RNA-binding Proteins Binding Sites Research Articles

POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

Recognizing binding sites of poorly characterized RNA-binding proteins on circular RNAs using attention Siamese network.

Circular RNAs (circRNAs) interact with RNA-binding proteins (RBPs) to play crucial roles in gene regulation and disease development. Computational approaches have attracted much attention to quickly predict highly potential RBP binding sites on circRNAs using the sequence or structure statistical binding knowledge. Deep learning is one of the popular learning models in this area but usually requires a lot of labeled training data. It would perform unsatisfactorily for the less characterized RBPs with a limited number of known target circRNAs. How to improve the prediction performance for such small-size labeled characterized RBPs is a challenging task for deep learning-based models. In this study, we propose an RBP-specific method iDeepC for predicting RBP binding sites on circRNAs from sequences. It adopts a Siamese neural network consisting of a lightweight attention module and a metric module. We have found that Siamese neural network effectively enhances the network capability of capturing mutual information between circRNAs with pairwise metric learning. To further deal with the small-sample size problem, we have performed the pretraining using available labeled data from other RBPs and also demonstrate the efficacy of this transfer-learning pipeline. We comprehensively evaluated iDeepC on the benchmark datasets of RBP-binding circRNAs, and the results suggest iDeepC achieving promising results on the poorly characterized RBPs. The source code is available at https://github.com/hehew321/iDeepC.

DiffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Advancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites.

Predicting RBP Binding Sites of RNA With High-Order Encoding Features and CNN-BLSTM Hybrid Model.

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.

An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability.

Genome-wide association studies have identified thousands of noncoding variants associated with human traits and diseases. However, the functional interpretation of these variants is a major challenge. Here, we constructed a multi-tissue atlas of human 3'UTR alternative polyadenylation (APA) quantitative trait loci (3'aQTLs), containing approximately 0.4 million common genetic variants associated with the APA of target genes, identified in 46 tissues isolated from 467 individuals (Genotype-Tissue Expression Project). Mechanistically, 3'aQTLs can alter poly(A) motifs, RNA secondary structure and RNA-binding protein-binding sites, leading to thousands of APA changes. Our CRISPR-based experiments indicate that such 3'aQTLs can alter APA regulation. Furthermore, we demonstrate that mapping 3'aQTLs can identify APA regulators, such as La-related protein 4. Finally, 3'aQTLs are colocalized with approximately 16.1% of trait-associated variants and are largely distinct from other QTLs, such as expression QTLs. Together, our findings show that 3'aQTLs contribute substantially to the molecular mechanisms underlying human complex traits and diseases.

Aging-associated lncRNAs are evolutionarily conserved and participate in NFκB signaling.

The transcriptome undergoes global changes during aging, including both protein-coding and noncoding RNAs. Using comparative genomics, we identify aging-associated long noncoding RNAs (lncRNAs) that are under evolutionary constraint and are more conserved than lncRNAs that do not change with age. Aging-associated lncRNAs are enriched for functional elements, including binding sites for RNA-binding proteins and transcription factors, in particular nuclear factor kappa B (NFκB). Using CRISPR screening, we discovered that 13 of the aging-associated lncRNAs were regulators of the NFκB pathway, and we named this family 'NFκB modulating aging-related lncRNAs (NFKBMARLs)'. Further characterization of NFκBMARL-1 reveals it can be traced to 29 Ma before humans and is induced by NFκB during aging, inflammation and senescence. Reciprocally, NFκBMARL-1 directly regulates transcription of the NFκB inhibitor NFKBIZ in cis within the same topologically associated domain by binding to the NFKBIZ enhancer and recruiting RELA to the NFKBIZ promoter. These findings reveal many aging-associated lncRNAs are evolutionarily conserved components of the NFκB pathway.

Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome

Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome. Downstream analysis of this catalog provides insight into the mechanisms underlying splicing regulation. We report that a core set of sQTLs is shared across multiple tissues. sQTLs often target the global splicing pattern of genes, rather than individual splicing events. Many also affect the expression of the same or other genes, uncovering regulatory loci that act through different mechanisms. sQTLs tend to be located in post-transcriptionally spliced introns, which would function as hotspots for splicing regulation. While many variants affect splicing patterns by altering the sequence of splice sites, many more modify the binding sites of RNA-binding proteins. Genetic variants affecting splicing can have a stronger phenotypic impact than those affecting gene expression.

A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections.

Background: Certain riboviruses can cause severe pulmonary complications leading to death in some infected patients. We propose that DNA damage induced-apoptosis accelerates viral release, triggered by depletion of host RNA binding proteins (RBPs) from nuclear RNA bound to replicating viral sequences. Methods: Information theory-based analysis of interactions between RBPs and individual sequences in the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), Influenza A (H3N2), HIV-1, and Dengue genomes identifies strong RBP binding sites in these viral genomes. Replication and expression of viral sequences is expected to increasingly sequester RBPs - SRSF1 and RNPS1. Ordinarily, RBPs bound to nascent host transcripts prevents their annealing to complementary DNA. Their depletion induces destabilizing R-loops. Chromosomal breakage occurs when an excess of unresolved R-loops collide with incoming replication forks, overwhelming the DNA repair machinery. We estimated stoichiometry of inhibition of RBPs in host nuclear RNA by counting competing binding sites in replicating viral genomes and host RNA. Results: Host RBP binding sites are frequent and conserved among different strains of RNA viral genomes. Similar binding motifs of SRSF1 and RNPS1 explain why DNA damage resulting from SRSF1 depletion is complemented by expression of RNPS1. Clustering of strong RBP binding sites coincides with the distribution of RNA-DNA hybridization sites across the genome. SARS-CoV-2 replication is estimated to require 32.5-41.8 hours to effectively compete for binding of an equal proportion of SRSF1 binding sites in host encoded nuclear RNAs. Significant changes in expression of transcripts encoding DNA repair and apoptotic proteins were found in an analysis of influenza A and Dengue-infected cells in some individuals. Conclusions: R-loop-induced apoptosis indirectly resulting from viral replication could release significant quantities of membrane-associated virions into neighboring alveoli. These could infect adjacent pneumocytes and other tissues, rapidly compromising lung function, causing multiorgan system failure and other described symptoms.

RIPiT-Seq: A tandem immunoprecipitation approach to reveal global binding landscape of multisubunit ribonucleoproteins.

RNA-binding proteins (RBPs) regulate all aspects of RNA metabolism. The ability to identify RNA targets bound by RBPs is critical for understanding RBP function. While powerful techniques are available to identify binding sites of individual RBPs at high resolution, it remains challenging to unravel binding sites of multicomponent ribonucleoproteins (RNPs) where multiple RBPs or proteins function cooperatively to bind to target RNAs. To fill this gap, we have previously developed RNA Immunoprecipitation in Tandem followed by high-throughput sequencing (RIPiT-seq) to characterize RNA targets of compositionally distinct RNP complexes by sequentially immunoprecipitating two proteins from the same RNP and sequencing the co-purifying RNA footprints. Here, we provide an updated and improved protocol for RIPiT-seq. In this protocol, we have used CRISPR-Cas9 to introduce affinity tag to endogenous protein of interest to capture a more representative state of an RNP complex. We present a modified protocol for library preparation for high-throughput sequencing so that it exclusively uses equipment and reagents available in a standard molecular biology lab. This updated custom library preparation protocol is compatible with commercial PCR multiplexing systems for Illumina sequencing platform for simultaneous and cost-effective analysis of large number of samples.

Transcriptome-wide analysis reveals spatial correlation between N6-methyladenosine and binding sites of microRNAs and RNA-binding proteins

N6-methyladenosine (m6A), the most prevalent epitranscriptomic modification in eukaryotes, is enriched in 3′-untranslated regions (3′UTRs) of mRNAs. As 3′UTRs are major binding sites of RNA-binding proteins (RBPs) and microRNAs (miRNAs), m6A-dependent local RNA structure change may alter the accessibility of RBPs and miRNAs to their target sites and regulate mRNA function. Using a human transcriptome-wide computational analysis to investigate the relation between m6A, RBPs and miRNAs, we find a strong positive correlation between number of m6A sites, miRNAs and RBPs binding to mRNAs, suggesting m6A-modified mRNAs are more targeted by miRNAs and RBPs. Moreover, m6A sites are located proximally to miRNA target sites and binding sites of multiple RBPs. Further, miRNA target sites and RBP-binding sites located close to each other are also located proximally to m6A. This study indicates three-way interplay between m6A, microRNA and RBP binding, suggesting the influence of mRNA modifications on the miRNA and RBP interactomes.

A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections

Background: Certain riboviruses can cause severe pulmonary complications leading to death in some infected patients. We propose that DNA damage induced-apoptosis accelerates viral release, triggered by depletion of host RNA binding proteins (RBPs) from nuclear RNA bound to replicating viral sequences. Methods: Information theory-based analysis of interactions between RBPs and individual sequences in the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), Influenza A (H3N2), HIV-1, and Dengue genomes identifies strong RBP binding sites in these viral genomes. Replication and expression of viral sequences is expected to increasingly sequester RBPs - SRSF1 and RNPS1. Ordinarily, RBPs bound to nascent host transcripts prevents their annealing to complementary DNA. Their depletion induces destabilizing R-loops. Chromosomal breakage occurs when an excess of unresolved R-loops collide with incoming replication forks, overwhelming the DNA repair machinery. We estimated stoichiometry of inhibition of RBPs in host nuclear RNA by counting competing binding sites in replicating viral genomes and host RNA. Results: Host RBP binding sites are frequent and conserved among different strains of RNA viral genomes. Similar binding motifs of SRSF1 and RNPS1 explain why DNA damage resulting from SRSF1 depletion is complemented by expression of RNPS1. Clustering of strong RBP binding sites coincides with the distribution of RNA-DNA hybridization sites across the genome. SARS-CoV-2 replication is estimated to require 32.5-41.8 hours to effectively compete for binding of an equal proportion of SRSF1 binding sites in host encoded nuclear RNAs. Significant changes in expression of transcripts encoding DNA repair and apoptotic proteins were found in an analysis of influenza A and Dengue-infected cells in some individuals. Conclusions: R-loop-induced apoptosis indirectly resulting from viral replication could release significant quantities of membrane-associated virions into neighboring alveoli. These could infect adjacent pneumocytes and other tissues, rapidly compromising lung function, causing multiorgan system failure and other described symptoms.

DeepA-RBPBS: A hybrid convolution and recurrent neural network combined with attention mechanism for predicting RBP binding site

It’s important to infer the binding site of RNA-binding proteins (RBP) for understanding the interaction between RBP and its RNA targets and decipher the mechanisms of transcriptional regulation. However, experimental detection of RBP binding sites is still time-intensive and expensive. Algorithms based on machine learning can speed up detection of RBP binding sites. In this article, we propose a new deep learning method, DeepA-RBPBS, which can use RNA sequences and structural features to predict RBP binding site. DeepA-RBPBS uses CNN and BiGRU to extract sequences and structural features without long-term dependence issues. It also utilizes an attention mechanism to enhance the contribution of key features. The comparison shows that the performance of DeepA-RBPBS is better than that of the state-of-the-art predictors. In the testing on 31 datasets of CLIP-seq experiments over 19 proteins, MCC (AUC) is 8% (5%) higher than those of the latest method based on deep learning, iDeepS. We also apply DeepA-RBPBS to the target RNA data of RBPs related to diabetes (LIN28, RBFOX2, FTO, IGF2BP2, CELF1 and HuR). The results show that DeepA-RBPBS correctly predicted 41,693 samples, where iDeepS predicted 31,381 samples. Communicated by Ramaswamy H. Sarma

KDeepBind: Prediction of RNA-Proteins binding sites using convolution neural network and k-gram features

RNA-binding proteins (RBPs) have a central role in different biological processes like gene regulation, containing transcription and alternative splicing, and provide necessary useful information for patient care. Therefore, identifying binding sites of the RBPs on RNA is the main research direction to understand the procedure of several biological processes. Through biochemical experiments, the RBPs binding sites identification has produced good results but these biochemical processes and exploratory methods consume time and money. Thus, it is important to propose an efficient computational model for the identification of RBPs binding sites. In this study, we propose an intelligent prediction model for RBPs binding sites using a deep learning approach. The proposed prediction model is called kDeepBind. We first apply the one-hot representation for the input sequence to permit successive convolution layers. To declare the binding hidden information from the recognized sequences, the convolution neural network (CNN) model is applied to automatically learn the abstract features. Then, we apply the k-Gram feature extraction technique and concatenate with CNN features. We evaluate the proposed predictor kDeepBind on verified RBP binding sites that are obtained from CLIP-seq datasets. Evaluation results show that the proposed prediction model obtains better performance in the term of ROC-AUC than comparative methods. It is thus highly expected that kDeepBind model will become a useful tool for academic research on RBPs binding sites prediction of RNA as well as in drug discovery. All training and testing data, learned motifs, and trained models can be downloaded at http://nsclbio.jbnu.ac.kr/tools/kDeepBind/

Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure

BackgroundRNA binding proteins (RBPs) play a vital role in post-transcriptional processes in all eukaryotes, such as splicing regulation, mRNA transport, and modulation of mRNA translation and decay. The identification of RBP binding sites is a crucial step in understanding the biological mechanism of post-transcriptional gene regulation. However, the determination of RBP binding sites on a large scale is a challenging task due to high cost of biochemical assays. Quite a number of studies have exploited machine learning methods to predict binding sites. Especially, deep learning is increasingly used in the bioinformatics field by virtue of its ability to learn generalized representations from DNA and protein sequences.ResultsIn this paper, we implemented a novel deep neural network model, DeepRKE, which combines primary RNA sequence and secondary structure information to effectively predict RBP binding sites. Specifically, we used word embedding algorithm to extract features of RNA sequences and secondary structures, i.e., distributed representation of k-mers sequence rather than traditional one-hot encoding. The distributed representations are taken as input of convolutional neural networks (CNN) and bidirectional long-term short-term memory networks (BiLSTM) to identify RBP binding sites. Our results show that deepRKE outperforms existing counterpart methods on two large-scale benchmark datasets.ConclusionsOur extensive experimental results show that DeepRKE is an efficacious tool for predicting RBP binding sites. The distributed representations of RNA sequences and secondary structures can effectively detect the latent relationship and similarity between k-mers, and thus improve the predictive performance. The source code of DeepRKE is available at https://github.com/youzhiliu/DeepRKE/.

Transcriptome-wide profiles of circular RNA and RNA-binding protein interactions reveal effects on circular RNA biogenesis and cancer pathway expression

BackgroundCircular RNAs (circRNAs) are stable, often highly expressed RNA transcripts with potential to modulate other regulatory RNAs. A few circRNAs have been shown to bind RNA-binding proteins (RBPs); however, little is known about the prevalence and distribution of these interactions in different biological contexts.MethodsWe conduct an extensive screen of circRNA-RBP interactions in the ENCODE cell lines HepG2 and K562. We profile circRNAs in deep-sequenced total RNA samples and analyze circRNA-RBP interactions using a large set of eCLIP data with binding sites of 150 RBPs. We validate interactions for select circRNAs and RBPs by performing RNA immunoprecipitation and functionally characterize our most interesting candidates by conducting knockdown studies followed by RNA-Seq.ResultsWe generate a comprehensive catalog of circRNA-RBP interactions in HepG2 and K562 cells. We show that KHSRP binding sites are enriched in flanking introns of circRNAs and that KHSRP depletion affects circRNA biogenesis. We identify circRNAs that are highly covered by RBP binding sites and experimentally validate individual circRNA-RBP interactions. We show that circCDYL, a highly expressed circRNA with clinical and functional implications in bladder cancer, is almost completely covered with GRWD1 binding sites in HepG2 cells, and that circCDYL depletion counteracts the effect of GRWD1 depletion. Furthermore, we confirm interactions between circCDYL and RBPs in bladder cancer cells and demonstrate that circCDYL depletion affects hallmarks of cancer and perturbs the expression of key cancer genes, e.g., TP53. Finally, we show that elevated levels of circCDYL are associated with overall survival of bladder cancer patients.ConclusionsOur study demonstrates transcriptome-wide and cell-type-specific circRNA-RBP interactions that could play important regulatory roles in tumorigenesis.

Improving CLIP-seq data analysis by incorporating transcript information

BackgroundCurrent peak callers for identifying RNA-binding protein (RBP) binding sites from CLIP-seq data take into account genomic read profiles, but they ignore the underlying transcript information, that is information regarding splicing events. So far, there are no studies available that closer observe this issue.ResultsHere we show that current peak callers are susceptible to false peak calling near exon borders. We quantify its extent in publicly available datasets, which turns out to be substantial. By providing a tool called CLIPcontext for automatic transcript and genomic context sequence extraction, we further demonstrate that context choice affects the performances of RBP binding site prediction tools. Moreover, we show that known motifs of exon-binding RBPs are often enriched in transcript context sites, which should enable the recovery of more authentic binding sites. Finally, we discuss possible strategies on how to integrate transcript information into future workflows.ConclusionsOur results demonstrate the importance of incorporating transcript information in CLIP-seq data analysis. Taking advantage of the underlying transcript information should therefore become an integral part of future peak calling and downstream analysis tools.

ICircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network.

Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those computational methods often suffer from the low discriminative power of feature representations, numerical instability and poor scalability. To address those limitations, we propose a novel computational method called iCircRBP-DHN using deep hierarchical network for discriminating circRNA-RBP binding sites. The network architecture can be regarded as a deep multi-scale residual network followed by bidirectional gated recurrent units (BiGRUs) with the self-attention mechanism, which can simultaneously extract local and global contextual information. Meanwhile, we propose novel encoding schemes by integrating CircRNA2Vec and the K-tuple nucleotide frequency pattern to represent different degrees of nucleotide dependencies. To validate the effectiveness of our proposed iCircRBP-DHN, we compared its performance with other computational methods on 37 circRNAs datasets and 31 linear RNAs datasets, respectively. The experimental results reveal that iCircRBP-DHN can achieve superior performance over those state-of-the-art algorithms. Moreover, we perform motif analysis on circRNAs bound by those different RBPs, demonstrating that our proposed CircRNA2Vec encoding scheme can be promising. The iCircRBP-DHN method is made available at https://github.com/houzl3416/iCircRBP-DHN.

A Survey of Regulatory Interactions Among RNA Binding Proteins and MicroRNAs in Cancer.

Recent advances in genomics and proteomics generated a large amount of trans regulatory data such as those mediated by RNA binding proteins (RBPs) and microRNAs. Since many trans regulators target 3′ UTR of mRNA transcripts, it is likely that there would be interactions, i.e., competitive or cooperative effect, among these trans factors. We compiled the available RBP and microRNA binding sites, mapped them to the mRNA transcripts, and correlated the binding data with mRNA expression data generated by The Cancer Genome Atlas (TCGA). We separated pairs of RBPs and microRNAs into three scenarios: those that have overlapping target sites on the same mRNA transcript (overlapping), those that have target sites on the same mRNA transcript but non-overlapping (neighboring), and those that do not target the same mRNA transcript (independent). Through a regression analysis on expression profiles, we indeed observed interaction effect between RBPs and microRNAs in the majority of the cancer expression data sets. We further discussed implication of such widespread interactions in the context of cancer and diseases.

A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections.

Background: Certain riboviruses can cause severe pulmonary complications leading to death in some infected patients. We propose that DNA damage induced-apoptosis accelerates viral release, triggered by depletion of host RNA binding proteins (RBPs) from nuclear RNA bound to replicating viral sequences. Methods: Information theory-based analysis of interactions between RBPs and individual sequences in the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), Influenza A (H3N1), HIV-1, and Dengue genomes identifies strong RBP binding sites in these viral genomes. Replication and expression of viral sequences is expected to increasingly sequester RBPs - SRSF1 and RNPS1. Ordinarily, RBPs bound to nascent host transcripts prevents their annealing to complementary DNA. Their depletion induces destabilizing R-loops. Chromosomal breakage occurs when an excess of unresolved R-loops collide with incoming replication forks, overwhelming the DNA repair machinery. We estimated stoichiometry of inhibition of RBPs in host nuclear RNA by counting competing binding sites in replicating viral genomes and host RNA. Results: Host RBP binding sites are frequent and conserved among different strains of RNA viral genomes. Similar binding motifs of SRSF1 and RNPS1 explain why DNA damage resulting from SRSF1 depletion is complemented by expression of RNPS1. Clustering of strong RBP binding sites coincides with the distribution of RNA-DNA hybridization sites across the genome. SARS-CoV-2 replication is estimated to require 32.5-41.8 hours to effectively compete for binding of an equal proportion of SRSF1 binding sites in host encoded nuclear RNAs. Significant changes in expression of transcripts encoding DNA repair and apoptotic proteins were found in an analysis of influenza A and Dengue-infected cells in some individuals. Conclusions: R-loop-induced apoptosis indirectly resulting from viral replication could release significant quantities of membrane-associated virions into neighboring alveoli. These could infect adjacent pneumocytes and other tissues, rapidly compromising lung function, causing multiorgan system failure and other described symptoms.

Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability.

N6-methyladenosine (m6A) plays important roles in regulating mRNA processing. Despite rapid progress in this field, little is known about genetic determinants of m6A modification and their role in common diseases. In this work, we mapped quantitative trait loci (QTLs) of m6A peaks in 60 Yoruba lymphoblast cell lines (LCLs). We find that m6A-QTLs are largely independent of expression and splicing QTLs, and are enriched with binding sites of RNA-binding proteins (RBPs), RNA structure-changing variants and transcriptional features. Joint analysis of QTLs of m6A and related molecular traits suggests that downstream effects of m6A are heterogeneous and context-dependent. We identified proteins that mediate m6A effects on translation. Integrating with data from genome-wide association studies (GWAS), we show that m6A-QTLs contribute to heritability of various immune and blood-related traits at levels comparable to splicing-QTLs and roughly half of eQTLs. Leveraging m6A-QTLs in a transcriptome-wide association study (TWAS) framework, we identified putative risk genes of these traits.