Transcriptome-wide analysis of protein–RNA interactions using high-throughput sequencing

Cataloguing and Selection of mRNAs Localized to Dendrites in Neurons and Regulated by RNA-Binding Proteins in RNA Granules

Spatiotemporal translational regulation plays a key role in determining cell fate and function. Specifically, in neurons, local translation in dendrites is essential for synaptic plasticity and long-term memory formation. To achieve local translation, RNA-binding proteins in RNA granules regulate target mRNA stability, localization, and translation. To date, mRNAs localized to dendrites have been identified by comprehensive analyses. In addition, mRNAs associated with and regulated by RNA-binding proteins have been identified using various methods in many studies. However, the results obtained from these numerous studies have not been compiled together. In this review, we have catalogued mRNAs that are localized to dendrites and are associated with and regulated by the RNA-binding proteins fragile X mental retardation protein (FMRP), RNA granule protein 105 (RNG105, also known as Caprin1), Ras-GAP SH3 domain binding protein (G3BP), cytoplasmic polyadenylation element binding protein 1 (CPEB1), and staufen double-stranded RNA binding proteins 1 and 2 (Stau1 and Stau2) in RNA granules. This review provides comprehensive information on dendritic mRNAs, the neuronal functions of mRNA-encoded proteins, the association of dendritic mRNAs with RNA-binding proteins in RNA granules, and the effects of RNA-binding proteins on mRNA regulation. These findings provide insights into the mechanistic basis of protein-synthesis-dependent synaptic plasticity and memory formation and contribute to future efforts to understand the physiological implications of local regulation of dendritic mRNAs in neurons.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Investigating RNA-Protein Interactions in Neisseria meningitidis by RIP-Seq Analysis

Deep sequencing technology has revolutionized transcriptome analyses of both prokaryotes and eukaryotes. RNA-sequencing (RNA-seq), which is based on massively parallel sequencing of cDNAs, has been used to annotate transcript boundaries and has revealed widespread antisense transcription as well as a wealth of novel noncoding transcripts in many bacterial pathogens. Moreover, RNA-seq is nowadays also widely used to comprehensively explore the interaction between RNA-binding proteins and their RNA targets on a genome-wide level in many human-pathogenic bacteria. In particular, immunoprecipitation of an RNA-binding protein (RBP) of interest followed by isolation and analysis of all bound RNAs (RNA immunoprecipitation (RIP)) allows rapid characterization of its RNA regulon. Here, we describe an experimental approach which employs co-immunoprecipitation (coIP) of the RNA-binding chaperone Hfq along with bound RNAs followed by deep-sequencing of co-purified RNAs (RIP-Seq) from a genetically modified strain of Neisseria meningitidis expressing a chromosomally encoded Hfq-3×FLAG protein. This approach allowed us to comprehensively identify both mRNAs and sRNAs as targets of Hfq and served as an excellent starting point for sRNA research in this human pathogenic bacterium.

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.

Computational approaches for the analysis of RNA-protein interactions: A primer for biologists

RNA-binding proteins (RBPs) play important roles in the control of gene expression and the coordination of different layers of post-transcriptional regulation. Interactions between certain RBPs and mRNA transcripts are notoriously difficult to predict, as any given protein-RNA interaction may rely not only on RNA sequence, but also on three-dimensional RNA structures, competitive inhibition from other RBPs, and input from cellular signaling pathways. Advanced and high-throughput technologies for the identification of RNA-protein interactions have come to the rescue, but the identification of binding sites and downstream functional effects of RBPs from the resulting data can be challenging. In this review, we discuss statistical inference and machine-learning approaches and tools relevant for the study of RBPs and the analysis of large-scale RNA-protein interaction datasets. This primer is intended for life scientists who are interested in incorporating these tools into their own research. We begin with the demystification of regression models, as used in the analysis of next-generation sequencing data, and progress to a discussion of Hidden Markov Models, which are of particular value in analyzing cross-linking followed by immunoprecipitation data. We then continue with examples of machine learning techniques, such as support vector machines and gradient tree boosting. We close with a brief discussion of current trends in the field, including deep learning architectures.

RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.

The 3' end of the story: deciphering combinatorial interactions that control mRNA fate

A new study investigates how microRNAs affect the binding of proteins to RNA.

Specific RNP capture with antisense LNA/DNA mixmers

RNA-binding proteins (RBPs) play essential roles in RNA biology, responding to cellular and environmental stimuli to regulate gene expression. Important advances have helped to determine the (near) complete repertoires of cellular RBPs. However, identification of RBPs associated with specific transcripts remains a challenge. Here, we describe "specific ribonucleoprotein (RNP) capture," a versatile method for the determination of the proteins bound to specific transcripts in vitro and in cellular systems. Specific RNP capture uses UV irradiation to covalently stabilize protein-RNA interactions taking place at "zero distance." Proteins bound to the target RNA are captured by hybridization with antisense locked nucleic acid (LNA)/DNA oligonucleotides covalently coupled to a magnetic resin. After stringent washing, interacting proteins are identified by quantitative mass spectrometry. Applied to in vitro extracts, specific RNP capture identifies the RBPs bound to a reporter mRNA containing the Sex-lethal (Sxl) binding motifs, revealing that the Sxl homolog sister of Sex lethal (Ssx) displays similar binding preferences. This method also revealed the repertoire of RBPs binding to 18S or 28S rRNAs in HeLa cells, including previously unknown rRNA-binding proteins.

Bioinformatic tools for analysis of CLIP ribonucleoprotein data

Investigating the interactions of RNA-binding proteins (RBPs) with RNAs is a complex task for molecular and computational biologists. The molecular biology techniques and the computational approaches to understand RBP-RNA (or ribonucleoprotein, RNP) interactions have advanced considerably over the past few years and numerous and diverse software tools have been developed to analyze these data. Accordingly, laboratories interested in RNP biology face the challenge of choosing adequately among the available software tools those that best address the biological problem they are studying. Here, we focus on state-of-the-art molecular biology techniques that employ crosslinking and immunoprecipitation (CLIP) of an RBP to study and map RNP interactions. We review the different software tools and databases available to analyze the most widely used CLIP methods, HITS-CLIP, PAR-CLIP, and iCLIP. WIREs RNA 2017, 8:e1404. doi: 10.1002/wrna.1404 For further resources related to this article, please visit the WIREs website.

Investigating miRNA-mRNA regulatory networks using crosslinking immunoprecipitation methods for biomarker and target discovery in cancer

INTRODUCTION

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. Recently, different experimental approaches, such as RNA Sequencing, crosslinking immunoprecipitation (CLIP) methods and its variations, together with computational approaches have been developed to elucidate the miRNA-mRNA targetome. Areas covered: This report focuses on comparing the different experimental and computational approaches, describing their advantages and disadvantages and providing several examples of preclinical (in vitro and in vivo) and clinical studies that have identified miRNA target genes in various tumour types, including breast, ovary, colorectal and pancreas. Expert commentary: The combination of CLIP methods with bioinformatic analyses is essential to better predict miRNA-mRNA interactions and associate their specific pathways within the extensive regulatory network. Nevertheless, further studies are needed to overcome the difficulties these methods have, in order to find a gold standard method that identifies, without any bias, the regulatory association between miRNAs and their target mRNAs.

A Pipeline for PAR-CLIP Data Analysis

Photo-activatable ribonucleoside cross-linking and immunoprecipitation (PAR-CLIP) is a method to detect binding sites of RNA-binding proteins (RBPs) transcriptome-wide. This chapter covers the computational analysis of the high-throughput sequencing reads generated from PAR-CLIP experiments. It explains how the reads are mutated due to UV cross-linking and how to appropriately pre-process and align them to a reference sequence. Aligned reads are then aggregated into clusters which represent putative RBP-binding sites. Mapping artifacts are a source of false positives, which can be controlled by means of a mapping decoy and adaptive quality filtering of the read clusters. A step-by-step explanation of this procedure is given. All necessary tools are open source, including the scripts presented and used in this chapter.

Systems biology of lens development: A paradigm for disease gene discovery in the eye

Over the past several decades, the biology of the developing lens has been investigated using molecular genetics-based approaches in various vertebrate model systems. These efforts, involving target gene knockouts or knockdowns, have led to major advances in our understanding of lens morphogenesis and the pathological basis of cataracts, as well as of other lens related eye defects. In particular, we now have a functional understanding of regulators such as Pax6, Six3, Sox2, Oct1 (Pou2f1), Meis1, Pnox1, Zeb2 (Sip1), Mab21l1, Foxe3, Tfap2a (Ap2-alpha), Pitx3, Sox11, Prox1, Sox1, c-Maf, Mafg, Mafk, Hsf4, Fgfrs, Bmp7, and Tdrd7 in this tissue. However, whether these individual regulators interact or their targets overlap, and the significance of such interactions during lens morphogenesis, is not well defined. The arrival of high-throughput approaches for gene expression profiling (microarrays, RNA-sequencing (RNA-seq), etc.), which can be coupled with chromatin immunoprecipitation (ChIP) or RNA immunoprecipitation (RIP) assays, along with improved computational resources and publically available datasets (e.g. those containing comprehensive protein-protein, protein-DNA information), presents new opportunities to advance our understanding of the lens tissue on a global systems level. Such systems-level knowledge will lead to the derivation of the underlying lens gene regulatory network (GRN), defined as a circuit map of the regulator-target interactions functional in lens development, which can be applied to expedite cataract gene discovery. In this review, we cover the various systems-level approaches such as microarrays, RNA-seq, and ChIP that are already being applied to lens studies and discuss strategies for assembling and interpreting these vast amounts of high-throughput information for effective dispersion to the scientific community. In particular, we discuss strategies for effective interpretation of this new information in the context of the rich knowledge obtained through the application of traditional single-gene focused experiments on the lens. Finally, we discuss our vision for integrating these diverse high-throughput datasets in a single web-based user-friendly tool iSyTE (integrated Systems Tool for Eye gene discovery) - a resource that is already proving effective in the identification and characterization of genes linked to lens development and cataract. We anticipate that application of a similar approach to other ocular tissues such as the retina and the cornea, and even other organ systems, will significantly impact disease gene discovery.

Biochemical Methods To Investigate lncRNA and the Influence of lncRNA:Protein Complexes on Chromatin

Long noncoding RNAs (lncRNAs), defined as nontranslated transcripts greater than 200 nucleotides in length, are often differentially expressed throughout developmental stages, tissue types, and disease states. The identification, visualization, and suppression/overexpression of these sequences have revealed impacts on a wide range of biological processes, including epigenetic regulation. Biochemical investigations on select systems have revealed striking insight into the biological roles of lncRNAs and lncRNA:protein complexes, which in turn prompt even more unanswered questions. To begin, multiple protein- and RNA-centric technologies have been employed to isolate lncRNA:protein and lncRNA:chromatin complexes. LncRNA interactions with the multi-subunit protein complex PRC2, which acts as a transcriptional silencer, represent some of the few cases where the binding affinity, selectivity, and activity of a lncRNA:protein complex have been investigated. At the same time, recent reports of full-length lncRNA secondary structures suggest the formation of complex structures with multiple independent folding domains and pave the way for more detailed structural investigations and predictions of lncRNA three-dimensional structure. This review will provide an overview of the methods and progress made to date as well as highlight new methods that promise to further inform the molecular recognition, specificity, and function of lncRNAs.

RIP: RNA Immunoprecipitation

The relevance of RNA-protein interactions in modulating mRNA and noncoding RNA function is increasingly appreciated and several methods have been recently developed to map them. The RNA immunoprecipitation (RIP) is a powerful method to study the physical association between individual proteins and RNA molecules in vivo. The basic principles of RIP are very similar to those of chromatin immunoprecipitation (ChIP), a largely used tool in the epigenetic field, but with some important caveats. The approach is based on the use of a specific antibody raised against the protein of interest to pull down the RNA-binding protein (RBP) and target-RNA complexes. Any RNA that is associated with this protein complex will also be isolated and can be further analyzed by polymerase chain reaction-based methods, hybridization, or sequencing.Several variants of this technique exist and can be divided into two main classes: native and cross-linked RNA immunoprecipitation. The native RIP allows to reveal the identity of RNAs directly bound by the protein and their abundance in the immunoprecipitated sample, while cross-linked RIP leads to precisely map the direct and indirect binding site of the RBP of interest to the RNA molecule.In this chapter both the protocols applied to mammalian cells are described taking into account the caveats and considerations required for designing, performing, and interpreting the results of these experiments.

Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression

Untranslated regions (UTRs) and, to a lesser extent, coding sequences of mRNAs are involved in defining the fate of the mature transcripts through the modulation of three primary control processes, mRNA localization, degradation and translation; the action of trans-factors such as RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) combined with the presence of defined sequence and structural cis-elements ultimately determines translation levels. Identifying functional regions in UTRs and uncovering post-transcriptional regulators acting upon these regions is thus of paramount importance to understand the spectrum of regulatory possibilities for any given mRNA. This tasks can now be approached computationally, to reduce the space of testable hypotheses and to drive experimental validation.This chapter focuses on presenting databases and tools allowing to study the various aspects of post-transcriptional regulation, including motif search (sequence and secondary structure), prediction of regulatory networks (e.g., RBP and ncRNA binding sites), profiling of the mRNAs translational state, and other aspects of this level of gene expression regulation. Two analysis pipelines are also presented as practical examples of how the described tools could be integrated and effectively employed.

Genome-Wide Profiling of RNA-Protein Interactions Using CLIP-Seq

UV crosslinking immunoprecipitation (CLIP) is an increasingly popular technique to study protein-RNA interactions in tissues and cells. Whole cells or tissues are ultraviolet irradiated to generate a covalent bond between RNA and proteins that are in close contact. After partial RNase digestion, antibodies specific to an RNA binding protein (RBP) or a protein-epitope tag is then used to immunoprecipitate the protein-RNA complexes. After stringent washing and gel separation the RBP-RNA complex is excised. The RBP is protease digested to allow purification of the bound RNA. Reverse transcription of the RNA followed by high-throughput sequencing of the cDNA library is now often used to identify protein bound RNA on a genome-wide scale. UV irradiation can result in cDNA truncations and/or mutations at the crosslink sites, which complicates the alignment of the sequencing library to the reference genome and the identification of the crosslinking sites. Meanwhile, one or more amino acids of a crosslinked RBP can remain attached to its bound RNA due to incomplete digestion of the protein. As a result, reverse transcriptase may not read through the crosslink sites, and produce cDNA ending at the crosslinked nucleotide. This is harnessed by one variant of CLIP methods to identify crosslinking sites at a nucleotide resolution. This method, individual nucleotide resolution CLIP (iCLIP) circularizes cDNA to capture the truncated cDNA and also increases the efficiency of ligating sequencing adapters to the library. Here, we describe the detailed procedure of iCLIP.

Accelerating Discovery and Functional Analysis of Small RNAs with New Technologies

Over the past decade, bacterial small RNAs (sRNAs) have gone from a biological curiosity to being recognized as a major class of regulatory molecules. High-throughput methods for sampling the transcriptional output of bacterial cells demonstrate that sRNAs are universal features of bacterial transcriptomes, are plentiful, and appear to vary extensively over evolutionary time. With ever more bacteria coming under study, the question becomes how can we accelerate the discovery and functional characterization of sRNAs in diverse organisms. New technologies built on high-throughput sequencing are emerging that can rapidly provide global insight into the numbers and functions of sRNAs in bacteria of interest, providing information that can shape hypotheses and guide research. In this review, we describe recent developments in transcriptomics (RNA-seq) and functional genomics that we expect to help us develop an integrated, systems-level view of sRNA biology in bacteria.

Isolation and analyses of axonal ribonucleoprotein complexes

Cytoskeleton-dependent RNA transport and local translation in axons are gaining increased attention as key processes in the maintenance and functioning of neurons. Specific axonal transcripts have been found to play roles in many aspects of axonal physiology including axon guidance, axon survival, axon to soma communication, injury response and regeneration. This axonal transcriptome requires long-range transport that is achieved by motor proteins carrying transcripts as messenger ribonucleoprotein (mRNP) complexes along microtubules. Other than transport, the mRNP complex plays a major role in the generation, maintenance, and regulation of the axonal transcriptome. Identification of axonal RNA-binding proteins (RBPs) and analyses of the dynamics of their mRNPs are of high interest to the field. Here, we describe methods for the study of interactions between RNA and proteins in axons. First, we describe a protocol for identifying binding proteins for an RNA of interest by using RNA affinity chromatography. Subsequently, we discuss immunoprecipitation (IP) methods allowing the dissection of protein-RNA and protein-protein interactions in mRNPs under various physiological conditions.

Specificity and nonspecificity in RNA-protein interactions

To fully understand the regulation of gene expression, it is critical to quantitatively define whether and how RNA-binding proteins (RBPs) discriminate between alternative binding sites in RNAs. Here, we describe new methods that measure protein binding to large numbers of RNA variants, and ways to analyse and interpret data obtained by these approaches, including affinity distributions and free energy landscapes. We discuss how the new methodologies and the associated concepts enable the development of inclusive, quantitative models for RNA-protein interactions that transcend the traditional binary classification of RBPs as either specific or nonspecific.

Trim25 Is an RNA-Specific Activator of Lin28a/TuT4-Mediated Uridylation

RNA binding proteins have thousands of cellular RNA targets and often exhibit opposite or passive molecular functions. Lin28a is a conserved RNA binding protein involved in pluripotency and tumorigenesis that was previously shown to trigger TuT4-mediated pre-let-7 uridylation, inhibiting its processing and targeting it for degradation. Surprisingly, despite binding to other pre-microRNAs (pre-miRNAs), only pre-let-7 is efficiently uridylated by TuT4. Thus, we hypothesized the existence of substrate-specific cofactors that stimulate Lin28a-mediated pre-let-7 uridylation or restrict its functionality on non-let-7 pre-miRNAs. Through RNA pull-downs coupled with quantitative mass spectrometry, we identified the E3 ligase Trim25 as an RNA-specific cofactor for Lin28a/TuT4-mediated uridylation. We show that Trim25 binds to the conserved terminal loop (CTL) of pre-let-7 and activates TuT4, allowing for more efficient Lin28a-mediated uridylation. These findings reveal that protein-modifying enzymes, only recently shown to bind RNA, can guide the function of canonical ribonucleoprotein (RNP) complexes in cis, thereby providing an additional level of specificity.

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Lin28a binding to a pre-miRNA is insufficient to trigger TuT4-mediated uridylation

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The E3 ligase Trim25 binds to the conserved terminal loop of pre-let-7

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Trim25 is an RNA-specific cofactor for Lin28a/TuT4-mediated uridylation

The GYF domain protein CD2BP2 is critical for embryogenesis and podocyte function

Scaffolding proteins play pivotal roles in the assembly of macromolecular machines such as the spliceosome. The adaptor protein CD2BP2, originally identified as a binding partner of the adhesion molecule CD2, is a pre-spliceosomal assembly factor that utilizes its glycine-tyrosine-phenylalanine (GYF) domain to co-localize with spliceosomal proteins. So far, its function in vertebrates is unknown. Using conditional gene targeting in mice, we show that CD2BP2 is crucial for embryogenesis, leading to growth retardation, defects in vascularization, and premature death at embryonic day 10.5 when absent. Ablation of the protein in bone marrow-derived macrophages indicates that CD2BP2 is involved in the alternative splicing of mRNA transcripts from diverse origins. At the molecular level, we identified the phosphatase PP1 to be recruited to the spliceosome via the N-terminus of CD2BP2. Given the strong expression of CD2BP2 in podocytes of the kidney, we use selective depletion of CD2BP2, in combination with next-generation sequencing, to monitor changes in exon usage of genes critical for podocyte functions, including VEGF and actin regulators. CD2BP2-depleted podocytes display foot process effacement, and cause proteinuria and ultimately lethal kidney failure in mice. Collectively, our study defines CD2BP2 as a non-redundant splicing factor essential for embryonic development and podocyte integrity.

Competition between target sites of regulators shapes post-transcriptional gene regulation

Post-transcriptional gene regulation (PTGR) of mRNA turnover, localization and translation is mediated by microRNAs (miRNAs) and RNA-binding proteins (RBPs). These regulators exert their effects by binding to specific sequences within their target mRNAs. Increasing evidence suggests that competition for binding is a fundamental principle of PTGR. Not only can miRNAs be sequestered and neutralized by the targets with which they interact through a process termed 'sponging', but competition between binding sites on different RNAs may also lead to regulatory crosstalk between transcripts. Here, we quantitatively model competition effects under physiological conditions and review the role of endogenous sponges for PTGR in light of the key features that emerge.

Leveraging the complementary nature of RNA-Seq and shotgun proteomics data

RNA sequencing (RNA-Seq) and MS-based shotgun proteomics are powerful high-throughput technologies for identifying and quantifying RNA transcripts and proteins, respectively. With the increasing affordability of these technologies, many projects have started to apply both to the same samples to achieve a more comprehensive understanding of biological systems. A major analytical challenge for such integrative projects is how to effectively leverage the complementary nature of RNA-Seq and shotgun proteomics data. RNA-Seq provides comprehensive information on mRNA abundance, alternative splicing, nucleotide variation, and structure alteration. Sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in cell and tissue samples and thus improve protein identification. Meanwhile, proteomics data provide essential confirmation of the validity and functional relevance of novel findings from RNA-Seq data. At the quantitative level, mRNA and protein levels are only modestly correlated, suggesting strong involvement of posttranscriptional regulation in controlling gene expression. Here, we review recent studies at the interface of RNA-Seq and proteomics data. We discuss goals, accomplishments, and challenges in RNA-Seq-based proteogenomics. We also examine the current status and future potential of parallel transcriptome and proteome quantification in revealing posttranscriptional regulatory mechanisms.

Genome-wide analysis of miRNA-mRNA interactions in marrow stromal cells

Regulation of hematopoietic stem cell proliferation, lineage commitment, and differentiation in adult vertebrates requires extrinsic signals provided by cells in the marrow microenvironment (ME) located within the bone marrow. Both secreted and cell-surface bound factors critical to this regulation have been identified, yet control of their expression by cells within the ME has not been addressed. Herein we hypothesize that microRNAs (miRNAs) contribute to their controlled expression. MiRNAs are small noncoding RNAs that bind to target mRNAs and downregulate gene expression by either initiating mRNA degradation or preventing peptide translation. Testing the role of miRNAs in downregulating gene expression has been difficult since conventional techniques used to define miRNA-mRNA interactions are indirect and have high false-positive and negative rates. In this report, a genome-wide biochemical technique (high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation or HITS-CLIP) was used to generate unbiased genome-wide maps of miRNA-mRNA interactions in two critical cellular components of the marrow ME: marrow stromal cells and bone marrow endothelial cells. Analysis of these datasets identified miRNAs as direct regulators of JAG1, WNT5A, MMP2, and VEGFA; four factors that are important to ME function. Our results show the feasibility and utility of unbiased genome-wide biochemical techniques in dissecting the role of miRNAs in regulation of complex tissues such as the marrow ME.

Computational Methods for CLIP-seq Data Processing

RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.

Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities

Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.

Biological and bioinformatical approaches to study crosstalk of long-non-coding RNAs and chromatin-modifying proteins

Long-non-coding RNA (lncRNA) regulates gene expression through transcriptional and epigenetic regulation as well as alternative splicing in the nucleus. In addition, regulation is achieved at the levels of mRNA translation, storage and degradation in the cytoplasm. During recent years, several studies have described the interaction of lncRNAs with enzymes that confer so-called epigenetic modifications, such as DNA methylation, histone modifications and chromatin structure or remodelling. LncRNA interaction with chromatin-modifying enzymes (CME) is an emerging field that confers another layer of complexity in transcriptional regulation. Given that CME-lncRNA interactions have been identified in many biological processes, ranging from development to disease, comprehensive understanding of underlying mechanisms is important to inspire basic and translational research in the future. In this review, we highlight recent findings to extend our understanding about the functional interdependencies between lncRNAs and CMEs that activate or repress gene expression. We focus on recent highlights of molecular and functional roles for CME-lncRNAs and provide an interdisciplinary overview of recent technical and methodological developments that have improved biological and bioinformatical approaches for detection and functional studies of CME-lncRNA interaction.

Crosslinking-immunoprecipitation (iCLIP) analysis reveals global regulatory roles of hnRNP L

Heterogeneous nuclear ribonucleoprotein L (hnRNP L) is a multifunctional RNA-binding protein that is involved in many different processes, such as regulation of transcription, translation, and RNA stability. We have previously characterized hnRNP L as a global regulator of alternative splicing, binding to CA-repeat, and CA-rich RNA elements. Interestingly, hnRNP L can both activate and repress splicing of alternative exons, but the precise mechanism of hnRNP L-mediated splicing regulation remained unclear. To analyze activities of hnRNP L on a genome-wide level, we performed individual-nucleotide resolution crosslinking-immunoprecipitation in combination with deep-sequencing (iCLIP-Seq). Sequence analysis of the iCLIP crosslink sites showed significant enrichment of C/A motifs, which perfectly agrees with the in vitro binding consensus obtained earlier by a SELEX approach, indicating that in vivo hnRNP L binding targets are mainly determined by the RNA-binding activity of the protein. Genome-wide mapping of hnRNP L binding revealed that the protein preferably binds to introns and 3' UTR. Additionally, position-dependent splicing regulation by hnRNP L was demonstrated: The protein represses splicing when bound to intronic regions upstream of alternative exons, and in contrast, activates splicing when bound to the downstream intron. These findings shed light on the longstanding question of differential hnRNP L-mediated splicing regulation. Finally, regarding 3' UTR binding, hnRNP L binding preferentially overlaps with predicted microRNA target sites, indicating global competition between hnRNP L and microRNA binding. Translational regulation by hnRNP L was validated for a subset of predicted target 3'UTRs.

Differential protein occupancy profiling of the mRNA transcriptome

Background

RNA-binding proteins (RBPs) mediate mRNA biogenesis, translation and decay. We recently developed an approach to profile transcriptome-wide RBP contacts on polyadenylated transcripts by next-generation sequencing. A comparison of such profiles from different biological conditions has the power to unravel dynamic changes in protein-contacted cis-regulatory mRNA regions without a priori knowledge of the regulatory protein component.

Results

We compared protein occupancy profiles of polyadenylated transcripts in MCF7 and HEK293 cells. Briefly, we developed a bioinformatics workflow to identify differential crosslinking sites in cDNA reads of 4-thiouridine crosslinked polyadenylated RNA samples. We identified 30,000 differential crosslinking sites between MCF7 and HEK293 cells at an estimated false discovery rate of 10%. 73% of all reported differential protein-RNA contact sites cannot be explained by local changes in exon usage as indicated by complementary RNA-seq data. The majority of differentially crosslinked positions are located in 3′ UTRs, show distinct secondary-structure characteristics and overlap with binding sites of known RBPs, such as ELAVL1. Importantly, mRNA transcripts with the most significant occupancy changes show elongated mRNA half-lives in MCF7 cells.

Conclusions

We present a global comparison of protein occupancy profiles from different cell types, and provide evidence for altered mRNA metabolism as a result of differential protein-RNA contacts. Additionally, we introduce POPPI, a bioinformatics workflow for the analysis of protein occupancy profiling experiments. Our work demonstrates the value of protein occupancy profiling for assessing cis-regulatory RNA sequence space and its dynamics in growth, development and disease.

Advancing the functional utility of PAR-CLIP by quantifying background binding to mRNAs and lncRNAs

BACKGROUND

Sequence specific RNA binding proteins are important regulators of gene expression. Several related crosslinking-based, high-throughput sequencing methods, including PAR-CLIP, have recently been developed to determine direct binding sites of global protein-RNA interactions. However, no studies have quantitatively addressed the contribution of background binding to datasets produced by these methods.

RESULTS

We measured non-specific RNA background in PAR-CLIP data, demonstrating that covalently crosslinked background binding is common, reproducible and apparently universal among laboratories. We show that quantitative determination of background is essential for identifying targets of most RNA-binding proteins and can substantially improve motif analysis. We also demonstrate that by applying background correction to an RNA binding protein of unknown binding specificity, Caprin1, we can identify a previously unrecognized RNA recognition element not otherwise apparent in a PAR-CLIP study.

CONCLUSIONS

Empirical background measurements of global RNA-protein crosslinking are a necessary addendum to other experimental controls, such as performing replicates, because covalently crosslinked background signals are reproducible and otherwise unavoidable. Recognizing and quantifying the contribution of background extends the utility of PAR-CLIP and can improve mechanistic understanding of protein-RNA specificity, protein-RNA affinity and protein-RNA association dynamics.

High-resolution profiling of protein occupancy on polyadenylated RNA transcripts

A key prerequisite to understand how gene regulatory processes are controlled by the interplay of RNA-binding proteins and ribonucleoprotein complexes with RNAs is the generation of comprehensive high-resolution maps of protein-RNA interactions. Recent advances in next-generation sequencing technology accelerated the development of various crosslinking and immunoprecipitation (CLIP) approaches to broadly identify RNA regions contacted by RNA-binding proteins. However these methods only consider single RNA-binding proteins and their contact sites, irrespective of the overall cis-regulatory sequence space contacted by other RNA interacting factors. Here we describe the application of protein occupancy profiling, a novel approach that globally displays the RNA contact sites of the poly(A)+ RNA-bound proteome. Protein occupancy profiling enables the generation of transcriptome-wide maps of protein-RNA interactions on polyadenylated transcripts and narrows the sequence search space for transcript regions involved in cis-regulation of gene expression in response to internal or external stimuli, altered cellular programs or disease.

Identifying and characterizing Hfq-RNA interactions

To regulate stress responses and virulence, bacteria use small regulatory RNAs (sRNAs). These RNAs can up or down regulate target mRNAs through base pairing by influencing ribosomal access and RNA decay. A large class of these sRNAs, called trans-encoded sRNAs, requires the RNA binding protein Hfq to facilitate base pairing between the regulatory RNA and its target mRNA. The resulting network of regulation is best characterized in Escherichia coli and Salmonella typhimurium, but the importance of Hfq dependent sRNA regulation is recognized in a diverse population of bacteria. In this review we present the approaches and methods used to discover Hfq binding RNAs, characterize their interactions and elucidate their functions.

RNA-binding protein Rbm47 binds to Nanog in mouse embryonic stem cells

Embryonic stem cells (ES cells) are pluripotent cells capable for self-renewal and to differentiate to all cell types. Finding the molecular mechanisms responsible for these unique characteristics of ES cells is important. RNA-binding proteins play important roles in post-transcriptional gene regulation by binding to specific mRNA targets. In this study, we investigated the targets of RNA-binding protein Rbm47 in mouse ES cells. Overexpression of HA epitope-tagged Rbm47 in mouse ES cells followed by RNA-binding protein immunoprecipitation, and then RT-PCR analysis of co-immunoprecipitated RNA showed that Rbm47 binds to Nanog transcript in mouse ES cells and doesn't bind to Sox2 and Oct4 transcripts in these cells. This finding can give rise to reveal molecular mechanisms underlying pluripotency and stemness of ES cells and will be necessary for efficient application of these cells in regenerative medicine and tissue engineering.

The "Observer Effect" in genome-wide surveys of protein-RNA interactions

Recent technological advances have spurred genome-wide studies that afford insights into ribonucleoprotein biology and transcript regulation on an unprecedented scale. Here we review techniques currently used to obtain genome-wide profiles of RNA-protein interactions in living cells. We highlight recent studies of the mRNA-bound proteome and address pitfalls inherent in such investigations.

mRNA fate: Life and death of the mRNA in the cytoplasm

The life of an mRNA molecule begins with transcription and ultimately ends in degradation. In the course of its life, however, mRNA is examined, modified in various ways and transported before eventually being translated into proteins. All these processes are performed by proteins and non-coding RNAs whose complex interplay in the cell contributes to determining the proteome changes and the phenotype of cells. On May 23‒26, 2012, over 150 scientists from around the world convened in the sunny shores of Riva del Garda, Italy, for the workshop entitled: "mRNA fate: Life and Death of mRNA in the Cytoplasm." Sessions included mRNA trafficking, mRNA translational control, RNA metabolism and disease, RNA-protein structures and systems biology of RNA. This report highlights some of the prominent and recurring themes at the meeting and emerging arenas of future research.

The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts

Protein-RNA interactions are fundamental to core biological processes, such as mRNA splicing, localization, degradation, and translation. We developed a photoreactive nucleotide-enhanced UV crosslinking and oligo(dT) purification approach to identify the mRNA-bound proteome using quantitative proteomics and to display the protein occupancy on mRNA transcripts by next-generation sequencing. Application to a human embryonic kidney cell line identified close to 800 proteins. To our knowledge, nearly one-third were not previously annotated as RNA binding, and about 15% were not predictable by computational methods to interact with RNA. Protein occupancy profiling provides a transcriptome-wide catalog of potential cis-regulatory regions on mammalian mRNAs and showed that large stretches in 3' UTRs can be contacted by the mRNA-bound proteome, with numerous putative binding sites in regions harboring disease-associated nucleotide polymorphisms. Our observations indicate the presence of a large number of mRNA binders with diverse molecular functions participating in combinatorial posttranscriptional gene-expression networks.