Being able to effectively target RNA with potent ligands will open up a large number of potential therapeutic options. The knowledge on how to achieve this is ever expanding but an important question that remains open is what chemical matter is suitable to achieve this goal. The high flexibility of an RNA as well as its more limited chemical diversity and featureless binding sites can be difficult to target selectively but can be addressed by well-designed cyclic peptides. In this review we will provide an overview of reported cyclic peptide ligands for therapeutically relevant RNA targets and discuss the methods used to discover them. We will also provide critical insights into the properties required for potent and selective interaction and suggestions on how to assess these parameters. The use of cyclic peptides to target RNA is still in its infancy but the lessons learned from past examples can be adopted for the development of novel potent and selective ligands. HoweverHydrogen bonding interactions between polar amino acids or the peptide backbone and nucleotide bases .Stacking interactions between aromatic amino acids and nucleotide bases .Cation-π interactions between positively charged amino acids and nucleotide bases .Electrostatic interactions between charged amino acids and the backbone phosphodiester .Hydrogen bonding interactions between amino acid side-chains or peptide backbone and the nucleotide ribose .The various interactions made between RNA-binding proteins (RBPs) and RNA provide both binding affinity and selectivity . Since pvia cyclization, potent RNA-binding cyclic peptides can be obtained.The first three categories provide binding affinity and nucleotide selectivity due to interactions that are specific for a nucleotide base. The last two categories provide binding affinity by interacting with parts that are the same for all of the nucleotides. FurthermPeptides are effective at interacting with shallow surfaces and grooves that are commonly found on RNA targets where pockets tend to be absent . The abiN- and/or C-termini through cyclization makes the peptide less susceptible to recognition by exopeptidases (via an endocytosis pathway and is driven by the presence of a high number of positive charges (mainly arginine)  RNA-binding cyclic peptides identified via library screening.2) RNA-binding cyclic peptides identified 3) RNA-binding cyclic peptides identified as natural products.The first hurdle to overcome when one wants to address an RNA target using cyclic peptides is to identify a suitable starting point. Various methods have been described to identify such starting points which we have divided in three categories:For each method we will first discuss its specific advantages and disadvantages followed by examples from the literature which have been further categorized by the target RNA. Interestingly, various RNA targets have been explored by more than one approach allowing for a comparison to be made.It is not surprising that cyclic peptides can potently interact with RNA since they can effectively mimic the RNA-binding domain (RBD) of an RBP . Various1 (N- and C-terminus so that cyclization could be achieved by oxidative disulfide bond formation (KD of 0.27 nM according to a fluorescence polarization (FP) assay. In comparison, the equivalent linear peptide had a KD of 1.32 nM indicating cyclization led to a 5-fold improvement. A similar peptide  transactivation response (TAR) element RNA has been widely explored as a target for macrocyclic peptides and is found in the 5′ untranslated region of the HIV mRNA . The TAR1  by startormation . The cycptide 2,  was lateptide 2, . The aff3, KD of 150 nM for the BIV TAR RNA in an electrophoretic mobility shift assay (EMSA). The affinity was however 3-fold lower than a linear wild-type sequence but peptide 3 did display a strong selectivity for BIV TAR over HIV TAR . Surprisingly, NMR analysis showed that the β-hairpin peptide bound in the opposite orientation in comparison to the wild-type peptide it was derived from  labelled TAR. Although peptide 8 has a very high affinity towards the TAR RNA, this did not translate to increased activity in cellular HIV replication and spread assays where it performed similar to peptide 4. Although 8 is able to displace the Tat domain that binds TAR, it still allows other SEC components to bind the TAR apical loop allowing transcription elongation explaining the low cellular activity. The impressively high affinity can be explained by the high-resolution NMR analysis of 8 bound to the TAR RNA . The RRM is a common RNA-binding domain found in many RNA-binding proteins . To deveent of 9   spanning residue 34–50 acts as both nuclear localization signal and RBD. The ARM is responsible for the regulatory activity of Rev by recognizing a cis-acting RNA target sequence known as the Rev response element (RRE) in the viral RNA and facilitating its transport to the cytoplasm for translation of viral proteins  inhibition constants. Furthermore, a four-fold higher Ki value for the cyclic peptides in comparison to the linear Tat sequence (Ki = 20 nM) indicated that conformational restriction is indeed needed for better inhibitory activity. The same group later applied the backbone cyclization strategy to the Rev ARM as well to prepare a library of constrained peptides and investigated the effect on nuclear import and Rev-induced gene expression  at a 50 μM concentration. Molecular modelling of RRE RNA—cyclic peptides complexes demonstrated that the peptides bind the major groove of the RRE RNA in a way that could interfere with the Rev-RRE interaction and thus reduce the level of transport of the full-length viral RNA in HIV-1 infected cells.By inhibiting the Rev:RRE interaction, viral replication can be reduced and the Loyter and Gilon groups reported the first cyclic peptides derived from the Tat ARM sequence to achieve this . Interesptide 13  as well pression . Interesfe cycle . Completet al. applied a template-nucleated α-helix stabilization approach by introducing a constrained cyclic pentapeptide at the N-terminus of the isolated ARM domain . The α-helical and Cys-rich regions are separated by a flexible linker not to interfere with each other’s activity and folding behavior. The cyclic peptide was synthesized using a head-to-side-chain cyclization strategy by coupling bromoacetic acid to the N-terminus of the peptide followed by intramolecular S-alkylation of the free Cys thiol on solid support. CD spectrum analysis confirmed that the ARM domain adopts an α-helical conformation upon binding with AuNps. EMSA analysis showed comparable affinity for both cyclic and linear peptide/AuNps hybrid material with the stem-loop IIB region of monovalent RRE RNA. The cyclic peptide hybrid material showed better binding with multivalent RRE RNA than the linear counterpart, but exact affinities could not be determined. The interaction between the peptide hybrid material and multivalent RRE RNA was demonstrated to be quite specific as a mutant peptide hybrid material in which essential amino acids were replaced by alanine showed reduced binding and poor selectivity. Cellular assays demonstrated that the cyclic version was able to recognize RRE RNA both in the cytoplasm and the nucleus while the linear equivalent was only capable of nuclear detection.Choi ve cells . The desMicroRNAs (miRNA) are short non-coding RNAs (∼22 nt) that usually interact with the 3′ untranslated region (3′UTR) of mature mRNAs and induce degradation or translational repression . Abnorma5 which was originally developed to target TAR RNA as described above, also binds the stem-loop of pre-miR-21 with a KD of 200 nM  that demonstrated the most potent affinity. Despite their higher degrees of flexibility, they were able to recognize the apical loop region of pre-miR-20b in an EMSA assay but accurate KD values could not be determined and inhibition of either Drosha or Dicer processing wasn’t evaluated.MiR-20b has also been described as an oncogenic miRNA by regulating the expression levels of hypoxia-inducible factor-1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF) in various cancer types . To targvia its helix-loop-helix domain as was demonstrated by the crystal structure of the complex  as determined by ITC. Since the TAV2b protein is known to non-specifically bind siRNAs, the authors hypothesized that the stapled peptide could also bind the highly analogous miRNAs. Interestingly, peptide 21 was able to bind miR-21 and pre-miR-21 with high affinity  as measured by ITC and a biotinylated variant was able to pull-down these targets efficiently from K562 cells. Simulations demonstrated that peptide 21 bound to a site on pre-miR-21 that overlaps with the Dicer cleavage site which was confirmed by a biochemical pre-miR maturation assay. A series of RNA hairpins with different stems was used to test the selectivity of 21 which demonstrated that the peptide interacts with RNA in a non-specific manner similar to the parent TAV2b protein. Although the applicability of this peptide in its current form is limited due to its low selectivity, it is a feasible starting point for further optimization by incorporating modifications that can recognize specific RNA sequences.Tomato aspermy virus 2b (TAV2b) protein is a suppressor of RNA silencing by binding the major groove of double stranded siRNA  complex . Recentl complex . Hydroca complex . CD spec complex . The mosplex 21,  featured5 and 21 would inhibit this nuclease as well  and demonstrated to be a crucial structural element for internal ribosome entry site (IRES)-mediated translation of this virus . The RBD5–1014 members)  . Besidesmembers) . The oppmembers) . The exp4 members) followed by on-bead screening and identified a tripeptide (KdKN) that repressed Tat-induced trans activation in cells with an IC50 of approximately 50 nM  against the BIV TAR RNA  of macrocyclic peptides was prepared via split-and-pool synthesis using the one bead two components (OBTC) method containing various γ-substituted-N-acylated-N-aminoethylamino acids (γ-AA) as building blocks . RT-qPCR analysis after cell treatment showed peptide 27 reduced the expression level of miR-155 by ∼70%, however, peptide 28 did not have any effect despite its high binding affinity. The lack of cellular activity for peptide 28 can potentially be explained by interaction with a pre-miR-155 site other than the ones involved in its maturation process since it demonstrated high cell-permeability. The hypothesis was supported by the fact that even peptide 27 was not able to completely inhibit pre-miR-155 cleavage in an in vitro Dicer assay at high concentrations (30 µM), further indicating that 27 inhibits miR-155 activity at a different stage . Interestingly, peptide 27 demonstrated a high selectivity for pre-miR-155 amongst a panel of six other miRNAs as well as a significant change in the mRNA and protein levels of the miR-155 target FOXO3A in MCF-7 breast cancer cells. Treatment of these cells with a 30 µM concentration of peptide 27 led to a 70% reduction of the viability together with an increase in the rate of apoptosis.The stable stem-loop structures of miRNA make them attractive targets for library screening techniques as they can easily be maintained in the correct fold during selection. An affinity-based screening protocol to identify macrocyclic peptide inhibitors to target pre-miR-155 was reported by g blocks . To faciLong non-coding RNA (lncRNA) has received an increasing amount of attention for its role in various biological processes . Althoug5 macrocyclic peptides against a GAS5 fragment isolated from the 3′ end. One potent peptide  the levels of GAS5 already at a 20 nM concentration in adipocytes while not affecting other lncRNAs (NEAT1 and MALAT1). Although the inhibition of UPF1 binding wasn’t directly proven, peptide 29 does have the desired effect of upregulating insulin receptor A and B as well as glucose uptake in diabetic adipocytes after cotreatment with insulin.The lncRNA GAS5 influences the expression of the insulin receptor and is depleted in patients with type 2 diabetes . The degtide 29,  was foun27–29 are especially interesting since they lack the polycationic nature that most other RNA targeting peptides have and incorporate novel fragments. Furthermore, the peptoid like nature of these compounds reduces the number of hydrogen-bond donors which can aid with cell-permeability  subunits . AlthougSome of the most potent RNA binding cyclic peptides have been identified as inhibitors of the bacterial ribosome . The rib30) and capreomycin (31) are analogous cyclic peptides -Phe-Leu-OH followed by side chain deprotection and coupling of the exocyclic lysine and thiopropionic acid  . A smaller bulge was tolerated (KD = 194 nM) but changing the bulge from the 3′ to the 5′ side abolished binding . Interestingly, the linear counterpart of peptide 37 was 6-fold less potent confirming that cyclization improved binding. The stem-loop of pre-miR-23b contains the motif of the intercalation site flanked by a small bulge and peptide 37 was found to bind this RNA albeit with a more moderate affinity (KD = 2.3 µM). Nonetheless, the method can be used to design peptides that selectively target certain RNA motifs by providing binding potency via the quinoline core and selectivity via the peptide.Although not directly identified as natural products, Beal and co-workers introduced a natural product inspired 2-phenylquinoline core into macrocyclic helix threading peptides (HTPs) to target RNA (37) . The hetvia three different methods. Each method has proven to be applicable to identify potent ligands and has its own advantages and disadvantages. Although the mimicking of RBPs has been the most widely explored strategy, it is limited to the available structural information. Generating conformationally constrained cyclic peptides with high affinity and selectivity requires a thorough understanding of the peptide fold upon binding with the RNA. Furthermore, the structure-based conversion of a linear peptide to a potent cyclic peptide followed by optimization via point mutations is a long and work intensive process as is illustrated by the TAR RNA binding peptides 3–8 whose reports span 15 years to achieve three orders of magnitude improvement in affinity. An interesting source for novel RBDs to excise peptides from could be provided by intrinsically disordered regions (IDRs). Various RBPs do not contain RNA-binding domains with stable folds but instead interact with their target RNAs through IDRs (The examples described above provide an exhaustive overview (with the exception of the antibiotic peptides) of known RNA-targeting macrocyclic peptides identified ugh IDRs . Recent 27–29 clearly demonstrate that this is feasible, and that interesting biological activity can be achieved. Alternatively, peptides derived from proteins with promiscuous but potent RNA-binding properties such as the TAV2b derived cell permeable stapled peptide 21 could also provide an excellent starting for library creation.The lack of structural information on therapeutically relevant protein-RNA interactions makes screening strategies an attractive alternative to identify cyclic peptide ligands for such RNAs. Although the examples are very limited, the advancement of screening techniques together with optimization of the intrinsic RNA-binding properties of the peptides in a library can provide potent and selective ligands. When designing a library one can also attempt to maintain properties that promote cellular uptake which is critically important to reach RNA targets. Peptides via stacking interactions as well as hydrogen bonding interactions of modified amino acids. These lessons can possibly be extrapolated to the optimization of peptide ligands or the design of new synthetic libraries for screening by including similar functional groups and heterocycles. Furthermore, one can use a naturally occurring cyclic peptide with intrinsic RNA binding affinity as a core scaffold for modification and testing against other RNA targets of interest. Such a screening could be performed by using methods similar to the Inforna platform described by the Disney group. Here, one first analyzes what type of RNA secondary structures a compound potently interacts with and then mines databases for RNAs that contain such structures , followed by reverse transcription and high-throughput sequencing to get an broad view of the selectivity of the ligand . Such highly positively charged peptides could be ineffective due to issues with membrane permeability or they could get trapped in the endosomes after inducing endosomal uptake (The RNA-binding cyclic peptide described above demonstrate that the targeting of RNA is feasible and can be effective in cellular systems. However, no animal experiments or even clinical trials have been reported for any of the described molecules with the exception of some of the ribosome targeting antibiotic peptides. In various cases there seems to be a disconnect between affinity and cellular activity which is clearly demonstrated by the low cellular activity of peptide l uptake . AnotherAlthough many lessons are still to be learned, the examples described above indicate that potent RNA binding can be achieved with macrocyclic peptides. We believe that by applying the right strategy to the right target it is highly conceivable that this class of molecules will be able to achieve the potency and selectivity required for therapeutic applicability. With the ongoing rise in interest in exploring RNA as a therapeutic target it will be important to explore a broad chemical space. The unique properties of cyclic peptides can make them a very suitable part of this space to explore further, and it is likely many more examples will appear in the near future.


The neural network method can obtain a higher precision of radar echo extrapolation than the traditional method. However, its application in radar echo extrapolation is still in the initial stage of exploration, and there is still much room for improvement in the extrapolation accuracy. To improve the utilization of radar echo information and extrapolation accuracy, this paper proposes a radar echo extrapolation model (ADC_Net) based on dilated convolution and attention convolution. In this model, dilated convolution, instead of the pooling operation, is used to downsample the feature matrix obtained after the standard convolution operation. In doing so, the internal data structure of the feature matrix is retained, and the spatial features of radar echo data from different scales are extracted as well. Besides, the attention convolution module is integrated in the ADC_Net model to improve its sensitivity to the target features in the feature matrix and suppress the interference information. The proposed model is tested in the extrapolation of radar echo images in the next 90 min from five aspects—extrapolated image, POD index, CSI index, FAR index, and HSS index. The experimental results show that the model can effectively improve the accuracy of radar echo extrapolation. Accurate short-term nowcasting has attracted much attention in the field of meteorological services. Its goal is to accurately and timely predict the local weather in the next 0 to 2 h, enabling weather stations to issue Urban Emergency Disaster Alerts in time. There are three main methods of nowcasting—radar echo extrapolation method, weather situation analysis and forecast method, and comprehensive analysis method, which is the most accurate and widely used method. However, the accuracy of comprehensive analysis method is based on the accuracy of the radar echo extrapolation method. Therefore, the research on the radar echo extrapolation plays a significant role in the forecast and prevention of severe weather.Nowcasting was first proposed by Browning2. The Strom cell identification and tracking (SCIT)3 and optical flow4. TREC divides two consecutive radar echo images at adjacent times into multiple image subsets, calculates the maximum correlation coefficient of different subsets in the two images, and uses the maximum correlation coefficient to determine the optimal matching area, which is the moving position of the image subset. The TREC method is applicable to extrapolation of steadily changing radar echoes, but not for rapidly changing ones, such as radar echoes of convective precipitation clouds5. Based on TREC, Zhang Yaping et al.6 proposed difference image based tracking radar echo by correlations (DITREC), which tracks radar echo motion on the basis of the correlation method of differential images. DITREC can eliminate the disordered vector in the TREC vector field due to the rapid change of echoes, but when using the derived DITREC field to predict the precipitation field, the prediction accuracy depends on the Z-I relationship it adopts8. However, the strong dependence of the Z-I relationship method on a specific climatic background results in the model’s weak generalization ability. Another radar echo extrapolation method, SCIT, calculates the centroid position and volume of the echo monomer, matches the calculated information with the scanned data before and after the echo monomer and track it, and then extrapolates with multiple consecutive moments of the matching and tracking results10. However, SCIT is only applicable to the identification, tracking and nowcasting of thunderstorm monomers11, which limits its application in weather forecasting to a certain extent. The third traditional radar echo extrapolation method, optical flow, finds the corresponding relationship between previous images and the current image by using the change of the target point in the image sequence in the time domain and the correlation between adjacent images. Then the motion information of objects between adjacent images is calculated according to their correspondence. Optical flow obtains the motion vector field of the echo by calculating the optical flow field of the radar echo, and extrapolates the radar echo based on the motion vector field. Different from TREC, the extrapolation of optical flow is based on the changes of radar echoes, rather than the invariant characteristics of the echoes12. As a result, optical flow can better track radar echo movement. The extrapolation ability of optical flow is superior to other extrapolation algorithms, especially in rapidly changing weather conditions. However, natural movement is dynamic, so radar echoes will be split, disappear, merge, etc., which is contradictory to the invariance assumption of the generation and disappearance of radar echoes by optical flow, greatly weakening the extrapolation of optical flow. In general, the traditional radar extrapolation method could only infer the echo position of the next moment according to the extrapolated images of several past radar echoes. It ignores the motion nonlinearity of the small and medium-scale atmospheric systems existing in the radar echoes under actual conditions. Therefore, historical radar echo data have not been used to full advantage.The traditional radar echo extrapolation methods mainly include tracking radar echoes by correlation (TREC)13. The application of neural network in meteorology sheds new light on the radar echo extrapolation method, and some scholars have conducted research in this area. Chen Jiahui et al.14 used BP neural network model to extrapolate radar echoes and demonstrated the effectiveness of neural network in radar echo extrapolation. Guo Hanyang et al.15 used convolutional gated recurrent unit (CGRU) to extrapolate radar echoes and compared them with TREC. The results show that the neural network model has more advantages than traditional methods. Klein16 added a dynamic convolutional layer to the convolutional neural networks (CNNs) structure and achieved accurate prediction of precipitation echoes by generating two prediction probability vectors. Based on Klein’s model17, added recurrent neural network (RNN) to the dynamic convolutional layer and successfully built recurrent dynamic convolutional neural networks (RDCNN). The neural network has achieved good results in both forecast accuracy and forecast timeliness. As traditional long short-term memory (LSTM) cannot extract spatial features, Shi et al.18 replaced the change of LSTM input to state and the change of state to state with convolution in their model and proposed ConvLSTM. To adapt to the spatial–temporal variableness, Shi et al. further improved ConvLSTM in 2017 and proposed TrajGRU19. To adapt to the spatio-temporal dependence of radar echo images, Singh et al.20 added a convolution structure to recurrent neural network and realized the radar-echo-image-sequence-based prediction. Zhang Lei et al.21 proposed an EDP model for sustainable e-agriculture. This weather radar echo prediction method is based on convolution neural network and long short-term memory networks. The results show that the neural network can effectively improve the prediction accuracy and effectiveness. He Liang et al.22 regarded precipitation forecasting as a picture-to-picture conversion problem, and used a U-net-structured convolutional neural network for forecasting. To extrapolate radar echoes, Zhang Dezheng et al.23 improved the model’s ability to represent the details of spatial structure information by deepening the convolutional layers in the neural network, which is composed of CGRUs.With the development of neural network, its powerful modeling ability and unique advantages in big data processing have attracted extensive attention of scholars, and it has been widely used in the field of meteorologyTo sum up, although the application of neural network in radar echo extrapolation is of great significance in utilizing historical radar echo data and improving the accuracy of radar echo extrapolation, the prediction results are still not satisfactory. They could be blurred or distorted, so the prediction accuracy needs to be improved. Therefore, this paper proposes a radar echo prediction model (ADC_Net) based on dilated convolution and attention convolution. To construct small object information and avoid the losing of internal data structure, the model substitutes dilated convolution for pooling in downsampling the feature matrix obtained after convolution. Also, the integration of an attention convolution module with an attention mechanism can improve the model’s sensitivity to target features and suppress interference information, thus enhancing the extrapolation accuracy of radar echoes.The structure of the ADC_Net model based on dilated convolution and attention convolution proposed in this paper is shown in Fig. As elementary building blocks, ADC_Net has nine recurrent convolution modules (RCNN), four dilated convolution (DCNN), four upsampling convolution modules (UPCNN), four attention convolution modules (ACNN), four skip connections, and a 1 * 1 standard convolution layer (cnn). RCNN is the basic module of the model, and its structure is shown in Fig. In the above module, dilated convolution instead of pooling operation is employed to downsample the feature matrix in the model and the attention convolution module is used to improve the model's sensitivity to the target features in the feature matrix and suppress the interference information. As the dilated convolution and the attention convolution module are the two main innovative structures of the ADC_Net model proposed in this paper, they will be illustrated in the following paragraphs.24. To increase the reception field, dilated convolution injects holes on the basis of the convolution map of standard convolution. Therefore, dilation rate, a hyper-parameter, is added to dilated convolution. The hyper-parameter refers to the number of intervals in the kernel, meaning filling the dilation rate-1 zeros in the convolution kernel. Figure Dilated convolution, also known as convolution kernel expansion operation, is a convolution method commonly used in pixel-wise output models25 takes a 3 * 3 convolution kernel as an example to show the difference between standard convolution and dilated convolution in the reception field and output. The dilation rate of dilated convolution is 2; the red part of the two figures on the left is the eigenvalue of the convolution operation with the convolution kernel, and the red part on the right is the output result after convolution.Dilated convolution expands the convolution kernel to the scale of the dilated scale constraint, and fills the area not occupied by the original convolution kernel with zeros. This ensures the exponential expansion of the reception field by dilated convolution without losing resolution or coverage. Therefore, in dilated convolution, the size of the feature matrix before and after convolution is not only related to the convolution kernel, convolution step size, and padding, but also affected by the dilation rate. For standard convolution, the calculation method of the feature matrix size after the convolution layer is shown in Eq. . Here, h4When using a neural network to process images, features are generally extracted through convolution operations in the neural network, and then the convolutional feature matrix is downsampled by a pooling operation to reduce the image scale and increase the reception field. However, the loss of information caused by downsampling in the pooling operation is irreversible, and this irreversible loss of information will cause the loss of the internal data structure of the feature matrix during the training process of the neural network. Usually only the probability of each class needs to be predicted in the classification model, so there is no need to consider the loss of image details caused by the pooling operation. When doing the pixel-level prediction task of radar echo extrapolation, it is necessary to restore the image to the same size as the input image through the upsampling operation, so the problem of irreversible information loss needs to be reconsidered. Therefore, in order t to keep resolution and still expand the receptive field, in this paper, dilated convolution, instead of the pooling operation, is used to downsample the feature matrix obtained after convolution in the model, and the reception field of the convolution kernel is expanded through dilated convolution without losing resolution or coverage. In this way, dilated convolution, together with standard convolution, can not only perform convolution operation on data and extract spatial features from radar echo data from multiple scales, but also retain internal data structure, making data information reconstruction possible, which cannot be realized by pooling operation. This is useful in radar echo extrapolation tasks. On the one hand, a larger reception field makes it possible to better learn the overall change trend of radar echoes, and on the other hand, a higher resolution makes it possible to accurately locate the pixel information of smaller radar echoes.27. The attention mechanism comes from the human visual attention mechanism. Humans usually observe and pay attention to a specific part of a scene according to the needs instead of seeing a scene throughout. Therefore, the essence of the attention mechanism is to filter out important information from a large amount and focus on them. In the neural network, a hidden vector is generated by the input image through convolution operation, activation function and pooling operation. The value of this hidden vector is calculated by referring to itself and several surrounding pixels. As a result, if standard convolution is employed, local information rather than global information will be used to calculate target pixels, which brings limitations to the training of neural networks, as is shown in Fig. 28. In contrast, low-level feature maps have more accurate target location but poor prediction ability and more virtual target information29.The attention mechanism has gradually become a research hotspot and an important part of the neural network structure in recent years. Initially applied to machine translation, now it has been used in image processing and recommendation systemsIn order to solve the above problems, this paper integrates the advantages of high-level feature maps and low-level feature maps. The attention convolution module is added to the ADC_Net neural network model. Considering each pixel in the feature map as a random variable, calculate the weight values of all pixels in the current prediction task, the pixels with high weights are used and the pixels with low weights are ignored in training and prediction. The use of the attention convolution module can effectively suppress the pixel information irrelevant to the current prediction task and enhance the related pixel information. Figure  in Eqs.  and 5)..\documen by Eqs.  and (5).21, so the prediction results are more accurate.To sum up, the application of the attention convolution module can simultaneously learn the advantages of high-level feature maps and low-level feature maps in the process of model training and prediction to achieve the global reference of feature values. According to the weight matrix obtained by referring to the global feature, each pixel of the feature matrix is given different weights. This allows the neural network to focus on the pixel information most relevant to the current task each time, making the neural network use pixel information more efficiently in the process of training and predictionThe data in the experiment are New generation of Doppler weather radar detection data, which are stored in polar coordinates. For the convenience of research, the radar echo data in polar coordinates are first stored in the three-dimensional Cartesian coordinate system by interpolation method. Considering the height of the representative average guided airflow in the troposphere, we select the CAPPI radar echo image at a vertical height of 3KM as the experimental data. In order to reduce the parameters in the training process and improve the training efficiency, the CAPPI images are grayscaled before the experiment. The processed grayscale image is then cropped, keeping a 256 * 256 size area in the center of the image. Because the echoes with areas too small are generally weak and evolve rapidly, they have little guiding significance for forecasting work. In order to avoid the influence of the sample quality on the training results, each radar echo image is screened during the construction of the sample dataset. The cases with the echo area coverage greater than 1/10 in the effective detection range of the radar are selected into the sample data set, and the radar echo range is controlled within the range of 0–70 DBZ. Figure hit shows the times that both the predicted value fake and the real value label are 1, far is the times that the predicted value fake is 1 while the true value label is 0, miss the times the predicted value fake is 0 but the true value label is 1, and cn is the times that both the predicted value fake and the true value label are 0. The calculation formulas of the experimental evaluation indicators POD, FAR, CSI and HSS are shown in Eqs. , false alarm rate (FAR), critical success index (CSI) and Heidke skill score (HSS). POD represents the probability that convective weather is predicted to occur and the prediction is successful. FAR represents the probability that convective weather is predicted to occur but the prediction fails. CSI is the prediction accuracy after weighing successful predications and wrong predications. HSS is a quantitative evaluation of the model after comprehensively considering various situations. Therefore, the higher the value of POD, CSI and HSS is, and the lower the value of FAR is, the more accurate the predicted result is, and the better the model effect is. Since strong convective weather usually occurs when the radar echo intensity is greater than 35DBZ, the threshold of the radar echo is set to 35DBZ in this experiment. The real label radar echo image and the predicted radar echo image are binarized respectively, with the pixels larger than 35DBZ in the radar echo image binarized to 1, and the pixels smaller than 35DBZ binarized to 0. When evaluating the experimental results, the binarized pixel value of the real image is named label, and the binarized pixel value of the predicted image is named fake,  in Eqs. , 7), , where s and y represent the predicted value and the true value, respectively. In the process of neural network training, the smaller the loss function value is, the more robust the model is. The prediction effect of the model can be measured through the definition and optimization of the loss function. In model training, considering that the use of the MSE loss function alone may cause a small loss value and result in a poor model training effect, the use of MAE alone may lead to the loss of data feature details. Therefore, in this paper, MSE + MAE is used as the loss function, which is conducive to the convergence of the function. Even if the learning factor is fixed, the function can quickly obtain the minimum value. The loss function of the proposed model is shown in Eq. .10\documDuring the training and testing of the neural network, the choice of different optimizers will impact the prediction results differently. In this paper, Adam is used as the optimizer. Integrating the advantages of the two optimization algorithms AdaGrad and RMSProp, Adam calculates the update step size by considering the First Moment Estimation of the gradient (the mean of the gradient) and the Second Moment Estimation (the uncentered variance of the gradient). Its advantages include easy operation, high computational efficiency, and parameter updates immune to the scaling transformation of gradients. Also, because of the good interpretability of Adam hyperparameters and little or no need for parameter adjustment, it is applicable to large-scale data and parameter scenarios. In this experiment, the initial learning rate of Adam is set to 0.0001.The language used in this experiment is Python, and the Pytorch framework is used for training on NVIDIA 2488 GPU. To evaluate the performance of the ADC_Net model based on dilated convolution and attentional convolution (ADC_Net) proposed in the paper, it is compared with the ADC_Net model based on dilated convolution (ADC_Net2), the ADC_Net model based on attention convolution module (ADC_Net3), the ADC_Net model based neither on dilated convolution nor on attentional convolution module (ADC_Net4), U_Net, EDP model, and ConvLSTM.Figure Tables This part compares the single training time and model parameters of ADC_Net model based on the ADC_Net model based on dilated convolution and attention convolution (ADC_Net), the ADC_Net model based on dilated convolution (ADC_Net2), the ADC_Net model based on attention convolution module (ADC_Net3), the ADC_Net model based neither on dilated convolution nor on attentional convolution module ADC_Net4) and U_Net, EDP model, ConvLSTM. The details are shown in Table  and U_NeThis paper proposes a radar echo extrapolation model (ADC_Net) based on dilated convolution and attention mechanism. Using dilated convolution instead of pooling operation can not only perform spatial feature extraction on radar echo data from multiple scales, but also preserve the internal data structure. It avoids the loss of the internal data structure in pooling and makes reconstruction of small object information possible. Also, by integrating an attention convolution module with attention mechanism into the model, the sensitivity to the target features is improved, and the interference information is suppressed, thereby improving the extrapolation accuracy of radar echoes. The radar echo extrapolation results of the ADC_Net model based on dilated convolution and attentional convolution (ADC_Net) proposed in the paper is compared with that of the ADC_Net model based on dilated convolution (ADC_Net2), the ADC_Net model based on attention convolution module (ADC_Net3), the ADC_Net model based neither on dilated convolution nor on attentional convolution module (ADC_Net4) and U_Net, EDP model, ConvLSTM. The results show that the proposed model has better prediction performance. Through comparative model experiments, it is found that combining dilated convolution with the attention convolution module can not only improve the prediction performance of the model, but also achieve better results than using dilated convolution or the attention convolution module alone. And through the use of the attention convolution module, the parameters and running time added by dilated convolution to the ADC_Net model can be reduced to a certain extent, and the running efficiency can be improved.Supplementary Information.


Furthermore, employing a mixture of four pairs of synthetic methylated and non-methylated small RNAs, we further refined this two-step method by optimizing the nuclease S1 treatment condition. With this method, we successfully purified miR-21-5p, miR-26-5p, piR-020485, and tsRNA from human lung and sperm tissue samples and analyzed their 2′-O-methylation modification at the 3′-end by LC-MS/MS.Methylation modification of small RNAs, including miRNA, piRNA, and tsRNA, is critical for small RNA biogenesis and biological function. Methylation of individual small RNA can be defined by liquid chromatography-coupled with mass spectrometry (LC-MS/MS). However, LC-MS/MS analysis requires a high purity of individual small RNA. Due to the difficulty of purifying specific small RNA from tissues or cells, the progress in characterizing small RNA methylation by LC-MS/MS is limited. Here, we report a novel method that can efficiently purify small RNA from human tissues for LC-MS/MS analysis. This method includes two steps: 1) pull down the target small RNA by incubating total small RNAs (18–24 nt) extracted from human tissues with a biotinylated antisense oligonucleotide of the target small RNA, followed by capturing the binding duplex of biotinylated antisense and small RNA  The 2′OArabidopsis  are two platforms to determine the 3′-terminal 2′Ome in specific RNAs, such as tRNA and rRNA . Howeverbidopsis , the effanalysis . HoweverIn the present study, we combined RNA pulldown assay and enzyme protection assay to develop a two-step strategy to isolate specific small RNA. Using this novel two-step method, we successfully purified specific small RNA including miRNA, piRNA, and tsRNA from tissue samples and accurately identified their 3′-terminal 2′Ome modification by LC-MS/MS.Human normal lung tissue and sperm samples were obtained from Nanjing Drum Tower Hospital of Nanjing University . This study has been approved by the Ethics Committee of the Nanjing Drum Tower Hospital of Nanjing University and a written consent form was obtained from each donor.4Cl and precipitated with glycogen and ethanol. The quality of the purified small RNAs was determined on an Agilent 2100 Bio-analyzer using a small RNA Kit (Agilent).As the previous reports , the smaCH3, miR-26-5p/miR-26-5pCH3, piR-020485/piR-020485CH3, and tsRNA/tsRNACH3) were synthesized in GenScript . Oligonucleotides and primers were dissolved in DEPC water to generate 20 μM/μL and 100 nM/μL stock solutions and stored at −70°C, respectively. For stem-loop primer RT-qPCR calibration, the reaction concentration was 0.1–1.0 µM. The sequences of oligonucleotides and primers used in the experiment are listed in Four pairs of unmethylated and methylated  human small RNAs  according to the manufacturer’s instructions. Briefly, the reverse transcription reaction was carried out in a 10 μL reaction buffer containing 1 µL of extract RNA, 1 µL of 10 mM dNTPs, 1 µL of AMV reverse transcriptase (TaKaRa), 1 µL of stem-loop RT probes/primers (Applied Biosystems), 2 µL of 5×reverse transcription buffer, and 4 µL of diethylpyrocarbonate (DEPC) water. For cDNA synthesis, the reaction mixtures were incubated at 16°C for 30 min, 42°C for 30 min, 85°C for 5 min, and then held at 4°C. Real-time PCR was performed (1 cycle of 95°C for 5 min and 40 cycles of 95°C for 15 s and 60°C for 1 min). The reaction was performed with a final volume of 20 µL containing 1 µL cDNA, 0.3 µL Taq, 0.33 µL hydrolysis probe (Applied Biosystems), 1.2 µL of 25 mM MgCl2, pH7.5), and then incubated at 95°C for 2 min, followed by slowly temperature-decreasing to 25°C. To eliminate the unpaired RNAs, 1000 units Nuclease S1 (Thermo Fisher) was added to the solution. The reaction was allowed for 30 min at room temperature and terminated by 70°C incubation. The ssDNA was then degraded by adding 4 units DNaseI (New England Biolabs) . The target small RNAs  were finally extracted by the RNA Clean and Concentrator-5 kit (Zymo Research) according to the manufacturer’s instructions. These nucleosides were then subjected to LC-MS/MS analysis using a 4600 UPLC/Triple TOF  processed two extra A residues at its 5′-end to ensure that its molecular mass is larger than that of the target small RNA. The biotinylated DNA oligonucleotide was then captured by streptavidin magnetic beads (Roche) and washed with ×0.5 SSC. The target small RNA was then eluted with DEPC water by incubating at 70°C for 5 min. Second, a 2 µL single-strand DNA oligonucleotide (10 µM), which sequence is complementary to the target human small RNA , was mixed with the isolated target small RNA (10 µg) in buffer  to purify the target small RNA from tissue or cell samples for LC-MS/MS analysis  containing 10 µg target small RNA. The mixture was incubated at 95°C for 2 min followed by decreasing to 25°C slowly. To eliminate unpaired RNAs, the mixture was treated with 1000 units nuclease S1 (Thermo Fisher) . Finally, the 21-nt single-stand DNA was then degraded by treatment with 4 units DNaseI (New England Biolabs) at 37°C for 30 min. The remaining small RNA was then extracted by RNA Clean and Concentrator-5 kit (Zymo Research) according to the manufacturer’s instructions. The quality of the purified target small RNA was evaluated by qRT-PCR or LC-MS/MS.The main obstacle of detection of small RNA modification is to purify specific small RNA to achieve the purity required by LC-MS/MS. Currently, the only major method to purify specific small RNA is the pulldown assay based on magnetic beads with single-stranded DNA/RNA (ssDNA/RNA) capture probes . Howeveranalysis . In stepCH3, miR-26-5pCH3, piR-020485CH3, and tsRNACH3 at the 3′-end to serve as the standards. CH3, miR-26-5p/miR-26-5pCH3, piR-020485/piR-020485CH3, and tsRNA/tsRNACH3. These synthetic small RNAs were analyzed by LC-MS/MS. As shown in CH3, miR-26-5pCH3, piR-020485CH3, and tsRNACH3 exhibited a peak “shift” of a methyl group compared to non-methylated miR-21-5p, miR-26-5p, piR-020485, and tsRNA, respectively.To validate this strategy, we synthesized non-methylated miR-21-2p, miR-26-5p, piR-020485, and tsRNA, as well as methylated miR-21-2pvia monitoring the purity and yield of isolated target small RNA under the different incubation times and concentrations of nuclease S1. As shown in . Considering the purity and yield of isolated small RNA, we optimized the treatment condition of nuclease S1 as 1000 units enzyme and 30 min incubation. Under this condition, miR-21-5p/miR-21-5pCH3, miR-26-5p/miR-26-5pCH3, piR-020485/piR-020485CH3, or tsRNA/tsRNACH3 were successfully purified from the mixture of synthetic small RNAs, as shown by stem-loop RT-qPCR analysis , tRNAVal(AAC), and tRNAGly(GCC), were able to be methylated by Dnmt2, which, in turn, protected tRNAs from cleavage under stress conditions (His(GTG)) are methylated at the 3′-end, we purified tsRNA from human sperms by the two-step strategy and performed LC-MS/MS analysis of purified tsRNA. As shown in Recent studies showed that several sperm tRNA-derived small RNAs (tsRNAs) were involved in the intergenerational transmission of disorders . The stunditions . To testPrevious studies by us and others have confirmed that mammalian small RNAs, such as piRNA and miRNA, are 2′-O-methylated at the 3′-end nucleotide . Given tThe current method for identifying the 2′-O-methylation of small RNA is sodium periodate oxidation in combination with Northern blot or RT-qPCR . However


N6-methyladenosine RNA binding proteins (YTHDFs) mediate the functional effects of N6-methyladenosine (m6A) on RNA. Recently, a report proposed that all YTHDFs work redundantly to facilitate RNA decay, raising questions about the exact functions of individual YTHDFs, especially YTHDF1 and YTHDF2. We show that YTHDF1 and YTHDF2 differ in their low-complexity domains (LCDs) and exhibit different behaviors in condensate formation and subsequent physiological functions. Biologically, we also find that the global stabilization of RNA after depletion of all YTHDFs is driven by increased P-body formation and is not strictly m6A dependent.The YTH The online version contains supplementary material available at 10.1186/s13059-023-02862-8. N6-methyladenosine (m6A) is the most prevalent internal mRNA modification in mammals. One of the major pathways through which m6A exerts its function is the preferential binding of “reader” proteins to methylated transcripts. Proteins containing the YT521-B homology (YTH) domain, including YTHDF1, 2, and 3 and YTHDC1 and 2 in mammals, are direct m6A readers possessing a dedicated m6A-binding domain [6A-modified mRNAs was shown to induce their translation, which has been linked to various physiologically relevant processes [Ythdf2 knockout is embryonically lethal in mice [Ythdf1 knockout mice develop normally within the first 3 months but exhibit defects in long-term learning and memory [RNA modification represents an important layer of post-transcriptional regulation of RNA metabolism. Among more than 170 distinct chemical modifications identified on cellular RNA [rocesses . YTHDF2 rocesses , 10. YTHrocesses , 12. Whi in mice , Ythdf1 d memory . Knockdod memory . These rWe have reported that the triple knockdown of YTHDF proteins leads to the highest mRNA stabilization compared to the single and double knockdown of YTHDFs . The molWe first examined the notion that YTHDF proteins are not involved in translation regulation but rather act redundantly to destabilize RNA. This model was presented alongside the concordant idea that YTHDF proteins share highly similar RNA targets, protein partners, and biological functions .6A modification status, not RNA binding of individual proteins. Specifically, there are 7105 m6A-modified genes among ~16,000 expressed genes in HeLa cells. Among 6814 mRNA with translation efficiency data acquired with Ribo-seq, 4424 m6A-modified mRNA were used by Zaccara et al. for their YTHDF1 knockdown analysis  samples Fig. b. This r6A-modified mRNA leads to the same conclusion  with YTHDF1-3 triple knockdown than with knockdown of any single YTHDF protein  from these two datasets to define the P-body transcriptome. Unlike P-body depleted (“Depl”) transcripts, m6A methylation does not cause a more significant stabilization effect for P-body enriched (“Enr”) transcripts after YTHDF1-3 triple knockdown  in cultured HeLa cells by DCP1A staining  < 0) as would be expected . The global stabilization effect of YTHDF triple knockdown was almost abolished in DDX6-depleted cells Fig. e. Over-eect Fig. f. Of notion Fig. e and f. 6A on RNA fate are heterogeneous and depend heavily on biological context. The context-dependent roles of m6A reader proteins enable the m6A-mediated multifaceted regulation of multiple biological processes. YTHDF1 and YTHDF2 proteins have notable sequence differences in their low-complexity regions and form different LLPS granules. Using YTHDF1 target transcripts instead of all methylated mRNA for analysis, we show that YTHDF1 indeed promotes translation in HeLa cells.The effects of m6A dependent. The triple knockdown of all three YTHDF proteins leads to increased cellular P-body formation and global stabilization of most mRNAs, regardless of their methylation status. Therefore, in addition to the individual functions of these YTHDF proteins that are affected by their relative levels, these abundant proteins also participate in a scaffolding role to maintain cellular RNP granules. Depriving these proteins may expose cellular mRNAs to induce P-body formation for global stabilization of the whole transcriptome.In this study, we confirm that depletion of all three YTHDF proteins exhibits a synergistic effect to stabilize mRNA , 15–17. AllStars negative control siRNA  was used as control siRNA in knockdown experiments. Cells were transfected by using Lipofectamine RNAiMAX (Invitrogen 13778075) for siRNAs  according to the manufacturer’s protocols.Homo sapiens were retrieved from UniProt Knowledgebase [Canonical amino acid sequences of YTHDF1-3 from edgebase . Sequencedgebase . ConservA protein solution (5 μL) of the prion-like domain of each YTHDF was loaded on an EM grid for 10 seconds and excess solution was removed via blotting with filter paper. The grid was then washed with water and stained with 5 μL uranyl acetate (2%) for 15 s. All negative staining samples were imaged on a JOEL 1400 microscope.Protein samples were prepared from respective zebrafish embryos by lysis in RIPA buffer (ThermoFisher Scientific 89900) containing 1 × Halt™ Protease and Phosphatase Inhibitor Cocktail (ThermoFisher Scientific 78441). Protein concentration was measured by NanoDrop 8000 Spectrophotometer (ThermoFisher Scientific). Lysates of equal total protein concentration were heated at 90°C in 1 × loading buffer (Bio-Rad 1610747) for ten minutes. Denatured protein was loaded into 4–12% NuPAGE Bis-Tris gels (Invitrogen NP0335BOX) and transferred to PVDF membranes (ThermoFisher Scientific 88585). Membranes were blocked in Tris-Buffered Saline, 0.1% Tween® 20 (TBST) with 3% BSA (MilliporeSigma A7030) for 30 min at room temperature, incubated in a diluted primary antibody solution at 4 °C overnight, and then washed and incubated in a dilution of secondary antibody conjugated to HRP for 1 h at room temperature. Protein bands were detected using SuperSignal West Dura Extended Duration Substrate kit (ThermoFisher Scientific 34075) on a FluroChem R (Proteinsimple).For imaging of P-bodies, HeLa cells were fixed with 4% paraformaldehyde and permeabilized with 0.3% Triton X-100. The blocked coverglass (ThermoFisher Scientific 155409PK) was incubated with an anti-DCP1A-AlexaFluor 488 conjugate (Abcam ab208275) at 4 °C overnight. After three washes with DPBS, the nucleus was counterstained with Hoechst 33342 (Abcam ab228551), and the coverglass was kept in DPBS at 4 °C before imaging. Samples were imaged on a Leica SP8 laser scanning confocal microscope at the University of Chicago. P-body numbers in each cell were quantified with Cellprofiler 3.0  with a cLibrary preparation was performed using a SMARTer Stranded Total RNA-Seq Kit v2  following the manufacturer’s protocols. Sequencing was carried out at the University of Chicago Genomics Facility on an Illumina NextSeq machine in single-end mode with 75 base pairs (bp) per read. Raw reads were trimmed with cutadapt (version 1.10)  and thenTM Reagent (Invitrogen 15596026) was added to the system and RNA was extracted following the manufacturer’s protocol. Following isopropanol precipitation, the RNA pellet was re-suspended in 50 μl RNase-free H2O.The protocol was adapted from a previous publication . For eacTM Reagent (Invitrogen) was added to the samples and RNA was extracted following the manufacturer’s protocol. Following isopropanol precipitation, the RNA pellet was re-suspended in 20 μl RNase-free H2O. After processing data similarly to the RNA-seq workflow described above through alignment, reads on each NCBI annotated gene were called using the DESeq2 package in R [The following steps were performed to isolate mammalian P-body cores and extract their RNA: (1) the 950 μl supernatant was spun at 18,000 × g for 20 min at 4 °C to pellet P-body cores. (2) The resulting supernatant was discarded, and the pellet was re-suspended in 1 ml SG lysis buffer. (3) Steps 1 and 2 were repeated to enrich for SG cores. (4) The resulting pellet was then re-suspended in 300 μl of SG lysis buffer and spun at 850 × g for 2 min at 4 °C. (5) The supernatant which represents the mammalian SG core enriched fraction was transferred to a new tube. (6) The enriched fraction was pre-cleared twice by adding 60 μL equilibrated DEPC-treated Protein A Dynabeads (ThermoFisher Scientific 10001D) and nutating at 4 °C for 30 min. Dynabeads were removed using a magnet. (7) Twenty micrograms of EDC3 or EDC4 antibody was added to the enriched fraction and nutated at 4 °C overnight to affinity purify P-body cores. (8) The solution was spun at 18,000 × g for 20 min at 4 °C and the supernatant was discarded to remove any unbound antibody. (9) The pellet was then re-suspended in 500 μl SG lysis buffer and 100 μl of equilibrated DEPC-treated Protein A Dynabeads was added. (10) The sample was nutated for 3 h at 4 °C. (11) The Dynabeads were washed three times with wash buffer 1  for 5 min, once with wash buffer 2  for 5 min, and once with wash buffer 3  for 2 min at 4 °C. (12) The beads were resuspended in 200 μl of 100 μg/ml Protease K solution  and incubated for 15 min at 37 °C. (13) TRIzolage in R . The folTM Reagent (Invitrogen 15596026), according to the manufacturer’s protocol. An amount of ERCC RNA spike-in control (Invitrogen 4456740) proportional to the total cell number was added to each purified total RNA sample before library preparation. After RNA-seq data alignment, reads on each NCBI annotated gene were converted to attomoles by dividing by the sum of reads aligned to the ERCC spike-in. Average log2(Fold changes) between attomole amounts were calculated and analyzed.The same number of cells were counted, and total RNA was isolated with TRIzolhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapters were trimmed using Cutadapt [N6-methyladenosine sites and differential expression of “translated” genes in HeLa was obtained directly from processed data files published on Gene Expression Omnibus from the study of interest. Further analyses were performed and plots were created in R v. 4.0.3 using the following packages: biomaRt v. 2.44.4 [6A sites contained only gene names and m6A site numbers. For boxplots, outliers were not shown but were included in statistical analyses.Raw fastq files were downloaded from Gene Expression Omnibus (accession number GSE134380) , 41 and Cutadapt . Reads wCutadapt  using HICutadapt . Read coCutadapt  with refCutadapt  in R v. . 2.44.4 , ggplot2. 2.44.4 , dplyr v. 2.44.4 , ggpubr . 2.44.4 , and for. 2.44.4 . When peAdditional file 1: Figure S1 describes discrepancies between experimental results and the unified model of YTHDFs. Figure S2 illustrates increased P-body formation after YTHDF1-3 triple knockdown. Figure S3 contains uncropped gel images shown in the figures.Additional file 2. It contains all processed data that was used to plot all the figures in this manuscript and YTHDF target definitions.Additional file 3. Review history.


Vasopressors and fluids are the cornerstones for the treatment of shock. The current international guidelines on shock recommend norepinephrine as the first-line vasopressor and vasopressin as the second-line vasopressor. In clinical practice, due to drug availability, local practice variations, special settings, and ongoing research, several alternative vasoconstrictors and adjuncts are used in the absence of precise equivalent doses. Norepinephrine equivalence (NEE) is frequently used in clinical trials to overcome this heterogeneity and describe vasopressor support in a standardized manner. NEE quantifies the total amount of vasopressors, considering the potency of each such agent, which typically includes catecholamines, derivatives, and vasopressin. Intensive care studies use NEE as an eligibility criterion and also an outcome measure. On the other hand, NEE has several pitfalls which clinicians should know, important the lack of conversion of novel vasopressors such as angiotensin II and also adjuncts such as methylene blue, including a lack of high-quality data to support the equation and validate its predictive performance in all types of critical care practice. This review describes the history of NEE and suggests an updated formula incorporating novel vasopressors and adjuncts. Shock is common and associated with mortality in patients admitted to intensive care units (ICUs) , 2. In tIt is common among clinicians and researchers to use the dose of vasopressor agents to grade the severity of shock. Norepinephrine has been recommended as the first-line vasopressor since 2004 , and theNorepinephrine equivalence (NEE) is a scale to quantify vasopressor exposure, which converts the dose of each vasopressor equivalent to that of norepinephrine. NEE has been used in clinical trials to set an inclusion criterion, define trial protocols, report baseline characteristics, and assess outcomes –19. For This review aims to describe the evolution of NEE, its utility in clinical research and practice, its pitfalls, and future perspectives and opportunities with a proposal to produce an updated version of the score.In 1995, the history of quantifying the amount of hemodynamic support began when the inotrope score (IS) was developed for neonates after congenital cardiac surgery . The IS Table NEE allows us to combine the dose of different vasopressor agents into a single scale, and this characteristic is advantageous when patients receive multiple vasopressors simultaneously. Although norepinephrine is the first-line vasopressor in critical care, adding secondary agents is suggested when norepinephrine alone cannot attain the target pressure or when the norepinephrine dose required to achieve the target MAP becomes excessive . From a Furthermore, NEE can serve as an eligibility criterion in clinical trials. Although clinical research, especially randomized controlled trials, should determine which patient is eligible for enrolment accurately and objectively, the precise definition of eligibility criteria is not always easy. For example, it would be difficult to decide whether a patient on norepinephrine of 0.3 µg/kg/min and vasopressin 0.03 U/min is eligible when “receiving norepinephrine ≥ 0.4 µg/kg/min” is listed in the inclusion criteria of a randomized trial. NEE can overcome this challenge by standardizing the potency of vasopressors, and several randomized trials used NE as an inclusion criterion , 15. NEE2/FiO2 ratio as a measure of oxygenation, MAP/NEE can be used as a measure of vasopressor responsiveness and severity of shock.A novel measure of hypotension using NEE has been recently proposed, i.e., the ratio of MAP and NEE . Like PaNEE has several pitfalls. First, with scarce evidence, the conversion ratio for each vasopressor agent is determined arbitrarily, either comparing the dose needed to achieve a target MAP or estimating the reduction in norepinephrine dose when used in combination. This drawback is especially important given the recent evidence supporting a multimodal vasopressor approach . The calSecond, NEE may sometimes not reflect the total amount of hemodynamic support. Since NEE only considers vasopressor effects, NEE fails to measure the effect of other hemodynamic interventions, such as mechanical circulatory support and drugs with predominantly inotropic profiles. For example, consider a patient with severe low cardiac output syndrome who receives veno-arterial extracorporeal membrane support and moderate to a high dose of inotropes in addition to low-dose norepinephrine. In that case, it will be obvious that the NEE for this patient is disproportionately low compared to the total hemodynamic support. However, it is quite challenging to integrate the intensity of mechanical circulatory support or inotropic agents with vasopressors. In general, MAP is the single measure of efficacy to guide vasopressors, while mechanical circulatory support or inotropes require other parameters  on top of MAP, where it becomes difficult to ascertain how much a combination of vasopressor and inotrope contributes to MAP or CI. Therefore, NEE should be used and interpreted cautiously in studies, including patients requiring mechanical circulatory support or high-dose inotropes. Although a scoping review proposed a calculation formula based on the available evidence on the equipotency of different vasopressors, the small number of included studies on each vasopressor limits its generalizability .Third, we need to renew the NEE equation whenever a new vasopressor emerges. Any new vasopressor comes with less evidence that, in most cases, is not enough to allow an accurate construct of a validated and updated NEE equation. This questions our traditional approach with complication derivations of the NEE equation and pushes us to think to simplify this process.We propose an updated NEE equation based on the best available evidence on the equipotency of various vasopressors. Two randomized controlled trials comparing epinephrine and norepinephrine found that the dose necessary to achieve the same MAP target was similar between the two vasopressors , 30. TheMost previous NEE formulas used 1/100 or 1/150 as the conversion ratio for dopamine , 20, 23.A small, non-randomized study in septic shock patients found that 3.2 µg/kg/min of phenylephrine was equivalent to 0.2 µg/kg/min to obtain MAP ≥ 65 mmHg . A randoThere are two large randomized controlled trials comparing vasopressin and norepinephrine in septic shock , 36. In Since ATHOS-3 is the only multicenter randomized controlled trial to assess the equipotency of angiotensin II in intensive care settings, we adopted the result of this trial to calculate a conversion factor for angiotensin II. This trial reported that 20 ng/kg/min of angiotensin II infusion resulted in a mean decrease of 0.05 µg/kg/min of norepinephrine compared with the placebo to maintain a target MAP of 75 mmHg or 10 mmHg greater than baseline in the first 3 h of drug initiation, and a target MAP of 65 mmHg afterward. Thus, we applied a correction factor of 0.0025 to the angiotensin II dose in ng/kg/min.Similarly, data from a recent multicenter randomized trial comparing terlipressin with norepinephrine in septic shock , we applA randomized trial compared metaraminol and norepinephrine in septic shock . Based oA recent randomized trial found that 5 g of hydroxocobalamin reduced norepinephrine by 0.08 µg/kg/min , which lA randomized trial comparing oral midodrine with intravenous norepinephrine found that 30 mg/day of midodrine reduced 73 mg of norepinephrine during six days in septic shock , which gOn the other hand, there is no randomized trial comparing the potency of methylene blue with that of other vasoconstrictors. A single-center randomized trial assessing the efficacy of methylene blue in septic shock  reportedTherefore, we have updated the NEE equation incorporating vasoconstrictors commonly used in recent years. Figure All the studies we used to update the NEE formula are listed in Table There are several unanswered questions concerning NEE. The current trend toward a multimodal vasopressor strategy will require a valuable measure to standardize the total amount of vasoconstrictor therapy. Although NEE is a helpful measure of vasopressor support, regular updates, e.g., the use of novel vasoconstrictors, are necessary. Since most correction ratios are arbitrarily determined, validation studies are also required to evaluate the predictive performance of NEE for worse clinical outcomes.Since its emergence in 2002, NEE has been increasingly used in intensive care research. Its importance will further increase if the catecholamine-sparing vasopressor strategy becomes prevalent. Regular renewal and validation are necessary to update NEE in line with clinical practice.


Membrane transporters are an important group of proteins in physiology and disease. Their functions make them common drug targets, but their location in the lipid bilayers poses a tremendous challenge to researchers. The current stage of development of structural biology, in addition to new research tools, has largely facilitated the acquisition of knowledge about transporters and mechanisms. This Collection presents recent studies, covering bioenergetics, structure and functional characterization of various transporters, lipids-protein interactions, and novel research tool development. Membrane-embedded transporters play essential roles in mediating the uptake of vital nutrients, and the removal of unwanted substances across the cell membranes, to maintain their critical concentrations. Their function and dysfunction are also associated with various diseases, such as obesity and cancers. Transport bioenergetics and mechanisms are therefore crucial for developing novel therapeutic drugs for treatments, and transporters can be used for effective drug delivery into cells or across critical barriers such as the blood/brain barrier. However, the hydrophobic nature of transporters poses significant problems for research, so the knowledge of membrane proteins generally lags far behind that of soluble proteins. In recent years, the availability of high-resolution 3-D structures determined by cryoEM techniques and crystallography has greatly facilitated the studies of transporters and their mechanisms of action. This Collection is dedicated to this line of research.1. The hypothesis stated that the electrochemical H+ gradient, the proton motive force (PMF), is the direct energy source for ion-coupled secondary active transport. This has been systematically and experimentally verified by H. Ronald Kaback2 and many other researchers.Peter Mitchell’s chemiosmotic hypothesis, proposed in 1961, transformed the field of bioenergetics and transport+ or Na+ electrochemical ion gradients to move substrates against concentration gradients. Recently, these transmembrane-electrostatically localized protons (TELP) have been recognized as a primary contributor to Mitchell’s PMF3. In addition, a follow-up study in this Collection identified a significant thermotropic component of PMF4. Lee found that mitochondria can isothermally utilize environmental heat through TELP to drive the synthesis of ATP, thus locking substantial amounts of the heat energy into ATP molecules. This work has refined and improved our knowledge of transport bioenergetics.As this implies, active transport can be categorized into primary and secondary active transport. The primary active transporters utilize the chemical energy from ATP hydrolysis. The secondary active transporters use the pre-established H5. Over the past decades, this hypothesis, also described as alternating-access actions, has been largely tested and confirmed by a battery of studies in the field, including in high-resolution structures, molecular dynamics (MD) simulations, and biophysical analysis9. Furthermore, cooperative binding between two transported solutes has also been recognized as the core mechanism for secondary active transport11.Mitchell also conceptualized a “mobile barrier” hypothesis to explain how the transporters mediate substrate translocations across proteins, and how the binding of substrate(s) dictates conformational changes12. The DEER distance measurements of a major facilitated superfamily exporter MdfA13 also offered an original insight into the conformational dynamics of this antiporter. Mutations at the ion-binding sites have often been found to cause fascinating effects, such as an asymmetric effect on Na+ and K+ binding to NaK-ATPase14, and altered stoichiometry in a NhaB Na+/H+ exchanger15. Specific elimination of Na+ binding from a common binding for H+, Na+, and Li+ was also reported in a melibiose transporter MelB16.In this Collection, MD simulations provide a new understanding of the kinetic coupling of ions and the conformational changes of Mhp1, a member of the nucleobase:cation symporter-1 family17 and is a recent hot research topic. Two articles stressed the important roles of cardiolipin in the stability and functions of a bacterial translocon18 and a NhaA Na+/H+ antiporter19, respectively. Another study characterized how the interaction between an Arg residue and phospholipids modulates the properties of a sarcoplasmic reticulum Ca2+-ATPase20.Beyond the interaction between transporter and substrate, the importance of the interactions between the transporter and the surrounding lipid bilayer has also been well recognized21. Recently, the lipid nanodiscs, which retain a lipid-bilayer core around membrane proteins and provide a more native-like environment than detergent micelles, have been shown to be a useful tool21. One article described a new class of copolymers that can extract membrane proteins directly as a form of lipid nanodiscs without the required membrane scaffold proteins23. Another study described a cell-free expression tool to study the co-translational folding of transporters without the presence of translocon insertase24.This Collection also recruited several studies on research tools. Membrane protein research often faces problems in identifying proper detergents due to difficulties with solubility. The high-throughput detergent studies in this Collection provide a large body of data on detergent effects on the stability of membrane proteins by determining two related parameters of protein unfolding and aggregation. It is noted that some detergents, such as phosphocholine detergents, could afford opposite effects on the two parametersEmerging techniques, including the cryoEM method and the artificial intelligence-based AlphaFold 2, have rapidly advanced our understanding of membrane transporters in a short period, and we expect continued growth of our knowledge. While this editorial cannot hope to discuss these important developments in detail, I hope it draws your attention to the Collection, and the recent progress in membrane transporter research.


To gain mechanistic insight, one of the identified upregulated circRNAs with a high reduction of editing in heart failure, AKAP13, was further characterized. ADAR2 reduced the formation of double-stranded structures in AKAP13 pre-mRNA, thereby reducing the stability of Alu elements and the circularization of the resulting circRNA. Overexpression of circAKAP13 impaired the sarcomere regularity of human induced pluripotent stem cell-derived cardiomyocytes. These data show that ADAR2 mediates A-to-I RNA editing in the human heart. A-to-I RNA editing represses the formation of dsRNA structures of Alu elements favoring canonical linear mRNA splicing and inhibiting the formation of circRNAs. The findings are relevant to diseases with reduced RNA editing and increased circRNA levels and provide insights into the human-specific regulation of circRNA formation.Alterations of RNA editing that affect the secondary structure of RNAs can cause human diseases. We therefore studied RNA editing in failing human hearts. Transcriptome sequencing showed that adenosine-to-inosine (A-to-I) RNA editing was responsible for 80% of the editing events in the myocardium. Failing human hearts were characterized by reduced RNA editing. This was primarily attributable to Alu elements in introns of protein-coding genes. In the failing left ventricle, 166 circRNAs were upregulated and 7 circRNAs were downregulated compared to non-failing controls. Most of the upregulated circRNAs were associated with reduced RNA editing in the host gene. ADAR2, which binds to RNA regions that are edited from A-to-I, was decreased in failing human hearts. In vitroThe online version contains supplementary material available at 10.1007/s00395-022-00940-9. Heart failure (HF) is characterized by high morbidity, mortality and increasing prevalence , 63, 78.Adenosine-to-inosine (A-to-I) RNA editing is a post-transcriptional modification process that enlarges transcriptome variety without altering the genome . This moTranscriptome-wide analysis has shown that A-to-I RNA editing primarily occurs in intronic regions of protein-coding genes and particularly in repetitive Alu sequences, which represent approximately 10% of the human genome . Alu seqPatient inclusion for the present study was approved by the ethics committee (appl. no. S-390/2011) . The stuBiopsies of 1–2 mm diameter were immediately washed in ice-cold saline (0.9% NaCl) and transferred and stored in liquid nitrogen until DNA and RNA was extracted. After diagnostic workup of the biopsies (histopathology), the remaining material was evenly dissected. DNA and RNA was extracted using the Allprep Kit (Qiagen) according to the manufacturer’s protocol. RNA purity and concentration were determined using the Bioanalyzer 2100 (Agilent Technologies) with the Eukaryote Total RNA Pico assay for RNA from biopsies.https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-).RNA-sequencing libraries were generated using the TruSeq Stranded Total RNA kit (Illumina) according to the standard protocol of the kit. Fastq-reads from paired-end sequencing (RNA and DNA) were generated using CASAVA v.1.82 (Illumina). Mapping was done against the human reference genome (GRCh38) using HISAT2-2.1.0, followed by transcript assembly using StringTie-1.3.3b. Individual GTF files with gene and transcript abundances per sample were then merged by applying StringTie with the ‘-e’ option. Finally, read count and transcript count information was generated using StringTie prepDE.py script. For genome sequencing, 1 µg of total gDNA was sheared using the CovarisTM S220 system, applying two treatments of 60 s each  with 200 cycles/burst. 500 ng of sheared gDNA was taken and whole-genome libraries were prepared using TruSeq DNA sample preparation kit according to manufacturer’s protocol . Mapping was done against the human reference genome (GRCh38) using HISAT2-2.1.0. Further processing and variant calling was done following “best-practice-guidelines” of the Genome Analysis Toolkit (GATK) (FASTQ files were used to identify and characterize RNA editing events in the transcriptome using the RNAEditor tool . The outn = 173) were extracted using UCSC genome browser. Scores of putative RNA editing events were overlapped with given coordinates to assess, whether RNA editing events were indeed found in flanking introns of regulated circRNAs BSS in HF patients (n = 19) and controls (n = 10).All genomic coordinates of up- and downstream introns flanking the back-splice site (BSS) of regulated circRNAs  was washed 3 times with ice-cold DPBS . The tissue was placed in 700 µL QIAzol lysis reagent. For homogenization, heart tissue was added to the Tissue Homogenizing CKMix  and processed in the Precellys 24 Tissue Homogenizer  at 5000 rpm, 4 °C until the tissue was fully homogenized. Lysate was centrifuged at 14,000 rpm, 4 °C for 10 min. RNA was extracted from the supernatant using the miRNeasy Mini Kit  with on-column DNase digestion according to manufacturer’s instructions.2 and 5% CO2 conditions and were split when they reached approximately 70% confluency. One day before transfection, 2 × 105 HCM were plated on a 60 mm tissue culture plate. siRNA was transfected using the Gene Trans II Transfection Reagent  with a final concentration of 60 nM in Opti-MEM I serum reduced medium  according to manufacturer’s instruction. After 4 h, cells were transferred to full growth medium and harvested after a total of 24 h or 48 h. The following siRNA sequences were used for lipofection Scr: UCUCUCACAACGGGCAUUU[dT][dT], siADAR1: GCUAUUUGCUGUCGUGUGA[dT][dT], siADAR2: GAUCGUGGCUUGCAUUAA [dT][dT]. siRNA was produced and predesigned by Merck, DE.Primary human cardiomyocytes (HCM) were cultured in Myocyte Basal Medium (C-22270) supplemented with Growth Medium Supplement Pack (C-39270) purchased at Promocell, DE. HEK-293 cells were cultivated in DMEM/F12 medium  supplemented with 10% FCS, 1% Penicillin/Streptomycin mix and 1% glutamine . Cells were cultured at 37 °C, 20% O2–104 was used for transduction with control AAV6-EGFP and AAV6-circAKAP13IR−Alu which were generated as described before [5 was used for the control AAV6-shscrambled  and the AAV-ADARB1-shRNA .Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) were generated as described before . Cardiacd before . A MOI o5 HEK293 cells were plated per well in a 6-well tissue culture plate. Cells were transfected with polyethylenimine (PEI) reagent  in a ratio of DNA:PEI 1:3 in Opti-MEM. After 4 h, cells were re-incubated in growth medium and harvested after 24 h. HEK293 cells were transfected with either ADAR1 , ADARB1  and PS100064  or the circAKAP13 mini-genes and pcDNA3.1( +) plasmids.One day before transfection, 6.6 × 10−∆Ct or the ∆∆Ct method using RPLP0 as a reference gene. The primer sequences are listed in Supplementary Table I.Total RNA was extracted from lysed cells using the miRNeasy Mini Kit  with on-column DNase digestion according to manufacturer’s instructions. 30 µL total RNA were eluted and 1 µg was reverse transcribed into cDNA using the following reagents at final concentrations: 2.5 U/µL M-MLV Reverse Transcriptase , 0.25 mM dNTP mix , 5 µM Random Hexamer Primers  and 1 U/µL RiboLock RNase Inhibitor . Generated cDNA was diluted to a working concentration of 5 ng/µL and qRT-PCR was performed with PowerUp SYBR Green Mastermix  according to manufacturer’s instruction. Relative gene expression was calculated with the 2Total artery (HR-810), atrium , brain (R1234035-50), kidney (R1234142-50), liver (R1234149-50), lung (R1234152-50), skeletal muscle (R1234171-50), stomach (R1234248-50), ventricle (R1234138-50) were purchased at Amsbio LLC, UK, quantified with the Denovix DS-11 spectrophotometer and 1 µg was transcribed into cDNA as described above. Generated cDNA was amplified with qRT-PCR and analyzed by gel electrophoresis.25 magnetic beads . Buffers were prepared as recommended by the manufacturer. After loading of RNA on magnetic beads, the mixture was incubated for 10 min. The supernatant was then analyzed as the oligo(dT)- negative fraction. Bound RNA was washed 3 times and then eluted as the oligo(dT)-positive fraction. RNA fractions were synthesized into cDNA as described above and analyzed by KOD XTreme HotStart PCR according to manufacturer’s instructions.Total RNA was extracted from lysed cells as described above. 5 µg of total RNA was selected with Oligo d (T)Total RNA (5 µg) was digested with 1 U/µg RNase R  for 10 min at 37 °C and 600 rpm on a thermo shaker. RNase R digestion was stopped at 95 °C for 3 min. RNA was transcribed into cDNA as described above and further analyzed with KOD Xtreme HotStart Polymerase PCR.2  and 0.05% NP-40 was prepared. Cells were harvested in ice-cold D-PBS  and centrifuged at 300 g and 4 °C for 10 min. The pellet was re-suspended in lysis buffer with RNase inhibitor and incubated on ice for 5 min. After another centrifugation at 1000 g and 4 °C for 3 min, the supernatant was mixed with 700 µL QIAzol and processed as the cytoplasmic fraction. The remaining cell pellet was re-suspended in 700 µL QIAzol and processed as the nucleus fraction. Total RNA from both fractions was extracted and cDNA was synthesized as described above. Analysis of fractions was performed with KOD HotStart Xtreme Polymerase according to manufacturer’s instructions.A lysis buffer containing 10 mM (pH = 8.0) Tris–HCl  containing 140 mM NaCl , 1.5 mM MgClFor immunoblotting of tissue samples, 50 mg heart tissue was washed 3 times with ice-cold DPBS . For homogenization, heart tissue was added to the Tissue homogenizing CKMix  and processed in the Precellys 24 Tissue Homogenizer  at 5000 rpm and 4 °C until the tissue was fully homogenized. The lysate was centrifuged at 14,000 rpm, 4 °C for 10 min. For immunoblotting of cell samples, HCM cells were lysed in RIPA buffer containing 1X HALT protease/phosphatase inhibitor cocktail . Proteins were loaded on a 10% SDS–Polyacrylamide gel. SDS-PAGE was performed in the Mini-PROTEAN System  and proteins were blotted on a 0.45 µm nitrocellulose membrane . The membrane was blocked in 5% non-fat dry milk (in TBS-T) and incubated with antibodies directed against ADAR1 , ADARB1  and ß-actin  as a loading control. Densiometric analysis was performed with ImageJ.6 HEK293 cells each were seeded on 4 × 100 mm tissue culture plates. After 24 h, each plate was transfected with 1800 ng ADAR2 using the PEI-method as described above. After another 24 h cells were harvested and pooled. RNA immunoprecipitation (RIP) was performed with the Magna-RIP RNA-Binding Protein Immunoprecipitation Kit  according to manufacturer’s instructions. RIP was performed with normal rabbit IgG  and rabbit anti ADARB1  antibodies. IP was functionally validated by immunoblotting using the same antibodies as described above. RNA bound to ADAR2 was reverse transcribed and tested with qRT-PCR as described above.A total of 3.7 × 10circAKAP13 mini-genes, the circAKAP13 sequence as well as sequences of Alu flanking the back-splice site  were amplified with KOD Xtreme HotStart PCR  according to manufacturer’s instructions. PCR products were visualized with GelStar Nucleic Acid Gel Stain  using agarose gel electrophoresis and bands with the expected size were purified with Monarch DNA Gel Extraction Kit . Purified PCR products were cloned into a linearized pcDNA3.1( +) vector using InFusion HD Cloning Kit . Plasmids were purified with Monarch Plasmid Miniprep Kit  and the insert was validated with Sanger sequencing. Primers are listed in Supplementary Table I. For PCR product cloning, desired targets were amplified and purified with KOD Xtreme HotStart PCR as described above. Purified PCR products were cloned into pMinit2.0 vector using NEB PCR Cloning Kit  according to manufacturer’s instructions. Purified plasmids were analyzed by Sanger sequencing to confirm PCR product amplification and sequences were screened for RNA editing using UCSC genome browser’s BLAT.For the construction of the For the preparation of RNA probes, 5 µg of template DNA amplified with DIG-tag primers (Supplementary table I) were transcribed with T7 polymerase from the DIG RNA labeling kit  for 2 h at 37 °C. Residual DNA was digested with DNase I for 15 min at 37 °C. DIG-labeled RNA probes were isolated using phenol–chloroform extraction and eluted in 25 µL EB . For northern blotting, 8 µg of RNA were loaded on a 1.2% agarose gel in 1X MOPS buffer  and RNA was separated by size through electrophoresis at 100 V. After electrophoresis, agarose gel was blotted onto positively charged nylon membrane . The RNA was then UV cross-linked to the membrane and pre-hybridized for 30 min at 68 °C with ultrasensitive hybridization buffer . The membrane was incubated with RNA probes at 50 ng/mL hybridization buffer over night at 68 °C. The membrane was then washed with high and low stringency wash buffer prior to blocking in 1X blocking solution from DIG wash and block buffer set . After blocking, membrane was incubated with 1:5000 Anti-DIG AP from DIG Luminescent Detection Kit . CSPD was used as a substrate for chemiluminescent detection.Sequencing data generated in this study and all other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.http://rnaeditor.uni-frankfurt.de/. All codes used in this study are publicly available.The RNAEditor tool used for RNA editing analysis is available from t-test for comparison of two groups. If more than two groups were compared, the one-way ANOVA with Tukey’s post hoc correction was performed. If the samples did not pass the normality test, two unpaired groups were analyzed using the Mann–Whitney U test and paired groups were analyzed using Wilcoxon matched-pairs signed-rank test. More than two groups were compared with the Kruskal–Wallis test. For correlation analysis, groups were first tested for Gaussian distribution. If the normality test was passed, Pearson correlation was performed, otherwise, Spearman correlation analysis was performed. P values under 0.05 were considered significant. Statistical analysis was performed using GraphPad Prism 8.First, sample values were tested for Gaussian distribution. If the normality test was passed, significance was tested with the student’s .To investigate the extent of RNA editing in HF, we performed transcriptome sequencing from left ventricle tissue from failing and non-failing hearts and identified RNA editing events. RNA editing events were detected in the nucleotide sequence of 1211 genes. Considering all genes with differential editing in failing left ventricles, we found the absolute decrease in RNA editing events to be more profound compared with the increase  and HF patients (n = 18) using quantitative real-time PCR (qRT-PCR) with primers spanning over the circRNA-specific back-splice site (BSS)  mRNA was not . All 5 circRNAs were enriched in the poly-A-negative and cytoplasmic fractions  was increased both in HF Fig. b and c aAKAP13 RNA transcript were confined to Alu elements  in intron 14 upstream of the 5’ BSS and one Alu element (AluSz) located downstream of the 3’ BSS in intron 20 of AKAP13 . The depletion of ADAR2 further resulted in a reduction of A-to-I RNA editing in the Alu element flanking the 3’ circAKAP13 BSS . Thus, the interaction of ADAR2 with Alu elements regulates A-to-I RNA editing, thereby inhibiting circAKAP13 RNA formation.ADAR2 protein levels and RNA editing events in Alu elements were significantly reduced in failing hearts. Therefore, we tested a potential interaction of ADAR2 with these Alu elements in AR2 Fig. a and b sBSS Fig. c–f. We nP13 Fig. g–j and oBSS Fig. k. Accordent Fig. l–p, whilcircAKAP13 BSS. The reduction of ADAR2 resulted in increased levels of 5’AluSx3 and 3’AluSz  when Alu elements were in the convergent orientation Supplementary file2 (DOCX 50 KB)Below is the link to the electronic supplementary material.