RNA editing, a wide-acknowledged post-transcriptional mechanism, has been reported to be involved in the occurrence and development of cancer, especially the abnormal alteration of adenosine to inosine. However, fewer studies focus on pancreaticcancer. Therefore, we aimed to explore the possible linkages between altered RNA editing events and the development of PDAC.We characterized the global A-to-I RNA editing spectrum from RNA and matched whole-genome sequencing data of 41 primary PDAC and adjacent normal tissues. The following analyses were performed: different editing level and RNA expression analysis,pathway analysis, motif analysis, RNA secondary structure analysis, alternative splicing events analysis, and survival analysis.The RNA editing of single-cell RNA public sequencing data was also characterized.A large number of adaptive RNA editing events with significant differences in editing levels were identified, which are mainly regulated by ADAR1. Moreover, RNA editing in tumors has a higher editing level and more abundant editing sites in general. 140genes were screened out since they were identified with significantly different RNA editing events and were significantly different in expression level between tumor and matched normal samples. Further analysis showed a preference that in the tumor-specific group, they are mainly enriched in cancer-related signal pathways, while in the normal tissue-specific group, they are mainly enriched in pancreatic secretion. At the same time, we also found positively selected differentially edited sites in a series of cancer immune genes, including EGF, IGF1R, and PIK3CD. RNA editing might participate in pathogenisis of PDAC through regulating the alternative splicing and RNA secondary structure of important genesto further regulate gene expression and protein synthesis, including RAB27B and CERS4. Furthermore, single cell sequencing results showed that type2 ductal cells contributed the most to RNA editing events in tumors.RNA editing is an epigenetic mechanism involved in the occurrence and development of pancreatic cancer, which has the potential to diagnose of PDAC and is closely related to the prognosis. RNA editing, one of the most common and abundant forms of posttranscriptional RNA modifications, has a vital impact on many biological processes , includiIn humans, the most prevalent RNA editing event is the conversion of adenosine (A) on the double-stranded RNAs to inosine (I) through oxidative deamination reaction under the catalysis of adenosine deaminase ADARs . There aVarious studies show that RNA editing participates in the pathogenesis of tumors and has the potential as a clinical indicator. Chigaev’s study has systematically characterized the RNA editing genomic landscape of various cancers conducted through The Cancer Genome Atlas (TCGA) project or Genotype-Tissue Expression (GTEx) Project , which pPancreatic cancer is one of the malignant tumors with the highest fatality rate, gradually increased incidence, difficult early diagnosis, poor treatment effect, and strong tumor heterogeneity. However, the pathogenesis of pancreatic cancer is still unclear. Here, we aim to explore the pathogenic mechanism of pancreatic ductal adenocarcinoma (PDAC) from the perspective of RNA editing. We obtained a global profile on RNA editing events of PDAC based on the paired RNA and WGS data from 41 patients and found specific RNA editing events, genes, and signal pathways related to PDAC. A series of differential RNA editing events (DREs) was identified to be located at the important function genes, including pancreatic secretion, tumor immune-related genes, most of which also showed significant prognosis associations in PDAC. Thus, RNA editing might take a role in pathogenesis and provide implications for clinical outcome in PDAC.The patients diagnosed with PDAC were recruited from Changhai Hospital, Shanghai. All patients have signed a written informed consent form for collection and use of samples. A total of 41 pairs of tumors and their distal normal tissues were obtained from surgical specimens and frozen at -80°C immediately before nucleic acid extraction. All procedures have complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and the guidelines of the institutional review committee of the Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences.Total RNA was isolated using the Qiagen RNeasy Kit  and treated with DNase I (New England Biolabs). Total DNA was extracted using the QIAamp DNA Mini Kit . The concentration was quantified by the NanoDrop One , and the RNA quality was analyzed by the 2100 Agilent Bioanalyzer . Libraries were generated using a standard library preparation kit (KAPA) according to the manufacturer’s instructions. Libraries were quantified on the Qubit4  with the High Sensitivity DNA Assay . Samples were equimolar pooled and sequenced on the Illumina NovaSeq  in 150-bp paired-end mode.https://github.com/s-andrews/FastQC), and subsequently the low-quality data were filtered by fastp (https://github.com/OpenGene/fastp). The hg19/GRCh37 of the human genome was used as reference. RNA-seq reads were aligned onto the reference genome using STAR  . WGS reads were aligned by BWA (https://github.com/lh3/bwa). All alignments were duplicated by Picard MarkDuplicates (http://broadinstitute.github.io/picard/), sorted, and indexed using SAMtools (https://github.com/samtools/samtools). Computationally, RNA editing is identified as a single-nucleotide base change between DNA and RNA. The RNA and DNA variants in each sample were both detected by REDItools (https://github.com/BioinfoUNIBA/REDItools2) under unstranded strategy. Then, the sites containing DNA variants were removed and the rest were considered as RNA editing sites. Meanwhile, we also used SPRINT (https://github.com/jumphone/SPRINT) to validate these RNA editing sites detected by REDItools.The quality of raw reads was assessed by FastQC  .http://ccb.jhu.edu/software/hisat2) and StringTie (http://ccb.jhu.edu/software/stringtie/). Differential expression analysis was performed underlying the Bioconductor package Deseq2 version 1.32.0 (http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), and the different expression genes were identified by filtering under log2|fold-change| ≥1 and padj <0.05.Read counts per gene were calculated for each sample using hisat2 (http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html). Statistical significance was defined as p.adjust < 0.05.We used Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) molecular pathway enrichment analysis for the genes with RNA editing sites between tumor and normal tissues through R package “clusterProfiler” (https://meme-suite.org/meme/) was identified using two motif widths of 30 and 50.The consensus motif in the MEME suite of motif-based sequence analysis tools , an online tool to analyze protein network characteristics and make protein network visualization, to evaluate the network of protein–protein interaction (PPI) and calculate K-means for clustering.We used the database of Search Tool for Retrieval of Interacting Genes (STRING) . Meanwhile, MXfold2 (https://github.com/mxfold/mxfold2) was also applied to predict the RNA secondary structure of RNA sequence surrounding the editing sites (± 250 nt).To predict the RNA secondary structure alteration derived from individual RNA editing events, we used the extended surrounding region (± 1000 nt around the editing site) to search for minimum free energy (MFE) and partition function using RNAfold (https://rnaseq-mats.sourceforge.net/index.html) to detect and analyze alternative splicing events, including skipped exon, alternative 5′ splice site (A5SS), alternative 3′ splice site (A3SS), mutually exclusive exons (MXE), and retained intron (RI).We used rMATS (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) and Seurat  were applied to cluster and identify the cell types based on known marker expression. The marker genes were PRSS1, CTRB1, CTRB2, and REG1B for acinar cells, MS4A1, CD79A, CD79B, and CD52 for B cells, AMBP, CFTR, and MMP7 for type1 ductal cells, KRT19, KRT7, TSPAN8, and SLPI for type 2 ductal cells, CHGB, CHGA, INS, and IAPP for endocrine cells, CDH5, PLVAP, VWF, and CLDN5 for endothelial cells, LUM, DCN, and COL1A1 for fibroblast cells, AIF1, CD64, CD14, and CD68 for macrophage cells, ACTA2, PDGFRB, and ADIRF for stellate cells, and CD3E, CD4, and CD8 T cells (Cell Ranger ( T cells . The reahttp://gepia2.cancer-pku.cn/#index) based on TCGA-PAAD datasets and ONCOMINE (https://www.oncomine.org) integrated pancreatic cancer data from TCGA and GEO.The survival analyses were calculated by GEPIA2  and R packages. A value of p < 0.05 was considered statistically significant. Data were expressed as mean ± SEM.t-test, p = 0.012) , which showed significant differences between tumor and normal samples    was slightly higher than that in normal tissues (average = 70812)  , 58.91% of all identified events were known and 79.35%, 85.19%, and 96.36% were known in the tumor-specific group, normal-specific group, and shared group, respectively   occurred in the tumor-specific and normal-specific groups, respectively     were identified, including 712 upregulated and 1,310 downregulated genes in tumor  . A set of 93 edDEGs were filtered out to showed similar expression levels with our results and showed a significant association with prognosis (p = 0.013) (p = 0.0122)   . LAMC2 c< 0.001) . ITGA3 h< 0.001) . MUC4-pr< 0.001) . In summIt was reported that RNA editing could affect RNA stability by altering the RNA secondary structure and alternative splicing . Thus, wA total of 3,647 significantly different alternative splicing events were identified between tumor and normal, of which 41.98% contained at least one RNA editing site Table S3These results suggest that RNA editing regulates gene expression and protein synthesis by affecting alternative splicing and the RNA secondary structure.STF for each DREs. A percentage of 5.48% of DREs belonging to 1,005 genes showed the signal of positive selection  of pancreatic cancer containing 24 tumors and 11 normal pancreases and identified 10 major clusters including type 1 ductal, type 2 ductal, acinar, endocrine, endothelial, fibroblast, stellate, macrophage, T, and B cells, through principal component analysis based on gene expression. We then extracted the RNA editing events for each cluster. In general, the significant difference in editing level occurred in type 2 ductal, macrophage, and T cells, whereas the significant difference in editing sites occurred in type 1 ductal, type 2 ductal, macrophage, stellate, and T cells . The positively selected RNA editing sites in cancer tissues are likely to be produced by self-regulation of constantly adapting to the microenvironment and changing the microenvironment during the survival and reproduction of cancer cells. In a word, the results further indicate that RNA editing may be driven by nature selection to play a role in pathogenesis of PDAC.RNA editing events have been reported to suffer under selection pressure. Zhang Rui’s team has found that RNA-edited SNP sites are highly enriched near autoimmune disease-related sites represented by Crohn’s disease, asthma, and allergic dermatitis and are subject to balanced selection . Duan’s election . We boldRNA editing may be involved in pathogenesis of PDAC by posttranscriptional regulation such as variable splicing and RNA secondary structure change. Sze Jing Tang et al. proposed a novel mechanism in which ADAR1-mediated editing can affect alternative splicing, which in turn affects protein binding . They foIn addition, our results suggest that RNA editing might act as a novel indicator for the diagnosis and prognosis of pancreatic cancer to some extent. We have observed that the RNA editing profile can more clearly distinguish tumors and normal samples, implying that RNA editing has potential for diagnosis of PDAC. The DEGs related to prognosis of PDAC Table S2These findings further expand our understanding of the role of RNA editing events in the occurrence and development of tumors. The specific occurrence of events in tumors may also have the potential to serve as important markers for PDAC diagnosis and prognosis. However, the understanding of the function of RNA editing is not comprehensive enough, and what kind of RNA editing events in which PDAC cells play a critical role in the occurrence and development of PDAC still needs a large amount of data for further research.https://www.ncbi.nlm.nih.gov/, GSE172356, https://db.cngb.org/, CNP0003871.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by Naval Medical University. The patients/participants provided their written informed consent to participate in this study.DY, GJ, and YM conceived the study. YM, DL, and TW processed the sequencing data. YM and BA analyzed the data. YM wrote the manuscript. SG and DY revised the manuscript. All authors contributed to the article and approved the submitted version.


For more than three decades, RNA has been known to be a relevant and attractive macromolecule to target but figuring out which RNA should be targeted and how remains challenging. Recent years have seen the confluence of approaches for screening, drug optimization, and target validation that have led to the approval of a few RNA-targeting therapeutics for clinical applications. This focused perspective aims to highlight — but not exhaustively review — key factors accounting for these successes while pointing at crucial aspects worth considering for further breakthroughs. They generally work by controlling or rectifying a biochemical process related to a particular ailment, thereby helping to restore normal functions. Several anti-cancer therapeutics also target DNA2. The reason why proteins and DNA have been the predominant target of medicine is historical: their three-dimensional structures3 and hence their role in disease were discovered earlier than that of RNA. But we now know that out of all the RNAs that get made in cells during transcription5, many actually have structure-related regulatory activities whose disruption leads to disease6. So, the same idea as for proteins applies to RNA: drugs that bind to RNA could help “correct” faults to support disease remission. RNA just had a later start at being recognized as a suitable drug target for small molecules (aside from being a prime target for antisense oligonucleotides7). But as the pool of functional — and hence druggable — RNAs continues to grow, RNA as a therapeutic target is receiving greater attention, as illustrated by an extensive survey published recently8.Most drugs in use today target protein enzymes and receptors11. That said, a ligand becomes a pharmaceutical only when binding to its target leads to a therapeutic effect. Most drugs actually work by competing with an endogenous metabolite12 or by targeting an allosteric site13. Fortunately, many naturally occurring compounds bind to RNA20, even though the chemical diversity of the four RNA building blocks is less rich than that of the 20 amino acids that make proteins. For example, metabolites bind to RNA riboswitches in bacteria to influence gene expression, representing potential systems for therapeutic intervention21. Furthermore, ribosomal RNA is the prime target of antibiotics like neomycin, streptomycin, and tetracyclin22. These examples of small molecules binding to RNA in nature indicate that RNA is as well suited as proteins for drug design or screening25.Like proteins, RNAs may be considered druggable when they possess clefts in which small molecules can bindany RNA can be readily targeted. As for proteins26, the process of validating suitable RNA targets can be expected to take many years. Most RNAs exist not as static folds but as ensembles of structurally and temporally dynamic molecules29. Every atom moves around an average position with possible larger local movements (like pucker changes or base-flipping events), possibly leading to interconversion between structures. To be effective, ligands ought to act by altering the conformational ensemble in a productive manner with favorable binding energetics30.Just because RNA is, in principle, a legitimate target for medicine does not mean that 31, and cofactors are required for the self-splicing of group I ribozymes33. Natural antibiotics also tend to act by disturbing conformational dynamics: paromomycin and related aminoglycosides interfere with the dynamics of the aminoacyl-tRNA decoding site in the ribosome36, and streptomycin stabilizes the ribosome in a conformation that supports constant GTPase activation37  transactivation response and frameshift stimulation elements led to the identification of many inhibitors41, some of which were shown to perturb the RNA dynamic ensemble42. Similarly, the severe acute respiratory syndrome (SARS) CoV-2 frameshifting element may constitute a suitable target, as this RNA has been proposed to alternate between several structures44.The task of developing drugs that interfere with RNA dynamics is much harder when the RNA has no known cognate ligand. It is then problematic to identify which RNA to target, at which location, and which shifts in the conformational ensembles would be associated with a therapeutic outcome. The genomes of RNA viruses were early recognized as attractive targetsa priori because this requires a deep understanding of the associated functional mechanisms, which takes time to acquire. For example, benzimidazole derivatives were reported to bind in vitro to a mimic of domain IIa from the hepatitis C virus (HCV) RNA49. Later, it was found that benzimidazoles act at the molecular level by disrupting a conformational switch, as seen in riboswitches50. In the end, many years later, it was also discovered that these compounds actually compete with a previously unidentified cognate ligand of the HCV RNA51.But determining which state should be targeted is difficult 52. It was also recently discovered that the translation readthrough-inducing drug ataluren — used to treat, for example, Duchenne muscular dystrophy — acts by binding to human ribosomal RNA in multiple sites, thereby competing with various factors54. Finally, the drug risdiplam (marketed as Evrysdi), recently approved by the US Food and Drug Administration (FDA), is a splicing modifier that binds to two locations within a duplex made of two complementary human RNAs without competing with any endogenous ligand. Specifically, risdiplam acts by stabilizing the intermediate complex formed by the pre-mRNA corresponding to the survival of the motor neuron 2 (SMN2) gene and the U1 snRNA from the splicing machinery  are not readily applicable to RNA drug discovery. Even when three-dimensional structures are available for the RNA target, the conformation captured, for example, in a crystal structure may not represent the best model to design drugs. In silico screening is attractive because of the level of atomic precision it allows, although this is generally still computationally expensive. Molecular dynamics approaches could overcome some of the issues of working with static targets by proposing transient states more relevant for ligand capture by the RNA58. Faster and cheaper coarse-grained predictions, though missing atomistic details and resolution, may also offer insights into conformational ensembles60. However, in silico dynamics may not recapitulate in vivo transitions, which happen according to precise kinetics and may require partners absent from simulations. Generally, current in silico approaches seem most useful on the back end, in combination with in vitro and in vivo assays, for rationally improving drug efficiency, stability, and bioavailability through structure-activity relationship studies64.As for protein binders, RNA ligands are often identified through In vitro screening approaches tend to look for a particular biochemical response, such as binding or an increase in expression using a particular reporter system65. Many of the currently studied small molecules that bind RNA have been identified by such means. For example, the affinity selection-mass spectrometry approach underlying the automated ligand detection system led to effective modulators of the flavin mononucleotide (FMN) riboswitch66 and, more recently, of the Xist long noncoding RNA67. Fragment-based approaches have become popular to identify small binders that may eventually be connected to create drugs because they allow an extensive search of the chemical space with limited compound library sizes71. However, hits from in vitro screening may not be clinically pursued if, for example, they elicit no effect or even present off-target effects in vivo73 or if they fail to elude bacterial resistance mechanisms (as seen with ribocil62 targeting the FMN riboswitch). Off-target effects, for example, may be spotted early upon counter-screening against such unwanted effects -protonation or tautomerization) may also occur87. Meeting such criteria is necessary for binding but not sufficient for selectivity and optimal potency in vivo56.Even when RNA targets have been validated, and effective RNA modulators have been identified, the design of drug leads and their optimizations for increased efficiency and potency brings new challenges. RNA binders benefit from having “RNA-like” physico-chemical characteristics, such as positively charged functional groups, and dipolar as well as hydrophobic/hydrophilic characters, hydrogen bond donors and acceptors, and base-like moieties92. The resulting chimeric molecule, now known as radezolid94 , is an illustration of what rational structure-based drug design approaches can achieve. Similarly, covalently linking thiamine and methylene diphosphonic acid increased the affinity to the thiamine phosphate riboswitch by 600-fold over thiamine alone97. Yet an equally rational approach aimed to conformationally restrain aminoglycosides in their bioactive conformation did not lead to the expected improvement in activity99. As it turns out, minimal overall conformational differences in the rigidified antibiotic led to the loss of key interactions with the target100, resulting in an even lower binding efficiency than the unconstrained molecule. These examples illustrate the power but also the limitations of our current rational approaches, which should encourage efforts in improving molecular dynamics strategies for drug discovery101. Cryo-electron microscopy represents a promising avenue for gaining structural along with dynamic information102, although when applied to RNA105, the currently achievable resolutions are not sufficient for structure-activity relationship studies in most cases (except for the ribosome89; What will likely improve the activity of small molecules is to consider their dynamics: ligands need to slide into cavities by taking advantage of correlated motions in the RNA . Failure106. Ligand atoms may take the place of water molecules around the RNA to different extents depending on the compound and its binding mode (108, it remains largely overlooked in the literature, probably because the contributions of water to stability around a ligand and its target are inherently challenging to fathom. Figuring out which water molecules can be displaced by which functional group to maximize the desired outcome is difficult to predict and measure109. Crystal structures of ligand-RNA complexes could help advance the drug optimization process by including solvent considerations, but few such crystals diffract to about 2.5 Å or higher resolution to visualize ordered water molecules. Overall, the free energy of binding results from a combination of enthalpy-driven and entropy-driven contributions, the ratio of which depends on the ligand chemical entities and the geometry and dynamics of the RNA target25. Here, biophysical approaches like isothermal calorimetry aimed at characterizing the properties not just of the RNA but also of the ligand and its interactions with the RNA target are key to pinpointing when the physico-chemical properties of both can synergize.Another aspect that may help direct further drug design strategies is to consider the contribution of the solvent. Both the RNA and its ligand are surrounded by ions and water molecules, which constantly exchange on a picosecond timescale. Binding necessitates the partial but complementary and coordinated loss of solvent molecules on both the RNA and the liganding mode . Althoug25. Every target system displays a particular set of specific molecular characteristics that need to be precisely grasped and measured. Patience, rigor, and humility are key as we progressively refine our understanding of how RNA structures and dynamics support RNA-mediated functions and how they are affected by ligand binding (110. Recent accomplishments in RNA targeting by small molecules can be traced back to structure-based drug design strategies against well-established targets. They were also made possible by phenotypic screening as it bypasses the false positives that typically arise when relying on binding assays only. In addition, such approaches owe their success to realistic expectations about what small molecules can reliably achieve upon binding RNA, such as interfering with a particular conformational changeover. It is likely that future strategies of a similar nature will lead to the discovery of more drugs that regulate RNA-based functions.Progress with therapeutic interventions aimed at targeting RNA has been slow, whether therapeutic agents are small molecules or antisense oligonucleotides binding . But theThe dynamics of specific and defined regions in RNA folds are critical for biological function.Identifying RNA modulators require functional and structural characterization of such regions.Combining biochemical and phenotypic screening approaches is optimal for identifying specific compounds that may become drugs.


RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5 Ribonucleic acids, once considered passive carriers of genetic information, are currently known to play a central role in virtually all cellular processes. They serve as templates for protein translation, regulate gene expression on many levels, and govern most aspects of cell metabolism, thus being crucial regulators of physiological and disease processes ,2,3. To Although RNA prediction and modeling develops rapidly and many new strategies have been introduced so far ,12,13,14Here, we present the analytical pipeline for reference-free high-throughput comparative analysis of RNA 3D structures. We show its first application in the RNA-Puzzles challenge that represented the community effort to predict the three-dimensional structures of functionally significant RNA elements in the SARS-CoV-2 genome, the 3parately . The SzaViral RNA genomes are the subject of extensive structural research. Currently, due to the COVID-19 pandemic, the most intensively studied one is that of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several research groups provided secondary structure models of entire SARS-CoV-2 genome or its fragments determined in various experimental states ,29,30,31The 5aviruses ,35—are saviruses ,37. SL3 cription . The downslation . SL5abc  virions . The reg virions .The 3 hairpin ,43,44, b hairpin . Furtherfunction .5R motifs ,25,47,48Here, we demonstrate the use of our computational pipeline for the SARS-CoV-2 challenge of RNA-Puzzles. The participants in this contest submitted 74 3D models of RNA, of which 34 concerned 5-UTR cf. .For 5m loops) ,25,27,493 nt see .The results obtained are described in more detail in the following.The analysis includes 34 structures predicted by four research groups .We have evaluated the stereochemical accuracy of the 3D structures submitted, and, based on the results obtained, we concluded that they are consistent with those presented in the summary of round IV of RNA-Puzzles . Models Using the RNAspider pipeline , we founIn the next step, the global pairwise comparison of all 3D models was conducted. We could observe that, in general, the submitted models were diverse in their global 3D folds. However, significant similarities can be detected between the 3D RNA structures submitted by a given group  with the average distance to it cf. .Based on the obtained results cf. , it can The major differences between models submitted by different groups could be detected for Bujnicki and Szachniuk teams, with an RMSD score equal to 64.384 Å. The RNAComposer-5UTRD_3 model was the most diverged one as compared to the ensembles of 3D structures submitted by different predictors .First, we extracted secondary structures from 3D structure atom coordinates, and we prepared conservation logos cf. . Next, aIn the next step, a consensus-driven approach was applied to identify RNA domains within submitted models. Consequently, seven conserved elements were identified cf. .Next, utilizing the domain-boundary-driven approach, we obtained 28 groups of domains, where 13 of them contained segments derived from models submitted by at least two different predictors cf. . MoreoveThe results of this step are presented and summarised in Next, we conducted a clustering-based analysis of coaxial helical stacking for SL5abc four-way junction (4WJ) and two domains, namely SL2 and SL3 (since it contains an important transcription-regulating (TRS-L) sequence required for subgenomic viral RNA synthesis ) within An SL5abc four-way junction occurred in 24 of 34 models. In each case, 4WJ had no single-stranded region between consecutive helices cf. .As a result, we could observe that most of the models were characterized by two coaxial helices. Members of the largest cluster (nine models submitted by the Szachniuk group) belonged to the family cH  with twoFinally, the analysis of Next, we conducted the analysis of the mutual arrangement of the SL2 and SL3 domains. Both of them appear together in 32 out of 34 such models. The models are characterized by a large variation in the mutual arrangement of SL2 and SL3. Most of them have a bend at the unpaired U60 connecting the SL2 and SL3. Only a few models have roughly coaxial stacking of SL2 and SL3 stems  ,29,30,31Therefore, the RNA-Puzzles community decided to contribute to these efforts and announced an additional prediction challenge regarding the determination of 3D structures of functionally important RNA elements in the SARS-CoV-2 genome, namely the 3As a result, it turned out that an aforementioned pipeline is especially useful in the case of complex, multi-domain models, where the RMSD-based global pairwise comparison of 3D structures is not informative since the global folds of molecules are too diverse, and thus it is necessary to focus on the 3D structure of single, key regulatory structural regions (domains).To characterize and identify common structural motifs in the generated 3D models of SARS-CoV-2 RNA, we computed consensus secondary structures of 5A genome ,28,29,31lication . Analysilication , which mNext, all structures contain a similar SL2 motif with a conserved pentaloop that has been shown to be critical for subgenomic RNA synthesis . In somen 3′-UTR . The menthe gRNA .In most models, SL4 adopts a bipartite domain structure that includes two stem-loop motifs, SL4 and SL4.5. The start codon of conserved uORF is found in the loop of SL4, while the 3le group . The othSARS-CoV . A singlThe SL5 motif has common features in most models, including 5equences .For the 3aviruses ,55, rece in vivo ,29,31,38The hypervariable region (HVR) containing the long-bulged stem covers almost the same range of nucleotides in all consensus structures, but differences can be observed in the number of mismatches and the location and size of the bulges. The HVR is defined as structurally dynamic; therefore, a different model is not surprising. The presence of multiple mutations in this region of 3lication . HVR is lication . Howeverfunction . This moThe consensus models of 3RS-CoV-1 . A strucThe 3D models of conserved structural RNA elements (domains) identified as a result of the comparative reference-free analysis presented in this paper can be used n ext, e.g., as new molecular targets to find drugs or in simulation experiments to investigate various biological hypothesis. For instance, SARS-CoV-2 5olecules . The RNAmulation . Furthermulation .The presented pipeline was applied to analyze the 3D structures predicted from the first complete sequence of SARS-CoV-2 (MN908947.3) reported in ,31. InpuThe modeling groups participating in the challenge applied different prediction methodologies and protocols. They are briefly described in the The proposed pipeline for reference-free comparative analysis of 3D RNA structures consists of seven fundamental steps that are performed sequentially .Step 1.Normalization and validation of 3D structure data.RNA 3D structure evaluation was conducted using rna-tools ,63. RCSBStep 2.RMSD-based global pairwise comparison of 3D RNA models. We compared the predicted models based on the root-mean-square deviation (RMSD) . It was Step 3.RNA 2D structure extraction from atom coordinate data and conservation analysis.RNApdbee ,70 was aStep 4.Determination of the 2D consensus structure.The RNAtive tool  togetherStep 5.Clustering of the 2D structure of RNA.In this step, all secondary structures considered were compared pairwise using RNAdistance . As RNAdStep 6.The 2D structure-based identification of RNA domains and their analysis.A two-step approach was applied to identify RNA structure domains. In the first step (driven by consensus), the secondary structures of all RNAs considered were aligned. Next, the nucleotide pairing statistics were calculated, and the consensus secondary structure was generated and encoded in the extended dot-bracket notation. The consensus obtained was divided into domains. Each continuous fragment closed by base pairs, appearing in ≥50% of the models considered, was recognized as a domain. The aim was to find the longest possible elements closed by the base pairs common to ≥50% models. In the second step (domain-boundary driven), each RNA secondary structure was recursively divided into continuous domains. We aimed to enlarge the domains previously identified and make them more accurate. This approach resulted in a larger number of domains; some of them were present in ≤50% of models, while some were overlapping or were part of the larger ones. The base pairs involved in pseudoknot formation were independently analyzed as both unpaired and paired. With pseudoknots considered, a domain was defined as a continuous fragment located between corresponding structural elements that included opening and closing pseudoknot brackets. Such a routine was performed recurrently to enable the handling of small domains nested in the larger ones. Then, a statistical analysis of the identified domains was performed. Color-scaled maps of the regions analyzed were prepared, where the localization of the domains (Y-axis) was presented within the input sequence (X-axis). To perform a detailed analysis of the results, each domain was described by residue range, sequence, secondary structure, number of residues, number of participants who submitted models supporting the domain, distribution of the number of models within modeling groups, total number of models in which the domain was identified, and list of model names. Finally, all identified domains were aligned by an RNA sequence to observe the distribution of their secondary structures over the models.Step 7.RMSD-based pairwise comparison and clustering of RNA 3D domains.All domains identified in the previous step, supported by at least three different 3D models, were selected for further analysis. For each of them, the corresponding 3D substructures were extracted from all 3D models in which the domain was identified. For each 3D substructure, a pairwise comparison of the RMSD scores was performed, and an RMSD score matrix was prepared in a color scale. Furthermore, for each RMSD matrix, the mean and standard deviations were computed. Finally, for each domain independently, clustering was performed using DBSCAN  with a dComputational experiments presented were run on a mobile workstation equipped with Intel Core i7-10850H CPU @ 2.70 GHz, RAM 64.0 GB, Ubuntu 18.04 LTS 64-bit. The consensus-driven evaluation was performed using RNAtive with all base pairs considered and a confidence level equal to 0.5. DBScan was executed with the minimal number of samples in a cluster equal to 2. The maximum distance between two samples for one to be counted as in the neighborhood of the other was 40 and max to cluster nonpseudoknotted secondary structures and domain RMSD scores. Scripts to perform the DBscan clustering and create secondary structure logos were developed in Python using the scikit-learn and weblogo libraries. Scripts to transform the data and process the results were developed in Bash and AWK.In this study, we presented the computational pipeline for a reference-free high-throughput comparative analysis of the ensemble of 3D RNA structures. To our knowledge, it is the first such extensive and holistic approach in RNA structural bioinformatics.We have demonstrated the first application of the pipeline in the RNA-Puzzles challenge and proved its contribution to understanding the structure of the SARS-CoV-2 virus and possible drug targets. Our analysis has shown that we are far from proposing reliable models for the entire UTR regions; however, individual domains can be modeled with high confidence, as shown by the consistency of 3D models for these domains obtained with different methods. Therefore, we have focused on the analysis of the three-dimensional structures of functionally important RNA elements (domains) in the SARS-CoV-2 genome.Or study is the first one in which 3D RNA models of SARS-CoV-2 UTR regions generated by different modeling groups were evaluated and compared. The resultant 2D RNA consensus structures generated for the submitted 3D RNA models for both the 5E or DMS ,28,29,31E or DMS ,25,27,49The presented pipeline has the potential for various applications. It can be useful in comparative analysis of homologs aimed at identifying templates for homology modeling. It can also be applied to classify three-dimensional structures leading to reliable function prediction of biological molecules.


Influenza virus and coronavirus are two important respiratory viruses, which often cause serious respiratory diseases in humans and animals after infection. In recent years, highly pathogenic avian influenza virus (HPAIV) and SARS-CoV-2 have become major pathogens causing respiratory diseases in humans. Thus, an in-depth understanding of the relationship between viral infection and host innate immunity is particularly important to the stipulation of effective control strategies. As the first line of defense against pathogens infection, innate immunity not only acts as a natural physiological barrier, but also eliminates pathogens through the production of interferon (IFN), the formation of inflammasomes, and the production of pro-inflammatory cytokines. In this process, the recognition of viral pathogen-associated molecular patterns (PAMPs) by host pattern recognition receptors (PRRs) is the initiation and the most important part of the innate immune response. In this review, we summarize the roles of RNA sensors in the host innate immune response to influenza virus and coronavirus infections in different species, with a particular focus on innate immune recognition of viral nucleic acids in host cells, which will help to develop an effective strategy for the control of respiratory infectious diseases. Innate immunity is the natural resistance of the host against pathogens, including viruses, bacteria, fungi, and others. The specific components of pathogens including nucleic acids, proteins, peptidoglycan, and so on, which are called pathogen-associated molecular patterns (PAMPs), can be recognized by the sensors of host cells that are called pattern recognition receptors (PRRs) to initiate the innate immune response. Up to now, at least five groups of PRRs have been identified, including toll-like receptors (TLRs), nucleotide-binding oligomerization domain-like receptors (NLRs), retinoic acid-inducible gene I-like helicases receptors (RLRs), c-type lectin receptors (CLRs), and DNA sensors including AIM2-like receptors (ALRs) and Cyclic-GMP-AMP Synthase (cGAS). These PRRs have different structures and are located differently in host cells. Upon engagement of PAMPs by host PRRs, the signal-transducing pathway is initiated with the recruitment of adaptors to the activating domains of PRRs, ultimately eliciting the innate immune response.As the transmembrane proteins, TLRs recognize the nucleic acids, lipoproteins, and lipopolysaccharides (LPS) of pathogens ,2,3,4. TViral respiratory diseases have always been serious threats to human health. After infection, the patients usually display fever, sore throat, cough, and wheezing. In severe cases, such as HPAIV, SARS-CoV, and SARS-CoV-2 infections, patients usually display more severe systemic inflammatory responses or even succumb to death. With the outbreak of SARS-CoV-2 in late 2019, a growing number of studies have focused on viral respiratory diseases. This review is mainly focused on the influenza virus and coronavirus, two commonly observed respiratory viruses in terms of their intracellular RNA sensor-mediated innate immune responses following infection.Orthomyxoviridae with high variability and transmissibility [Influenza virus is a segmented single-stranded negative RNA virus of sibility . Segmentsibility ,38,39,40sibility . Specifisibility  and PA-Xsibility , plays asibility ,45; (ii)sibility ,47 or dssibility  to supprsibility . On the sibility ,51. It wsibility . Thus, aIn humans, viral RNA is primarily sensed by three types of PRRs, namely TLRs, RLRs, and NLRs ,54,55. A−/− mice displayed a higher viral titer and fewer inflammatory mediators upon H3N2 infection than wild-type (WT) mice, indicating the crucial role of TLR3 in resisting IAV infection [−/− mice [It was reported that IAV replication intermediates could be recognized by TLR3 in human respiratory epithelial cells, which recruits TRIF and PI3K adapters and further activates IRFs and NF-κB signaling pathways ,64. In vnfection ,65. Howenfection . Notablynfection . For IAVnfection ,68. Furt−/− mice . Althoug−/− mice ,71, the −/− mice , which s−/− mice .In addition to TLRs, RLRs, as classical cytoplasmic RNA sensors, also play an important role in sensing viral RNA and combating viral infection ,74. It wIn recent years, the research on inflammasomes has become a hot area. It is now believed that two pathways concern the formation of inflammasomes, including the classical pathway mediated by caspase-1 and the non-classical pathway by caspase-11 ,95,96,97Since the avian is one of the main hosts of IAV infection, the IAV from the avian is usually described as the avian influenza virus (avian IAV). Avian IAV is divided into HPAIV and low pathogenic avian influenza virus (LPAIV) according to its pathogenicity. HPAIVs mainly consist of H5 or H7 subtypes , not onlIn addition to humans and chickens, IAV also replicates in other species. In comparison with chickens, the homolog of RIG-I was present in ducks, which mediates innate antiviral responses through its CARD domain ,130. It It was found that varied miRNAs were involved in the innate immune response upon IAV infection in humans . On the Corona-virus in the family Coronaviridae [Coronavirus is a linear single-stranded RNA virus, belonging to the genus of aviridae . Accordiaviridae . Coronavaviridae . TherefoUp to now, three types coronavirus pose a severe threat to public health, namely SARS-CoV, SARS-CoV-2 and MERS-CoV, all of which belong to β-Coronavirus ,172,173.−/− or TRIF−/− mice showed an increased susceptibility to SARS-CoV infection compared to that of wild-type (WT) mice, displaying a more severe lesion in the lung and a higher viral titer in the serum than the WT mice [Similar to IAV, the RNA sensors for coronavirus are TLRs and RLRs ,182. In  WT mice . It was, WT mice . These f WT mice . Further WT mice . These fIt was found that TLR7/8 played an indispensable role in the recognition of corona-virus ssRNA, triggering host response ,188. SAR−/− CalU 3 cells [Different from IAV, coronavirus genome in the cytoplasm of host cells is primarily sensed by MDA5 and LGP2, consequently activating innate immune response ,197,198. 3 cells . Further 3 cells , suggestEvidence has shown that severe forms of SARS-CoV or SARS-CoV-2 infections were closely related to acute severe inflammatory reactions ,203. MulInterestingly, it was found that the cGAS-STING signaling pathway, a signaling pathway activated by recognition of DNA by cellular cGAS, was also activated to drive a robust IFN-I response after SARS-CoV-2 infection ,219. DifAvian infectious bronchitis virus (IBV), a representative avian coronavirus, mainly causes respiratory symptoms, but the lack of available cell lines for viral infection has limited its further investigation . Thus, tPorcine coronavirus consists of porcine respiratory coronavirus (PRCV), porcine delta coronavirus (PDCoV), porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), etc. It was reported that viral RNA could be sensed by TLR3 and TLR9 to activate the NF-κB signaling pathway in PEDV-infected IECs, which is independent of RIG-I . MeanwhiUp to now, there are few reports regarding the regulation of innate immune response by miRNAs during coronavirus infection in humans. It was found that miRNAs were involved in innate immune response, viral binding, viral invasion, and intracellular stress after coronavirus infection ,237,238.In contrast to miRNAs, the roles of coronavirus proteins in the innate immune response have been widely investigated across the species .Innate immune response is highly different between species for both IAV and coronavirus infections. On the one hand, the differences in PRRs between species directly influence the host’s innate immune recognition. For instance, the congenital deficiency of RIG-I in chicken enhances its susceptibility to these two viruses greatly . On the Up to now, IAV and coronavirus have posed a serious threat to animals and public health. As the first line of host defense against virus infection, innate immune response is particularly important. TLRs, NLRs, and RLRs play pivotal roles in innate immune response via recognizing IAV and coronavirus RNAs to trigger innate immune signaling pathways. Both IAV and coronavirus RNAs in endosomes are sensed by TLR3 and TLR7. However, IAV appears to trigger the innate immune response mainly through RIG-I, whereas coronavirus prefers to rely on MDA5. It is still unclear whether there are any RNA sensors currently unknown to us and are involved in the innate immune response to IAV and coronavirus infections. However, the innate immune recognition of IAV/coronavirus RNA also varies considerably across species, including the differences in PRRs and PPIs modulated by innate immune recognition of viral RNA. This review primarily focuses on PRRs-mediated innate immune signaling and provides an overview of the roles of RNA sensors in host innate response to IAV and coronavirus infections in different species. A comprehensive understanding of the mechanisms by which these two respiratory viral pathogens initiate the innate immune response via RNA sensors will help us better understand the difference in susceptibility across the species and develop novel vaccines or antiviral drugs for their effective control.


Bacillus halodurans, OLE RNA is a component of a ribonucleoprotein (RNP) complex involving at least two proteins named OapA and OapB, but additional components may exist that could point to functional roles for the RNA. Disruption of the genes for either OLE RNA, OapA, or OapB result in the inability of cells to overcome cold, alcohol, or Mg2+ stresses. In the current study, we used in vivo crosslinking followed by OLE RNA isolation to identify the protein YbxF as a potential additional partner in the OLE RNP complex. Notably, a mutation in the gene for this same protein was also reported to be present in a strain wherein the complex is nonfunctional. The B. halodurans YbxF (herein renamed OapC) is homologous to a bacterial protein earlier demonstrated to bind kink turn (k-turn) RNA structural motifs. In vitro RNA-protein binding assays reveal that OLE RNA forms a previously unrecognized k-turn that serves as the natural binding site for YbxF/OapC. Moreover, B. halodurans cells carrying OLE RNAs with disruptive mutations in the k-turn exhibit phenotypes identical to cells lacking functional OLE RNP complexes. These findings reveal that the YbxF/OapC protein of B. halodurans is important for the formation of a functional OLE RNP complex.Ornate, large, extremophilic (OLE) RNAs represent a class of noncoding RNAs prevalent in Gram-positive, extremophilic/anaerobic bacterial species. OLE RNAs (∼600 nt), whose precise biochemical functions remain mysterious, form an intricate secondary structure interspersed with regions of highly conserved nucleotides. In the alkali-halophilic bacterium  Large noncoding RNAs (ncRNAs) are rare in bacteria , 2, but Despite these difficulties, certain ncRNAs exhibit intriguing structural or functional characteristics that have driven further investigation. One such ncRNA class is OLE  RNA . OLE RNABacillus halodurans  (ole) is embedded in a large operon   revealede operon , but it e operon  and is pe operon . OLE RNAnditions . When cenditions , suggestB. halodurans OLE RNA has been shown to form a ribonucleoprotein (RNP) complex with two proteins: OapA  cause reduced ability to grow in cold temperatures  or in the presence of modest amounts of short chain alcohols, such as ethanol (5% v/v) (2+ (as low as 2 mM) . Th. ThB. harganisms . Deletio(5% v/v) , as wellas 2 mM) . This lal growth , 17.ole and oapA genes are deleted  carrying D100A and D104A mutations are cultured under the three stress conditions aforementioned, the phenotypes are more severe than those observed when the  deleted . A suppr deleted . This se deleted  14, 15), 15ole alodurans , 14. Onein vivo.Given its size, structural complexity, and connections to diverse phenotypes, we reasoned that the OLE RNP complex potentially has many additional protein partners. Identification of additional interacting partners, particularly those with established biological and biochemical functions, would greatly benefit the effort to determine the precise functions of OLE RNA. A complete inventory of protein partners in the RNP complex also would aid in establishing a comprehensive understanding of the biological roles of this unusual particle. To pursue this goal, we directly purified OLE RNA from cells using an ‘RNA pull-down’ technique coupled with proteomic mass spectrometry (MS) analysis. Our initial dataset under nonstress growth conditions affirmed that OapB binds OLE RNA B. halodurans YbxF protein, herein renamed OapC, indeed binds OLE RNA by recognizing a previously unidentified k-turn near the P5 stem of OLE RNA , which is a widespread bacterial homolog (B). The YbxF protein from Bacillus subtilis was previously demonstrated to bind to k-turns  YbxF proteins were N-terminally His6-tagged, overexpressed, purified  and determined that YbxF indeed exhibits binding . In contrast, the G15D mutant that is predicted to disrupt RNA binding fails to exhibit evidence of binding .To validate the OLE RNA CHART findings and prior genetic suppressor selection results , B. halopurified , and useenotypes . By usinenotypes , we wereonstruct  that is subtilis . However OLE RNA A and det binding B, top. I binding B, bottomKD) values with the filter-binding assay. The use of high protein concentrations results in saturation of the protein-binding nitrocellulose membrane, which precludes accurate measurement of the fraction of RNA bound to protein under the highest concentrations of protein tested. We attempted to circumvent this issue by performing filter-binding assays using smaller volumes but were still unable to employ concentrations of YbxF sufficient to permit precise KD determinations. Nevertheless, the assay could reliably report the ability of an RNA construct to serve as a ligand for YbxF.Note that we did not attempt to establish precise dissociation constant . The data from these initial binding assays revealed that YbxF bound to the fragment of OLE RNA comprising nucleotides 78 through 290 (fragment OLE78–290) but not to other fragments of the OLE RNA (fragments OLE293–394 and OLE449–608). This finding indicated that the YbxF-binding site likely resided only within the OLE78–290 fragment.We next sought to map the minimal binding site for the YbxF protein in OLE RNA. Because s of RNA , 32, we s of RNA . Therefos of RNA C. The da78–290 RNA were systematically generated and evaluated for their ability to serve as a ligand for YbxF. The joining region between stems P4a and P5  as well as nucleotides in J5/6 and the left shoulder of P6 (A) were required for robust YbxF binding . The fact that the P6 stem structure as originally predicted is supported by nucleotide sequence covariation, wherein base-pairing interactions are maintained through evolution, suggests that this structure is important at some point for the formation or function of OLE RNA.Indeed, the consensus sequence and structural model for OLE RNA near the P5 stem is consistent with two possible structures. The first is a structure where the P5 stem and a long P6 stem are flanked by single-stranded joining regions A, left. A, center). This alternative structure includes a C-G basepair in the canonical stem (C helix) of the k-turn motif nearest to the 3-nt internal bulge. The nucleotides forming this putative base-pair, which is an essential feature of k-turns . This finding suggests that a k-turn is also an important structural feature among OLE RNAs from many species. As the OLE RNA consensus model predicts, the B. halodurans sequence in this same region also conforms to a consensus k-turn motif .The second structure includes an extension of the P5 stem to form a k-turn structure using its flanking joining regions (J4a/5 and J5/6) along with a portion of the left shoulder of P6 A, center k-turns , 20, was k-turns A, centerrn motif A, right.B), encompassing only the k-turn substructure of the B. halodurans OLE RNA, spanning nucleotides 83 to 96 and 136 to 147. The intervening 38 nucleotides of the P5.1 stem were replaced by six nucleotides forming a hairpin loop. As predicted, the truncated k-turn construct based on the B. halodurans OLE RNA sequence is bound by YbxF  with an apparent affinity that is similar to that observed for longer OLE RNA constructs  exhibits a pattern of spontaneous RNA cleavage products from an in-line probing reaction (B) that are consistent with the formation of the P5 and P6 stems as originally predicted for OLE RNAs . Specifically, nucleotides in the J4a/5 that form a major portion of the k-turn become more structured in the presence of the protein. In addition, nucleotides 187 to 190 become less structured, presumably because they are displaced from the original P6 stem when their base-pairing partners are used to form another portion of the k-turn (A). These results strongly indicate that the presence of YbxF induces a precise shape change in OLE RNA between two mutually exclusive but conserved RNA substructures. Thus, both sequence conservation and protein mediated structural switching suggests that the full-length P6 stem and the k-turn structures might be relevant to OLE RNA under different biological conditions.Upon the addition of YbxF to an in-line probing reaction, the pattern of spontaneous RNA cleavage products changes in a manner that is largely consistent with k-turn formation B. Specife k-turn A. These B. subtilis YbxF protein  affect YbxF binding. The U84C mutant, which converts the natural U-G wobble basepair into a C-G Watson–Crick basepair, retains robust binding by the protein (C). In contrast, both the U84A and C85A constructs exhibit little or no evidence of protein binding at a YbxF concentration of 1 μM. However, when higher concentrations of protein were tested, YbxF was observed to bind the U84A construct but not the C85A construct . These results are consistent with the hypothesis that YbxF is a selective binding partner for OLE RNA, as was indicated by the presence of this protein on the list of OLE RNP candidate components derived from the CHART enrichment described previously. Although we did not derive Hill coefficient values from the binding data, we speculate that a 1-to-1 interaction between YbxF protein and OLE RNA is formed because the minimal binding site adopts only a single k-turn. A cocrystal structure of the similar B. subtilis YbxF protein docked to RNA involves a single k-turn motif and a 1-to-1 interaction  under certain stress conditions (Although we have not precisely defined the ructures  18). Th. ThKD vanditions . Given tin vitro (C and 7A). If this RNA–protein interaction is necessary for the biological function of the OLE RNP complex, then introduction of this same C85A mutation in vivo should yield a B. halodurans strain that exhibits the same phenotypes as those observed when essential components of the OLE RNP complex are disrupted or deleted.Through the experiments described previously, we have demonstrated that biochemical disruption of a conserved putative basepair in a k-turn of OLE RNA (construct C85A) abolishes binding YbxF in vitro C and 7A.B. halodurans Δole-oapA strain and inserted a plasmid that carries the PM1 version of the oapA gene and an ole gene carrying mutations U84C, U84A, or C85A. As described previously, the PM1 version of the OapA protein causes more severe cold-, alcohol-, and Mg2+-sensitive phenotypes, unless the OLE RNP complex is rendered inactive . Notably, the consensus model for OLE RNA . Such findings suggest that other protein partners natural to the OLE RNP complex might avoid detection in our CHART dataset. Experiments that isolate OLE RNA in complex with OapA might prove critical in revealing the complete set of proteins that bind OLE RNA, which could provide the clues necessary to ultimately establish the biochemical function of this unusual ncRNA.Regardless of why OapA is not observed in the CHART dataset, other evidence cited before indicates it is a natural partner of the OLE RNP complex. In addition, a modified OapA protein tagged with three FLAG tags  and exprAlthough we now know that at least three proteins are required to yield functional OLE RNP complexes, the known functions of these proteins do not yet provide apparent insights into the predominant biochemical function(s) of OLE RNA. Thus, it appears important to continue the effort to define additional components of the RNP complex to generate additional hypotheses regarding the biochemical and biological roles of this enigmatic RNA. Efforts are currently underway to assess whether other protein candidates can be definitively associated with the OLE RNP complex using bioinformatic, biochemical, and genetic approaches.The consensus model of OLE RNA sequences was generated as previously described  using thA collection of 1000 YbxF sequences was compiled using BLAST  with WP_Covariation analysis of the k-turn in OLE RNA was completed by manual assessment of the OLE RNA alignment. R2R  was usedB. halodurans strains, including the mutant PM1 strain, were created previously  and His6-ybxF/oapC-G15D were synthesized and cloned into pET11a at NdeI and BamHI sites by GenScript. The plasmids were transformed into Escherichia coli BL21(DE3) (New England BioLabs) cells according to the manufacturer’s instructions. Transformants were confirmed by colony PCR and DNA sequencing of the amplified product.Constructs for expression of WT and G15D YbxF/OapC proteins were designed each with an N terminus hexahistidine tag followed by a PreScission protease cleavage site. Inserts of His−1 carbenicillin. Cultures were incubated overnight with shaking at 37 °C. The resulting culture was used to inoculate 1 l of LB medium with 100 μg ml−1 carbenicillin. The culture was incubated with shaking at 37 °C until the absorbance at 600 nm (A600) attained a value between 0.4 and 0.6. Protein expression was then induced by addition of IPTG to a final concentration of 1 mM and the temperature was reduced to 16 °C and incubated with shaking overnight. Cells were harvested by centrifugation at 4 °C for 20 min at 6000g. Pelleted cells were resuspended in 50 ml of cell wash buffer  and harvested by centrifugation at 4 °C for 20 min at 6000g. Pelleted cells were resuspended in 5 ml of cell lysis buffer phosphine [TCEP], 10 mM imidazole, EDTA-free protease inhibitors ) per gram of cell pellet. Cells, kept on ice, were lysed by sonication. Nonlysed cells and debris were removed by centrifugation at 4 °C for 30 min at 16,000g. The supernatant was transferred to a new tube chilled on ice.For protein expression, each strain was plated, and a single colony was used to inoculate a 10 ml culture of Luria Bertani (LB) medium with 100 μg mlA280 were analyzed by SDS-PAGE, pooled, and concentrated as described before. Protein purity was assessed by SDS-PAGE with Coomassie staining  using an Akta Pure FPLC system (Cytiva). The column was washed with 20 column volumes of wash buffer  and then eluted with a gradient of elution buffer  in 1.5 ml fractions. SDS-PAGE of the fractions was used to detect YbxF/OapC. Fractions containing YbxF/OapC were pooled and concentrated with an Amicon Ultra-4 Centrifugal Filter Unit (Millipore) with molecular weight cutoff of 3 kDa. The protein was further purified by using a HiLoad 16/600 Superdex 200 pg SEC column (Cytiva). The sample was loaded onto the column using an Akta Pure FPLC system (Cytiva) and eluted with buffer comprised of 50 mM Tris (pH 7.5 at 20 °C), 250 mM NaCl, 1 mM TCEP. Fractions with high staining . Proteinole-oapA as the DNA template. DNA templates encoding RNAs shorter than 65 nt were prepared either by annealing two complementary synthetic DNA oligomers or by overlap extension as described previously . The cell pellet was resuspended in room temperature formaldehyde (1% v/v) in PBS and rotated end-over-end for 1 h. Glycine was added to a final concentration of 0.3 M to quench the formaldehyde and the cells were washed once more with 50 ml PBS. The resulting cells were harvested by centrifugation, flash frozen, and stored at −80 °C until used.One liter of LB medium (pH 10) was used to culture WT B. halodurans cells were thawed on ice and resuspended in 5 ml WB100 . The cells were lysed with sonication and the lysate was clarified by centrifugation at 4 °C for 30 min at 17,000g. Next, 1 ml of lysate was diluted to 3 ml using solutions described previously  was added in place of the capture oligonucleotide mixture for the ‘beads-only’ control. Insoluble material was removed via centrifugation at ∼20 °C for 10 min at 17,000g. Then, 200 μl of pre-equilibrated MyOne Streptavidin C1 Dynabeads (Invitrogen) were added and the mixture was rotated end over end at ∼20 °C for 2 h. The beads were captured with a magnetic stand and washed either three (low stringency) or nine (high stringency) times with 2 ml of WB250 . The beads were washed once more with 0.5 ml RNase H elution buffer  to pre-equilibrate. The beads were then resuspended in 50 μl of RNase H elution buffer containing 10 U RNase H (New England BioLabs). The reaction proceeded at ∼20 °C for 1 h with end-over-end rotation to prevent the beads from settling. The beads were captured and the supernatant containing eluted proteins was reverse crosslinked via incubation at 55 °C for 16 h.A cocktail of 3′-biotin-TEG DNA oligos  were addB. halodurans to identify protein candidates. The false discovery rate was determined by searching the peptide sequences against a control database containing scrambled sequences from B. halodurans and was calculated to be less than 1%.High stringency CHART pull-down experiments were conducted three independent times using biotinylated capture oligonucleotides and likewise three times in the absence of capture oligonucleotides. Similarly, low stringency CHART pull-down experiments were conducted six independent times using either biotinylated capture oligonucleotides that were complementary to OLE RNA or noncomplementary (‘scramble’ and poly(T); See MS data were analyzed using Scaffold 5 (Proteome Software) with protein identifications being assigned if both peptide and protein identification probabilities were greater than 95% and 99%, respectively, and at least two peptides were identified from each protein. The number of times a protein candidate was identified across biological replicates (n = 3 or N = 6) was counted and the hit was highly ranked if it was identified in OLE RNA-enriched samples but never in a “beads only” control (high stringency experiments) or in at most one noncomplementary capture oligonucleotide control (low stringency experiments). OapB (YbzG) and OapC (YbxF) were prominent hits in the resulting CHART dataset.2O (MilliQ) twice and then once with binding buffer . After assembling the apparatus with pre-equilibrated membranes, each well was washed with 0.2 ml binding buffer.Filter-binding assays were performed using a 48-well slot-blot filter apparatus (Bio-Rad) with both nitrocellulose  and nylon  membranes positioned in the filtration liquid flow in series. Prior to assembly, the membranes were pre-equilibrated by soaking in dH32P-labeled RNA and YbxF/OapC in binding buffer with a final volume of 0.2 ml. Assays were assembled at ∼20 °C and incubated for 15 min before being loaded onto the slot-blot apparatus. After filtration, each well was washed once with 0.2 ml binding buffer. The slot-blot apparatus was then disassembled, and the membranes were dried with a blow dryer before being exposed to a phosphorimager screen . The resulting band intensities were quantified using ImageQuant TL 8.1 . The binding data for each experiment were normalized by first subtracting the amount of radioactivity measured on the nitrocellulose membrane in the absence of protein from the values recorded for all other incubations. The background value was also added to the radioactivity measured for each nylon membrane blot. These corrections are based on the assumption that the RNA adhered to the nitrocellulose membrane actually represents free RNA, which is a known artefact (Binding assays were performed using ∼10 pM 5′ artefact . Percent32P-radiolabeled OLE78–290 RNA were refolded in deionized H2O via incubation at 75 °C for 1 min followed by cooling to ∼20 °C over 5 min. Unlabeled OLE449–608 RNA (not bound by YbxF/OapC) was added to a final concentration 1 μM to serve as a decoy for possible RNase contaminants from the YbxF/OapC protein preparation. This addition was expected to decrease RNase degradation of the 5′ 32P-radiolabeled OLE78–290 RNA that would obscure the banding pattern generated by spontaneous RNA cleavage. In-line probing assays  and then subjected to denaturing (8 M urea) PAGE for 2 h at 40 W. The gel was dried, exposed to a phosphorimager screen , and visualized with a Typhoon phosphorimager .Trace amounts (∼5 nM) of 5′ g assays , 34 alsoB. halodurans growth assays under standard, cold, or ethanol stress conditions were performed as previously described  was calculated and normalized to the performance of WT B. halodurans cells under identical culture conditions.escribed . Growth The data that support the findings of this study are available from the corresponding author upon reasonable request.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


RNA structure and function are intimately tied to RNA binding protein recognition and regulation. Posttranslational modifications are chemical modifications which can control protein biology. The role of PTMs in the regulation RBPs is not well understood, in part due to a lacking analysis of PTM deposition on RBPs. Herein, we present an analysis of posttranslational modifications (PTMs) on RNA binding proteins . We curate published datasets and primary literature to understand the landscape of PTMs and use protein–protein interaction data to understand and potentially provide a framework for understanding which enzymes are controlling PTM deposition and removal on the RBP landscape. Intersection of our data with The Cancer Genome Atlas also provides researchers understanding of mutations that would alter PTM deposition. Additional characterization of the RNA–protein interface provided from in-cell UV crosslinking experiments provides a framework for hypotheses about which PTMs could be regulating RNA binding and thus RBP function. Finally, we provide an online database for our data that is easy to use for the community. It is our hope our efforts will provide researchers will an invaluable tool to test the function of PTMs controlling RBP function and thus RNA biology. RNA molecules have emerged as critical players in nearly every biological process, from cell division to the regulation of chromatin state and gene expression ,2. Many During their lifetimes, RNA molecules are constantly in contact with RNA-binding proteins (RBPs) ,8. RNA-RProtein molecules are subject to a high level of regulation through chemical modifications termed posttranslational modifications (PTMs) . PTMs arThere has been some important headway in characterizing PTM modifications and their role in regulating RNA binding proteins. The limited cases illustrate just how impactful changes to the chemical composition of RBPs can be . For exahttp://PTM-RBP-ATLAS.igb.uci.edu) where this data can be easily gathered by the community for inspection to help with their research programs. Overall, we envision the data generated herein will serve as a powerful analysis for the growing labs interested in understanding how RBPs control the fate of RNA molecules.Expanding the suite of known and addressable of PTMs on RBPs requires rigorous curation and interrogation of existing proteome-scale mass spectrometry data coupled to other facets of RNA–protein interaction biology; together this could serve as a platform for novel hypotheses related to functional types and sites of PTMs on RBPs. Herein, we perform a thorough analysis of experimentally derived PTMs. Using recent large-scale datasets for the identification of RNA binding proteins, we investigate PTMs on the set of known RBPs (RBP-ome). We intersect this dataset with published protein–protein interaction databases to provide potential models for how PTMs are deposited and removed by cellular enzymes. We utilize mass spectrometry data on in-cell crosslinked RNA–protein interactions, that are close to identified RNA–protein interfaces, to provide the community with PTMs that have the potential to regulate RNA–protein binding. We investigate the potential disease relevance of PTM sites by intersecting our data with The Cancer Genome Atlas. We catalog novel PTM sites that are mutated in these datasets and provide experimentally-derived evidence that a key and well known RBP is mutated in cancer. Finally, we provide the community with an online searchable database  were obtained from the UCSC Genome Browser [citation]. For each RNA binding protein, phastCons scores from 100 random positions were selected from its exons for background in comparison to scores at PTM positions.https://www.encodeproject.org/). eCLIP peak regions overlapping genes were identified using . Phosphorylation was the most common modification, though acetylation, ubiquitination, and methylation were also present in over half of RBPs . These data highlight the potential complexity of PTM sites on RBPs.To classify proteins as RNA-binding (the RBP-ome), we combined three existing sets of annotated RBPs from ,10,34,35s Figure . A totalt, P = 4.93 × 10–60). The most common set of modifications was phosphorylation, ubiquitination, acetylation, and methylation . This high evolutionary conservation of many PTM sites indicates they may have important functions that remain intact through evolutionary time.The presence of PTM sites is often regarded to suggest conserved function. It has been observed that PTM sites can be important for regulation of protein–protein interactions and regulatory hot spots for signaling in other classes of functional proteins . We workOverall, our analysis of PTM sites from curated datasets and published manuscripts suggests that RBPs have highly complex experimentally observed PTM profiles, that RBPs have many different types of PTMs, and that the sites of PTM deposition are highly conserved. These resources provide a large dataset focused on RNA binding proteins for analysis by the community.PTM modifications on proteins are deposited, read and removed by distinct classes of enzymes that ultimately control PTM stoichiometry ,54. To iOur approach identified tens of thousands of potentially modifying interactions, or cases in which a modified RBP interacts with an enzyme capable of making the same modification. Most modified proteins had at least one potentially modifying interaction; however, the percentage of proteins with such an interaction varied by PTM type Figure . For theThe large landscape of identified RBP-PTMs in which a potential enzyme pair was identified through analysis of protein–protein interactions implored us to look deeper at the identified enzyme-RBP pairs. We focused on phosphorylation as it is well studied and altered kinase activities are known to be associated with tumorigenesis . We rankhttps://www.cancer.gov/tcga). We identified 9446 cancer mutations that coincide with the position of categorized PTM modified sites in 1727 RBPs . For individual PTMs, each had a negative association between frequency and distance from crosslink site, which was significant for 16/33 PTMs ..45–48). /33 PTMs . The str/33 PTMs . Further/33 PTMs  and as st-test P-value < 2.2e–16). We worked to analyze PTM sites at RNA–protein crosslinks from available crystal structures in the PDB. Among those, hnRNP-C stood out due to the high number of PTMs and those that intersect with cross link peptides from RNA–protein crosslinking experiments  together with the corresponding information about 47 types of post-translational modification (PTMs). In total, there are 280 708 entries in the database, and each entry contains information about the RBP’s Uniprot ID, gene name, position, PMID, Ssource, Amino Acid (AA), modification type, cell line, sample type, body site, and disease state. The database is publicly searchable by RBP Uniprot ID or by gene name and corresponding PTM type through the web server located at: RNA biology is tightly controlled through its interactions with RNA binding proteins. Chemical modifications, termed posttranslational modifications, can control protein function. Herein we provide the community with the first global analysis of PTM marks on RNA binding proteins. Our analysis, using experimentally-determined RNA binding proteins and PTM databases reveals that RBPs are modified by a wide variety of PTMs, in all totaling 69 658 distinct modifications. We also demonstrate that the sites of PTM deposition are conserved, with many RNA binding proteins have a high level of diversity of their cataloged PTM modification types.We have attempted to create the broadest possible sets of data for PTMs and PPIs; however, sources of bias still exist. Our data are derived from existing literature, and PTMs are not studied evenly—phosphorylation is the focus of far more studies than other PTMs, and just four modifications  make up the bulk of the available data. Less-studied PTMs may not have enough data to draw significant conclusions observed with more common modifications. For PPIs, different methodologies may miss certain interactions, including transient interactions with substrates. We included eight PPI databases, which include both high-throughput and more focused experiments, using both physical and genetic techniques, to collect the most complete set of interactions possible; however, it is possible that difficult to detect interactions are underrepresented in our interaction data.Intersection of protein–protein interaction data onto RBP modifications reveals complexity of PTM-depositing enzymes and those that could be responsible for PTM removal. Focusing on phosphorylation, an analysis of overlapping protein–protein interactions suggests master regulators that could be controlling PTM deposition and removal. This analysis highlights the potential of such enzymes, which have known roles in regulating cancer phenotypes, at center stage, thus likely controlling signaling through RBP function. Two enzymes highlighted, PTEN and SRC kinase, are known to be critically important in controlling oncogenic phenotypes, but there has yet to be a connection in their role as master regulators of RBP function and as such the potential for re-wiring the transcriptome. Our database provides a framework for generating hypotheses about potential communication between PTM regulation and RBP function ripe for testing.We also analyzed the relationship between PTM deposition sites and mutations identified in The Cancer Genome Atlas. Our analysis revealed that many of the catalogued RBPs contain mutations, with some familiar nucleic acid binding proteins such as p53 dominating the landscape, but further analysis revealed lesser-known yet likely important mutations. For example, the SF3B1 gene encodes part of the U2 snRNP portion of the spliceosome B complex. Consistent with the literature, we observe that SF3B1 is enriched in mutations; one in particular, K700E, is now established to play an important role in regulating alternative splicing through an unknown mechanism . Our PTMPTM sites control protein function, but it is unclear if proximity to the RNA–protein interface would allow them to influence RNA–protein interactions. We provide the first comparison of PTM data in the context of in-cell captured RNA–protein crosslinks. This is critical to understand if there is an overlap between the sites of PTM deposition and RNA binding. Through this analysis, the complexity of RNA recognition motifs, experimentally validated, and the relationship of PTM sites is revealed. PTM complexity and number of the hnRNP proteins, a critically important class of RBPs, comes to light. Highlighting this relationship suggests an enrichment for arginine methylation among these proteins, suggesting that neutralization of the arginine positive charge could be critically important for controlling the RBP–RNA interface. Such analysis in a fuller context of these modifications and their relationship to controlled RBP function will be invaluable to fully understand their roles.Finally, we provide the community with an online database that is fully searchable. We hope this database, and the analyses herein provide a catalyst for experiments to fully understand the role of PTMs in regulating RBP and RNA function.http://PTM-RBP-ATLAS.igb.uci.edu/. This website is free, open to all users and no login or password is required.The web interface to the database is available at: gkac243_Supplemental_FilesClick here for additional data file.


Adding complexity to the biology of transformed cells and its RNA and protein landscape, it has recently been shown that cancer cells also exhibit a distorted pattern of chemical modifications of the RNA molecule , 2, the tabolism . The ideser, FTO , represe6A RNA modification and alterations in its associated proteins impair physiological hematopoiesis and are also observed in hematological malignancies [6A marking was shown to be important for the resolution of the naive state of embryonic stem cells to primed cells, the control of cell fate decisions in early hematopoiesis and the maintainance of hematopoietic stem cell identity and symmetric commitment [6A RNA decoration in transition of differentiated stages of more mature hematopoietic cells in physiological and pathological microenvironmental conditions. Examples of hematopoietic cell lineage conversion and plasticity include B-cell lymphomas that transdifferentiate to histiocytic/dendritic cell sarcoma and B-cell acute lymphoblastic leukemia (ALL) patients that escape both antibody treatments and chimeric antigen receptor (CAR) T-cell therapy against CD19 by converting to AML. We have recently identified that myeloid-lineage transdifferentiation is associated with a reconfiguration of the DNA methylation landscape [6A RNA epitranscriptome.In hematopoiesis, the generation of all the different types of mature blood cells from hematopoietic stem cells (HSCs) requires a tight control of RNA activity and disrupted patterns of mgnancies . In thismmitment , 9. Lessandscape . We now 6A RNA landscape upon cell conversion, we have herein studied the human precursor B-ALL cell line RCH-ACV transfected with the transgene C/EBPαER, designated below as BLaER1, at the start (0 h) and end (168 h) of transdifferentiation timepoints upon 17β-estradiol exposure, using m6A-sequencing (m6A-seq) based on immunocapturing and massive parallel sequencing (Supplementary Methods) [6A-seq raw data have been deposited in the Sequence Read Archive (SRA) BioProject (accession number PRJNA734010).Our experimental model involves transdifferentiation of pre-B cells into macrophages. Following early work showing that murine B-cell precursors can be induced by C/EBPα to convert into functional macrophages, we used murine and human cellular models of pre-B cells containing C/EBPα fused with the estrogen receptor hormone-binding domain (C/EBPαER) that transdifferentiate to macrophages upon 17β-estradiol exposure , 12. PriMethods) . The obt6A profiling of efficiently transdifferentiated cells, which was validated by the shift in the corresponding CD19 and CD11B markers  were stable between the 0 h and 168 h time points, 6072 m6A peaks corresponding to 3056 RNA transcripts changed upon cell conversion  were localized in gene-body related sequences, corresponding to 31.6% (1922) and 30.9% (1874) intron and exon RNA sequences, respectively; whereas 19% (1153) were in 5′-untranslated regions (UTRs) and 18.5% (1123) in 3′-UTRs  corresponding to 1664 transcripts . We observed an overrepresentation of Gene Ontology (GO) biological processes, molecular function, KEGG pathways and reactome pathways related to RNA ribosome protein translation, such as “ribosome biogenesis”, “ribosomal RNA processing”, “nucleolus”, “translation” and “ribosome”; in addition to RNA regulation (such as “RNA binding” and “RNA transport”)  Fig.  [hypergeons Fig. . These p6A marks in mRNA function relates to the location of the modification [6A is to induce mRNA instability [6A sites on gene expression in our model, we took advantage of the available microarray expression data of the start (0 h) and end (168 h) cell conversion timepoints [6A peaks and expression levels , even when stratified for losses and gains of m6A vs upregulation or downregulation of the corresponding transcripts  . Most importantly, and in agreement with the GO results . Among these genes, many ribosomal proteins are some of the most significantly downregulated upon m6A 3-UTR increased deposition accompanying the transdifferentiation process   Fig. . We obse and end 68 h celllts Fig. , we foun Dataset . Importa Dataset  and prot Dataset  levels i Dataset . This re and end 68 h cellns) Fig. , support6A in pre-B cell to macrophage transdifferentiation, particularly targeting 3′-UTRs of ribosome-associated transcripts, we experimentaly validated this model by knocking down the main m6A RNA methyltransferase, METTL3 [6A content and impairing transdifferentiation (Fig. 6A-mediated downregulation of the seven ribosomal proteins observed in our B-cell to macrophage conversion model (Fig. 6A mark, exhibited an increased stability of the RPS25 mRNA transcript according to the Actinomycin D assay (Fig. Having shown the above associations that support a functional role for m, METTL3 . Efficie, METTL3 , mimickeion Fig. . Most imdel Fig. , that indel Fig. . Interesdel Fig. . Finallysay Fig. .6A RNA marks in the succesfull generation of a macrophage from pre-B cells, particularly by decorating the 3′-UTRs of genes related to the ribosome translational machinery. These findings highlight the role of this epitranscriptomic signal, and the proteins controling and mediating its deposition and downstream effects, in hematological transdifferentiation pathways.Overall, these results indicate a relevant activity of mSupplementary Figure S1Supplementary Figure S2Supplementary Figure S3Supplementary Figure S4Supplementary Figure S5Supplementary Figure S6Supplementary Figure S7Supplementary Figure S8Supplementary Figure S9Supplementary Figure S10Supplementary Figure S11Supplementary Figure S12Supplementary Figure S13Dataset S1Dataset S2