Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non‐model Organisms

Abstract

Short (“seed”) or extended base pairing between microRNAs (miRNAs) and their target RNAs enables post‐transcriptional silencing in many organisms. These interactions allow the computational prediction of potential targets. In model organisms, predicted targets are frequently validated experimentally; hence meaningful miRNA‐regulated processes are reported. However, in non‐models, these reports mostly rely on computational prediction alone. Many times, further bioinformatic analyses such as Gene Ontology (GO) enrichment are based on these in silico projections. Here such approaches are reviewed, their caveats are highlighted and the ease of picking false targets from predicted lists is demonstrated. Discoveries that shed new light on how miRNAs evolved to regulate targets in various phyletic groups are discussed, in addition to the pitfalls of target identification in non‐model organisms. The goal is to prevent the misuse of bioinformatic tools, as they cannot bypass the biological understanding of miRNA–target regulation.

Unravelling the developmental and functional significance of an ancient Argonaute duplication

“…Whereas their mode of action and functional roles are extensively-studied in bilaterian animals such as vertebrates, nematodes and arthropods, they are vastly understudied in non-bilaterian animals such as cnidarians and sponges despite being identified in these animal phyla more than a decade ago 5 . Markedly, until now, there is only little experimental evidence supporting any developmental or physiological role for miRNAs in organisms other than bilaterian animals and land plants, despite their wide phyletic distribution 6,7 . Our previous studies revealed that the miRNAs of Cnidaria (sea anemones, corals, jellyfish and hydroids), the sister group of Bilateria, exhibit remarkable similarities to plant miRNA biogenesis and mode of action [8][9][10] .…”

Using exosomal miRNAs extracted from follicular fluid to investigate their role in porcine oocyte development

“…Based on the signi cant expression of the above miRNAs, we selected ten relevant miRNAs-miR-10a-5 ps, miR-200b, miR-141, miR-92a, miR-221-3p, miR-21-5p, miR-26a, let-7d-5p, miR-125b and miR-99a-5p-related to porcine oocyte development for further target genes analysis. Currently, there is no gold standard for miRNA gene target analysis 36,37 . Given this, we used Miranda and RNA software to predict the targeting regulation for further GO and pathway analyses according to previously reported papers 38,39 . This analysis found selected miRNAs (miR-125b, let-7d-5p,miR-200b, miR-26a, and miR-92a) were expressed in PFF exosomes obtained from different-sized follicles., These miRNAs were also very closely related to oocyte development, which has been previously reported 40 .…”

Using Exosomal miRNAs Extracted from Follicular Fluid to Investigate Their Role in Porcine Oocyte Development

“…Based on the signi cant expression of the above miRNAs, we selected ten relevant miRNAs-miR-10a-5 ps, miR-200b, miR-141, miR-92a, miR-221-3p, miR-21-5p, miR-26a, let-7d-5p, miR-125b and miR-99a-5p-related to porcine oocyte development for further target genes analysis. Currently, there is no gold standard for miRNA gene target analysis 36,37 . Given this, we used Miranda and RNA software to predict the targeting regulation for further GO and pathway analyses according to previously reported papers 38,39 . This analysis found selected miRNAs (miR-125b, let-7d-5p,miR-200b, miR-26a, and miR-92a) were expressed in PFF exosomes obtained from different-sized follicles., These miRNAs were also very closely related to oocyte development, which has been previously reported 40 .…”