A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity
| Autor: | Ron Weiss, Jonathan Babb, Jeremy J Gam |
|---|---|
| Přispěvatelé: | 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, Gam, Jeremy Jonathan, Babb, Jonathan, Weiss, Ron |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Untranslated region Regulation of gene expression Reporter gene Multidisciplinary Science HEK 293 cells General Physics and Astronomy General Chemistry Computational biology Biology General Biochemistry Genetics and Molecular Biology Article 3. Good health 03 medical and health sciences Synthetic biology 030104 developmental biology 0302 clinical medicine 030220 oncology & carcinogenesis microRNA lcsh:Q lcsh:Science Gene Psychological repression |
| Zdroj: | Nature Communications, Vol 9, Iss 1, Pp 1-12 (2018) Nature Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. National Institutes of Health (U.S.) (Grant R01CA173712) National Institutes of Health (U.S.) (Grant P50GM098792) |
| Databáze: | OpenAIRE |
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