Making use of transcription factor enrichment to identify functional microRNA-regulons.

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.