Skip to main content
SpringerLink
Log in
Book cover

Omics Approaches to Understanding Muscle Biology pp 109–133Cite as

Approaches to Studying the microRNAome in Skeletal Muscle

  • Chapter
  • First Online:

  • 516 Accesses

Part of the book series: Methods in Physiology ((METHPHYS))

Abstract

Muscle is a highly plastic tissue that needs to rapidly undergo dramatic changes in gene expression patterns in order to maintain homeostasis. This requires a delicate balance between satellite cell proliferation, myotube formation and differentiation, and muscle degeneration/regeneration. The disruption of these pathways drives muscle disorders and diseases; this includes dystrophies, inflammatory myopathies, sarcopenia, and cachexia. Thus, identifying factors that regulate muscle gene expression programs is essential to understanding muscle health and function and may uncover new therapeutic targets. Since the discovery of microRNAs (miRNAs), it has become well established that they are key regulatory factors which fine-tune gene expression patterns in all cell and tissue types. As we gain new insight into the function of miRNAs, their essential role as posttranscriptional regulatory elements that drive proper muscle function becomes increasingly apparent. As has been observed in the X-linked genetic diseases Duchenne and Becker muscular dystrophies (DMD and BMD, respectively), the chronic dysregulation of miRNAs can exacerbate disease. In this chapter we will explore the role of miRNAs in skeletal muscle and the importance of harnessing the power of miRNA profiling to understand how different perturbations to muscle (i.e. exercise, injury, or genetic defects) affect the muscle miRNAome and how the miRNAome, in turn, can yield valuable information about the overall health of muscle.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843–854.

    Article  CAS  PubMed  Google Scholar 

  2. Reinhart, B. J., Slack, F. J., Basson, M., Pasquinelli, A. E., Bettinger, J. C., Rougvie, A. E., Horvitz, H. R., & Ruvkun, G. (2000). The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 403, 901–906.

    Article  CAS  PubMed  Google Scholar 

  3. Wightman, B., Ha, I., & Ruvkun, G. (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 75, 855–862.

    Article  CAS  PubMed  Google Scholar 

  4. Cacchiarelli, D., Incitti, T., Martone, J., Cesana, M., Cazzella, V., Santini, T., Sthandier, O., & Bozzoni, I. (2011). miR-31 modulates dystrophin expression: New implications for Duchenne muscular dystrophy therapy. EMBO Reports, 12, 136–141.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cacchiarelli, D., Legnini, I., Martone, J., Cazzella, V., D’Amico, A., Bertini, E., & Bozzoni, I. (2011). miRNAs as serum biomarkers for Duchenne muscular dystrophy. EMBO Molecular Medicine, 3, 258–265.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cacchiarelli, D., Martone, J., Girardi, E., Cesana, M., Incitti, T., Morlando, M., Nicoletti, C., Santini, T., Sthandier, O., Barberi, L., Auricchio, A., Musaro, A., & Bozzoni, I. (2010). MicroRNAs involved in molecular circuitries relevant for the Duchenne muscular dystrophy pathogenesis are controlled by the dystrophin/nNOS pathway. Cell Metabolism, 12, 341–351.

    Article  CAS  PubMed  Google Scholar 

  7. Eisenberg, I., Eran, A., Nishino, I., Moggio, M., Lamperti, C., Amato, A. A., Lidov, H. G., Kang, P. B., North, K. N., Mitrani-Rosenbaum, S., Flanigan, K. M., Neely, L. A., Whitney, D., Beggs, A. H., Kohane, I. S., & Kunkel, L. M. (2007). Distinctive patterns of microRNA expression in primary muscular disorders. Proceedings of the National Academy of Sciences of the United States of America, 104, 17016–17021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fiorillo, A. A., Heier, C. R., Novak, J. S., Tully, C. B., Brown, K. J., Uaesoontrachoon, K., Vila, M. C., Ngheim, P. P., Bello, L., Kornegay, J. N., Angelini, C., Partridge, T. A., Nagaraju, K., & Hoffman, E. P. (2015). TNF-alpha-Induced microRNAs control dystrophin expression in Becker muscular dystrophy. Cell Reports, 12, 1678–1690.

    Article  CAS  PubMed  Google Scholar 

  9. Fiorillo, A. A., Tully, C. B., Damsker, J. M., Nagaraju, K., Hoffman, E. P., & Heier, C. R. (2018). Muscle miRNAome shows suppression of chronic inflammatory miRNAs with both prednisone and vamorolone. Physiological Genomics, 50, 735–745.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Greco, S., De Simone, M., Colussi, C., Zaccagnini, G., Fasanaro, P., Pescatori, M., Cardani, R., Perbellini, R., Isaia, E., Sale, P., Meola, G., Capogrossi, M. C., Gaetano, C., & Martelli, F. (2009). Common micro-RNA signature in skeletal muscle damage and regeneration induced by Duchenne muscular dystrophy and acute ischemia. FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 23, 3335–3346.

    Article  CAS  Google Scholar 

  11. Liu, N., Williams, A. H., Maxeiner, J. M., Bezprozvannaya, S., Shelton, J. M., Richardson, J. A., Bassel-Duby, R., & Olson, E. N. (2012). microRNA-206 promotes skeletal muscle regeneration and delays progression of Duchenne muscular dystrophy in mice. The Journal of Clinical Investigation, 122, 2054–2065.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Valencia-Sanchez, M. A., Liu, J., Hannon, G. J., & Parker, R. (2006). Control of translation and mRNA degradation by miRNAs and siRNAs. Genes and Development, 20, 515–524.

    Article  CAS  PubMed  Google Scholar 

  13. Ezkurdia, I., Juan, D., Rodriguez, J. M., Frankish, A., Diekhans, M., Harrow, J., Vazquez, J., Valencia, A., & Tress, M. L. (2014). Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Human Molecular Genetics, 23, 5866–5878.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Londin, E., Loher, P., Telonis, A. G., Quann, K., Clark, P., Jing, Y., Hatzimichael, E., Kirino, Y., Honda, S., Lally, M., Ramratnam, B., Comstock, C. E., Knudsen, K. E., Gomella, L., Spaeth, G. L., Hark, L., Katz, L. J., Witkiewicz, A., Rostami, A., Jimenez, S. A., Hollingsworth, M. A., Yeh, J. J., Shaw, C. A., SE, M. K., Bray, P., Nelson, P. T., Zupo, S., Van Roosbroeck, K., Keating, M. J., Calin, G. A., Yeo, C., Jimbo, M., Cozzitorto, J., Brody, J. R., Delgrosso, K., Mattick, J. S., Fortina, P., & Rigoutsos, I. (2015). Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proceedings of the National Academy of Sciences of the United States of America, 112, E1106–E1115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 120, 15–20.

    Article  CAS  PubMed  Google Scholar 

  16. Mitchell, P. S., Parkin, R. K., Kroh, E. M., Fritz, B. R., Wyman, S. K., Pogosova-Agadjanyan, E. L., Peterson, A., Noteboom, J., O'Briant, K. C., Allen, A., Lin, D. W., Urban, N., Drescher, C. W., Knudsen, B. S., Stirewalt, D. L., Gentleman, R., Vessella, R. L., Nelson, P. S., Martin, D. B., & Tewari, M. (2008). Circulating microRNAs as stable blood-based markers for cancer detection. Proceedings of the National Academy of Sciences of the United States of America, 105, 10513–10518.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Friedman, R. C., Farh, K. K., Burge, C. B., & Bartel, D. P. (2009). Most mammalian mRNAs are conserved targets of microRNAs. Genome Research, 19, 92–105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Krutzfeldt, J., Rajewsky, N., Braich, R., Rajeev, K. G., Tuschl, T., Manoharan, M., & Stoffel, M. (2005). Silencing of microRNAs in vivo with ‘antagomirs’. Nature, 438, 685–689.

    Article  PubMed  CAS  Google Scholar 

  19. Davis-Dusenbery, B. N., & Hata, A. (2010). Mechanisms of control of microRNA biogenesis. Journal of Biochemistry, 148, 381–392.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kim, V. N., Han, J., & Siomi, M. C. (2009). Biogenesis of small RNAs in animals. Nature Reviews. Molecular Cell Biology, 10, 126–139.

    Article  CAS  PubMed  Google Scholar 

  21. Wang, Y., Juranek, S., Li, H., Sheng, G., Wardle, G. S., Tuschl, T., & Patel, D. J. (2009). Nucleation, propagation and cleavage of target RNAs in Ago silencing complexes. Nature, 461, 754–761.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chi, S. W., Zang, J. B., Mele, A., & Darnell, R. B. (2009). Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature, 460, 479–486.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hafner, M., Landthaler, M., Burger, L., Khorshid, M., Hausser, J., Berninger, P., Rothballer, A., Ascano, M., Jr., Jungkamp, A. C., Munschauer, M., Ulrich, A., Wardle, G. S., Dewell, S., Zavolan, M., & Tuschl, T. (2010). Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell, 141, 129–141.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Djuranovic, S., Nahvi, A., & Green, R. (2012). miRNA-mediated gene silencing by translational repression followed by mRNA deadenylation and decay. Science, 336, 237–240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Meijer, H. A., Kong, Y. W., Lu, W. T., Wilczynska, A., Spriggs, R. V., Robinson, S. W., Godfrey, J. D., Willis, A. E., & Bushell, M. (2013). Translational repression and eIF4A2 activity are critical for microRNA-mediated gene regulation. Science, 340, 82–85.

    Article  CAS  PubMed  Google Scholar 

  26. Bang, C., Batkai, S., Dangwal, S., Gupta, S. K., Foinquinos, A., Holzmann, A., Just, A., Remke, J., Zimmer, K., Zeug, A., Ponimaskin, E., Schmiedl, A., Yin, X., Mayr, M., Halder, R., Fischer, A., Engelhardt, S., Wei, Y., Schober, A., Fiedler, J., & Thum, T. (2014). Cardiac fibroblast-derived microRNA passenger strand-enriched exosomes mediate cardiomyocyte hypertrophy. The Journal of Clinical Investigation, 124, 2136–2146.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Meijer, H. A., Smith, E. M., & Bushell, M. (2014). Regulation of miRNA strand selection: Follow the leader? Biochemical Society Transactions, 42, 1135–1140.

    Article  CAS  PubMed  Google Scholar 

  28. Pritchard, C. C., Cheng, H. H., & Tewari, M. (2012). MicroRNA profiling: Approaches and considerations. Nature Reviews Genetics, 13, 358–369.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bartel, D. P., & Chen, C. Z. (2004). Micromanagers of gene expression: The potentially widespread influence of metazoan microRNAs. Nature Reviews Genetics, 5, 396–400.

    Article  CAS  PubMed  Google Scholar 

  30. Ballarino, M., Morlando, M., Fatica, A., & Bozzoni, I. (2016). Non-coding RNAs in muscle differentiation and musculoskeletal disease. The Journal of Clinical Investigation, 126, 2021–2030.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Guller, I., & Russell, A. P. (2010). MicroRNAs in skeletal muscle: Their role and regulation in development, disease and function. The Journal of Physiology, 588, 4075–4087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lagos-Quintana, M., Rauhut, R., Yalcin, A., Meyer, J., Lendeckel, W., & Tuschl, T. (2002). Identification of tissue-specific microRNAs from mouse. Current Biology: CB, 12, 735–739.

    Article  CAS  PubMed  Google Scholar 

  33. Lee, R. C., & Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis elegans. Science, 294, 862–864.

    Article  CAS  PubMed  Google Scholar 

  34. McCarthy, J. J. (2008). MicroRNA-206: The skeletal muscle-specific myomiR. Biochimica et Biophysica Acta, 1779, 682–691.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. McCarthy, J. J., & Esser, K. A. (2007). MicroRNA-1 and microRNA-133a expression are decreased during skeletal muscle hypertrophy. Journal of Applied Physiology (1985), 102, 306–313.

    Article  CAS  Google Scholar 

  36. Sempere, L. F., Freemantle, S., Pitha-Rowe, I., Moss, E., Dmitrovsky, E., & Ambros, V. (2004). Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation. Genome Biology, 5, R13.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Small, E. M., O'Rourke, J. R., Moresi, V., Sutherland, L. B., McAnally, J., Gerard, R. D., Richardson, J. A., & Olson, E. N. (2010). Regulation of PI3-kinase/Akt signaling by muscle-enriched microRNA-486. Proceedings of the National Academy of Sciences of the United States of America, 107, 4218–4223.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. van Rooij, E., Quiat, D., Johnson, B. A., Sutherland, L. B., Qi, X., Richardson, J. A., Kelm, R. J., Jr., & Olson, E. N. (2009). A family of microRNAs encoded by myosin genes governs myosin expression and muscle performance. Developmental Cell, 17, 662–673.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. van Rooij, E., Sutherland, L. B., Qi, X., Richardson, J. A., Hill, J., & Olson, E. N. (2007). Control of stress-dependent cardiac growth and gene expression by a microRNA. Science, 316, 575–579.

    Article  PubMed  CAS  Google Scholar 

  40. Pradervand, S., Weber, J., Thomas, J., Bueno, M., Wirapati, P., Lefort, K., Dotto, G. P., & Harshman, K. (2009). Impact of normalization on miRNA microarray expression profiling. RNA, 15, 493–501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rao, P. K., Kumar, R. M., Farkhondeh, M., Baskerville, S., & Lodish, H. F. (2006). Myogenic factors that regulate expression of muscle-specific microRNAs. Proceedings of the National Academy of Sciences of the United States of America, 103, 8721–8726.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rosenberg, M. I., Georges, S. A., Asawachaicharn, A., Analau, E., & Tapscott, S. J. (2006). MyoD inhibits Fstl1 and Utrn expression by inducing transcription of miR-206. The Journal of Cell Biology, 175, 77–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Dey, B. K., Gagan, J., Yan, Z., & Dutta, A. (2012). miR-26a is required for skeletal muscle differentiation and regeneration in mice. Genes and Development, 26, 2180–2191.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Crist, C. G., Montarras, D., Pallafacchina, G., Rocancourt, D., Cumano, A., Conway, S. J., & Buckingham, M. (2009). Muscle stem cell behavior is modified by microRNA-27 regulation of Pax3 expression. Proceedings of the National Academy of Sciences of the United States of America, 106, 13383–13387.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wei, W., He, H. B., Zhang, W. Y., Zhang, H. X., Bai, J. B., Liu, H. Z., Cao, J. H., Chang, K. C., Li, X. Y., & Zhao, S. H. (2013). miR-29 targets Akt3 to reduce proliferation and facilitate differentiation of myoblasts in skeletal muscle development. Cell Death and Disease, 4, e668.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ge, Y., Sun, Y., & Chen, J. (2011). IGF-II is regulated by microRNA-125b in skeletal myogenesis. The Journal of Cell Biology, 192, 69–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Seok, H. Y., Tatsuguchi, M., Callis, T. E., He, A., Pu, W. T., & Wang, D. Z. (2011). miR-155 inhibits expression of the MEF2A protein to repress skeletal muscle differentiation. The Journal of Biological Chemistry, 286, 35339–35346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Motohashi, N., Alexander, M. S., Shimizu-Motohashi, Y., Myers, J. A., Kawahara, G., & Kunkel, L. M. (2013). Regulation of IRS1/Akt insulin signaling by microRNA-128a during myogenesis. Journal of Cell Science, 126, 2678–2691.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Naguibneva, I., Ameyar-Zazoua, M., Polesskaya, A., Ait-Si-Ali, S., Groisman, R., Souidi, M., Cuvellier, S., & Harel-Bellan, A. (2006). The microRNA miR-181 targets the homeobox protein Hox-A11 during mammalian myoblast differentiation. Nature Cell Biology, 8, 278–284.

    Article  CAS  PubMed  Google Scholar 

  50. Sun, Q., Zhang, Y., Yang, G., Chen, X., Cao, G., Wang, J., Sun, Y., Zhang, P., Fan, M., Shao, N., & Yang, X. (2008). Transforming growth factor-beta-regulated miR-24 promotes skeletal muscle differentiation. Nucleic Acids Research, 36, 2690–2699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gagan, J., Dey, B. K., Layer, R., Yan, Z., & Dutta, A. (2011). MicroRNA-378 targets the myogenic repressor MyoR during myoblast differentiation. The Journal of Biological Chemistry, 286, 19431–19438.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cardinali, B., Castellani, L., Fasanaro, P., Basso, A., Alema, S., Martelli, F., & Falcone, G. (2009). Microrna-221 and microrna-222 modulate differentiation and maturation of skeletal muscle cells. PLoS One, 4, e7607.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Shi, K., Lu, J., Zhao, Y., Wang, L., Li, J., Qi, B., Li, H., & Ma, C. (2013). MicroRNA-214 suppresses osteogenic differentiation of C2C12 myoblast cells by targeting Osterix. Bone, 55, 487–494.

    Article  CAS  PubMed  Google Scholar 

  54. Ibberson, D., Benes, V., Muckenthaler, M. U., & Castoldi, M. (2009). RNA degradation compromises the reliability of microRNA expression profiling. BMC Biotechnology, 9, 102.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Doleshal, M., Magotra, A. A., Choudhury, B., Cannon, B. D., Labourier, E., & Szafranska, A. E. (2008). Evaluation and validation of total RNA extraction methods for microRNA expression analyses in formalin-fixed, paraffin-embedded tissues. The Journal of molecular diagnostics: JMD, 10, 203–211.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Aryani, A., & Denecke, B. (2015). In vitro application of ribonucleases: Comparison of the effects on mRNA and miRNA stability. BMC Research Notes, 8, 164.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Xi, Y., Nakajima, G., Gavin, E., Morris, C. G., Kudo, K., Hayashi, K., & Ju, J. (2007). Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalin-fixed paraffin-embedded samples. RNA, 13, 1668–1674.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yablonka-Reuveni, Z., & Nameroff, M. (1987). Skeletal muscle cell populations. Separation and partial characterization of fibroblast-like cells from embryonic tissue using density centrifugation. Histochemistry, 87, 27–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gautam, V., & Sarkar, A. K. (2015). Laser assisted microdissection, an efficient technique to understand tissue specific gene expression patterns and functional genomics in plants. Molecular Biotechnology, 57, 299–308.

    Article  CAS  PubMed  Google Scholar 

  60. Iyer-Pascuzzi, A. S., & Benfey, P. N. (2010). Fluorescence-activated cell sorting in plant developmental biology. Methods in Molecular Biology, 655, 313–319.

    Article  CAS  PubMed  Google Scholar 

  61. Coll, M., El Taghdouini, A., Perea, L., Mannaerts, I., Vila-Casadesus, M., Blaya, D., Rodrigo-Torres, D., Affo, S., Morales-Ibanez, O., Graupera, I., Lozano, J. J., Najimi, M., Sokal, E., Lambrecht, J., Gines, P., van Grunsven, L. A., & Sancho-Bru, P. (2015). Integrative miRNA and gene expression profiling analysis of human quiescent hepatic stellate cells. Scientific Reports, 5, 11549.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H., & Yang, X. W. (2006). FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains. Nature Neuroscience, 9, 443–452.

    Article  CAS  PubMed  Google Scholar 

  63. Pritchard, C. C., Kroh, E., Wood, B., Arroyo, J. D., Dougherty, K. J., Miyaji, M. M., Tait, J. F., & Tewari, M. (2012). Blood cell origin of circulating microRNAs: A cautionary note for cancer biomarker studies. Cancer Prevention Research (Philadelphia, Pa.), 5, 492–497.

    Article  CAS  Google Scholar 

  64. Kroh, E. M., Parkin, R. K., Mitchell, P. S., & Tewari, M. (2010). Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods, 50, 298–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chen, Y., Gelfond, J. A., McManus, L. M., & Shireman, P. K. (2009). Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis. BMC Genomics, 10, 407.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Mestdagh, P., Feys, T., Bernard, N., Guenther, S., Chen, C., Speleman, F., & Vandesompele, J. (2008). High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA. Nucleic Acids Research, 36, e143.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Chen, C., Ridzon, D. A., Broomer, A. J., Zhou, Z., Lee, D. H., Nguyen, J. T., Barbisin, M., Xu, N. L., Mahuvakar, V. R., Andersen, M. R., Lao, K. Q., Livak, K. J., & Guegler, K. J. (2005). Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Research, 33, e179.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Shi, R., Sun, Y. H., Zhang, X. H., & Chiang, V. L. (2012). Poly(T) adaptor RT-PCR. Methods in Molecular Biology, 822, 53–66.

    Article  CAS  PubMed  Google Scholar 

  69. Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25, 402–408.

    Article  CAS  PubMed  Google Scholar 

  70. Gee, H. E., Buffa, F. M., Camps, C., Ramachandran, A., Leek, R., Taylor, M., Patil, M., Sheldon, H., Betts, G., Homer, J., West, C., Ragoussis, J., & Harris, A. L. (2011). The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis. British Journal of Cancer, 104, 1168–1177.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Pfaffl, M. W., Tichopad, A., Prgomet, C., & Neuvians, T. P. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper – Excel-based tool using pair-wise correlations. Biotechnology Letters, 26, 509–515.

    Article  CAS  PubMed  Google Scholar 

  72. Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., & Speleman, F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3. https://doi.org/10.1186/gb-2002-3-7-research0034.

  73. Andersen, C. L., Jensen, J. L., & Orntoft, T. F. (2004). Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, 64, 5245–5250.

    Article  CAS  PubMed  Google Scholar 

  74. Mestdagh, P., Van Vlierberghe, P., De Weer, A., Muth, D., Westermann, F., Speleman, F., & Vandesompele, J. (2009). A novel and universal method for microRNA RT-qPCR data normalization. Genome Biology, 10, R64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Roberts, T. C., Coenen-Stass, A. M., & Wood, M. J. (2014). Assessment of RT-qPCR normalization strategies for accurate quantification of extracellular microRNAs in murine serum. PLoS One, 9, e89237.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Liu, C. G., Calin, G. A., Meloon, B., Gamliel, N., Sevignani, C., Ferracin, M., Dumitru, C. D., Shimizu, M., Zupo, S., Dono, M., Alder, H., Bullrich, F., Negrini, M., & Croce, C. M. (2004). An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. Proceedings of the National Academy of Sciences of the United States of America, 101, 9740–9744.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Thomson, J. M., Parker, J., Perou, C. M., & Hammond, S. M. (2004). A custom microarray platform for analysis of microRNA gene expression. Nature Methods, 1, 47–53.

    Article  CAS  PubMed  Google Scholar 

  78. Goff, L. A., Yang, M., Bowers, J., Getts, R. C., Padgett, R. W., & Hart, R. P. (2005). Rational probe optimization and enhanced detection strategy for microRNAs using microarrays. RNA Biology, 2, 93–100.

    Article  CAS  PubMed  Google Scholar 

  79. Git, A., Dvinge, H., Salmon-Divon, M., Osborne, M., Kutter, C., Hadfield, J., Bertone, P., & Caldas, C. (2010). Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA, 16, 991–1006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Maroney, P. A., Chamnongpol, S., Souret, F., & Nilsen, T. W. (2008). Direct detection of small RNAs using splinted ligation. Nature Protocols, 3, 279–287.

    Article  CAS  PubMed  Google Scholar 

  81. Nelson, P. T., Baldwin, D. A., Scearce, L. M., Oberholtzer, J. C., Tobias, J. W., & Mourelatos, Z. (2004). Microarray-based, high-throughput gene expression profiling of microRNAs. Nature Methods, 1, 155–161.

    Article  CAS  PubMed  Google Scholar 

  82. Berezikov, E., van Tetering, G., Verheul, M., van de Belt, J., van Laake, L., Vos, J., Verloop, R., van de Wetering, M., Guryev, V., Takada, S., van Zonneveld, A. J., Mano, H., Plasterk, R., & Cuppen, E. (2006). Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Research, 16, 1289–1298.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Yeung, M. L., Bennasser, Y., Myers, T. G., Jiang, G., Benkirane, M., & Jeang, K. T. (2005). Changes in microRNA expression profiles in HIV-1-transfected human cells. Retrovirology, 2, 81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Castoldi, M., Schmidt, S., Benes, V., Noerholm, M., Kulozik, A. E., Hentze, M. W., & Muckenthaler, M. U. (2006). A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA). RNA, 12, 913–920.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Bissels, U., Wild, S., Tomiuk, S., Holste, A., Hafner, M., Tuschl, T., & Bosio, A. (2009). Absolute quantification of microRNAs by using a universal reference. RNA, 15, 2375–2384.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Risso, D., Massa, M. S., Chiogna, M., & Romualdi, C. (2009). A modified LOESS normalization applied to microRNA arrays: A comparative evaluation. Bioinformatics, 25, 2685–2691.

    Article  CAS  PubMed  Google Scholar 

  87. Hua, Y. J., Tu, K., Tang, Z. Y., Li, Y. X., & Xiao, H. S. (2008). Comparison of normalization methods with microRNA microarray. Genomics, 92, 122–128.

    Article  CAS  PubMed  Google Scholar 

  88. Geiss, G. K., Bumgarner, R. E., Birditt, B., Dahl, T., Dowidar, N., Dunaway, D. L., Fell, H. P., Ferree, S., George, R. D., Grogan, T., James, J. J., Maysuria, M., Mitton, J. D., Oliveri, P., Osborn, J. L., Peng, T., Ratcliffe, A. L., Webster, P. J., Davidson, E. H., Hood, L., & Dimitrov, K. (2008). Direct multiplexed measurement of gene expression with color-coded probe pairs. Nature Biotechnology, 26, 317–325.

    Article  CAS  PubMed  Google Scholar 

  89. Baras, A. S., Mitchell, C. J., Myers, J. R., Gupta, S., Weng, L. C., Ashton, J. M., Cornish, T. C., Pandey, A., & Halushka, M. K. (2015). miRge – A multiplexed method of processing small RNA-seq data to determine microRNA entropy. PLoS One, 10, e0143066.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Chen, C., Khaleel, S. S., Huang, H., & Wu, C. H. (2014). Software for pre-processing Illumina next-generation sequencing short read sequences. Source Code for Biology and Medicine, 9, 8.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Langmead, B. (2010). Aligning short sequencing reads with Bowtie. Current Protocols in Bioinformatics, Chapter 11, Unit 11.7.

    PubMed  Google Scholar 

  92. Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Klambauer, G., Unterthiner, T., & Hochreiter, S. (2013). DEXUS: Identifying differential expression in RNA-Seq studies with unknown conditions. Nucleic Acids Research, 41, e198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Mackowiak, S. D. (2011). Identification of novel and known miRNAs in deep-sequencing data with miRDeep2. Current Protocols in Bioinformatics Chapter 12, Unit 12.10.

    Google Scholar 

  95. Hackenberg, M., Sturm, M., Langenberger, D., Falcon-Perez, J. M., & Aransay, A. M. (2009). miRanalyzer: A microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Research, 37, W68–W76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Backes, C., Meder, B., Hart, M., Ludwig, N., Leidinger, P., Vogel, B., Galata, V., Roth, P., Menegatti, J., Grasser, F., Ruprecht, K., Kahraman, M., Grossmann, T., Haas, J., Meese, E., & Keller, A. (2016). Prioritizing and selecting likely novel miRNAs from NGS data. Nucleic Acids Research, 44, e53.

    Article  PubMed  Google Scholar 

  97. Kapranov, P., Ozsolak, F., & Milos, P. M. (2012). Profiling of short RNAs using Helicos single-molecule sequencing. Methods in Molecular Biology, 822, 219–232.

    Article  CAS  PubMed  Google Scholar 

  98. Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., & Enright, A. J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research, 34, D140–D144.

    Article  CAS  PubMed  Google Scholar 

  99. Griffiths-Jones, S., Saini, H. K., van Dongen, S., & Enright, A. J. (2008). miRBase: Tools for microRNA genomics. Nucleic Acids Research, 36, D154–D158.

    Article  CAS  PubMed  Google Scholar 

  100. Agarwal, V., Bell, G. W., Nam, J. W., & Bartel, D. P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4. https://doi.org/10.7554/eLife.05005.

  101. Vlachos, I. S., Paraskevopoulou, M. D., Karagkouni, D., Georgakilas, G., Vergoulis, T., Kanellos, I., Anastasopoulos, I. L., Maniou, S., Karathanou, K., Kalfakakou, D., Fevgas, A., Dalamagas, T., & Hatzigeorgiou, A. G. (2015). DIANA-TarBase v7.0: Indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Research, 43, D153–D159.

    Article  CAS  PubMed  Google Scholar 

  102. Chou, C. H., Shrestha, S., Yang, C. D., Chang, N. W., Lin, Y. L., Liao, K. W., Huang, W. C., Sun, T. H., Tu, S. J., Lee, W. H., Chiew, M. Y., Tai, C. S., Wei, T. Y., Tsai, T. R., Huang, H. T., Wang, C. Y., Wu, H. Y., Ho, S. Y., Chen, P. R., Chuang, C. H., Hsieh, P. J., Wu, Y. S., Chen, W. L., Li, M. J., Wu, Y. C., Huang, X. Y., Ng, F. L., Buddhakosai, W., Huang, P. C., Lan, K. C., Huang, C. Y., Weng, S. L., Cheng, Y. N., Liang, C., Hsu, W. L., & Huang, H. D. (2018). miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions. Nucleic Acids Research, 46, D296–D302.

    Article  CAS  PubMed  Google Scholar 

  103. Hsu, S. D., Lin, F. M., Wu, W. Y., Liang, C., Huang, W. C., Chan, W. L., Tsai, W. T., Chen, G. Z., Lee, C. J., Chiu, C. M., Chien, C. H., Wu, M. C., Huang, C. Y., Tsou, A. P., & Huang, H. D. (2011). miRTarBase: A database curates experimentally validated microRNA-target interactions. Nucleic Acids Research, 39, D163–D169.

    Article  CAS  PubMed  Google Scholar 

  104. Wang, X. (2008). miRDB: A microRNA target prediction and functional annotation database with a wiki interface. RNA, 14, 1012–1017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Wong, N., & Wang, X. (2015). miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Research, 43, D146–D152.

    Article  CAS  PubMed  Google Scholar 

  106. Dweep, H., & Gretz, N. (2015). miRWalk2.0: A comprehensive atlas of microRNA-target interactions. Nature Methods, 12, 697.

    Article  CAS  PubMed  Google Scholar 

  107. Parveen, A., Gretz, N., & Dweep, H. (2016). Obtaining miRNA-Target Interaction Information from miRWalk2.0. Current Protocols in Bioinformatics, 55, 12.15.11–12.15.27.

    Article  Google Scholar 

  108. Vlachos, I. S., Zagganas, K., Paraskevopoulou, M. D., Georgakilas, G., Karagkouni, D., Vergoulis, T., Dalamagas, T., & Hatzigeorgiou, A. G. (2015). DIANA-miRPath v3.0: Deciphering microRNA function with experimental support. Nucleic Acids Research, 43, W460–W466.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., Li, M., Wang, G., & Liu, Y. (2009). miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acids Research, 37, D98–D104.

    Article  CAS  PubMed  Google Scholar 

  110. Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, D. (2002). The human genome browser at UCSC. Genome Research, 12, 996–1006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Riffo-Campos, A. L., Riquelme, I., & Brebi-Mieville, P. (2016). Tools for sequence-based miRNA target prediction: What to choose? International Journal of Molecular Sciences, 17. https://doi.org/10.3390/ijms17121987.

  112. Vlachos, I. S., & Hatzigeorgiou, A. G. (2013). Online resources for miRNA analysis. Clinical Biochemistry, 46, 879–900.

    Article  CAS  PubMed  Google Scholar 

  113. Sykes, P. J., Neoh, S. H., Brisco, M. J., Hughes, E., Condon, J., & Morley, A. A. (1992). Quantitation of targets for PCR by use of limiting dilution. BioTechniques, 13, 444–449.

    CAS  PubMed  Google Scholar 

  114. Vogelstein, B., & Kinzler, K. W. (1999). Digital PCR. Proceedings of the National Academy of Sciences of the United States of America, 96, 9236–9241.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Bustin, S. A., & Nolan, T. (2004). Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. Journal of Biomolecular Techniques: JBT, 15, 155–166.

    PubMed  PubMed Central  Google Scholar 

  116. Li, X., Li, Y., Zhao, L., Zhang, D., Yao, X., Zhang, H., Wang, Y. C., Wang, X. Y., Xia, H., Yan, J., & Ying, H. (2014). Circulating muscle-specific miRNAs in Duchenne muscular dystrophy patients. Molecular Therapy Nucleic acids, 3, e177.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Heier, C. R., Fiorillo, A. A., Chaisson, E., Gordish-Dressman, H., Hathout, Y., Damsker, J. M., Hoffman, E. P., & Conklin, L. S. (2016). Identification of pathway-specific serum biomarkers of response to glucocorticoid and infliximab treatment in children with inflammatory Bowel disease. Clinical and Translational Gastroenterology, 7, e192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Bak, R. O., Hollensen, A. K., Primo, M. N., Sorensen, C. D., & Mikkelsen, J. G. (2013). Potent microRNA suppression by RNA Pol II-transcribed ‘Tough Decoy’ inhibitors. RNA, 19, 280–293.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Hollensen, A. K., Bak, R. O., Haslund, D., & Mikkelsen, J. G. (2013). Suppression of microRNAs by dual-targeting and clustered tough decoy inhibitors. RNA Biology, 10, 406–414.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Ebert, M. S., Neilson, J. R., & Sharp, P. A. (2007). MicroRNA sponges: Competitive inhibitors of small RNAs in mammalian cells. Nature Methods, 4, 721–726.

    Article  CAS  PubMed  Google Scholar 

  121. Choi, W. Y., Giraldez, A. J., & Schier, A. F. (2007). Target protectors reveal dampening and balancing of Nodal agonist and antagonist by miR-430. Science, 318, 271–274.

    Article  CAS  PubMed  Google Scholar 

  122. Christopher, A. F., Kaur, R. P., Kaur, G., Kaur, A., Gupta, V., & Bansal, P. (2016). MicroRNA therapeutics: Discovering novel targets and developing specific therapy. Perspectives in Clinical Research, 7, 68–74.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA

    Alyson A. Fiorillo & Christopher R. Heier

  2. Center for Genetic Medicine Research, Children’s National Medical Center, Washington, DC, USA

    Alyson A. Fiorillo & Christopher R. Heier

Authors
  1. Alyson A. Fiorillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher R. Heier
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alyson A. Fiorillo .

Editor information

Editors and Affiliations

  1. Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, Merseyside, UK

    Jatin George Burniston

  2. Children’s National Health System, George Washington University, Washington, DC, USA

    Yi-Wen Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The American Physiological Society

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fiorillo, A.A., Heier, C.R. (2019). Approaches to Studying the microRNAome in Skeletal Muscle. In: Burniston, J., Chen, YW. (eds) Omics Approaches to Understanding Muscle Biology. Methods in Physiology. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-9802-9_6

Download citation

Publish with us

Policies and ethics

Search

Navigation