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BMC genomics2017; 18(1); 187; doi: 10.1186/s12864-017-3571-3

Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses.

Abstract: Endurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise. For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition. We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition. To the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses.
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Publication Date: 2017-02-17 PubMed ID: 28212624PubMed Central: PMC5316211DOI: 10.1186/s12864-017-3571-3Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • N.I.H.
  • Extramural
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  • Non-U.S. Gov't

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.

This study investigates the physiological response of horses to endurance exercise by examining metabolites, genes and microRNAs in the blood. The researchers analyzed blood samples from ten horses before and after a 160 km endurance race, identifying significant changes which provided insight into the molecular mechanisms underlying the response to such intense exercise.

Experimental Design and Findings

  • The researchers gathered blood samples from ten horses before and after they participated in a 160 km endurance competition. The main intention was to compare the blood components before and after the endurance competition in an attempt to identify how the body reacts to such intense exercise.
  • By looking at the metabolome (the total set of metabolites), transcriptome (the total set of gene transcripts), and microRNA (miRNome) of the horses’ blood samples, the team was able to understand the changes and regulatory networks that took place.
  • They found that eleven unique metabolites, 263 metabolic genes, and five microRNAs showed notable changes from before to after the endurance competition. These changes pointed to significant alterations in certain molecular pathways including energy and oxygen sensing, oxidative stress response, and inflammation.

Insights from the Data

  • The team observed that individual metabolites and gene transcripts were part of several biochemical processes, showing that the body’s response to endurance exercise is complex and involves multiple shared pathways.
  • By using a method known as regulatory impact factor metric analysis, they were able to further validate these regulatory networks at the transcription factor and microRNA levels.
  • More extended study with 31 additional animals confirmed strong associations between specific blood components (such as lactate, methylene derivatives, specific microRNAs, and genes coding proteins involved in metabolic reactions) and the body’s response to the endurance competition.

Implications and Potential Biomarkers

  • The researchers identified possible biomarkers at the baseline (before the competition) that could predict a higher likelihood of not finishing the endurance competition, which could help in preparing horses for such events in the future and improving their performance.
  • This research is the first to offer an integrated view of the metabolome, transcriptome, and miRNome networks in response to endurance exercise in horses. It provides valuable insights for further understanding and potentially enhancing the performance of horses in endurance events.

Cite This Article

APA
Mach N, Ramayo-Caldas Y, Clark A, Moroldo M, Robert C, Barrey E, López JM, Le Moyec L. (2017). Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses. BMC Genomics, 18(1), 187. https://doi.org/10.1186/s12864-017-3571-3

Publication

BMC genomics
ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 18
Issue: 1
Pages: 187

Researcher Affiliations

Mach, Núria
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France. nuria.mach@inra.fr.
Ramayo-Caldas, Yuliaxis
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
Clark, Allison
  • Health Science Department, Open University of Catalonia (UOC), Barcelona, Spain.
Moroldo, Marco
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
Robert, Céline
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
  • Paris-Est University, National Veterinary School of Alfort, Maisons-Alfort, France.
Barrey, Eric
  • Animal Genetics and Integrative Biology unit (GABI), INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
López, Jesús Maria
  • Health Science Department, Open University of Catalonia (UOC), Barcelona, Spain.
Le Moyec, Laurence
  • Integrative Biology of Exercise Adaptations unit, UBIAE, EA7362, Evry Val d'Essone University, Evry, France.

MeSH Terms

  • Adaptation, Physiological / genetics
  • Animals
  • Biomarkers / blood
  • Gene Expression Profiling
  • Gene Regulatory Networks
  • Horses
  • Metabolomics
  • MicroRNAs / genetics
  • Physical Conditioning, Animal / physiology
  • Physical Endurance / genetics
  • Systems Biology
  • Transcription Factors / metabolism

Grant Funding

  • U01 DK097430 / NIDDK NIH HHS

References

This article includes 62 references
  1. Joyner MJ, Coyle EF. Endurance exercise performance: the physiology of champions.. J Physiol Lond 2008;586(1):35–44.
    doi: 10.1113/jphysiol.2007.143834pmc: PMC2375555pubmed: 17901124google scholar: lookup
  2. Mach N, Fuster-Botella D. Endurance exercise and gut microbiota: A review.. J Sport Health Sci 2016.
    pmc: PMC6188999pubmed: 30356594
  3. Clark A, Mach N. Exercise-induced stress behavior, gut-microbiota-brain axis and diet: a systematic review for athletes.. J Int Soc Sports Nutr 2016.
    pmc: PMC5121944pubmed: 27924137
  4. Russell AP, Lamon S, Boon H, Wada S, Guller I, Brown EL, Chibalin AV, Zierath JR, Snow RJ, Stepto N. Regulation of miRNAs in human skeletal muscle following acute endurance exercise and short-term endurance training.. J Physiol 2013;591(Pt 18):4637–53.
    doi: 10.1113/jphysiol.2013.255695pmc: PMC3784204pubmed: 23798494google scholar: lookup
  5. Munoz A, Riber C, Trigo P, Castejon-Riber C, Castejon FM. Dehydration, electrolyte imbalances and renin-angiotensin-aldosterone-vasopressin axis in successful and unsuccessful endurance horses.. Equine Vet J 2010;42:83–90.
    doi: 10.1111/j.2042-3306.2010.00211.xpubmed: 21058987google scholar: lookup
  6. Snow DH, Baxter P, Rose RJ. Muscle fibre composition and glycogen depletion in horses competing in an endurance ride.. Vet Record 1981;108(17):374–8.
    doi: 10.1136/vr.108.17.374pubmed: 7292903google scholar: lookup
  7. Davies KJ, Packer L, Brooks GA. Biochemical adaptation of mitochondria, muscle, and whole-animal respiration to endurance training.. Arch Biochem Biophys 1981;209(2):539–54.
    doi: 10.1016/0003-9861(81)90312-Xpubmed: 7294809google scholar: lookup
  8. Mach N, Plancade S, Pacholewska A, Lecardonnel J, Riviere J, Moroldo M, Vaiman A, Morgenthaler C, Beinat M, Nevot A. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.. Sci Rep 2016;6:22932.
    doi: 10.1038/srep22932pmc: PMC4785432pubmed: 26960911google scholar: lookup
  9. Capomaccio S, Vitulo N, Verini-Supplizi A, Barcaccia G, Albiero A, D’Angelo M, Campagna D, Valle G, Felicetti M, Silvestrelli M. RNA sequencing of the exercise transcriptome in equine athletes.. PLoS One 2013;8(12):e83504.
    doi: 10.1371/journal.pone.0083504pmc: PMC3877044pubmed: 24391776google scholar: lookup
  10. Baggish AL, Hale A, Weiner RB, Lewis GD, Systrom D, Wang F, Wang TJ, Chan SY. Dynamic regulation of circulating microRNA during acute exhaustive exercise and sustained aerobic exercise training.. J Physiol 2011;589(Pt 16):3983–94.
    doi: 10.1113/jphysiol.2011.213363pmc: PMC3179997pubmed: 21690193google scholar: lookup
  11. Bye A, Rosjo H, Aspenes ST, Condorelli G, Omland T, Wisloff U. Circulating microRNAs and aerobic fitness--the HUNT-Study.. PLoS One 2013;8(2):e57496.
    doi: 10.1371/journal.pone.0057496pmc: PMC3585333pubmed: 23469005google scholar: lookup
  12. Nielsen S, Akerstrom T, Rinnov A, Yfanti C, Scheele C, Pedersen BK, Laye MJ. The miRNA plasma signature in response to acute aerobic exercise and endurance training.. PLoS One 2014;9(2):e87308.
    doi: 10.1371/journal.pone.0087308pmc: PMC3929352pubmed: 24586268google scholar: lookup
  13. Mooren FC, Viereck J, Kruger K, Thum T. Circulating micrornas as potential biomarkers of aerobic exercise capacity.. Am J Physiol Heart Circul Physiol 2014;306(4):H557–63.
    doi: 10.1152/ajpheart.00711.2013pmc: PMC3920240pubmed: 24363306google scholar: lookup
  14. Sawada S, Kon M, Wada S, Ushida T, Suzuki K, Akimoto T. Profiling of circulating MicroRNAs after a bout of acute resistance exercise in humans.. PLoS One 2013;8(7):e70823.
    doi: 10.1371/journal.pone.0070823pmc: PMC3726615pubmed: 23923026google scholar: lookup
  15. Wardle SL, Bailey MES, Kilikevicius A, Malkova D, Wilson RH, Venckunas T, Moran CN. Plasma MicroRNA levels differ between endurance and strength athletes.. PLoS One 2015;10(4):e0122107.
    doi: 10.1371/journal.pone.0122107pmc: PMC4400105pubmed: 25881132google scholar: lookup
  16. Uhlemann M, Mobius-Winkler S, Fikenzer S, Adam J, Redlich M, Mohlenkamp S, Hilberg T, Schuler GC, Adams V. Circulating microRNA-126 increases after different forms of endurance exercise in healthy adults.. Eur J Prev Cardiol 2014;21(4):484–91.
    doi: 10.1177/2047487312467902pubmed: 23150891google scholar: lookup
  17. Nicholson JK, Wilson ID. Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism.. Nat Rev Drug Discov 2003;2(8):668–76.
    doi: 10.1038/nrd1157pubmed: 12904817google scholar: lookup
  18. Huang CC, Lin WT, Hsu FL, Tsai PW, Hou CC. Metabolomics investigation of exercise-modulated changes in metabolism in rat liver after exhaustive and endurance exercises.. Eur J Appl Physiol 2010;108(3):557–66.
    doi: 10.1007/s00421-009-1247-7pubmed: 19865828google scholar: lookup
  19. Le Moyec L, Robert C, Triba MN, Billat VL, Mata X, Schibler L, Barrey E. Protein catabolism and high lipid metabolism associated with long-distance exercise are revealed by plasma NMR metabolomics in endurance horses.. PLoS One 2014;9(3):e90730.
    doi: 10.1371/journal.pone.0090730pmc: PMC3962349pubmed: 24658361google scholar: lookup
  20. 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
  21. Pechlivanis A, Kostidis S, Saraslanidis P, Petridou A, Tsalis G, Veselkov K, Mikros E, Mougios V, Theodoridis GA. 1H NMR study on the short- and long-term impact of two training programs of sprint running on the metabolic fingerprint of human serum.. J Proteome Res 2013;12(1):470–80.
    doi: 10.1021/pr300846xpubmed: 23198909google scholar: lookup
  22. Chorell E, Moritz T, Branth S, Antti H, Svensson MB. Predictive metabolomics evaluation of nutrition-modulated metabolic stress responses in human blood serum during the early recovery phase of strenuous physical exercise.. J Proteome Res 2009;8(6):2966–77.
    doi: 10.1021/pr900081qpubmed: 19317510google scholar: lookup
  23. Brugnara L, Vinaixa M, Murillo S, Samino S, Rodriguez MA, Beltran A, Lerin C, Davison G, Correig X, Novials A. Metabolomics approach for analyzing the effects of exercise in subjects with type 1 diabetes mellitus.. PLoS One 2012;7(7):e40600.
    doi: 10.1371/journal.pone.0040600pmc: PMC3394718pubmed: 22792382google scholar: lookup
  24. Duggan GE, Hittel DS, Sensen CW, Weljie AM, Vogel HJ, Shearer J. Metabolomic response to exercise training in lean and diet-induced obese mice.. J Appl Physiol (1985) 2011;110(5):1311–8.
    doi: 10.1152/japplphysiol.00701.2010pubmed: 21270351google scholar: lookup
  25. Lehmann R, Zhao X, Weigert C, Simon P, Fehrenbach E, Fritsche J, Machann J, Schick F, Wang J, Hoene M. Medium chain acylcarnitines dominate the metabolite pattern in humans under moderate intensity exercise and support lipid oxidation.. PLoS One 2010;5(7):e11519.
    doi: 10.1371/journal.pone.0011519pmc: PMC2902514pubmed: 20634953google scholar: lookup
  26. Chevion S, Moran DS, Heled Y, Shani Y, Regev G, Abbou B, Berenshtein E, Stadtman ER, Epstein Y. Plasma antioxidant status and cell injury after severe physical exercise.. Proc Natl Acad Sci U S A 2003;100(9):5119–23.
    doi: 10.1073/pnas.0831097100pmc: PMC154308pubmed: 12702774google scholar: lookup
  27. Krol J, Loedige I, Filipowicz W. The widespread regulation of microRNA biogenesis, function and decay.. Nat Rev Genet 2010;11(9):597–610.
    pubmed: 20661255
  28. Hudson NJ, Reverter A, Dalrymple BP. A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.. PLoS Comput Biol 2009;5(5):e1000382.
    doi: 10.1371/journal.pcbi.1000382pmc: PMC2671163pubmed: 19412532google scholar: lookup
  29. Reverter A, Hudson NJ, Nagaraj SH, Perez-Enciso M, Dalrymple BP. Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data.. Bioinformatics 2010;26(7):896–904.
    doi: 10.1093/bioinformatics/btq051pubmed: 20144946google scholar: lookup
  30. Scott JM, Esch BTA, Shave R, Warburton DER, Gaze D, George K. Cardiovascular consequences of completing a 160-km Ultramarathon.. Med Sci Sports Exerc 2009;41(1):25–33.
    pubmed: 19092706
  31. Bartel J, Krumsiek J, Schramm K, Adamski J, Gieger C, Herder C, Carstensen M, Peters A, Rathmann W, Roden M. The human blood metabolome-transcriptome interface.. Plos Genetics 2015;11(6):e1005274.
    doi: 10.1371/journal.pgen.1005274pmc: PMC4473262pubmed: 26086077google scholar: lookup
  32. Eijkelenboom A, Burgering BM. FOXOs: signalling integrators for homeostasis maintenance.. Nat Rev Mol Cell Biol 2013;14(2):83–97.
    doi: 10.1038/nrm3507pubmed: 23325358google scholar: lookup
  33. Samuel BS, Shaito A, Motoike T, Rey FE, Backhed F, Manchester JK, Hammer RE, Williams SC, Crowley J, Yanagisawa M. Effects of the gut microbiota on host adiposity are modulated by the short-chain fatty-acid binding G protein-coupled receptor, Gpr41.. Proc Natl Acad Sci U S A 2008;105(43):16767–72.
    doi: 10.1073/pnas.0808567105pmc: PMC2569967pubmed: 18931303google scholar: lookup
  34. Wong JM, de Souza R, Kendall CW, Emam A, Jenkins DJ. Colonic health: fermentation and short chain fatty acids.. J Clin Gastroenterol 2006;40(3):235–43.
    doi: 10.1097/00004836-200603000-00015pubmed: 16633129google scholar: lookup
  35. Chaudhry MA, Omaruddin RA, Brumbaugh CD, Tariq MA, Pourmand N. Identification of radiation-induced microRNA transcriptome by next-generation massively parallel sequencing.. J Radiat Res 2013;54(5):808–22.
    doi: 10.1093/jrr/rrt014pmc: PMC3766286pubmed: 23447695google scholar: lookup
  36. Zhao C, Brown RS, Tang CH, Hu CC, Schlieker C. Site-specific proteolysis mobilizes TorsinA from the membrane of the Endoplasmic Reticulum (ER) in Response to ER Stress and B cell stimulation.. J Biol Chem 2016;291(18):9469–81.
    doi: 10.1074/jbc.M115.709337pmc: PMC4850287pubmed: 26953341google scholar: lookup
  37. Jamart C, Gomes AV, Dewey S, Deldicque L, Raymackers JM, Francaux M. Regulation of ubiquitin-proteasome and autophagy pathways after acute LPS and epoxomicin administration in mice.. BMC Musculoskelet Disord 2014;15:166.
    doi: 10.1186/1471-2474-15-166pmc: PMC4041039pubmed: 24885455google scholar: lookup
  38. Zhang T, Gunther S, Looso M, Kunne C, Kruger M, Kim J, Zhou YG, Braun T. Prmt5 is a regulator of muscle stem cell expansion in adult mice.. Nat Commun 2015;6:7140.
    doi: 10.1038/ncomms8140pmc: PMC4458870pubmed: 26028225google scholar: lookup
  39. Leke R, Escobar TD, Rao KV, Silveira TR, Norenberg MD, Schousboe A. Expression of glutamine transporter isoforms in cerebral cortex of rats with chronic hepatic encephalopathy.. Neurochem Int 2015;88:32–7.
    doi: 10.1016/j.neuint.2015.03.005pubmed: 25842041google scholar: lookup
  40. Keller MD, Pollitt CC, Marx UC. Nuclear magnetic resonance-based metabonomic study of early time point laminitis in an oligofructose-overload model.. Equine Vet J 2011;43(6):737–43.
    doi: 10.1111/j.2042-3306.2010.00336.xpubmed: 21496096google scholar: lookup
  41. Tan K, Fujimoto M, Takii R, Takaki E, Hayashida N, Nakai A. Mitochondrial SSBP1 protects cells from proteotoxic stresses by potentiating stress-induced HSF1 transcriptional activity.. Nat Commun 2015;6:6580.
    doi: 10.1038/ncomms7580pmc: PMC4558571pubmed: 25762445google scholar: lookup
  42. Lamprecht M, Bogner S, Schippinger G, Steinbauer K, Fankhauser F, Hallstroem S, Schuetz B, Greilberger JF. Probiotic supplementation affects markers of intestinal barrier, oxidation, and inflammation in trained men; a randomized, double-blinded, placebo-controlled trial.. J Int Soc Sports Nutr 2012;9(1):45.
    doi: 10.1186/1550-2783-9-45pmc: PMC3465223pubmed: 22992437google scholar: lookup
  43. Jeukendrup AE, Vet-Joop K, Sturk A, Stegen JH, Senden J, Saris WH, Wagenmakers AJ. Relationship between gastro-intestinal complaints and endotoxaemia, cytokine release and the acute-phase reaction during and after a long-distance triathlon in highly trained men.. Clin Sci (Lond) 2000;98(1):47–55.
    doi: 10.1042/cs0980047pubmed: 10600658google scholar: lookup
  44. Radom-Aizik S, Zaldivar F Jr, Leu SY, Adams GR, Oliver S, Cooper DM. Effects of exercise on microRNA expression in young males peripheral blood mononuclear cells.. Clin Transl Sci 2012;5(1):32–8.
    doi: 10.1111/j.1752-8062.2011.00384.xpmc: PMC4664183pubmed: 22376254google scholar: lookup
  45. Tonevitsky AG, Maltseva DV, Abbasi A, Samatov TR, Sakharov DA, Shkurnikov MU, Lebedev AE, Galatenko VV, Grigoriev AI, Northoff H. Dynamically regulated miRNA-mRNA networks revealed by exercise.. BMC Physiol 2013;13:9.
    doi: 10.1186/1472-6793-13-9pmc: PMC3681679pubmed: 24219008google scholar: lookup
  46. Radom-Aizik S, Zaldivar F Jr, Oliver S, Galassetti P, Cooper DM. Evidence for microRNA involvement in exercise-associated neutrophil gene expression changes.. J Appl Physiol (1985) 2010;109(1):252–61.
    doi: 10.1152/japplphysiol.01291.2009pmc: PMC2904199pubmed: 20110541google scholar: lookup
  47. Iliopoulos D, Hirsch HA, Struhl K. An epigenetic switch involving NF-kappaB, Lin28, Let-7 MicroRNA, and IL6 links inflammation to cell transformation.. Cell 2009;139(4):693–706.
    doi: 10.1016/j.cell.2009.10.014pmc: PMC2783826pubmed: 19878981google scholar: lookup
  48. Pacholewska A, Mach N, Mata X, Vaiman A, Schibler L, Barrey E, Gerber V. Novel equine tissue miRNAs and breed-related miRNA expressed in serum.. BMC Genomics 2016;17(1):831.
    doi: 10.1186/s12864-016-3168-2pmc: PMC5080802pubmed: 27782799google scholar: lookup
  49. Williams EG, Wu Y, Jha P, Dubuis S, Blattmann P, Argmann CA, Houten SM, Amariuta T, Wolski W, Zamboni N. Systems proteomics of liver mitochondria function.. Science 2016;352(6291):aad0189.
    doi: 10.1126/science.aad0189pmc: PMC10859670pubmed: 27284200google scholar: lookup
  50. Zheng C, Zhang S, Ragg S, Raftery D, Vitek O. Identification and quantification of metabolites in (1)H NMR spectra by Bayesian model selection.. Bioinformatics 2011;27(12):1637–44.
    doi: 10.1093/bioinformatics/btr118pmc: PMC3106181pubmed: 21398670google scholar: lookup
  51. Balayssac S, Dejean S, Lalande J, Gilard V, Malet-Martino M. A toolbox to explore NMR metabolomic data sets using the R environment.. Chemometr Intell Lab Syst 2013;126:50–9.
    doi: 10.1016/j.chemolab.2013.04.015google scholar: lookup
  52. Wehrens R. Orthogonal signal correction and OPLS.. Chemometrics 2011;pp. 240–3.
  53. Worley B, Powers R. A sequential algorithm for multiblock orthogonal projections to latent structures.. Chemometr Intell Lab Syst 2015;149(Pt B):33–9.
    doi: 10.1016/j.chemolab.2015.10.018pmc: PMC4668594pubmed: 26640310google scholar: lookup
  54. Xia J, Wishart DS. MetPA: a web-based metabolomics tool for pathway analysis and visualization.. Bioinformatics 2010;26(18):2342–4.
    doi: 10.1093/bioinformatics/btq418pubmed: 20628077google scholar: lookup
  55. Watson E, Yilmaz LS, Walhout AJM. Understanding metabolic regulation at a systems level: metabolite sensing, mathematical predictions, and model organisms.. Ann Rev Gen 2015;49:553–75.
    doi: 10.1146/annurev-genet-112414-055257pubmed: 26631516google scholar: lookup
  56. Ge Y, Sealfon SC, Speed TP. Some step-down procedures controlling the false discovery rate under dependence.. Stat Sin 2008;18(3):881–904.
    pmc: PMC2583793pubmed: 19018297
  57. Janky R, Verfaillie A, Imrichova H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M, Potier D. iRegulon: from a gene list to a gene regulatory network using large motif and track collections.. PLoS Comput Biol 2014;10(7):e1003731.
    doi: 10.1371/journal.pcbi.1003731pmc: PMC4109854pubmed: 25058159google scholar: lookup
  58. . Drugs to treat overweight and obesity.. J Psychosoc Nurs Ment Health Serv 2014;52(8):21-22.
    pubmed: 25106053
  59. Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM. A census of human transcription factors: function, expression and evolution.. Nat Rev Gen 2009;10(4):252–63.
    doi: 10.1038/nrg2538pubmed: 19274049google scholar: lookup
  60. Abdi H, Williams L, Valentin D. Multiple factor analysis: principal component analysis for multitable and multiblock data sets.. Wiley Interd Rev Comput Stat 2013;5:149–79.
    doi: 10.1002/wics.1246google scholar: lookup
  61. Le S, Josse J, Husson F. FactoMineR: An R package for multivariate analysis.. J Stat Soft 2008;25(1):1–18.
    doi: 10.18637/jss.v025.i01google scholar: lookup
  62. Kruiswijk F, Labuschagne CF, Vousden KH. p53 in survival, death and metabolic health: a lifeguard with a licence to kill.. Nat Rev Mol Cell Biol 2015;16(7):393–405.
    doi: 10.1038/nrm4007pubmed: 26122615google scholar: lookup

Citations

This article has been cited 24 times.
  1. Sisia G, Giudice E, Attanzio A, Briglia M, Piccione G, Trunfio C, Arfuso F. Exosome and miRNA Content Engagement in the Physical Exercise Response: What Is Known to Date in Atheltic Horses?. Int J Mol Sci 2026 Jan 4;27(1).
    doi: 10.3390/ijms27010520pubmed: 41516392google scholar: lookup
  2. Stefaniuk-Szmukier M, Szmatoła T, Ropka-Molik K. Molecular Signatures of Exercise Adaptation in Arabian Racing Horses: Transcriptomic Insights from Blood and Muscle. Genes (Basel) 2025 Apr 4;16(4).
    doi: 10.3390/genes16040431pubmed: 40282391google scholar: lookup
  3. Yuan X, Yao X, Zeng Y, Wang J, Ren W, Wang T, Li X, Yang L, Yang X, Meng J. The Impact of the Competition on miRNA, Proteins, and Metabolites in the Blood Exosomes of the Yili Horse. Genes (Basel) 2025 Feb 15;16(2).
    doi: 10.3390/genes16020224pubmed: 40004554google scholar: lookup
  4. Gotić J, Špelić L, Kuleš J, Horvatić A, Gelemanović A, Ljubić BB, Mrljak V, Bottegaro NB. Proteomic analysis emphasizes the adaptation of energy metabolism in horses during endurance races. BMC Vet Res 2025 Feb 15;21(1):67.
    doi: 10.1186/s12917-025-04518-0pubmed: 39955578google 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 May 10;25(10).
    doi: 10.3390/ijms25105210pubmed: 38791248google scholar: lookup
  6. Laus F, Bazzano M, Spaterna A, Laghi L, Marchegiani A. Nuclear Magnetic Resonance (NMR) Metabolomics: Current Applications in Equine Health Assessment. Metabolites 2024 May 7;14(5).
    doi: 10.3390/metabo14050269pubmed: 38786746google scholar: lookup
  7. Reißmann M, Rajavel A, Kokov ZA, Schmitt AO. Identification of Differentially Expressed Genes after Endurance Runs in Karbadian Horses to Determine Candidates for Stress Indicators and Performance Capability. Genes (Basel) 2023 Oct 24;14(11).
    doi: 10.3390/genes14111982pubmed: 38002925google scholar: lookup
  8. Meng S, Zhang Y, Lv S, Zhang Z, Liu X, Jiang L. Comparison of muscle metabolomics between two Chinese horse breeds. Front Vet Sci 2023;10:1162953.
    doi: 10.3389/fvets.2023.1162953pubmed: 37215482google scholar: lookup
  9. Mach N, Midoux C, Leclercq S, Pennarun S, Le Moyec L, Rué O, Robert C, Sallé G, Barrey E. Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes. Commun Biol 2022 Oct 3;5(1):1032.
    doi: 10.1038/s42003-022-03977-7pubmed: 36192523google scholar: lookup
  10. Rau A, Passet B, Castille J, Daniel-Carlier N, Asset A, Lecardonnel J, Moroldo M, Jaffrézic F, Laloë D, Moazami-Goudarzi K, Vilotte JL. Potential genetic robustness of Prnp and Sprn double knockout mouse embryos towards ShRNA-lentiviral inoculation. Vet Res 2022 Jul 7;53(1):54.
    doi: 10.1186/s13567-022-01075-4pubmed: 35799279google scholar: lookup
  11. 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.3245pubmed: 35194967google scholar: lookup
  12. 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.2021.718866pubmed: 34733900google scholar: lookup
  13. Mach N, Moroldo M, Rau A, Lecardonnel J, Le Moyec L, Robert C, Barrey E. Understanding the Holobiont: Crosstalk Between Gut Microbiota and Mitochondria During Long Exercise in Horse. Front Mol Biosci 2021;8:656204.
    doi: 10.3389/fmolb.2021.656204pubmed: 33898524google scholar: lookup
  14. de Meeûs d'Argenteuil C, Boshuizen B, Oosterlinck M, van de Winkel D, De Spiegelaere W, de Bruijn CM, Goethals K, Vanderperren K, Delesalle CJG. Flexibility of equine bioenergetics and muscle plasticity in response to different types of training: An integrative approach, questioning existing paradigms. PLoS One 2021;16(4):e0249922.
    doi: 10.1371/journal.pone.0249922pubmed: 33848308google scholar: lookup
  15. Lee S, Baker ME, Clinton M, Taylor SE. Use of Omics Data in Fracture Prediction; a Scoping and Systematic Review in Horses and Humans. Animals (Basel) 2021 Mar 30;11(4).
    doi: 10.3390/ani11040959pubmed: 33808497google scholar: lookup
  16. Belhaj MR, Lawler NG, Hoffman NJ. Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology. Metabolites 2021 Mar 7;11(3).
    doi: 10.3390/metabo11030151pubmed: 33799958google scholar: lookup
  17. Halama A, Oliveira JM, Filho SA, Qasim M, Achkar IW, Johnson S, Suhre K, Vinardell T. Metabolic Predictors of Equine Performance in Endurance Racing. Metabolites 2021 Jan 31;11(2).
    doi: 10.3390/metabo11020082pubmed: 33572513google scholar: lookup
  18. Bazzano M, Laghi L, Zhu C, Lotito E, Sgariglia S, Tesei B, Laus F. Exercise Induced Changes in Salivary and Serum Metabolome in Trained Standardbred, Assessed by (1)H-NMR. Metabolites 2020 Jul 21;10(7).
    doi: 10.3390/metabo10070298pubmed: 32708237google scholar: lookup
  19. Mach N, Ruet A, Clark A, Bars-Cortina D, Ramayo-Caldas Y, Crisci E, Pennarun S, Dhorne-Pollet S, Foury A, Moisan MP, Lansade L. Priming for welfare: gut microbiota is associated with equitation conditions and behavior in horse athletes. Sci Rep 2020 May 20;10(1):8311.
    doi: 10.1038/s41598-020-65444-9pubmed: 32433513google scholar: lookup
  20. Ropka-Molik K, Stefaniuk-Szmukier M, Musiał AD, Velie BD. The Genetics of Racing Performance in Arabian Horses. Int J Genomics 2019;2019:9013239.
    doi: 10.1155/2019/9013239pubmed: 31565654google scholar: lookup
  21. Plancade S, Clark A, Philippe C, Helbling JC, Moisan MP, Esquerré D, Le Moyec L, Robert C, Barrey E, Mach N. Unraveling the effects of the gut microbiota composition and function on horse endurance physiology. Sci Rep 2019 Jul 3;9(1):9620.
    doi: 10.1038/s41598-019-46118-7pubmed: 31270376google scholar: lookup
  22. Cappelli K, Capomaccio S, Viglino A, Silvestrelli M, Beccati F, Moscati L, Chiaradia E. Circulating miRNAs as Putative Biomarkers of Exercise Adaptation in Endurance Horses. Front Physiol 2018;9:429.
    doi: 10.3389/fphys.2018.00429pubmed: 29740341google scholar: lookup
  23. Zhang Z, Li H, Jiang S, Li R, Li W, Chen H, Bo X. A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data. Brief Bioinform 2019 Jul 19;20(4):1524-1541.
    doi: 10.1093/bib/bby023pubmed: 29617727google scholar: lookup
  24. Clark A, Mach N. The Crosstalk between the Gut Microbiota and Mitochondria during Exercise. Front Physiol 2017;8:319.
    doi: 10.3389/fphys.2017.00319pubmed: 28579962google scholar: lookup
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