Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
권호 표지
DOI QR코드

DOI QR Code

Postmortem skeletal muscle metabolism of farm animals approached with metabolomics

  • Susumu Muroya (Animal Products Research Group, NARO Institute of Livestock and Grassland Science (NILGS))
  • Received : 2022.09.20
  • Accepted : 2022.11.07
  • Published : 2023.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

Skeletal muscle metabolism regulates homeostatic balance in animals. The metabolic impact persists even after farm animal skeletal muscle is converted to edible meat through postmortem rigor mortis and aging. Muscle metabolites resulting from animal growth and postmortem storage have a significant impact on meat quality, including flavor and color. Metabolomics studies of postmortem muscle aging have identified metabolisms that contain signatures inherent to muscle properties and the altered metabolites by physiological adaptation, with glycolysis as the pivotal metabolism in postmortem aging. Metabolomics has also played a role in mining relevant postmortem metabolisms and pathways, such as the citrate cycle and mitochondrial metabolism. This leads to a deeper understanding of the mechanisms underlying the generation of key compounds that are associated with meat quality. Genetic background, feeding strategy, and muscle type primarily determine skeletal muscle properties in live animals and affect post-mortem muscle metabolism. With comprehensive metabolite detection, metabolomics is also beneficial for exploring biomarker candidates that could be useful to monitor meat production and predict the quality traits. The present review focuses on advances in farm animal muscle metabolomics, especially postmortem muscle metabolism associated with genetic factors and muscle type.

Keywords

Acknowledgement

I greatly appreciate Dr. K. Ojima and Dr. M. Oe for their valuable suggestions and technical support; Dr. T. Gotoh and Dr. K. Matsukawa for their outstanding collaborative work; and Ms. C. Shindo, Ms. M. Ichimura, and Ms. Y. Eguchi (NILGS) for their technical assistance.

References

  1. Calkins CR, Hodgen JM. A fresh look at meat flavor. Meat Sci 2007;77:63-80. https://doi.org/10.1016/j.meatsci.2007.04.016
  2. Dayton WR, White ME. Cellular and molecular regulation of muscle growth and development in meat animals. J Anim Sci 2008;86:E217-25. https://doi.org/10.2527/jas.2007-0456
  3. Braun T, Gautel M. Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis. Nat Rev Mol Cell Biol 2011;12:349-61. https://doi.org/10.1038/nrm3118
  4. Mohammadabadi M, Bordbar F, Jensen J, Du M, Guo W. Key genes regulating skeletal muscle development and growth in farm animals. Animals (Basel) 2021;11:835. https://doi.org/10.3390/ani11030835
  5. Rudar M, Fiorotto ML, Davis TA. Regulation of muscle growth in early postnatal life in a swine model. Annu Rev Anim Biosci 2019;7:309-35. https://doi.org/10.1146/annurevanimal-020518-115130
  6. Muroya S, Ueda S, Komatsu T, Miyakawa T, Ertbjerg P. MEATabolomics: muscle and meat metabolomics in domestic animals. Metabolites 2020;10:188. https://doi.org/10.3390/metabo10050188
  7. Berri C, Picard B, Lebret B, et al. Predicting the quality of meat: myth or reality? Foods 2019;8:436. https://doi.org/10.3390/foods8100436
  8. Zampiga M, Flees J, Meluzzi A, Dridi S, Sirri F. Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: a review. J Anim Sci Biotechnol 2018;9:61. https://doi.org/10.1186/s40104018-0278-5
  9. Picard B, Lebret B, Cassar-Malek I, et al. Recent advances in omic technologies for meat quality management. Meat Sci 2015;109:18-26. https://doi.org/10.1016/j.meatsci.2015.05.003
  10. Rubert J, Zachariasova M, Hajslova J. Advances in highresolution mass spectrometry based on metabolomics studies for food--a review. Food Addit Contam: Part A 2015;32:1685708. https://doi.org/10.1080/19440049.2015.1084539
  11. Consonni R, Cagliani LR. The potentiality of NMR-based metabolomics in food science and food authentication assessment. Magn Reson Chem 2019;57:558-78. https://doi.org/10.1002/mrc.4807
  12. Garcia-Canas V, Simo C, Herrero M, Ibanez E, Cifuentes A. Present and future challenges in food analysis: foodomics. Anal Chem 2012;84:10150-9. https://doi.org/10.1021/ac301680q
  13. Junot C, Fenaille F, Colsch B, Becher F. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass Spectrom Rev 2014;33:471-500. https://doi.org/10.1002/mas.21401
  14. Ramautar R, Somsen GW, de Jong GJ. CE-MS for metabolomics: Developments and applications in the period 2016- 2018. Electrophoresis 2019;40:165-79. https://doi.org/10.1002/elps.201800323
  15. Stolz A, Jooss K, Hocker O, Romer J, Schlecht J, Neususs C. Recent advances in capillary electrophoresis-mass spectrometry: Instrumentation, methodology and applications. Electrophoresis 2019;40:79-112. https://doi.org/10.1002/elps.201800331
  16. Guitton Y, Dervilly-Pinel G, Jandova R, Stead S, Takats Z, Le Bizec B. Rapid evaporative ionisation mass spectrometry and chemometrics for high-throughput screening of growth promoters in meat producing animals. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2018;35:90010. https://doi.org/10.1080/19440049.2017.1421778
  17. Welzenbach J, Neuhoff C, Looft C, Schellander K, Tholen E, Grosse-Brinkhaus C. Different statistical approaches to investigate porcine muscle metabolome profiles to highlight new biomarkers for pork quality assessment. PLoS One 2016;11:e0149758. https://doi.org/10.1371/journal.pone.0149758
  18. Welzenbach J, Neuhoff C, Heidt H, et al. Integrative analysis of metabolomic, proteomic and genomic data to reveal functional pathways and candidate genes for drip loss in pigs. Int J Mol Sci 2016;17:1426. https://doi.org/10.3390/ijms17091426
  19. Gredell DA, Schroeder AR, Belk KE, et al. Comparison of machine learning algorithms for predictive modeling of beef attributes using rapid evaporative ionization mass spectrometry (REIMS) data. Sci Rep 2019;9:5721. https://doi.org/10.1038/s41598-019-40927-6
  20. Cameron SJS, Alexander JL, Bolt F, et al. Evaluation of direct from sample metabolomics of human feces using rapid evaporative ionization mass spectrometry. Anal Chem 2019;91:13448-57. https://doi.org/10.1021/acs.analchem.9b02358
  21. Balog J, Sasi-Szabo L, Kinross J, et al. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med 2013;5:194ra93. https://doi.org/10.1126/scitranslmed.3005623
  22. Sugimoto M, Kaneko M, Onuma H, et al. Changes in the charged metabolite and sugar profiles of pasteurized and unpasteurized Japanese sake with storage. J Agric Food Chem 2012;60:2586-93. https://doi.org/10.1021/jf2048993
  23. Ren S, Hinzman AA, Kang EL, Szczesniak RD, Lu LJ. Computational and statistical analysis of metabolomics data. Metabolomics 2015;11:1492-513. https://doi.org/10.1007/s11306-015-0823-6
  24. Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M. Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform 2012;7:96-108. https://doi.org/10.2174/157489312799304431
  25. Kim YHB, Ma D, Setyabrata D, et al. Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies. Meat Sci 2018;144: 74-90. https://doi.org/10.1016/j.meatsci.2018.04.031
  26. Khan MI, Jo C, Tariq MR. Meat flavor precursors and factors influencing flavor precursors--a systematic review. Meat Sci 2015;110:278-84. https://doi.org/10.1016/j.meatsci.2015.08.002
  27. Huff Lonergan E, Zhang W, Lonergan SM. Biochemistry of postmortem muscle - lessons on mechanisms of meat tenderization. Meat Sci 2010;86:184-95. https://doi.org/10.1016/j.meatsci.2010.05.004
  28. Sitz BM, Calkins CR, Feuz DM, Umberger WJ, Eskridge KM. Consumer sensory acceptance and value of domestic, Canadian, and Australian grass-fed beef steaks. J Anim Sci 2005;83:2863-8. https://doi.org/10.2527/2005.83122863x
  29. Gruber SL, Tatum JD, Scanga JA, Chapman PL, Smith GC, Belk KE. Effects of postmortem aging and USDA quality grade on Warner-Bratzler shear force values of seventeen individual beef muscles1. J Anim Sci 2006;84:3387-96. https://doi.org/10.2527/jas.2006-194
  30. Bratcher CL, Johnson DD, Littell RC, Gwartney BL. The effects of quality grade, aging, and location within muscle on Warner-Bratzler shear force in beef muscles of locomotion. Meat Sci 2005;70:279-84. https://doi.org/10.1016/j.meatsci.2005.01.013
  31. Thompson JM, Perry D, Daly B, Gardner GE, Johnston DJ, Pethick DW. Genetic and environmental effects on the muscle structure response post-mortem. Meat Sci 2006;74:59-65. https://doi.org/10.1016/j.meatsci.2006.04.022
  32. Busch WA, Jr FCP, Goll DE. Molecular properties of postmortem muscle. 4. Effect of temperature on adenosine triphosphate degradation, isometric tension parameters, and shear resistance of bovine muscle. J Food Sci 1967;32:390-4. https://doi.org/10.1111/j.1365-2621.1967.tb09692.x
  33. Muroya S, Oe M, Nakajima I, Ojima K, Chikuni K. CE-TOF MS-based metabolomic profiling revealed characteristic metabolic pathways in postmortem porcine fast and slow type muscles. Meat Sci 2014;98:726-35. https://doi.org/10.1016/j.meatsci.2014.07.018
  34. Meinert L, Tikk K, Tikk M, et al. Flavour development in pork. Influence of flavour precursor concentrations in longissimus dorsi from pigs with different raw meat qualities. Meat Sci 2009;81:255-62. https://doi.org/10.1016/j.meatsci.2008.07.031
  35. Lundberg P, Vogel HJ, Ruderus H. Carbon-13 and proton NMR studies of post-mortem metabolism in bovine muscles. Meat Sci 1986;18:133-60. https://doi.org/10.1016/0309-1740(86)90089-6
  36. Maga JA. Organoleptic properties of umami substances. In: Kawamura Y, Kare MR, editors. Umami : A Basic Taste. New York, USA: Marcel Dekker Inc.; 1987. p. 255-70.
  37. Terasaki M, Kajikawa M, Fujita E, Ishii K. Studies on the flavor of meats, Part I. Formation and degradation of inosinic acids in meats. Agric Biol Chem 1965;29:208-15. https://doi.org/10.1080/00021369.1965.10858377
  38. Watanabe A, Tsuneishi E, Takimoto Y. Analysis of ATP and its breakdown products in beef by reversed-phase HPLC. J Food Sci 1989;54:1169-72. https://doi.org/10.1111/j.13652621.1989.tb05948.x
  39. Yaylayan VA, Keyhani A, Wnorowski A. Formation of sugarspecific reactive intermediates from (13)C-labeled L-serines. J Agric Food Chem 2000;48:636-41. https://doi.org/10.1021/jf990687a
  40. Martins SIFS, Jongen WMF, van Boekel MAJS. A review of Maillard reaction in food and implications to kinetic modelling. Trends Food Sci Technol 2000;11:364-73. https://doi.org/10.1016/S0924-2244(01)00022-X
  41. Gratacos-Cubarsi M, Lametsch R. Determination of changes in protein conformation caused by pH and temperature. Meat Sci 2008;80:545-9. https://doi.org/10.1016/j.meatsci.2007.11.019
  42. Joo ST, Kauffman RG, Kim BC, Park GB. The relationship of sarcoplasmic and myofibrillar protein solubility to colour and water-holding capacity in porcine longissimus muscle. Meat Sci 1999;52:291-7. https://doi.org/10.1016/S0309-1740(99)00005-4
  43. Scheffler TL, Gerrard DE. Mechanisms controlling pork quality development: The biochemistry controlling postmortem energy metabolism. Meat Sci 2007;77:7-16. https://doi.org/10.1016/j.meatsci.2007.04.024
  44. Kristensen L, Purslow PP. The effect of ageing on the waterholding capacity of pork: role of cytoskeletal proteins. Meat Sci 2001;58:17-23. https://doi.org/10.1016/S0309-1740(00)00125-X
  45. Melody JL, Lonergan SM, Rowe LJ, Huiatt TW, Mayes MS, Huff-Lonergan E. Early postmortem biochemical factors influence tenderness and water-holding capacity of three porcine muscles. J Anim Sci 2004;82:1195-205. https://doi.org/10.2527/2004.8241195x
  46. Zhang WG, Lonergan SM, Gardner MA, Huff-Lonergan E. Contribution of postmortem changes of integrin, desmin and μ-calpain to variation in water holding capacity of pork. Meat Sci 2006;74:578-85. https://doi.org/10.1016/j.meatsci.2006.05.008
  47. Yu Q, Tian X, Shao L, Li X, Dai R. Targeted metabolomics to reveal muscle-specific energy metabolism between bovine longissimus lumborum and psoas major during early postmortem periods. Meat Sci 2019;156:166-73. https://doi.org/10.1016/j.meatsci.2019.05.029
  48. Muroya S, Oe M, Ojima K, Watanabe A. Metabolomic approach to key metabolites characterizing postmortem aged loin muscle of Japanese Black (Wagyu) cattle. Asian-Australas J Anim Sci 2019;32:1172-85. https://doi.org/10.5713/ajas.18.0648
  49. Muroya S, Nomura R, Nagai H, Ojima K, Matsukawa K. Metabolomic profiling of postmortem aged muscle in Japanese Brown beef cattle revealed an interbreed difference from Japanese Black beef. Anim Biosci 2033 Jul 26 [Epub]. https://doi.org/10.5713/ab.22.0202
  50. Kodani Y, Miyakawa T, Komatsu T, Tanokura M. NMRbased metabolomics for simultaneously evaluating multiple determinants of primary beef quality in Japanese Black cattle. Sci Rep 2017;7:1297. https://doi.org/10.1038/s41598-01701272-8
  51. Graham SF, Kennedy T, Chevallier O, et al. The application of NMR to study changes in polar metabolite concentrations in beef longissimus dorsi stored for different periods post mortem. Metabolomics 2010;6:395-404. https://doi.org/10.1007/s11306-010-0206-y
  52. England EM, Matarneh SK, Mitacek RM, et al. Presence of oxygen and mitochondria in skeletal muscle early postmortem. Meat Sci 2018;139:97-106. https://doi.org/10.1016/j.meatsci.2017.12.008
  53. Ma D, Suh DH, Zhang J, et al. Elucidating the involvement of apoptosis in postmortem proteolysis in porcine muscles from two production cycles using metabolomics approach. Sci Rep 2021;11:3465. https://doi.org/10.1038/s41598-02182929-3
  54. Yu Q, Tian X, Shao L, Li X, Dai R. Mitochondria changes and metabolome differences of bovine longissimus lumborum and psoas major during 24 h postmortem. Meat Sci 2020;166:108112. https://doi.org/10.1016/j.meatsci.2020.108112
  55. Wang LL, Han L, Ma XL, Yu QL, Zhao SN. Effect of mitochondrial apoptotic activation through the mitochondrial membrane permeability transition pore on yak meat tenderness during postmortem aging. Food Chem 2017;234:323-31. https://doi.org/10.1016/j.foodchem.2017.04.185
  56. Wang LL, Yu QL, Han L, et al. Study on the effect of reactive oxygen species-mediated oxidative stress on the activation of mitochondrial apoptosis and the tenderness of yak meat. Food Chem 2018;244:394-402. https://doi.org/10.1016/j.foodchem.2017.10.034
  57. Ma D, Kim YHB, Cooper B, et al. Metabolomics profiling to determine the effect of postmortem aging on color and lipid oxidative stabilities of different bovine muscles. J Agric Food Chem 2017;65:6708-16. https://doi.org/10.1021/acs.jafc.7b02175
  58. Cortassa S, Caceres V, Bell LN, O'Rourke B, Paolocci N, Aon MA. From metabolomics to fluxomics: a computational procedure to translate metabolite profiles into metabolic fluxes. Biophys J 2015;108:163-72. https://doi.org/10.1016/j.bpj.2014.11.1857
  59. Hazard D, Fernandez X, Pinguet J, et al. Functional genomics of the muscle response to restraint and transport in chickens. J Anim Sci 2011;89:2717-30. https://doi.org/10.2527/jas.20103288
  60. Mitacek RM, Ke Y, Prenni JE, et al. Mitochondrial degeneration, depletion of NADH, and oxidative stress decrease color stability of wet-aged beef longissimus steaks. J Food Sci 2019;84:38-50. https://doi.org/10.1111/1750-3841.14396
  61. Chandel NS. Navigating metabolism. Cold Spring Harbor, New York, USA: Cold Spring Harbor Laboratory Press; 2015.
  62. Sergeeva TF, Shirmanova MV, Zlobovskaya OA, et al. Relationship between intracellular pH, metabolic co-factors and caspase-3 activation in cancer cells during apoptosis. Biochim Biophys Acta Mol Cell Res 2017;1864:604-11. https://doi.org/10.1016/j.bbamcr.2016.12.022
  63. Ma J, Yu Q, Han L. The effect of postmortem pH decline rate on caspase-3 activation and tenderness of bovine skeletal muscle during aging. J Food Biochem 2022;46:e14215. https://doi.org/10.1111/jfbc.14215
  64. Kim YHB, Kemp R, Samuelsson LM. Effects of dry-aging on meat quality attributes and metabolite profiles of beef loins. Meat Sci 2016;111:168-76. https://doi.org/10.1016/j.meatsci.2015.09.008
  65. Endo A, Koizumi R, Nakazawa Y, et al. Characterization of the microbiota and chemical properties of pork loins during dry aging. MicrobiologyOpen 2021;10:e1157. https://doi.org/10.1002/mbo3.1157
  66. Setyabrata D, Wagner AD, Cooper BR, Kim YHB. Effect of dry-aging on quality and palatability attributes and flavorrelated metabolites of pork loins. Foods 2021;10:2503. https://doi.org/10.3390/foods10102503
  67. Zhang R, Ross AB, Yoo MJY, Farouk MM. Metabolic fingerprinting of in-bag dry- and wet-aged lamb with rapid evaporative ionisation mass spectroscopy. Food Chem 2021;347:128999. https://doi.org/10.1016/j.foodchem.2020.128999
  68. Li X, Babol J, Bredie WLP, Nielsen B, Tomankova J, Lundstrom K. A comparative study of beef quality after ageing longissimus muscle using a dry ageing bag, traditional dry ageing or vacuum package ageing. Meat Sci 2014;97:433-42. https://doi.org/10.1016/j.meatsci.2014.03.014
  69. Schiaffino S, Reggiani C. Fiber types in mammalian skeletal muscles. Physiol Rev 2011;91:1447-531. https://doi.org/10.1152/physrev.00031.2010
  70. Eggert JM, Depreux FFS, Schinckel AP, Grant AL, Gerrard DE. Myosin heavy chain isoforms account for variation in pork quality. Meat Sci 2002;61:117-26. https://doi.org/10.1016/S0309-1740(01)00154-1
  71. Lefaucheur L. A second look into fibre typing - Relation to meat quality. Meat Sci 2010;84:257-70. https://doi.org/10.1016/j.meatsci.2009.05.004
  72. Bourdeau Julien I, Sephton CF, Dutchak PA. Metabolic networks influencing skeletal muscle fiber composition. Front Cell Dev Biol 2018;6:125. https://doi.org/10.3389/fcell.2018.00125
  73. Muroya S, Oe M, Ojima K. Thiamine accumulation and thiamine triphosphate decline occur in parallel with ATP exhaustion during postmortem aging of pork muscles. Meat Sci 2018;137:228-34. https://doi.org/10.1016/j.meatsci.2017.11.035
  74. Stepuro AI, Adamchuk RI, Oparin AY, Stepuro II. Thiamine inhibits formation of dityrosine, a specific marker of oxidative injury, in reactions catalyzed by oxoferryl forms of hemoglobin. Biochemistry (Moscow) 2008;73:1031-41. https://doi.org/10.1134/S0006297908090113
  75. Mkrtchyan G, Aleshin V, Parkhomenko Y, et al. Molecular mechanisms of the non-coenzyme action of thiamin in brain: biochemical, structural and pathway analysis. Sci Rep 2015;5:12583. https://doi.org/10.1038/srep12583
  76. Terjung RL, Dudley GA, Meyer RA. Metabolic and circulatory limitations to muscular performance at the organ level. J Exp Biol 1985;115:307-18. https://doi.org/10.1242/jeb.115.1.307
  77. Warner RD, Greenwood PL, Pethick DW, Ferguson DM. Genetic and environmental effects on meat quality. Meat Sci 2010;86:171-83. https://doi.org/10.1016/j.meatsci.2010.04.042
  78. Lobo RR, Correia BSB, Pena-Bermudez YA, et al. Effects of dietary inclusion of yerba mate (Ilex paraguariensis) extract on lamb muscle metabolomics and physicochemical properties in meat. J Anim Sci 2021;99:skab244. https://doi.org/10.1093/jas/skab244
  79. de Zawadzki A, Arrivetti LOR, Vidal MP, et al. Mate extract as feed additive for improvement of beef quality. Food Res Int 2017;99:336-47. https://doi.org/10.1016/j.foodres.2017.05.033
  80. Yang Y, Li J, Jia X, et al. Characterization of the flavor precursors and flavor fingerprints in grazing lambs by foodomics. Foods 2022;11:191. https://doi.org/10.3390/foods11020191