Dataset Information

Trans-regulation of RNA-binding protein motifs by microRNA.

ABSTRACT: The wide array of vital functions that RNA performs is dependent on its ability to dynamically fold into different structures in response to intracellular and extracellular changes. RNA-binding proteins regulate much of this activity by targeting specific RNA structures or motifs. One of these structures, the 3-way RNA junction, is characteristically found in ribosomal RNA and results from the RNA folding in cis, to produce three separate helices that meet around a central unpaired region. Here we demonstrate that 3-way junctions can also form in trans as a result of the binding of microRNAs in an unconventional manner with mRNA by splinting two non-contiguous regions together. This may be used to reinforce the base of a stem-loop motif being targeted by an RNA-binding protein. Trans interactions between non-coding RNA and mRNA may be used to control the post-transcriptional regulatory code and suggests a possible role for some of the recently described transcripts of unknown function expressed from the human genome.

SUBMITTER: Doyle F

PROVIDER: S-EPMC4006066 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Trans-regulation of RNA-binding protein motifs by microRNA.

Doyle Francis F Tenenbaum Scott A SA

Frontiers in genetics 20140415

The wide array of vital functions that RNA performs is dependent on its ability to dynamically fold into different structures in response to intracellular and extracellular changes. RNA-binding proteins regulate much of this activity by targeting specific RNA structures or motifs. One of these structures, the 3-way RNA junction, is characteristically found in ribosomal RNA and results from the RNA folding in cis, to produce three separate helices that meet around a central unpaired region. Here ...[more]

PMID: 24795744

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Project description:RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.

Project description:BackgroundRNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico.ResultsWe have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59.ConclusionsOur experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

Dataset Information

Trans-regulation of RNA-binding protein motifs by microRNA.

Publications

Trans-regulation of RNA-binding protein motifs by microRNA.

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