Dynamic enhancers control skeletal muscle identity and reprogramming.

Dynamic enhancers control skeletal muscle identity and reprogramming.

Ramachandran, Krithika;Senagolage, Madhavi D;Sommars, Meredith A;Futtner, Christopher R;Omura, Yasuhiro;Allred, Amanda L;Barish, Grant D;
plos biology 2019 Vol. 17 pp. e3000467
296
ramachandran2019dynamicplos

Abstract

Skeletal muscles consist of fibers of differing metabolic activities and contractility, which become remodeled in response to chronic exercise, but the epigenomic basis for muscle identity and adaptation remains poorly understood. Here, we used chromatin immunoprecipitation sequencing of dimethylated histone 3 lysine 4 and acetylated histone 3 lysine 27 as well as transposase-accessible chromatin profiling to dissect cis-regulatory networks across muscle groups. We demonstrate that in vivo enhancers specify muscles in accordance with myofiber composition, show little resemblance to cultured myotube enhancers, and identify glycolytic and oxidative muscle-specific regulators. Moreover, we find that voluntary wheel running and muscle-specific peroxisome proliferator-activated receptor gamma coactivator-1 alpha (Pgc1a) transgenic (mTg) overexpression, which stimulate endurance performance in mice, result in markedly different changes to the epigenome. Exercise predominantly leads to enhancer hypoacetylation, whereas mTg causes hyperacetylation at different sites. Integrative analysis of regulatory regions and gene expression revealed that exercise and mTg are each associated with myocyte enhancer factor (MEF) 2 and estrogen-related receptor (ERR) signaling and transcription of genes promoting oxidative metabolism. However, exercise was additionally associated with regulation by retinoid X receptor (RXR), jun proto-oncogene (JUN), sine oculis homeobox factor (SIX), and other factors. Overall, our work defines the unique enhancer repertoires of skeletal muscles in vivo and reveals that divergent exercise-induced or PGC1α-driven epigenomic programs direct partially convergent transcriptional networks.

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