RNA-protein Interaction Sites Research Articles

Revised iCLIP-seq Protocol for Profiling RNA-protein Interaction Sites at Individual Nucleotide Resolution in Living Cells.

Individual nucleotide resolution UV cross-linking and immunoprecipitation followed by high-throughput sequencing (iCLIP-seq) is a powerful technique that is used to identify RNA-binding proteins' (RBP) binding sites on target RNAs and to characterize the molecular basis of posttranscriptional regulatory pathways. Several variants of CLIP have been developed to improve its efficiency and simplify the protocol [e.g., iCLIP2 and enhanced CLIP (eCLIP)]. We have recently reported that transcription factor SP1 functions in the regulation of alternative cleavage and polyadenylation through direct RNA binding. We utilized a modified iCLIP method to identify RNA-binding sites for SP1 and several of the cleavage and polyadenylation complex subunits, including CFIm25, CPSF7, CPSF100, CPSF2, and Fip1. Our revised protocol takes advantage of several features of the eCLIP procedure and also improves on certain steps of the original iCLIP method, including optimization of circularization of cDNA. Herein, we describe a step-by-step procedure for our revised iCLIP-seq protocol, that we designate as iCLIP-1.5, and provide alternative approaches for certain difficult-to-CLIP proteins. Key features Identification of RNA-binding sites of RNA-binding proteins (RBPs) at nucleotide resolution. iCLIP-seq provides precise positional and quantitative information on the RNA-binding sites of RBPs in living cells. iCLIP facilitates the identification of sequence motifs recognized by RBPs. Allows quantitative analysis of genome-wide changes in protein-RNA interactions. Revised iCLIP-1.5 protocol is more efficient and highly robust; it provides higher coverage even for low-input samples. Graphical overview.

RNAapt3D: RNA aptamer 3D-structural modeling database

RNA aptamers are oligonucleotides with high binding affinity and specificity for target molecules and are expected to be a new generation of therapeutic molecules and targeted delivery materials. The tertiary structure of RNA molecules and RNA-protein interaction sites are increasingly important as potential targets for new drugs. The pathological mechanisms of diseases must be understood in detail to guide drug design. In developing RNA aptamers as drugs, information about the interaction mechanisms and structures of RNA aptamer-target protein complexes are useful. We constructed a database, RNA aptamer 3D-structural modeling (RNAapt3D), consisting of RNA aptamer data that are potential drug candidates. The database includes RNA sequences and computationally predicted RNA tertiary structures based on secondary structures and implements methods that can be used to predict unknown structures of RNA aptamer-target molecule complexes. RNAapt3D should enable the design of RNA aptamers for target molecules and improve the efficiency and productivity of candidate drug selection. RNAapt3D can be accessed at https://rnaapt3d.medals.jp.

Single Nucleotide Resolution RNA-Protein Cross-Linking Mass Spectrometry: A Simple Extension of the CLIR-MS Workflow.

RNA–protein interactions mediate many intracellular processes. CLIR-MS (cross-linking of isotope-labeled RNA and tandem mass spectrometry) allows the identification of RNA–protein interaction sites at single nucleotide/amino acid resolution in a single experiment. Using isotopically labeled RNA segments for UV-light-induced cross-linking generates characteristic isotope patterns that constrain the sequence database searches, increasing spatial resolution. Whereas the use of segmentally isotopically labeled RNA is effective, it is technically involved and not applicable in some settings, e.g., in cell or tissue samples. Here we introduce an extension of the CLIR-MS workflow that uses unlabeled RNA during cross-linking and subsequently adds an isotopic label during sample preparation for MS analysis. After RNase and protease digests of a cross-linked complex, the nucleic acid part of a peptide–RNA conjugate is labeled using the enzyme T4 polynucleotide kinase and a 1:1 mixture of heavy 18O4-γ-ATP and light ATP. In this simple, one-step reaction, three heavy oxygen atoms are transferred from the γ-phosphate to the 5′-end of the RNA, introducing an isotopic shift of 6.01 Da that is detectable by mass spectrometry. We applied this approach to the RNA recognition motif (RRM) of the protein FOX1 in complex with its cognate binding substrate, FOX-binding element (FBE) RNA. We also labeled a single phosphate within an RNA and unambiguously determined the cross-linking site of the FOX1-RRM binding to FBE at single residue resolution on the RNA and protein level and used differential ATP labeling for relative quantification based on isotope dilution. Data are available via ProteomeXchange with the identifier PXD024010.

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

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

TRIP-seq reveals repression of premature polyadenylation by co-transcriptional FUS-U1 snRNP assembly.

RNA processing occurs co-transcriptionally through the dynamic recruitment of RNA processing factors to RNA polymerase II (RNAPII). However, transcriptome-wide identification of protein-RNA interactions specifically assembled on transcribing RNAPII is challenging. Here, we develop the targeted RNA immunoprecipitation sequencing (tRIP-seq) method that detects protein-RNA interaction sites in thousands of cells. The high sensitivity of tRIP-seq enables identification of protein-RNA interactions at functional subcellular levels. Application of tRIP-seq to the FUS-RNA complex in the RNAPII machinery reveals that FUS binds upstream of alternative polyadenylation (APA) sites of nascent RNA bound to RNAPII, which retards RNAPII and suppresses the recognition of the polyadenylation signal by CPSF. Further tRIP-seq analyses demonstrate that the repression of APA is achieved by a complex composed of FUS and U1 snRNP on RNAPII, but not by either one alone. Moreover, our analysis reveals that FUS mutations in familial amyotrophic lateral sclerosis (ALS) that impair the FUS-U1 snRNP interaction aberrantly activate the APA sites. tRIP-seq provides new insights into the regulatory mechanism of co-transcriptional RNA processing by RNA processing factors.

SliceIt: A genome-wide resource and visualization tool to design CRISPR/Cas9 screens for editing protein-RNA interaction sites in the human genome.

Several protein-RNA cross linking protocols have been established in recent years to delineate the molecular interaction of an RNA Binding Protein (RBP) and its target RNAs. However, functional dissection of the role of the RBP binding sites in modulating the post-transcriptional fate of the target RNA remains challenging. CRISPR/Cas9 genome editing system is being commonly employed to perturb both coding and noncoding regions in the genome. With the advancements in genome-scale CRISPR/Cas9 screens, it is now possible to not only perturb specific binding sites but also probe the global impact of protein-RNA interaction sites across cell types. Here, we present SliceIt (http://sliceit.soic.iupui.edu/), a database of in silico sgRNA (single guide RNA) library to facilitate conducting such high throughput screens. SliceIt comprises of ~4.8 million unique sgRNAs with an estimated range of 2-8 sgRNAs designed per RBP binding site, for eCLIP experiments of >100 RBPs in HepG2 and K562 cell lines from the ENCODE project. SliceIt provides a user friendly environment, developed using advanced search engine framework, Elasticsearch. It is available in both table and genome browser views facilitating the easy navigation of RBP binding sites, designed sgRNAs, exon expression levels across 53 human tissues along with prevalence of SNPs and GWAS hits on binding sites. Exon expression profiles enable examination of locus specific changes proximal to the binding sites. Users can also upload custom tracks of various file formats directly onto genome browser, to navigate additional genomic features in the genome and compare with other types of omics profiles. All the binding site-centric information is dynamically accessible via "search by gene", "search by coordinates" and "search by RBP" options and readily available to download. Validation of the sgRNA library in SliceIt was performed by selecting RBP binding sites in Lipt1 gene and designing sgRNAs. Effect of CRISPR/Cas9 perturbations on the selected binding sites in HepG2 cell line, was confirmed based on altered proximal exon expression levels using qPCR, further supporting the utility of the resource to design experiments for perturbing protein-RNA interaction networks. Thus, SliceIt provides a one-stop repertoire of guide RNA library to perturb RBP binding sites, along with several layers of functional information to design both low and high throughput CRISPR/Cas9 screens, for studying the phenotypes and diseases associated with RBP binding sites.

Using Protein Interaction Profile Sequencing (PIP-seq) to Identify RNA Secondary Structure and RNA-Protein Interaction Sites of Long Noncoding RNAs in Plants.

From the moment of transcription, RNA molecules are continuously bound by RNA-binding proteins (RBPs). While the majority of research has focused on how these RBPs regulate posttranscriptional gene regulation of messenger RNAs (mRNAs), the majority of cellular RNAs do not code for proteins, such as ribosomal RNAs, transfer RNAs, and microRNAs. Since these RNAs do not code for protein, their function is mainly determined by their interactions with RBPs as well as their intramolecular base pairing, or RNA secondary structure. One class of noncoding RNAs termed long noncoding RNAs (lncRNAs) have recently become the subject of intense research interest. To study the function of lncRNAs in eukaryotic cells, it is important to examine both their interactions with RBPs as well as their RNA secondary structure. Protein interaction profile sequencing (PIP-seq) is a genome-wide method that uses structure-specific ribonucleases (RNases) to identify regions of double-stranded and single-stranded RNA as well as regions that are protected from these RNases, which represent sites of RBP binding. This method is a very powerful way to examine RNA-protein interactions and RNA secondary structure of all lncRNAs expressed in cells and tissues and can reveal cell-type-specific or tissue-specific patterns of these RNA features. Here, we give a step-by-step account of performing this technique for comprehensively analyzing RNA-protein interactions and RNA secondary structure in plant transcriptomes.

Computationally Characterizing Protein-Bound Long Noncoding RNAs and Their Secondary Structure Using Protein Interaction Profile Sequencing (PIP-Seq) in Plants.

Two major components of posttranscriptional regulation are RNA-protein interactions and RNA secondary structure. While noncoding RNAs are far more abundant than messenger RNAs in eukaryotic systems, their functions remain largely unstudied. Evidence suggests that RNA-protein interactions and RNA secondary structure also regulate the function of long noncoding RNAs (lncRNAs), which are noncoding RNAs over 200 nucleotides (nt) in length. Protein interaction profile sequencing (PIP-seq) allows researchers to perform an unbiased screen of protein-bound regions and secondary structure of RNAs throughout a transcriptome of interest. Using a peak calling approach, our pipeline is able to identify protein-protected sites (PPSs), which are putative RNA-protein interaction sites. Additionally, by taking the ratio of read coverages in double-stranded RNA (dsRNA)-seq compared to single-stranded RNA (ssRNA)-seq libraries, our analysis can also calculate an RNA secondary structure score that reflects the likelihood of a region being comprised of double- or single-stranded ribonucleotides. Researchers can also use this pipeline to look at specific regions of interest, such as known lncRNAs, and determine their protein-bound status as well as elucidate their secondary structure.

Mapping of In Vivo RNA-Binding Sites by Ultraviolet (UV)-Cross-Linking Immunoprecipitation (CLIP).

RNA "CLIP" (cross-linking immunoprecipitation), the method by which RNA-protein complexes are covalently cross-linked and purified and the RNA sequenced, has attracted attention as a powerful means of developing genome-wide maps of direct, functional RNA-protein interaction sites. These maps have been used to identify points of regulation, and they hold promise for understanding the dynamics of RNA regulation in normal cell function and its dysregulation in disease.

High-Resolution, High-Throughput Analysis of Hfq-Binding Sites Using UV Crosslinking and Analysis of cDNA (CRAC).

Small regulatory nonprotein-coding RNAs (sRNAs) have emerged as ubiquitous and abundant regulators of gene expression in a diverse cross section of bacteria. They play key roles in most aspects of bacterial physiology, including central metabolism, nutrient acquisition, virulence, biofilm formation, and outer membrane composition. RNA sequencing technologies have accelerated the identification of bacterial regulatory RNAs and are now being employed to understand their functions. Many regulatory RNAs require protein partners for activity, or modulate the activity of interacting proteins. Understanding how and where proteins interact with the transcriptome is essential to elucidate the functions of the many sRNAs. Here, we describe the implementation in bacteria of a UV-crosslinking technique termed CRAC that allows stringent, transcriptome-wide recovery of bacterial RNA-protein interaction sites in vivo and at base-pair resolution. We have used CRAC to map protein-RNA interaction sites for the RNA chaperone Hfq and ribonuclease RNase E in pathogenic E. coli, and toxins from toxin-antitoxin systems in Mycobacterium smegmatis, demonstrating the broad applicability of this technique.

PureCLIP: capturing target-specific protein\u2013RNA interaction footprints from single-nucleotide CLIP-seq data

The iCLIP and eCLIP techniques facilitate the detection of protein–RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP (https://github.com/skrakau/PureCLIP), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.

A three-dimensional model of RNase P in the hyperthermophilic archaeon Pyrococcus horikoshii OT3

Ribonuclease P (RNase P) is an endoribonuclease involved in maturation of the 5′-end of tRNA. We found previously that RNase P in the hyperthermophilic archaeon Pyrococcus horikoshii OT3 consists of a catalytic RNase P RNA (PhopRNA) and five protein cofactors designated PhoPop5, PhoRpp21, PhoRpp29, PhoRpp30, and PhoRpp38. The crystal structures of the five proteins have been determined, a three-dimensional (3-D) model of PhopRNA has been constructed, and biochemical data, including protein-RNA interaction sites, have become available. Here, this information was combined to orient the crystallographic structures of the proteins relative to their RNA binding sites in the PhopRNA model. Some alterations were made to the PhopRNA model to improve the fit. In the resulting structure, a heterotetramer composed of PhoPop5 and PhoRpp30 bridges helices P3 and P16 in the PhopRNA C-domain, thereby probably stabilizing a double-stranded RNA structure (helix P4) containing catalytic Mg2+ ions, while a heterodimer of PhoRpp21 and PhoRpp29 locates on a single-stranded loop connecting helices P11 and P12 in the specificity domain (S-domain) in PhopRNA, probably forming an appropriate conformation of the precursor tRNA (pre-tRNA) binding site. The fifth protein PhoRpp38 binds each kink-turn (K-turn) motif in helices P12.1, P12.2, and P16 in PhopRNA. Comparison of the structure of the resulting 3-D model with that of bacterial RNase P suggests transition from RNA-RNA interactions in bacterial RNase P to protein-RNA interactions in archaeal RNase P. The proposed 3-D model of P. horikoshii RNase P will serve as a framework for further structural and functional studies on archaeal, as well as eukaryotic, RNase Ps.

RNA structure, binding, and coordination in Arabidopsis.

From the moment of transcription, up through degradation, each RNA transcript is bound by an ever-changing cohort of RNA binding proteins. The binding of these proteins is regulated by both the primary RNA sequence, as well as the intramolecular RNA folding, or secondary structure, of the transcript. Thus, RNA secondary structure regulates many post-transcriptional processes. With the advent of next generation sequencing, several techniques have been developed to generate global landscapes of both RNA-protein interactions and RNA secondary structure. In this review, we describe the current state of the field detailing techniques to globally interrogate RNA secondary structure and/or RNA-protein interaction sites, as well as our current understanding of these features in the transcriptome of the model plant Arabidopsis thaliana. WIREs RNA 2017, 8:e1426. doi: 10.1002/wrna.1426 For further resources related to this article, please visit the WIREs website.

IRPIS-PseNNC: identifying RNA-protein interaction sites by incorporating the position-specific dinucleotide propensity into ensemble random forest approach

As the pile of RNA-Protein complexes sequences mounted, in order to overcome time-consuming problem of the traditional identify RNA-Protein interaction sites (RPIS) method, it is urgent need develop intelligent recognition approach for quickly and reliable recognition of the RNA-Protein interaction sites (RPIS). To settle the question, we developed a new method named iRPIS-PseNNC, in which each sample is a nineteen nucleotides segment that for positive the centre of the segments is RPIS and for negative the segments centre is non-RPIS, and the sample was obtained by sliding window. The RNA sample was formulated by combining the dipeptide position-specific propensity into random forest approach, and by using the random sampling to balance the training dataset. According the voting system, we combine eleven random forest together to construct an ensemble classifier. It is shown that via the rigorous cross validations that the new predictor “iRPIS-PseNNC” achieved very high percentage of accuracy than any other existing algorithms in this field, indicating that the iRPIS-PseNNC predictor will be an effective tool for prediction RNA-Protein interaction sites.

Finding RNA-Protein Interaction Sites Using HMMs.

RNA-binding proteins play important roles in the various stages of RNA maturation through binding to its target RNAs. Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Several Hidden Markov model-based (HMM) approaches have been suggested to identify protein-RNA binding sites from CLIP-Seq datasets. In this chapter, we describe how HMM can be applied to analyze CLIP-Seq datasets, including the bioinformatics preprocessing steps to extract count information from the sequencing data before HMM and the downstream analysis steps following peak-calling.

Protein Interaction Profile Sequencing (PIP-seq).

Every eukaryotic RNA transcript undergoes extensive post-transcriptional processing from the moment of transcription up through degradation. This regulation is performed by a distinct cohort of RNA-binding proteins which recognize their target transcript by both its primary sequence and secondary structure. Here, we describe protein interaction profile sequencing (PIP-seq), a technique that uses ribonuclease-based footprinting followed by high-throughput sequencing to globally assess both protein-bound RNA sequences and RNA secondary structure. PIP-seq utilizes single- and double-stranded RNA-specific nucleases in the absence of proteins to infer RNA secondary structure. These libraries are also compared to samples that undergo nuclease digestion in the presence of proteins in order to find enriched protein-bound sequences. Combined, these four libraries provide a comprehensive, transcriptome-wide view of RNA secondary structure and RNA protein interaction sites from a single experimental technique. © 2016 by John Wiley & Sons, Inc.

Easier, Better, Faster, Stronger: Improved Methods for RNA-Protein Interaction Studies

The RNA field has been revolutionized by methods that allow genome-scale identification of RNA-protein interaction sites. Two reports now introduce more efficient approaches, opening the technology to wider adoption (Van Nostrand etal., 2016; Zarnegar etal., 2016).

Protein Interaction Profile Sequencing (PIP-seq) in Plants.

RNA secondary structure and RNA-protein interactions are necessary for maintaining biological functionality and regulatory mechanisms within eukaryotic transcriptomes. Determining the structural characteristics and protein-bound sites of RNA molecules has therefore become a major research objective and requires the development of global methods for probing intra- and intermolecular RNA interaction sites. Sequencing RNAs treated with single-strand- and double-strand-specific ribonucleases in the absence of proteins allows the inference of RNA secondary structure. These samples can be compared to samples treated with nucleases in the presence of interacting proteins to identify protein-bound sequences. Thus, these four libraries reveal a comprehensive, transcriptome-wide view of RNA secondary structure and RNA protein interaction sites in a single experiment for any plant species of interest. © 2016 by John Wiley & Sons, Inc.

BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data.

Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations. Therefore, rigorous statistical analysis is required in order to separate true T-to-C base changes, following cross-linking, from noise. So far, most of the existing PAR-CLIP data analysis methods focus on discarding the low-frequency errors and rely on high-frequency substitutions to report binding sites, not taking into account the possibility of high-frequency false positive substitutions. Here, we introduce BMix, a new probabilistic method which explicitly accounts for the sources of noise in PAR-CLIP data and distinguishes cross-link induced T-to-C substitutions from low and high-frequency erroneous alterations. We demonstrate the superior speed and accuracy of our method compared with existing approaches on both simulated and real, publicly available human datasets. The model is freely accessible within the BMix toolbox at www.cbg.bsse.ethz.ch/software/BMix, available for Matlab and R. Supplementary data is available at Bioinformatics online. niko.beerenwinkel@bsse.ethz.ch.

Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data.

BackgroundPAR-CLIP is a recently developed Next Generation Sequencing-based method enabling transcriptome-wide identification of interaction sites between RNA and RNA-binding proteins. The PAR-CLIP procedure induces specific base transitions that originate from sites of RNA-protein interactions and can therefore guide the identification of binding sites. However, additional sources of transitions, such as cell type-specific SNPs and sequencing errors, challenge the inference of binding sites and suitable statistical approaches are crucial to control false discovery rates. In addition, a highly resolved delineation of binding sites followed by an extensive downstream analysis is necessary for a comprehensive characterization of the protein binding preferences and the subsequent design of validation experiments.ResultsWe present a statistical and computational framework for PAR-CLIP data analysis. We developed a sensitive transition-centered algorithm specifically designed to resolve protein binding sites at high resolution in PAR-CLIP data. Our method employes a Bayesian network approach to associate posterior log-odds with the observed transitions, providing an overall quantification of the confidence in RNA-protein interaction. We use published PAR-CLIP data to demonstrate the advantages of our approach, which compares favorably with alternative algorithms. Lastly, by integrating RNA-Seq data we compute conservative experimentally-based false discovery rates of our method and demonstrate the high precision of our strategy.ConclusionsOur method is implemented in the R package wavClusteR 2.0. The package is distributed under the GPL-2 license and is available from BioConductor at http://www.bioconductor.org/packages/devel/bioc/html/wavClusteR.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0470-y) contains supplementary material, which is available to authorized users.