Inferring microRNA regulation: A proteome perspective

Abstract

Post-transcriptional regulation in multicellular organisms is mediated by microRNAs. However, the mechanisms that determine if a gene is regulated by miRNAs are poorly understood. Previous works focused mostly on miRNA seed matches and other features of the 3-UTR of transcripts. These common approaches relied on knowledge of the miRNA families, and computational approaches still yield poor, inconsistent results, with many false positives. In this work, we present a different paradigm for predicting miRNA-regulated genes based on proteins. In a novel, automated machine learning framework, we use sequence as well as diverse functional annotations to train models on multiple organisms using experimentally validated data. We present insights from tens of millions of features extracted and ranked from different modalities. We show high predictive performance per organism and in generalization across species. We provide a list of novel predictions for Danio rerio (zebrafish) and Arabidopsis thaliana (mouse-ear cress). We compare genomic models, and observe that our protein model outperforms genomics, while a unified model improves on both. While most membranous and disease related proteins are regulated by miRNAs, we observe the G-protein coupled receptor (GPCR) family is an exception, being mostly unregulated by miRNAs, and we raise possible explanations for this. We further show that the evolutionary conservation among duplicated genes does not imply a coherence in miRNA regulation. We conclude that duplicated genes diverge in their tendency to be miRNA regulated. However, protein function is informative across species in predicting post-transcriptional miRNA regulation in living cells.