An RNA structure prediction from a single‐sequence RNA folding program is not evidence for an RNA whose structure is important for function. Random sequences have plausible and complex predicted structures not easily distinguishable from those of structural RNAs. How to tell when an RNA has a conserved structure is a question that requires looking at the evolutionary signature left by the conserved RNA. This question is important not just for long noncoding RNAs which usually lack an identified function, but also for RNA binding protein motifs which can be single stranded RNAs or structures. Here we review recent advances using sequence and structural analysis to determine when RNA structure is conserved or not. Although covariation measures assess structural RNA conservation, one must distinguish covariation due to RNA structure from covariation due to independent phylogenetic substitutions. We review a statistical test to measure false positives expected under the null hypothesis of phylogenetic covariation alone (specificity). We also review a complementary test that measures power, that is, expected covariation derived from sequence variation alone (sensitivity). Power in the absence of covariation signals the absence of a conserved RNA structure. We analyze artifacts that falsely identify conserved RNA structure such as the misuse of programs that do not assess significance, the use of inappropriate statistics confounded by signals other than covariation, or misalignments that induce spurious covariation. Among artifacts that obscure the signal of a conserved RNA structure, we discuss the inclusion of pseudogenes in alignments which increase power but destroy covariation.RNA Structure and Dynamics > RNA Structure, Dynamics and ChemistryRNA Evolution and Genomics > Computational Analyses of RNARNA Evolution and Genomics > RNA and Ribonucleoprotein EvolutionThis article is categorized under: Covariation together with covariation power allow to distinguish between three different conservation patterns in RNA alignments. Structural RNAs with evolutionary signal for a conserved structure have covariation and power (green). Non structural RNAs have power but no covariation (red). RNAs that lack both covariation and power are too conserved in sequence to be able to decide whether they have a conserved structure or not (blue). We show the covariation versus power observed in structural RNAs from the database Rfam (black), and the covariation and power for the long non‐coding RNAs HOTAIR, RepA‐Xist, and ncSRA that do not support a conserved RNA structure (brown) Conserved structural RNAs are involved in many cellular functions from translation , to RNA cleavage , protein localization (SRP), or gene regulation at both transcriptional and translational levels . RNA‐Seq probing experiments have revealed numerous expressed and uncharacterized RNAs for which it seems imperative to interrogate whether they perform any relevant function by means of an RNA structure. Here we address the question of how to identify new evolutionarily conserved RNA structures. We will show that a plausible computational prediction by a standard single‐sequence folding method is not evidence of a conserved RNA structure, nor is a computational prediction complemented with chemical probing data. In addition to computational predictions and chemical probing, another standard technique for analyzing an uncharacterized RNA is to compare similar sequences in related organisms. A conserved RNA sequence does not necessarily imply a conserved RNA structure. We will show that it requires a particular pattern of variation to support a conserved RNA structure. We will also show that there are variation patterns that support the absence of a conserved structure as well.2to predict its structure. Information about the structure can bring important clues to decipher the RNA function. Computational methods to predict a secondary structure from the sequence of a structural RNA have a long history  when pseudoknots are allowed by the model  and the fraction of predicted base pairs that are correct   to be able to distinguish between real RNA folds and predicted foldings of random sequences.Given an uncharacterized RNA transcript, the statistical question is not finding a structure, but rather Unfortunately, the situation is that in most cases we cannot. Even random sequences have plausible folds, often with similar stability, number of base pairs, and structural complexity as the structures of real RNAs. Thus, predicted structures of random sequences are often effectively indistinguishable from those of real RNAs. As an example, for the SAM‐I riboswitch in Figure An example of a predicted structure of a shuffled SAM‐I sequence with a free energy of −27.4 kcal/mol and 25 base pairs fold using ViennaRNA is shown in Figure The number of possible helices with at least three base pairs for a random sequence of the length (94 nts) and the base composition  of the SAM‐I riboswitch is 29 ± 12 helices. For a randomized sample of the human telomerase RNA, the total number of potential helices with at least three base pairs is 1572 ± 150. These numbers are not just large, they are comparable to those of the real sequences (32 and 1716 respectively). RNA structure prediction from a single sequence cannot reliably distinguish real structural RNAs from unstructured RNAs or even random sequences.An additional source of information typically used to predict the structure of an RNA comes from experimentally determined reactivities per‐residue of the RNA molecule when subjected to some chemical modification. For instance, SHAPE‐Seq performs selective 2‐hydroxyl acylation analyzed by primer extension  unconstrained substitutions at the two positions. This effect is better observed when the comparison is restricted to a clade. The slower rate of substitutions at base pairs results from most mutations being rejected, except for double compensatory substitutions that produce 3D functionally equivalent molecules.For a conserved RNA sequence, the pattern of substitutions observed in an alignment is informative of whether there is a conserved RNA structure or not. Figure 3.1Conservation patterns like the one presented in Figure i, j is defined asa, b are (non‐gap) residues; Obsijab is the observed count of a : b pairs in positions i, j , and Obsia is the observed count of residue a in position i. G‐test and MI have similar expressions; the G‐test uses observed counts where MI uses residue frequencies. These two differ when the positions have gaps. Two pairs of positions with the same residue frequencies will have the same MI, but the pair with fewer gaps will have higher G‐test value.Different covariation measures can be used to quantify the sequence variation observed in Figure i, j), for an alignment of length L and 1 ≤ i, j ≤ L, the APC correction is defined ascovi=1L−1∑j=1j≠iLcovi,j and cov=2LL−1∑i=1L−1∑j=i+1Lcovi,j. Most measure of covariation after subtracting a background correction improves their performance for detecting covariation in RNA alignments  such as the eutherian HOTAIR  to the presence of pseudogenes in a structural RNA alignment.6Other artifacts can have the opposite effect of erroneously creating spurious covariation. We illustrate some of those artifacts using examples from the recent literature.6.1The original analysis of the HOTAIR lncRNA alignments reported a structure which the authors say is “evolutionarily conserved” , an inconsistent substitution (A:C in Mustela), and many inconsistent substitutions at other pairs in the proposed helix. In addition, the mouse/human pairwise comparison in this HOTAIR region shows six substitutions inconsistent with the proposed structure and no compensatory base pair substitutions. Overall, the analysis of the proposed HOTAIR structure is a combination of helices such as proposed helix 10  used by the program RNAalifold  can erroneously assign significant “covariation” support to completely conserved base pairs.An example of the inappropriate use of the RAFS statistic to provide support for an evolutionarily conserved RNA structure is Tavares et al. . Figure ‐‐RAFSp ‐‐window 500 ‐‐slide 100 analysis on the complete HOTAIR D1 alignment, similar numbers of “significantly covarying pairs” are detected in permuted alignments  as in the original alignment . More details about this analysis are given in Rivas and Eddy  first identified in zebrafish with homology in vertebrates  as well as in mouse . The fact that the sequence seems to be mostly consistent with the proposed cloverleaf structure is also not enough, as sequences, even random ones, usually conform with consistent structures. The covariation and power analysis is the way to assess whether the RNA structure is conserved or not. The alignment in Figure 6.4Observing structural covariation strongly relies on having an alignment where homologous base pairs are aligned respecting the structural constraints of the molecule. Conserved RNA structures can have important differences from species to species: sometimes helices have different number of base pairs; even entire helices may be missing in particular groups of species. The alignment of structural RNAs is further complicated by the fact that the pattern of mutations is more often dictated by base pairing correlations than by simple position conservation. Because helices can have variable number of base pairs and compensatory substitutions, alignments built without taking into account the conserved structure can easily misalign residues in helical regions.Perhaps counterintuitively, misalignments can create spurious covariations. Figure Alignments with base pair misalignments can be identified by performing structural realignments. A technique to identify spurious covariations consists of realigning the sequences using an Infernal model build with the proposed structure. An Infernal realignment of the COOLAIR sequences in Figure cmsearch), and to make an alignment of the homologs . An Infernal model simultaneously scores conservation, covariation, and consistency with the proposed RNA structure. Using Infernal, the database Rfam has collected reliable alignments of over 3000 structural RNA families  is a method of describing residue‐to‐residue correlations by inferring the direct pairwise statistical interactions in a biological sequence, as opposed to indirect interactions, where two residues that do not directly interact are correlated because both interact to a third residue resulting in a network effect  base pairs. Triple base pairs form when a base directly interacts with two other bases using two different edges (a found example is an A forming a cis WC pair with a U and a Hoogsteen‐type pair with another U). Base triples can induce indirect correlations between the other two bases. Because non‐WC direct interactions tend to show low covariation signal, indirect interactions due to base triples which involve at least one non‐WC pair are not expected to have strong covariation signal.Indirect interactions in RNA can appear due to base triples and other tertiary interactions  and fij observed in the alignment, that is,si ≤ K, for an alphabet of size K.The Potts model in Equation  is the mi, j using the direct information (DI) statistic , and on some modified parameters h^ia,h^jb defined by the conditions ∑j∑bPdirija,b=fia, and ∑i∑aPdirija,b=fjb. The DI scores are usually corrected using the APC  for each of the three methods Figure . The lisOverall, our experiments show that for structural RNAs, there is little performance difference using DCA methods versus a simpler G‐test statistic. For most RNAs, we observe a sensitivity threshold at which there is a sharp transition from high PPVs to low PPVs Figure . This seFigure 8A conserved RNA structure results in covariation, but a conserved RNA structure is not the only source of covariation in an RNA alignment. The method R‐scape . RNA structure prediction can be made more reliable by using both positive and negative evolutionary information in the form of base pairs that covary and base pairs that vary but do not covary. Given an RNA alignment, the method CaCoFold infers a consensus RNA structure that incorporates all covarying base pairs and avoids all negative base pairs Rivas, . CaCoFolCovariation analysis helps identify structural elements that otherwise would remain undetected, such as a single base pair pseudoknot in the glutamine riboswitch Figure . The pseCovariation helps predict new structural elements. For instance, for the coronavirus 3′UTR pseudoknot, the covariation analysis of the proposed Rfam structure identifies a new hairpin loop with an AA bulge, a standard RNA motif Lilley, . The extEscherichia coli tyrT operon). Covariation helps identify one additional helix with covariation support which frames a tandem GA, another recurrent RNA motif  do not reliably identify conserved RNA structure. Covariation and variation information found in alignments of conserved RNAs help distinguish conserved RNA structures from conserved RNAs not supporting a structure. On the other hand, conserved RNA structure cannot be inferred from conserved RNAs without variability.Covariation analysis requires a covariation measure and a test to distinguish covariation due to RNA structure versus covariation due to other sources. Acceptable covariation statistics are those that measure covariation alone, such as MI, G‐test, or DCA couplings. Covariation measures that also score conservation and consistency with an RNA structure are not acceptable to decide on the presence of a conserved RNA structure as both conservation and consistency occur frequently in RNA sequences, almost with independence of the presence or not of an RNA structure.R‐scape provides a statistical test to identify base pairs with covariation above phylogenetic expectation. Statistically significant pairwise covariation could still sometimes be due to other sources than a conserved RNA structure. The method CaCoFold which proposed an RNA structure with arbitrary topology using all covariation information present in the alignment is able to display the RNA structure supported by covariation, other covarying interactions not attributed to RNA base pairing, and the regions of the structure with poor determination due to the absence of covariation support.The accuracy of alignments is fundamental in recovering the evolutionary signals left by a conserved RNA structure. Most alignment algorithms assume a position independent pattern of substitutions which often disrupts the pattern of structural RNAs and may even induce spurious covariation. Starting from a one‐sequence/structure Infernal model to produce the alignment of all conserved sequences is usually better  than using alignments by other standard methods. Emphasis on improving RNA structural alignment methods so that they can be informed not just by the nested base pairs but by all arbitrary RNA base pairing will greatly help the identification of novel conserved structural RNAs.There are other RNA homology methods comparable to CMfinder that attempt to extract from a genome regions with a conserved RNA structure such as QRNA , obtained by the program R‐scape using option: –pdb. For a given RNA family, both the Seed and Full alignments have annotated the same structure. The structure includes both the Watson‐Crick (WC) base pairs as well as the non‐canonical base pairs (nonWC). G‐test scores are calculated using the program R‐scape (using option: –naive), and they include the average product correction (APC) . Scores from DCA couplings trained using Boltzmann Learning optimization (BL‐DCA) are calculated using the code provided with Ref. Cuturello et al., 2020. ECs scores trained using pseudo‐maximum likelihood optimization come directly from Ref. Weinreb et al., 2016. Running times for ECs are unavailable. PPV stands for positive predictive value, and it is the fraction of the detected basepairs that are true base pairs.Click here for additional data file.FIGURE S1 Comparison of different measures of covariation on 19 structural RNAs with experimentally determined structures. For each structural RNA, we compare the performance of three different methods, on two different alignments. One of the methods is G‐test, a simple covariation measure calculated directly from the alignment (generated by the software R‐scape using option ––naive). The other two are DCA methods: EC_RNA  uses pseudo maximum likelihood, and BL‐DCA  uses a Boltzmann Machine to optimize the DCA coupling. All covariation methods use an APC correction . The Seed alignments are from Rfam v14.2. The Full alignments are those used in . The base pairs has been inferred from a crystal structure . The dashed lines correspond to 50% PPV  and 50% sensitivity .Click here for additional data file.


Chronic hyperglycemia manifests in a variety of different micro- and macrovascular disorders such as diabetes mellitus and cardiovascular disease and has been shown to have links to post-transcriptional dysregulation. Equally, the development and progress of other devastating disorders such as tumorigenesis and neurodegenerative disease have also been associated with dysfunction of molecules involved in epigenetics such as RNA-binding proteins. Recent advances, especially on an analytical systemic level, have revealed new roles for these proteins and their contribution in maintaining the balance between normal function and dysfunction/disease.RNA-binding proteins (RBPs) are multi-faceted proteins in the regulation of RNA or its RNA splicing, localisation, stability, and translation. Amassing proof from many recent and dedicated studies reinforces the perception of RBPs exerting control through differing expression levels, cellular localization and post-transcriptional alterations. However, since the regulation of RBPs is reliant on the micro-environment and events like stress response and metabolism, their binding affinities and the resulting RNA-RBP networks may be affected. Therefore, any misregulation and disruption in the features of RNA and its related homeostasis can lead to a number of diseases that include diabetes, cardiovascular disease, and other disorders such as cancer and neurodegenerative diseases. As such, correct regulation of RNA and RBPs is crucial to good health as the effect RBPs exert through loss of function can cause pathogenesis. In this review, we will discuss the significance of RBPs and their typical function and how this can be disrupted in disease. RNA-binding proteins (RBPs) are critical RNA regulators responsible for modulating post-transcriptional events in the cell. RBPs can recognize and interact with binding motifs called RNA recognition motifs (RRM) and/or RNA structure to form ribonucleoprotein (RNP) complexes for the regulation of various RNA processes such as RNA stability, alternative pre-mRNA splicing, mRNA decay, translocation, post-translational nucleotide modifications, and RNA localization  1,2].,2.1,2].The mRNA life cycle from newly transcribed mRNA molecules to the generation of functioning mature mRNA transcripts is an intricate system which is governed by many different RBPs. In the human genome there are at least 1200 verified RBPs as well as several newly discovered ones . RBP-RNADiabetes mellitus (DM) is an increasingly prevalent global health burden . DM is aFor instance, RNA Binding Fox-1 Homolog 2 (RBFOX2), regulates alternative splicing and is upregulated in the diabetic heart, controlling splicing of genes involved in diabetic cardiomyopathy by binding to target RNA motifs associated with protein trafficking and cell apoptosis ,13. AddiRBPs are also implicated in the dysfunction of ECs under diabetic conditions in relation to their association with non-protein coding RNAs (ncRNAs), which include long noncoding RNAs (lncRNAs). The latter are, in fact, responsible for the preponderance of gene transcripts and act as positive or negative regulators based on their interactions with RBPs . The impThe above examples demonstrate the close association of lncRNAs and RBPs and the importance of this relationship in diabetic pathogenesis. At the same time, it is becoming clearer that advances in RNA biology uncover more roles for RBPs in the progression of diabetes where the dysregulation of RBPs controlling alternative splicing, RNA decay, and destabilization can have disastrous downstream effects on cardiovascular genes.SF3B1) has been found to be mutated in a lot of patients suffering from chronic lymphocytic leukemia (CLL)  prions which are based on amyloid formation  while itIn conclusion, disruption to the function of RBPs and the ensuing post-transcriptional dysregulation in gene expression can lead to significant events in the development and progression of distinct human diseases. Any associated therapeutic approaches are still limited by the ambiguity surrounding the roles of RBPs which have not been fully understood yet and are still under investigation, given the complexity of their interaction with other cellular networks, pathways, and processes involved in disease. Nevertheless, newly emerging technologies that involve high throughput analyses allow for the uncovering and deciphering of further interconnections and the discovery of new RBP and RNA targets. The results of such analysis will shed light on the mechanisms underpinning human disorders that currently plague patients worldwide and provide assurance on the efficacy and safety of any novel corrective options. Future medicinal advances will grace us with the possibility of using these remedial strategies in a clinical setting.


Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them. Precise identification of RNA modification sites is therefore of crucial importance to understanding the functions and regulatory mechanisms of various RNAs. More than 100 different types of RNA modifications have been identified2, and among them, N6-methyladenosine (m6A) is the most common eukaryotic mRNA modification. M6A occurs on nascent pre-mRNA, regulating its stability and translation. It is involved in many biological processes such as the circadian clock, differentiation from naïve pluripotency, and the heat shock response. It also plays various roles in disease pathogenesis such as carcinoma, breast tumor, gastric cancer, and anti-tumor immunity. Besides m6A, there are also a number of RNA modifications with crucial biological functions. For instance, N1-methyladenosine (m1A) can block the Watson-Crick interface and is vital for tRNA stability and the replication of HIV-1.Post-transcriptional RNA modifications increase the structural and functional diversity of RNA molecules and regulate all stages of RNA life11, SRAMP12, DeepPromise13, WHISTLE14, Gene2vec15, m6A-Atlas16, RMDisease17, PEA18, PPUS19, BERMP20, m5Upred21, and m6AmPred22. Special attention has also been paid to the prediction of RNA modifications in introns23, lncRNAs24 as well as various tissues and cell lines27. Together, these works greatly advanced our understanding of the localization of multiple RNA modification types in different species under various conditions28. However, existing approaches suffered from the following limitations.To date, a number of computational approaches have been proposed for in silico prediction of RNA modification sites from the primary RNA sequences, including: the iRNA toolkits6A, but failed to support multiple RNA modifications simultaneously through an integrated predictive model. Therefore, the study of the interplay between different modifications is limited. The iRNA toolkit11 developed primarily by Chen, Lin and Chou are the earliest as well as the most comprehensive approaches that support the prediction of various RNA modifications from RNA sequences and have been widely adopted as the gold standard for benchmarking the performance of different RNA modification prediction methods. However, the iRNA toolkit was presented in the form of multiple independent studies, each targeting a single modification. The iMRM web server29 was aimed to support five RNA modifications simultaneously with a friendly web graphical user interface; however, it is still based on five independent binary predictors corresponding to the five RNA modifications, respectively, without considering potential interactions among different modifications. Given the intrinsic biochemical and biophysical properties of different RNA modifications, the predictive framework established for one type of modification can often be conveniently migrated for the prediction of another modification. It is thus beneficial and efficient to test the computational framework on multiple RNA modifications simultaneously. Very recently, by taking advantage of the generative adversarial network (GAN), the MR-GAN approach was developed to predict eight RNA modifications30. However, some of the modifications supported may be rare modifications, such as m1G (only 29 sites), m2G (only 59 sites), and D (only 162 sites)30, whose wide occurrence in human transcriptome has not yet been confirmed. Given a large number of negative (non-modifiable) sites of such rare RNA modifications, the sequence-based prediction is likely to produce a substantial proportion of false-positive predictions in practice and should be used with extra caution.Firstly, most existing studies only focused on a single RNA modification type, mainly m5U modification has been previously confirmed with thousands of m5U sites reported by two different approaches (miCLIP and FICC-seq)31. Nevertheless, MR-GAN used only 30 sites for its training, which is likely to seriously limit its predictive capability for this specific modification. In addition, substantial discrepancies have been reported previously between different epi-transcriptome profiling technologies, e.g., for m5C32 and Ψ33. Thus, it is crucial to take advantage of the data generated from multiple orthogonal technologies to minimize the potential technological bias whenever such datasets are available.Secondly, most existing works relied on a limited amount of data from a single source (a single database or dataset generated from a single experiment), failing to fully take advantage of the available epi-transcriptome information. For example, the wide occurrence of m12 and iMRM29 focused on prediction accuracy but failed to provide a clear and intuitive interpretation of their prediction results. Although some existing approaches carefully interpreted their trained predictive model34, to the best of our knowledge, none of the existing works provided insights into their decision-making process for individual predictions. Recent advances in interpretable RNA/DNA models enabled the extraction of low-level CNN kernels and visualizing them as position weight matrices (PWM). These patterns, however, provide only vague insights, especially for multiple-layer DNNs, and cannot provide nucleotide level interpretation. However, it remains of significant interest to identify the critical sequence contents that directly contribute to positive RNA modification predictions, which should help facilitate our understanding of the sequence-dependent forming mechanisms of individual RNA modification sites.Thirdly, most of the work in the field, such as SRAMP6Am, even though its base-resolution epi-transcriptome profiling technology miCLIP has been developed, and the profiling data is publicly available.Lastly, a predictive framework has not been developed for some RNA modification types such as mFor these reasons, there is strong motivation to take advantage of state-of-the-art deep learning techniques to develop a unified predictive framework that supports multiple RNA modifications by integrating datasets generated from multiple technologies.6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um. To the best of our knowledge, these are the only widely occurring RNA modifications that can be profiled transcriptome-wide with existing base-resolution technologies, which are highly desired characteristics of RNA modification for reliable large-scale prediction. The multi-label architecture of our approach enables accommodation of the shared structure of different modifications while fully exploiting their distinct features. As some modifications are still more abundant than the others, to handle the imbalanced training data issue in multi-label learning, online hard examples mining (OHEM)35 and Uncertain Weighting36 were utilized. Some widely adopted state-of-art machine learning algorithms XGBoost37 and CatBoost38 were also implemented as the benchmarks. Importantly, we used the integrated gradient (IG)39 and the attention weights40 to gain insights into the trained overall model and to explain every individual prediction. Finally, a web server was developed and made freely accessible to serve the research community.We present here MultiRM, an attention-based multi-label neural network approach for integrated prediction and interpretation of RNA modifications from the primary RNA sequence (or the corresponding DNA sequence). Twelve RNA modification types are supported by our model, including mOur framework predicts twelve types of widely occurring RNA modifications using a deep neural network, as shown in Fig. MultiRM consists of an embedding module representing the input RNA sequences using the inherent short- and long-range interactions among nucleotides. The embedded representation is fed to an LSTM layer to extract the underlying sequence features shared by all modifications. Then, the attention mechanism enables the model to focus on the relevant region of the input RNA sequence for each specific modification type as needed. Finally, the multi-label module containing two fully connected (FC) layers predicts the multiple modifiable sites simultaneously. The framework is trained using a cross-entropy loss enhanced by OHEM and Uncertain Weighting.The primary purpose of our study is to establish an interpretable predictor that could achieve state-of-the-art accuracy in the identification of multiple widely occurring RNA modifications from the primary RNA sequences.b (b stands for binary). AUCb is the area under ROC curves calculated based on the positive and the corresponding negative samples  of each modification. Using the Word2vec embedding, we evaluated our multi-label model with 21-bp, 51-bp, and 101-bp RNA sequences as the input. As shown in Table We firstly tried to optimize the length of the input sequences according to AUCb values based on the optimized model were then achieved, as shown in Table 7G prediction by 0.08 of the AUCb score, and UW raised the AUCb for m1A by 0.06. After we combined both of them, it helped to enhance the average AUCb score by 0.0145.Subsequently, to address the unbalanced training data problem, we implemented OHEM, uncertainty weighting (UW), and focal loss on our optimized multi-label model and tested their performance with 51-bp of input sequences. Improved AUCb of 0.8361 and 0.8581, respectively, and achieved the best performance on six of the twelve RNA modifications considered  with an average ranking of 1.667 among the five approaches tested. The widely adopted XGBoost algorithm obtained the best performance on four modifications  and achieved the mean and median performance with AUCb of 0.8035 and 0.8122 with an average ranking of 2.25 among the five approaches considered.With the optimized settings , we then compared the newly developed approach MultiRM with the baseline approaches and other embedding techniques. The optimized hyper-parameters for each model to be compared can be found in Supplementary Table 41 value based on their respective ROC curves. The corresponding performance evaluation metrics, including Sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) for each modification, was calculated and provided in Table 42.Subsequently, we selected the optimal thresholds for each modification with the largest G-MeanSo far, the results have emphasized the performance of our method in terms of classification. To gain insights into the driving features behind the predictions, we applied techniques that are capable of providing model interpretability so as to identify key input sequence contents that are significant for predicting RNA modifications (see “Methods”). The sequence contents within the attention have a greater impact on RNA modifications, and mutations within these regions are more likely to lead to the gain or loss of RNA modification sites, as shown in Supplementary Fig. 43 and STREME44. To further quantify the similarity between the motifs obtained through MultiRM and DREME/STREME, motif comparison tool TOMTOM45 was applied to produce a p-value. Sufficient small p-values indicate a certain degree of consistency  of each pair of vectors was calculated to reveal the relevance of two arbitrary RNA modifications unveiled by the integrated prediction model. A surprising finding is the RNA modifications all show strong and significant positive associations among each other, including those, originated from different nucleotides , I and m1A , Ψ and m5U . Notably, m6A showed only mild association with other modifications, implying its relatively special role in post-transcriptional regulation as the most abundant mRNA modification. It is also worth noting that the above analysis did not consider the context-specificity of RNA modifications . It does not directly suggest that different RNA modifications should co-occur in the same biological context, or they work with synergetic effects, even though the latter is highly probable as what we have seen in the epi-genetic regulation of histone modifications.A major advantage of the proposed integrated model is the capability to learn the underlying association among different RNA modifications. It was shown previously that there exist clear evolutionary and functional cross-talk among different post-translational modifications of proteinsee Fig. . It suggTo further validate the above finding, we calculated the pairwise distance between two arbitrary RNA modifications and compared it to the random. Although we could not completely rule out the possibility of experimental bias, e.g., polyA selection, we did observe strong aggregation effects among most RNA modifications considered in this analysis. It is clear that the distance between two arbitrary RNA modifications is likely to be closer than random , the proportion of false-positive predictions varies substantially between the more abundant RNA modifications (such as m6A) and the less abundant ones (such as m1A), i.e., with a much higher false-positive rate for the less abundant RNA modifications. The problem is partially due to the limited consistency among existing biotechnologies for profiling RNA modifications. For example, while more than 10,581 m5C sites were reported from bisulfite sequencing, only 617 and 1084 m5C sites were reported by AzaIP and miCLIP, respectively32, probably due to their different technical preference and sensitivity. We provided in Supplementary Fig. It is important to note that, MultiRM currently does not consider the distinct abundance of different RNA modifications. So even under the same setting  or methods (such as ordinary MeRIP-seq combined with motif search) were not used.6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), as shown in Table We ultimately obtained 20 epi-transcriptome profiles generated from 15 different base-resolution technologies for 12 different types of RNA modifications  sites were collected from the RADAR databaseK-fold cross-validation is used to mitigate overfitting in many Machine Learning problems, especially for those who have small training data. This is because using K-fold to validate a model can better estimate how the results of the model will be generalized to an independent data set, especially in a limited dataset, where a small test cannot reflect the entire distribution of the data. In our scenario, however, we have ~300k training data in total. Consequently, a 5% testing/validation set will already give us a good estimate.In the end, over 300k sites were collected. We separated the RNA sequences in each class (corresponding to a type of RNA modification) into three sets, i.e., the training set, validation set, and test set. Here, the training set is unbalanced across different classes (modification types), i.e., the number of sites is different for different RNA modifications, while the validation and test set have balanced samples with sizes 150 and 50, respectively. In general, the hyper-parameters were optimized based on the validation sets, while the reported final prediction performance was achieved on the test set. Traditionally, To develop high-precision computational methods, it is essential to wisely represent or embed sequence data. Suppose we have raw data 57 is a simple yet very effective encoding method to represent sequence data. For each RNA sequence Traditionally, one-hot58, which uses a neural network to speed up the parameterization in HMM. It built a nonlinear feature embedding Because of the ability to capture long-range interaction, Hidden Markov Model is suitable for modeling sequence data. It has been successfully applied by Seq2vec59 has enhanced the performance of various NLP tasks. As a statistical language model, it follows skip-gram and continuous-bag-of-words (CBOW) architectures and uses neural networks to learn word embeddings based on context relationships. We trained our own RNA embeddings by treating each RNA sequence as a sentence and the k consecutive RNA nucleotides (k-mer) as words in that sentences. Mathematically, we define a mapping from single nucleotides to the vector representation of k-mers as 15 that 3-mers has the best predictive performance on m6A sites. Therefore, in our work, we follow that 3-mers convention to embed our input data. More specifically, a 3-nt sliding window moves over 1001-nt sample sequences with stride 1-nt to create sequences of 999 words with overlap. Each word corresponds to an index from the collection of all possible 3-mer combinations (104 different combinations in our training data). Then, Word2vec was implemented by Gensim package60 with a five-word-long window of neighboring words to learn the inherent relationship and generate a 300-dimensional feature vector. Finally, each embedded RNA sequence is converted into a 999 by 300 matrix.Since first invented by Mikolov in 2013, Word2vecIn this work, two types of DNN architecture, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) were utilized to learn the sequence features of RNA modifications. Specifically, long short-term memory (LSTM) was implemented to account for possible long-range dependencies of the features.39. Both the hidden states The model mainly consists of three parts see Fig. . The firImportantly, to assess the contribution of the embedding methods used in our model, we exploited three variations of embedding. For one-hot encoding, a CNN has added ahead and executed as part of the embedding module. Since the original one-hot encoding of RNA sequences is a sparse input, CNNs will help extract sequence patterns in a dense manner and generate high dimensional representations of these motifs. Besides, pooling layers are utilized to trim less informative features.6A and I) and the less abundant ones (such as Am and Gm). Traditionally, this problem has been addressed in two ways. One way is to alter the original imbalanced data to balance it using an oversampling algorithm like SMOTE61. Another potentially more effective way is to weigh the loss of each class at the end of the network. Since our inputs are RNA sequences that were rigorously generated, generating artificial RNA sequences may degrade the credibility of the original dataset. Therefore, we focused on the second option. As a benchmark, we first used a constant weight for each task based on the Effective Numbers of Samples62. Subsequently, we designed our multi-label model to be self-paced by learning the weights of each task during training and only back-propagated the samples with higher loss (OHEM)35 so that it could jointly prioritize tasks and samples through the whole training process. We also tested the performance of focal loss63, which basically down-weights the loss assigned to well-classified samples on the common binary cross-entropy loss.It is worth noting that the data among different labels (classes) was highly imbalanced, i.e., the number of sites varied substantially between the more abundant modifications  and assessed by the area under the ROC curves (denoted as AUROC or AUC), which is a non-parametric indicator that reflects the performance of a model.b and AUCm. AUCb was computed from the positive and the corresponding negative samples of each modification (with b representing binary), while AUCm was calculated using all other labels, including all other modifications and all the unmodifiable nucleotides as the negative (with m representing multiple). Consequently, AUCb represents the goodness of prediction for one particular modification versus its original non-modifiable base, e.g., m1A vs. A, while AUCm generalizes to all other cases, including other modifications and non-modifiable bases. Although AUCm is the matched evaluation scheme for multi-label learning tasks, AUCb was provided to maintain comparability with existing works, most of which perform binary classification with the unmodifiable original nucleotide as the negative samples. It is worth noting that nucleotides other than the original one were also considered when calculating AUCm. Because it is straightforward to make correct negative predictions according to the non-original nucleotide of a particular modification , AUCm is substantially higher than AUCb.Specifically, we calculated two types of AUC: AUC40 for each class to classify the positive and negative samples of a particular modification. These metrics were all calculated under the binary classification scenario, i.e., using the positive and negative samples of a specific modification, and are thus comparable to the reported performance in most of the existing works on RNA modification site prediction. AUCb is used as the primary evaluation metric for its nonparametric characteristics and comparability to the reported performance of related works in the existing literature.The widely adopted assessment metrics, including sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (Mcc), were also implemented to assess the prediction performance, and can be expressed as,p-value of this site is 0.01. This is used as the cut-off of the prediction. However, it is important to note that the cut-off controls only type I errors. Even with the same p-value cut-off, the proportion of false-positive predictions are still substantially different between the more abundant RNA modifications and the less abundant ones.The statistical significance of a predicted probability is assessed by an upper bound of the p-value, indicating how extreme the observed probability is among all the occurrences of the same nucleotide. It is calculated from the relative ranking of the putative RNA modification sites, i.e., if only 1% of nucleotides report a probability larger than a specific site, then the upper bound of the In addition to the accurate prediction of RNA modifications, it is often appealing to grasp the idea behind the model’s prediction. In our model, we used attention weights and IG to explain visually how the model makes specific decisions. Specifically, we focused on what our model valued most while making different predictions andacquired the nucleotide which contributed most while making the positive prediction through attention weights and IGs.40 was originally introduced as a solution to handle the long input sequences of the sequence-to-sequence model. Here, we transplanted it to our method by mapping the input RNA sequences to 12 context vectors. Since it has access to the entire input RNA sequences and is capable of picking out specific elements from the sequence to produce output, the mechanism thus gives the model freedom to focus and place more or less attention on the relevant nucleotide of the input RNA sequence for each prediction task as needed. Consequently, by visualizing the attention weights, which represent the weights of each nucleotide of the input RNA sequences in each prediction task, we can identify the most critical part of the input sequences in our model while making different predictions.Bahdanau attention39. Here, the target neuron of interest is the classification layer of each modification. The IG computes the averaged gradients of the output neuron when the input varies along a linear path from a baseline or reference to the input. It measures the contribution of each input to modification prediction and assigns higher scores to important nucleotides in the input sequences. Based on the contribution scores in each input nucleotide position, we visualized the attribution map as sequence logos where the height represents the importance of that position in the prediction. The size of nucleotides in a positive direction represents an important level in predicting the appearance of RNA modifications.By calculating the gradient of an output neuron with respect to its input, the gradient-based attribution method can reflect how much the input features contributed to a particular output through the networks. In our work, we used an attribution method called IGs64, we accumulated the attribution values in each position corresponding to all true positive samples with prediction scores in the top 10%. Then, for each sample, we search for the top k motifs across the attribution map by taking the highest mean scores in sliding windows of the desired length, removing its neighborhood, and repeating again for the next motif. After multiple sequence alignment, UMAP65 was used to embed the top-ranking motifs and DBSCAN66 was used to cluster these embedded motifs. Finally, we aggregated these motifs in each cluster by calculating their PWM and visualized them using sequence logos.Visualization of the attribution maps of each input sequence for a specific RNA modification not only gives the important positions while making positive predictions but also reveals the potential target motif (or recurring patterns) of its corresponding modifications. In order to calculate the consensus motif contributed most for each RNA modification, following a previous study56, in this work, we compared our model with two gradient-boost decision trees, i.e., XGBoost37 and CatBoost38. XGBoost has been widely used for bioinformatics predictions. CatBoost is used here because it has built-in techniques to deal with categorical variables thus avoided the extra step to convert nucleotides to one-hot encoding in this problem. The gradient-boost decision trees were used as multiclass classifiers over all 13 classes, including the 12 wide occurring RNA modifications and the non-modifiable nucleotide class. To generate optimal results, we searched the hyper-parameters of each method by coordinated decent, and the optimized choices were provided in Supplementary Table Since tree-based classification algorithms often have the best off-the-shelf accuracy for many bioinformatics problems67 and a mini-batch size of 128 during training. Meanwhile, exponential and cosine annealing68 learning rate decay were implemented for suitable models, and early stopping69 was introduced when the generalization loss increased in five successive epochs to prevent overfitting on the training data. Finally, the validation set was used to search for the best hyper-parameters for a single model, and the test set was used to choose the best model among various models with their best performance.We trained our attention-based DNNs (see Model Design subsection) over the short RNA sequences of 101-bp, 51-bp, or 21-bp windows. For Word2vec (see Embeddings section), we pre-trained the RNA 3-mers over 1001-bp sequence and then extracted the short RNA subsequence of the corresponding length from it. For CNN and HMM, we designed them in an end-to-end manner with the one-hot encoding of RNA sequences as the input. During training, we used a mini-batch size of 128 as the input and trained on 1 NVIDIA RTX 2080Ti over 100 epochs. In addition, we used an Adam optimizerFurther information on research design is available in the Supplementary InformationReporting Summary


Atg7) were increased, together with the activation of autophagy. These findings shed some light on the epigenetic regulation of gene expression, which may provide a new opinion to promote functional recovery after PNI.N6-methyladenosine (m6A) is one of the most plentiful internal RNA modifications, especially in eukaryotic messenger RNA (mRNA), which plays pivotal roles in the regulation of mRNA life cycle and nerve development. However, the mRNA m6A methylation pattern in peripheral nervous injury (PNI) has not been investigated. In this study, sciatic nerve samples were collected from 7 days after sciatic nerve injury (SNI) and control rats. Quantitative real-time PCR demonstrated that m6A-related methyltransferase/demethylase genes were remarkably upregulated in SNI group compared with control group. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) was performed to reveal the m6A methylation landscape. The results showed that 4,014 m6A peaks were significantly altered, including 2,144 upregulated and 1,870 downregulated m6A peaks, which were corresponded to 1,858 genes. Moreover, 919 differentially expressed genes were identified by the conjoint analysis of MeRIP-seq and RNA-seq. GO and KEGG pathway analyses were performed to determine the biological functions and signaling pathways of the m6A-modified genes. Notably, these genes were mainly related to the immune system process, cell activation, and nervous system development in GO analysis. KEGG pathway analysis revealed that these genes were involved in the cell cycle, B cell receptor signaling pathway, axon guidance pathway, and calcium signaling pathway. Furthermore, the m6A methylation and protein expression levels of autophagy-related gene ( Peripheral nerve repair following injury is a complicated process. The distal stump of injured nerve undergoes Wallerian degeneration due to the loss of nutrition and energy support . NeuronaThe roles of epigenetic modifications in the peripheral nervous system are being actively studied, especially histone acetylation , and DNAvia methylated RNA immunoprecipitation sequencing (MeRIP-seq) and identified differentially methylated peaks. After that, the data were combined with RNA sequencing (RNA-seq) to further analyze the relationship between mRNA expression and m6A methylation. Finally, we found that the mRNA m6A methylation and protein expression levels of Atg7 were increased after SNI. ATG7 acts as the hub of microtubule-associated protein light chain 3 (LC3)-phosphatidylethanolamine and ATG12, thereby facilitating the expansion of autophagosomal membranes  and control groups. Then, we compared the m6A-tagged transcription profiles before and after SNI embranes . This prFemale Wistar rats  were randomly divided into control and SNI groups at different time points. The animal model of SNI was constructed as described previously . We collThe mRNA expression levels of m6A writers and erasers were detected by quantitative real-time PCR (qRT-PCR). Total RNA was isolated from the sciatic nerve tissues in control (uninjured) and SNI (7 days after injury) groups using a TRIzol Reagent . The purity and quantity of RNA samples were assessed by NanoDrop ND-1000 instrument. cDNA was prepared by reverse transcription using a FastKing RT kit with gDNase . QRT-PCR was performed using the Ultra SYBR Mixture  according to the manufacturer’s specifications. GAPDH was used as an internal control. Each experiment was repeated at least three times, and all data were presented as mean ± standard deviation (SD). The primer sequences are shown in TM m6A RNA IP kit . The input samples without immunoprecipitation were used as a control group. The following reagents were used for elution and purification in sequence: 1 × IP buffer, LB Buffer, HS Buffer, RLT Buffer, 100% and 75% ethanol.The sequencing experiments were conducted at Cloud-seq Biotech Inc. . First, we collected and quantified total RNA from the sciatic nerve tissues of control (uninjured) and SNI (7 days after injury) groups. Then, Arraystar Seq-StarTMpoly(A) mRNA Isolation Kit  was used to extract poly(A) mRNA from total RNA. The tubes with 1 μg/μL RNA and fragmentation buffer  were incubated at 70°C for 6 min in a preheated thermal cycler block. Subsequently, 30 μL of 3 M sodium acetate (NaOAc), 1 μL of glycoBlue, and 750 μL volumes of 100% ethanol were added, mixed and kept at −80°C overnight. After centrifugation and pellet resuspension, RNA fragmentation was conducted by running 0.5 μg of RNA through 1.5% (wt/v) agarose gel. The fragmented RNA was immunoprecipitated with magnetic beads containing m6A antibody (1 μL m6A antibody in 50 μL 1 × IP buffer) using the GenSeq® Ultra II Directional RNA Library Prep Kit . For RNA-seq, total RNA (1 μg) was used to remove the rRNAs via Ribo-Zero rRNA Removal Kit . RNA-seq libraries were constructed using the TruSeq Stranded Total RNA Library Prep Kit . Then, both libraries were controlled and evaluated with the Bio Analyzer 2100 system . The 10 pM libraries were denatured as single-stranded DNA molecules and amplified in situ as clusters. MeRIP-seq and RNA-seq procedures were carried out on an Illumina NovaSeq 6000 instrument with 150-bp paired-end reads. The quality control metrics of the sequencing data are listed in MeRIP-seq libraries were constructed using the NEBNextvia Illumina NovaSeq 6000 sequencer and were quality checked with Q30. Cutadapt software (v1.9.3) was used to remove 3′ adaptor-trimming and low-quality reads (via Hisat2 software (v2.0.4)  . Peaks w(v2.0.4) . Differe(v2.0.4) . The pea(v2.0.4) . GO and via Hisat2. Cuffdiff software (part of Cufflinks) was used to calculate the gene level FPKM (fragments per kilobase of exon per million fragments mapped reads) as the expression profiles of mRNA. Differentially expressed genes were identified based on the fold-change and p-value calculated from FPKM values  . Then, tM values . GO and Atg7 gene was verified by qPCR analysis. The primer sequences are listed in Gene-specific m6A qPCR was carried out at Cloud-seq Biotech Inc. First, we obtained and quantified the total RNA from both control (uninjured) and SNI (7 days after injury) groups. Then, metal-ion was used to fragment the extracted RNA to approximately 100 nt in length and reserved one-tenth of fragmented RNA as INPUT control. Next, the fragmented RNA were subjected to immunoprecipitation with m6A antibody-coated magnetic beads. Methylated RNA bound to the magnetic beads was eluted and purified. Finally, the m6A modification of The sciatic nerve samples obtained from control (uninjured) and SNI  groups were ground using an automatic grinder, and then lysed with RIPA lysis buffer. BCA protein assay kit  was used to measure the concentration of total protein. An equal amount of protein sample was separated by 10% SDS-PAGE, and then transferred onto polyvinylidene difluoride membranes. After blocking with 5% non-fat milk for 1 h, the membranes were incubated with primary antibody overnight at 4°C. Subsequently, the membranes were incubated with the corresponding secondary antibody for 1 h. Enhanced chemiluminescent substrate (Bio-Rad) was used to visualize the protein bands. The antibodies used for Western blotting were as follows: ATG7 , LC3 , and GAPDH .The fresh samples of control and SNI (2 days after injury) groups were fixed with 2.5% glutaraldehyde overnight at 4°C and then with OsO4 at 4°C for 1 h. Different concentrations of ethanol solution  were used for gradient dehydration. The samples were permeated with acetone and Epon embedding agent, sectioned using an ultramicrotome, and stained with lead citrate solution and uranyl acetate saturated in 50% ethanol. Autophagosomes were observed under a JEM-1010 electron microscope  at × 3,000 magnification.t test, ANOVA, and post hoc analysis. Fisher’s exact test was used for all bioinformatic analyses.All statistical analyses were performed using the GraphPad Prism software. The results were presented as mean ± SD. ImageJ software was used to calculate the gray values of Western blot images. Significant differences between two groups were assessed by Student’s To investigate the effects of epigenetic modulators on rat sciatic nerve (7 days after SNI), we determined the expression profiles of m6A writers  and erasers . As shown in GSE171866). In total, SNI samples had 2,144 and 1,870 significantly upregulated and downregulated m6A peaks, which corresponded to the transcripts of 1,083 and 775 genes, respectively , and there were significantly enriched peaks at the beginning of 3′ untranslated region (3′UTRs) and near the stop codon . Motif edetected . These rTo clarify the biological functions and signaling pathways of m6A modification during SNI, both GO enrichment and KEGG pathway analyses were carried out. In GO analysis, the biological functions of differentially methylated genes can be classified into three parts: biological processes (BP), cellular component (CC), and molecular function (MF). The top 10 most remarkably enriched BPs, CCs, and MFs of the genes with up/downregulated m6A peaks are listed in GSE171866). The differentially expressed transcripts consisted of 2,055 upregulated transcripts and 785 downregulated transcripts  were associated with functional recovery after PNI. In Atf3 mutant mice, the axon regeneration ability of DRG neurons is weakened  can be found below: NCBI Gene Expression Omnibus, accession no: The animal study was reviewed and approved by Ethics Committee of Radiation Medicine Chinese Academy of Medical Sciences.LZ and DH participated in the design of the research and the writing of the manuscript. PM, BM, and JQ conducted the experiments. GT and ZL analyzed the data. XZ revised the manuscript and were responsible for supervision of study. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Arabidopsis, functioning in physical association with DNA-dependent Pol IV to synthesize the second strands of double-stranded siRNA precursors. Base-pairing between the DNA template strand transcribed by Pol IV and the nontemplate DNA strand is needed to induce Pol IV arrest and Pol IV/RDR2 transcriptional coupling, but how this occurs is unknown. We report the structure of RDR2 and experimental evidence for how RDR2 engages its RNA templates and initiates transcription. RDR2 engages the ends of RNAs displaced from RNA/DNA hybrids, suggesting a model in which Pol IV arrest and backtracking, accompanied by DNA strand reannealing, extrudes the 3′ end of the Pol IV transcript, allowing RNA engagement and second-strand synthesis by RDR2.RDR2 is critical for siRNA-directed DNA methylation in  Arabidopsis, RNA-DEPENDENT RNA POLYMERASE 2 (RDR2) physically interacts with DNA-dependent NUCLEAR RNA POLYMERASE IV (Pol IV) and their activities are tightly coupled, with Pol IV transcriptional arrest, induced by the nontemplate DNA strand, somehow enabling RDR2 to engage Pol IV transcripts and generate double-stranded RNAs. The double-stranded RNAs are then released from the Pol IV–RDR2 complex and diced into short-interfering RNAs that guide RNA-directed DNA methylation and silencing. Here we report the structure of full-length RDR2, at an overall resolution of 3.1 Å, determined by cryoelectron microscopy. The N-terminal region contains an RNA-recognition motif adjacent to a positively charged channel that leads to a catalytic center with striking structural homology to the catalytic centers of multisubunit DNA-dependent RNA polymerases. We show that RDR2 initiates 1 to 2 nt internal to the 3′ ends of its templates and can transcribe the RNA of an RNA/DNA hybrid, provided that 9 or more nucleotides are unpaired at the RNA’s 3′ end. Using a nucleic acid configuration that mimics the arrangement of RNA and DNA strands upon Pol IV transcriptional arrest, we show that displacement of the RNA 3′ end occurs as the DNA template and nontemplate strands reanneal, enabling RDR2 transcription. These results suggest a model in which Pol IV arrest and backtracking displaces the RNA 3′ end as the DNA strands reanneal, allowing RDR2 to engage the RNA and synthesize the complementary strand.RNA-dependent RNA polymerases play essential roles in RNA-mediated gene silencing in eukaryotes. In  RNA-dependent RNA polymerases (RDRs) are encoded within the genomes of many eukaryotes and function in gene silencing by converting single-stranded RNAs (ssRNAs) into double-stranded (ds) precursors of short-interfering RNAs (siRNAs) , 2. The 3Neurospora, and nematodes , which associates with DNA-dependent NUCLEAR RNA POLYMERASE IV (Pol IV) , 7 via d1115ematodes –23.Important mechanistic insights into the Pol IV–RDR2 partnership have come from recapitulation of siRNA biogenesis in vitro . AlthougNeurospora crassa enzyme, missing amino acids 1 to 376, and the other is a partial structure for Thermothielavioides terrestris QDE-1 missing amino acids 1 to 363 . Striking structural similarities are apparent between the active centers of RDR2, QDE-1 enzymes, and multisubunit DNA-dependent RNA polymerases. The N-terminal region of RDR2 includes an RNA recognition motif (RRM) proposed to help define the RNA template path. Using oligonucleotides that recapitulate the configuration of the DNA template, DNA nontemplate, and RNA strands upon Pol IV arrest, we show that template–nontemplate DNA strand reannealing displaces the RNA 3′ end from the template DNA, making it available for RDR2 engagement. Because RNA strand displacement and extrusion also coincides with template–nontemplate DNA strand reannealing when multisubunit RNA polymerases undergo backtracking, we propose that Pol IV arrest and backtracking are the likely mechanisms by which Pol IV transcripts are channeled to RDR2.Structural information for eukaryotic cellular RDRs is currently limited to crystal structures for two fungal QDE-1 (Quelling Defective-1) enzymes. One is a partial structure for the 1 to 363 , 25. We SI Appendix, Figs. S1–S4). Processing of the same datasets using cryoSPARC v3.2.0 . The higher-resolution RELION map was then used for model building, employing Buccaneer . The three highly conserved aspartates (Asp triad) required for phosphodiester bond catalysis are located within a loop of DPBB2.The N-terminal region (amino acids 1 to 360) of RDR2 begins with an N-terminal domain (amino acids 1 to 100) that includes an RRM followed by a β-barrel–like domain and a four-helical bundle domain C and D. 32lymerase , which eFollowing DPBB2, a loop passes across a potential secondary channel and connects to the bridge helix domain, followed by the trigger loop. Neck helix 3, part of which was not modeled due to flexibility in the loop, connects to the C-terminal head domain, composed of two long α-helices and a loop  that protrudes into the cleft.Saccharomyces cerevisiae Pol II is shown in Thermus thermophilus) and yeast Pol II (RMSD = 0.431 Å). Key domains of the catalytic core, including the two DPBBs, bridge helix, trigger loop, and Asp-triad align nearly perfectly  (A superimposition of the RDR2 active site loop (amino acids 828 to 836) of DPBB2 onto the corresponding structure of erfectly B. The Aserfectly . Extra d , providing conformational flexibility during translocation of the RNA and DNA chains  is located at the junction between one of these helices and the intervening loop. The same structural arrangement is observed for the bridge helix of Neurospora QDE-1 , results in a null mutant phenotype . In multisubunit RNAPs, an invariant histidine residue (His-β′1242 for T. thermophilus RNAP and His-1085 for yeast Rpb1) is critical for interaction with the β-phosphate of incoming NTPs. A lysine is present at the corresponding position in RDR2 (Lys-923), Neurospora QDE-1 (Lys-1119), and the crAss-like phage RNA polymerase, phi14:2 (Lys-1615) (Beyond the bridge helix (amino acids 885 to 896) in the C-terminal direction is an α-helix (amino acids 906 to 922) followed by a loop (amino acids 923 to 939) that likely constitutes the trigger loop. In multisubunit RNAPs, the trigger loop is a flexible module that helps position substrate nucleotide triphosphates (NTPs) in the catalytic center during each nucleotide addition cycle , 42. Theys-1615) , suggestRDR2 amino acids 7 to 95 are predicted to form a β-α-β-β-α-β–type RRM domain based on Genome3D analysis . AlignmeEscherichia coli RDR2-RRM fused, in-frame, to trigger-factor (TF). A fusion with a mutant form of RDR2-RRMmut, having five aromatic or positively charged amino acids of the RNP1 or RNP2 motifs changed to alanine, was also generated. Electrophoretic mobility shift assays (EMSA) demonstrated that TF-RDR2-RRM exhibits ssRNA binding that is substantially impaired in TF-RDR2-RRMmut, consistent with RNA binding activity  displays RNA-binding activity, we expressed in activity B.SI Appendix, Fig. S12).A structure for RDR2 engaged with RNA has not yet been achieved, but structural and functional data suggest a potential path for the template RNA within RDR2. Examination of the electrostatic surface potential shows a positively charged surface, starting with the RDR2-RRM and continuing along the cleft and into the active site D. If an 32P-labeled RNA transcripts were detected using the 5-nt template , suggesting that transcription may initiate internal to the 3′ ends of template RNAs. To test this hypothesis, we designed three 15-nt RNA templates that differ in the 3 nt present at their 3′ ends: either 3′-CCU, 3′-CUU, or 3′-UUU  , mutations at numerous amino acid positions conserved in RDR2 disrupt posttranscriptional gene silencing, including: P611L within the DPBB1 domain; G825E, D826N, and S860F within the DPBB2 domain; G866E within the ASP-triad loop; G921D within the bridge helix; and E429K and E453K within the slab domain .In RDR6, a paralog of RDR2 . Collectively, these mutations provide genetic evidence for the importance of the head domain, yet the function of the domain remains unclear. In the RDR2 structure, the tip of the head domain faces toward the cleft (Movie S1). We speculate that the tip of the head may interact with incoming template RNA, helping guide the RNA to the active site.The RDR2 head domain is composed of five α-helices. A nonsense mutation, W1083* that truncates the last 51 residues of the C-terminal head domain, was recovered as a silencing defective mutant . In Zea function , 52, andfunction  . The fork domain composed of loop 1 to 3 (amino acids 466 to 546) of Rpb2 contacts the RNA/DNA hybrid, the template strand, and downstream DNA  corresponds well with fork loop 3 of yeast Rpb2 (amino acids 521 to 541), whereas RDR2 amino acids 511 to 521 form an α-helix, which may correspond to the Rpb2 link domain helix (amino acids 757 to 776)  by base-stacking interactions with the template base at position −1  and by water-mediated interactions between a phosphate group of the iNTP and template bases at the −1 and −2 positions  (T. thermophilus Lys-846) and Gln-582  are present in the equivalent iNTP α-phosphate interacting positions  and are invariably conserved among the RDRs examined. However, potential iNTP γ-phosphate interacting amino acids are less conserved in RDR2 and other RDRs . In the position equivalent to T. thermophilus His-999, RDR2 has Arg-622, whereas other RDRs have Lys, Asn, or other amino acids . Polar amino acids are conserved at RDR positions corresponding to RDR2 Ser-525 and may correspond to T. thermophilus Gln-567. These structural comparisons suggest that initiation site selection by RDR2 may have some mechanistic similarities to initiation site selection in multisubunit RNAPs, a possibility worthy of further structural investigation.For multisubunit RNAPs, the initiating NTP (iNTP) is stabilized by interactions with conserved positively charged or polar amino acids  . We quesImportantly, the fact that RDR2 initiates internal to the 3′ ends of its RNA templates has biological significance, because it causes the resulting dsRNA to have a 1- or 2-nt 3′ overhang. In other studies , we haveThe transcription reactions of Pol IV and RDR2 are tightly coupled, with Pol IV arrest and RDR2 initiation of second-strand RNA synthesis being linked processes dependent on base-pairing interactions between the DNA template and DNA nontemplate strands . RDR2 caImportantly, multisubunit RNAPs in eukaryotes and bacteria have been shown to catalyze this very reaction, a phenomenon known as backtracking , 60. UpoSI Appendix, Detailed Methods. Generation of baculovirus expressing RDR2 using the resulting pSEP10-RDR2 vector was performed as described previously  to a final concentration of 4.8 mg/mL, as measured using a Bradford assay.Expression of recombinant RDR2 was optimized using the TEQC method  in whichSI Appendix, Detailed Methods.Grid preparation, grid screening, and data collection were performed in the cryo-EM facility at the HHMI Janelia Research Campus. Datasets were collected with no stage tilt or with 30° of stage tilt using a Titan Krios microscope (Thermo Fisher) operated at 300 kV and equipped with a K3 camera (Gatan). Detailed methods for grid preparation and data collection are described in SI Appendix, Detailed Methods and Figs. S1 and S2. Briefly, after beam-induced motion correction and CTF estimation, an initial 3D model at ∼4.3-Å resolution was generated from 2D-reference–picked particles, which was then used as a 3D-reference to select a subset of particles. Two-dimensional classifications, 3D classifications, iterative CTF refinement, Bayesian polishing, and metadata filtering of these selected particles led to the final map, “Map1” of ∼3.10 Å. Independent processing of the same dataset by cryoSPARC v3.2.0, which resulted in a nearly identical 3D reconstruction at ∼3.57 Å, “Map2”, is described in SI Appendix, Detailed Methods and Fig. S3. All figures for cryo-EM density maps, models and model surfaces were generated using Chimera v1.15  t gateway  and Emap gateway  was used gateway . Detaile32P]-labeled ATP, GTP or CTP, and RDR2. After incubating at room temperature to 27 °C for 1 to 2 h, the reactions were either treated by proteinase K or passed through PERFORMA spin columns (Edge Bio), precipitated, and resolved by 15% or 17% denaturing PAGE. The gels were then subjected to either phosphorimaging or autoradiography using X-ray films. In SI Appendix, Detailed Methods.RDR2 transcription assays were conducted by mixing templates (RNA or RNA/DNA hybrids), NTPs supplemented with [E. coli, synthesized by GenScript, and cloned into the pCold-TF vector (Takara) BamHI and HindIII site. The resulting construct was transformed into ArcticExpress competent cells (Agilent). Protein expression was induced by cold shock. The fusion protein was affinity purified using Ni-TNA agarose (Qiagen). Details are provided in SI Appendix, Detailed Methods.To produce recombinant the TF-RDR2-RRM fusion protein, the DNA sequence encoding RDR amino acids 1 to 100 . After 30 min at room temperature, reactions were subjected to 6% native PAGE and fluorescence imaging followed by staining with Coomassie brilliant blue.Recombinant TF-RDR2-RRM proteins  were incubated with a 37-nt ssRNA , 5′ end-labeled with Alexa647, in 25 mM Hepes-KOH pH7.6, 50 mM NaCl, 2 mM MgClSupplementary FileSupplementary File


RNAlater ice and pulverization of the samples with a simple homogenizer, followed by a phenol-chloroform-based RNA extraction. Spectrophotometric analysis indicated high purity of the isolated RNA. Electrophoresis on agarose gel revealed two well-defined ribosomal RNA bands. Herein, we present a method that consistently yields RNA of high purity and integrity from rat bone.Research on bone diseases often requires investigation of bone gene expression. Isolating high-quality RNA is essential to obtain reliable and accurate gene expression data. In an effort to analyze the expression of genes related to osteoporosis in rat bones, we developed an improved method for extraction of high-quality RNA without the need for liquid nitrogen or specialized equipment. This method involved transitioning frozen bone tissues to a more pliable state with  Research on bone diseases, including osteoporosis, metabolic bone disorders, and inherited syndromes, often requires investigation of bone gene expression. The similarities between human and rodent skeleton have made the laboratory rat a valuable model in bone disease research. Obtaining high-quality RNA from bone remains challenging due to the hypocellular, mineralized nature of the tissue.Tissue homogenization is the first step in RNA isolation. The conventional approach involves grinding frozen bone samples wrapped in foil using a mortar and pestle, pre-chilled with liquid nitrogen -4. AlthoSeveral approaches have been proposed as alternatives to the conventional pestle and mortar homogenization of bone tissues. Carter et al. developed a one-way method for RNA isolation using the Bullet Blender Centrifuge Technology  that produced high-integrity RNA yields . In the In our laboratory, we conduct research on the effect of various biologic agents on bone mineral density in rats with experimental autoimmune arthritis. We flash-froze tibias and femurs from rats treated with those compounds and stored them at -80°C to analyze the expression of osteoporosis-related genes. Our initial efforts to isolate RNA from those tissues resulted in low yields and, occasionally, highly degraded RNA. Therefore, we developed an improved method that consistently yields high-quality RNA from bone while eliminating the need for liquid nitrogen and specialized equipment.AnimalsAnimal work was approved by the Directorate of Veterinary Services of the Region of Central Macedonia according to national legislation , in conformance with the European Directive (2010/63/EU) (protocol number 10/6-19:1). Male Wistar rats (7- to 8-week-old) provided by the Animal Facility of the Department of Pharmacology of the National and Kapodistrian University of Athens and housed in the animal house of the Clinical Pharmacology Laboratory at Aristotle University of Thessaloniki were anesthetized with 5% isoflurane and euthanized by decapitation. Femur and tibia bones were rapidly harvested. The attached muscle tissue was removed with a scalpel and any remaining tissue was rubbed away with a phosphate buffer saline-soaked gauze. Bones were flash-frozen and stored at -80°C prior to RNA isolation.RNA isolationTM, Carlsbad, CA, USA) and then stored at -20°C for at least 16 hours before RNA isolation. Once the initial overnight soak at -20°C was complete, bone samples were removed from RNAlater ice, sectioned into small pieces, weighted, and the desired amount (100 mg) was placed into homogenization tubes containing 1.5 mL of pre-chilled TRIzol Reagent (InvitrogenTM). The remaining bone tissue was returned to the RNAlater ice Eppendorf tubes and stored at -20°C.Bone samples were transferred from -80°C storage to 2 mL Eppendorf tubes containing 1.5 mL of pre-chilled RNAlater ice -treated water. Finally, RNA was incubated in a heat block set at 55°C for 10 minutes and stored at -80°C.The lysate of each sample was centrifuged for 5 minutes at 12,000 x RNA assessmentTM, Winooski, VT, USA) at 260 and 280 nm. To evaluate whether there was any protein or chemical contamination of the samples, 260/280 ratios were obtained. Spectrophotometric analysis indicated high purity of the isolated RNA, with 260/280 ratios ranging from 1.8 to 2.1. Example spectrophotometry results from samples obtained with our method are presented in Table RNA concentration was measured in the Epoch Microplate Spectrophotometer (BiotekRNA integrity was determined by agarose gel electrophoresis based on the absence of “smearing" on 28S and 18S ribosomal RNA bands. Sample volumes corresponding to 1.25 μg RNA were analyzed on 1.2% agarose gel stained with ethidium bromide. Two clear ribosomal RNA bands were visualized. The absence of “smearing” indicates high RNA quality (Figure In this paper, we present an improved method for RNA isolation from rat bone tissue without the need for liquid nitrogen or specialized equipment. This method was developed in an effort to analyze the expression of genes related to osteoporosis in rat bones. Extracting high-quality RNA is essential to obtain reliable and accurate gene expression data. However, there is a limited number of studies on RNA extraction from bone tissue and they all involve the use of specialized equipment -7.Our initial attempts involved grinding bone samples with a pestle and mortar, pre-chilled with liquid nitrogen. Apart from the health and safety hazards associated with liquid nitrogen use, the technique resulted in particularly low RNA yields. We found that bone samples frozen with liquid nitrogen became brittle and typically shattered into small pieces during the grinding process. Fragmented pieces of the samples frequently flied out of the mortar. Also, a significant quantity of the sample was practically lost in the surface of the mortar and could not be recovered. Another possible explanation for the poor RNA yields obtained with this approach is that even though we used a pre-chilled spatula for the collection of the powdered tissue, samples potentially thawed during transfer from the mortar to the Eppendorf tubes containing TRIzol.We then attempted to homogenize bone samples in TRIzol using a power homogenizer. We had successfully used this method in the past for RNA isolation from soft tissues, such as rat liver and brain. Briefly, we transferred bone samples immediately from -80°C storage to homogenization tubes containing pre-chilled TRIzol. During the homogenization process we submerged the tubes in an ice bath to maintain sample at near freezing temperature. Analysis of those samples by agarose gel electrophoresis indicated significant RNA degradation. We assume that this method failed to produce high-quality RNA from bone tissue, owning to the fact that homogenization of hard tissues requires more time that possibly generates heat that leads to RNA degradation.In order to avoid RNA degradation caused by heat generation during the homogenization process, we incorporated RNAlater ice in our protocol. RNAlater ice is a reagent for transitioning samples from a frozen to a more pliable state that allows sample thawing with no loss of RNA integrity. RNAlater ice has been tested with soft animal tissues, such as brain, liver, kidney, and spleen, and with frozen cell pellets. RNAlater ice has not been tested on mineralized tissues, on the rationale that it might not penetrate them adequately. Our experiments confirm that RNAlater ice is, also, effective at protecting hard tissues, such as bone, from RNA degradation during thawing. RNAlater ice eliminates the need to work with liquid nitrogen and even allows to further dissect and weight the tissues before homogenization. We believe that this method achieved RNA yields of high purity and integrity due to the inclusion of RNAlater ice in our protocol.Many science laboratories, nowadays, struggle to operate with limited resources. Acquiring new equipment is not always feasible for many researchers. Herein, we propose a method for RNA isolation from bone tissue without any specialized equipment. This approach consistently yields RNA of high purity and integrity from rat bone. This method could facilitate gene expression studies related not only to osteoporosis, but also bone tumors, osteoarthritis, rheumatic diseases, and bone metabolic disorders.


Drosophila, piRNA-mediated cleavage of transposon RNA triggers the production of responder piRNAs via ping-pong amplification. Responder piRNA 3′ end formation by the nuclease Zucchini is coupled to the production of downstream trailer piRNAs, expanding the repertoire of transposon piRNA sequences. In Aedes aegypti mosquitoes, piRNAs are generated from viral RNA, yet, it is unknown how viral piRNA 3′ ends are formed and whether viral RNA cleavage gives rise to trailer piRNA production. Here we report that in Ae. aegypti, virus- and transposon-derived piRNAs have sharp 3′ ends, and are biased for downstream uridine residues, features reminiscent of Zucchini cleavage of precursor piRNAs in Drosophila. We designed a reporter system to study viral piRNA 3′ end formation and found that targeting viral RNA by abundant endogenous piRNAs triggers the production of responder and trailer piRNAs. Using this reporter, we identified the Ae. aegypti orthologs of Zucchini and Nibbler, two nucleases involved in piRNA 3′ end formation. Our results furthermore suggest that autonomous piRNA production from viral RNA can be triggered and expanded by an initial cleavage event guided by genome-encoded piRNAs.In the germline of animals, PIWI interacting (pi)RNAs protect the genome against the detrimental effects of transposon mobilization. In  Aedes genus are responsible for the transmission of arthropod-borne (arbo)viruses that cause severe diseases, such as dengue, Zika, chikungunya and yellow fever. For efficient transmission to occur, arboviruses have to actively replicate in several mosquito tissues to eventually infect the salivary gland RNA pathway, suggesting that two independent small RNA pathways act in parallel to combat viral infections  the pathway is active in somatic tissues as well as germline tissues  mosquito piRNA clusters contain large numbers of endogenous viral elements (EVEs), sequences of non-retroviral RNA viruses inserted in host genomes osophila ,22,23, oosophila ,25,  at 25°C. For knockdown experiments, dsRNA was transfected using X-tremeGENE HP DNA Transfection Reagent (Roche) according to the manufacturer's instructions. Where indicated, cells were infected with Sindbis virus (SINV) at a multiplicity of infection (MOI) of 0.1. For further details, see Supplemental Information.gypsy- and EVE-initiator piRNAs and trailer cassettes were introduced into an infectious cDNA clone of Sindbis virus downstream of a duplicated subgenomic promoter. Site-directed mutagenesis was used to introduce target site mutations. Subsequently, viruses were grown as described previously . For RT-qPCR analyses, RNA was DNaseI treated, reverse transcribed, and PCR amplified in the presence of SYBR green. For small RNA northern blotting, RNA was resolved by denaturing urea polyacrylamide gel electrophoresis and transferred to nylon membranes. See Supplemental Information for experimental details and oligonucleotide sequences.Ae. aegypti transcripts, the Phasi Charoen like virus genome and pre-miRNA sequences using Bowtie  and sequenced on an Illumina Hiseq4000. Sequence data have been deposited in the NCBI sequence read archive under SRA accession SRP272125. Sequencing data were analyzed in Galaxy . Reads wg Bowtie . FurtherAag2 cells were transfected with a plasmid expressing 3 × flag tagged Zuc using X-tremeGENE HP DNA Transfection Reagent (Roche), and fixed 48 hours after transfection. Cells were incubated with a mouse anti-flag antibody , followed by goat anti-mouse IgG Alexa fluor 568 . Mitochondria were stained using Mitoview Green (Biotium). For further information, see Supplemental Information.A 3 × flag tagged Zuc expression plasmid was transfected into Aag2 cells using X-tremeGENE HP DNA Transfection Reagent (Roche). Cells were lysed and lysates incubated with M2-Flag beads (Sigma) to immunoprecipitate 3 × flag tagged Zuc and interacting proteins. For western blot analyses, samples were resolved on polyacrylamide gels, blotted to nitrocellulose membranes and stained with the following antibodies generated in our laboratory ,38: rabbP < 0.05; ** P < 0.005; *** P < 0.0005) using Prism 8 (GraphPad Software). For statistical analysis of sharpness scores in Figure Unless indicated otherwise, unpaired two tailed t-tests with Holm-Sidak correction for multiple comparisons were used for statistical analyses . Zuc uses a sequence motif to preferentially cleave upstream of uridine residues in vivo (Ae. aegypti Aag2 cells infected with Sindbis virus (SINV)  . We firss (SINV) . Specifis (SINV) , stronglTogaviridae family. During its replication cycle, genomic sense (+) strand RNA serves as a template for the production of antigenomic antisense (-) strand RNA, which in turn provides a template for production of genomic and subgenomic RNA species piRNAs derived from the SINV genome. SINV is a positive-strand RNA virus of the  species . Strikin species . In addi species . These f species ,25, are  species , a negatg2 cells . These fgypsy LTR retrotransposon gypsy73   Figure . These rgypsy-25U, 28U, and 30U according to the distance of responder piRNA 5′ end to the + 1U). Responder piRNAs were readily detected by high resolution northern blotting for all reporter viruses  resulted in a more diffuse pattern of responder piRNA lengths  were depleted , and around the putative slice site (nt 10–11) of the e Figure . Respond, and aroosophila  and mosqosophila . AltogetAe. aegypti PLDc_2-domain containing proteins with those of Zuc orthologs from fruit flies, silkworm and mouse  and found that AAEL011385 had the highest similarity to the various Zuc orthologs , as well as weak associations between Zuc and Ago3/Piwi have been observed  insertions at the same position, which corresponds to the location of a helix that sticks out of core structure of Drosophila Zuc (Culicidae (mosquitoes) but not in other vector species such as ticks, lice, and tsetse flies, indicating that it is a variable region beyond Aedine mosquitoes, the function of which remains to be understood.To our surprise, we found that AAEL011385/Zuc contains a sizeable insertion directly downstream of the catalytic HKD-motif . Moreovehila Zuc . MultiplDmZuc. Indeed, knockdown of AAEL011385/Zuc in Aag2 cells resulted in longer viral responder piRNAs with a broader size distribution . This analysis identified AAEL005527 as a one-to-one ortholog of DmNbr . These non-retroviral sequence elements are enriched in piRNA clusters and, accordingly, give rise to abundant piRNAs , responder piRNAs but no trailer piRNAs are detected, indicating that trailer piRNAs are specifically derived from the trailer cassette (Figure Since vpiRNAs were biased for downstream uridines Figure  and posig Figure . Interesg Figure , suggeste Figure . Moreovee Figure , indicate Figure .gypsy and EVE initiator piRNA targeted viruses (Figure To assess phased piRNA biogenesis beyond the first trailer piRNA, we sequenced small RNAs produced in Aag2 cells infected with interval and desert viruses, as well as their respective scrambled control viruses. Mapping piRNAs onto the trailer cassette reveals production of additional piRNAs in cells infected with the s Figure . In contAltogether, our results indicate that during acute infection with a cytoplasmic RNA virus, endogenous piRNAs can initiate piRNA production from viral genomic RNA via the ping-pong amplification loop . The endSmall RNA sequencing data has been deposited to the NCBI Sequence Read Archive under accession number SRP272125.gkab640_Supplemental_FileClick here for additional data file.