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.

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 Functional characterization of splicing regulatory elements

2021 ◽  
Author(s):  
Scott I Adamson ◽  
Lijun Zhan ◽  
Brenton R Graveley
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Functional Characterization ◽  
Regulatory Elements ◽  
Small Subset ◽  
General Sequence ◽  
Splicing Regulators ◽  
Splicing Regulatory Elements

Background: RNA binding protein-RNA interactions mediate a variety of processes including pre-mRNA splicing, translation, decay, polyadenylation and many others. Previous high-throughput studies have characterized general sequence features associated with increased and decreased splicing of certain exons, but these studies are limited by not knowing the mechanisms, and in particular, the mediating RNA binding proteins, underlying these associations. Results: Here we utilize ENCODE data from diverse data modalities to identify functional splicing regulatory elements and their associated RNA binding proteins. We identify features which make splicing events more sensitive to depletion of RNA binding proteins, as well as which RNA binding proteins act as splicing regulators sensitive to depletion. To analyze the sequence determinants underlying RBP-RNA interactions impacting splicing, we assay tens of thousands of sequence variants in a high-throughput splicing reporter called Vex-seq and confirm a small subset in their endogenous loci using CRISPR base editors. Finally, we leverage other large transcriptomic datasets to confirm the importance of RNA binding proteins which we designed experiments around and identify additional RBPs which may act as additional splicing regulators of the exons studied. Conclusions: This study identifies sequence and other features underlying splicing regulation mediated specific RNA binding proteins, as well as validates and identifies other potentially important regulators of splicing in other large transcriptomic datasets.

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.

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 CASowary: CRISPR-Cas13 guide RNA predictor for transcript depletion

2021 ◽  
Author(s):  
Alexander Krohannon ◽  
Mansi Srivastava ◽  
Simone Rauch ◽  
Rajneesh Srivastava ◽  
Bryan Dickinson ◽  
...  
Keyword(s):  
Rna Binding ◽  
Rna Binding Proteins ◽  
Training Data ◽  
Therapeutic Interventions ◽  
Hek293 Cells ◽  
Data Set ◽  
Guide Rna ◽  
Target Transcript ◽  
Protein Occupancy ◽  
Rna Interaction

Recent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which further limit the scope of effective target sequences. As a result, there currently exists no method to predict whether a specific sgRNA will effectively knockdown a transcript. Here we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from Cas13 characterization experiments for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information on RNA. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 70%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting Cas system - CIRTS, confirming the robustness and reproducibility of our model's sgRNA predictions. Utilizing transcriptome wide protein occupancy map generated using POP-seq in Hela cells against publicly available protein-RNA interaction map in Hek293 cells, we show that CASowary can predict high quality guides for numerous transcripts in a cell line specific manner. Application of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.

scholarly journals An interaction network of RNA-binding proteins involved in Drosophila oogenesis

2020 ◽  
Author(s):  
Prashali Bansal ◽  
Johannes Madlung ◽  
Kristina Schaaf ◽  
Boris Macek ◽  
Fulvia Bono
Keyword(s):  
Translational Regulation ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Direct Interaction ◽  
Interaction Network ◽  
Splicing Factors ◽  
Mrna Regulation ◽  
Interaction Partners ◽  
Maternal Transcripts

AbstractDuring Drosophila oogenesis, the localization and translational regulation of maternal transcripts relies on RNA-binding proteins (RBPs). Many of these RBPs localize several mRNAs and may have additional direct interaction partners to regulate their functions. Using immunoprecipitation from whole Drosophila ovaries coupled to mass spectrometry, we examined protein-protein associations of 6 GFP-tagged RBPs expressed at physiological levels. Analysis of the interaction network and further validation in human cells allowed us to identify 26 previously unknown associations, besides recovering several well characterized interactions. We identified interactions between RBPs and several splicing factors, providing links between nuclear and cytoplasmic events of mRNA regulation. Additionally, components of the translational and RNA decay machineries were selectively co-purified with some baits, suggesting a mechanism for how RBPs may regulate maternal transcripts. Given the evolutionary conservation of the studied RBPs, the interaction network presented here provides the foundation for future functional and structural studies of mRNA localization across metazoans.

scholarly journals High-throughput mutagenesis identifies mutations and RNA binding proteins controlling CD19 splicing and CART-19 therapy resistance

2021 ◽  
Author(s):  
Mariela Cortés-López ◽  
Laura Schulz ◽  
Mihaela Enculescu ◽  
Claudia Paret ◽  
Bea Spiekermann ◽  
...  
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Single Point ◽  
Point Mutations ◽  
Therapy Resistance ◽  
Regulatory Elements ◽  
Splice Isoforms ◽  
Exon 2

During CART-19 immunotherapy for B-cell acute lymphoblastic leukaemia (B-ALL), many patients relapse due to loss of the cognate CD19 epitope. Since epitope loss can be caused by aberrant CD19 exon 2 processing, we herein investigate the regulatory code that controls CD19 splicing. We combine high-throughput mutagenesis with mathematical modelling to quantitatively disentangle the effects of all mutations in the region comprising CD19 exons 1-3. Thereupon, we identify ~200 single point mutations that alter CD19 splicing and thus could predispose B-ALL patients to CART-19 resistance. Furthermore, we report almost 100 previously unknown splice isoforms that emerge from cryptic splice sites and likely encode non-functional CD19 proteins. We further identify cis-regulatory elements and trans-acting RNA-binding proteins that control CD19 splicing (e.g., PTBP1 and SF3B4) and validate that loss of these factors leads to enhanced CD19 mis-splicing. Our dataset represents a comprehensive resource for potential prognostic factors predicting success of CART-19 therapy.

scholarly journals An Interaction Network of RNA-Binding Proteins Involved in Drosophila Oogenesis

2020 ◽  
Vol 19 (9) ◽  
pp. 1485-1502
Author(s):  
Prashali Bansal ◽  
Johannes Madlung ◽  
Kristina Schaaf ◽  
Boris Macek ◽  
Fulvia Bono
Keyword(s):  
Translational Regulation ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Direct Interaction ◽  
Interaction Network ◽  
Splicing Factors ◽  
Mrna Regulation ◽  
Interaction Partners ◽  
Maternal Transcripts

During Drosophila oogenesis, the localization and translational regulation of maternal transcripts relies on RNA-binding proteins (RBPs). Many of these RBPs localize several mRNAs and may have additional direct interaction partners to regulate their functions. Using immunoprecipitation from whole Drosophila ovaries coupled to mass spectrometry, we examined protein-protein associations of 6 GFP-tagged RBPs expressed at physiological levels. Analysis of the interaction network and further validation in human cells allowed us to identify 26 previously unknown associations, besides recovering several well characterized interactions. We identified interactions between RBPs and several splicing factors, providing links between nuclear and cytoplasmic events of mRNA regulation. Additionally, components of the translational and RNA decay machineries were selectively co-purified with some baits, suggesting a mechanism for how RBPs may regulate maternal transcripts. Given the evolutionary conservation of the studied RBPs, the interaction network presented here provides the foundation for future functional and structural studies of mRNA localization across metazoans.

scholarly journals Rapidly Growing Protein-Centric Technologies to Extensively Identify Protein–RNA Interactions: Application to the Analysis of Co-Transcriptional RNA Processing

2021 ◽  
Vol 22 (10) ◽  
pp. 5312
Author(s):  
Akio Masuda ◽  
Toshihiko Kawachi ◽  
Kinji Ohno
Keyword(s):  
Rna Polymerase Ii ◽  
Rna Processing ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Detection Efficiency ◽  
Fine Tuning ◽  
Interaction Sites ◽  
Rnap Ii ◽  
Rna Interaction ◽  
In Cells

During mRNA transcription, diverse RNA-binding proteins (RBPs) are recruited to RNA polymerase II (RNAP II) transcription machinery. These RBPs bind to distinct sites of nascent RNA to co-transcriptionally operate mRNA processing. Recent studies have revealed a close relationship between transcription and co-transcriptional RNA processing, where one affects the other’s activity, indicating an essential role of protein–RNA interactions for the fine-tuning of mRNA production. Owing to their limited amount in cells, the detection of protein–RNA interactions specifically assembled on the transcribing RNAP II machinery still remains challenging. Currently, cross-linking and immunoprecipitation (CLIP) has become a standard method to detect in vivo protein–RNA interactions, although it requires a large amount of input materials. Several improved methods, such as infrared-CLIP (irCLIP), enhanced CLIP (eCLIP), and target RNA immunoprecipitation (tRIP), have shown remarkable enhancements in the detection efficiency. Furthermore, the utilization of an RNA editing mechanism or proximity labeling strategy has achieved the detection of faint protein–RNA interactions in cells without depending on crosslinking. This review aims to explore various methods being developed to detect endogenous protein–RNA interaction sites and discusses how they may be applied to the analysis of co-transcriptional RNA processing.

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