Abstract

Skeletal muscle comprises a family of diverse tissues with highly specialized functions. Many acquired diseases, including HIV and COPD, affect specific muscles while sparing others. Even monogenic muscular dystrophies selectively affect certain muscle groups. These observations suggest that factors intrinsic to muscle tissues influence their resistance to disease. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNAseq to profile mRNA expression in skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. We identify candidate genes that may drive tissue specialization, including Smarca4, Vegfa, and Myostatin. By demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for studying the mechanisms of tissue specialization.

Document Type

Article

Publication Date

5-29-2018

Notes/Citation Information

Published in eLife, v. 7, e34613, p. 1-27.

© 2018, Terry et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Digital Object Identifier (DOI)

https://doi.org/10.7554/eLife.34613.001

Funding Information

National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR069266) to Karyn A. Esser and Michael E. Hughes.

National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR066082) to Karyn A. Esser.

Related Content

All raw data and. bed files are available on NCBI’s Gene Expression Omnibus (accession number: GSE100505), and transcript-level expression values can also be downloaded from MuscleDB (http://muscledb.org/), a web application built using the ExpressionDB platform (Hughes et al., 2017).

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