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.

scholarly journals dSreg: A bayesian model to integrate changes in splicing and RNA binding protein activity

2019 ◽  
Author(s):  
Carlos Martí-Gómez ◽  
Enrique Lara-Pezzi ◽  
Fátima Sánchez-Cabo
Keyword(s):  
Alternative Splicing ◽  
Bayesian Model ◽  
Rna Binding ◽  
Environmental Changes ◽  
Rna Binding Proteins ◽  
Simulated Data ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Protein Activity ◽  
Enrichment Methods

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. To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNAseq with data from regulatory features, e.g. binding sites of RNA binding proteins (RBPs). 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 alternative splicing changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RBPs experiments from ENCODE, as opposed to traditional enrichment methods such as Over-representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). 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. dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg.

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 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 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.

scholarly journals SEQing: web-based visualization of iCLIP and RNA-seq data in an interactive python framework

2019 ◽  
Author(s):  
Martin Lewinski ◽  
Yannik Bramkamp ◽  
Tino Köster ◽  
Dorothee Staiger
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Target Genes ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Seq ◽  
Web Based ◽  
Genome Wide ◽  
Functional Relevance ◽  
Nucleotide Resolution

AbstractBackgroundRNA-binding proteins interact with their target RNAs at specific sites. These binding sites can be determined genome-wide through individual nucleotide resolution crosslinking immunoprecipitation (iCLIP). Subsequently, the binding sites have to be visualized. So far, no visualization tool exists that is easily accessible but also supports restricted access so that data can be shared among collaborators.ResultsHere we present SEQing, a customizable interactive dashboard to visualize crosslink sites on target genes of RNA-binding proteins that have been obtained by iCLIP. Moreover, SEQing supports RNA-seq data that can be displayed in a diffrerent window tab. This allows, e.g. crossreferencing the iCLIP data with genes differentially expressed in mutants of the RBP and thus obtain some insights into a potential functional relevance of the binding sites. Additionally, detailed information on the target genes can be incorporated in another tab.ConclusionSEQing is written in Python3 and runs on Linux. The web-based access makes iCLIP data easily accessible, even with mobile devices. SEQing is customizable in many ways and has also the option to be secured by a password. The source code is available at https://github.com/malewins/SEQing.

scholarly journals Identification and validation of an immune-associated RNA-binding proteins signature to predict clinical outcomes and therapeutic responses in colon cancer patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Di Sun ◽  
Kui-Sheng Yang ◽  
Jian-Liang Chen ◽  
Zheng-bing Wang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Binding Proteins ◽  
Drug Sensitivity ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Infiltration ◽  
Tcga Dataset

Abstract Background The immune infiltration of patients with colon cancer (CC) is closely associated with RNA-binding proteins (RBPs). However, immune-associated RBPs (IARBPs) in CC remain unexplored. Methods The data were downloaded from The Cancer Genome Atlas (TCGA) and the patients were divided into four immune subgroups by single sample gene set enrichment analysis (ssGSEA), in which weighted gene correlation network analysis (WGCNA) identified modules of co-expressed genes correlated with immune infiltration. Univariate (UCR) and multivariate Cox regression (MCR) analyses were applied to screen survival-associated IARBPs. Then, a prognostic signature was performed on TCGA dataset. Risk model was constructed based on the TCGA dataset. Based on the median risk score, CC patients were subdivided into low- and high-risk groups. Furthermore, the accuracy and prognostic value of this signature were validated by using Kaplan-Meier (K-M) curve, receiver operating characteristic (ROC). We further validated the findings in Gene Expression Omnibus (GEO) database. Finally, we evaluated the association between gene expression level and drug sensitivity. Results Based on the infiltration of immune cells, the TCGA patients were divided into four subgroups. In total, we identified 25 IARBPs, after differential expression and WGCNA analysis. Subsequently, two IARBP signatures (FBXO17 and PPARGC1A) were identified to be significantly associated with the overall survival (OS) of CC patients. K-M survival analysis revealed that the low-risk group correlated with prolonged OS. The prognostic signature was an independent prognostic factor and reflects the immune status of CC patients. Finally, FBXO17 was related with drug sensitivity of bleomycin, gemcitabine, and lenvatinib. PPARGC1A was related to drug sensitivity of dabrafenib, vemurafenib, and trametinib. Conclusion A novel two immune-associated RBPs that was established that may be useful in predicting survival and individualized treatment.

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 RNA-seq analysis of galaninergic neurons from ventrolateral preoptic nucleus identifies expression changes between sleep and wake

2020 ◽  
Author(s):  
Xiaofeng Guo ◽  
Xiaoling Gao ◽  
Brendan T. Keenan ◽  
Jingxu Zhu ◽  
Dimitra Sarantopoulou ◽  
...  
Keyword(s):  
Dna Repair ◽  
Er Stress ◽  
Differentially Expressed Genes ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Preoptic Nucleus ◽  
Ventrolateral Preoptic Nucleus

Abstract Background : Previous studies show that galanin neurons in ventrolateral preoptic nucleus (VLPO-Gal) are essential for sleep regulation. Here, we explored the function of the VLPO-Gal neurons in sleep by comparing their transcriptional responses between sleeping mice and those kept awake, sacrificed at the same diurnal time. Results : RNA-sequencing (RNA-seq) analysis was performed on eGFP(+) galanin neurons isolated using laser captured microdissection (LCM) from VLPO. Expression of Gal was assessed in our LCM eGFP(+) neurons via real time qPCR and showed marked enrichment when compared to LCM eGFP(-) neurons and to bulk VLPO samples. Gene set analysis utilizing data from a recent single-cell RNA-seq study of the preoptic area demonstrated that our VLPO-Gal samples were highly enriched with galanin-expressing inhibitory neurons, but not galanin-expressing excitatory neurons. A total of 263 genes were differentially expressed between sleep and wake in VLPO-Gal neurons. When comparing differentially expressed genes in VLPO-Gal neurons to differentially expressed genes in a wake-active neuronal region (the medial prefrontal cortex), evidence indicates that both systemic and cell-specific mechanisms contribute to the transcriptional regulation in VLPO-Gal neurons. In both wake-active and sleep-active neurons, ER stress pathways are activated by wake and cold-inducible RNA-binding proteins are activated by sleep. In contrast, expression of DNA repair genes is increased in VLPO-Gal during wakefulness, but increased in wake-active cells during sleep. Conclusion : Our study identified transcriptomic responses of the galanin neurons in the ventrolateral preoptic nucleus (VLPO) during sleep and sleep deprivation. Data indicate that VLPO contains mainly sleep-active inhibitory galaninergic neurons. The VLPO galanin neurons show responses to sleep and wake similar to wake-active regions, indicating these responses, such as ER stress and cold-inducible RNA-binding proteins, are systemic affecting all neuronal populations. Region-specific differences in sleep/wake responses were also identified, in particular DNA repair, suggesting these could be driven by neuronal activity. Our study expands knowledge about the transcriptional response of a distinct group of neurons essential for sleep.

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