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Exploring the miRNA regulatory network using evolutionary correlations

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Item Type:Article
Title:Exploring the miRNA regulatory network using evolutionary correlations
Creators Name:Obermayer, B. and Levine, E.
Abstract:Post-transcriptional regulation by miRNAs is a widespread and highly conserved phenomenon in metazoans, with several hundreds to thousands of conserved binding sites for each miRNA, and up to two thirds of all genes under miRNA regulation. At the same time, the effect of miRNA regulation on mRNA and protein levels is usually quite modest and associated phenotypes are often weak or subtle. This has given rise to the notion that the highly interconnected miRNA regulatory network exerts its function less through any individual link and more via collective effects that lead to a functional interdependence of network links. We present a Bayesian framework to quantify conservation of miRNA target sites using vertebrate whole-genome alignments. The increased statistical power of our phylogenetic model allows detection of evolutionary correlation in the conservation patterns of site pairs. Such correlations could result from collective functions in the regulatory network. For instance, co-conservation of target site pairs supports a selective benefit of combinatorial regulation by multiple miRNAs. We find that some miRNA families are under pronounced co-targeting constraints, indicating a high connectivity in the regulatory network, while others appear to function in a more isolated way. By analyzing coordinated targeting of different curated gene sets, we observe distinct evolutionary signatures for protein complexes and signaling pathways that could reflect differences in control strategies. Our method is easily scalable to analyze upcoming larger data sets, and readily adaptable to detect high-level selective constraints between other genomic loci. We thus provide a proof-of-principle method to understand regulatory networks from an evolutionary perspective.
Keywords:Base Sequence, Bayes Theorem, Computational Biology, Gene Regulatory Networks, Genetic Models, MicroRNAs, Molecular Evolution, Molecular Sequence Data, Phylogeny, Sequence Alignment, Animals, Mice, Zebrafish
Source:PLoS Computational Biology
Publisher:Public Library of Science
Page Range:e1003860
Date:9 October 2014
Additional Information:Erratum in: PLoS Comput Biol 10(11): e1004015.
Official Publication:https://doi.org/10.1371/journal.pcbi.1003860
PubMed:View item in PubMed

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