Computational Prediction of RNA Structural Motifs Involved in Post-Transcriptional Regulatory Processes

Abstract

Messenger RNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. Here, we develop RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying posttranscriptional regulations. By analyzing experimental data on mRNA decay rates, we identify common structural elements in fastdecaying and slow-decaying mRNAs and link them with binding preferences of several RNA binding proteins. We also predict structural elements in sets of mRNAs with common subcellular localization in mouse neurons and fly embryos. Finally, by analyzing premicroRNA stem-loops, we identify structural differences between pre-microRNAs of animals and plants, which provide insights into the mechanism of microRNA biogenesis. Together, our results reveal unexplored layers of posttranscriptional regulations in groups of RNAs and are therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules. Our new RNA motif discovery tool is available online.bioinformatics ͉ motif prediction ͉ posttranscriptional regulation ͉ RNA secondary structure ͉ SCFGs R NA molecules undergo diverse posttranscriptional regulation of gene expression, including regulation of RNA transport and localization, mRNA translation, and RNA decay (1-3). In many cases, such posttranscriptional regulation occurs through elements on the mRNA molecule that interact with the hundreds of RNA binding proteins (RBPs) that exist in the cell (4). A well-known example is the iron-responsive element (IRE), a secondary structure RNA motif located on UTRs of members of the iron metabolism and transport pathway (5). The binding of the RBPs Irp1 and Irp2 to IRE elements affects the translation rate of the mRNA, and by that coordinates the response to changing levels of iron in the environment. Other examples include a 118-nucleotide stem-loop structure through which mRNAs are transported to the yeast bud tip by the RBP She2 (6) and the RBP Sbp2 that is involved in mediating UGA redefinition from a stop codon to selenocysteine by binding specific stem-loop structures, termed selenocystein insertion site (SECIS) elements, in the 3Ј UTR of selenoproteins (7). In other cases, elements on the mRNA molecule interact with other RNAs that direct the regulatory effect. For example, the recognition and binding affinity of a microRNA to its mRNA target is determined by both the sequence and structure of the target mRNA (8-11).The examples above suggest that the posttranscriptional regulation of mRNAs is determined not only by its linear nucleotide sequence but also by its secondary structure. Thus, a key goal is to understand the involvement of mRNA secondary structures in such regulation. One approach is to identify recurring patterns, termed m...

RESA identifies mRNA-regulatory sequences at high resolution

“…4a). For example, RESA can improve upon the identification of mRNA localization elements 34–35 , in combination with existing methods to isolate specific embryonic compartments, cell types, or subcellular structures. Similarly, polysome fractionation can distinguish highly and poorly translated mRNAs, allowing high-throughput identification of sequences directing translation activation or repression.…”

Computational Analysis of Conserved RNA Secondary Structure in Transcriptomes and Genomes

“…These programs have been used to predict structural RNA regions in large eukaryotic genomes, especially in the human genome (71,72,79,91,95,114,117,123). In one recent screen of the human genome, for example, Smith et al predicted 4.1 million structural RNAs in the human genome, at an estimated sensitivity of about 30%, and an estimated false positive rate of about 170 per megabase.…”

“…These methods have been used to predict hundreds, thousands, even millions of novel structural ncRNAs, especially in large mammalian genomes (71,72,79,91,95,114,117,123). With computational predictions and experimental transcriptomics both producing lots of candidate noncoding RNAs, this has sometimes been seen as independent confirmation of the existence of a vast hidden complexity of functional RNA, but the computational approaches are subject to their own list of potential artifacts.…”

Systematic Identification of Regulatory Elements in Conserved 3′ UTRs of Human Transcripts

“…These characteristics make them difficult to predict and experimentally validate. The characterization of functional elements has benefited from computational strategies that predict a large number of putative elements (Foat and Stormo, 2009; Kazan et al, 2010; Li et al, 2010; Rabani et al, 2011) along with in vitro methods that systematically identify binding site specificities of RBPs for short RNAs (Martin et al, 2012; Tenenbaum et al, 2000) and engineer new RNA-protein interactions (Chen et al, 2008). However, current in vitro and in vivo genome-wide experimental methods characterizing RNA regulation, including SELEX (Tuerk and Gold, 1990), RNACompete (Ray et al, 2013), PARCLIP (Hafner et al, 2010), and HITS-CLIP (Darnell, 2010) are labor intensive and require knowledge of specific RNA-binding proteins.…”