Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

It is well known that viruses make extensive use of the host cell's machinery, hijacking it for the purpose of viral replication and interfere with the activity of master regulatory proteins - including RNA binding proteins (RBPs). RBPs recognize and bind RNA molecules to control several steps of cellular RNA metabolism, such as splicing, transcript stability, translation and others, and recognize their targets by means of sequence or structure motifs. Host RBPs are critical factors for viral replication, especially for RNA viruses, and have been shown to influence viral RNA stability, replication and escape of host immune response. While current research efforts have been centered around identifying mechanisms of host cell-entry, the role of host RBPs in the context of SARS-CoV-2 replication remains poorly understood. Few experimental studies have started mapping the SARS-CoV-2 RNA-protein interactome in infected human cells, but they are limited in the resolution and exhaustivity of their output. On the other hand, computational approaches enable screening of large numbers of human RBPs for putative interactions with the viral RNA, and are thus crucial to prioritize candidates for further experimental investigation. Here, we investigate the role of RBPs in the context of SARS-CoV-2 by constructing a first single-nucleotide \textit{in silico} map of human RBP / viral RNA interactions by using deep learning models trained on RNA sequences. Our framework is based on Pysster and DeepRiPe, two deep learning method which use a convolutional neural network to learn sequence-structure preferences of a specific RBP. Models were trained using eCLIP and PAR-CLIP datasets for >150 RBP generated on human cell lines and applied cross-species to predict the propensity of each RBP to bind the SARS-CoV-2 genome. After extensive validation of predicted binding sites, we generate RBP binding profiles across different SARS-CoV-2 variants and 6 other betacoronaviruses. We address the questions of (1) conservation of binding between pathogenic betacoronaviruses, (2) differential binding across viral strains and (3) gain and loss of binding events in novel mutants which can be linked to disease severity and spread in the population. In addition, we explore the specific pathways hijacked by the virus, by integrating host factors linked to these virus-binding RBPs through protein-protein interaction networks or genome wide CRISPR screening. We believe that identifying viral RBP binding sites will give valuable insights into the mechanisms of host-virus interaction, thus giving us a deeper understanding of the life cycle of SARS-CoV-2 but also opening new avenues for the development of new therapeutics.

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RBPsuite: RNA-protein binding sites prediction suite based on deep learning

BMC Genomics ◽

10.1186/s12864-020-07291-6 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Xiaoyong Pan ◽

Yi Fang ◽

Xianfeng Li ◽

Yang Yang ◽

Hong-Bin Shen

Keyword(s):

Deep Learning ◽

Binding Sites ◽

Rna Binding ◽

Rna Binding Proteins ◽

Circular Rnas ◽

Biological Processes ◽

Learning Models ◽

Protein Binding Sites ◽

Full Length Sequence ◽

Computationally Intensive

Abstract Background RNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive. Results Here we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence. Conclusions RBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/.

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Conceptual Advances in Control of Inflammation by the RNA-Binding Protein Tristetraprolin

Frontiers in Immunology ◽

10.3389/fimmu.2021.751313 ◽

2021 ◽

Vol 12 ◽

Author(s):

Pavel Kovarik ◽

Annika Bestehorn ◽

Jeanne Fesselet

Keyword(s):

Immune Responses ◽

Mrna Stability ◽

Binding Sites ◽

Molecular Mechanisms ◽

Rna Binding ◽

Rna Binding Proteins ◽

Rna Degradation ◽

Decay Rates ◽

Mrna Destabilization

Regulated changes in mRNA stability are critical drivers of gene expression adaptations to immunological cues. mRNA stability is controlled mainly by RNA-binding proteins (RBPs) which can directly cleave mRNA but more often act as adaptors for the recruitment of the RNA-degradation machinery. One of the most prominent RBPs with regulatory roles in the immune system is tristetraprolin (TTP). TTP targets mainly inflammation-associated mRNAs for degradation and is indispensable for the resolution of inflammation as well as the maintenance of immune homeostasis. Recent advances in the transcriptome-wide knowledge of mRNA expression and decay rates together with TTP binding sites in the target mRNAs revealed important limitations in our understanding of molecular mechanisms of TTP action. Such orthogonal analyses lead to the discovery that TTP binding destabilizes some bound mRNAs but not others in the same cell. Moreover, comparisons of various immune cells indicated that an mRNA can be destabilized by TTP in one cell type while it remains stable in a different cell linage despite the presence of TTP. The action of TTP extends from mRNA destabilization to inhibition of translation in a subset of targets. This article will discuss these unexpected context-dependent functions and their implications for the regulation of immune responses. Attention will be also payed to new insights into the role of TTP in physiology and tissue homeostasis.

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GraphProt2: A novel deep learning-based method for predicting binding sites of RNA-binding proteins

10.1101/850024 ◽

2019 ◽

Cited By ~ 2

Author(s):

Michael Uhl ◽

Van Dinh Tran ◽

Rolf Backofen

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Deep Learning ◽

Binding Sites ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

State Of The Art ◽

Prediction Method ◽

Superior Performance

AbstractCLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This necessitates the use of computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction method based on graph convolutional neural networks (GCN). In contrast to current CNN methods, GraphProt2 supports variable length input as well as the possibility to accurately predict nucleotide-wise binding profiles. We demonstrate its superior performance compared to GraphProt and a CNN-based method on single as well as combined CLIP-seq datasets.

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Staufen1 Protein Participates Positively in the Viral RNA Replication of Enterovirus 71

Viruses ◽

10.3390/v11020142 ◽

2019 ◽

Vol 11 (2) ◽

pp. 142 ◽

Cited By ~ 1

Author(s):

Young-Mao Chen ◽

Bo-Ting Ou ◽

Chao-Ying Chen ◽

Han-Hsiang Chan ◽

Chih-Jung Chen ◽

...

Keyword(s):

Rna Binding ◽

Rna Virus ◽

Rna Stability ◽

Enterovirus 71 ◽

Viral Rna ◽

Viral Translation ◽

Rna Translation ◽

Rna Accumulation

The double-stranded RNA-binding protein Staufen1 (Stau1) has multiple functions during RNA virus infection. In this study, we investigated the role of Stau1 in viral translation by using a combination of enterovirus 71 (EV-A71) infection, RNA reporter transfection, and in vitro functional and biochemical assays. We demonstrated that Stau1 specifically binds to the 5′-untranslated region of EV-A71 viral RNA. The RNA-binding domain 2-3 of Stau1 is responsible for this binding ability. Subsequently, we created a Stau1 knockout cell line using the CRISPR/Cas9 approach to further characterize the functional role of Stau1’s interaction with viral RNA in the EV-A71-infected cells. Both the viral RNA accumulation and viral protein expression were downregulated in the Stau1 knockout cells compared with the wild-type naïve cells. Moreover, dysregulation of viral RNA translation was observed in the Stau1 knockout cells using ribosome fractionation assay, and a reduced RNA stability of 5′-UTR of the EV-A71 was also identified using an RNA stability assay, which indicated that Stau1 has a role in facilitating viral translation during EV-A71 infection. In conclusion, we determined the functional relevance of Stau1 in the EV-A71 infection cycle and herein describe the mechanism of Stau1 participation in viral RNA translation through its interaction with viral RNA. Our results suggest that Stau1 is an important host factor involved in viral translation and influential early in the EV-A71 replication cycle.

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When Ribonucleases Come into Play in Pathogens: A Survey of Gram-Positive Bacteria

International Journal of Microbiology ◽

10.1155/2012/592196 ◽

2012 ◽

Vol 2012 ◽

pp. 1-18 ◽

Cited By ~ 13

Author(s):

Brian C. Jester ◽

Pascale Romby ◽

Efthimia Lioliou

Keyword(s):

Pathogenic Bacteria ◽

Rna Binding ◽

Rna Binding Proteins ◽

Rna Stability ◽

Genetic Material ◽

Gram Positive ◽

Major Health ◽

Gram Positive Bacteria ◽

New Concepts

It is widely acknowledged that RNA stability plays critical roles in bacterial adaptation and survival in different environments like those encountered when bacteria infect a host. Bacterial ribonucleases acting alone or in concert with regulatory RNAs or RNA binding proteins are the mediators of the regulatory outcome on RNA stability. We will give a current update of what is known about ribonucleases in the model Gram-positive organismBacillus subtilisand will describe their established roles in virulence in several Gram-positive pathogenic bacteria that are imposing major health concerns worldwide. Implications on bacterial evolution through stabilization/transfer of genetic material (phage or plasmid DNA) as a result of ribonucleases' functions will be covered. The role of ribonucleases in emergence of antibiotic resistance and new concepts in drug design will additionally be discussed.

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Profiling the binding sites of RNA-binding protein by LACE-seq

10.21203/rs.3.pex-1499/v1 ◽

2021 ◽

Author(s):

Ruibao Su ◽

Di Wang ◽

Changchang Cao ◽

Yuanchao Xue

Keyword(s):

Binding Sites ◽

Rna Binding ◽

Rna Binding Proteins ◽

Transcription Termination ◽

Early Embryo ◽

Linear Amplification ◽

Single Nucleotide ◽

Nucleotide Resolution ◽

Single Nucleotide Resolution

Abstract RNA-binding proteins (RBPs) directly interact with various RNAs in living cells to regulate their processing, translation, and stability. Identifying the precise binding sites of RBPs is critical for appreciating their physiological or pathological roles in germline and early embryo development. Current methods typically need millions of cells to map RBP binding positions, which prevents us from appreciating the crucial role of RBPs in early development. Here, we present the LACE-seq method for unbiased mapping of RBP-binding sites at single-nucleotide resolution in fewer cells or even single oocytes. LACE-seq depends on RBP-mediated reverse transcription termination, and linear amplification of the cDNA ends for deep sequencing. To further promote its application, we describe a step-by-step protocol about how to construct a successful LACE-seq library.

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Minireview: Global Regulation and Dynamics of Ribonucleic Acid

Endocrinology ◽

10.1210/en.2009-1250 ◽

2010 ◽

Vol 151 (4) ◽

pp. 1391-1397 ◽

Cited By ~ 61

Author(s):

Jack D. Keene

Keyword(s):

Posttranscriptional Regulation ◽

Rna Binding ◽

Rna Binding Proteins ◽

Rna Stability ◽

Insulin Receptors ◽

Dynamic Features ◽

Rna Regulation ◽

Environmental Signals ◽

The Many

Gene expression starts with transcription and is followed by multiple posttranscriptional processes that carry out the splicing, capping, polyadenylation, and export of each mRNA. Interest in posttranscriptional regulation has increased recently with explosive discoveries of large numbers of noncoding RNAs such as microRNAs, yet posttranscriptional processes depend largely on the functions of RNA-binding proteins as well. Glucocorticoid nuclear receptors are classical examples of environmentally reactive activators and repressors of transcription, but there has also been a significant increase in studies of the role of posttranscriptional regulation in endocrine responses, including insulin and insulin receptors, and parathyroid hormone as well as other hormonal responses, at the levels of RNA stability and translation. On the global level, the transcriptome is defined as the total RNA complement of the genome, and thereby, represents the accumulated levels of all expressed RNAs, because they are each being produced and eventually degraded in either the nucleus or the cytoplasm. In addition to RNA turnover, the many underlying posttranscriptional layers noted above that follow from the transcriptome function within a dynamic ribonucleoprotein (RNP) environment of global RNA-protein and RNA-RNA interactions. With the exception of the spliceosome and the ribosome, thousands of heterodispersed RNP complexes wherein RNAs are dynamically processed, trafficked, and exchanged are heterogeneous in size and composition, thus providing significant challenges to their investigation. Among the diverse RNPs that show dynamic features in the cytoplasm are processing bodies and stress granules as well as a large number of smaller heterogeneous RNPs distributed throughout the cell. Although the localization of functionally related RNAs within these RNPs are responsive to developmental and environmental signals, recent studies have begun to elucidate the global RNA components of RNPs that are dynamically coordinated in response to these signals. Among the factors that have been found to affect coordinated RNA regulation are developmental signals and treatments with small molecule drugs, hormones, and toxins, but this field is just beginning to understand the role of RNA dynamics in these responses.

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Sequence dependent phase separation of protein-polynucleotide mixtures elucidated using molecular simulations

Nucleic Acids Research ◽

10.1093/nar/gkaa1099 ◽

2020 ◽

Vol 48 (22) ◽

pp. 12593-12603 ◽

Cited By ~ 2

Author(s):

Roshan Mammen Regy ◽

Gregory L Dignon ◽

Wenwei Zheng ◽

Young C Kim ◽

Jeetain Mittal

Keyword(s):

Phase Separation ◽

Intrinsically Disordered Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Experimental Studies ◽

Coarse Grained ◽

Disordered Proteins ◽

Sequence Specificity ◽

Intrinsically Disordered

Abstract Ribonucleoprotein (RNP) granules are membraneless organelles (MLOs), which majorly consist of RNA and RNA-binding proteins and are formed via liquid–liquid phase separation (LLPS). Experimental studies investigating the drivers of LLPS have shown that intrinsically disordered proteins (IDPs) and nucleic acids like RNA and other polynucleotides play a key role in modulating protein phase separation. There is currently a dearth of modelling techniques which allow one to delve deeper into how polynucleotides play the role of a modulator/promoter of LLPS in cells using computational methods. Here, we present a coarse-grained polynucleotide model developed to fill this gap, which together with our recently developed HPS model for protein LLPS, allows us to capture the factors driving protein-polynucleotide phase separation. We explore the capabilities of the modelling framework with the LAF-1 RGG system which has been well studied in experiments and also with the HPS model previously. Further taking advantage of the fact that the HPS model maintains sequence specificity we explore the role of charge patterning on controlling polynucleotide incorporation into condensates. With increased charge patterning we observe formation of structured or patterned condensates which suggests the possible roles of polynucleotides in not only shifting the phase boundaries but also introducing microscopic organization in MLOs.

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DeepCLIP: Predicting the effect of mutations on protein-RNA binding with Deep Learning

10.1101/757062 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alexander Gulliver Bjørnholt Grønning ◽

Thomas Koed Doktor ◽

Simon Jonas Larsen ◽

Ulrika Simone Spangsberg Petersen ◽

Lise Lolle Holm ◽

...

Keyword(s):

Deep Learning ◽

Protein Binding ◽

Binding Sites ◽

Antisense Oligonucleotides ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

List Type ◽

Link Type ◽

Sequence Variations

ABSTRACTNucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modelling of protein-RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modelling RNA-protein binding. Importantly, we demonstrate that DeepCLIP is able to reliably predict the functional effects of contextually dependent nucleotide variants in independent wet lab experiments. Furthermore, we show how DeepCLIP binding profiles can be used in the design of therapeutically relevant antisense oligonucleotides, and to uncover possible position-dependent regulation in a tissue-specific manner. DeepCLIP can be freely used at http://deepclip.compbio.sdu.dk.HighlightsWe have designed DeepCLIP as a simple neural network that requires only CLIP binding sites as input. The architecture and parameter settings of DeepCLIP makes it an efficient classifier and robust to train, making high performing models easy to train and recreate.Using an extensive benchmark dataset, we demonstrate that DeepCLIP outperforms existing tools in classification. Furthermore, DeepCLIP provides direct information about the neural network’s decision process through visualization of binding motifs and a binding profile that directly indicates sequence elements contributing to the classification.To show that DeepCLIP models generalize to different datasets we have demonstrated that predictions correlate with in vivo and in vitro experiments using quantitative binding assays and minigenes.Identifying the binding sites for regulatory RNA-binding proteins is fundamental for efficient design of (therapeutic) antisense oligonucleotides. Employing a reported disease associated mutation, we demonstrate that DeepCLIP can be used for design of therapeutic antisense oligonucleotides that block regions important for binding of regulatory proteins and correct aberrant splicing.Using DeepCLIP binding profiles, we uncovered a possible position-dependent mechanism behind the reported tissue-specificity of a group of TDP-43 repressed pseudoexons.We have made DeepCLIP available as an online tool for training and application of proteinRNA binding deep learning models and prediction of the potential effects of clinically detected sequence variations (http://deepclip.compbio.sdu.dk/). We also provide DeepCLIP as a configurable stand-alone program (http://www.github.com/deepclip).

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