Murach KA, Liu Z, Jude B, Figueiredo VC, Wen Y, Khadgi S, Lim S, Morena da Silva F, Greene NP, Lanner JT, McCarthy JJ, Vechetti IJ, von Walden F
J. Biol. Chem. 298 (11) 102515 [2022-11-00; online 2022-09-21]
Myc is a powerful transcription factor implicated in epigenetic reprogramming, cellular plasticity, and rapid growth as well as tumorigenesis. Cancer in skeletal muscle is extremely rare despite marked and sustained Myc induction during loading-induced hypertrophy. Here, we investigated global, actively transcribed, stable, and myonucleus-specific transcriptomes following an acute hypertrophic stimulus in mouse plantaris. With these datasets, we define global and Myc-specific dynamics at the onset of mechanical overload-induced muscle fiber growth. Data collation across analyses reveals an under-appreciated role for the muscle fiber in extracellular matrix remodeling during adaptation, along with the contribution of mRNA stability to epigenetic-related transcript levels in muscle. We also identify Runx1 and Ankrd1 (Marp1) as abundant myonucleus-enriched loading-induced genes. We observed that a strong induction of cell cycle regulators including Myc occurs with mechanical overload in myonuclei. Additionally, in vivo Myc-controlled gene expression in the plantaris was defined using a genetic muscle fiber-specific doxycycline-inducible Myc-overexpression model. We determined Myc is implicated in numerous aspects of gene expression during early-phase muscle fiber growth. Specifically, brief induction of Myc protein in muscle represses Reverbα, Reverbβ, and Myh2 while increasing Rpl3, recapitulating gene expression in myonuclei during acute overload. Experimental, comparative, and in silico analyses place Myc at the center of a stable and actively transcribed, loading-responsive, muscle fiber-localized regulatory hub. Collectively, our experiments are a roadmap for understanding global and Myc-mediated transcriptional networks that regulate rapid remodeling in postmitotic cells. We provide open webtools for exploring the five RNA-seq datasets as a resource to the field.