Comparative analysis of the transcriptomes of EDL, psoas, and soleus muscles from mice

  • Matthew J. Gage (University of Massachusetts Lowell) (Contributor)
  • Uzma Tahir (Contributor)
  • Pabodha Hettige (Contributor)
  • Kiisa C. Nishikawa (Northern Arizona University) (Contributor)



Abstract Background Individual skeletal muscles have evolved to perform specific tasks based on their molecular composition. In general, muscle fibers are characterized as either fast-twitch or slow-twitch based on their myosin heavy chain isoform profiles. This approach made sense in the early days of muscle studies when SDS-PAGE was the primary tool for mapping fiber type. However, Next Generation Sequencing tools permit analysis of the entire muscle transcriptome in a single sample, which allows for more precise characterization of differences among fiber types, including distinguishing between different isoforms of specific proteins. We demonstrate the power of this approach by comparing the differential gene expression patterns of extensor digitorum longus (EDL), psoas, and soleus from mice using high throughput RNA sequencing. Results EDL and psoas are typically classified as fast-twitch muscles based on their myosin expression pattern, while soleus is considered a slow-twitch muscle. The majority of the transcriptomic variability aligns with the fast-twitch and slow-twitch characterization. However, psoas and EDL exhibit unique expression patterns associated with the genes coding for extracellular matrix, myofibril, transcription, translation, striated muscle adaptation, mitochondrion distribution, and metabolism. Furthermore, significant expression differences between psoas and EDL were observed in genes coding for myosin light chain, troponin, tropomyosin isoforms, and several genes encoding the constituents of the Z-disk. Conclusions The observations highlight the intricate molecular nature of skeletal muscles and demonstrate the importance of utilizing transcriptomic information as a tool for skeletal muscle characterization.
Date made availableJan 1 2020
Publisherfigshare Academic Research System

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