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Approaches to Studying the microRNAome in Skeletal Muscle

  • Alyson A. FiorilloEmail author
  • Christopher R. Heier
Chapter
Part of the Methods in Physiology book series (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.

References

  1. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  2. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  3. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  4. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  7. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  9. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  13. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Davis-Dusenbery, B. N., & Hata, A. (2010). Mechanisms of control of microRNA biogenesis. Journal of Biochemistry, 148, 381–392.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Kim, V. N., Han, J., & Siomi, M. C. (2009). Biogenesis of small RNAs in animals. Nature Reviews. Molecular Cell Biology, 10, 126–139.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Meijer, H. A., Smith, E. M., & Bushell, M. (2014). Regulation of miRNA strand selection: Follow the leader? Biochemical Society Transactions, 42, 1135–1140.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Pritchard, C. C., Cheng, H. H., & Tewari, M. (2012). MicroRNA profiling: Approaches and considerations. Nature Reviews Genetics, 13, 358–369.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Lee, R. C., & Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis elegans. Science, 294, 862–864.PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    McCarthy, J. J. (2008). MicroRNA-206: The skeletal muscle-specific myomiR. Biochimica et Biophysica Acta, 1779, 682–691.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  37. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  40. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  44. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  46. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  48. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  50. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  53. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  54. 54.
    Ibberson, D., Benes, V., Muckenthaler, M. U., & Castoldi, M. (2009). RNA degradation compromises the reliability of microRNA expression profiling. BMC Biotechnology, 9, 102.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  56. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    Iyer-Pascuzzi, A. S., & Benfey, P. N. (2010). Fluorescence-activated cell sorting in plant developmental biology. Methods in Molecular Biology, 655, 313–319.PubMedCrossRefPubMedCentralGoogle Scholar
  61. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  63. 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.CrossRefGoogle Scholar
  64. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  68. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  69. 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.CrossRefGoogle Scholar
  70. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  72. 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. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  74. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  77. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  78. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  79. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  81. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  85. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  87. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  88. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  89. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Langmead, B. (2010). Aligning short sequencing reads with Bowtie. Current Protocols in Bioinformatics, Chapter 11, Unit 11.7.PubMedPubMedCentralGoogle Scholar
  92. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Klambauer, G., Unterthiner, T., & Hochreiter, S. (2013). DEXUS: Identifying differential expression in RNA-Seq studies with unknown conditions. Nucleic Acids Research, 41, e198.PubMedPubMedCentralCrossRefGoogle Scholar
  94. 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. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  96. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  97. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  98. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  99. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  100. 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. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  102. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  103. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  104. 104.
    Wang, X. (2008). miRDB: A microRNA target prediction and functional annotation database with a wiki interface. RNA, 14, 1012–1017.PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Wong, N., & Wang, X. (2015). miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Research, 43, D146–D152.CrossRefGoogle Scholar
  106. 106.
    Dweep, H., & Gretz, N. (2015). miRWalk2.0: A comprehensive atlas of microRNA-target interactions. Nature Methods, 12, 697.PubMedCrossRefPubMedCentralGoogle Scholar
  107. 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.CrossRefGoogle Scholar
  108. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  109. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  110. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  111. 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. 112.
    Vlachos, I. S., & Hatzigeorgiou, A. G. (2013). Online resources for miRNA analysis. Clinical Biochemistry, 46, 879–900.PubMedCrossRefPubMedCentralGoogle Scholar
  113. 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.PubMedPubMedCentralGoogle Scholar
  114. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  115. 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.PubMedPubMedCentralGoogle Scholar
  116. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  117. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  118. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  119. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  120. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  121. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  122. 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.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© The American Physiological Society 2019

Authors and Affiliations

  1. 1.Department of Genomics and Precision MedicineGeorge Washington University School of Medicine and Health SciencesWashington, DCUSA
  2. 2.Center for Genetic Medicine ResearchChildren’s National Medical CenterWashington, DCUSA

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