Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments. Nucleotide conversion approaches facilitate metabolic RNA labelling experiments but complicate computational analysis. Here, the authors develop a methodology and software package to enable specific analysis methods for nucleotide conversion RNA-seq data. The RNA expression level of a gene is governed by the interplay of RNA synthesis and degradation. While RNA-seq can easily obtain transcriptome-wide snapshots of gene expression profiles in a single experiment, it remained difficult to directly measure the temporal dynamics of gene regulation as consequences of changes in the rates of RNA synthesis and degradation, e.g. due to external stimuli.1, transient RNA expression2, kinetics of RNA polymerases3 or the dynamics of RNA expression4. Existing protocols for purification of labeled RNA are highly laborious and require substantial amounts of RNA5. In addition, contamination with background total RNA in the labeled RNA fraction must be controlled for4, and normalization is challenging6.To overcome this limitation, techniques involving metabolic labeling of RNA have been developed. Metabolic RNA labeling uses 4-thiouridine (4sU) or other nucleoside analogs, which are introduced into living cells and incorporated into nascent RNA. First approaches physically separated labeled and unlabeled RNA by thiol-specific biotinylation and affinity purification and sequenced separate libraries of these fractions. This approach has been used to study RNA processing9: Before sequencing, RNA is treated with chemical agents to specifically convert 4sU into cytosines or cytosine analogs. Thus, labeled and unlabeled RNAs can in principle be differentiated based on T-to-C mismatches in sequencing reads without the need to physically purify labeled RNA. A major advantage of the nucleotide conversion approach, aside from lower requirements of starting material and a simplified experimental workflow, is that it can be combined with more specialized protocols, e.g. to profile transcription start sites10 or ribosome occupancy11. Furthermore, we and others have combined 4sU conversion with single cell RNA-seq to study the heterogeneity of gene regulation14.Recently, several approaches have been proposed that circumvent the purification7. Thus, a considerable number of reads originating from labeled RNA does not cover any 4sU incorporation site. The percentage of such reads is in the order of 20-80% and depends on the ratio of 4sU and normal uridine available for incorporation into nascent RNA, the read length, and the uridine content of RNA. The pioneering studies employing 4sU conversion exclusively focused on T-to-C reads. Despite underestimating labeled RNA, T-to-C reads alone can be used to estimate unbiased RNA half-lives in pulse-chase experiments7 and to detect rapid changes of transcription upon drug treatment or acute depletion of transcription factors16.A limitation of 4sU conversion approaches is that concentrations of 4sU that are tolerated by cells commonly only replace 1 in 40 uridines by 4sU17. The posterior represents uncertainties in the quantification, mainly due to the scarcity of 4sU incorporation events, and has so far been used to filter out genes with inaccurate quantification12. Importantly, however, our Bayesian framework in principle allows to take these uncertainties further to downstream analyses such as estimation of RNA kinetics or gene expression changes.We previously proposed a statistical solution to quantify labeled and unlabeled RNA without bias due to limited 4sU incorporation: By using a binomial mixture model, our GRAND-SLAM approach provides unbiased estimates of the percentage of labeled RNA per gene and its posterior distribution18 or uncovering short-term gene regulation15 require additional analysis steps. For experiments involving affinity purification of labeled RNA, several software packages including pulseR19, INSPEcT20 and DRiLL4 have been developed for such downstream analyses. However, all these packages are designed to work with separate sequencing libraries corresponding to the labeled and unlabeled fraction of a sample, and the models implemented in these packages are blind towards the challenges that come along with nucleotide conversion approaches. As a notable exception, pulseR has later been adapted for analyzing nucleotide conversion data by adding an additional nuisance parameter for the limited labeling efficiency21. However, none of these methods take advantage of the output of the mixture modeling approach as implemented in GRAND-SLAM or the fact that uncertainties of the percentage of labeled RNA can be estimated for nucleotide conversion approaches.GRAND-SLAM is designed for the primary processing of nucleotide conversion RNA-seq data, i.e. to estimate the percentage of labeled RNA among the total RNA for each gene. Estimating RNA half-lives22. Here, we compare these methods and show that the most accurate results are obtained by directly utilizing the posteriors from GRAND-SLAM to estimate the kinetic model. Furthermore, we propose a Bayesian hierarchical model to dissect the mode of gene regulation from snapshot experiments. To facilitate collaborative work and exploratory data analysis, grandR provides a comprehensive web-based data visualization and exploration tool.To close this gap, we here present grandR, an R package to facilitate analyses of nucleotide conversion RNA-seq experiments and taking full advantage of the uncertainty estimates. It includes new methods for quality control and recalibrating labeling times. grandR implements several methods to estimate RNA synthesis and degradation rates from progressive labeling experiments that have been applied previously by us and others7, Timelapse-seq8 or TUC-seq9. The inputs for grandR are in principle matrices (genes × samples) of (i) total read counts and (ii) information derived from metabolic RNA labeling . grandR includes several functions to read data into a common internal data structure, and most of the implemented methods are therefore agnostic how raw data has been preprocessed. The only exceptions are the Bayesian methods described below, as they require the posterior distributions of the percentage of labeled RNA. We thus recommend using grandR after primary processing by our GRAND-SLAM tool17, which provides these posterior distributions. We put special emphasis on designing our package to be usable by non-experts in R programming: All data and metadata belonging to a project are stored in a single variable, and there are clearly defined, and expressively named, high-level functions to proceed with analysis pipelines , or for multiple pairwise comparisons , either for total, labeled or unlabeled RNA. Testing for more complex designs is also possible based on the likelihood ratio test. grandR implements all kinetic modeling approaches discussed below , it supports both pulse-chase and progressive labeling designs and can also fit non-steady state data  with low concentrations of 4sU (100 µM) for a 24 h pulse phase, followed by washing out 4sU and sequencing at several time points during this chase phase. Notably, cell viability was assessed to be ~80% after 24 h. Quality control using grandR revealed that samples treated with 4sU for 24 h and untreated controls segregate in a principal component analysis  and strongly (absolute log2 fold change >124) regulated , which is here equal to the percentage of labeled RNA, was significantly correlated with the log2 fold change with respect to the (4sU naïve) 0 h time point . (ii) In Narain et al.3, we used non-linear least squares regression (NLLS) to fit the full model including 18. (iii) Boileau et al.21 adapted their pulseR package19 that was originally designed for affinity purified labeled RNA. (iv) Finally, we have presented a Bayesian method to estimate the degradation rate 17. The main difference among methods (i)-(iv) is the error model employed, which might result in differing parameter estimates even though the same kinetic model is used. To compare the accuracy of the four methods, we implemented in-silico simulation of nucleotide conversion RNA-seq experiments in grandR. This uses a previously developed method29 to simulate read counts. Furthermore, individual 4sU incorporation and conversion events as well as background mismatches as introduced by sequencing errors are simulated for each read, and GRAND-SLAM17 is used to estimate the NTR for each gene. The simulations are also subject to biological variability (see Methods). All parameters including the read count, overdispersion and half-life distributions as well as the 4sU incorporation and background T-to-C mismatch rates are matched to a recent SLAM-seq data set of SARS-CoV-2 infection22 as reference. Taken together, we designed the simulation in grandR to mimic real data as closely as possible.Different variants of this model have been used to estimate kinetic parameters using nucleotide conversion data with progressive labeling designs: (i) Finkel et al.p < 2.2 × 10−16 for all methods, Pearson correlation; Supplementary Fig. P < 2.2 × 10−16 for all comparisons, two-sided Kolmogorov-Smirnov test; Fig. While the estimated RNA half-lives correlated well with the ground-truth for all methods . Poorer performance with time points not matching to the true RNA half-lives were indeed not unexpected based on previous theoretical considerations27. To investigate the effect of missing time points in more detail, we simulated a broad range of potential experimental settings , or whether single samples at 6 time points (1 × 0 + 1 + 2 + 4 + 6 + 8 h) provide more accurate results. Clearly, the “1 × 0 + 1 + 2 + 4 + 6 + 8 h” design resulted in significantly less variable half-life estimates for genes having RNA half-lives <1 h . Thus, the concentration of active 4sU increases until saturation, and RNA that was transcribed significantly before reaching saturation contains fewer 4sU than RNA transcribed later. Such an increase in the 4sU concentration has been described recently30. Therefore, especially for earlier time points, the effective labeling time is expected to be much shorter than the nominal labeling time. For example, the effective labeling time of a nominally 1 h sample might only by 40 min, since during the first 20 min, the concentration of activated 4sU has been too low to induce many 4sU incorporation events.4sU is not available for transcription immediately once the cells are cultured on 4sU media, but first crosses cell membranes and is then processed by the pyrimidine salvage pathway before it is available as substrate for transcription  by Herzog et al. using Actinomycin-D (Act-D) chase RNA-seq data, (iii) by grandR using the same pulse-chase data, (iv) by grandR using progressive labeling data without or (v) with recalibration  and magnitude . Interestingly, the Act-D data were well correlated as well , as described7, but showed substantially longer half-lives (median: 8.19 h). With respect to all three references (i–iii), recalibration slightly improved the correlation of half-lives estimated from progressive labeling . More importantly, recalibration rectified the magnitude of the estimated half-lives  being either <P-values 23 for new and old RNA, respectively.We analyzed “regions of practical equivalence” (ROPE)n = 10,835 genes, virtually none had n = 127, 1.2%) or n = 0) independent of the true change of RNA synthesis  and n = 3174 (93.7%) genes had We first analyzed our simulated data (2 h labeling) where RNA synthesis rates were perturbed. As expected from overall sis Fig. . Notablyely Fig. . Thus, Rn = 10,835 genes, n = 40 (0.4%) had n = 407 (4.0%) had n = 302 out of overall 1641 genes with >2-fold downregulated RNA half-lives, 18.4%). Thus, as shown above, new RNA for some genes exhibited significant changes when only RNA half-lives are changed. By contrast, our Bayesian approach can accurately differentiate between changes in synthesis and degradation. Moreover, out of n = 3390 genes with >2-fold regulated half-lives, n = 1692 (49.9%) had n = 1351 (39.8%) had Next, we focused on simulated data where RNA half-lives were perturbed. From overall .01 Fig. . These h.01 Fig. , indicatInterestingly, the sensitivities of our Bayesian approach for detecting changes in RNA stability were asymmetric  after depletion of BANP were labeled with 4sU prior to sequencing. Importantly, the samples from the 4 h timepoint and later were labeled for 2 h, but shorter labeling of 30 and 90 min was applied for the 1 h and 2 h timepoints, respectively. Due to these different labeling times, new RNA is not directly comparable among the samples and inference of BANP depletion across the timepoints. Thus, we adapted the effective labeling time such that the median log fold change of the half-lives resulting from the adapted labeling time vs the half-lives from the control samples without BANP depletion was 0 for each sample. Indeed, after this recalibration by matching the half-life distribution to this reference, the distribution for RNA half-lives estimated for each timepoint were largely indistinguishable  bias due to limited labeling  and (ii) the inherent quantification uncertainty (the NTR model) are directly taken into account. Another disadvantage of adapting methods that have been developed for the affinity purification approach is that they are agnostic of the highly variable number of uridines across the reads, which impacts on the likelihood of observing one of the infrequent 4sU incorporation events. Notably, this is a different, and more severe, issue than the differences in uridine content among full length RNAs (“uridine bias”) that is relevant to purification approaches and taken into account by some of the previous tools19.All so far available packages for analyzing experiments involving metabolic RNA labeling have been designed to work with two (or three) fractions per sample, one sequenced library of purified labeled RNA, and one of total (and/or unlabeled) RNA. In principle, these packages can also be used for nucleotide conversion data after splitting labeled and unlabeled RNA by extracting reads containing T-to-C mismatches. This bioinformatic separation comes with disadvantages: The error models used by previous packages are not designed to handle the enormous bias of underestimating labeled RNA by counting reads with mismatches. The pulseR package, as a notable exception, has been adapted to include an additional nuisance parameter for this26. Toxicity of too highly concentrated 4sU has previously been assessed by testing for cell viability. However, here we show that expression estimates can be affected before cell viability suffers. We therefore advocate that all studies employing metabolic RNA labeling must report the extent of any effect of 4sU on expression. Since levels of short-lived RNAs quickly decline when transcription is globally inhibited, a correlation of the fold changes for 4sU treated samples vs. corresponding 4sU naïve controls with the NTRs can be used as surrogate marker for transcriptional defects due to 4sU treatment, as implemented in grandR. However, it is important to note that also technical effects such as decreased efficiency of reverse transcription or less accurate mappability of reads with many mismatches can result in such a correlation.It is well described in the literature that nucleoside concentrations must be optimized for specific cell types and desired labeling timesRNA degradation rates have previously been estimated using progressive labeling time courses using different computational methods. All these approaches employed the kinetic model described in the Methods section but they differ in their choice of the error model. Due to noise introduced by the inference of the NTR, the actual errors of normalized new and old RNA likely are differently distributed than standard RNA-seq data. Our simulations indicate that the errors are well approximated by a gaussian distribution. We recommend using the non-linear least squares fitting procedure as the general tool for fitting the kinetics of RNA expression. The Bayesian approach provides slightly more accurate results and better error bounds but can only be used under steady state conditions.BANP data set, we made the assumption that globally, RNA half-lives are not affected by acute depletion of BANP, and therefore used the estimated half-lives of the untreated control sample as a reference. Testing for effective labeling is critical when samples with distinct labeling times are compared, and for estimating RNA degradation and, to a lesser extent, synthesis rates in absolute terms.A major caveat of metabolic RNA labeling experiments is that the effective labeling time might not correspond to the nominal labeling time. grandR provides tools to test for this critical issue and recalibrate labeling times: For progressive labeling time courses, asymmetric residuals of early time points indicate shorter effective labeling times. Using the labeling times as additional independent variables when jointly fitting the kinetics for all genes, as implemented in grandR, can be used to estimate the effective labeling times. For snapshot experiments, labeling times can be recalibrated based on additional assumptions, e.g. based on reference RNA half-lives that must be known a-priori. For the 20. However, the amount of intronic RNA among total RNA is relatively low in comparison to 4sU purified RNA after a short labeling pulse. Thus, to obtain accurate parameters of RNA processing, we recommend affinity purification and the use of either DRiLL or INSPEcT. More work is required to explore how intronic reads in nucleotide conversion RNA-seq can be used to infer parameters about RNA processing. Another limitation is that the kinetic model employed by all methods in grandR inherently assumes constant rates of synthesis and degradation during the time of labeling. This might not be true in some scenarios. For example, the reduced RNA half-lives observed for SARS-CoV-2 likely result from the general host shutoff protein nsp1 encoded by SARS-CoV-232. It is therefore very likely that degradation of cellular mRNAs depends on the abundance of nsp1, which increases substantially over the first few hours of infection. Thus, the degradation rate at 4 h post infection (corresponding to the 1 h labeling time point in the SLAM-seq data from ref. 22) likely is different at 7 h post infection , and the estimated RNA half-lives estimated across the progressive labeling time course represent averages. When multiple snapshots over time have been measured, grandR can estimate rates for each snapshot. In such a setting, rates are only assumed to be constant within the time in between each snapshot and its reference . Thus, dynamically changing rates over time can be detected by analyzing their posterior distributions using the Bayesian model implemented in grandR )17. Due to this one-to-one correspondence, the test in bakR can be used to identify significant differences in RNA half-lives, importantly, however, only under steady state conditions. bakR does not attempt to model non-steady state conditions, which is an inherent part of the Bayesian hierarchical model implemented in grandR.While our manuscript was under review, the bakR package was publishedNucleotide conversion approaches greatly reduced the burden on the wet-lab side for conducting metabolic RNA labeling experiments but introduced the need for more sophisticated tools for their computational analysis. Complementing our GRAND-SLAM software for primary processing of such data, we developed the grandR package as a general toolkit to aid researchers to further analyze and interpret such data. Here, we demonstrated that additional quality control measures are necessary for such data to exclude effects of 4sU on transcription, and that short labeling times often require recalibration. For both methods, grandR provides high-level functions. Furthermore, grandR enables researchers to estimate synthesis and degradation rates for both progressive labeling as well as snapshot experiments without requiring steady state assumptions. Finally, grandR provides a web-based interface for exploratory data analysis.17. Fastq files were downloaded from the SRA database. The accession number were: GSE99970 for the pulse-chase 4sU labeling data set in mESCs from ref. 7, GSE99974 (only wt cells) and GSE99972 for the progressive labeling in mESCs7, GSE139151 for the NXF1 knockdown data set from ref. 18, GSE162323 for the SARS-CoV-2 data set from ref. 22, and GSE155604 for the BANP depletion data set from ref. 16. Adapter sequences were trimmed using cutadapt (version 3.4) using parameters “-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT” for the SARS-CoV-2 data. For the other data sets, reads were already pre-trimmed on SRA. Then, bowtie2 (version 2.3.0) was used to map read against an rRNA  and Mycoplasma database using default parameters. Remaining reads were mapped against target databases using STAR (version 2.5.3a) using parameters “–outFilterMismatchNmax 20–outFilterScoreMinOverLread 0.4–outFilterMatchNminOverLread 0.4–alignEndsType Extend5pOfReads12–outSAMattributes nM MD NH”. We used the murine genome for mESCs and BANP, the human genome for NXF1, and the combined human and SARS-CoV-2 (NC_045512) genome for SARS-CoV-2. All genome sequences were taken from the Ensembl database (version 90). Bam files for each data set were merged and converted into a CIT file using the GEDI toolkit34 and then processed using GRAND-SLAM  with parameters “-trim5p 15 -modelall” to generate read counts and NTR values on the gene level, taking into account all reads that are compatible with at least one isoform of a gene. For all mESC data sets, we performed a second GRAND-SLAM run utilizing the RNA 3’ end annotation provided in ref. 7 and setting the parameters -conv 0.025 -err 0.00035  since GRAND-SLAM did not estimate these parameters for several of the individual samples  due to extremely low amounts of labelled RNA. These runs utilizing the custom 3’ end annotation were used for the the analyses shown in Fig. GSE99975.All SLAM-seq data used here were processed using the GRAND-SLAM pipelinet-test for the differences of correlation coefficients was computed using the psych package (version 2.2.9).All correlation coefficients were computed using the cor function of R (version 4.2.1). Wald tests for differential expression and principal analyses were computed using DESeq2 with default parameters (version 1.36) using the PairwiseDESeq2 or PlotPCA functions of grandR, respectively. The To simulate a nucleotide conversion RNA-seq experiment for 17, i.e. we used a uniform prior. In addition, it also computes the Beta approximation of the posterior distribution of 17.To simulate the “measured” NTR for gene SimulateReadsForSample of grandR and simulates the read count SimulateTimeCourse. Based on a time point SimulateReadsForSample as follows: To model biological variability, we define This procedure is implemented in the function estimateDispersions from the DESeq2 package. The reference synthesis rates SimulateTimeCourseNonConstant.For all simulations here, we used the “mock” samples from the SARS-CoV-2 data set to compute the relative abundances mentclass2pt{minimThe commonly used kinetic model of RNA expression assumes zero-order kinetics for RNA synthesis and first-order kinetics for degradation:Here, entclass1pt{minima22 focused on unlabeled RNA and performed simple linear regression on Eq.  to fit the full model in Eq. (18. (iii) Boileau et al.21 proposed to fit the full model using their pulseR package19 based on the raw counts of reads showing T > C mismatches, and to remove bias of this approach using an additional nuisance parameter. (iv) Finally, we have presented a Bayesian method to estimate the degradation rate 17.Different variants of this model have been used to estimate RNA stability represented by the degradation rate n on Eq.  after loThe main difference among methods (i)-(iv) is the error model employed. While (i) and (ii) assume homoscedastic gaussian errors of estimated pre-existing and newly synthesized RNA levels, either in log space (i) or of the levels directly (ii), pulseR models read counts using a negative binomial distribution assuming a gene specific overdispersion parameter that is jointly estimated from all samples. The Bayesian approach (iv) assumes that all data points are generated from a single degradation rate constant To model the abundance of RNA at time d on Eq. :4\documeUnder steady state assumptions, we have d of Eq. :6\documeWe have data given in the form of total expression values We define the random variables for old and new RNA as entclass1pt{minimarom Eqs.  remain cFitKinetics, which will fit either the LM, the NLLS or NTR model for each gene by calling the respective functions mentioned below.All methods described below, except for pulseR, can conveniently be used in grandR using the function FitKineticsGeneLogSpaceLinear function in grandR using the lm function of R. Confidence intervals are estimated using the confint function.For the linear model approach (method (i) in the text), we note that we have a linear function ing Eqs.  or 6). . \documeFitKineticsGeneLeastSquares using the nls.lm function from the minpack.lm package. Confidence intervals are estimated using confint.nls.lm.For the non-linear least squares approach (method (ii) in the text), we assume 19. It was later adapted to also handle nucleotide conversion RNA-seq data21. pulseR operates on labeled and unlabeled read counts (i.e. reads with and without observed T-to-C conversions), and includes additional nuisance parameters to model reads from unlabeled RNA with T-to-C conversions  and reads from labeled RNA without T-to-C conversions (reads not covering 4sU incorporation sites). In our notation, the pulseR model isFitKineticsPulseR using the code from ref. 21 provided on github (https://github.com/dieterich-lab/ComparisonOfMetabolicLabeling).pulseR (method (iii) in the text) originally was developed for 4sU labeling experiments where labeled and unlabeled RNA was physically purified and sequenced separately17. We assume For the Bayesian NTR method (method (iv) in the text), we note that under the assumption of steady state By logarithmizing and setting the derivative to 0, we see that the MAP estimator isWe can also transform the MAP estimator of Thus, transforming from the optimize function built into R. For approximate 21. The rationale here is, that as we use a uniform prior, the posterior distribution is equal to the likelihood function. The CI is found by finding the values of integrate function and report the FitKineticsGeneNtr. It also provides an estimate of We use the CalibrateEffectiveLabelingTimeKineticFit in grandR.grandR implements two ways to recalibrate labeling times. The first can only be used with progressive labeling data and makes use of the fact that our kinetic model poses some constraints on how the temporal dynamics can behave. For that, we fit the NLLS model simultaneously for all genes, and consider the labeling time as additional variables that are jointly optimized. To make this procedure more efficient and less prone to noise, we first make a rough estimate of the half-lives using the uncalibrated labeling times and use the top 200 expressed genes from the following half-life classes: 0–2 h, 2–4 h, 6–8 h, >8 h. Stratifying by half-life classes is important as many of the most strongly expressed genes have very long RNA half-lives. Importantly, however, the uniroot function of R. This procedure is implemented in the function CalibrateEffectiveLabelingTimeMatchHalflives in grandR.The second method for temporal recalibration requires reference half-lives. For each biological sample the observed data can be transformed into half-lives for any labeling time . We choose the labeling time such that the median log fold change between the reference and transformed half-lives across all genes is 0 by using the By solving Eqs.  and 5),,5), we owith We use Eq.  to get rSubstituting this into Eq. :21\documuniroot function. Of note, this assumes TransformSnapshot function.We solve this numerically for With a common reference sample with abundance  use Eq.  to derivTransforming snapshot data). We define snapshot data for a single biological sample Sample the unobserved parameters Sample Sample Sample We assume that snapshot data control condition. We prefer to invert the log fold change of the degradation rates, which then corresponds to the more intuitive log fold change of the RNA half-lives Here, we are mainly concerned with snapshot data Draw a sample Draw a sample Draw a sample However, we show here that we can efficiently draw We then transform these samples into estimateDispersions from the DESeq2 package for estimation. There is no obvious conjugate prior for We assume that read counts It is easy to see that the Beta distribution is a conjugate prior for Thus, for the prior We use the full distribution of all genes to inform the prior distribution as follows. We first transform the expression value 23. This can be achieved in grandR by first calling the Normalize function, which places the normalized read counts into the default data slot of the grandR object.So far, we have ignored normalization. For practical applications, this must be taken into account. We do this by the same approach as DESeq2, i.e. by rescaling read counts using a size factor to obtain normalized read countsThus, to sample from 17. This posterior only quantifies technical variance of measuring the true The number of conversions on a read For each biological sample Here, If the marginal posteriors This can be integrated:is a proper prior. 35. Since we want to sample 35. Note that To sample d in Eq.  with prid in Eq. , and, du35. To determine the grid bounds, we first find the maximum using R’s optim function, and then go into positive and negative uniroot function. To sample 35. This value For numerical stability, we compute everything in log space, then subtract the maximal grid value and exponentiateFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Software 1Reporting Summary


The highest screening utility in differentiation between angioinvasion and metastasis presence and absence in PTC patients was presented for DNA/RNA OSDP (AUC = 0.71), SIRT3 (AUC = 0.70), and TAC (AUC = 0.67) . Our study suggests that peripheral concentration of oxidative stress markers could be useful as angioinvasion and metastasis indicator in PTC patients.Angioinvasion remains the important prognostic feature in papillary thyroid cancer (PTC) patients. Literature data indicates several markers that may be associated with oxidative stress and/or angioinvasion. Therefore, we assessed the utility of selected parameters in angioinvasion and metastasis screening in serum of PTC patients. Serum antioxidant capacity (TAC) and sirtuin 3 (SIRT3) levels were decreased (all  PTC originating from the follicular epithelium usually presents a good prognosis with an excellent survival rate. However, the diagnosis of aggressive types, manifested by local invasion, recurrence, and distant metastases, is still a significant clinical problem2. Angioinvasion has been identified as an important and independent prognostic factor in many cancers, including PTC. The diagnosis of thyroid cancer dedifferentiation with local angioinvasion is of great importance in further clinical management3. The molecular criteria for angioinvasion have varied both in practice as well as in studies assessing the clinical significance of these findings. This inconsistency may be attributed to application of inappropriate criteria4.Papillary thyroid cancer (PTC) is one of the most commonly occurring endocrine neoplasms worldwide5. It has been demonstrated that ROS promote angiogenesis, either directly or via the generation of active oxidation products, including peroxidized lipids7.Oxidative stress is predicted to have a major impact both in the development and the progression of thyroid cancer, since, due to the process of hormonogenesis, the thyroid gland is exposed to a high level of reactive oxygen species (ROS). What should be emphasized, PTC is the most prevalent thyroid cancer associated with oxidative stress9. Moreover, growing literature data links oxidative stress with DNA methylation in cancer development and progression. Nuclear factor kappa B (NF-κB) and interleukin-6 (IL-6) play a significant role in immune and inflammatory processes. In turn, sirtuin 1 and 3 (SIRT1 and SIRT3) and forkhead box protein 01 (FOXO) act as regulators of important metabolic pathways including redox homeostasis10. Protein 53 (p53), 5′-AMP-activated protein kinase catalytic subunit alpha-1 (PRKAA1), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) regulate metabolic reprogramming and antioxidant/mitochondrial function11. Their usefulness in angioinvasion screening in PTC remains to be elucidated.Several markers may be associated with oxidative stress and/or angioinvasion. The total oxidative stress (TOS) and antioxidant capacity (TAC) are used to estimate the general oxidation status and antioxidant ability of the body, respectively. Malondialdehyde (MDA), the product of lipid peroxidation, is a commonly used as biological marker of this process. DNA/RNA oxidative stress damage products (DNA/RNA OSDP) show the extent of nucleic acid oxidative modificationsIt can be assumed the interplay between oxidative stress, chronic inflammation, and angiogenesis in cancer progression. Thus, our hypothesis is that circulating oxidative stress markers, inflammation and/or angiogenesis related parameters can be helpful in angioinvasion screening in order to support clinical evaluation in questionable PTC cases. Thus, the aim of this study was to assess the utility of selected parameters in angioinvasion and metastasis screening in serum of PTC patients.12(Table1).To test the working hypothesis the group of PTC patients was divided into 2 subgroups: PTC patients with both angioinvasion and metastasis (study group) and PTC patients without these features p < 0.05; p < 0.001; p < 0.05; respectively). Yet, other parameters did not differ among groups (Table The cholesterol (CHOL) and low-density lipoprotein (LDL) concentrations were increased, and high-density lipoprotein (HDL) was decreased in study group when compared with reference group (ps Table .Table 2Tp < 0.05) and both DNA OSDP and MDA levels were increased in study group when compared with reference PTC patients . The study and reference groups did not differ in terms of TOC, SIRT1, DNA methylation, FOXO, PRKAA1, PGC1α, p53, NF-κB, and IL-6   measurements. However, the AUC for MDA (AUC = 0.66), somewhat high, did not reach significance when compared with AUC = 0.50 (p > 0.01) , SIRT3 (AUC = 0.70), TAC (AUC = 0.67)  and between p53 and FOXO  was shown. Moreover, medium negative correlation between TAC and MDA measurements was demonstrated . Medium positive correlation between p53 and PRKAA1  and between NF-κB and PGC1α  was also observed. Additionally, medium positive correlation between PRKAA1 and PGC1α  was shown.Considering all the biochemical parameters in PTC patients and their significant associations, strong positive correlation between p53 and NF-κB . Moreover, we also observed that both angioinvasion and metastasis presence influence serum TAC, DNA/RNA OSDP, SIRT3, and NF-κB concentrations in PTC patients (p < 0.05)  Table .Table 4T15. This may be due to the inconsistent criteria for angioinvasion detection in endocrine tumors, especially thyroid cancer13. Assessment of angioinvasion in primary tumors is currently based upon examination of sections stained with haematoxylin and eosin. Moreover, several vascular markers have been characterized and studied to detect cancer angioinvasion, e.g., a commonly used endothelial marker CD31, platelet-associated protein CD61, and von Willebrand factor (factor VIII-related antigen)19.The literature data based results assessing the predictive value of angioinvasion in distant metastasis development, especially in well-differentiated thyroid cancer, are discrepant20. Janion et al.21 showed significantly lower serum MDA and TAS levels and higher TOS levels in the colorectal cancer (CRC) group compared to the control group. Oxidative stress has been also suggested as significant PTC risk factor and the level of ROS generation correlated with cancer aggressiveness22. Therefore, potential modulation of oxidative status may influence the cancer progression. It has been hypothesized that oxidative stress markers in thyroid tumors may have diagnostic, prognostic, and therapeutic relevance22. To date, little studies have been performed to establish the tools for angioinvasion detection in cancer. Thus, new potential circulating biomarkers indicative of angioinvasion and metastasis are needed.There is clear evidence for a higher level of oxidative stress, including a compromised antioxidative defense system in cancer23. It has been observed that oxidative stress stimulates the secretion of angiogenic modulators in cancer cells via hypoxia-dependent and -independent pathways24. In our study, the level of serum oxidative stress markers was evaluated to assess the potential screening utility in PTC-related angioinvasion with metastasis. PTC patients with both angioinvasion and metastasis presented decreased serum TAC and SIRT3 and increased DNA OSDP and MDA levels than PTC patients without these features. The results indicate that oxidative stress is involved in angioinvasion development among PTC patients and TAC, SIRT3, DNA/RNA OSDP, and MDA could be potentially considered as PTC angioinvasion screening tools. TAC measures the peroxyl-scavenging capacity of the extracellular antioxidant system and indirectly reflects the level of oxidative stress. A low serum TAC has been reported to have a strong association with various cancers25. Reduced TAC in studied PTC patients when compared with reference PTC patients could reflect the consumption of antioxidants in free radical reactions by the cancer invasion. This suggests that TAC may have a protective role against angioinvasion and metastasis.Accumulating evidence suggest that ROS function as signaling molecules to mediate various growth-related responses including angiogenesis26 demonstrated that pre chemotherapy serum MDA level was significantly higher in patients with primary ocular carcinoma having larger tumor and having lymph node metastasis than those without lymph node metastasis suggestive of its potential significance in cancer progression.Based on MDA, lipid peroxidation was significantly increased in PTC patients with angioinvasion and metastasis compared with respective reference group. These results also point to increased oxidative stress in those patients. Furthermore, Maurya et al.27. The roles of SIRT3 vary in different types of cancers and they have been thoroughly discussed. In some types of cancer SIRT3 functions as a tumor promoter by lowering ROS levels to maintain cell proliferation, other studies describe SIRT3 as a tumor suppressor, as it can promote apoptosis in cancer cells under stress conditions28. SIRT3 plays a crucial role by mediating interactions between mitochondria and intracellular signaling29. It has been observed that increased ROS production in SIRT3 null cells contributes to increased HIF1α stabilization leading to the transcriptional activation of multiple metastasis-related pathways31. Our study suggests that SIRT3 plays a role as angiogenic factor in PTC, however, its involvement in PTC progression is yet to be elucidated.SIRT3, the major deacetylase in mitochondria, plays a crucial role in regulating glucose metabolism, modulating ROS, and limiting the oxidative damage in cellular components35. 8-Oxo-7,8-dihydro-2’-deoxyguanosine, 8-oxo-7,8-dihydroguanosine, and 8-hydroxy-2′-deoxyguanosine (8-OHdG) are generated by the oxidative damage to DNA. Increased serum 8-oxodG concentration compared to the control group has been observed in CRC patients21. Moreover, it has been demonstrated increased levels of urinary 8-OHdG in CRC patients and patients with tumor metastasis, compared to healthy controls and patients without tumor metastasis, respectively36. This line of evidence suggest that peripheral concentration of DNA/RNA OSDP may be useful in noninvasive screening of angioinvasion and metastasis in cancer patients.Interestingly, oxidative damage to nucleic acid has been associated with the development of various diseases including cancer37. FOXO proteins are growth factor and stress regulated transcription factors known to cooperate with p5338. The reciprocal regulation of these transcription factors can influence several important cancer pathways: tumor cell metabolism, DNA damage, mitochondrial function, and ATP production39. Depending on the cellular context, the activation of NF-κB can have different related output to the p53 function. Our study confirmed strong positive correlation between p53 and NF-κB  and between p53 and FOXO  in PTC patients. FOXO mediates cellular oxidative stress to maintain metabolic stability by promoting transcription of several genes coding for antioxidant proteins40.Oxidative stress can also activate a variety of transcription factors including NF-κB, FOXO, and p53. P53 is the most extensively studied tumor suppressor deregulated in most human cancers. In turn, NF-κB transcription factors play essential regulatory function in inflammation, cell proliferation, and apoptosisp < 0.001). As MDA is a marker for lipid peroxidation, the results indicate the imbalance between oxidant and antioxidant capacity in PTC, and, therefore, oxidative damage in cells and tissues. Positive correlation between p53 and PRKAA1  and between NF-κB and PGC1α  was also observed in all PTC patients. Additionally, medium positive correlation between PRKAA1 and PGC1α  was shown. PRKAA1 is a major cellular energy sensor implicated in angiogenesis in hypoxia-induced diseases41. In turn, PGC1α plays a pivotal role in mitochondrial biogenesis and multiple other pathways 42. As no difference in serum PRKAA1 and PGC1α concentration was observed between groups, it can be concluded that their peripheral level is not influenced by angioinvasion development as they are produced and exert effects locally.Moreover, medium negative correlation between TAC and MDA measurements was demonstrated . In our regression analysis results, a significant influence of angioinvasion and metastasis on various peripheral parameters, which showed statistical significance (p < 0.05), was observed. Serum parameters such as CHOL, LDL, HDL, TAC, OSDP, SIRT3, and NFκB showed significant association with the presence of both angioinvasion and metastasis in PTC patients. Thus, it can be concluded that the concentration change of these parameters originates from the cancer itselft. In previously conducted studies, we also observed a positive correlation between the concentration of MDA measured among advanced PTC patients and their lipid profile. This finding suggests that individuals with advanced PTC may be at risk of having deregulated lipid levels, which, in turn, is associated with the metastasis process47. On the other hand, in our analysis, it has been shown the difference in the MDA concentration between groups, however, our regression analysis did not demonstrate the association between angioinvasion and metastasis status and serum MDA level. Therefore, it can be assumed that the change in MDA concentration results from the peripheral oxidative stress rather than the cancer. It can be hypothesized that additional mechanisms involving lipids and MDA are associated with PTC progression, which need further detailed evaluation. Additional investigations on these peripheral metabolic processes will enable to better understand their role and potential clinical significance. Larger studies are also needed to confirm the results and assess the possibility to support of serum oxidative stress markers in challenging PTC screening.To assess the potential screening utility of the parameters, ROC curves were constructed. DNA/RNA OSDP (AUC = 0.71), SIRT3 (AUC = 0.70), and TAC (AUC = 0.67) have been demonstrated to differentiate between PTC patients with both angioinvasion and metastasis and PTC patients without these features , followed by histopathological examination. For this study 80 patients diagnosed with various stages of PTC after total thyroidectomy were enrolled Table . To asseVenous blood (5.5 mL) was obtained and centrifuged, with serum subsequent separation and then frozen at − 80 °C. The procedures were approved by the Local Ethics Committee of the Medical University of Bialystok, Poland, and written informed consent was obtained from each participant (R-I-002/491/2019).The triglyceride (TG), LDL, HDL, CHOL, and C-reactive protein (CRP) concentrations were assayed using the enzymatic colorimetric method on a Roche C111 device . The thyroid-stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4), and antithyroglobulin antibody (TGBAb) concentrations were measured by a Roche E411 device  using the electrochemiluminescence (ECLIA) method. The TOS status was assessed using photometric immunodiagnostic assay (PerOx (TOS/TOC) kit, KC5100, 64625 Bensheim, Germany) and the TAC status was determined by photometric assay (ImAnOx (TAS/TAC) Kit, KC5200, 64625 Bensheim, Germany). P53, NF-κB, FOXO, SIRT1, PRKAA1, and PGC1α concentrations were determined using an enzyme-linked immunosorbent assay (ELISA)  according to the manufacturer’s instructions. SIRT3 and IL-6 concentrations were determined using ELISA method . Moreover, DNA/RNA OSDP and DNA methylation were assessed using an immunoassay kit (DNA/RNA Oxidative Damage (High Sensitivity) ELISA Kit, Cayman Chemicals, 589320, Ann Arbor, Michigan, MI, USA; DNA Methylation ELISA Kit, Cayman Chemicals, 589324, Ann Arbor, Michigan, MI, USA).Samples and controls were measured in duplicate using the blind analysis method in the same run.p < 0.05 was considered statistically significant. To assess the screening utility, the receiver operating characteristic (ROC) curves were determined and the area under the ROC curves (AUC) was analyzed. Moreover, Spearman correlations and logistic regression analyses were performed.Statistical analyses were performed using GraphPad Prism v. 9.0 . The lack of normality in data distribution using the Shapiro–Wilk test was demonstrated. Thus, the groups were compared using the nonparametric tests were used and The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Bialystok, Poland (R-I-002/491/2019).Written informed consent has been obtained from the patients to publish this paper.Reporting angioinvasion as prognostic factor in PTC patients should be performed to ensure proper management and, therefore, screening tools to for objective evaluation of the process are of great importance. Our study confirmed the screening utility of serum oxidative stress markers as angioinvasion and metastasis indicators in PTC patients. The mechanisms that may connect to the angioinvasion process and oxidative stress in PTC need to be studied in depth.


Results: The current study found that CHD patients have significantly lower Conscientiousness (t(565) = −3.84, p < 0.001, 95% C.I. , Cohen’s d = −0.16) and SRH (t(565) = −13.83, p < 0.001, 95% C.I. , and Cohen’s d = −0.58) scores compared to age and sex-matched healthy controls. Moreover, health status (controls vs. CHD patients) moderated the links between Neuroticism, Extraversion and SRH. Specifically, Neuroticism , Openness , and Conscientiousness  were significant predictors of SRH in healthy controls, whereas Conscientiousness  and Extraversion  were significant predictors of SRH in CHD patients. Conclusion: Based on the close associations between personality traits and SRH, and the subsequent impact on patient outcomes, the results of this study should be taken into consideration by clinicians and health professionals when developing tailored treatment and intervention programs for their patients.Objective: The objective of the present study is to examine the association between Big Five personality traits and self-rated health (SRH) among individuals with coronary heart disease (CHD), and to compare this relationship with that of healthy control participants, which is of importance as SRH can be a determinant of outcomes. Methods: The current study used data from 566 participants with CHD with a mean age of 63.00 (S.D. = 15.23) years old  and 8608 age- and sex-matched healthy controls with a mean age of 63.87 (S.D.= 9.60) years old  from the UKHLS. The current study used predictive normative modelling approaches, one-sample  The most common cause of mortality in the world is coronary heart disease (CHD) . As a reThese findings are being applied to the cardiological field ,7,8, wheHowever, the incorporation of information into subjective ratings of health, either consciously or subconsciously, has been a topic of debate. Recent research suggests that, in addition to the use of demographic and medical data, individuals’ conscious representation of health complaints, such as fatigue, tiredness, and weakness, which reflect the acknowledged severity of symptoms ,15,16,17Despite their importance, prior research has not investigated the personality factors associated with SRH among individuals with CHD. We wondered if CHD could moderate the associations between personality traits and SRH. Therefore, the current study aims to examine the relationship between Big Five personality traits and SRH in CHD patients, and to compare these findings to the results obtained from healthy controls. This investigation is important because SRH is a crucial determinant of outcomes.https://www.understandingsociety.ac.uk for the details of this study (accessed on 25 August 2022). In Wave 1, all participants initially answered a question about whether they had received a clinical diagnosis of CHD (collected between 2009 and 2010). Next, up to Wave 3, individuals were once more asked if they had just received a CHD diagnosis. Participants who indicated that they had been diagnosed with CHD at Wave 1, Wave 2, or Wave 3 were considered as people with CHD and vice versa. Wave 3 also collected information about personality, demographics, and psychiatric distress items (collected between 2011 and 2012). The patients who had not received a clinical diagnosis of CHD were chosen as controls since they were age and sex matched. Participants with any relevant missing data were also excluded from further analysis. Consequently, there were 8,608 healthy controls with a mean age of 63.87 ± 9.60 years old (61.93% men) and 566 patients with CHD who had a mean age of 63.00 ± 15.23 years . In Understanding society: the UK Household Longitudinal Study (UKHLS), which has been gathering yearly data from the original sample of UK homes since 1991, was the source of the data utilized in the study . Please Self-reported cardiovascular disease’s veracity has been confirmed, as indicated by its consistency with medical records . The quhttps://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation/term/personality-traits?search_api_views_fulltext= (accessed on 25 August 2022).The 15-item Big Five Inventory , with a On a scale from 1 (excellent) to 5 (very poor), participants replied to the question, “In general, would you describe your health is...” This particular subjective health measurement has a modest level of reliability . When tAge, sex, monthly income, greatest level of education attained, married status, and psychological distress as determined by the GHQ-12 were among the demographic factors . Please t tests. This approach gives more advantages than paired-sample t tests because it can control for demographic confounders and does not require equal sample sizes. To analyze how personality traits relate to SRH in healthy controls and CHD patients differently, a hierarchical regression was conducted. We performed this by taking age, monthly income, highest educational qualification, marital status, psychological distress, CHD status , and personality traits, including Neuroticism, Agreeableness, Openness, Conscientiousness, and Extraversion, as well as personality traits by CHD status interactions , Cohen’s d = −0.16) and SRH (t(565) = −13.83, p < 0.001, 95% C.I. , Cohen’s d = −0.58) scores than healthy controls after controlling for demographics. Moreover, significant interactions were observed between Neuroticism  and Extraversion by CHD status , with the overall hierarchical regression model explaining 24.4% of total variances. The multiple regression model accounted for 22.3% (R2 = 0.223) of the total variance in SRH among healthy controls, with Neuroticism , Openness , and Conscientiousness  serving as significant predictors. However, in CHD patients, the regression model accounted for 28.9% (R2 = 0.289) of the total variance, with Conscientiousness  and Extraversion  as significant predictors of SRH  and compare it with that of healthy controls. The results indicated that CHD patients had significantly lower Conscientiousness (t(565) = −3.84, s of SRH .The current study’s objective was to examine the relationship between the Big Five personality characteristics and SRH in CHD patients and to compare the findings to those from healthy controls. According to the current study, compared to healthy controls of the same age and sex, CHD patients’ Conscientiousness and SRH scores are considerably lower. Moreover, CHD status moderates the links between Neuroticism, Extraversion and SRH. Specifically, Openness, Neuroticism, and Conscientiousness are significant predictors of SRH in healthy controls, whereas Conscientiousness and Extraversion are significant predictors of SRH in CHD patients.Big Five personality traits and CHD have not yet been fully studied in the literature, although prior research has revealed that Type A  ,36,37,38In both healthy controls and CHD patients, we found that Conscientiousness emerges as the personality trait that exhibits a positive correlation with SRH, which is in line with prior research findings. Previous studies have suggested that Conscientiousness is associated with health-promoting practices like physical activity , and fewHowever, Neuroticism was found to be negatively associated with SRH, and Openness was positively associated with SRH in healthy controls but not in CHD patients, which seems to be consistent with previous studies. Neuroticism has been found to have a negative association with objective health measures, which include behavioral markers like walking speed e.g., ,54) and  and 54])Despite the notable strengths of the present study, such as a robust sample size, utilization of age and sex-matched healthy controls, and rigorous control of sociodemographic variables and psychological distress, a few limitations remain. One limitation of the present study is its cross-sectional design, which precludes the establishment of causal relationships. Future studies should use a longitudinal approach to understand whether personality traits cause better or worse SRH and to determine if SRH causes higher or lower personality trait scores. A second limitation is that the current study relied on self-reported measures , which cIn sum, the present study aimed to examine the relationship between personality traits and SRH among individuals with CHD, and to compare these associations with those observed in healthy controls. The findings revealed that, in comparison to age- and sex-matched healthy controls, CHD patients reported significantly lower scores on measures of Conscientiousness and SRH. Moreover, CHD status moderated the links between Neuroticism and Extraversion and SRH. Specifically, Openness and Conscientiousness were significant predictors of SRH in healthy controls, whereas Conscientiousness and Extraversion were significant predictors of SRH in CHD patients. There are some implications of the current study as well. As personality traits are directly associated with SRH, which is itself related to outcomes , the res


Hepatic failure induced by immune checkpoint inhibitors (ICIs) has been reported in only a few case series and case reports.We aimed to explore the association between ICIs and hepatic failure and characterize the clinical features of ICI‐associated hepatic failure in the pharmacovigilance database.Data from the first quarter (Q1) of 2015 to the fourth quarter (Q4) of 2021 in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database were retrieved for disproportionality and Bayesian analysis. Reporting odds ratios (ROR) and information component (IC) were used to evaluate correlations between ICIs and hepatic failure.025 = 2.70, IC025 = 1.39). For the three categories of ICIs, programmed cell death 1 ligand 1 inhibitors  had a higher risk signal than programmed cell death protein 1 inhibitors and cytotoxic T lymphocyte‐associated protein 4 inhibitors. For monotherapy, atezolizumab showed the strongest risk signal . The combination of nivolumab and ipilimumab showed stronger signals of hepatic failure compared with nivolumab or ipilimumab alone . Considering the concomitant agents used with ICIs, the majority of these regimens showed stronger signals than ICI monotherapy, such as acetaminophen .Hepatic failure occurred in 0.19%  of all cases in the FAERS database, of which 654 cases were associated with ICIs. The overall median time from ICIs initiation to hepatic failure onset was 38 days, 72.3% of the adverse events occurred within the first 3 months, and 68.65% of the cases died after developing hepatic failure. In general, a strong signal was shown between ICIs and hepatic failure  and information component (IC). Liver transplantation is the most effective treatment, but donor livers are scarce, and the procedure is expensive. Therefore, early prediction of the risk of hepatic failure and timely intervention are very important. However, the detailed characteristics of ICI‐associated hepatic failure have not been thoroughly investigated, and the differences in risk between different ICI regimens are still controversial. The impact of combinations with other drugs on ICI‐associated hepatic failure is also still unknown. In addition, data from clinical trials might not comprehensively represent the real world, and only a few cases reported in the literature were inadequate to characterize hepatic failure. Therefore, we have contributed in a small way to solving the above problems based on the US Food and Drug Administration Adverse Event Reporting System (FAERS) database,22.1https://open.fda.gov/data/faers/.Data were retrieved based on the FAERS database from the first quarter (Q1) of 2015 to the fourth quarter (Q4) of 2021. The FAERS database is a spontaneous reporting system, and adverse events are reported by physicians, consumers, health professionals, and pharmacists from different regions. FAERS data files contain demographic and administrative information, drug information, preferred terms (PTs) coded for the adverse event, patient outcomes, report sources, therapy start dates and end dates for reported drugs, and indications for use. The FAERS database is free and publicly available at the website (shown in the data source): 2.2In the FAERS database, adverse event reports were coded by PTs from Standardized MedDRA Queries (SMQ) of Medical Dictionary for Regulatory Activities . We considered the following mutually exclusive PTs as related to hepatic failure: “hepatic failure [10019663],” “acute hepatic failure [10000804]”, “subacute hepatic failure [10056956],” “acute on chronic liver failure [10077305],” and “chronic hepatic failure [10057573].”Both generic names and brand names were used to identify 8 FDA approved ICIs Table . ICIs caWe removed duplicated records by selecting the latest FDA_DT when the CASEID and FDA_DT were the same. Among the retrieved reports of ICI‐associated hepatic failure, we extracted information including age, gender, reporting country, reporting region, reporting year, reporter type, indication, drug name, and outcome. The time to onset (TTO) of hepatic failure for different ICIs, defined as the interval time between the adverse event onset date and the first dose date, were also evaluated. Additionally, we calculated the fatality proportion from the total number of ICI‐associated hepatic failures.2.32.3.1In order to estimate the association between ICIs and hepatic failure, we designed four comparison groups including: (1) comparing different categories of ICIs with all other drugs in the full database; (2) comparing ICI monotherapy with all other drugs; (3) comparing ICI combination therapy with ICI monotherapy; and (4) comparing combinations of ICIs and the most frequently used concomitant agents with ICI monotherapy.2.3.2Subgroup analyses were also conducted, including female versus male; and younger group (age < 65 years) versus older group (age ≥ 65 years). We only calculated the ROR when comparing disproportionality signal differences in different ICI subgroups.2.4025) was greater than 1 with at least 3 cases, or the lower limit of the 95% two‐sided confidence interval of the IC (IC025) was greater than 0. The equations of these two algorithms are listed in Table We used descriptive analysis to summarize the clinical features of ICI‐associated hepatic failure. Reporting odds ratio (ROR) and information component (IC), two specific indices of disproportionality analysis, were calculated to identify potential signals. Strong associations were defined when the lower limit of the 95% two‐sided confidence interval of the ROR  had a larger proportion of hepatic failure than females , and gender information was unavailable for 54 patients. The largest proportion of cases was aged 45–64 years , with a median age of 64 (range 14–90) years. Most reports were from Europe , Asia , and North America , with the number of cases increasing from 2015 to 2021, and more than one‐half of these cases were reported by physicians . The most common indication for ICIs was lung cancer . Only 5 cases  had liver metastasis, with 1 case of metastatic esophageal adenocarcinoma to the liver, 1 case of metastatic lung malignancy to the liver, 1 case of metastatic kidney cancer to the liver, and the other 2 cases had unknown primary disease. Of the three categories of ICIs, anti‐PD‐1s  were reported more than anti‐PD‐L1s  and anti‐CTLA‐4s . Among all ICIs, nivolumab was associated with the highest number of cases , followed by ipilimumab  and pembrolizumab . The combination of nivolumab and ipilimumab was most frequently reported in ICI combination therapy .Hepatic failure occurred in 0.19%  of all cases in the FAERS database, of which 654 cases were associated with ICIs Figure , and thep = 0.038), but two‐by‐two comparisons showed no statistical difference (p > 0.05)  days, and 72.30% (214/296) occurred within the first 3 months. The median TTO was 29 (range 1–174) days for pembrolizumab, 35.5 (range 1–380) days for atezolizumab, 38 (range 1–863) days for nivolumab, 39 (range 2–196) days for durvalumab, 54.5 (range 1–424) days for ipilimumab, 66 (range 4–775) days for avelumab, and 172 (range 34–196) days for tremelimumab. However, we performed the Kruskal–Wallis test for two‐by‐two comparisons for groups with a case number greater than 20, and the results showed an overall significant difference in the distribution of TTO for these groups of ICI monotherapy (–914 daysp = 0.282) , and the highest fatality proportion was associated with avelumab . Monofactor analysis was performed for ICIs with case numbers greater than 20, and Pearson's chi‐squared test showed no statistical difference (2) Table .3.2To estimate the association between ICIs and hepatic failure, we compared different ICIs with all other drugs in the full database Figure . In geneFor monotherapy Figure , most ICFor combination therapy, the most common therapy was the combination of nivolumab and ipilimumab, and stronger signals of this regimen were observed when compared to all other drugs , ipilimumab , and nivolumab   extreme malaise or visible gastrointestinal symptoms, (2) rapid deepening of jaundice, (3) with or without hepatic encephalopathy, (4) bleeding, with other causes excluded.Hepatic failure is rare but fatal. In a meta‐analysis that included 19,127 patients treated with ICIs, 8 patients developed fatal liver damage.Our study has some limitations. Firstly, the FAERS database is a spontaneous reporting system, without strict regulation; therefore, data in the FAERS database are not as reliable as clinical trials and cohort studies. Secondly, this database does not cover all real‐world data and most mild cases may be underreported, which may have resulted in bias in our analysis. Thirdly, this database can neither identify causality for ICIs nor exclude cofactors, such as alcohol, hepatitis or liver disease, cardiopulmonary complications and decompensation, sepsis, and additional comorbidities. No subgroup analysis of the patients' primary diseases was performed. Fourthly, uncertainties in diagnostic classifications, as well as eventual outcomes, and others, pose significant restrictions. Fifthly, the total number of patients treated with ICIs was not available in the FAERS database; thus, it cannot be used to calculate incidence rates.5This study aimed to evaluate the association between ICIs and hepatic failure based on the FAERS database. ICIs had possible strong signals associated with hepatic failure. Most cases with hepatic failure occurred within the first 3 months after ICIs initiation and had a poor outcome, which suggests that liver function monitoring needs to be enhanced after the administration of ICIs.Ye Xu: Data curation (lead); methodology ; visualization (lead); writing – original draft (lead); writing – review and editing . cilin Yan: Data curation (supporting); software (lead). Ying Zhao: Supervision ; writing – review and editing . Dandan Li: Supervision ; writing – review and editing . mingxing Guo: Supervision ; writing – review and editing . xiangli Cui: Conceptualization ; resources ; supervision ; writing – review and editing .This work was supported by Beijing Hospital Authority (PG2020002) and Beijing Science and Technology Planning Project (KJ2022CX039).All authors declare that they have no conflicts of interest.Table S1.Table S2.Table S3.Table S4.Table S5.Figure S1.Figure S2.Click here for additional data file.


Kaposi’s sarcoma-associated herpesvirus (KSHV) and the Epstein–Barr virus (EBV) are double-stranded DNA oncogenic gammaherpesviruses. These two viruses are associated with multiple human malignancies, including both B and T cell lymphomas, as well as epithelial- and endothelial-derived cancers. KSHV and EBV establish a life-long latent infection in the human host with intermittent periods of lytic replication. Infection with these viruses induce the expression of both viral and host RNA transcripts and activates several RNA sensors including RIG-I-like receptors (RLRs), Toll-like receptors (TLRs), protein kinase R (PKR) and adenosine deaminases acting on RNA (ADAR1). Activation of these RNA sensors induces the innate immune response to antagonize the virus. To counteract this, KSHV and EBV utilize both viral and cellular proteins to block the innate immune pathways and facilitate their own infection. In this review, we summarize how gammaherpesviral infections activate RNA sensors and induce their downstream signaling cascade, as well as how these viruses evade the antiviral signaling pathways to successfully establish latent infection and undergo lytic reactivation. KSHV  and EBV  are members of the gammaherpesvirus family and these viruses are associated with multiple human malignancies. KSHV is the etiological agent of Kaposi’s sarcoma (KS), in addition to two B-cell-derived malignancies: primary effusion lymphoma (PEL) and multicentric Castleman’s disease (MCD) ,2. More EBV and KSHV are oncogenic double-stranded DNA (dsDNA) viruses with both viruses exhibiting two distinct phases of their life cycles: latency and lytic replication. During latency, the KSHV genome is replicated as a circular episome by the cellular DNA polymerase and only expresses a limited set of latency-associated proteins and pre-microRNAs . SimilarThe innate immune response triggered by nucleic acid recognition plays an important role during viral infection. This process is initiated by nucleic acid sensors that recognize foreign DNA and RNA, such as viral genomes. Upon sensing the viral nucleic acids, the DNA/RNA sensors and their signaling cascades are activated to produce type I interferons (IFNs) and proinflammatory cytokines, which establish an antiviral state and inhibit viral infection. Several host DNA and RNA sensors have been reported to limit KSHV or EBV infection, including endosomal Toll-like receptors (TLRs), cytosolic DNA and dsRNA sensors cyclic GMP–AMP synthase (cGAS) and retinoic acid-inducible gene I protein (RIG-I)-like receptors (RLRs) ,13. RLRsIn this review, we summarize the RNA sensors regulating gammaherpesviruses and their signaling pathways involved in the innate immune system. During KSHV and EBV replication, the viral- and host-derived immunostimulatory RNAs are abundant and some of them are able to bind and activate cellular RNA sensors, leading to the induction of the RNA-activated innate immune response to control viral replication and infection. While the host cell uses these RNA sensors to limit virus infection, KSHV and EBV utilize various viral and/or host proteins or noncoding RNAs to evade innate immune responses and facilitate viral replication. RLRs are RNA sensors localized in the cytosol which include RIG-I, MDA5 and the laboratory of genetics and physiology (LGP2). All three RLRs share similar RNA binding domains, including the conserved DExD/H helicase domain and C-terminal domain (CTD) . The N-t543561–43650, ORF810420–10496 and the repeat region LIR1119059–119204, through high-throughput sequencing of RNA isolated by the immunoprecipitation approach [Both EBV and KSHV infections trigger the RLR signaling pathway through viral/ host RNA binding to the RNA sensors, RIG-I and MDA5. West et al. first reported that dsRNA is induced during KSHV lytic replication by demonstrating its accumulation in reactivating cells using a dsRNA-specific antibody J2 . Furtherapproach . In addiapproach . Host vaapproach . vtRNAs approach ,27. Howeapproach . Furtherapproach ,25, suggEBV-encoded RNA1 (EBER1) and EBER2 are small noncoding RNAs that are transcribed by host RNA polymerase III (Pol III) and are abundantly expressed in latent EBV-infected cells. In spite of EBER1 and EBER2 being short RNAs of 167 and 172 nucleotides, respectively, both have secondary structures consisting of intermolecular base-pairing and several stem-loops that can trigger RLR sensing ,29. EBERKSHV and EBV deploy multiple viral proteins to disrupt RLR activation during de novo infection and lytic reactivation in order to efficiently evade the antiviral response and establish their life cycle. The protein homologs BPLF1 and ORF64 are viral deubiquitinating enzymes (DUBs) of EBV and KSHV, respectively, that target the RLR sensor RIG-I ,35,36. ROriP transcripts can bind to ADAR1 and promote EBV viral lytic replication [In addition to utilizing virus-derived proteins and noncoding RNAs, EBV and KSHV also hijack host proteins to evade the RLR signaling pathway. ADARs are RNA-editing enzymes that bind to dsRNA and convert adenosine to inosine in dsRNA. There are three members of the human ADAR family, designated ADAR1 (ADAR), ADAR2 (ADARB1), and ADAR3 (ADARB2), where ADAR1 is responsible for the majority of the A-to-I editing activity in mammalian cells . Widesprlication . Hence, TLR3 (Toll-like receptor 3) is a dsRNA sensor that localizes on the endosomal membrane and recognizes dsRNA structures in the lumen of the endosome. Upon binding to dsRNA, TLR3 recruits and activates its adaptor protein TIR-domain-containing adapter inducing IFN-β (TRIF), leading to the induction of its downstream signaling pathway to promote the production of inflammatory cytokines and type I IFN . PrimaryTLRs 7 and 8 are located on the endosomal membrane and recognize single-stranded RNA (ssRNA) with guanosine and uridine-rich sequences . Upon biProtein kinase R (PKR) is a dsRNA-dependent protein kinase that is induced by interferons. dsRNA binding induces PKR dimerization and autophosphorylation, leading to its activation as a kinase. Activated PKR phosphorylates the α subunit of eukaryotic translation initiation factor 2 (elF2α), causing global translation shutdown and cell growth inhibition. In addition, PKR activation also elicits the formation of stress granules (SGs), which are able to trigger the host cell antiviral response and inhibit virus production ,83. To cN6-methyladenosine (m6A), 5-methylcytosine (m5C), and pseudouridylation comprising some of the most well-studied RNA modifications [Within a cell, the addition of chemical moieties to RNA, i.e., RNA modifications, can affect its stability, secondary structure, and protein binding activity, which impact the recognition of RNA molecules by innate immune sensors, including RLRs, TLRs, and PKRs in the immune signaling pathway . More thications . 6A) modifications in both viral and cellular RNA molecules [6A modification is carried out by the m6A methyltransferases (METTL3/14/16), also called writers of the m6A [6A RNA is recognized by m6A-binding proteins, also known as readers in the RNA epigenetic pathway, such as YTHDF1/2/3, YTHDC1/2, and HNRNPA2B1, and can be removed by demethylases called erasers such as FTO and ALKBH5 [6A readers, writers and erasers regulate the lytic reactivation of KSHV [6A modification of IL-6 mRNA and the recruitment of reader YTHDC2 to the mRNA is required for protecting IL-6 from degradation induced by the SOX protein [6A modification of cellular mRNA, GPRC5A, enhances its RNA stability causing an increase in GPRC5A protein which subsequently inhibits NF-κB signaling pathway and promotes KSHV lytic reactivation [In addition to the increase in A-to-I editing of viral and host RNAs during KSHV and EBV infection, as described above, infections with these two viruses also lead to enhanced N6 methyladenosine (molecules ,92. m6A  the m6A . The modd ALKBH5 . Various of KSHV ,92,93,94 of KSHV . The m6A protein . Additiotivation . 6A editing proteins influence EBV latency and lytic replication [6A modification of the cellular transcripts DTX4 and TYK2 mRNA by decreasing the expression of demethylase ALKBH5 that results in the attenuation of innate immunity and facilitates EBV lytic reactivation [Similar to KSHV, many mlication ,99. Impotivation . Recently, another abundant RNA modification, pseudouridylation, has been identified in EBER2 where pseudouridylation of EBER2 is important for the viral lytic replication for EBV . PseudouWe have discussed how KSHV and EBV infection activates several cellular RNA sensors, including RIG-I, MDA5, PKR, TLR3, TLR7 and TLR8 to induce the downstream signal pathways and innate immune responses . In addiAlthough various RNA sensing pathways are induced during KSHV and EBV infection, only a small number of RNA ligands recognized by these RNA sensors have been identified and further studied. One of the most well-known transcripts are EBV-noncoding EBERs, which can activate several RNA sensors including TLR3, TLR7 and RIG-I. Thus, EBERs can be utilized as an RNA ligand to learn about the crosstalk among the RNA signaling pathways. This could also contribute to clarifying the question of whether the inhibition of IFN-induced apoptosis by the EBERs is dependent on the activation of PKR. EBERs are expressed in the latent state and act as a ligand for RNA sensing. Therefore, EBV must employ various strategies to avoid the EBER activation of RNA sensing pathways to prevent apoptosis and maintain viral latent infection within host cells. In addition to LMP1 and miBART6 promoting the degradation of RLRs to decrease IFN induction, EBERs themselves play a role in the inhibition of IFN-induced apoptosis, but the mechanism is still unclear. Hence, studying the structure, binding affinity and functions of the RNA ligands that are induced during KSHV and EBV infection will advance our understanding of virus–host interactions during KSHV/EBV infection.6A modifications of viral and host RNAs influence KSHV and EBV infections, future work should be geared towards determining how the various m6A readers, writers and erasers mechanistically alter various m6A sites in both viral and cellular mRNAs to modulate the innate immune response and whether they exhibit functional redundancy. Overall, elucidating how KSHV and EBV activate and escape the RNA-mediated innate immune response may contribute to a better understanding of the virus-associated pathologies and provide new insights into developing effective strategies against viral infection.Recently, it has been shown that many KSHV and EBV RNAs undergo RNA modifications including methylation, A-to-I editing and pseudouridylation, which affect RNA stability, secondary structure, and protein association as well as the recognition of RNAs by RNA sensors ,101,102.


E-cigarette (E-cig) inhalation affects health status by modulating inflammation profiles in several organs, including the brain, lung, heart, and colon. The effect of flavored fourth-generation pod-based E-cigs (JUUL) on murine gut inflammation is modulated by both flavor and exposure period. Exposure of mice to JUUL mango and JUUL mint for one month upregulated inflammatory cytokines, particularly TNF-α, IL-6, and Cxcl-1 (IL-8). JUUL Mango effects were more prominent than those incurred by JUUL Mint after one month of exposure. However, JUUL Mango reduced the expression of colonic inflammatory cytokines after three months of exposure. In this protocol, we detail the process of RNA isolation from the mouse colon and the use of extracted RNA in profiling the inflammatory milieu. Efficient RNA extraction from the murine colon is the most important step in the evaluation of inflammatory transcripts in the colon. E-cigarettes (E-cigs) were first introduced to the international market in the mid-2000s as an alternative to conventional tobacco smoking . E-cigs produce an aerosol  upon heating and the aerosolization of vehicle solvents propylene glycol and vegetable glycerin. In addition, a large variety of flavors are added to E-cigs to add appeal to people of all kinds, including women, children, and minorities .JUUL is one of the most popular pod-based E-cig brands. They sell pods containing e-liquids in different flavors, such as mint and mango . The effMoshensky et al. (2022) observed in vivo mouse exposures of daily JUUL aerosol inhalation with different flavors (mango and mint) for one and three months to evaluate the effects of JUUL aerosol inhalation on the function and inflammation of different organs . The autExtraction of RNA from the murine colon is the most important step in the relative quantification of colon transcripts. Several factors affect the quality of extracted RNA used in qRT-PCR, such as the method used in the extraction, RNA purity and concentration, and the presence of other impurities, including guanidinium isothiocyanate, phenolic compounds, ethanol, tissue DNA, and protein. The extraction procedure should be performed efficiently, since the purity of the extracted RNA affects downstream processing, such as cDNA synthesis and PCR . In addiBiological materialsFemale C57BL/6 mice (6–8 weeks)Materialshttps://www.vaporauthority.com/products/genuine-kanger-mini-protank-3-glassomizerE-cigarette devices, Kanger Mini ProTank glassomizer, https://www.juul.com/shopJuul mango and mint pods, JUUL, Microcentrifuge tube (1.5 mL) 50 mL centrifuge tubes 15 mL centrifuge tubes TM wood handled cotton swabs and applicators FisherbrandPetri dishes, stackable TM optical 96-well reaction plate MicroAmpOptical adhesive covers GPLE .TM BRANDTM thin wall 0.2 mL PCR tubes with attached caps BrandTechReagentsqScript cDNA SuperMix 2× SYBR Green qPCR Master Mix TM RNA Miniprep kit Direct-zolTRI reagent TM PBS, pH 7.4 GibcoTM DNase/RNase-free distilled water UltraPureEthanol, pure for molecular biology RNAlater DNase I treatment preparation (this enzyme is provided with Direct-zolTM RNA Miniprep kit) (see Recipes)Synthesis of cDNA from RNA using qScript cDNA Synthesis kit (see Recipes)qPCR reaction mixture (see Recipes)Noyes spring scissors, angled Fine forceps Micropipette E-cigarette InExpose system (SCIreQ)Balance TM 150 handheld homogenizer motor FisherbrandST8 microcentrifuge MiniAmp Plus thermal cycler TM 3 real-time PCR system, 96-well, 0.2 mL QuantStudioSCR_002798GraphPad Prism, RRID: QuantStudio Design & Analysis SoftwareIsolation of colon from the exposed mice Expose female C57BL/6 mice (6–8 weeks) to E-cig aerosols produced from JUUL mango or JUUL mint or to air for one and three months using the InExpose system.Note: A detailed protocol on mouse exposure and composition of JUUL pods is present in the original article . BrieflyAt the end of exposure, euthanize mice by regulated slow (~50% flow rate) carbon dioxide inhalation followed by cervical dislocation..Using scissor and forceps, cut the whole colon of a mouse from the anus to the cecum Put the colon in a Petri dish containing PBS to rinse away any blood. Remove any attached adipose tissue from the colon.Use a cotton swab to empty the colon from remaining stool.Cut the colon into small pieces and transfer part of the colon into a 1.5 mL Eppendorf containing 400–500 μL of RNAlater.Notes:Saving the whole colon in RNAlater solution is an option if no other procedures/techniques are required, such as processing of the mouse colon into formalin-fixed paraffin-embedded blocks. Therefore, step A6 is optional.In case histology is required for the colon, cut it into three parts: proximal, mid, and distal colon. The distal colon is used to assess the transcripts of inflammatory cytokines. Mid and proximal colons are used for histology and other purposes.The amount of RNAlater can be increased (600 μL to 1 mL) if a larger part or the whole colon will be saved. The RNAlater should cover the part/whole colon saved as shown in Store the colon samples at -80 °C.Note: If RNA extraction is done on the same day of euthanasia, step A7 is not required. Saving the tissue in RNAlater is important when the downstream processing will be performed later.Extraction of RNA from the colon tissues Thaw the colon samples on ice; then, use scissors and forceps to cut the colon tissues into small pieces.Note: Ensure that no precipitated salts are around the tissue. Wash the colon with PBS to remove any precipitated salts.Weigh the colon piece using a scale/balance and ensure that the weight is ≤ 20 mg/piece.Notes:It is very important to weigh the mouse colon; ideally, tissues between 5 and 20 mg give good RNA yield. Using larger tissue pieces gives a poor RNA yield due to column blockage during the RNA extraction process.It is recommended to mince/cut the colon into small pieces after weighing to speed up the homogenization step.Transfer the colon piece into a 1.5 mL Eppendorf containing 600 μL of TRI reagent solution.Homogenize the colon piece in TRI reagent solution using the 150 handheld homogenizer for 1–2 min until complete disruption of tissue.Notes:It is important to clean and disinfect the homogenizer using 70% ethanol and water between samples to avoid cross contamination.Make sure that the weighted piece is completely disintegrated and no visible tissue remains.In case of tough tissue, you can increase the volume of TRI reagent to 800 μL and/or increase the homogenization time.The homogenizer has three different speeds . It is recommended to use the middle speed, since the highest could be destructive to tissues if it is continued for a long time and the lowest could take a long period of time to disintegrate the tissue.× g for 2 min.Centrifuge the tissue suspension at a maximum speed of 13,000 . Collect the supernatant into a new 1.5 mL Eppendorf and discard the tube containing the precipitated debris.After the centrifugation step, you can see a precipitated debris at the bottom of the Eppendorf and a clear supernatant solution above it Mix the supernatant from step B6 with an equal volume (approximately 600 μL) of ethanol (95–100%).TM IICR column in a collection tube (this column is a part of Direct-zolTM RNA Miniprep kit).Transfer the previous mixture into a Zymo-SpinTM IICR is 700 μL and the volume of each sample is approximately 1,200 μL (600 μL TRI/tissue mix + 600 μL ethanol). Therefore, step B8 is repeated twice on the same column.Note: The maximum capacity of Zymo-Spin× g for 1 min. Then, discard the flow through waste into 15 or 50 mL falcon tubes and discard the collection tube.Centrifuge the mixture from step B8 at 13,000 Note: At this step, the flow through waste can be used to purify the protein from the mouse colon.TM IICR column to a new collection tube.Transfer the Zymo-SpinTM RNA Miniprep kit) (400 µL/sample) to the column and centrifuge at 13,000 × g for 30 s to 1 min. Discard the flow through and transfer the column to a new collection tube.Add RNA wash buffer (a part of Direct-zolRecipe 1) per column. Incubate the mixture with the column at room temperature for 15 min.Add 80 µL of DNase I treatment preparation (Notes:TM RNA Miniprep kit) is provided as lyophilized powder. Reconstitute it using with DNase/RNase-free water according to the manufacturer’s instructions. Mix well and then store the aliquots at -20 °C until use.DNase I (part of Direct-zolThis step is highly recommended to get rid of DNA present in the sample.TM RNA PreWash buffer (part of Direct-zolTM RNA Miniprep kit) to the column and centrifuge at 13,000 × g for 1 min. Discard the flow through and repeat this step one more time.Add 400 µL of Direct-zolNote: RNA PreWash buffer is provided as a concentrate buffer. You should dilute it with ethanol according to the manufacturer’s instructions.× g for 1 min to remove the wash buffer.Add 700 µL of RNA wash buffer to the column and centrifuge at 13,000 Note: RNA wash buffer is provided as a concentrate buffer. You should dilute it with ethanol according to the manufacturer’s instructions.× g for 5 min to ensure complete removal of the ethanol that is present in RNA PreWash buffer and RNA wash buffer.Centrifuge the empty column present in a new collection tube at 13,000 Note:It is important to get rid of ethanol since any residual amount can impact the downstream qPCR steps and/or the yield and purity of extracted RNA.× g for 1 min.Transfer the column carefully into an RNase-free tube, add 50 µL of DNase/RNase-free water directly to the column matrix, incubate at room temperature for 2–5 min, and then centrifuge at 13,000 × g for 1 min.Repeat step B16 using the RNA eluted from the column. Transfer the eluted RNA into the same column, incubate at room temperature for 2 min, and then centrifuge at 13,000 RT-qPCR for the inflammatory transcript in the mouse colonConvert the extracted RNA (approximately 500–750 ng) into cDNA using qScript cDNA SuperMix according to manufacturer’s instructions.Recipe 2. The volume of RNA added depends on the concentration. Total RNA added should be between 500 and 750 ng. The volume of free water added depends on the volume of RNA, according to Recipe 2.To a 0.2 mL thin-walled PCR tube placed on ice, add the components as shown in Note: When comparing samples, it is preferred to use the same amount of RNA. For example, to compare the transcripts from mouse colon derived from air-exposed groups and from JUUL mango pods, use the same amount of RNA to form cDNA; either 500 or 750 ng for all samples or in between concentrations.Vortex the mixture gently and then centrifuge for 10 s to collect contents.Place the PCR tubes in a MiniAmp Plus thermal cycler using the following program :Dilute the cDNA product into 1/5 or 1/10 using nuclease-free water and store the diluted cDNA at -20 °C until analysis.Use the synthesized cDNA as a template for qPCR reaction with 2× SYBR Green qPCR Master Mix.TM optical 96-well reaction plate, add the PCR reaction mixtures according to Recipe 3.Using a MicroAmpAdd optical adhesive covers GPLE to the plate, put in the machine, and run the following PCR program :. The previous step aids in the extraction of many samples at the same time to minimize the inter-assay procedure. It is important to weigh the colon piece undergoing the RNA extraction process . Since each column has a maximum capacity, including too large samples could lead to blockage of the column, and therefore negatively impact all the downstream process of RNA extraction. The addition of TRI reagent stops RNase enzymes and neutralizes any infectious agents in the samples; therefore, it helps in improving the quality and stability of extracted RNA. Following the previously described procedure, you can assess the concentration and purity of RNA using a Nanodrop spectrophotometer. For the concentration, you may obtain ~100 ug of total RNA from the previous procedure. The purity of extracted RNA can be assessed by the measurement of absorbance (A260/280 and A260/230). The acceptable value for pure RNA in terms of A260/280 is 1.8–2.2 or a value >1.8. A value lower than 1.8 indicates the presence of contaminants that absorb at 280 nm, such as protein and phenol. Similarly, 260/230 values >1.8 indicate pure RNA, while values lower than 1.8 indicate contamination with TRI reagent that absorbs at 230 nm.High-yield purified RNA extraction is possible from mouse colon tissue. Using the RNAlater solution helps preserve tissues and/or the RNA for a longer period at -80 °C For the qRT-PCR, the cycle threshold (Ct) value for the target gene was calculated using QuantStudio Design & Analysis Software, and then normalized to the housekeeping gene (∆CT). For comparison of the relative transcript expression between the different groups of mice, we used the formula ∆∆CT. For example, to compare the relative IL-6 transcript expression, we determined:IL6 - CT housekeeping gene)∆CT for IL-6 in JUUL exposed mice = CT IL6 - CT housekeeping gene)∆CT for IL-6 in air-exposed mice = CT ∆∆CT = ∆CT for IL-6 in JUUL exposed mice - ∆CT for IL-6 in air-exposed miceFurther data analysis for relative gene expression can be done using GraphPad Prism.This method is suitable for RNA extraction from different mouse organsSalmonella  to the Green Master Mix according to the manufacturer's instructions, to reach a 1× final concentration.b The amount of cDNA formed in Recipe 2 is 500 ng, and dilution (1/5 or 1/10) is done to synthesized cDNA to become 50–100 ng. In general, 5-100 ng cDNA per reaction is acceptable.c For the sequences of primers used in the amplification of inflammatory transcripts in the colon please refer to Moshensky et al. .