Duchenne Muscular Dystrophy (DMD) is a complex process involving multiple pathways downstream of the primary genetic insult leading to fatal muscle degeneration. Aging muscle is a multifactorial neuromuscular process characterized by impaired muscle regeneration leading to progressive atrophy. We hypothesized that these chronic atrophying situations may share specific myogenic adaptative responses at transcriptional level according to tissue remodeling. Muscle biopsies from four young DMD and four AGED subjects were referred to a group of seven muscle biopsies from young subjects without any neuromuscular disorder and explored through a dedicated expression microarray. We identified 528 differentially expressed genes (out of 2,745 analyzed), of which 328 could be validated by an exhaustive meta-analysis of public microarray datasets referring to DMD and Aging in skeletal muscle. Among the 328 validated co-expressed genes, 50% had the same expression profile in both groups and corresponded to immune/fibrosis responses and mitochondrial metabolism. Generalizing these observed meta-signatures with large compendia of public datasets reinforced our results as they could be also identified in other pathological processes and in diverse physiological conditions. Focusing on the common gene signatures in these two atrophying conditions, we observed enrichment in motifs for candidate transcription factors that may coordinate either the immune/fibrosis responses (ETS1, IRF1, NF1) or the mitochondrial metabolism (ESRRA). Deregulation in their expression could be responsible, at least in part, for the same transcriptome changes initiating the chronic muscle atrophy. This study suggests that distinct pathophysiological processes may share common gene responses and pathways related to specific transcription factors.
References
[1]
Sandri M (2008) Signaling in muscle atrophy and hypertrophy. Physiology (Bethesda) 23: 160–170.
[2]
Fry AC (2004) The role of resistance exercise intensity on muscle fibre adaptations. Sports Med 34: 663–679.
[3]
Mitch WE, Goldberg AL (1996) Mechanisms of muscle wasting. The role of the ubiquitin-proteasome pathway. N Engl J Med 335: 1897–1905.
[4]
Evans WJ (1995) What is sarcopenia? J Gerontol A Biol Sci Med Sci 50 Spec No: 5–8.
[5]
Ozawa E, Yoshida M, Suzuki A, Mizuno Y, Hagiwara Y, et al. (1995) Dystrophin-associated proteins in muscular dystrophy. Hum Mol Genet 4 Spec No: 1711–1716.
[6]
Petrof BJ, Shrager JB, Stedman HH, Kelly AM, Sweeney HL (1993) Dystrophin protects the sarcolemma from stresses developed during muscle contraction. Proc Natl Acad Sci U S A 90: 3710–3714.
[7]
Turner PR, Schultz R, Ganguly B, Steinhardt RA (1993) Proteolysis results in altered leak channel kinetics and elevated free calcium in mdx muscle. J Membr Biol 133: 243–251.
[8]
Blake DJ, Weir A, Newey SE, Davies KE (2002) Function and genetics of dystrophin and dystrophin-related proteins in muscle. Physiol Rev 82: 291–329.
[9]
Kamel HK (2003) Sarcopenia and aging. Nutr Rev 61: 157–167.
[10]
Dirks AJ, Hofer T, Marzetti E, Pahor M, Leeuwenburgh C (2006) Mitochondrial DNA mutations, energy metabolism and apoptosis in aging muscle. Ageing Res Rev 5: 179–195.
[11]
Lee CM, Lopez ME, Weindruch R, Aiken JM (1998) Association of age-related mitochondrial abnormalities with skeletal muscle fiber atrophy. Free Radic Biol Med 25: 964–972.
[12]
Marzetti E, Leeuwenburgh C (2006) Skeletal muscle apoptosis, sarcopenia and frailty at old age. Exp Gerontol 41: 1234–1238.
[13]
Mecocci P, Fano G, Fulle S, MacGarvey U, Shinobu L, et al. (1999) Age-dependent increases in oxidative damage to DNA, lipids, and proteins in human skeletal muscle. Free Radic Biol Med 26: 303–308.
[14]
Sohal RS, Agarwal S, Candas M, Forster MJ, Lal H (1994) Effect of age and caloric restriction on DNA oxidative damage in different tissues of C57BL/6 mice. Mech Ageing Dev 76: 215–224.
[15]
Baron D, Montfort J, Houlgatte R, Fostier A, Guiguen Y (2007) Androgen-induced masculinization in rainbow trout results in a marked dysregulation of early gonadal gene expression profiles. BMC Genomics 8: 357.
[16]
Baron D, Dubois E, Bihouee A, Teusan R, Steenman M, et al. (2011) Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns 1. BMC Genomics 12: 113.
[17]
Virtanen C, Takahashi M (2008) Muscling in on microarrays. Appl Physiol Nutr Metab 33: 124–129.
[18]
Bakay M, Chen YW, Borup R, Zhao P, Nagaraju K, et al. (2002) Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics 3: 4.
[19]
Dupuy A, Simon RM (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99: 147–157.
[20]
Ein-Dor L, Kela I, Getz G, Givol D, Domany E (2005) Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics 21: 171–178.
[21]
Jafari P, Azuaje F (2006) An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med Inform Decis Mak 6: 27.
[22]
Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365: 488–492.
[23]
Cahan P, Rovegno F, Mooney D, Newman JC, St Laurent G III, et al. (2007) Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization. Gene 401: 12–18.
[24]
Fierro AC, Vandenbussche F, Engelen K, Van de Peer Y, Marchal K (2008) Meta Analysis of Gene Expression Data within and Across Species. Curr Genomics 9: 525–534.
[25]
Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 5: e184.
[26]
Fischer MD, Budak MT, Bakay M, Gorospe JR, Kjellgren D, et al. (2005) Definition of the unique human extraocular muscle allotype by expression profiling. Physiol Genomics 22: 283–291.
[27]
Dudley JT, Tibshirani R, Deshpande T, Butte AJ (2009) Disease signatures are robust across tissues and experiments. Mol Syst Biol 5: 307.
[28]
Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, et al. (2001) Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294: 1704–1708.
[29]
Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL (2001) Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98: 14440–14445.
[30]
Sacheck JM, Hyatt JP, Raffaello A, Jagoe RT, Roy RR, et al. (2007) Rapid disuse and denervation atrophy involve transcriptional changes similar to those of muscle wasting during systemic diseases. FASEB J 21: 140–155.
[31]
Lecker SH, Jagoe RT, Gilbert A, Gomes M, Baracos V, et al. (2004) Multiple types of skeletal muscle atrophy involve a common program of changes in gene expression. FASEB J 18: 39–51.
[32]
Rifai Z, Welle S, Kamp C, Thornton CA (1995) Ragged red fibers in normal aging and inflammatory myopathy. Ann Neurol 37: 24–29.
[33]
Fayet G, Jansson M, Sternberg D, Moslemi AR, Blondy P, et al. (2002) Ageing muscle: clonal expansions of mitochondrial DNA point mutations and deletions cause focal impairment of mitochondrial function. Neuromuscul Disord 12: 484–493.
[34]
Aure K, Fayet G, Leroy JP, Lacene E, Romero NB, et al. (2006) Apoptosis in mitochondrial myopathies is linked to mitochondrial proliferation. Brain 129: 1249–1259.
[35]
Hiona A, Sanz A, Kujoth GC, Pamplona R, Seo AY, et al. (2010) Mitochondrial DNA mutations induce mitochondrial dysfunction, apoptosis and sarcopenia in skeletal muscle of mitochondrial DNA mutator mice. PLoS One 5: e11468.
[36]
Lamirault G, Meur NL, Roussel JC, Cunff MF, Baron D, et al. (2010) Molecular risk stratification in advanced heart failure patients. J Cell Mol Med 14: 1443–1452.
[37]
Carmeli E, Moas M, Reznick AZ, Coleman R (2004) Matrix metalloproteinases and skeletal muscle: a brief review. Muscle Nerve 29: 191–197.
[38]
Cossu G, Mavilio F (2000) Myogenic stem cells for the therapy of primary myopathies: wishful thinking or therapeutic perspective?. J Clin Invest 105: 1669–1674.
[39]
Gussoni E, Blau HM, Kunkel LM (1997) The fate of individual myoblasts after transplantation into muscles of DMD patients. Nat Med 3: 970–977.
[40]
Hawke TJ, Garry DJ (2001) Myogenic satellite cells: physiology to molecular biology. J Appl Physiol 91: 534–551.
[41]
Kayo T, Allison DB, Weindruch R, Prolla TA (2001) Influences of aging and caloric restriction on the transcriptional profile of skeletal muscle from rhesus monkey. Proc Natl Acad Sci U S A 98: 5093–5098.
[42]
Schreiber SN, Knutti D, Brogli K, Uhlmann T, Kralli A (2003) The transcriptional coactivator PGC-1 regulates the expression and activity of the orphan nuclear receptor estrogen-related receptor alpha (ERRalpha). J Biol Chem 278: 9013–9018.
[43]
Hudson NJ, Reverter A, Wang Y, Greenwood PL, Dalrymple BP (2009) Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks. PLoS One 4: e7249.
[44]
Danko CG, Pertsov AM (2009) Identification of gene co-regulatory modules and associated cis-elements involved in degenerative heart disease. BMC Med Genomics 2: 31.
[45]
Giguere V (2008) Transcriptional control of energy homeostasis by the estrogen-related receptors. Endocr Rev 29: 677–696.
[46]
Dittmer J (2003) The biology of the Ets1 proto-oncogene. Mol Cancer 2: 29.
[47]
Bhat NK, Thompson CB, Lindsten T, June CH, Fujiwara S, et al. (1990) Reciprocal expression of human ETS1 and ETS2 genes during T-cell activation: regulatory role for the protooncogene ETS1. Proc Natl Acad Sci U S A 87: 3723–3727.
[48]
Saha B, Jyothi PS, Chandrasekar B, Nandi D (2010) Gene modulation and immunoregulatory roles of interferon gamma. Cytokine 50: 1–14.
[49]
Trovo-Marqui AB, Tajara EH (2006) Neurofibromin: a general outlook. Clin Genet 70: 1–13.
[50]
Gutmann DH, Geist RT, Rose K, Wright DE (1995) Expression of two new protein isoforms of the neurofibromatosis type 1 gene product, neurofibromin, in muscle tissues. Dev Dyn 202: 302–311.
[51]
Kossler N, Stricker S, Rodelsperger C, Robinson PN, Kim J, et al. (2011) Neurofibromin (Nf1) is required for skeletal muscle development. Hum Mol Genet.
[52]
Mirebeau-Prunier D, Le Pennec S, Jacques C, Gueguen N, Poirier J, et al. (2010) Estrogen-related receptor alpha and PGC-1-related coactivator constitute a novel complex mediating the biogenesis of functional mitochondria. FEBS J 277: 713–725.
[53]
Flanigan KM, Lauria G, Griffin JW, Kuncl RW (1998) Age-related biology and diseases of muscle and nerve. Neurol Clin 16: 659–669.
[54]
Deschenes MR (2011) Motor Unit and Neuromuscular Junction Remodeling with Aging. Curr Aging Sci. Apr 29. [Epub ahead of print].
[55]
Steenman M, Lamirault G, Le Meur N, Le Cunff M, Escande D, et al. (2005) Distinct molecular portraits of human failing hearts identified by dedicated cDNA microarrays. Eur J Heart Fail 7: 157–165.
[56]
Le Meur N, Lamirault G, Bihouee A, Steenman M, Bedrine-Ferran H, et al. (2004) A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication. Nucleic Acids Res 32: 5349–5358.
[57]
Baron D, Bihouee A, Teusan R, Dubois E, Savagner F, et al. (2011) MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets. Bioinformatics 27: 725–726.
[58]
Maglott D, Ostell J, Pruitt KD, Tatusova T (2007) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 35: D26–D31.
[59]
Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7: 203–214.
[60]
Baron D, Houlgatte R, Fostier A, Guiguen Y (2005) Large-scale temporal gene expression profiling during gonadal differentiation and early gametogenesis in rainbow trout. Biol Reprod 73: 959–966.
[61]
Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM (2002) Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 62: 4427–4433.
[62]
French L, Lane S, Law T, Xu L, Pavlidis P (2009) Application and evaluation of automated semantic annotation of gene expression experiments. Bioinformatics 25: 1543–1549.
[63]
Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P (2004) Coexpression analysis of human genes across many microarray data sets. Genome Res 14: 1085–1094.
[64]
Chen R, Mallelwar R, Thosar A, Venkatasubrahmanyam S, Butte AJ (2008) GeneChaser: identifying all biological and clinical conditions in which genes of interest are differentially expressed. BMC Bioinformatics 9: 548.
[65]
Lopez F, Textoris J, Bergon A, Didier G, Remy E, et al. (2008) TranscriptomeBrowser: a powerful and flexible toolbox to explore productively the transcriptional landscape of the Gene Expression Omnibus database. PLoS One 3: e4001.
[66]
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550.
