Inferring the evolutionary history of primate microRNA binding sites: overcoming motif counting biases

Evolution of miRNA binding sites and regulatory networks in cichlids

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analysing the selective constraints driving evolution of miRNA and transcription factor (TF) binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA and TF binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared to subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA and TF binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired e.g. clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

Evolutionary dynamics of microRNA target sites across vertebrate evolution

MicroRNAs (miRNAs) control the abundance of the majority of the vertebrate transcriptome. The recognition sequences, or target sites, for bilaterian miRNAs are found predominantly in the 3' untranslated regions (3'UTRs) of mRNAs, and are amongst the most highly conserved motifs within 3'UTRs. However, little is known regarding the evolutionary pressures that lead to loss and gain of such target sites. Here, we quantify the selective pressures that act upon miRNA target sites. Notably, selective pressure extends beyond deeply conserved binding sites to those that have undergone recent substitutions. Our approach reveals that even amongst ancient animal miRNAs, which exert the strongest selective pressures on 3'UTR sequences, there are striking differences in patterns of target site evolution between miRNAs. Considering only ancient animal miRNAs, we find three distinct miRNA groups, each exhibiting characteristic rates of target site gain and loss during mammalian evolution. The first group both loses and gains sites rarely. The second group shows selection only against site loss, with site gains occurring at a neutral rate, whereas the third loses and gains sites at neutral or above expected rates. Furthermore, mutations that alter the strength of existing target sites are disfavored. Applying our approach to individual transcripts reveals variation in the distribution of selective pressure across the transcriptome and between miRNAs, ranging from strong selection acting on a small subset of targets of some miRNAs, to weak selection on many targets for other miRNAs. miR-20 and miR-30, and many other miRNAs, exhibit broad, deeply conserved targeting, while several other comparably ancient miRNAs show a lack of selective constraint, and a small number, including mir-146, exhibit evidence of rapidly evolving target sites. Our approach adds valuable perspective on the evolution of miRNAs and their targets, and can also be applied to characterize other 3'UTR regulatory motifs.

Prediction of human miRNA target genes using computationally reconstructed ancestral mammalian sequences

MicroRNAs (miRNA) are short single-stranded RNA molecules derived from hairpin-forming precursors that play a crucial role as post-transcriptional regulators in eukaryotes and viruses. In the past years, many microRNA target genes (MTGs) have been identified experimentally. However, because of the high costs of experimental approaches, target genes databases remain incomplete. Although several target prediction programs have been developed in the recent years to identify MTGs in silico, their specificity and sensitivity remain low. Here, we propose a new approach called MirAncesTar, which uses ancestral genome reconstruction to boost the accuracy of existing MTGs prediction tools for human miRNAs. For each miRNA and each putative human target UTR, our algorithm makes uses of existing prediction tools to identify putative target sites in the human UTR, as well as in its mammalian orthologs and inferred ancestral sequences. It then evaluates evidence in support of selective pressure to maintain target site counts (rather than sequences), accounting for the possibility of target site turnover. It finally integrates this measure with several simpler ones using a logistic regression predictor. MirAncesTar improves the accuracy of existing MTG predictors by 26% to 157%. Source code and prediction results for human miRNAs, as well as supporting evolutionary data are available at http://cs.mcgill.ca/∼blanchem/mirancestar.

Evolutionary Transitions of MicroRNA-Target Pairs

How newly generated microRNA (miRNA) genes are integrated into gene regulatory networks during evolution is fundamental in understanding the molecular and evolutionary bases of robustness and plasticity in gene regulation. A recent model proposed that after the birth of a miRNA, the miRNA is generally integrated into the network by decreasing the number of target genes during evolution. However, this decreasing model remains to be carefully examined by considering in vivo conditions. In this study, we therefore compared the number of target genes among miRNAs with different ages, combining experiments with bioinformatics predictions. First, we focused on three Drosophila miRNAs with different ages. As a result, we found that an older miRNA has a greater number of target genes than a younger miRNA, suggesting the increasing number of targets for each miRNA during evolution (increasing model). To further confirm our results, we also predicted all target genes for all miRNAs in D. melanogaster, considering co-expression of miRNAs and mRNAs in vivo. The results obtained also do not support the decreasing model but are reasonably consistent with the increasing model of miRNA-target pairs. Furthermore, our large-scale analyses of currently available experimental data of miRNA-target pairs also showed a weak but the same trend in humans. These results indicate that the current decreasing model of miRNA-target pairs should be reconsidered and the increasing model may be more appropriate to explain the evolutionary transitions of miRNA-target pairs in many organisms.