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The Role of Omics Approaches in Muscle Research

  • Stefano SchiaffinoEmail author
  • Carlo Reggiani
  • Marta Murgia
Chapter
Part of the Methods in Physiology book series (METHPHYS)

Abstract

A thorough understanding of skeletal muscle physiology requires knowledge of the molecular composition of muscle tissue. So far, this has been mostly obtained through biochemical investigations focused on selected components of the muscle contractile and metabolic machinery. More recently, a systems biology approach, based on the emergence of omics technologies, has been introduced to get global views of all muscle components. These approaches have been used to explore the DNA (genomics and epigenomics), RNA (transcriptomics and non-coding RNA analyses), proteins (proteomics and global analyses of post-translational protein modifications, e.g. phosphoproteomics) and small molecules (metabolomics).

References

  1. 1.
    Camera, D. M., Burniston, J. G., Pogson, M. A., Smiles, W. J., & Hawley, J. A. (2017). Dynamic proteome profiling of individual proteins in human skeletal muscle after a high-fat diet and resistance exercise. The FASEB Journal, 31, 5478–5494.CrossRefGoogle Scholar
  2. 2.
    Barrès, R., & Zierath, J. R. (2016). The role of diet and exercise in the transgenerational epigenetic landscape of T2DM. Nature Reviews Endocrinology, 12, 441–451.CrossRefGoogle Scholar
  3. 3.
    Yang, X., Coulombe-Huntington, J., Kang, S., Sheynkman, G. M., Hao, T., Richardson, A., Sun, S., Yang, F., Shen, Y. A., Murray, R. R., Spirohn, K., Begg, B. E., Duran-Frigola, M., MacWilliams, A., Pevzner, S. J., Zhong, Q., Trigg, S. A., Tam, S., Ghamsari, L., Sahni, N., Yi, S., Rodriguez, M. D., Balcha, D., Tan, G., Costanzo, M., Andrews, B., Boone, C., Zhou, X. J., Salehi-Ashtiani, K., Charloteaux, B., Chen, A. A., Calderwood, M. A., Aloy, P., Roth, F. P., Hill, D. E., Iakoucheva, L. M., Xia, Y., & Vidal, M. (2016). Widespread expansion of protein interaction capabilities by alternative splicing. Cell, 164, 805–817.CrossRefGoogle Scholar
  4. 4.
    Tapial, J., Ha, K. C. H., Sterne-Weiler, T., Gohr, A., Braunschweig, U., Hermoso-Pulido, A., Quesnel-Vallières, M., Permanyer, J., Sodaei, R., Marquez, Y., Cozzuto, L., Wang, X., Gómez-Velázquez, M., Rayon, T., Manzanares, M., Ponomarenko, J., Blencowe, B. J., & Irimia, M. (2017). An atlas of alternative splicing profiles and functional associations reveals new regulatory programs and genes that simultaneously express multiple major isoforms. Genome Research, 27(10), 1759–1768.CrossRefGoogle Scholar
  5. 5.
    Capitanchik, C., Dixon, C. R., Swanson, S. K., Florens, L., Kerr, A. R. W., & Schirmer, E. C. (2018). Analysis of RNA-Seq datasets reveals enrichment of tissue-specific splice variants for nuclear envelope proteins. Nucleus, 9, 410–430.CrossRefGoogle Scholar
  6. 6.
    Sharma, K., D’Souza, R. C., Tyanova, S., Schaab, C., Wiśniewski, J. R., Cox, J., & Mann, M. (2014). Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Reports, 8, 1583–1594.CrossRefGoogle Scholar
  7. 7.
    Hoffman, N. J., Parker, B. L., Chaudhuri, R., Fisher-Wellman, K. H., Kleinert, M., Humphrey, S. J., Yang, P., Holliday, M., Trefely, S., Fazakerley, D. J., Stöckli, J., Burchfield, J. G., Jensen, T. E., Jothi, R., Kiens, B., Wojtaszewski, J. F., Richter, E. A., & James, D. E. (2015). Global phosphoproteomic analysis of human skeletal muscle reveals a network of exercise-regulated kinases and AMPK substrates. Cell Metabolism, 22, 922–935.CrossRefGoogle Scholar
  8. 8.
    Jin, Y., Diffee, G. M., Colman, R. J., Anderson, R. M., & Ge, Y. (2019). Top-down mass spectrometry of sarcomeric protein post-translational modifications from non-human primate skeletal muscle. Journal of the American Society for Mass Spectrometry.  https://doi.org/10.1007/s13361-019-02139-0.
  9. 9.
    Dyar, K. A., Ciciliot, S., Wright, L. E., Bienso, R. S., Tagliazucchi, G. M., Patel, V. R., Forcato, M., Paz, M. I., Gudiksen, A., Solagna, F., Albiero, M., Moretti, I., Eckel-Mahan, K. L., Baldi, P., Sassone-Corsi, P., Rizzuto, R., Bicciato, S., Pilegaard, H., Blaauw, B., & Schiaffino, S. (2014). Muscle insulin sensitivity and glucose metabolism are controlled by the intrinsic muscle clock. Molecular Metabolism, 3, 29–41.CrossRefGoogle Scholar
  10. 10.
    Dyar, K. A., Hubert, M. J., Mir, A. A., Ciciliot, S., Lutter, D., Greulich, F., Quagliarini, F., Kleinert, M., Fischer, K., Eichmann, T. O., Wright, L. E., Pena Paz, M. I., Casarin, A., Pertegato, V., Romanello, V., Albiero, M., Mazzucco, S., Rizzuto, R., Salviati, L., Biolo, G., Blaauw, B., Schiaffino, S., & Uhlenhaut, N. H. (2018). Transcriptional programming of lipid and amino acid metabolism by the skeletal muscle circadian clock. PLoS Biology, 16, e2005886.CrossRefGoogle Scholar
  11. 11.
    Loizides-Mangold, U., Perrin, L., Vandereycken, B., Betts, J. A., Walhin, J. P., Templeman, I., Chanon, S., Weger, B. D., Durand, C., Robert, M., Paz Montoya, J., Moniatte, M., Karagounis, L. G., Johnston, J. D., Gachon, F., Lefai, E., Riezman, H., & Dibner, C. (2017). Lipidomics reveals diurnal lipid oscillations in human skeletal muscle persisting in cellular myotubes cultured in vitro. Proceedings of the National Academy of Sciences of the United States of America, 114, E8565–E8574.CrossRefGoogle Scholar
  12. 12.
    Dyar, K. A., Lutter, D., Artati, A., Ceglia, N. J., Liu, Y., Armenta, D., Jastroch, M., Schneider, S., de Mateo, S., Cervantes, M., Abbondante, S., Tognini, P., Orozco-Solis, R., Kinouchi, K., Wang, C., Swerdloff, R., Nadeef, S., Masri, S., Magistretti, P., Orlando, V., Borrelli, E., Uhlenhaut, N. H., Baldi, P., Adamski, J., Tschop, M. H., Eckel-Mahan, K., & Sassone-Corsi, P. (2018). Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell, 174, 1571–1585.e1511.CrossRefGoogle Scholar
  13. 13.
    Schmalbruch, H., & Hellhammer, U. (1977). The number of nuclei in adult rat muscles with special reference to satellite cells. The Anatomical Record, 189, 169–175.CrossRefGoogle Scholar
  14. 14.
    Murgia, M., Nagaraj, N., Deshmukh, A. S., Zeiler, M., Cancellara, P., Moretti, I., Reggiani, C., Schiaffino, S., & Mann, M. (2015). Single muscle fiber proteomics reveals unexpected mitochondrial specialization. EMBO Reports, 16, 387–395.CrossRefGoogle Scholar
  15. 15.
    Murgia, M., Toniolo, L., Nagaraj, N., Ciciliot, S., Vindigni, V., Schiaffino, S., Reggiani, C., & Mann, M. (2017). Single muscle fiber proteomics reveals fiber-type-specific features of human muscle aging. Cell Reports, 19, 2396–2409.CrossRefGoogle Scholar
  16. 16.
    Lang, F., Khaghani, S., Türk, C., Wiederstein, J. L., Hölper, S., Piller, T., Nogara, L., Blaauw, B., Günther, S., Müller, S., Braun, T., & Krüger, M. (2018). Single muscle fiber proteomics reveals distinct protein changes in slow and fast fibers during muscle atrophy. Journal of Proteome Research, 17, 3333–3347.CrossRefGoogle Scholar
  17. 17.
    Schiaffino, S., Reggiani, C., Kostrominova, T. Y., Mann, M., & Murgia, M. (2015). Mitochondrial specialization revealed by single muscle fiber proteomics: Focus on the Krebs cycle. Scandinavian Journal of Medicine and Science in Sports, 25(Suppl 4), 41–48.CrossRefGoogle Scholar
  18. 18.
    Glass, D. J. (2010). A critique of the hypothesis, and a defense of the question, as a framework for experimentation. Clinical Chemistry, 56, 1080–1085.CrossRefGoogle Scholar
  19. 19.
    Van Helden, P. (2013). Data-driven hypotheses. EMBO Reports, 14, 104.CrossRefGoogle Scholar

Copyright information

© The American Physiological Society 2019

Authors and Affiliations

  • Stefano Schiaffino
    • 1
    Email author
  • Carlo Reggiani
    • 2
  • Marta Murgia
    • 2
    • 3
  1. 1.Venetian Institute of Molecular Medicine (VIMM)PadovaItaly
  2. 2.Department of Biomedical SciencesUniversity of PadovaPadovaItaly
  3. 3.Max-Planck-Institute of BiochemistryMartinsriedGermany

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