ProQ-dependent activation of Salmonella virulence genes mediated by post-transcriptional control of PhoP synthesis

Gastrointestinal disease caused by Salmonella enterica is associated with the pathogen's ability to replicate within epithelial cells and macrophages. Upon host cell entry, the bacteria express a type-three secretion system encoded within Salmonella pathogenicity island 2, through which host-manipulating effector proteins are secreted to establish a stable intracellular niche. Transcription of this intracellular virulence program is activated by the PhoPQ two-component system that senses the low pH and the reduced magnesium concentration of host cell vacuoles. In addition to transcriptional control, Salmonella commonly employ RNA-binding proteins (RBPs) and small regulatory RNAs (sRNAs) to regulate gene expression at the post-transcriptional level. ProQ is a globally acting RBP in Salmonella that promotes expression of the intracellular virulence program, but its RNA repertoire has previously been characterized only under standard laboratory growth conditions. Here, we provide a high-resolution ProQ interactome during conditions mimicking the environment of the Salmonella-containing vacuole (SCV), revealing hundreds of previously unknown ProQ binding sites in sRNAs and mRNA 3'UTRs. ProQ positively affected both the levels and the stability of many sRNA ligands, some of which were previously shown to associate with the well-studied and infection-relevant RBP Hfq. We further show that ProQ activates the expression of PhoP at the post-transcriptional level, which, in turn, leads to upregulation of the intracellular virulence program.

IMPORTANCE

Salmonella enterica is a major pathogen responsible for foodborne gastroenteritis, and a leading model organism for genetic and molecular studies of bacterial virulence mechanisms. One key trait of this pathogen is the ability to survive within infected host cells. During infection, the bacteria employ a type three secretion system that deliver effector proteins to target and manipulate host cell processes. The transcriptional regulation of this virulence program is well understood. By contrast, the factors and mechanisms operating at the post-transcriptional level to control virulence gene expression are less clear. In this study, we have charted the global RNA ligand repertoire of the RNA-binding protein ProQ during in vitro conditions mimicking the host cell environment. This identified hundreds of binding sites and revealed ProQ-dependent stabilization of intracellular-specific small RNAs. Importantly, we show that ProQ post-transcriptionally activates the expression of PhoP, a master transcriptional activator of intracellular virulence in Salmonella.

Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes

RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein-RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.

Oncogenic K-Ras suppresses global miRNA function

K-Ras frequently acquires gain-of-function mutations (K-RasG12D being the most common) that trigger significant transcriptomic and proteomic changes to drive tumorigenesis. Nevertheless, oncogenic K-Ras-induced dysregulation of post-transcriptional regulators such as microRNAs (miRNAs) during oncogenesis is poorly understood. Here, we report that K-RasG12D promotes global suppression of miRNA activity, resulting in the upregulation of hundreds of targets. We constructed a comprehensive profile of physiological miRNA targets in mouse colonic epithelium and tumors expressing K-RasG12D using Halo-enhanced Argonaute pull-down. Combining this with parallel datasets of chromatin accessibility, transcriptome, and proteome, we uncovered that K-RasG12D suppressed the expression of Csnk1a1 and Csnk2a1, subsequently decreasing Ago2 phosphorylation at Ser825/829/832/835. Hypo-phosphorylated Ago2 increased binding to mRNAs while reducing its activity to repress miRNA targets. Our findings connect a potent regulatory mechanism of global miRNA activity to K-Ras in a pathophysiological context and provide a mechanistic link between oncogenic K-Ras and the post-transcriptional upregulation of miRNA targets.

Analyzing RNA-Protein Interactions by Cross-Link Rates and CLIP-seq Libraries

UV cross-linking-based methods are the most common tool to explore in vivo RNA-protein interactions. UV cross-linking enables the freezing of direct interactions in the cell, which can then be mapped by high-throughput sequencing through a family of methods termed CLIP-seq. CLIP-seq measures the distribution of cross-link events by purifying a protein of interest and sequencing the covalently bound RNA fragments. However, there are disagreements and ambiguities as to which proteins are RNA-binding proteins and what interactions are significant as all proteins contact all RNAs at some frequency. Here we describe a protocol for both determining RNA-protein interactions through a combination of RNA library preparation and the measurement of absolute cross-link rates, which helps determine what proteins are RNA-binding proteins and what interactions are significant. This protocol, comprising an updated form of the easyCLIP protocol, describes guidelines for RNA library preparation, oligo and protein standard construction, and the measurement of cross-link rates. These methods are easily visualizable through their fluorescent labels and can be adapted to study RNA-binding properties of both functional, high affinity RNA-binding proteins, and the accidental RNA interactions of non-RNA-binding proteins. © 2023 Wiley Periodicals LLC. Basic Protocol 1: RNA library construction Basic Protocol 2: Determining UV cross-link rates Support Protocol 1: Cross-linking and lysing cells Support Protocol 2: Adapter preparation Support Protocol 3: Preparation of cross-linked RBP standard.

Distinct binding pattern of EZH2 and JARID2 on RNAs and DNAs in hepatocellular carcinoma development

Hepatocellular carcinoma (HCC) is one of the most malignant cancers worldwide, with high mortality. However, the molecular regulatory mechanisms of liver cancer, especially transcriptional and post-transcriptional mechanisms, should be further studied. Here we used chromatin and cross-linking immunoprecipitation with high throughput sequencing methods (ChIP-seq and CLIP-seq) to capture the global binding profiles on RNAs and DNAs of Enhancer of zeste homolog 2 (EZH2) and its partner Jumonji And AT-Rich Interaction Domain Containing 2 (JARID2) in liver carcinoma cell lines (HepG2) and normal liver cell line (THLE-2), respectively. We also integrated HCC transcriptome data from the TCGA to analyze the expression pattern of bound genes. We found that EZH2 and JARID2 both showed distinct binding profiles between HepG2 and THLE-2 cells. By binding to the primary RNAs, bound transcripts of EZH2 and JARID2 in HepG2 showed significantly increased transcriptional levels in HCC patients. By performing gene set enrichment analysis (GSEA), the bound transcripts were also highly related to HCC development. We also found EZH2 and JARID2 could specifically bind to several long noncoding RNAs (lncRNAs), including H19. By exploring the DNA binding profile, we detected a dramatically repressed DNA binding ability of EZH2 in HepG2 cells. We also found that the EZH2-bound genes showed slightly increased transcriptional levels in HepG2 cells. Integrating analysis of the RNA and DNA binding profiles suggests EZH2 and JARID2 shift their binding ability from DNA to RNA in HepG2 cells to promote cancer development in HCC. Our study provided a comprehensive and distinct binding profile on RNAs and DNAs of EZH2 and JARID2 in liver cancer cell lines, suggesting their potential novel functional manners to promote HCC development.

A family of C. elegans VASA homologs control Argonaute pathway specificity and promote transgenerational silencing

Germline Argonautes direct transcriptome surveillance within perinuclear membraneless organelles called nuage. In C. elegans, a family of Vasa-related Germ Line Helicase (GLH) proteins localize in and promote the formation of nuage. Previous studies have implicated GLH proteins in inherited silencing, but direct roles in small-RNA production, Argonaute binding, or mRNA targeting have not been identified. Here we show that GLH proteins compete with each other to control Argonaute pathway specificity, bind directly to Argonaute target mRNAs, and promote the amplification of small RNAs required for transgenerational inheritance. We show that the ATPase cycle of GLH-1 regulates direct binding to the Argonaute WAGO-1, which engages amplified small RNAs. Our findings support a dynamic and direct role for GLH proteins in inherited silencing beyond their role as structural components of nuage.

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.

Identification of RNA-RBP Interactions in Subcellular Compartments by CLIP-Seq

Cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) allows the identification of RNA targets bound by a specific RNA-binding protein (RBP) in in vivo and ex vivo experimental models with high specificity. Due to the little RNA yield obtained after cross-linking, immunoprecipitation, polyacrylamide gel electrophoresis, membrane transfer, and RNA extraction, CLIP-seq is usually performed from relatively large amounts of starting material, like cell lysates or tissue homogenates. However, RBP binding of its specific RNA targets depends on its subcellular localization, and a different set of RNAs may be bound by the same RBP within distinct subcellular sites. To uncover these RNA subsets, preparation of CLIP-seq libraries from specific subcellular compartments and comparison to CLIP-seq datasets from total lysates is necessary, yet there are currently no available protocols for this. Here we describe the adaptation of CLIP-seq to identify the specific RNA targets of an RBP (FUS) at a small subcompartment, that is, neuronal synapses, including subcompartment isolation, RBP-RNA complex enrichment, and upscaling steps.

Two novel RNA-binding proteins identification through computational prediction and experimental validation

Since RBPs play important roles in the cell, it's particularly important to find new RBPs. We performed iRIP-seq and CLIP-seq to verify two proteins, CLIP1 and DMD, predicted by RBPPred whether are RBPs or not. The experimental results confirm that these two proteins have RNA-binding activity. We identified significantly enriched binding motifs UGGGGAGG, CUUCCG and CCCGU for CLIP1 (iRIP-seq), DMD (iRIP-seq) and DMD (CLIP-seq), respectively. The computational KEGG and GO analysis show that the CLIP1 and DMD share some biological processes and functions. Besides, we found that the SNPs between DMD and its RNA partners may be associated with Becker muscular dystrophy, Duchenne muscular dystrophy, Dilated cardiomyopathy 3B and Cardiovascular phenotype. Among the thirteen cancers data, CLIP1 and another 300 oncogenes always co-occur, and 123 of these 300 genes interact with CLIP1. These cancers may be associated with the mutations occurred in both CLIP1 and the genes it interacts with.

Time-Resolved Analysis of N-RNA Interactions during RVFV Infection Shows Qualitative and Quantitative Shifts in RNA Encapsidation and Packaging

Rift Valley fever virus (RVFV) is a negative-sense, tripartite RNA virus that is endemic to Africa and the Arabian Peninsula. It can cause severe disease and mortality in humans and domestic livestock and is a concern for its potential to spread more globally. RVFV's nucleocapsid protein (N) is an RNA-binding protein that is necessary for viral transcription, replication, and the production of nascent viral particles. We have conducted crosslinking, immunoprecipitation, and sequencing (CLIP-seq) to characterize N interactions with host and viral RNAs during infection. In parallel, to precisely measure intracellular N levels, we employed multiple reaction monitoring mass spectrometry (MRM-MS). Our results show that N binds mostly to host RNAs at early stages of infection, yielding nascent virus particles of reduced infectivity. The expression of N plateaus 10 h post-infection, whereas the intracellular viral RNA concentration continues to increase. Moreover, the virions produced later in infection have higher infectivity. Taken together, the detailed examination of these N-RNA interactions provides insight into how the regulated expression of N and viral RNA produces both infectious and incomplete, noninfectious particles.

CLIP-Seq to identify targets and interactions of RNA binding proteins and RNA modifying enzymes

The study of RNA chemical modifications is currently one of the most rapid-growing fields. Many types of RNA modifications in diverse RNA species have been shown to play versatile roles in a wide array of cellular processes. These modifications are installed and erased by writer and eraser enzymes, respectively. Additionally, RNA chemical modifications have downstream biological effects through either influencing changes in the chemistry or structure of RNA molecules or through recognition of the modification; these functions are primarily executed by the modification reader proteins. Reader proteins may bind to the modification site and cause a downstream signal cascade. One of the essential tools for studying erasers, writers, and readers is cross-linking immunoprecipitation followed by high-throughput sequencing (CLIP-seq). This method can detect the sites on endogenous RNAs bound by RNA-binding proteins or RNA modifying enzymes. Essentially, this strategy allows for snapshots of the epitranscriptome and molecular events occurring within the cell. In this article, we go through in detail the various steps involved in CLIP-seq.

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor

BACKGROUND

Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used 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 creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features.

RESULTS

Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP.

CONCLUSIONS

RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.

Comprehensive profiling of mRNA polyadenylation in specific cell types in vivo by cTag-PAPERCLIP

The ability to generate cell-type specific mRNA polyadenylation (pA) maps from complex tissues is crucial for understanding how alternative polyadenylation (APA) is regulated in individual cell types in their physiological environment under different conditions. In this chapter, we discuss cTag-PAPERCLIP, a recently developed method combining the well-established CLIP (crosslinking immunoprecipitation) technique and the Cre-lox system to achieve customized cell-type specific APA profiling from mouse tissue without cell purification or enrichment. In cTag-PAPERCLIP, selective expression of GFP-tagged poly(A) binding protein (PABP-GFP) in the desired cell type is achieved through Cre-mediated activation of a latent knock-in allele of PABP-GFP. Immunoprecipitation of PABP-GFP then allows mRNA 3' end fragments in the desired cell type to be specifically retrieved from ultraviolet (UV)-irradiated whole tissue lysate. The mRNA fragments are subsequently turned into a cDNA library to provide a comprehensive APA map and an mRNA expression profile of the chosen cell type through deep sequencing.

Global identification of RsmA/N binding sites in Pseudomonas aeruginosa by in vivo UV CLIP-seq

Pseudomonas aeruginosa harbours two redundant RNA-binding proteins RsmA/RsmN (RsmA/N), which play a critical role in balancing acute and chronic infections. However, in vivo binding sites on target transcripts and the overall impact on the physiology remains unclear. In this study, we applied in vivo UV crosslinking immunoprecipitation followed by RNA-sequencing (UV CLIP-seq) to detect RsmA/N-binding sites at single-nucleotide resolution and mapped more than 500 binding sites to approximately 400 genes directly bound by RsmA/N in P. aeruginosa. This also verified the ANGGA sequence in apical loops skewed towards 5'UTRs as a consensus motif for RsmA/N binding. Genetic analysis combined with CLIP-seq results suggested previously unrecognized RsmA/N targets involved in LPS modification. Moreover, the RsmA/N-titrating RNAs RsmY/RsmZ may be positively regulated by the RsmA/N-mediated translational repression of their upstream regulators, thus providing a possible mechanistic explanation for homoeostasis of the Rsm system. Thus, our study provides a detailed view of RsmA/N-RNA interactions and a resource for further investigation of the pleiotropic effects of RsmA/N on gene expression in P. aeruginosa.

The KH domain facilitates the substrate specificity and unwinding processivity of DDX43 helicase

The K-homology (KH) domain is a nucleic acid-binding domain present in many proteins. Recently, we found that the DEAD-box helicase DDX43 contains a KH domain in its N-terminus; however, its function remains unknown. Here, we purified recombinant DDX43 KH domain protein and found that it prefers binding ssDNA and ssRNA. Electrophoretic mobility shift assay and NMR revealed that the KH domain favors pyrimidines over purines. Mutational analysis showed that the GXXG loop in the KH domain is involved in pyrimidine binding. Moreover, we found that an alanine residue adjacent to the GXXG loop is critical for binding. Systematic evolution of ligands by exponential enrichment, chromatin immunoprecipitation-seq, and cross-linking immunoprecipitation-seq showed that the KH domain binds C-/T-rich DNA and U-rich RNA. Bioinformatics analysis suggested that the KH domain prefers to bind promoters. Using 15N-heteronuclear single quantum coherence NMR, the optimal binding sequence was identified as TTGT. Finally, we found that the full-length DDX43 helicase prefers DNA or RNA substrates with TTGT or UUGU single-stranded tails and that the KH domain is critically important for sequence specificity and unwinding processivity. Collectively, our results demonstrated that the KH domain facilitates the substrate specificity and processivity of the DDX43 helicase.

Improving CLIP-seq data analysis by incorporating transcript information

BACKGROUND

Current peak callers for identifying RNA-binding protein (RBP) binding sites from CLIP-seq data take into account genomic read profiles, but they ignore the underlying transcript information, that is information regarding splicing events. So far, there are no studies available that closer observe this issue.

RESULTS

Here we show that current peak callers are susceptible to false peak calling near exon borders. We quantify its extent in publicly available datasets, which turns out to be substantial. By providing a tool called CLIPcontext for automatic transcript and genomic context sequence extraction, we further demonstrate that context choice affects the performances of RBP binding site prediction tools. Moreover, we show that known motifs of exon-binding RBPs are often enriched in transcript context sites, which should enable the recovery of more authentic binding sites. Finally, we discuss possible strategies on how to integrate transcript information into future workflows.

CONCLUSIONS

Our results demonstrate the importance of incorporating transcript information in CLIP-seq data analysis. Taking advantage of the underlying transcript information should therefore become an integral part of future peak calling and downstream analysis tools.

DeepA-RBPBS: A hybrid convolution and recurrent neural network combined with attention mechanism for predicting RBP binding site

It's important to infer the binding site of RNA-binding proteins (RBP) for understanding the interaction between RBP and its RNA targets and decipher the mechanisms of transcriptional regulation. However, experimental detection of RBP binding sites is still time-intensive and expensive. Algorithms based on machine learning can speed up detection of RBP binding sites. In this article, we propose a new deep learning method, DeepA-RBPBS, which can use RNA sequences and structural features to predict RBP binding site. DeepA-RBPBS uses CNN and BiGRU to extract sequences and structural features without long-term dependence issues. It also utilizes an attention mechanism to enhance the contribution of key features. The comparison shows that the performance of DeepA-RBPBS is better than that of the state-of-the-art predictors. In the testing on 31 datasets of CLIP-seq experiments over 19 proteins, MCC (AUC) is 8% (5%) higher than those of the latest method based on deep learning, iDeepS. We also apply DeepA-RBPBS to the target RNA data of RBPs related to diabetes (LIN28, RBFOX2, FTO, IGF2BP2, CELF1 and HuR). The results show that DeepA-RBPBS correctly predicted 41,693 samples, where iDeepS predicted 31,381 samples.Communicated by Ramaswamy H. Sarma.

DDX56 Binds to Chikungunya Virus RNA To Control Infection

DEAD box RNA helicases regulate diverse facets of RNA biology. Proteins of this family carry out essential cellular functions, and emerging literature is revealing additional roles in immune defense. Using RNA interference screening, we identified an evolutionarily conserved antiviral role for the helicase DDX56 against the alphavirus Sindbis virus (SINV), a mosquito-transmitted pathogen that infects humans. Depletion of DDX56 enhanced infection in Drosophila and human cells. Furthermore, we found that DDX56 also controls the emerging alphavirus chikungunya virus (CHIKV) through an interferon-independent mechanism. Using cross-linking immunoprecipitation (CLIP-Seq), we identified a predicted stem-loop on the viral genomic RNA bound by DDX56. Mechanistically, we found that DDX56 levels increase in the cytoplasm during CHIKV infection. In the cytoplasm, DDX56 impacts the earliest step in the viral replication cycle by binding and destabilizing the incoming viral genomic RNA, thereby attenuating infection. Thus, DDX56 is a conserved antiviral RNA binding protein that controls alphavirus infection.IMPORTANCE Arthropod-borne viruses are diverse pathogens and include the emerging virus chikungunya virus, which is associated with human disease. Through genetic screening, we found that the conserved RNA binding protein DDX56 is antiviral against chikungunya virus in insects and humans. DDX56 relocalizes from the nucleus to the cytoplasm, where it binds to a stem-loop in the viral genome and destabilizes incoming genomes. Thus, DDX56 is an evolutionarily conserved antiviral factor that controls alphavirus infection.

tRFTar: Prediction of tRF-target gene interactions via systemic re-analysis of Argonaute CLIP-seq datasets

tRNA-derived fragments (tRFs), which by definition are cleaved from tRNAs, comprise a novel class of regulatory small non-coding RNAs. Recent evidence has revealed that tRFs can be loaded onto Argonaute (AGO) family proteins to perform post-transcriptional regulations via substantial tRF-target gene interactions (TGIs). However, there is no resource that systematically profiles potential AGO-mediated TGIs. To this end, we performed a systemic computational screening of potential AGO-mediated TGIs by a re-analysis of 146 crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) datasets in which 920,690 TGIs between 12,102 tRFs and 5,688 target genes were identified. The predicted TGIs have superior signal-to-noise ratio and good consistency with TGIs identified from an orthogonal technique. AGO-bound tRFs are not evenly distributed, where the 5'-tRF and 3'-tRF are enriched and some commonly expressed tRFs are also overrepresented. The tRFs tend to target conserved regions of transcripts and co-express with their target genes. Filtering TGIs with consistent co-expression with target genes results in a set of regulatory TGIs that contains 25,281 tRF-target pairs. Together, our results unveiled the extensive regulatory interactions between tRFs and target genes. Finally, the CLIP-derived TGIs were incorporated in a user-friendly online platform termed as tRFTar, where various functions like custom searching, co-expressed TGI filtering, genome browser and TGI-based tRF functional enrichment analysis are enabled to help users to investigate the functions of tRFs. The tRFTar is freely available at http://www.rnanut.net/tRFTar/.

Chemical crosslinking enhances RNA immunoprecipitation for efficient identification of binding sites of proteins that photo-crosslink poorly with RNA

In eukaryotic cells, proteins that associate with RNA regulate its activity to control cellular function. To fully illuminate the basis of RNA function, it is essential to identify such RNA-associated proteins, their mode of action on RNA, and their preferred RNA targets and binding sites. By analyzing catalogs of human RNA-associated proteins defined by ultraviolet light (UV)-dependent and -independent approaches, we classify these proteins into two major groups: (i) the widely recognized RNA binding proteins (RBPs), which bind RNA directly and UV-crosslink efficiently to RNA, and (ii) a new group of RBP-associated factors (RAFs), which bind RNA indirectly via RBPs and UV-crosslink poorly to RNA. As the UV crosslinking and immunoprecipitation followed by sequencing (CLIP-seq) approach will be unsuitable to identify binding sites of RAFs, we show that formaldehyde crosslinking stabilizes RAFs within ribonucleoproteins to allow for their immunoprecipitation under stringent conditions. Using an RBP (CASC3) and an RAF (RNPS1) within the exon junction complex (EJC) as examples, we show that formaldehyde crosslinking combined with RNA immunoprecipitation in tandem followed by sequencing (xRIPiT-seq) far exceeds CLIP-seq to identify binding sites of RNPS1. xRIPiT-seq reveals that RNPS1 occupancy is increased on exons immediately upstream of strong recursively spliced exons, which depend on the EJC for their inclusion.

SURF: integrative analysis of a compendium of RNA-seq and CLIP-seq datasets highlights complex governing of alternative transcriptional regulation by RNA-binding proteins

Advances in high-throughput profiling of RNA-binding proteins (RBPs) have resulted inCLIP-seq datasets coupled with transcriptome profiling by RNA-seq. However, analysis methods that integrate both types of data are lacking. We describe SURF, Statistical Utility for RBP Functions, for integrative analysis of large collections of CLIP-seq and RNA-seq data. We demonstrate SURF's ability to accurately detect differential alternative transcriptional regulation events and associate them to local protein-RNA interactions. We apply SURF to ENCODE RBP compendium and carry out downstream analysis with additional reference datasets. The results of this application are browsable at http://www.statlab.wisc.edu/shiny/surf/.

Large-scale analysis of the position-dependent binding and regulation of human RNA binding proteins

Background

RNA binding proteins (RBPs) play essential roles in the regulation of RNA metabolism. Recent studies have disclosed that RBPs achieve their functions via binding to their targets in a position-dependent pattern on RNAs. However, few studies have systematically addressed the associations between the RBP's functions and their positional binding preferences.

Methods

Here, we present large-scale analyses on the functional targets of human RBPs by integrating the enhanced cross-linking and immunoprecipitation followed by sequencing (eCLIP-seq) datasets and the shRNA knockdown followed by RNA-seq datasets that are deposited in the integrated ENCyclopedia of DNA Elements in the human genome (ENCODE) data portal.

Results

We found that (1) binding to the translation termination site and the 3'untranslated region is important to most human RBPs in the RNA decay regulation; (2) RBPs' binding and regulation follow a cell-type specific pattern.

Conclusions

These analysis results show the strong relationship between the binding position and the functions of RBPs, which provides novel insights into the RBPs' regulation mechanisms.

Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins

BACKGROUND

A critical step in uncovering rules of RNA processing is to study the in vivo regulatory networks of RNA binding proteins (RBPs). Crosslinking and immunoprecipitation (CLIP) methods enable mapping RBP targets transcriptome-wide, but methodological differences present challenges to large-scale analysis across datasets. The development of enhanced CLIP (eCLIP) enabled the mapping of targets for 150 RBPs in K562 and HepG2, creating a unique resource of RBP interactomes profiled with a standardized methodology in the same cell types.

RESULTS

Our analysis of 223 eCLIP datasets reveals a range of binding modalities, including highly resolved positioning around splicing signals and mRNA untranslated regions that associate with distinct RBP functions. Quantification of enrichment for repetitive and abundant multicopy elements reveals 70% of RBPs have enrichment for non-mRNA element classes, enables identification of novel ribosomal RNA processing factors and sites, and suggests that association with retrotransposable elements reflects multiple RBP mechanisms of action. Analysis of spliceosomal RBPs indicates that eCLIP resolves AQR association after intronic lariat formation, enabling identification of branch points with single-nucleotide resolution, and provides genome-wide validation for a branch point-based scanning model for 3' splice site recognition. Finally, we show that eCLIP peak co-occurrences across RBPs enable the discovery of novel co-interacting RBPs.

CONCLUSIONS

This work reveals novel insights into RNA biology by integrated analysis of eCLIP profiling of 150 RBPs with distinct functions. Further, our quantification of both mRNA and other element association will enable further research to identify novel roles of RBPs in regulating RNA processing.

Human protein-RNA interaction network is highly stable across mammals

BACKGROUND

RNA-binding proteins (RBPs) are crucial in modulating RNA metabolism in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Although previous studies on the conservation of RBP targets have been carried out in lower eukaryotes such as yeast, relatively little is known about the extent of conservation of the binding sites of RBPs across mammalian species.

RESULTS

In this study, we employ CLIP-seq datasets for 60 human RBPs and demonstrate that most binding sites for a third of these RBPs are conserved in at least 50% of the studied vertebrate species. Across the studied RBPs, binding sites were found to exhibit a median conservation of 58%, ~ 20% higher than random genomic locations, suggesting a significantly higher preservation of RBP-RNA interaction networks across vertebrates. RBP binding sites were highly conserved across primates with weak conservation profiles in birds and fishes. We also note that phylogenetic relationship between members of an RBP family does not explain the extent of conservation of their binding sites across species. Multivariate analysis to uncover features contributing to differences in the extents of conservation of binding sites across RBPs revealed RBP expression level and number of post-transcriptional targets to be the most prominent factors. Examination of the location of binding sites at the gene level confirmed that binding sites occurring on the 3' region of a gene are highly conserved across species with 90% of the RBPs exhibiting a significantly higher conservation of binding sites in 3' regions of a gene than those occurring in the 5'. Gene set enrichment analysis on the extent of conservation of binding sites to identify significantly associated human phenotypes revealed an enrichment for multiple developmental abnormalities.

CONCLUSIONS

Our results suggest that binding sites of human RBPs are highly conserved across primates with weak conservation profiles in lower vertebrates and evolutionary relationship between members of an RBP family does not explain the extent of conservation of their binding sites. Expression level and number of targets of an RBP are important factors contributing to the differences in the extent of conservation of binding sites. RBP binding sites on 3' ends of a gene are the most conserved across species. Phenotypic analysis on the extent of conservation of binding sites revealed the importance of lineage-specific developmental events in post-transcriptional regulatory network evolution.

Conditional Hfq Association with Small Noncoding RNAs in Pseudomonas aeruginosa Revealed through Comparative UV Cross-Linking Immunoprecipitation Followed by High-Throughput Sequencing

The Gram-negative bacterium P. aeruginosa is ubiquitously distributed in diverse environments and can cause severe biofilm-related infections in at-risk individuals. Although the presence of a large number of putative sRNAs and widely conserved RNA chaperones in this bacterium implies the importance of posttranscriptional regulatory networks for environmental fluctuations, limited information is available regarding the global role of RNA chaperones such as Hfq in the P. aeruginosa transcriptome, especially under different environmental conditions. Here, we characterize Hfq-dependent differences in gene expression and biological processes in two physiological states: the planktonic and biofilm forms. A combinatorial comparative CLIP-seq and total RNA-seq approach uncovered condition-dependent association of RNAs with Hfq in vivo and expands the potential direct regulatory targets of Hfq in the P. aeruginosa transcriptome.

Bacterial small noncoding RNAs (sRNAs) play posttranscriptional regulatory roles in cellular responses to changing environmental cues and in adaptation to harsh conditions. Generally, the RNA-binding protein Hfq helps sRNAs associate with target mRNAs to modulate their translation and to modify global RNA pools depending on physiological state. Here, a combination of in vivo UV cross-linking immunoprecipitation followed by high-throughput sequencing (CLIP-seq) and total RNA-seq showed that Hfq interacts with different regions of the Pseudomonas aeruginosa transcriptome under planktonic versus biofilm conditions. In the present approach, P. aeruginosa Hfq preferentially interacted with repeats of the AAN triplet motif at mRNA 5′ untranslated regions (UTRs) and sRNAs and U-rich sequences at rho-independent terminators. Further transcriptome analysis suggested that the association of sRNAs with Hfq is primarily a function of their expression levels, strongly supporting the notion that the pool of Hfq-associated RNAs is equilibrated by RNA concentration-driven cycling on and off Hfq. Overall, our combinatorial CLIP-seq and total RNA-seq approach highlights conditional sRNA associations with Hfq as a novel aspect of posttranscriptional regulation in P. aeruginosa.

IMPORTANCE The Gram-negative bacterium P. aeruginosa is ubiquitously distributed in diverse environments and can cause severe biofilm-related infections in at-risk individuals. Although the presence of a large number of putative sRNAs and widely conserved RNA chaperones in this bacterium implies the importance of posttranscriptional regulatory networks for environmental fluctuations, limited information is available regarding the global role of RNA chaperones such as Hfq in the P. aeruginosa transcriptome, especially under different environmental conditions. Here, we characterize Hfq-dependent differences in gene expression and biological processes in two physiological states: the planktonic and biofilm forms. A combinatorial comparative CLIP-seq and total RNA-seq approach uncovered condition-dependent association of RNAs with Hfq in vivo and expands the potential direct regulatory targets of Hfq in the P. aeruginosa transcriptome.

Hfq Globally Binds and Destabilizes sRNAs and mRNAs in Yersinia pestis

Hfq is a ubiquitous Sm-like RNA-binding protein in bacteria involved in physiological fitness and pathogenesis, while its in vivo binding nature remains elusive. Here we reported genome-wide Hfq-bound RNAs in Yersinia pestis, a causative agent of plague, by using cross-linking immunoprecipitation coupled with deep sequencing (CLIP-seq) approach. We show that the Hfq binding density is enriched in more than 80% mRNAs of Y. pestis and that Hfq also globally binds noncoding small RNAs (sRNAs) encoded by the intergenic, antisense, and 3' regions of mRNAs. An Hfq U-rich stretch is highly enriched in sRNAs, while motifs partially complementary to AGAAUAA and GGGGAUUA are enriched in both mRNAs and sRNAs. Hfq-binding motifs are enriched at both terminal sites and in the gene body of mRNAs. Surprisingly, a large fraction of the sRNA and mRNA regions bound by Hfq and those downstream are destabilized, likely via a 5'P-activated RNase E degradation pathway, which is consistent with a model in which Hfq facilitates sRNA-mRNA base pairing and the coupled degradation in Y. pestis These results together have presented a high-quality Hfq-RNA interaction map in Y. pestis, which should be important for further deciphering the regulatory role of Hfq-sRNAs in Y. pestis IMPORTANCE Discovered in 1968 as an Escherichia coli host factor that was essential for replication of the bacteriophage Qβ, the Hfq protein is a ubiquitous and highly abundant RNA-binding protein in many bacteria. With the assistance of Hfq, small RNAs in bacteria play important roles in regulating the stability and translation of mRNAs by base pairing. In this study, we want to elucidate the Hfq-assisted sRNA-mRNA regulation in Yersinia pestis A global map of Hfq interaction sites in Y. pestis was obtained by sequencing cDNAs converted from the Hfq-bound RNA fragments using UV cross-linking coupled immunoprecipitation technology. We demonstrate that Hfq could bind to hundreds of sRNAs and the majority of mRNAs in Y. pestis The enriched binding motifs in sRNAs and mRNAs are complementary to each other, suggesting a general base-pairing mechanism for sRNA-mRNA interaction. The Hfq-bound sRNA and mRNA regions were both destabilized. The results suggest that Hfq binding facilitates sRNA-mRNA base pairing and coordinates their degradation, which might enable Hfq to surveil the homeostasis of most mRNAs in bacteria.

Integration of CLIP experiments of RNA-binding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data

Background

Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein.

Results

In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down.

Conclusions

The methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5900-1) contains supplementary material, which is available to authorized users.

The dynamics of FTO binding and demethylation from the m6A motifs

N⁶-methyladenosine (m⁶A) is considered as a reversible RNA modification occurring more frequently on the GAC than AAC context in vivo, which regulates post-transcriptional gene expression in mammalian cells. m⁶A 'writers' METTL3 and METTL14 demonstrate a strong preference for binding AC-containing motifs in living cells. However, this evidence is currently lacking for m⁶A erasers, leaving the dynamics of the internal m⁶A modification under debate recently. We analysed three recently published FTO CLIP-seq data sets and two generated in this study, one of the two known m⁶A 'erasers'. FTO binding peaks from all cell lines contain RRACH motifs. Only those from K562, 3T3-L1and HeLa cells were enriched in AC-containing motifs, while those from HEK293 were not. The exogenously overexpressed FTO effectively binds to m⁶A motif-containing RNA sites. FTO overexpression specifically removed m⁶A modification from GGACU and RRACU motifs in a concentration-dependent manner. These findings underline the dynamics of FTO in target selection, which is predicted to contribute to both the m⁶A dynamics and the FTO plasticity in biological functions and diseases.

Discovery of Allele-Specific Protein-RNA Interactions in Human Transcriptomes

Gene 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.

Computational Analysis of RNA-Protein Interactions via Deep Sequencing

RNA-binding proteins (RBPs) function in all aspects of RNA processes including stability, structure, export, localization and translation, and control gene expression at the posttranscriptional level. To investigate the roles of RBPs and their direct RNA ligands in vivo, recent global approaches combining RNA immunoprecipitation and deep sequencing (RIP-seq) as well as UV-cross-linking (CLIP-seq) have become instrumental in dissecting RNA-protein interactions. However, the computational analysis of these high-throughput sequencing data is still challenging. Here, we provide a computational pipeline to analyze CLIP-seq and RIP-seq datasets. This generic analytic procedure may help accelerate the identification of direct RNA-protein interactions from high-throughput RBP profiling experiments in a variety of bacterial species.

Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data

We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).

omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data

CLIP-seq methods allow the generation of genome-wide maps of RNA binding protein – RNA interaction sites. However, due to differences between different CLIP-seq assays, existing computational approaches to analyze the data can only be applied to a subset of assays. Here, we present a probabilistic model called omniCLIP that can detect regulatory elements in RNAs from data of all CLIP-seq assays. omniCLIP jointly models data across replicates and can integrate background information. Therefore, omniCLIP greatly simplifies the data analysis, increases the reliability of results and paves the way for integrative studies based on data from different assays.

CLIP-Seq in Bacteria: Global Recognition Patterns of Bacterial RNA-Binding Proteins

RNA-protein interactions are at the heart of many central cellular processes, and RNA-binding proteins (RBPs) associate with virtually all RNA molecules in a cell. In bacteria, global RBPs, often in conjunction with small regulatory RNAs, affect physiology and virulence by controlling transcription, translation, and RNA decay. To understand how these regulatory proteins orchestrate global gene expression, detailed maps of their cellular RNA binding sites are required. To this end, cross-linking and immunoprecipitation followed by deep sequencing (CLIP-seq) has revolutionized RBP studies by providing knowledge about global recognition patterns of RBPs in both eukaryotic and bacterial cells. In this chapter, we provide a step-by-step protocol for global mapping of bona fide RBP binding sites using CLIP-seq in bacteria. This protocol has been successfully applied for charting the binding sites of Hfq, CsrA, and ProQ, three global regulatory RBPs in Salmonella enterica and Escherichia coli, and should be readily applicable to other RBPs and bacterial species.

Ecdysone-responsive microRNA-252-5p controls the cell cycle by targeting Abi in Drosophila

The steroid hormone ecdysone has a central role in the developmental transitions of insects through its control of responsive protein-coding and microRNA (miRNA) gene expression. However, the complete regulatory network controlling the expression of these genes remains to be elucidated. In this study, we performed cross-linking immunoprecipitation coupled with deep sequencing of endogenous Argonaute 1 (Ago1) protein, the core effector of the miRNA pathway, in Drosophila S2 cells. We found that regulatory interactions between miRNAs and their cognate targets were substantially altered by Ago1 in response to ecdysone signaling. Additionally, during the larva-to-adult metamorphosis, miR-252-5p was up-regulated via the canonical ecdysone-signaling pathway. Moreover, we provide evidence that miR-252-5p targets Abelson interacting protein ( Abi) to decrease the protein levels of cyclins A and B, controlling the cell cycle. Overall, our data suggest a potential role for the ecdysone/miR-252-5p/Abi regulatory axis partly in cell-cycle control during metamorphosis in Drosophila.-Lim, D.-H., Lee, S., Han, J. Y., Choi, M.-S., Hong, J.-S., Seong, Y., Kwon, Y.-S., Lee, Y. S. Ecdysone-responsive microR-252-5p controls the cell cycle by targeting Abi in Drosophila.

Medulloblastoma-associated DDX3 variant selectively alters the translational response to stress

DDX3X encodes a DEAD-box family RNA helicase (DDX3) commonly mutated in medulloblastoma, a highly aggressive cerebellar tumor affecting both children and adults. Despite being implicated in several facets of RNA metabolism, the nature and scope of DDX3′s interactions with RNA remain unclear. Here, we show DDX3 collaborates extensively with the translation initiation machinery through direct binding to 5′UTRs of nearly all coding RNAs, specific sites on the 18S rRNA, and multiple components of the translation initiation complex. Impairment of translation initiation is also evident in primary medulloblastomas harboring mutations in DDX3X, further highlighting DDX3′s role in this process. Arsenite-induced stress shifts DDX3 binding from the 5′UTR into the coding region of mRNAs concomitant with a general reduction of translation, and both the shift of DDX3 on mRNA and decreased translation are blunted by expression of a catalytically-impaired, medulloblastoma-associated DDX3^R534H variant. Furthermore, despite the global repression of translation induced by arsenite, translation is preserved on select genes involved in chromatin organization in DDX3^R534H-expressing cells. Thus, DDX3 interacts extensively with RNA and ribosomal machinery to help remodel the translation landscape in response to stress, while cancer-related DDX3 variants adapt this response to selectively preserve translation.

Analysis of Circular RNAs Using the Web Tool CircInteractome

Circular RNAs (circRNAs) are generated through nonlinear back splicing, during which the 5' and 3' ends are covalently joined. Consequently, the lack of free ends makes them very stable compared to their counterpart linear RNAs. By selectively interacting with microRNAs and RNA-binding proteins (RBPs), circRNAs have been shown to influence gene expression programs. We designed a web tool, CircInteractome, in order to (1) explore potential interactions of circRNAs with RBPs, (2) design specific divergent primers to detect circRNAs, (3) study tissue- and cell-specific circRNAs, (4) identify gene-specific circRNAs, (5) explore potential miRNAs interacting with circRNAs, and (6) design specific siRNAs to silence circRNAs. Here, we review the CircInteractome tool and explain recent updates to the site. The database is freely accessible at http://circinteractome.nia.nih.gov .

The Human CCHC-type Zinc Finger Nucleic Acid-Binding Protein Binds G-Rich Elements in Target mRNA Coding Sequences and Promotes Translation

The CCHC-type zinc finger nucleic acid-binding protein (CNBP/ZNF9) is conserved in eukaryotes and is essential for embryonic development in mammals. It has been implicated in transcriptional, as well as post-transcriptional, gene regulation; however, its nucleic acid ligands and molecular function remain elusive. Here, we use multiple systems-wide approaches to identify CNBP targets and function. We used photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) to identify 8,420 CNBP binding sites on 4,178 mRNAs. CNBP preferentially bound G-rich elements in the target mRNA coding sequences, most of which were previously found to form G-quadruplex and other stable structures in vitro. Functional analyses, including RNA sequencing, ribosome profiling, and quantitative mass spectrometry, revealed that CNBP binding did not influence target mRNA abundance but rather increased their translational efficiency. Considering that CNBP binding prevented G-quadruplex structure formation in vitro, we hypothesize that CNBP is supporting translation by resolving stable structures on mRNAs.

Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP–RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.

Variation in single-nucleotide sensitivity of eCLIP derived from reverse transcription conditions

Crosslinking and immunoprecipitation (CLIP) followed by high-throughput sequencing identifies the binding sites of RNA binding proteins on RNAs. The covalent RNA-amino acid adducts produced by UV irradiation can cause premature reverse transcription termination and deletions (referred to as crosslink-induced mutation sites (CIMS)), which may decrease overall cDNA yield but are exploited in state-of-the-art CLIP methods to identify these crosslink sites at single-nucleotide resolution. Here, we show the ratio of both crosslinked base deletions and read-through versus termination are highly dependent on the identity of the reverse transcriptase enzyme as well as on buffer conditions used. AffinityScript and TGIRT showed a lack of deletion of the crosslinked base with other enzymes showing variable rates, indicating that utilization and interpretation of CIMS analysis requires knowledge of the reverse transcriptase enzyme used. Commonly used enzymes, including Superscript III and AffinityScript, show high termination rates in standard magnesium buffer conditions, but show a single base difference in the position of termination for TARDBP motifs. In contrast, manganese-containing buffer promoted read-through at the adduct site. These results validate the use of standard enzymes and also propose alternative enzyme and buffer choices for particularly challenging samples that contain extensive RNA adducts or other modifications that inhibit standard reverse transcription.

Six GU-rich (6GUR) FUS-binding motifs detected by normalization of CLIP-seq by Nascent-seq

FUS, an RNA-binding protein (RBP), is mutated or abnormally regulated in neurodegenerative disorders. FUS regulates various aspects of RNA metabolisms. FUS-binding sites are rich in GU contents and are highly degenerative. FUS-binding motifs of GGU, GGUG, GUGGU and CGCGC have been previously reported. These motifs, however, are applicable to a small fraction of FUS-binding sites. As CLIP-seq tags are enriched in genes that are highly expressed, we normalized CLIP-seq tags by Nascent-seq tags or RNA-seq tags of mouse N2a cells. Nascent-seq identifies nascent transcripts before being processed for splicing and polyadenylation. We extracted frequently observed 4-nt motifs from Nascent-seq-normalized CLIP regions, RNA-seq-normalized CLIP regions, and native CLIP regions. Specific GU-rich motifs were best detected in Nascent-seq-normalized CLIP regions. Analysis of structural motifs using Nascent-seq-normalized CLIP regions also predicted GU-rich sequence forming a stem structure. Sensitivity and specificity were calculated by examining whether the extracted motifs were present at the cross-linking-induced mutation sites (CIMS), where FUS was directly bound. We found that a combination of six motifs (UGUG, CUGG, UGGU, GCUG, GUGG, and UUGG), which were extracted from Nascent-seq-normalized CLIP-regions, had a better discriminative power than (i) motifs extracted from RNA-seq-normalized CLIP regions, (ii) motifs extracted from native CLIP regions, (iii) previously reported individual motifs, or (iv) 15 motifs in SpliceAid 2. Validation of the 6 GU-rich (6GU_R) motifs using CLIP-seq of the cerebrum and the whole brain showed that the 6GU_R motifs were specifically enriched in CIMS. The number of the 6GU_R motifs in an uninterrupted region was counted and multiplied by four to calculate the area, which was defined as the 6GU_R-Score. The 6GU_R-Score of 8 or more best discriminated CIMS from CIMS-flanking regions. We propose that the 6GU_R motifs predict FUS-binding sites more efficiently than previously reported individual motifs or 15 motifs in SpliceAid 2.

A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data

RNA binding proteins (RBPs) play an important role in regulating many processes in the cell. RBPs often recognize their RNA targets in a specific manner. In addition to the RNA primary sequence, the structure of the RNA has been shown to play a central role in RNA recognition by RBPs. In recent years, many experimental approaches, both in vitro and in vivo, were developed and employed to identify and characterize RBP targets and extract their binding specificities. In vivo binding techniques, such as CrossLinking and ImmunoPrecipitation (CLIP)-based methods, enable the characterization of protein binding sites on RNA targets. However, these methods do not provide information regarding the structural preferences of the protein. While methods to obtain the structure of RNA are available, inferring both the sequence and the structure preferences of RBPs remains a challenge. Here we present SMARTIV, a novel computational tool for discovering combined sequence and structure binding motifs from in vivo RNA binding data relying on the sequences of the target sites, the ranking of their binding scores and their predicted secondary structure. The combined motifs are provided in a unified representation that is informative and easy for visual perception. We tested the method on CLIP-seq data from different platforms for a variety of RBPs. Overall, we show that our results are highly consistent with known binding motifs of RBPs, offering additional information on their structural preferences.

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

Background

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation.

Results

In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications.

Conclusion

The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep

Electronic supplementary material

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Robust, Cost-Effective Profiling of RNA Binding Protein Targets with Single-end Enhanced Crosslinking and Immunoprecipitation (seCLIP)

Profiling of RNA binding protein targets in vivo provides critical insights into the mechanistic roles they play in regulating RNA processing. The enhanced crosslinking and immunoprecipitation (eCLIP) methodology provides a framework for robust, reproducible identification of transcriptome-wide protein-RNA interactions, with dramatically improved efficiency over previous methods. Here we provide a step-by-step description of the eCLIP method, along with insights into optimal performance of critical steps in the protocol. In particular, we describe improvements to the adaptor strategy that enables single-end enhanced CLIP (seCLIP), which removes the requirement for paired-end sequencing of eCLIP libraries. Further, we describe the observation of contaminating RNA present in standard nitrocellulose membrane suppliers, and present options with significantly reduced contamination for sensitive applications. These notes further refine the eCLIP methodology, simplifying robust RNA binding protein studies for all users.

CRISPR/Cas9-mediated integration enables TAG-eCLIP of endogenously tagged RNA binding proteins

Identification of in vivo direct RNA targets for RNA binding proteins (RBPs) provides critical insight into their regulatory activities and mechanisms. Recently, we described a methodology for enhanced crosslinking and immunoprecipitation followed by high-throughput sequencing (eCLIP) using antibodies against endogenous RNA binding proteins. However, in many cases it is desirable to profile targets of an RNA binding protein for which an immunoprecipitation-grade antibody is lacking. Here we describe a scalable method for using CRISPR/Cas9-mediated homologous recombination to insert a peptide tag into the endogenous RNA binding protein locus. Further, we show that TAG-eCLIP performed using tag-specific antibodies can yield the same robust binding profiles after proper control normalization as eCLIP with antibodies against endogenous proteins. Finally, we note that antibodies against commonly used tags can immunoprecipitate significant amounts of antibody-specific RNA, emphasizing the need for paired controls alongside each experiment for normalization. TAG-eCLIP enables eCLIP profiling of new native proteins where no suitable antibody exists, expanding the RBP-RNA interaction landscape.

Analysis of HIV-1 Gag-RNA Interactions in Cells and Virions by CLIP-seq

Next-generation sequencing-based methodologies have revolutionized the analysis of protein-nucleic acid complexes; yet these novel approaches have rarely been applied in virology. Because it has an RNA genome, RNA-protein interactions play critical roles in human immunodeficiency virus type 1 (HIV-1) replication. In many cases, the binding sites of proteins on HIV-1 RNA molecules in physiologically relevant settings are not known. Cross-linking-immunoprecipitation sequencing (CLIP-seq) methodologies, which combine immunoprecipitation of covalently crosslinked protein-RNA complexes with high-throughput sequencing, is a powerful technique that can be applied to such questions as it provides a global account of RNA sequences bound by a RNA-binding protein of interest in physiological settings at near-nucleotide resolution. Here, we describe the application of the CLIP-seq methodology to identify the RNA molecules that are bound by the HIV-1 Gag protein in cells and in virions. This protocol can easily be applied to other viral and cellular RNA-binding proteins that influence HIV-1 replication.

Improvements to the HITS-CLIP protocol eliminate widespread mispriming artifacts

Background

 High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP) allows for high resolution, genome-wide mapping of RNA-binding proteins. This methodology is frequently used to validate predicted targets of microRNA binding, as well as direct targets of other RNA-binding proteins. Hence, the accuracy and sensitivity of binding site identification is critical. 

Results

We found that substantial mispriming during reverse transcription results in the overrepresentation of sequences complementary to the primer used for reverse transcription. Up to 45 % of peaks in publicly available HITS-CLIP libraries are attributable to this mispriming artifact, and the majority of libraries have detectable levels of mispriming. We also found that standard techniques for validating microRNA-target interactions fail to differentiate between artifactual peaks and physiologically relevant peaks.

Conclusions

Here, we present a modification to the HITS-CLIP protocol that effectively eliminates this artifact and improves the sensitivity and complexity of resulting libraries.

Electronic supplementary material

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Identification of CELF1 RNA targets by CLIP-seq in human HeLa cells

The specific interactions between RNA-binding proteins and their target RNAs are an essential level to control gene expression. By combining ultra-violet cross-linking and immunoprecipitation (CLIP) and massive SoliD sequencing we identified the RNAs bound by the RNA-binding protein CELF1, in human HeLa cells. The CELF1 binding sites deduced from the sequence data allow characterizing specific features of CELF1-RNA association. We present therefore the first map of CELF1 binding sites in human cells.

CLIPSeqTools--a novel bioinformatics CLIP-seq analysis suite

Immunoprecipitation of RNA binding proteins (RBPs) after in vivo crosslinking, coupled with sequencing of associated RNA footprints (HITS-CLIP, CLIP-seq), is a method of choice for the identification of RNA targets and binding sites for RBPs. Compared with RNA-seq, CLIP-seq analysis is widely diverse and depending on the RBPs that are analyzed, the approaches vary significantly, necessitating the development of flexible and efficient informatics tools. In this study, we present CLIPSeqTools, a novel, highly flexible computational suite that can perform analysis from raw sequencing data with minimal user input. It contains a wide array of tools to provide an in-depth view of CLIP-seq data sets. It supports extensive customization and promotes improvization, a critical virtue, since CLIP-seq analysis is rarely well defined a priori. To highlight CLIPSeqTools capabilities, we used the suite to analyze Ago-miRNA HITS-CLIP data sets that we prepared from human brains.

Characterization of RNA-Protein Interactions: Lessons from Two RNA-Binding Proteins, SRSF1 and SRSF2

SR proteins are a class of RNA-binding proteins whose RNA-binding ability is required for both constitutive and alternative splicing. While members of the SR protein family were once thought to have redundant functions, in-depth biochemical analysis of their RNA-binding abilities has revealed distinct binding profiles for each SR protein, that often lead to either synergistic or antagonistic functions. SR protein family members SRSF1 and SRSF2 are two of the most highly studied RNA-binding proteins. Here we examine the various methods used to differentiate SRSF1 and SRSF2 RNA-binding ability. We discuss the benefits and type of information that can be determined using each method.