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Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer
A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to i...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9836395/ https://www.ncbi.nlm.nih.gov/pubmed/36503705 http://dx.doi.org/10.7554/eLife.73395 |
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author | Kim, Yang Joon Rhee, Kaitlin Liu, Jonathan Jeammet, Selene Turner, Meghan A Small, Stephen J Garcia, Hernan G |
author_facet | Kim, Yang Joon Rhee, Kaitlin Liu, Jonathan Jeammet, Selene Turner, Meghan A Small, Stephen J Garcia, Hernan G |
author_sort | Kim, Yang Joon |
collection | PubMed |
description | A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity captures the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature. |
format | Online Article Text |
id | pubmed-9836395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-98363952023-01-13 Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer Kim, Yang Joon Rhee, Kaitlin Liu, Jonathan Jeammet, Selene Turner, Meghan A Small, Stephen J Garcia, Hernan G eLife Physics of Living Systems A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity captures the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature. eLife Sciences Publications, Ltd 2022-12-12 /pmc/articles/PMC9836395/ /pubmed/36503705 http://dx.doi.org/10.7554/eLife.73395 Text en © 2022, Kim et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Physics of Living Systems Kim, Yang Joon Rhee, Kaitlin Liu, Jonathan Jeammet, Selene Turner, Meghan A Small, Stephen J Garcia, Hernan G Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
title | Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
title_full | Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
title_fullStr | Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
title_full_unstemmed | Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
title_short | Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
title_sort | predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer |
topic | Physics of Living Systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9836395/ https://www.ncbi.nlm.nih.gov/pubmed/36503705 http://dx.doi.org/10.7554/eLife.73395 |
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