A quantitative model of transcriptional regulation reveals the influence binding location on expression
Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the fu...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2010
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| Online Access: | http://hdl.handle.net/1721.1/57501 https://orcid.org/0000-0001-9249-8181 |
| _version_ | 1826202696180301824 |
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| author | MacIsaac, Kenzie Daniel Lo, Kinyui Alice Gordon, William Motola, Shmulik Mazor, Tali Fraenkel, Ernest |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory MacIsaac, Kenzie Daniel Lo, Kinyui Alice Gordon, William Motola, Shmulik Mazor, Tali Fraenkel, Ernest |
| author_sort | MacIsaac, Kenzie Daniel |
| collection | MIT |
| description | Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events. |
| first_indexed | 2024-09-23T12:14:54Z |
| format | Article |
| id | mit-1721.1/57501 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:14:54Z |
| publishDate | 2010 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/575012022-10-01T08:46:53Z A quantitative model of transcriptional regulation reveals the influence binding location on expression MacIsaac, Kenzie Daniel Lo, Kinyui Alice Gordon, William Motola, Shmulik Mazor, Tali Fraenkel, Ernest Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Fraenkel, Ernest MacIsaac, Kenzie Daniel Lo, Kinyui Alice Gordon, William Motola, Shmulik Mazor, Tali Fraenkel, Ernest Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events. 2010-08-12T19:51:46Z 2010-08-12T19:51:46Z 2010-04 2009-09 Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/57501 MacIsaac, Kenzie D. et al. “A Quantitative Model of Transcriptional Regulation Reveals the Influence of Binding Location on Expression.” PLoS Comput Biol 6.4 (2010): e1000773. © 2010 MacIsaac et al. https://orcid.org/0000-0001-9249-8181 en_US http://dx.doi.org/10.1371/journal.pcbi.1000773 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | MacIsaac, Kenzie Daniel Lo, Kinyui Alice Gordon, William Motola, Shmulik Mazor, Tali Fraenkel, Ernest A quantitative model of transcriptional regulation reveals the influence binding location on expression |
| title | A quantitative model of transcriptional regulation reveals the influence binding location on expression |
| title_full | A quantitative model of transcriptional regulation reveals the influence binding location on expression |
| title_fullStr | A quantitative model of transcriptional regulation reveals the influence binding location on expression |
| title_full_unstemmed | A quantitative model of transcriptional regulation reveals the influence binding location on expression |
| title_short | A quantitative model of transcriptional regulation reveals the influence binding location on expression |
| title_sort | quantitative model of transcriptional regulation reveals the influence binding location on expression |
| url | http://hdl.handle.net/1721.1/57501 https://orcid.org/0000-0001-9249-8181 |
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