MiRNA Targets: From Prediction Tools to Experimental Validation.

Giulia Riolo,Silvia Cantara,Claudia Ricci,Carlotta Marzocchi

doi:10.3390/mps4010001

Abstract

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

Highlights

Among all RNAs, there are non-coding RNAs
These can be divided into two main categories: algorithms derived from characteristics of the mRNA sequence and/or based on the miRNA-mRNA interaction, and statistical inference based on Machine Learning
The first category includes procedures investigating the existence of an interaction between miRNA and its target, by studying directly the miRNA-mRNA pair or introducing a specific target site bound by miRNAs, known as microRNA responsive element, into a reporter gene, in order to measure potential miRNA-induced changes at protein levels

Summary

Introduction

Among all RNAs, there are non-coding RNAs (ncRNAs). These are functional RNA molecules that do not code for proteins but can modulate protein levels through posttranscriptional regulation. Non-coding RNA genes consist of highly abundant and functionally important RNAs such as transfer RNA (tRNA) and ribosomal RNA (rRNA), as well as RNAs such as small interfering RNAs (siRNAs) and microRNAs (miRNAs) [1]. Over 35,000 miRNA sequences have been identified in 271 organisms [2]. MiRNAs, approximately 18–26 nucleotides long, are expressed in plants, fungi, animals, and unicellular organisms and their synthesis is a finely regulated multi-step process [3]. A primary miRNA (pri-miRNA) is transcribed into the nucleus by an RNA polymerase II. The microprocessor complex, consisting of Drosha and DiGeorge

Objectives

Methods

Findings

Conclusion