Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies
Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE),...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier
2018
|
| Online Access: | http://hdl.handle.net/1721.1/116736 |
| _version_ | 1826203010703818752 |
|---|---|
| author | Koch, Christopher Konieczka, Jay Delorey, Toni Socha, Amanda Davis, Kathleen Knaack, Sara A. Thompson, Dawn O'Shea, Erin K. Regev, Aviv Roy, Sushmita Lyons, Ana M. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Koch, Christopher Konieczka, Jay Delorey, Toni Socha, Amanda Davis, Kathleen Knaack, Sara A. Thompson, Dawn O'Shea, Erin K. Regev, Aviv Roy, Sushmita Lyons, Ana M. |
| author_sort | Koch, Christopher |
| collection | MIT |
| description | Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species. Keywords: regulatory networks;
network inference; evolution of gene regulatory networks; evolution of stress response; yeast; probabilistic graphical model; phylogeny; comparative functional genomics |
| first_indexed | 2024-09-23T12:30:18Z |
| format | Article |
| id | mit-1721.1/116736 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:30:18Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1167362022-10-01T09:23:32Z Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies Koch, Christopher Konieczka, Jay Delorey, Toni Socha, Amanda Davis, Kathleen Knaack, Sara A. Thompson, Dawn O'Shea, Erin K. Regev, Aviv Roy, Sushmita Lyons, Ana M. Massachusetts Institute of Technology. Department of Biology Lyons, Ana M. Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species. Keywords: regulatory networks; network inference; evolution of gene regulatory networks; evolution of stress response; yeast; probabilistic graphical model; phylogeny; comparative functional genomics National Science Foundation (U.S.) (Grant DBI-1350677) National Institutes of Health (U.S.) (Grant R01CA119176-01) National Institutes of Health (U.S.) (Grant DP1OD003958-01) 2018-07-02T20:01:10Z 2018-07-02T20:01:10Z 2017-05 2018-07-02T19:17:10Z Article http://purl.org/eprint/type/JournalArticle 2405-4712 http://hdl.handle.net/1721.1/116736 Koch, Christopher et al. “Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies.” Cell Systems 4, 5 (May 2017): 543–558 © 2017 The Author(s) http://dx.doi.org/10.1016/J.CELS.2017.04.010 Cell Systems Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Elsevier |
| spellingShingle | Koch, Christopher Konieczka, Jay Delorey, Toni Socha, Amanda Davis, Kathleen Knaack, Sara A. Thompson, Dawn O'Shea, Erin K. Regev, Aviv Roy, Sushmita Lyons, Ana M. Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies |
| title | Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies |
| title_full | Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies |
| title_fullStr | Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies |
| title_full_unstemmed | Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies |
| title_short | Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies |
| title_sort | inference and evolutionary analysis of genome scale regulatory networks in large phylogenies |
| url | http://hdl.handle.net/1721.1/116736 |
| work_keys_str_mv | AT kochchristopher inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT konieczkajay inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT deloreytoni inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT sochaamanda inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT daviskathleen inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT knaacksaraa inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT thompsondawn inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT osheaerink inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT regevaviv inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT roysushmita inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies AT lyonsanam inferenceandevolutionaryanalysisofgenomescaleregulatorynetworksinlargephylogenies |