Machine learning-based identification of endogenous cellular microRNA sponges against viral microRNAs.

Abstract

A "miRNA sponge" is an artificial oligonucleotide-based miRNA inhibitor containing multiple binding sites for a specific miRNA. Each miRNA sponge can bind and sequester several miRNA copies, thereby decreasing the cellular levels of the target miRNA. In addition to developing artificial miRNA sponges, scientists have sought endogenous RNA transcripts and found that long non-coding RNAs, competing endogenous RNAs, pseudogenes, circular RNAs, and coding RNAs could act as miRNA sponges under precise conditions. Here we present a computational approach for the prediction of endogenous human miRNA sponge candidates targeting viral miRNAs derived from pathogenic human viruses. Viral miRNA binding sites were predicted using a newly-developed machine learning-based method, and candidate interactions between miRNAs and sponge RNAs were experimentally validated using luciferase reporter assay, western blot analysis, and flow cytometry. We found that BX649188.1 functions as a potential natural miRNA sponge against kshv-miR-K12-7-3p.