risk factor of 28-day mortality.
Caution is required when using the M-AspICU criteria in ICU, especially in patients with non-specific infiltration and non-classical host factors.
Introduction
Invasive pulmonary aspergillosis (IPA), a common opportunistic infection in the intensive care unit (ICU), is associated with increased mortality [1–4]. The reported incidence of IPA among critically ill patients varies widely from 9.4% to 69% [2–5]. The significant differences in these values can be attributed to the lack of lung histopathology, heterogeneity among different types of ICU patients, and most importantly, the lack of consensus criteria on how to define IPA in the ICU population. Rapid and accurate diagnosis of IPA is critical for early administration of appropriate antifungal therapy and improved prognosis.
The European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) first proposed diagnostic criteria for IPA in 2002 and updated these criteria in 2008 and 2020 for better use in research and clinical care [6–8]. However, these criteria are aimed primarily at immunocompromised populations. In 2021, EORTC/MSG proposed IPA criteria for the ICU population [9]. Blot et al. proposed a clinical algorithm for IPA (AspICU criteria) that relies on a positive Aspergillus culture in lower respiratory tract specimens (LRTs) and is used to differentiate infection from colonization [10]. Schauwvlieghe et al. modified the AspICU criteria (M-AspICU) with the purpose of improving low sensitivity to culture and ICU population inapplicability [3]. Both the EORTC/MSG and M-AspICU criteria classify patients with IPA as “proven” and “probable” (EORTC/MSG)/“putative” (M-AspICU) cases. “Proven” cases are only defined with positive lung histopathology results, but this invasive test is not suitable for ICU patients with severe respiratory failure, coagulation disorders, or other complications. The diagnosis of probable IPA, which relies on a combination of host factors, clinical symptoms, mycological findings, and radiological findings, is relatively non-invasive and thus more suitable for ICU patients. The updated EORTC/MSG and M-AspICU criteria focus on probable cases with enlargement of host factors and imaging results and the revision of mycological criteria to identify more IPA cases.
To date, only a few studies have compared different criteria for IPA diagnosis in critically ill patients. In this study, we compared the rates of IPA according to the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU criteria in an ICU population and evaluated the effects of the three different IPA criteria on patient outcomes.
Methods
Study Design and Population
We conducted a retrospective single-center study in two ICUs (medical and surgical) of Peking Union Medical College Hospital between November 10, 2016 and November 10, 2021. Patients with the following characteristics were included: (1) age over 18 years; (2) length of ICU stay longer than 48 h; (3) had suspected pneumonia; (4) had any of the following tests done: Aspergillus galactomannan (GM) enzyme immunoassay test in serum or bronchoalveolar lavage fluid (BALF), LRT fungal culture (sputum, BALF, bronchial brush, or aspirate), or lung histopathology; (5) had a chest computed tomography (CT) examination, and the interval between CT and mycological test was less than 7 days.
Data Collection
The following variables were collected from patient records: age, gender, comorbidities, Acute Physiology and Chronic Health (APACHE II) score on admission, and Sequential Organ Failure Assessment (SOFA) score on the day of first retained GM; if the patient did not have a GM test, we selected the day of the first retained fungal culture of the LRTs. We also collected mycological tests, including fungal cultures of LRTs, serum GM, and BALF GM and chest CT data, and lung histopathology. Chest CT data were obtained by two senior ICU doctors who reviewed the chest images. If there were any disagreements, the two doctors had a discussion, and another senior ICU doctor was consulted if a consensus could not be reached. Finally, we collected invasive mechanical ventilation and survival status at 28 days.
Diagnostic Criteria
We selected three recent diagnostic criteria to diagnose IPA in critically ill patients: (1) the EORTC/MSG criteria published by Donnelly et al. in 2020 (2020 EORTC/MSG) [7]; (2) the EORTC/MSG ICU Working Group criteria published by Bassetti et al. in 2021 (2021 EORTC/MSG ICU) [9]; and (3) the algorithms published by Schauwvlieghe et al. in 2018 (M-AspICU) [3]. The detailed diagnostic criteria are listed in Supplementary Material 1. We defined “proven” and “probable” IPA as IPA cases, while “possible” IPA and cases that could not be classified by any of the diagnostic criteria were defined as patients without IPA (“No-IPA”).
Statistical Analysis
Continuous variables are presented as mean with standard deviation or median with interquartile range and were compared by t test or Mann–Whitney U test, respectively. Categorical data are reported as proportions and compared using the chi-square test. Diagnostic agreement was calculated using Cohen’s kappa. We performed a multivariate binary logistic regression analysis to detect independent risk factors of 28-day mortality and to assess the association of each criterion with 28-day mortality. P < 0.05 was considered significant. Data were analyzed using SPSS version 21.0.1. Figures were designed using Prism version 9.0.
Ethics Statement
The study was approved by the institutional review boards and Ethics Committee of Peking Union Medical College Hospital. As a result of the retrospective nature of the study, informed written consent was waived. The study was conducted in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Results
Study Population and Characteristics
A total of 2403 patients met the inclusion criteria between November 10, 2016 and November 10, 2021. (Supplementary Material 2). The patient characteristics are shown in Table 1. Of the 2403 patients, 1462 (60.8%) were male, and the mean age was 59 years. Almost one-third of the patients were admitted to the ICU for medical conditions. The patients in our study had a median APACHE II score of 18, of whom 1376 (57.3%) required invasive mechanical ventilation. None of the patients had lung histopathology, but all had fungal cultures in LRTs. Serum GM tests were performed in 2286 (95.1%) patients, and 315 also underwent BALF GM testing.Table 1 Patient characteristics
Characteristic of patients (n = 2403)
Mean age (SD), years 59 (17.1)
Male, n (%) 1462 (60.8)
Admission type, n (%)
Medical admission 759 (31.6)
Surgical admission 1644 (68.4)
Severity of illness
APACHE II score on ICU admission, median (IQR) 18 (13–22)
SOFA score, median (IQR) 7 (6–10)
IMV, n (%) 1376 (57.3)
Comorbidities, n (%)
Diabetes mellitus 596 (24.8)
COPD 96 (4.0)
Liver cirrhosis 51 (2.1)
AKI 483 (20.1)
Influenza 50 (2.1)
Immune deficiency state
Neutropeniaa 79 (2.8)
Hematologic malignancy 120 (5.0)
Solid tumor 227 (9.4)
Solid organ transplant 24 (4.9)
Glucocorticoidb 261 (10.9)
Mycological findings
Performed serum GM, n (%) 2286 (95.1)
Serum GM ≤ 0.5 1880 (78.2)
0.5 < serum GM ≤ 1.0 309 (12.9)
Serum GM > 1.0 97 (4.0)
Serum GM, median (IQR) 0.25 (0.17–0.44)
Performed BALF GM, n (%) 315 (13.1)
BALF GM ≤ 0.8 195 (8.1)
0.8 < BALF GM ≤ 1.0 17 (0.7)
BALF GM > 1.0 103 (4.3)
BALF GM, median (IQR) 0.32 (0.25–1.91)
Performed fungal culture, n (%) 2403 (100)
Positive fungal culture, n (%) 208 (8.7)
Radiological findings n (%)
Nodule 167 (6.9)
Cavity 83 (3.5)
Consolidation 451 (18.8)
Other infiltrates 1834 (76.3)
ICU intensive care unit, APACHE Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment, IMV invasive mechanical ventilation, COPD chronic obstructive pulmonary disease, AKI acute kidney injury, GM galactomannan, BALF bronchoalveolar lavage fluid
aPresent on the day of BALF and/or first sample of culture
bGlucocorticoid treatment with prednisone equivalent of 20 mg or more per day during the past 90 days
IPA Diagnostic Rates
The frequency of IPA varied substantially among the three criteria. According to the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU criteria, IPA was present in 81 (3.37%), 157 (6.53%), and 555 (23.10%) cases, respectively. Patients with IPA diagnosed according to the 2020 EORTC/MSG also met 2021 EORTC/MSG ICU criteria, while patients with IPA diagnosed according to the 2021 EORTC/MSG ICU also met the M-AspICU criteria (Fig. 1). The characteristics of patients with IPA diagnosed using the three criteria are shown in Table 2. Compared to the M-AspICU, the 2020 EORTC/MSG and 2021 EORTC/MSG ICU criteria diagnosed more severely ill patients. There were no differences in serum GM and BALF GM among patients with IPA diagnosed by the three criteria. Patients with IPA diagnosed by the 2020 EORTC/MSG had the highest rate of positive Aspergillus culture (71.6%), while patients diagnosed using the M-AspICU had the lowest rate (37.5%).Fig. 1 The distribution of invasive pulmonary aspergillosis by single criteria and the overlap of three diagnostic criteria in all patients
Table 2 Characteristics and clinical outcomes of patients classified as IPA according to the three criteria
IPA according to 2020 EORTC/MSG (n = 81) IPA according to 2021 EORTC/MSG ICU (n = 157) IPA according to M-AspICU (n = 555) P value
Mean age (SD), years 56 (16.4) 58 (16.1) 58 (17.5) 0.263
Male, n (%) 41 (50.6) 92 (59.0) 320 (57.7) 0.448
Admission type, n (%)
Medical admission 62 (76.5) †‡ 98 (62.4)*‡ 264 (47.6)*† < 0.001
Surgical admission 19 (23.5) †‡ 59 (37.6)*‡ 291 (52.4)*† < 0.001
Severity of illness
APACHE II score on ICU admission, median (IQR) 22 (17–29)‡ 21 (16–26)‡ 19 (15–25)*† 0.001
SOFA score, median (IQR) 9 (7–13)‡ 9 (6–13) 8 (6–11)* 0.009
IMV, n (%) 66 (81.5)‡ 129 (82.2)‡ 386 (69.5)*† 0.002
Comorbidities, n (%)
Diabetes mellitus 13 (16.0)‡ 24 (15.3)‡ 153 (27.6)*† 0.001
COPD 5 (6.2)† 34 (21.7)*‡ 46 (8.3)† < 0.001
Liver cirrhosis 1 (1.2)† 16 (10.2)*‡ 24 (4.3)† 0.003
AKI 40 (49.4) 77 (49.0)‡ 220 (39.6)† 0.045
Influenza 10 (12.3)‡ 23 (14.6)‡ 27 (4.9)*† < 0.001
Immune deficiency state
Neutropeniaa 21 (25.9)†‡ 25 (15.9)*‡ 51 (9.2)*† < 0.001
Hematologic malignancy 18 (22.2) ‡ 23 (14.7) ‡ 46 (8.3)*† < 0.001
Solid tumor 2 (2.5)‡ 7 (4.5)‡ 58 (10.5)*† 0.007
Solid organ transplant 8 (9.9)‡ 11 (7.1)‡ 14 (2.5)*† 0.001
HIV 0 (0) 0 (0) 4 (0.7) 0.422
Glucocorticoidb 55 (67.9)†‡ 72 (45.9)*‡ 142 (25.6)*† < 0.001
Mycological findings
GM in serum, median (IQR) 0.56 (0.34–1.21) 0.59 (0.36–0.82) 0.61 (0.51–0.86) 0.288
GM in BALF, median (IQR) 4.05 (1.01–5.00) 4.35 (1.04–5.00) 3.34 (0.95–5.00) 0.759
Positive fungal culture, n (%) 58 (71.6)†‡ 84 (53.5)*‡ 208 (37.5)*† < 0.001
Radiological findings n (%)
Nodule 23 (28.4)‡ 42 (26.8)‡ 81 (14.6)*† < 0.001
Cavity 17 (21.0)‡ 29 (18.5)‡ 50 (9.0)*† < 0.001
Consolidation 62 (76.5)‡ 121 (77.1)‡ 252 (45.4)*† < 0.001
Clinical outcome
Length of stay in ICU, median (IQR), days 10 (5–20) 10 (5–21) 9 (4–19) 0.312
ICU mortality, n (%) 37 (45.7)‡ 61 (38.9)‡ 125 (22.5)*† < 0.001
28-day mortality, n (%) 33 (40.7)‡ 54 (34.4)‡ 110 (19.8)*† < 0.001
EORTC/MSG Criteria for IPA by The European Organization for Research and Treatment of Cancer/ Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group
IPA invasive pulmonary aspergillosis, ICU intensive care unit, M-AspICU modified algorithm for IPA in ICU, APACHE Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment, IMV invasive mechanical ventilation, HIV human immunodeficiency virus, COPD chronic obstructive pulmonary disease, GM galactomannan, BALF bronchoalveolar lavage fluid
aPresent on the day of BALF and/or first sample of culture
bGlucocorticoid treatment with prednisone equivalent of 20 mg or more per day during the past 90 days
*P < 0.05, compared with IPA according to 2020 EORTC/MSG
†P < 0.05, compared with IPA according to 2021 EORTC/MSG ICU
‡P < 0.05, compared with IPA according to M-AspICU
Diagnostic Agreement Among the Three Criteria
The diagnostic agreement between the three diagnostic criteria was poor, with a Cohen’s kappa of only 0.328 (P < 0.001). The diagnostic agreements between any two of the three criteria are shown in Fig. 2.Fig. 2 Cohen’s kappa correlation between any of the three criteria
2021 EORTC/MSG ICU Criteria vs. 2020 EORTC/MSG Criteria
Compared with the 2020 EORTC/MSG criteria, the 2021 EORTC/MSG ICU criteria diagnosed 76 additional patients with IPA. Among these patients, 37 (48.7%) were diagnosed because of the broader host criteria alone, which included chronic obstructive pulmonary disease (COPD; n = 20), severe influenza (n = 12), and decompensated liver cirrhosis (n = 10), 29 (38.2%) were diagnosed because of the lower serum GM positive threshold alone, with a median of 0.59 (0.41–0.71), and 10 (13.1%) were diagnosed owing to a combination of both.
2021 EORTC/MSG ICU Criteria vs. M-AspICU Criteria
Compared with the 2021 EORTC/MSG ICU criteria, the M-AspICU criteria diagnosed 398 additional patients with IPA. Among them, 154 (38.7%) were diagnosed because of broader host factors alone, which included solid tumors (n = 28), diabetes mellitus (n = 57), acute kidney injury (n = 66), 119 (29.9%) patients were diagnosed because of the broader radiological factors alone, and the remaining 125 (31.4%) patients were diagnosed owing to both of the above. Although the BALF GM threshold is higher for the M-AspICU criteria (1.0) than for the 2021 EORTC/MSG ICU criteria (0.8), this small difference had no effect on IPA diagnosis in our study.
Outcomes of IPA Diagnosed Using the Three Criteria
Compared to those without IPA, 28-day mortality was significantly higher in patients with IPA, regardless of the criteria used: 2020 EORTC/MSG criteria (40.7% vs. 11.4%, P < 0.001); 2021 EORTC/MSG ICU criteria (34.4% vs. 10.6%, P < 0.001); and M-AspICU criteria (19.8% vs. 9.9%, P < 0.001).
2021 EORTC/MSG ICU Criteria vs. 2020 EORTC/MSG Criteria
There was no difference in 28-day mortality between patients with IPA diagnosed using the 2021 EORTC/MSG ICU and 2020 EORTC/MSG criteria (34.4% vs. 40.7%, P = 0.335) (Fig. 3). Patients diagnosed by the 2021 EORTC/MSG ICU criteria but missed by the 2020 EORTC/MSG criteria had higher 28-day mortality than patients without IPA based on the 2021 EORTC/MSG ICU criteria (27.6% vs. 10.6%, P < 0.001). The 28-day mortality rates of the additional patients diagnosed by the 2021 EORTC/MSG ICU criteria (as a result of broader host factors, lower GM threshold, or both) were higher than the patients without IPA based on the EORTC-ICU criteria (Supplementary Material 3).Fig. 3 Kaplan–Meier for 28-day survival of patients with IPA
2021 EORTC/MSG ICU Criteria vs. M-AspICU Criteria
The 28-day mortality rate of patients with IPA diagnosed by the M-AspICU criteria was lower than that of patients diagnosed by the 2021 EORTC/MSG ICU criteria (19.8% vs. 34.4%, P < 0.001) (Fig. 3). Patients diagnosed with IPA using the M-AspICU criteria but missed by the 2021 EORTC/MSG ICU criteria had higher 28-day mortality than patients without IPA based on the M-AspICU criteria (14.1% vs. 9.9%, P = 0.014). The 28-day mortality rate of the additional patients diagnosed by the M-AspICU criteria as a result of either broader host factors or broader radiological factors alone was higher than patients without IPA based on the M-AspICU criteria. However, the 28-day mortality rate of additional patients diagnosed by the M-AspICU criteria owing to both broader host factors and broader radiological factors was similar to patients without IPA based on M-AspICU criteria (8.8% vs. 9.9%, P = 0.689) (Supplementary Material 3).
Predictive Ability of the Three Criteria for Mortality
IPA diagnosed by the 2020 EORTC/MSG or 2021 EORTC/MSG ICU criteria was independently associated with 28-day mortality, even after adjustment for intensive mechanical ventilation, acute kidney injury, and SOFA score (Fig. 4). IPA diagnosed by the M-AspICU criteria is an independent risk factor of 28-day mortality [odds ratio (OR) = 1.431, 95% confidence interval (CI) = 1.034–1.981, P = 0.031] only when excluding patients who fulfilled neither the host factors nor radiological features of 2021 EORTC/MSG ICU.Fig. 4 Forest plots of risk factors for 28-day mortality. Model 1: Analysis of risk factors based on the model includes IPA diagnosed by the 2020 EORTC/MSG criteria and other univariable factors. Model 2: Analysis risk factors based on the model includes IPA diagnosed by the 2021 EORTC/MSG ICU criteria and other univariable factors. Model 3: Analysis of risk factors based on the model includes IPA diagnosed by the M-AspICU criteria and other univariable factors. Displayed are only those variables that remained in the model during variable selection. OR odds ratio, DM diabetes mellitus, IMV invasive mechanical ventilation, SOFA Sequential Organ Failure Assessment, IPA invasive pulmonary aspergillosis, EORTC/MSG Consensus Definitions of IPA from European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group, M-AspICU modified algorithm for IPA
Discussion
We compared the diagnostic and prognostic performance of the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU criteria for IPA in an ICU population. The main findings of our study can be summarized as follows: (1) the diagnostic rate of IPA in the ICU population ranged from 3.34% based on the 2020 EORTC/MSG criteria to 23.1% based on the M-Asp ICU criteria; (2) IPA diagnosed by either the 2020 EORTC/MSG or 2021 EORTC/MSG ICU criteria was independently associated with 28-day mortality; (3) IPA diagnosed by the M-AspICU criteria is an independent risk factor of 28-day mortality when excluding patients who fulfilled neither the host factors nor radiological features of 2021 EORTC/MSG ICU.
The diagnostic rate of IPA according to the M-AspICU criteria was four times higher than that based on the 2021 EORTC/MSG ICU criteria. These findings are in alignment with the results of Schroeder et al., who compared the two criteria in critically ill patients with positive LRT Aspergillus cultures (Cohen’s kappa = 0.12) [11]. The poor diagnostic agreement between the 2020 EORTC/MSG and M-AspICU criteria was similar to the results of Szabo et al.’s study of patients with corona virus disease 2019 (COVID-19) (Cohen’s kappa = 0.154) [12].
The 2020 EORTC/MSG criteria are not suitable for ICU patients because they only can identify patients with hematological malignancies and severe immunosuppression. Patients with IPA would be missed in the ICU according to the 2020 EORTC/MSG criteria. To reduce the likelihood of missing IPA diagnoses in the ICU, the diagnostic criteria were broadened. Compared with the 2020 EORTC/MSG criteria, additional host factors including influenza and COPD were added to the 2021 EORTC/MSG ICU criteria, and the positive threshold for serum GM was decreased from 1.0 to 0.5. Schauwvlieghe et al. concluded that influenza was independently associated with IPA [3]. An autopsy of 67 patients with proven IPA indicated that half had underlying diseases with COPD [13]. According to our findings, the use of the 2021 EORTC/MSG ICU criteria increased the rate of IPA diagnosis by 48.4% compared with the 2020 EORTC/MSG criteria. Among the additional patients diagnosed with IPA using the 2021 EORTC/MSG ICU criteria, those diagnosed because of expanded host factors alone had higher mortality than patients without IPA. Similarly, those diagnosed because of the lower GM threshold also had higher mortality than those without IPA. These findings support the diagnostic value of the 2021 EORTC/MSG ICU criteria in the ICU setting.
The 2021 EORTC/MSG ICU criteria add COPD and influenza in addition to the classic host factors. ICU patients with diabetes, solid tumors, and other underlying conditions, on the other hand, are susceptible to IPA [14, 15]. We may require broader IPA criteria for ICU patients. Compared to the 2021 EORTC/MSG ICU criteria, the M-AspICU criteria further broaden the diagnostic criteria with no host factor limitations and any infiltrates on lung imaging. The M-AspICU criteria diagnosed more than three times as many patients with IPA in this study. These patients with IPA had higher mortality than patients without IPA based on the M-AspICU criteria. More than one-third of the patients were diagnosed with IPA using the M-AspICU as a result of the broadened host factors alone. Among them, 37% had DM, and 19% had solid tumors. Several studies have supported diabetes mellitus (DM) and solid tumors as risk factors for IPA in critically ill patients. This finding is consistent with the results of Taccone et al., who found that either DM or solid tumors were present in nearly 20% of patients with proven IPA [16]. Cornillet et al. reported that ground-glass opacities and pleural effusions were more common in ICU patients with IPA [14]. Huang et al. demonstrated that patchiness was the most common CT sign among critically ill patients with both IPA and COPD [17]. In the present study, nearly one-third of the patients that were diagnosed with IPA using the M-AspICU criteria but missed by the 2021 EORTC/MSG ICU criteria as a result of non-specific infiltration alone had higher mortality than patients without IPA.
Although the M-AspICU criteria diagnosed the largest cohort of patients with IPA among the three criteria, IPA based on the criteria was not an independent risk factor of mortality. Schroder et al. found a high false positive rate of IPA (up to 71%) based on M-AspICU criteria in patients with proven IPA [11]. In the present study, we found that the subgroup of additional patients diagnosed by the M-AspICU criteria owing to both the expanded host factors and radiological features had a low mortality rate that did not differ from the rate among patients without IPA. Although all of these patients met the mycological criteria, Aspergillus colonization and false positives should be considered. After we excluded these additional patients mentioned above, IPA diagnosed by M-AspICU became an independent risk factor of 28-day mortality. Thus, the clinical significance of this subgroup of patients warrants further evaluation.
Our study has some limitations. First, all patients did not have lung histopathology. Second, the 2020 EORTC/MSG criteria included Aspergillus PCR; however, this technique is not available in our hospital, which may have caused us to underestimate the actual diagnostic rate of the 2020 EORTC/MSG criteria. Siopi et al. confirmed that cases of probable IPA increased by 22% when PCR was used [18]. Third, there were no patients with COVID-19 in our study. Although the inclusion dates from November 2016 to November 2021, our hospital is not a designated hospital for COVID-19 patients. However, there are other influenza data in our study. Fourth, BALF GM is more valuable than serum GM for diagnosing IPA in the ICU population [2, 19]. However, only 13.1% of patients in our study had BALF GM. Fifth, this was a single-center study, and the results may be biased; finally, radiologic features other than nodes, cavities, and consolidation were not collected.
Conclusion
The diagnostic rate of IPA in critically ill patients varied considerably based on the diagnostic criteria used, with very poor agreement between the three evaluated criteria. Compared with the 2020 EORTC/MSG criteria, the 2021 EORTC/MSG ICU criteria identified a group of patients with similarly devastating outcomes. Although the M-AspICU criteria have the highest sensitivity since they identify most patients as “probable” given the broadened criteria, this study shows that these patients are less ill. Whether broadened host factors and/or broadened radiologic features should be incorporated into the diagnostic criteria should be further evaluated in prospective cohort studies.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (PDF 917 KB)
Acknowledgements
Funding
This study was supported by the National Key Research and Development Program of China (2021YFC2500804). The Rapid Service Fee was funded by the authors.
Authorship
All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole and have given their approval for this version to be published.
Authors' Contributions
Bin Du and Rui-ting Liu designed the study. Shan Li, Yan Chen and Xi-xi Wan interpreted the clinical data. Jin-min Peng and Li Weng directed the writing. The first draft of the manuscript was written by Rui-ting Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Disclosures
Rui-ting Liu, Yan Chen, Shan Li, Xi-xi Wan, Li Weng, Jin-min Peng and Bin Du declare that they have no conflicts of interest to this work.
Compliance with Ethics Guidelines
The study was approved by the institutional review boards and Ethics Committee of Peking Union Medical College Hospital. As a result of the retrospective nature of the study, informed written consent was waived. The study was conducted in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Data Availability
The data sets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Oxford University Press
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10.1093/narcan/zcad020
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Critical Reviews and Perspectives
Current proteomics methods applicable to dissecting the DNA damage response
Muralidharan Monita Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA 94158, USA
Krogan Nevan J Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA 94158, USA
Bouhaddou Mehdi Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA 94158, USA
Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90024, USA
Quantitative and Computational Biosciences Institute (QCBio), University of California, Los Angeles, CA 90024, USA
https://orcid.org/0000-0001-9317-1658
Kim Minkyu Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA 94158, USA
Department of Biochemistry and Structural Biology, University of Texas Health Science Center, San Antonio, TX 78229, USA
To whom correspondence should be addressed. Tel: +1 210 450 7346; Email: [email protected]
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© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Cancer.
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Abstract
The DNA damage response (DDR) entails reorganization of proteins and protein complexes involved in DNA repair. The coordinated regulation of these proteomic changes maintains genome stability. Traditionally, regulators and mediators of DDR have been investigated individually. However, recent advances in mass spectrometry (MS)-based proteomics enable us to globally quantify changes in protein abundance, post-translational modifications (PTMs), protein localization, and protein-protein interactions (PPIs) in cells. Furthermore, structural proteomics approaches, such as crosslinking MS (XL-MS), hydrogen/deuterium exchange MS (H/DX-MS), Native MS (nMS), provide large structural information of proteins and protein complexes, complementary to the data collected from conventional methods, and promote integrated structural modeling. In this review, we will overview the current cutting-edge functional and structural proteomics techniques that are being actively utilized and developed to help interrogate proteomic changes that regulate the DDR.
Graphical Abstract
Graphical Abstract
National Institutes of Health 10.13039/100000002 U54CA274502 P50AI150476 U19AI135990 Martha and Bruce Atwater Breast Cancer Research Fund Benioff Initiative for Prostate Cancer Research UTHSCSA 10.13039/100008635 F. Hoffmann-La Roche 10.13039/100007013 Vir Biotechnology 10.13039/100019650
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pmcINTRODUCTION
Liquid chromatography followed by tandem mass spectrometry (LC–MS/MS) is being widely used to quantitatively measure both protein levels and post translational modifications. LC–MS/MS approaches can be performed in two different ways: ‘top-down’ approaches profile intact proteins and protein complexes, whereas ‘bottom-up’ or shotgun approaches profile proteins that have been digested to peptides. Peptides are then separated over a column (e.g. reversed phase C18) using increasing concentrations of organic solvent (e.g. acetonitrile), and as peptides elute from the column, they go through electrospray ionization (ESI) and the intact mass to charge ratio (m/z) of precursor or MS1 ions are measured. Precursor MS1 ions are then subjected to MS/MS or MS2 analysis, involving fragmentation of the peptide ion and subsequent measurement of fragment or product ion m/z (1,2). Through this tandem MS process, peptides in the samples are identified by comparing the collected data to a species-specific proteome database of in silico-digested proteins and scoring the resulting matches (3,4).
LC–MS/MS allows simultaneous identification and quantification of thousands of proteins or PTM sites on peptides in a single run. Abundance of each peptide across samples can be quantified and compared in several different ways: sample comparison can be assessed by using stable isotope labeling of amino acids in cells (SILAC) prior to tryptic digestion (5). Similarly, samples can be labeled post-digestion using isobaric tags (e.g. iTRAQ, TMT) (6–8) and multiple samples can be combined (up to 18 for TMT and 8 for iTRAQ) (9–11) for multiplexed quantification. Each tag possesses identical chemical structures and is differentiated via isotopes at different positions that generate peptides with the same m/z in MS1. However, upon MS2 fragmentation the tags produce different mass reporter ions, allowing relative quantification of peptide abundance. Alternatively, the relative abundance of each peptide in two or more biological samples can be directly measured without utilizing any labeling or special growth conditions (label-free).
Regarding MS data acquisition, there are two different acquisition modes for collecting MS data. In Data-Dependent Acquisition (DDA) mode, MS instrument selects the most abundant (typically top 10–20) peptide ions from MS1 scan in each column retention time window, and then these selected precursor peptide ions are fragmented and analyzed in MS2. However, the stochastic precursor selection of DDA could result in inconsistent detection and undersampling of less abundant peptides, which cause higher variation across replicates. Alternatively, in Data-Independent Acquisition (DIA) mode, MS instrument gathers MS2 data from all precursor ions in a narrow mass window (e.g. 400–425 m/z), and then repeats this process across the entire mass range (e.g. 400–1200 m/z) to systematically collect MS2 from all detected peptide ions in each retention time window (e.g. 2–4 s). MS instrument continues collecting MS2 data across the entire retention time range, thus generating highly multiplexed and comprehensive maps of fragment ions that elute on the chromatography. DIA uses a spectral library to extract peptide information from MS2 data, which leads to more accurate peptide quantification, lower variation across replicates, and fewer missing peptide identification, compared to DDA. But DDA is preferred for targeted analysis and library generation as it offers more accurate identification than DIA due to longer acquisition time per data point and more robust database-based search methods (12–14).
Interrogating the structure-function correlation of proteins and protein complexes has been crucial for understanding several cellular processes and disease mechanisms and developing various therapies. It is established that protein conformations are not just single specific three-dimensional quaternary structures but have multiple transient conformations with dynamic properties (15). Protein dynamics facilitate its functions, such as macromolecular associations and other downstream processes in cellular pathways (16). Conventional structural biology tools such as X-ray crystallography (XRC), cryo-electron microscopy (cryoEM), and nuclear magnetic resonance (NMR) solve structures of important protein complexes but need to consistently deal with dynamics and heterogenous assemblies and require large quantities of protein, which limits their application. Protein chemistry combined with various mass spectrometry-based structural proteomics approaches, including XL-MS, H/DX-MS, and nMS, provide large structural information of proteins and protein complexes, in part due to their wide applicability to many different types of proteins, including structured, disordered, monomeric, and multimeric proteins (17). Furthermore, structural proteomics methods enable studying transient interactions of proteins and their complexes up to the megadalton range in vitro and in vivo under physiological conditions (18–22). Although structural proteomics tools alone do not solve structures, they provide invaluable information complementary to conventional methods, promoting integrated structural modeling (23). This review covers a repertoire of various proteomics approaches that can further our understanding of dynamic changes in proteome and mechanistic basis of PPIs that regulate DNA repair (Figure 1; Table 1).
Figure 1. Schematic representation of functional and structural proteomics methods. (A) Proteins in cell lysates are digested and peptides are subsequently ionized and fragmented through nLC–MS/MS either directly (abundance proteomics) or after enrichment of PTM peptides (PTM proteomics). (B) Affinity Purification-MS (AP-MS) is to systematically identify PPIs by expressing and purifying affinity-tagged ‘bait’ proteins in cells and detecting stably bound protein interactors by MS. (C) Proximity labeling MS relies on a promiscuous biotin protein ligase (BirA/BioID/TurboID) or an engineered ascorbic acid peroxidase (APEX) fused to a protein of interest (bait) to label proteins in close proximity to the bait protein through covalent transfer of biotin or biotin derivatives in a distance-dependent manner. Upon addition of biotin, the proximal proteins are biotinylated, enriched with streptavidin beads, and identified by quantitative MS. These MS datasets from functional proteomics are integrated by network modeling and analysis to extract biological insight. (D) Protein crosslinking MS (XL-MS) involves crosslinking or surface modification of protein complexes followed by proteolysis and MS identifications of amino acid residue pairs that are within close spatial proximity, thus providing structural insights into proteins and protein assemblies. (E) Hydrogen/Deuterium exchange MS (H/DX-MS) measures dynamic changes in protein structures over a specified time scale by measuring the relative uptake of protein backbone amide hydrogen for deuterium (and vice versa). Residue-level information can be derived from gas-phase fragmentation of the deuterated peptides. (F) Native MS (nMS) involves analyzing and characterizing protein complexes, while retaining their inter and intramolecular interactions. The stoichiometry of a protein complex can be deduced from the calculated mass, and structural preference can be informed by the number of charges it carries. Data from the diverse structural proteomics methodologies can be combined to obtain maximum structural insight through integrative modeling. nLC, nano liquid chromatography; UPLC, ultra-performance liquid chromatography. Edges and arrows connecting proteins to other proteins and/or biological processes in the interactomes and networks are not only based on MS-driven proteomics data but also inferred from network modeling and analysis.
Table 1. Exemplary Proteomic Studies of the DNA Damage Response
Proteomic method Pros Cons Study Reference
Abundance proteomics Quantitatively measure changes in abundance of thousands of proteins simultaneously Proteome coverage could be limited for high complexity samples, in which fractionation before LC-MS/MS is recommended Comparative proteomic analysis between BRCA1-deficient and BRCA1-proficient mouse tumors upon cisplatin treatment Comparative proteomic analysis between BRCA1-deficient and BRCA1-proficient mouse tumors upon cisplatin treatment (28) (28)
Phospho-proteomics Determine the identity of phosphorylated proteins and the amino acid residues (and their level) which hold the phosphate group Phospho-peptide enrichment step may not recover low abundant peptides and some enrichment beads (IMAC, TiO2) have higher efficiency for Ser/Thr than Tyr Global changes in protein level and phosphorylation site profiles bySILAC with cisplatin treatment ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage Site-specific phosphorylation dynamics of the nuclear proteome during the DNA damage response Analyzing the phospho-proteome regulated by Spinophilin (PP1 regulatory subunit) that interacts with BRCA1 (29,30) (43) (44) (46)
Ub-proteomics Enables the large-scale analysis of ubiquitinated proteins and amino acid residues in cells K-GG remnant profiling after trypsin digestion fails to recognize -GG- modified protein N-termini; loses Ub chain topology information; and may recognizes ISG15- and NEDD8-modified proteins, which also yield K-GG remnant peptides upon cleavage by trypsin Quantitative proteomic assessment of Ubiquitination and Acetylation in the DNA Damage Response Profiling of Proteome and Ubiquitome Changes in Human Lens Epithelial Cell Line after Ultraviolet-B Irradiation (47) (49)
ADPr-proteomics Provides global information of ADPr proteins and amino acid residues Numerous modifiable amino acid residues and the labile nature of ADPr bond pose a challenge for site-specific localization of ADPr Requires electron transfer dissociation (ETD)-based methods to preserve labile ADPr Systems-wide analysis of Serine ADP-Ribosylation in response to oxidative stress Proteome-wide identification of poly(ADP-Ribosyl)ation targets in different genotoxic stress responses (62) (63,64)
AP-MS Allows to identify in vivo PPIs by affinity purification using antibodies recognizing endogenous proteins of interest or affinity tags Data could vary by antibodies employed Transient or weak PPIs may not be captured effectively Cannot distinguish between direct and indirect PPIs Analyzing the effect of pathogenic mutations on the BRCA1 interactome TEADs associate with DNA repair proteins to facilitate recovery from DNA damage PPARγ interacts with the MRN complex and the E3 ubiquitin ligase UBR5 (46) (76) (127)
iPOND, NCC Enables to identify proteins specifically localized to chromosomal loci under active DNA replication DNA labeling with EdU or biotin-dUTP and de/crosslinking steps are required NCC requires cell membrane permeabilization Analysis of protein dynamics at active, stalled, and collapsed replication forks (78,82–86)
ChIP-MS Capture PPIs on crosslinked chromatin at specific gene loci and/or genome-wide Relies on antibodies Requires de/crosslinking steps Telomeres were immunoprecipitated and known and novel telomere-binding proteins were identified by MS (79,80)
Proximity proteomics Detects transient and/or weak interactions Biotin-streptavidin binding is compatible with purification under harsh denaturing conditions Offers spatial and temporal resolution of PPIs Proteins within proximity of a bait are identified, some of which are not necessarily interacting proteins May have high backgrounds from abundant cellular proteins and/or endogenous biotinylation Biotinylation labeling time varies (1 min - 24 hr) depending on the enzyme used APEX reveals Shieldin as a 53BP1 effector complex in DSB repair BioID identifies Ku70-associated proteins BioID analysis of ATRX reveals its association with SLF2 that helps inhibit telomere exchanges (91) (96) (97)
XL-MS Elucidates PPIs and protein complex information by providing distance constraints within a protein or between proteins Depending on application needs (target functional group, solubility, membrane permeability, MS-cleavability, etc), a correct crosslinker can be selected Crosslinking efficiency is often low (1–5%), leading to marginal crosslinks Long crosslinking time may induce protein aggregates Hybrid dipeptides generated by crosslinkers could greatly expand the search space during spectra matching PPARγ interact with the MRN complex via NBS1 Mapping the regions involved in the Timeless–Tipin interaction (127) (128)
H/DX-MS Offers to analyze protein structures, dynamics, folding, and interactions Not limited by the size of proteins or protein complexes Requires few microliters of low micromolar concentration of proteins Detects co-existing protein conformations Deuterium/hydrogen scrambling effect (redistribution of the exchanged protons within a peptide) could complicate the analysis Back exchange of deuterium label during chromatographic separation due to intrinsic amino acid hydrogen exchange rate differences could lead to loss and alteration of signal and biased measurement Dynamic changes in DNA-binding domains of RPA upon ssDNA binding is revealed Helical subdomain (HD) of the PARP1 catalytic domain undergoes rapid unfolding when PARP1 encounters a DNA break, relieving the autoinhibition (149) (150)
nMS Widely applicable to samples varying in mass, degree of flexibility, symmetry, and polydispersity Multiple oligomeric states of proteins and complexes can be analyzed simultaneously Requires few microliters of low micromolar concentration of proteins Does not require samples to be chemically labeled or crosslinked. The relative abundance of detected complexes may deviate from that in solution because of different complexes' distinct ionization, transmission, and detection probabilities In the gas phase, hydrophobic interactions weaken, and electrostatic interactions become stronger than in-solution making detecting specific assemblies impossible without prior crosslinking A MutS intermediate state that is simultaneously bound to a DNA mismatch and a nucleoside triphosphate is detected The effect of mutations in Staphylococcus aureus uracil-DNA glycosylase inhibitor on its association with uracil-DNA glycosylase is analyzed (160) (161)
Integrative structural modeling Computationally integrates structural data from multiple sources to enable structure determination of macromolecules and their complexes Accuracy of output models relies on quantity and resolution of the input information Structures of protein complexes are derived by integrative modeling using structural proteomics data as well as data collected from conventional methods (167,170,174,176)
FUNCTIONAL PROTEOMICS
Abundance proteomics
Profiling of total proteome in cells can be performed by digesting proteins to peptides with trypsin (and/or other peptidases) and applying them to LC–MS/MS. The relative abundance of each identified peptide is then measured and individual peptide data for each protein are combined to give protein-level quantification. This analysis allows us to quantitatively measure changes in abundance of thousands of proteins in cells and tissues simultaneously (Figure 1). To increase the proteome coverage (i.e. number of peptides and associated proteins identified in a given MS run), samples are often fractionated by ion exchange or reversed phase chromatography (24,25) and each fraction is individually analyzed by LC–MS/MS to maximize peptide identification. Furthermore, to globally map cellular macromolecular complexes, Complex-Centric Proteome Profiling method was recently developed (26). In this method, cell lysates are fractionated by size exclusion chromatography, and native protein complexes in each fraction are digested into peptides and analyzed by DIA MS. Quantification and composition of protein complexes are then determined based on protein patterns across the fractions by error-controlled, complex-centric analysis. This technique revealed the mitotic proteome reorganization, including disassembly of the nuclear pore complex, by quantitatively comparing human protein complexes during transition from interphase to mitosis (27).
Comparative proteomic analysis between BRCA1-deficient and BRCA1-proficient mouse tumors upon cisplatin treatment revealed that proteins involved in centrosome organization, chromosome condensation, HR repair and nucleotide metabolism were up-regulated in cisplatin-sensitive BRCA1-deficient tumors (28). However, several studies show that overall changes in protein abundance during or after DNA damage exposure are much less conspicuous than PTM changes (29,30), necessitating the profiling of PTMs of proteins in addition to protein levels.
Post-translational modification (PTM) proteomics
PTMs (e.g. phosphorylation, ubiquitylation, and ADP-ribosylation among others) of many DDR proteins are known to induce changes in protein stability, cellular localization, enzymatic activity, and/or interaction with DNA and/or other proteins (31–38). To properly profile and quantify PTMs of proteins by MS, digested peptides from lysates, bearing a given type of PTMs, need to be enriched prior to LC–MS/MS analysis (Figure 1). For phospho-peptide enrichment, Fe3+-based Immobilized Metal Affinity Chromatography (IMAC) beads are widely used and about 10 000–20 000 phosphorylated peptides (phospho-peptides) can be typically detected in a single LC–MS/MS run (39). Similarly, other metal-based (e.g. Ti4+, Zr4+) IMAC beads and titanium dioxide (TiO2) can be also utilized to enrich phospho-peptides (39,40). To enrich ubiquitylated peptides, an antibody-bead conjugate that recognizes a di-glycine remnant of ubiquitin left on Lysine residues (K-GG) of protein substrates after trypsin digestion is used (41,42). This enrichment followed by LC–MS/MS allows quantitative analysis of thousands of non-redundant phosphorylated or ubiquitinated peptides and exact sites of modification after searching MS spectra against putative protein sequences in a species-specific proteome database.
Phospho-proteomic studies reveal that there is a considerable variation in the kinetics and directionality of phospho-proteome changes following DNA damage (43,44), and that over one-third of the captured phospho-peptides are dephosphorylated within minutes of DNA damage (43), indicating that phosphatases are not only involved in counter-balancing DNA double-strand break (DSB)-induced phosphorylation events by resetting activated DDR factors to the initial homeostatic state following the repair of damaged DNA, but also play a primary role in initiating the repair process by removing constitutive phosphorylation that inhibits the function of DNA repair factors to activate these proteins (45,46). Ubiquitome analysis has been also successfully used to profile changes in the ubiquitome in response to DNA damage (47). Because ubiquitination often regulates protein stability, pairing ubiquitin proteomics with abundance proteomics and proteasome inhibitor treatments (e.g. MG132) is often used to identify proteins whose levels are regulated by ubiquitination (48,49).
ADP-ribose (ADPr) unit is transferred by poly(ADP-ribose) polymerases (PARPs) from NAD+ to a carboxylic acid residues (Glu, Asp) (50–52), but other residues containing a thiol group (Cys), an alcohol group (Ser, Thr, Tyr), a guanidinium group (Arg) or Lys are also found to serve as acceptors (53–63). Despite the role of PARylation in DDR, high-throughput profiling of ADPr-modified proteins has been hindered due to the difficulty of PAR enrichment. However, the Af1521 macrodomain, which has a strong ADPr-binding affinity, was shown to successfully enrich and identify ADP-ribosylated proteins from cell lysates induced under different genotoxic stress conditions (64). Interestingly, another study reveals that Ser residues are the major ADPr site in response to oxidative stress, and that significant portion of these Ser residues are co-targeted by mutually exclusive ADP-ribosylation and phosphorylation, indicating a crosstalk between the two PTMs (62) that remains to be further validated for their roles in cellular stress response.
For the MS analysis of ADPr proteins, numerous modifiable amino acid residues and the labile nature of the bond between ADPr and the amino acid residues pose a challenge for site-specific localization using the conventional collision-induced dissociation (CID) MS/MS method. Instead, the radical-driven electron transfer dissociation (ETD) method has proven to preserve labile ADPr and allow confident determination of the exact ADPr-modified amino acid residues (62). Recently, the activated ion ETD (AI-ETD) method that uses infrared photoactivation during the ETD reaction was developed, which allows to overcome the high degree of non-dissociative electron transfer (ETnoD) during regular ETD, especially for ADPr-peptide precursors with low m/z ratio (63). AI-ETD was shown to map significantly more ADPr sites than ETD and identify more physiological ADPr sites with less input materials, many of which were not previously reported.
Protein-protein interaction mapping by affinity purification MS
Coordinated regulation of protein complex formation is central to the DDR response. For example, BRCA1, a hereditary breast and ovarian cancer susceptibility gene (65–67) that plays a crucial role in the repair of DNA double-strand breaks (DSB) (68), carries out its repair functions in concert with a large number of proteins involved at distinct steps of homologous recombination (69). Affinity Purification-MS (AP-MS) is a proteomics method to systematically identify PPIs by expressing and purifying affinity-tagged ‘bait’ proteins in cells and detecting stably bound protein interactors (prey) by MS (46,70–72). Samples that express the affinity tag alone or repress the expression of tagged bait protein are often used in parallel as negative control for statistical PPI scoring (Figure 1). Alternatively, direct immunoprecipitation of the endogenous protein of interest may also be performed if a sensitive and specific antibody exists (73).
Recently, Kim et al. applied AP-MS to comprehensively analyze the BRCA1 interactome and how these interaction profiles change with pathogenic BRCA1 mutations (46). This AP-MS study revealed a number of previously unidentified BRCA1-interacting proteins in addition to known interactors and showed that these interactions are altered by mutations in different domains of BRCA1: C-terminal BRCT domain mutants (S1655F, 5382insC, M1775R) completely or significantly lost the interaction with multiple homologous recombination repair proteins (including FAM175A, BRIP1, RBBP8, UIMC, BRE), whereas N-terminal RING domain mutants (I26A, C61G, R71G) maintain these interactions although they are not as strong as with wild-type BRCA1. These results concur with previous suggestions that RING domain mutants may retain residual activity; for example, the C61G variant is only moderately sensitive to cisplatin and PARP inhibitors and becomes readily resistant to these drugs (74,75). AP-MS of transcriptional enhanced associate domain family members 1–4 (TEADs) also revealed their association with DNA repair proteins (XRCC5, XRCC6, PARP1, RIF1) to facilitate cellular recovery from DNA damage (76).
Protein–DNA interaction mapping
The DDR also involves choreographed interactions between DNAs and proteins and cataloging the dynamic spatial and temporal changes in DNA/chromatin-associated proteome is essential to thoroughly understand the DNA repair and replication processes. To identify proteins enriched at DNA replication forks, isolation of Proteins on Nascent DNA (iPOND) and Nascent Chromatin Capture (NCC) techniques were developed (77,78). Additionally, chromatin immunoprecipitation (ChIP) followed by MS (ChIP-MS) was devised to probe chromatin-bound proteins at specific gene loci as well as genome-wide (79,80).
In iPOND, newly synthesized DNAs are labeled with 5-ethynyl-2’-deoxyuridine (EdU) incorporation by the replisome DNA polymerases. After crosslinking proteins to DNAs with formaldehyde, biotin is covalently conjugated to an alkyne group of EdU using click chemistry, and 100–200 bp protein-bound chromatin fragments are generated by sonication. Finally, proteins bound on the EdU labeled chromatin fragments are captured by streptavidin beads and analyzed by quantitative MS after protein-DNA decrosslinking. Duration of EdU labeling (pulse) and subsequent chase after removal of EdU determines the spatial information of proteins relative to the replication fork. Furthermore, the pulse and chase samples are critical to distinguish proteins engaged in DNA replication from those that are simply associated with bulk chromatin (81). NCC is similar to iPOND except it utilizes biotin-dUTP instead of EdU, such that NCC does not require a biotin conjugation step. However, due to impermeability of biotin-dUTP, it is necessary to permeabilize cell membranes in a hypotonic buffer during biotin-dUTP labeling. Quantitative MS analysis of enriched proteins in iPOND and NCC has been performed by either label-free or SILAC/iTRAQ/TMT-based protocols (78,82–86) to identify new DNA replication proteins. In these studies, labeling protocols tend to provide more quantitatively precise and reproducible datasets than label-free methods (81). ChIP-MS is similar to AP-MS, in that both methods rely on antibody-based immunoprecipitation/purification, but ChIP-MS is used to specifically identify PPIs on crosslinked chromatin. Proteins in the male-specific lethal (MSL) complex on the chromatin template as well as proteins enriched in telomeres were monitored by ChIP-MS and successfully identified novel proteins relevant to the function of MSL complex and telomere maintenance, respectively (79,80).
Proximity-dependent labeling MS
Proximity-dependent labeling has emerged as a complementary method to AP-MS to study PPIs and relies on a promiscuous biotin protein ligase (BirA/BioID/TurboID) (87,88) or an engineered ascorbic acid peroxidase (APEX) (89) fused to a protein of interest (bait) to label proteins in close proximity to the bait protein through covalent transfer of biotin or biotin derivatives in a distance-dependent manner. Upon addition of biotin, the proximal proteins are biotinylated, enriched with streptavidin beads, and identified by quantitative MS (Figure 1). The covalent biotin labeling of proximal proteins is particularly useful to capture transient or weaker PPIs, compatible with purification under harsh cell lysis conditions (e.g. higher detergent and urea concentrations in lysis buffer), and offers spatial and temporal resolution of PPIs within various subcellular locations (87,89,90). APEX-based proximity proteomics characterized the endogenous network of BRCA1, 53BP1, and MDC1 and revealed Shieldin is a 53BP1 effector complex in DSB repair (91). Recently, biotin ligase enzymes have been engineered to split into two parts which enable contact-specific proximity-dependent labeling. While the two split enzyme parts remain inactive apart, they become active when bound together by PPIs. (92–94). These split enzyme-based approaches offer greater targeting specificity of biotinylation than full-length enzymes alone and provide powerful tools for validating and functionally characterizing high-confidence PPIs identified by AP-MS and/or other methods (95). Abbasi and Schild-Poulter applied BioID to the Ku interactome analysis and identified ∼250 Ku70-associated proteins in HEK293 cells, many of which are involved in RNA metabolism, chromatin remodeling, and microtubule dynamics as well as DNA repair and replication, revealing additional cellular roles of the Ku complex (96). Another BioID analysis of ATRX revealed its association with SLF2 that helps inhibit telomere exchanges (97).
MS data quantification, interpretation and integration using bioinformatics and network modeling
Once data is collected, they first pass through quality control. Specifically, one must ensure reproducibility in peptide detections and intensity quantifications between biological replicates. Replicates that fail quality control are discarded. Global proteomics data of whole cell lysates (e.g. abundance proteomics, phosphoproteomics, ubiquitin proteomics, or similar) can be subjected to relative quantification to compare each experimental condition to a control. One software, called MSstats, offers quantitative and comparative measurement of protein levels, PTMs, or PPIs between two biological states, such as diseased versus healthy or mutant versus wild-type (98). This data preprocessing step generates log2 fold changes and P-values, and thus defines differentially expressed or regulated proteins (DEP). For AP-MS data, additional algorithms are used to remove background signal; even after performing an affinity purification, some proteins detected may be non-specifically binding the tube or other reagents used during sample preparation. The analysis workflow typically utilizes software packages (e.g. compPASS, SAINT, MIST) (99–103) to assign quantitative scoring metrics (i.e. abundance, specificity, and reproducibility) to all pairwise PPIs and filter high-confidence interacting proteins (e.g. SAINTscore ≥ 0.9, Bayesian false discovery rate ≤ 0.05). These quantitative, differential, preprocessed data are subsequently interpreted using more sophisticated computational algorithms, which enable the integration of new data with prior knowledge.
A highly-used, and often first-line, approach to data interpretation is gene enrichment analysis, which enables the assignment of biological terms to differentially abundant proteins. Gene sets have been defined by several groups, many of which are distributed by the Molecular Signatures Database (MSigDB, www.gsea-msigdb.org). Commonly used databases include Reactome (104), KEGG (105), Wikipathways (106) and Gene Ontology (107). One enrichment analysis algorithm, called Gene Set Enrichment Analysis (GSEA), creates a ranked list of proteins derived from experiment (often ranked by log2 fold change) and compares it to annotated gene sets (e.g. cellular pathways) to determine whether genes in each gene set occur toward the top or bottom (large or small fold changes) of the list. If so, this indicates the gene set is ‘correlated’ with proteins under regulation in the experiment (108). A similar approach uses a Fisher's exact or hypergeometric test to assess the overlap of DEP to each gene set (109). The end result is the same: significantly enriched gene sets represent cellular pathways that are regulated in the experiment. These results provide general biological understanding and often guide subsequent validation experiments. Importantly, these gene set databases possess many terms related to the DNA damage response. In Gene Ontology, there are over 40 terms that either possess ‘DNA repair’ or ‘DNA damage,’ spanning pathways such as ‘double strand break repair via non-homologous end joining’ and ‘single strand break repair via break induced replication’. Gene enrichment analysis is a good way to gain a high-level summary of the data but is often insufficient to more deeply interrogate the data to reveal mechanistic hypotheses for further experimental testing.
Network modeling, a branch of computational modeling that uses pre-defined networks of gene-gene interactions, can be used to integrate MS datasets and extract biological insight at the interface between distinct proteomics datasets. Several freely-available, large (often genome-wide scale) biological networks exist [e.g. PathwayCommons (110), STRING (111), ReactomeFI (112)], which capture decades of experimental knowledge detailing how genes and proteins physically and/or functionally interact in protein complexes or functional pathways. Data from an experiment can be overlaid onto these networks and analyzed using network modeling algorithms to understand biological interconnectivity across multiple datasets (Figure 1).
One approach is called network propagation (113). Briefly, proteins of interest from the experiment (‘nodes’) are labeled in the network. This signal is then propagated through each proteins’ molecular interactions (‘edges’) to nearby nodes in an iterative manner, often using a random walk or heat kernel mathematical process. A permutation test is subsequently performed by shuffling node labels or randomizing network structure in order to assign a p-value to each node, which represents the network proximity, and functional connectivity, of each gene to the initial labeled nodes. Network propagation can be used to identify the extent of intersection between two or more gene lists by evaluating their shared neighborhoods within the network. This approach is particularly useful when two gene lists have poor overlap at the gene level but high potential overlap at the pathway level. We and others have previously used this approach to discover links between the human papillomavirus (HPV) virus-host physical interactome and the mutational landscape of HPV-negative tumors, revealing converging routes to oncogenesis (114), and to integrate genome-wide CRISPR hits from human cells infected with distinct coronaviruses, revealing host pathway targets for pan-coronavirus therapeutics (115).
Data integration using network propagation can be especially useful when a single dataset is insufficient to extract the level of mechanistic insight desired. It can also be used as an exploratory tool to screen for interesting connections between distinct data types. Furthermore, network propagation is useful when data is sparse in nature, which can be the case for proteomics datasets; in these cases, network propagation can be used to explore how proteins coalesce into the same complex or pathway. However, because network propagation provides a wealth of information, the results can be difficult to interpret. Additional approaches are needed to make it easier for users to pinpoint the most relevant mechanisms from the resulting network.
Network modeling tools could be transformative for discovering new mechanisms underlying DDR signaling. For example, one might be interested in understanding the mechanisms underlying how a specific cancer driver point mutation (e.g. BRCA1 mutation) drives cancer progression. To study this, one could perform global proteomics (abundance proteomics and PTM analysis) comparing wild-type cells to cells that possess the mutation of interest. Additionally, one could perform differential AP-MS or proximity-dependent labeling to quantitatively compare how a protein-coding point mutant alters PPIs. The protein-level overlap between these datasets might be small; indeed, proteins detected to be differentially regulated in the global proteomics data may reflect gene expression responses several steps downstream the mutant, and thus may not be contained within the physical interactome. Network propagation could be used here to reveal the molecular networks that ‘connect’ the differential PPIs to the downstream signaling events. Specifically, network propagation could be performed in two parts. First, label and propagate nodes corresponding to differentially interacting PPIs. Second, label and propagate nodes corresponding to differentially regulated proteins from the global proteomics studies. Genes that are significant (P < 0.05) in both propagation analyses represent molecular networks that lie at the interface between BRCA1 mutant protein interactions and the downstream signaling effects of the BRCA1 mutation. Such an analysis may delineate a novel cellular pathway linking a BRCA1 mutation to its signaling ramifications, and thereby suggest subsequent validation experiments to further understand BRCA1 function. This approach is specifically applicable to DDR signaling because one important question in the field is how specific mutants (especially variants of uncertain significance of key DDR proteins) impact protein complex formation and DNA repair outcomes, and may provide a more comprehensive view of proteomic changes and signaling pathways induced by a given mutation, which is often difficult to capture by any single proteomics attempt.
STRUCTURAL PROTEOMICS
Crosslinking MS (XL-MS)
XL-MS is a powerful technique that informs protein-protein interactions and provides structural insights into proteins and protein assemblies by identifying amino acid residue pairs that are within close spatial proximity (116–121). Typically, this approach involves crosslinking or surface modification of protein complexes followed by proteolysis and MS identifications (Figure 1). Applications of XL-MS vary from studying macromolecular complexes (122,123) to their structural dynamics (124,125).
Qiu et al. conducted a comprehensive investigation of DNA-binding proteins in vivo using XL-MS and identified >100 proteins, including those involved in DNA replication and repair (126). Another study found that peroxisome proliferator activated receptor γ (PPARγ) interacts with the MRE11-RAD50-NBS1 (MRN) complex and the E3 ubiquitin ligase UBR5 by AP-MS and that PPARγ promotes UBR5-mediated degradation of ATM interactor (ATMIN), leading to ATM activation and the initiation of DNA repair upon DNA damage (127). Subsequent XL-MS experiment identified PPARγ peptides crosslinked to NBS1, which are located in the DNA-binding domain and the ligand-binding domain of PPARγ, indicating that PPARγ interact with the MRN complex via NBS1, and that NBS1 binding may interfere with PPARγ transcription activator function. Holzer et al. elucidated the structural basis for the human Timeless–Tipin complex using various complementary tools including XL-MS as well as XRC and nMS. They used lysine-specific crosslinker and demonstrated that the Timeless–Tipin interaction is extensive, such that three distinct regions of Timeless between amino acids 400 and 1100 are crosslinked to three different sites in Tipin. Using this integrated approach they were able to show that Timeless forms a binding surface capable of surrounding the smaller protein Tipin (128).
There are a few inherent challenges of XL-MS: (i) the combinatorial nature of the crosslinks and mixture of unmodified peptides increases the complexity of the spectra; (ii) sequencing of crosslinked peptides often results in low-quality or uninterpretable MS/MS spectra because of insufficient or complex fragmentation; (iii) identification and filtering of nonspecific interactions are challenging especially in validating novel observed PPIs. Thus, high-throughput bioinformatic approaches are required to decipher complete structural information. Some of these challenges can be overcome by increasing data acquisition rates, higher sequencing capacity, and novel hybrid fragmentation techniques like electron transfer/higher-energy collision dissociation (EThcD) and ultraviolet photodissociation (UV-PD) (129). Regardless, XL-MS is a valuable tool for structural characterization of protein complexes by providing real-time insight into the PPIs and protein-DNA/RNA interactions that may undergo structural changes in response to DNA damage.
Hydrogen/deuterium exchange MS (H/DX-MS)
H/DX-MS measures dynamic changes in protein structures over a specified time scale by measuring the relative uptake of protein backbone amide hydrogen for deuterium (and vice versa) (Figure 1). The rate of this reaction is dependent on various factors like solvent accessibility, chemical properties of the underlying amino acid sequence, stability of hydrogen bonding networks, and inductive effect of the neighboring groups (130–133). It has been applied to explore protein/protein complex conformations (134), protein-ligand binding sites (135), allosteric effects of proteins (136), protein folding dynamics (137), intrinsically disordered proteins (138), and protein − membrane interactions (139). The resolution of data depends on the fragmentation technique adopted during the MS data acquisition. Residue-level information can be derived from gas-phase fragmentation of the deuterated peptides using ETD or electron-capture dissociation (ECD) (140,141). The vibrationally cold energy utilized in these techniques results in minimized hydrogen scrambling (i.e. redistribution of the exchanged protons within a peptide) as opposed to other methods like collision-induced dissociation (CID) (142–144). In 2013, Resetca et al. developed a method called time-resolved electrospray ionization mass spectrometry (TRESI-MS), which uses a microfluidic chip in-line with all the steps involved in a ‘bottom-up’ H/DX workflow (145). This development provided faster sample preparation times and improved reproducibility, making it feasible to characterize rapid structural transitions that occur during protein folding (146), ligand binding (147), or post-translational modification (148).
Ahmad et al. applied H/DX-MS to explore ssDNA-driven dynamics of the individual domains of single-strand DNA (ssDNA)-binding protein, replication protein A (RPA) (149). They observed changes in DNA binding domains (DBD) A-E of RPA upon ssDNA binding (149): DBD-A and B were shown to be dynamic and no protection was observed upon ssDNA binding, while DBD-C displayed extensive changes signifying a major role in stabilizing RPA on ssDNA binding. These H/DX-MS data proposed that DBD-A and -B of RPA serve as the dynamic half and DBD-C, -D and -E function as the less-dynamic half, such that ssDNA buried in DBD-A and B could be more accessible to RPA-interacting proteins (149). Dawicki-McKenna et al. monitored conformational dynamics of PARP1 using H/DX-MS and observed a >10 000-fold faster exchange within specific portions of the helical subdomain (HD) of the catalytic domain, which undergoes rapid unfolding when PARP1 encounters a DNA break (150). This result indicates that DNA damage detection by PARP1 relieves the autoinhibition posed by the HD.
Native MS (nMS) and intact protein analysis
nMS and intact protein MS involve analyzing and characterizing macromolecules, predominantly protein complexes, while retaining their inter and intramolecular interactions (Figure 1). To ionize protein complexes directly from aqueous solutions, buffers in which protein complexes are suspended during purification steps are exchanged into volatile buffers, such as ammonium acetate which has a pH range of 6–8 and evaporates readily during ionization. nMS resorts to a soft transfer of the analyte from the solution to the gas phase using nano-electrospray ionization (nESI). During ionization the macromolecules are charged, and the solvent molecules are stripped off before the ions reach the mass analyzer under (ultra)high-vacuum conditions. One may argue that proteins may not always retain a fully native state during nMS. However, under careful and optimized conditions, nMS provides gas-phase ions of proteins that retain many of their native features (151,152). The stoichiometry of a protein complex can be deduced from the calculated mass, and structural preference can be informed by the number of charges it carries (153). Structured protein conformations typically carry limited charges per unit mass due to the limited solvent accessibility owing to their compact nature. In contrast, unfolded/denatured proteins have a larger solvent-accessible surface area which enables them to accumulate higher charges (154–156). nMS combined with CID or surface-induced dissociation (SID) disrupts noncovalent interactions between protein subunits based on their strength and thus can help decipher the stoichiometry and topology of protein complexes (157,158). SID is a rapid dissociation that is almost a single-step energy deposition process when ions collide with a surface. No extensive unfolding of proteins and subunits is observed in this process (159).
nMS analysis of Escherichia coli MutS detected a MutS dimer state that is simultaneously bound to a DNA mismatch and a nucleoside triphosphate, which has been thought as an intermediate for faithful initiation of mismatch repair (160). Furthermore, this analysis could distinguish between binding of nucleoside diphosphate and nucleoside triphosphate in the two composite ATPase sites of MutS dimer, providing mechanistic and structural insight into how mismatched DNA binding and release are controlled by binding and hydrolysis of ATP. This study demonstrates that nMS is a powerful tool to detect mechanistically relevant reaction intermediates of allosteric enzyme complexes that cannot be easily addressed by other techniques. Besides, nMS was utilized to quantitatively measure the macromolecular association between uracil–DNA glycosylase and its inhibitor in Staphylococcus aureus and revealed that naturally occurring mutations in the inhibitor lead to appreciable changes in the dissociation constants for the complex (161).
Despite significant advances in nMS methods, there are some analytical challenges that remain to be addressed. For example, complexes expressed and purified from mammalian cells often carry PTMs and/or small ligands, which cause small mass differences and result in a heterogeneous mixture. It is challenging to resolve heterogeneous protein complexes, although charge changing experiments can sometimes help solve this problem (162,163). Additionally, biomolecules need to be prepared to be amenable to native electrospray ionization. But they often require nMS-incompatible components, including metal ions, small molecules, or lipids to maintain the integrity. Improving gas-phase desalting and adduct removal techniques would not only maintain the biomolecule in a ‘native-like’ environment but also reduce adduct formation and aid solvent removal at low activation energies, resulting in clean native spectra (164). Alternative ionization methods, such as desorption electrospray ionization (DESI) that enable biomolecules to directly ionize from biological tissues (165) may also help mitigate this problem. DESI involves directing a fast-moving charged solvent stream at an angle relative to the sample surface under atmospheric conditions to extract analytes from the surfaces resulting in the secondary ions being propelled toward the mass analyzer (166).
Integrative structural modeling
Data from the diverse structural proteomics methodologies discussed above can be combined to obtain maximum structural insight through integrative modeling (IM) (23,167,168). Structure determination of protein assemblies is vital for a mechanistic understanding of their function. IM takes experimental input data from structural, biochemical, proteomic, and genetic studies to optimize and compute a comprehensive model of protein complexes with properties that satisfy information within the uncertainty of the data (23,167,169,170). The primary sources of input information for IM are XRC, NMR spectroscopy or comparative modeling, XL-MS, H/DX-MS, stoichiometric data from nMS, solution scattering data from small-angle X-ray scattering (SAXS), and protein–protein interactions from AP-MS. Structures of several protein complexes have been derived using integrative methods to explain their architecture and evolutionary principles of large assemblies (167,171–173), rationalize the effect of disease mutations (167,174), and derive structural heterogeneity of flexible protein complexes (170,175,176).
Gutierrez et al. applied the IM approach to investigate in-solution architecture of CSN9-dependent structural changes and conformational dynamics of the COP9 signalosome (CSN) complex (170). Based on XL-MS data, XRC structures, and comparative models, they proposed that CSN9 binding triggers the CSN complex to adopt a configuration that facilitates CSN–CRL interactions, thereby enhancing CSN deneddylase activity. Most importantly, they observed additional conformations and configurations of CSN in solution that were absent in the static structure.
TFIIH is a conserved 10-subunit complex that plays a critical role in both RNA polymerase II transcription (174,177) and DNA repair (178). Mutations in TFIIH subunits lead to different cancers (179,180) and autosomal recessive disorders, such as xeroderma pigmentosum (XP), and trichothiodystrophy (TTD) (178). EM studies derived structural information of yeast and human TFIIH (181–184). By adopting an integrative approach using data from XL-MS, biochemical analyses, and previously reported EM maps, Luo et al. determined the molecular architecture of both human and yeast TFIIH (174). They identified four new conserved regions that function as hubs for TFIIH assembly and >35 conserved topological features within TFIIH, illuminating a network of interactions involved in TFIIH assembly and regulation of its activities.
FUTURE PERSPECTIVES
Development and application of myriads of tools over the past few years have helped in shaping our understanding of how protein complexes, PTM signaling, PPIs and protein structure/function regulate the DDR. This opens up an arena of DDR research in the future that can be decoded with integrated multidisciplinary approaches to gain a deeper mechanistic understanding of these genome maintenance processes both in vitro and in vivo.
There are several areas of mass spectrometry that are currently transforming the field. One is single-cell proteomics, which is a relatively new field focusing on analyzing the proteome of individual cells. This technique can provide important insights into cellular heterogeneity in signaling responses (185,186), which could reveal cell-to-cell variability in DDR.
Ion mobility mass spectrometry (IM-MS) is another powerful technique that is used to further separate ions based on their collisional cross section, affected by their size, shape, and charge (187). IM-MS is particularly useful for deconvolving complex mixtures where traditional mass spectrometry may struggle to differentiate between closely related species (188). Furthermore, IM-MS can be used in combination with other mass spectrometry-based techniques, such as native MS and top-down proteomics, to obtain structural information about proteins and protein complexes (189,190).
Additionally, data independent acquisition (DIA) methods such as SWATH and diaPASEF allow for the simultaneous quantification of thousands of proteins in complex mixtures (191–194). DIA methods are particularly useful for large-scale proteomics studies, where traditional DDA methods may be limited by their dynamic range and sensitivity. SWATH and diaPASEF are both highly reproducible and can provide accurate and precise quantitative measurements, making them valuable tools for proteome profiling.
Although further innovation and addressing limitations in existing technologies remain crucial, we are about to unravel the complexity of regulatory mechanisms in DNA repair at an extraordinary level of detail and at a rapid pace.
DATA AVAILABILITY
No new data were generated or analysed in support of this research.
ACKNOWLEDGEMENTS
We would like to thank members of the Krogan lab for critical reading of the manuscript. Figure and graphical abstract were created with Biorender.com.
FUNDING
National Institutes of Health [U54CA274502, P50AI150476, U19AI135990 to N.J.K.]; Martha and Bruce Atwater Breast Cancer Research Fund; Benioff Initiative for Prostate Cancer Research; UTHSCSA Startup Research Fund (to M.K.); F. Hoffmann-La Roche and Vir Biotechnology and gifts from The Ron Conway Family (to N.J.K.).
Conflict of interest statement. None declared.
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PMC010xxxxxx/PMC10206585.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37222933
816
10.1007/s40121-023-00816-y
Original Research
Remdesivir for Hospitalized COVID-19 Patients in the United States: Optimization of Health Care Resources
Barnieh Lianne 1
Beckerman Rachel 1
Jeyakumar Sushanth 1
Hsiao Alice 2
Jarrett James [email protected]
3
Gottlieb Robert L. 45
1 grid.518606.c 0000 0005 0588 2337 Maple Health Group, New York, NY USA
2 grid.418227.a 0000 0004 0402 1634 Gilead Sciences, Foster City, CA USA
3 grid.476328.c 0000 0004 0383 8490 Gilead Sciences, 2 Roundwood Ave, Hayes, Uxbridge, UB11 1AF UK
4 grid.411588.1 0000 0001 2167 9807 Baylor University Medical Center, Dallas, TX USA
5 grid.486749.0 0000 0004 4685 2620 Baylor Scott and White Research Institute, Dallas, TX USA
24 5 2023
24 5 2023
6 2023
12 6 16551665
7 2 2023
27 4 2023
© The Author(s) 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Introduction
In addition to significant morbidity and mortality, the coronavirus disease (COVID-19) has strained health care systems globally. This study investigated the cost-effectiveness of remdesivir + standard of care (SOC) for hospitalized COVID-19 patients in the USA.
Methods
This cost-effectiveness analysis considered direct and indirect costs of remdesivir + SOC versus SOC alone among hospitalized COVID-19 patients in the US. Patients entered the model stratified according to their baseline ordinal score. At day 15, patients could transition to another health state, and on day 29, they were assumed to have either died or been discharged. Patients were then followed over a 1-year time horizon, where they could transition to death or be rehospitalized.
Results
Treatment with remdesivir + SOC avoided, per patient, a total of 4 hospitalization days: two general ward days and a day for both the intensive care unit and the intensive care unit plus invasive mechanical ventilation compared to SOC alone. Treatment with remdesivir + SOC presented net cost savings due to lower hospitalization and lost productivity costs compared to SOC alone. In increased and decreased hospital capacity scenarios, remdesivir + SOC resulted in more beds and ventilators being available versus SOC alone.
Conclusions
Remdesivir + SOC alone represents a cost-effective treatment for hospitalized patients with COVID-19. This analysis can aid in future decisions on the allocation of healthcare resources.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40121-023-00816-y.
Keywords
Cost-effectiveness
COVID-19
Hospitalization
Remdesivir
http://dx.doi.org/10.13039/100005564 Gilead Sciences issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
==== Body
pmcKey Summary Points
Why carry out this study?
While the COVID-19 healthcare paradigm has evolved, infections and hospitalizations continue to occur.
What was learned from the study?
Treatment with remdesivir, in addition to standard of care, reduced time spent in hospital and presented net cost savings compared to standard of care alone.
Across various hospital capacity scenarios, compared to standard of care alone, treatment with remdesivir resulted in more beds and ventilators being available.
In the context of increasingly constrained healthcare resources, remdesivir is a cost-effective treatment for hospitalized patients with COVID-19 and provides good value for health systems.
Introduction
Severe acute respiratory syndrome corona virus type 2 (SARS-CoV-2) emerged at the end of 2019 as a novel coronavirus disease (COVID-19) and spread rapidly worldwide. Since the virus was first detected in the USA in March 2020, over 1 million Americans have died [1]. Though multiple vaccines are available, the pandemic is far from over, particularly given ongoing immune evasiveness from humoral immunity, even as cellular immunity from vaccination and/or prior recovery alters the relative risk [2]. Despite approximately two thirds of the American population being considered fully vaccinated, > 120,000 new cases are reported daily, resulting in > 400 daily deaths and > 4500 patients hospitalized in the intensive care unit [1]. Strains on the health system resources have resulted in postponed elective inpatient surgical admissions and workforce shortages [3, 4]. While many services have resumed with lower COVID-19 hospitalizations, local surges still cause disruption.
Patients infected with COVID-19 can experience a variety of symptoms and, in some cases, can be managed in the outpatient setting or at home. However, for others, disease progression can occur rapidly, requiring hospitalization and the potential for invasive mechanical ventilation (IMV), leading to substantial clinical burden. Remdesivir (RDV), an antiviral therapy that received emergency use authorization in May 2020 from the Food and Drug Administration, has since received full approval as a treatment for hospitalized and non-hospitalized COVID-19 patients, including pediatric patients aged ≥ 28 days, weighing at least 3 kg [5]. The inpatient approval was primarily based on the pivotal ACTT-1 trial [6], which compared RDV plus standard of care (SOC) versus placebo plus SOC and found that treatment with RDV led to a shorter time to recovery (15 versus 10 days), with an increased recovery rate of 29%. Furthermore, in a post hoc analysis, RDV was associated with a 70% reduction in mortality for patients who required low-flow oxygen support at baseline [6]. The findings of the ACTT-1 trial have been supported by similar results from other trials [7] and in real-world comparative effectiveness studies [8, 9]. More recently, efficacy has been demonstrated for the early use of remdesivir for patients with one or more risk factors for progression [10].
The COVID-19 pandemic has strained health care systems globally, impacting staffing, supplies, and space [11]. In the context of increasingly constrained healthcare resources, the relative value of treatments needs to be considered. Furthermore, as the pandemic continues, new evidence emerges for novel and existing treatments, with guidelines being constantly reviewed and revised [12]. This study aims to investigate the cost-effectiveness of RDV + SOC for hospitalized COVID-19 patients in the US.
Methods
Model Overview
To assess the cost-effectiveness of treatment with RDV + SOC, a health economic model was developed in Microsoft Excel 2016 following the International Society for Pharmacoeconomic and Outcomes Research Modelling Good Practice Guidelines [13] from a societal perspective (Fig. 1). Hospitalized COVID-19 patients enter a decision tree model, stratified according to their baseline status, as defined by the World Health Organization ordinal score (OS), as follows: 0, no clinical or virological evidence of infection; 1, ambulatory, no activity limitation; 2, ambulatory, activity limitation; 3, hospitalized, no oxygen therapy; 4, hospitalized, oxygen mask or nasal prongs; 5, hospitalized, non-invasive ventilation (NIV) or high-flow oxygen; 6, hospitalized, intubation with IMV; 7, hospitalized, IMV + additional support such as pressors, renal replacement therapy, or extracorporeal membrane oxygenation (ECMO); 8, deceased [14]. In this model, on day 15, patients could then transition into one of the following health states: discharged, ward with no supplemental oxygen (OS 3), ward on low-flow oxygen (OS 4), intensive care unit (ICU) with NIV or high-flow oxygen (OS 5), or ICU with IMV (OS 6) or ECMO (OS 7). On day 29 in this model, patients are assumed to have either died or been discharged to rehabilitation or home. Patients can also be re-hospitalized post-discharge, incurring hospital costs and outcomes. After day 29, patients enter a Markov model, subject to age- and sex-adjusted mortality derived from US-specific lifetables. Patients can transition to death at any time. Long-term effects of COVID-19 are not considered as part of this model due to a lack of information on the impact of treatment over time. The model time horizon was 1 year; thus, no discounting for costs or outcomes was applied. No ethics or patient consent was necessary for this study; this study is based on previously conducted studies and does not contain any new studies with human participants performed by the authors.Fig. 1 Model structure
Population Inputs
The number of COVID-19 cases in the model base case is calculated using national epidemiological data in adults for 2022 [15]. The average age and gender split were taken from a recent retrospective cohort analysis of over 850,000 patients from Premier Healthcare Data [16]. Based on surveillance data, a hospitalization rate of 10% was applied to the total number of confirmed COVID-19 cases [15], recognizing that this rate may differ based on the evolving virus.
Clinical Inputs
The clinical distribution by OS at baseline was informed by the retrospective cohort analysis of Premier Healthcare Data, which included adult patients, with first admission to the hospital between May 2020 and December 2021 and with primary or secondary discharge diagnosis of COVID-19 [16]. The proportion of patients with OS4, OS5, OS6, and OS7 was determined to be 60.0%, 21.0%, 14.0%, and 5.0% over the time period of the study [16]. As transition probabilities for the two treatment arms (RDV + SOC and SOC alone) were not available from Premier Healthcare Data, they were calculated using data from Table 2 from Beigel et al. of the ACTT-1 trial [6], which provided a breakdown of participants by OS at baseline and their respective improvements by day 15 (see Table S1 Supplementary Material).
On day 29, the proportion of patients alive in the base case was derived from the OS-specific hazard ratios reported in Table 2 from Beigel et al. of the ACTT-1 trial (Table 1). It was assumed that patients with an OS of between 1 and 3 at day 29 were at no higher risk than the general population for mortality; thus, post-discharge, US-specific background mortality rates apply to these patients [17].Table 1 Key model inputs
Parameters Value Distribution References
Day 28 mortality HR (95% CI) RDV vs. SOC
OS 1–3 1 Gamma Assumption
OS 4 0.42 (0.04, 0.67) Beigel et al. [6]
OS 5 0.28 (0.12, 0.66)
OS 6 0.82 (0.40, 1.69)
OS 7 0.76 (0.39, 1.50)
Length of stay at day 15 (days)
OS 1–3 General ward
ICU
ICU + IMV
5
0
0
Gamma Ohsfeldt et al. [18]
OS 4 General ward
ICU
ICU + IMV
5
0.5
0
Ohsfeldt et al. [18]
OS 5 General ward
ICU
ICU + IMV
7
1
0
Ohsfeldt et al. [18]
OS 6 General ward
ICU
ICU + IMV
10
2
0
Ohsfeldt et al. [18]
OS 7 General ward
ICU
ICU + IMV
16
,0
11
Ohsfeldt et al. [18]
OS 8 General ward
ICU
ICU + IMV
10
0
8
Ohsfeldt et al. [18]
Readmission General ward
ICU
ICU + IMV
5
0
0
Assumption
Cost inputs (USD)
RDV per vial $520.00 Gamma GSI data on file
General ward per day $1772.00 Ohsfeldt et al. [18]
ICU per day $2902.00
ICU including IMV per day $3598.00
Readmission per day $1772.00 Assumption
Average hourly wage $11.00 Statista [22]
Utility decrements
General ward 0.27 Beta Sheinson et al. [23]
ICU 0.36
MIV 0.56
CI confidence interval, HR hazard ratio, ICU intensive care unit, MIV mechanical invasive ventilation, OS ordinal score, RDV remdesivir, SOC standard of care, USD US dollars
Length of stay (LOS) for each OS was derived from an analysis of the Premier Healthcare Database, which evaluated hospital costs, LOS and discharge status among adult COVID-19 patients between April 1 and December 31, 2020 (Table 1) [18]. LOS for rehospitalization was assumed to be 5.0 days. The rate of rehospitalization was elicited from an internal analysis and was verified by clinical experts; this rate is in line with other published sources [19]. Rate of rehospitalization in the SOC arm was 12.0% [20] with a RDV rate ratio of rehospitalization of 1.67 taken from the ACTT-1 trial [6].
Cost Inputs
Costs are reported in 2021 US dollars (USD). The base case model considered both direct costs to the health care setting, including drug costs and inpatient stays, and indirect costs (Table 1). It was assumed that a treatment course of RDV would consist of six vials. No cost was assigned to SOC. The cost per hospital day by OS was obtained from a study on COVID-19 hospital admissions using national data from the Premier Health Database, which stratified costs by level of care and ward; the overall cost per day for the hospital (general ward) and ICU informed this analysis [18]. For the cost of ICU including IMV, an average of the overall hospital and ICU costs for OS 7 was used [18]. Costs included health care professional time, tests and monitoring, and hotel costs. The cost per day of a hospital readmission was assumed to be the same as the general ward.
Productivity losses were included in the base case. From the US Bureau of Labor Statistics, it was assumed that 96.0% of the population would be economically active and working [21]. For those working, a loss cost per day due to hospitalization/rehabilitation of $221.20 was applied, based on the average hourly wage for all employees in the US [22].
Utilities
Utility decrements associated with hospitalization were incorporated by level of care, based on the highest level of care received (Table 1). It was assumed that the utility decrement for the general ward was equivalent to “COVID-19 symptoms only” and, for ICU, was equivalent to “oxygen support without ventilation,” based on a cost-effectiveness framework that evaluated acute treatments for hospitalized patients with COVID-19 [23]. This model assumed that upon discharge from the hospital (and upon entering the Markov model), patients are subject to background age-adjusted US population utility norms from the EQ-5D [24].
Outcomes
The number of hospital days avoided (by ward, ICU, and ICU + IMV) and the number needed to treat to avoid 1 day in the general ward, the ICU, or ICU + IMV (derived by determining how many patients needed to receive RDV + SOC to avoid 1 day in hospital) were estimated and presented on a per patient basis. Treatment costs, hospitalization costs, and the cost per quality-adjusted life year (QALY) were calculated.
Sensitivity and Scenario Analyses
The model estimated parameter uncertainty through both one-way and probabilistic sensitivity analyses. For one-way analyses, all parameters were varied individually by ± 10% to determine the top ten most influential inputs on the model. The probabilistic sensitivity analysis, where all inputs are varied simultaneously across pre-specified distributions, was repeated over 5000 iterations. For the probabilistic analysis, population settings, clinical inputs, and costs were varied using the gamma distribution; proportions and utility decrements were varied using the beta distribution.
In addition, various scenarios were investigated. Scenarios included the use of the ACTT-1 clinical trial data to inform OS baseline distribution [6], surge capacity for peak infection periods where bed (general, ICU and ICU + IMV) increased to 90.0%, increased hospital costs of 30%, and a scenario where only direct costs are considered (payer perspective).
As caring for COVID-19 patients has downstream effects on the resource use for hospitals, the model also considered the implications of the capacity of hospital resources used to treat COVID-19 patients as a scenario analysis. Treatment capacity was assessed based on the total population, and the number of general ward and ICU beds, along with mechanical ventilators, was taken from a survey done by the American Hospital Association on capacity in 2020 [25]. Of the total available resources, it was assumed that 64.0% of general ward beds and 63.0% of both ICU beds and mechanical ventilators would be available for COVID-19 patients at baseline [3].
Results
In the base case, the model estimated that treatment with RDV + SOC avoided, per patient, a total of 4 hospitalization days: 2 general ward days and 1 day for both the ICU and ICU + IMV compared to SOC alone (Fig. 2). The number needed to treat to avoid 1 general ward day, 1 ICU day, and 1 ICU + MIV day was 0.55, 1.05, and 1.10, respectively. Per patient estimated cost outcomes are presented in Table 2. Treatment with RDV + SOC resulted in incremental costs for treatment and rehabilitation as well as cost savings for hospitalization and lost productivity. There were net total savings for treatment with RDV + SOC.Fig. 2 Estimated hospitalization days per patient by treatment
Table 2 Estimated cost outcomes by treatment, per patient (USD)
Treatment Hospitalization Rehabilitation Productivity Total
RDV + SOC $3116 $18,273 $1376 $1617 $24,382
SOC 0 $27,562 $1262 $2192 $31,015
Difference vs. SOC $3116 − $9289 $114 − $574 − $6633
RDV remdesivir, SOC standard of care
Negative numbers indicate a cost-savings
The life years (LYs) and QALYs of treatment with RDV + SOC versus SOC along with the cost-effectiveness results are presented in Table 3. Treatment with RDV + SOC was less costly and more effective than SOC alone and thus is considered dominant.Table 3 Cost-effectiveness results
LYs QALYs Total costs (USD)
RDV + SOC 0.90 0.72 $24,382
SOC 0.83 0.66 $31,015
Difference vs. SOC 0.07 0.06 − $6633
LY life years, QALY quality-adjusted life years, RDV remdesivir, SOC standard of care, USD US dollars
Negative numbers indicate a cost-savings
Sensitivity and Scenario Analyses
The top ten drivers of the incremental cost-effectiveness ratio are displayed in Figure S1, Supplementary Material. One-way sensitivity analyses found that the model was most sensitive to the relative risk reduction associated with LOS across the OS 4–8, along with the cost per day of IMV. The probabilistic sensitivity analysis indicated that RDV + SOC was dominant (cost less and was more effective) in 99.8% of the 5000 iterations versus SOC alone (see Figure S2, Supplementary Material).
Scenario analyses exploring the alternate assumptions for using the baseline distribution in ACTT-1, increased hospital costs, and considering direct costs only are presented in Table 4. Across all scenarios, RDV + SOC remained dominant (less costly and more effective).Table 4 Scenario analysis results, difference of RDV + SOC vs SOC alone (per patient)
Base case OS baseline distribution from ACTT-1 [6] Hospital costs increased by 30% Direct costs only
Total ward days avoided 2 2 2 2
Total ICU days avoided 1 1 1 1
Total ICU + MIV days avoided 1 < 1 1 1
Total costs − $6633 − $3500 − $9408 − $6056
Total QALYs 0.06 0.04 0.06 0.06
Negative values indicates cost savings
Scenario analysis of the impact of RDV + SOC with varying proportions of hospital capacity being used for COVID-19, estimated at a total population level, is presented in Table S2, Supplementary Material. In the base case, treatment with RDV + SOC requires 10% more general ward bed days versus 47% more when treated with SOC alone to manage patients with COVID-19. Across both increased and decreased treatment capacity scenarios, treatment with RDV + SOC compared to SOC alone results in more general ward and ICU bed days along with more ventilators available.
Discussion
This study demonstrated the cost-effectiveness of inpatient RDV + SOC versus SOC alone for the treatment of hospitalized COVID-19 patients from a societal perspective. We found that under base case assumptions, treatment with RDV + SOC reduced the LOS and avoided days in hospital, resulting in cost savings compared to SOC alone. The results were robust across multiple scenarios, and RDV + SOC was cost-effective in almost all iterations in the probabilistic sensitivity analysis. Treatment with RDV + SOC, under these assumptions, is a cost-effective option for US health payers, while also minimizing lost productivity due to illness.
This study contributes to the growing body of literature reporting that remdesivir is cost-effective for the treatment of hospitalized patients with COVID-19. Furthermore, these results can be considered highly generalizable given the various sources used to populate the inputs in the model [26–30]. Although the direct quantification of the impact of hospitalizations due to COVID-19 on lost productivity costs and absenteeism is not yet available, the impact of COVID-19 on the global economic and financial markets has been significant, with reductions in income and a rise in unemployment [31]. Furthermore, RDV reduces the number of days in hospital and thus increases the capacity in the ICU by freeing up bed space [32, 33]. Not only does this allow patients to return to work faster following discharge from hospital, but this may also aid in offsetting losses in revenue due to reduced elective surgeries [34]. This study may underestimate the benefit on RDV on reducing healthcare resource use as it focuses on the use of RDV in hospitalized patients only. Given the recent data on the use of RDV in preventing progression to severe COVID-19 among outpatients [10], the cost-effectiveness of RDV in other settings should be explored to fully understand the benefit of treatment.
While this model is informed by multiple inputs from the literature, the COVID-19 pandemic is continually evolving. Clinical estimates may vary based on regional/seasonal surges and new COVID variants within the US. Furthermore, as observed over the last 3 years, the impact on hospitalizations and severity of disease changes as new variants emerge. This has wide-reaching health system impacts: when hospitals approach and exceed capacity because of COVID bed utilization, even hospital transfers for non-COVID-related healthcare needs are affected; these impacts are not yet well quantified. However, while recent data have shown that LOS, a key driver in the current model, has not changed significantly over time across the observed variants [16], the science and understanding of COVID-19 continue to evolve, which may impact other inputs in the model. While we explored an increase of 30% in costs in a scenario analysis, this may not fully address the actual increases in costs that hospitals are currently facing. As of this writing, inflation has hit a 40-year high in the US, and in the face of rising costs and labor shortages, many hospitals have turned to contract workers (travel nurses) to fill the gaps at significant additional costs [35, 36].
While the model should be interpreted in the context of its limitations and assumptions, many of the assumptions were conservative. The transition probabilities informing the model for the current analysis were taken from an earlier time in the pandemic, and disease transmission rates may be different as new variants emerge. Furthermore, these disease transmission rates were taken from a time before vaccinations emerged. The long-term effects of COVID-19 (post-discharge) were not considered, as data in this area are not well understood and were thus considered too uncertain to be included at this time. While further work on identifying the risk factors, groups disproportionately affected, and health and financial costs associated with post-COVID would help inform these inputs in the future, preliminary data indicate that treatment with RDV leads to a reduction in long-term COVID-19 symptoms [37]. While the base case analysis considered the societal perspective, this model did not explicitly consider burnout—physical and mental exhaustion—among healthcare workers during the pandemic [38]. In addition to the significant impact on the mental well-being of healthcare workers, burnout can generate inefficiencies in the healthcare organizations and lead to staffing shortages. Finally, our model did not estimate the number of deaths avoided when patients were treated with RDV + SOC. Given the benefit in survival observed with treatment with RDV + SOC [6, 39], this model may underestimate the benefit of RDV in the inpatient setting.
Conclusions
Despite the availability of multiple vaccines and treatments, the health and economic burden of the COVID-19 pandemic remain significant, and the ongoing need to deploy effective interventional therapies to treat patients remains an urgent need. This model found that RDV + SOC is a cost-effective treatment for hospitalized patients with COVID-19, both reducing the disease burden of patients and representing good value for health systems. This model can aid in guiding future decisions for the allocation of healthcare resources.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (PDF 408 KB)
Acknowledgements
Funding
This study was supported by Gilead Sciences; Gilead Sciences funded the rapid service fee. The Maple Health Group received consulting fees from Gilead Sciences for this work.
Author Contributions
Alice Hsiao and James Jarrett conceptualized the work; Rachel Beckerman, Sushanth Jeyakumar, Alice Hsiao, James Jarrett and Robert L. Gottlieb participated in the methodology; Lianne Barnieh, Rachel Beckerman, Sushanth Jeyakumar were involved in the analysis and investigation. Lianne Barnieh was responsible for preparation of the original draft. All authors participated in the review and editing of the final manuscript.
Disclosures
Robert L. Gottlieb was on Scientific Advisory Boards with Eli Lilly, Hoffman-La Roche, Gilead Sciences, and GlaxoSmithKline and was on the Academic Steering Committee with Kinevant Sciences. Robert L. Gottlieb’s institution has received an in-kind gift of medication from Gilead Sciences to facilitate unrelated research. Alice Hsiao and James Jarrett are employees of Gilead Sciences. Lianne Barnieh, Rachel Beckerman, and Sushanth Jeyakumar have no competing interests to declare.
Compliance with ethics guidelines
This article is based on previously conducted studies and does not contain any new studies with human participants performed by any of the authors.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Efficient deep-blue luminescence based on dual-channel intra/intermolecular exciplexes
Exciplex emission for high-performance deep-blue electroluminescent devices
https://orcid.org/0000-0002-9783-4815
Zhang Zhen Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Validation Visualization Writing - original draft Writing - review & editing 1 †
Dou Dehai Conceptualization Formal analysis Investigation Methodology Resources Validation Visualization Writing - review & editing 1 †
Xia Rongrong Investigation Methodology Validation 1
Wu Peng Investigation Validation Visualization 1
Spuling Eduard Data curation Writing - review & editing 2
Wang Ke Methodology 1
Cao Jin Methodology 1
Wei Bin Project administration 1
Li Xifeng Project administration Resources 1
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Zhang Jianhua Data curation Formal analysis Funding acquisition Project administration Resources Validation Writing - review & editing 1 *
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Bräse Stefan Conceptualization Data curation Funding acquisition Project administration Supervision Visualization Writing - original draft Writing - review & editing 2 3 *
https://orcid.org/0000-0002-0814-8822
Wang Zixing Conceptualization Data curation Formal analysis Funding acquisition Methodology Project administration Resources Supervision Validation Writing - original draft Writing - review & editing 1 *
1 Key Laboratory of Advanced Display and System Applications, Ministry of Education, Shanghai University, 149 Yanchang Rd, 200072 Shanghai, China.
2 Institute of Organic Chemistry, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 6, 76131 Karlsruhe, Germany.
3 Institute of Biological and Chemical Systems-Functional Molecular Systems (IBCS-FMS), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, D-76344 Eggenstein-Leopoldshafen, Germany.
* Corresponding author. Email: [email protected] (J.Z.); [email protected] (S.B.); [email protected] (Z.W.)
✝ These authors contributed equally to this work.
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Highly efficient and stable blue organic light-emitting diodes (OLEDs) cannot be easily obtained simultaneously. In particular, the efficiency roll-off as a reference index to evaluate the lifetime of deep-blue OLED at high luminescence is still severe. A novel molecule (CzSiTrz) connected with carbazole and triazine fragments by a nonconjugated silicon atom is designed. An intramolecular charge transfer emission and intermolecular exciplex luminescence in the aggregated state are obtained, resulting in a dual-channel intra/intermolecular exciplex (DCIE) emission with fast and efficient reverse intersystem crossing (RISC). Deep-blue OLED with Commission Internationale de l'Eclairage (CIE) coordinates of (0.157, 0.076) and a record-high external quantum efficiency (EQE) of 20.35% at high luminance (5000 cd m−2) is accomplished. Simple molecular synthesis and device fabrication of this strategy give a unique approach to realizing high-performance deep-blue electroluminescence.
Dual-channel intra/intermolecular exciplex emissions achieve high-performance deep-blue electroluminescent devices.
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 21975152 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 51725505 http://dx.doi.org/10.13039/501100002858 China Postdoctoral Science Foundation 2022M722028 State’s Key Project of Research and Development Plan 2022YFE0109000 Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy via the Excellence Cluster 3D Matter Made to Order EXC-2082/1–390761711 CopyeditorJudith Urtula
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pmcINTRODUCTION
As one of the most popular display and solid-state lighting solutions, organic light-emitting diodes (OLEDs) have attracted enormous attention and efforts to explore and accelerate their practical applications (1–3). In the current commercial products, phosphorescent emissive materials are used for green and red emissions thanks to their high efficiencies and stable device performances (4, 5). However, because of the lack of suitable emitters, pure/deep-blue devices still rely on the first generation of purely fluorescent molecules with an internal quantum efficiency (IQE) below 25%, resulting in inferior device performances compared with its red and green counterparts (6–9). To solve this issue and meet the requirement of the National Television Standards Committee (NTSC) coordinates for “pure” blue of (0.14, 0.08) in commercial products, many efforts have been made by academia and industry all over the world. Similar to development strategies for green and red phosphorescent emitters, materials containing metal atoms (Ir, Pt, Au, Cu et al.) were designed and synthesized to obtain deep-blue emitters. Among them, the representative achievements were conducted by the group of Li (10), Forrest (11), Zysman-Colman (12), Credgington, and Thompson et al. (13, 14). Some deep-blue phosphorescent OLEDs (PhOLEDs) with Commission Internationale de l'Eclairage y coordinates (CIEy) of 0.05 to 0.11 and external quantum efficiencies (EQEs) of 11 to 24.8% have been accessed. Supposing the transport of electrons and holes can be more balanced, the stability of these phosphorescent emissive materials can be further improved, and the energy transmission and conversion efficiency can also be improved. In that case, it is possible to solve the problem of efficiency roll-off in PhOLEDs. In 2012, Ma and coworkers (15–17) proposed that hot exciton materials can convert the high triplet-state excitons (Tn, n > 1, hot excitons) to singlet-state ones (Sm, m ≥ 1) through hybridized local and charge-transfer (HLCT) process, leading to 100% IQE. HLCT provides a strategy to obtain donor-π-acceptor type deep-blue molecules with low CIEy, and the highest value of EQE can reach up to 10.8% with relatively low efficiency roll-off at present, which has great potential to obtain higher efficiency and stable deep-blue OLEDs.
Another important method to access deep-blue emission is the thermally activated delayed fluorescence (TADF) process. Adachi et al. achieved the breakthrough of TADF materials for OLEDs (18–24). In TADF molecules, the nonradiative triplet-state excitons (Tn, n ≥ 1) can be upconverted to radiative S1 excitons through reverse intersystem crossing (RISC), which is conducive to obtaining blue emission accompanied by 100% IQE in theory. The classic TADF molecules are based on the donor-acceptor (D-A) system. It has been proven that the intramolecular charge transfer (CT) via through-bond interaction effectively achieves good TADF emitters for OLEDs. For example, Kwon and Lee (25) reported that a deep-blue device based on a D-A molecule, TDBA–Ac, exhibited CIE coordinates of (0.15, 0.06) with a high EQEmax of 21.50 ± 0.22%. Besides the D-A type TADF molecules, Hatakeyama (26, 27) designed and synthesized TADF molecules based on the multiple resonance (MR) effects of B and N atoms to achieve deep-blue OLED with CIEy < 0.1 (0.09) and high EQE. Further study shows that deep-blue OLEDs based on MR-TADF could be constructed by using the MR effect of N/O and C/O atom pairs (28, 29). Exciplex is a unique complex generated in situ through intramolecular or intermolecular charge transitions induced by light or electric fields (30–33). Holes on the highest occupied molecular orbital (HOMO) of the donor fragment and electrons on the lowest unoccupied molecular orbital (LUMO) of the acceptor motif are cross-recombined to form excitons and then emit light. Therefore, the exciplex strategy has the intrinsic potential to obtain the full color and TADF luminescence, including deep-blue emission, by independently adjusting the energy levels of the HOMO of the donor and LUMO of the acceptor. Currently reported representative intermolecular exciplexes connected by spiro- or oxygen bridge generally used acridines or diarylamines as donors, as reported by Jiang and Zhang, making the HOMO energy level of the molecule relatively shallow, which is conducive to achieving sky-blue luminescence (34, 35).
To pursue ideal emitters for deep-blue OLEDs, we explored a novel material design strategy and synthesized the molecule CzSiTrz (Fig. 1). A silicon atom was used to connect the carbazole (Cz) and 1,3,5-triazine (Trz) moieties building a nonconjugated system. The efficient dual-channel intra/intermolecular exciplex (DCIE) TADF emission with fast RISC (kRISC = 9.8 × 106 s−1) is demonstrated. Using CzSiTrz as an emitter, deep-blue OLEDs with an emission peak at 440 nm, CIE coordinates of (0.157, 0.076), and record-high EQEs of 20.35% at high luminance (5000 cd m−2) were realized.
Fig. 1. Molecules and theoretical calculations.
(Left) Chemical structures of CzSiTrz, CzSiCz, and TrzSiTrz. (Right) The optimized structure, frontier orbital densities (HOMO and LUMO), and energy states of CzSiTrz.
RESULTS AND DISCUSSION
Molecule design and synthesis
We started our work on high-performance deep-blue OLEDs by designing and synthesizing the donor-Si-acceptor (D-Si-A) molecule, CzSiTrz. For comparison and emission property study purposes, the D-Si-D molecule, CzSiCz, and A-Si-A molecule, TrzSiTrz, were also synthesized (Fig. 1). The synthetic steps and characterization details are described in the Supplementary Materials. A carbazole donor was selected because of its planar structural geometry and deep HOMO energy level. In addition, the electron delocalization of planar Cz and Trz units enhances the electronic vibration coupling between them. At the same time, both Cz and Trz segments have high triplet energy levels of locally excited states (3LE). After connecting these two components with silicon atoms, the first excited singlet state of charge transfer (1CT) of CzSiTrz can be spin-orbit coupled well with their 3LE, which can further enhance the RISC process (36).
Moreover, unlike the reported linking units, oxygen, hexafluoroisopropylidene, spirobifluorene bridge, etc., we use a silicon bridge as the central linking group to break the conjugation between donor and acceptor. The use of a Si bridge does not affect the electron cloud distribution of the donor and acceptor fragments, keeping them with a large energy gap for deep-blue emission. As reported by Sun and coworkers (37), the phenylsilyl group can stabilize the excited and polaronic states of the organic molecules through the hyperconjugation effect, which is favorable to producing efficient and stable intermolecular exciplex emission. In addition, the introduction of a silicon atom can play a positive role in improving the thermal performance of organic materials.
The glass transition temperature (Tg) and the thermal decomposition temperature (Td) of CzSiTrz are 165° and 540°C, respectively (fig. S2 and Table 1). High Tg and Td indicate that CzSiTrz can maintain good thermodynamic and morphological stability during the device manufacturing process.
Table 1. Photophysical, electroluminescent, and thermal properties of CzSiTrz.
DSC, differential scanning calorimetry.
Compound λPL* (nm) S1/T1 (eV) ΔEST (eV) HOMO/ LUMO (eV)† Tg/Td (°C)‡ PLQY (%) τp (ns) τd (μs) kISC kRISC (×106 s−1) (×106 s−1)
CzSiTrz 377*/380§/445║/448¶ 3.12/3.06 0.06 5.62/2.88 165/540 83 35.5 0.11 2.6 9.8
*PL measured in cyclohexane.
†The HOMO and LUMO values were determined using the following equations: HOMO (eV) = −(Eox + 4.8), LUMO (eV) = EHOMO + Eg. The value of Eg was calculated from the absorption edges of films.
‡Tg, glass transition temperatures; Td, thermal decomposition temperatures of 5% weight loss, obtained from DSC and TGA measurements.
§PL measured in 0.1 wt % PMMA.
║PL of neat CzSiTrz film at 298 K.
¶PL of neat CzSiTrz film at 77 K.
Theoretical calculations and electrochemical properties
Density functional theory (DFT) and time-dependent DFT calculations at the B3LYP/6-31G** level were carried out to reveal the electron cloud distributions and energy levels of CzSiTrz. The results are shown in Fig. 1 and Table 1. The HOMO is mainly located on the electron-donating carbazole fragment, and the LUMO is dispersed on the triazine moiety. The theoretically calculated HOMO and LUMO levels of CzSiTrz are −5.74 and −2.56 eV, respectively. Because of the nonconjugation effect of the Si atom, there is almost no overlap between HOMO and LUMO, resulting in a small ΔES1T1 of 0.18 eV. In addition, it was found that the T2 was identical to S1 (2.74 eV), and the difference between T3 and S1 was also very small (ΔES1T3 = −0.05 eV), which is conducive to realizing efficient RISC.
Cyclic voltammetry (CV) using the Fc+/Fc redox pair as an internal standard was carried out to explore the electrochemical properties of CzSiTrz (fig. S3 and Table 1). As is typically observed for carbazole-based oxidations, CzSiTrz also showed an irreversible oxidation process with a potential peak at 0.82 eV. The reduction potential of −2.27 and −2.49 eV was observed, which could be assigned to the triazine unit. On the basis of these data, HOMO and LUMO levels were estimated at −5.62 and −2.88 eV, respectively.
Photophysical properties
The photoluminescent (PL) properties of CzSiTrz in solution were evaluated in different solvents first (Fig. 2A). It exhibited structured emission spectra with maximum emission peaks located at 359/377 nm in cyclohexane and 364/381 nm in toluene, which can be ascribed to the LE state transition of Cz or Trz units. PL emissions were also carried out in a dichloromethane (DCM) and 2-methyltetrahydrofuran (2-Me-THF) solution. As shown in Fig. 2A, besides emission peaks at 359 and 381 nm, another emission peak appeared at 506 nm (in 2-Me-THF) and 549 nm (in DCM) because of their higher polarities of them, which could be ascribed to the intramolecular through-space CT transition between Cz and Trz unit. Furthermore, the PL behaviors of CzSiTrz in mixed THF/water solvents were performed and analyzed (Fig. 2B). When water fraction (fw) was below 30%, the fluorescence emission behavior was quite weak. The emission spectra were almost the same as in 2-Me-THF, with two emission ranges between 359 to 381 and 475 to 500 nm ascribed to the LE transition and intramolecular CT transition, respectively, between Cz or Trz units. When the fw of water was increased to 50%, the emission intensity increased, and the emission at 484 nm became the stronger peak rather than that around 359 to 381 nm.
Fig. 2. Photophysical properties and molecular interactions.
(A) PL spectra of CzSiTrz in solution. a.u., arbitrary units. (B) CzSiTrz in THF/water mixtures (10 μM) with water fraction (fw) from 0 to 90% at room temperature. (C) CzSiTrz in PMMA with doped concentration from 0.1 to 100 wt % (neat film). (D) PL spectra of CzSiTrz, CzSiCz, TrzSiTrz, and CzSiCz:TrzSiTrz (1:1) film. (E) The possible processes of DICE in CzSiTrz film.
Further increasing the fw of water, the longer emission became the dominant peak, and the intensity increased. However, when fw is 90%, the strongest emission with a peak around 450 nm was obtained. In addition, the emission was blue-shifted with the fw increase from 50% (482 nm) to 90% (450 nm). This could be ascribed to the reduction of Stokes shift, which may be derived from the crowded space and suppressed geometric relaxation. Therefore, CzSiTrz exhibited enhanced intensity and blue-shifted PL spectra peaks in the aggregation state. These results suggest that there are two channels of intramolecular and intermolecular CT emission processes to form their mixed luminescence when CzSiTrz gets aggregated to form nanoparticles as the distance between Cz and Trz units gets short. As a result, an efficient dual-channel intra/intermolecular exciplex (DCIE) emission was achieved.
To further confirm the DCIE features of CzSiTrz in neat film, PL emissions with different doping concentrations in polymethyl methacrylate (PMMA) film were performed and are shown in Fig. 2C. The PMMA films with 0.1 and 1% doping concentrations showed similar emission behaviors as in cyclohexane and toluene with LE transition emission and a tiny intramolecular CT emission. Notably, the PL spectrum of the film with 1 wt % doping concentration clearly exhibited a shoulder peak around 436 nm. CzSiTrz was doped into a nonpolar polymer, polystyrene, at a very low concentration (5 × 10−6 M); the emission bands between 400 and 460 nm were also observed after 40-ns decay (fig. S4) to verify the intramolecular CT emission too. As the doping concentration increases, the peaks between 350 and 385 nm become smaller and smaller, while the peaks at 436 nm become dominant. This suggests that there are strong LE state transitions and weak intramolecular CT transitions of CzSiTrz at lower doping concentrations, and strong intermolecular CT transitions dominate as the doping concentration increases while the molecules are relatively close aggregated. Ultimately, only an unstructured emission at 445 nm in the neat film was observed (Fig. 2C). Compared with the emission in solution with 90% fw of water, the film emission shows almost the same shape and peak around 445 nm.
Moreover, as shown in Fig. 2D, the emission of CzSiTrz film at 445 nm is consistent with that of a 1:1 physical mixture of CzSiCz and TrzSiTrz (peak at 445 nm), which further confirmed the intermolecular interactions in nondoped CzSiTrz film. Therefore, the emission of the neat film should be attributed to DCIE transitions of CzSiTrz molecules. The PL emission of neat CzSiTrz film at 77 K was performed to explore its fluorescence and phosphorescence emission, and the excited singlet (1CT) and triplet (3CT) states were estimated at 3.12 and 3.06 eV on the basis of the onset of spectra, respectively (Fig. 3A). As a result, a small ΔEST of 0.06 eV was accessed. This value is very small enough to activate a TADF process.
Fig. 3. Photophysical properties of CzSiTrz.
(A) PL spectra of CzSiTrz neat film at room temperature and 77 K. (B) Transient PL decay spectra of CzSiTrz in PMMA. (C) Transient PL decay spectra of CzSiTrz neat film.
The photoluminescent quantum yield (PLQY) in the PMMA matrix with a different doping concentration of CzSiTrz was performed. The results showed that PLQYs increased from 13% (0.1 wt %) to 43% (neat film). When the oxygen was excluded (under a nitrogen atmosphere), the PLQY was further increased to 83% (table S1). The transient decay of these PMMA films was explored to reveal the emission properties. The transient emission decay of 1 wt % CzSiTrz in PMMA film showed a delayed component at 436 nm from intramolecular CT emission, while it only showed the single exponential decay of 1LE at 365 nm. As the doping concentration of CzSiTrz increased, the delayed component increased too. For the neat film, as shown in (Fig. 3C), the emission could be ascribed to the prompt fluorescence (PF) with a lifetime of 35.5 ns and the delayed fluorescence (DF) with a lifetime of 0.11 μs. On the basis of the lifetime and PLQY of CzSiTrz neat film, the rate of constant of PF (kp) is calculated as 2.8 × 107 s−1, which is higher than the rate of constant of DF (kd) of 8.9 × 106 s−1, because DF proceeds via a RISC of exciton from 3CT to 1CT and then returns to ground state. The intersystem crossing rate kISC is calculated to be 2.6 × 106 s−1. Notably, the kRISC of CzSiTrz is as fast as 9.8× 106 s−1 based on the literature equation (S1.1 to S1.6 and fig. S6), which is one of the largest values of current reported results, including conventional D-A, unique MR, and intramolecular exciplex TADF molecules (table S2). Emission decays of neat film from 80 to 290 K (fig. S5) were measured and showed a temperature dependence typical for a TADF mechanism. The triplet states of the Cz donor and Trz acceptor segments are at 3.04 eV of 3LED and 3.01 eV of 3LEA, respectively (36). Because the connection of silicon atom completely breaks the conjugation between Cz and Trz, 3LED and 3LEA are very close to the 1CT and 3CT of CzSiTrz, and the RISC process of 3LED or 3LEA → 1CT is easy to occur and dominates at low temperature resulting in large kRISC value and little temperature dependence of decays (fig. S5). The large kRISC indicates that the triplet state converting to the singlet state is very fast, and the exciton stays in the triplet state only for a very short time, which can suppress the exciton quenching. This confirms that DCIE is an efficient strategy for fast and efficient upconversion from T1 to S1, leading to an efficient TADF process.
Electroluminescent properties
The deep-blue emission with a maximum peak at 445 nm and unique DCIE TADF features of CzSiTrz inspired us to explore its electroluminescent performances. Considering its aggregation-enhanced emission phenomenon, nondoped hole-only and electron-only device were fabricated first (fig. S8). It showed that CzSiTrz had better electron transport ability than hole transport ability. On the basis of these results, we fabricated nondoped OLED devices with the architecture of ITO/HATCN (10 nm)/NPB (15 nm)/TCTA (10 nm)/mCP (20 nm)/ CzSiTrz (X nm)/ASFT (Y nm)/ASFT:Liq (1:1) (20 nm)/Liq (1 nm)/Al (80 nm) (Fig. 4A). ITO (indium tin oxide) and Liq/Al were used as the anode, and the cathode, respectively; NPB (N,N′-di(naphthalen-1-yl)-N,N′diphenyl-benzidine) and TCTA (4,4′,4′′-tris(carbazol-9-yl)triphenylamine) were used as the hole-transporting layers; 4-(3-(4,6-diphenyl-1,3,5-triazin-2-yl)phenyl)spiro[fluorene-9,5′-indeno[1,2-c]pyridine] (ASFT) was also used as the electron-transporting layer. 1,3-di(9H-carbazol-9-yl)benzene (mCP) was used as the exciton-blocking layer (Fig. 4A). The nondoped emissive layer was constructed using a neat CzSiTrz film.
Fig. 4. Deep-blue devices based on CzSiTrz.
(A) Device architecture and materials used in the devices A to E. (B) Current density–voltage–luminance characteristics (J-V-L) for devices A to C (J-V characteristics for device C from 0 to 9 V in fig. S12). (C) EQE versus luminescence curves for devices A to C. (D) EL spectra for devices C were obtained at 8 V.
Devices A, B, and C were fabricated with neat CzSiTrz of 5 nm and ASFT of 10, 15, and 20 nm, respectively. The turn-on voltages (Von) of these devices were around 2.8 to 2.9 V (Table 2 and table S3), which is a new record for deep-blue OLEDs (emission peak <450 nm). The low voltages were due to using a nonconjugated connection mode that allowed to regulation of the injection of holes and electrons of molecules independently by D and A units. Their maximum EL emission peak was located at around 440 nm, which is consistent with their PL emissions. No other emission peaks were observed in devices A, B, and C, indicating that the emissions are only from CzSiTrz through the DCIE transition process. When ASFT was 15 nm (device B), maximum current efficiency (CEmax) of 17.3 cd A−1, power efficiency (PEmax) of 16.8 lm W−1, EQEmax of 21.5%, and CIE coordinates of (0.153, 0.079) were obtained. These performances were better than that of device A with ASFT of 10 nm because of more balanced charge transport. Moreover, the CIE coordinates of device B were very close to the standards of blue primaries (0.14, 0.08) of the NTSC. The high EQEmax of 21.5% is one of the best results for deep-blue OLEDs with peak emission <450 nm and CIEy < 0.08. However, device B demonstrated a notable efficiency roll-off at a high luminance of 1000 cd m−2 (EQE = 17.6%, 18% roll-off) and 5000 cd m−2 (EQE = 8.5%, 60% roll-off). We further increased the ASFT to 20 nm in device C to enhance the electron transporting abilities. As a result, deep-blue device C with CIE coordinates of (0.157, 0.076) shows very stable efficiency performance, and its EQE gradually increases as the brightness increases. For a practical high luminance of 1000 cd m−2, the EQE was 19.7%, comparable to the most efficient deep-blue OLEDs with maximum emission peak <450 nm and CIEy < 0.08. The variation of CIE versus applied voltage is small, indicating that the emission spectra are relatively stable (fig. S9). To our best knowledge, the record-high EQE of 20.35% at 5000 cd m−2 and 20.43% at 7493 cd m−2 are the best performances at high luminance so far (Fig. 5 and table S3) (38–43). Its angular-resolved EL was measured and found to be very close to the standard Lambertian emission (fig. S10). In addition, the maximum luminance (Lmax, 11,662 cd m−2) of device C was also the best value of current reported deep-blue OLEDs with a maximum emission peak <450 nm, CIEy < 0.08. The high luminance, EQE, and low-efficiency roll-off could be attributed to the DCIE TADF and the large kRISC value (9.8 × 106 s−1) through inhibiting the triplet-triplet and singlet-triplet exciton annihilation processes in the device. We also investigated the effect of CzSiTrz thickness on device performance. We constructed another nondoped device with the CzSiTrz thicknesses of 7 nm (device D, fig. S11). The maximum emission peak of device D is almost identical to device C, around 440 nm. The EQEmax of device D can reach up to 17.5%, lower than device C. Yet, it suffered from server efficiency roll-off at a high luminesce of 1000 cd m−2 (EQE = 16.1%, 8% roll-off) and 5000 cd m−2 (EQE = 9.0%, 66% roll-off for device D).
Table 2. Electroluminescent device performances of deep-blue OLEDs based on CzSiTrz.
FWHM, full width at half maximum.
Devices CE/PE/EQE (cd A−1/lm W−1/%)
Von (V) EL (nm) Luminance (cd m−2) Max at 1000 cd m−2 at 5000 cd m−2 CIE(x,y) FWHM (nm)
A 2.9 444 7814 13.1/15.3/13.8 3.3/1.9/4.6 2.8/1.1/3.9 (0.153, 0.079) 59
B 2.9 444 6666 17.3/16.8/21.5 13.5/8.3/17.6 6.2/2.6/8.5 (0.154,0.079) 59
C 2.8 440 11662 14.1/12.0/20.43 14.0/9.1/19.7 13.7/7.2/20.35 (0.157,0.076) 58
D 3.1 444 6120 14.4/11.2/17.6 12.1/5.9/16.1 6.5/2.2/9.0 (0.155,0.081) 57
Fig. 5. Representative of purely organic emitters for deep-blue OLEDs with EL wavelength < 450 nm, CIEy < 0.10 (details in table S5).
(A) EQE versus the maximum luminescence. (B) CIEy versus luminance at 1000 cd m−2 (the violet star, ★, for device C at 5000 cd m−2 in this work).
Notably, there is no very obvious difference in turn-on voltage of the four devices, which indicates that the charge injection is efficient and energy level differences between each functional layer are small too. The thickness of ASFT in device A is 10 nm; compared to many conventional OLED devices, device A showed the highest EQE at the lowest current density. With the increase in voltage, the current density also increases rapidly. Its EQE immediately decreases, and its efficiency roll-off is very obvious. With the increase of the thickness of ASFT, devices B, C, and D show a trend of EQE rising first with the increase of voltage and then roll-off. In these cases, the holes and electron pairs in the emitting layer are not balanced when applying voltage for the thicker ASFT, while, as the increase of voltage, the transmission of electrons increases rapidly, the holes and electron pairs in the emitting layer gradually tend to balance, and their EQEs reach the maximum value. Especially for device C, by adjusting the thickness of the hole and electron transport layers on both sides of the device, the carrier balance is further improved, resulting in a relatively high EQE at high luminance (5000 cd m−2). As the voltage continues to increase, the holes and electrons are imbalanced again, resulting in the EQE decreasing with the brightness increase. Rapid aging at the same brightness is a conventional method to evaluate the lifetime of OLED devices. Devices with reduced efficiency roll-off usually have lower operating voltage and current density at high brightness, indicating that the hole and electron are well balanced in the emitting layer. Obviously, device C operated under lower voltage and current density is expected to have a long lifetime.
In this work, we connected substituted carbazole and 1,3,5-triazine as donor and acceptor, respectively, with nonconjugated Si atoms to construct a novel molecular system and obtained the representative molecule CzSiTrz. It is found that CzSiTrz forms intramolecular CT emission between D and A fragments by through-space exciplex. Strong intermolecular exciplex luminescence can also be formed in the solid state. Last, a DCIE emission with ultrafast RISC (kRISC of 9.8 × 106 s−1) was produced in its neat film because of efficient 3CT-1CT upconversion and spin-orbit coupling between 3LED,A, and 1CT. On the basis of the strategy, deep-blue OLEDs with CIEy < 0.08 and a record-high EQE over 20% at 5000 cd m−2 were demonstrated. Compared with the traditional D-π-A type TADF molecules, using nonconjugated silane to connect D and A segments with higher T1 energy levels has obtained efficient deep-blue materials with good thermal stability. D and A groups are more abundant and various; this effective and unique design concept of chemical structure opens up an imaginary space for TADF materials with high kRISC. It offers a promising method to achieve blue OLED devices with high performance in the future.
MATERIALS AND METHODS
All chemicals and reagents were used as received from commercial sources without further purification unless stated otherwise. The auxiliary materials for OLED fabrication were purchased from Yurui (Shanghai) Chemical Co. Ltd. 1H nuclear magnetic resonance (NMR) and 13C NMR spectra were recorded on a Bruker AV-500 spectrometer at room temperature. High-resolution mass spectra were determined on a Thermo Fisher Scientific LTQ FT Ultra mass spectrometer.
PL spectra were taken using an FLSP980 fluorescence spectrophotometer. The films on quartz substrate for photoluminescent quantum yield (PLQY) measurement were fabricated through vacuum thermal evaporation and encapsulated in the glovebox.
CV was carried out using a CH Instrument 660C electrochemical analyzer with an Ag/AgCl electrode as reference electrode, with tetra(n-butyl)ammonium hexafluorophosphate (TBAPF6) in DMF as supporting electrolytes.
The glass transition temperature (Tg) of the compounds was determined under a nitrogen atmosphere by differential scanning calorimetry on a TA Q500 HiRes thermal analyzer using a scanning rate of 10°C/min with nitrogen flushing from 20° to 600°C. The decomposition temperature (Td) corresponding to 5% weight loss was conducted on a TA Q500 HiRes TGA thermal analyzer.
All calculations in this study used Gauss 09 software. The molecular structures were optimized using the B3LYP algorithm 6-31G** basis set, and the HOMO and LUMO energy levels and the corresponding electron cloud distribution were calculated.
For OLED device fabrication, the organic layers were deposited consecutively on the precleaned ITO-coated glass substrates in vacuum conditions with a pressure of 8 × 10−5 Pa. The deposition rates of LiF, the organic materials, and aluminum were 0.6, 0.1, and 5 Å/s. The organic layers and the Al cathode were deposited without exposure to the atmosphere. The current density–voltage–luminescence (J-V-L) characteristics were measured by a Keithley 2400 source meter and a PR-670 Spectra Colorimeter in the direction perpendicular to the substrate at room temperature under ambient conditions.
Acknowledgments
Funding: This work is financially supported by the State’s Key Project of Research and Development Plan (no. 2022YFE0109000), the National Science Fund of China (nos. 51725505 and 21975152), and the China Postdoctoral Science Foundation (no. 2022 M722028). This research is also funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy via the Excellence Cluster 3D Matter Made to Order (EXC-2082/1–390761711) and the KIT-Publication Fund of the Karlsruhe Institute of Technology.
Author contributions: Conceptualization: Z.Z., S.B., and Z.W. Methodology: Z.Z., D.D., R.X., K.W., E.S., J.C., S.B., and Z.W. Investigation: Z.Z., D.D., R.X., and P.W. Visualization: Z.Z., D.D., R.X., and P.W. Funding acquisition: J.Z. and Z.W. Project administration: Z.Z., Z.W., B.W., X.L., and J.Z. Supervision: S.B. and Z.W. Writing—original draft: Z.Z. and Z.W. Writing—review and editing: Z.Z., E.S., S.B., and Z.W.
Competing interests: The authors declare that they have no competing interests.
Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Correction (21 June 2023): Due to a production error, a paragraph of the main text was incorrectly captured as part of the Figure 3 caption. In the corrected version, the text has been removed from the caption and reinserted in the main text as the last paragraph of the subsection “Photophysical properties”.
Supplementary Materials
This PDF file includes:
Synthesis and Characterization
Supplementary Text
Figs. S1 to S20
Tables S1 to S3
References
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PMC010xxxxxx/PMC10233178.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37261612
815
10.1007/s40121-023-00815-z
Commentary
Rapid Diagnostic Test Value and Implementation in Antimicrobial Stewardship Across Low-to-Middle and High-Income Countries: A Mixed-Methods Review
http://orcid.org/0000-0001-7095-7922
Moore Luke S. P. [email protected]
123
Villegas Maria Virginia 45
Wenzler Eric 6
Rawson Timothy M. 37
Oladele Rita O. 8
Doi Yohei 91011
Apisarnthanarak Anucha 1213
1 grid.428062.a 0000 0004 0497 2835 Clinical Infection Department, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH UK
2 Imperial College Healthcare NHS Trust, North West London Pathology, London, UK
3 grid.7445.2 0000 0001 2113 8111 NIHR Health Protection Research Unit in Healthcare Associated Infections & Antimicrobial Resistance, Imperial College London, London, UK
4 grid.412195.a 0000 0004 1761 4447 Grupo de Resistencia Antimicrobiana y Epidemiología Hospitalaria, Universidad El Bosque, Bogotá, Colombia
5 grid.412195.a 0000 0004 1761 4447 Molecular Genetics and Antimicrobial Resistance Unit, International Center for Microbial Genomics, Universidad El Bosque, Bogotá, Colombia
6 grid.185648.6 0000 0001 2175 0319 College of Pharmacy, University of Illinois at Chicago, Chicago, IL USA
7 grid.7445.2 0000 0001 2113 8111 Centre for Antimicrobial Optimisation, Imperial College London, London, UK
8 grid.411782.9 0000 0004 1803 1817 Department of Medical Microbiology and Parasitology, College of Medicine, University of Lagos, Lagos, Nigeria
9 grid.256115.4 0000 0004 1761 798X Department of Infectious Diseases, Fujita Health University School of Medicine, Toyoake, Aichi Japan
10 grid.256115.4 0000 0004 1761 798X Department of Microbiology, Fujita Health University School of Medicine, Toyoake, Aichi Japan
11 grid.21925.3d 0000 0004 1936 9000 Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
12 grid.412434.4 0000 0004 1937 1127 Research Group in Infectious Diseases Epidemiology and Prevention, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand
13 grid.412434.4 0000 0004 1937 1127 Division of Infectious Diseases, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand
1 6 2023
1 6 2023
6 2023
12 6 14451463
27 2 2023
20 4 2023
© The Author(s) 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Despite technological advancements in infectious disease rapid diagnostic tests (RDTs) and use to direct therapy at the per-patient level, RDT utilisation in antimicrobial stewardship programmes (ASPs) is variable across low-to-middle income and high-income countries. Key insights from a panel of seven infectious disease experts from Colombia, Japan, Nigeria, Thailand, the UK, and the USA, combined with evidence from a literature review, were used to assess the value of RDTs in ASPs. From this, a value framework is proposed which aims to define the benefits of RDT use in ASPs, separate from per-patient benefits. Expert insights highlight that, to realise the value of RDTs within ASPs, effective implementation is key; actionable advice for choosing an RDT is proposed. Experts advocate the inclusion of RDTs in the World Health Organization Model List of essential in vitro diagnostics and in iterative development of national action plans.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40121-023-00815-z.
Keywords
Antimicrobial resistance
Antimicrobial stewardship
Antimicrobial stewardship programmes
High-, middle-, and low-income countries
Infectious disease RDT
Point-of-care testing
Rapid diagnostic tests
issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
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pmcKey Summary Points
Rapid diagnostic test (RDT) utilisation in antimicrobial stewardship programmes (ASPs) is variable across low-to-middle income and high-income countries.
The 5P (programme support, preserve, practicable, population health, and precision) Value Framework is proposed to enable a wider analysis of the value of RDTs in an ASP beyond per-patient outcome measures.
Effective implementation across a range of resourcing, communication, education, logistic, and interfacing activities is key to maximise RDT adoption.
Experts advocate the inclusion of RDTs in the World Health Organization Model List of essential in vitro diagnostics and in iterative development of national action plans.
Introduction
Infectious disease rapid diagnostic tests (RDTs) are fast and accurate medical tests used to identify infectious organisms and evaluate antimicrobial susceptibility [1]. RDTs are one of the most important tools the global community can use to preserve the value of antimicrobials [2, 3]. Generally, RDTs are undervalued, as their utility is viewed by their per-patient impact, rather than by the wider societal benefit of faster diagnosis and reduction in onward transmission of communicable diseases [4, 5]. In some way, the coronavirus disease 2019 pandemic has highlighted the global societal value of RDTs and raised awareness of the importance of diagnostic capabilities [6–8]. However, global use of RDTs as part of antimicrobial stewardship programmes (ASPs) remains low [9, 10].
Implementation and use of RDTs in ASPs is associated with multiple clinical benefits; similarly, ASPs play an important role in the effective implementation of RDTs [11]. Thus, ASPs must evolve alongside technological advancements of RDTs and be continually re-evaluated [12]. Goal 3 of the national action plan (NAP) for combating antibiotic-resistant bacteria in the USA calls for the development and use of rapid and innovative diagnostic tests for identification and characterisation of resistant bacteria [13]. Despite increasing recognition and emphasis of the importance of RDTs to ASPs, other NAPs do not include mandates to implement RDTs in ASPs. For example, the NAP for antimicrobial stewardship (AMS) in the UK acknowledges the need for RDTs but does not provide specific recommendations for their use [14].
Whilst the value of novel antimicrobials has recently begun to be reconsidered by recognising wider clinical, societal, and economic impacts [15], similar perspectives on valuing infectious disease RDTs are not as well developed. In this paper, the value of RDTs as part of an ASP, beyond per-patient clinical and cost outcomes, is triangulated through an expert working group (EWG) and synthesis of published literature. The reasons behind the potential underuse of RDTs in ASPs across low-to-middle income countries (LMICs) and high-income countries (HICs) are also explored.
Methods
A panel of seven experienced infectious disease physicians, translational pharmacists, academics, and microbiologists from Colombia, Japan, Nigeria, Thailand, the UK, and the USA was identified using a stratified snowball recruitment method. Snowball recruitment is a sampling method where one interviewee provides the name of at least one more potential interviewee. The subsequent interviewee then provides the name of at least one more potential interviewee, and so on [16]. Purposeful selection was used to establish an EWG with representation across low-to-middle and high-income resource settings as well as different global regions. Three experts represented LMICs, including Colombia, Nigeria, and Thailand, and four experts represented HICs including Japan, the UK (two experts), and the USA. Country income levels correspond to classifications determined by the World Bank [17].
A mixed-methods approach was used to capture experiential knowledge from the EWG as well as from published literature. During semi-structured one-on-one interviews, from 26 May 2022 to 12 August 2022, experts provided ‘on-the-ground’ opinions gained from implementing RDTs, treating patients with suspected infectious disease, and managing ASPs. Experts also guided the search strategy required for a targeted literature review (TLR) to evaluate the clinical and economic outcomes associated with RDT use as part of ASPs.
PubMed was searched from 24 June 2010 to 24 June 2022 using the search terms described in the ‘Search Strategy and Selection Criteria’ section (Table S1 in the supplementary material). Articles were reviewed using predefined inclusion and exclusion criteria (Table S2 in the supplementary material). Screening stages were conducted by a single expert reviewer, with a 10% check conducted by a second reviewer. Of note, articles were excluded if they did not present data in the context of an ASP or an AMS initiative, such as infectious disease physician support alone. The quality of evidence for outcomes selected for inclusion in this TLR was considered and is described in further detail in the supplementary material.
Results from the literature review were discussed by the EWG during a meeting on 9 September 2022, together with considerations for RDT implementation across a range of hospital settings and global regions. A meeting on 20 October 2022 validated the recommendations in this paper.
Compliance with Ethics Guidelines
This review does not contain any new data with human participants or animals performed by any of the authors.
Search Strategy and Selection Criteria
A combination of keywords was used: (“antimicrobial stewardship” OR “antimicrobial program*” OR stewardship OR program*) AND (“point-of-care testing” OR “rapid diagnostic test” OR “rapid diagnostic” OR “rapid test” OR “rapid antigen” OR “rapid assay” OR RDT); NOT (“immunisation program*” OR “immunization program*”). Non-human study participants and non-English literature were excluded. Grey literature searches were conducted using keyword website searching, including the European Congress of Clinical Microbiology & Infectious Diseases, IDWeek, American Society for Microbiology Microbe, and national Health Technology Assessment sites.
Results and Discussion
To explore the value and impact of RDTs within an ASP, one must first consider the global definition of an RDT. The EWG agreed that a wide range of definitions exist for ‘rapid’, varying from 90 min up to 24 h. According to a Delphi Consensus in Asia–Pacific, RDTs should provide results within 4–6 h to determine treatment before administration of the second dose of an antimicrobial [18]. To accommodate a variety of settings, where this is not possible delivery of results within 24 h may be acceptable [2].“If we can do [an RDT] from a positive blood culture, get an answer from there in an hour or two, that’s a day or two ahead of conventional microbiology… that’s rapid.”
Clinical Pharmacist in Infectious Diseases, USA
“Whenever you’re thinking around the word rapid, you’ve got to add on front time for transport and back time for result generation onto an IT platform and transmission back to where it can be actioned.”
Clinical Fellow in Infectious Diseases, UK
Members of the EWG from HICs noted that some non-molecular tests such as matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) and VITEK®2 may not be regarded as RDTs in some settings, since HICs have access to molecular RDTs that give results within several hours. Whilst ‘rapidity’ is important, the accuracy, performance, and information provided by the diagnostic test are equally vital characteristics. Moreover, although molecular RDTs add granularity to microorganism identification, they are not used in isolation, and further diagnostic tests are usually needed for phenotypic characterisation to guide antimicrobial prescription. Therefore, a global definition of RDT needs to encompass a multitude of RDT modalities, ever-improving turnaround times, and where the test is conducted [9].
The EWG proposes the following global definition of RDTs for use in ASPs: Infectious disease RDTs include both microbial and host assays which can be conducted and actioned within a 24 h period that can substantially support ASPs.
Whilst some regions may not consider biomarkers as RDTs, the EWG agreed that biomarkers of infection are key to both antimicrobial initiation and cessation decision nodes when turnaround time is appropriate. Biomarkers of infection, including C-reactive protein, erythrocyte sedimentation rate, and procalcitonin, help to determine host response and stratify risk of patients with infection. In some LMIC settings, use of RDTs is limited to evaluation of biomarkers.
Why is the Global Use of RDTs Low?
The EWG agreed that global use of RDTs in infection-related management strategies could be increased, particularly the use of molecular RDTs in Africa and the Asia–Pacific region. The EWG outlined several barriers to RDT use across HICs and LMICs (Fig. 1).Fig. 1 Main barriers to use of RDTs, derived from EWG semi-structured interviews. EWG expert working group, RDT rapid diagnostic test
Although some biomarkers like procalcitonin and C-reactive protein are included in the World Health Organization (WHO) Model List of essential in vitro diagnostics (EDLs), they are not mandated for use [19]. The LMIC representatives of the EWG advocated for the inclusion of non-molecular RDTs and other biomarkers in the WHO Model List of EDLs [19]. Furthermore, insufficient quality of evidence has resulted in a general lack of guidance from professional societies and guidelines. For example, the updated Infectious Diseases Society of America guidelines for AMS make a weak recommendation for ASPs to advocate RDTs for bloodstream infections (BSIs) as a result of moderate-quality evidence [20]. Additionally, RDTs are perceived as expensive compared to non-rapid laboratory-based diagnostic assays [21].“In our day-to-day practice, it’s almost exclusively culture based for the majority of infections.”
Infectious Diseases Physician and Clinical Microbiologist, UK
“Right now, most of the RDTs need to be confirmed by conventional methods, which means additional time and costs.”
Professor of Infectious Diseases, Japan
Providers tend to prioritise antimicrobial prescriptions over diagnostic testing, and such prescribing behaviours can be entrenched and resistant to change [22]. Generally, RDTs are complementary to conventional culture-based methods [23, 24]. RDTs help provide information more quickly and allow changes to be made sooner but do not yet replace traditional culture-based methods which are considered the ‘gold standard’ for detection of many infectious pathogens and crucial for determining antimicrobial susceptibility. Using both techniques requires more human resources and incurs additional costs.“Due to the National Insurance programme [in Thailand], patients pay $1 and can be admitted to hospital for any treatment]; in that situation, the hospital tries to limit tests.”
Professor and Chief of Infectious Diseases, Thailand
“The system we have [in Japan] where AMS activities are reimbursed is fairly unique. The national insurance pays us a fixed amount per patient to do AMS and we use that money to run the programmes. There is the possibility to pay for some [RDTs] out of this of funding. This could be a model for steady support for activities and testing [elsewhere].”
Professor of Infectious Diseases, Japan
“Patients pay out of pocket for healthcare [in Nigeria]. Cost affects availability. Clinicians tend to think that the patient needs to buy the drugs first… How do you know this [patient] needs this antibiotic? It’s a behaviour that will need to change...”
Associate Professor, Nigeria
Reimbursement models differ significantly in each country, and these policies impact RDT use. Although overall costs are typically higher for molecular RDTs than for non-molecular RDTs, in 2020, state-based government insurance reimbursement rates for a molecular RDT were higher than for a non-molecular RDT in Georgia, USA [25]. The additional cost of molecular testing may be offset by lower costs associated with appropriate prescribing and decreased time of performing cultures [25]. However, such cost savings are not commonly redirected to the laboratory that initially invested capital to purchase the RDT, resulting in disconnected cost silos.
What Value Do RDTs Bring to AMS?
The value of RDTs was explored via the TLR and discussed by the EWG. The following results summarise the data identified from the TLR. The searches retrieved 1829 articles and conference abstracts. Of these, 69 reported clinical and economic outcome data recorded in hospital or long-term care facility patients with suspected infection tested with any RDT as part of an ASP or an AMS initiative. Full details are shown in Fig. S1 in the supplementary material. Most data were from retrospective and observational studies (n = 39) and randomised controlled trials (RCTs) were scarce (n = 4) (Fig. S2 in the supplementary material). There was a gap in globally representative data, with most published research focused on the USA. Additionally, almost all studies reported RDT use for suspected BSIs. The overall quality of the evidence for outcomes selected for inclusion in this review was rated low using an objective assessment (see Tables S4–S7 in the supplementary material).
Reduced Mortality
Many studies identified in the TLR reported infection-related and all-cause mortality across a variety of time points including 7-, 15-, 28-, 30-, and 60-day mortality. Of these, four quasi-experimental studies and two retrospective studies demonstrated that RDTs plus ASPs were associated with improved all-cause 30-day mortality compared with conventional or standard-of-care interventions [26–31]. For example, MALDI-TOF combined with antimicrobial susceptibility testing (AST) intervention significantly decreased time to organism identification and was associated with a significant reduction in 30-day all-cause mortality (12.7% vs. 20.3%, p = 0.021), when retrospectively compared to conventional identification methods [27]. However, two RCTs and three quasi-experimental studies did not demonstrate an additional benefit of RDT use on 30-day mortality [32–36].
Improved AMS Metrics
AMS metrics may be indirectly positive for patients but not directly associated with clinical (morbidity or mortality) impact; outcomes related to AMS were highly variable across the literature review. Overall, studies demonstrated a reduction in time to optimal therapy [27, 29, 37–44] and time to effective therapy following an RDT plus an ASP [27, 37, 40–44].
Five studies (one RCT [33], three retrospective studies [38, 45, 46], and one quasi-experimental study [47]) demonstrated significant reductions in time to de-escalation following RDT plus ASP intervention when compared to conventional diagnostic methods. Only three studies (one RCT [33] and two retrospective studies [38, 46]) demonstrated significant reductions in time to escalation following RDT plus ASP intervention. One quasi-experimental study [34] and one retrospective study [45] demonstrated a numerical reduction in time to escalation following RDT plus ASP, and both state that ASP intervention was only performed on weekdays.
Decreased Length of Stay
Twelve studies identified in the TLR showed that RDTs combined with ASPs were associated with significant decreases in hospitalisation and/or intensive care unit (ICU) length of stay (LOS) [27, 28, 35, 40, 42, 43, 46, 48–52]. In a retrospective study, use of Gram-negative blood culture test (BC-GN) plus an active ASP intervention was associated with a significant decrease in hospital LOS (7 vs. 9 days [interquartile range 5–15 vs. 4.5–21], p = 0.001), when compared to the preintervention group [46]. LOS was the most common economic outcome identified in the targeted literature review; however, despite a general trend towards reduced LOS after RDT intervention in ASPs, most findings were not significant. This may be due to factors such as study design, e.g. an underpowered study; only looking at intensive care units where infection may not be the only issue governing LOS; or inappropriate population selection.
Other commonly reported economic outcomes included internal hospital costs and hospital readmission rates. Although economic outcomes were reported across most studies identified, only two were economic models [24, 53]. Most data and methodology utilised could not demonstrate a direct economic benefit of RDT use, which may be due to the study design, e.g. if ASP metrics were not included as primary outcomes. Whilst some savings can be made by reducing antibiotic use, it was noted that RDTs are expensive to implement and use as an adjunct.
Cost-Effectiveness
In a cost-effectiveness analysis of competing strategies for the diagnosis of BSI alone or combined with an ASP, molecular RDTs were determined to be cost-effective for the diagnosis of patients with suspected BSI [24]. The economic model compared the cost-effectiveness of different strategies used for the identification of bacterial organisms and/or the presence of antibiotic resistance among these patients. MALDI-TOF plus an ASP was the most cost-effective strategy, resulting in savings of $29,205 per quality-adjusted life year (QALY) and preventing one death per 14 patients with suspected BSI, compared to conventional laboratory methods without an ASP (incremental cost-effectiveness ratio [ICER], − $29,205/QALY) [24]. Other cost-effective options included BC-GN plus an ASP (ICER, − $23,587/QALY) and polymerase chain reaction plus an ASP (ICER, − $19,833/QALY); RDT alone without ASP was not cost-effective [24].
Another study assessed the cost-effectiveness of treating malaria based on microscopy, RDT, or presumptive diagnosis as part of a malaria control programme in Uganda. Malaria RDTs (primarily histidine-rich protein II-based tests) were conducted in healthcare centres with no laboratory infrastructure [53]. Overall, malaria RDTs were the most cost-effective with the lowest ICER (US $5.0), compared to microscopy (US $9.61 per case correctly diagnosed and treated) [53]. Malaria RDTs were cost-effective in both low- and high-transmission settings [53].
The EWG noted that economic analyses are important and must consider the broader ASP in the local area. Cost benefits of RDT use are difficult to translate across settings, and most available economic evidence is not applicable to LMICs.
Nine studies identified in the TLR suggest that RDTs plus ASPs are associated with reduced hospital costs [30, 42, 43, 54–58]. One study evaluated total hospital costs following MALDI-TOF implementation and dedicated pharmacy stewardship personnel time [30]. Data used in the cost analysis were derived from patients; and factors considered included the cost of RDT implementation, the cost of pharmacist and microbiology technologist time, laboratory costs, and costs associated with LOS. As a result of MALDI-TOF implementation and ASP pharmacy stewardship, total hospital costs decreased by $2439 per BSI, giving an approximate annual cost saving of $2.34 million [30].
Overall, most studies in the TLR indicated that RDTs have a positive impact on AMS metrics, but not on clinical outcomes such as mortality. A wide variety of AMS metrics were reported, including time to optimal therapy, time to effective therapy, time to escalation, and time to de-escalation. Future studies, including RCTs, with more rigorous designs (e.g. higher statistical power, or with primary outcomes focused on key AMS metrics and clinical outcomes) are needed to better capture the impact and evaluate the value of RDTs in ASPs. Demonstrating outcomes not only at the individual level but also at the population level requires studies developed in collaboration with many stakeholder organisations, clinical and physician societies, and government groups [5].
Value Benefits of RDT Use in ASPs
Reflecting the themes derived above, to define the value of RDT use, the EWG proposed five aspects of value benefits of RDT use in an ASP, as part of the 5P (programme support, preserve, practicable, population health, and precision) framework (Table 1). The value framework for RDTs was modelled on the STEDI (spectrum, transmission, enablement, diversity, and insurance) principles, which were used as the evaluation framework for the de-linkage pilot in England, and re-valued infection therapies [59, 60]. The proposed 5P framework will allow future wider analysis of the value of RDTs in an ASP beyond per-patient outcome measures.Table 1 5P framework: value benefits of RDT use in an ASP
Value Definition of benefit
Programme support Enable specific ASP interventions and provision of meta-data for delineating ASP outcome KPIs
Preserve Quantifiable changes in antimicrobial consumption, appropriateness of antimicrobial prescriptions, and potential antimicrobial resistance
Practicable Impact on laboratory and clinical area sample flow (including logistics, information technology, and personnel) and patient flow (including admission avoidance, and length of stay) across LMIC and HIC settings
Population health Quantifiable impact on population health through both impact on infection transmission and speed of return to work
Precision Evaluable test performance characteristics which may supersede existing traditional laboratory ‘gold standard’ diagnostics
ASP antimicrobial stewardship programme, HIC high-income country, KPI key performance indicator, LMIC low- to middle-income country, RDT rapid diagnostic test
Across both HICs and LMICs, the EWG agreed that RDTs bring value to ASPs and aid in the fight against antimicrobial resistance and clinically challenging pathogens. Even where RDTs are not widely available, there is value seen in diagnostics in general as they aid surveillance and provide epidemiological data [61]. Beyond the clinical and economic value described in the literature, the EWG recognised a broader impact and value of RDT use in ASPs to a variety of stakeholders including hospitals, clinicians, patients, and wider society (Fig. 2).Fig. 2 The value of RDT use in ASPs to key stakeholders: hospitals, clinicians, patients, and society. AMS antimicrobial stewardship, ASP antimicrobial stewardship programme, RDT rapid diagnostic test
Effective Implementation is Key to Realise the Value of RDTs in ASPs
In this section, opinions from the EWG were combined with supporting data from the literature. “We have all these very nice gadgets and tools which we can implement. But we’ve learned from our experience in terms of trying to develop and implement technology that you can’t force these into someone’s decision-making pathway. They must be developed and designed to fit in with current processes in a way that isn’t burdensome on the end user.”
Clinical Fellow in Infectious Diseases, UK
To achieve optimal antimicrobial use, RDTs must be used to facilitate decision-making at various stages of the clinical pathway, i.e. antimicrobial initiation, on-treatment, and de-escalation [18 ]. Use of RDTs as part of bundled interventions that support decision-making is key. Generally, experts acknowledge molecular RDTs to be most valuable in the initiation phase of the patient care pathway (Fig. 3). Early, safe de-escalation of antimicrobials based on RDT results can also play an important role in ASPs by reducing antimicrobial consumption [34, 38, 45–47].“[RDTs] bring a lot of clinical benefits of course because they can make a quick diagnosis, especially in the ICU. It’s very important to start with the right antibiotic, the right one will decrease mortality.”
Scientific Advisor, Colombia
Fig. 3 EWG perspectives on RDT use across the patient care pathway. EWG expert working group, HCP healthcare professional, RDT rapid diagnostic test
Implementation requires more than the provision of the test: it requires support and training, ensuring the test fits within the clinical decision-making pathway and ASP, and the provision of available resources including staff and laboratory capabilities. Successful implementation in both LMICs and HICs is dependent on five main aspects: communication, resources, justification, education, and behaviour.
Communication
Alongside physicians and pharmacists trained in infectious disease, the EWG recognised the importance of the microbiology laboratory and information technology staff in the implementation of RDTs within ASPs. Close communication between the laboratory and the ASP team results in savings which can offset the added RDT cost [51].“If you implement an RDT [but] don’t have the support staff from stewardship to do the active intervention, and [call] providers and translate outputs, you might as well not even have [that RDT]. That’s where a lot of people struggle even if they can afford to bring in the test.”
Clinical Pharmacist in Infectious Diseases, USA
Resources and Justification
The EWG reinforced that each region is affected by different economic development levels and infrastructure and therefore RDT implementation is specific to each setting. When considering resources, this should be based on prioritisation of areas of greatest ASP impact or improvement within that setting. Achieving this requires effective utilisation of hospital and laboratory data to help prioritise ASP goals.“Our intensive care colleagues are really interested in these types of tools and there is a willingness to engage with them. It becomes a business case [to] demonstrate the benefit.”
Infectious Diseases Physician and Clinical Microbiologist, UK
“Studies of cost-effectiveness [are needed] to try to show the administrators how important [RDTs] are [alongside] the ASP.”
Scientific Advisor, Colombia
At the national level, country-specific economic models can help to justify and guide decision-making processes regarding RDT use. For example, a cost-effectiveness analysis conducted from a Brazilian Public Health System perspective, used data on direct medical costs to evaluate molecular RDTs to diagnose methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Gram-negative bacteria (CRGNB), and vancomycin-resistant Enterococcus spp. (VRE) [62]. Although the model did not evaluate RDT use as part of ASPs, complementary use of molecular RDTs and conventional methods were shown to be more cost-effective than conventional methods alone for the detection of antimicrobial drug-resistant bacteria in BSI [62].
Education and Behaviour
More education and training of clinical teams outside infectious disease specialties is needed to increase awareness and optimal use of RDTs.“Awareness should be driven among clinicians to know that [RDTs] are available. A lot of doctors were taught in medical school about biomarkers. They’ve never seen them. Their labs don’t have them, so they don’t request them, and they’ve forgotten about them.”
Associate Professor, Nigeria
“I would say that education is key. Some [hospitals] have [RDTs], but [clinicians] try not to use them very often. Many physicians don’t know how to use them.”
Scientific Advisor, Colombia
“There is no expertise to help with the interpretation of the results with some diagnostics.”
Professor and Chief of Infectious Diseases, Thailand
Diagnostic stewardship must be incorporated in ASPs to guide the use of RDTs in the patient pathway and to support interpretation of RDT results. ASP-supported interpretation of RDT results helps expediate antimicrobial therapy optimisation and decrease broad-spectrum antimicrobial use for several organisms. Some studies indicate that procalcitonin guidance can reduce duration of treatment and daily defined dose (DDD) and is associated with significant decreases in mortality [63, 64].
RDT Selection and Implementation
Here, the EWG outline approaches for RDT selection and implementation in ASPs. When deciding which RDTs should be implemented, ASP teams should consider the prevalent or problematic organisms within their hospital setting, as well the sensitivity and specificity of each test [23]. The EWG compiled a checklist to facilitate hospital RDT implementation in any hospital setting and geographical region (Table S3 in the supplementary material). Experts agreed on the imporance of considering existing interventions and processes that the candidate RDT can complement. Microbiologist-validated RDT instruments should be used, and laboratory staff should guide discussions on implementation of RDTs and new AST interpretive criteria [23, 65]. Test location, i.e. at point of care versus in the laboratory, impacts the governance of test use as well as internal and external quality assurance. Ultimately, this determines who runs the test, who pays for the test, and who oversees the quality assurance of the test [25].
The ASP team and microbiology laboratory must elucidate RDT processes within local protocols and algorithms, detailing what RDT to perform, where to send specimens, and how to respond to RDT results [25, 43]. Thus, total process efficiency is key to effective implementation. This includes end-to-end efficiency from the moment a test is ordered, through to specimen collection, testing in the lab, and communication of the result outside the lab that will impact or change patient care. If a hospital is expecting value from an RDT, the hospital must make sure there is nothing that negates that value in the workflow, such as process inefficiencies. To aid RDT selection in any hospital setting and geographical region, the EWG summarised the main implementation factors for each RDT, ordered by lowest to highest sophistication (Table 2).Table 2 RDT selection and implementation factors
Technology type Global usea Implementation factors
Biomarker e.g. procalcitonin, CRP, ESR High Biomarkers are useful in both HICs and LMICs. Procalcitonin suggests whether a patient has a bacterial infection versus a viral infection. Biomarkers can reduce antibiotic prescribing for respiratory infections but not for other issues like pharyngitis
Antigen test e.g. PBP2a, CrAg® LFA, Tb-LAM Ag High Lateral flow assays benefit both HICs and LMICs owing to their compact size and low upfront cost. PBP2a antigen testing can be used to detect methicillin resistance in S. aureus [66]. Some antigen tests can be provided near the patient. Most have high specificity rates and lower sensitivity rates, and therefore a high number of false negatives
Biochemical chromogenic assay e.g. RAPIDEC® Carba NP Low Carba NP can be useful in resource-limited regions or LMICs without access to or expertise in molecular RDTs. Carba NP is reliable for detection of carbapenemase activity from species of Enterobacterales and Pseudomonas aeruginosa [67]. Carba NP allows for fast screening and easy handling as well as optimal performance to detect carbapenemase-producing bacteria; however, subtle colour changes can be missed during analysis of results
Automated identification/AST e.g. VITEK®2, BD Phoenix™ High Automated identification and antimicrobial susceptibility systems are accessible to both LMICs and HICs. VITEK®2 can be used for AST in combination with MALDI-TOF (for organism identification) in a centralised laboratory, resulting in significantly shorter LOS and significantly shorter length of antimicrobial therapy [48]. This technology is automated and compact. It can estimate resistance in Gram-negative and Gram-positive bacteria, and can detect ESBL-producing strains. The biggest advantage of this technology is its ability to provide phenotypic, as opposed to genotypic, information for which translation of resistance markers is needed
Mass spectrometry (MALDI-TOF) e.g. Vitek MS®, Microflex® Intermediate MALDI-TOF can be useful in resource-limited regions or LMICs. Generally, HICs have access to molecular RDTs producing results within several hours. The integration of MALDI-TOF MS with AST, and near-real-time AMS practices for patients with Gram-negative BSIs can significantly improve time to optimal therapy [42]. MALDI-TOF detects both bacterial and fungal microbes and is useful in settings with many uncommon infections. Direct analysis is performed on biological samples. For now, MALDI-TOF does not detect resistance mechanisms, but promising studies are emerging. Moreover, MALDI-TOF cannot provide susceptibility reports
PCR e.g. Verigene® System, ePlex system, BIOFIRE® FilmArray® System Intermediate PCR is an established and useful technology in both HICs and LMICs, but high upfront costs can limit its use in more rural regions of LMICs. PCR is well suited to detect the presence or absence of resistance genes. However, PCR is less suited for detection of point mutations within target genes. Multiplex PCR can detect several resistance genes simultaneously. The VERIGENE® System, ePlex system, and BIOFIRE BCID2 can detect key Gram-negative resistance markers. Dependent on the platform, a comprehensive number of targets can be tested, including bacteria, viruses, and fungi. Panels can be used to provide pathogen-specific results. BIOFIRE® FilmArray® Pneumonia Plus tests for pneumonia and other lower respiratory tract infections
NMR e.g. T2MR, T2Candida Low NMR technologies are currently limited to some centres in HICs. Traditional blood cultures are the gold standard for the diagnosis of candidemia, but these take 2–3 days for results and require further species identification. T2MR has a short turnaround time of 8 h and supports safe early discontinuation of empiric antifungal therapy in ICU patients with suspected candidemia [68]. T2 systems have a rapid turnaround time and can use blood specimens directly. De-escalation is possible with negative results
AST antimicrobial susceptibility testing, BCID2 blood culture identification 2, BSI bloodstream infection, CRP C-reactive protein, ESBL extended-spectrum beta-lactamase, ESR erythrocyte sedimentation rate, EWG expert working group, HIC high-income country, ICU intensive care unit, LMIC low-to-medium income country, LOS length of stay, MALDI-TOF matrix-assisted laser desorption/ionisation time-of-flight, MS mass spectrometry, NMR nuclear magnetic resonance, PBP2a penicillin-binding protein 2a, PCR polymerase chain reaction, RDT rapid diagnostic test, T2MR T2 magnetic resonance
aGlobal use is defined as use across HICs and LMICs and was determined by EWG feedback. High = ≥ 5 experts across HICs and LMICs reported use of RDTs in their setting; intermediate = three or four experts across HICs and LMICs reported use of RDTs in their setting; and low = ≤ 2 experts reported use of RDTs in their setting
The EWG agreed that robust RDT post-implementation follow-up processes are required to ensure adoption upon implementation. ASPs must document clinical and economic outcomes associated with each RDT and consequent impact on patient care [36, 65]; the 5P approach enables a whole-healthcare economy lens through which to make these evaluations. Hospitals and long-term healthcare facilities must establish key performance indicators (KPIs) to measure RDT effectiveness [18].“It is important to tailor [KPIs] to what you can accomplish [in your setting]. For example, maybe you have overuse of vancomycin [in your setting] and that’s your main target. [This] makes data a little easier to collect.”
Clinical Pharmacist in Infectious Diseases, USA
Most hospitals focus on easily accessible pharmacy metrics, i.e. DDD, concordance with policy, concordance with AST results, and time to effective antimicrobial therapy. However, these KPIs can be harder to extract in some settings and the most appropriate KPI for each ASP is dependent on the capabilities on the resources of the setting. In hospitals with minimal ASPs, a focus for the laboratory could be turnaround time, whereas a setting with advanced ASP may be able to focus on hospital level outcomes and data collated for LOS and mortality. Moreover, KPIs must reflect effectiveness of RDTs against local data [18, 69]. The combination of passive guidance through evidence-based institutional guidelines, active monitoring, and feedback of prescribing patterns by ASP teams, as well as real-time data, can improve local antimicrobial management [47].
Future Directions
The EWG discussed that, to realise the true impact of RDTs on ASPs, there needs to be investment in appropriately designed studies (e.g. ASP metrics as primary outcomes) which are adequately powered to evaluate this impact. The EWG have identified the following areas for focus: more robust clinical studies in a range of non-academic or community-based settings; larger studies that capture representative patients; targeted cost-effectiveness studies in a range of geographical locations; and studies to investigate the value of RDTs to ASPs and wider society.
Limitations and Strengths
This work has some limitations. Firstly, the EWG was represented by one expert per country across selected HICs and LMICs (except for the UK which was represented by two experts). Although the EWG was recruited from a variety of geographical regions, within each region there remains a high degree of intercountry variability. Therefore, the views expressed in the EWG may not be representative of all possible issues around RDT use in ASPs in each country. Secondly, snowball recruitment for selecting group members may risk exclusion of experts with alternative views. This was mitigated by one-on-one interviews that enabled group members to provide independent opinions before validating a consensus during EWG meetings. Thirdly, the targeted literature review results are based on a single database (PubMed) and were developed to capture articles reporting clinical or economic data covering RDT use within an ASP, and therefore studies without programmes or AMS initiatives were not included. In addition, the majority of the literature identified in the TLR is based on evidence in HICs, which has implications for the applicability of findings to LMICs. The EWG are aware that additional studies exist investigating biomarkers as RDTs; however, biomarkers are often not identified as RDTs within the literature. All literature was limited to English language, thus excluding literature from non-English speaking regions.
Conclusion
Experts across HICs and LMICs agree that RDTs have significant potential to bring substantial value to patients, clinicians, ASPs, healthcare providers, and wider society. However, supporting published evidence, at least for perspectives beyond just the per-patient impact, remains sparse. Results from this review indicate that published evidence for the clinical and economic value of RDT use in ASPs is weighted towards BSIs in the USA; more evidence must be generated across other disease areas and regions. The use of RDTs is low across HICs and LMICs, and a lack of setting-specific clinical and economic outcome data is a key barrier to RDT uptake. A wider evidence base, combined with robust clinical and economic outcome data, is needed to drive uptake of RDTs. Moreover, effective implementation across a range of resourcing, communication, education, logistic, and interfacing activities is key to maximise RDT adoption. The EWG developed an evaluation framework through which the value of RDTs in an ASP may be more optimally realised than per-patient outcomes only. This paper is a call to action for global stakeholders (i.e. clinicians, laboratory staff, hospital administrators, payers, and policymakers) to:Raise awareness of RDTs available in their setting and maintain close communication with both the ASP team and the microbiology laboratory personnel.
Provide and share practical support, training, and resources to facilitate uptake and effective implementation of RDTs in their setting.
Encourage the use of RDTs in national reimbursement models, and provide recommendations on RDT use in NAPs and the WHO Model List of EDL.
Supplementary Information
Below is the link to the electronic supplementary material.Supplementary file1 (PDF 407 KB)
Acknowledgements
Funding
The one-to-one interviews were funded by bioMérieux, Marcy-l'Étoile, France. The study sponsor funded the journal’s Rapid Service Fee. The study sponsor had no role in study design, data collection, analysis, or preparation of the manuscript. No payments or honoraria were made to the authors in respect of manuscript preparation.
Medical Writing and Editorial Assistance
Writing and editorial support was provided by Anna L. McCormick, PhD, Cecilia R. F. Silva, MSci, and Rachael M. Kershaw, PhD from Prime Access (Prime Global) and funded by bioMérieux.
Authorship
All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
Author Contributions
All authors contributed to the study design. Material preparation, data collection and analysis were performed by Anna L. McCormick, Cecilia R. F. Silva, and Rachael M. Kershaw in collaboration with Anucha Apisarnthanarak, Eric Wenzler, Luke S. P. Moore, Maria Virginia Villegas, Rita O. Oladele, Timothy M. Rawson and Yohei Doi. Medical writing support was provided by Cecilia R. F. Silva, Rachael M. Kershaw and Anna L. McCormick. All authors contributed to the development of the manuscript and approved the final manuscript.
Disclosures
Luke S. P. Moore has consulted for or received speaker fees from bioMerieux (2013–2022), Pfizer (2018–2022), Eumedica (2016–2022), Kent Pharma (2021), Umovis Lab (2020–2021), Shionogi (2021–2022), Pulmocide (2021), Sumitovant (2021–2022), and received research grants from the National Institute for Health Research (2013–2022), CW+ Charity (2018–2022), Infectopharm (2022), and LifeArc (2020–2022). Anucha Apisarnthanarak declares no competing interests. Eric Wenzler reports grants from Qiagen and Shionogi Inc (research grant paid to institution), and personal fees from Abbvie Inc, Ferring Pharmaceuticals, Qiagen, Melinta Therapeutics, Astellas Pharma and Venatorx Pharmaceuticals. Maria Virginia Villegas reports grants from Pfizer and bioMerieux, personal fees from Pfizer, MSD and bioMerieux and support for meetings from Pfizer and bioMerieux. Rita O. Oladele declares no competing interests. Timothy M. Rawson reports personal fees from bioMeriuex, Roche Diagnostics Ltd and Pfizer. Yohei Doi reports personal fees from Gilead Sciences, MSD, GSK, Shionogi, Meiji Seika Pharma, Moderna, bioMerieux, Chugai, FujiFilm, MSD and Eiken Chemical.
Compliance with Ethics Guidelines
This review does not contain any new data with human participants or animals performed by any of the authors.
Data Availability
The data supporting the content of this review is from publicly available sources. Some of the articles are from journal sites that require paid access and are not freely available.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC010xxxxxx/PMC10234830.txt |
==== Front
Vaccine X
Vaccine X
Vaccine: X
2590-1362
Elsevier
S2590-1362(23)00066-9
10.1016/j.jvacx.2023.100325
100325
Regular paper
Benefit-risk assessment of Covid-19 vaccine, MRNA (MRNA-1273) for males age 18–64 years
Yogurtcu Osman N. a
Funk Patrick R. a
Forshee Richard A. a
Anderson Steven A. a
Marks Peter W. bc
Yang Hong [email protected]
a⁎
a Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
b Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
c Office of the Center Director, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
⁎ Corresponding author at: Office of Biostatistics & Pharmacovigilance, FDA – Center for Biologics Evaluation and Research, 10903 New Hampshire Ave, WO71, Silver Spring, MD 20993-0002, USA. [email protected]
02 6 2023
8 2023
02 6 2023
14 10032519 5 2022
13 9 2022
30 5 2023
© 2023 The Authors
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights
• Publication promotes vaccination to fight against the COVID-19 pandemic.
• We assessed the benefits and risks per million male population of age 18–64 stratified by age groups.
• Results show CVmRNA vaccine benefits outweigh risks for all model scenarios.
• If the trend of pandemic changes significantly or a new variant emerges, benefits-risks must be reassessed.
Since the authorization of the Moderna mRNA COVID-19 vaccine, real-world evidence has indicated its effectiveness in preventing COVID-19 cases. However, increased cases of mRNA vaccine-associated myocarditis/pericarditis have been reported, predominantly in young adults and adolescents. The Food and Drug Administration conducted a benefit-risk assessment to inform the review of the Biologics License Application for use of the Moderna vaccine among individuals ages 18 and older. We modeled the benefit-risk per million individuals who receive two complete doses of the vaccine. Benefit endpoints were vaccine-preventable COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and deaths. The risk endpoints were vaccine-related myocarditis/pericarditis cases, hospitalizations, ICU admissions, and deaths. The analysis was conducted on the age-stratified male population due to data signals and previous work showing males to be the main risk group. We constructed six scenarios to evaluate the impact of uncertainty associated with pandemic dynamics, vaccine effectiveness (VE) against novel variants, and rates of vaccine-associated myocarditis/pericarditis cases on the model results. For our most likely scenario, we assumed the US COVID-19 incidence was for the week of December 25, 2021, with a VE of 30% against cases and 72% against hospitalization with the Omicron-dominant strain. Our source for estimating vaccine-attributable myocarditis/pericarditis rates was FDA’s CBER Biologics Effectiveness and Safety (BEST) System databases. Overall, our results supported the conclusion that the benefits of the vaccine outweigh its risks. Remarkably, we predicted vaccinating one million 18–25 year-old males would prevent 82,484 cases, 4,766 hospitalizations, 1,144 ICU admissions, and 51 deaths due to COVID-19, comparing to 128 vaccine-attributable myocarditis/pericarditis cases, 110 hospitalizations, zero ICU admissions, and zero deaths. Uncertainties in the pandemic trajectory, effectiveness of vaccine against novel variants, and vaccine-attributable myocarditis/pericarditis rate are important limitations of our analysis. Also, the model does not evaluate potential long-term adverse effects due to either COVID-19 or vaccine-attributable myocarditis/pericarditis.
Keywords
COVID-19 vaccine
FDA
Benefit-risk analysis
Males
Myocarditis
Pericarditis
==== Body
pmc1 Introduction
The Moderna COVID-19 mRNA (mRNA-1273 or CVmRNA) vaccine has been available for use in persons 18 years of age and older in the United States under Emergency Use Authorization (EUA) and was recently licensed in January 2022. Since the authorization of mRNA COVID-19 vaccines (Pfizer-BioNTech and Moderna) in December of 2020, real-world evidence from the pre-Omicron period has indicated the vaccines effectively prevent COVID-19 cases, hospitalizations, and deaths. However, cases of myocarditis and pericarditis associated with mRNA COVID-19 vaccines have been reported in the United States (US), especially in adolescents (for whom the Pfizer-BioNTech vaccine is authorized) and young adult males[1], [2], [3] through the Biologics Effectiveness and Safety (BEST) system, which is an active post-market surveillance system at the US Food and Drug Administration’s (FDA) Center for Biologics Evaluation and Research (CBER). CBER follows FDA’s structured framework[16] when making decisions on product licensure. FDA previously conducted and published a benefit-risk assessment for the use of COMIRNATY among ages 16 years and older[6]. In this paper, to advance transparency regarding FDA’s decision-making process, we report its benefit-risk (B-R) assessment (using principles similar to FDA[6] and CDC’s[4] earlier assessment for a different vaccine) to inform regulatory decisions related to the Biologics License Application (BLA) for use of the Moderna vaccine among individuals 18 years of age and older.
The regulatory question addressed by our analyses is whether the benefits of vaccination outweigh the risks for the target population, considering the uncertainties of the evolving pandemic (changes in disease incidence and emergence of new variants) and the risk of myocarditis/pericarditis after vaccination, predominantly among young males, identified by post-authorization safety surveillance. Our assessment used the structured benefit-risk framework (BRF) which includes four key dimensions - Analysis of Condition, Current Treatment Options, Benefits, and Risks and Risk Management[5]. We assessed the benefits and risks per million males ages 18–64 years who are vaccinated with two complete doses of the Moderna vaccine. The metric is fixed at per one million individuals with two complete primary doses of vaccination) to compare benefits and risks consistently among the groups without knowing how vaccine uptake may vary. We estimated COVID-19 cases, hospitalizations, ICU admissions, and deaths prevented by the vaccination (benefits) and myocarditis/pericarditis cases, related hospitalizations, ICU admissions, and deaths attributable to the vaccine (risks). We chose these endpoints as they are the most clear and measurable endpoints for benefits and risks that have the greatest public health significance. Modeling was not conducted for females and individuals 65 years of age and older due to previous work showing a relative lower risk[6] among these groups, resulting in too few cases of myocarditis/pericarditis after vaccination in these groups to reliably estimate myocarditis/pericarditis rates. These groups are also expected to have a more favorable benefit-risk compared to males age 18–64 years based on available evidence.
2 Methods
2.1 Model overview
We used a similar methodology as presented in our earlier paper[6] and the Microsoft Excel model file for our analysis is available upon request. We assessed the benefits and risks per million male population stratified by age: 18–25, 26–35, 36–45, 46–55 and 56–64 years since data indicated an age-dependent risk of myocarditis and pericarditis post vaccination. Those age groups aligned with the data collection stratification for COVID-19 outcomes. The model benefit endpoints were vaccine-preventable COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and deaths. The model risk endpoints were vaccine related myocarditis/pericarditis cases, hospitalizations, ICU admissions, and deaths (Fig. 1). Key model inputs include duration of vaccine protection, VE against COVID-19 cases and hospitalizations, age-specific COVID-19 case and hospitalization incidence rates, age-specific vaccine-attributable myocarditis case rate, hospitalizations, ICU admissions, and death rate.Fig. 1 Benefits-risks value tree. Description: A tree from left to right pointing out the benefits and risks of Moderna CVmRNA.
Our model produced benefit-risk outcomes for six different scenarios (Table 1) as a sensitivity analysis of the uncertainties related to three major model inputs: COVID-19 incidence rate (Scenarios 1, 2, and 3), VE (Scenarios 1 and 4), and vaccine-attributable myocarditis/pericarditis rate (Scenarios 1, 5, and 6). We decide to use Scenario 1 as a base scenario since it represents the most up-to-date COVID-19 incidence at the time of this analysis. For the remaining scenarios, only one of the three major model inputs from Scenario 1 is modified at a time (summarized in Table 1). Model predictions are provided in Table 2. Visual benefit-risk plot for age groups 18–25, 26–35, and 36–45 for Scenario 1 is shown in Fig. 2.Table 1 Six model scenarios with varying COVID-19 incidence rates, vaccine effectiveness against cases and hospitalizations, and myocarditis/pericarditis rates.
COVID-19 Incidence Vaccine Effectiveness Excess Myocarditis Risk
Scenario 1 As of December 2021 Omicron dominant:
30% against cases
72% against hospitalization
72% against death Mean of BEST meta-analysis
Scenario 2 Average COVID-19 pandemic incidence in 2021 Same as Scenario 1 Same as Scenario 1
Scenario 3 Lowest COVID-19 pandemic incidence (June 5, 2021) Same as Scenario 1 Same as Scenario 1
Scenario 4 Same as Scenario 1 Delta dominant:
80% against cases
90% against hospitalization
90% against death Same as Scenario 1
Scenario 5 Same as Scenario 1 Same as Scenario 1 2.5th percentile of BEST meta-analysis
Scenario 6 Same as Scenario 1 Same as Scenario 1 97.5th percentile of BEST meta-analysis
Table 2 Benefit-risk outcomes per million males vaccinated with two primary series doses of Moderna CVmRNA under each of the six scenarios described in Table 1.
BENEFITS RISKS
Ages COVID Cases COVID Hospitalizations COVID ICUs COVID Deaths Myo/Peri Cases Myo/Peri Hospitalizations Myo/Peri ICUs Myo/Peri Deaths
Scenario 1 18-25yo 82,484 4,766 1,144 51 128 110 0 0
26-35yo 87,557 4,766 1,220 247 32 26 0 0
36-45yo 90,939 4,766 1,258 378 23 18 0 0
46-55yo 77,624 10,921 3,200 1,423 13 10 0 0
56-64yo 68,747 15,025 4,928 2,120 10 7 0 0
Scenario 2 18-25yo 26,705 2,088 501 48 128 110 0 0
26-35yo 29,718 2,088 535 235 32 26 0 0
36-45yo 31,727 2,088 551 359 23 18 0 0
46-55yo 29,174 4,607 1,350 1,087 13 10 0 0
56-64yo 27,472 6,286 2,062 1,573 10 7 0 0
Scenario 3 18-25yo 3,903 635 152 7 128 110 0 0
26-35yo 4,307 635 163 33 32 26 0 0
36-45yo 4,576 635 168 50 23 18 0 0
46-55yo 4,311 1,127 330 158 13 10 0 0
56-64yo 4,134 1,456 477 230 10 7 0 0
Scenario 4 18-25yo 219,958 5,957 1,430 63 128 110 0 0
26-35yo 233,486 5,957 1,525 309 32 26 0 0
36-45yo 242,504 5,957 1,573 472 23 18 0 0
46-55yo 206,998 13,652 4,000 1,779 13 10 0 0
56-64yo 183,326 18,781 6,160 2,650 10 7 0 0
Scenario 5 18-25yo 82,484 4,766 1,144 51 68 58 0 0
26-35yo 87,557 4,766 1,220 247 9 7 0 0
36-45yo 90,939 4,766 1,258 378 11 9 0 0
46-55yo 77,624 10,921 3,200 1,423 7 5 0 0
56-64yo 68,747 15,025 4,928 2,120 4 3 0 0
Scenario 6 18-25yo 82,484 4,766 1,144 51 241 207 0 0
26-35yo 87,557 4,766 1,220 247 119 97 0 0
36-45yo 90,939 4,766 1,258 378 47 36 0 0
46-55yo 77,624 10,921 3,200 1,423 23 18 0 0
56-64yo 68,747 15,025 4,928 2,120 23 18 0 0
Fig. 2 Benefit-risk outcomes per million males in age groups within 18–45 years for scenario 1 Description: Six bar graphs examining benefits (e.g., prevented deaths) and risks (e.g., excess deaths).
2.2 Benefits
2.2.1 Data and assumptions
2.2.1.1 Duration of vaccine protection
We estimated protection over a 5-month period after completion of the 2-dose primary series, since a 5-month interval between completion of the primary series and booster dose is authorized by FDA and recommended by the Centers for Disease Control and Prevention (CDC). For simplicity, the model did not account for the benefits of partial vaccination and it assumed a constant VE during the 5-month period post second dose. We did not factor in protection from potential exposure or infection with COVID-19 among unvaccinated individuals into our analysis since the degree of this protection and its time-dependency were not well understood at the time of the analysis.
2.2.1.2 Incidences of COVID-19 case, hospitalization, ICU, and death
The COVID-19 case and hospitalization incidence rates were assumed to remain constant over the assessment period (within 5 months post-second dose). For Scenarios 1, 4, 5, and 6, the crude incidence rates of COVID-19 cases and deaths in the unvaccinated population from the week of December 25, 2021, were obtained from the COVID Data Tracker for all male age groups[8]. The hospitalization rates for these scenarios were for the week of December 25, 2021, collected by COVID-NET[7]. The percentage of hospitalizations with ICU admissions was estimated based on the cumulative rates of ICU admissions for each male age group from March 2020 to October 2021 provided by the CDC (collected by COVID-NET). We used the average 2021 US COVID-19 incidence rate and the lowest US COVID-19 incidence rate (June 2021) in Scenarios 2 and 3, respectively. All the incidence-related model inputs are summarized in Table 3.Table 3 End of December 2021 US COVID incidences in the unvaccinated population listed by age groups (Male population only) and percentage of hospitalized going to ICU. Source: 1- COVID Data Tracker, 2-COVID-NET.
Age group Daily COVID-19 cases/100 k persons[1] Daily Hospitalizations/100 k persons[2] Daily deaths/100 k persons[1] Percent of hospitalized going to ICU[2]
18–25 180.89 4.35 0.05 24.0
26–35 192.01 4.35 0.23 25.6
36–45 199.43 4.35 0.34 26.4
46–55 170.23 9.98 1.30 29.3
56–64 150.76 13.73 1.94 32.8
2.2.1.3 Vaccine effectiveness
We assumed Omicron as the dominant strain in Scenarios 1, 2, 3, 5, and 6 and assumed averages of 30% VE against COVID-19 cases and 72% VE against COVID-19 hospitalizations during the 5-month period post second dose. The data from a UK surveillance report were used to derive these VEs for Omicron[9]. For Scenario 4 we assumed Delta as the dominant strain and used the US averages of 80% VE against cases and 90% VE against hospitalizations[10]. Many studies conducted in the US and other countries during the Delta-dominant period showed consistently high VE of the Moderna vaccine against both COVID-19 cases and related hospitalization[10], [11]. For all scenarios, we assumed the vaccine effectiveness against death due to COVID-19 would be equal to the vaccine effectiveness against hospitalizations due to COVID-19.
2.3 Risks
2.3.1 Calculation of risks
Our benefit-risk model had four risk endpoints (Fig. 1): vaccine-attributable myocarditis/pericarditis cases, hospitalizations, ICU admissions, and deaths. Estimates of vaccine-attributable cases of myocarditis/pericarditis (per 1 million person-days with a risk window of 7 days post vaccination) were based on a meta-analysis of four health claims databases in BEST, which combined data from four data partners (DP) starting from December 10, 2020 (Table 4). Data cutoff dates for the sources were as follows: DP1 (August 21, 2021), DP2 (July 10, 2021), DP3 (July 31, 2021), and DP4 (June 30, 2021). The age-specific vaccine-attributable incidences of myocarditis/pericarditis were calculated for dose 2 and used as the model input for the risk of individuals with a complete two-dose vaccination. The risk post dose 1 was ignored since CDC has suggested people who have myocarditis after dose 1 take precaution to receive dose 2, and the majority of myocarditis cases were reported post dose 2. For Scenarios 1, 2, 3, and 4 we used the mean meta-analysis predicted myocarditis/pericarditis rate for each male age group, while for Scenario 5 we used the 2.5th percentile rate, and for Scenario 6 we used the 97.5th percentile rate. We used Equation (1) to calculate total myocarditis/pericarditis (MPred) per one million individuals who completed the primary vaccination series (two doses of vaccine).(1) MPred=MPred2∗F
Table 4 Estimated rates of vaccine-attributable myocarditis/pericarditis cases post second dose of the primary series, by age subgroup, for 1 million fully vaccinated male individuals with Moderna CVmRNA.
Age Group Moderna CVmRNA
Adjusted Rate per 1 M Doses
Point Estimate and 95% CI Data Source
(DP = CBER BEST Data Partner)
18–25 127.8 [67.8, 241.2] DP1-4
26–35 31.8 [8.5, 118.9] DP1-4
36–45 23.1 [11.3, 47.2] DP1-4
46–55 12.5 [6.9, 22.8] DP1-4
56–64 9.6 [3.9, 23.3] DP1-4
MPred2 is the meta-analysis estimated myocarditis/pericarditis case rates post dose 2. As presented previously[6], these rates given in units of per 100 k person-years were divided by 365 days and multiplied by 7 days (the risk window), which derives the total number of myocarditis/pericarditis cases within a 7-days risk window among the 100 k people who completed their primary vaccination series. Next, we multiplied that number by 10 and derived the total number of cases among the one million individuals who completed their primary vaccination series. Consequently, when converting from 100 k person-years to one million vaccinated individuals’ risk post vaccination (in a 7-day risk window), the rates were multiplied by a factor F = (7*10)/365 as seen in Equation (1). We calculated the confidence intervals for the myocarditis cases with the chi-square method for the Poisson distribution of rare events[12]. The number of myocarditis hospitalization (MH), ICU visits (MICU), and deaths (MD) are fractions of predicted myocarditis/pericarditis cases (MPred), such that MH = MPred* FH, MICU = MPred * FICU, and MD = MPred * FD where the scalars FH, FICU, and FD here are fractions of myocarditis cases that result in the more severe outcomes: hospitalizations, ICU visits, and deaths, respectively.
2.3.2 Data and assumptions
2.3.2.1 Myocarditis/pericarditis attributable to vaccine
We used myocarditis/pericarditis incidence data for male age groups derived from four BEST health claim databases (Table 4). Meta-analysis results are available for the incidence of myocarditis case rates within the risk window of 7 days for vaccine dose 1 and 2 separately. We limited this study to males 18–64 because the reported cases of myocarditis/pericarditis attributable to vaccine among female age groups and male and female age 65 years or above are very rare, leading to unstable estimates of case rates. Therefore, we are not able to provide reliable estimates of the risks for females in all age groups and individuals 65 years of age and older. Previous work on other mRNA vaccines has shown that the risks are lower for females and those over 65[15].
2.3.2.2 Myocarditis/pericarditis hospitalization and death rate
Vaccine Safety Datalink (VSD, a vaccine safety monitoring partnership between the CDC and 9 integrated healthcare organizations) data[13] showed that 86% of myocarditis cases are hospitalized, and none required admission to the ICU for ages 18–29 years. The rate of hospitalization fell to 77% for ages 30–39 years. Since the age ranges in VSD differ from those in our analysis, we made the following adjustment for our age ranges. We used hospitalization rates of 86% for ages 18–25 years, 81.5% (the midpoint between ages 18–29 years and ages 30–39 years in VSD data) for ages 26–35 years, and 77% for ages 36 years and above in our analysis. The hospitalizations were mainly for monitoring patients’ condition as a precaution, and no additional treatment is needed. Vaccine Safety Datalink (VSD) data show a median of a one-day stay during hospitalization[13]. No death confirmed to be caused by vaccine-attributable myocarditis/pericarditis has been identified. In this model, we used zero-death rate for vaccine attributable myocarditis/pericarditis as a model input.
3 Results
For Scenario 1 (base scenario), the model predicted that vaccination of 1 million males 18–25 years of age with two primary series doses of the vaccine would prevent 82,484 COVID-19 cases, 4,766 hospitalizations, 1,144 ICU admissions, and 51 deaths due to COVID-19\ while causing 128 vaccine-attributable myocarditis/pericarditis cases, 110 hospitalizations and no ICU admissions. No death due to vaccine attributed myocarditis/pericarditis is expected. These results represented the benefit-risk of the groups with the highest potential myocarditis/pericarditis risk under the current scenario (Omicron dominant, most recent peak incidence, and the mean rate of vaccine-attributable myocarditis/pericarditis case), and we considered that the benefits of vaccine clearly outweigh the risks. The results for all six model scenarios are summarized in Table 2, and we considered that the benefits of the vaccine clearly outweigh the risks for each of the additional 5 scenarios as well. Scenarios 1, 2, and 3 represented the uncertainty in the case incidence (the week of December 25, 2021, average and the lowest incidence) in the future of the pandemic, Scenarios 1 and 4 represented the uncertainty in VE against the emerging variants (Omicron vs Delta), and Scenarios 1, 5, and 6 represented the uncertainty in the incidence rate of vaccine-attributable myocarditis/pericarditis.
4 Conclusions and discussion
Based on available data, our results support the conclusion that the benefits of the Moderna vaccine clearly outweigh its risks for all the model scenarios for all males 18–64 years of age. Furthermore, based on consistent evidence that indicates a lower risk of vaccine-associated myocarditis in females of all ages and in males 65 years and older, it is reasonable to expect that the benefit-risk balance of vaccination with Moderna in these demographic groups would be even more favorable compared with males 18–64 years of age. Therefore, we concluded that the benefits of the Moderna vaccine outweigh its risks for the overall target population.
Our modeling approach has a few limitations in the estimation of benefits. In this analysis, we conducted sensitivity analyses to test the impact of the COVID-19 incidence rates at the recent peak, average, and lowest incidence level of the pandemic. Nevertheless, given the uncertain trajectory of the pandemic, our constant COVID-19 incidence rate assumption creates high uncertainty in the benefit estimates. Furthermore, the percentage of hospitalizations resulting in ICU admission and the percentage of hospitalized patients who die are estimated based on cumulative rates of hospitalizations, ICU admissions, and deaths for each age group reported on COVID-NET from March 2020 to October 2021 (pre-Omicron period). The COVID-19 incidence rate might have changed since Omicron surged, but the more recent data were not yet available at the time of this analysis. The rate of ICU admission and death associated with Omicron may be lower compared to Delta, which may lead to an overestimation of the benefits.
Also, the estimated benefits of the vaccine would decrease if it became less effective against emerging SARS-CoV-2 variants. In this analysis, we evaluated the impact of different VEs for Omicron and Delta strains. However, there is uncertainty associated with future new variants or the composition of the variants. Furthermore, the 30% VE for Omicron used in this analysis was obtained from a UK study. A similar US study with a smaller sample size showed an approximately 23% VE for Omicron within a 5-month period post second dose[14]. However, we do not expect that this difference in VE estimates would change our B-R conclusion. Another uncertainty for the model is the durability of vaccine protection. In this analysis, we assumed a 5-month protection period after completion of two primary doses of the vaccine overall. If vaccine-induced immunity significantly wanes within five months post second dose, potentially differentially for infection versus serious disease, that may reduce the benefit of the vaccine. Another limitation to the benefits is that we do not factor in the potential for a COVID-19 infection to impart additional protection. Antibody seroprevalence and its correlation to protection are not well understood yet. This could lead to an overestimation of the benefits of the vaccine. We assumed that the vaccine effectiveness against death would be equal to the vaccine effectiveness against COVID-19 hospitalizations in all scenarios. This could lead to an underestimation of the benefits.
Our approach to estimating risks has limitations as well. First, neither the female group nor age 65 years or older of both sexes were included in this analysis due to the rarity of cases of myocarditis/pericarditis in these populations leading to an unstable estimate of case rate. However, the B-R for females and individuals 65 years of age or older is expected to be more favorable compared to male age 18–65 years, for whom clear favorable benefit-risk was demonstrated by this analysis. Second, there is uncertainty about the risk of myocarditis cases attributable to the vaccine. To estimate myocarditis/pericarditis risk attributable to the vaccine, health claims data were used, which have inherent limitations such as small sample sizes due to rare events among age groups. The reported cases in BEST had not been validated by a complete review of the patients’ medical charts and therefore may be an overestimate. To address some of these limitations, the crude myocarditis rate in our model was adjusted using the myocarditis background rate in 2019 (measured by the BEST data partners), meta-analysis of four claims databases was conducted to increase the sample size, and a sensitivity analysis was conducted to test the impact on the benefit-risk confidence interval of the estimated vaccine-attributable myocarditis/pericarditis case rate. Also, we use VSD data to calculate the rates of hospitalization and death associated with vaccine-attributable myocarditis/pericarditis. However, the actual rates we used for myocarditis came from the FDA's BEST system, not VSD. The reason for this was that the VSD did not provide an age/sex specific myocarditis/pericarditis rate, and the young male group was our primary concern for the mRNA-based vaccine review. The BEST system, on the other hand, lacked information on hospitalizations and deaths caused by vaccine-attributable myocarditis and pericarditis. We did not estimate the proportions' accuracy, which is especially important because the rates of myocarditis for VSD and BEST can differ.
Furthermore, some benefit-risk endpoints in our assessment are difficult to compare directly, for example, hospitalizations from COVID-19 and myocarditis hospitalizations. Our B-R assessment did not include potential long-term adverse effects due to either COVID-19 or myocarditis or second-order benefits and risks, including a potential impact on the public trust in COVID-19 vaccines. In this analysis, we did not investigate the benefits and risks for specific subpopulations, such as those with comorbidities, due to limited information for these populations. The health condition of individuals may remarkably impact the B-R profile and its evaluation.
Our model and analyses helped inform FDA’s licensure decision on Moderna’s vaccine. FDA considered the impact of the COVID-19 pandemic on public health, the unavailability of treatment options, the urgent need for a vaccine to prevent the disease and control the pandemic, together with the available evidence and uncertainty associated with vaccine effectiveness and vaccine-attributable myocarditis/pericarditis risk. The FDA Review Committee agreed that the benefit/risk balance for the vaccine was favorable and supported approval for use of a 2-dose primary series in individuals 18 years of age and older[17]. The purpose of publication of this benefit-risk assessment is to increase the transparency of our regulatory action by communicating to the public that the benefits of vaccination with the Moderna COVID-19 vaccine clearly outweigh the risks, even among the male adolescent population that is at a higher risk of myocarditis/pericarditis. This will hopefully increase public confidence in the vaccine and promote vaccination to fight COVID-19.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
We provide link and reference for the data in the paper
Acknowledgements
We thank Christian Reed, Ph.D., Rituparna Moitra, Ph.D., and Rosser Matthews, Ph.D. for editing this manuscript. We thank Joyce Obidi, Ph.D., and Hui-Lee Wong, Ph.D. for helpful discussions. We thank Diane Gubernot, Ph.D. and Marisabel Rodriguez Messan, Ph.D. for reviewing the manuscript. We thank FDA CBER OBPV data and analysis partners ACUMEN and OPTUM. Also, we thank the Centers for Disease Control and Prevention, particularly the Vaccine Task Force, for sharing the initial benefit-risk assessment model and data on the COVID-19 pandemic.
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References
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10 Jones J. COVID-19 Vaccine Effectiveness for Moderna and Janssen Vaccines. ACIP meeting October 21, 2021, Retrieved December 17, 2021, from https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2021-10-20-21/10-COVID-jones-508.pdf ; 2021.
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13 Klein N. Rapid Cycle Analysis to Monitor the Safety of COVID-19 Vaccines in Near Real-Time within the Vaccine Safety Datalink: Myocarditis and Anaphylaxis. ACIP Meeting August 30, 2021, Retrieved February 4, 2022 from https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2021-08-30/04-COVID-Klein-508.pdf.
14 Tseng H.F. Ackerson B.K. Luo Y. Effectiveness of mRNA-1273 against SARS-CoV-2 Omicron and Delta variants Nat Med 2022 10.1038/s41591-022-01753-y
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PMC010xxxxxx/PMC10239310.txt |
==== Front
Brain Behav Immun Health
Brain Behav Immun Health
Brain, Behavior, & Immunity - Health
2666-3546
Elsevier
S2666-3546(23)00062-5
10.1016/j.bbih.2023.100648
100648
Full Length Article
Neurophenotypes of COVID-19: Risk factors and recovery outcomes
Prabhakaran Divya ab
Day Gregory S. c
Munipalli Bala d
Rush Beth K. e
Pudalov Lauren e
Niazi Shehzad K. e
Brennan Emily f
Powers Harry R. g
Durvasula Ravi g
Athreya Arjun hi
Blackmon Karen [email protected]
e∗
a Mayo Clinic, Center for Individualized Medicine, Jacksonville, FL, USA
b University of California, San Diego, Radiation Medicine and Applied Sciences, San Diego, CA, USA
c Mayo Clinic, Department of Neurology, Jacksonville, FL, USA
d Mayo Clinic, Department of General Internal Medicine, Jacksonville, FL, USA
e Mayo Clinic, Department of Psychiatry and Psychology, Jacksonville, FL, USA
f Mayo Clinic, Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Jacksonville, FL, USA
g Mayo Clinic, Division of Infectious Diseases, Jacksonville, FL, USA
h Mayo Clinic, Department of Molecular Pharmacology and Experimental Therapeutics, Rochester, MN, USA
i Mayo Clinic, Department of Psychiatry and Psychology, Rochester, MN, USA
∗ Corresponding author. Department of Psychiatry and Psychology, Mayo Clinic Florida 4315 Pablo Oaks Ct., Jacksonville, FL, 32224, USA. [email protected]
04 6 2023
7 2023
04 6 2023
30 10064819 2 2023
24 4 2023
3 6 2023
© 2023 The Authors. Published by Elsevier Inc.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this “normal cognition” phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the “memory-speed impaired” neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder “dysexecutive” neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of COVID-19, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.
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pmc1 Introduction
Cognitive and psychiatric symptoms are among the most common, persistent, and disabling consequences of COVID-19 (Taquet et al., 2022; Hastie et al., 2022; Davis et al., 2021). Post-COVID cognitive impairment is reported in 15–35% of patients during the chronic recovery phase (Ceban et al., 2022; Becker et al., 2021), with higher rates reported across patients who were hospitalized versus home-isolated during the acute stage of illness (Pihlaja et al., 2023). Self-reported cognitive complaints include problems with concentration, memory, and slowed thinking (Davis et al., 2021; Ceban et al., 2022), commonly referred to as “brain fog”. Objective neuropsychological assessment shows predominant impairment in attention, executive functioning, and memory, with relative preservation of language and visuospatial functions (Bertuccelli et al., 2022). The most common post-COVID neuropsychiatric manifestations (new onset and chronic) include fatigue, anxiety, depression, insomnia, and posttraumatic stress disorder (PTSD) (Nalbandian et al., 2021). At this point, it remains unclear whether these heterogenous cognitive and psychiatric sequelae are uniformly elevated or clustered into distinct post-COVID neurophenotypes. This is important to determine, as different post-acute neurophenotypic presentations could point backwards to different etiological factors and forwards to different recovery outcomes.
Several mechanisms may contribute to persistent cognitive and psychiatric sequelae following COVID-19 infection. These include neuroinvasion of SARS-CoV-2 into brain or neuroepithelial tissue and indirect damage from respiratory failure (hypoxic-ischemic effects), stroke, multi-organ system dysfunction, inflammasome activation, and complex pandemic-related psychosocial factors, such as social isolation, fear, altered sleep, diet, exercise, and other health behaviors (Crunfli et al., 2022; Mehandru and Merad, 2022; Evans et al., 2021; Ollila et al., 2022; Udeh-Momoh et al., 2021). Risk factors for long COVID are heterogeneous. Higher COVID-19 disease severity is associated with greater risk of long COVID (Shir and Day, 2022) but even mild to moderate infections can increase risk of adverse neuropsychiatric outcomes (Taquet et al., 2022; Zhao et al., 2022). Cognitive profiles vary across studies, and it remains unclear whether disease-specific features (i.e., acute COVID-19 severity, presence of anosmia, encephalopathy, stroke, etc.) and disease-nonspecific features (sociodemographics, medical comorbidities, etc.) differentially contribute to distinct post-acute neuropsychological profiles. Given the variety of explanatory factors and ways in which they may interact, it is likely that post-COVID neuropsychological manifestations present as a multiform rather than a uniform cognitive syndrome.
Recognizing this, we examined objective and subjective neuropsychological outcomes of SARS-CoV-2 infection in ambulatory and hospitalized patients using standardized assessment procedures across successive COVID-19 variant waves. We characterized multivariate neuropsychological clusters (i.e., “neurophenotypes”) during the post-acute recovery stage, identified their associative features, and re-examined recovery outcomes six months later with the same symptom inventories and computerized cognitive testing platform. We hypothesized that neurocognitive and neuropsychiatric sequelae of COVID-19 would cluster into distinct neurophenotypes, each related to unique disease-specific and non-specific factors, and each differing in their longitudinal recovery trajectories.
2 Materials and methods
2.1 Participants
The Mayo Clinic Institutional Review Board approved this prospective longitudinal cohort study. We electronically obtained informed consent from all participants, recruited between July 2020 and February 2022 from a hospital-wide registry of Mayo Clinic Florida patients who tested positive for SARS-CoV-2 infection. All participants were ≥18 years of age and had no history of major neurocognitive disorders. Participants completed the outcome assessment within 12 weeks of PCR-confirmed infection (post-acute recovery stage) and follow-up assessments six months later (chronic recovery stage). Participation required access to a computer for consent, test, and survey completion. All participants received a digital link to complete assessments and respond to questionnaires.
We abstracted participant demographics and medical history from the electronic health record (EHR) at time of initial post-acute outcome assessment. Medical comorbidities were summarized with the Elixhauser Van-Walraven Index (EVCI). The EVCI includes 31 common chronic medical conditions (including depression) and was calculated from EHR data up to 1 year before the PCR-positive test date (van Walraven et al., 2009). Higher scores indicate a greater number of medical comorbidities. Vascular risk factors (history of smoking, diabetes, hypertension, and obesity) and psychiatric disorders were identified by ICD-10 diagnostic codes in the patient's EHR (Kloppenborg et al., 2008) and separately summarized due to the specialized role that these factors play in adverse COVID-19 outcomes (Taquet et al., 2021). Sociodemographic disadvantage was summarized with the 2019 Area Deprivation Index (ADI). ADI scores for each participant were retrieved from the University of Wisconsin-Madison's Neighborhood Atlas (2019), which derives national percentile rankings of socioeconomic disadvantage at the (US) Census Block Group neighborhood level from 1 (least disadvantaged) to 100 (most disadvantaged) based on unemployment rates, poverty, education, and housing (Kind and Buckingham, 2018).
COVID-19 disease severity was determined by an infectious disease specialist (HRP), using the National Institute of Allergy and Infection Disease Ordinal Scale (NIAID-OS) (Dodd et al., 2020), with lower scores indicating higher illness severity. The following COVID-19 disease-specific factors were assessed: hospitalization status (ambulatory versus hospitalized), symptom status (symptomatic versus asymptomatic), and presence of anosmia (yes/no). Vaccination status was coded at three levels: vaccine not available (prior to FDA approval); unvaccinated (vaccine FDA approved but participant remained unvaccinated); and vaccinated. Finally, COVID-19 variant type was estimated from peak variant prevalence data at covariants.org (Hodcroft, 2021) by test region (Florida) and time span (binned in 2-week intervals).
2.2 Neurocognitive assessment
We assessed objective cognitive performance with the CNS-Vital Signs (CNSVS) computerized neurocognitive assessment during the post-acute and chronic stages of recovery. The CNSVS takes approximately 30 min and includes the following neurocognitive domains: (a) verbal memory (immediate and delayed word recognition); (b) visual memory (immediate and delayed design recognition); (c) psychomotor speed (finger tapping and symbol digit coding tests); (d) reaction time (averaged across Stroop congruent and incongruent trials); (e) complex attention (sum of errors from continuous performance, shifting attention, and Stroop tests); and (f) cognitive flexibility (correct responses on the shifting attention test minus the number of errors on the shifting attention test and Stroop test), as previously described (Gualtieri and Johnson, 2006). It allows for serial testing by drawing from a large item bank with random stimuli selection. Test-retest reliability coefficients are comparable to traditional test batteries but practice effects are relatively high for the cognitive flexibility, psychomotor speed, and reaction time domains at 3-month retest (Rijnen et al., 2018). It has been normed in >1000 individuals ages 7–90 (prior to COVID-19); and validated in various clinical populations (Gualtieri and Johnson, 2006). It includes embedded validity indicators that show overall high accuracy in identifying intentional attempts to underperform (Anderson et al., 2020). As a medium-sized multi-domain test battery with an online/remote option, it is considered appropriate for Harmonization Level 2 (HL2), which is within the recommended approach to first follow-up assessment of post-acute infection (Cysique et al. 2022). Domain scores were age-adjusted by comparison to a normative reference group (mean = 100, standard deviation = 15). Scores that were flagged as invalid were removed before analyses. Classification of impairment (<9th percentile) was based on the American Academy of Clinical Neuropsychology consensus conference statement on uniform labeling of performance test scores (Guilmette et al., 2020).
2.3 Neuropsychiatric symptom inventories
We assessed subjective neuropsychological symptoms with the Neuropsych Questionnaire-45 (NPQ-45) during the post-acute and chronic stages of recovery. The NPQ-45 is a self-report symptom inventory that probes 12 neuropsychiatric symptom domains (Gualtieri, 2007). Scores from the following domains were summed and scaled as minimal (0–74), mild (75–149), or moderate to severe (150–300): attention (e.g., concentration difficulties), memory (e.g., forgetfulness), anxiety (e.g., nervousness, restlessness), depression (e.g., feeling discouraged, lack of interest), fatigue (e.g., low energy, weakness), and pain (e.g., headaches, muscle pain). The NPQ-45 scales show a high degree of internal consistency (Cronbach's alpha >0.90), test-retest reliability (two sessions within three months) and interrater reliability (Gualtieri, 2007).
During the chronic recovery stage, participants also completed the Medical Outcomes Survey (MOS-SF 36) (Ware and Sherbourne, 1992); and a posttraumatic stress disorder (PTSD) civilian checklist (PCL-C 17); (Weathers et al., 1993). The MOS-SF 36 is a self-report questionnaire with eight subscales: functioning (physical and social), bodily pain, emotional well-being, general health perceptions and perceived change in health, role limitations (physical, emotional, and social) and energy/fatigue. It has been validated as a measure of physical and mental/social health in several medical and psychiatric conditions, with consistently good psychometric properties (McHorney et al., 1993, 1994). MOS SF-36 section scores are standardized to mean of 100 based on comparison to a U.S. normative reference group that ranges in age from 18 to 94 years (Ware and Sherbourne, 1992). Lower scores indicate greater functional disability. The PCL-C 17 is a 17-item self-report checklist of PTSD symptoms for the generic general population, with excellent internal consistency (Cronbach's alpha = 0.96) and a consistent factor structure across diverse populations (Overstreet et al., 2023). The 17 items are summed to create a severity score that ranges from 17 to 85, with higher scores indicating greater PTSD symptom severity. Scores greater than 29 indicate moderate to severe PTSD and cut-off scores ranging from 30 to 50 have been used to define PTSD in prior research studies (Gerrity et al., 2007).
2.4 Data imputation
Multivariate Imputation via Chained Equations (MICE) with predictive mean matching for five imputations and 50 iterations (van Buuren and Groothuis-Oudshoorn, 2011) was used to complete missing data from the post-acute neurocognitive assessment and neuropsychiatric symptom inventories, with a <15% missingness threshold (Jakobsen et al., 2017). Little's test statistic was used to assess whether data was missing completely at random (MCAR) between cognitive domains (Little, 1988). Pearson χ2 and analysis of variance (ANOVA) statistical tests for parametric data and Kruskal-Wallis tests for non-parametric data were used to assess clinical and sociodemographic factors associated with cognitive domain data completeness.
2.5 Unsupervised machine learning: K-means clustering
Unsupervised machine learning methods were used to perform cluster analyses, given that they allow for the inference of subgroups (referred to as clusters) within a dataset. Algorithms in unsupervised learning strive to maximize inter-cluster separation and minimize separation among samples within a cluster. Objective and subjective neuropsychological measures collected during the post-acute illness stage were used as input features in a K-means clustering analysis. No domain bias was applied to inputs. We determined the optimal number of k-means clusters with the elbow method (Kaufman and Rousseeuw, 1990). Model fitting was performed with NbClust (Charrad et al., 2014), implemented in R studio build 492 (Rstudio 2020), with R v4.1.1 (R Core Team, 2021). IBM SPSS Statistics 27 was used to perform K-means clustering and remaining data analyses. Pearson χ2 statistical test identified significant data missingness across clusters.
2.6 Cross-sectional and longitudinal cluster features
Clinical and sociodemographic factors associated with cluster membership were identified through Pearson χ2 and analysis of variance (ANOVA) statistical tests for parametric data and Kruskal-Wallis tests for non-parametric data. Normality was determined by kurtosis, skewness, and Shapiro-Wilk tests. Friedman non-parametric tests were used to evaluate longitudinal change in cognitive test scores and NPQ-45 symptom inventory scores within each cluster. Analysis of variance (ANOVA) statistical tests were used to compare functional (MOS-36 scores) and psychiatric (PTSD scores) outcomes between clusters at the chronic (6-month) recovery stage. Significance was set at p < 0.05. Post-hoc analysis was conducted using Bonferroni correction to counteract Type I errors. Significance was set at p < 0.05.
3 Results
During the study period, 205 participants (171 ambulatory, 34 hospitalized) completed post-acute neuropsychological outcome assessments 5.2 (±3.7 weeks) following positive laboratory confirmation of SARS-CoV-2 infection. Of these, 101 participants completed the 6-month outcome assessment (87 ambulatory, 14 hospitalized). Attrition analyses are described in Supplementary Materials and Methods (Supplementary Fig. 1 and Table 1).Table 1 Neurophenotypes: associated features and risk factors.
Table 1Feature Cluster 1 Dysexecutive (N = 31) Cluster 2 Memory-Speed Impaired (N = 32) Cluster 3 Normal Cognition (N = 142) Test statistic p-value
Age, M (SD), y 50.06a (13.12) 47.22a (14.49) 56.81b (13.50) F = 8.25 <0.001*
Sex, n (%) χ2 = 2.77 0.25
Female 22 (71%)a 21 (66%)a 80 (56%) a
Male 9 (29%)a 11 (34%)a 62 (44%) a
Covid Variant Type, n (%) χ2 = 5.56 0.24
Initial/Alpha 14 (45%) a 21 (65%) a 63 (44%) a
Delta 9 (29%) a 5 (16%) a 47 (33%) a
Omicron 8 (26%) a 6 (19%) a 32 (23%) a
Hospitalization Status, n (%) χ2 = 5.92 0.05
Ambulatory 27 (87%)a 22 (69%)a 122 (86%)a
Hospitalized 4 (13%)a 10 (31%)a 20 (14%)a
NIAID Score, median (range) 8 (3, 8)a 7 (3, 8)b 8 (3, 8)a 0.006*
Anosmia, n (%) χ2 = 12.02 0.002*
No Anosmia 19 (63%)a 9 (30%)b 87 (64%)a
Anosmia 11 (37%)a 21 (70%)b 49 (36%)a
Vaccination Status, n (%) χ2 = 11.64 0.02*
Vaccine Unavailable 14 (45%)a,b 21 (66%)a 59 (43%)b
Unvaccinated 5 (16%)a,b 4 (12%)a 8 (6%)b
Vaccinated 12 (39%)a,b 7 (22%)a 71 (51%)b
Area Deprivation Index (ADI), median (range) 37 (4, 80)a 31 (8, 94)a,b 30 (2, 94)b H = 7.18 0.03*
Elixhauser van-Walraven Index (EVCI), median (range) 0 (−4, 21)a 0 (−4, 31)a 0 (−4, 27)a H = 0.68 0.71
History of Smoking, n (%) χ2 = 0.15 0.93
Yes 7 (25%) a 6 (21%) a 30 (23%) a
No 21 (75%) a 23 (79%) a 103 (77%)a
Diabetes, n (%) χ2 = 0.68 0.71
Yes 2 (6%) a 4 (13%) a 13 (9%) a
No 29 (94%) a 28 (87%) a 127 (91%) a
Hypertension, n (%) χ2 = 2.55 0.28
Yes 8 (26%) a 14 (44%) a 44 (31%) a
No 23 (74%) a 18 (56%) a 96 (69%) a
Obesity, n (%) χ2 = 8.82 0.01*
Yes 9 (29%)a 7 (22%)a,b 14 (10%)b
No 22 (71%)a 25 (78%)a,b 126 (90%)b
History of Psychiatric Diagnoses, n (%) χ2 = 4.29 0.12
Yes 13 (42%) a 13 (41%) a 38 (27%) a
No 18 (58%) a 19 (59%) a 104 (73%) a
NIAID: National Institute of Allergy and Infectious Diseases (NIAID) severity scale: 8) Death; 7) Hospitalized, on invasive mechanical ventilation or extracorporeal membrane oxygenation (ECMO); 6) Hospitalized, on non-invasive ventilation or high flow oxygen devices; 5) Hospitalized, requiring supplemental oxygen; 4) Hospitalized, not requiring supplemental oxygen - requiring ongoing medical care (COVID-19 related or otherwise); 3) Hospitalized, not requiring supplemental oxygen - no longer requires ongoing medical care; 2) Not hospitalized, limitation on activities and/or requiring home oxygen; 1) Not hospitalized, no limitations on activities.
Area Deprivation Index (ADI): Socioeconomic disadvantage at the (US) Census Block Group neighborhood level ranging from 1 (least disadvantaged) to 100 (most disadvantaged) based on unemployment rates, poverty, education, and housing.
Elixhauser van-Walraven Index (EVCI): Summary index of 31 common chronic medical conditions abstracted from the electronic health record up to 1 year prior to PCR-positive test date.
Cluster columns not sharing subscripts indicate mean or median differs significantly at p < 0.05 as indicated by Bonferroni correction.
3.1 Missing data analyses
The Little's test result (test-statistic (29) = 20.97, p = 0.86) indicated that data was MCAR between cognitive domains. There were no differences associated with patient age, sex, education, ADI, hospitalization status, or NIAID score (p > 0.05) in cognitive domain data completeness. All input features met a <14% missingness threshold before MICE imputation.
3.2 Post-acute neuropsychological outcomes K-means cluster analysis
We determined with the elbow method that the optimal number of clusters was k = 3. Clusters did not differ in data missingness (χ2 = 3.43, p = 0.22). Cluster 1 (N = 31) was characterized as “dysexecutive” due to impaired cluster centers for cognitive flexibility and complex attention (Fig. 1, Supplementary Table 2). This dysexecutive cluster was also characterized by mild-to-moderate complaint severity for anxiety, attention, memory, fatigue, and pain. Cluster 2 (N = 32) was characterized as “memory-speed impaired” due to impaired cluster centers for verbal memory, psychomotor speed, and reaction time, as well as low average cluster centers for visual memory and cognitive flexibility. This memory-speed impaired cluster was also characterized by mild complaint severity for memory, attention, anxiety, depression, and pain, as well as moderate-severe fatigue. Cluster 3 was the largest cluster (N = 142) and was characterized as “normal cognition” due to cluster centers in the average/normal range for all cognitive domains. Notably, despite normal objective cognitive performance, participants in this cluster still reported mild complaint severity for attention, memory, fatigue, and pain.Fig. 1 Violin plots show cluster centers for each cognitive domain score from the CNS Vital Signs computerized test battery. Scores were age-adjusted based on a normative reference sample with a mean of 100 and standard deviation of 15. Cluster 1 shows impaired cluster centers in complex attention and cognitive flexibility (dysexecutive group). Cluster 2 scores impaired cluster centers in verbal memory, psychomotor speed, and reaction time (memory-speed impaired group). Cluster 3 showed average/normal cluster centers for all cognitive domains (normal group).
Fig. 1
To facilitate comparison of cognitive impairment rates with other studies reported in the literature, we calculated the rates of subjective and objective cognitive impairment by cluster and by domain in the post-acute recovery stage (Supplementary Table 3).
3.3 Disease-specific risk factors for cluster membership
There were no significant associations between cluster membership and COVID-19 variant type (χ2 = 5.56, p = 0.24) or symptom status (χ2 = 0.42, p = 0.81). There was a marginal relationship between cluster membership and hospitalization status (χ2 = 5.92, p = 0.05), with the highest hospitalization rates in the memory-speed impaired cluster (31%). Cluster membership was associated with vaccination status (χ2 = 11.64, p = 0.02); the normal cognition cluster had the highest vaccination rate (51%), while the memory-speed impaired cluster had the lowest vaccination rate (22%). Lack of vaccine availability at time of infection (i.e., infection before 12/17/20) was the most common reason why patients were unvaccinated across all 3 clusters. There was a strong association between cluster membership and anosmia (χ2 = 12.02, p = 0.002), as well as NIAID scores (H (2) = 10.20, p = 0.006), with the memory-speed impaired cluster showing the highest rate of anosmia (70%) and lowest median NIAID scores (higher disease severity). Full results are presented in Table 1.
3.4 Disease-nonspecific risk factors for cluster membership
There were no differences in sex distribution (χ2 = 2.77, p = 0.25) or years of education (F = 0.78, p = 0.46) between clusters. Participant age differed across clusters (F = 8.25, p < 0.001), with the memory-speed impaired cluster showing the youngest mean age. Regarding medical risk factors, there were no associations between cluster membership and history of smoking (χ2 = 0.15, p = 0.93), hypertension (χ2 = 2.55, p = 0.28), diabetes (χ2 = 0.68, p = 0.71), total EVCI score (H (2) = 0.68, p = 0.71), or history of psychiatric diagnoses (χ2 = 4.29, p = 0.12). There was an association between cluster membership and obesity (χ2 = 8.82, p = 0.01) and ADI scores (H (2) = 7.18, p = 0.03). The dysexecutive cluster had the highest obesity rate (29%) while the normal cognition cluster had the lowest obesity rate (10%). ADI scores were highest in the dysexecutive cluster, indicating greater socioeconomic disadvantage. Summary results are depicted in Fig. 2.Fig. 2 Clustering outcomes. Unsupervised machine learning methods were used to perform cluster analyses on input features collected during the post-acute recovery stage in 205 patients with PCR-confirmed COVID-19 infections. Patients ranged in illness severity from asymptomatic to mild (ambulatory) to severe (hospitalized) cases. The optimal number of k-means clusters was determined with the elbow method. The resulting clusters were found to be associated with different disease-specific (anosmia, illness severity) and disease-nonspecific (obesity and area deprivation) risk factors.
Fig. 2
3.5 Longitudinal recovery outcomes
Of the original 205 participants who completed assessments in the post-acute recovery stage, 101 (49%) completed a follow-up assessment in the 6-month chronic recovery stage (dysexecutive cluster 1: N = 11; memory-speed impaired cluster 2: N = 12; normal cognition cluster 3: N = 78). To facilitate comparison of chronic cognitive impairment rates with other studies reported in the literature, we calculated the rate of subjective and objective cognitive impairment by cluster and by domain in the chronic 6-month recovery stage (Supplementary Table 4). We compared impairment rates across clusters using chi-square analyses. Results show that cluster 1 (dysexecutive neurophenotype) no longer differs in rates of complex attention or cognitive flexibility impairment relative to the other 2 clusters at the 6-month recovery stage. The rate of verbal memory impairment in cluster 2 (memory-speed impaired neurophenotype) does not differ from the other clusters. However, cluster 2 does demonstrate higher rates of objective visual memory and psychomotor speed impairment at the 6-month recovery stage, as well as self-reports a higher rate of subjective memory impairment.
To examine within-subject change in objective performance and subjective symptoms over time, we used non-parametric Friedman test of differences among repeated measures. Results for each cluster are provided in Supplementary Table 5. Within cluster 1 (dysexecutive neurophenotype), there was marginal improvement in complex attention (p = 0.06) and significant improvement in cognitive flexibility (p = 0.01) but no change in subjective symptom report. Cluster 2 (memory-speed impaired neurophenotype) had no significant changes in objective cognitive test score, but they did self-report improved attention (p = 0.03). Cluster 3 (“normal cognition” neurophenotype) significantly improved in the domains of verbal memory (p = 0.01) and psychomotor speed (p = 0.003) but had no change in subjective symptom inventory scores.
Comparison of functional outcomes between clusters at the 6-month chronic recovery stage revealed that cluster 2 (memory-speed impaired neurophenotype) had worse functional outcomes (MOS-36 scores) compared to the other two clusters (Table 2), with particularly strong effects for energy/fatigue, general health, and health change (i.e., decline in health relative to one year ago). There were no differences in PTSD PCL-17 scores between clusters.Table 2 Neurophenotypes: 6-month functional outcomes.
Table 2 Cluster 1 - Dysexecutive Function (N = 11) Cluster 2 - Memory-Speed Impaired (N = 12) Cluster 3 – Normal Cognition (N = 76) Test statistic p-value
Medical Outcomes Survey (MOS SF-36), M (SD)
Physical functioning 108.09 (10.72)a 94.17 (13.99)b 105.62 (11.30)a H = 8.24 0.02*
Role functioning/physical 109.64 (11.25)ab 94.50 (16.05)a 106.82 (14.28)b H = 7.24 0.03*
Role functioning/emotional 106.09 (11.62)a 99.50 (16.19)a 105.61 (12.69)a H = 2.20 0.33
Energy/fatigue 103.18 (10.80)ab 89.08 (19.90)b 102.55 (16.57)a F = 3.57 0.03*
Emotional well-being 104.09 (8.25)a 97.42 (16.00)a 104.97 (11.32)a H = 3.57 0.17
Social functioning 105.55 (11.06)ab 91.92 (16.58)a 103.32 (13.41)b H = 7.80 0.02*
Pain 103.91 (11.20)a 90.42 (17.24)b 102.13 (12.45)a F = 4.56 0.01*
General health 105.00 (13.01)a 87.58 (21.16)b 106.61 (16.63)a F = 6.60 0.002*
Health change 101.36 (13.31)a 83.33 (14.20)b 94.91 (13.95)a H = 9.91 0.007*
PCL-C 17, median (range) 21 (17–37)a 25 (18–65)a 20 (0–59)a H = 3.34 0.19
MOS SF-36 scores are standardized to mean of 100 (SD = 10) based on comparison to a U.S. normative reference group that ranges in age from 18 to 94 years (Ware and Sherbourne, 1992). Lower scores indicate greater functional disability.
The PCL-C 17 contains 17 items that are summed as a severity score (1 = not at all, 5 = extremely) from a range of 17–85, with higher scores indicating greater PTSD symptom severity. Scores greater than 29 indicate moderate to severe PTSD and cut-off scores ranging from 30 to 50 have been used to define PTSD in prior research studies (Gerrity et al., 2007).
Cluster columns not sharing subscripts indicate mean or median differs significantly at p < 0.05 as indicated by Bonferroni correction.
4 Discussion
In the current study, we identified three distinct neurophenotypes from multivariate neuropsychological data collected in adults recovering from SARS-CoV-2 infection. Risk factors and 6-month recovery outcomes differed across neurophenotypes, which provides preliminary validation of this approach. Several findings emerged that can potentially be used to guide evaluations of post-COVID patients and clinical trials of therapeutics designed to target persistent cognitive sequelae of COVID-19.
First, most participants (69%) performed within normal limits on objective cognitive measures during the post-acute recovery stage. These participants were classified in the “normal cognition” cluster, although they did report mild severity inattention, fatigue, memory, and pain complaints. Such complaints are often sufficient to prompt evaluation in post-COVID care clinics (Graham et al., 2021), particularly if there is subjective experience of health change/decline. On average, this neurophenotype showed improvement in memory and psychomotor speed over time, although this may have been at least partially due to practice effects, which are high on the psychomotor speed domain (Rijnen et al., 2018). Membership in this group predicted normal functional outcomes 6 months after SARS-CoV-2 infection, which is a point that can be used to counsel patients with mild post-COVID neuropsychiatric complaints who perform normally on objective cognitive testing.
Second, we found a rate of cognitive impairment (31%) among our participants that is consistent with that reported in the literature (Ceban et al., 2022; Becker et al., 2021; Pihlaja et al., 2023). Among the 31% of participants who showed cognitive impairment, there were two distinct clusters: a memory-speed impaired cluster and a dysexecutive cluster. This is consistent with the types of deficits that have been reported (Bertuccelli et al., 2022) but suggests two distinct patterns of impairment with different clinical implications.
The memory-speed impaired cluster can be considered the most severe neurophenotype. In addition to impaired performance on verbal memory, psychomotor speed, and reaction time measures, there was also subtly reduced performance on visual memory and cognitive flexibility measures. Individuals in this group reported the highest rates of subjective inattention, poor memory, and fatigue. They exhibited persistent impairment at 6-month follow-up and reported the highest rate of functional limitations and health change (decline) over the past year. Although medical comorbidities can increase the risk of more severe COVID-19 infection and contribute to overall health decline, membership in this cluster was not associated with medical comorbidity status in the 1-year leading up to infection. Rather, risk factors included higher COVID-19 symptom severity, lower vaccination rate (largely due to the lack of vaccine availability at time of infection), and the presence of anosmia during acute infection, all of which are disease-specific factors.
This raises the possibility that cognitive impairment in the memory-speed-impaired neurophenotype may be due to pathologic mechanisms directly related to SARS-CoV-2 infection. Higher disease severity in COVID-19 reflects an increased need for respiratory support, which suggests that hypoxic-ischemic damage is an important etiological factor to consider (Almeria et al., 2020; Thakur et al., 2021), especially as this is an established risk factor for memory impairment following critical illness in general (Pandharipande et al., 2013) and COVID-19 specifically (Thakur et al., 2021). Direct and indirect neuroinvasion must also be considered. Post-mortem investigations of SARS-CoV-2 infected patients have shown neural invasion and cell death through infected astrocytes (Crunfli et al., 2022) in regions that are part of the suspected neural–mucosal CNS entry route (Meinhardt et al., 2021) and are proximal to regional atrophy patterns implicated by neuroimaging of living patients, such as the piriform cortex, parahippocampal gyrus, and orbitofrontal cortex (Dondaine et al., 2022; Douaud et al., 2022), all of which are known to support memory and neuropsychiatric functions. An increasing number of studies also establish the inflammatory consequences of COVID-19 within the central nervous system (Vora et al., 2021). Biofluid biomarkers of astroglial activation (YKL-40) and pro-inflammatory cytokines (e.g., IL-1β, IL-6, IL-8, and TNF-α) distinguish cases from healthy uninfected controls (Pilotto et al., 2021), while markers of neuroaxonal loss (e.g., neurofilament light, total-tau) rise in proportion with disease severity, with higher levels identifying patients with worse outcomes at hospital discharge (Virhammar et al., 2021; Prudencio et al., 2021). Collectively, these findings suggest that post-COVID cognitive sequelae in the memory-speed impaired cluster may arise from the combined direct and indirect effects of COVID-19 infection on the brain.
Surprisingly, younger individuals had a higher risk of membership in the memory-speed impairment cluster. A similar association between younger age and post-COVID cognitive impairment has been previously observed (Matias-Guiu et al., 2023). This has two important implications. One is that the memory impairment in this group is unlikely to reflect unmasking of an incipient age-related neurodegenerative disease. The second is that these are individuals who would be otherwise working and/or raising families; thus, persistent cognitive impairment in this cohort is likely to result in greater functional impairment, raising per capita and indirect costs of disability, similar to what has been documented in conventional brain injury groups (Lo et al., 2021). For these young patients, early and intensive cognitive rehabilitation efforts are essential, not just for recovery and community integration, but for minimizing the financial impact of COVID-19 infection.
The dysexecutive neurophenotype was characterized by impairment in complex attention and cognitive flexibility. This was a milder neurophenotype that showed recovery over six months in complex attention and cognitive flexibility. The base rates for impairment in complex attention dropped from 36% to 9.1% and for cognitive flexibility from 52% to 9.1%. However, attrition and practice effects may have inflated improvements, as those who completed 6-month follow-up had higher baseline complex attention and cognitive flexibility than those who were lost to follow-up and practice effects are high on the cognitive flexibility domain. Risk factors for cluster membership included COVID-nonspecific factors such as neighborhood deprivation and obesity. Participants from communities with higher ADI scores are more likely to experience systemic disadvantage, potentially manifesting as reduced access to physical and mental healthcare, food insecurity, reduced exercise opportunities, more air pollution and unsafe housing, social discrimination, and increased worry about pandemic-related factors (Abrams and Szefler, 2020; McNeely et al., 2020; Case et al., 2022). They are more likely to be concerned about the varied economic effects of the pandemic, school closures and coordination of work and childcare responsibilities, occupational exposure to the virus, access to and cost of healthcare, ability to socially distance, and concern for older family members potentially living in the same household, all stressors that could impact cognitive performance (Valdes et al., 2022; Bernardini et al., 2021). Finally, obesity is more common in areas of lower socioeconomic status (Wang and Beydoun, 2007), which suggests that these may not be independent risk factors.
4.1 Treatment considerations
Our findings emphasize differences and similarities across patients with long COVID symptoms. Post-acute neuropsychological profiles clustered into three distinct neurophenotypes, each associated with distinct risk factors and 6-month recovery outcomes. These findings can inform phenotype-specific approaches to treatment, highlighting the need for different treatment approaches rather than a “one size fits all” response to post-COVID symptoms. This is important for prudent programmatic resource allocation and financial effect modeling within medical provider teams and for minimizing out-of-pocket expenses incurred by patients. Importantly, we found that more than two-thirds of patients ascertained from a hospital registry do not have objective cognitive impairment. For many, inefficiencies in attention and executive functioning resolved within six months of infection. For the normal and dysexecutive neurophenotypes, reassurance and lifestyle counseling will likely be essential to improve long-term wellness, along with public and private health initiatives to strengthen pandemic childcare policies, employee sick time policies, and healthcare access. Cognitive Behavioral Therapy (CBT) is also likely to provide benefits for those reporting persistent anxiety, depression, insomnia, and fatigue (Adamson et al., 2020).
For the memory-speed neurophenotype, a comprehensive interdisciplinary rehabilitation approach that incorporates physical therapy, occupational therapy, nursing, and psychology may be particularly important, as demonstrated in comprehensive pain clinics (Engelberg-Cook et al., 2021; Kurklinsky et al., 2016). Realistic goal setting, activity pacing, and empowered self-management of symptoms are essential components of therapy (Skilbeck, 2022). Targeted cognitive rehabilitation in long COVID patients is effective for remediation of memory impairment (García-Molina et al., 2022). Rehabilitative therapies can focus on recovery strategies and compensatory memory strategies to attenuate frustration and facilitate adjustment to life with memory dysfunction. Individualized recommendations from cognitive rehabilitation specialists can inform accommodations to support a successful return to work, school, or community reintegration.
4.2 Limitations
A significant study limitation was high participant attrition. Although there were no significant differences in 6-month follow-up rates by cluster, the mild and severe neurophenotypes had much smaller sample sizes than the normal cluster. Disproportionate cluster size was not predicted in advance due to the unknown nature of the disease. This naturalistic observational study was designed in early phase of the COVID-19 pandemic. The nature of the post-COVID neurophenotypes, the associated prevalence (i.e., proportion in a population sample), and anticipated dropout rates in the visits for online assessments were not known a priori. Furthermore, these proportions were expected to vary with subsequent variants of the virus and the availability of vaccines. These factors challenged accurate sample size calculation but results do provide valuable information for prospective study planning. Larger cohorts will be necessary to obtain sufficient sample size for the dysexecutive and memory-speed impaired neurophenotypes in future longitudinal outcome investigations. Cluster analysis will need to be replicated across unique datasets in order to further validate neurophenotypes.
Another limitation is use of a remote computerized cognitive screening test battery, which is a practical solution to reduce exposure-risk during a pandemic but does not offer the breadth, detail, and flexibility of a Harmonization Level 3 (HL3) comprehensive neuropsychological evaluation (Cysique et al., 2022). With that said, the range of cognitive impairment rates we observed during the post-acute stage [verbal memory: 26%; visual memory: 15%; psychomotor speed: 24%; reaction time: 18%; complex attention: 20%; cognitive flexibility: 31%] is within the range reported in studies utilizing HL3 measures (Ceban et al., 2022; Becker et al., 2021; Matias-Guiu et al., 2023), which suggests sufficient sensitivity to post-COVID cognitive impairment. An additional limitation is the absence of a locally derived uninfected control group, which is important for distinguishing the effects of COVID-19 infection from the effects of pandemic-related psychosocial factors. Also, we did not include pre-infection baseline cognitive scores or a test of premorbid function, which makes it difficult to discern whether low performances are longstanding and unrelated to COVID-19. Although we did find an association between disease-specific factors and membership in the memory-speed impaired cluster, we cannot rule out confounding factors that may have contributed to both disease susceptibility and cognitive impairment. In addition, this study was limited to the cognitive and psychiatric manifestations of COVID-19 and did not probe the range of physical (e.g., pulmonary, autonomic, etc.) symptoms that have been associated with long COVID. Finally, we did not evaluate whether participants received formal interventions or therapeutics between post-acute and chronic assessments; therefore, we cannot attribute recovery to the “natural course” of the disease.
5 Conclusion
The neurologic manifestations of COVID-19 present as distinct neurophenotypes with different risk factors and recovery trajectories. Future efforts should seek to replicate these neurophenotypes and their associated features in independent samples. It will be important to directly test whether the efficacy of various post-COVID therapeutics differs across neurophenotypes, given the high likelihood that different etiological factors contribute to post-COVID cognitive sequelae and influence recovery.
Author contributions
KB conceptualized the study and study design, with input from GSD, SN, and AA. DP, GSD, BM, BR, SN, EB, HP, RD, AA and KB contributed to data acquisition, analysis, and interpretation. DP and KB drafted the manuscript with important intellectual contributions and revisions from GSD, BM, BR, LP, EB, and AA.
Funding
The present study was funded in part by the 10.13039/100014535 Mayo Clinic Center for Individualized Medicine , a research grant from the Donald and Lucy Dayton Professorship to Teresa Rummans, M.D., and the Mayo Clinic in Florida Research Accelerator for Clinicians Engaged in Research Program (RACER) award to KB. GSD is supported by a career development grant from the 10.13039/100000002 NIH (K23AG064029), and by funding from the Alzheimer's Association and Chan Zuckerberg Foundation. The funding body did not advise on the design of the study, data collection, analysis, and interpretation, or writing of the manuscript.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: GS Day's research is supported by NIH [K23AG064029, U01AG057195, U19AG032438], the Alzheimer's Association, and Chan Zuckerberg Initiative. He serves as a consultant for Parabon Nanolabs Inc, as a Topic Editor [Dementia] for DynaMed (EBSCO), and as the Clinical Director of the Anti-NMDA Receptor Encephalitis Foundation [Inc, Canada; uncompensated]. He is the co-Project PI for a clinical trial in anti-NMDAR encephalitis, which receives support from Horizon Pharmaceuticals. He has developed educational materials for PeerView Media, Inc, and Continuing Education Inc. He owns stock in ANI pharmaceuticals. Dr. Day's institution has received support from EliLilly for Dr. Day's development and participation in an educational event promoting early diagnosis of symptomatic Alzheimer disease.
The remaining authors have nothing to disclose.
Appendix A Supplementary data
The following is the Supplementary data to this article.Multimedia component 1
Multimedia component 1
Data availability
Data will be made available on request.
Acknowledgments
The authors thank the participants, Teresa Rummans, MD, for her generous support of this project, the Mayo Clinic Center for Individualized Medicine which supported a Graduate Research Experience Fellowship for Ms. Prabhakaran, and Tiffany Cribb B.S., for her assistance with data collection.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbih.2023.100648.
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PMC010xxxxxx/PMC10240484.txt |
==== Front
Infect Dis Ther
Infect Dis Ther
Infectious Diseases and Therapy
2193-8229
2193-6382
Springer Healthcare Cheshire
37273072
820
10.1007/s40121-023-00820-2
Review
Antimicrobial Resistance in Ventilator-Associated Pneumonia: Predictive Microbiology and Evidence-Based Therapy
http://orcid.org/0000-0001-5105-6002
Alnimr Amani [email protected]
grid.411975.f 0000 0004 0607 035X Department of Microbiology, College of Medicine, King Fahad Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
5 6 2023
5 6 2023
6 2023
12 6 15271552
11 3 2023
9 5 2023
© The Author(s) 2023
https://creativecommons.org/licenses/by-nc/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Ventilator-associated pneumonia (VAP) is a serious intensive care unit (ICU)-related infection in mechanically ventilated patients that is frequent, as more than half of antibiotics prescriptions in ICU are due to VAP. Various risk factors and diagnostic criteria for VAP have been referred to in different settings. The estimated attributable mortality of VAP can go up to 50%, which is higher in cases of antimicrobial-resistant VAP. When the diagnosis of pneumonia in a mechanically ventilated patient is made, initiation of effective antimicrobial therapy must be prompt. Microbiological diagnosis of VAP is required to optimize timely therapy since effective early treatment is fundamental for better outcomes, with controversy continuing regarding optimal sampling and testing. Understanding the role of antimicrobial resistance in the context of VAP is crucial in the era of continuously evolving antimicrobial-resistant clones that represent an urgent threat to global health. This review is focused on the risk factors for antimicrobial resistance in adult VAP and its novel microbiological tools. It aims to summarize the current evidence-based knowledge about the mechanisms of resistance in VAP caused by multidrug-resistant bacteria in clinical settings with focus on Gram-negative pathogens. It highlights the evidence-based antimicrobial management and prevention of drug-resistant VAP. It also addresses emerging concepts related to predictive microbiology in VAP and sheds lights on VAP in the context of coronavirus disease 2019 (COVID-19).
Keywords
Early effective therapy
Window
Drug-resistant organism
Novel diagnostics
issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2023
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pmcKey Summary Points
A literature review was conducted to critically evaluate the evidence on antimicrobial resistance in ventilator-associated pneumonia (VAP) in adults due to the continuous evolvement of resistance mechanisms and availability of newer antimicrobials.
Rapid testing for VAP can potentially provide results in real time to tailor therapy appropriately with rapid identification of pathogens and detection of their multidrug resistance (MDR) determinants. Supporting clinical data are still limited as a result of potential confounders with an inflated estimation of the diagnostic performance.
The local ICU ecology remains a significant risk factor for acquiring MDR infections regardless of the time of intubation, and may be linked to the global increase in MDR pathogens.
Mathematical models and scoring systems are still not sufficiently developed for operational application given their limitations in predicting MDR-VAP.
Areas for priority research studies in VAP caused by resistant pathogens include the clinical utility of rapid diagnostics and validation of prediction scores in making clinical decisions to accelerate the developments in this emerging field.
Introduction
Ventilator-associated pneumonia (VAP) continues to be a major cause of morbidity and mortality despite advancements in prevention, antimicrobial therapy, and supportive care. VAP imposes a significant economic burden on healthcare systems, and more than half of antibiotics prescriptions in intensive care units (ICU) are found to be administered for VAP cases [1–3]. The attributable cost of VAP was estimated by a Monte Carlo simulation model in a meta-analysis to be around $40,144 per case [4]. Antimicrobial resistance in the ICU environment is highly dynamic and the rates of VAP caused by resistant pathogens can be additionally costly. Around 12% of the implicated pathogens in a retrospective study of adult patients with hospital-associated pneumonia (HAP) and VAP (N = 8969) between 2009 and 2016 in the USA were carbapenem-resistant. The median VAP hospital length of stay in that study was 26 days with an additional cost of $30,000 (median $105,947 vs $72,810) [5]. ICU infections caused by Gram-negative bacteria account for the majority of cases worldwide [6]. Of these, 37% are estimated to be multidrug-resistant (MDR) pathogens, demonstrating acquired nonsusceptibility to at least one agent in three different antimicrobial classes. An extensively drug-resistant (XDR) status is defined as nonsusceptibility to at least one agent in all but two or fewer antimicrobial categories (isolates remain susceptible to only one or two therapeutic categories) [7]. ICU infection is an independent predictor for poor prognosis, and VAP caused by MDR and XDR strains is very challenging to cure [8]. The frequency of MDR pathogens varies between healthcare centers and within centers among different patient populations, as some patients with complex comorbidities are likely to have higher rates of antimicrobial resistance [9].
In this review, the evidence for antimicrobial therapy in cases of adult VAP in the era of antimicrobial resistance (AMR) is summarized. In particular, the review provides a brief description of risk factors for antimicrobial-resistant VAP, followed by a comprehensive overview of AMR mechanisms in the commonly implicated pathogens and their detection tools. Since pneumonia in mechanically ventilated patients represents a consortium of diverse pathological events and is not restricted to VAP, the current review highlights other forms of lower respiratory tract infections in the ICU and describes studies underlying the evidence for coronavirus disease 2019 (COVID-19)-associated VAP. Finally, challenges and key areas for future research are presented. The review was conducted on the basis of a comprehensive search using electronic databases and reference checks. The Cochrane Library, MEDLINE, EMBASE, and Web of Science were searched from 1990 until March 2023. Various common terms related to VAP diagnostics in ICU were used to conduct the search. A checklist was adopted from the critical appraisal skills program checklist for diagnostic studies to assess the quality of the included studies prior to citing them.
This review article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by the author.
VAP Definition and Risk Factors
While pneumonia is identified by using a combination of imaging, clinical, and laboratory criteria, establishing a definitive diagnosis of VAP can be challenging. A recently adopted definition of VAP is pneumonia in which the patient is on mechanical ventilation for at least two consecutive days, and the ventilator was in place on the date of the event or the day before [10]. On the other hand, ventilated hospital-acquired pneumonia (vHAP) refers to severe HAP in patients requiring mechanical ventilation, and non-ventilated intensive care unit-acquired pneumonia (NV-ICUAP) stands for pneumonia that occurs at least 48 h after ICU admission [11, 12]. Hospital-acquired pneumonia is designated for other cases of pneumonia occurring at least 48 h after hospital admission, not incubating at the time of admission, and not associated with mechanical ventilation [10]. This new classification endorsed by the National Healthcare Safety Network (NHSN) categorizes all adverse ventilator-associated events (VAE) into a tier-based system on the basis of the rapidly evolving data in order to consider all potential conditions affecting morbidity in ventilated patients other than VAP as the only significant pathology. The updated surveillance definition also minimizes subjectivity and enables automated data collection which streamlines the analysis and benchmarking, and comparison of VAP rates across institutions. vHAP is associated with higher mortality rates than VAP, and nonventilator hospital-acquired pneumonia (nvHAP) is an equally frequent subvariant of HAP that tends to occur after a longer admission and results in higher costs [13, 14]. Notably, these conditions are not independent entities and, in many cases, they may represent a continuum of the disease in hospitalized patients (Fig. 1) [12, 15].Fig. 1 Progression of nosocomial pneumonia in hospitalized patients [15]. HAP hospital-acquired pneumonia, ICU intensive care unit, NV-ICUAP non-ventilated intensive care unit acquired pneumonia, VAP ventilator-associated pneumonia, vHAP ventilated hospital-acquired pneumonia
Risk Factors for VAP
Critically ill patients tend to have more comorbidities and severe acute physiologic disturbance [16]. The frequent use of catheters and other supportive devices among ICU patients can bypass natural host defense mechanisms and provide a portal of entry for organisms into various sites. In addition, medical equipment can be a reservoir for MDR organisms, facilitating horizontal transmission across ICU patients. Frequent contact with healthcare workers is another risk for infection with nosocomial, drug-resistant pathogens. Furthermore, patients in the ICU are more likely to be under antimicrobial selective pressure in comparison with other patient populations. All these factors lead to the emergence of MDR bacterial clones and increased colonization pressure among ICU patients [17].
Risk Factors for Drug-Resistant VAP in Adult Patients
VAP caused by an MDR pathogen acquired in the ICU depends on both host comorbidities as well as factors related to the healthcare system. Several patients factors were found to be linked to resistant infections reported from the ICU including older age, limited mobility (e.g., bedridden status), underlying comorbid conditions such as diabetes mellitus, end-stage renal disease, immunosuppression, and malignancies, recent surgery or other invasive procedures, antimicrobial therapy in the 90 days prior to the ICU admission (odds ratio, OR 13.5), prior use of broad-spectrum antibiotic (OR 4.1), previous colonization, and increased severity of acute illness (mortality risk > 15%) (Table 1). The length of stay in the ICU and the long duration of hospitalization (> 5 days) prior to the ICU admission, including being in long-term care facilities, increase the risks of VAP and other ICU infections, particularly with a pathogen that is MDR [10, 18–21]. Additionally, prolonged mechanical ventilation for more than 7 days is an independent risk factor for MDR-VAP (OR 6.0) [22]. Regardless of onset, the initial VAP severity is another risk factor for MDR infections [22]. Other potential factors include history of MDR infection, recurrent hospitalization, and the presence of structural pulmonary disease [23]. The MDR epidemiology in an ICU environment plays an additional role where more than 25% local prevalence of MDR pathogens in a unit’s antibiogram is considered a risk factor that predisposes patients to MDR-VAP [22]. Although most data on VAP infections originate from developed countries, the available evidence suggests the rates of infection can be higher in the developing world. A prospective, multicenter surveillance cohort of 55 hospital ICUs in 46 countries including India, Turkey, Morocco, and Central and South American states reported an overall rate of 22.5 infections/1000 ICU days, with a high rate of VAP equivalent to 24.1 episodes/1000 ventilator days (10.0 to 52.7) [24]. Antimicrobial resistance was alarmingly high in the study (84% methicillin resistance among Staphylococcus aureus infections, and more than 50% resistance to third-generation cephalosporin in Enterobacterales and fluoroquinolones among Pseudomonas aeruginosa isolates), with a VAP mortality rate of 44.9%. The Infectious Diseases Society of America (IDSA) guidelines for adult hospital-acquired and ventilator-associated pneumonia suggest a target threshold of at least 95% for empiric coverage of MDR pathogens when treating a high-risk patient population based on the listed risk factors, while a 90% threshold is endorsed by the European Respiratory Society (ERS), European Society of Intensive Care Medicine (ESICM), European Society of Clinical Microbiology and Infectious Diseases (ESCMID), and Asociación Latinoamericana del Tórax (ALAT) [10, 22]. Nevertheless, these risk factors have been criticized for not accurately forecasting for VAP caused by MDR while the local unit ecology and prior antimicrobial usage were shown to be the main significant predictive variables [25].Table 1 Risk factors for antimicrobial-resistant ventilator-associated pneumonia [10, 22]
Risk factors for VAP caused by an MDR pathogen Local epidemiology: MDR resistance rate > 25%
Long duration of hospitalization prior to VAP
Parenteral antibiotics in the preceding 90 days
Hospital stay prior to VAP > 5 days
Previous MDR colonization
Severity of illness:
ARDS prior to VAP
Concurrent septic shock
Acute renal replacement therapy prior to VAP
Chance of mortality ≈ 15%
Specific risk factors for VAP caused by MRSA Local MRSA rate > 20% or unknown
MRSA colonization
Specific risk factors for VAP caused by MDR Gram-negative bacilli, e.g., P. aeruginosa Local resistance rates to first-line agents > 10% or unknown
Colonization with MDR Pseudomonas or other MDR Gram-negative bacilli
ARDS acute respiratory distress syndrome, MDR multidrug resistant, MRSA methicillin-resistant S. aureus, VAP ventilator-associated pneumonia
VAP Surveillance Cultures and Local Epidemiology
The role of surveillance cultures for VAP remains debatable. Nosocomial infections, including VAP, are significantly influenced by prior colonization with potential pathogens [12, 26, 27]. For instance, ICU patients with S. aureus colonization have a 15-fold increased chance of developing VAP in contrast to patients who are not colonized [28]. A meta-analysis by Brusselaers et al. (N = 791) implies that the likelihood of developing VAP can be predicted by the colonization of the respiratory tract shown in sequential surveillance cultures with a high negative predictive value (> 90%), particularly in the case of Enterobacterales [29]. The area under the curve (AUC) of the hierarchical summary receiver operating characteristic (ROC) curve showed a moderate level of accuracy (AUC 0.90) in predicting multidrug resistance with improved accuracy when recent cultures are used, although heterogeneity was evident in the analysis. On the other hand, a few studies revealed that upper airway samples positive for Enterobacterales are more predictive for VAP than gastric samples, while selective oral decontamination still reduced the colonization and the rate of VAP without influencing the gastric colonization with Gram-negative rods [30, 31]. Therefore, a proactive approach to VAP employs surveillance cultures to identify colonizing bacteria including those MDR strains. Collecting endotracheal aspirates is a relatively noninvasive procedure that can be performed on intubated patients. The prediction of these surveillance microbiological cultures was shown to be most accurate when they were routinely conducted at least twice a week [29, 32]. These samples are frequently utilized to guide empirical treatment when patients develop signs of VAP. Yet, there is inconclusive evidence that this approach improves clinical outcomes or reduces costs, even when lower respiratory tract surveillance cultures can help to predict the involvement of MDR bacteria in patients with VAP [30, 33]. Furthermore, approximately 30% of antimicrobial prescriptions in ICU are estimated to target colonization leading to inappropriate usage of antibiotics since surveillance cultures alone cannot distinguish colonization from infection even in the presence of high bacterial loads (positive predictive value of 75%) [29, 34]. In addition, the frequent use of broad-spectrum agents to treat susceptible community-acquired pathogens is frequent in the ICU environment owing to the acuity of illness [29]. Since many patients in ICU are admitted directly from the community such as trauma cases, the prevalence of resistant bacterial colonization can be initially low at the time of admission in geographical areas with low backgrounds of MDR organisms. This highlights the significance of incorporating the local epidemiology when selecting an empirical regimen along with the surveillance cultures. The complex interaction of naturally occurring changes in the prevalence of MDR colonization is created in ICU patient populations on the basis of individualized factors which is further complicated by rapid turnover of cases and different transmission routes [26].
Microbiology in VAP
Multiple variables, such as the length of mechanical ventilation, the length of hospital and ICU stay prior to VAP, the timing and cumulative antimicrobial exposure, the local ecology, and the occurrence of any potential epidemic in a particular ICU, all affect the type of organisms isolated in VAP cases. P. aeruginosa, Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii are commonly involved Gram-negative species while S. aureus is the main Gram-positive pathogen [34–36]. In previously healthy patients who are not receiving antibiotics, early-onset VAP, defined as VAP onset of less than 5 days of hospitalization without MDR risk factors, typically involves normal upper airway flora, whereas late-onset VAP, occurring after at least 5 days of hospitalization, or VAP in patients with a high index for MDR pathogens (Table 1) are more likely to be caused by nosocomial organisms [10, 22, 23]. These include methicillin-resistant S. aureus (MRSA), Gram-negative enteric pathogens that produce extended-spectrum β-lactamases and/or carbapenemases (ESBL and CRE, respectively), and the carbapenem-resistant non-fermentative pathogens P. aeruginosa and A. baumannii. Infections caused by these resistant pathogens are difficult to treat and are associated with increased morbidity, mortality, and costs [37].
The leading VAP pathogens may change over time and from unit to unit, with variable local epidemiology of drug-resistant cases, although there has been an increasing global trend of VAP caused by MDR pathogens over time [5]. In an observational 24-h point prevalence study of 1150 centers across 88 countries that included 44% mechanically ventilated patients, 37% of Gram-negative infections causing VAP were MDR with a predominance of Klebsiella spp. (38%), followed by Acinetobacter spp. (33%), E. coli (18%) and Pseudomonas spp. (10%) [6]. Another multicenter study of more than 200 ICU in 56 countries found P. aeruginosa as the leading Gram-negative respiratory pathogen isolated (25%) among 7171 isolates. Multi-resistance was frequently encountered as 38% of P. aeruginosa isolates were nonsusceptible to piperacillin–tazobactam or meropenem. Around 68% of the strains were reported as susceptible to ceftolozane–tazobactam in the cohort [38]. Surveillance data from the USA demonstrated more than 100% increased rates of ESBL production and 800% rise in carbapenem resistance in Enterobacterales, resulting in limited therapeutic options [39]. High resistance rates were also reported in VAP cases in other geographical areas. For example, up to 89% of VAP isolates were MDR, XDR, or pan-drug resistant in Greece, and 89% of A. baumannii isolated from VAP cases from Saudi Arabia were XDR [40, 41]. In a recently published cohort from a tertiary care hospital in Northern Saudi Arabia, XDR A. baumannii, XDR P. aeruginosa, and K. pneumoniae with an ESBL profile were predominant etiologies of VAP [42]. Around 87.5% of Gram-negative VAP pathogens were described as XDR over a 4-year observational study in a tertiary care cancer center in India [9]. Colistin/polymyxin resistance in Gram-negative pathogens is being increasingly reported following increased consumption of the drug to treat VAP caused by XDR organisms, which is further complicated by non-availability of susceptibility testing to the drug in many routine laboratories as it requires special laborious assays [42]. The IDSA guidelines recommend against the routine use of colistin in treating VAP while European guidelines similarly state the empiric therapy to be guided by the mortality risk, local ecology, and other MDR selection risk factors [10, 22]. Yet, colistin-based combination therapy is still used in some critical care centers to empirically treat XDR pathogens [43]. Less common VAP bacterial pathogens include Stenotrophomonas maltophilia, Burkholderia cepacia, Legionella, and anaerobes which are known for their intrinsic resistance to multiple antimicrobial classes [44, 45]. VAP can be polymicrobial in origin which further complicates the therapeutic approach.
Key Mechanisms of Antimicrobial Resistance in VAP
Bacterial resistance to antibiotics may result from alteration of the antibacterial target, decreasing intracellular concentrations as a result of reduced permeability or efflux pumps, or enzymatic inactivation of the drug [46]. In some instances, intrinsic antimicrobial resistance can exist where every strain of a species exhibits reduced susceptibility to a particular agent as in the case of S. maltophilia expressing a metallo-β-lactamase that is chromosomally encoded, rendering the organism resistant to carbapenems [45]. Alternately, formerly sensitive species might acquire resistance genes from foreign DNA or undergo genetic mutation to become resistant.
Antibacterial use can lead to the selection and expansion of drug-resistant bacterial clones through disturbing the individual’s microbiome. Following the increased usage of carbapenems, Gram-negative bacteria with chromosomally encoded β-lactamases, such as A. baumannii and S. maltophilia, have become more frequent, especially in patients receiving mechanical ventilation [44–46]. Chromosomal and plasmid DNA replicate as bacteria propagate through a mechanism that is highly prone to errors during nucleic base incorporation, with the potential of producing functional gene products in favor of bacterial survival in the presence of antibiotics such as SHV-2, an ESBL which imparted resistance to expanded-spectrum cephalosporins that was created when serine replaced glycine at position 238 in the SHV-1 β-lactamase [47]. The importation of insertion sequences (IS) can also inactivate several genes affecting their functions. For instance, the ccrA gene in Bacteroides fragilis expresses a metallo-β-lactamase that is only produced if an IS has been placed upstream of this structural gene [48]. Subsequent antibiotic exposure favors growth of strains with mutations that enable the bacteria to survive in the presence of high concentrations of the drug.
A variety of mechanisms of antimicrobial resistance in VAP pathogens have been described that explain the genetic basis of acquired reduced susceptibility to antimicrobials (Table 2). The molecular basis of resistance listed in Table 1 can co-exist in one bacterium, rendering therapy challenging. It is important to note that resistance to one β-lactam in cases of P. aeruginosa may not predict resistance to others since a wide variety of genetic mechanisms are frequently encountered in this organism [49, 50]. In addition to these genetic determinants, there has been debatable evidence for the relationship between the ability of the organism to form a biofilm and a significant reduction in its antimicrobial susceptibility. Biofilm formation and distribution of virulence genes did not appear to differ between VAP and non-VAP pseudomonal isolates in an in vitro study, although the VAP isolates were less virulent in an in vivo model [51, 52]. Difficult-to-treat resistance (DTR) in P. aeruginosa is a new terminology that was introduced to describe strains exhibiting nonsusceptibility to all of piperacillin–tazobactam, ceftazidime, cefepime, aztreonam, meropenem, imipenem, and fluoroquinolones [50]. Therapeutic agents that may retain activity on VAP caused by DTR P. aeruginosa are limited and mainly based on ceftolozane–tazobactam, ceftazidime–avibactam, imipenem–relebactam, or cefiderocol, although the evidence supporting the use of these agents is still being collected.Table 2 Mechanisms of acquired antimicrobial resistance in common Gram-negative pathogens causing ventilator-associated pneumonia [46, 49, 50]
Organism Mechanism of resistance Features
P. aeruginosa AmpC β-lactamase Most common β-lactamase observed in P. aeruginosa
Resistance to penicillins, cephalosporins other than cefepime
Extended-spectrum β-lactamase Confer resistance to penicillin, all cephalosporins, aztreonam
e.g., OXA-11, 14,16, 19, 28, PER-1, VEB
Metallo-β-lactamases Resistance to penicillin, all cephalosporins, carbapenems
Can not hydrolyze aztreonam
e.g., NDM, VIM, IMP, SPM-1
OprD downregulation (porin loss) Major cause of carbapenem nonsusceptibility, e.g., imipenem resistance and reduced susceptibility to meropenem
Susceptibility maintained to cephalosporins
MexAB-OprM upregulation (efflux) Reduced susceptibility to meropenem but not imipenem
Reduced susceptibility/or resistance to penicillin
Resistance to cephalosporins and fluoroquinolones
Topoisomerase II/IV mutations Fluoroquinolone resistance
A. baumannii AmpC β-lactamase Chromosomally encoded cephalosporinases intrinsic to all strains
Low level of expression does not cause clinical failure unless co-existing with a promoter insertion sequence ISAba1
Serine-carbapenemase Resistance to penicillin, all cephalosporins, carbapenems
OXA-23 and OXA-24/40-like and OXA-23-like
Metallo-β-lactamases Very rare
Extended-spectrum β-lactamase Not widely spread among the species
Porin downregulation Poorly characterized mechanism
Reduced susceptibility to β-lactams
Efflux overexpression Reduced susceptibility to β-lactams, fluoroquinolones, chloramphenicol, tetracyclines, and tigecycline
Usually in association with AmpC or carbapenemases overexpression
Enterobacterales Carbapenemase High level resistance to carbapenems
e.g., KPC, NDM, VIM, OXA-48, SME
Isolates may also co-produce other β-lactamases, e.g.,ESBL, AmpC
Extended-spectrum β-lactamase Low level resistance to β-lactams
AmpC β-lactamase Low level resistance to β-lactams
Porin downregulation or
Efflux overexpression
Low level resistance to β-lactams
S. maltophilia Zinc-containing penicillinase (L1), cephalosporinase (L2) Resistance to β-lactams including carbapenems, and aztreonam
Acetyl-transferase and temperature-dependent changes in the outer membrane LPS Aminoglycoside resistance
Efflux pumps Resistance to multiple antimicrobial classes
Biofilm formation Resistance to multiple antimicrobial classes
Carbapenem-resistant A. baumannii (CRAB) is a critical-priority pathogen that is linked to persistent outbreaks in critical care settings [53]. It requires combination therapy of two active agents even if a single drug demonstrates in vitro activity, where ampicillin/sulbactam in high dose is a preferred agent in combination with either polymyxin, colistin, a tetracycline, or meropenem in extended infusion [50]. These recommendations are based on two meta-analyses which evaluated different therapeutic regimens, and in 23 studies (N > 2100 patients) ampicillin–sulbactam-based combination therapy reduced mortality relative to polymyxin- or tigecycline-based treatment [54]. A more recent meta-analysis of 18 studies (N > 1800 patients) has also concurred that ampicillin–sulbactam-containing regimens reduced mortality and nephrotoxicity in critically ill patients in comparison to colistin-based protocols [55]. Cefiderocol may constitute a base for the combination regimen for CRAB with very limited evidence currently to support its use [56]. Interventional trials are still underway to evaluate its potential role in the treatment of MDR-VAP. Table 2 summarizes the most commonly reported mechanisms of resistance in A. baumannii.
There are few therapeutic choices for VAP caused by CRE, and no antibiotic regimen has been firmly established as being superior to another [57]. The isolate’s genotype and phenotypic susceptibility profile both affect the therapeutic choice. Meropenem may remain a viable second agent in a combined regimen if the CRE isolate has a minimal inhibitory concentration (MIC) for meropenem of 8 μg/mL or lower [58]. For the treatment of infections by organisms that produce a serine carbapenemase, such as K. pneumoniae carbapenemase (KPC) or oxacillinase 48 (OXA-48), ceftazidime–avibactam is among novel β-lactam–β-lactamase inhibitor combination drugs that can be used with an overall good clinical experience [50]. Of note, ceftazidime–avibactam resistance can develop within 10–19 days during treatment, necessitating retesting the isolate in the laboratory on clinical demand and the addition of a second agent, usually a carbapenem, for possible synergistic effects based on a few in vitro and in vivo reports [59–61]. Further studies are required to examine the clinical significance of higher MIC values for serine-CRE on ceftazidime–avibactam therapy since MIC to β-lactam agents could be altered, leading to restored susceptibility in variant isolates [59]. A polymyxin-based (colistin or polymyxin B) combination regimen is used when the strain is susceptible in vitro based on broth microdilution assays and no other medication is available for use [50]. The rationale for this combination is based on reducing mortality associated with invasive CRE infections and the possibility of emergence of colistin resistance during monotherapy as seen in observational studies in which meropenem use with colistin favored better clinical outcomes whenever its MIC was below 16 μg/mL [58]. In comparison to polymyxins, the β-lactam–β-lactamase inhibitor combinations offer better safety profiles, more consistent dosage, and higher in vitro susceptibility rates based on little but encouraging data which support their superior effectiveness [62, 63]. Randomized controlled trials (RCT) are yet to corroborate these observational studies which are prone to selection bias. Alternative effective options for treating serine-CRE include meropenem–vaborbactam and imipenem–cilastatin–relebactam, and the siderophore cefiderocol; the last of these agents has been approved for clinical use in VAP [10]. Tigecycline can also be used for CRE infections particularly for respiratory infections with its ability to penetrate tissues [64]. A polymyxin in combination with tigecycline had a mortality rate of 30% (N = 23) in comparison with 12.5% mortality (N = 16) seen with a combined regimen of colistin, tigecycline, and extended-infusion meropenem [58, 65]. Because resistance to polymyxins is an increasingly reported problem, laboratory testing is crucial to optimize its use with a possibility of retesting as development of polymyxin resistance during therapy has been described in Enterobacterales [66, 67]. Polymyxin-resistant CRE infections have been shown to be an independent risk factor for mortality [66].
For the metallo-β-lacatamase (MBL)-CRE infections, an aztreonam-based regimen with either ceftazidime–avibactam or cefiderocol is proposed while, as in the case of serine-CRE, a polymyxin-based regimen offers an alternative option when the novel β-lactams cannot be used for MBL-producing Enterobacterales [50]. No β-lactams other than these two agents, cefiderocol and aztreonam, possess powerful antibacterial activity against MBL-harboring CRE isolates [68]. The combination of ceftazidime–avibactam and aztreonam may potentially exhibit a synergistic effect because the avibactam inactivates the enzymes, increasing aztreonam activity. Despite the fact that MBL-producing isolates frequently produce other β-lactamases conferring resistance to aztreonam, this combination maintained its therapeutic effectiveness in a small number of reports of cases with highly resistant MBL-producing CRE infections [69, 70]. On the other hand, there is very limited clinical experience with cefiderocol treatment of MBL-producing pathogens [71]. Figure 2 summarizes the currently available options for treating VAP caused by CRAB, DTR P. aeruginosa, and CRE.Fig. 2 Recommendations for management of ventilator-associated pneumonia caused by MDR Gram-negative pathogens [50]
Microbiological Diagnostics for VAP and Detection of Resistance
The diagnosis of VAP is suspected in patients with a new or progressive pulmonary infiltrate on imaging and supportive clinical signs of infection like fever and leukocytosis. The microbiological diagnosis is established when a pathogen is identified from a lower respiratory tract sample in a clinically compatible case. Sampling is preferably performed prior to the empiric initiation or changing of antimicrobial therapy to optimize the diagnostic yields of the respiratory microscopy and culture, and blood cultures should be also routinely collected [72]. As a result of the lack of a gold standard for the diagnosis of VAP, clinical practice may largely vary among institutions, practitioners, and also from case to case.
Invasive, Quantitative Versus Noninvasive, Semiquantitative Lower Respiratory Tract Testing
The recommendations from the ERS, ESICM, ESCMID, and ALAT endorse quantitative cultures conducted on invasive samples such as mini-bronchoalveolar lavage (BAL) or equivalents [22]. The justification for this strategy is the significant impact on diagnostic accuracy and reduction of unnecessary antimicrobial use. Given the lack of definitive evidence that either methodology has an influence on mortality or duration of stay, the IDSA and the American Thoracic Society (ATS) still advocate noninvasive sampling techniques with semiquantitative VAP cultures instead [10]. Yet, as demonstrated in numerous studies, bronchoscope-based sampling and quantitative cultures are more helpful in optimizing the use of antibiotics and de-escalating therapy without adverse effects on prognosis or length of ICU stay [73, 74]. When quantitative cultures are performed, cutoff thresholds should be carefully assessed to avoid missing VAP events or false positives leading to unjustified therapy. Thresholds of 106 or more colony forming units (cfu)/mL, 104 cfu/mL, and 103 cfu/mL have been proposed for endotracheal aspirates, mini-BAL or bronchoscopic BAL, and protected brushing to minimize overdiagnosis of VAP [22, 75]. Quantitative cultures are more labor-intensive, time-consuming, and expensive than semiquantitative cultures from a laboratory perspective.
Molecular Diagnostics and Novel Assays
In VAP care, rapid diagnostic testing is occasionally employed to shorten the time to appropriate therapy. Antimicrobial escalation is not thought to be beneficial for Gram-negative infection in adults who remained critically ill after appropriate empirical therapy which may support the use of rapid diagnostics such as molecular tests that can be helpful tools for the rapid identification of pathogens and the detection of antibiotic resistance markers [76]. Multiplex syndromic testing platforms are commercially available for VAP using lower respiratory and blood specimens [77]. Table 3 outlines the currently available instruments for rapid detection of pathogens from blood and lower respiratory samples. These assays, however, in their current setup are considered an adjunct testing option that cannot replace standard microbiological tests, with the main advantage of the short turnaround time (TAT). While several rapid diagnostic platforms are available to detect common Gram-negative and Gram-positive respiratory pathogens which are widely acceptable by intensivists, validation of a systematic methodology is still required in order to make the comparison of various studies possible [78]. Additionally, the evidence for clinical utility of those rapid tools is still evolving with debatable impact on clinical outcomes [79, 80]. A multiplex respiratory panel was shown to be helpful to reduce unnecessary antimicrobial escalation between 72 h and 7 days (7.8% versus 14.2%; p = 0.007) and marginally lower overall hospital costs ($1413.5 ± 1438.0 vs $1759.4 ± 1929.2; p = 0.008) according to a retrospective case–control study (N = 692) evaluating the clinical utility of molecular point-of-care diagnostics in respiratory infections in China [80]. In contrast, a systematic review by the Cochrane collaboration that assessed the effects of rapid antimicrobial susceptibility testing (AST), defined by TAT ≤ 8 h, versus conventional assays for detecting resistance determinants in pathogens isolated from bacteremic infections found little or no difference in time-to-discharge (relative risk (RR) 1.0, 95% CI 0.7–1.5; low-certainty evidence), time-to-appropriate therapy (RR − 17.3, CI − 45.1 to 10.5; low-certainty evidence), and mortality (RR 1.1, 95% CI 0.8–1.5; low-certainty evidence) in the qualitative and subgroup analysis of six RCTs (N = 1638) [81]. It endorsed the urgent need for future large prospective studies designed to focus on clinically relevant outcomes rather than the assay characteristics. Furthermore, false negative molecular testing results can lead to delayed effective therapy and may not justify additional costs [82]. Nevertheless, molecular testing methods can be useful in selected cases as they have the potential to identify respiratory pathogens that are not evident in routine culture growth as a result of their fastidious nature or prior antimicrobial therapy. The integration of those novel tools into clinical practice remains a challenge for several institutions, and their implementation strategies should reinforce indication while assuaging the potential limitations [78]. Ongoing research is under development in the rapid diagnostics field which can not only solve some VAP diagnostic challenges but also elucidate new concepts in its pathogenesis such as the detection of pathogens in the human lung parenchyma through an optical fiber-based endoscope [83, 84]. Metagenomic signatures have been recently introduced and employed to accurately identify VAP-associated pathogens and their resistance markers with an overall accuracy of 98.1% in less than 5 h [85].Table 3 Commercially available diagnostic platforms useful for detection of ventilator-associated pneumonia and multidrug-resistant pathogens
Platform TAT Testing panel Cost
Routine microbiology tests 48–72 h
Can be longer in polymicrobial VAP
Potentially any culturable organism
Provides susceptibility testing for most organisms
$
Routine cultures + MALDI-TOF 24–48 h
Can be longer in polymicrobial VAP
Potentially any culturable organism
Subtyping and antibiotic susceptibility testing requires additional processing and access to other databases
$$
BioFire FilmArray
Respiratory and pneumonia panels
1 h 9 viruses, 18 bacteria (pneumonia panel)
Resistance markers for CRE/ESBL/MRSA:
mecA/mecC, MREJ/MREJb
KPCc, NDMc, OXA-48-like
VIMc, IMPc, CTX-Mc
$$$
Curetis Unyvero
Respiratory (P50, P55) and pneumonia panels
5 h Pneumonia panel:
No viral targets, 19 bacteria, 1 fungus (Pneumocystis jirovecii)
22 Resistance markers
mecA, blaTEM, CTX-Mc
OXA-48-like, OXA-23, OXA-24, OXA-58,
KPCc, NDMc, VIMc
$$$
Nanosphere Verigene 2.5 h 6 viruses, Bordetella, no common bacteria
No resistance markers
$$$
Hologic Gen-Probe Prodesse assay 5 h ProFluþ, ProFASTþ, ProAdenoþ, ProParafluþ, Pro hMPVþ
4 viruses, no bacteria
$$$
Verigene 2.5 h 6 viruses, Bordetella
ePlex respiratory panel 1.5 h Pneumonia panel 2:
9 viruses, atypical bacteria (Mycoplasma, Chlamydia)
$$$
eSensor respiratory panel 6 h 6 viruses, no bacteria $$$
NxTAG 5 h 8 viruses, atypical bacteria (Mycoplasma, Chlamydia, Legionella) $$$
Seegene
Respiratory panel
2.5 h 9 viruses, 7 bacteria
No resistance markers
$$$
Carba-R cartridge-based PCR 50 min after growth of culture colonies No organism identification
5 resistance markers
KPC, OXA-48, NDM, IMP, VIM in Gram-negative bacteria: Enterobacterales, P. aeruginosa, and A. baumannii
$$$
accelerate Pheno® 1.5 h after flagging of blood cultures and 7 h for AST 16 bacteria
MIC for 25 antibiotics
2 Resistance genes (mecA, MLSb)
$$$
TAT turnaround time, AST antimicrobial susceptibility testing
Role of Biomarkers
Several biomarkers, like procalcitonin (PCT), C-reactive protein (CRP), mid-region fragment of pro-adrenomedullin (MR-proADM), interleukin-1 beta (IL-1β), and soluble triggering receptor sTREM-1, have been assessed as potential aids in the diagnosis of VAP. None of these proved to be reliable for confirming or ruling out the diagnosis in a suspected case, nor can they differentiate drug-resistant VAP. Currently, the IDSA/ATS guidelines endorse incorporating PCT as well as clinical criteria in antibiotic de-escalation decisions while the European guidelines recommend against PCT routine use in determining VAP cases who can undergo de-escalation and advocate for utilizing the Clinical Pulmonary Infection Score (CPIS) to determine low-risk patients in whom antimicrobials can be ceased in 72 h [10, 22].
Predictive Scores
Recently, logistic regression has been used in multiple studies to predict VAP caused by MDR species with a main focus on VAP caused by CRE [5]. In a study by Lodise et al. to create predictive models for the probability of VAP caused by resistant species, logistic regression models were transformed into an Excel-based user-friendly interface to estimate the risk of resistance by launching a bedside tool that predicts the likelihood of six phenotypes of MDR pathogens among hospitalized adult patients with Gram-negative infections [86]. This initiative will require further clinical evaluation of its utility. Another simple scoring system was proposed by Richter et al. who demonstrate the utilization of clinical heuristics from existing electronic medical records to inform clinical decision-making [87]. A report from China by Gao et al. examined predictors of CRE-VAP and proposed utilizing length of hospital stay greater than 7 days and the use of β-lactamase inhibitor combination agents or carbapenems to measure the MDR index [88]. Another recent US-based study conducted by Weston et al. utilized prior resistant nonbacteremic cultures and exposure to skilled nursing facility as predictors of carbapenem resistance in one institution to guide empirical therapy [89]. Although the modeling studies have enabled understanding of the evolutionary dynamics of MDR-VAP, further research should focus on validating the usefulness of these various scores in different populations and institutions to produce operational predictions and assessing their cost-effectiveness. Once an optimal score model is identified, it needs to be thoroughly examined in an interventional trial to determine its clinical impact where a proposed model needs to be calibrated and finetuned in order to replicate its empirical observations. Of importance, the mathematical models have predictive limitations. Certain elements can influence the selection and co-selection of AMR in bacteria which may not be accounted for in existing models [90]. Furthermore, it is not yet fully understandable which antimicrobial class has the greatest influence on the emergence of AMR. It is unknown how the competition with sensitive strains impacts the occurrence of persistent counterparts after the establishment of MDR status and how their coexistence over extended periods of time affects prognosis [91–93]. Fitting the models to various types of pathogens remains computationally challenging as the structure of MDR models becomes more complex. Spillover is another significant barrier for prediction that has not been adequately addressed where horizontal transmission of AMR between places predisposes a patient without known risk factors for infections by MDR pathogens, highlighting the impact of local ecology of a unit [94]. Additionally, reliable routine screening for MDR colonization is suboptimally performed in several ICUs with limited resources, making it challenging to predict MDR-VAP using such models. Simplified artificial intelligence-based systems may support the integration of various laboratory and clinical data and streamline the VAP diagnosis in the future.
Empirical Antimicrobial Therapy
Early Effective Therapy
Once respiratory samples has been collected, the likely pathogens should be the focus of empiric antibiotic therapy for VAP and the choice of a specific regimen should be based on the epidemiology of circulating pathogens and their susceptibility profiles in an ICU, local antibiograms, as well as the individualized patient index for MDR, which includes previous microbiology results and initial microscopy findings, although the latter alone cannot differentiate VAP pathogens from each other and its diagnostic yield is also variable. A recent open-label, multicenter RCT of VAP (N = 206) demonstrated that the clinical cure in cases of microscopy-guided empiric regimen was non-inferior to the guideline-based group (77% vs 72%; 95% CI − 0.07 to 0.17) [95]. The Gram stain-based group had lower consumption of anti-MRSA and anti-pseudomonal agents (61% vs 100% and 70% vs 100%, respectively) without significantly impacting the mortality, ICU-free days, and ventilator-free days. Local unit antibiogram and epidemiologic resistance data should be taken into consideration if empiric dual coverage for VAP pathogens is being explored as in the case of P. aeruginosa, although this is still debatable [96].
Earlier time to appropriate therapy is associated with reduced mortality in cases of VAP [10]. Nonetheless, MDR pathogens are frequently encountered in respiratory samples of mechanically ventilated patients and treatment decision for suspected VAP should take into account a multi-consideration approach based on the clinical presentation, the specimen type, diagnostic yield, and risk of increasing AMR.
Risk Stratification Models and Empiric Windows
The first empiric window refers to the short time between sampling and retrieving microscopic findings which is usually around an hour (Fig. 3). Appropriate empiric antibiotic selection should be maximized during the first empiric window when the patient is in severe illness using predictive microbiology and clinical heuristics, while utility of the predictive scores in later empiric windows is likely to be limited [97]. Determining empiric therapy requires an understanding of the variables that predict clinical resistance. For instance, prior cultures with antimicrobial resistance demonstrated a suboptimal sensitivity and positive predictive value (PPV) (48% and 61%) with moderate specificity and negative predictive value (NPV) (88% and 81%) as predictors of AMR in subsequent infections [98]. In a recent quasi-experimental study, Elligsen et al. evaluated the occurrence of clinically significant discordance in empiric treatment and described shorter times of discordance post-intervention for Gram-negative infections (25 h versus 55 h; p = 0.001; adjusted hazard ratio 1.95, 95% CI − 1.4 to 2.8). More intricate measurements combine the local microbiology with patient-specific variables [99]. More complex metrics were developed in the last decade to incorporate patient-specific factors in combination with the local epidemiology of organisms. Multivariable logistic regression is applied in predictive models for the appropriate treatment regimen taking into account variables such as age, gender, the patient’s preceding microbiology, admitting unit, concurrent bloodstream infection, the source of infection, previous hospitalization, and prior ICU admission in addition to antimicrobial exposures [100]. An example of such a decision-assisting tool is the weighted-incidence syndromic combination antibiograms (WISCA). This advanced multicomponent antibiogram has an advantage over conventional laboratory antibiograms by showing the likelihood of adequate antimicrobial coverage in a particular patient with VAP while accounting for polymicrobial etiology and combination therapy [101, 102]. The narrowest possible adequate therapy is the end goal for any predictive tool without compromising clinical outcomes.Fig. 3 Empirical therapeutic windows where earlier targeted therapy is the aim [97, 100]
Prognosis, Mortality, and Preventive Tools
Antimicrobial resistance may contribute to the higher mortality rates associated with VAP, although this is difficult to measure because of the multiple underlying factors [103]. The available evidence shows that VAP is associated with a variably high risk of crude all-cause mortality (13–50%) [8, 10]. Population attributable fraction of VAP is frequently used to calculate the difference between observed ICU mortality and ICU mortality that would have been observed for the same population if all VAP cases were prevented [8, 104]. A meta-analysis from 24 randomized prevention trials (N = 6284) estimated an overall VAP-related mortality of 13%, while a contradicting multicenter European study by Steen et al. estimated 3.6% VAP attributed mortality among the 60-day ICU mortality, which was linked to the effectiveness of modern preventive tools in developed countries [105, 106]. VAP caused by an MDR pathogen is associated with increased mortality, which can be related to the delayed initiation of effective antimicrobial therapy when risk stratification is not implemented [107–109]. Delays in the administration of effective treatment are independent predictors of mortality in serious illnesses and are involved in the high mortality rates linked to resistant infections [87, 110]. Further, the use of second- and third-line antimicrobial agents, indicated by the presence of an MDR pathogen, leads in certain cases to inferior antimicrobial activity and less favorable pharmacological properties as in the case of vancomycin use to treat staphylococcal VAP [111]. Mortality is also influenced by variables such as the severity of presenting illness, primary diagnosis, underlying comorbidities, and concurrent bacteremia [112].
Efforts need to be intensified in order to reduce selective pressures, avoid unnecessary treatment in colonized patients, and prevent the emergence and spread of resistant clones in an ICU through effective antimicrobial stewardship programs (ASP) and thus reducing antimicrobial-resistant VAP [113–115]. In a study that evaluated the rates of MDR infections in two ICUs in the USA, more than fourfold reduction in MDR Gram-negative infections was achieved after implementing a comprehensive ASP over a 7-year period [116]. The longitudinal impact of introducing an effective ASP through significant rise in the rates of infections caused by carbapenem-susceptible P. aeruginosa was evident in another 7-year study of an Australian ICU [117]. Thus, it is increasingly being recognized that an antimicrobial team is a necessity in the modern ICU [114, 118]. The total incidence of VAP cases should also be reduced by routinely implementing high-impact interventional strategies such as the VAP prevention bundles, although accurate assessment of their impact is difficult because of the lack of specific VAP diagnostic criteria and the subjectivity of some components in these bundles [119, 120]. A meta-analysis of 13 observational studies examined the impact of VAP bundles on mortality and reported a 10% reduction following implementation (OR 0.9, 95% CI 0.8–0.9) [121]. Thus, active VAP surveillance and prevention strategies within a unit will lead to lower mortality and reflux reduction in VAP caused by MDR pathogens, although it is challenging to assess the effectiveness of control strategies intended to lower the incidence of antibiotic-resistant bacteria in ICU because of the naturally occurring high changes in the prevalence of colonization [26].
VAP and COVID-19
It is thought that COVID-19 is linked to a higher risk of VAP, which is not entirely accounted for by the prolonged duration of ventilation time. This is hypothesized to be connected to the secondary invaders that produce superinfection as well as the pulmonary dysbiosis brought on by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [122]. VAP in patients with COVID-19 has been associated with shock, bacteremia, and polymicrobial infections [123, 124]. With a crude mortality rate of greater than 40%, the rate of VAP in hospitalized COVID-19 cases varies significantly, ranging from 7% to 86% [125]. The etiologies of VAP in COVID-19 cases have been variable in different studies. In a review of 171 patients with COVID-19 and VAP, the median mechanical ventilation duration before VAP onset was 9 days (95% CI − 5 to 15 days). Of those, 45% of the cases underwent microbiological sampling and grew P. aeruginosa (35%) and S. aureus (23%) as the leading two pathogens causing VAP in the cohort study [126]. A study by Pickens et al. (N = 179), in which BAL was routinely obtained at the point of intubation and upon clinical suspicion of superinfection, estimated the rate of VAP as 45 cases/1000 mechanical ventilation days (44%). MDR organisms were not frequently isolated (three cases) which can reflect the variation in local epidemiology [127]. Another study by Alnimr et al., in which 57.4% of 67 non-survivor COVID-19 cases were described to develop VAP, showed that most of the cases were caused by XDR A. baumannii, P. aeruginosa, and S. aureus (56.4%) [124]. MDR pathogens were also reported as etiological agents in 35% of VAP cases in 774 patients with COVID-19 [128]. de Macedo et al. investigated the healthcare facility as an independent risk factor for MDR organisms in critically ill patients with COVID-19 and found a significant link between admission to a newly opened hospital and the acquisition of MDR VAP in Brazil (OR 3.24; 95% CI 1.39–7.57; p = 0.006). Other non-modifiable risk factors were also identified for MDR superinfections in COVID-19 cases which included male gender and hypoxemia at presentation [129]. It should be noted that noninvasive sampling was used most often in the literature documenting VAP in patients with COVID-19 to prevent the pathogen from being aerosolized. Tracheal aspirates were collected utilizing closed-circuit suctioning under airborne isolation measures. Mini-BAL and BAL were less commonly used alternatives to avoid aerosol-generating procedures that were performed only when they were likely to alter the prognosis or in emergency situations. Lower respiratory tract sampling is not required for the diagnosis of viral pneumonia including COVID-19 as nasopharyngeal samples are optimal specimens for both the molecular assays and antigen detection kits developed for SARS-CoV-2 [130, 131]. It is reserved for severely ill patients with a high suspicion of SARS-CoV-2 infection but repeatedly negative tests on upper airway specimens. Sputum induction is also not advised for safety consideration. It is important to consider that most SARS-CoV-2 laboratory kits were not validated for lower respiratory specimens during the emergency use authorization in the pandemic. Thus, a laboratory that opts to test and report results of SARS-CoV-2 from these samples needs to adequately validate specimens for various assay characteristics including the lower limit of detection (analytical sensitivity) and interference (analytical specificity). Point of care tests were evidently useful during the COVID-19 pandemic by providing rapid microbiological tools that support patient care under surge circumstances.
Conclusions and Future Perspectives
VAP caused by MDR pathogens is associated with high mortality rates, length of hospital stays, and hospital costs. It can be caused by a variety of pathogens which harbor various resistance mechanisms, making treatment challenging. Rapid testing for VAP has developed and can potentially provide results in real time to tailor therapy appropriately with rapid identification of pathogens and detection of their resistance determinants. In the era of widespread AMR, use of rapid diagnostics has the potential to reduce the use of antibiotics and improve clinical outcomes in patients with VAP. However, robust clinical data from well-designed RCTs are still evolving to assess the impact of these novel diagnostic platforms on antimicrobial usage and prognosis. Considerations remain for potential confounding bias leading to an inflated estimation of their diagnostic performance especially in studies where the outcome measures did not include clinically pertinent parameters. When used, rapid diagnostic tools should be integrated into effective antimicrobial stewardship processes. Nevertheless, the local epidemiological patterns and patient-specific risk factors for MDR pneumonia can support the clinical decision toward a broader therapy and minimize delays to appropriate empiric treatment with its prognostic impacts. Various elements are currently incorporated in predictive scores which are hot research topics for which various models are under development with a variable level of complexity and resourcing. Clinical validation of those evolving models in large, multicenter prospective studies is urgently needed to increase the utility of potentially useful tools for a serious ICU infection. As the prevalence of drug-resistant VAP continues to increase, ASP must be activated in critical care areas to reduce selective pressures in circulating clones and prevent colonization by MDR and XDR species, which is often
a source of serious MDR infections or outbreaks. ICU should invest in establishing effective antimicrobial teams, and the laboratory needs to play an active role in liaison with the ICU to develop a facility-specific model of diagnostic stewardship that can create reflex, rapid testing and reporting of critical cases. This will facilitate timely initiation of appropriate targeted therapy with less collateral damage. Emphasis should be put on preventive tools for VAP including cases caused by MDR organisms along with audit and feedback tools to incorporate institution-specific factors based on available evidence. Areas for future research in VAP caused by MDR pathogens include the clinical efficacy and cost-effectiveness of rapid VAP diagnostics and validation of AMR clinical prediction scores as well as their actual use in making treatment decisions in various settings.
Acknowledgements
The author would like to thank Imam Abdulrahman bin Faisal University for providing access to the articles cited in this review through its digital library.
Funding
No funding was obtained for this work. The author funded the rapid service fee.
Author Contributions
Amani Alnimr designed, drafted and amended the manuscript.
Disclosures
Amani Alnimr reports no conflicts of interest in any aspect related to this review.
Compliance with Ethics Guidelines
This review article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by the author.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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PMC010xxxxxx/PMC10241326.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
10.47487/apcyccv.v3i4.240
Carta Al Editor
Riesgo cardiovascular en pacientes con crisis gotosa: ¿existe alguna asociación y necesidad de tratamiento?
Cardiovascular risk in patients with acute gout: is there any association and need for treatment?https://orcid.org/0000-0002-7498-5346
Picón-Jaimes Yelson Alejandro 1 Médico general, Magíster en Epidemiología y Salud Pública; Candidato a doctor en Salud, Bienestar y Bioética; España
https://orcid.org/0000-0003-3933-3876
Merchán-Cepeda Manuel Alejandro 2 Médico general
https://orcid.org/0000-0002-2387-841X
Castro-Córdoba Juan Camilo 3 Médico general
https://orcid.org/0000-0002-0599-5014
Jaramillo-Acosta Manuel Alejandro 4 Médico general
https://orcid.org/0000-0002-7479-3539
Peñaranda-Ocampo Hector Jaime 5 Médico general
1 Programa de Doctorado en Salud, Bienestar y Bioética, Universidad Ramón Llul. Barcelona, Universitat Ramon Llull Programa de Doctorado en Salud, Bienestar y Bioética Universidad Ramón Llul Barcelona Spain
2 Departamento de Medicina, Fundación Universitaria Ciencias de la Salud. Bogotá, Colombia, Departamento de Medicina Fundación Universitaria Ciencias de la Salud Bogotá Colombia
3 Departamento de Medicina, Universidad Antonio Nariño. Bogotá, Colombia, Universidad Antonio Nariño Departamento de Medicina Universidad Antonio Nariño Bogotá Colombia
4 Departamento de Medicina, Universidad del Quindío. Armenia, Colombia, Universidad del Quindío Departamento de Medicina Universidad del Quindío Armenia Colombia
5 Departamento de Medicina, Universidad Libre. Cali, Colombia, Universidad Libre Departamento de Medicina Universidad Libre Cali Colombia
Correspondencia Yelson Alejandro Picón-Jaimes, Calle de José Antonio de Armona Num. 24 Pl 2 Pt E. Barrio Palos de Moguer, Distrito Arganzuela, 28012 Madrid. España. [email protected]
Contribución de los autores. Todos los autores participaron en la realización del manuscrito
Conflictos de interés: Los autores declaran no tener algún conflicto de interés
31 12 2022
Oct-Dec 2022
3 4 226227
27 11 2022
30 12 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
==== Body
pmc Señor editor
Las enfermedades cardiovasculares siguen siendo la primera causa de muerte en el mundo, con una prevalencia muy alta a nivel global, situación que ocasiona una carga de enfermedad insostenible, sobre todo en países de bajos y medianos ingresos donde existen dificultades en el acceso oportuno y especializado al cuidado de la salud 1. Esto ha reforzado la necesidad de colocar todos los esfuerzos en la atención primaria, con el fin de prevenir el avance rápido del riesgo cardiovascular en edades tempranas. La gota es una artritis inflamatoria causada por el depósito de cristales de urato en los tejidos que puede desencadenar un cuadro abrupto de dolor intenso y edema en las articulaciones, principalmente en miembros inferiores 2. Se ha descrito que la prevalencia de gota varía entre las poblaciones, llegando hasta aproximadamente un 7% (tres casos por cada mil habitantes por año) de la población 2. De acuerdo con el estudio de epidemiologia global de Dehlin et al. 2, Latinoamérica no posee datos fiables sobre la prevalencia y desenlaces de esta condición. Sin embargo, la obesidad y otras comorbilidades cardiometabólicas se asocian fuertemente con la gota, por lo que se presume, entonces 3, que la prevalencia es alta. Ahora bien, este es un círculo vicioso negativo sobre el riesgo y enfermedad cardiovascular, puesto que la hiperuricemia y crisis gotosa generan hipertensión vasoactiva inducida por ácido úrico, a expensas de lesión microvascular e intersticial del parénquima renal y arterial 3,4. Entonces, surge el interrogante, ¿existe evidencia que soporte la asociación causal y la necesidad de tratamiento de crisis gotosa y riesgo cardiovascular?
Recientemente, Cipolletta et al.3) llevaron a cabo un estudio de casos y controles donde evaluaron la asociación entre crisis gotosa y evento cardiovascular subsecuente en 62 574 pacientes con gota y 10 475 pacientes con gota y eventos. Los autores encontraron que aquellos pacientes con eventos cardiovasculares tienen mayor probabilidad de presentar una crisis gotosa hasta 60 días (OR 1,93; IC 95%: 1,57 - 2,38) y 120 días (OR 1,57; IC 95%: 1,26 - 1,96) previos al evento. Al calcular la razón de eventos cardiovascular por 1000 personas por día en una serie autocontrolada de 1421 pacientes con gota y eventos cardiovasculares, encontraron que la mayor razón se presentó entre los 0 y 60 días previos al evento (2,49; IC 95%: 2,16 - 2,82). Al comparar los eventos 150 días previos o 180 - 540 días posterior a la crisis gotosa, se evidenció una diferencia de razón de incidencia de 1,17 (IC 95%: 0,83 - 1,52) por cada 1000 personas por día, y una razón de incidencia de 1,89 (IC 95%: 1,54 - 2,30) 3. De esta forma, los autores concluyeron que aquellos pacientes con crisis gotosa tienen una mayor probabilidad de presentar eventos cardiovasculares en días posteriores. Entonces, al existir una asociación, ¿debe tratarse? y ¿cómo?
Saag et al.4 ejecutaron un ensayo controlado aleatorizado con el objetivo de buscar la relación entre los niveles de urato séricos, manifestaciones de gota y muerte cardiovascular, en pacientes bajo tratamiento con febuxostat o alopurinol, donde se siguieron 6190 pacientes (febuxostat, n=3098 vs. alopurinol, n=3092) por 32 meses. Se encontró que los niveles promedio de urato sérico fueron menores en aquellos que recibieron febuxostat y que no existió asociación entre estos niveles y muerte cardiovascular en este grupo. También, el número de tratamientos requeridos fue comparable posterior al año de tratamiento en el grupo febuxostat vs. el grupo alopurinol (incidencia media de crisis gotosa por año de 0,35 vs. 0,34). Finalmente, la resolución de tofos gotosos fue > 50% en ambos grupos 4. Wang et al.5 llevaron a cabo una revisión sistemática y metaanálisis en el cual evaluaron la incidencia de eventos cardiovasculares mayores en pacientes con gota o hiperuricemia asintomática, y tratados con alopurinol o febuxostat. Se incluyó un total de 18 estudios con más de 270 000 pacientes, evidenciando que no existió diferencias significativas entre alguno de los dos agentes, respecto a la incidencia de eventos cardiovasculares, pero sí sobre los niveles de ácido úrico sérico (DM -0,83; IC 95%: -1,22 a -0,44; p <0,0001) y reacciones adversas dermatológicas (OR 0,55; IC 95%: 0,42 - 0,73; p <0,0001), a favor de febuxostat. No obstante, debido a la heterogeneidad de los estudios, los autores sugirieron la necesidad de nuevos estudios de mayor calidad con la misma evaluación de desenlaces 5. Parece ser, entonces, que no existe diferencia en cuanto al desenlace de evento cardiovascular mayor con el uso de estos agentes. Pero ¿y en otras comorbilidades cardiovasculares?
Por su parte, Roth et al. 6) evaluaron la utilidad de la colchicina para la crisis gotosa y su relación con desenlaces clínicos en falla cardiaca descompensada; evidenciaron que de 1047 pacientes (de los cuales 237 recibieron colchicina), existió una reducción significativa de la mortalidad intrahospitalaria en el grupo colchicina (2,1% vs. 6,5%; p=0,009), sin diferencias en la readmisión a 30 días (21,5% vs. 19,5%; p=0,495) 6. Esto permitió concluir a los autores que la colchicina podría impactar sobre la mortalidad intrahospitalaria en aquellos con crisis gotosa y falla cardiaca descompensada.
Así, es evidente que existe una tendencia del tratamiento agudo de la crisis gotosa, en los desenlaces cardiovasculares y muerte cardiovascular 7. La evidencia es heterogénea y la gran mayoría de estudios han sido ejecutados en países de altos ingresos. De acuerdo con lo planteado por Lozada-Martinez et al. 8 es necesario diseñar y realizar estudios en Latinoamérica que consideren la ecología genética y epigenética de las enfermedades, para ser más precisos en su abordaje y poder garantizar resultados desde la prevención primaria y secundaria. Actualmente, bajo la integración y desarrollo de modelos biomédicos de investigación traslacional 8,9, sería posible ir más allá y poder conocer con mayor profundidad qué otros factores inciden sobre la crisis gotosa y los desenlaces cardiovasculares, para determinar si en la población latinoamericana se pueden conseguir resultados similares. Pero, a la fecha, parece ser que es necesario tratar de forma agresiva la crisis gotosa, ejecutar estudios epidemiológicos sobre la relación entre la gota y la enfermedad cardiovascular y evaluar qué intervenciones son reproducibles y más favorables para nuestra población.
Financiamiento: Autofinanciado
Citar como: Picón-Jaimes YA, Merchán-Cepeda MA, Castro-Córdoba JC, Jaramillo-Acosta MA, Peñaranda-Ocampo HJ. Riesgo cardiovascular en pacientes con crisis gotosa: ¿existe alguna asociación y necesidad de tratamiento? Arch Peru Cardiol Cir Cardiovasc. 2022;3(4). doi: 10.47487/apcyccv.v3i4.240
==== Refs
Referencias bibliográficas
1 Organización Mundial de la Salud Enfermedad Cardiovascular [Internet] Ginebra OMS c2022 Disponible en: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
2 Dehlin M Jacobsson L Roddy E Global epidemiology of gout prevalence, incidence, treatment patterns and risk factors Nat Rev Rheumatol 2020 16 7 380 390 10.1038/s41584-020-0441-1 32541923
3 Cipolletta E Tata LJ Nakafero G Avery AJ Mamas MA Abhishek A Association Between Gout Flare and Subsequent Cardiovascular Events Among Patients With Gout JAMA 2022 328 5 440 450 10.1016/j.rec.2016.05.009 35916846
4 Saag KG Becker MA White WB Whelton A Borer JS Gorelick PB Evaluation of the Relationship Between Serum Urate Levels, Clinical Manifestations of Gout, and Death From Cardiovascular Causes in Patients Receiving Febuxostat or Allopurinol in an Outcomes Trial Arthritis Rheumatol 2022 74 9 1593 1601 10.1002/art.42160 35536764
5 Wang M Zhang Y Zhang M Li H Wen C Zhao T The major cardiovascular events of febuxostat versus allopurinol in treating gout or asymptomatic hyperuricemia a systematic review and meta-analysis Ann Palliat Med 2021 10 10 10327 10337 10.21037/apm-21-1564 34498481
6 Roth ME Chinn ME Dunn SP Bilchick KC Mazimba S Association of colchicine use for acute gout with clinical outcomes in acute decompensated heart failure Clin Cardiol 2022 45 7 733 741 10.1002/clc.23830 35481608
7 Lim GB Gout flares can precede cardiovascular events Nat Rev Cardiol 2022 19 10 642 642 10.1038/s41569-022-00766-x
8 Lozada-Martínez ID Suarez-Causado A Solana-Tinoco JB Ethnicity, genetic variants, risk factors and cholelithiasis The need for eco-epidemiological studies and genomic analysis in Latin American surgery Int J Surg 2022 99 106589 106589 10.1016/j.ijsu.2022.106589 35248763
9 Pérez-Fontalvo NM De Arco-Aragón MA Jimenez-García JDC Lozada-Martinez ID Molecular and computational research in low- and middle-income countries Development is close at hand J Taibah Univ Med Sci 2021 16 6 948 949 10.1016/j.jtumed.2021.06.010 34381325
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PMC010xxxxxx/PMC10241327.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37284564
10.47487/apcyccv.v3i4.239
Reporte De Caso
Estimulación de rama izquierda en enfermedad eléctrica difusa cardiaca en una paciente pediátrica
Left bundle branch stimulation in diffuse electrical heart disease in a pediatric patienthttps://orcid.org/0000-0002-9548-4428
Cueva-Parra Ángel 1 Cardiólogo electrofisiólogo
https://orcid.org/0000-0003-4734-8023
Ploneda-Valencia Ruy 1 Cardiólogo electrofisiólogo
https://orcid.org/0000-0002-2048-1206
Levinstein Moisés 1 Cardiólogo electrofisiólogo
https://orcid.org/0000-0002-9176-7885
Gómez-Flores Jorge 1 Cardiólogo electrofisiólogo
https://orcid.org/0000-0001-9903-6537
Nava Santiago 1 Cardiólogo electrofisiólogo
1 Departamento de Electrofisiología, Instituto Nacional de Cardiología “Ignacio Chávez”. Ciudad de México, México. Departamento de Electrofisiología, Instituto Nacional de Cardiología “Ignacio Chávez” Ciudad de México México
Correspondencia: Santiago Nava Juan Badiano 1, Belisario Domínguez Sec. 16, Tlalpan, 14080. Ciudad de México, CDMX, [email protected], +52 5514519574
Contribución de los autores: Todos los autores participaron en la elaboración del manuscrito.
Declaración de conflicto de intereses: Los autores declaran no tener ningún conflicto de intereses.
31 12 2022
Oct-Dec 2022
3 4 210214
16 11 2022
28 12 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
Resumen
La estimulación de rama izquierda es una estrategia de segunda línea en pacientes donde la estimulación del haz de His no es la óptima. En la actualidad no se han reportado casos de estimulación de rama izquierda en pacientes con enfermedad eléctrica difusa cardiaca ni en población pediátrica.
Abstract
Left bundle branch stimulation is a second-line strategy in patients where His bundle stimulation is not optimal. Currently, no cases of left bundle branch stimulation have been reported in patients with diffuse electrical cardiac disease or in the pediatric population.
Palabras clave:
Fascículo Atrioventricular
Estimulación Cardiaca Artificial
Arritmia Cardiaca
Disfunción del Nodo Sino-Atrial
Pediatría
Keywords:
Bundle of His
Cardiac Pacing, Artificial
Cardiac Arhythmia
Sinus Node Dysfunction
Pediatrics
==== Body
pmcINTRODUCCIÓN
La estimulación de rama izquierda fue descrita por primera vez en 2017 por Huang et al.1. Es una técnica útil en pacientes donde la estimulación fisiológica del haz de His no es factible debido a umbrales de captura elevados, bloqueos de conducción distales al His, o por dificultades técnicas; además, es segura y reproducible 2. Una indicación de estimulación es la disfunción sinusal la cual puede estar o no acompañada a otras alteraciones en el sistema de conducción, cuando un paciente presenta anormalidades en la propagación del impulso eléctrico tanto en el sistema especializado de conducción como en el miocardio auricular y ventricular, el paciente presenta enfermedad eléctrica difusa cardiaca 3. En la actualidad no se han reportado casos de estimulación de rama izquierda con enfermedad eléctrica difusa cardiaca y en población pediátrica.
REPORTE DE CASO
Paciente mujer de 5 años, sin antecedentes familiares de importancia, con historia de síncope, con 3 meses de evolución de incremento en la frecuencia de estos eventos motivo por el cual acudió a nuestro instituto. A su ingreso presentaba bradicardia y compromiso hemodinámico, además de crisis de Stoke Adams, en el electrocardiograma (ECG) se documentó ritmo de la unión y pausas (Figura 1). Por lo anterior, se le colocó un marcapasos temporal con un cable de fijación activa a través de la vena yugular derecha conectado a fuente de marcapasos definitivo externa, como previamente se ha reportado 4. El ecocardiograma mostró ausencia de cardiopatía estructural, con llenado ventricular monofásico.
Figura 1 Electrocardiograma de doce derivadas en reposo. A. Ritmo de la unión AV a una frecuencia de 42 latidos por minuto con QRS de 140 ms y bloqueo bifascicular, sin evidencia de actividad auricular. B. Pausa de 2,3 segundos durante un episodio de fibrilación auricular.
Se decidió realizar un estudio electrofisiológico con el marcapasos temporal programado en VVI 40, donde se evidenció ausencia de actividad eléctrica en la aurícula derecha, así como falla de captura en distintas posiciones y a máxima salida (Figura 2A); así mismo, presentó despolarizaciones auriculares muy esporádicas que permitieron documentar intervalos AH y HV prolongados (Figura 2B). Debido a la posibilidad de coexistencia entre la enfermedad eléctrica difusa cardiaca y arritmias ventriculares, se realizó protocolo de inducción de taquicardia ventricular el cual fue negativo.
Figura 2 Estudio electrofisiológico. Inicialmente se posicionó un catéter cuadripolar en la aurícula derecha (AD) y otro en el ventrículo derecho (VD) A. Estimulación en múltiples puntos de la AD sin captura a máxima salida. B. Se posicionó intencionadamente el catéter AD en la región del haz de His y se logró documentar un latido auricular que generó intervalos AH y HV largos (AH 181 ms, HV 154 ms), así como un bloqueo infrahisiano.
Posteriormente, se programó implante de marcapasos unicameral con estimulación del haz de His usando el electrodo SelectSecure MRI SureScan 3830 (Medtronic, Minneapolis, MN, USA) y la vaina C315 (Medtronic, Minneapolis, MN, USA); durante el procedimiento se obtuvieron umbrales elevados en la región anatómica del haz de His, motivo por el cual se procedió a implantar el cable 1 cm por debajo, en la región anatómica de la rama izquierda; la estimulación en esta localización logró capturarla y generar un QRS de 115 ms con un umbral de 0,7V/1,0 ms e impedancia de 1200 ohms (Figura 3A). La inyección de contraste a través de la vaina permitió evidenciar adecuada penetración del electrodo en el septum interventricular (Figura 3B), el procedimiento culminó sin complicaciones. En el seguimiento a los 6 meses no ha vuelto a presentar síncope.
Figura 3 Estimulación de rama izquierda. A. Estimulación subselectiva de la rama izquierda que generó un QRS de 115 ms con morfología de BCRD en V2, desde el electrodo de estimulación se aprecia potencial de rama izquierda. B. Inyección de contraste a través de la vaina C315 que permite apreciar una adecuada penetración del cable 3038 en el septum interventricular.
DISCUSIÓN
El implante de dispositivos electrónicos de estimulación cardiaca en población pediátrica constituye un reto debido al menor tamaño de los pacientes, a las limitaciones de los accesos vasculares y a posibles complicaciones asociadas a futuros recambios de generador 5, en estos pacientes la estimulación fisiológica o del haz de His es lo ideal ya que previene el efecto nocivo de la estimulación crónica del VD 5. Otra opción de estimulación fisiológica es la de la rama izquierda o técnica de Huang 1. Recientemente, Ponnusamy et al. reportaron un caso de estimulación de rama izquierda en un paciente de 13 años con bloqueo AV completo 6.
La enfermedad eléctrica difusa cardiaca es una causa poco frecuente de implante de marcapasos en niños 3. En este caso, debido a la edad de la paciente, a la necesidad de estimulación ventricular de manera crónica, parálisis eléctrica auricular derecha y a la falla de obtener umbrales adecuados en la región del haz de His, optamos por estimulación de la rama izquierda con un marcapasos unicameral en modo VVIR.
Actualmente existen criterios de estimulación de rama izquierda 2, en el caso descrito nosotros obtuvimos captura subselectiva de la misma con un adecuado umbral, cumpliendo los siguientes criterios. 1) morfología de rama derecha en V2; 2) muesca en la mitad del QRS en V1; 3) presencia onda S en V6; y 4) tiempo de activación ventricular (TIDI) menor de 90 ms en V5. Además, esta estimulación, la cual logró reducir la duración del QRS en comparación al QRS basal de la paciente (de 140 a 115 ms) con el mismo eje que el ritmo intrínseco (Figura 4). Así mismo, durante la estimulación se evidenció potencial de rama izquierda desde el electrodo de estimulación, este potencial se logra documentar en el 25 a 30% de los casos (Figura 4) 2.
Figura 4 A. Electrocardiograma de doce derivaciones con estimulación subselectiva de rama izquierda. B. Radiografía de tórax posprocedimiento.
En conclusión, este tipo de estimulación es una alternativa a la estimulación tradicional del haz de His 1, en el caso presentado la indicación fue por presencia de umbrales de captura elevados en la región anatómica del haz de His. Si bien el área para estimular la rama izquierda es relativamente amplia, debido a la conformación anatómica de esta estructura, hay que asegurar una adecuada penetración del electrodo en el septum interventricular 2, por ello, se aplicó una inyección de medio de contraste a través del sistema de entrega y se corroboró la adecuada fijación del electrodo (video 1-material suplementario y Figura 3). Asegurar una adecuada fijación reduce el riesgo de desplazamiento del electrodo, así como variaciones importantes del umbral de captura.
Fuente de financiamiento: El trabajo fue autofinanciado.
Citar como: Cueva-Parra A, Ploneda-Valencia R, Levinstein M, Gómez-Flores J, Nava S. Estimulación de rama izquierda en enfermedad eléctrica difusa cardiaca en una paciente pediátrica. Arch Peru Cardiol Cir Cardiovasc. 2022;3(4). doi: 10.47487/apcyccv.v3i4.239
==== Refs
REFERENCIAS BIBLIOGRÁFICAS
1 Huang W Su L Wu S Xu L Xiao F Zhou X A novel pacing strategy with low and stable output pacing the left bundle branch immediately beyond the conduction block Can J Cardiol 2017 33 12 1736 1736 10.1016/j.cjca.2017.09.013
2 Li X Li H Ma W Ning X Liang E Pang K Permanent left bundle branch area pacing for atrioventricular block Feasibility, safety, and acute effect Heart Rhythm 2019 16 12 1766 1773 10.1016/j.hrthm.2019.04.043 31048065
3 Iturralde-Torres P Nava-Townsend S Gómez-Flores J Medeiros-Domingo A Colín-Lizalde L Hermosillo AG Association of congenital, diffuse electrical disease in children with normal heart sick sinus syndrome, intraventricular conduction block, and monomorphic ventricular tachycardia J Cardiovasc Electrophysiol 2008 19 5 550 555 10.1111/j.1540-8167.2007.01006.x 17971134
4 Kawata H Pretorius V Phan H Mulpuru S Gadiyaram V Patel J Utility and safety of temporary pacing using active fixation leads and externalized re-usable permanent pacemakers after lead extraction Europace 2013 15 9 1287 1291 10.1093/europace/eut045 23482613
5 Lyon S Dandamudi G Kean A Permanent His-bundle Pacing in Pediatrics and Congenital Heart Disease J Innov Card Rhythm Manag 2020 11 2 4005 4012 10.19102/icrm.2020.110205 32368373
6 Ponnusamy SS Muthu G Bopanna D Selective left bundle branch pacing for pediatric complete heart block Indian Pacing Electrophysiol J 2020 20 2 78 80 10.1016/j.ipej.2019.12.012 31866553
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PMC010xxxxxx/PMC10241328.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37284566
10.47487/apcyccv.v3i4.243
Reporte De Caso
Cor triatriatum sinister, reporte de un caso
Cor triatriatum sinister, case reporthttps://orcid.org/0000-0002-5768-3856
Conde Salazar José Luis Médico cirujano, residente Cirugía Cardiovascular
https://orcid.org/0000-0001-5413-6032
Sisniegas Razón Aldrix Josué Médico cirujano, residente Cirugía Cardiovascular
https://orcid.org/0000-0003-2100-3537
Soplopuco Palacios Franz Médico cirujano de tórax y cardiovascular
1 Cirugía Cardiovascular, Instituto Nacional Cardiovascular - INCOR. Lima, Perú. Cirugía Cardiovascular, Instituto Nacional Cardiovascular - INCOR Lima Perú
Correspondencia: Jose Luis Conde Salazar, Calle Chinchaysuyo Mz Q Lot 79, ENACE. Lima, Perú, +51 988788282, Correo: [email protected]
Contribución de los autores: Todos los autores participaron en el diseño del estudio, revisión, redacción y aprobación de la versión final del manuscrito.
Declaración de conflictos de interés: No hay conflicto de intereses.
31 12 2022
Oct-Dec 2022
3 4 215219
16 11 2022
28 12 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
Resumen
Se reporta un caso de cor triatriatum sinister asociado a drenaje venoso pulmonar anómalo, en paciente adulto que debutó con palpitaciones, edema de miembros inferiores, disnea, posteriormente ortopnea, bendopnea y ascitis. El cuadro clínico se inició con episodios de fibrilación auricular, asociado a rehospitalizaciones por falla cardiaca derecha, por lo que se solicitó angiotomografía y ecografía transesofágica que dieron con el diagnostico final. El abordaje quirúrgico se realizó mediante exéresis total del septum fibromuscular multifenestrado y doble plastia valvular, debido a que presentaba insuficiencia mitral y tricuspidea severas; lo que mejoró la condición clínica del paciente. Se reconoce la importancia de considerar dentro del diagnóstico diferencial de las causas de insuficiencia cardiaca derecha originadas en el atrio izquierdo a esta cardiopatía congénita acianótica.
Abstract
We report a case of cor triatriatum sinister associated with anomalous pulmonary venous drainage in an adult patient who presented with palpitations, lower limb edema, dyspnea, orthopnea, bendopnea and ascites. The clinical picture began with episodes of atrial fibrillation, associated with rehospitalizations for right heart failure, so angiotomography and transesophageal echography were requested, which led to the final diagnosis. The surgical approach was performed by total excision of the multifenestrating fibromuscular septum and double valvular plasty, due to severe mitral and tricuspid insufficiency, which improved the patient's clinical condition. The importance of considering this acyanotic congenital heart disease within the differential diagnosis of the causes of right heart failure originating in the left atrium is recognized.
Palabras clave:
Cardiopatía Congénita Acianótica
Cor Triatriatum
Membrana Atrial
Keywords:
Acyanotic Congenital Heart Disease
Cor Triatriatum
Atrial Membrane
==== Body
pmcINTRODUCCIÓN
El cor triatriatum, una anomalía congénita acianótica de la septación atrial, puede ser izquierda (cor triatriatum sinister: CTS) o derecha (cor triatriatum dexter: CTD). El CTS, inicialmente descrito en 1868, representa el 0,1-0,4% de todos los pacientes con defectos cardiacos congénitos; el CTD, descrita en 1875, es aun más rara y su prevalencia es desconocida 1. El posible desarrollo de esta membrana sería la fusión embriológica incorrecta de la vena pulmonar primitiva o un atrapamiento de estructuras primitivas del seno venoso 2, pero la base embriológica no ha sido establecida 3. Por otro lado, también se ha considerado que la causa podría ser una persistencia anómala de la vena cava superior izquierda. Histológicamente, corresponde a un septum fibromuscular que se inserta medialmente en el margen de la fosa oval y lateralmente puede unirse a la orejuela izquierda del atrio. Esta membrana es incompleta y puede variar desde tener una amplia apertura central hasta presentar múltiples fenestras 4. De esta manera forma dos compartimentos (anteroinferior y posterosuperior), la cámara posterosuperior está en comunicación con las venas pulmonares y reciben su drenaje; la cámara anteroinferior está en contacto con la válvula mitral y contiene al apéndice auricular izquierdo 5.
La historia natural depende de la fisiología obstructiva y de las anomalías congénitas asociadas al CTS. En la forma obstructiva, debuta como falla cardiaca e hipertensión pulmonar, la severidad de la insuficiencia cardiaca va en función del grado de obstrucción que produce la membrana, infrecuentemente puede ocluirse completamente el drenaje venoso hacia la cámara anteroinferior. No hay predilección por el sexo, la edad de diagnóstico en población adulta usualmente se da entre los 30-60 años, siendo el debut infantil en aquellos con membrana obstructiva 6. Presentamos el caso de un paciente con evolución natural del CTS con fibrilación auricular permanente, insuficiencia mitral funcional severa e insuficiencia tricuspidea severa con anuloectasia con buena respuesta al tratamiento quirúrgico.
REPORTE DEL CASO
Varón de 47 años, natural de Lima, con antecedente de diabetes mellitus hace diez años y tratamiento regular con metformina (850mg al día). Inicia cuadro clínico con palpitaciones hace 8 años, recibe el diagnostico de fibrilación auricular y es manejado con bisoprolol (5mg al día) y rivaroxabán (20mg al día); posteriormente se añade disnea CF-II, ascitis y edema de miembros inferiores que requirió hospitalizaciones repetidas y fue compensado con diuréticos (espironolactona 25mg al día, furosemida 40mg cada 12 horas) sin un diagnóstico especifico. Hace dos años aumenta la ascitis, y la disnea progresa a CF-III, se añade ortopnea, bendopnea y llenura precoz; por lo que es hospitalizado para amplíar estudios.
Al realizarle una angiotomografía (Figura 1) se observó la aurícula izquierda dilatada (diámetro 50mm, área 45cm2) y en su interior una membrana localizada a 13mm por encima del velo anterior de válvula mitral que se extendía hasta después del origen de la orejuela y ocupaba el 90% de la luz auricular. Se evidenció el septum interauricular integro y cercano a éste la membrana atrial izquierda dejaba una comunicación a nivel de la pared inferior auricular. Las dimensiones de esta comunicación eran de 1.9cm2 de área, con 19x12.5mm de diámetros longitudinal y transversal, respectivamente. La membrana descrita era móvil y estaba parcialmente calcificada y poseía 2 adherencias en su extremo superior. El drenaje venoso pulmonar no tenía obstrucción y constaba de 5 venas pulmonares (3 derechas 2 izquierdas) que drenaban en la aurícula izquierda, por lo que se hace el diagnostico de CTS obstructivo. En este momento el cateterismo derecho no encontró hipertensión pulmonar.
Figura 1 Tomografía contrastada de corazón y grandes vasos: (A) Membrana atrial izquierda alejándose del plano valvular mitral durante la fase de sístole. (B) Membrana atrial izquierda acercándose al plano de la válvula mitral durante la fase de diástole.
Ocho meses después se realizó una ecocardiografía transesofágica (ETE) (Figura 2) donde se evidenció el CTS restrictivo con un gradiente máximo de 5 mmHg, y cuya membrana dejaba un área efectiva de 0.75cm2 cercano al septum atrial. La orejuela tenía morfología chicken wing y velocidad de vaciado 36 cm/s. Se evidenció insuficiencia mitral moderada (2 jets) tipo mixto (degenerativo leve y funcional predominante) con anillo de 36 x 44 mm y patrón de llenado monofásico. También se observó insuficiencia tricúspidea severa funcional con vena contracta de 8 mm y su anillo de 48 mm; fracción de eyección de ventrículo izquierdo 57%; ventrículo derecho dilatado con 61 x 66 x 84mm de diámetros basal, medio y longitudinal, respectivamente y su fracción de acortamiento en 38%. Se evidenció alta probabilidad de hipertensión pulmonar por lo que se realizó cateterismo cardiaco derecho en el cual se evidencia hipertensión pulmonar postcapilar aislada (PAPm 38 mmHg ,CAP 25 mmHg, RVP 1.5 U Wood) en el cateterismo izquierdo las arterias coronarias sin lesiones significativas.
Figura 2 (A) Ecografía transesofágica vista medio esofágica dos cámaras, se evidencia AL Doppler color dos jets de regurgitación mitral, además membrana en atrio izquierdo y (B) vista medioesofágica eje largo, se evidencia jet de turbulencia en atrio izquierdo con membrana atrial tipo obstructiva. (C) Vista apical de cuatro cámaras con Doppler color a nivel de válvula tricúspide demostrando jet de regurgitación tricúspidea severa.
En junta de cirugía cardiovascular se decidío abordaje quirúrgico con recomendación final de resección de membrana y plastia mitral-tricúspidea.
Técnica quirúrgica
Se realizo esternotomía media, canulación central aorto-bicaval, abordaje del atrio izquierdo a través del surco de sondergaard y transeptalmente se procedió a realizar la exéresis total de la membrana fibromuscular multifenestrada que ocupaba el 90% del diámetro atrial. Al evidenciar dilatación del anillo mitral se realizó anuloplastía mitral con anillo número 30, atrioseptoplastia y por anuloectasia tricúspidea severa se realizó anuloplastia con banda número 30. Se logro salir de circulación extracorporea (CEC) sin dificultad y sin ninguna intercurrencia. El tiempo de CEC fue de 133 min y el de clampaje aórtico de 93 min. En la ETE pos-CEC se constata normofunción de plastías mitral y tricúspidea, función biventricular conservada y aurícula izquierda libre de membranas (Figura 3). La membrana extraída fue enviada a anatomía patológica (Figura 4)
Figura 3 Ecocardiografía transesofágica vista medio esofágica, cuatro cámaras. (A) Ausencia de jet de regurgitación en plastias mitral y tricúspidea durante la fase de sístole. (B) Ausencia de restricción en plastias mitral y tricúspidea durante la fase de diástole.
Figura 4 Patología microscópica de membrana extraída. Aumento 4x (Coloración Hematoxilina - Eosina) A) Fragmento de tejido fibroconjuntivo con cardiomiocitos algunos de aspecto degenerativo. B) Fibrosis y áreas de calcificación distrófica (asterisco)
Durante el control, nueve días posteriores a la cirugía, se observa insuficiencia tricuspidea moderada, en un segundo tiempo operatorio se realizó, mediante reesternotomía media, canulación central arterial y venosa bicaval, atriotomia derecha, se evidencia banda número 30 integra, con velos tricúspideo anterior y posterior íntegros con prolapso de velo septal, se extrae la banda tricúspidea para proceder a colocar puntos de afronte con pleget en la región de la comisura entre el velo anterior y septal (puntos key); para, a continuación, proceder a colocar una banda tricúspidea número 30, testeo con solución fisiológica con insuficiencia nula, se cierra atriotomia derecha, desclampaje y salida de CEC; tiempo de clampaje 48 min, tiempo de CEC 80 min. Fue trasladado a cuidados intensivos, extubado a las 5 h con estancia total de 36 h en la unidad y dado de alta al quinto día del posoperatorio asintomático con evolución satisfactoria.
A los 2 meses se realizó ecocardiograma transtorácico donde se evidencia fracción de eyección de ventrículo izquierdo 61%, aurícula izquierda diámetro 47mm y área 29cm2, ventrículo derecho 44 x 39 x 64 mm; anuloplastia mitral competente no restrictiva, plastia tricuspidea no restrictiva con insuficiencia leve a moderada, no presencia de membrana interauricular izquierda.
DISCUSIÓN
El CTS en el adulto junto a la clínica de nsuficiencia mitral y fibrilación auricular está asociado con anomalías del tabique interatrial, como el foramen oval permeable (FOP). Si el FOP está a nivel de la cámara posterosuperior, dará un importante shunt Izquierda-derecha (I-D) imitando al drenaje venoso pulmonar anómalo total con lo que el ventrículo derecho puede dilatarse. Si el FOP esta en la cámara anteroinferior, la clínica será de estenosis mitral y el shunt I-D será menor 7. A diferencia de lo anterior, en nuestro paciente el septo interauricular se mantuvo íntegro y la insuficiencia mitral fue por dilatación de anillo a pesar de que le membrana fue obstructiva, esto podría explicarse por la sobrecarga de volumen en la cámara posterosuperior que progresivamente causaría dilatación en conjunto del anillo mitral y del ventrículo izquierdo similar al caso reportado por Nakajima et al.8.
El CTS se asocia comúnmente con anomalías del retorno venoso en forma de drenaje venoso pulmonar anómalo parcial o total, el paciente tuvo la variante de tres venas pulmonares derechas y dos izquierdas 9. Los síntomas predominantes fueron disnea, edema de miembros inferiores y palpitaciones, que correspondían a la insuficiencia mitral severa con insuficiencia tricúspidea severa, esta última secundaria a anuloectasia propiciada por la fibrilación auricular a su vez producida por la dilatación del atrio izquierdo por la fisiología obstructiva de la membrana. Se han observado reportes de CTS resaltando que la CF de disnea depende del número y tamaño de las fenestraciones en la membrana atrial 10. Como se observó en la angiotomografía inicial y en la ETE posterior el agujero que dejaba la membrana fue disminuyendo de dimensión, lo que explicaría el diagnóstico tardío y permitiría la avanzada clínica de falla cardiaca derecha.
El gold standard diagnóstico es la ecocardiografía transtorácica 2D 7 y también esta descrita la ETE; porque revelan en tiempo real la dinámica y el tamaño de las fenestras. La angiotomografía proporciona anatomía detallada y se prefiere al cateterismo cardíaco por no ser invasivo 11. El cateterismo determina el gradiente entre ambas cámaras, siendo el promedio de 20 a 25 mmHg 7 en el paciente el gradiente calculado fue de 5 mmHg. Se ha reportado el diagnóstico prenatal asociado a derrame pericárdico evaluado durante el primer trimestre de la gestación 12.
Existen dos abordajes quirúrgicos, si la cámara posterosuperior está agrandada (niños grandes y adultos) se incide por la parte derecha de la cámara detrás del surco interauricular. Cuando la cámara posterosuperior es pequeña y la aurícula derecha es grande (neonatos infantes) es preferible abordarlo por el atrio derecho 7. El paciente fue abordado por el atrio derecho para facilitar la anuloplastia tricúspidea. La tasa de supervivencia a 5 y 15 años es 96 y 88% respectivamente 1. Está descrita la reestenosis cuando la resección de la membrana es incompleta.
En conclusión, se reporta el presente caso al ser una cardiopatía congénita acianotica de muy rara frecuencia, más aún infrecuente en su debut en la edad adulta.
Fuente de financiamiento: Autofinanciado.
Citar como: Conde Salazar JL, Sisniegas Razón AJ, Soplopuco Palacios F. Cor triatriatum sinister, reporte de un caso. Arch Peru Cardiol Cir Cardiovasc. 2022;3(4). doi: 10.47487/apcyccv.v3i4.243.
Este artículo fue presentado a la VI Jornada Anual de casos clínicos para residentes del INCOR.
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2 Manenti A Sighinolfi P Mattioli A Deep Inside Pathology of Cor Triatriatum Sinister Ann Thorac Surg 2021 112 3 1038 1039 10.1016/j.athoracsur.2020.10.074 33485916
3 Zheng Y Zhu W Huang X Lin D Loeffler's group 2 cor triatriatum sinistrum with mobile left atrial thrombus - a case report and literature review Cardiol Young 2021 31 4 666 668 10.1017/S1047951120004497 33323159
4 Kokotsakis J Anagnostakou V Almpanis G Paralikas I Nenekidis I Kratimenos T Prapa E Tragotsalou N Lioulias A Mazarakis A Cor triatriatum presenting as heart failure with reduced ejection fraction a case report J Cardiothorac Surg 2011 6 83 83 10.1186/1749-8090-6-83 21672201
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6 Rudiene V Hjortshoj CMS Glaveckaite S Zakarkaite D Petrulioniene Z Gumbiene L Cor triatriatum sinistrum diagnosed in the adulthood a systematic review Heart 2019 105 15 1197 1202 10.1136/heartjnl-2019-314714 31171629
7 Kouchoukos NT Blackstone EH Hanley FL Cor Triatiatum.Kirklin / Barratt-Boyes Cardiac Surgery. 4ta ed 2012 Oxford Elsevier Books
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12 Cánovas E Cazorla E Alonzo MC Jara R Álvarez L Beric D Prenatal diagnosis of cor triatriatum sinister associated with early pericardial effusion A case report World J Clin Cases 2021 9 17 4395 4399 10.12998/wjcc.v9.i17.4395 34141806
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PMC010xxxxxx/PMC10241329.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
10.47487/apcyccv.v3i4.236
Original Article
Acute and long-term success of ventricular tachycardia ablation in patients with ischemic heart disease in a Mexican center
https://orcid.org/0000-0002-9548-4428
Cueva-Parra Angel 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0003-3861-0560
Neach-De La Vega Diego 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0002-2584-925X
Yañez-Guerrero Paola 1 Medical trainee
https://orcid.org/0000-0002-6650-6042
Bustillos-García Gabriela 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0002-9176-7885
Gómez-Flores Jorge 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0002-2048-1206
Levinstein Moisés 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0001-5942-3081
Morales José L. 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0002-9118-3409
Iturralde-Torres Pedro 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0001-7294-7330
Márquez Manlio F. 1 Cardiologist, electrophysiologist
https://orcid.org/0000-0001-9903-6537
Nava Santiago 1 Cardiologist, electrophysiologist
1 Departamento de Electrofisiología, Instituto Nacional de Cardiología «Ignacio Chávez». Mexico City, Mexico. Departamento de Electrofisiología Instituto Nacional de Cardiología «Ignacio Chávez Mexico City Mexico
Correspondence: Santiago Nava Juan Badiano 1, Belisario Domínguez Sec. 16, Tlalpan, 14080 Ciudad de México, CDMX, [email protected], +52 5514519574
Authors contribution: ACP: conception of the idea, data collection, data analysis, manuscript writing, literature review. DND, PYG: data collection, data analysis, manuscript writing, literature review. GBG: data collection, data analysis, literature review. JGF, ML, JLM: advice, manuscript writing. MFM: advice, data analysis, manuscript writing. PI: review of bibliography and discussion. SN: manuscript writing, literature review, and discussion
Conflict of interests: The authors declare no conflict of interest
31 12 2022
Oct-Dec 2022
3 4 179187
05 10 2022
12 12 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
RESUMEN
Objetivo.
Informar los resultados de la ablación con catéter de taquicardia ventricular (TV) en la cardiopatía isquémica (CI) e identificar los factores de riesgo asociados a la recurrencia en un centro mexicano.
Materiales y métodos
. Se realizó una revisión retrospectiva de los casos de ablación de TV ejecutados en nuestro centro desde 2015 hasta 2022. Se analizó por separado las características de los pacientes y las de los procedimientos y se determinaron los factores asociados a la recidiva.
Resultados
. Se realizaron 50 procedimientos en 38 pacientes (84% varones; edad media 58,1 años). La tasa de éxito agudo fue del 82%, con un 28% de recurrencia. Sexo femenino (OR 3,33, IC 95% 1,66-6,68, p=0,006); fibrilación auricular (OR 3,5, IC 95% 2,08-5,9, p=0,012); tormenta eléctrica (OR 2.4, IC 95% 1.06-5.41, p =0,045); la clase funcional mayor que II (OR 2,86, IC 95% 1,34-6,10, p=0,018) fueron factores de riesgo para recurrencia y la presencia de TV clínica en el momento de la ablación (OR 0,29, IC 95% 0,12- 0,70, p=0,004) y el uso de más de dos técnicas de mapeo (OR 0,64, IC 95% 0,48 - 0,86, p=0,013) fueron factores protectores.
Conclusiones.
La ablación de taquicardia ventricular en cardiopatía isquémica ha tenido buenos resultados en nuestro centro. La tasa de recurrencia es similar a lo reportado por otros autores y existen algunos factores asociados a ella.
ABSTRACT
Objective
. To report the results of ventricular tachycardia (VT) catheter ablation in ischemic heart disease (IHD), and to identify risk factors associated with recurrence in a Mexican center.
Materials and methods
. We made a retrospective review of the cases of VT ablation performed in our center from 2015 to 2022. We analyzed the characteristics of the patients and those of the procedures separately and we determined factors associated with recurrence.
Results
. Fifty procedures were performed in 38 patients (84% male; mean age 58.1 years). Acute success rate was 82%, with a 28% of recurrences. Female sex (OR 3.33, IC 95% 1.66-6.68, p=0.006), atrial fibrillation (OR 3.5, IC 95% 2.08-5.9, p=0.012), electrical storm (OR 2.4, IC 95% 1.06-5.41, p=0.045), functional class greater than II (OR 2.86, IC 95% 1.34-6.10, p=0.018) were risk factors for recurrence and the presence of clinical VT at the time of ablation (OR 0.29, IC 95% 0.12-0.70, p=0.004) and the use of more than 2 techniques for mapping (OR 0.64, IC 95% 0.48-0.86, p=0.013) were protective factors.
Conclusions
. Ablation of ventricular tachycardia in ischemic heart disease has had good results in our center. The recurrence is similar to that reported by other authors and there are some factors associated with it.
Keywords:
Ventricular Tachycardia
Ischemic Heart Disease
Catheter Ablation
Palabras clave:
Taquicardia Ventricular
Cardiopatía Isquémica
Ablación con Catéter
==== Body
pmcIntroduction
Ischemic heart disease (IHD) is one of the leading causes of heart failure worldwide and is the most frequent cause of ventricular tachycardia (VT) in patients with structural heart disease 1; in this context, reentry is the most common mechanism of VT. In these patients the dense scar and borderline tissue are common findings, being these areas the most relevant in reentry circuits 1,2.
In 1999 the MUSTT trial demonstrated that catheter ablation reduces VT recurrence in patients with IHD 3. Subsequent studies corroborated these findings and reaffirmed that catheter ablation reduces recurrence of VT 4,5 and also reduced the composite of mortality, hospitalizations, and discharges of the implantable cardioverter defibrillator (ICD) 6,7. Nowadays, catheter ablation is the procedure of choice in patients with structural heart disease and recurrent VT despite optimal medical treatment and in patients with multiple ICD discharges 1,2,8,9. It should be noted that VT ablation in the presence of structural heart disease is a very complex procedure that requires an electroanatomic mapping system 10.
Because IHD in our country is highly prevalent, as it is worldwide, we considered appropriate to report our experience in VT ablation in patients with IHD and to determine which factors are associated with VT recurrence.
Materials and methods
We performed a descriptive, retrospective study of patients older than 18 years diagnosed with IHD who underwent electrophysiological study and catheter ablation from January 2015 through April 2022 at the Instituto Nacional de Cardiología Ignacio Chavez in México city.
Patients were included if they had a diagnosis of IHD and VT documented by 12-lead electrocardiogram or ICD review, with indication of ablation according to current international guidelines and with informed consent. Patients were cataloged as having IHD if they had a history of acute myocardial infarction (AMI), with or without revascularization, significant coronary disease (diagnosed by coronary angiography or by tomography) or if they had evidence of ischemia or fibrosis with an ischemic pattern in other imaging studies (cardiac magnetic resonance imaging or nuclear medicine studies). Patients without evidence of ischemia or without fibrosis with ischemic pattern or with mixed pattern were excluded. Clinical VT was defined as the VT documented before ablation on a 12-lead ECG or diagnosed after ICD revision. Electrical storm was defined as the presence of 3 or more episodes of sustained VT spaced at least 5 minutes apart in a 24-hour period.
All electrophysiological studies were performed using an electroanatomical mapping system CARTO (Biosense - Webster, Diamond Bar, California) or ENSITE (St. Jude Medical, St. Paul, Minnesota), all studies were performed either with general anesthesia or sedation. In all cases, a decapolar catheter was placed in the coronary sinus and a quadripolar catheter in the right ventricle apex (RVA). Access to the left ventricle (LV) was performed by retro-aortic way or by transseptal puncture. If necessary, the epicardial approach was obtained in the conventional manner previously described 11. Unfractionated heparin infusion was administered to maintain an activated clotting time (ACT) between 250 to 350 seconds.
At the beginning of the procedure, we performed pacing maneuvers from the catheter positioned at the RVA. We used conventional mapping techniques, if the VT was hemodynamically tolerated, we performed activation mapping or entrainment, but if it was not hemodynamically tolerated, substrate-guided mapping or pace mapping was used. The combination of these techniques was performed at the discretion of the operator.
Regarding the ablation, we employed techniques previously described 1,2 such as homogenization, dechanelling or linear ablation to achieve an adequate substrate modulation, the combination of these techniques was performed according to the operator discretion. All ablations were performed using an irrigated radiofrequency catheter. The radiofrequency application was guided by impedance drop (>10 ohms) for a variable time, between 20 and 60 seconds at the discretion of the operator.
After the ablation, we perform the same stimulation maneuvers from the RVA catheter, an ablation was classified as successful if at the end of the procedure it was not possible to induce the clinical VT. A major complication was defined as one that prolonged the hospital stay and/or required another procedure to solve it. Recurrence was defined as the reappearance of clinical VT for more than 30 seconds (with the same morphology and/or the same cycle length) either by a 12-lead ECG, 24-hour Holter or by ICD review.
After VT ablation, patients discharge depended on their clinical condition, and could be discharged the day after the ablation or remaining hospitalized until all their clinical problems were resolved. After discharge, a first assessment was made 3 months, and then every 6 or 12 months, all patients with ICDs received a follow-up every 6 months. A 24-hour Holter study were performed according to the clinical judgment of the treating physician.
Statistical analysis
Being aware that the recurrence of tachycardia greatly impacts the patient's prognosis. After the follow-up, we studied which variables were associated with recurrence. To do this, we studied the most important variables, both clinical and associated with the electrophysiological study and ablation. Among the clinical variables studied are age, sex, LVEF, NYHA functional class, smoking, systemic arterial hypertension, diabetes mellitus, dyslipidemia, obesity, chronic kidney disease, previous AMI, atrial fibrillation, the use of drugs, presentation as electrical storm. Among the variables related to the electrophysiological study are the number of mapping techniques, use during intracardiac echocardiography, presence of clinical VT during ablation, presence of VT other than clinical, and type of anesthesia.
The data was analyzed in the IBM SPSS statistics database software. Baseline characteristics were presented as numbers and percentages for categorical variables and mean and standard deviation (±SD) for continuous variables. We entered the data of the aforementioned variables in double entry tables (2x2) where the results of patients with recurrence and without recurrence were compared to find the Odds ratio and the 95% confidence interval. For this analysis we only consider the first procedure for each patient, (38 procedures). Data obtained from re-ablations were not included for statistical analysis
Results
From 2015 to 2022, we performed 50 electrophysiological studies and VT ablation in 38 patients with IHD. The mean age was 58.08 ± 9.12 years, and 84.2% were male. Mean left ventricle ejection fraction (LVEF) was 33.09 ± 12.3%, most patients were in functional class NYHA II and the average QRS length in sinus rhythm was 124.7 ± 34.3 msec. Other clinical characteristics of the patients are shown in Table 1.
Table 1 Basal characteristics of the patients
Patients (n=38)
n %
Principal characteristics
Male sex 32 84.2
Age (y) (mean ± SD) 58.1 ± 9.1
LVEF (%)(mean ± SD) 33.1 ± 12.3
Electrical storm 10 26.3
ICD before the ablation 14 36.8
Functional class
NYHA I 11 29.0
NYHA II 20 52.6
NYHA III 6 15.8
NYHA IV 1 2.6
Coronary risk factors
Systemic arterial hypertension 17 44.7
Diabetes Mellitus 16 42.1
Smoking 14 36.8
Dyslipidemia 10 26.3
Chronic kidney disease 9 23.7
Obesity 7 18.4
Cardiovascular history
Prior AMI 34 88.5
PCI 22 57.9
CABG 5 13.2
Atrial fibrillation 3 7.9
Cardiovascular drugs
Betablockers 25 65.8
ACEI / ARB / ARNI 23 60.5
MRA 14 36.7
ASA 31 81.6
P2Y12 inhibitor 21 55.3
OAC 10 26.3
Amiodarone 25 65.8
AMI=acute myocardial infarction, ACEI= Angiotensin-converting enzyme inhibitor, ARB=Angiotensin receptor blocker, ARNI= angiotensin receptor neprilysin inhibitor, ASA=acetylsalicylic acid, CBAG=coronary artery bypass grafting ICD=implantable cardioverter defibrillator, LVEF= left ventricle ejection fraction, MRA=Mineralocorticoid receptor antagonist, PCI= percutaneous coronary intervention, OAC=oral anticoagulant. SD= standard deviation
Three patients had paroxysmal atrial fibrillation (AF), five patients had another arrythmia, two of them typical atrial flutter and the others atrial ventricle node reentry tachycardia (AVNRT). Thirty seven percent of patients had an ICD prior to ablation. The median follow-up time was 13 months (IQ range 18.9 moths), during the follow-up 3 patients died.
Clinical VT was documented in a 12-lead electrocardiogram in 32 patients (81.5%). The mean cycle length (CL) was 410 ± 85.1 msec. The mean QRS duration was 150.7 ± 34.8 msec. Most patients (57.8%) presented VT with right-bundle branch block morphology (Table 2). Figure 1 shows the electroanatomical reconstruction of the left ventricle with abnormal potentials of a patient with inferior infarction, as well as his clinical VT with left bundle branch block morphology.
Table 2 Ventricular tachycardia characteristics
Characteristic Mean Range
Cycle length 410 ± 85.1 ms 240 - 595
QRS wide 150.7 ± 34.8 ms 120 - 220
Morphology Number Percentage
LBBB morphology 8 21.0%
RBBB morphology 22 57.9%
V1 Isoelectric 1 2.6%
LBBB Left bundle branch block. RBBB Right bundle branch block.
Figure 1 A. Voltage map showing a dense scar on the posterior wall of the left ventricle (yellow arrow), fragmented potentials were found in this area (white arrow). Ablation was performed with the substrate modulation technique. This study corresponds to a 49-year-old male patient with dilated ischemic cardiomyopathy. B. Clinical VT of the patient, the QRS is negative in V1 with superior axis, its origin was from the lower basal wall of the left ventricle.
We performed 50 procedures, 28 patients underwent only one ablation procedure, 9 patients underwent two, and one patient underwent up to four ablations. Retro-aortic access was the most used and most procedures were performed under general anesthesia. The characteristics of the procedures are shown in Table 3.
Table 3 Characteristics of the procedures
Procedures (n=50)
n %
Principal Characteristics
Retro aortic access 44 88.0
General anesthesia 15 30.0
Epicardial approach 3 6.0
Clinical VT at the procedure 33 66.0
No clinical VT at the procedure 13 26.0
ICE 13 26.0
Outcomes
Acute success 41 82.0
Recurrence 14 28.0
Major complications 9 18.0
Electroanatomical mapping
CARTO® 45 90.0
ENSITE® 5 10.0
Mapping technique
Mapping by substrate 43 86.0
Activation mapping 29 58.0
Pace mapping 21 42.0
Entrainment 3 6.0
More than one technique 35 70.0
More than two techniques 9 18.0
ICE=Intracardiac echocardiography, VT= Ventricular tachycardia.
Considering only the first ablation procedure for each patient, the acute success rate was 82% and the recurrence rate during follow-up was 33%, but the recurrence rate decreased to 28%, including re-ablations. There were nine cases with a major complication: four patients had atrioventricular block during the procedure as a result of the application of radiofrequency in regions with Purkinje potentials, three patients had complications related to vascular access (a significant hematoma, an arterial pseudo aneurysm, and a dissection of the iliac artery), one patient presented a significant pericardial effusion, and the last one died due to incessant VT.
The most frequent mapping technique was substrate-guided mapping (Table 3). We use entrainment only in 3 cases (6%), because most patients did not tolerate tachycardia due to hemodynamic decompensation. Once the myocardial sites involved in the tachycardia circuits were identified, substrate modulation was performed using the scar homogenization technique to eliminate fragmented and late potentials. Likewise, decanalization or linear ablation was performed in cases where the circuit was well defined.
The analysis of factors associated with VT recurrence showed that female sex (OR 3.33, IC 95% 1.66-6.68, p=0.006), atrial fibrillation (OR 3.5, IC 95% 2.08-5.9, p=0.012), NYHA functional class > II (OR 2.4, IC 95% 1.06-5.41, p=0.045) and electrical storm (OR 2.4, IC 95% 1.06-5.41, p=0.045) were the main risk factors for recurrence. (Table 4).
Table 4 Factors associated with ventricular tachycardia recurrence
Factor No recurrence (n=25) Recurrence (n = 13) OR IC 95% p value
Age 58.2 ± 8.7 57.85 ± 10.25
Age > 65 5 (20.0%) 4 (30.7%) 1.43 0.58 - 3.54 0.459
Female 1 (4.2%) 5 (44.4%) 3.33 1.66 - 6.68 0.006
LVEF 31.9 ± 12.4 33.7 ± 12.5%
LVEF < 35% 14 (56.0%) 8 (61.5%) 1.16 0.47 - 2.89 0.743
NYHA 1.9 ± 0.7 2.2 ± 0.7
NYHA > II 2 (8%) 5 (44.4%) 2.86 1.34 - 6.10 0.018
Smoking 10 (40.0%) 4 (30.7%) 0.76 0.29 - 2.01 0.516
Systemic arterial hypertension 11 (44.0%) 6 (46.2%) 1.06 0.44 - 2.55 0.899
Diabetes Mellitus 12 (48.0%) 4 (30.7%) 0.61 0.23 - 1.63 0.307
Dyslipidemia 6 (24.0%) 4 (30.7%) 1.24 0.49 - 3.14 0.653
Obesity 5 (20.0%) 2 (11.1%) 0.81 0.23 - 2.83 0.728
Chronic kidney disease 5 (20.0%) 4 (30.7%) 1.43 0.58 - 3.54 0.459
Previous AMI 22 (88.0%) 12 (92.3%) 1.41 0.25 - 8.12 0.681
AF 0 (0.0%) 3 (23.7%) 3.5 2.08 - 5.9 0.012
Betablockers 19 (76.0%) 6 (46.2%) 0.45 0.19 - 1.05 0.066
ACEI / ARB / ARNI 14 (56.0%) 9 (69.23%) 1.47 0.55 - 3.90 0.429
MRA 9 (26.0%) 5 (44.4%) 1.0 0.44 - 2.63 0.881
Amiodarone 16 (64.0%) 9 (69.23%) 1.17 0.45 - 3.07 0.747
Electrical storm 4 (16.0%) 6 (46.2%) 2.4 1.06 - 5.41 0.045
>1 technique for mapping 18 (72.0%) 7 (53.8%) 0.61 0.26 - 1.43 0.263
>2 technique for mapping 9 (36.0%) 0 (0.0%) 0.64 0.48 - 0.86 0.013
ICE 5 (20.3%) 1 (11.1%) 0.56 0.09 - 3.66 0.519
Clinical VT at the procedure 21 (84.0%) 5 (44.4%) 0.29 0.12 - 0.70 0.004
No clinical VT 6 (24.0%) 4 (30.7%) 1.24 0.49 - 3.14 0.456
General Anesthesia 7 (28.0%) 1 (7.7%) 0.31 0.05 - 2.04 0.145
LVEF= left ventricle ejection fraction, AMI = Acute myocardial infarction, AF= Atrial fibrillation , ACEI= Angiotensin-ACEI: Angiotensin converting enzyme inhibitor, ARB=Angiotensin receptor blocker, ARNI= angiotensin receptor neprilysin inhibitor, MRA=Mineralocorticoid receptor antagonist, ICE=Intracardiac echocardiography, VT= Ventricular tachycardia.
On the other hand, within the variables related to ablation, the presence of the clinical VT at the time of the procedure either spontaneously or induced was associated with less recurrence (OR 0.29, IC 95% 0.12-0.70, p=0.004). Figure 2 highlights the importance of mapping during tachycardia. This figure shows an electroanatomical reconstruction of the left ventricle performed during tachyardia, where it was possible to identify its critical isthmus, which allowed us to stop it with a single application of radiofrequency. Consequently, the fact of being able to use multiple techniques in the presence of clinical VT reduced the recurrence rate, the use of more than two mapping techniques was also a protective factor (OR 0.64, IC 95% 0.48-0.86, p=0.013) (Table 4).
Figure 2 A. Clinical VT of a 62-year-old male with arterial hypertension and diabetes mellitus and multiarterial coronary artery disease. B. Pre-ablation position, the ablation catheter is positioned at the critical point of the tachycardia, where diastolic potentials are observed. The artifact of radiofrequency starts is also observed. C. TV stop after 2.9 seconds of RF start. D. Voltage map on an unconventional scale, where the critical isthmus of TV is identified, right in the middle of the isthmus is the ablation point that managed to end TV.
Discussion
Our series is the largest TV ablation series in IC in Mexico. with a high acute success rate, greater than 80%, and an acceptable recurrence rate of 28%, like that reported by other authors in industrialized countries. Likewise, we found that there are some risk factors associated with VT recurrence and also the existence of protective factors such as the presence of clinical VT during ablation and the use of more than two mapping techniques. These findings highlight the importance of induction of VT during the electrophysiological study in case it is not present spontaneously and the importance of mapping during clinical VT.
Our findings have some similarities and some differences compared to those published by other authors. For example, Di Biase et al. and Nakara et al. in their respective series of patients with IHD and VT, found that, most patients were male, but the LVEF was lower compared to our population 12,13. Also, the percentage of systemic arterial hypertension and diabetes mellitus was lower in our study compared to others from USA and Europe 12-16.
An important aspect is that not all the patients in our study have a history of previous AMI, since some patients had only chronic stable angina and even in a minority, VT was the first manifestation of IHD. This is an important difference compared to studies conducted in industrialized countries where all patients with IHD and VT have a history of AMI.
Ischemic cardiomyopathy scars have a sub-endocardial distribution but can also have a transmural extension, which can make ablation challenging 2,8. The endocardial-epicardial approach in patients with IHD generates better results in the follow-up since it reduces recurrence 12,17. Because the epicardial approach is not free of complications, we use this approach only in cases when the electrocardiogram suggested an epicardial origin, in our series, the epicardial approach was necessary in three patients with no recurrences.
It is necessary to use an electroanatomic mapping system in patients with ischemic cardiomyopathy, which will allow us to define the areas of dense scar, normal myocardium, and borderline tissue 8,9. On the other hand, there are multiple types of strategies that can be used for mapping, one of the techniques that has proven to be very useful is mapping guided by substrate, which consists of identify areas with local abnormal ventricular activities (LAVA) and apply radiofrequency in these areas until the disappearance or dissociation of these potentials 14-17. This technique was the most used in our study and in all cases, we performed substrate modulation.
The success rate, and major complications in our study were similar to that reported by Vergara et al. 18. Previously, Wolf et al. 15 reported in a series of 57 patients with IHD and VT who were taken to ablation that the incidence of clinical VT during the procedure was 73% 15, while in our study was 66%, on the other hand, it should be noted that a significant percentage of patients presented a VT different from the clinical one (26%). This is an important finding since the presence of non-clinical ventricular tachycardias is associated with a poor prognosis in these patients and even with recurrence of clinical VT 15.
Despite advances in VT ablation, recurrence remains one of the major challenges. It remains unknown whether VT recurrence reflects disease progression or failure of the procedure, several reports found than an adverse prognosis depends on clinical variables, such as the inability to eliminate all VTs during the ablation procedure, advanced age, NYHA class and the presence of AF. Also, there is evidence that early recurrences are associated with high risk of adverse prognosis, and the risk decreases gradually with later recurrences 19.
We found that female sex was a significant risk factor for VT recurrence, however in the VISTA Randomized Multicenter Trial no statistically significant differences were found between both sexs 14. The incidence of ventricular arrhythmias is usually lower in women than in men with ischemic heart disease, but women experience greater adverse effects with optimal medical therapy, which can lead to its abandonment, and this could be associated with an increase in recurrence of the VT, however this is only a hypothesis since women are underrepresented in most clinical trials 20,21. It has also been reported that diabetes mellitus is a risk factor for recurrence, however despite that in our population the prevalence of diabetes was higher compared to other studies, it did not influence recurrence 14,19. Unlike other studies, we did not find differences in terms of age.
One of the very common comorbidities in patients with IHD is AF, which reflex atrial fibrosis. In the context of IHD, AF can reflex more advanced disease and could be associated with greater recurrence, it is also known that the association of these two pathologies worsens the prognosis of patients. Like Siontis et al., we found that AF increase the recurrence rate 19.
Another factor that had a significant impact on recurrence was the presence of clinical VT during the procedure. This suggests that, although hemodynamically poorly tolerated in many cases, it is always important to try to induce clinical VT to improve success and reduce recurrence. Previously, it had been reported that the presence of clinical VT at the time of ablation could be associated with less recurrence 19. Likewise, the presence of clinical VT allows the use of more mapping techniques, such as entrainment or activation mapping 1,2. In this series, there were 9 patients in which we used more than two mapping techniques, none of whom presented recurrence.
Haanschoten et al. (22 also reported that failure of antiarrhythmic drugs, total revascularization, ablation type and electrical storm before ablation were important factors of recurrence. In our series we find an association between the electrical storm and recurrence but not with the revascularization and the ablation type, which may be due to the size of the sample and differences between populations. Another important factor for recurrence and mortality is the low LVEF, which is part of the novel predictive score for survival and recurrence proposed by Vergara et al in 2018, despite that, we did not find a statistically significant association between the LVEF and recurrence 23, but if we find a relationship between the functional class and the recurrence, the NYHA functional class greater than 2 was associated with greater recurrence, this finding is consistent with that previously reported by other authors 19.
Finally, in patients with previous AMI, is very important to assess transmural scar with imaging studies (resonance or nuclear medicine) since in the group of patients with transmural infarction, the epicardial approach has been associated with less recurrence 24. Unfortunately, in our series few patients underwent epicardial ablation.
This study has several limitations: the inherent limitations of a retrospective analysis, the fact that was a single-center observational study which included a small sample of patients, that limits the statistical power to detect independent predictors of mortality and recurrence. Another important limitation is the low rate of epicardial approaches performed. Is important to consider the development of the operators’ skills, since their experience has increased over the years.
In conclusion VT catheter ablation in IHD has a good effectiveness and a relatively low recurrence rate despite some major complications. In Mexican population, female sex, atrial fibrillation, electrical storm, NYHA functional class greater than 2 increase the rate of recurrence, while the presence of the clinical VT during the ablation and the use of more than 2 techniques for mapping procedure are protective factors.
Financing: This research has not received any specific grant from agencies in the public, commercial, or non-profit sectors
Cite as: Cueva-Parra A, Neach-De La Vega D, Yañez-Guerrero P, Bustillos-García G, Gómez-Flores J, Levinstein M, et al. Acute and long-term success of ventricular tachycardia ablation in patients with ischemic heart disease in a Mexican center. Arch Peru Cardiol Cir Cardiovasc. 2022;3(4). doi: 10.47487/apcyccv.v3i4.236
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References
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6 Sapp JL Wells GA Parkash R Stevenson WG Blier L Sarrazin JF Ventricular Tachycardia Ablation versus Escalation of Antiarrhythmic Drugs N Engl J Med 2016 375 2 111 121 10.1056/NEJMoa1513614 27149033
7 Kuck KH Tilz RR Deneke T Hoffmann BA Ventura R Hansen PS Impact of Substrate Modification by Catheter Ablation on Implantable Cardioverter-Defibrillator Interventions in Patients With Unstable Ventricular Arrhythmias and Coronary Artery Disease Results From the Multicenter Randomized Controlled SMS (Substrate Modification Study) Circ Arrhythm Electrophysiol 2017 10 3 e004422 10.1161/CIRCEP.116.004422 28292751
8 Al-Khatib SM Stevenson WG Ackerman MJ Bryant WJ Callans DJ Curtis AB 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death Executive summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society Heart Rhythm 2018 15 10 e190 e252 10.1016/j.hrthm.2017.10.035 29097320
9 Vergara P Roque C Oloriz T Mazzone P Della Bella P Substrate mapping strategies for successful ablation of ventricular tachycardia a review Arch Cardiol Mex 2013 83 2 104 111 10.1016/j.acmx.2013.02.001 23668956
10 Kim YH Chen SA Ernst S Guzman CE Han S Kalarus Z 2019 APHRS expert consensus statement on three-dimensional mapping systems for tachycardia developed in collaboration with HRS, EHRA, and LAHRS J Arrhythm 2020 36 2 215 270 10.1002/joa3.12308 32256872
11 Sosa E Scanavacca M d'Avila A Pilleggi F A new technique to perform epicardial mapping in the electrophysiology laboratory J Cardiovasc Electrophysiol 1996 7 6 531 536 10.1111/j.1540-8167.1996.tb00559.x 8743758
12 Di Biase L Santangeli P Burkhardt DJ Bai R Mohanty P Carbucicchio C Endo-epicardial homogenization of the scar versus limited substrate ablation for the treatment of electrical storms in patients with ischemic cardiomyopathy J Am Coll Cardiol 2012 60 2 132 141 10.1016/j.jacc.2012.03.044 22766340
13 Nakahara S Tung R Ramirez RJ Michowitz Y Vaseghi M Buch E Characterization of the arrhythmogenic substrate in ischemic and nonischemic cardiomyopathy implications for catheter ablation of hemodynamically unstable ventricular tachycardia J Am Coll Cardiol 2010 55 21 2355 2365 10.1016/j.jacc.2010.01.041 20488307
14 Di Biase L Burkhardt JD Lakkireddy D Carbucicchio C Mohanty S Mohanty P Ablation of Stable VTs Versus Substrate Ablation in Ischemic Cardiomyopathy The VISTA Randomized Multicenter Trial J Am Coll Cardiol 2015 66 25 2872 2882 10.1016/j.jacc.2015.10.026 26718674
15 Wolf M Sacher F Cochet H Kitamura T Takigawa M Yamashita S Long-Term Outcome of Substrate Modification in Ablation of Post-Myocardial Infarction Ventricular Tachycardia Circ Arrhythm Electrophysiol 2018 11 2 e005635 10.1161/CIRCEP.117.005635 29440186
16 Briceño DF Romero J Villablanca PA Londoño A Diaz JC Maraj I Long-term outcomes of different ablation strategies for ventricular tachycardia in patients with structural heart disease: systematic review and meta-analysis Europace 2018 20 1 104 115 10.1093/europace/eux109 28575378
17 Tung R Michowitz Y Yu R Mathuria N Vaseghi M Buch E Epicardial ablation of ventricular tachycardia an institutional experience of safety and efficacy Heart Rhythm 2013 10 4 490 498 10.1016/j.hrthm.2012.12.013 23246598
18 Vergara P Trevisi N Ricco A Petracca F Baratto F Cireddu M Late potentials abolition as an additional technique for reduction of arrhythmia recurrence in scar related ventricular tachycardia ablation J Cardiovasc Electrophysiol 2012 23 6 621 627 10.1111/j.1540-8167.2011.02246.x 22486970
19 Siontis KC Kim HM Stevenson WG Fujii A Bella PD Vergara P Prognostic Impact of the Timing of Recurrence of Infarct-Related Ventricular Tachycardia After Catheter Ablation Circ Arrhythm Electrophysiol 2016 9 12 e004432 10.1161/CIRCEP.116.004432 27923805
20 Lam CSP Arnott C Beale AL Chandramouli C Hilfiker-Kleiner D Kaye DM Sex differences in heart failure Eur Heart J 2019 40 47 3859 3868c 10.1093/eurheartj/ehz835 31800034
21 Ehdaie A Cingolani E Shehata M Wang X Curtis AB Chugh SS Sex Differences in Cardiac Arrhythmias Clinical and Research Implications Circ Arrhythm Electrophysiol 2018 11 3 e005680 10.1161/CIRCEP.117.005680 29874167
22 Haanschoten DM Smit JJJ Adiyaman A Ramdat Misier AR Hm Delnoy PP Elvan A Long-term outcome of catheter ablation in post-infarction recurrent ventricular tachycardia Scand Cardiovasc J 2019 53 2 62 70 10.1080/14017431.2019.1601253 30916588
23 Vergara P Tzou WS Tung R Brombin C Nonis A Vaseghi M Predictive Score for Identifying Survival and Recurrence Risk Profiles in Patients Undergoing Ventricular Tachycardia Ablation The I-VT Score Circ Arrhythm Electrophysiol 2018 11 12 e006730 10.1161/CIRCEP.118.006730 30562104
24 Acosta J Fernández-Armenta J Penela D Andreu D Borras R Vassanelli F Infarct transmurality as a criterion for first-line endo-epicardial substrate-guided ventricular tachycardia ablation in ischemic cardiomyopathy Heart Rhythm 2016 13 1 85 95 10.1016/j.hrthm.2015.07.010 26165946
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PMC010xxxxxx/PMC10241331.txt |
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Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37283604
10.47487/apcyccv.v3i2.207
Reporte De Caso
Amiloidosis cardíaca: reporte de caso
Cardiac amyloidosis: a case reporthttp://orcid.org/0000-0003-1018-8695
Viñas-Mendieta Adriana E. 1 Residente de cardiología clínica
http://orcid.org/0000-0002-3534-5119
García-Saavedra Mario B. 1 Médico cardiólogo
1 . Centro Médico Naval ‘’Cirujano Mayor Santiago Távara’’. Bellavista, Lima-Perú. Centro Médico Naval ‘’Cirujano Mayor Santiago Távara Bellavista, Lima Perú
Correspondencia Adriana E. Viñas-Mendieta. Av. Venezuela cdra. 24 S/N, Bellavista-Callao. (+51) 990021887. [email protected]
Conflictos de interés: Ninguno
Contribuciones de los autores: Todos los autores participaron en la elaboración, redacción y corrección del manuscrito.
27 6 2022
Apr-Jun 2022
3 2 121126
11 5 2022
29 6 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
RESUMEN
La amiloidosis es una enfermedad multisistémica por infiltración de proteínas mal plegadas, la afectación cardíaca determina su pronóstico. Existen varios tipos de proteínas precursoras capaces de causar la enfermedad; sin embargo, solo dos afectan al corazón, por cadenas ligeras de inmunoglobulina clonales (AL) y proteína tetramérica transtiretina (TTR). Es una enfermedad subdiagnosticada y que, en etapas tardías, es de mal pronóstico. Se presenta el caso de un paciente adulto mayor con manifestaciones cardíacas y extracardíacas progresivas, así como criterios laboratoriales y ecocardiográficos que nos acercaron al diagnóstico de amiloidosis cardíaca y que además permitió valorar el pronóstico. Tuvo una evolución tórpida y con desenlace mortal. Los estudios de anatomía patológica permitieron confirmar la presunción diagnóstica.
ABSTRACT
Amyloidosis is a multisystem disease caused by infiltration of misfolded proteins; cardiac involvement determines its prognosis. There are several types of precursor proteins capable of causing the disease; however, only two affect the heart, clonal immunoglobulin light chains (AL) and tetrameric transthyretin (TTR) protein. It is an underdiagnosed disease and, in late stages, it has a poor prognosis. We present the case of an older adult patient with progressive cardiac and extracardiac manifestations, as well as laboratory and echocardiographic criteria that brought us closer to the diagnosis of cardiac amyloidosis and also allowed us to assess the prognosis. The patient had a torpid evolution with a fatal outcome. The pathological anatomy studies allowed us to confirm our diagnostic presumption.
Palabras clave:
Amiloidosis
Insuficiencia Cardíaca
Ecocardiografía
Keywords:
Amyloidosis
Heart Failure
Echocardiography
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pmcINTRODUCCIÓN
La amiloidosis cardíaca (AC) es una miocardiopatía infiltrativa, cuya incidencia es incierta por ser una entidad poco reportada en Perú y Latinoamérica. Suele ser subdiagnosticada y, en etapas avanzadas, de pronóstico sombrío. La presentación clínica más frecuente es falla cardíaca, la mortalidad en nuestro país es aproximadamente 25% por año 1. Existen más de 30 proteínas precursoras, las más frecuentes que afectan al corazón son por cadenas ligeras (AL) y proteína tetramérica transtiretina (TTR) con dos subtipos hereditaria (ATTRh) y salvaje (ATTRwt) 2.
REPORTE DE CASO
Varón de 79 años natural de Lima-Perú, antecedente de insuficiencia venosa periférica. Ingresa por emergencia con tiempo de enfermedad de un mes, de inicio insidioso, caracterizado por disnea progresiva NYHA IV, y edema de miembros inferiores; una semana antes de su ingreso, ortopnea y bendopnea. Además, presentaba episodios frecuentes de epistaxis, parestesias y dolor tipo hincada en miembros inferiores.
Al examen físico presenta presión arterial 110/60, frecuencia cardíaca 70 por minuto, frecuencia respiratoria 25 por minuto, SatO2 95%, sin signos de hipoperfusión periférica, edemas +++/+++ en miembros inferiores y en pared abdominal. Murmullo vesicular abolido en ambas bases pulmonares y crepitantes bilaterales. A nivel cardiovascular, ruidos cardíacos rítmicos de baja intensidad S3(-), S4(-), ingurgitación yugular de segundo grado, reflujo hepatoyugular, soplo sistólico mitral III/VI irradiado a axila, pulsos periféricos presentes y simétricos. LOTEP, sensibilidad superficial de miembros inferiores, hipoestesia bilateral a predominio distal y ROT +/+++ bilateral.
El electrocardiograma mostró ritmo sinusal, bloqueo completo de rama izquierda y complejos QRS de bajo voltaje en derivaciones del plano frontal (Figura 1). La radiografía de tórax evidenció redistribución de flujo y derrame pleural bilateral. Fue evaluado por neurología y diagnosticado de neuropatía sensitivo motora periférica con electromiografía.
Figura 1 EKG en ritmo sinusal, a frecuencia de 60 por minuto, eje +60°, PR 200ms, complejo QRS 150ms, QT 480ms, bloqueo completo de rama izquierda del haz de his, complejos de bajo voltaje en derivaciones de plano frontal, signos de sobrecarga de ventrículo izquierdo.
La ecocardiografía transtorácica mostró engrosamiento difuso de paredes del ventrículo izquierdo (VI), septum interventricular 22 mm (masa 256 g/m2, grosor parietal relativo 0,8), miocardio de aspecto moteado difuso. Aurículas severamente dilatadas (AI 51 mL/m2 AD 26 cm2) con septum interatrial engrosado y deformación de reservorio francamente disminuida en 7% (Figura 2, 3C). Las cuatro válvulas con velos engrosados, insuficiencia mitral mixta severa con efecto coanda, válvula aórtica normofuncionante AVA 2,3 cm2 por planimetría e insuficiencia tricúspidea moderada con jet central (Figura 2). Ventrículo derecho con paredes engrosadas, función sistólica disminuida TAPSE 12 mm, FE 3D 36,1% y SG -8,6% (Figura 2, 3B). Disfunción diastólica patrón restrictivo E/e´27 y presiones de llenado del VI incrementadas. Vena cava inferior dilatada sin colapso y signos indirectos de hipertensión pulmonar con PSAP aproximada 62 mmhg.
Figura 2 Ecocardiografía transtorácica muestra gran hipertrofia que compromete todas las cavidades cardiacas y válvulas, miocardio hiperrefringente de aspecto infiltrativo. (A) vista paraesternal eje corto a nivel de músculos papilares. (B) Vista apical 4 cámaras con dilatación biauricular, engrosamiento de septum interauricular y paredes libres, ventrículo izquierdo hipertrofiado y con cavidad reducida. (C) Vista apical 3 cámaras. (D) vista apical 4 cámaras modificada enfocada en ventrículo derecho el cual también presenta engrosamiento de su pared libre.
El análisis de mecánica de VI mostró gran deterioro de fibras subendocárdicas por strain longitudinal global (SGL) en -7,6%, mapa polar de deformación sistólica pico con mayor disminución en segmentos basales y medios, gradiente basal-apical relativo (RELAPS) en 1,2 (sensibilidad 93%, especificidad 82% para AC), fracción de eyección 36%, relación FE/SGL 4,75% (>4% característico de AC) e hipocinesia global (Figura 3, video 1).
Figura 3 (A) función de fibras subendocárdicas evaluadas por SGL severamente disminuida en -7.6% con afectación predominante en segmentos medios y basales (RELAPS 1.2). (B) Strain global de ventrículo derecho severamente disminuida en -8.6%. (C) Strain auricular izquierdo muestra gran disminución de la función auricular en sus tres fases reservorio 7%, conducto -6% y contracción activa -1%.
En los exámenes de laboratorio, anemia leve (Hb 10,1 gr/dL), leucopenia (3850 x mm3), bilirrubina total 1,1 mg/dL (0,0-1,0), urea 39,7 mg/dL (16,6-48,5), creatinina 0,8 mg/dL (0,6-1,4), gamma-glutamil transpeptidasa 86 U/L (0,0-71), transaminasa oxalacética 20,4 U/L (0,0-37), transaminasa pirúvica 15 U/L (0,0-41), proteínas totales 6,7 g/dL (6,4-8,3), albúmina 2,97 g/dL (3,5-5,2), globulina 3,7 g/dL (2-3,5), PRO-BNP 3625 pg/mL (<100), Troponina T 0,07 ng/mL (0,0-0,04), alfa fetoproteína 1,2 ng/mL (0,0-7), antígeno carcinoembrionario 2,6 ng/mL (0,0-3,4), CA 72-4 1,7 U/mL (0,0-8,2), CA 19,9 4,5 U/mL (0-39), CYFRA 21-1 1,0 ng/mL (0-3,3), DHL 178 U/L (135-225), cloro 96,5 mmol/L (98-107), potasio 4,2 mmol/L (3,5-5,3), sodio 136,6 mmol/L (135-148), IgA 1251 mg/dL (70-400), IgG 1492 mg/dL (700-1600), IgM 59,4 mg/dL (40-310), Beta 2 microglobulina 2,2 mg/L (0,8-2,2), cadenas ligeras libres séricas kappa 37,7 mg/L (3,3-19,4), lambda 104,8 mg/L (5,7-26,3), relación kappa/lambda 0,3 (0,2-1,6).
Hallazgos clínicos, electrocardiográficos y ecocardiográficos compatibles con miocardiopatía infiltrativa sugestiva de AC. El aumento policlonal de cadenas ligeras descartó el subtipo AL. Por la edad y ausencia de antecedentes familiares nos enfrentamos, probablemente, al subtipo ATTRwt.
Se inició terapia diurética endovenosa con buena respuesta clínica. Una vez compensado, se inició carvedilol escalándose hasta 6,25 mg bid como dosis máxima tolerada y furosemida vía oral 40 mg qd. El paciente, estable NYHA III y sin síntomas ni signos de bajo gasto, fue dado de alta por inicio de la segunda ola de covid-19 con el plan de realizarse ambulatoriamente resonancia cardíaca, gammagrafía ósea y biopsia de tejido periumbilical. Dos semanas después del alta, reingresa a emergencia con choque cardiogénico por falla cardíaca aguda; un familiar mencionó que no tomó medicación indicada; tuvo mala respuesta al tratamiento y evolución tórpida, falleció pocos días después de su ingreso.
Se realizó la necropsia, y las muestras de tejido cardíaco confirmaron el reemplazo de fibras miocárdicas por material eosinofílico amorfo que corresponde a amiloidosis cardíaca (Figura 4).
Figura 4 VAnatomía patológica (A) Anatomía macroscópica de corazón con engrosamiento de pared de VI, elevaciones subendocárdicas en gotas de rocío color gris-rosa en cavidades auriculares (flecha). (B) Presencia de haces de colágeno denso y material acelular que corresponde a fibrillas de amilode, depositado en el espacio extracelular de distribución difusa extensa y perivascular, asociado a sustitución y destrucción parcial de miocitos (H-EX20). (C) Histoquímica con coloración fucsina ácida a nivel del miocardio, presencia de proteínas en el área de depósito amiloide (X10).
DISCUSIÓN
Presentamos el caso de un paciente adulto mayor con AC, basándonos en los hallazgos clínicos y paraclínicos, finalmente confirmado por anatomía patológica. El paciente se encontraba en estadio III de la clínica Mayo y además estadio D de falla cardíaca; dado lo avanzado de su enfermedad y la poca tolerancia a los medicamentos, su evolución fue tórpida y su desenlace mortal.
La AC es una enfermedad multisistémica por infiltración de proteínas mal plegadas, su pronóstico lo determina la afectación cardíaca (3. El subtipo AL es más frecuente en varones, se presenta a edad más temprana que otros subtipos; causada por discrasias sanguíneas asociadas con frecuencia a células plasmáticas en médula ósea, es rápidamente progresiva y de pronóstico sombrío por formación de especies reactivas de oxígeno y disfunción lisosomal 4. Por otro lado, el subtipo ATTR es causado por incremento exagerado en la producción hepática de transtiretina y su depósito a nivel miocárdico, su variante ATTRwt es más frecuente en varones de edad avanzada, lentamente progresiva, como sugiere la clínica de este paciente 3.
La manifestación más frecuente de AC es la falla cardíaca, otras formas de presentación son síncope, hipotensión ortostática, trastornos del sistema de conducción, taquiarritmias supraventriculares y ventriculares, angina o asociarse a valvulopatías como estenosis aórtica 5.
La manifestación extracardíaca más frecuente es polineuropatía sensitivo-motora bilateral de miembros inferiores, el paciente presentó síntomas compatibles con esta entidad desde un año antes de su ingreso confirmado por electromiografía. Otros hallazgos incluyen gastroparesia, síndrome del túnel carpiano, enfermedad renal, proteinuria, sangrado, hematomas, macroglosia, entre otras 6.
Los hallazgos electrocardiográficos tienen baja sensibilidad, pero encontrar voltajes normales en un paciente hipertrófico incrementa sospecha diagnóstica 4. Dentro de los exámenes de laboratorio, los valores de troponinas I y BNP/NTproBNP permiten determinar el estadio de la enfermedad y predecir supervivencia 5. La detección de cadenas ligeras en suero y orina con una relación anormal kappa/lambda genera sospecha de AL con 99% de sensibilidad 7.
Actualmente, la imagen cardíaca multimodal desempeña un rol esencial. La gammagrafía nuclear con radiotrazadores óseos marcados con TC 99 con captación >= grado 2 o relación captación corazón/pulmón contralateral >=1,5 hace el diagnóstico de ATTR con especificidad y valor predictivo positivo cercana al 100%, sin necesidad de biopsia luego de haber descartado la replicación monoclonal 8. Técnicas ecocardiográficas como SGL tienen un rol fundamental para el diagnóstico diferencial, así como para establecer pronóstico y seguimiento 9, se han identificado predictores independientes de mal pronóstico como RELAPS >2, índice FEVI/SGL > 4,1, y SGL menor de -11,78% 8. El paciente cumplió con la mayoría de parámetros asociados con el mal pronóstico.
A pesar de que no se realizó resonancia magnética cardíaca por problemas logísticos y el pronto deceso del paciente, destacamos la importancia de esta técnica de imagen, pues tiene mayor resolución espacial y permite la caracterización tisular 7.
La biopsia endomiocárdica con tinción rojo congo o hematoxilina-eosina es el estándar de oro para confirmar el diagnóstico; biopsias menos invasivas como del tejido periumbilical o mucosa oral tienen sensibilidad variable entre 14 a 90% 5. Es importante tipificar la amiloidosis para el tratamiento específico con inmunohistoquímica, inmunofluorescencia o microdisección láser con espectrometría de masas 3.
Con respecto al manejo sintomático, la restricción de sodio y diuréticos son el pilar fundamental de la terapia descongestiva. La seguridad y eficacia de terapias médica que disminuyen mortalidad en falla cardíaca son limitadas y mal toleradas por un buen porcentaje de pacientes. Estos pacientes tienen mayor riesgo de generar trombos intracavitarios, se considera más seguro el uso de los nuevos anticoagulantes orales 4.
La terapia modificadora de la enfermedad en amiloidosis AL consiste en quimioterapia e inmunoterapia, el esquema CyBorD es de primera elección 10) En ATTR, el tratamiento con tafamidis, en el estudio ATTR-ACT, redujo la mortalidad por todas las causas, cardiovascular, hospitalización cardiovascular y mejoró calidad de vida, sobre todo en etapas tempranas de enfermedad 11.
Existe poca evidencia respecto al uso de terapia con dispositivos; sin embargo, su indicación no es diferente que para los pacientes con falla cardíaca de otra etiología; la supervivencia postrasplante cardíaco es similar al resto de población trasplantada 11.
CONCLUSIONES
Presentamos a un paciente adulto mayor con AC avanzada con síntomas extracardíacos como polineuropatía sensitivo-motora, epistaxis y síntomas progresivos de falla cardíaca; con mala tolerancia al tratamiento y de progresión rápida y mortal. Es una enfermedad generalmente subdiagnosticada y que en etapas avanzadas es de pronóstico sombrío y difícil manejo. Actualmente, contamos con diferentes scores que incluyen datos clínicos, imagenológicos y laboratoriales que permiten un enfoque diagnóstico precoz y además establecer un pronóstico, siendo buenos predictores de mortalidad. Se están realizando importantes avances terapéuticos que conducirán a mejores resultados clínicos.
Consideraciones éticas: Los autores declaran que para la presentación del caso clínico no se realizaron experimentos en humanos ni animales; se han seguido los protocolos del centro sobre la publicación de datos de pacientes. Se declara, además, que en el presente artículo no aparecen datos personales de pacientes
Fuente de financiamiento: Autofinanciado
Citar como: Viñas-Mendieta AE, García-Saavedra MB. Amiloidosis cardíaca: reporte de caso. Arch Peru Cardiol Cir Cardiovasc. 2022;3(2):121-126. doi: 10.47487/apcyccv.v3i2.207
==== Refs
REFERENCIAS BIBLIOGRÁFICAS
1 Moreno JMM Ocmin JA García SE Aguilar C Alarco W Amiloidosis cardíaca Experiencia en un Instituto Cardiovascular de referencia nacionaI Arch Peru Cardiol Cir Cardiovasc 2020 1 2 95 104 10.47487/apcyccv.v1i2.40
2 Rapezzi C Lorenzini M Longhi S Milandri A Gagliardi C Bartolomei I Cardiac amyloidosis the great pretender Heart Fail Rev 2015 20 2 117 124 10.1007/s10741-015-9480-0 25758359
3 Fine NM Davis MK Anderson K Delgado DH Giraldeau G Kitchlu A Canadian Cardiovascular Society/Canadian Heart Failure Society Joint Position Statement on the Evaluation and Management of Patients with Cardiac Amyloidosis Can J Cardiol 2020 36 3 322 334 10.1016/j.cjca.2019.12.034 32145862
4 Witteles RM Liedtke M AL Amyloidosis for the Cardiologist and Oncologist Epidemiology, Diagnosis, and Management JACC CardioOncol 2019 1 1 117 130 10.1016/j.jaccao.2019.08.002 34396169
5 Garcia-Pavia P Rapezzi C Adler Y Arad M Basso C Brucato A Diagnosis and treatment of cardiac amyloidosis a position statement of the ESC Working Group on Myocardial and Pericardial Diseases Eur Heart J 2021 42 16 1554 1568 10.1002/ejhf.2140 33825853
6 Witteles RM Bokhari S Damy T Elliott PM Falk RH Fine NM Screening for Transthyretin Amyloid Cardiomyopathy in Everyday Practice JACC Heart Fail 2019 7 8 709 716 10.1016/j.jchf.2019.04.010 31302046
7 Rapezzi C Aimo A Serenelli M Barison A Vergaro G Passino C Critical Comparison of Documents From Scientific Societies on Cardiac Amyloidosis JACC State-of-the-Art Review J Am Coll Cardiol 2022 79 13 1288 1303 10.1016/j.jacc.2022.01.036 35361352
8 Lee Chuy K Drill E Yang JC Landau H Hassoun H Nahhas O Incremental Value of Global Longitudinal Strain for Predicting Survival in Patients with Advanced AL Amyloidosis JACC CardioOncol 2020 2 2 223 231 10.1016/j.jaccao.2020.05.012 33117993
9 Palladini G Sachchithanantham S Milani P Gillmore J Foli A Lachmann H A European collaborative study of cyclophosphamide, bortezomib, and dexamethasone in upfront treatment of systemic AL amyloidosis Blood 2015 126 5 612 615 10.1182/blood-2015-01-620302 25987656
10 Maurer MS Schwartz JH Gundapaneni B Elliott PM Merlini G Waddington-Cruz M Tafamidis Treatment for Patients with Transthyretin Amyloid Cardiomyopathy N Engl J Med 2018 379 11 1007 1016 10.1056/NEJMoa1805689 30145929
11 Griffin JM Rosenthal JL Grodin JL Maurer MS Grogan M Cheng RK ATTR Amyloidosis Current and Emerging Management Strategies: JACC: CardioOncology State-of-the-Art Review. JACC: CardioOncology JACC CardioOncol 2021 3 4 488 505 10.1016/j.jaccao.2021.06.006 34729521
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PMC010xxxxxx/PMC10241332.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37284565
10.47487/apcyccv.v3i4.249
Reporte De Caso
Progresión cardiaca de amiloidosis sistémica de cadera ligera
Cardiac progression of systemic light chain amyloidosishttps://orcid.org/0000-0002-6376-3051
Chango Azanza Diego Xavier 1 2 Cardiólogo
https://orcid.org/0000-0001-7237-6136
Fernández Tirado Ruth Lizbeth 3 Cardiólogo
https://orcid.org/0000-0001-6524-8558
López Pillaga Valeria Verenisse 3 Cardiólogo
https://orcid.org/0000-0002-0179-5189
Tello Ochoa José David 3 Cardiólogo
https://orcid.org/0000-0003-3922-9702
Pinos Vásquez Javier Fernando 1 2 4 5 Cardiólogo
1 Hospital del Rio, Cuenca, Ecuador. Hospital del Rio Cuenca Ecuador
2 Cardiológico del Austro, Cuenca, Ecuador. Cardiológico del Austro Cuenca Ecuador
3 Universidad Catolica de Cuenca, Cuenca, Ecuador. Universidad Católica de Cuenca Universidad Catolica de Cuenca Cuenca Ecuador
4 Universidad de Cuenca, Cuenca, Ecuador. Universidad de Cuenca Universidad de Cuenca Cuenca Ecuador
5 Hospital Vicente Corral Moscoso, Cuenca, Ecuador. Hospital Vicente Corral Moscoso Cuenca Ecuador
Correspondencia: Dr. Diego Chango Emilio Carrera y 12 de octubre, Cuenca - Ecuador. [email protected]
Contribuciones de los autores: DXCHA y JFPV han participado en la extracción y análisis de los datos, revisión, redacción y aprobación de la versión final del manuscrito. RLFT, VVLP y JDTO realizaron la extracción y análisis de los datos, revisión y redacción del manuscrito.
Conflictos de interés: Los autores declaran no tener conflictos de intereses.
31 12 2022
Oct-Dec 2022
3 4 220225
01 11 2022
28 12 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
RESUMEN
La amiloidosis sistémica de cadena ligera es una enfermedad caracterizada por la acumulación de proteína amiloide en múltiples órganos y sistemas. Presentamos el caso de un paciente de sexo masculino de 52 años con diagnóstico de amiloidosis sistémica de cadena ligera con compromiso cardiaco y renal. Una biopsia renal mostró la presencia de amiloidosis renal asociada a proteinuria. El electrocardiograma inicial mostró microvoltaje en derivaciones frontales discordante con la hipertrofia ventricular izquierda evidenciada en el ecocardiograma transtorácico (ETT). La resonancia magnética cardiaca (RMC) confirmó la presencia de infiltración amiloide cardiaca con un patrón de realce tardío de gadolinio extenso ventricular. A pesar de recibir tratamiento específico sistémico quimioterápico, la evolución no fue favorable a los cuatro meses de seguimiento, con empeoramiento de la infiltración cardíaca, aumento de los biomarcadores y progresión de la disnea. El ETT permitió poner en manifiesto el empeoramiento de parámetros de función diastólica e incremento de los espesores parietales en contexto de la infiltración. El electrocardiograma y ecocardiograma fueron herramientas de fácil acceso que permitieron el monitoreo de la respuesta al tratamiento.
ABSTRACT
Systemic light chain amyloidosis is a disease characterized by the accumulation of amyloid protein in multiple organs and systems. We present the case of a 52-year-old male patient with a diagnosis of systemic light chain amyloidosis associated with cardiac and renal involvement. A renal biopsy showed the presence of renal amyloidosis associated with proteinuria, and the patient was referred for cardiovascular evaluation. The baseline electrocardiogram showed micro voltage in frontal leads that were discordant with the left ventricular hypertrophy evidenced in the transthoracic echocardiogram (TTE). Cardiac magnetic resonance imaging (CMR) confirmed the presence of cardiac amyloid infiltration with a pattern of extensive ventricular late-gadolinium enhancement. Despite being referred and receiving specific systemic chemotherapy treatment, the evolution was not favorable after four months of follow-up with worsening cardiac infiltration, increasing values of biomarkers, and progression of dyspnea. The TTE was useful in revealing the unfavorable evolution and worsening of diastolic function parameters and increased wall thickness in the context of infiltration. The electrocardiogram and echocardiogram were easily accessible tools that allowed the monitoring of the response to treatment.
Palabras clave:
Amiloidosis AL
Insuficiencia Cardiaca
Miocardiopatias
Electrocardiograma
Ecocardiografía
Keywords:
Inmunoglobulin Light-chain Amyloidosis
Heart Failure
Cardiomyopathies
Electrocardiography
Echocardiography
==== Body
pmcINTRODUCCIÓN
La amiloidosis sistémica de cadena ligera es la formación clonal de cadenas ligeras de inmunoglobulinas que sufren alteraciones y que provocan un mal plegamiento provocando su acumulación a nivel sistémico. Está asociada a una discrasia de células plasmáticas, lo que provoca fallas en la contractilidad a nivel miocárdico; además, las cadenas ligeras provocan un efecto miocardio tóxico de forma directa. Representa aproximadamente el 80% de todos los casos de amiloidosis cardiaca, no existe predilección por el sexo, y se cree que existe afectación cardiaca en el 90% de los casos 1-3.
La amiloidosis sistémica de cadena ligera frecuentemente afecta al riñón con presencia de proteinuria en valores nefróticos con o sin la presencia de insuficiencia renal, mientras que en el corazón el principal efecto es la miocardiopatía restrictiva, en donde se puede evidenciar un engrosamiento del septo interventricular y pared ventricular, lo cual se asocia a disfunción tanto sistólica como diastólica, por lo que el paciente puede presentar insuficiencia cardiaca y valores elevados de troponinas y péptidos natriuréticos. Puede existir también compromiso gastrointestinal y del sistema nervioso central. Frecuentemente, dentro de los efectos autonómicos se puede presentar hipotensión postural, impotencia sexual y alteraciones de motilidad gastrointestinal 4-6.
REPORTE DE CASO
Paciente de sexo masculino de 53 años, sin factores de riesgo cardiovascular, ni antecedentes cardiológicos conocidos, que se presentó con proteinuria y disnea de medianos a grandes esfuerzos de varios meses de evolución. Al examen cardiovascular presentaba ligera ingurgitación yugular, campos pulmonares ventilados sin ruidos sobreagregados, ruidos cardiacos normales sin soplos, y discreto edema de miembros inferiores.
Se realizó un electrocardiograma (ECG) que mostró ritmo sinusal, frecuencia cardiaca de 75 lpm, microvoltaje del QRS en derivaciones frontales, progresión lenta de la onda R en derivaciones precordiales, repolarización normal e intervalo QTc normal (Figura 1).
Figura 1: Electrocardiograma basal de 12 derivaciones. (Editado con @PMcardioBot)
En la paraclínica se observó niveles de troponinas normales de 0,06 ng/mL y valores aumentados de péptido natriurético (NT-pro-BNP) de 1303 pg/dL. Se estableció el diagnóstico de insuficiencia cardiaca y se procedió a realizar un ecocardiograma transtorácico (ETT) que mostró hipertrofia ligera ventricular izquierda con espesor septal de 12 mm, fracción de eyección del ventrículo izquierdo (FEVI) del 66% con signos de disfunción diastólica e incremento de las presiones de llenado del ventrículo izquierdo (VI) (Figura 2).
Figura 2: Ecocardiograma transtorácico: A: vista de eje largo paraesternal izquierda que muestra un ventrículo izquierdo no dilatado con ligera hipertrofia septal basal de 12mm. B: Vista apical de cuatro cámaras con dilatación ligera de ambas aurículas. C y D: Doppler pulsado y Doppler tisular con patrón de relajación del ventrículo izquierdo de tipo pseudonormal con velocidades tisulares disminuidas, lo que permite estimar una relación E/e´ de 13.
La discordancia entre la hipertrofia ventricular izquierda y el microvoltaje en el ECG, sugirió la presencia de una miocardiopatía infiltrativa por amiloide como causa de la falla cardiaca, por lo que se procedió a la determinación de cadenas ligeras y realización de resonancia magnética cardiaca (RMC). Este estudio mostró una hipertrofia ventricular izquierda con espesor septal de 12,2 mm, una FEVI preservada de 66% con volúmenes ventriculares normales, cavidades derechas no dilatadas con ventrículo derecho de tamaño y función sistólica conservada. La caracterización tisular posterior a la administración de contraste de gadolinio mostró un realce tardío con patrón de tipo no isquémico con compromiso global biventricular altamente sugestivo de infiltración cardíaca de tipo amiloide (Figura 3).
Figura 3: RMC. A, B, C: imágenes en sangre blanca de cuatro, dos cámaras y eje corto mostrando hipertrofia ventricular izquierda y dilatación ligera de ambas aurículas. D: Imágenes de caracterización tisular en sangre negra potenciada en T1 para valorar anatomía (inferior media) y realce tardío de gadolinio extenso y global a nivel bi-ventricular (flechas azules) (inferior derecha). RMC: resonancia magnética cardiaca, AI: aurícula izquierda, AD: aurícula derecha, VI: ventrículo izquierdo, VD: ventrículo derecho.
La biopsia renal fue compatible con infiltración amiloide y las cadenas ligeras se encontraron alteradas, realizándose el diagnóstico de amiloidosis de cadena ligera con compromiso cardiaco en estadio II, por lo que fue derivado a oncohematología para tratamiento específico. La biopsia de médula ósea no fue categórica para mieloma múltiple asociado y se decidió iniciar tratamiento quimioterápico sistémico con esquema CYBORD (ciclofosfamida, dexametasona y bortezomib) por seis ciclos previo a la valoración de trasplante de médula ósea.
Posterior a cuatro ciclos de quimioterapia, el paciente evolucionó de forma desfavorable, con progresión de la disnea (NYHA III) y signos de retención hídrica a los cuatro meses de iniciado el tratamiento. Al examen físico presentó aumento en la ingurgitación yugular y en el edema de los miembros inferiores. A nivel pulmonar presentaba estertores crepitantes bibasales, y a la auscultación cardiaca la presencia de un tercer ruido. Se repitió el ECG que mostró reducción del voltaje de los complejos QRS en derivaciones frontales y precordiales, en comparación al estudio previo, además de morfología QS de V1 a V4 en derivaciones precordiales (Figura 4).
Figura 4: Electrocardiograma de 12 derivaciones evolutivo.
Se realizó un nuevo ETT de control. En relación con el previo, mostró incremento de los espesores parietales, mayor dilatación auricular, incremento de las presiones de llenado ventriculares, ligero derrame pericárdico, además de derrame pleural derecho (Figura 5). Los biomarcadores también mostraron progresión de la enfermedad cardiaca con elevación de la troponina (0,09 ng/mL) y del NT-pro-BNP (4069 pg/mL). Tras nueva valoración se decidió cambiar el esquema quimioterápico empleado. En la espera de nuevo tratamiento el paciente presentó neumonía por COVID-19 y falleció por complicaciones relacionadas al cuadro infeccioso.
Figura 5: Ecocardiograma evolutivo. A: vista eje largo paraesternal derecha (arriba) y apical de cuatro cámaras (abajo). B: Hallazgos del Doppler tisular con incremento de la relación E/e´ estimada en 17. C: vista subcostal con presencia de derrame pleural (triangulo azul). ETT: ecocardiograma transtorácico.
DISCUSIÓN
La amiloidosis es una anomalía de las células plasmáticas caracterizada por la producción clonal de inmunoglobulinas de la cadena ligera, generando depósitos de fibrillas de amiloide en el espacio intersticial del miocardio 5,7. En el caso descrito se relata la progresión de la amiloidosis sistémica primaria de cadena ligera. En esta patología, además de los síntomas de insuficiencia cardíaca, puede aparecer angina producto de la infiltración amiloide en los vasos intramiocardicos sin alteración de las arterias epicárdicas, además de neuropatía sensitivo-motora, derrame pleural debido a la infiltración en la pleura parietal, síndrome nefrótico, macroglobulinemia de Waldenstrom o mieloma múltiple asociado 8.
El algoritmo diagnóstico incluye el ECG, pudiendo encontrarse microvoltaje, alteraciones de la conducción del estímulo eléctrico y fibrilación auricular, mientras que en las manifestaciones del ETT se encuentra la hipertrofia del VI, disfunción diastólica y alteración de la deformación longitudinal global 6,9. Para poner en evidencia la infiltración amiloide a nivel cardiaco, la RMC es una técnica versátil, que evalúa específicamente la presencia de realce tardío subendocárdico difuso o transmural y la cinética anormal del gadolinio 10. El estudio inicial para evaluar la afectación cardiaca generada por los depósitos de amiloide es el ETT; este examen permite identificar el incremento de espesores parietales, la disminución de la función tanto sistólica como diastólica y alteraciones en el Doppler tisular 11. Otros hallazgos frecuentes son la dilatación auricular, apariencia moteada del miocardio, engrosamiento valvular y del tabique interauricular, generados por la acumulación de amiloide; también se puede acompañar de derrame pericárdico pequeño; sin embargo, estas alteraciones son inespecíficas y también se las podría encontrar en otras miocardiopatías infiltrativas 5,8,12.
El ECG puede ser una herramienta útil en el diagnóstico y progresión de amiloidosis definiendo al microvoltaje como una amplitud QRS disminuida de 0,5 mV en todas las derivaciones de las extremidades o de menos de 1 mV en las derivaciones precordiales. El 84% de los pacientes suele presentar bajo voltaje. La identificación de un patrón de microvoltaje está relacionado con una menor supervivencia 6,13. Como se ha demostrado en este caso, tanto el ETT como el ECG, son herramientas de fácil acceso que permiten evaluar la progresión de esta enfermedad.
CONCLUSIÓN
La amiloidosis sistémica primaria de cadena ligera es una patología que produce acumulación de proteína amiloide en diferentes órganos y sistemas. El compromiso cardíaco cambia el pronóstico y evolución clínica de los pacientes. La progresión de la enfermedad es rápida y la respuesta al tratamiento puede ser evaluada mediante estudios complementarios. El ECG y el ETT son herramientas de fácil acceso que permiten la evaluación de la progresión estructural de esta enfermedad, como la disminución del voltaje de los complejos QRS, incremento de los espesores parietales, dilatación auricular, disfunción diastólica y la caída de la FEVI.
Fuentes de financiamiento: Autofinanciado.
Citar como: Chango Azanza DX, Fernández Tirado RL, López Pillaga VV, Tello Ochoa JD, Pinos Vásquez JF. Progresión cardiaca de amiloidosis sistémica de cadera ligera. Arch Peru Cardiol Cir Cardiovasc. 2022;3(4). doi: 10.47487/apcyccv.v3i4.249
==== Refs
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2 Clemmensen TS Eiskjær H Ladefoged B Mikkelsen F Sørensen J Granstam SO Prognostic implications of left ventricular myocardial work indices in cardiac amyloidosis Eur Heart J Cardiovasc Imaging 2021 22 6 695 704 10.1093/ehjci/jeaa097 32529207
3 Olea-sánchez EG Sánchez-moreno EC Ramón D Valencia-lópez R Luque-hernández A Síndrome nefrótico por amiloidosis AL asociado con gammapatia monoclonal de cadenas ligeras lambda Nephrotic syndrome by amyloidosis AL associated to lambda light-chain monoclonal Med Interna México 2020 36 5 722 726 10.24245/mim.v36i5.3236
4 Castillo-Velarde ER Amiloidosis primaria Rev la Fac Med Humana 2019 19 3 81 85 10.25176/RFMH.v19i3.2151
5 Magro Checa C Rosales Alexander JL Salvatierra J Raya Álvarez E Amyloidosis Med 2013 11 34 2065 2075 10.1016/S0304-5412(13)70581-X
6 Lazo-Soldevilla M Meza- Centeno L Santos-Julca C Maita-Hinostroza R Miocardiopatía por amiloidosis una causa imprevista de accidente cerebrovascular Rev la Soc Peru Med Interna 2022 35 1 37 40 10.36393/spmi.v35i1.655
7 Liao R Jain M Teller P Connors LH Ngoy S Skinner M Infusion of light chains from patients with cardiac amyloidosis causes diastolic dysfunction in isolated mouse hearts Erratum in Circulation. 2013/09/10;128(11):e174 Circulation 2001 104 14 1594 1597 11581134
8 Muñoz Moreno JM Añorga Ocmin J Espinola García S Aguilar C Alarco W Amiloidosis cardíaca Experiencia en un Instituto Cardiovascular de referencia nacionaI Arch Peru Cardiol y Cirugía Cardiovasc 2020 1 2 95 104 10.47487/apcyccv.v1i2.40
9 Dorbala S Cuddy S Falk RH How to Image Cardiac Amyloidosis A Practical Approach JACC Cardiovasc Imaging 2020 13 6 1368 1383 10.1016/j.jcmg.2019.07.015 31607664
10 Garcia-Pavia P Rapezzi C Adler Y Arad M Basso C Brucato A Diagnosis and treatment of cardiac amyloidosis a position statement of the ESC Working Group on Myocardial and Pericardial Diseases Eur Heart J 2021 42 16 1554 1568 10.1093/eurheartj/ehab072 33825853
11 Phelan D Collier P Thavendiranathan P Popovic ZB Hanna M Plana JC Relative apical sparing of longitudinal strain using two-dimensional speckle-tracking echocardiography is both sensitive and specific for the diagnosis of cardiac amyloidosis Heart 2012 98 19 1442 1448 10.1136/heartjnl-2012-302353 22865865
12 Peña C González JT López-Vidal H Donoso J Contreras C Vergara CG AL amyloidosis in the Chilean public health system A pending debt. Multicenter study of the Chilean Monoclonal Gammopathies Cooperative Group Rev Med Chil 2019 147 10 1239 1246 10.4067/s0034-98872019001001239 32186631
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PMC010xxxxxx/PMC10241335.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37283600
10.47487/apcyccv.v3i2.222
Artículo Original
Predicción de lesiones coronarias significativas mediante perfusión miocárdica con SPECT. Resultados de un centro de referencia nacional en Lima-Perú
Prediction of significant coronary lesions by SPECT myocardial perfusion. Results from a national reference hospital in Lima-Peruhttp://orcid.org/0000-0002-7225-7695
Talledo-Paredes Luisa S. 1 2 Cardiólogo nuclear
http://orcid.org/0000-0001-9316-6664
Guerrero-Ramírez Delia M.T. 1 Licenciado en Tecnología Médica - Radiología
http://orcid.org/0000-0001-8091-5179
Mendoza-Paulini Aurelio 1 Cardiólogo especialista en imágenes cardiacas
http://orcid.org/0000-0002-6565-8917
Rodríguez-Urteaga Zoila 1 Cardiólogo especialista en imágenes cardiacas
http://orcid.org/0000-0002-3885-9924
Angulo-Poblete Daniel 2
http://orcid.org/0000-0002-4396-7954
Ríos-Ortega Josías C. 3 Cirujano cardiovascular
1 Servicio de Ayuda al Diagnóstico y Tratamiento. Instituto Nacional Cardiovascular. EsSalud. Lima. Perú. Servicio de Ayuda al Diagnóstico y Tratamiento Instituto Nacional Cardiovascular. EsSalud Lima Perú
2 Programa de Maestría de Investigación y Docencia en Salud. Universidad Nacional de San Marcos. Lima, Perú Universidad Nacional Mayor de San Marcos Programa de Maestría de Investigación y Docencia en Salud Universidad Nacional de San Marcos Lima Peru
3 Servicio de cirugía cardiovascular. Instituto Nacional Cardiovascular. EsSalud. Lima. Perú. Servicio de cirugía cardiovascular Instituto Nacional Cardiovascular. EsSalud Lima Perú
Correspondencia Luisa S. Talledo - Paredes. Jirón Coronel Zegarra 417. Jesús María. Lima, Perú. +511 4111560. [email protected]
Conflicto de intereses: Ninguno
27 6 2022
Apr-Jun 2022
3 2 7481
07 5 2022
30 6 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
RESUMEN
Objetivo.
Determinar la capacidad discriminativa de la perfusión miocárdica con tomografía de emisión de fotón único (SPECT) para predecir obstrucciones coronarias por coronariografía. Determinar la mortalidad y eventos cardiovasculares mayores en el seguimiento.
Materiales y métodos.
Estudio observacional retrospectivo con seguimiento clínico en pacientes sometidos a SPECT y luego coronariografía. Excluimos pacientes con infarto de miocardio y revascularización percutánea y/o quirúrgica en los seis meses previos.
Resultados.
Se incluyeron 105 casos en el estudio. El protocolo de SPECT más usado fue el farmacológico (70%). Los pacientes que tenían defecto de perfusión mixto (isquemia e infarto) ≥10% de la masa ventricular total (MVT) presentaron en el 88% de los casos lesiones coronarias significativas (LAS) (sensibilidad 87,5% y especificidad 83%). Por otro lado, el tener isquemia ≥10% de la MVT se relacionó con 80% de LAS (sensibilidad: 72%, especificidad: 65%). El seguimiento clínico a 48 meses evidenció que un defecto de perfusión ≥ 10% fue predictor de eventos cardiovasculares mayores (ECM) tanto en el análisis univariado (HR=5,3; IC95% 1,2 - 22,2; p=0,022) como multivariado (HR= 6,1; IC95% 1,3 - 26,9; p= 0,017).
Conclusiones.
El tener un defecto de perfusión ≥10% de la MVT en el estudio SPECT predijo con alta probabilidad y sensibilidad la existencia de LAS (>80%); además, este grupo tuvo mayores ECM en el seguimiento.
ABSTRACT
Objective.
To determine the discriminative capacity of myocardial perfusion with single photon emission tomography (SPECT) to predict coronary obstructions by coronary angiography. To determine mortality and major cardiovascular events at follow-up.
Materials and methods
. Retrospective observational study with clinical follow-up in patients undergoing SPECT and then coronary angiography. We excluded patients with myocardial infarction and percutaneous and/or surgical revascularization in the previous 6 months.
Results:
105 cases were included in the study. The most commonly used SPECT protocol was pharmacological (70%). Patients with perfusion defect ≥10% of total ventricular mass (TVM) had significant coronary lesions (SCL) in 88% of cases (sensitivity 87.5% and specificity 83%). On the other hand, having ischemia ≥10% of the TVM was associated with 80% SCL (sensitivity: 72%, specificity: 65%). Clinical follow-up at 48 months evidenced that a perfusion defect ≥ 10% was predictive of major cardiovascular events (MACE) in both univariate (HR=5.3; 95%CI 1.2 - 22.2; p=0.022) and multivariate (HR= 6.1; 95%CI 1.3 - 26.9; p= 0.017) analyses.
Conclusions
. Having a perfusion defect ≥10% of the MVT in the SPECT study predicted with high probability and sensitivity the existence of SCL (>80%); moreover, this group had higher MACE at follow-up.
Palabras clave:
Perfusión Miocárdica
Infarto de Miocardio
Perú
Keywords:
Myocardial Perfusion
Myocardial Infarction
Peru
==== Body
pmcINTRODUCCIÓN
Las enfermedades cardiovasculares son la principal causa de muerte en el mundo y, en nuestro país, ocupan el segundo lugar, representando el 16% de muertes prematuras en adultos entre 30-69 años 1,2. Sin embargo, dicha tendencia se ha ido reduciendo debido al diagnóstico precoz de la enfermedad coronaria crónica (ECC) con el mayor uso de las imágenes cardiovasculares, las pruebas funcionales no invasivas; así como mayor disponibilidad de los métodos de revascularización 3,4.
El gold standard para el diagnóstico de la ECC es la coronariografía; empero, este procedimiento tiene serias complicaciones, por ejemplo, aquellas derivadas de la administración de contraste (reacciones alérgicas, nefropatía), lesiones vasculares, hemorragia, alteraciones en el ritmo cardiaco e, inclusive, riesgo de muerte 4,5. Por otro lado, en el Perú este procedimiento no es de fácil acceso porque existen pocos centros que cuentan con salas de coronariografía y, según el registro PERSTEMI, esa fue una de las causas de la intervención tardía en pacientes con infarto de miocardio ST elevado 6.
Por lo anterior, es que los métodos funcionales no invasivos para la detección de la ECC son de gran utilidad. Los más comunes son la ecocardiografía de estrés, la resonancia magnética cardiaca de estrés, la tomografía de emisión de fotón único (SPECT, por sus siglas en inglés), la tomografía de emisión de positrones, entre otros. Estas pruebas han demostrado tener alta sensibilidad para determinar obstrucciones coronarias significativas comparadas con la coronariografía con reserva fraccional de flujo 4,7.
En nuestro centro, el Instituto Nacional Cardiovascular de EsSalud de Lima, Perú, el cual es un centro de referencia nacional, especializado en cardiología y cirugía cardiovascular, solo contamos con la ecocardiografía de estrés y la SPECT como pruebas funcionales de imágenes para detectar la ECC. La SPECT tiene dos fases, de estrés y de reposo y lo que se busca son los cambios de la perfusión del músculo cardiaco al inyectar un radiofármaco (habitualmente Tc99m) luego de inducir estrés miocárdico con uso de vasodilatadores como la adenosina (protocolo farmacológico) o con una prueba de esfuerzo (protocolo ergométrico) o con ambos (mixto) 8. Los defectos de la perfusión en la SPECT pueden ser interpretados como infarto si estos persisten en el reposo; sin embargo, traducen isquemia miocárdica si solo ocurren en la fase de estrés 8.
A pesar de que la SPECT es uno de los métodos poco invasivos más utilizados en nuestro centro para la detección de la ECC, hasta la fecha no se ha evaluado la precisión de esta prueba en el diagnóstico de la enfermedad coronaria severa. Es por este motivo, que el objetivo principal del estudio es evaluar la capacidad discriminativa del SPECT con defectos de perfusión >= 10% de MVT de predecir lesiones coronarias significativas en la coronariografía. Como objetivo secundario se fijó el comparar en el seguimiento a 48 meses la mortalidad y los eventos adversos cardiovasculares mayores (MACE, por sus siglas en inglés) en pacientes con defecto total de perfusión (isquemia + infarto) ≥10% de la MVT versus aquellos con < 10% de MVT.
MATERIALES Y METODOS
Diseño y población
Se realizó un estudio observacional, retrospectivo, en el Instituto Nacional Cardiovascular de EsSalud en Lima, Perú, en pacientes mayores de 18 años, con sospecha de enfermedad coronaria, a los que se les realizó SPECT miocárdico y que, además, posteriormente, fueron sometidos a coronariografía diagnóstica en el periodo comprendido entre enero del 2016 a diciembre del 2019. El muestreo fue no aleatorio por conveniencia. Se excluyó a los pacientes que en los últimos 6 meses tuvieron diagnóstico de infarto de miocardio y/o aquellos sometidos a procedimientos de revascularización quirúrgica o percutánea previos al SPECT. Además, se realizó seguimiento clínico, revisando la historia clínica física y electrónica de cada uno de ellos.
Variables
Defecto de perfusión: presencia de disminución de la captación del radiofármaco (Tc99) durante la prueba de SPECT miocárdica, la cual puede ser solo durante la fase de estrés (isquemia) o en ambas fases (infarto). Esta variable fue dividida en forma categórica en <10% o ≥10% de la MVT.
Lesiones coronarias significativas (LAS): definidas en la coronariografía como una obstrucción mayor del 50% del tronco de la coronaria izquierda (TCI) o más del 70% en las arterias descendente anterior (DA), circunfleja (CX) y coronaria derecha (CD).
Mortalidad: consideramos la mortalidad por todas las causas.
Eventos cardiovasculares mayores: consideramos como indicadores el infarto de miocardio de novo, nueva revascularización coronaria, stroke y amputación de miembros inferiores.
Protocolo de la SPECT miocárdica
Siguiendo las recomendaciones de las guías actuales 8 la obtención de imágenes de perfusión miocárdica se realizó en dos fases, de estrés y de reposo, con la inyección del radiofármaco tecnecio 99 m-sestamibi con un maximo de 30 milicuries (1110 megabequerelios) en total. En la fase de estrés se realizaron protocolos farmacológicos con infusión de adenosina (140 ug/kg/min), durante 6 min, inyectando al pico máximo de la infusión (entre el 3.er y 4.to minuto) o protocolo mixto, con ejercicio, en su mayoría protocolo Naughton modificado. En ambas fases la adquisición de las imágenes se realizó entre 60-90 min después de la inyección del radiofármaco asociado a una dieta rica en grasa. Se realizaron los exámenes en una cámara gamma de doble cabezal (SIEMENS-ECAM), que utiliza un colimador de baja energía y alta resolución, una matriz de 64 x 64; zoom de 1,45; detectores en configuración de 90º, sincronizados con el electrocardiograma y con una ventana de latidos de 50%, en una cobertura de 180º, órbita no circular, modo avance y toma de 20 s por imagen, ocho frames para cada ciclo R-R cardiaco. Las imágenes fueron interpretadas por un médico cardiólogo con entrenamiento en cardiología nuclear y fueron utilizados los softwares de procesamiento de imágenes CEDARS QGS (Cedars-Sinai Medical Center) y EMORY, los que permiten evaluaciones tridimensionales dinámicas del ventrículo izquierdo, como valores numéricos de volúmenes y FEVI.
Protocolo de la coronariografía
Este procedimiento fue realizado por un cardiólogo especialista en hemodinámica, siguiendo los protocolos establecidos en nuestro centro, con un equipo de cineangiografía ArtisZee de Siemens Medical System-Alemania. Posteriormente, se revisaron las imágenes en reunión clínica del Servicio de Hemodinámica, donde un grupo de cardiólogos hemodinamistas determinó la localización y el porcentaje de las lesiones coronarias. Se definió como lesiones angiográficas significativas (LAS) a la estenosis de más del 50% del tronco de la coronaria izquierda (TCI) y en las arterias descendente anterior (DA), circunfleja (Cx) y coronaria derecha (CD) más de 70%.
Análisis estadístico
Análisis descriptivo: se emplearon métodos gráficos y numéricos para expresar las variables. Las numéricas que cumplían criterios de normalidad, según la prueba de Kolmogórov-Smirnov, fueron expresadas como media y desviación estándar, y aquellas que no cumplían criterios de normalidad se expresaron como mediana y rango intercuartil (RIC). Para las variables categóricas se emplearon porcentajes. Para mortalidad y MACE se estimó la incidencia acumulada en el periodo de seguimiento. Para la sobrevida se utilizó el análisis del hazard ratio con sus intervalos de confianza y sus respectivas curvas de Kaplan Meier.
Análisis comparativo: la prueba de chi cuadrado fue usada para la prueba de hipótesis entre los resultados de la perfusión y las lesiones angiográficas significativas (LAS) en la cineangiografía. Además, se determinó la sensibilidad y especificidad de la prueba de SPECT comparándola con los resultados de la coronariografía. Se realizó un análisis multivariable mediante regresión logística, en esta prueba la variable dependiente fue la mortalidad, mientras que las variables independientes fueron la FEVI<40%, diabetes mellitus en insulinoterapia, presencia de más de 10% de defecto total en la perfusión, entre otras. Todo el análisis estadístico se realizó en el programa Stata versión 17.
Ética
El comité de ética e investigación del Instituto Nacional Cardiovascular aprobó el protocolo de este estudio (Certificado de Aprobación 16/2021-CEI mayo 2021). El estudio no implicó riesgos para los pacientes por ser de tipo observacional.
RESULTADOS
Incluimos 105 pacientes en nuestro análisis. En la Tabla 1 se muestran las características de base de la población de estudio, la media de edad fue de 67 años (±11), principalmente fueron varones (84%). La mayoría tenía hipertensión arterial (69%) como antecedente. Un 51% tenía diagnóstico previo de infarto de miocardio y 50% había sido sometido a angioplastía coronaria o revascularización quirúrgica. El 74% recibía betabloqueantes y antiagregantes en el momento del estudio.
Tabla 1 Características de la población (n=105)
Características n (%)
Edad media ± DE 67±11
Sexo n (%)
Masculino 88 (84)
Femenino 17 (16)
Factores de riesgo cardiovascular
HTA 72 (69)
DM 48 (46)
Dislipidemia 65 (62)
Tabaquismo 41(39)
Enfermedad renal 8 (8)
Eventos cardiovasculares n (%)
Stroke 6 (6)
Infarto 54 (51)
Angioplastia previa 34 (32)
Bypass coronario previo 29 (28)
Características del ECG n (%)
Bloqueo de rama izquierda 8 (8)
Bloqueo de rama derecha 5 (5)
Fibrilación auricular 19 (18)
Marcapasos 4 (5)
Terapia recibida n (%)
Betabloqueantes 78 (74)
Antiagregantes 78 (74)
IECAs 23 (22)
BRA II 53 (50)
Estatinas 65 (62)
Anticoagulantes 10 (10)
Diuréticos 13 (12)
Nitratos orales 30 (29)
Todos los valores han sido redondeados a números enteros, *valores expresados como media y desviación estándar, HTA=hipertensión arterial. DM= diabetes mellitus. ECC= electrocardiograma. IECA= inhibidores de la enzima convertidora de angiotensina. BRA=bloqueadores de los receptores de angiotensina. DE= desviación estándar.
El protocolo farmacológico fue el más usado (70% de los pacientes). Encontramos que 89% de los pacientes presentaron algún grado de defecto (isquemia y/o infarto). La mediana del defecto en estrés (isquemia y/o infarto) representó 15% de la masa ventricular total (MVT), mientras que la mediana del defecto en reposo (solo infarto) fue de 4% de la MVT. La mediana de la isquemia alcanzó el 10% de la MVT. La FEVI en reposo fue de 48±15%, mientras que 28 pacientes (27%) tenían FEVI<40% (Tabla 2). En la coronariografía el 76% de los pacientes presentó LAS, la mayoría de ellos en la DA (60%).
Tabla 2 Características de la perfusión y de la coronariografía (n=105)
Protocolo de inducción de IM
Farmacológico n (%) 73 (70)
Mixto n (%) 32 (30)
Defecto de perfusión (isquemia + infarto)
Sin defecto n (%) 12 (11)
Con defecto n (%) 93 (89)
Defecto en estrés (%MVT) mediana (RIC) 15 (3,5-26,5)
Defecto en reposo (Infarto)(%MVT) mediana (RIC) 4 (0-14)
Isquemia (%MVT) mediana (RIC) 10 (3-17)
FEVI
Reposo media ± DE 48±15
Estrés media ± DE 50±16
TID media ± DE 1,07±0,17
FEVI≤40% n (%) 28 (27)
Defecto total (% MVT)
≥10% n (%) 80 (76)
<10% n (%) 25 (24)
Características de la coronariografía
Sin lesiones n (%) 25 (24)
Con lesiones n (%) 80 (76)
TCI 12 (11)
ADA 63 (60)
ACX 46 (44)
ACD 56 (53)
Todos los valores han sido redondeados a números enteros, IM: isquemia miocárdica. FEVI: fracción de eyección del ventrículo izquierdo. TCI: tronco de coronaria izquierda. ADA: arteria descendente anterior. ACX: arteria circunfleja. ACD: arteria coronaria derecha. TID: dilatación isquémica transitoria. DE: desviación estándar. RIC: rango intercuartil.
Relación entre el defecto de perfusión y LAS en la coronariografía (Tabla 3)
La población de estudio fue dividida en dos grupos, aquellos con menos de 10% de defecto total (incluimos defecto 0) y aquellos con ≥10% de la MVT. Los pacientes que tenían defecto de perfusión ≥10% de la MVT presentaron en el 88% de los casos LAS en la coronariografía; mientras que en aquellos con defecto <10% de la MVT, solo 40% presentaban LAS (p<0,001). Cuando estudiamos la sensibilidad de la prueba para tener LAS, con un resultado positivo de más de 10% de defecto, esta resultó 87,5% y la especificidad 83%, mientras que el valor predictivo positivo (VPP) resultó en 88%, y valor predictivo negativo (VPN) en 60%.
Tabla 3 Relación entre defecto de perfusión y LAS
Sin LAS n (%) Con LAS n (%) P*
Defecto total (%MVT) (n=101) <0,001
<10 (n=25) 15 (60) 10 (40)
≥10 (n=80) 10 (12) 70 (88)
Isquemia (%MVT) (n=50) 0,01
<10 (n=20) 11 (55) 9 (45)
≥10 (n= 30) 6 (20) 24 (80)
Isquemia DA (n=50)
<10 (n=32) 22 (69) 10 (31) 0,005
≥10 (n=18) 5 (28) 13 (72)
Isquemia Cx (n=50) <0,001
<10 (n=34) 32 (94) 2 (6)
≥10 (n=16) 7 (44) 9 (56)
Isquemia CD (n=50) 0,097
<10 (n=29) 24 (83) 5 (17)
≥10 (n=21) 13 (62) 8 (38)
*Chi cuadrado para variables categóricas. LAS: lesión angiográfica significativa. MVT: masa ventricular total. DA: descendente anterior. Cx: circunfleja. CD: coronaria derecha.
Al excluir a los pacientes con defecto en reposo (es decir, con infarto previo), nos quedamos con aquellos que solo presentaban isquemia, en total 50 pacientes. Los sujetos que presentaban isquemia ≥10% de la MVT tuvieron LAS en el 80% de los casos (sensibilidad: 72%, especificidad: 65%, VPP 80%, VPN 55%) un porcentaje mucho mayor en relación a los que tenían isquemia <10% (p<0,01). La isquemia ≥10% en el territorio de la DA se relacionó con 72% de LAS en dicha arteria (p=0,005) y en la Cx se relacionó con 56% de LAS (p<0,001), mientras que en la CD solo con el 38%, lo cual no fue significativo (p=0,097). En la Figura 1 se muestran los resultados de SPECT de un paciente aleatorio con más de 10% de isquemia de la MVT, los defectos de la perfusión en la cara lateral se correlacionaron con una LAS en la arteria Cx.
Figura 1 A. Hipocaptación severa del radiofármaco en el ápex del ventrículo izquierdo y en su pared inferolateral, reversibles en reposo, en relación a isquemia severa territorio arteria circunfleja. B. Imagen de lesión severa en la arteria circunfleja (círculo) en el mismo paciente.
En cuanto a la lesión de tronco coronario se encontraron siete pacientes, de los cuales seis presentaron defecto de perfusión ≥10%. Mientras que los pacientes con LAS en tres vasos, fueron 19, de los cuales 17 presentaron defectos de perfusión mayor al 10%.
Mortalidad y MACE
El seguimiento clínico, luego de revisar la historia clínica física y electrónica de los sujetos del estudio, con el fin de determinar la mortalidad por todas las causas y la ocurrencia de MACE, fue de 48 meses. Se encontró que 25% de los pacientes que tuvieron defecto de perfusión ≥10% de la MVT fallecieron; empero, solo un paciente (4%) de los que tuvieron defecto <10% falleció en el mismo periodo; esta diferencia resultó estadísticamente significativa (p=0,022) en el análisis univariable; sin embargo, no hubo diferencia estadísticamente significativa en la ocurrencia de stroke o de infarto de novo en ambos grupos, a pesar de haber una mayor ocurrencia de estos en el grupo con defecto ≥10%. Al sumar los eventos de mortalidad y MACE (stroke, infarto de novo), estos se presentaron en 41% de los casos del grupo con defecto ≥10% y solo en 8% del otro grupo (p=0,002) (Tabla 4).
Tabla 4 Relación entre complicaciones y defecto de perfusión
<10% MVT (n=25) ≥10%MVT (n=80) p*
Mortalidad n (%) 1 (4) 20 (25) 0,022
Stroke n (%) - 4 (5) 0,254
Infarto de novo n (%) 1 (4) 15 (19) 0,073
Mortalidad + stroke+ infarto n (%) 2 (8) 33 (41) 0,002
MVT: masa ventricular total. p=chi-cuadrado
En la Figura 2A se muestra la curva de sobrevida de Kaplan Meier de los pacientes, según el porcentaje de defecto mixto, y en la Figura 2B se grafica la ocurrencia de MACE y mortalidad por todas las causas según el método de Kaplan Meier. En el seguimiento se revascularizaron 40 de los 58 pacientes (69%) con presencia de isquemia ≥10% en la perfusión.
Figura 2 A. Curva de sobrevida de Kaplan Meier según el porcentaje de defecto de perfusión. B. Función de riesgo acumulado para MACE y Mortalidad por todas las causas según el porcentaje de defecto de perfusión defecto de perfusión.
En el análisis multivariado, tomando como variables independientes la FEVI<40% en reposo al momento de la prueba de perfusión, defecto total ≥10% de la MVT, diabetes mellitus en terapia con insulina, reducción ≥10% de la FEVI al estrés, presentar LAS en el cateterismo o tener más de 70 años al momento de la perfusión; no encontramos relación entre el defecto ≥10% de la MVT y la mortalidad (HR= 5,8; IC95%: 0,7- 47,3; p= 0,098). Sin embargo si se relacionó a mayor frecuencia eventos cardiovasculares mayores. (HR= 6,1; IC95% 1,3 - 26,9; p=0,017). (Tabla 5)
Tabla 5 Predictores de eventos cardiovasculares mayores - análisis multivariable (regresión de Cox)
Variable Hazard Ratio Error estándar p IC 95%
FEVI<40% 1,65 0,60 0,167 0,81 - 3,40
Defecto total ≥10% MVT 6,11 4,6 0,017 1,38 - 26,95
DM insulinoterapia 1,41 0,50 0,346 0,69 - 2,83
Reducción 10% FEVI 1,26 0,98 0,767 0,27 - 5,82
Lesiones en cateterismo 3,03 1,75 0,056 0,97 - 9.45
Edad ≥70 años 1,15 0,41 0,680 0,57 - 2,31
DM: diabetes mellitus; MVT: masa ventricular total. FEVI: fracción de eyección del ventrículo izquierdo; IC: intervalo de confianza.
DISCUSION
En el presente estudio se encontró que un defecto de perfusión (isquemia y/o infarto) ≥10% tuvo una sensibilidad de 87,5% y una especificidad de 83% para detectar LAS. Al estudiar solamente la isquemia, la sensibilidad fue de 72% y la especificidad de 65%. Además, el hallazgo de un defecto total ≥ 10% se relacionó con mayor mortalidad en el seguimiento (25% vs. 4%, p=0,022), y de MACE (41% vs. 8%, p=0,002) comparado a un defecto menor.
Los primeros metanálisis que compararon la SPECT con la angiografía encontraron una sensibilidad de 87% y una especificidad del 73%, lo cual se condice con nuestros resultados. La especificidad del SPECT es más baja que la sensibilidad, dado que es un estudio funcional comparado con un estudio totalmente anatómico, que no hace referencia a la reserva fraccional de flujo 9-11.
La prueba de perfusión miocárdica con SPECT es un método recomendado en pacientes con infarto previo, revascularización previa o FEVI baja antes del cateterismo, dado que su precisión es alta en el grupo de pacientes con enfermedad coronaria conocida, según la guía europea para el diagnóstico de síndrome coronario crónico 2019 4.
Hay varias condiciones que influyen en los resultados de la perfusión miocárdica con SPECT, una de ellas es la lesión del tronco de la coronaria izquierda (TCI). Al producir un déficit de circulación uniforme (balanceada) en todo el miocardio del ventrículo izquierdo puede condicionar falsos negativos del SPECT. Ciertos estudios reportan solo alrededor de 50% de resultados positivos (≥10% de isquemia) en lesiones de TCI de más de 50% 12-14. En casos de alta sospecha de enfermedad coronaria, con resultados de SPECT negativos, se recomienda usar estrategias de multimodalidad de imágenes (angiotomografía) 12. En el presente estudio se tuvo 25 pacientes que resultaron con <10% de defecto total, de ellos, diez presentaron LAS en la angiografía, ninguno presentó lesión de TCI; evidentemente, tener menos de 10% de defecto no excluye tener LAS; empero, debido a que la gravedad de la enfermedad es menor, el pronóstico de la enfermedad no es tan sombrío 12,15.
En ocho pacientes de la muestra se encontró bloqueo de rama izquierda, esta condición, así como la obesidad y las miocardiopatías hipertróficas o infiltrativas, también alteran los resultados de la SPECT, produciendo, en algunos casos, falsos negativos o falsos positivos 12. En la presente serie, siete de estos pacientes presentaron defectos de perfusión y seis presentaron LAS.
Diez pacientes tuvieron ≥ 10% de defecto de perfusión total, de ellos, seis tuvieron solo isquemia ≥10%, no presentaron LAS en la coronariografía; en estos casos se suele sospechar enfermedad microvascular. La disfunción coronaria microvascular no es condición benigna como se pensaba anteriormente. Se ha demostrado que esta patología incrementa el riesgo cardiovascular, altera la calidad de vida y se relaciona con hospitalizaciones frecuentes y con mayores eventos cardiovasculares 16,17.
Se ha demostrado una clara relación entre la extensión y severidad de la isquemia inducida en el SPECT y el pronóstico (mortalidad y eventos cardiovasculares) 12. En el analisis de supervivencia encontramos un HR=6.09 (IC 95: 0,82-45,39, p=0,07) para mortalidad en pacientes con mas del 10% de defecto de MVT. Probablemente la falta de significancia estadística se deba a la poca cantidad de pacientes en estudio, sin embargo es evidente el riesgo incrementado de mortalidad en relación al defecto de más del 10%. Sin embargo, el análisis del HR para MACE indica un resultado de HR=5,31 (IC 95%: 1,27-22,25, p=0,02), lo que demuestra una mayor probabilidad de presentar eventos cardiovasculares mayores (infarto, stroke y muerte) en pacientes con más del 10% de defecto de la MVT.
Piccini et al. demostraron en una cohorte de 6383 pacientes con enfermedad coronaria documentada con coronariografía sometidos a SPECT, que tener un defecto significativo en la perfusión se asoció con un incremento del riesgo de muerte por todas las causas, muerte cardiovascular, y muerte súbita, esta asociación fue independiente de la FEVI 18. Navare et al., en un metanálisis de casi 15 000 pacientes en 24 estudios, demostraron que la ocurrencia de muerte cardiaca y de infarto de miocardio fueron significativamente mayores en pacientes con alteraciones de la perfusión tanto con protocolo farmacológico o con ejercicio. Las tasas de eventos cardíacos fueron significativamente mayores con resultados de prueba normales y anormales con estrés farmacológico que con ejercicio (1,78 % frente a 0,65 % [p < 0,001] para resultados normales, y 9,98 % frente a 4,3 % [p< 0,001] para resultados anormales). Este estudio demostró que los resultados de la SPECT con estrés por ejercicio y farmacológico son comparables en su capacidad para estratificar el riesgo de los pacientes; sin embargo, los pacientes sometidos a los estudios de estrés farmacológico tienen un mayor riesgo de eventos cardíacos posteriores. Esto fue cierto incluso para aquellos con resultados de imágenes de perfusión normales 15. Resultados similares se han encontrado en otro metanálisis 19) y es por esto que las guías de manejo de revascularización de miocardio recomiendan realizar estrategias percutáneas o quirúrgicas para aumentar la sobrevida cuando el defecto de perfusión por isquemia es mayor de 10% 20.
Nuestro estudio tiene algunas limitaciones, la principal es que refleja los resultados de un solo centro, además, es un estudio retrospectivo en el que los datos fueron extraídos de la historia clínica, de tal manera que la calidad del registro de los datos podría tener fallas; asimismo, la muestra es relativamente pequeña por lo que genera un bajo poder estadístico. El seguimiento de los pacientes no es uniforme y no evaluamos el efecto de la revascularización en aquellos pacientes que fueron sometidos a estos procedimientos luego de tener un resultado de SPECT positivo y LAS.
Por otro lado, este es el primer estudio a nivel nacional que compara los resultados obtenidos en la SPECT para predecir LAS en la coronariografía. Debido a que es el primer estudio es aún exploratorio y marca el inicio para realizar un estudio con mayor población. Empero, a pesar de las limitaciones nos muestra data nacional en la evolución de los pacientes que tienen un grado de isquemia/infarto significativo en la perfusión miocárdica. La fiabilidad de los datos obtenidos en la SPECT y en la angiografía coronaria se fundamenta en que ambos procedimientos fueron realizados e interpretados por cardiólogos entrenados y certificados tanto en Medicina Nuclear y Hemodinámica, respectivamente.
En conclusión, la presencia de un defecto de perfusión ≥10% de la MVT por SPECT en nuestro centro, tiene alta sensibilidad para predecir el hallazgo de LAS en el estudio de coronariografía; además, este grupo de pacientes tuvo más eventos cardiovasculares mayores en el seguimiento.
Financiamiento: Autofinanciado
Citar como: Talledo-Paredes LS, Guerrero-Ramírez DMT, Mendoza-Paulini A, Rodríguez-Urteaga Z, Angulo-Poblete D, Ríos-Ortega JC. Predicción de lesiones coronarias significativas mediante perfusión miocárdica con SPECT. Resultados de un centro de referencia nacional en Lima-Perú. Arch Peru Cardiol Cir Cardiovasc. 2022;3(2). doi: 10.47487/apcyccv.v3i2.222
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13 Berman DS Kang X Slomka PJ Gerlach J de Yang L Hayes SW Underestimation of extent of ischemia by gated SPECT myocardial perfusion imaging in patients with left main coronary artery disease J Nucl Cardiol 2007 14 4 521 528 10.1016/j.nuclcard.2007.05.008 17679060
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PMC010xxxxxx/PMC10241336.txt |
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Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37283599
10.47487/apcyccv.v3i2.219
Artículo Original
Aortic valve replacement through minithoracotomy. Results from the Peruvian experience
Reemplazo de válvula aórtica mediante minitoracotomía. Resultados de la experiencia peruanahttp://orcid.org/0000-0002-4396-7954
Ríos-Ortega Josías C. 1 Medical Doctor
http://orcid.org/0000-0001-5413-6032
Sisniegas-Razón Josué 1 Medical Doctor
http://orcid.org/0000-0002-6972-159X
Conde-Moncada Roger 1 Medical Doctor
http://orcid.org/0000-0001-7942-2895
Pérez-Valverde Yemmy 1 Medical Doctor
http://orcid.org/0000-0002-7964-7311
Morón-Castro Julio 1 Medical Doctor
1 Departamento de Cirugía Cardiovascular, Instituto Nacional Cardiovascular INCOR- EsSalud. Lima, Perú. Departamento de Cirugía Cardiovascular Instituto Nacional Cardiovascular INCOR- EsSalud Lima Perú
Corresponding author Josías Caleb Ríos Ortega. Jirón Coronel Zegarra 417. Jesús María. Lima. Perú. +511 985794462; +511 4111560. [email protected]
Authors contribution: All authors contributed to the writing and analysis of the data
Conflict of interest: None declared
27 6 2022
Apr-Jun 2022
3 2 6973
01 5 2022
30 6 2022
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License
ABSTRACT
Objectives.
To assess mortality, major valve-related events (MAVRE), and other complications in the perioperative period and follow up in patients with aortic valve replacement (AVR) through mini-thoracotomy (MT).
Methods.
We retrospectively analyzed patients aged <80 who underwent AVR through MT between January 2017 and December 2021 in a national reference center in Lima, Peru. Patients undergoing other surgical approaches (mini-sternotomy, etc.), other concomitant cardiac procedures, redo, and emergency surgeries were excluded. We measured the variables (MAVRE, mortality, and other clinical variables) at 30 days and a mean follow-up of 12 months.
Results.
Fifty-four patients were studied, the median age was 69.5 years, and 65% were women. Aortic valve (AV) stenosis was the main indication for surgery (65%), and bicuspid AV represented 55.6% of cases. At 30-days, MAVRE occurred in two patients (3.7%), with no in-hospital mortality. One patient had an intraoperative ischemic stroke, and one required a permanent pacemaker. No patient underwent reoperation due to prosthesis dysfunction or endocarditis. In a mean follow-up of one year, MAVRE occurrence did not show variations with the perioperative period, most patients remained in NYHA I (90.7%) or II (7.4%) compared to the preoperative period (p<0.001).
Conclusions.
AV replacement through MT is a safe procedure in our center for patients under 80 years.
RESUMEN
Objetivos.
Evaluar la mortalidad, los eventos mayores relacionados con la válvula (EMRV) y otras complicaciones en el período perioperatorio y de seguimiento en pacientes con sustitución de la válvula aórtica (SVA) mediante minitoracotomía (MT).
Métodos.
Analizamos retrospectivamente a pacientes menores de 80 años, a quienes se les realizó SVA por MT entre enero de 2017 y diciembre de 2021 en un centro de referencia nacional en Lima, Perú. Se excluyeron pacientes sometidos a otros abordajes quirúrgicos (miniesternotomía, etc.), otros procedimientos cardíacos concomitantes, cirugías de reoperación y de emergencia. Medimos las variables (mortalidad, EMRV y otras variables clínicas) a los 30 días y un seguimiento medio de 12 meses.
Resultados.
Se estudiaron 54 pacientes, la mediana de edad fue de 69,5 años y el 65% fueron mujeres. La estenosis de la válvula aórtica (VA) fue la principal indicación para la cirugía (65%) y la VA bicúspide representó el 55,6% de los casos. A los 30 días, se produjeron dos EMRV (3,7%) y no hubo mortalidad intrahospitalaria. Un paciente tuvo un accidente cerebrovascular isquémico intraoperatorio y uno requirió un marcapasos permanente. Ningún paciente fue reoperado por disfunción de la prótesis o endocarditis. En un seguimiento medio de un año, la aparición de EMRV no mostró variaciones con el periodo perioperatorio; la mayoría de los pacientes permaneció en clase funcional NYHA I (90,7%) o II (7,4%) en comparación con el periodo preoperatorio (p<0,001).
Conclusiones.
La sustitución de la válvula aórtica mediante minitoracotomía es un procedimiento seguro en nuestro centro en pacientes<80 años.
Keywords:
Aortic valve
Thoracotomy
Cardiac Surgery
Peru
Palabras clave:
Válvula Aórtica
Toracotomía
Cirugía Cardíaca
Perú
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pmcINTRODUCTION
Aortic valve (AV) stenosis is the most common heart valve disease and is an important public health problem (1,2; Despite advances in trans-catheter therapy, surgical AV replacement remains the gold standard therapy, especially in young and low-risk patients 3. For this surgery, full median sternotomy (FMS) has been the approach of choice, offering wide access to cardiac anatomy and extensive exposure to the great vessels.
However, FMS has serious complications, such as disruption of the sternum and mediastinal infection that occur in 0.3-5% of cases, and these problems are associated with a mortality rate between 14 and 47% 4. Pain is another complication that in many cases is disabling for a long period 5. Formation of abnormal scars is a significant source of morbidity following sternotomy and this the presence of this scar causes alterations in body image, self-esteem, and cosmetic outcomes 6,7.
Therefore, surgical approaches have been sought to avoid FMS (mini-J or T sternotomy, mini-thoracotomy [MT]) since few decades. There are currently many centers worldwide that perform minimally invasive procedures for AV replacement, and these approaches have been shown to reduce pain, hospital stay, postoperative atrial fibrillation rate, and surgical site infectious complications 8-10.
We present the surgical results in patients who underwent AV replacement through MT approach performed in our center. The objective of this first report in our country, is to determine procedure´s safety measured through total mortality and major adverse valve-related events (MAVRE).
MATERIALS AND METHODS
Design
We conducted a retrospective research of patients who were submitted to AV replacement through MT approach from January 2017 to December 2021 in the Instituto Nacional Cardiovascular - EsSalud, Lima, Peru. Patients older than 80 years, patients undergoing other surgical approaches (mini-sternotomy, etc.), other concomitant cardiac procedures (replacement of the ascending aorta, other valve surgeries, coronary bypass, etc.), redo and emergency surgeries were excluded.
Operative technique
All surgeries were performed during cardioplegic arrest on cardiopulmonary bypass (CPB) and underwent intraoperative transesophageal echocardiography in addition to standard monitoring for cardiac surgery.
For MT we opened the third intercostal space (Figure 1A, 1B) and detach the upper or lower rib from its junction with the sternum, previously ligating the right internal mammary artery, in all cases for CPB we cannulated right femoral artery and vein (Figure 1D). Aortic cross clamping was performed with percutaneous clamp through the second/third intercostal space (anterior axillary line) or with a flexible-articulated clamp (Figure 1C). We use crystalloid cardioplegic solution for myocardial protection (HTK Custodiol®) and placed it in the aortic root or directly into coronary ostia. Aortotomy was performed conventionally and then we performed AV replacement using surgical instruments for MT (Figure 1E, 1F).
Figure 1 A. Incision site (arrow). B. View of the ascending aorta after mini-thoracotomy. C. Aortic cross clamping with a flexible clamp. D. Femoral artery and vein cannulation. E. Aortic ring after removing the AV and placing sutures. F. Final result.
Data collection
Data was collected using the physical and electronic medical records of the patients at three times: baseline (during hospital admission before to the surgical procedure), during the perioperative period (surgery procedures, in the intensive care unit (ICU) and during the first 30 postoperative days), and in the follow-up to 12 months after surgery.
Outcomes
The primary clinical outcome was procedure´s safety, measured through total mortality and MAVRE 10) which included valve-related mortality, any structural or nonstructural prosthesis dysfunction, valve thrombosis, embolism, bleeding, prosthetic valve endocarditis, reoperation, or permanent pacemaker insertion. The secondary endpoint of the study was determining the hospital stay, surgical re-interventions due to excessive bleeding, among other clinical variables.
Statistical analysis
We explored the distribution of variables using analytical and graphical methods and reported numerical data. Variables that meet normality criteria were expressed as mean and standard deviation, and those that do not meet normality criteria were expressed as median and interquartile range (IQR). Categorical variables were expressed in absolute and relative frequencies in the baseline, perioperative and follow-up periods. We estimated the cumulative incidence of mortality and MAVRE in the perioperative period and during follow up. Also, we used chi-square test for comparing functional class.
RESULTS
Baseline and surgical characteristics
We included 54 patients in our analysis. Preoperative baseline profiles of the patients are listed in Table 01. Sixty-five percent were women, and the median age was 69.5 years (IQR: 64.5 - 74.5). Most patients had functional class III before surgery (63%). AV stenosis was the main indication for surgery (65%) and bicuspid AV represented 55.6% of cases. Median EuroScore II before surgery was 0.87.
Table 1 Baseline characteristics (n=54)
Item Frecuency
Age (years)--------------------------median (IQR) 69.5 (64.5-74.5)
Sex
male--------------------------------n (%) 19 (35)
Female---------------------------n (%) 35 (65)
BMI (kg/m2) ---------------------median (IQR) 25 (22.65-27.35)
BMI≥30 -------------------n (%) 5 (9.3)
Functional Class (NYHA) ---n (%)
II 20 (37)
III 34 (63)
AV disease--------------------------n (%)
Stenosis 35 (65)
Regurgitation 19 (35)
AV morphology------------------n (%)
Bicuspid 30 (55.6)
Tricuspid 24 (44.4)
AV annulus (mm)-----------------median (IQR) 23.5 (22.0-26.5)
≤21mm ---------------------n (%) 10 (18.5)
AV peak gradient (mmHg) ---median (IQR) 90 (78-114)
AV mean gradient (mmHg) -median (IQR) 57.5 (47-78.5)
LVEF (%)----------------------------median (IQR) 65 (60-70)
Other basal characteristics ----n (%)
Hypertension 24 (44)
Diabetes 10 (19)
Chronic atrial fibrillation 1 (1.9)
Dialysis 0 (0)
EuroScore II (%)------------------median (IQR) 0.87 (0.685-1.24)
BMI=Body mass index; kg=Kilogram; m=Meter; NYHA=New Year Heart Association; AV= Aortic valve; LVEF=Left ventricular ejection fraction; SD=Standard deviation, replacement; IQR=Interquartile range.
Early Postoperative results
We mainly perform biological AV replacements (81.5%), median aortic cross-clamping (ACC) and cardiopulmonary bypass (CPB) times were 109 and 145 min, respectively. In seven patients we performed aortic root enlargement. Table 02 shows others surgical characteristics.
Table 2 Surgical procedures characteristics (n=54)
Item Frecuency
CPB time (min)-----------------median (IQR) 145 (128-162)
ACC time (min)------------- median (IQR) 109 (92-126)
Prosthesis type ---------------n (%)
Mechanical 10 (18.5)
Biological 45 (81.5)
Aortic root enlargement-----------n (%) 7 (13)
Prosthesis size --------------------------n (%)
19 mm 1 (1.9)
21 mm 25 (46.3)
23 mm 15 (27.8)
25 mm 13 (24.1)
CPB=Cardiopulmonary Bypass; ACC= Aortic cross clamp.
At 30 days, MAVRE occurred in two patients (3.7%), we had no in-hospital mortality, one patient had an intraoperative ischemic stroke, and another required a permanent pacemaker. No patient underwent reoperation due to prosthesis dysfunction or endocarditis. Two patients (3%) required surgical re-intervention due to excessive bleeding. Prolonged intubation (>48h) was present in 3 patients (5.56%). No mediastinitis or perioperative infarction were observed. Other characteristics are shown in Table 03.
Table 3 Early post-operative evolution (first 30 days) (n=54)
Primary end-point
Total Mortality-----------------------n (%) 0 (0)
MAVRE----------------------------------n (%) 2 (3.7%)
Valve-related mortality 0 (0)
Valve-related morbidity
Stroke 1 (1.9)
Structural dysfunction 0 (0)
Nonstructural dysfunction 0 (0)
Major Bleeding 0 (0)
Definitive Pacemaker 1 (1.9)
Secondary end-point----------------------median (IQR)
ICU stay (days) 3 (2-4)
In hospital stay (days) 12 (9-15)
Postoperative bleeding(ml) 500 (400-600)
Other clinical variables--------------n (%)
Redo-surgery for excessive bleeding 2 (3.7)
Prolonged mechanical ventilation 3 (5.56)
Perioperative myocardial infarction 0 (0)
Mediastinitis 0 (0)
Echocardiographic findings
Peak AV gradient-------------------median (IQR) 29.5 (21.5-37.5)
Mean AV gradient-----------------median (IQR) 15 (9-21)
LVEF------------------------------------median (IQR) 61.5 (58.5-64.5)
Severe mismatch ------------------n (%) 0 (0)
MAVRE=Major adverse valvar relative event; ICU=Intensive care unit; LVEF=Left ventricular ejection fraction; SD=Standard deviation.
Table 4 Follow-up evolution (average 12 months) (n=54)
Primary end-point
Total Mortality-------n (%) 0 (0)
MAVRE --------------------n (%) 2 (3.7)
Valve-related mortality 0 (0)
Valve-related morbidity
Stroke 1 (1.9)
Structural dysfunction 0 (0)
Nonstructural dysfunction 0 (0)
Major Bleeding 0 (0)
Redo surgery for infective endocarditis 0 (0)
Definitive Pacemaker 1 (1.9)
Functional class (NYHA) ---n (%)
I 49 (90.7)
II 4 (7.4)
III 1 (1.8)
MAVRE=Major adverse valve relative event; NYHA=New Year Heart Association.
Follow-up results
In a mean follow-up of one year, we had two MAVRE: one patient with stroke and one permanent pacemaker insertion (3.7% of cases), no mortality cases were reported. Regarding symptomatology, most patients remained in NYHA I (90.7%) or II (7.4%) functional class, compared to the preoperative period (NYHA I: 0%, NYHA II: 37%), this difference was significant (p<0.001).
DISCUSSION
Symptomatic severe aortic stenosis is associated with high mortality rates, ~ 50% at 1 year, and the prevalence will likely increase as the population ages. In this pathology, interventional procedures (AV replacement) have been shown to drastically reduce mortality and improve quality of life 2,11. The development of trans catheter AV replacement has changed the treatment of patients with severe aortic stenosis. However, for young and low risk individuals, surgery remains the preferred treatment option. Moreover, the advent of sutureless aortic prostheses has increased the ease of minimally invasive surgery for AV replacement 2,12. Minimally invasive cardiac surgeries have been performed since the early 1990s, seeking alternatives to reduce the complications of FMS 9,13. In our study, the first in our country, and one of the few published in Latin-American, we found excellent mortality and MAVRE rates (0% and 3.7%, respectively) in patients undergoing AVR through MT.
Mortality and MAVRE
Mini-invasive AV replacement had shown no mortality rates differences compared to conventional aortic valve replacement 14,15. In our series, which includes only patients under 80 years of age, we had no 30-day mortality; however, one patient suffered an ischemic stroke with severe cognitive sequelae and another patient required pacemaker placement due to complete atrioventricular block. A meta-analysis found a crude incidence of early/hospital mortality of 1.4% and 2.2% with minimally invasive and conventional approaches, respectively, and the incidence of postoperative stroke was 1.5% -1.7% 17.
Other clinical Variables
Patients undergoing AVR through mini-invasive approaches spent on average 2.1 (1.6 to 2.7) days less in the hospital 17, mini-invasive approach for AV replacement has been shown to reduce the stay in the ICU, the hospital stay, the pain and the postoperative bleeding 14-17. In our series the median of ICU stay was 3 days, and the median of postoperative bleeding was 500 ml in the first 24 hours. These data are comparable with other series 14-17. CPB and ACC times are longer than FMS in some series, however this has not resulted in an increase in postoperative complications 14-16. In our study the medians of ACC and CPB times were comparable with those studies.
Regarding the mini-invasive approach, there are two techniques: MT and upper mini-sternotomy (MS). In various meta-analyses there were no difference in operative mortality or stroke incidence between both techniques 17,18. However Meta‐analyses favored MT over MS in reoperation for bleeding (OR: 0.42, 95% CI: 0.28‐0.63; P < 0.001), aortic cross‐clamp time (standardized mean difference: −0.12, 95% CI: −0.20 to 0.029; P = 0.009), and the rate of conversion to sternotomy (OR: 0.32, 95% CI: 0.11‐0.93; P = 0.036). The rate of permanent pacemaker insertion approached borderline significance in favor of MS (OR: 0.54, 95% CI: 0.26‐1.12; P = 0.097) 18.
One benefit that has been overlooked by most studies is the better cosmetic results that minimally invasive surgery obviously has. Although this was not one our objectives, some studies have shown that a minimally invasive approach has advantages in terms of body image, self-esteem, and aesthetic results over the conventional approach in patients undergoing cardiac surgery 7.
Despite the advantages of minimally invasive surgery in AVR described above, the experience is still scarce in Latin America. Some reports have been previously published for AVR through MS o MT showing good results and low rates of mortality and postoperative complications; however, experience is still scant 19,20.
Limitations and strengths
Our study must be interpreted in the light of its limitations. First, despite few patients were included and it may suppose a low statistical power and high risk of random error. Second, the follow-up time was short and it was not standardized among participants. Third, the data was extracted from medical records, so we cannot guarantee data quality control. On the other hand, our study also has several strengths: it is the first published in our country to show results in AV replacement using a minimally invasive approach. Future analytic studies with national data comparing AVR via MT versus FMS are needed.
In conclusion, AV replacement by mini-thoracotomy is a safe procedure in our center, in patients under 80 years of age, with good mortality and MAVRE rates.
Funding statements: This work was supported by self-financing
Cite as: Ríos-Ortega JC, Sisniegas-Razón J, Conde-Moncada R, Pérez-Valverde Y, Morón-Castro J. Aortic valve replacement through minithoracotomy. Results from the Peruvian experience. Arch Peru Cardiol Cir Cardiovasc. 2022;3(2). doi: 10.47487/apcyccv.v3i2.219
==== Refs
REFERENCES
1 Iung B Delgado V Rosenhek R Price S Prendergast B Wendler O Contemporary presentation and management of valvular heart disease The EURObservational Research Programme Valvular Heart Disease II Survey Circulation 2019 140 14 1156 1169 10.1161/CIRCULATIONAHA.119.041080 31510787
2 Yadgir S Johnson CO Aboyans V Adebayo OM Adedoyin RA Afarideh M Global, regional, and national burden of calcific aortic valve and degenerative mitral valve diseases, 1990-2017 Circulation 2020 141 21 1670 1680 10.1161/CIRCULATIONAHA.119.043391 32223336
3 Vahanian A Beyersdorf F Praz F Milojevic M Baldus S Bauersachs J 2021 ESC/EACTS Guidelines for the management of valvular heart disease Developed by the Task Force for the management of valvular heart disease Eur Heart J 2022 43 7 561 632 10.1093/eurheartj/ehab395 34453165
4 Losanoff JE Richman BW Jones JW Disruption and infection of median sternotomy a comprehensive review Eur J Cardiothorac Surg 2002 21 5 831 839 10.1016/s1010-7940(02)00124-0 12062271
5 Huang AP Sakata RK Pain after sternotomy - review Braz J Anesthesiol 2016 66 4 395 401 10.1016/j.bjane.2014.09.013 27343790
6 King KM McFetridge Durdle J LeBlanc P Anzarut A Tsuyuki RT A Descriptive Examination of the Impact of Sternal Scar Formation in Women Eur J Cardiovasc Nurs 2009 8 2 112 118 10.1016/j.ejcnurse.2008.08.001 18793872
7 Iyigün T Kaya M Gülbeyaz SÖ Fistikçi N Uyanik G Yilmaz B Patient body image, self-esteem, and cosmetic results of minimally invasive robotic cardiac surgery Int J Surg 2017 39 88 94 10.1016/j.ijsu.2017.01.105 28143731
8 Svensson LG Minimal-access "J" or "j" sternotomy for valvular, aortic, and coronary operations or reoperations Ann Thorac Surg 1997 64 5 1501 1503 10.1016/S0003-4975(97)00927-2 9386741
9 Moreno-Cabral RJ Mini-T sternotomy for cardiac operations J Thorac Cardiovasc Surg 1997 113 4 810 811 10.1016/S0022-5223(97)70252-6
10 Akins CW Miller DC Turina MI Kouchoukos NT Blackstone EH Grunkemeier GL Guidelines for reporting mortality and morbidity after cardiac valve interventions J Thorac Cardiovasc Surg 2008 135 4 732 738 10.1016/j.jtcvs.2007.12.002 18374749
11 Bevan GH Zidar DA Josephson RA Al-Kindi SG Mortality Due to Aortic Stenosis in the United States, 2008-2017 JAMA 2019 321 22 2236 2238 10.1001/jama.2019.6292 31184728
12 Powell R Pelletier MP Chu MWA Bouchard D Melvin KN Adams C The Perceval Sutureless Aortic Valve Review of Outcomes, Complications, and Future Direction Innovations (Phila) 2017 12 3 155 173 10.1097/IMI.0000000000000372 28570342
13 Glauber M Ferrarini M Miceli A Minimally invasive aortic valve surgery state of the art and future directions Ann Cardiothorac Surg 2015 4 1 26 32 10.3978/j.issn.2225-319X.2015.01.01 25694973
14 Phan K Xie A Di Eusanio M Yan TD A meta-analysis of minimally invasive versus conventional sternotomy for aortic valve replacement Ann Thorac Surg 2014 98 4 1499 1511 10.1016/j.athoracsur.2014.05.060 25064516
15 Brown ML McKellar SH Sundt TM Schaff HV Ministernotomy versus conventional sternotomy for aortic valve replacement a systematic review and meta-analysis J Thorac Cardiovasc Surg 2009 137 3 670 679 10.1016/j.jtcvs.2008.08.010 19258087
16 Ariyaratnam P Loubani M Griffin SC Minimally invasive aortic valve replacement Comparison of long-term outcomes Asian Cardiovasc Thorac Ann 2015 23 7 814 821 10.1177/0218492315587606 25991010
17 Chang C Raza S Altarabsheh SE Delozier S Sharma UM Zia A Minimally Invasive Approaches to Surgical Aortic Valve Replacement A Meta-Analysis Ann Thorac Surg 2018 106 6 1881 1889 10.1016/j.athoracsur.2018.07.018 30189193
18 Yousuf Salmasi M Hamilton H Rahman I Chien L Rival P Benedetto U Vohra HA Mini-sternotomy vs right anterior thoracotomy for aortic valve replacement J Card Surg 2020 35 7 1570 1582 10.1111/jocs.14607 32652784
19 Fortunato J Jeronimo A Sesca J Paludo R Paz M Paludo L Troca valvar aórtica minimamente invasiva uma alternativa à técnica convencional Braz J Cardiovasc Surg 2012 27 4 570 582 10.5935/1678-9741.20120099
20 Benetti F Rizzardi JL Concetti C Bergese M Zappetti A Minimally aortic valve surgery avoiding sternotomy Eur J Cardiothorac Surg 1999 16 2 S84 S85 10613564
|
PMC010xxxxxx/PMC10241338.txt |
==== Front
Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
10.47487/apcyccv.v3i4.235
Reporte De Caso
Síndrome de Takotsubo: complicación cardiovascular del accidente cerebrovascular
Takotsubo Syndrome: cardiovascular complication of strokehttps://orcid.org/0000-0003-2060-4648
Leiva Abanto Claudio 1 Médico residente de cardiología
https://orcid.org/0000-0002-5854-1032
Medina Palomino Félix 1 Médico asistente del servicio de cardiología
https://orcid.org/0000-0001-9318-4918
Anchante Hernández Henry 1 Médico asistente del servicio de cardiología
https://orcid.org/0000-0003-4966-867X
Dueñas Carvajal Roy 1 Médico asistente del servicio de cardiología
1 Hospital Nacional Cayetano Heredia. Lima, Perú. Hospital Nacional Cayetano Heredia Lima Perú
Correspondencia: Claudio Leiva Abanto Tiziano Muñoz 467, San Martin de Porres, Lima, Perú, +51 967311377, [email protected]
Contribución de los autores: todos los autores participaron en la redacción y aprobación de la versión final del manuscrito
Declaración de conflictos de interés: Los autores declaramos no tener conflictos de interés.
31 12 2022
Oct-Dec 2022
3 4 204209
16 10 2022
28 11 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
Resumen
Mujer adulta mayor, 82 años, Katz A, con antecedente de diabetes mellitus tipo 2 e hipertensión arterial; ingresa por accidente cerebrovascular isquémico que se complica con síndrome de Takotsubo con posterior reingreso por fibrilación auricular tras el alta médica. Estos tres eventos clínicos tienen criterios para integrarse como un síndrome cerebro-corazón, el cual es una condición de alto riesgo para mortalidad.
Abstract
An 82-year-old woman, Katz A, with a history of type 2 diabetes mellitus and high blood pressure, was admitted for ischemic stroke complicated by Takotsubo´s syndrome with subsequent readmission for atrial fibrillation after discharge. These three clinical events have criteria to be integrated as a Brain Heart Syndrome, which is a high-risk condition for mortality.
Palabras clave:
Cardiomiopatía de Takotsubo
Accidente Cerebrovascular
Fibrilación Auricular
Infarto de Miocardio
Keywords:
Takotsubo Cardiomyopathy
Stroke
Atrial Fibrillation
Myocardial Infarction
==== Body
pmcINTRODUCCIÓN
El síndrome cerebro-corazón es una patología que se describió por primera vez en las décadas de 1950 y 1960, se informan aproximadamente en el 20% de los pacientes con accidente cerebrovascular isquémico, y es responsable de más de 1,5 millones de muertes en todo el mundo, incluidas las complicaciones cardiovasculares posteriores al ictus, la muerte súbita inesperada en la epilepsia, el síndrome de Takotsubo y la muerte cardíaca súbita neurogénica. Es así que las complicaciones cardiacas son la segunda causa de muerte en las primeras semanas después del evento. Pero no solo se limita a complicaciones fatales, sino también son frecuentes las arritmias cardíacas, la insuficiencia cardíaca y los síndromes coronarios no mortales. A pesar de las mejoras en la descripción del síndrome cerebro-corazón no hay avances en el desarrollo de terapias específicas dirigidas al eje cerebro-corazón para prevenir las complicaciones cardiovasculares y la muerte después de un accidente cerebrovascular 1,2.
REPORTE DE CASO
Paciente mujer de 82 años, Katz A, con antecedente de diabetes mellitus tipo 2 e hipertensión arterial; tres horas antes de ingresar a emergencia pierde súbitamente la fuerza muscular en el hemicuerpo derecho asociado con bradilalia, sin pérdida de la conciencia ni relajación de esfínteres. Al examinarla presentaba presión arterial:114/70 mmHg; frecuencia cardíaca: 92 por minuto, regular; frecuencia respiratoria 20 por minuto; saturación de oxígeno: 95%; temperatura de 36,5 °C; escala de Glasgow 11/15; pupilas isocóricas, fotorreactivas; hemiparesia derecha; paresia de hemicara derecha; babinski bilateral, escala NIHSS (National Institutes of Health Stroke Scale) 17 puntos. El electrocardiograma (EKG) de ingreso (Figura 1) muestra ritmo sinusal con crecimiento de aurícula izquierda y en la tomografía cerebral sin contraste no se aprecia lesión hemorrágica ni isquémica, se inicia trombólisis a las cuatro horas de inicio de los síntomas sin una mejora del NIHSS; el control tomográfico a las 24 h reporta hipodensidad heterogénea en ganglio basal izquierdo sugerente de isquemia aguda.
Figura 1 EKG de ingreso. Ritmo sinusal con crecimiento de aurícula izquierda.
Al cuarto día de hospitalización se detecta pulso irregular sin repercusión hemodinámica, el EKG muestra ritmo sinusal con bigeminismo supraventricular y supradesnivel del segmento ST V2-V5 con amputación de ondas R, además troponina T ultrasensible incrementada en 154 ng/L (Figura 2). La ecocardiografía (Figura 3) revela akinesia de las cara anterior, inferior, ánteroinferolateral y ánteroinferoseptal a nivel medial y apical, e hipercontractilidad basal, con fracción de eyección de ventrículo izquierdo preservado, orientando a una miocardiopatía de Takotsubo. Se realiza angiografía coronaria que muestra ausencia de lesiones obstructivas significativas de arterias epicárdicas, en tanto que la ventriculografia muestra imagen en forma de «olla de Takotsubo» (Figura 4). Con leve mejoría neurológica y estado cardiovascular estable, fue dada de alta al octavo día de hospitalización; se le receta la siguiente medicación: ácido acetil salicílico 100 mg cada 24 h; atorvastatina 40 mg cada 24 h; enalapril 10 mg cada 12 h; metformina 850 mg cada 12 h.
Figura 2 EKG al cuarto día de hospitalización. Ritmo sinusal con bigeminismo supraventricular (circulo) y supradesnivel del segmento ST V2-V5 (flecha roja) con amputación de ondas R (rayo amarillo).
Figura 3 Ecocardiografía en vista apical de 3, 2 y 4 cámaras. Evidencia acinesia de las cara anterior, inferior, ánteroinferolateral y ánteroinferoseptal a nivel medial y apical (flecha verde) e hipercontractilidad basal (flecha roja) (patrón de Takotsubo). AD: aurícula derecha. AI: aurícula izquierda. Ao: aorta. VD: ventrículo derecho. VI: ventrículo izquierdo.
Figura 4 Ventriculografía. A, Hipercontractilidad de los segmentos basales (flecha anaranjada) y acinesia de los segmentos medios y apicales del ventrículo izquierdo (fecha verde). B, olla para capturar pulpos «Takotsubo».
Dos días después reingresa por episodio de inicio súbito que incluye palpitaciones y disnea en reposo. Al ingreso, PA: 118/85 mmHg; taquipneica, pulso irregular; taquicárdicos sin signos de hipoperfusión ni congestión pulmonar. EKG (Figura 5) muestra fibrilación auricular (FA) con respuesta ventricular alta y troponina T ultrasensible incrementada a 89 ng/L. En esta hospitalización se agrega deterioro del nivel de conciencia, se indica tomografía cerebral que demuestra conversión hemorrágica a nivel de lesión isquémica anterior. Posteriormente, es dada de alta con atorvastatína 40 mg cada 24 h; bisoprolol 2,5 mg cada 24 h e insulina largina 30 UI cada 24 h. Finalmente, la paciente fallece a los dos meses de seguimiento por causa no conocida.
Figura 5 EKG dos días después del alta. Fibrilación auricular con respuesta ventricular alta.
DISCUSIÓN
Se presenta una paciente con factores de alto riesgo cardiovascular que pasa por tres momentos. En primer lugar, el síndrome focal agudo, donde el accidente cerebrovascular agudo (ACV) isquémico es la principal causa, y los eventos cardiembólicos son responsable del 35% de las etiologías, entre las cuales la FA es el origen del 20 al 30% de todos los ictus isquémicos y el 10% de los ictus criptogénicos 3-5; se comprueba la causa isquémica en la tomografía cerebral; sin embargo, no se encuentran signos de origen cardioembólico en el examen físico ni en el electrocardiograma de ingreso. Se le administró trombolítico, el cual no fue efectivo, resultado esperable puesto que al ser administrado a las cuatro horas de enfermedad solo el 52% son efectivos y, en mayores de 80 años, la efectividad es alrededor del 50% 6,7.
Un segundo momento, cuando el electrocardiograma muestra una clara elevación del segmento ST en derivadas precordiales (Figura 2) pudiendo esta alteración de la repolarización ser secundario al ACV isquémico 8,9; sin embargo, en el contexto de una paciente con muy alto riesgo cardiovascular se necesita descartar lesión miocárdica, teniendo presente que el ACV genera una respuesta inflamatoria sistémica con liberación de citosinas capaz de inestabilizar las placas coronarias y, además, ocasionar necrosis miocárdica por la liberación de calcio intracelular, finalizando en un síndrome coronario agudo (SCA) o en una miocardiopatía 10; es aquí que el estudio de enzimas cardiacas cumplió un rol importante 11 pues indicó daño miocárdico agudo lo cual se corroboró con la ecocardiografía, la cual demuestra alteración de múltiples territorios vasculares haciendo presumir de lesión multivaso, pero, sorprendentemente el cateterismo cardiaco reporta ausencia de lesiones obstructivas significativas dando un giro diagnóstico hacia el infarto de miocardio con arterias coronarias no obstruidas (MINOCA) 12, que junto al infarto agudo de miocardio demostrado además de la ausencia de diagnóstico alternativo encaja en los criterios de MINOCA 13-15.
Ante un MINOCA, el cual es un síndrome clínico que tiene una prevalencia que va desde 1 al 15% en pacientes con SCA especialmente en jóvenes, mujeres e infarto de miocardio ST no elevado (IMASTNE), y basados en los hallazgos ecocardiográficos, se realizó una ventriculografía 15,16 que evidencia un ventrículo en forma de «olla de Takotsubo» (Figura 4). Se concluye que estamos ante un síndrome de Takotsubo, con controversias respecto al uso de Betabloqueadores e inhibidores de la enzima convertidora de angiotensina, además del uso de tratamiento antiplaquetario único 13,14,17.
Finalmente, en un tercer momento se demuestra FA con respuesta ventricular alta pudiendo ser un evento incidental, ya que la paciente reunió condiciones para desarrollarla; formar parte del síndrome cerebro-corazón; ser secundaria al infarto de miocardio 18 o tratarse de FA preexistente, las cuales se detectan con mayor frecuencia después de un ACV; esto genera un debate para demostrar que los episodios de FA se desencadenan por el síndrome cerebro-corazón o son la causa del ictus inicial 1,2. Estos tres eventos (ACV isquémico, síndrome de Takotsubo y FA) forman parte del síndrome cerebro-corazón que ocurre con mayor frecuencia dentro de los primeros tres días del evento cerebrovascular, estando asociado a la edad avanzada, prevalencia de comorbilidades cardíacas y factores de riesgo cardiovascular 1,2, lo cual explica el daño cardíaco por la interacción a través del eje nerviosos cerebro-corazón causando inflamación, desregulación autónoma central, liberación de catecolaminas, cambios estructurales del miocardio y anomalías de la pared vascular 2,19.
El síndrome de Takotsubo, descrito por primera vez por Sato en 1990, desencadenado por eventos estresantes físicos o emocionales las cuales generan una «tormenta de catecolaminas» 20 afecta con más frecuencia a mujeres posmenopáusicas, las cuales representan el 2% de SCA o 10% si solo se considera mujeres. Su diagnóstico requiere alteración de la contractilidad miocárdica transitoria (aproximadamente 6 semanas); anomalías electrocardiográficas evolutivas 21,22; disparidad entre la elevación de troponinas y el miocardio disfuncional y ausencia de miocarditis, feocromocitoma o enfermedad coronaria epicárdica significativa 23; con complicaciones tan frecuentes, como presencia de lesiones obstructivas, preferentemente si el factor desencadénate es físico, enfermedad neurológica aguda, primera troponina mayor a 10 veces el límite superior normal y la fracción de eyección del ventrículo izquierdo es menor al 45%; condicionado un alto riesgo de mortalidad 24,25.
En nuestra opinión, el evento primario se trató de una FA paroxística que origina un ACV isquémico cardioembólico que se complicó con un síndrome de Takotsubo, cerrando el círculo del síndrome cerebro-corazón.
En conclusión, en pacientes con una presentación frecuente como el ACV isquémico, a pesar de no demostrar arritmias al ingreso, es obligatorio buscar la presencia de FA, además de evaluar la coexistencia de lesión miocárdica, pues no siempre son secundaros a lesiones coronarias significativas como en MINOCA o Sd. de Takotsubo que se presenta en el contexto de un cuadro emergente como síndrome cerebro-corazón, con alto riesgo de mortalidad, el cardiólogo está en la obligación de buscarlos, y en ello cumplen una función fundamental las troponinas ultrasensibles.
Fuente de financiamiento: El trabajo fue autofinanciado.
Citar como: Leiva Abanto C, Medina Palomino F, Anchante Hernández H, Dueñas Carvajal R. Síndrome de Takotsubo: complicación cardiovascular del accidente cerebrovascular. Arch Peru Cardiol Cir Cardiovasc. 2022;3(4). doi: 10.47487/apcyccv.v3i4.235
==== Refs
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17 Nordenskjöld AM Agewall S Atar D Baron T Beltrame J Bergström O Randomized evaluation of beta blocker and ACE-inhibitor/angiotensin receptor blocker treatment in patients with myocardial infarction with non-obstructive coronary arteries (MINOCA-BAT) Rationale and design Am Heart J 2021 231 96 104 10.1016/j.ahj.2020.10.059 33203618
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19 Budnik M Piatkowski R Ochijewicz D Zaleska M Grabowski M Opolski G Pathophysiology of Takotsubo Syndrome as A Bridge to Personalized Treatment J Pers Med 2021 11 9 879 879 10.3390/jpm11090879 34575656
20 Goico A Chandrasekaran M Herrera CJ Novel developments in stress cardiomyopathy From pathophysiology to prognosis Int J Cardiol 2016 223 1053 1058 10.1016/j.ijcard.2016.08.241 27611570
21 Johnson NP Chavez JF Mosley 2nd WJ Flaherty JD Fox JM Performance of electrocardiographic criteria to differentiate Takotsubo cardiomyopathy from acute anterior ST elevation myocardial infarction Int J Cardiol 2013 164 3 345 348 10.1016/j.ijcard.2011.07.029 21802749
22 Kosuge M Kimura K Electrocardiographic findings of takotsubo cardiomyopathy as compared with those of anterior acute myocardial infarction J Electrocardiol 2014 47 5 684 689 10.1016/j.jelectrocard.2014.03.004 24735641
23 Akashi YJ Nef HM Lyon AR Epidemiology and pathophysiology of Takotsubo syndrome Nat Rev Cardiol 2015 12 7 387 397 10.1038/nrcardio.2015.39 25855605
24 Ghadri JR Kato K Cammann VL Gili S Jurisic S Di Vece D Long-Term Prognosis of Patients With Takotsubo Syndrome J Am Coll Cardiol 2018 72 8 874 882 10.1016/j.jacc.2018.06.016 30115226
25 Templin C Ghadri JR Diekmann J Napp LC Bataiosu D Jaguszewski M Clinical Features and Outcomes of Takotsubo (Stress) Cardiomyopathy N Engl J Med 2015 373 10 929 938 10.1056/nejmoa1406761 26332547
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PMC010xxxxxx/PMC10241339.txt |
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Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37283602
10.47487/apcyccv.v3i2.211
Artículo De Revisión
Agresión mitral reumática. utilidad de la ecocardiografía transesofágica 3D
Rheumatic mitral aggression. Usefulness of 3d transesophageal echocardiographyhttp://orcid.org/0000-0002-7497-3898
Cupe-Chacalcaje Kelly 1 Médico cardiólogo
http://orcid.org/0000-0002-3846-0868
Benites-Yshpilco Lindsay 1 Médico residente de Cardiología
http://orcid.org/0000-0001-7908-5200
Cachicatari-Beltrán Angela 1 Médico cardiólogo
http://orcid.org/0000-0002-1061-3893
Urdanivia-Ruiz Dante 1 Médico cardiólogo
http://orcid.org/0000-0002-3670-0776
Rafael-Horna Eliana 1 Médico cardiólogo
http://orcid.org/0000-0002-2652-211X
Rojas Paol 1 Médico cardiólogo
http://orcid.org/0000-0002-4136-582
Lévano-Pachas Gerald 1 Médico cardiólogo
http://orcid.org/0000-0002-7538-2125
Baltodano-Arellano Roberto 1 2 Médico cardiólogo, Catedrático
1 . Hospital Nacional Guillermo Almenara, LimaPerú. Hospital Nacional Guillermo Almenara Lima Perú
2 . Universidad Nacional Mayor de San Marcos, Lima, Perú. Universidad Nacional Mayor de San Marcos Universidad Nacional Mayor de San Marcos Lima Peru
Correspondencia Galeón 150-203 San Borja, Lima, Perú. +51 996723803. [email protected]
Participación de autores KCC, RBA: concepción, recolección de datos, análisis, redacción, aprobación final. LBY: recolección de datos, análisis, redacción. ACB: recolección de datos, análisis, redacción, aprobación final. DUR, ERH, PR: recolección de datos, análisis, redacción. GLP: logística, redacción.
27 6 2022
Apr-Jun 2022
3 2 98111
20 5 2022
28 6 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
RESUMEN
La cardiopatía reumática es la causa líder de enfermedad cardiovascular en menores de 25 años en el mundo, con una mayor prevalencia en países de bajos ingresos. El hallazgo habitual y distintivo de agresión reumática es la estenosis mitral, lo cual conlleva a graves consecuencias cardiovasculares. Las guías internacionales establecen la ecocardiografía transtorácica (ETT) como prueba diagnóstica de la cardiopatía reumática; sin embargo, presenta limitaciones en la medición de la planimetría y las inherentes al Doppler. La ecocardiografía transesofágica 3D (ETE-3D) es una novel modalidad que muestra imágenes realísticas de la válvula mitral y que presenta como valor agregado el localizar con precisión el plano de máxima estenosis y determinar mejor el compromiso comisural.
ABSTRACT
Rheumatic heart disease is the leading cause of cardiovascular disease in children under 25 years of age worldwide, with the highest prevalence in low-income countries. The usual and distinctive finding of rheumatic aggression is mitral stenosis, which leads to serious cardiovascular consequences. International guidelines establish transthoracic echocardiography (TTE) as the diagnostic test for rheumatic heart disease; however, it has limitations in the measurement of planimetry and those inherent to Doppler. Transesophageal 3D echocardiography (TTE-3D) is a new modality that shows realistic images of the mitral valve and has the added value of accurately locating the plane of maximum stenosis and better determining commissural involvement.
Palabras clave:
Cardiopatía Reumática
Válvula Mitral
Estenosis de la Válvula Mitral
Ecocardiografía Transesofágica
Key words:
Rheumatic Heart Disease
Mitral Valve
Mitral Valve Stenosis
Echocardiography, Transesophageal
==== Body
pmcINTRODUCCIÓN
La fiebre reumática es, hoy en día, la causa más común de enfermedad cardiovascular en menores de 25 años teniendo como manifestación habitual la estenosis mitral 1. Prácticamente erradicada en países de altos ingresos, la enfermedad se concentra en países en vías de desarrollo debido, principalmente, a inadecuadas condiciones sanitarias 1-3. Con una prevalencia mundial de 15 millones de pacientes con cardiopatía reumática, se estima que anualmente aparecen cerca de 300 000 casos nuevos, mientras que el número de fallecimientos anuales alcanza los 233 000 4.
La prueba diagnóstica de cardiopatía reumática es la ecocardiografía 5,6, y justamente fue esta enfermedad una de las primeras en estudiarse en los albores de esta técnica. Edler y colaboradores, en el afán por determinar de forma no invasiva la insuficiencia mitral en pacientes con estenosis mitral reumática, encontraron en la aplicación del modo M del ultrasonido la solución y, de esa forma, sin imaginárselo, descubrieron la herramienta diagnóstica no invasiva mas poderosa de la cardiología 7. Gracias al avance tecnológico, hoy en día es posible visualizar la válvula mitral de forma realística tal cual la observa el cirujano en campo operatorio mediante la utilización de la ETE-3D 8.
El objetivo de esta revisión es describir la utilidad de la ETE-3D en el estudio de la agresión reumática mitral y mostrar su aplicación en una serie de casos clínicos (figuras 1 a 10, videos 1 a 10).
Figura 1 Caso 1: Mujer de 57 años con disnea y palpitaciones. Accidente cerebrovascular reciente con secuelas. La evaluación neurológica determinó afasia de expresión. Sin terapia anticoagulante previa. (A) Utilizando Doppler, el área de apertura mitral por tiempo de hemipresión fue calculada en 0,8 cm2. (B) ETE-3D con adquisición del volumen en un ciclo cardíaco. La válvula mitral en face muestra apertura muy limitada con engrosamiento de los velos y anillo, fusión parcial bicomisural, además de contraste espontáneo (Video 1). (C) Imagen 3D de la válvula mitral vista desde el ventrículo izquierdo evidencia abombamiento y engrosamiento bivalvar a predominio del velo anterior y engrosamiento de las cuerdas tendíneas, todo ello impide ver el orificio de apertura mitral (Video 2). (D) Reconstrucción multiplanar determina el área de apertura mitral en 0,5 cm2. (E) Imágenes ortogonales (factible con sonda volumétrica) de la orejuela izquierda determinando su morfología de bolsa de viento y su contenido de abundante contraste espontáneo (Video 3).
Figura 2 Caso 2: mujer de 35 años refiere disnea y ortopnea. Al examen de tórax se auscultó soplo diastólico en rodamiento en foco mitral. Fue intervenida exitosamente de remplazo con prótesis mitral mecánica. (A) La valoración Doppler determinó un gradiente medio transmitral en 12 mmHg. (B) ETE con adquisición volumétrica en dos ciclos cardíacos. La vista auricular de la válvula mitral en el cual se evidencia marcado engrosamiento y rugosidad bivalvar, fusión parcial de comisuras y, por consiguiente, apertura limitada severamente con morfología de boca de pez (Video 4). (C) En la inspección tridimensional, desde el aspecto ventricular, las valvas mitrales muestran engrosamiento, superficie abombada y rugosa, fusión de comisuras y engrosamiento de las cuerdas tendíneas (Video 5). (D) La reconstrucción multiplanar determinó el área de apertura mitral por planimetría en 0,8 cm2. (E) Trazo del área de vena contracta en 0,6 cm2 de regurgitación mitral (severo >0,4 cm2) a partir de reconstrucción multiplanar de un volumen adquirido con Doppler color.
Figura 3 Caso 3: Paciente mujer de 70 años, con disnea clase funcional NYHA III y palpitaciones por fibrilación atrial. En la inspección física resalta la ingurgitación yugular y edema de miembros inferiores. (A) Valoración Doppler en espectro intermedio determina gradientes transmitrales elevados (gradiente medio: 11,8 mmHg). (B) ETE, adquisición volumétrica en un ciclo cardíaco. Vista de cirujano de valvas mitrales engrosadas con gran prominencia en valva anterior correspondiente a proceso cálcico extenso, originando severo estrechamiento valvular (Video 6). (C) Vista ventricular en 3D del aparato mitral, evidencia severo engrosamiento y abombamiento de valva anterior, fusión de comisuras casi completa con apertura casi puntiforme (Video 7). (D) ETE-2D en color a 0° muestra dilatación biauricular severa con regurgitaciones mitrotricúspidea relevantes. (E) Modo M de vena cava inferior dilatada (29 mm) con colapso inferior a 50%. (F) Doppler continuo de regurgitación tricúspidea severa con gradiente alto (31,9 mmHg). E y F determinan probabilidad alta de hipertensión pulmonar.
Figura 4 Caso 4: Paciente varón de 62 años refiere disnea clase funcional NYHA II y palpitaciones de un año de evolución. Antecedente de ablación de flutter atrial exitoso. No adherencia a terapia anticoagulante. (A) Mediante Doppler se determinó estenosis mitral severa (gradiente medio 20,8 mmHg). (B) ETE, adquisición volumétrica en un ciclo cardíaco. Vista del cirujano de la válvula mitral con presencia de protuberancias cálcicas en comisuras con fusión parcial, además de engrosamiento y rugosidad de las valvas, los cuales ocasionan evidente estrechamiento valvular (Video 8). (C) Adquisición 3D a un ciclo de la orejuela izquierda, en la vista auricular se muestra ocupada por trombo sedimentado (Video 9). (D) Reconstrucción multiplanar con área de apertura mitral trazada en 0,6 cm2. (E) Múltiples cortes de la orejuela evidencian la presencia de trombo que ocupa casi su totalidad (Video 10).
Figura 5 Caso 5: Paciente mujer de 69 años con disnea y palpitaciones. Antecedentes: hipertensión arterial, diabetes mellitus, fibrilación auricular y Stroke secuelar reciente. (A) Gradiente medio transmitral elevado (9,2 mmHg) por valoración Doppler. (B) ETE, adquisición volumétrica en un ciclo cardíaco. Vista auricular de la válvula mitral evidencia severo engrosamiento del anillo y valvas, y fusión parcial comisural, los cuales originan estenosis severa (Video 11). (C) Vista ventricular con severo engrosamiento irregular de valvas mitrales con predominio de los bordes libres, originando fusión de comisuras y así estrechamiento valvular (Video 12). (D) Reconstrucción multiplanar, muestra un área de apertura mitral por planimetría en 0,5 cm2. (E) Imagen volumétrica de la orejuela con morfología en bolsa de viento que evidencia marcado contraste espontáneo (Video 13). (F) Tomografía cerebral que muestra lesión isquémica extensa temporoccipital izquierdo correspondiente a embolismo cardíaco.
Figura 6 Caso 6: Paciente de 56 años con estenosis mitral reumática en espera de intervención, acude por empeoramiento de disnea (NYHA III), palpitaciones y alza térmica. (A) Interrogación Doppler en espectro intermedio muestra gradiente medio transmitral en 7,9 mmHg. (B) ETE, adquisición volumétrica en un ciclo cardíaco. Vista en face de la válvula mitral con múltiples protuberancias en anillo congruente con esclerocalcificación, engrosamiento de los velos, fusión parcial bicomisural y consiguiente estenosis mitral (Video 14). (C) Vista en face de la válvula mitral cerrada, se aprecia estructura filiforme valvular originada en el aspecto auricular compatible con vegetación. (D) En la vista ventricular se aprecia valva anterior mitral engrosada y abombada, asimismo las cuerdas tendíneas se visualizan severamente engrosadas, demostrando la gran agresividad reumática (Video 15). (E) Reconstrucción multiplanar de la válvula mitral, se traza el área de apertura en 1,2 cm2. (F) ETE-2D en nivel medio esofágico a 64°, evidencia vegetación de 16 mm en válvula mitral (Video 16).
Figura 7 Caso 7: Paciente mujer de 60 años ingresa por disnea NYHA III asociada a palpitaciones. Al examen físico se evidencia ingurgitación yugular y edema de miembros inferiores. (A) Evaluación por Doppler del gradiente transmitral en 4,2 mmHg. (B) ETE, adquisición volumétrica en un ciclo cardíaco. Vista auricular de la válvula mitral que evidencia engrosamiento bivalvar y anular con fusión mínima bicomisural, configurando la apertura mitral en morfología boca de pez (Video 17). (C) Vista ventricular de la válvula mitral muestra engrosamiento de bordes libres bivalvar, abombamiento de la valva anterior, engrosamiento de las cuerdas tendíneas y fusión de comisuras (Video 18). (D) Reconstrucción multiplanar para estudiar la apertura mitral trazada en 1,1 cm2. (E) ETE-2D color en nivel esofágico medio a 0° evidencia marcada dilatación de cavidades derechas, repercusión hemodinámica de la estenosis mitral (Video 19).
Figura 8 Caso 8: Paciente mujer de 59 años con disnea y palpitaciones por fibrilación auricular. Sin terapia antiarrítmica y anticoagulante. Al examen físico resalta soplo diastólico en foco mitral y crepitantes en ambos campos pulmonares. (A) Interrogación con Doppler continuo en múltiples espectros del llenado mitral determina un gradiente medio promedio en 9,6 mmHg. (B) ETE, adquisición volumétrica en un ciclo cardíaco. Mitral en face muestra marcado engrosamiento severo del anillo y de ambas valvas, fusión parcial bicomisural y apertura severamente limitada. Asimismo, se evidencia contraste espontáneo (Video 20). (C) Válvula mitral desde el aspecto ventricular en 3D, muestra engrosamiento bivalvar con preferencia de borde libre de velo anterior; igualmente, engrosamiento de cuerdas y fusión comisural (Video 21). (D) Reconstrucción multiplanar evidencia estenosis severa (área: 0,6 cm2). (E) Vistas ortogonales de la aurícula izquierda evidencian trombo gigante (59 mm de longitud mayor) implantado en casi la totalidad de la pared lateral (Video 22). (F) Imagen 3D del trombo gigante en aurícula izquierda (Video 23).
Figura 9 Caso 9: Paciente varón de 58 años ingresa por disnea exacerbada y palpitaciones. Al examen físico resaltan ingurgitación yugular (+++/+++), ascitis y edema de miembros inferiores. (A) Valoración Doppler transmitral muestra un gradiente medio en 3,12 mmHg. (B) ETE, adquisición volumétrica en un ciclo cardíaco. Vista del cirujano de válvula mitral, destacan engrosamientos y protuberancias de valva anterior, así como engrosamiento anular y fusión parcial de ambas comisuras, lo cual origina una apertura mitral restringida (Video 24). (C) Vista ventricular de válvula mitral evidencia marcado engrosamiento de bordes libres valvares, engrosamiento de cuerdas tendíneas y fusión de comisuras (Video 25). (D) Reconstrucción multiplanar de válvula mitral, permite un trazo de apertura valvular en 2 cm2. (E) Área de vena contracta de regurgitación mitral en 1,1 cm2 (severa >0,4 cm2), estimada de reconstrucción multiplanar con color. (F) Dilatación de ventrículo derecho y regurgitación tricúspidea severa funcional con gradiente elevado (40 mmHg), ambos signos de alta probabilidad de hipertensión pulmonar.
Figura 10 Caso 10: Paciente mujer 44 años con diagnóstico de estenosis mitral reumática, intervenida hace 10 años de valvuloplastia percutánea, ingresa por disnea exacerbada y palpitaciones intermitentes. (A) Valoración Doppler de gradiente medio transmitral elevado (10,2 mmHg). (B) ETE, adquisición volumétrica en un ciclo cardíaco. Técnica de transiluminación de la válvula mitral en face, en el cual se definen mejor la profundidad y el relieve originado por esclerocalcificación severa del anillo y las valvas; asimismo, se aprecia fusión completa bicomisural, hallazgos que desvirtúan la apertura mitral (Video 26). (C) Vista ventricular de válvula mitral con transiluminación, que muestra severo engrosamiento y calcificación del aparato subvalvular con una apertura mitral no perceptible (Video 27). (D) Reconstrucción multiplanar precisa área de apertura mitral en 0,5 cm2. (E) ETE en nivel medio esofágico con rotación horaria de la sonda a 131°, muestra defecto septal auricular iatrogénico debido a valvuloplastia percutánea previa.
FISIOPATOLOGÍA DE LA ESTENOSIS MITRAL
La valvulitis reumática originada en la infancia es producto de procesos inmunológicos tipo reacción cruzada, debido a las similitudes estructurales de proteínas de la válvula nativa con los antígenos estreptocócicos (mimetismo molecular) 9.El continuum inflamatorio en el complejo mitral produce cambios degenerativos progresivos caracterizados por engrosamiento, esclerosis y calcificación del aparato valvular lo cual ocasiona, en etapas avanzadas predominantemente, obstrucción en el flujo sanguíneo hacia el ventrículo izquierdo.
La consiguiente elevación de presiones en la aurícula izquierda incrementa el gradiente transmitral para mantener el llenado ventricular. La transmisión pasiva de presiones hacia las venas pulmonares y capilar pulmonar promueve el incremento de las presiones de la arteria pulmonar, que en el tiempo originará cambios estructurales en el árbol pulmonar llevando a hipertensión pulmonar fija, la segunda estenosis 10. Esta condición favorece una menor congestión a expensas de una reducción del gasto cardíaco con las consecuencias clínicas que ello implica. Asimismo, la poscarga incrementada para el ventrículo derecho origina su remodelamiento que, en fases avanzadas, conlleva a perpetuar el ciclo de falla derecha - regurgitación tricúspidea 11.
ECOCARDIOGRAFÍA TRANSTORÁCICA 2D EN LA ESTENOSIS MITRAL
La afectación reumática mitral se inicia con el engrosamiento de los bordes libres valvares, lo cual es progresivo hacia las bases, llevando en fases posteriores a deformar la valva anterior en forma de palo de hockey mientras que la valva posterior se torna rígida 12,13. La vista paraesternal eje largo muestra tales hallazgos, incluyendo las alteraciones cordales que se inician en la unión valvar y progresan a músculos papilares. En el abordaje paraesternal eje corto, el plano mitral es de vital importancia, pues muestra la fusión comisural con apertura en boca de pez, distintivo de agresión reumática 5. Asimismo, nos permite precisar el área de apertura por planimetría, el cual es el Gold Standard para medir el área de estenosis, dado que no es influenciado por confusores presentes en métodos basados en el Doppler11.
La estimación de severidad por valoración Doppler, como la gradiente transmitral y el tiempo de hemipresión, están sujetos a variaciones ocasionadas por el flujo sanguíneo, y la frecuencia cardiaca; adicionalmente, la segunda se afecta por Compliance reducida 14, por ejemplo, los puntos de corte de gradiente transmitral para determinar severidad son aplicables solo en frecuencia cardíaca entre 60 y 80 latidos por minuto, pues mayores frecuencias sobreestimarán la severidad 15.
La ETT tiene un rol esencial en el diagnóstico de la estenosis mitral reumática, en la determinación de severidad y en la obtención de scores útiles para la indicación de valvuloplastia percutánea 16,17. Desafortunadamente tiene limitaciones como el riesgo de imprecisión para encontrar el plano de mayor estrechez mitral, los confusores del Doppler y la baja reproducibilidad de los scores de valoración de valvuloplastia por ser estos semicuantitativos 5.
EXPLORACIÓN TRANSESOFÁGICA 3D DE LA AGRESIÓN MITRAL
Indudablemente la ecocardiografía transesofágica (ETE) es la técnica de imagen que brinda la más alta resolución espacial de la válvula mitral, debido a la mayor frecuencia de transmisión, proximidad del transductor y al plano de mejor recepción de insonación de ultrasonido que se encuentra la válvula mitral 18. Precisamente, el barrido de 0 a 120 grados en el nivel medio esofágico asegura la exploración en detalle de los festones anteriores y posteriores, dando cuenta de su anatomía y funcionalidad. La ETE-2D esta indicada en presencia de imágenes transtorácicas técnicamente limitadas y en la valoración de valvuloplastía percutánea, para determinar severidad de la regurgitación mitral y presencia de trombos en orejuela izquierda 6. Sin embargo, carece de valor agregado en el estudio de la estenosis mitral.
El avance tecnológico promovió el desarrollo del transductor matricial, constituida por más de 3000 cristales piezoeléctricos (modo M de cristal único) que, junto a potentes procesadores, favorecieron el desarrollo de la ecocardiografía tridimensional en tiempo real. La incorporación de la tecnología 3D en la sonda transesofágica optimizó la valoración de la válvula mitral, al superar en calidad de imagen y resolución a la ETT-3D 19. De esta forma, la ETE-3D prevé imágenes realísticas de la válvula mitral, demuestra en detalle valvas y comisuras y, finalmente, permite obtener una planimetría mitral en la máxima estenosis 18. Estas cualidades hacen de esta modalidad una excelente elección para la planeación quirúrgica y la intervención percutánea 19.
Adquisición de imagen
La adquisición volumétrica transesofágica se realiza en el nivel medioesofágico en una vista entre 0 y 120 donde las valvas mitrales se disponen en el plano mas horizontal, exactamente perpendicular al ultrasonido, asegurando de esa forma la máxima resolución espacial. Los modos de adquisición, volumen a latido único o a múltiples latidos tienen los beneficios y vicios inherentes a ellos. El primero factible tanto en ritmo sinusal como en fibrilación auricular, sin embargo, evidencia limitaciones en las resoluciones temporal y espacial, mientras que el segundo puede alcanzar altas resoluciones, pero su aplicación requiere un ritmo cardíaco regular y control respiratorio 20.
Planimetría
La medición del área valvular mitral es determinante en el manejo de la estenosis mitral reumática, y es el método recomendado dado que es independiente de la hemodinamia 12. A diferencia de la planimetría transtorácica 2D, que usualmente sobreestima al área de apertura mitral, la planimetría obtenida a partir de la ETE-3D con la herramienta reconstrucción multiplanar identifica con exactitud la parte más estrecha del orificio valvular mitral haciéndolo, además, con baja variabilidad intra e interobservador 18,21,22. Debido a ello, algunos investigadores la consideran el Gold Standard23. Una ventaja adicional de la ETE-3D es la factibilidad para hacer la planimetría de la apertura mitral, la cual es superior que la ETT-2D (95% vs 60%) 18.
Estudio de comisuras
La visualización privilegiada de las comisuras mitrales en la agresión reumática constituye la principal ventaja de la ETE-3D en comparación con los estudios en 2D 18. Esta valoración es posible en los todos los pacientes estudiados con ETE-3D, a diferencia de la ETT-2D que en el 40% de pacientes no se logra visualizar por completo las comisuras. Adicionalmente, la ETT-2D subestima el grado de fusión comisural en el 19% de casos cuando se compara con la ETE-3D 24. Dado que la separación de comisuras en la valvuloplastía es el principal mecanismo que incrementa la apertura mitral, su visualización óptima con ETE 3D podría ser superior a técnicas 2D en predecir resultados de valvuloplastias con balón 18,25.
CONCLUSIONES
La fiebre reumática es una enfermedad aún prevalente en países de bajos ingresos como el nuestro. La ecocardiografía es la prueba diagnóstica de la cardiopatía reumática, no obstante, su desarrollo en la modalidad ETE 3D ha permitido mostrar la agresión mitral con alto realismo, similar a la visualización del cirujano en campo operatorio. La presentación comisural privilegiada y la planimetría de la estenosis mitral constituyen el valor agregado de la ETE 3D, convirtiéndola en una herramienta útil para la planeación de la intervención percutánea o quirúrgica
Fuente de financiamiento: Autofinanciado
Citar como: Cupe-Chacalcaje K, Benites-Ys hpilco L, Cachicatari-Beltrán A, Urdanivia-Ruiz D, Rafael-Horna E, Rojas P, et al. Agresión mitral reumá tica. Utilidad de la ecocardiografía transesofágica 3D. Arch Peru Cardiol Cir Cardiovasc. 2022;3(2):98-111. doi: 10.47487/apcyccv.v3i2.211
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4 Seckeler MD Hoke TR The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease Clin Epidemiol 2011 3 67 84 10.2147/CLEP.S12977 21386976
5 Wunderlich NC Beigel R Siegel RJ Management of mitral stenosis using 2D and 3D echo-Doppler imaging JACC Cardiovasc Imaging 2013 6 11 1191 1205 10.1016/j.jcmg.2013.07.008 24229772
6 Otto CM Nishimura RA Bonow RO Carabello BA Erwin JP 3rd Gentile F 2020 ACC/AHA Guideline for the Management of Patients with Valvular Heart Disease Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines J Am Coll Cardiol 2021 77 4 450 500 10.1016/j.jacc.2020.11.035 33342587
7 Singh S Goyal A The origin of echocardiography a tribute to Inge Edler Tex Heart Inst J 2007 34 4 431 438 18172524
8 Zamorano J de Agustín JA Three-dimensional echocardiography for assessment of mitral valve stenosis Curr Opin Cardiol 2009 24 5 415 419 10.1097/HCO.0b013e32832e165b 19550310
9 Cunningham MW Rheumatic fever, autoimmunity, and molecular mimicry the streptococcal connection Int Rev Immunol 2014 33 4 314 329 10.3109/08830185.2014.917411 24892819
10 Tandon HD Kasturi J Pulmonary vascular changes associated with isolated mitral stenosis in India Br Heart J 1975 37 1 26 36 10.1136/hrt.37.1.26 1111557
11 Silbiger JJ Advances in Rheumatic Mitral Stenosis Echocardiographic, Pathophysiologic, and Hemodynamic Considerations J Am Soc Echocardiogr 2021 34 7 709 722 10.1016/j.echo.2021.02.015 33652082
12 Baumgartner H Hung J Bermejo J Chambers JB Evangelista A Griffin BP Iung B Otto CM Pellikka PA Quiñones M EAE/ASE Echocardiographic assessment of valve stenosis EAE/ASE recommendations for clinical practice Eur J Echocardiogr 2009 10 1 1 25 10.1093/ejechocard/jen303 19065003
13 Jain S Mankad SV Echocardiographic assessment of mitral stenosis echocardiographic features of rheumatic mitral stenosis Cardiol Clin 2013 31 2 177 191 10.1016/j.ccl.2013.03.006 23743071
14 Gorlin R Gorlin SG Hydraulic formula for calculation of the area of the stenotic mitral valve, other cardiac valves, and central circulatory shunts I. Am Heart J 1951 41 1 1 29 10.1016/0002-8703(51)90002-6 14799435
15 Nishimura RA Rihal CS Tajik AJ Holmes DR Jr Accurate measurement of the transmitral gradient in patients with mitral stenosis a simultaneous catheterization and Doppler echocardiographic study J Am Coll Cardiol 1994 24 1 152 158 10.1016/0735-1097(94)90556-8 8006259
16 Wilkins GT Weyman AE Abascal VM Block PC Palacios IF Percutaneous balloon dilatation of the mitral valve an analysis of echocardiographic variables related to outcome and the mechanism of dilatation Br Heart J 1988 60 4 299 308 10.1136/hrt.60.4.299 3190958
17 Iung B Cormier B Ducimetière P Porte JM Nallet O Michel PL Immediate results of percutaneous mitral commissurotomy A predictive model on a series of 1514 patients Circulation 1996 94 9 2124 2130 10.1161/01.cir.94.9.2124 8901662
18 Schlosshan D Aggarwal G Mathur G Allan R Cranney G Real-time 3D transesophageal echocardiography for the evaluation of rheumatic mitral stenosis JACC Cardiovasc Imaging 2011 4 6 580 588 10.1016/j.jcmg.2010.12.009 21679891
19 Sugeng L Shernan SK Salgo IS Weinert L Shook D Raman J Live 3-dimensional transesophageal echocardiography initial experience using the fully-sampled matrix array probe J Am Coll Cardiol 2008 52 6 446 449 10.1016/j.jacc.2008.04.038 18672165
20 Lang RM Badano LP Tsang W Adams DH Agricola E Buck T EAE/ASE recommendations for image acquisition and display using three-dimensional echocardiography Eur Heart J Cardiovasc Imaging 2012 13 1 1 46 10.1093/ehjci/jer316 22275509
21 Min SY Song JM Kim YJ Park HK Seo MO Lee MS Discrepancy between mitral valve areas measured by two dimensional planimetry and three-dimensional transoesophageal echocardiography in patients with mitral stenosis Heart 2013 99 4 253 258 10.1136/heartjnl-2012-302742 23125249
22 Dreyfus J Brochet E Lepage L Attias D Cueff C Detaint D Realtime 3D transoesophageal measurement of the mitral valve area in patients with mitral stenosis Eur J Echocardiogr 2011 12 10 750 755 10.1093/ejechocard/jer118 21824874
23 Zhong X Chen W Shi Z Huan Z Ma L Liu W Three-dimensional transesophageal echocardiography measurement of mitral valve area in patients with rheumatic mitral stenosis ¿multiplanar reconstruction or 3D direct planimetry? Int J Cardiovasc Imaging 2021 37 1 99 107 10.1007/s10554-020-01950-1 32719991
24 Messika-Zeitoun D Brochet E Holmin C Rosenbaum D Cormier B Serfaty JM Three-dimensional evaluation of the mitral valve area and commissural opening before and after percutaneous mitral commissurotomy in patients with mitral stenosis Eur Heart J 2007 28 1 72 79 10.1093/eurheartj/ehl206 16935871
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Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37283603
10.47487/apcyccv.v3i2.209
Case Report
Giant cardiac hydatid cyst causing sustained ventricular tachycardia. Successful surgical treatment
Quiste hidatídico cardíaco gigante causante de taquicardia ventricular sostenida. Tratamiento quirúrgico exitosohttp://orcid.org/0000-0002-0339-3431
Callalli Edmy 1 Cardiovascular surgeon
http://orcid.org/0000-0002-0481-7400
Pelaez Christian 2 Cardiologist
http://orcid.org/0000-0003-4379-3058
Armendáriz-Ferrari José Carlos 2 Cardiologist
http://orcid.org/0000-0002-7675-7726
Gonzales Silvana 1 Cardiovascular surgeon
http://orcid.org/0000-0002-3162-7362
Salas Mercedes 1 Cardiovascular surgeon
http://orcid.org/0000-0002-3505-2949
Niño Martín 1 Resident
http://orcid.org/0000-0001-5501-0108
Bombilla Anibal 1 Resident
1 Cardiothoracic Surgery Department, Hospital Nacional Hipólito Unanue. Lima, Peru. Cardiothoracic Surgery Department Hospital Nacional Hipólito Unanue Lima Peru
2 Cardiology Service, Hospital Nacional Hipólito Unanue. Lima, Peru. Cardiology Service Hospital Nacional Hipólito Unanue Lima Peru
Corresponding author: Edmy Callalli Mattos Av. Salaverry 575; Edificio DALI - Departamento 2501. +51 992774437. [email protected]
Conflict of interest statement: None declared
27 6 2022
Apr-Jun 2022
3 2 117120
02 5 2022
25 6 2022
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License
ABSTRACT
Cardiac involvement of hydatid disease is rare. In Peru, a country with a high prevalence of this infectious disease, few cases of cardiac hydatid disease have been reported. We present the case of a man with a cardiac hydatid cyst of more than 10 cm in diameter that debuted with malignant arrhythmia and successfully treated with surgery.
RESUMEN
La afectación cardíaca de la enfermedad hidatídica es rara. En Perú, país con alta prevalencia de esta enfermedad infecciosa, se han reportado pocos casos de hidatidosis cardíaca. Presentamos el caso de un varón con un quiste hidatídico cardíaco de más de 10 cm de diámetro que debutó con arritmia maligna, tratado exitosamente con cirugía.
Keywords:
Echinococcosis
Heart
Arrhythmias, cardiac
Palabras clave:
Equinococosis
Corazón
Arritmias cardiacas
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pmcINTRODUCTION
Echinococcosis is a zoonosis endemic in Peru, and an estimated 1,139 disability-adjusted life years are lost annually from surgical treatment 1. It is caused by the larval form of Echinococcus granulosus. The larval development leads to hydatid cyst formation. The most frequent site of the disease is in the liver (50-70%), followed by the lungs (25-40%). Cardiac location is uncommon and represents 0-5-2% of all sites (2. The clinical manifestations of cardiac echinococcosis can range from the absence of symptoms to life-threatening complications. In the cases described, surgical management is the treatment of choice (2,3.
CASE REPORT
We present a 48-year-old male patient, born in Cerro de Pasco (Peruvian Andean), resident in Lima 20 years ago, with no relevant medical history. One year before his admission to our hospital, he presented palpitations and two spontaneously resolved presyncopal episodes. Two weeks before admission, the patient presented sustained palpitations that led to loss of consciousness and motivated his admission to the emergency department of our hospital. The emergency physical examination showed a Glasgow scale 12/15, blood pressure 120/80 mmHg, heart rate: 120 beats per minute, and temperature 36.5 ºC. Chest examination revealed rhythmic heart sounds, absence of murmurs, and deviation of the apex impulse to the left. The electrocardiogram revealed monomorphic ventricular tachycardia. With these data, the patient underwent electrical cardioversion with two shocks of 200 J. Emergency laboratory tests showed Leukocytes: 9000; Hb: 15 g/dL; neutrophils: 10%; Platelets: 200 000; Troponins: 0.2; creatinine: 0.7 mg/dL. Hydatest (indirect hemagglutination test for the detection of antibodies to Echinococcus Granulosum) was Negative. Chest X-ray: lung fields without alveolar or interstitial infiltrates, growth of cardiac cavities with a deviation of the apex upwards, and cardiothoracic index of 0.78 (Figure 1A). The transthoracic echocardiogram showed a heterogeneous hyperechoic intrapericardial image with defined borders located at the apex with compression of the right and left ventricles. The left and right valves were not affected and the function of both ventricles was preserved. Cardiac tomography revealed a heterogeneous image with defined walls, without contrast enhancement, located in the apical part of the right ventricle, with a mean density of 18 HU, dimensions of 10.8x9x8cm (Figure 1B), with the apex of the left ventricle deviated towards the left. After discussing the case with the Heart Team, surgical treatment was decided.
Figure 1 A. Frontal chest X-ray showing cardiomegaly, with regular cardiac silhouette and elevated heart apex, lung parenchyma with complete expansion, and free costophrenic and diaphragmatic angles. B. Chest tomography showing the approximate dimensions of the cardiac cyst tumor, longitudinal 10.85 cm x anteroposterior 8.47 cm.
Surgical findings
We found a pedunculated and cystic tumor in the right apex compatible with a hydatid cyst measuring 10.5x9x8 cm (Figure 2A). When we open the cystic cavity, we show purulent and fetid material, after aspirating said content. The cyst was found in the muscular wall of the apex of the right ventricle without compromising the anterior or inferior wall of the said ventricle, nor did it have communication with the ventricular cavity.
Figure 2 A. Surgical treatment: Opening of the cardiac cyst and evacuation of the content, no communication with the cardiac chambers is evident, and involvement of the interventricular septum is identified. B. Surgical treatment: partial capitonage of the cystic cavity with right ventriculoplasty. C. Pathology image. Hematoxylin eosin stain showing the hydatid membranes.
Surgical Technique
We approached throughout a total median sternotomy. Ascending aorta and inferior and superior vena cava were cannulated for cardiopulmonary bypass (CPB) at normothermia. We used anterograde cardioplegic solution (HTK Custodiol®) for cardiac protection. Superior and inferior vena cava were snared to prevent air from entering the CPB system. The apex of the right ventricle anteriorly was placed anteriorly, we opened the cyst wall longitudinally, observing the surgical findings. After performing a meticulous cleaning, we resected as much of the external membrane of the cyst as possible. Then, we closed the continuity solution of the ventricle wall with stitches (Polypropylene 3/0) with two Teflon rods simulating the Dor ventriculoplasty (Figure 2B). CPB and aortic cross-clamp times were 124 and 100 minutes, respectively.
Follow-up
The patient was disconnected from the mechanical ventilator 12 hours after surgery; however, he remained in the intensive care unit for 4 days due to right ventricular dysfunction. Postoperative transthoracic echocardiography showed preserved left ventricular ejection fraction (55%), and non-dilated right ventricle with right ventricular outflow tract fractional shortening of 36%. During the postoperative evolution, there were no arrhythmia events. The patient was discharged two weeks after surgery. Discharge treatment included Albendazole 400mg/ 12h for three months and Bisoprolol.
Pathological findings
The pathological analysis confirmed the presence of non-viable hydatid membranes (Figure 2C).
DISCUSSION
Peru is one of the countries with an endemic prevalence of hydatid disease in Latin America. Junín, Cerro de Pasco (our patient's birthplace), and Huancavelica are the provinces with the highest incidence of hydatid disease (1. It is reported that the most frequent cases of Cardiac Hydatid (CH) are non-infected cases, with reports of infected cases such as ours being exceptional 2,3.
Due to the slow growth of CH cysts, a growth rate of 0.5-1 cm/year, only 10% are symptomatic. The clinical manifestations are nonspecific: palpitations, syncope, atypical chest pain, dyspnea, and even cough and fever at the beginning of the disease. The electrocardiographic manifestations are also nonspecific, with the most frequent pattern in the different case reports being deep negative T waves, which can be explained by the inflammatory and mechanical effects of the cyst. Other electrocardiographic manifestations described are ST-segment elevation, isolated ventricular extrasystoles, sustained and non-sustained ventricular tachycardia. Depending on the location, acute cyst rupture can cause pulmonary embolism with right heart failure and arterial embolism into the brain, aorta, and arteries of the extremities. Serological tests have a limited diagnostic value, with ELISA being the one with the highest sensitivity and specificity 2-4.
Transthoracic echocardiography is the initial diagnostic test, showing the cysts, their location, number, size, as well as hemodynamic compromise, and probable complications such as pericardial effusion. In case of diagnostic doubt, cardiac CT and cardiac MRI could be performed (4-6.
The heart is infected when hexacanth embryos reach the systemic circulation and the myocardium via the coronary circulation, pulmonary artery, or patent foramen oval. The left ventricle is most frequently involved (55% to 60%), followed by the right ventricle (15% to 25%), the left atrium (8%), the pericardium (8% to 11%), the septum interventricular (5% to 9%) and the pulmonary arteries (7%) due to their greater vascularization. Hydatid cysts of the left ventricle are usually located in the sub-epicardium, while those of the interventricular septum are intra-myocardial (3,7.
Several cases of cardiac hydatid disease have been reported in Peru. Huerta-Obando et al., in 2017, reported a case of a 10-year-old patient with CH disease in the interventricular septum measuring 7.2x5.0 cm with associated liver cyst 7. In 2012, Castillo et al. published a case of an 11-year-old patient with a 3.5x3.1 cm cardiac hydatid cyst in the posterolateral wall of the left ventricle associated with a pulmonary cyst 8. Ramirez et al., in 2010, reported a case of a 45-year-old obese patient with CH disease in the left cardiac apex measuring 7.3x5.7 cm associated with a hepatic cyst 9. In these three cases, surgical treatment followed by cycles of Albendazole was the successful treatment, which is consistent with other published cases 2,3. However, Montero et al., reported a case of a 21-year-old patient with CH disease in the interventricular septum measuring 5.4x4.4 cm but, they do not mention surgery as a definitive treatment 10.
In conclusion, CH disease is a rare presentation of Echinococcosis, we should suspect it when cardiac cystic tumors are found in patients with the appropriate epidemiological context. Surgical management followed by cycles of Albendazole is the treatment of choice.
Funding statements : This work was supported by self-financing
Ethics statement: Authors declare that they have respected the ethical principles of the Declaration of Helsinki
Citar como: Callalli E, Pelaez C, Armendáriz-Ferrari JC, Gonzales S, Salas M, Niño M, et al. Giant cardiac hydatid cyst causing sustained ventricular tachycardia. Successful surgical treatment. Arch Peru Cardiol Cir Cardiovasc. 2022;3(2). doi: 10.47487/apcyccv.v3i2.209
==== Refs
BIBLIOGRAPHIC REFERENCES
1 Tamarozzi F Hou A Morales ML Giordani MT Vilca F Mozo K Prevalence and Risk Factors for Human Cystic Echinococcosis in the Cusco Region of Peruvian Highlands Diagnosed Using Focused Abdominal Ultrasound National Library of Medicine Am J Trop Med Hyg 2017 96 6 1472 1477 10.4269/ajtmh.16-0882 28719254
2 Jamli M Cherif T Ajmi N Besbes T Mgarrech I Jerbi S Surgical Management and Outcomes of Cardiac and Great Vessels Echinococcosis A 16-Year Experience Ann Thorac Surg 2020 110 4 1333 1338 10.1016/j.athoracsur.2020.01.065 32145201
3 KahlfuB S Flieger R Roepke T Yilmaz K Diagnosis and treatment of cardiac echinococcosis Heart 2016 102 17 1348 1353 10.1136/heartjnl-2016-309350 27199228
4 Fennira S Kamoun S Besbes B Ben Mrad I Zairi I Ben Moussa F Cardiac hydatid cyst in the interventricular septum A literature review Int J Infect Dis 2019 88 120 126 10.1016/j.ijid.2019.09.004 31499211
5 Dursun M Terzibasioglu E Yilmaz R Cekrezi B Olgar S Nisli K Cardiac hydatid disease CT and MRI findings AJR Am J Roentgenol 2008 190 1 226 232 10.2214/AJR.07.2035 18094316
6 Canpolat U Yorgun H Sunman H Aytemir K Cardiac hydatid cyst mimicking left ventricular aneurysm and diagnosed by magnetic resonance imaging Turk Kardiyol Dern Ars 2011 39 1 47 51 21358231
7 Huerta-Obando AV Olivera-Baca EY Silva-Díaz J Salazar-Díaz A Quiste hidatídico intracardiaco en una niña reporte de caso Rev Peru Med Exp Salud Publica 2018 35 2 338 343 10.17843/rpmesp.2018.352.3258 30183910
8 Castillo De la Cadena L Cuba Y Mendoza C Mispireta M Parra J Pacheco P Quiste cardiaco hidatídico en un niño Presentación de un caso Rev Med Hered 2012 23 36 40
9 Ramírez J Hernández J Orlovac A Málaga G Cáceres J Equinococcosis cardiaca y obesidad mórbida Consideraciones quirúrgicas Acta Med Peru 2010 27 123 126
10 Montero F Contreras O Barriga E Curi Y Cosme P Delgado C A propósito de un caso de quiste hidatídico cardiaco Rev Per Cardiol 2005 31 3 180 185
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PMC010xxxxxx/PMC10241342.txt |
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Arch Peru Cardiol Cir Cardiovasc
Arch Peru Cardiol Cir Cardiovasc
apcyccv
Archivos Peruanos de Cardiología y Cirugía Cardiovascular
2708-7212
2708-7212
Instituto Nacional Cardiovascular - INCOR
37283601
10.47487/apcyccv.v3i2.202
Reporte De Caso
Complicaciones cardiovasculares en gestante con linfoma primario mediastinal de células B
Cardiovascular complications in pregnant woman with Primary Mediastinal B-Cell Lymphomahttp://orcid.org/0000-0001-9368-842X
Inquilla Coyla Melisa 1 Médica residente del Servicio de Cardiología
http://orcid.org/0000-0001-9318-4918
Anchante Hernández Henry 1 Médico asistente de Servicio Cardiología
http://orcid.org/0000-0002-5854-1032
Medina Palomino Félix 1 Médico asistente de Servicio Cardiología
1 Hospital Cayetano Heredia, Lima - Perú. Hospital Cayetano Heredia Lima Perú
Correspondencia: Melisa Jessi Inquilla Coyla. Av. Tomás Valle 1250 E11 Dpto. 505. [email protected]
Contribución de los autores MIC, HAH y FMP participaron en la redacción, revisión y aprobación de la versión final del manuscrito
Conflictos de interés: Sin conflicto de intereses
27 6 2022
Apr-Jun 2022
3 2 112116
27 3 2022
03 6 2022
https://creativecommons.org/licenses/by-nc/4.0/ Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
RESUMEN
Gestante de 26 años con 29 semanas de edad gestacional, refirió 2 meses de tos, disnea, ortopnea y palpitaciones. La tomografía de tórax reveló una masa sólida de 10x12 cm en el pulmón derecho; la ecocardiografía mostró que la tumoración comprometía la aurícula y el ventrículo derecho; mediante biopsia transcutánea se diagnosticó linfoma primario mediastinal de células B (LPMCB). La paciente presentó flutter auricular 2:1, bradicardia sinusal y bradicardia auricular ectópica; por la rápida y mala evolución se decidió el término de la gestación por cesárea y el inicio de quimioterapia, posterior a la cual las complicaciones cardiovasculares resolvieron. El LPMCB es un linfoma muy raro que puede afectar gestantes en cualquier trimestre, sus síntomas están relacionados con su rápido crecimiento y comprometen el corazón provocando diversas manifestaciones cardiovasculares (insuficiencia cardíaca, efusión pericárdica, arritmias cardiacas). El LPMCB es característicamente quimiosensible y de buen pronóstico.
ABSTRACT
A 26-year-old pregnant woman with 29 weeks of gestational age reported two months of cough, dyspnea, orthopnea, and palpitations. Chest tomography revealed a 10x12cm solid mass in the right lung. Echocardiography showed that the tumor compromised the right atrium and ventricle, and was diagnosed by transcutaneous biopsy as Primary Mediastinal B-Cell Lymphoma (PMCBL). The patient presented 2:1 atrial flutter, sinus bradycardia, and ectopic atrial bradycardia. Due to the rapid poor evolution, it was decided to terminate the pregnancy by cesarean section and start chemotherapy, after which the cardiovascular complications resolved. PCML is a very rare lymphoma that can affect pregnant women in any trimester, its symptoms are related to its rapid growth and compromise of the heart, causing various cardiovascular manifestations (heart failure, pericardial effusion, cardiac arrhythmias). PCMLC is characteristically chemosensitive and has a good prognosis.
Palabras clave:
Linfoma de Células B Grandes Difuso
Gestación
Metástasis
Taquiarritmia
Bradicardia
Keywords:
Lymphoma, Large B-Cell Diffuse
Pregnancy
Metastasis
Tachyarrhythmia
Bradycardia
==== Body
pmcINTRODUCCIÓN
Los tumores cardiacos son hallazgos infrecuentes, y son los metastásicos o secundarios los predominantes; los de tipo hematológico (incluyendo linfomas) ocupan el cuarto lugar en frecuencia 1; el compromiso cardíaco por linfomas es poco común y afecta a diferentes niveles como el pericardio, el miocardio, el endocardio y el sistema de conducción 2.
Sabiendo que la incidencia de los linfomas ocurre en la población joven, y por tanto en mujeres en edad fértil, la presentación en la gestación es posible, pudiendo representar hasta el 25% de las neoplasias en esta etapa, siendo el linfoma no Hodgkin el menos frecuente con una incidencia estimada de 0,8 por 100 000 gestaciones 3 que demandan consideraciones diagnósticas y terapéuticas particulares.
REPORTE DE CASO
Paciente multípara de 26 años y 29,5 semanas de gestación; admitida al servicio de emergencia, refiere tos no productiva, disnea progresiva, ortopnea y palpitaciones ocasionales durante los últimos 2 meses. Además, aumento de volumen del rostro y cuello; dilatación venosa en la cara anterior del tórax, abdomen, rostro y cuello; disfonía y pérdida de peso aproximadamente de 15 kg durante el último año. El examen físico de ingreso reveló presión arterial 90/60 mmHg, frecuencia cardiaca 102 lpm y frecuencia respiratoria 28 rpm, con saturación de oxígeno en 95% a FiO2 ambiental. Igualmente, se evidenció palidez, circulación colateral en la región anterior del tórax, ingurgitación yugular y murmullo vesicular abolido en mitad inferior en ambos campos pulmonares, ruidos cardiacos taquicárdicos rítmicos; el abdomen estuvo distendido en relación al útero grávido.
Los exámenes auxiliares mostraron: electrocardiograma con taquicardia sinusal y elevación marcada del nivel sérico de deshidrogenasa láctica 1053 U/L (VN: 120 - 246). En el resto de los parámetros no se encontró otra alteración significativa. La biometría fetal ecocardiográfica reveló gestación única de 29,3 semanas y peso ponderado de 1491 g.
La tomografía de tórax (Fig.1) mostró una masa mediastinal sólida de 10x12 cm, la ecocardiografía encontró una tumoración auricular derecha, no móvil, de 35x40 mm sésil, de ecogenicidad heterogénea, efusión pericárdica leve, engrosamiento e hipocinesia de cara lateral de ventrículo derecho, con función sistólica de ventrículo izquierdo preservada (FEVI en 51%) (Fig. 2). La evolución clínica fue tórpida demandando soporte oxigenatorio por distrés marcado y presentando palpitaciones de inicio súbito documentándose flutter auricular 2:1 que revirtió espontáneamente a ritmo sinusal (Fig. 3). Se inició la corticoterapia y quimioterapia con doxorrubicina, ciclofosfamida y vincristina bajo la alta sospecha de malignidad, por decisión de junta médica multidisciplinaria.
Figura 1 Tomografía de tórax sin contraste: masa sólida de 11x12 cm que compromete mediastino anterior y lóbulo pulmonar superior y medio derechos con atelectasias compresivas y pasivas en parénquima adyacente; presencia de ganglios mediastinales de hasta 16 mm y axilares de 9 mm.
Figura 2 Vista apical cuatro cámaras y subcostal: tumoración que invade aurícula derecha e infiltra pared ventrículo derecho (Flechas).
Figura 3 Flutter con bloqueo 2:1 con frecuencia ventricular en 136 lpm.
Se realizó una biopsia percutánea por vía transtorácica anterior y se estableció el diagnóstico anatomopatológico e inmunohistoquímico de linfoma primario mediastinal de células B grandes estadio II de Ann Bor. Debido a la rápida mala evolución se procedió con el término del embarazo por vía cesárea segmentaria, obteniéndose un neonato de 1775 g. Luego de la intervención se agregó rituximab; 48 h después la paciente presentó bradicardia asintomática (40- 43 lpm) y posteriormente ritmo auricular ectópico con bradicardia en 40 lpm (Fig. 4), el cual revirtió espontáneamente a ritmo sinusal luego de tres días.
Figura 4 Ritmo auricular ectópico con bradicardia en 40 lpm.
Posterior a la segunda sesión de quimioterapia la paciente presento remisión de las taquiarritmias y bradiarritmias permaneciendo en ritmo sinusal a frecuencia de 60-65 lpm promedio; en su control ecocardiográfico (Fig. 5) se evidenció disminución del tamaño de la tumoración auricular, y mejoría en la motilidad de pared lateral de VD. La paciente fue dada de alta para completar su quimioterapia de forma ambulatoria.
Figura 5 Vista subcostal y apical cuatro cámaras: control ecocardiográfico post quimioterapia con reducción de tumoración de aurícula derecha.
DISCUSIÓN
El linfoma primario mediastinal de células B grandes (LPMCB) es un tipo raro de linfoma (3% de todos los linfomas no Hodgkin y 6 a 10% de los linfomas de células B difusos); es más frecuente en mujeres (relación 3:1), en la tercera y cuarta década de la vida 4. Puede presentarse en cualquier trimestre de la gestación y esta condición pareciera no afectar el curso natural del linfoma, pero dificulta la estadificación y decisiones del manejo 5, como se presentaron en el caso reportado.
Este tipo de linfoma es de rápido crecimiento y su cuadro clínico es de pocos meses, sus principales síntomas y signos son relacionados a la compresión de órganos adyacentes: disnea, disfagia, tos, voz ronca, compresión de vasos y síndrome de vena cava superior; los síntomas generales como fiebre o pérdida de peso se encuentran en menos del 20% de los casos. Al momento del diagnóstico, esta tumoración generalmente es de tipo masa voluminosa (bulky o >10cm) en mediastino anterior en el 80% de los casos, en estadios tempranos 2-4.
Los linfomas pueden comprometer al corazón y mostrar clínica cardiovascular. Los mecanismos de compromiso cardiaco descritos por McDonnell et al. en 1982 son vía hematógena, diseminación linfangítica retrógrada e infiltración directa. Los síntomas son variables dependiendo nivel comprometido (pericardio, miocardio); sistema de conducción cardiaco: efusión pericárdica, insuficiencia cardiaca, arritmias, masas intracardiacas, obstrucción del flujo cardiaco y disfunción valvular 2.
El estudio imagenológico de inicio es la ecocardiografía, por su disponibilidad y bajo costo; la resonancia magnética cardiaca complementa este estudio determinando la ubicación, extensión, movilidad, funcionalidad y caracterización de los tejidos comprometidos, mediante sus diferentes secuencias (6, 7).
Se sospechará de trastornos de conducción si hay una diseminación y crecimiento rápido, la proximidad al sistema de conducción, especialmente en pacientes con trastornos de conducción previos 8. Se han reportado casos en pacientes no gestantes con este tipo de linfoma que manifiestan taquiarritmias; Tsay et al. reportaron un caso de fibrilación auricular que alternó con disfunción del nodo sinusal con ritmo de escape auricular ectópico 9; Khalid K et al. reportaron fibrilación auricular paroxística y sin recurrencia luego de la quimioterapia 10. También se reportaron bradiarritmias: Subramanyam et al. reportaron bloqueo auriculoventricular (BAV) de segundo grado con pausas sinusales y otro caso de BAV tercer grado 8. El presente informe es sobre el primer caso reportado en una paciente gestante.
El LPMCB es un tumor característicamente quimiosensible con una muy buena respuesta al esquema rituximab, ciclofosfamida, doxorrubicina, vincristina y prednisona 7, aunque se han propuesto otros esquemas de quimioterapia, no existen estudios que comparen estos tratamientos.
La decisión de administración de quimioterapia en gestantes es compleja y dependerá de la agresividad de la presentación clínica y la edad gestacional: en el primer trimestre, la quimioterapia es altamente teratogénica por lo que se recomienda el término de la gestación. En el segundo y tercer trimestre los mayores riesgos son la restricción del crecimiento intrauterino, mortinatos, prematuridad y mielosupresión neonatal y materna, considerando el término del embarazo a las 2 a 3 semanas de la última quimioterapia para reducción de riesgo de las citopenias, demorando más el término de la gestación si la respuesta a la quimioterapia y el estado fetal es favorable; esto basado en estudios retrospectivos y reportes de series de casos. Además, debe preferirse la culminación del parto por vía vaginal si no existen contraindicaciones obstétricas (11, 12).
El pronóstico de la gestante, entre el segundo y tercer trimestre, y del neonato es favorable, y aparentemente no inferior en comparación a pacientes no gestantes de acuerdo con las series reportadas de Gelb et al. de doce gestantes de las cuales tres tenían LNH mediastinal, con resultados buenos para la madre y el neonato 13; de igual manera Manasorava et al. reportaron siete gestantes en un estudio retrospectivo y en un segundo estudio tres gestantes con LPMCB con remisión de la tumoración y con neonatos sin malformaciones, vivos y saludables (14, 15).
En conclusión, presentamos el caso de una gestante en tercer trimestre con LPMCB con metástasis cardíaca y síntomas de insuficiencia cardíaca, taqui y bradiarritmias. El inicio pronto de la quimioterapia tuvo una respuesta favorable reduciendo el tamaño de la tumoración y logrando mejoría cínica e imagenológica con resolución de las complicaciones cardiovasculares secundarias.
Financiamiento: Autofinanciado
Aspectos éticos: La paciente brindo el consentimiento verbal y escrito para la publicación de este reporte de caso
Citar como:Inquilla Coyla M, Anchante Hernández H, Medina Palomino F. Complicaciones cardiovasculares en gestante con linfoma primario mediastinal de células B. Arch Peru Cardiol Cir Cardiovasc. 2022;3(2):112-116. doi: 10.47487/apcyccv.v3i2.202
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1 Poterucha TJ Kochav J OConnor DS Rosner GF Cardiac Tumors Clinical Presentation, Diagnosis, and Management Curr Treat Options Oncol 2019 20 8 66 66 10.1007/s11864-019-0662-1 31250250
2 Chinen K Izumo T Cardiac Involvement by malignant lymphoma a clinicopathologic study of 25 autopsy cases based on the WHO classification Ann Hematol 2005 84 8 498 505 10.1007/s00277-005-1009-5 15782345
3 Hurley TJ McKinnell JV Irani MS Hematologic Malignancies in Pregnancy Obstet Gynecol Clin North Am 2005 32 4 595 614 10.1016/j.ogc.2005.08.008 16310674
4 Martelli M Ferreri A Di Rocco A Ansuinelli M Johnson PWM Primary mediastinal large B-cell lymphoma Crit Rev Oncol Hematol 2017 113 318 327 10.1016/j.critrevonc.2017.01.009 28318892
5 Buchholtz ML Buclein V Brendel M Paal M Superior vena cava syndrome related to mediastinal lymphoma in late pregnancy A case report Case Rep Womens Health 2018 19 e00065 10.1016/j.crwh.2018.e00065 30094193
6 Tyebally S Chen D Bhattacharyya S Mughrabi A Hussain Z Manisty C Cardiac Tumors JAAC CardioOncology State of the Art Review JAAC CardioOncol 2020 2 2 293 311 10.1016/j.jaccao.2020.05.009
7 Broccoli A Zinzani PL The unique biology and treatment of primary mediastinal B-cell lymphoma Best Pract Res Clin Haematol 2018 31 3 241 250 10.1016/j.beha.2018.07.001 30213393
8 Subramanyam P Mahmood SS Dinsfriend W Pastore RD Martin P Chan AT Clinical Case Challenges Infiltrative Lymphoma-Associated bradycardia and Cardiac Conduction Abnormalities JAAC CardioOncol 2020 2 1 135 138 10.1016/j.jaccao.2020.01.002
9 Tsay J Manjad S Miller F When you infiltrate, you fibrilate: Diffuse large B cell Lymphoma presenting as right atrial mass with atrial arrhythmias JAAC 2019 73 9 2349 2349 10.1016/S0735-1097(19)32955-9
10 Khalid K Faza N Lakkis NM Tabbaa R Cardiac Involvement by Burkitt Lymphoma in 49-Year-Old Man Tex Heart Ins J 2020 47 3 210 212 10.14503/THIJ-17-6448
11 Fiascone S Datkhaeva I Winer ES Rizack T Primary Mediastinal Large B-Cell Lymphoma in Pregnancy Leuk Lymphoma 2016 57 1 240 243 10.3109/10428194.2015.1049168 25956044
12 Pentheroudakis G Pavlidis N Cancer and pregnancy poema magna, not anymore Eur J Cancer 2006 42 2 126 140 10.1016/j.ejca.2005.10.014 16326099
13 Gelb AB Van de Rijn M Warnke RA Kamel OW Pregancy-associated lymphomas A clinicopathologic study Cancer 1996 78 2 304 310 10.1002/(SICI)1097-0142(19960715)78:2304:AID-CNCR183.0.CO;2-# 8674008
14 Mangasarova IK Bariakh EA Vorobev VI Khlavno AB Magomedova AU Shmakov RG [Primary mediastinal large B-cel Lymphona in pregnant women] Terk Arkh 2014 86 7 53 58
15 Mangasarova IK Magomedova AU Kravchenko SK Shmakov RG Bariakh EA Vorobev VI [Eight-year experience in trating aggressive mediastinal large B- cell lymphomas] Ter Arkh 2013 85 7 50 56 24137947
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PMC010xxxxxx/PMC10249367.txt |
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J Migr Health
J Migr Health
Journal of Migration and Health
2666-6235
Elsevier
S2666-6235(23)00042-9
10.1016/j.jmh.2023.100192
100192
Article
Health Care for All: Undocumented Migrants and the COVID-19 Pandemic in Alberta, Canada—A Scoping Review
Sanchez Marian C. PhD Candidate [email protected]
a⁎
Nyarko Deborah [email protected]
b
Mulji Jenna [email protected]
b
Džunić Anja [email protected]
b
Surti Monica [email protected]
b
Mangat Avneet [email protected]
b
Mainali Dikshya [email protected]
b
Spitzer Denise L. Professor [email protected]
b
a School of Public Health, University of Alberta, ECHA 3-300, 11405 87 Ave NW, Edmonton, AB T6G 1C9, Canada
b School of Public Health, University of Alberta
⁎ Corresponding author: Marian C. Sanchez, PhD Candidate, School of Public Health, University of Alberta, ECHA 3-300 11405 87 Ave NW, Edmonton, AB T6G 1C9, Canada [email protected]
08 6 2023
2023
08 6 2023
7 10019230 6 2022
4 6 2023
7 6 2023
© 2023 The Authors. Published by Elsevier Ltd.
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights
• What can be learned about the healthcare access of undocumented workers? How can health equity be advanced through sensitivity to the process of precaritization and the precarities informing their lives?• Thailand and Spain are the only countries in the world that offer the same healthcare access to undocumented migrants as citizens. Most European countries only offer emergency services: France, the Netherlands, Portugal, Spain, and Switzerland allow undocumented migrants to access similar services to citizens if they meet conditions (proof of identity; length of residence in the country). European cities such as Ghent, Frankfurt, and Dusseldorf, offer barrier-free healthcare. Throughout the USA, Federally Qualified Health Centers support care to the uninsured regardless of immigration status.
• In Canada, Ontario and Quebec, provide a base level of healthcare access to undocumented migrants, and a small number of stand-alone community-based clinics offer additional care and specialized services.
• To promote healthcare for undocumented migrants in Alberta, barrier-free access to vaccination, COVID-19 treatment, and proof of vaccinations are essential, but an equity lens to healthcare service— informed by analytic understanding and robust approach to precaritization as a social determinant, is most needed.
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pmcIntroduction
COVID-19 has drawn attention to the various inequities embedded in our societies highlighting the necessity for governance and advocacy work that will advance health inclusion. In many countries, migrants have been disproportionately affected by direct and indirect effects of the pandemic—in the first instance from infection often due to their status as essential workers and/or crowded living conditions, and in the latter due to loss of livelihoods and reduced access to other health determinants. [Laundry, et al., 2021, Etowa & Hyman, 2021, Machado & Goldenberg, 2021, Spitzer, in press] The World Health Organization 1948 Constitution included the right to health for all migrants. In recent years, the 2017 World Health Assembly furthered this agenda by highlighting the contributions of migrants to host and natal countries and the critical importance of migrant health to the Sustainable Development Goals. [WHO 2017] In Canada, the Charter of Rights and Freedoms affirms that all persons, regardless of citizenship status, are entitled to “life, liberty, and security of person” and “the right to human dignity, respect, equality and justice.” [Alcaraz, et al., 2021, p. 9] These tenets underpin the call for pandemic healthcare including vaccination for all—encompassing the undocumented migrants.
The term undocumented refers to individuals who do not possess the state-sponsored authorization to reside or work in the confines of the nation-state. Although the exact number of undocumented migrants in Canada is difficult to ascertain, as of 2016 over 500,000 undocumented migrants lived in Ontario alone. Those numbers are thought to have increased in the intervening years. [Bains, 2021] Undocumented workers often enter a country legally, but may slip into and out of legal status as they navigate systems of border controls and employment authorizations. [Goldring & Landholt, 2013a, United Nations, 2015] Under conditions of the COVID-19 pandemic, the shuttering of businesses, government offices, and lockdowns, created more possibilities for migrant workers to become undocumented. As a result, there is slippage between the categories of temporary and undocumented migrant workers. Employers in the western Canadian province of Alberta are believed to be “among the most enthusiastic users of temporary foreign workers” in the country, [Barnetson & Foster, 2017, p. 27] suggesting that a considerable number of undocumented migrants may reside in the Province.
Our work is informed by the deployment of critical race theory, an eco-social framework, and migrant health equity as primary and reinforcing theoretical lenses-critical race, eco-social, and migrant health equity.. Critical race theory highlights the historically and politically constructed racialized inequalities that are embedded in and reinforced by socio-political systems. [Crenshaw, 2016] Eco-social theory examines how social environments, interacting with exposures, agency, and resistance, are embodied and configure the health and well-being of individuals and communities.[Krieger & Gruskin, 2001] Migrant health equity employs both lenses to situate migrant well-being within the skeins of neoliberal globalization and the unequal gendered division of labour that serves global capitalism. [Spitzer, 2022, Tuyisenge & Goldenberg, 2021] As it also advocates for the adoption of fairer social and economic policies that meaningfully address the unmet needs and rights of migrants and the undocumented, migrant health equity is highly aligned with the UN Sustainable Development Goals’ principle of leaving no one behind as well as public health's moral imperative of social justice. Migrant health equity, however, can only be wrought through sustained and meaningful disruption of upstream forces that currently engender social, economic, and gendered inequalities that contour access to social determinants of health and social location, which for undocumented migrant workers is characterized by their precarious lives. [Spitzer, 2022, Spitzer, 2020]
Anchored by these perspectives, we summarize evidence in support of health care for all, with a view towards promoting health equity for undocumented workers in Alberta. We ask: What can be learned about the healthcare access of undocumented workers? How can health equity be advanced through sensitivity to the process of precaritization and forms of precarities that inform undocumented migrants’ lives? We respond to these questions by first providing an overview of temporary foreign workers’ (TFWs) contributions to the Canadian labour market and economy as the context in which undocumented migrants are situated. Next, and using Alberta as an illustrative case, we contrast these gains to the barriers that undocumented workers face in accessing health determinants and healthcare services including vaccination during the COVID-19 pandemic and beyond. We then examine international policies and programs implemented to facilitate undocumented migrants’ pandemic health rights, as well as models of service delivery and policy found across Canada to ground our recommendations for action.
Methods
Working from a feminist methodological perspective that challenges the singular epistemic privilege according to Western bio-scientific knowledge, [Brooks, 2007] we produced this scoping review through a collaborative, robust, and multi-thematic process of environmental scanning. We included multiple viewpoints, particularly from people with lived expertise, to integrate diverse information and strengthen our evidentiary base. Given the emerging nature of our topic and recognizing that there is rich research beyond peer-reviewed publications, we also utilized both grey and peer-reviewed literature to promote inclusion and conversation of multiple knowledge sources.
Contributing to the call for literature review that goes beyond seeking and filling the gap, [Tynan & Bishop, 2023] we used a modified mini-delphi process—an iterative research and group facilitation technique, [Hasson et al., 2000] to start from our positionality and to tap on the team members’ public health perspectives. Through an online discussion, we generated an outline of topics to be covered. Utilizing an editable google document and spending a span of one week for a participatory thematic analysis, each team member then contributed to the review and reduction of the initial outline into key words and conceptual relationships. We followed this with a facilitated online meeting where we discussed and established consensus on the final search strings: (i) “temporary foreign workers” “economic contributions” “Alberta” and “Canada”; (ii) “policy” “healthcare” “undocumented” “uninsured” “immigrant” and “migrant”; (iii) “medically uninsured”, “migrants'', “immigrants”, “healthcare access”, “access to health services”, “migrant worker”, “uninsured”, “undocumented”, “illegal”, “barrier”; and “Alberta”; and (iv)“undocumented”, “migrants”, “healthcare access”, “refugee”, and “immigrants.
We searched a total of seven databases between March to May 2021. These included Scopus, PubMed, PAIS International, OVID, EMBASE, CINAHL, and EBSCO. We also conducted a general search of published and grey literature using Google Scholar and the University of Alberta Academic Search portals. We utilized “Connected Papers,” a visual search tool to identify policy related papers, and consulted governmental and policy websites to complement and verify our findings. Limiting our scan to an 11-year period (2010-2021), our search attempts generated a total of 443 publications that we subjected to initial screening. The reading, annotation, and cross referencing of these materials eventually led to the inclusion of 83 articles in our final analysis.
Results
Generating Precarity
From a political economic and critical social science perspective, precarity and precaritization are multi-dimensional constructs that characterize TFWs’ employment, lives, and, subsequently, health conditions. [Schierup, Ålund, and Likic-Brboric, 2015, Syed, 2020] Precarity is often articulated as the experience of limited job opportunity, reduced social protection, and induced income insecurity [Standing Committee on Human Resources, Skills and Social Development and the Status of Persons with Disabilities, 2019] and the resulting effects of indecent living conditions, growing social isolation, and constant mental stress. [Brown-McLaughlin, 2020] Precarity needs to be understood, however, as a condition driven by contextual factors beyond employment. Because while the narrow and traditional conceptualization of precarity often fails to account for the system and structural level creation of both ontological and labour conditions that deprive TFWs of predictability and stability, these attendant processes position migrant workers into existential and economic locations of risks and uncertainties. [Syed, 2020]
In Canada, the gendered, neoliberal and capitalist work and migration regimes, bolstered by the state's sustenance of societal, political, economic, and legal structures that overtly construct and reinforce categories of differences—status vs. non-status, citizen vs. non-citizen—are mechanisms for the distribution of power, privilege, and access to health and wellbeing. [Spitzer, 2022] As a case in point, under the auspices of the Canadian Temporary Foreign Workers’ Programs, migrant workers from the Global South are brought to the country to fill labour shortages via issuance of employer-specific work permits (ESWP). This restricts ESWP holders to particular employers and ensures that they “inhabit[s] social and political categories that carry ideological weight and institutional logic, which in turn generate specific administrative practices that subjugate them in Canada.” [Abboud, 2013; p.134] Being tied to a single employer leads them to a life of indentured labour because they are not free to circulate in the labour market. “[C]losed work permits, coupled with inadequate monitoring and enforcement of labour standards, create the conditions that allow unscrupulous employers and recruiters to abuse…(TFWs) with impunity. Closed work permits facilitate employer control and exploitation of workers including working excessive hours without payment for overtime, unpaid hours of work and often less than minimum wage pay.” [Migrante Alberta, 2016, para. 8] Closed work permits and the two-tiered process of permanent residency in Canada, wherein some TFWs may apply for permanent residency status, have become instrumentalities in forcing ESWP holders to be docile bodies as they are not only pushed into indentureship, but also impelled to be silent about their experiences of abuse due to their employers’ power to control their pathways to citizenship.[Torres, et al., 2012, Tungohan, 2018] As Brown-McLaughlin [Brown-McLaughlin, 2020] argues, Canada intentionally uses TFWs to create a pool of legally free or coerced labour by systematically denying participants’ access to citizenship. This in turn produces predominantly racialized non-citizens or denizens of foreigners with residency rights to work, but who are denied full citizen rights. TFWs are therefore deemed ‘good enough to work’, but not to stay in Canada because gate-keeping policies are well placed to keep TFWs out of spaces for citizenship rights.
As the non-citizenship status is a system induced social location, being undocumented is also a fluid condition that migrants are forced to move into and/or out of. Being undocumented embodies “the authorized and unauthorized forms of non-citizenship that are institutionally produced and [that] share a precarity rooted in the conditionality of presence and access” [GOLDRING & LANDOLT, 2013b]. Oftentimes undocumented migrants are ‘created’ when their work permits cannot be renewed leaving them with the option of either returning with few funds or continuing to earn for their families while running the risk of deportation. [Brown-McLaughlin, 2020] Given the precarity of their migration status and their lives in Canada, undocumented migrants try to minimize contact with authorities who represent the nation-state, including health services. Resultantly, despite their elevated risk of occupational injuries, stress, and noxious social and physical environmental exposures, [Alcaraz, et al., 2021, Burton-Jeangros, et al., 2020, Ridde, et al., 2020] they report a high number of unmet healthcare needs or a failure to obtain healthcare when needed—including health services for pregnancy and conditions requiring tertiary care.
Situating Undocumented Migrants in Canada
Primarily subsumed under the category of temporary foreign workers (TFWs), undocumented migrants are among the thousands of foreign-born Canadian residents who contribute to—and sustain—Canadian society and the Canadian economy, including those whose labours have been deemed essential during the COVID-19 pandemic. Overall, the literature suggests that TFWs benefit Canada in four important ways by: (1) Supplying human resources for different sectors and industries; (2) Providing essential services in the time of the pandemic; (3) Contributing quantifiable resources and other enabling mechanisms for Canada's economy; and (4) Enriching national and transboundary discourses on migration, citizenship, and development.
Undocumented Migrant's Health and Health Care in Alberta
Undocumented migrants in Alberta are among those with the poorest health outcomes. [Foster & Luciano, 2020, Salami, et al., 2020] As Fig. 1 suggests, their social location as non-status individuals position them into multi-level and reinforcing risks and uncertainties that shape their healthcare seeking behaviors and consequently overall health and wellbeing.Fig. 1 The Rippling and Reinforcing Effects of a Non-Status Position on Migrant Health and Wellbeing
Figure 1
As elsewhere in the country, hospitals and emergency rooms in Alberta have the ethical responsibility to provide services to all patients regardless of their residency status and may be seen as facilitators of undocumented migrants’ health; however, many migrants without status remain afraid to seek health services as they do not possess a valid Alberta Healthcard and, as a result, are not covered by the publicly funded healthcare system. [Mattatall, 2017] This often leads to tolerance of minor ailments or outright avoidance of health services. When seeking treatment in most urgent situations, uninsured migrants face enormous out of pocket expenses, which they are unable to afford as they already struggle financially due to precarious employment conditions.
Policy barriers also pose additional threats to safe and continuous healthcare access for undocumented migrants. While there is no specific policy prohibiting access to healthcare for undocumented migrants, they are not eligible to apply for the Alberta Health Card. An extension of the Card for those who entered Canada legally but are waiting for their residency or work permits to be renewed, is also limited to a few months. [Gov. Alberta, 2021] Afterwards, they are on their own. Many migrants with precarious status have tried to challenge their healthcare coverage in court arguing that access to healthcare is a basic human right covered under Canadian law, but to date their fights have been unsuccessful. [Chen, 2017]
Additionally, undocumented migrants consistently live in fear of deportation as a consequence of being reported to authorities by healthcare providers they visit. [Government of Alberta 2021] The most common reason for undocumented migrants to seek medical care is to give birth. In Calgary, a continuous increase in deliveries by non-Canadians including uninsured migrants has been observed over the past decade. [Chen, 2017] These pregnant people often do not have another choice and must go to a hospital to deliver their babies, but afterwards they face bills of thousands of dollars that they have to pay back over many months, putting them in even greater financial straits. Importantly, in many cases these individuals do not receive any prenatal care, which may enhance their risk for various birth complications and poor maternal health outcomes.
Some healthcare practitioners and clinics do not charge non-status migrants as they feel uncomfortable billing their patients once they learn that they are uninsured. Only a small number of undocumented migrants, however, is able to locate health care providers who will not levy fees; thus, the majority still ends up being charged for services. [Mattatall, 2017, Chen, 2017] Moreover, when uninsured migrants receive a prescription from their healthcare provider, they immediately face another challenge as most pharmacies request a valid Alberta Health Card to dispense medications. [Mattatall, 2017]
COVID-19 and Migrant Workers in Alberta
COVID-19 also exposed and widened the jarring healthcare disparities that migrant workers experience in the province. [Benjamen, et al., 2021] For instance, the COVID-19 outbreak in the Cargill meat-packing plant in High River, Alberta, was linked to 25% of the province's cases in October 2020. Most of the workers in this plant are TFWs [Green, 2020] who are said to take on 3D jobs—difficult, dirty, dangerous, just to survive [Mattatall, 2017]. Migrant workers in Alberta also reported heightened physical and mental health concerns as they confront COVID-19 related issues. These include language barriers limiting understanding of public health guidelines, loss of employment and income, and problems obtaining documentation. [Baum, et al., 2020]
In the early days of the pandemic, when social distancing was the primary measure implemented by the Alberta health authorities to stop the spread of the virus, many migrant workers in precarious employment were unable to comply. [Baum, et al., 2020, Bragg, 2020] Amongst meat packing and food processing workers for instance, carpooling, close living conditions, and cramped working conditions were inherent to their jobs; neither did they have the option to work from home because they were not eligible to receive employment insurance. For some, missing days of work, even if exhibiting symptoms, would mean losing work, which would mean losing income and, for those who possessed it, legal status in Canada. [Dryden, 2021] Overall, as federal benefits and health supports were widely unavailable to migrant workers, it became clear as to how exploitation and fear has continued to plague migrant workers’ rights to health during the pandemic. It is also not difficult to imagine how adverse the condition was for their undocumented counterparts.
In the case of COVID-19 vaccinations, many workers reported language barriers, inaccessibility to clinics and a mere lack of knowledge that vaccines were being offered—signaling the ineffectiveness of generic public health messaging. [Baum, et al., 2020, Beck, et al., 2019] Health messages were not tailored for many TFWs who did not speak English well, were hesitant to access the healthcare system, or were lacking access to the Internet. Additionally, in the case that workers knew of vaccinations, clear messaging on how to access these services without an Alberta Health Card was inconsistent and identified as a deterrent. [Baum, et al., 2020, Beck, et al., 2019] Although some messaging stated a valid Health Card would not be requested upon arrival, this was not always the case when one arrived at their appointment. Signages at clinics would, in fact, often indicate where people needed to present their Alberta Health Cards. For an undocumented worker, the possibility of being asked for a valid health card was enough to deter one from seeking vaccination, despite the presence of such vaccination services and assurances of vaccination providers. Lastly, fear of deportation and the vaccine database being available to the Canadian Border Services, was also a reason to avoid vaccinations. [Bains, 2021, Somos, 2021] Overall, a lack of transparency and adequate messaging represented a failure of public health efforts to protect this population. In the face of a pandemic, where healthcare is of utmost importance, undocumented migrants faced major stressors that had severe impacts on their physical, mental and social health (see Fig. 2).Fig. 2 Factors that exacerbated Alberta migrant workers’ plight for physical, mental, and social wellbeing during the Pandemic
Figure 2
A global glimpse of Healthcare and Pandemic Service Access for Undocumented Migrants
Undocumented Migrants’ Health Care Access: A Pan-Canadian picture
Many countries do not have federal or state policies addressing healthcare for undocumented immigrants; however, local governments and organizations working towards providing accessible health services for those without state-sanctioned healthcare may be found. For example, the US government funds Federally Qualified Health Centers across the country that support care to the uninsured regardless of immigration status. [Beck, et al., 2019] Major cities in the European Union have created localized systems that grant healthcare access to undocumented immigrants without enforcement interactions. In Ghent, Belgium, undocumented immigrants are given access to a medical card that is valid for three months and does not require the provision of an address. [PICUM, 2017] In Germany, Frankfurt and Dusseldorf employ local governments and organizations to fund the provision of uninsured healthcare for undocumented migrants, bypassing their need to register under the national German health system and face potential exposure to immigration authorities. [Bahar Özvarış, et al., 2020] At the federal level, both Italy and Spain provide universal access to healthcare for undocumented immigrants through government programs that support all levels of healthcare. [National Resource Center for Refugees, 2021] In Spain, Madrid strengthens the federal system of universal healthcare at the local level through facilitating an ongoing campaign which educates undocumented migrants about their rights to access the public health system in that country. The campaign also reminds practitioners of their duty to care for patients irrespective of status and provides identity cards for undocumented residents to ensure they have access to public services offered by the city, including healthcare. [Bahar Özvarış, et al., 2020]
During the COVID-19 pandemic, governments across the world started to increase their healthcare access to undocumented migrants due to the nature of this public health pandemic; however, some initiatives have had untoward impacts. Pop-up vaccine clinics have circulated throughout the USA, enabling easier access to testing and vaccination services for the undocumented. These pop-up clinics work in conjunction with a variety of institutions, including local organizations, religious institutions, local government committees, and cultural services. [National Resource Center for Refugees, 2021] In Turkey, refugees and undocumented immigrants were granted access to COVID-19 testing and treatment. This response was led by the Turkish government with support from local authorities, local and international non-governmental organizations (NGOs) and community organizations. While this initiative provided COVID-19 related health services to undocumented migrants, the service created was hampered by a number of issues including language and information obstacles, which prevented migrants from accessing the specific services they required. [Bahar Özvarış, et al., 2020] In light of intersectional barriers related to the COVID-19 pandemic, Portugal granted undocumented migrants who have applied for permanent residency, citizenship rights, enabling full access to the nation's healthcare services as a method of decreasing risks associated with public health. Undocumented migrants are given access to health services while awaiting the outcome of their applications. [Cotovio, 2020]
Undocumented Migrants’ Health Care Access: A Pan-Canadian picture
Across Canada, only two of ten provinces, Ontario and Quebec, provide a base level of healthcare access to undocumented immigrants. Ontario has 74 community health centres and an additional 22 health centres in Toronto that provide medical services to undocumented immigrants. [Association of Ontario Health Centres, 2016] Furthermore, the FCJ Refugee Centre in the City of York and a medical clinic in Scarborough have also been providing free access to healthcare. [FCJ Refugee Centre, 2012, Kennedy, 2021] In Montreal, Quebec, the community-based perinatal health and social centre Maison Bleue, operated by the non-governmental organization Medicins du Monde, offers medical treatment and maternal and child health services to marginalized populations. [Aubé, et al., 2019, CBC News, 2017] Maison Bleue also offers services for uninsured migrants with the exception of prenatal care and follow-through. Operating on donations and grants, the clinic cannot address complex needs of its target population; resultantly, unmet healthcare needs, primarily due to financial barriers remain. [Salami, et al., 2020] Unfortunately, while we only found these few examples within Canada, many of these centres also do not have the capacity nor the resources to take on a large number of patients. This highlights the need for more support from the government for these organizations and for greater overall access to healthcare for undocumented immigrants.
The Public Health Agency of Canada stated that COVID-19 vaccines would be available to all regardless of insurance and migration status. However, the identification procedures were determined by provincial and territorial governments, and as of March 2021, only Ontario and British Columbia announced that undocumented and migrant workers would not be required to present health cards to access vaccines. [Bains, 2021] Many vaccination clinics in Toronto, Montréal, Edmonton, and Calgary did not check for an individual's health card, although name and date of birth were required. [FCJ Refugee Centre 2012, TCRI, 2020] Although legally only a first name, date of birth, and contact information (email address or phone number) were required and could be affirmed without documentary evidence, some sites requested confirmation of address. The Government of Quebec also designated medical clinics that provided free access for the diagnosis and treatment of COVID-19. [Steps to Justice, 2021] Local health authorities and community organizations in partnership with individual physicians and advocacy groups also launched ‘barrier and surveillance free’ vaccination clinics in community and workplace settings. [Poncana, 2021, Tait & Graney, 2021]
The issuance of proof of vaccination certificates has added another layer of complication for undocumented migrants. In Ontario, an Ontario Health Insurance Plan (OHIP) card is needed to obtain a receipt via the Province's vaccination portal. Those who do not possess OHIP could procure a receipt through their local public health unit. Venues that ask for vaccination receipts require accompanying identification that verifies name and date of birth. Although some may not be aware of the regulations, these documents do not need to be government-issued nor include photo ID. [Steps to Justice, 2021]
Conclusion
COVID-19 has exposed and exacerbated the health, social, and economic inequities embedded in Canada's political-economic structures. Despite undocumented migrants’ contributions to Canadian society, especially feeding and keeping Canada safe in this unprecedented time of a global health emergency, they have not been afforded the same access to critical preventative healthcare and services as other Canadian residents. Financial barriers, fear of deportation, logistical problems, and lack of information about accessible barrier-free migrant-responsive services that do not require documentation of residency status or provincial health, are all obstacles for care. Even where undocumented migrants are able to obtain free healthcare services, they are often limited in their capacity to address complex conditions and care needs.
As a result, undocumented migrants report a high rate of unmet healthcare needs, which may lead to more severe conditions, higher rates of disability, and, potentially, death. Health and wellbeing and access to healthcare, however, cannot be extracted from issues of social location, working conditions, income, migration status, and familial separation. Moreover, undocumented workers may be unable to lodge complaints about poor working and living conditions; some may be challenged to reach out to migrant advocacy groups who are able to channel their voices to policymakers and the public.
The plight of TFWs and undocumented migrants echo the long existing calls and demands for the revision of Canada's labour and economic policies as well its overall stance and participation in global processes of migration and development. TFWs have been—and continue to be integral to the nation-building of this country and to the functioning of specific sectors and industries despite the structural constraints that render TFWS as indentured labour and technically Canadians without passports. [Burton-Jeangros, et al., 2020] The temporary nature of their stays in Canada and the contingencies inherent in their work visas make them vulnerable to shifts in their documented status — a process that we illustrated in this paper as induced and systemic precaritization.
Models of better healthcare access for undocumented immigrants exist both around the world and in Canada (see Fig. 3). The Province of Alberta has the responsibility to follow these examples and provide healthcare access including vaccination services to people irrespective of their status. Barriers to accessing healthcare, which include fear of being deported, delayed access to health services, and healthcare costs, can be mitigated using the specific strategies outlined above in creating programs and services for undocumented migrants to access health services.Fig. 3 Some Promising Policies and Strategies for Improving the Undocumented's Healthcare Access and Overall Wellbeing
Figure 3
Healthcare for all requires attention not only on migrant workers’ work, life and health conditions, but also on the role of economic and migration models and how they facilitate enslaved, cheapened and extractive wage labour systems that breed conditions for poor migrant health and healthcare access. Overall, applying an equity lens to pandemic healthcare necessitates an analytic understanding and robust approach to precaritization as a social determinant of health and wellbeing. In the context of the World Health Organization's founding documents, the Sustainable Development Goals, and Canada's own Charter of Rights and Freedom, we offer the following recommendations to promote healthcare for undocumented migrants in Alberta, based on the findings in this report:1 Eliminate waiting periods for provincial insurance coverage and provide universal healthcare coverage for all residents of Canada regardless of status;
2 Expand barrier-free access to health care services including access to vaccination, COVID-19 treatment, and proof of vaccinations
3 Support community-based migrant-responsive healthcare; and
4 Grant temporary foreign workers permanent residency status upon arrival.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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|
PMC010xxxxxx/PMC10249368.txt |
==== Front
Vaccine X
Vaccine X
Vaccine: X
2590-1362
Elsevier
S2590-1362(23)00065-7
10.1016/j.jvacx.2023.100324
100324
Regular paper
Choice preference and willingness to pay for COVID-19 vaccination in Namibia
Konstantinus Abisai [email protected]
a⁎
Konstantinus Iyaloo [email protected]
ab
a Ndatara Surveys, Swakopmund, Namibia
b Namibia Institute of Pathology, Windhoek, Namibia
⁎ Corresponding author. [email protected]
08 6 2023
8 2023
08 6 2023
14 10032411 5 2022
12 3 2023
30 5 2023
© 2023 The Authors
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights
• The study assesses vaccine uptake behavior of the general population in Namibia.
• The study uses stated choice experiment and latent class discrete choice models.
• Provides insights into preference for vaccine characteristics on uptake.
• Provides insights into psychological factors driving vaccine hesitancy and risk perceptions.
• Enumerates willingness -to-pay and willingness-to-wait measures for vaccines.
• Provides insights for government strategy to promote future vaccine uptake.
Background
Namibia has not been spared from the coronavirus (COVID-19) pandemic, and as intervention the Namibian government has rolled out vaccination programmes. This study was conducted before the roll out of these vaccines to assess the preference for COVID-19 vaccinations. Stated preference studies provide information about social demand, access, willingness-to-pay and financing for future COVID-19 vaccination.
Methods
A stated choice experiment (SCE) survey was administered to a sample of 506 participants from Namibia's general population between October 2020 and December 2020. Participants were asked to make a series of hypothetical choices and estimate their preference for different attributes of a vaccine. A latent class model was used to analyse the SCE data. The study also assessed anti-vaccination behaviour, past vaccination behaviour, impacts of COVID-19 on mental and physical health and Willingness-To-Pay (WTP) measures. The WTP measures were captured as out-of-pocket and further calculated using the marginal rate of substitution method in SCE.
Results
Data from 269 participants was included in the analysis. Vaccine side effects (40.065), population coverage (4.688), payment fee to receive vaccine immediately (3.733) were the top three influential attributes for vaccine preferences. Accordingly, increases in mild and severe side effects of vaccine options had negative impacts on utility; with an average WTP of N$728.26 to reduce serious side effects. The average WTP to receive a high-quality vaccine with 90% efficient was found to be N$233.11 (US$15.14). Across classes, there was a strong preference for vaccines with high effectiveness over longer durations of time.
Conclusions
The results provide useful information for the Namibian government to improve the current strategies for vaccine rollout interventions.
Keywords
COVID-19
Vaccine hesitancy
Willingness-To-Pay
Vaccination preference
Public health
Stated choice
Abbreviations
MoHSS Ministry of Health and Social Services
WTP Willingness-To-Pay
WTW Willingness-To-Wait
SCE Stated Choice Experiment
MRS Marginal Rate of Substition
==== Body
pmc1 Introduction
The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has reached all countries and devastated the livelihoods of people, including Namibia. The socio-economic impacts of the COVID-19 pandemic are varied and amongst many, include: a reduction of routine child and maternal services [39], additional child and maternal deaths [8], increased food insecurity particularly in low and middle-income countries [4], reduced services from routine essential health services [1]; and increased morbidity and mortality from other diseases due to diversion of resources [7]. The impacts of the COVID-19 pandemic on health and development is expected to last for a prolonged period [30].
Namibia has been hit hard by COVID-19 starting from the second wave. By the third wave which was the worst, the country’s daily new cases peaked at approximately 2500 during the month of July 2021 (worldometers.com). Consequently, the government of Namibia, through the Ministry of Health and Social Services (MoHSS) applied different interventions in place, including a total lockdown of the country during the third wave [6], [32]. To date, Namibia reported 171,000 positive cases and 4,090 deaths. Immunization against COVID-19 has been prioritized to minimize the impact of the pandemic, however there has been a general low vaccine uptake in the country. The MoHSS projected to vaccinate a target population of 1,501,041 (60%) of the total population to reach herd immunity [31]. Although vaccination started around March 2021, just over 500 000 Namibians have been vaccinated with the first dose thus far representing 33% of the population (worldometers.com).
Immunization is one of the most cost-effective and successful prevention interventions for infectious diseases [36]. While some studies have shown a willingness to receive COVID-19 vaccination amongst groups such as healthcare workers, students and pregnant women [10], [35], [40], others show mass uncertainty regarding vaccines and the influence of modern political movement [5], [11], [34]. In particular, the fast-tracked vaccine development and approval process for COVID-19 vaccines known as Operation Warp Speed is perceived to be for political gain rather than science [27]. The scepticism about the safety of the COVID-19 vaccine [37]; and other social factors such as the lack of knowledge leading to misinformation and rumor mongering about COVID-19 [33], [40] have huge implications for coronavirus vaccine acceptance [13]. Vaccine refusal and delays are contributing to an increasing number of vaccine-preventable diseases outbreaks globally. For this reason, WHO named vaccine hesitancy as one of the top ten threats to global health in 2019 [12]. Thus, studies on the preference of COVID-19 vaccines are considered vital to inform governmental strategies for vaccine take-up.
A number of studies have looked at the acceptance and willingness-to-pay for COVID-19 vaccines in African countries [3], [26], [42], and in particular a few have focused on Namibia [47], [48]. However, none of these studies have considered trade-offs between vaccine attributes. Hess et al, (2022) employed advanced discrete choice models on stated choice data collected from 18 countries including Namibia, which compared the influence of vaccine attributes across countries [46]. Other applications of SCE in Namibia have been applied to mode choice in freight transport [2], [28].
The present paper considers the trade-offs between COVID-19 vaccine attributes, that people make when choosing to be vaccinated, and associate factors that drive vaccine uptake. The study employs stated choice surveys and discrete choice models to assess the choices that people make when faced with a discrete set of alternatives [21]. The study furthermore employs the models developed to compute willingness-to-pay (WTP) and willingness-to-wait (WTW) measures for different aspects of the vaccines.
2 Methods
2.1 Survey design
The study employed a Stated Choice Experiment (SCE) design that was developed by the Centre for Choice Modelling [46] and adopted locally by the authors. The objective of the SCE was to find value of the attributes (or characteristics) of a product or service, where the product or service comprises several attributes [43]. Participants were faced with several hypothetical vaccination choice scenarios. They were asked to imagine a situation where several vaccines for COVID-19 have been developed and have undergone all required testing and received regulatory approval for use in humans from the health authorities. Participants were informed that vaccination protects those who received it against infection and against passing the virus on to others. In addition, participants were informed that while vaccination cannot completely eliminate the risk of contracting the virus, it would also reduce the risk of serious illness should a vaccinated person become infected.
The choice design presented respondents with two sets of six choice sets of each choice task, where there were two hypothetical profiles of unknown vaccines against COVID-19 (labelled as Vaccine A or Vaccine B). For each choice set, respondents had three options: to indicate their preferred vaccine profile between Vaccine A or B; or remain unvaccinated. The details of the attributes and levels are displayed in Table 1. An example choice scenario is shown in Fig. 1.Table 1 Levels used in experimental design for SC scenarios.
Attribute Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Risk of infection out of 100,000 people 500
(0.5%) 1,500 (1.5%) 3,000 (3%) 4,000 (4%) 5,000 (5%) – 7,500
(7.5%)
Risk of serious illness: 2,000 (2%) 4,000 (4%) 6,000 (6%) 10,000 (10%) 15,000 (15%) – 20,000 (20%)
Estimated protection duration five years two years one year 6 months Unknown – –
Population coverage More than 80% 60% 40% 20% Fewer than 10% – –
Risk of mild side effects out of 100,000 people 100 (0.1%) 500 (0.5%) 1,000 (1%) 5,000 (5%) 10,000 (10%) – –
Risk of severe side effects out of 100,000 people 1 (0.001%) 5 (0.005%) 10 (0.01%) 15 (0.015%) 20 (0.02%) – –
Exemption from international travel restrictions no restrictions no exemptions – – – – Restrictions on international travel
Waiting time (for free option) 2 weeks 1 month 2 month 3 month 6 months –
Fee (for paid option) N$186 N$464 N$928 N$1,624 N$2,088 N$3,931 –
Fig. 1 Example of SC scenario.
2.2 COVID-19 specific questions
Following completion of the SC scenarios, several questions were used to collect additional information on preferences in relation to COVID-19 vaccination. These questions related to the preferred location for being vaccinated (for example hospital vs special COVID-19 vaccination centres), and the preferred person for administering the vaccine (for example doctors vs nurses). This was followed by questions relating to the reason for vaccination, for example the relative importance of protecting oneself vs protecting others, and the influence of recommendations received by friends and family, medical experts, and politicians. The same respondents were next faced with a willingness-to-pay question, where they were asked how much they would be willing to pay to avoid a six-month waiting period for a COVID-19 vaccine with desirable characteristics (over 90% efficacy, five-year protection, and low side effects). Separate willingness-to-pay levels were also captured for the same vaccine with and without vaccines providing exemptions from travel restrictions. Finally, using a 5-point Likert scale, respondents were asked how likely they thought it was that an infected person a) develops symptoms; b) develops a serious illness that does not require hospitalization; and c) develops a serious illness that requires hospitalization, and c) dies.
2.3 Socio-demographics, risk exposure and past vaccination choices
Key socio-demographic measures were captured, including gender, age, income, ethnicity, education, and employment status (and change therein since the pandemic start). Respondents were also asked to indicate if they had previously been vaccinated against influenza, whether they have had any other elective vaccines, and if they have had all recommended vaccines. Finally, they were asked for a personal rating of their physical and mental health (separately), and how these had changed since the start of the COVID-19 pandemic.
2.4 Ethical approval and data collection
Ethical approval was obtained from the Ministry of Health and Social Services (MoHSS; ref: 17/3/3/AK). The survey was conducted across all the 14 regions of Namibia between October-December 2020. The questionnaire was administered through Qualtrics (https://www.qualtrics.com) and presented in all the major languages of Namibia including: Oshiwambo, English, Afrikaans, Otjiherero, Khoe-khoe (Damara/Nama), Silozi and Rukwangali. Survey participants were recruited through Ndatara Surveys (https://www.ndatara.com). Participants were recruited via social media platforms (online) or through field interviewers (CAPI). To encourage participation, the main incentive for participating was a prize draw given to four lucky participants, while each participant recruited was also awarded a small token for their time after completing the survey. Only participants older than 18 who could consent took part in the study.
3 Data analysis
3.1 Initial data analysis
The core of the analysis was concerned with modelling the choices from the SC component of the survey, however prior to this, we undertook a detailed analysis of other parts of the data. We analysed the responses for questions used to gauge respondents’ views in relation to the risk of COVID-19 infection, past vaccination behaviour, as well as the impact COVID-19 has had on their physical and mental health.
3.2 Model estimation
Given the likely high levels of heterogeneity in preferences in the sample, we used Latent Class (LC) models (cf. [25]) to analyse the data, where the discrete choice model in each class was of the Multinomial Logit (MNL) type [22]. In general terms, for person n in study area c, we write the deterministic component of utility for alternative i in choice scenario t and in class c as:(1) Vncjt=δcj+β′cjXncjt
where δj is an alternative specific constant (ASC) used in class c for alternative j; βc is a vector of coefficients to be estimated and Xncj the vector of attributes of alternative j in choice scenario t faced by respondent n. Some differences arise across alternatives and across attributes as follow:• For the constants, we used the no vaccine option as the base, normalising its constant to zero. Separate constants were estimated for free and paid vaccine options, along with an effects coded position constant to distinguish between the left and right vaccine in the survey.
• For the no vaccine option, the only attributes that entered the utility function were the risk of infection and the risk of illness, using the baseline levels from Table 1.
• After initial tests for non-linearity, all attributes were treated as continuous, with two exceptions: For protection duration, a separate term was estimated for unknown protection duration, alongside the continuous term for known durations, while the travel exemption attribute, which has only two levels, was dummy coded (with no exemption as the base).
The LC model was estimated using maximum likelihood routines in Apollo v0.2.5 [23]. No weighting was used in estimation, and the results were instead reweighted after estimation.
3.3 Willingness-to-pay calculations
A very important concern that face vaccine uptake is whether the public is willing to accept and willing to purchase the vaccine [18]. In this study, two MRS measures of willingness-to-pay (WTP) and willingness-to-wait (WTW) were computed substitution from the ratio of the parameter of interest over the parameter for payment fee or waiting time [29]. For example, taking the relative importance of risk of mild side effects on waiting time, the MRS in class s is:(2) MRSs,mildvswaitθ=βmildsideeffects,sβwaitingtime,s
Subsequent to this, another value of WTP was captured from the survey through the iterative bidding method of the out-of-pocket expense. The survey asked respondents to specify their willingness-to-pay (WTP) for two paid vaccination options. The first option was to avoid a six month wait for a high-quality vaccine (over 90% efficacy, five-year protection, and low side effects). The second option was to pay for a vaccine to avoid travel restrictions. This was done to find the hypothetical value of WTP that respondents are willing to pay for a vaccine [24]. The mean WTP was then calculated by summing the bidding prices, and averaging them across the sample.
4 Results
4.1 Participant demographics
The survey registered 506 complete responses, of which 212 were male, 281 females; and 13 preferred not to state their sex (Fig. 2, Table 2). Sample sizes varied substantially across regions, with the majority of the respondents from Erongo (21.3%; n = 120), Oshana (26.9%; n = 137), Khomas (14.9%; n = 76) and ||Karas (22.7%; n = 115). The age group between 18 and 30 years old made up 56% of the respondents. Compared to the overall Namibian population, the resulting data was generally balanced in relation to age and gender (Table 2).Fig. 2 Survey respondents in the 14 regions of Namibia.
Table 2 Distribution of age in sample versus population (source: Namibia Statistics Agency).
Female age 18–30 age 31–50 age 51 and over
Data Adult pop Data Pop Data Pop Data Pop
54.01% 52.7% 56.13% 41.0% 38.34% 39.3% 5.52% 19.7%
4.2 Anti-Vaccination behaviour
Due to the choice options presented in the survey (see Fig. 1), the overall vaccine uptake in the study was made of three groups, namely: respondents who always choose a vaccine across the six SCE scenarios, respondents who choose a vaccine in some but not all scenarios, and respondents who never choose a vaccine (vaccine hesitant), where this is not due to the characteristics of the vaccines presented to them. Table 3 shows the share of vaccine uptake in the sample, and reveals that individuals who always choose a vaccine, regardless of the characteristics of the vaccine, formed the largest group at 58.7%, while the anti-vaccination group formed 18.8% of the sample.Table 3 Share of Vaccine uptake in the sample.
Overall vaccine uptake 72.0%
Share likely to accept any reasonable vaccine 58.7%
Share open to vaccination depending on characteristics 22.5%
Share of vaccine − hesitant individuals (anti-vaccine) 18.8%
The anti-vaccination group of individuals is of particular concern in relation to herd immunity as it relates to a growth in vaccine hesitancy in many countries [12]. Fig. 3 shows the reasons submitted by the vaccine hesitant group for vaccine refusal. The reasons: Vaccine requires more testing before I can trust, I prefer natural immunity, I don’t believe in vaccines, and Options presented are not good enough; informed most of the choices. We also observed differences across reasons in terms of the share of respondents indicating that they choose not to be vaccinated due to ‘options presented are not good enough’ compared to other ‘anti-vax’ reasons. The high share of vaccine-hesitant individuals in the sample can thus be attributed to a lack of trust in the vaccination procurement and dissemination process, the presence of underlying beliefs that fuel the anti-vaccination behaviour, and a lack of information and clarity surrounding vaccination [11], [44]. These are key areas that require intervention by the MoHSS.Fig. 3 Reasons for anti-vaccination behaviour.
4.3 Past vaccination behaviour
Past vaccination choices often persist into the future. An initial indication of differences in vaccination preferences is thus given by looking at past vaccination choices made by the respondents. As shown in Fig. 4, about 45% of the sample reported to have taken all recommended government vaccinations, 25% report to have taken past influenza vaccination and 17% have additionally taken other elective vaccines. When these results are compared to the 58.8% of respondents who indicated willingness to take any reasonable vaccine (see Table 3); it indicates that that general vaccination rates are likely to increase over time.Fig. 4 Past Vaccination choices.
4.4 Impact of COVID-19 on mental and physical health
The COVID-19 pandemic and associated restrictions are often said to have caused substantial harm to physical and mental health of people; even for those not directly affected by the pandemic [16], [20]. Our data shows that majority of respondents did not in fact report a change in their mental or physical health (cf. Fig. 6). Slightly more respondents reported a deterioration in mental health (12.5%) than physical health (5%). Smaller numbers of respondents’ report improvements, potentially because of home working and reduced stress levels [41]. In Namibia, the prohibition of alcohol sales during the initial lockdown has also led to dramatic reductions in-patient admissions due to assault and accidents (cf. [38]).
4.5 Public risk perception of COVID-19 pandemic
Experience from past pandemics shows that the success of policies to slow down the rapid transmission of highly infectious disease rely, in part, on the public having accurate perceptions of personal and societal risk factors [14], [15], [17]. Fig. 5 shows the risk perceptions from the sample, which measures four areas: likelihood of infection leading to symptoms, likelihood of infection leading to serious illness, likelihood of infection leading to hospitalisation and likelihood of infection leading to death. We plotted the mean risk perception scores from the data (Fig. 6), and the results show that risk perception across the four areas varied between 3.1 and 3.2 on a 5-point scale. The proportionally large standard deviation measured across all four questions furthermore indicate that risk perceptions are spread out and diverse in the sample, but the general risk perception remains fairly high.Fig. 5 Perceived impact of COVID-19 on own Physical and Mental health.
Fig. 6 Risk perception in relation to COVID-19.
4.6 Latent class analysis
4.6.1 Model formulation
The core of the empirical work was concerned with the analysis of vaccine uptake behaviour using the latent class model. As mentioned in s.3, the overall vaccine uptake was composed of three patterns of preferences, namely respondents who always choose a vaccine across their six scenarios, respondents who choose a vaccine in some but not all the scenarios, and respondents who never choose a vaccine (vaccine-hesitant). These groupings translated to the respective class allocations, namely: class 1, class 2 and class 3 in the latent class model.
A key aim of the study in this part was to understand the impact of vaccine characteristics on uptake. Thus, to achieve this aim, individuals classified as ‘vaccine-hesitant’ were excluded from the data during estimation. This reduced the sample used for estimation to 256 respondents. The result of the latent class model are presented in Table 4.Table 4 Results of Latent Class Model.
Class 1 Class 2 Class 3
Attribute coeff r.s.e r.t-r coeff r.s.e r.t-r coeff r.s.e r.t-r
ASC position (δ pos) [base] −0.006170 0.063317 −0.09745 0.056055 0.047496 −1.18021 −0.031935 0.276709 −0.11541
ASC free (δ free) 1.603373 0.500102 3.20609 1.585343 0.506360 3.13086 −9.027259 2.098749 −4.30126
ASC paid (δ paid) 2.297221 0.478364 4.80224 −0.926700 0.485035 −1.91058 −8.358209 2.61881 −3.19159
ASC no vaccination (δ nv) 0.000000 NA NA 0.000000 NA NA 0.000000 NA NA
Vaccine attributes
Risk of infection (βinfection) −0.005172 0.044605 −0.11595 −0.022839 0.037242 −0.61326 0.0000 NA NA
Risk of illness (βillness) 0.0000 NA NA −0.043294 0.014203 −3.04818 −0.355028 0.124661 −2.84795
Protection duration unknown (βdu) 0.0000 NA NA 0.00000 NA NA −0.396795 1.494417 −0.26552
Protection duration (βdur) 0.002493 0.003813 0.65371 0.003003 0.003426 0.87673 0.00000 NA NA
Vaccine side effects: mild −0.022488 0.022348 −1.00631 −0.025652 0.017865 −1.43589 0.000000 NA NA
Vaccine side effects: severe −40.06515 9.042884 −4.43057 0.0000 NA NA 0.0000 NA NA
Vaccine administration variables
Waiting time to receive vaccine −0.055851 9.042884 −4.43057 −0.007521 0.008425 −0.89271 −0.010271 0.036214 −0.28361
Fee [to receive vaccine immediately] −3.7334e-04 8.791e-05 −4.24713 −1.6909e-04 1.4503e-04 −1.16584 −3.5512e-04 4.2202e-04 −0.84148
Population coverage of vaccinated 4.6879e-04 04 0.012488 04 0.03754 0.009904 0.009198 1.07678 0.006133 0.012074 0.50798
Exempt from international travel (ΔOT_RL) 0.428344 0.499399 0.85772 0 NA NA 0 NA NA
Delta (ΔSHIP_Freq_RD) [base] 0 NA NA 0.239298 0.166410 1.43800 −1.575445 0.252125 −6.24866
Model Statistics
Observations 1614
Parameters 31
Class allocation: mean probability 0.40367 0.51280 0.08353
Model LL (Start) −2597.633 −2597.633
Model specific LL (final) −2890.17 −3026.383 −5928.619
Overall LL (Start) −2135.333
Overall LL(0) −2597.633
Overall LL(final) −2072.276
Pseudo R2 0.2022
Adjusted R2 0.1903
AIC 4206.55
BIC 4373.53
Notes: coeff = coefficient, rob.s.e = robust standard error, rob.t-r = robust t-ratio, *insignificant.
4.6.2 Model outcomes
Payment fee to receive vaccine immediately, vaccine side effects, and population coverage emerged as the top three influential attributes for vaccine preferences in the model and across classes (Table 4). Overall, we also found that:• Risk of infection and risk of illness have negative impacts on utility, meaning that vaccines with a higher efficacy obtain a greater utility. The (per percentage point) impact of changes in the risk of infection is larger than the impact of changes in the risk of illness.
• Increases in the length of time that a vaccine protects from infection/illness have a positive impact on the utility of vaccination, where there is an additional disutility if the protection duration is unknown.
• Increases in mild and severe side effects have negative impacts on utility.
• Increases in waiting time reduce the utility of vaccines, as do increases in the cost for paid vaccine options.
• Increases in the share of the population already vaccinated have a positive impact on the utility of vaccination. This behaviour could be explained based on risk averseness, by being less willing to accept vaccination when it has not been ‘tested’ on a large share of the population.
• If vaccination implies an exemption from travel restrictions, then this has a positive impact on the utility of vaccination.
• Accurate public risk perception is also critical to effectively manage public health risks.
In addition to the above description of the overall effects, the models also uncovered substantial heterogeneity in preferences across individuals, with different sensitivities obtained in the different classes of the LC structures.
4.7 Willingness-to-pay analysis
The average out-of-pocket expense to obtain a high-quality vaccine was found to be N$233.11 (equivalent of US$12.83), while the out-of-pocket expense to avoid travel restrictions was N$377.39 (equivalent of US$20.77). The value obtained for the high-quality vaccine of N$233.11, was found to be higher than values obtained in other African countries such as Ethiopia at US$4.9 [42]) and Nigeria at US$1.9 [3]; but was much lower than WTP values obtained in Chile at US$232 [9], Indonesia at US$ 57.20 [19]and Malaysia at US$30 [45].
Marginal rates of substitution analysis from the model are presented in Fig. 6, Fig. 7. We see that while vaccine performance was presented to respondents as the percentage point risk of infection and illness in the survey, for the MRS calculations, we translated the values into efficacy, looking at how many additional weeks respondents are willing to wait for a vaccine with a 10% increase in efficacy. Some factors were found to have an impact on the WTP of respondents. Overall, we found the following:• There are substantial differences across the different vaccine attributes, where these differences vary also between WTW and WTP measures.
• Protection against illness is seen as more important than protection against infection.
• There is a high willingness to wait for a vaccine until it becomes clear how long the vaccine will offer protection.
• We also present the MRS for a reduction from the highest to the lowest risks presented in the SC scenarios, which implies going from 10% to 0.1% for mild side effects, and 0.02% to 0.001% for severe side effects. On this, we see that there is both marginally high sensitivities to go both from serious and mild side effects to the low or no side effects.
• The attribute relating to exemptions from travel restrictions was also found to have a meaningful role. We note that respondents would be willing to wait several months for a vaccine that would exempt them from such restrictions (see Fig. 7, and similarly a higher WTP value to obtain exemption for international travel (see Fig. 8).Fig. 8 WTP measures.
Fig. 7 WTW measures.
5 Discussion and conclusion
This is the first study to use DCE to explore the preferences of Namibians towards potential vaccines. Although this study was conducted before vaccines were available, Namibia has to date vaccinated just over 500 000 individuals [31]. Therefore, understanding preferences for COVID-19 vaccines is a major need.
The current study revealed a number of outcomes. Several psychological factors were assessed including reasons for not taking the vaccine, past vaccination behavior, perceived impact of mental and physical health, and public risk perception of COVID-19. The study reveals that vaccine hesitancy is overall decreasing in Namibia, but there is still high share of vaccine-hesitant Namibians who demonstrate a lack of trust in the vaccination procurement and dissemination process. Thus, we suggest government to overcome this by improving information and clarity surrounding vaccination. Local studies of monitoring vaccinated individuals can also increase the trust towards COVID-19 vaccination, where this information is shared to elucidate vaccine safety and efficacy in the local population.
The results of the latent class model show that payment fee to receive vaccine immediately, vaccine side effects, and population coverage are the most influential attributes of the vaccines. Despite the considerable heterogeneity in the study sample, we found that characteristics of vaccines matter, both in terms of the decision to be vaccinated or not, and in the choice between different vaccines. A key implication of this work is that some individuals clearly will accept any reasonable vaccine while others are hesitant to be vaccinated. Importantly, we found a third group who are open to vaccination only if the characteristics of the vaccine are right for them. What defines “right” differs across individuals, but efficacy is of especially great importance and low risk of both mild and severe side effects.
A major output of the research is the ability to make predictions of the uptake of vaccination against COVID-19, and this was demonstrated both by the enumeration of WTP and WTW measures. Overall, there is high willingness to wait for a vaccine until it becomes clear how long the vaccine will offer protection, and a high willingness to pay to reduce vaccine side effects. The average WTP for a high-quality vaccine recorded was N$233.11 (US$ equivalent of US$15.14), which corroborates with the model output. An effective behavioural change strategy for COVID-19 vaccines uptake can now be devised to address multiple beliefs and behavioural determinants, and thereby reduce barriers and leverage enablers identified in this study.
There are a number of potentially fruitful avenues for future research. For example, we explored rationales for vaccine hesitancy but our work does not offer solutions in terms of encouraging vaccination uptake amongst this group, which should be explored in further research. Similarly, given our findings in terms of the impact that vaccine characteristics have on uptake, and previous work on the preference and willingness-to-pay for COVID-19 vaccines in Africa [3], [42], another area of work relates to developing the most effective messaging for encouraging uptake by the most hesitant subgroups of the population. A study in Asia found healthcare workers more willing to be vaccinated due to perceived COVID-19 susceptibility and low potential risk of vaccine harm [40], and hence it will be important to understand the hesitance according to different groups including healthcare workers, the elderly and pregnant women to ensure a targeted approach to addressing vaccine hesitancy.
A key limitation is that this survey was rolled out before Namibia had COVID-19 vaccines, and moreover, before Namibia had its worse COVID-19 wave between March and July 2021, which was fuelled by the Delta variant which resulted in more infections and deaths. Preferences of people might have changed due to these factors, and this warrants another wave of data collection using the same survey tools. Nonetheless, the results of this study can inform government and policy makers in refining the vaccine delivery plan to overcome vaccine hesitancy in Namibia.
6 Disclosures about potential conflict of interests
No potential conflict of interests is declared by the authors.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data will be made available on request.
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