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Equine veterinary journal2025; doi: 10.1002/evj.70146

Exercise-specific plasma proteomic signatures in racehorses: Candidates for training adaptation and peak load monitoring.

Abstract: Racehorses undergo profound physiological changes with training and competition, but current biomarkers inadequately capture the complex molecular dynamics of exercise. This study aimed to identify novel plasma biomarkers of training adaptation and peak load using high-throughput proteomics. Objective: We hypothesised that systematic training and racing induce distinct plasma proteomic signatures, enabling the discovery of candidate biomarkers linked to training status, oxidative stress, inflammation and metabolic remodelling. Methods: In vivo longitudinal study. Methods: Forty-nine Arabian and Thoroughbred racehorses underwent standardised high-intensity training. Plasma samples were collected at rest, immediately post-exercise and after recovery during three phases: initial training (T1), mid-season conditioning (T2) and race-phase (R). In total, 314 samples were analysed using tandem mass tags based quantitative proteomics and Orbitrap mass spectrometry. Protein abundance changes were assessed with multiple-testing correction (q < 0.05), and pathway enrichment was performed using STRING and ShinyGO. Results: Proteomic responses differed by phase. T1 showed broad activation of inflammatory (S100A8/A9), antioxidant (superoxide dismutase 1, catalase) and metabolic proteins (glucose-6-phosphate dehydrogenase, phosphoglycerate kinase 1). T2 displayed a more refined profile with remodelling and redox regulators (decorin, thymosin β4, glutathione S-transferase). Racing elicited the strongest response, with over 100 up-regulated proteins linked to energy metabolism, oxidative defense and cytoskeletal adaptation. Several proteins: including S100A8, thymosin β4, prothymosin-α, cofilin-1 and lipocalins, were consistently modulated across phases, highlighting their biomarker potential. Conclusions: Breed imbalance and incomplete follow-up sampling may affect generalisability. Validation in larger, diverse cohorts with targeted assays is required. Conclusions: This study identifies a panel of promising plasma proteins as candidate biomarkers of exercise adaptation and overload in racehorses. These findings may support improved monitoring of performance, training load and early detection of overtraining in equine athletes.
Publication Date: 2025-12-29 PubMed ID: 41461583DOI: 10.1002/evj.70146Google Scholar: Lookup
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  • Journal Article

Summary

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Overview

  • This study investigated plasma proteins that change in response to training and racing in racehorses, aiming to discover new biomarkers that reflect training adaptation and peak physical load.
  • Using high-throughput proteomics across different training phases, the researchers identified specific protein signatures linked to inflammation, oxidative stress, metabolism, and muscle remodeling that could help monitor equine athletic performance and overtraining.

Study Objective and Hypothesis

  • The researchers sought to overcome the limitations of current biomarkers in capturing the complex molecular changes in horses during training and competition.
  • They hypothesized that systematic training and racing induce distinct plasma proteomic signatures, which can be identified as candidate biomarkers associated with:
    • Training status
    • Oxidative stress
    • Inflammation
    • Metabolic remodeling

Study Design and Methods

  • The study was an in vivo longitudinal design involving 49 Arabian and Thoroughbred racehorses undergoing standardized high-intensity training.
  • Plasma samples were collected at three key time points during three different training phases:
    • Rest, immediately post-exercise, and after recovery
    • Phases: initial training (T1), mid-season conditioning (T2), and race-phase (R)
  • In total, 314 plasma samples were analyzed.
  • Proteomic analysis was performed using:
    • Tandem Mass Tags (TMT)-based quantitative proteomics
    • Orbitrap mass spectrometry
  • Statistical analysis included multiple-testing correction (q < 0.05) to identify significant protein abundance changes.
  • Pathway enrichment analysis was done using STRING and ShinyGO tools to interpret functional changes at the molecular level.

Key Findings and Proteomic Signatures

  • Proteomic responses varied significantly across the different training phases:
  • Initial Training (T1):
    • Broad activation of inflammatory proteins such as S100A8 and S100A9.
    • Upregulation of antioxidant enzymes like superoxide dismutase 1 and catalase.
    • Increase in metabolic proteins involved in glucose metabolism such as glucose-6-phosphate dehydrogenase and phosphoglycerate kinase 1.
  • Mid-Season Conditioning (T2):
    • More refined protein profile indicating tissue remodeling and redox regulation.
    • Increased levels of decorin and thymosin β4 linked to extracellular matrix modulation and cytoskeletal organization.
    • Elevated glutathione S-transferase, an enzyme involved in oxidative stress defense.
  • Race Phase (R):
    • Strongest proteomic response with over 100 proteins upregulated.
    • Proteins related to energy metabolism, oxidative defense, and cytoskeletal adaptation showed marked increases.
  • Certain proteins like S100A8, thymosin β4, prothymosin-α, cofilin-1, and various lipocalins were consistently modulated across all phases, suggesting strong potential as biomarkers.

Interpretation and Biological Insights

  • The findings highlight complex molecular adaptations involving inflammation modulation, enhanced antioxidant defenses, alterations in energy metabolism, and structural reorganization of muscles during the course of training and racing.
  • Proteins identified reflect physiological processes critical to adapting to sustained high-intensity exercise and peak physical load in racehorses.
  • Proteins like S100A8/A9 indicate inflammatory responses, while enzymes such as superoxide dismutase and catalase point towards increased oxidative stress handling.
  • Changes in cytoskeletal proteins and extracellular matrix components reveal ongoing tissue remodeling essential for performance improvements and injury prevention.

Limitations and Future Directions

  • The study acknowledges potential limitations including:
    • Breed imbalance between Arabian and Thoroughbred horses, which may influence protein expression profiles.
    • Incomplete follow-up sampling could affect the comprehensiveness of the longitudinal data.
  • To improve generalizability, validation studies in larger and more diverse horse cohorts are needed.
  • Future research should focus on targeted assays for the candidate proteins to confirm their utility as reliable biomarkers.

Conclusions and Practical Applications

  • This study presents a novel panel of plasma proteins as promising candidate biomarkers for monitoring exercise adaptation and peak load in racehorses.
  • These biomarkers could have practical applications in:
    • Tracking training progress and physiological adaptations in equine athletes.
    • Optimizing training loads to maximize performance while minimizing risk of overtraining or injury.
    • Early detection of overtraining syndrome through biomolecular signatures.
  • Overall, the integrative proteomic approach deepens understanding of the molecular underpinnings of equine performance and provides a foundation for precision management of racehorse training and health.

Cite This Article

APA
Grzędzicka J, Świderska B, Sitkiewicz E, Dąbrowska I, Witkowska-Piłaszewicz O. (2025). Exercise-specific plasma proteomic signatures in racehorses: Candidates for training adaptation and peak load monitoring. Equine Vet J. https://doi.org/10.1002/evj.70146

Publication

ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English

Researcher Affiliations

Grzędzicka, Jowita
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.
Świderska, Bianka
  • Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.
Sitkiewicz, Ewa
  • Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.
Dąbrowska, Izabela
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.
Witkowska-Piłaszewicz, Olga
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.

Grant Funding

  • 2021/41/B/NZ7/03548 / Narodowe Centrum Nauki
  • Science Development Fund of the Warsaw University of Life Sciences-SGGW

References

This article includes 58 references
  1. Leisson K, Jaakma Ü, Seen T. Adaptation of equine locomotor muscle fiber types to endurance and intensive high speed training.. J Equine Vet Sci 2008;28:395–401.
  2. Rivero J, Hill E. Skeletal muscle adaptations and muscle genomics of performance horses.. Vet J 2016;209:5–13.
  3. Wang T, Meng J. Differential metabolomics and cardiac function in trained vs. untrained Yili performance horses.. Animals 2025;15(16):2444.
    doi: 10.3390/ani15162444google scholar: lookup
  4. Bryan K, McGivney BA, Farries G, McGettigan PA, McGivney CL, Gough KF. Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components.. BMC Genomics 2017;18:595.
    doi: 10.1186/s12864-017-4007-9google scholar: lookup
  5. Witkowska‐Piłaszewicz O, Malin K, Dąbrowska I, Grzędzicka J, Ostaszewski P, Carter C. Immunology of physical exercise: is Equus caballus an appropriate animal model for human athletes?. Int J Mol Sci 2024;25(10):5210.
    doi: 10.3390/ijms25105210google scholar: lookup
  6. Lee EC, Fragala MS, Kavouras SA, Queen RM, Pryor JL, Casa DJ. Biomarkers in sports and exercise: tracking health, performance, and recovery in athletes.. J Strength Cond Res 2017;31(10):2920–2937.
  7. Ichibangase T, Imai K. Application of fluorogenic derivatization‐liquid chromatography‐tandem mass spectrometric proteome method to skeletal muscle proteins in fast Thoroughbred horses.. J Proteome Res 2009;8(4):2129–2134.
    doi: 10.1021/pr801004sgoogle scholar: lookup
  8. Bouwman FG, van Ginneken MME, Noben JP, Royackers E, de Graaf‐Roelfsema E, Wijnberg ID. Differential expression of equine muscle biopsy proteins during normal training and intensified training in young standardbred horses using proteomics technology.. Comp Biochem Physiol D 2010;5(1):55–64.
    doi: 10.1016/j.cbd.2009.11.001google scholar: lookup
  9. Scoppetta F, Tartaglia M, Renzone G, Avellini L, Gaiti A, Scaloni A. Plasma protein changes in horse after prolonged physical exercise: a proteomic study.. J Proteomics 2012;75(14):4494–4506.
  10. Gotić J, Špelić L, Kuleš J, Horvatić A, Gelemanović A, Ljubić BB. Proteomic analysis emphasizes the adaptation of energy metabolism in horses during endurance races.. BMC Vet Res 2025;21:67.
  11. Johansson L, Ringmark S, Bergquist J, Skiöldebrand E, Widgren A, Jansson A. A proteomics perspective on 2 years of high‐intensity training in horses: a pilot study.. Sci Rep 2024;14(1):23684.
  12. Amiri Roudbar M, Rosengren MK, Mousavi SF, Fegraeus K, Naboulsi R, Meadows JRS. Effect of an endothelial regulatory module on plasma proteomics in exercising horses.. Comp Biochem Physiol D Genomics Proteomics 2024;52:101265.
    doi: 10.1016/j.cbd.2024.101265google scholar: lookup
  13. Sullivan GM, Feinn R. Using effect size—or why the p value is not enough.. J Grad Med Educ 2012;4(3):279–282.
    doi: 10.4300/jgme-d-12-00156.1google scholar: lookup
  14. Hughes CS, Moggridge S, Müller T, Sorensen PH, Morin GB, Krijgsveld J. Single‐pot, solid‐phase‐enhanced sample preparation for proteomics experiments.. Nat Protoc 2019;14(1):68–85.
    doi: 10.1038/s41596-018-0082-xgoogle scholar: lookup
  15. Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometry‐based shotgun proteomics.. Nat Protoc 2016;11(12):2301–2319.
    doi: 10.1038/nprot.2016.136google scholar: lookup
  16. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T. The Perseus computational platform for comprehensive analysis of (prote)omics data.. Nat Methods 2016;13(9):731–740.
    doi: 10.1038/nmeth.3901google scholar: lookup
  17. FDA‐NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) resource [Internet].. 2016 [cited 2025 Nov 3].
  18. Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility.. Nat Biotechnol 2006;24(8):971–983.
  19. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes.. Nucleic Acids Res 2000;28(1):27–30.
    doi: 10.1093/nar/28.1.27google scholar: lookup
  20. Ryckman C, Vandal K, Rouleau P, Talbot M, Tessier PA. Proinflammatory activities of S100 proteins: S100A8, S100A9, and S100A8/A9 induce neutrophil chemotaxis and adhesion.. J Immunol 2003;170(6):3233–3242.
  21. Lajqi T, Köstlin‐Gille N, Bauer R, Zarogiannis SG, Lajqi E, Ajeti V. Training vs. tolerance: the Yin/Yang of the innate immune system.. Biomedicines 2023;11(3):766.
  22. Tozaki T, Kikuchi M, Kakoi H, Hirota KI, Mukai K, Aida H. Profiling of exercise‐induced transcripts in the peripheral blood cells of Thoroughbred horses.. J Equine Sci 2016;27(4):157–164.
    doi: 10.1294/jes.27.157google scholar: lookup
  23. Mihelić K, Vrbanac Z, Bojanić K, Kostanjšak T, Ljubić BB, Gotić J. Changes in acute phase response biomarkers in racing endurance horses.. Animals 2022;12(21):2993.
    doi: 10.3390/ani12212993google scholar: lookup
  24. Minamijima Y, Niwa H, Uchida E, Yamamoto K. Comparison of the proteomes in sera between healthy Thoroughbreds and Thoroughbreds with respiratory disease associated with transport using mass spectrometry‐based proteomics.. J Equine Sci 2021;32(1):11–15.
    doi: 10.1294/jes.32.11google scholar: lookup
  25. Bishop RC, Arrington JV, Wilkins PA, McCoy AM. Alterations in the peritoneal fluid proteome of horses with colic attributed to ischemic and non‐ischemic intestinal disease.. Animals 2025;15(11):1604.
    doi: 10.3390/ani15111604google scholar: lookup
  26. Cerón JJ, Ortín‐Bustillo A, López‐Martínez MJ, Martínez‐Subiela S, Eckersall PD, Tecles F. S‐100 proteins: basics and applications as biomarkers in animals with special focus on calgranulins (S100A8, A9, and A12).. Biology 2023;12(6):881.
    doi: 10.3390/biology12060881google scholar: lookup
  27. Powers SK, Duarte J, Kavazis AN, Talbert EE. Reactive oxygen species are signalling molecules for skeletal muscle adaptation.. Exp Physiol 2010;95(1):1–9.
  28. Margaritelis NV, Paschalis V, Theodorou AA, Kyparos A, Nikolaidis MG. Redox basis of exercise physiology.. Redox Biol 2020;35:101499.
  29. Koju N, Qin ZH, Sheng R. Reduced nicotinamide adenine dinucleotide phosphate in redox balance and diseases: a friend or foe?. Acta Pharmacol Sin 2022;43(8):1889–1904.
  30. Wang J, Ren W, Li Z, Li L, Wang R, Ma S. Plasma lipidomics and proteomics analyses pre‐ and post‐5000 m race in Yili horses.. Animals 2025;15(7):994.
    doi: 10.3390/ani15070994google scholar: lookup
  31. Tokura Y, Nakayama Y, Fukada S, Nara N, Yamamoto H, Matsuda R. Muscle injury‐induced thymosin β4 acts as a chemoattractant for myoblasts.. J Biochem 2011;149(1):43–48.
    doi: 10.1093/jb/mvq115google scholar: lookup
  32. Bock‐Marquette I, Maar K, Maar S, Lippai B, Faskerti G, Gallyas F Jr. Thymosin beta‐4 denotes new directions towards developing prosperous anti‐aging regenerative therapies.. Int Immunopharmacol 2023;116:109741.
  33. Xing Y, Ye Y, Zuo H, Li Y. Progress on the function and application of thymosin β4.. Front Endocrinol 2021;12:767785.
    doi: 10.3389/fendo.2021.767785google scholar: lookup
  34. Robinson KA, Sun M, Barnum CE, Weiss SN, Huegel J, Shetye SS. Decorin and biglycan are necessary for maintaining collagen fibril structure, fiber realignment, and mechanical properties of mature tendons.. Matrix Biol 2017;64:81–93.
  35. Siqueira RF, Weigel RA, Nunes GR, Mori CS, Fernandes WR. Oxidative profiles of endurance horses racing different distances.. Arq Bras Med Vet Zootec 2014;66(2):455–461.
    doi: 10.1590/1678-41625760google scholar: lookup
  36. Wagner EL, Potter GD, Gibbs PG, Eller EM, Scott BD, Vogelsang MM. Copper, zinc‐superoxide dismutase activity in exercising horses fed two forms of trace mineral supplements.. J Equine Vet Sci 2010;30(1):31–37.
    doi: 10.2527/jas.2010-2871google scholar: lookup
  37. Allen J, Sun Y, Woods JA. Exercise and the regulation of inflammatory responses.. Prog Mol Biol Transl Sci 2015;135:337–354.
  38. Sugimoto MA, Vago JP, Teixeira MM, Sousa LP. Annexin A1 and the resolution of inflammation: modulation of neutrophil recruitment, apoptosis, and clearance.. J Immunol Res 2016;2016:8239258.
    doi: 10.1155/2016/8239258google scholar: lookup
  39. Wang S, Song R, Wang Z, Jing Z, Wang S, Ma J. S100A8/A9 in inflammation.. Front Immunol 2018;9:1298.
    doi: 10.3389/fimmu.2018.01298google scholar: lookup
  40. Zhou Y, Zhang X, Baker JS, Davison GW, Yan X. Redox signaling and skeletal muscle adaptation during aerobic exercise.. iScience 2024;27(5):109643.
  41. Iozzo RV, Schaefer L. Proteoglycan form and function: a comprehensive nomenclature of proteoglycans.. Matrix Biol 2015;42:11–55.
  42. Eisner LE, Rosario R, Andarawis‐Puri N, Arruda EM. The role of the non‐collagenous extracellular matrix in tendon and ligament mechanical behavior: a review.. J Biomech Eng 2022;144(5):050801.
    doi: 10.1115/1.4053086google scholar: lookup
  43. Goh J, Hofmann P, Aw NH, Tan PL, Tschakert G, Mueller A. Concurrent high‐intensity aerobic and resistance exercise modulates systemic release of alarmins (HMGB1, S100A8/A9, HSP70) and inflammatory biomarkers in healthy young men: a pilot study.. Transl Med Commun 2020;5:4.
  44. Elokda AS, Nielsen DH. Effects of exercise training on the glutathione antioxidant system.. Eur J Cardiovasc Prev Rehabil 2007;14(5):630–637.
  45. Parker BL, Kiens B, Wojtaszewski JFP, Richter EA, James DE. Quantification of exercise‐regulated ubiquitin signaling in human skeletal muscle identifies protein modification cross talk via NEDDylation.. FASEB J 2020;34(4):5906–5916.
    doi: 10.1096/fj.202000075rgoogle scholar: lookup
  46. Grzędzicka J, Dąbrowska I, Kiełbik P, Perzyna M, Witkowska‐Piłaszewicz O. Are proteins such as MMp2, IGF1, IL‐13, and IL‐1ra valuable as markers of fitness status in racehorses? A pilot study.. Agriculture 2023;13(11):2134.
  47. Noda K, Kitagawa K, Miki T, Horiguchi M, Akama TO, Taniguchi T. A matricellular protein fibulin‐4 is essential for the activation of lysyl oxidase.. Sci Adv 2020;6(48):eabc1404.
    doi: 10.1126/sciadv.abc1404google scholar: lookup
  48. Frystyk J. Exercise and the growth hormone‐insulin‐like growth factor axis.. Med Sci Sports Exerc 2010;42(1):58–66.
  49. Netea MG, Domínguez‐Andrés J, Barreiro LB, Chavakis T, Divangahi M, Fuchs E. Defining trained immunity and its role in health and disease.. Nat Rev Immunol 2020;20(6):375–388.
    doi: 10.1038/s41577-020-0285-6google scholar: lookup
  50. Darragh IAJ, O'Driscoll L, Egan B. Exercise training and circulating small extracellular vesicles: appraisal of methodological approaches and current knowledge.. Front Physiol 2021;12:738333.
    doi: 10.3389/fphys.2021.738333google scholar: lookup
  51. Milczek‐Haduch D, Żmigrodzka M, Witkowska‐Piłaszewicz O. Extracellular vesicles in sport horses: potential biomarkers and modulators of exercise adaptation and therapeutics. Int J Mol Sci 2025;26(9):4359.
    doi: 10.3390/ijms26094359google scholar: lookup
  52. Masini A, Tedeschi D, Baragli P, Masini AP, Sighieri C, Lubas G. Exercise‐induced intravascular haemolysis in standardbred horses. Comp Clin Pathol 2003;12:45–48.
    doi: 10.1007/s00580-002-0470-ygoogle scholar: lookup
  53. Pakula PD, Halama A, Al‐Dous EK, Johnson SJ, Filho SA, Suhre K. Characterization of exercise‐induced hemolysis in endurance horses. Front Vet Sci 2023;10:1115776.
  54. Gegner HM, Naake T, Dugourd A, Müller T, Czernilofsky F, Kliewer G. Pre‐analytical processing of plasma and serum samples for combined proteome and metabolome analysis. Front Mol Biosci 2022;9:961448.
    doi: 10.3389/fmolb.2022.961448google scholar: lookup
  55. Blume JE, Manning WC, Troiano G, Hornburg D, Figa M, Hesterberg L. Rapid, deep and precise profiling of the plasma proteome with multi‐nanoparticle protein corona. Nat Commun 2020;11:3662.
  56. Schoos AM, Nwaru BI, Borres MP. Component‐resolved diagnostics in pet allergy: current perspectives and future directions. J Allergy Clin Immunol 2021;147(4):1164–1173.
  57. McDonald RE, Fleming RI, Beeley JG, Bovell DL, Lu JR, Zhao X. Latherin: a surfactant protein of horse sweat and saliva. PLoS One 2009;4(5):e5726.
  58. Espinosa‐López EM, Ortiz‐Guisado B, Diez de Castro E, Durham A, Aguilera‐Tejero E, Gómez‐Baena G. Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome. BMC Vet Res 2025;21(1):425.

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