scholarly journals RNA regulons and the RNA-protein interaction network

2012 ◽  
Vol 3 (5) ◽  
pp. 403-414 ◽  
Author(s):  
Jochen Imig ◽  
Alexander Kanitz ◽  
André P. Gerber
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Network ◽  
Coordinated Control ◽  
Non Coding Rnas ◽  
Rna Interaction

AbstractThe development of genome-wide analysis tools has prompted global investigation of the gene expression program, revealing highly coordinated control mechanisms that ensure proper spatiotemporal activity of a cell’s macromolecular components. With respect to the regulation of RNA transcripts, the concept of RNA regulons, which – by analogy with DNA regulons in bacteria – refers to the coordinated control of functionally related RNA molecules, has emerged as a unifying theory that describes the logic of regulatory RNA-protein interactions in eukaryotes. Hundreds of RNA-binding proteins and small non-coding RNAs, such as microRNAs, bind to distinct elements in target RNAs, thereby exerting specific and concerted control over posttranscriptional events. In this review, we discuss recent reports committed to systematically explore the RNA-protein interaction network and outline some of the principles and recurring features of RNA regulons: the coordination of functionally related mRNAs through RNA-binding proteins or non-coding RNAs, the modular structure of its components, and the dynamic rewiring of RNA-protein interactions upon exposure to internal or external stimuli. We also summarize evidence for robust combinatorial control of mRNAs, which could determine the ultimate fate of each mRNA molecule in a cell. Finally, the compilation and integration of global protein-RNA interaction data has yielded first insights into network structures and provided the hypothesis that RNA regulons may, in part, constitute noise ‘buffers’ to handle stochasticity in cellular transcription.

scholarly journals Systematic discovery and functional interrogation of SARS-CoV-2 viral RNA-host protein interactions during infection

2020 ◽  
Author(s):  
Ryan A. Flynn ◽  
Julia A. Belk ◽  
Yanyan Qi ◽  
Yuki Yasumoto ◽  
Cameron O. Schmitz ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Viral Rna ◽  
Host Protein ◽  
List Type ◽  
Genome Wide ◽  
A Genome

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a pandemic with growing global mortality. There is an urgent need to understand the molecular pathways required for host infection and anti-viral immunity. Using comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS), we identified 309 host proteins that bind the SARS-CoV-2 RNA during active infection. Integration of this data with viral ChIRP-MS data from three other positive-sense RNA viruses defined pan-viral and SARS-CoV-2-specific host interactions. Functional interrogation of these factors with a genome-wide CRISPR screen revealed that the vast majority of viral RNA-binding proteins protect the host from virus-induced cell death, and we identified known and novel anti-viral proteins that regulate SARS-CoV-2 pathogenicity. Finally, our RNA-centric approach demonstrated a physical connection between SARS-CoV-2 RNA and host mitochondria, which we validated with functional and electron microscopy data, providing new insights into a more general virus-specific protein logic for mitochondrial interactions. Altogether, these data provide a comprehensive catalogue of SARS-CoV-2 RNA-host protein interactions, which may inform future studies to understand the mechanisms of viral pathogenesis, as well as nominate host pathways that could be targeted for therapeutic benefit.Highlights· ChIRP-MS of SARS-CoV-2 RNA identifies a comprehensive viral RNA-host protein interaction network during infection across two species· Comparison to RNA-protein interaction networks with Zika virus, dengue virus, and rhinovirus identify SARS-CoV-2-specific and pan-viral RNA protein complexes and highlights distinct intracellular trafficking pathways· Intersection of ChIRP-MS and genome-wide CRISPR screens identify novel SARS-CoV-2-binding proteins with pro- and anti-viral function· Viral RNA-RNA and RNA-protein interactions reveal specific SARS-CoV-2-mediated mitochondrial dysfunction during infection

scholarly journals Transcriptome-wide high-throughput mapping of protein–RNA occupancy profiles using POP-seq

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansi Srivastava ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Network ◽  
K562 Cells ◽  
Genomic Variation ◽  
Interaction Sites ◽  
Protein Occupancy ◽  
Rna Interaction

AbstractInteraction between proteins and RNA is critical for post-transcriptional regulatory processes. Existing high throughput methods based on crosslinking of the protein–RNA complexes and poly-A pull down are reported to contribute to biases and are not readily amenable for identifying interaction sites on non poly-A RNAs. We present Protein Occupancy Profile-Sequencing (POP-seq), a phase separation based method in three versions, one of which does not require crosslinking, thus providing unbiased protein occupancy profiles on whole cell transcriptome without the requirement of poly-A pulldown. Our study demonstrates that ~ 68% of the total POP-seq peaks exhibited an overlap with publicly available protein–RNA interaction profiles of 97 RNA binding proteins (RBPs) in K562 cells. We show that POP-seq variants consistently capture protein–RNA interaction sites across a broad range of genes including on transcripts encoding for transcription factors (TFs), RNA-Binding Proteins (RBPs) and long non-coding RNAs (lncRNAs). POP-seq identified peaks exhibited a significant enrichment (p value < 2.2e−16) for GWAS SNPs, phenotypic, clinically relevant germline as well as somatic variants reported in cancer genomes, suggesting the prevalence of uncharacterized genomic variation in protein occupied sites on RNA. We demonstrate that the abundance of POP-seq peaks increases with an increase in expression of lncRNAs, suggesting that highly expressed lncRNA are likely to act as sponges for RBPs, contributing to the rewiring of protein–RNA interaction network in cancer cells. Overall, our data supports POP-seq as a robust and cost-effective method that could be applied to primary tissues for mapping global protein occupancies.

scholarly journals Transcriptome-wide high-throughput mapping of protein-RNA occupancy profiles using POP-seq

2020 ◽  
Author(s):  
Mansi Srivastava ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Network ◽  
Genomic Variation ◽  
P Value ◽  
Interaction Sites ◽  
Protein Occupancy ◽  
Rna Interaction

AbstractInteraction between proteins and RNA is critical for post-transcriptional regulatory processes. Existing high throughput methods based on crosslinking of the protein-RNA complexes and polyA pull down are reported to contribute to biases and are not readily amenable for identifying interaction sites on non polyA RNAs. We present Protein Occupancy Profile-Sequencing (POP-seq), a phase separation based method in three versions, one of which does not require crosslinking, thus providing unbiased protein occupancy profiles on whole cell transcriptome without the requirement of polyA pulldown. Our study demonstrates that ~68% of the total POP-seq peaks exhibited an overlap with publicly available protein-RNA interaction profiles of 97 RNA binding proteins (RBPs) in K562 cells. We show that POP-seq variants consistently capture protein-RNA interaction sites across a broad range of genes including on transcripts encoding for transcription factors (TFs), RNA-Binding Proteins (RBPs) and long non-coding RNAs (lncRNAs). POP-seq identified peaks exhibited a significant enrichment (p value < 2.2e-16) for GWAS SNPs, phenotypic, clinically relevant germline as well as somatic variants reported in cancer genomes, suggesting the prevalence of uncharacterized genomic variation in protein occupied sites on RNA. We demonstrate that the abundance of POP-seq peaks increases with an increase in expression of lncRNAs, suggesting that highly expressed lncRNA are likely to act as sponges for RBPs, contributing to the rewiring of protein-RNA interaction network in cancer cells. Overall, our data supports POP-seq as a robust and cost-effective method that could be applied to primary tissues for mapping global protein occupancies.

PlncRNADB: A Repository of Plant lncRNAs and lncRNA-RBP Protein Interactions

2019 ◽  
Vol 14 (7) ◽  
pp. 621-627 ◽  
Author(s):  
Youhuang Bai ◽  
Xiaozhuan Dai ◽  
Tiantian Ye ◽  
Peijing Zhang ◽  
Xu Yan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Populus Trichocarpa ◽  
Noncoding Rnas ◽  
Reference Database ◽  
Protein Coding ◽  
Arabidopsis Lyrata ◽  
User Friendly

Background: Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants. Objective: PlncRNADB is a searchable database of lncRNA sequences and annotation in plants. Methods: We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts. Results: More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface. Conclusion: PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.

scholarly journals Posttranscriptional regulation of lipid metabolism by non-coding RNAs and RNA binding proteins

2018 ◽  
Vol 81 ◽  
pp. 129-140 ◽  
Author(s):  
Abhishek K. Singh ◽  
Binod Aryal ◽  
Xinbo Zhang ◽  
Yuhua Fan ◽  
Nathan L. Price ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Posttranscriptional Regulation ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Non Coding Rnas

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

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.

scholarly journals Roles of long non-coding RNAs and emerging RNA-binding proteins in innate antiviral responses

Theranostics ◽  
2020 ◽  
Vol 10 (20) ◽  
pp. 9407-9424 ◽  
Author(s):  
Yiliang Wang ◽  
Yun Wang ◽  
Weisheng Luo ◽  
Xiaowei Song ◽  
Lianzhou Huang ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Antiviral Responses ◽  
Non Coding Rnas

scholarly journals The latent geometry of the human protein interaction network

2017 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Pablo Mier ◽  
Miguel A. Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Biologically Relevant ◽  
Human Protein Interaction ◽  
Connection Probability ◽  
Cellular Localisation ◽  
Complex Architecture

AbstractTo mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

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