FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Frontiers in veterinary science2023; 10; 1162953; doi: 10.3389/fvets.2023.1162953

Comparison of muscle metabolomics between two Chinese horse breeds.

Abstract: With their enormous muscle mass and athletic ability, horses are well-positioned as model organisms for understanding muscle metabolism. There are two different types of horse breeds-Guanzhong (GZ) horses, an athletic breed with a larger body height (~148.7 cm), and the Ningqiang pony (NQ) horses, a lower height breed generally used for ornamental purposes-both inhabited in the same region of China with obvious differences in muscle content. The main objective of this study was to evaluate the breed-specific mechanisms controlling muscle metabolism. In this study, we observed muscle glycogen, enzyme activities, and LC-MS/MS untargeted metabolomics in the gluteus medius muscle of six, each of GZ and NQ horses, to explore differentiated metabolites that are related to the development of two muscles. As expected, the glycogen content, citrate synthase, and hexokinase activity of muscle were significantly higher in GZ horses. To alleviate the false positive rate, we used both MS1 and MS2 ions for metabolite classification and differential analysis. As a result, a total of 51,535 MS1 and 541 MS2 metabolites were identified, and these metabolites can separate these two groups from each other. Notably, 40% of these metabolites were clustered into lipids and lipid-like molecules. Furthermore, 13 significant metabolites were differentially detected between GZ and NQ horses (fold change [FC] value ≥ 2, variable important in projection value ≥1, and value ≤ 0.05). They are primarily clustered into glutathione metabolism (GSH, = 0.01), taurine, and hypotaurine metabolism ( < 0.05) pathways. Seven of the 13 metabolites were also found in thoroughbred racing horses, suggesting that metabolites related to antioxidants, amino acids, and lipids played a key role in the development of skeleton muscle in horses. Those metabolites related to muscle development shed a light on racing horses' routine maintenance and improvement of athletic performance.
Copyright © 2023 Meng, Zhang, Lv, Zhang, Liu and Jiang.
Get Full Text
Publication Date: 2023-05-05 PubMed ID: 37215482PubMed Central: PMC10196265DOI: 10.3389/fvets.2023.1162953Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article provides insights on the differing muscle metabolism of two Chinese horse breeds, the Guanzhong (GZ), and Ningqiang pony (NQ). Spartanly speaking, the researchers analyzed muscle glycogen and enzyme activities, as well as untargeted metabolomics, achieving significant findings regarding differential metabolites and their role in muscle development.

Objective and Methodology

The primary aim of this study was to investigate the distinct mechanisms controlling muscle metabolism in the GZ and NQ horses. These two horse breeds – GZ being athletic with a larger body and NQ mostly used for ornamental purposes with a lower body height – provide an exciting comparative study, not least due to their similar environment.

The researchers used the gluteus medius muscle of six horses from each breed to explore differentiated metabolites related to muscle development. This examination included analyzing muscle glycogen content and the activity of enzymes such as citrate synthase and hexokinase.

Findings and Analysis

As anticipated, the muscle’s glycogen content and citrate synthase and hexokinase activity were remarkably higher in the athletic GZ horses compared to the ornamental NQ horses. Following that, the researchers embarked on metabolite classification and differential analysis through both MS1 and MS2 ions.

  • A total of 51,535 MS1 and 541 MS2 metabolites were identified that were able to segregate the two horse groups. Interestingly, approximately 40% of these metabolites were lipid and lipid-like molecules.
  • Between the GZ and NQ horses, 13 significant metabolites were differentially detected, many related to glutathione (GSH) metabolism, taurine, and hypotaurine metabolism pathways.
  • Out of these 13 metabolites, seven were also present in thoroughbred racing horses, drawing a link to metabolites associated with antioxidants, amino acids, and lipids important for the skeleton muscle development in horses.

Significance and Implications

This study demystifies the distinct muscle metabolism between GZ and NQ horses, shedding light on significant aspects of horse physiology and performance, especially for racing horses. The identified metabolites related to muscle development, such as those connected to antioxidants, amino acids, and lipids, turned out to be key players in the horses’ muscle development.

Findings from this research are invaluable for enhancing the routine maintenance and improving athletic performance of racing horses. Understanding the specific metabolites and their roles allows for more informed decisions about horse nutrition and training, leading to healthier and better-performing horses.

Cite This Article

APA
Meng S, Zhang Y, Lv S, Zhang Z, Liu X, Jiang L. (2023). Comparison of muscle metabolomics between two Chinese horse breeds. Front Vet Sci, 10, 1162953. https://doi.org/10.3389/fvets.2023.1162953

Publication

Frontiers in veterinary science
ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 10
Pages: 1162953
PII: 1162953

Researcher Affiliations

Meng, Sihan
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Zhang, Yanli
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Lv, Shipeng
  • College of Animal Science, Xinjiang Agricultural University, Urumqi, China.
Zhang, Zhengkai
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Liu, Xuexue
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
  • Centre d'Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, Toulouse, France.
Jiang, Lin
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

This article includes 74 references
  1. Klein DJ, Anthony TG, McKeever KH. Metabolomics in equine sport and exercise.. J Anim Physiol Anim Nutr (Berl) 2021 Jan;105(1):140-148.
    doi: 10.1111/jpn.13384pubmed: 32511844google scholar: lookup
  2. Goldansaz SA, Guo AC, Sajed T, Steele MA, Plastow GS, Wishart DS. Livestock metabolomics and the livestock metabolome: A systematic review.. PLoS One 2017;12(5):e0177675.
    doi: 10.1371/journal.pone.0177675pmc: PMC5439675pubmed: 28531195google scholar: lookup
  3. Liu X, Zhang Y, Li Y, Pan J, Wang D, Chen W, Zheng Z, He X, Zhao Q, Pu Y, Guan W, Han J, Orlando L, Ma Y, Jiang L. EPAS1 Gain-of-Function Mutation Contributes to High-Altitude Adaptation in Tibetan Horses.. Mol Biol Evol 2019 Nov 1;36(11):2591-2603.
    doi: 10.1093/molbev/msz158pmc: PMC6805228pubmed: 31273382google scholar: lookup
  4. Wouters CP, Toquet MP, Renaud B, François AC, Fortier-Guillaume J, Marcillaud-Pitel C, Boemer F, De Tullio P, Richard EA, Votion DM. Metabolomic Signatures Discriminate Horses with Clinical Signs of Atypical Myopathy from Healthy Co-grazing Horses.. J Proteome Res 2021 Oct 1;20(10):4681-4692.
    doi: 10.1021/acs.jproteome.1c00225pubmed: 34435779google scholar: lookup
  5. Steelman SM, Johnson P, Jackson A, Schulze J, Chowdhary BP. Serum metabolomics identifies citrulline as a predictor of adverse outcomes in an equine model of gut-derived sepsis.. Physiol Genomics 2014 May 15;46(10):339-47.
    doi: 10.1152/physiolgenomics.00180.2013pubmed: 24619519google scholar: lookup
  6. Luck MM, Le Moyec L, Barrey E, Triba MN, Bouchemal N, Savarin P, Robert C. Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics.. Front Physiol 2015;6:198.
    doi: 10.3389/fphys.2015.00198pmc: PMC4544308pubmed: 26347654google scholar: lookup
  7. Mach N, Ramayo-Caldas Y, Clark A, Moroldo M, Robert C, Barrey E, López JM, Le Moyec L. Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses.. BMC Genomics 2017 Feb 17;18(1):187.
    doi: 10.1186/s12864-017-3571-3pmc: PMC5316211pubmed: 28212624google scholar: lookup
  8. de Meeûs d'Argenteuil C, Boshuizen B, Vidal Moreno de Vega C, Leybaert L, de Maré L, Goethals K, De Spiegelaere W, Oosterlinck M, Delesalle C. Comparison of Shifts in Skeletal Muscle Plasticity Parameters in Horses in Three Different Muscles, in Answer to 8 Weeks of Harness Training.. Front Vet Sci 2021;8:718866.
    doi: 10.3389/fvets.2021.718866pmc: PMC8558477pubmed: 34733900google scholar: lookup
  9. Cloteau C, Dervilly G, Kaabia Z, Bagilet F, Delcourt V, Loup B, Guitton Y, Royer AL, Monteau F, Garcia P, Popot MA, Le Bizec B, Bailly-Chouriberry L. From a non-targeted metabolomics approach to a targeted biomarkers strategy to highlight testosterone abuse in equine. Illustration of a methodological transfer between platforms and laboratories.. Drug Test Anal 2022 May;14(5):864-878.
    doi: 10.1002/dta.3221pubmed: 35001538google scholar: lookup
  10. Keen B, Cawley A, Reedy B, Fu S. Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues.. Drug Test Anal 2022 May;14(5):794-807.
    doi: 10.1002/dta.3245pmc: PMC9544538pubmed: 35194967google scholar: lookup
  11. Stojiljkovic N, Leroux F, Bubanj S, Popot MA, Paris A, Tabet JC, Junot C. Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography-high-resolution mass spectrometry.. Eur J Mass Spectrom (Chichester) 2019 Jun;25(3):339-353.
    doi: 10.1177/1469066719839034pubmed: 31096786google scholar: lookup
  12. Klein DJ, McKeever KH, Mirek ET, Anthony TG. Metabolomic Response of Equine Skeletal Muscle to Acute Fatiguing Exercise and Training.. Front Physiol 2020;11:110.
    doi: 10.3389/fphys.2020.00110pmc: PMC7040365pubmed: 32132934google scholar: lookup
  13. Ohmura H, Mukai K, Takahashi Y, Takahashi T. Metabolomic analysis of skeletal muscle before and after strenuous exercise to fatigue.. Sci Rep 2021 May 27;11(1):11261.
    doi: 10.1038/s41598-021-90834-ypmc: PMC8160181pubmed: 34045613google scholar: lookup
  14. Middleton RP, Lacroix S, Scott-Boyer MP, Dordevic N, Kennedy AD, Slusky AR, Carayol J, Petzinger-Germain C, Beloshapka A, Kaput J. Metabolic Differences between Dogs of Different Body Sizes.. J Nutr Metab 2017;2017:4535710.
    doi: 10.1155/2017/4535710pmc: PMC5684564pubmed: 29225968google scholar: lookup
  15. Brookes PS, Jimenez AG. Metabolomics of aging in primary fibroblasts from small and large breed dogs.. Geroscience 2021 Aug;43(4):1683-1696.
    doi: 10.1007/s11357-021-00388-0pmc: PMC8492862pubmed: 34132979google scholar: lookup
  16. Shi K, Zhao Q, Shao M, Duan Y, Li D, Lu Y, Tang Y, Feng C. Untargeted Metabolomics Reveals the Effect of Selective Breeding on the Quality of Chicken Meat.. Metabolites 2022 Apr 19;12(5).
    doi: 10.3390/metabo12050367pmc: PMC9144515pubmed: 35629871google scholar: lookup
  17. Liao X, Shi X, Hu H, Han X, Jiang K, Liu Y, Xiong G. Comparative Metabolomics Analysis Reveals the Unique Nutritional Characteristics of Breed and Feed on Muscles in Chinese Taihe Black-Bone Silky Fowl.. Metabolites 2022 Sep 27;12(10).
    doi: 10.3390/metabo12100914pmc: PMC9611261pubmed: 36295816google scholar: lookup
  18. Ritota M, Casciani L, Failla S, Valentini M. HRMAS-NMR spectroscopy and multivariate analysis meat characterisation.. Meat Sci 2012 Dec;92(4):754-61.
    doi: 10.1016/j.meatsci.2012.06.034pubmed: 22819725google scholar: lookup
  19. Zhu C, Petracci M, Li C, Fiore E, Laghi L. An Untargeted Metabolomics Investigation of Jiulong Yak (Bos grunniens) Meat by (1)H-NMR.. Foods 2020 Apr 12;9(4).
    doi: 10.3390/foods9040481pmc: PMC7230376pubmed: 32290528google scholar: lookup
  20. Lisuzzo A, Bonelli F, Sgorbini M, Nocera I, Cento G, Mazzotta E, Turini L, Martini M, Salari F, Morgante M, Badon T, Fiore E. Differences of the Plasma Total Lipid Fraction from Pre-Foaling to Post-Foaling Period in Donkeys.. Animals (Basel) 2022 Jan 26;12(3).
    doi: 10.3390/ani12030304pmc: PMC8833319pubmed: 35158628google scholar: lookup
  21. Lisuzzo A, Laghi L, Fiore F, Harvatine K, Mazzotta E, Faillace V, Spissu N, Zhu C, Moscati L, Fiore E. Evaluation of the metabolomic profile through (1)H-NMR spectroscopy in ewes affected by postpartum hyperketonemia.. Sci Rep 2022 Oct 1;12(1):16463.
    doi: 10.1038/s41598-022-20371-9pmc: PMC9526738pubmed: 36183000google scholar: lookup
  22. Mukai K, Kitaoka Y, Takahashi Y, Takahashi T, Takahashi K, Ohmura H. Moderate-intensity training in hypoxia improves exercise performance and glycolytic capacity of skeletal muscle in horses.. Physiol Rep 2021 Dec;9(23):e15145.
    doi: 10.14814/phy2.15145pmc: PMC8661515pubmed: 34889527google scholar: lookup
  23. He Y, Nadeau J, Reed S, Hoagland T, Bushmich S, Aborn S, Jones AK, Martin D. The Effect of Season on Muscle Growth, Fat Deposition, Travel Patterns, and Hoof Growth of Domestic Young Horses.. J Equine Vet Sci 2020 Feb;85:102817.
    doi: 10.1016/j.jevs.2019.102817pubmed: 31952631google scholar: lookup
  24. Raspa F, Dinardo FR, Vervuert I, Bergero D, Bottero MT, Pattono D, Dalmasso A, Vinassa M, Valvassori E, Bruno E, De Palo P, Valle E. A Fibre- vs. cereal grain-based diet: Which is better for horse welfare? Effects on intestinal permeability, muscle characteristics and oxidative status in horses reared for meat production.. J Anim Physiol Anim Nutr (Berl) 2022 Mar;106(2):313-326.
    doi: 10.1111/jpn.13643pmc: PMC9292821pubmed: 34553422google scholar: lookup
  25. Liu X, Zhang Y, Liu W, Li Y, Pan J, Pu Y, Han J, Orlando L, Ma Y, Jiang L. A single-nucleotide mutation within the TBX3 enhancer increased body size in Chinese horses.. Curr Biol 2022 Jan 24;32(2):480-487.e6.
    doi: 10.1016/j.cub.2021.11.052pmc: PMC8796118pubmed: 34906355google scholar: lookup
  26. Zeng L, Chen N, Yao Y, Dang R, Chen H, Lei C. Analysis of Genetic Diversity and Structure of Guanzhong Horse Using Microsatellite Markers.. Anim Biotechnol 2019 Jan;30(1):95-98.
    doi: 10.1080/10495398.2017.1416392pubmed: 29463179google scholar: lookup
  27. Pu Y, Zhang Y, Zhang T, Han J, Ma Y, Liu X. Identification of Novel lncRNAs Differentially Expressed in Placentas of Chinese Ningqiang Pony and Yili Horse Breeds.. Animals (Basel) 2020 Jan 11;10(1).
    doi: 10.3390/ani10010119pmc: PMC7022612pubmed: 31940795google scholar: lookup
  28. Zhang T, Lu H, Cao L, Zeng Z. Genetic diversity of microsatellite on ningqiang pony. Hubei Agricultural Sciences (2008) 868–70.
    pubmed: 0
  29. National Livestock and Poultry Genetic Resources . (2011). Animal genetic resources in China-Horses Donkeys. Camels: China Agricultural Press.
  30. Kearns CF, McKeever KH, Abe T. Overview of horse body composition and muscle architecture: implications for performance.. Vet J 2002 Nov;164(3):224-34.
    doi: 10.1053/tvjl.2001.0702pubmed: 12505395google scholar: lookup
  31. Rivero JL, Hill EW. Skeletal muscle adaptations and muscle genomics of performance horses.. Vet J 2016 Mar;209:5-13.
    doi: 10.1016/j.tvjl.2015.11.019pubmed: 26831154google scholar: lookup
  32. Gika HG, Theodoridis GA, Plumb RS, Wilson ID. Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics.. J Pharm Biomed Anal 2014 Jan;87:12-25.
    doi: 10.1016/j.jpba.2013.06.032pubmed: 23916607google scholar: lookup
  33. Ribbenstedt A, Ziarrusta H, Benskin JP. Development, characterization and comparisons of targeted and non-targeted metabolomics methods.. PLoS One 2018;13(11):e0207082.
    doi: 10.1371/journal.pone.0207082pmc: PMC6237353pubmed: 30439966google scholar: lookup
  34. Otter D, Cao M, Lin HM, Fraser K, Edmunds S, Lane G, Rowan D. Identification of urinary biomarkers of colon inflammation in IL10-/- mice using Short-Column LCMS metabolomics.. J Biomed Biotechnol 2011;2011:974701.
    doi: 10.1155/2011/974701pmc: PMC3005964pubmed: 21188174google scholar: lookup
  35. Myers OD, Sumner SJ, Li S, Barnes S, Du X. Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.. Anal Chem 2017 Sep 5;89(17):8689-8695.
    doi: 10.1021/acs.analchem.7b01069pubmed: 28752757google scholar: lookup
  36. Wu IL, Turnipseed SB, Storey JM, Andersen WC, Madson MR. Comparison of data acquisition modes with Orbitrap high-resolution mass spectrometry for targeted and non-targeted residue screening in aquacultured eel.. Rapid Commun Mass Spectrom 2020 Apr 15;34(7):e8642.
    doi: 10.1002/rcm.8642pmc: PMC7722469pubmed: 31702084google scholar: lookup
  37. Thies C. Insight into traditional Chinese medicine obtained from an October 2004 visit to China organized by the People to People Ambassador Program.. Pharm Dev Technol 2005;10(4):447-50.
    doi: 10.1080/10837450500411892pubmed: 16370173google scholar: lookup
  38. Bergh A, Nordlöf H, Essén-Gustavsson B. Evaluation of neuromuscular electrical stimulation on fibre characteristics and oxidative capacity in equine skeletal muscles.. Equine Vet J Suppl 2010 Nov;(38):671-5.
    doi: 10.1111/j.2042-3306.2010.00180.xpubmed: 21059079google scholar: lookup
  39. Kitaoka Y, Mukai K, Aida H, Hiraga A, Masuda H, Takemasa T, Hatta H. Effects of high-intensity training on lipid metabolism in Thoroughbreds.. Am J Vet Res 2012 Nov;73(11):1813-8.
    doi: 10.2460/ajvr.73.11.1813pubmed: 23106469google scholar: lookup
  40. Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.. Anal Chem 2006 Feb 1;78(3):779-87.
    doi: 10.1021/ac051437ypubmed: 16448051google scholar: lookup
  41. Adusumilli R, Mallick P. Data Conversion with ProteoWizard msConvert.. Methods Mol Biol 2017;1550:339-368.
    doi: 10.1007/978-1-4939-6747-6_23pubmed: 28188540google scholar: lookup
  42. Kessner D, Chambers M, Burke R, Agus D, Mallick P. ProteoWizard: open source software for rapid proteomics tools development.. Bioinformatics 2008 Nov 1;24(21):2534-6.
    doi: 10.1093/bioinformatics/btn323pmc: PMC2732273pubmed: 18606607google scholar: lookup
  43. Kuhl C, Tautenhahn R, Böttcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.. Anal Chem 2012 Jan 3;84(1):283-9.
    doi: 10.1021/ac202450gpmc: PMC3658281pubmed: 22111785google scholar: lookup
  44. Wen B, Mei Z, Zeng C, Liu S. metaX: a flexible and comprehensive software for processing metabolomics data.. BMC Bioinformatics 2017 Mar 21;18(1):183.
    doi: 10.1186/s12859-017-1579-ypmc: PMC5361702pubmed: 28327092google scholar: lookup
  45. Shen B, Yi X, Sun Y, Bi X, Du J, Zhang C, Quan S, Zhang F, Sun R, Qian L, Ge W, Liu W, Liang S, Chen H, Zhang Y, Li J, Xu J, He Z, Chen B, Wang J, Yan H, Zheng Y, Wang D, Zhu J, Kong Z, Kang Z, Liang X, Ding X, Ruan G, Xiang N, Cai X, Gao H, Li L, Li S, Xiao Q, Lu T, Zhu Y, Liu H, Chen H, Guo T. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.. Cell 2020 Jul 9;182(1):59-72.e15.
    doi: 10.1016/j.cell.2020.05.032pmc: PMC7254001pubmed: 32492406google scholar: lookup
  46. Coletto E, Latousakis D, Pontifex MG, Crost EH, Vaux L, Perez Santamarina E, Goldson A, Brion A, Hajihosseini MK, Vauzour D, Savva GM, Juge N. The role of the mucin-glycan foraging Ruminococcus gnavus in the communication between the gut and the brain.. Gut Microbes 2022 Jan-Dec;14(1):2073784.
    doi: 10.1080/19490976.2022.2073784pmc: PMC9122312pubmed: 35579971google scholar: lookup
  47. Liang L, Rasmussen MH, Piening B, Shen X, Chen S, Röst H, Snyder JK, Tibshirani R, Skotte L, Lee NC, Contrepois K, Feenstra B, Zackriah H, Snyder M, Melbye M. Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women.. Cell 2020 Jun 25;181(7):1680-1692.e15.
    doi: 10.1016/j.cell.2020.05.002pmc: PMC7327522pubmed: 32589958google scholar: lookup
  48. Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, Greiner R, Wishart DS. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.. J Cheminform 2016;8:61.
    doi: 10.1186/s13321-016-0174-ypmc: PMC5096306pubmed: 27867422google scholar: lookup
  49. Aoki-Kinoshita KF, Kanehisa M. Gene annotation and pathway mapping in KEGG.. Methods Mol Biol 2007;396:71-91.
    doi: 10.1007/978-1-59745-515-2_6pubmed: 18025687google scholar: lookup
  50. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K, Tanaka S, Aoshima K, Oda Y, Kakazu Y, Kusano M, Tohge T, Matsuda F, Sawada Y, Hirai MY, Nakanishi H, Ikeda K, Akimoto N, Maoka T, Takahashi H, Ara T, Sakurai N, Suzuki H, Shibata D, Neumann S, Iida T, Tanaka K, Funatsu K, Matsuura F, Soga T, Taguchi R, Saito K, Nishioka T. MassBank: a public repository for sharing mass spectral data for life sciences.. J Mass Spectrom 2010 Jul;45(7):703-14.
    doi: 10.1002/jms.1777pubmed: 20623627google scholar: lookup
  51. Kind T, Liu KH, Lee DY, DeFelice B, Meissen JK, Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification.. Nat Methods 2013 Aug;10(8):755-8.
    doi: 10.1038/nmeth.2551pmc: PMC3731409pubmed: 23817071google scholar: lookup
  52. Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques PÉ, Li S, Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.. Nucleic Acids Res 2021 Jul 2;49(W1):W388-W396.
    doi: 10.1093/nar/gkab382pmc: PMC8265181pubmed: 34019663google scholar: lookup
  53. Zsoldos RR, Voegele A, Krueger B, Schroeder U, Weber A, Licka TF. Long term consistency and location specificity of equine gluteus medius muscle activity during locomotion on the treadmill.. BMC Vet Res 2018 Apr 6;14(1):126.
    doi: 10.1186/s12917-018-1443-ypmc: PMC5889605pubmed: 29625573google scholar: lookup
  54. Vigh-Larsen JF, Ørtenblad N, Spriet LL, Overgaard K, Mohr M. Muscle Glycogen Metabolism and High-Intensity Exercise Performance: A Narrative Review.. Sports Med 2021 Sep;51(9):1855-1874.
    doi: 10.1007/s40279-021-01475-0pubmed: 33900579google scholar: lookup
  55. Valberg SJ, Macleay JM, Billstrom JA, Hower-Moritz MA, Mickelson JR. Skeletal muscle metabolic response to exercise in horses with 'tying-up' due to polysaccharide storage myopathy.. Equine Vet J 1999 Jan;31(1):43-7.
    doi: 10.1111/j.2042-3306.1999.tb03789.xpubmed: 9952328google scholar: lookup
  56. Vaz FM, Wanders RJ. Carnitine biosynthesis in mammals.. Biochem J 2002 Feb 1;361(Pt 3):417-29.
    doi: 10.1042/bj3610417pmc: PMC1222323pubmed: 11802770google scholar: lookup
  57. Brevetti G, Fanin M, De Amicis V, Carrozzo R, Di Lello F, Martone VD, Angelini C. Changes in skeletal muscle histology and metabolism in patients undergoing exercise deconditioning: effect of propionyl-L-carnitine.. Muscle Nerve 1997 Sep;20(9):1115-20.
    pubmed: 9270666doi: 10.1002/(sici)1097-4598(199709)20:9<1115::aid-mus4>3.0.co;2-bgoogle scholar: lookup
  58. Ma Y, Maruta H, Sun B, Wang C, Isono C, Yamashita H. Effects of long-term taurine supplementation on age-related changes in skeletal muscle function of Sprague-Dawley rats.. Amino Acids 2021 Feb;53(2):159-170.
    doi: 10.1007/s00726-020-02934-0pubmed: 33398526google scholar: lookup
  59. Seidel U, H P, Rimbach G. Taurine: A Regulator of Cellular Redox Homeostasis and Skeletal Muscle Function.. Mol Nutr Food Res 2019 Aug;63(16):e1800569.
    doi: 10.1002/mnfr.201800569pubmed: 30211983google scholar: lookup
  60. Vargiu R, Licheri D, Carcassi AM, Naimi S, Collu M, Littarru GP, Mancinelli R. Enhancement of muscular performance by a coformulation of propionyl-L-carnitine, coenzyme Q10, nicotinamide, riboflavin and pantothenic acid in the rat.. Physiol Behav 2002 Jun 1;76(2):257-63.
    doi: 10.1016/S0031-9384(02)00717-5pubmed: 12044598google scholar: lookup
  61. Carbonin PU, Ramacci MT, Pahor M, Di Gennaro M, Gambassi G Jr, Lo Giudice P, Sgadari A, Pacifici L. Antiarrhythmic effect of L-propionylcarnitine in isolated cardiac preparations.. Cardioscience 1991 Jun;2(2):109-14.
    pubmed: 1878484
  62. Fischer M, Keller J, Hirche F, Kluge H, Ringseis R, Eder K. Activities of gamma-butyrobetaine dioxygenase and concentrations of carnitine in tissues of pigs.. Comp Biochem Physiol A Mol Integr Physiol 2009 Jul;153(3):324-31.
    doi: 10.1016/j.cbpa.2009.03.005pubmed: 19285565google scholar: lookup
  63. Hoppel C. The role of carnitine in normal and altered fatty acid metabolism.. Am J Kidney Dis 2003 Apr;41(4 Suppl 4):S4-12.
    doi: 10.1016/S0272-6386(03)00112-4pubmed: 12751049google scholar: lookup
  64. Siliprandi N, Di Lisa F, Menabó R, Ciman M, Sartorelli L. Transport and functions of carnitine in muscles.. J Clin Chem Clin Biochem 1990 May;28(5):303-6.
    pubmed: 2199594
  65. Ferrari R, Merli E, Cicchitelli G, Mele D, Fucili A, Ceconi C. Therapeutic effects of L-carnitine and propionyl-L-carnitine on cardiovascular diseases: a review.. Ann N Y Acad Sci 2004 Nov;1033:79-91.
    doi: 10.1196/annals.1320.007pubmed: 15591005google scholar: lookup
  66. Brevetti G, Perna S, Sabbá C, Martone VD, Condorelli M. Propionyl-L-carnitine in intermittent claudication: double-blind, placebo-controlled, dose titration, multicenter study.. J Am Coll Cardiol 1995 Nov 15;26(6):1411-6.
    doi: 10.1016/0735-1097(95)00344-4pubmed: 7594063google scholar: lookup
  67. Tomin T, Schittmayer M, Birner-Gruenberger R. Addressing Glutathione Redox Status in Clinical Samples by Two-Step Alkylation with N-ethylmaleimide Isotopologues.. Metabolites 2020 Feb 16;10(2).
    doi: 10.3390/metabo10020071pmc: PMC7074022pubmed: 32079090google scholar: lookup
  68. Wen C, Li F, Zhang L, Duan Y, Guo Q, Wang W, He S, Li J, Yin Y. Taurine is Involved in Energy Metabolism in Muscles, Adipose Tissue, and the Liver.. Mol Nutr Food Res 2019 Jan;63(2):e1800536.
    doi: 10.1002/mnfr.201800536pubmed: 30251429google scholar: lookup
  69. Veeravalli S, Phillips IR, Freire RT, Varshavi D, Everett JR, Shephard EA. Flavin-Containing Monooxygenase 1 Catalyzes the Production of Taurine from Hypotaurine.. Drug Metab Dispos 2020 May;48(5):378-385.
    doi: 10.1124/dmd.119.089995pubmed: 32156684google scholar: lookup
  70. Warskulat U, Flögel U, Jacoby C, Hartwig HG, Thewissen M, Merx MW, Molojavyi A, Heller-Stilb B, Schrader J, Häussinger D. Taurine transporter knockout depletes muscle taurine levels and results in severe skeletal muscle impairment but leaves cardiac function uncompromised.. FASEB J 2004 Mar;18(3):577-9.
    doi: 10.1096/fj.03-0496fjepubmed: 14734644google scholar: lookup
  71. Hamilton EJ, Berg HM, Easton CJ, Bakker AJ. The effect of taurine depletion on the contractile properties and fatigue in fast-twitch skeletal muscle of the mouse.. Amino Acids 2006 Oct;31(3):273-8.
    doi: 10.1007/s00726-006-0291-4pubmed: 16583307google scholar: lookup
  72. Ito T, Oishi S, Takai M, Kimura Y, Uozumi Y, Fujio Y, Schaffer SW, Azuma J. Cardiac and skeletal muscle abnormality in taurine transporter-knockout mice.. J Biomed Sci 2010 Aug 24;17 Suppl 1(Suppl 1):S20.
    doi: 10.1186/1423-0127-17-S1-S20pmc: PMC2994367pubmed: 20804595google scholar: lookup
  73. Ito T, Yoshikawa N, Inui T, Miyazaki N, Schaffer SW, Azuma J. Tissue depletion of taurine accelerates skeletal muscle senescence and leads to early death in mice.. PLoS One 2014;9(9):e107409.
    doi: 10.1371/journal.pone.0107409pmc: PMC4167997pubmed: 25229346google scholar: lookup
  74. Barbiera A, Sorrentino S, Lepore E, Carfì A, Sica G, Dobrowolny G, Scicchitano BM. Taurine Attenuates Catabolic Processes Related to the Onset of Sarcopenia.. Int J Mol Sci 2020 Nov 23;21(22).
    doi: 10.3390/ijms21228865pmc: PMC7700215pubmed: 33238549google scholar: lookup

Citations

This article has been cited 4 times.
  1. Wang T, Meng J, Peng X, Huang J, Huang Y, Yuan X, Li X, Yang X, Chang X, Zeng Y, Yao X. Metabolomics analysis and mRNA/miRNA profiling reveal potential cardiac regulatory mechanisms in Yili racehorses under different training regimens. PLoS One 2025;20(7):e0322468.
    doi: 10.1371/journal.pone.0322468pubmed: 40658689google scholar: lookup
  2. Shah SAUR, Tang B, He D, Ahmad M, Nabi G, Wang C, Kou Z, Wang K, Hao Y. Seasonal Breeding Alters Fecal Microbiota and Metabolome in the Male Captive Yangtze Finless Porpoise (Neophocaena asiaeorientalis asiaeorientalis). Ecol Evol 2025 Jun;15(6):e71611.
    doi: 10.1002/ece3.71611pubmed: 40552099google scholar: lookup
  3. Wang J, Feng Y, Xu S, Tenzin N, Han H, Gong D, Liu F, Sun Y, Liu S. Non-targeted LC-MS metabolomics reveals serum metabolites for high-altitude adaptation in Tibetan donkeys. Sci Rep 2025 Jan 2;15(1):46.
    doi: 10.1038/s41598-024-83544-8pubmed: 39747337google scholar: lookup
  4. Vidal Moreno de Vega C, de Meeûs d'Argenteuil C, Boshuizen B, De Mare L, Gansemans Y, Van Nieuwerburgh F, Deforce D, Goethals K, De Spiegelaere W, Leybaert L, Verdegaal EJMM, Delesalle C. Baselining physiological parameters in three muscles across three equine breeds. What can we learn from the horse?. Front Physiol 2024;15:1291151.
    doi: 10.3389/fphys.2024.1291151pubmed: 38384798google scholar: lookup
Subscribe to our newsletter
Join 99,780 horse owners like you who receive our email updates on horse health and nutrition.