RNA-binding Proteins Binding Sites Research Articles

Decoding protein binding landscape on circular RNAs with base-resolution transformer models

Circular RNAs (circRNAs), a class of endogenous RNA with a covalent loop structure, can regulate gene expression by serving as sponges for microRNAs and RNA-binding proteins (RBPs). To date, most computational methods for predicting RBP binding sites on circRNAs focus on circRNA fragments instead of circRNAs. These methods detect whether a circRNA fragment contains binding sites, but cannot determine where are the binding sites and how many binding sites are on the circRNA transcript. We report a hybrid deep learning-based tool, CircSite, to predict RBP binding sites at single-nucleotide resolution and detect key contributed nucleotides on circRNA transcripts. CircSite takes advantage of convolutional neural networks (CNNs) and Transformer for learning local and global representations of circRNAs binding to RBPs, respectively. We construct 37 datasets of circRNAs interacting with proteins for benchmarking and the experimental results show that CircSite offers accurate predictions of RBP binding nucleotides and detects key subsequences aligning well with known binding motifs. CircSite is an easy-to-use online webserver for predicting RBP binding sites on circRNA transcripts and freely available at http://www.csbio.sjtu.edu.cn/bioinf/CircSite/.

Profiling of RNA-binding protein binding sites by in situ reverse transcription-based sequencing.

RNA-binding proteins (RBPs) regulate diverse cellular processes by dynamically interacting with RNA targets. However, effective methods to capture both stable and transient interactions between RBPs and their RNA targets are still lacking, especially when the interaction is dynamic or samples are limited. Here we present an assay of reverse transcription-based RBP binding site sequencing (ARTR-seq), which relies on in situ reverse transcription of RBP-bound RNAs guided by antibodies to identify RBP binding sites. ARTR-seq avoids ultraviolet crosslinking and immunoprecipitation, allowing for efficient and specific identification of RBP binding sites from as few as 20 cells or a tissue section. Taking advantage of rapid formaldehyde fixation, ARTR-seq enables capturing the dynamic RNA binding by RBPs over a short period of time, as demonstrated by the profiling of dynamic RNA binding of G3BP1 during stress granule assembly on a timescale as short as 10 minutes.

Quantifying negative selection in human 3ʹ UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3ʹ untranslated regions (3ʹ UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3ʹ UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3ʹ UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3ʹ UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3ʹ UTRs.

GCLiPP: global crosslinking and protein purification method for constructing high-resolution occupancy maps for RNA binding proteins

GCLiPP is a global RNA interactome capture method that detects RNA-binding protein (RBP) occupancy transcriptome-wide. GCLiPP maps RBP-occupied sites at a higher resolution than phase separation-based techniques. GCLiPP sequence tags correspond with known RBP binding sites and are enriched for sites detected by RBP-specific crosslinking immunoprecipitation (CLIP) for abundant cytosolic RBPs. Comparison of human Jurkat T cells and mouse primary T cells uncovers shared peaks of GCLiPP signal across homologous regions of human and mouse 3′ UTRs, including a conserved mRNA-destabilizing cis-regulatory element. GCLiPP signal overlapping with immune-related SNPs uncovers stabilizing cis-regulatory regions in CD5, STAT6, and IKZF1.

Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq.

Enhanced crosslinking and immunoprecipitation (eCLIP) sequencing is a method for transcriptome-wide detection of binding sites of RNA-binding proteins (RBPs). However, identified crosslink sites can deviate from experimentally established functional elements of even well-studied RBPs. Current peak-calling strategies result in low replication and high false positive rates. Here, we present the R/Bioconductor package DEWSeq that makes use of replicate information and size-matched input controls. We benchmarked DEWSeq on 107 RBPs for which both eCLIP data and RNA sequence motifs are available and were able to more than double the number of motif-containing binding regions relative to standard eCLIP processing. The improvement not only relates to the number of binding sites (3.1-fold with known motifs for RBFOX2), but also their subcellular localization (1.9-fold of mitochondrial genes for FASTKD2) and structural targets (2.2-fold increase of stem-loop regions for SLBP. On several orthogonal CLIP-seq datasets, DEWSeq recovers a larger number of motif-containing binding sites (3.3-fold). DEWSeq is a well-documented R/Bioconductor package, scalable to adequate numbers of replicates, and tends to substantially increase the proportion and total number of RBP binding sites containing biologically relevant features.

FLARE: a fast and flexible workflow for identifying RNA editing foci

BackgroundFusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors.ResultsWe present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing.ConclusionsFLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE.

Optimized infrared photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (IR-PAR-CLIP) protocol identifies novel IGF2BP3-interacting RNAs in colon cancer cells.

The conserved family of RNA-binding proteins (RBPs), IGF2BPs, plays an essential role in posttranscriptional regulation controlling mRNA stability, localization, and translation. Mammalian cells express three isoforms of IGF2BPs: IGF2BP1-3. IGF2BP3 is highly overexpressed in cancer cells, and its expression correlates with a poor prognosis in various tumors. Therefore, revealing its target RNAs with high specificity in healthy tissues and in cancer cells is of crucial importance. Photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) identifies the binding sites of RBPs on their target RNAs at nucleotide resolution in a transcriptome-wide manner. Here, we optimized the PAR-CLIP protocol to study RNA targets of endogenous IGF2BP3 in a human colorectal carcinoma cell line. To this end, we first established an immunoprecipitation protocol to obtain highly pure endogenous IGF2BP3-RNA complexes. Second, we modified the protocol to use highly sensitive infrared (IR) fluorescent dyes instead of radioactive probes to visualize IGF2BP3-crosslinked RNAs. We named the modified method "IR-PAR-CLIP." Third, we compared RNase cleavage conditions and found that sequence preferences of the RNases impact the number of the identified IGF2BP3 targets and introduce a systematic bias in the identified RNA motifs. Fourth, we adapted the single adapter circular ligation approach to increase the efficiency in library preparation. The optimized IR-PAR-CLIP protocol revealed novel RNA targets of IGF2BP3 in a human colorectal carcinoma cell line. We anticipate that our IR-PAR-CLIP approach provides a framework for studies of other RBPs.

RBP-Tar – a searchable database for experimental RBP binding sites

Background: RNA-binding proteins (RBPs) play a critical role in regulating gene expression by binding to specific sites on RNA molecules. Identifying these binding sites is crucial for understanding the many functions of RBPs in cellular function, development and disease. Current experimental methods for identifying RBP binding sites, such as ultra-violet (UV) crosslinking and immunoprecipitation (CLIP), and especially the enhanced CLIP (eCLIP) protocol, were developed to identify authentic RBP binding sites experimentally. Methods: To make this data more accessible to the scientific community, we have developed RBP-Tar (https://ncbr.muni.cz/RBP-Tar), a web server and database that utilises eCLIP data for 167 RBPs mapped on the human genome. The web server allows researchers to easily search and retrieve binding site information by genomic location and RBP name. Use case: Researchers can produce lists of all known RBP binding sites on a gene of interest, or produce lists of binding sites for one RBP on different genomic loci. Conclusions: Our future goal is to continue to populate the web server with additional experimental datasets from CLIP experiments as they become available and processed, making it an increasingly valuable resource for the scientific community.

CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion.

Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful learning ability, the various deep learning frameworks have been used to predict the binding sites of RNA-binding protein (RPB) on circRNAs. These methods usually perform only single-level feature extraction of sequence information. However, the feature acquisition may be inadequate for single-level extraction. Generally, the features of deep and shallow layers of neural network can complement each other and are both important for binding site prediction tasks. Based on this concept, we propose a method that combines deep and shallow features, namely CRBP-HFEF. Specifically, features are first extracted and expanded for different levels of network. Then, the expanded deep and shallow features are fused and fed into the classification network, which finally determines whether they are binding sites. Compared to several existing methods, the experimental results on multiple datasets show that the proposed method achieves significant improvements in a number of metrics (with an average AUC of 0.9855). Moreover, much sufficient ablation experiments are also performed to verify the effectiveness of the hierarchical feature expansion strategy.

Alternative Splicing Outcomes Across an RNA-Binding Protein Concentration Gradient

Alternative splicing (AS) is a dynamic RNA processing step that produces multiple RNA isoforms from a single pre-mRNA transcript and contributes to the complexity of the cellular transcriptome and proteome. This process is regulated through a network of cis-regulatory sequence elements and trans-acting factors, most-notably RNA binding proteins (RBPs). The muscleblind-like (MBNL) and RNA binding fox-1 homolog (RBFOX) are two well characterized families of RBPs that regulate fetal to adult AS transitions critical for proper muscle, heart, and central nervous system development. To better understand how the concentration of these RBPs influences AS transcriptome wide, we engineered a MBNL1 and RBFOX1 inducible HEK-293 cell line. Modest induction of exogenous RBFOX1 in this cell line modulated MBNL1-dependent AS outcomes in 3 skipped exon events, despite significant levels of endogenous RBFOX1 and RBFOX2. Due to background RBFOX levels, we conducted a focused analysis of dose-dependent MBNL1 skipped exon AS outcomes and generated transcriptome wide dose–response curves. Analysis of this data demonstrates that MBNL1-regulated exclusion events may require higher concentrations of MBNL1 protein to properly regulate AS outcomes compared to inclusion events and that multiple arrangements of YGCY motifs can produce similar splicing outcomes. These results suggest that rather than a simple relationship between the organization of RBP binding sites and a specific splicing outcome, that complex interaction networks govern both AS inclusion and exclusion events across a RBP gradient.

Cumulative effects of weakly repressive regulatory regions in the 3’ UTR maintain PD-1 expression homeostasis in mammals

PD-1 has become a common target for cancer treatment. However, the molecular regulation of PD-1 expression homeostasis remains unclear. Here we report the PD-1 3’ UTR can dramatically repress gene expression via promoting mRNA decay. Deletion of the PD-1 3’ UTR inhibits T cell activity and promotes T-ALL cell proliferation. Interestingly, the robust repression is attributable to cumulative effects of many weak regulatory regions, which we show together are better able to maintain PD-1 expression homeostasis. We further identify several RNA binding proteins (RBPs) that modulate PD-1 expression via the 3’ UTR, including IGF2BP2, RBM38, SRSF7, and SRSF4. Moreover, despite rapid evolution, PD-1 3’ UTRs are functionally conserved and strongly repress gene expression through many common RBP binding sites. These findings reveal a previously unrecognized mechanism of maintaining PD-1 expression homeostasis and might represent a general model for how small regulatory effects play big roles in regulation of gene expression and biology.

Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

Analysis of RBP expression and binding sites identifies PTBP1 as a regulator of CD19 expression in B-ALL

ABSTRACT Despite massive improvements in the treatment of B-ALL through CART-19 immunotherapy, a large number of patients suffer a relapse due to loss of the targeted epitope. Mutations in the CD19 locus and aberrant splicing events are known to account for the absence of surface antigen. However, early molecular determinants suggesting therapy resistance as well as the time point when first signs of epitope loss appear to be detectable are not enlightened so far. By deep sequencing of the CD19 locus, we identified a blast-specific 2-nucleotide deletion in intron 2 that exists in 35% of B-ALL samples at initial diagnosis. This deletion overlaps with the binding site of RNA binding proteins (RBPs) including PTBP1 and might thereby affect CD19 splicing. Moreover, we could identify a number of other RBPs that are predicted to bind to the CD19 locus being deregulated in leukemic blasts, including NONO. Their expression is highly heterogeneous across B-ALL molecular subtypes as shown by analyzing 706 B-ALL samples accessed via the St. Jude Cloud. Mechanistically, we show that downregulation of PTBP1, but not of NONO, in 697 cells reduces CD19 total protein by increasing intron 2 retention. Isoform analysis in patient samples revealed that blasts, at diagnosis, express increased amounts of CD19 intron 2 retention compared to normal B cells. Our data suggest that loss of RBP functionality by mutations altering their binding motifs or by deregulated expression might harbor the potential for the disease-associated accumulation of therapy-resistant CD19 isoforms.

Transcriptomics-proteomics Integration reveals alternative polyadenylation driving inflammation-related protein translation in patients with diabetic nephropathy

BackgroundDiabetic nephropathy (DN) is a complex disease involving the upregulation of many inflammation-related proteins. Alternative polyadenylation (APA), a crucial post-transcriptional regulatory mechanism, has been proven to play vital roles in many inflammatory diseases. However, it is largely unknown whether and how APA exerts function in DN.MethodsWe performed transcriptomics and proteomics analysis of glomeruli samples isolated from 50 biopsy-proven DN patients and 25 control subjects. DaPars and QAPA algorithms were adopted to identify APA events from RNA-seq data. The qRT-PCR analysis was conducted to verify 3′UTR length alteration. Short and long 3ʹUTRs isoforms were also overexpressed in podocytes under hyperglycemia condition for examining protein expression.ResultsWe detected transcriptome-wide 3′UTR APA events in DN, and found that APA-mediated 3ʹUTR lengthening of genes (APA genes) increased their expression at protein but not mRNA level. Increased protein level of 3′UTR lengthening gene was validated in podocytes under hyperglycemia condition. Pathway enrichment analysis showed that APA genes were enriched in inflammation-related biological processes including endoplasmic reticulum stress pathways, NF-κB signaling and autophagy. Further bioinformatics analysis demonstrated that 3′UTR APA of genes probably altered the binding sites for RNA-binding proteins, thus enhancing protein translation.ConclusionThis study revealed for the first time that 3′UTR lengthening of APA genes contributed to the progression of DN by elevating the translation of corresponding proteins, providing new insight and a rich resource for investigating DN mechanisms.

PAR-CLIP Assay in Ferroptosis.

Ferroptosis is a regulatory cell death process that is accompanied by large amounts of iron ion accumulation and lipid peroxidation. Photoactivated ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) is a method used to identify the binding sites of RNA-binding proteins (RBPs) on target RNAs with high resolution at the nucleotide level. By inserting photosensitive ribonucleoside analogs into new RNA transcripts of living cells, characteristic mutations can be generated during reverse transcription and be used to accurately locate the crosslinking position of RNAs and RBPs. The use of PAR-CLIP to detect interactions and determine precise crosslinking sites between RNAs and RBPs, or to search for RNAs upstream or downstream of ferroptosis pathways genes through known proteins, can help to clarify and verify the occurrence and regulation mechanisms of the various signaling pathways of ferroptosis. Furthermore, it may reveal new targets for ferroptosis detection and improve the treatment efficiency of ferroptosis-related diseases such as cancer and neurodegenerative diseases. Here, we introduce a specific PAR-CLIP protocol for monitoring the ferroptosis process.

Determinants of Functional MicroRNA Targeting.

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

Multiplexed transcriptome discovery of RNA-binding protein binding sites by antibody-barcode eCLIP

Ultraviolet crosslinking and immunoprecipitation (CLIP) methodologies enable the identification of RNA binding sites of RNA-binding proteins (RBPs). Despite improvements in the library preparation of RNA fragments, the enhanced CLIP (eCLIP) protocol requires 4 days of hands-on time and lacks the ability to process several RBPs in parallel. We present a new method termed antibody-barcode eCLIP that utilizes DNA-barcoded antibodies and proximity ligation of the DNA oligonucleotides to RBP-protected RNA fragments to interrogate several RBPs simultaneously. We observe performance comparable with that of eCLIP with the advantage of dramatically increased scaling while maintaining the same material requirement of a single eCLIP experiment.

CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features.

Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.

Transcriptome - wide bioinformatics analysis of the binding sites of RNA - binding proteins and their putative role in mendelian diseases

Background: Post-transcriptional regulation is the control of gene expression at the RNA level. After produced, the stability and distribution of the different transcripts are regulated by means of RNA-binding proteins (RBPs). Mutations in RNA-binding proteins can cause Mendelian diseases - prominently neuromuscular disorders and cancers. This study determines the interaction between RBPs and target-RNA complexes from public data of the ENCODE project and identifies mutations associated with Mendelian diseases that could disrupt the RBP-RNA interactions. Materials and methods: we performed a transcriptome - wide bioinformatics prediction of the binding sites of RBPs in the human transcriptome from public data of the ENCODE project. Results: The majority (54%) of pathogenic mutation putatively affecting the binding sites of RBPs are located in protein - coding genes and are mainly classified as loss - of - function mutations. Mutations located in the binding sites of RBPs related to RNA processing. For 13 diseases, Familial hypercholesterolemia is the most significant disease with about 40% of mutations in ClinVar database located into the binding sites of RBPs (p=2.3e-65), but congenital hypogonadotropic hypogonadism is the disease with the highest percentage of mutations affecting the binding sites of RBPs (98%, p=2.7e-25). The RBPs most involved in human Mendelian diseases by binding sites-disrupting mutations are YBX3, AQR and PRPF8. Conclusions: A large number of Mendelian diseases are potentially mediated by disease - causing variants that potentially disrupt the binding sites of RBPs. This will provide insight sharper on post - transcriptional mechanisms. Besides, it is useful to know the role of protein - RNA interactome networks in pathologies, thereby serving the treatment of diseases. Key words: bioinformatics analysis, ENCODE project, ClinVar, RNA-binding proteins, Mendelian diseases

JLCRB: A unified multi-view-based joint representation learning for CircRNA binding sites prediction

CircRNAs usually bind to the corresponding RBPs(RNA Binding proteins) and play a key role in gene regulation. Therefore, it is important to identify the binding sites of RBPs on CircRNAs for the regulation of certain diseases. Due to the information provided by the single view feature is limited, the current mainstream methods are mainly to detect the RBP binding sites by constructing multi-view models. However, with the number of view features increases, the invalid information also increases, and the existing methods only simply concatenate together various features from different views, while ignoring the intrinsic connection between multi-view data. To solve this problem, we propose a new multi-view joint representation learning network by improving the consistency of multi-view feature information. First, the network uses different feature encoding methods to fully extract the feature information of RNA, respectively. Then we construct the intrinsic connection between the views by generating a global joint representation of multiple views, and this is used for feature calibration of each view to highlight important features and suppress unimportant ones. Finally, the depth features obtained from the fusion of multiple views are used to detect the binding sites of RNAs. The average AUC of our method is 93.68% in 37 CircRNA-RBP datasets. The experimental results show that the prediction performance of the method is better than existing methods. The code and datasets are obtained at https://github.com/Xuezg/JLCRB. In addition, we also provide a free web server that is freely available at http://82.157.188.204/JLCRB/.