A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity

Jeremy J. Gam, Jonathan Babb and Ron Weiss ()
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Jeremy J. Gam: Massachusetts Institute of Technology (MIT)
Jonathan Babb: Massachusetts Institute of Technology (MIT)
Ron Weiss: Massachusetts Institute of Technology (MIT)

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract MicroRNAs (miRNAs) regulate a majority of protein-coding genes, affecting nearly all biological pathways. However, the quantitative dimensions of miRNA-based regulation are not fully understood. In particular, the implications of miRNA target site location, composition rules for multiple target sites, and cooperativity limits for genes regulated by many miRNAs have not been quantitatively characterized. We explore these aspects of miRNA biology at a quantitative single-cell level using a library of 620 miRNA sensors and reporters that are regulated by many miRNA target sites at different positions. Interestingly, we find that miRNA target site sets within the same untranslated region exhibit combined miRNA activity described by an antagonistic relationship while those in separate untranslated regions show synergy. The resulting antagonistic/synergistic computational model enables the high-fidelity prediction of miRNA sensor activity for sensors containing many miRNA targets. These findings may help to accelerate the development of sophisticated sensors for clinical and research applications.

Date: 2018
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DOI: 10.1038/s41467-018-04575-0

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