Two-dimensional gel electrophoresis provides robust comparative analysis of skeletal muscle, but this technique is laborious and limited by its inability to resolve all proteins. In contrast, orthogonal separation by SDS-PAGE and reverse-phase liquid chromatography (RPLC) coupled to mass spectrometry (MS) affords deep mining of the muscle proteome, but differential analysis between samples is challenging due to the greater level of fractionation and the complexities of quantifying proteins based on the abundances of their tryptic peptides. Here we report simple, semi-automated and time efficient ( i.e., 3 h per sample) proteome profiling of skeletal muscle by 1-dimensional RPLC electrospray ionisation tandem MS. Solei were analysed from rats (n = 5, in each group) bred as either high- or low-capacity runners (HCR and LCR, respectively) that exhibited a 6.4-fold difference (1,625 ± 112 m vs. 252 ± 43 m, p < 0.0001) in running capacity during a standardized treadmill test. Soluble muscle proteins were extracted, digested with trypsin and individual biological replicates (50 ng of tryptic peptides) subjected to LC-MS profiling. Proteins were identified by triplicate LC-MS/MS analysis of a pooled sample of each biological replicate. Differential expression profiling was performed on relative abundances (RA) of parent ions, which spanned three orders of magnitude. In total, 207 proteins were analysed, which encompassed almost all enzymes of the major metabolic pathways in skeletal muscle. The most abundant protein detected was type I myosin heavy chain (RA = 5,843 ± 897) and the least abundant protein detected was heat shock 70 kDa protein (RA = 2 ± 0.5). Sixteen proteins were significantly ( p < 0.05) more abundant in HCR muscle and hierarchal clustering of the profiling data highlighted two protein subgroups, which encompassed proteins associated with either the respiratory chain or fatty acid oxidation. Heart-type fatty acid binding protein (FABPH) was 1.54-fold ( p = 0.0064) more abundant in HCR than LCR soleus. This discovery was verified using selective reaction monitoring (SRM) of the y5 ion (551.21 m/z) of the doubly-charged peptide SLGVGFATR (454.19 m/z) of residues 23–31 of FABPH. SRM was conducted on technical replicates of each biological sample and exhibited a coefficient of variation of 20%. The abundance of FABPH measured by SRM was 2.84-fold greater ( p = 0.0095) in HCR muscle. In addition, SRM of FABPH was performed in vastus lateralis samples of young and elderly humans with different habitual activity levels (collected during a previous
References
[1]
Holloway, K.V.; O’Gorman, M.; Woods, P.; Morton, J.P.; Evans, L.; Cable, N.T.; Goldspink, D.F.; Burniston, J.G. Proteomic investigation of changes in human vastus lateralis muscle in response to interval-exercise training. Proteomics 2009, 9, 5155–5174, doi:10.1002/pmic.200900068.
[2]
Staunton, L.; Zweyer, M.; Swandulla, D.; Ohlendieck, K. Mass spectrometry-based proteomic analysis of middle-aged vs. aged vastus lateralis reveals increased levels of carbonic anhydrase isoform 3 in senescent human skeletal muscle. Int. J. Mol. Med. 2012, 30, 723–733.
[3]
Moriggi, M.; Vasso, M.; Fania, C.; Capitanio, D.; Bonifacio, G.; Salanova, M.; Blottner, D.; Ritweger, J.; Felsenberg, D.; Cerretelli, P.; et al. Long term bed rest with and without vibration exercise countermeasures: Effects on human muscle protein dysregulation. Proteomics 2010, 10, 3756–3774, doi:10.1002/pmic.200900817.
[4]
Hittel, D.S.; Hathout, Y.; Hoffman, E.P.; Houmard, J.A. Proteome analysis of skeletal muscle from obese and morbidly obese women. Diabetes 2005, 54, 1283–1288, doi:10.2337/diabetes.54.5.1283.
[5]
Isfort, R.J.; Hinkle, R.T.; Jones, M.B.; Wang, F.; Greis, K.D.; Sun, Y.; Keough, T.W.; Anderson, N.L.; Sheldon, R.J. Proteomic analysis of the atrophying rat soleus muscle following denervation. Electrophoresis 2000, 21, 2228–2234, doi:10.1002/1522-2683(20000601)21:11<2228::AID-ELPS2228>3.0.CO;2-V.
[6]
Donoghue, P.; Doran, P.; Wynne, K.; Pedersen, K.; Dunn, M.J.; Ohlendieck, K. Proteomic profiling of chronic low-frequency stimulated fast muscle. Proteomics 2007, 7, 3417–3430, doi:10.1002/pmic.200700262.
[7]
Jungblut, P.; Holzhütter, H.; Apweiler, R.; Schlüter, H. The speciation of the proteome. Chem. Cent. J. 2008, 2, 1–10, doi:10.1186/1752-153X-2-1.
[8]
Parker, K.C.; Walsh, R.; Salajegheh, M.; Amato, A.; Krastins, B.; Sarracino, D.A.; Greenberg, S.A. Characterization of human skeletal muscle biopsy samples using shotgun proteomics. J. Proteome Res. 2009, 8, 3265–3277, doi:10.1021/pr800873q.
[9]
Hojlund, K.; Yi, Z.; Hwang, H.; Bowen, B.; Lefort, N.; Flynn, C.R.; Langlais, P.; Weintraub, S.T.; Mandarino, L.J. Characterization of the human skeletal muscle proteome by one-dimensional Gel electrophoresis and HPLC-ESI-MS/MS. Mol. Cell. Proteomics 2007, 7, 257–267, doi:10.1074/mcp.M700304-MCP200.
[10]
Hussey, S.E.; Sharoff, C.G.; Garnham, A.; Zhengping, Y.; Bowen, B.P.; Mandarino, L.J.; Hargreaves, M. Effect of exercise on the skeletal muscle proteome in patients with type 2 diabetes. Med. Sci. Sports Exerc. 2012, 45, 1069–1076.
[11]
Pette, D. Metabolic heterogeneity of muscle fibres. J. Exp. Biol. 1985, 115, 179–189.
[12]
Gelfi, C.; Vigano, A.; Ripamonti, M.; Pontoglio, A.; Begum, S.; Pellegrino, M.A.; Grassi, B.; Bottinelli, R.; Wait, R.; Cerretelli, P. The human muscle proteome in aging. J. Proteome Res. 2006, 5, 1344–1353, doi:10.1021/pr050414x.
[13]
Holloszy, J.O. Biochemical adaptations in muscle. Effects of exercise on mitochondrial oxygen uptake and respiratory enzyme activity in skeletal muscle. J. Biol. Chem. 1967, 242, 2278–2282.
Koch, L.G.; Britton, S.L. Artificial selection for intrinsic aerobic endurance running capacity in rats. Physiol. Genomics 2001, 5, 45–52.
[16]
Burniston, J.G.; Kenyani, J.; Wastling, J.M.; Burant, C.F.; Qi, N.R.; Koch, L.G.; Britton, S.L. Proteomic analysis reveals perturbed energy metabolism and elevated oxidative stress in hearts of rats with inborn low aerobic capacity. Proteomics 2011, 11, 3369–3379, doi:10.1002/pmic.201000593.
[17]
Kivel？, R.; Silvennoinen, M.; Lehti, M.; Rinnankoski-Tuikka, R.; Purhonen, T.; Ketola, T.; Pullinen, K.; Vuento, M.; Mutanen, N.; Sartor, M.A.; et al. Gene expression centroids that link with low intrinsic aerobic exercise capacity and complex disease risk. FASEB J. 2010, 24, 4565–4574, doi:10.1096/fj.10-157313.
[18]
Huang, D.A.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44–57.
[19]
Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30, doi:10.1093/nar/28.1.27.
[20]
Zhen, E.Y.; Berna, M.J.; Jin, Z.; Pritt, M.L.; Watson, D.E.; Ackermann, B.L.; Hale, J.E. Quantification of heart fatty acid binding protein as a biomarker for drug-induced cardiac and musculoskeletal necroses. Proteomics Clin. Appl. 2007, 1, 661–671, doi:10.1002/prca.200700006.
[21]
Cobley, N.; Bartlett, J.D.; Kayani, A.; Murray, S.W.; Louhelainen, J.; Donovan, T.; Waldron, S.; Gregson, W.; Burniston, J.G.; Morton, J.P.; et al. PGC-1α transcriptional response and mitochondrial adaptation to acute exercise is maintained in skeletal muscle of sedentary elderly males. Biogerontology 2012, 13, 621–631, doi:10.1007/s10522-012-9408-1.
[22]
Koch, L.G.; Kemi, O.J.; Qi, N.; Leng, S.X.; Bijma, P.; Gilligan, L.J.; Wilkinson, J.E.; Wisloff, H.; Hoydal, M.A.; Rolim, N.; et al. Intrinsic aerobic capacity sets a divide for aging and longevity. Circ. Res. 2011, 109, 1162–1172, doi:10.1161/CIRCRESAHA.111.253807.
[23]
Rivas, D.A.; Lessard, S.J.; Saito, M.; Friedhuber, A.M.; Koch, L.G.; Britton, S.L.; Yasoelkis, B.B.; Hawley, J.A. Low intrinsic running capacity is associated with reduced skeletal muscle substrate oxidation and lower mitochondrial content in white skeletal muscle. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2011, 300, R835–R843, doi:10.1152/ajpregu.00659.2010.
[24]
Seifert, E.L.; Bastianelli, M.; Aguer, C.; Moffat, C.; Estey, C.; Koch, L.G.; Britton, S.L.; Harper, M.-E. Intrinsic aerobic capacity correlates with inherent mitochondrial oxidative and H2O2 emission capacities without major shifts in myosin heavy chain isoform. J. Appl. Physiol. 2012, 113, 1624–1634.
[25]
Noland, R.C.; Thyfault, J.P.; Henes, S.T.; Whitfield, B.R.; Woodlief, T.L.; Evan, J.R.; Lust, J.A.; Britton, S.L.; Koch, L.G.; Dudek, R.W.; et al. Artificial selection for high-capacity endurance running is protective against high-fat diet-induced insulin resistance. Am. J. Physiol. Endocrinol. Metab. 2007, 293, E31–E41, doi:10.1152/ajpendo.00500.2006.
[26]
Wisloff, U.; Najjar, S.M.; Ellingsen, O.; Haram, P.M.; Swoap, S.; Al-Sahre, Q.; Frenstrom, M.; Razaei, K.; Lee, S.J.; Koch, L.G.; et al. Cardiovascular risk factors emerge after artificial selection for low aerobic capacity. Science 2005, 307, 418–420, doi:10.1126/science.1108177.
[27]
Burniston, J.G.; Hoffman, E.P. Proteomic responses of skeletal and cardiac muscle to exercise. Expert Rev. Proteomics 2011, 8, 361–377, doi:10.1586/epr.11.17.
[28]
Bell, R.; Hubbard, A.; Chettier, R.; Chen, D.; Miller, J.P.; Kapahi, P.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A human protein interaction network shows conservation of aging processes between human and invertebrate species. PLoS Genet. 2009, 5, e1000414, doi:10.1371/journal.pgen.1000414.
[29]
Han, B.; Higgs, R.E. Proteomics: From hypothesis to quantitative assay on a single platform. Guidelines for developing MRM assays using ion trap mass spectrometers. Brief. Funct. Genomic Proteomic. 2008, 7, 340–354, doi:10.1093/bfgp/eln032.
[30]
Binas, B.; Erol, E. FABPs as determinants of myocellular and hepatic fuel metabolism. Mol. Cell. Biochem. 2007, 299, 75–84, doi:10.1007/s11010-005-9043-0.
[31]
Shearer, J.; Fueger, P.T.; Rottman, J.N.; Bracy, D.P.; Binas, B.; Wasserman, D.H. Heart-type fatty acid-binding protein reciprocally regulates glucose and fatty acid utilization during exercise. Am. J. Physiol. Endocrinol. Metab. 2005, 288, E292–E297, doi:10.1152/ajpendo.00287.2004.
[32]
Shaw, C.S.; Clark, J.; Wagenmakers, A.J. The effect of exercise and nutrition on intramuscular fat metabolism and insulin sensitivity. Annu. Rev. Nutr. 2010, 30, 13–34, doi:10.1146/annurev.nutr.012809.104817.
[33]
Doran, P.; Donoghue, P.; O’Connell, K.; Gannon, J.; Ohlendieck, K. Proteomics of skeletal muscle aging. Proteomics 2009, 9, 989–1003, doi:10.1002/pmic.200800365.
[34]
Gannon, J.; Doran, P.; Kirwan, A.; Ohlendieck, K. Drastic increase of myosin light chain MLC-2 in senescent skeletal muscle indicates fast-to-slow fibre transition in sarcopenia of old age. Eur. J. Cell. Biol. 2009, 88, 685–700, doi:10.1016/j.ejcb.2009.06.004.
