Long non-coding RNAs: spatial amplifiers that control nuclear structure and gene expression

Over the past decade, it has become clear that mammalian genomes encode thousands of long non-coding RNAs (lncRNAs), many of which are now implicated in diverse biological processes. Recent work studying the molecular mechanisms of several key examples — including Xist, which orchestrates X chromosome inactivation — has provided new insights into how lncRNAs can control cellular functions by acting in the nucleus. Here we discuss emerging mechanistic insights into how lncRNAs can regulate gene expression by coordinating regulatory proteins, localizing to target loci and shaping three-dimensional (3D) nuclear organization. We explore these principles to highlight biological challenges in gene regulation, in which lncRNAs are well-suited to perform roles that cannot be carried out by DNA elements or protein regulators alone, such as acting as spatial amplifiers of regulatory signals in the nucleus

Global analysis reveals SRp20- and SRp75-specific mRNPs in cycling and neural cells.

Members of the SR protein family of RNA-binding proteins have numerous roles in mRNA metabolism, from transcription to translation. To understand how SR proteins coordinate gene regulation, comprehensive knowledge of endogenous mRNA targets is needed. Here we establish physiological expression of GFP-tagged SR proteins from stable transgenes. Using the GFP tag for immunopurification of mRNPs, mRNA targets of SRp20 and SRp75 were identified in cycling and neurally induced P19 cells. Genome-wide analysis showed that SRp20 and SRp75 associate with hundreds of distinct, functionally related groups of transcripts that change in response to neural differentiation. Knockdown of either SRp20 or SRp75 led to up- or downregulation of specific transcripts, including identified targets, and rescue by the GFP-tagged SR proteins proved their functionality. Thus, SR proteins contribute to the execution of gene-expression programs through their association with distinct endogenous mRNAs

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

Background: RNA-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. Results: We 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. Conclusions: Our 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.This work has been funded by research grants from the University of Trento, Ital