scholarly journals Discovery of allele-specific protein-RNA interactions in human transcriptomes

2018 ◽  
Author(s):  
Emad Bahrami-Samani ◽  
Yi Xing
Keyword(s):  
Genetic Variants ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Specific Protein ◽  
Computational Method ◽  
Joint Analysis ◽  
Transcriptional Level ◽  
Rna Seq ◽  
Allele Specific ◽  
Rna Interaction

AbstractGene expression is tightly regulated at the post-transcriptional level through splicing, transport, translation, and decay. RNA-binding proteins (RBPs) play key roles in post-transcriptional gene regulation, and genetic variants that alter RBP-RNA interactions can affect gene products and functions. We developed a computational method ASPRIN (Allele-Specific Protein-RNA Interaction), that uses a joint analysis of CLIP-seq (cross-linking and immunoprecipitation followed by high-throughput sequencing) and RNA-seq data to identify genetic variants that alter RBP-RNA interactions by directly observing the allelic preference of RBP from CLIP-seq experiments as compared to RNA-seq. We used ASPRIN to systematically analyze CLIP-seq and RNA-seq data for 166 RBPs in two ENCODE (Encyclopedia of DNA Elements) cell lines. ASPRIN identified genetic variants that alter RBP-RNA interactions by modifying RBP binding motifs within RNA. Moreover, through an integrative ASPRIN analysis with population-scale RNA-seq data, we showed that ASPRIN can help reveal potential causal variants that affect alternative splicing via allele-specific protein-RNA interactions.

scholarly journals Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA

2018 ◽  
Author(s):  
Ei-Wen Yang ◽  
Jae Hoon Bahn ◽  
Esther Yun-Hua Hsiao ◽  
Boon Xin Tan ◽  
Yiwei Sun ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Specific Binding ◽  
Specific Protein ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Allele Specific ◽  
Rna Interaction

AbstractAllele-specific protein-RNA binding is an essential aspect that may reveal functional genetic variants influencing RNA processing and gene expression phenotypes. Recently, genome-wide detection of in vivo binding sites of RNA binding proteins (RBPs) is greatly facilitated by the enhanced UV crosslinking and immunoprecipitation (eCLIP) protocol. Hundreds of eCLIP-Seq data sets were generated from HepG2 and K562 cells during the ENCODE3 phase. These data afford a valuable opportunity to examine allele-specific binding (ASB) of RBPs. To this end, we developed a new computational algorithm, called BEAPR (Binding Estimation of Allele-specific Protein-RNA interaction). In identifying statistically significant ASB sites, BEAPR takes into account UV cross-linking induced sequence propensity and technical variations between replicated experiments. Using simulated data and actual eCLIP-Seq data, we show that BEAPR largely outperforms often-used methods Chi-Squared test and Fisher’s Exact test. Importantly, BEAPR overcomes the inherent over-dispersion problem of the other methods. Complemented by experimental validations, we demonstrate that ASB events are significantly associated with genetic regulation of splicing and mRNA abundance, supporting the usage of this method to pinpoint functional genetic variants in post-transcriptional gene regulation. Many variants with ASB patterns of RBPs were found as genetic variants with cancer or other disease relevance. About 38% of ASB variants were in linkage disequilibrium with single nucleotide polymorphisms from genome-wide association studies. Overall, our results suggest that BEAPR is an effective method to reveal ASB patterns in eCLIP and can inform functional interpretation of disease-related genetic variants.

scholarly journals APA-Scan: Detection and Visualization of 3’-UTR APA with RNA-seq and 3’-end-seq Data

2020 ◽  
Author(s):  
Naima Ahmed Fahmi ◽  
Jae-Woong Chang ◽  
Heba Nassereddeen ◽  
Khandakar Tanvir Ahmed ◽  
Deliang Fan ◽  
...  
Keyword(s):  
Rna Binding ◽  
Rna Binding Proteins ◽  
Alternative Polyadenylation ◽  
Transcriptional Level ◽  
Cleavage Sites ◽  
Read Coverage ◽  
Rna Seq ◽  
Regulate Gene Expression ◽  
Polyadenylation Sites ◽  
Polyadenylation Signals

AbstractThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3’-untranslated region (3’-UTR) of mRNA produces transcripts with shorter 3’-UTR. Often, 3’-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3’-UTR APA provides a means to regulate gene expression at the post-transcriptional level and is known to promote translation. Current bioinformatics pipelines have limited capability in profiling 3’-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3’-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3’-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3’-UTR transcripts in the RNA-seq data. The performance of APA-Scan was validated by qPCR.ImplementationAPA-Scan is implemented in Python. Source code and a comprehensive user’s manual are freely available at https://github.com/compbiolabucf/APA-Scan

CELF1 Promotes Matrix Metalloproteinases Gene Expression at Transcriptional Level in Lens Epithelial Cells

2021 ◽  
Author(s):  
Jun Xiao ◽  
Xi Tian ◽  
Siyan Jin ◽  
Yanhui He ◽  
Meijiao Song ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Epithelial Cells ◽  
Matrix Metalloproteinases ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Lens Epithelial Cells ◽  
Transcriptional Level ◽  
Rna Seq ◽  
Whole Transcriptome Sequencing ◽  
Underlying Mechanisms

Abstract Background: RNA binding proteins (RBPs)-mediated regulation plays important roles in many eye diseases, including the canonical RBP CELF1 in cataract. While the definite molecular regulatory mechanisms of CELF1 on cataract still remain elusive. Methods: In this study, we overexpressed CELF1 in lens epithelial SRA01/04 cells and applied whole transcriptome sequencing (RNA-seq) method to analyze the global differences mediated by CELF1. We then analyzed public RNA-seq and CELF1-RNA interactome data to decipher the underlying mechanisms.Results: The results showed that transcriptome profile was globally changed by CELF1 overexpression (CELF1-OE). Functional analysis revealed CELF1 specifically increased the expression of genes in extracellular matrix disassembly, extracellular matrix organization, and proteolysis, which could be classified into matrix metalloproteinases (MMPs) family. This finding was also validated by RT-qPCR and public mouse early embryonic lens data. Integrating analysis with public CELF1-RNA interactome data revealed that no obvious CELF1-binding peak was found on the transcripts of these genes, indicating an indirectly regulatory role of CELF1 in lens epithelial cells. Conclusions: Our study demonstrated that CELF1-OE promotes transcriptional level of MMP genes; and this regulation may be completed by other ways except for binding to RNA targets. These results suggest that CELF1-OE is implicated in the development of lens, which is associated with cataract and expands our understanding of CELF1 regulatory roles as an RNA binding protein.

scholarly journals PremPRI: Predicting the Effects of Single Mutations on Protein-RNA Interactions

2020 ◽  
Author(s):  
Ning Zhang ◽  
Haoyu Lu ◽  
Yuting Chen ◽  
Zefeng Zhu ◽  
Qing Yang ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Pearson Correlation ◽  
Regulation Of Gene Expression ◽  
Scoring Function ◽  
Computational Method ◽  
Missense Mutations ◽  
Rna Interaction

ABSTRACTProtein-RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein-RNA interaction may contribute to the pathogenesis of many diseases. Here we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein-RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of 11 sequence- and structure-based features, and is parameterized on 248 mutations from 50 protein-RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal mol−1, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein-RNA interaction inhibitors. PremPRI is freely available at http://lilab.jysw.suda.edu.cn/research/PremPRI/.

scholarly journals PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions

2020 ◽  
Vol 21 (15) ◽  
pp. 5560
Author(s):  
Ning Zhang ◽  
Haoyu Lu ◽  
Yuting Chen ◽  
Zefeng Zhu ◽  
Qing Yang ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Pearson Correlation ◽  
Regulation Of Gene Expression ◽  
Scoring Function ◽  
Computational Method ◽  
Missense Mutations ◽  
Rna Interaction

Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol−1, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.

scholarly journals Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ei-Wen Yang ◽  
Jae Hoon Bahn ◽  
Esther Yun-Hua Hsiao ◽  
Boon Xin Tan ◽  
Yiwei Sun ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Specific Binding ◽  
Allele Specific

scholarly journals RNA-binding proteins and circadian rhythms in Arabidopsis thaliana

2001 ◽  
Vol 356 (1415) ◽  
pp. 1755-1759 ◽  
Author(s):  
Dorothee Staiger
Keyword(s):  
Arabidopsis Thaliana ◽  
Feedback Loop ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Transcriptional Level ◽  
Reverse Genetic ◽  
Genetic Approach ◽  
Circadian Oscillators ◽  
Clock Proteins ◽  
Reverse Genetic Approach

An Arabidopsis transcript preferentially expressed at the end of the daily light period codes for the RNA–binding protein At GRP7. A reverse genetic approach in Arabidopsis thaliana has revealed its role in the generation of circadian rhythmicity: At GRP7 is part of a negative feedback loop through which it influences the oscillations of its own transcript. Biochemical and genetic experiments indicate a mechanism for this autoregulatory circuit: At grp7 gene transcription is rhythmically activated by the circadian clock during the day. The At GPR7 protein accumulates with a certain delay and represses further accumulation of its transcript, presumably at the post–transcriptional level. In this respect, the At GRP7 feedback loop differs from known circadian oscillators in the fruitfly Drosophila and mammals based on oscillating clock proteins that repress transcription of their own genes with a 24 h rhythm. It is proposed that the At GRP7 feedback loop may act within an output pathway from the Arabidopsis clock.

scholarly journals Multiple roles of heterogeneous nuclear ribonucleoprotein K during skeletal muscle cell differentiation

2021 ◽  
Author(s):  
Keisuke Hitachi ◽  
Yuri Kiyofuji ◽  
Masashi Nakatani ◽  
Kunihiro Tsuchida
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Myogenic Differentiation ◽  
Cell Physiology ◽  
Transcriptional Level ◽  
Loss Of Function ◽  
Independent Manner ◽  
Multiple Functions

RNA-binding proteins (RBPs) regulate cell physiology via the formation of ribonucleic-protein complexes with coding and non-coding RNAs. RBPs have multiple functions in the same cells; however, the precise mechanism through which their pleiotropic functions are determined remains unknown. In this study, we revealed the multiple inhibitory functions of hnRNPK for myogenic differentiation. We first identified hnRNPK as a lncRNA Myoparr binding protein. Gain- and loss-of-function experiments showed that hnRNPK repressed the expression of myogenin at the transcriptional level via binding to Myoparr. Moreover, hnRNPK repressed the expression of a set of genes coding for aminoacyl-tRNA synthetases in a Myoparr-independent manner. Mechanistically, hnRNPK regulated the eIF2α/Atf4 pathway, one branch of the intrinsic pathways of the endoplasmic reticulum sensors, in differentiating myoblasts. Thus, our findings demonstrate that hnRNPK plays multiple lncRNA-dependent and -independent roles in the inhibition of myogenic differentiation, indicating that the analysis of lncRNA-binding proteins will be useful for elucidating both the physiological functions of lncRNAs and the multiple functions of RBPs.

scholarly journals Nucleocytoplasmic Transport of RNA-Binding Proteins Regulates Neural Stem Cell Fates 

2020 ◽  
Author(s):  
Melissa J. MacPherson ◽  
Sarah L Erickson ◽  
Drayden Kopp ◽  
Pengqiang Wen ◽  
Mohammadreza Aghanoori ◽  
...  
Keyword(s):  
Progenitor Cells ◽  
Cell Fate ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Neurodevelopmental Disorder ◽  
Neural Progenitor ◽  
Nucleocytoplasmic Transport ◽  
Transcriptional Level ◽  
Cell Fates ◽  
Cell Fate Decisions

Abstract The formation of the cerebral cortex requires balanced expansion and differentiation of neural progenitor cells, the fate choice of which requires regulation at many steps of gene expression. As progenitor cells often exhibit transcriptomic stochasticity, the ultimate output of cell fate-determining genes must be carefully controlled at the post-transcriptional level, but how this is orchestrated is poorly understood. Here we report that de novo missense variants in an RNA-binding protein CELF2 cause human cortical malformations and perturb neural progenitor cell fate decisions in mice by disrupting the nucleocytoplasmic transport of CELF2. In self-renewing neural progenitors, CELF2 is localized in the cytoplasm where it binds and coordinates mRNAs that encode cell fate regulators and neurodevelopmental disorder-related factors. The translocation of CELF2 into the nucleus releases mRNAs for translation and thereby triggers neural progenitor differentiation. Our results reveal a mechanism by which transport of CELF2 between discrete subcellular compartments orchestrates an RNA regulon to instruct cell fates in cerebral cortical development.

scholarly journals A deep learning framework to predict binding preference of RNA constituents on protein surface

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Jordy Homing Lam ◽  
Yu Li ◽  
Lizhe Zhu ◽  
Ramzan Umarov ◽  
Hanlun Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Binding Preference ◽  
Rna Backbone ◽  
Binding Pockets ◽  
Rna Interaction

Abstract Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.

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