Metabonomic study of the biochemical profiles of heterozygous myostatin knockout swine
Jianxiang XU, Dengke PAN, Jie ZHAO, Jianwu WANG, Xiaohong HE, Yuehui MA, Ning LI
Metabonomic study of the biochemical profiles of heterozygous myostatin knockout swine
Myostatin is a transforming growth factor-β family member that normally acts to limit skeletal muscle growth. Myostatin gene (MSTN) knockout (KO) mice show possible effects for the prevention or treatment of metabolic disorders such as obesity and type 2 diabetes. We applied chromatography and mass spectrometry based metabonomics to assess system-wide metabolic response of heterozygous MSTN KO (MSTN+/-) swine. Most of the metabolic data for MSTN+/- swine were similar to the data for wild type (WT) control swine. There were, however, metabolic changes related to fatty acid metabolism, glucose utilization, lipid metabolism, as well as BCAA catabolism caused by monoallelic MSTN depletion.The statistical analyses suggested that: (1) most metabolic changes were not significant in MSTN+/- swine compared to WT swine; (2) only a few metabolic properties were significantly different between KO and WT swine, especially for lipid metabolism. Significantly, these minor changes were most evident in female KO swine and suggested differences in gender sensitivity to myostatin.
myostatin / transforming growth factor-β family / skeletal muscle / metabolic disorders / chromatography / mass spectrometry / metabonomics
[1] |
Nicholson J K, Connelly J, Lindon J C, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nature Reviews Drug Discovery, 2002, 1(2): 153–161
CrossRef
Pubmed
Google scholar
|
[2] |
Nicholson J K, Lindon J C, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 1999, 29(11): 1181–1189
CrossRef
Pubmed
Google scholar
|
[3] |
Holmes E, Nicholls A W, Lindon J C, Ramos S, Spraul M, Neidig P, Connor S C, Connelly J, Damment S J, Haselden J, Nicholson J K. Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition. NMR in Biomedicine, 1998, 11(4–5): 235–244
CrossRef
Pubmed
Google scholar
|
[4] |
Beckwith-Hall B M, Nicholson J K, Nicholls A W, Foxall P J, Lindon J C, Connor S C, Abdi M, Connelly J, Holmes E. Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chemical Research in Toxicology, 1998, 11(4): 260–272
CrossRef
Pubmed
Google scholar
|
[5] |
Robertson D G, Reily M D, Sigler R E, Wells D F, Paterson D A, Braden T K. Metabonomics: evaluation of nuclear magnetic resonance (NMR) and pattern recognition technology for rapid in vivo screening of liver and kidney toxicants. Toxicological Sciences, 2000, 57(2): 326–337
CrossRef
Pubmed
Google scholar
|
[6] |
Williams R E, Cottrell L, Jacobsen M, Bandara L R, Kelly M D, Kennedy S, Lock E A. 1H-Nuclear magnetic resonance pattern recognition studies with N-phenylanthranilic acid in the rat: time- and dose-related metabolic effects. Biomarkers, 2003, 8(6): 472–490
CrossRef
Pubmed
Google scholar
|
[7] |
Idborg-Björkman H, Edlund P O, Kvalheim O M, Schuppe-Koistinen I, Jacobsson S P. Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis. Analytical Chemistry, 2003, 75(18): 4784–4792
CrossRef
Pubmed
Google scholar
|
[8] |
Lenz E M, Bright J, Knight R, Wilson I D, Major H. A metabonomic investigation of the biochemical effects of mercuric chloride in the rat using 1H NMR and HPLC-TOF/MS: time dependent changes in the urinary profile of endogenous metabolites as a result of nephrotoxicity. Analyst, 2004, 129(6): 535–541
CrossRef
Pubmed
Google scholar
|
[9] |
Lamers R J, van Nesselrooij J H, Kraus V B, Jordan J M, Renner J B, Dragomir A D, Luta G, van der Greef J, DeGroot J. Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis and Cartilage, 2005, 13(9): 762–768
CrossRef
Pubmed
Google scholar
|
[10] |
Brindle J T, Antti H, Holmes E, Tranter G, Nicholson J K, Bethell H W, Clarke S, Schofield P M, McKilligin E, Mosedale D E, Grainger D J. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine, 2002, 8(12): 1439–1444
CrossRef
Pubmed
Google scholar
|
[11] |
Yang J, Xu G, Hong Q, Liebich H M, Lutz K, Schmülling R M, Wahl H G. Discrimination of type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. Journal of Chromatography B, 2004, 813(1–2): 53– 58
CrossRef
Pubmed
Google scholar
|
[12] |
Robertson D G, Reily M D, Baker J D. Metabonomics in pharmaceutical discovery and development. Journal of Proteome Research, 2007, 6(2): 526–539
CrossRef
Pubmed
Google scholar
|
[13] |
Louden D, Handley A, Taylor S, Lenz E, Miller S, Wilson I D, Sage A. Reversed-phase high-performance liquid chromatography combined with on-line UV diode array, FT infrared, and 1H nuclear magnetic resonance spectroscopy and time-of-flight mass spectrometry: application to a mixture of nonsteroidal antiinflammatory drugs. Analytical Chemistry, 2000, 72(16): 3922–3926
CrossRef
Pubmed
Google scholar
|
[14] |
Plumb R S, Stumpf C L, Gorenstein M V, Castro-Perez J M, Dear G J, Anthony M, Sweatman B C, Connor S C, Haselden J N. Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Communications in Mass Spectrometry, 2002, 16(20): 1991–1996
CrossRef
Pubmed
Google scholar
|
[15] |
Lenz E M, Bright J, Knight R, Wilson I D, Major H. Cyclosporin A-induced changes in endogenous metabolites in rat urine: a metabonomic investigation using high field 1H NMR spectroscopy, HPLC-TOF/MS and chemometrics. Journal of Pharmaceutical and Biomedical Analysis, 2004, 35(3): 599–608
CrossRef
Pubmed
Google scholar
|
[16] |
Piccioni F, Capitani D, Zolla L, Mannina L. NMR metabolic profiling of transgenic maize with the Cry1Ab gene. Journal of Agricultural and Food Chemistry, 2009, 57(14): 6041–6049
CrossRef
Pubmed
Google scholar
|
[17] |
Chassy B M. Can-omics inform a food safety assessment? Regulatory Toxicology and Pharmacology, 2010, 58(3 Suppl): S62–S70
CrossRef
Pubmed
Google scholar
|
[18] |
Raamsdonk L M, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh M C, Berden J A, Brindle K M, Kell D B, Rowland J J, Westerhoff H V, van Dam K, Oliver S G. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnology, 2001, 19(1): 45–50
CrossRef
Pubmed
Google scholar
|
[19] |
Delneri D, Brancia F L, Oliver S G, Brancia F L. Towards a truly integrative biology through the functional genomics of yeast. Current Opinion in Biotechnology, 2001, 12(1): 87–91
CrossRef
Pubmed
Google scholar
|
[20] |
Fiehn O, Kopka J, Dörmann P, Altmann T, Trethewey R N, Willmitzer L. Metabolite profiling for plant functional genomics. Nature Biotechnology, 2000, 18(11): 1157–1161
CrossRef
Pubmed
Google scholar
|
[21] |
Gavaghan C L, Holmes E, Lenz E, Wilson I D, Nicholson J K. An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Letters, 2000, 484(3): 169–174
CrossRef
Pubmed
Google scholar
|
[22] |
Lenz E M, Bright J, Wilson I D, Hughes A, Morrisson J, Lindberg H, Lockton A. Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects. Journal of Pharmaceutical and Biomedical Analysis, 2004, 36(4): 841–849
CrossRef
Pubmed
Google scholar
|
[23] |
Williams R E, Lenz E M, Rantalainen M, Wilson I D. The comparative metabonomics of age-related changes in the urinary composition of male Wistar-derived and Zucker (fa/fa) obese rats. Molecular BioSystems, 2006, 2(3–4): 193–202
CrossRef
Pubmed
Google scholar
|
[24] |
Williams R E, Lenz E M, Lowden J S, Rantalainen M, Wilson I D. The metabonomics of aging and development in the rat: an investigation into the effect of age on the profile of endogenous metabolites in the urine of male rats using 1H NMR and HPLC-TOF MS. Molecular BioSystems, 2005, 1(2): 166–175
CrossRef
Pubmed
Google scholar
|
[25] |
Tate A R, Damment S J, Lindon J C. Investigation of the metabolite variation in control rat urine using 1H NMR spectroscopy. Analytical Biochemistry, 2001, 291(1): 17–26
CrossRef
Pubmed
Google scholar
|
[26] |
Bollard M E, Holmes E, Lindon J C, Mitchell S C, Branstetter D, Zhang W, Nicholson J K. Investigations into biochemical changes due to diurnal variation and estrus cycle in female rats using high-resolution 1H NMR spectroscopy of urine and pattern recognition. Analytical Biochemistry, 2001, 295(2): 194–202
CrossRef
Pubmed
Google scholar
|
[27] |
Gavaghan C L, Wilson I D, Nicholson J K. Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and PLS-DA. FEBS Letters, 2002, 530(1–3): 191–196
CrossRef
Pubmed
Google scholar
|
[28] |
Griffin J L, Walker L A, Garrod S, Holmes E, Shore R F, Nicholson J K. NMR spectroscopy based metabonomic studies on the comparative biochemistry of the kidney and urine of the bank vole (Clethrionomys glareolus), wood mouse (Apodemus sylvaticus), white toothed shrew (Crocidura suaveolens) and the laboratory rat. Comparative Biochemistry and Physiology Part B: Biochemistry & Molecular Biology, 2000, 127(3): 357–367
CrossRef
Pubmed
Google scholar
|
[29] |
Holmes E, Nicholson J K, Tranter G. Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks. Chemical Research in Toxicology, 2001, 14(2): 182–191
CrossRef
Pubmed
Google scholar
|
[30] |
Gavaghan McKee C L, Wilson I D, Nicholson J K. Metabolic phenotyping of nude and normal (Alpk:ApfCD, C57BL10J) mice. Journal of proteome research, 2006, 5(2): 378–384
CrossRef
Pubmed
Google scholar
|
[31] |
McPherron A C, Lawler A M, Lee S J. Regulation of skeletal muscle mass in mice by a new TGF-β superfamily member. Nature, 1997, 387(6628): 83–90
CrossRef
Pubmed
Google scholar
|
[32] |
Lee S J. Regulation of muscle mass by myostatin. Annual Review of Cell and Developmental Biology, 2004, 20(1): 61–86
CrossRef
Pubmed
Google scholar
|
[33] |
Lin J, Arnold H B, Della-Fera M A, Azain M J, Hartzell D L, Baile C A. Myostatin knockout in mice increases myogenesis and decreases adipogenesis. Biochemical and Biophysical Research Communications, 2002, 291(3): 701–706
CrossRef
Pubmed
Google scholar
|
[34] |
Rebbapragada A, Benchabane H, Wrana J L, Celeste A J, Attisano L. Myostatin signals through a transforming growth factor β-like signaling pathway to block adipogenesis. Molecular and Cellular Biology, 2003, 23(20): 7230–7242
CrossRef
Pubmed
Google scholar
|
[35] |
Bogdanovich S, Krag T O, Barton E R, Morris L D, Whittemore L A, Ahima R S, Khurana T S. Functional improvement of dystrophic muscle by myostatin blockade. Nature, 2002, 420(6914): 418– 421
CrossRef
Pubmed
Google scholar
|
[36] |
Bogdanovich S, Perkins K J, Krag T O, Whittemore L A, Khurana T S. Myostatin propeptide-mediated amelioration of dystrophic pathophysiology. FASEB Journal, 2005, 19(6): 543–549
CrossRef
Pubmed
Google scholar
|
[37] |
Wagner K R, McPherron A C, Winik N, Lee S J. Loss of myostatin attenuates severity of muscular dystrophy in mdx mice. Annals of Neurology, 2002, 52(6): 832–836
CrossRef
Pubmed
Google scholar
|
[38] |
McPherron A C, Lee S J. Suppression of body fat accumulation in myostatin-deficient mice. Journal of Clinical Investigation, 2002, 109(5): 595–601
CrossRef
Pubmed
Google scholar
|
[39] |
Lee S J. Quadrupling muscle mass in mice by targeting TGF-β signaling pathways. PLoS ONE, 2007, 2(8): e789
CrossRef
Pubmed
Google scholar
|
[40] |
Grobet L, Martin L J, Poncelet D, Pirottin D, Brouwers B, Riquet J, Schoeberlein A, Dunner S, Ménissier F, Massabanda J, Fries R, Hanset R, Georges M. A deletion in the bovine myostatin gene causes the double-muscled phenotype in cattle. Nature Genetics, 1997, 17(1): 71–74
CrossRef
Pubmed
Google scholar
|
[41] |
Kambadur R, Sharma M, Smith T P, Bass J J. Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle. Genome Research, 1997, 7(9): 910–916
Pubmed
|
[42] |
McPherron A C, Lee S J. Double muscling in cattle due to mutations in the myostatin gene. Proceedings of the National Academy of Sciences of the United States of America, 1997, 94(23): 12457–12461
CrossRef
Pubmed
Google scholar
|
[43] |
Grobet L, Poncelet D, Royo L J, Brouwers B, Pirottin D, Michaux C, Ménissier F, Zanotti M, Dunner S, Georges M. Molecular definition of an allelic series of mutations disrupting the myostatin function and causing double-muscling in cattle. Mammalian Genome, 1998, 9(3): 210–213
CrossRef
Pubmed
Google scholar
|
[44] |
Clop A, Marcq F, Takeda H, Pirottin D, Tordoir X, Bibé B, Bouix J, Caiment F, Elsen J M, Eychenne F, Larzul C, Laville E, Meish F, Milenkovic D, Tobin J, Charlier C, Georges M. A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nature Genetics, 2006, 38(7): 813–818
CrossRef
Pubmed
Google scholar
|
[45] |
Mosher D S, Quignon P, Bustamante C D, Sutter N B, Mellersh C S, Parker H G, Ostrander E A. A mutation in the myostatin gene increases muscle mass and enhances racing performance in heterozygote dogs. PLOS Genetics, 2007, 3(5): e79
CrossRef
Pubmed
Google scholar
|
[46] |
Schuelke M, Wagner K R, Stolz L E, Hübner C, Riebel T, Kömen W, Braun T, Tobin J F, Lee S J. Myostatin mutation associated with gross muscle hypertrophy in a child. New England Journal of Medicine, 2004, 350(26): 2682–2688
CrossRef
Pubmed
Google scholar
|
[47] |
Li H, 2, Feng C, Wang N, Yan J, Ha F, Chen H, Fan B, Pan D. Construction of a myostatin gene-targeting vector and myostatin gene knockout of porcine fetal fibroblasts cells. Letters in Biotechnology, 2010, 21(5): 699–704
|
[48] |
Pan D, Zhang L, Zhou Y, Feng C, Long C, Liu X, Wan R, Zhang J, Lin A, Dong E, Wang S, Xu H, Chen H. Efficient production of omega-3 fatty acid desaturase (sFat-1)-transgenic pigs by somatic cell nuclear transfer. Life Sciences, 2010, 53(4): 517–523
CrossRef
Pubmed
Google scholar
|
[49] |
Lawton K A, Berger A, Mitchell M, Milgram K E, Evans A M, Guo L, Hanson R W, Kalhan S C, Ryals J A, Milburn M V. Analysis of the adult human plasma metabolome. Pharmacogenomics, 2008, 9(4): 383–397
CrossRef
Pubmed
Google scholar
|
[50] |
Evans A M, DeHaven C D, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Analytical Chemistry, 2009, 81(16): 6656–6667
CrossRef
Pubmed
Google scholar
|
[51] |
Welch B L. The generalisation of student’s problems when several different population variances are involved. Biometrika, 1947, 34(1–2): 28–35
CrossRef
Pubmed
Google scholar
|
[52] |
Breiman L. Random Forests. Machine Learning, 2001, 45(1): 5–32
CrossRef
Google scholar
|
[53] |
Goldstein B A, Hubbard A E, Cutler A, Barcellos L F. An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genetics, 2010, 11(1): 49
CrossRef
Pubmed
Google scholar
|
[54] |
Plumb R, Granger J, Stumpf C, Wilson I D, Evans J A, Lenz E M. Metabonomic analysis of mouse urine by liquid-chromatography-time of flight mass spectrometry (LC-TOFMS): detection of strain, diurnal and gender differences. Analyst, 2003, 128(7): 819–823
CrossRef
Pubmed
Google scholar
|
[55] |
Zhao B, Wall R J, Yang J. Transgenic expression of myostatin propeptide prevents diet-induced obesity and insulin resistance. Biochemical and Biophysical Research Communications, 2005, 337(1): 248–255
CrossRef
Pubmed
Google scholar
|
[56] |
Guo T, Jou W, Chanturiya T, Portas J, Gavrilova O, McPherron A C. Myostatin inhibition in muscle, but not adipose tissue, decreases fat mass and improves insulin sensitivity. PLoS ONE, 2009, 4(3): e4937
CrossRef
Pubmed
Google scholar
|
[57] |
McPherron A C. Metabolic Functions of Myostatin and Gdf11. Immunology, Endocrine & Metabolic Agents in Medicinal Chemistry, 2010, 10(4): 217–231
CrossRef
Pubmed
Google scholar
|
/
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