bioRxivEvolutionary Biology
The role of chromatin accessibility in cis-regulatory evolution
P.-C. Peng, P. Khoueiry, C. Girardot, J. P. Reddington, D. A. Garfield, E. E. M. Furlong, S. Sinha
Abstract
Transcription factor (TF) binding is determined by sequence as well as chromatin accessibility. While 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 transcription factors (TFs) in the core network of mesoderm specification, between D. melanogaster and D. virilis, and examined its relationship to accessibility and sequence-level changes. We generated chromatin accessibility data from three important stages of embryogenesis in both D. melanogaster and D. virilis, and recorded conservation and divergence patterns. We then used multi-variable models to correlate accessibility and sequence changes to TF binding divergence. We found that accessibility changes can in some cases, e.g., 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 co-divergence 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.
- organism: Drosophila
- assay: ATAC-seq
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
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