Predicting effective microRNA target sites in mammalian mRNAs

Abstract

MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.

DOI: http://dx.doi.org/10.7554/eLife.05005.001

eLife digest

Proteins are built by using the information contained in molecules of messenger RNA (mRNA). Cells have several ways of controlling the amounts of different proteins they make. For example, a so-called ‘microRNA’ molecule can bind to an mRNA molecule to cause it to be more rapidly degraded and less efficiently used, thereby reducing the amount of protein built from that mRNA. Indeed, microRNAs are thought to help control the amount of protein made from most human genes, and biologists are working to predict the amount of control imparted by each microRNA on each of its mRNA targets.

All RNA molecules are made up of a sequence of bases, each commonly known by a single letter—‘A’, ‘U’, ‘C’ or ‘G’. These bases can each pair up with one specific other base—‘A’ pairs with ‘U’, and ‘C’ pairs with ‘G’. To direct the repression of an mRNA molecule, a region of the microRNA known as a ‘seed’ binds to a complementary sequence in the target mRNA. ‘Canonical sites’ are regions in the mRNA that contain the exact sequence of partner bases for the bases in the microRNA seed. Some canonical sites are more effective at mRNA control than others. ‘Non-canonical sites’ also exist in which the pairing between the microRNA seed and mRNA does not completely match. Previous work has suggested that many non-canonical sites can also control mRNA degradation and usage.

Agarwal et al. first used large experimental datasets from many sources to investigate microRNA activity in more detail. As expected, when mRNAs had canonical sites that matched the microRNA, mRNA levels and usage tended to drop. However, no effect was observed when the mRNAs only had recently identified non-canonical sites. This suggests that microRNAs primarily bind to canonical sites to control protein production.

Based on these results, Agarwal et al. further developed a statistical model that predicts the effects of microRNAs binding to canonical sites. The updated model considers 14 different features of the microRNA, microRNA site, or mRNA—including the mRNA sequence around the site—to predict which sites within mRNAs are most effectively targeted by microRNAs. Tests showed that Agarwal et al.'s model was as good as experimental approaches at identifying the effective target sites, and was better than existing computational models.

The model has been used to power the latest version of a freely available resource called TargetScan, and so could prove a valuable resource for researchers investigating the many important roles of microRNAs in controlling protein production.

DOI: http://dx.doi.org/10.7554/eLife.05005.002

Affiliations

[1 ]Howard Hughes Medical Institute, Whitehead Institute for Biomedical Research , Cambridge, United States

[2 ]deptDepartment of Biology , Massachusetts Institute of Technology , Cambridge, United States

[3 ]deptComputational and Systems Biology Program , Massachusetts Institute of Technology , Cambridge, United States

[4 ]deptBioinformatics and Research Computing , Whitehead Institute for Biomedical Research , Cambridge, United States

[5 ]deptDepartment of Life Science, College of Natural Sciences , Hanyang University , Seoul, Republic of Korea

Max Planck Institute Development Biology , Germany

Max Planck Institute Development Biology , Germany

Author notes

[* ]For correspondence: dbartel@ 123456wi.mit.edu

Contributors

Elisa Izaurralde: Role: Reviewing editor,

Max Planck Institute Development Biology , Germany

Journal

Journal ID (nlm-ta): eLife

Journal ID (hwp): eLife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Print): 2050-084X

ISSN (Electronic): 2050-084X

Publication date (electronic) 12 August 2015

Publication date Collection 2015

Volume: 4

ID: 26267216 ID: 4532895 ID: 05005 ID: 10.7554/eLife.05005

Copyright statement: © 2015, Agarwal et al

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Product

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health (NIH);

Award ID: GM067031

Award Recipient : Vikram Agarwal George W Bell Jin-Wu Nam David P Bartel

Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute (HHMI);

Award ID: Investigator

Award Recipient : David P Bartel

Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation (NSF);

Award ID: Graduate Research Fellowship

Award Recipient : Vikram Agarwal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Categories

Subject: Research Article

Subject: Computational and Systems Biology

Subject: Genomics and Evolutionary Biology

Custom metadata

elife-xml-version 2.3

Author impact statement Many experimentally identified microRNA-binding sites are ineffective at mediating repression, and an improved quantitative model for predicting the effective sites is as informative as high-throughput experimental approaches.