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The Role of Chromatin Accessibility in cis-Regulatory Evolution
Author(s) -
Pei-Chen Peng,
Pierre Khoueiry,
Charles Girardot,
James P. Reddington,
David Garfield,
Eileen E. M. Furlong,
Saurabh Sinha
Publication year - 2019
Publication title -
genome biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.702
H-Index - 74
ISSN - 1759-6653
DOI - 10.1093/gbe/evz103
Subject(s) - enhancer , biology , drosophila melanogaster , transcription factor , chromatin , melanogaster , evolutionary biology , drosophila virilis , computational biology , genetics , sequence motif , binding site , motif (music) , regulatory sequence , gene , physics , acoustics
Transcription factor (TF) binding is determined by sequence as well as chromatin accessibility. Although the role of accessibility in shaping TF-binding landscapes is well recorded, its role in evolutionary divergence of TF binding, which in turn can alter cis-regulatory activities, is not well understood. In this work, we studied the evolution of genome-wide binding landscapes of five major TFs in the core network of mesoderm specification, between Drosophila melanogaster and Drosophila virilis, and examined its relationship to accessibility and sequence-level changes. We generated chromatin accessibility data from three important stages of embryogenesis in both Drosophila melanogaster and Drosophila virilis and recorded conservation and divergence patterns. We then used multivariable models to correlate accessibility and sequence changes to TF-binding divergence. We found that accessibility changes can in some cases, for example, for the master regulator Twist and for earlier developmental stages, more accurately predict binding change than is possible using TF-binding motif changes between orthologous enhancers. Accessibility changes also explain a significant portion of the codivergence of TF pairs. We noted that accessibility and motif changes offer complementary views of the evolution of TF binding and developed a combined model that captures the evolutionary data much more accurately than either view alone. Finally, we trained machine learning models to predict enhancer activity from TF binding and used these functional models to argue that motif and accessibility-based predictors of TF-binding change can substitute for experimentally measured binding change, for the purpose of predicting evolutionary changes in enhancer activity.

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