Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues
2017
Skeletal muscle comprises a family of diverse tissues with highly specialized morphology, function, and metabolism. Many acquired diseases, including HIV, COPD, cancer cachexia, critical illness myopathy, and sepsis, affect specific muscles while sparing others. Even monogenic muscular dystrophies tend to selectively affect certain muscle groups, despite their causative genetic mutations being present in all tissues. These observations suggest that factors intrinsic to muscle tissues influence their susceptibility to various disease mechanisms. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use unbiased RNA sequencing (RNA-seq) to profile global mRNA expression in a wide array of 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. This diversity is only partly explained by fiber type composition and developmental history, suggesting that specialized transcriptional profiles establish the functional identity of muscle tissues. We find conservation in the transcriptional profiles across species as well as between males and females, indicating that these data may be useful in predicting gene expression in related species, such as humans. Notably, thousands of differentially expressed genes in skeletal muscle are associated with human disease, and hundreds of these genes encode targets of drugs on the market today. Related to this observation, we suggest a mechanistic explanation for how myotonic dystrophy induces weakness in the extensor digitorum longus (EDL) while sparing nearby muscles. These data may therefore provide the means by which muscle-specific sensitivity to disease may be unraveled. In addition, we detect hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. We anticipate that in conjunction with transcriptional modeling, this resource will catalyze more sophisticated tissue engineering of skeletal muscle to improve the efficacy of regenerative medicine.
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