Cargando…
Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic
Patterns of gene expression are primarily determined by proteins that locally enhance or repress transcription. While many transcription factors target a restricted number of genes, others appear to modulate transcription levels globally. An example is MeCP2, an abundant methylated-DNA binding prote...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660794/ https://www.ncbi.nlm.nih.gov/pubmed/31289233 http://dx.doi.org/10.1073/pnas.1903549116 |
_version_ | 1783439362434793472 |
---|---|
author | Cholewa-Waclaw, Justyna Shah, Ruth Webb, Shaun Chhatbar, Kashyap Ramsahoye, Bernard Pusch, Oliver Yu, Miao Greulich, Philip Waclaw, Bartlomiej Bird, Adrian P. |
author_facet | Cholewa-Waclaw, Justyna Shah, Ruth Webb, Shaun Chhatbar, Kashyap Ramsahoye, Bernard Pusch, Oliver Yu, Miao Greulich, Philip Waclaw, Bartlomiej Bird, Adrian P. |
author_sort | Cholewa-Waclaw, Justyna |
collection | PubMed |
description | Patterns of gene expression are primarily determined by proteins that locally enhance or repress transcription. While many transcription factors target a restricted number of genes, others appear to modulate transcription levels globally. An example is MeCP2, an abundant methylated-DNA binding protein that is mutated in the neurological disorder Rett syndrome. Despite much research, the molecular mechanism by which MeCP2 regulates gene expression is not fully resolved. Here, we integrate quantitative, multidimensional experimental analysis and mathematical modeling to indicate that MeCP2 is a global transcriptional regulator whose binding to DNA creates “slow sites” in gene bodies. We hypothesize that waves of slowed-down RNA polymerase II formed behind these sites travel backward and indirectly affect initiation, reminiscent of defect-induced shockwaves in nonequilibrium physics transport models. This mechanism differs from conventional gene-regulation mechanisms, which often involve direct modulation of transcription initiation. Our findings point to a genome-wide function of DNA methylation that may account for the reversibility of Rett syndrome in mice. Moreover, our combined theoretical and experimental approach provides a general method for understanding how global gene-expression patterns are choreographed. |
format | Online Article Text |
id | pubmed-6660794 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-66607942019-08-02 Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic Cholewa-Waclaw, Justyna Shah, Ruth Webb, Shaun Chhatbar, Kashyap Ramsahoye, Bernard Pusch, Oliver Yu, Miao Greulich, Philip Waclaw, Bartlomiej Bird, Adrian P. Proc Natl Acad Sci U S A Biological Sciences Patterns of gene expression are primarily determined by proteins that locally enhance or repress transcription. While many transcription factors target a restricted number of genes, others appear to modulate transcription levels globally. An example is MeCP2, an abundant methylated-DNA binding protein that is mutated in the neurological disorder Rett syndrome. Despite much research, the molecular mechanism by which MeCP2 regulates gene expression is not fully resolved. Here, we integrate quantitative, multidimensional experimental analysis and mathematical modeling to indicate that MeCP2 is a global transcriptional regulator whose binding to DNA creates “slow sites” in gene bodies. We hypothesize that waves of slowed-down RNA polymerase II formed behind these sites travel backward and indirectly affect initiation, reminiscent of defect-induced shockwaves in nonequilibrium physics transport models. This mechanism differs from conventional gene-regulation mechanisms, which often involve direct modulation of transcription initiation. Our findings point to a genome-wide function of DNA methylation that may account for the reversibility of Rett syndrome in mice. Moreover, our combined theoretical and experimental approach provides a general method for understanding how global gene-expression patterns are choreographed. National Academy of Sciences 2019-07-23 2019-07-09 /pmc/articles/PMC6660794/ /pubmed/31289233 http://dx.doi.org/10.1073/pnas.1903549116 Text en Copyright © 2019 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences Cholewa-Waclaw, Justyna Shah, Ruth Webb, Shaun Chhatbar, Kashyap Ramsahoye, Bernard Pusch, Oliver Yu, Miao Greulich, Philip Waclaw, Bartlomiej Bird, Adrian P. Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic |
title | Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic |
title_full | Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic |
title_fullStr | Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic |
title_full_unstemmed | Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic |
title_short | Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic |
title_sort | quantitative modelling predicts the impact of dna methylation on rna polymerase ii traffic |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660794/ https://www.ncbi.nlm.nih.gov/pubmed/31289233 http://dx.doi.org/10.1073/pnas.1903549116 |
work_keys_str_mv | AT cholewawaclawjustyna quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT shahruth quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT webbshaun quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT chhatbarkashyap quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT ramsahoyebernard quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT puscholiver quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT yumiao quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT greulichphilip quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT waclawbartlomiej quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic AT birdadrianp quantitativemodellingpredictstheimpactofdnamethylationonrnapolymeraseiitraffic |