scholarly journals First Identification of RNA-Binding Proteins That Regulate Alternative Exons in the Dystrophin Gene

2020 ◽  
Vol 21 (20) ◽  
pp. 7803
Author(s):  
Julie Miro ◽  
Anne-Laure Bougé ◽  
Eva Murauer ◽  
Emmanuelle Beyne ◽  
Dylan Da Cunha ◽  
...  
Keyword(s):  
Large Scale ◽  
Binding Proteins ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Dystrophin Gene ◽  
Rna Seq ◽  
Spatio Temporal ◽  
Dmd Gene ◽  
Shed Light

The Duchenne muscular dystrophy (DMD) gene has a complex expression pattern regulated by multiple tissue-specific promoters and by alternative splicing (AS) of the resulting transcripts. Here, we used an RNAi-based approach coupled with DMD-targeted RNA-seq to identify RNA-binding proteins (RBPs) that regulate splicing of its skeletal muscle isoform (Dp427m) in a human muscular cell line. A total of 16 RBPs comprising the major regulators of muscle-specific splicing events were tested. We show that distinct combinations of RBPs maintain the correct inclusion in the Dp427m of exons that undergo spatio-temporal AS in other dystrophin isoforms. In particular, our findings revealed the complex networks of RBPs contributing to the splicing of the two short DMD exons 71 and 78, the inclusion of exon 78 in the adult Dp427m isoform being crucial for muscle function. Among the RBPs tested, QKI and DDX5/DDX17 proteins are important determinants of DMD exon inclusion. This is the first large-scale study to determine which RBP proteins act on the physiological splicing of the DMD gene. Our data shed light on molecular mechanisms contributing to the expression of the different dystrophin isoforms, which could be influenced by a change in the function or expression level of the identified RBPs.

scholarly journals In heart failure reactivation of RNA-binding proteins drives the transcriptome into a fetal state

2021 ◽  
Author(s):  
Matteo D'Antonio ◽  
Jennifer P. Nguyen ◽  
Timothy D. Arthur ◽  
Hiroko Matsui ◽  
Margaret K.R. Donovan ◽  
...  
Keyword(s):  
Heart Failure ◽  
Binding Proteins ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Healthy Adult ◽  
Rna Binding Proteins ◽  
Expression Profiles ◽  
Fetal Tissue ◽  
Cellular Heterogeneity ◽  
Rna Seq

Transcriptome-wide expression changes occur during heart failure, including reactivation of fetal-specific isoforms. However, the underlying molecular mechanisms and the extent to which a fetal gene program switch occurs remains unclear. Limitations hindering transcriptome-wide analyses of alternative splicing differences (i.e. isoform switching) in cardiovascular system (CVS) tissues between fetal and adult (healthy and diseased) stages have included both cellular heterogeneity across bulk RNA-seq samples and limited availability of fetal tissue for research. To overcome these limitations, we have deconvoluted the cellular compositions of 996 RNA-seq samples representing heart failure, healthy adult (heart and arteria), and fetal-like (iPSC-derived cardiovascular progenitor cells) CVS tissues. Comparison of the expression profiles revealed that RNA-binding proteins (RBPs) are highly overexpressed in fetal-like compared with healthy adult and are reactivated in heart failure, which results in expression of thousands fetal-specific isoforms. Of note, isoforms for 20 different RBPs were among those that reverted in heart failure to the fetal-like expression pattern. We determined that, compared with adult-specific isoforms, fetal-specific isoforms are more likely to bind RBPs, have canonical sequences at their splice sites and encode proteins with more functions. Our findings suggest targeting RBP fetal-specific isoforms could result in novel therapeutics for heart failure.

scholarly journals A Deep Boosting Based Approach for Capturing the Sequence Binding Preferences of RNA-Binding Proteins from High-Throughput CLIP-Seq Data

2016 ◽  
Author(s):  
Shuya Li ◽  
Fanghong Dong ◽  
Yuexin Wu ◽  
Sai Zhang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Binding Proteins ◽  
Rna Binding ◽  
Gene Expression Regulation ◽  
Rna Binding Proteins ◽  
False Negative ◽  
Functional Roles ◽  
Potential Applications ◽  
Validation Tests

AbstractCharacterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from the false positive and false negative issues. Here, we develop a deep boosting based machine learning approach, called DeBooster, to accurately model the binding sequence preferences and identify the corresponding binding targets of RBPs from CLIP-seq data. Comprehensive validation tests have shown that DeBooster can outperform other state-of-the-art approaches in predicting RBP targets and recover false negatives that are common in current CLIP-seq data. In addition, we have demonstrated several new potential applications of DeBooster in understanding the regulatory functions of RBPs, including the binding effects of the RNA helicase MOV10 on mRNA degradation, the influence of different binding behaviors of the ADAR proteins on RNA editing, as well as the antagonizing effect of RBP binding on miRNA repression. Moreover, DeBooster may provide an effective index to investigate the effect of pathogenic mutations in RBP binding sites, especially those related to splicing events. We expect that DeBooster will be widely applied to analyze large-scale CLIP-seq experimental data and can provide a practically useful tool for novel biological discoveries in understanding the regulatory mechanisms of RBPs.

The expanding world of metabolic enzymes moonlighting as RNA binding proteins

2021 ◽  
Author(s):  
Nicole J. Curtis ◽  
Constance J. Jeffery
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Binding Proteins ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Molecular Structures ◽  
Intermediary Metabolism ◽  
The Many ◽  
And Function

RNA binding proteins play key roles in many aspects of RNA metabolism and function, including splicing, transport, translation, localization, stability and degradation. Within the past few years, proteomics studies have identified dozens of enzymes in intermediary metabolism that bind to RNA. The wide occurrence and conservation of RNA binding ability across distant branches of the evolutionary tree suggest that these moonlighting enzymes are involved in connections between intermediary metabolism and gene expression that comprise far more extensive regulatory networks than previously thought. There are many outstanding questions about the molecular structures and mechanisms involved, the effects of these interactions on enzyme and RNA functions, and the factors that regulate the interactions. The effects on RNA function are likely to be wider than regulation of translation, and some enzyme–RNA interactions have been found to regulate the enzyme's catalytic activity. Several enzyme–RNA interactions have been shown to be affected by cellular factors that change under different intracellular and environmental conditions, including concentrations of substrates and cofactors. Understanding the molecular mechanisms involved in the interactions between the enzymes and RNA, the factors involved in regulation, and the effects of the enzyme–RNA interactions on both the enzyme and RNA functions will lead to a better understanding of the role of the many newly identified enzyme–RNA interactions in connecting intermediary metabolism and gene expression.

scholarly journals dSreg: a Bayesian model to integrate changes in splicing and RNA-binding protein activity

2019 ◽  
Vol 36 (7) ◽  
pp. 2134-2141
Author(s):  
Carlos Martí-Gómez ◽  
Enrique Lara-Pezzi ◽  
Fátima Sánchez-Cabo
Keyword(s):  
Bayesian Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Environmental Changes ◽  
Rna Binding Proteins ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Rna Seq ◽  
Protein Activity ◽  
Enrichment Methods

Abstract Motivation Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two-step framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis. Results To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNA-seq with data from regulatory features, e.g. binding sites of RNA-binding proteins. dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified AS changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RNA-binding proteins’ experiments from ENCODE, as opposed to traditional enrichment methods, such as over-representation analysis and gene set enrichment analysis. dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments. Availability and implementation dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg. Supplementary information Supplementary data are available at Bioinformatics online.

P5398CircRNAs deregulation in heart failure patients

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Greco ◽  
A Made' ◽  
M Longo ◽  
R Tikhomirov ◽  
S Castelvecchio ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Functional Characterization ◽  
Enrichment Analysis ◽  
Host Gene ◽  
Circular Rnas ◽  
Amplification Efficiency ◽  
Rna Seq ◽  
Bioinformatics Pipeline

Abstract Background Circular RNAs (circRNAs) are an emerging class of noncoding RNAs stemming from the splicing and circularization of pre-mRNAs exons. CircRNAs can regulate transcription and splicing, sequester microRNAs acting as “sponge” and inducing the respective targets, and bind to RNA binding proteins. Recently, they have been found deregulated in dilated cardiomyopathies (DCM), one of the cardiovascular diseases with the worst rate of morbidity and mortality, and whose molecular mechanisms are only partially known. Purpose Therein, we will evaluate in ischemic DCM patients the modulation of 17 circRNAs, 14 out of them obtained from literature data on DCM ischemic or not, while the other 3 were circRNAs not characterized in the heart previously. The study aims to identify circRNAs candidates for further functional characterization in DCM. In addition, as differential expression (DE) analysis is not easily performed for circRNAs in RNA-seq datasets, the validated circRNAs will be used to set up the most specific and sensitive bioinformatics pipeline for circRNA-DE analysis. Methods We designed divergent and convergent specific primers for 17 circRNAs and their host gene, respectively, and their amplification efficiency was measured by RT-qPCR. Transcripts expression was measured in left ventricle biopsies of 12 patients affected by non end-stage ischemic HF and of 12 matched controls. Results We identified cPVT1, cANKRD17, cBPTF as DE, and validated the modulation of 5 out of the 14 DCM-related circRNAs (cHIPK3, cALPK2, cPCMTD1, cNEBL, cSLC8A1), while cPDRM5, cTTN1 showed opposite modulation, which may be due to the specific disease condition. All of them were modulated differently from the respective host gene. CircRNA/miRNA interactions were predicted using Starbase 3.0. Next, mRNAs-targets of the identified miRNAs were predicted by mirDIP 4.1 and intersected with gene expression datasets of the same patients, previously obtained by microarray analysis. We found that cBPTF and cANKRD17 might sponge 12 and 2 miRNAs, respectively. Enrichment analysis of the relevant targets identified several important pathways implicated in DCM, such as MAPK, FoxO, EGFR, VEGF and Insulin/IGF pathways. In addition, deep RNA-Seq analysis that is currently ongoing and the validated circRNAs will be used to optimize the bioinformatics pipeline for circRNA DE analysis. Conclusions We identified a subset of circRNAs deregulated in ischemic HF potentially implicated in HF pathogenesis.

scholarly journals RNA-seq reveals the RNA binding proteins, Hfq and RsmA, play various roles in virulence, antibiotic production and genomic flux in Serratia sp. ATCC 39006

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 822 ◽  
Author(s):  
Nabil M Wilf ◽  
Adam J Reid ◽  
Joshua P Ramsay ◽  
Neil R Williamson ◽  
Nicholas J Croucher ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Antibiotic Production ◽  
Rna Seq

scholarly journals RBPMetaDB: A comprehensive annotation of mouse RNA-Seq datasets with perturbations of RNA-binding proteins

2018 ◽  
Author(s):  
Jin Li ◽  
Su-Ping Deng ◽  
Jacob Vieira ◽  
James Thomas ◽  
Valerio Costa ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Dna Binding ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Critical Role ◽  
Experimental Studies ◽  
Easy Access ◽  
Rna Seq ◽  
Mrna Stabilization

AbstractRNA-binding proteins may play a critical role in gene regulation in various diseases or biological processes by controlling post-transcriptional events such as polyadenylation, splicing, and mRNA stabilization via binding activities to RNA molecules. Due to the importance of RNA-binding proteins in gene regulation, a great number of studies have been conducted, resulting in a large amount of RNA-Seq datasets. However, these datasets usually do not have structured organization of metadata, which limits their potentially wide use. To bridge this gap, the metadata of a comprehensive set of publicly available mouse RNA-Seq datasets with perturbed RNA-binding proteins were collected and integrated into a database called RBPMetaDB. This database contains 278 mouse RNA-Seq datasets for a comprehensive list of 163 RNA-binding proteins. These RNA-binding proteins account for only ∼10% of all known RNA-binding proteins annotated in Gene Ontology, indicating that most are still unexplored using high-throughput sequencing. This negative information provides a great pool of candidate RNA-binding proteins for biologists to conduct future experimental studies. In addition, we found that DNA-binding activities are significantly enriched among RNA-binding proteins in RBPMetaDB, suggesting that prior studies of these DNA- and RNA-binding factors focus more on DNA-binding activities instead of RNA-binding activities. This result reveals the opportunity to efficiently reuse these data for investigation of the roles of their RNA-binding activities. A web application has also been implemented to enable easy access and wide use of RBPMetaDB. It is expected that RBPMetaDB will be a great resource for improving understanding of the biological roles of RNA-binding proteins.Database URL: http://rbpmetadb.yubiolab.org

scholarly journals miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258550
Author(s):  
Upendra Kumar Pradhan ◽  
Nitesh Kumar Sharma ◽  
Prakash Kumar ◽  
Ashwani Kumar ◽  
Sagar Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Mature Mirnas ◽  
Learning System ◽  
Mirna Biogenesis ◽  
Rna Seq ◽  
Human Mirnas ◽  
Research Areas ◽  
Spatio Temporal

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

scholarly journals RNA binding protein RBM3 augments kissing loop formation with lncRNAs to enhance translational control

2021 ◽  
Author(s):  
Afreen Asif Ali Sayed ◽  
Sonali Choudhury ◽  
Dharmalingam Subramaniam ◽  
Sumedha Gunewardena ◽  
Sivapriya Ponnurangam ◽  
...  
Keyword(s):  
Translational Control ◽  
Binding Proteins ◽  
Rna Binding ◽  
Epithelial Mesenchymal Transition ◽  
Rna Binding Proteins ◽  
Tumor Xenograft ◽  
Rna Seq ◽  
Loop Formation ◽  
Mesenchymal Transition ◽  
Kissing Loop

Background and Aims: Translational regulation involve the coordinated actions of RNA binding proteins (RBPs) and non-coding RNAs. For efficient translation, the mRNA needs to be circularized. While RNA binding proteins and translation factors have been shown to regulate the circularization, the role of lncRNAs in the process is not yet defined. Methods: We first performed RNA-seq and RNA-immunoprecipitation coupled-Seq (RIP-Seq) to identify differentially expressed lncRNA and mRNA in RBM3 overexpressing cell lines. We manipulated lncRNA expression in the cells and determined effects on gene expression and cell viability and motility. The studies were confirmed in vivo in intestine specific RBM3 transgenic and RBM3 knockout mouse models. Results: In comparing the RNA-Seq and RIP-Seq datasets, we identified increased expression of lncRNA LSAMP-3 and Flii-1 that bind to RBM3. In addition, there was an increase in expression of epithelial mesenchymal transition and angiogenesis markers following RBM3 overexpression. Moreover, modeling studies suggest that these lncRNAs formed kissing-loop interactions on target mRNAs including transcripts that encode epithelial mesenchymal transition and angiogenesis. While RBM3 transgenic mice showed increased LSAMP-3 and Flii-1, this was reduced in the RBM3 knockout mice. Also, RBM3 overexpression increased tumor xenograft growth, which was suppressed by knockdown of the lncRNAs. Also, knockdown of endogenous RBM3 specifically in the intestine suppressed azoxymethane-dextran sodium sulfate driven colitis-associated cancers, with a corresponding reduction in the expression of lncRNAs and transcripts that encode epithelial mesenchymal transition and angiogenesis. Conclusion: We propose that RBPs such as RBM3 mediate their function through regulatory lncRNAs that enable circularization to control translation.

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