A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity
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 site...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Nature Publishing Group
2018
|
| Online Access: | http://hdl.handle.net/1721.1/117727 https://orcid.org/0000-0003-4600-0383 https://orcid.org/0000-0003-0396-2443 |
| _version_ | 1811088232435154944 |
|---|---|
| author | Gam, Jeremy Jonathan Babb, Jonathan Weiss, Ron |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Gam, Jeremy Jonathan Babb, Jonathan Weiss, Ron |
| author_sort | Gam, Jeremy Jonathan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:58:48Z |
| format | Article |
| id | mit-1721.1/117727 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:58:48Z |
| publishDate | 2018 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1177272022-10-01T18:20:27Z A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity Gam, Jeremy Jonathan Babb, Jonathan Weiss, Ron 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 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) 2018-09-12T18:00:24Z 2018-09-12T18:00:24Z 2018-06 2018-01 2018-09-12T14:14:23Z Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/117727 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) https://orcid.org/0000-0003-4600-0383 https://orcid.org/0000-0003-0396-2443 http://dx.doi.org/10.1038/s41467-018-04575-0 Nature Communications Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Nature Publishing Group Nature |
| spellingShingle | Gam, Jeremy Jonathan Babb, Jonathan Weiss, Ron A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity |
| title | A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity |
| title_full | A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity |
| title_fullStr | A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity |
| title_full_unstemmed | A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity |
| title_short | A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity |
| title_sort | mixed antagonistic synergistic mirna repression model enables accurate predictions of multi input mirna sensor activity |
| url | http://hdl.handle.net/1721.1/117727 https://orcid.org/0000-0003-4600-0383 https://orcid.org/0000-0003-0396-2443 |
| work_keys_str_mv | AT gamjeremyjonathan amixedantagonisticsynergisticmirnarepressionmodelenablesaccuratepredictionsofmultiinputmirnasensoractivity AT babbjonathan amixedantagonisticsynergisticmirnarepressionmodelenablesaccuratepredictionsofmultiinputmirnasensoractivity AT weissron amixedantagonisticsynergisticmirnarepressionmodelenablesaccuratepredictionsofmultiinputmirnasensoractivity AT gamjeremyjonathan mixedantagonisticsynergisticmirnarepressionmodelenablesaccuratepredictionsofmultiinputmirnasensoractivity AT babbjonathan mixedantagonisticsynergisticmirnarepressionmodelenablesaccuratepredictionsofmultiinputmirnasensoractivity AT weissron mixedantagonisticsynergisticmirnarepressionmodelenablesaccuratepredictionsofmultiinputmirnasensoractivity |