A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity
Name
s41467-018-04575-0.pdf
Size
3.9 MB
Format
Adobe PDF
Checksum (MD5)
2f8ee53e27d62ff171410bd5ee532661
Author(s)
Gam, Jeremy Jonathan
Babb, Jonathan
Weiss, Ron
Date Issued
June 2018
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Gam, Jeremy J. et al. “A Mixed Antagonistic/synergistic miRNA Repression Model Enables Accurate Predictions of Multi-Input miRNA Sensor Activity.” Nature Communications 9, 1 (June 2018): 2430 © 2018 The Author(s)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Synthetic Biology Center
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-018-04575-0
