Analyze Diet
Communications biology2022; 5(1); 1032; doi: 10.1038/s42003-022-03977-7

Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes.

Abstract: Emerging evidence indicates that the gut microbiome contributes to endurance exercise performance. Still, the extent of its functional and metabolic potential remains unknown. Using elite endurance horses as a model system for exercise responsiveness, we built an integrated horse gut gene catalog comprising ~25 million unique genes and 372 metagenome-assembled genomes. This catalog represents 4179 genera spanning 95 phyla and functional capacities primed to exploit energy from dietary, microbial, and host resources. The holo-omics approach shows that gut microbiomes enriched in Lachnospiraceae taxa are negatively associated with cardiovascular capacity. Conversely, more complex and functionally diverse microbiomes are associated with higher glucose concentrations and reduced accumulation of long-chain acylcarnitines and non-esterified fatty acids in plasma, suggesting increased ß-oxidation capacity in the mitochondria. In line with this hypothesis, more fit athletes show upregulation of mitochondrial-related genes involved in energy metabolism, biogenesis, and Ca cytosolic transport, all of which are necessary to improve aerobic work power, spare glycogen usage, and enhance cardiovascular capacity. The results identify an associative link between endurance performance and gut microbiome composition and gene function, laying the basis for nutritional interventions that could benefit horse athletes.
Publication Date: 2022-10-03 PubMed ID: 36192523PubMed Central: PMC9529974DOI: 10.1038/s42003-022-03977-7Google 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.
  • Journal Article
  • Research Support
  • 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.

The research explores the link between gut microbiome composition and endurance performance, using elite endurance horses as the model system. The researchers generated a comprehensive gut gene catalog that may pave the way for nutritional interventions aimed at enhancing athletic performance in horses.

Integrated Horse Gut Gene Catalog

  • The researchers constructed a complete horse gut gene catalog consisting of approximately 25 million unique genes and 372 metagenome-assembled genomes.
  • This catalog represents an expansive range of microbiome diversity, covering 4179 genera across 95 phyla.
  • The comprehensive catalog showcases functional capacities designed to extract energy from various sources including the diet, microbes themselves, and the host body (in this case, the horse).

Association with Cardiovascular Capacity

  • The researchers found a negative relationship between the presence of Lachnospiraceae taxa (a family of bacteria) in the gut microbiome and cardiovascular capacity — as the abundance of these bacteria increased, cardiovascular fitness decreased.
  • On the other hand, richer and functionally diverse microbiomes were associated with higher glucose concentrations and reduced accumulation of certain fatty acids in the plasma which suggested an increased capacity for beta-oxidation (a metabolic process) in the mitochondria of cells.

Enhanced Endurance Performance and Mitochondrial Activity

  • A higher level of fitness was observed in horses that exhibited upregulation of specific mitochondrial-related genes. These genes are involved in energy metabolism, biogenesis, and calcium cytosolic transport – all crucial processes for improving aerobic work power, conserving glycogen usage, and enhancing cardiovascular capacity.
  • Altogether, this research positions the gut microbiome as a key factor influencing endurance performance, potentially providing a basis for the development of nutritional interventions that could improve the performance of equine athletes.

Cite This Article

APA
Mach N, Midoux C, Leclercq S, Pennarun S, Le Moyec L, Rué O, Robert C, Sallé G, Barrey E. (2022). Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes. Commun Biol, 5(1), 1032. https://doi.org/10.1038/s42003-022-03977-7

Publication

ISSN: 2399-3642
NlmUniqueID: 101719179
Country: England
Language: English
Volume: 5
Issue: 1
Pages: 1032
PII: 1032

Researcher Affiliations

Mach, Núria
  • Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France. nuria.mach@inrae.fr.
  • Université de Toulouse, INRAE, ENVT, IHAP, Toulouse, France. nuria.mach@inrae.fr.
Midoux, Cédric
  • Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France.
  • Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France.
  • Université Paris-Saclay, INRAE, PROSE, Antony, France.
Leclercq, Sébastien
  • Université François Rabelais de Tours, INRAE, ISP, Nouzilly, France.
Pennarun, Samuel
  • INRAE, Genomic facility, 31326, Castanet-Tolosan, France.
Le Moyec, Laurence
  • Université d'Évry Val d'Essonne, Université Paris-Saclay, Évry, France.
  • Muséum National d'Histoire Naturelle, CNRS, MCAM, Paris, France.
Rué, Olivier
  • Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France.
  • Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France.
Robert, Céline
  • Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France.
  • École Nationale Vétérinaire d'Alfort, Maisons-Alfort, France.
Sallé, Guillaume
  • Université François Rabelais de Tours, INRAE, ISP, Nouzilly, France.
Barrey, Eric
  • Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France.

MeSH Terms

  • Animals
  • Athletes
  • Fatty Acids
  • Glucose
  • Glycogen
  • Horses
  • Humans
  • Metagenome

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 101 references
  1. Hawley JA, Lundby C, Cotter JD, Burke LM. Maximizing Cellular Adaptation to Endurance Exercise in Skeletal Muscle.. Cell Metab 2018 May 1;27(5):962-976.
    doi: 10.1016/j.cmet.2018.04.014pubmed: 29719234google scholar: lookup
  2. Mach N, Fuster-Botella D. Endurance exercise and gut microbiota: A review.. J Sport Health Sci 2017 Jun;6(2):179-197.
    doi: 10.1016/j.jshs.2016.05.001pmc: PMC6188999pubmed: 30356594google scholar: lookup
  3. Capomaccio S, Vitulo N, Verini-Supplizi A, Barcaccia G, Albiero A, D'Angelo M, Campagna D, Valle G, Felicetti M, Silvestrelli M, Cappelli K. RNA sequencing of the exercise transcriptome in equine athletes.. PLoS One 2013;8(12):e83504.
  4. Shave R, Howatson G, Dickson D, Young L. Exercise-Induced Cardiac Remodeling: Lessons from Humans, Horses, and Dogs.. Vet Sci 2017 Feb 12;4(1).
    doi: 10.3390/vetsci4010009pmc: PMC5606617pubmed: 29056668google scholar: lookup
  5. Ricard A, Robert C, Blouin C, Baste F, Torquet G, Morgenthaler C, Rivière J, Mach N, Mata X, Schibler L, Barrey E. Endurance Exercise Ability in the Horse: A Trait with Complex Polygenic Determinism.. Front Genet 2017;8:89.
    doi: 10.3389/fgene.2017.00089pmc: PMC5488500pubmed: 28702049google scholar: lookup
  6. van der Kolk JH, Thomas S, Mach N, Ramseyer A, Burger D, Gerber V, Nuoffer JM. Serum acylcarnitine profile in endurance horses with and without metabolic dysfunction.. Vet J 2020 Jan;255:105419.
    doi: 10.1016/j.tvjl.2019.105419pubmed: 31982078google scholar: lookup
  7. Cottin F, Metayer N, Goachet AG, Julliand V, Slawinski J, Billat V, Barrey E. Oxygen consumption and gait variables of Arabian endurance horses measured during a field exercise test.. Equine Vet J Suppl 2010 Nov;(38):1-5.
  8. Goachet AG, Julliand V. Implementation of field cardio-respiratory measurements to assess energy expenditure in Arabian endurance horses.. Animal 2015 May;9(5):787-92.
    doi: 10.1017/S1751731114003061pubmed: 25496768google scholar: lookup
  9. Hargreaves M, Spriet LL. Skeletal muscle energy metabolism during exercise.. Nat Metab 2020 Sep;2(9):817-828.
    doi: 10.1038/s42255-020-0251-4pubmed: 32747792google scholar: lookup
  10. Young LE, Rogers K, Wood JL. Left ventricular size and systolic function in Thoroughbred racehorses and their relationships to race performance.. J Appl Physiol (1985) 2005 Oct;99(4):1278-85.
  11. Fischbach MA. Microbiome: Focus on Causation and Mechanism.. Cell 2018 Aug 9;174(4):785-790.
    doi: 10.1016/j.cell.2018.07.038pmc: PMC6094951pubmed: 30096310google scholar: lookup
  12. Scheiman J, Luber JM, Chavkin TA, MacDonald T, Tung A, Pham LD, Wibowo MC, Wurth RC, Punthambaker S, Tierney BT, Yang Z, Hattab MW, Avila-Pacheco J, Clish CB, Lessard S, Church GM, Kostic AD. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism.. Nat Med 2019 Jul;25(7):1104-1109.
    doi: 10.1038/s41591-019-0485-4pmc: PMC7368972pubmed: 31235964google scholar: lookup
  13. Lundberg JO, Moretti C, Benjamin N, Weitzberg E. Symbiotic bacteria enhance exercise performance.. Br J Sports Med 2021 Mar;55(5):243.
    doi: 10.1136/bjsports-2020-102094pubmed: 32447320google scholar: lookup
  14. Chen H, Nwe PK, Yang Y, Rosen CE, Bielecka AA, Kuchroo M, Cline GW, Kruse AC, Ring AM, Crawford JM, Palm NW. A Forward Chemical Genetic Screen Reveals Gut Microbiota Metabolites That Modulate Host Physiology.. Cell 2019 May 16;177(5):1217-1231.e18.
    doi: 10.1016/j.cell.2019.03.036pmc: PMC6536006pubmed: 31006530google scholar: lookup
  15. Donia MS, Fischbach MA. HUMAN MICROBIOTA. Small molecules from the human microbiota.. Science 2015 Jul 24;349(6246):1254766.
    doi: 10.1126/science.1254766pmc: PMC4641445pubmed: 26206939google scholar: lookup
  16. Frampton J, Murphy KG, Frost G, Chambers ES. Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function.. Nat Metab 2020 Sep;2(9):840-848.
    doi: 10.1038/s42255-020-0188-7pubmed: 32694821google scholar: lookup
  17. Ticinesi A, Lauretani F, Tana C, Nouvenne A, Ridolo E, Meschi T. Exercise and immune system as modulators of intestinal microbiome: implications for the gut-muscle axis hypothesis.. Exerc Immunol Rev 2019;25:84-95.
    pubmed: 30753131
  18. Husted AS, Trauelsen M, Rudenko O, Hjorth SA, Schwartz TW. GPCR-Mediated Signaling of Metabolites.. Cell Metab 2017 Apr 4;25(4):777-796.
    doi: 10.1016/j.cmet.2017.03.008pubmed: 28380372google scholar: lookup
  19. Mach N, Lansade L, Bars-Cortina D, Dhorne-Pollet S, Foury A, Moisan MP, Ruet A. Gut microbiota resilience in horse athletes following holidays out to pasture.. Sci Rep 2021 Mar 3;11(1):5007.
    doi: 10.1038/s41598-021-84497-ypmc: PMC7930273pubmed: 33658551google scholar: lookup
  20. 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/s41598-020-65444-9pmc: PMC7239938pubmed: 32433513google scholar: lookup
  21. Estaki M, Pither J, Baumeister P, Little JP, Gill SK, Ghosh S, Ahmadi-Vand Z, Marsden KR, Gibson DL. Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions.. Microbiome 2016 Aug 8;4(1):42.
    doi: 10.1186/s40168-016-0189-7pmc: PMC4976518pubmed: 27502158google scholar: lookup
  22. Durk RP, Castillo E, Márquez-Magaña L, Grosicki GJ, Bolter ND, Lee CM, Bagley JR. Gut Microbiota Composition Is Related to Cardiorespiratory Fitness in Healthy Young Adults.. Int J Sport Nutr Exerc Metab 2019 May 1;29(3):249-253.
    doi: 10.1123/ijsnem.2018-0024pmc: PMC6487229pubmed: 29989465google scholar: lookup
  23. Yang Y, Shi Y, Wiklund P, Tan X, Wu N, Zhang X, Tikkanen O, Zhang C, Munukka E, Cheng S. The Association between Cardiorespiratory Fitness and Gut Microbiota Composition in Premenopausal Women.. Nutrients 2017 Jul 25;9(8).
    doi: 10.3390/n邀792pmc: PMC5579588pubmed: 28757576google scholar: lookup
  24. 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/s41598-019-46118-7pmc: PMC6610142pubmed: 31270376google scholar: lookup
  25. 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.2021.656204pmc: PMC8063112pubmed: 33898524google scholar: lookup
  26. Janabi AHD, Biddle AS, Klein D, McKeever KH. Exercise training-induced changes in the gut microbiota of Standardbred racehorses. Comp. Exerc. Physiol. 2016;12:119–130.
    doi: 10.3920/CEP160015google scholar: lookup
  27. Janabi AHD, Biddle AS, Klein DJ, McKeever KH. The effects of acute strenuous exercise on the faecal microbiota in Standardbred racehorses. Comp. Exerc. Physiol. 2017;13:13–24.
    doi: 10.3920/CEP160030google scholar: lookup
  28. Gilroy R, Ravi A, Getino M, Pursley I, Horton DL, Alikhan NF, Baker D, Gharbi K, Hall N, Watson M, Adriaenssens EM, Foster-Nyarko E, Jarju S, Secka A, Antonio M, Oren A, Chaudhuri RR, La Ragione R, Hildebrand F, Pallen MJ. Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture.. PeerJ 2021;9:e10941.
    doi: 10.7717/peerj.10941pmc: PMC8035907pubmed: 33868800google scholar: lookup
  29. Li J, Zhong H, Ramayo-Caldas Y, Terrapon N, Lombard V, Potocki-Veronese G, Estellé J, Popova M, Yang Z, Zhang H, Li F, Tang S, Yang F, Chen W, Chen B, Li J, Guo J, Martin C, Maguin E, Xu X, Yang H, Wang J, Madsen L, Kristiansen K, Henrissat B, Ehrlich SD, Morgavi DP. A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environment.. Gigascience 2020 Jun 1;9(6).
    doi: 10.1093/gigascience/giaa057pmc: PMC7260996pubmed: 32473013google scholar: lookup
  30. Ang L, Vinderola G, Endo A, Kantanen J, Jingfeng C, Binetti A, Burns P, Qingmiao S, Suying D, Zujiang Y, Rios-Covian D, Mantziari A, Beasley S, Gomez-Gallego C, Gueimonde M, Salminen S. Gut Microbiome Characteristics in feral and domesticated horses from different geographic locations.. Commun Biol 2022 Feb 25;5(1):172.
    doi: 10.1038/s42003-022-03116-2pmc: PMC8881449pubmed: 35217713google scholar: lookup
  31. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju.. Nat Commun 2016 Apr 13;7:11257.
    doi: 10.1038/ncomms11257pmc: PMC4833860pubmed: 27071849google scholar: lookup
  32. Hu D, Yang J, Qi Y, Li B, Li K, Mok KM. Metagenomic Analysis of Fecal Archaea, Bacteria, Eukaryota, and Virus in Przewalski's Horses Following Anthelmintic Treatment.. Front Vet Sci 2021;8:708512.
    doi: 10.3389/fvets.2021.708512pmc: PMC8416479pubmed: 34490397google scholar: lookup
  33. Barton W, Cronin O, Garcia-Perez I, Whiston R, Holmes E, Woods T, Molloy CB, Molloy MG, Shanahan F, Cotter PD, O'Sullivan O. The effects of sustained fitness improvement on the gut microbiome: A longitudinal, repeated measures case-study approach.. Transl Sports Med 2021 Mar;4(2):174-192.
    doi: 10.1002/tsm2.215pmc: PMC8317196pubmed: 34355132google scholar: lookup
  34. O' Donnell MM, Harris HM, Jeffery IB, Claesson MJ, Younge B, O' Toole PW, Ross RP. The core faecal bacterial microbiome of Irish Thoroughbred racehorses.. Lett Appl Microbiol 2013 Dec;57(6):492-501.
    doi: 10.1111/lam.12137pubmed: 23889584google scholar: lookup
  35. Kauter A, Epping L, Semmler T, Antao EM, Kannapin D, Stoeckle SD, Gehlen H, Lübke-Becker A, Günther S, Wieler LH, Walther B. The gut microbiome of horses: current research on equine enteral microbiota and future perspectives.. Anim Microbiome 2019 Nov 13;1(1):14.
    doi: 10.1186/s42523-019-0013-3pmc: PMC7807895pubmed: 33499951google scholar: lookup
  36. Dougal K, de la Fuente G, Harris PA, Girdwood SE, Pinloche E, Newbold CJ. Identification of a core bacterial community within the large intestine of the horse.. PLoS One 2013;8(10):e77660.
  37. El Kaoutari A, Armougom F, Gordon JI, Raoult D, Henrissat B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota.. Nat Rev Microbiol 2013 Jul;11(7):497-504.
    doi: 10.1038/nrmicro3050pubmed: 23748339google scholar: lookup
  38. Hu Y, Yang X, Qin J, Lu N, Cheng G, Wu N, Pan Y, Li J, Zhu L, Wang X, Meng Z, Zhao F, Liu D, Ma J, Qin N, Xiang C, Xiao Y, Li L, Yang H, Wang J, Yang R, Gao GF, Wang J, Zhu B. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota.. Nat Commun 2013;4:2151.
    doi: 10.1038/ncomms3151pubmed: 23877117google scholar: lookup
  39. Munk P, Knudsen BE, Lukjancenko O, Duarte ASR, Van Gompel L, Luiken REC, Smit LAM, Schmitt H, Garcia AD, Hansen RB, Petersen TN, Bossers A, Ruppé E, Lund O, Hald T, Pamp SJ, Vigre H, Heederik D, Wagenaar JA, Mevius D, Aarestrup FM. Abundance and diversity of the faecal resistome in slaughter pigs and broilers in nine European countries.. Nat Microbiol 2018 Aug;3(8):898-908.
    doi: 10.1038/s41564-018-0192-9pubmed: 30038308google scholar: lookup
  40. Wang C, Song Y, Tang N, Zhang G, Leclercq SO, Feng J. The shared resistome of human and pig microbiota is mobilized by distinct genetic elements.. Appl Environ Microbiol 2021 Mar 1;87(5).
    doi: 10.1128/AEM.01910-20pmc: PMC8090867pubmed: 33310720google scholar: lookup
  41. Sabino YNV, Santana MF, Oyama LB, Santos FG, Moreira AJS, Huws SA, Mantovani HC. Characterization of antibiotic resistance genes in the species of the rumen microbiota.. Nat Commun 2019 Nov 20;10(1):5252.
    doi: 10.1038/s41467-019-13118-0pmc: PMC6868206pubmed: 31748524google scholar: lookup
  42. Rands CM, Starikova EV, Brüssow H, Kriventseva EV, Govorun VM, Zdobnov EM. ACI-1 beta-lactamase is widespread across human gut microbiomes in Negativicutes due to transposons harboured by tailed prophages.. Environ Microbiol 2018 Jun;20(6):2288-2300.
    doi: 10.1111/1462-2920.14276pubmed: 30014616google scholar: lookup
  43. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database.. Bioinformatics 2019 Nov 15;36(6):1925-7.
  44. Gilroy R, Leng J, Ravi A, Adriaenssens EM, Oren A, Baker D, La Ragione RM, Proudman C, Pallen MJ. Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity.. PeerJ 2022;10:e13084.
    doi: 10.7717/peerj.13084pmc: PMC8957277pubmed: 35345588google scholar: lookup
  45. Youngblut ND, de la Cuesta-Zuluaga J, Reischer GH, Dauser S, Schuster N, Walzer C, Stalder G, Farnleitner AH, Ley RE. Large-Scale Metagenome Assembly Reveals Novel Animal-Associated Microbial Genomes, Biosynthetic Gene Clusters, and Other Genetic Diversity.. mSystems 2020 Nov 3;5(6).
    doi: 10.1128/mSystems.01045-20pmc: PMC7646530pubmed: 33144315google scholar: lookup
  46. Poulsen M, Schwab C, Jensen BB, Engberg RM, Spang A, Canibe N, Højberg O, Milinovich G, Fragner L, Schleper C, Weckwerth W, Lund P, Schramm A, Urich T. Methylotrophic methanogenic Thermoplasmata implicated in reduced methane emissions from bovine rumen.. Nat Commun 2013;4:1428.
    pubmed: 23385573doi: 10.1038/ncomms2432google scholar: lookup
  47. Mizrahi I, Wallace RJ, Moraïs S. The rumen microbiome: balancing food security and environmental impacts.. Nat Rev Microbiol 2021 Sep;19(9):553-566.
    doi: 10.1038/s41579-021-00543-6pubmed: 33981031google scholar: lookup
  48. Fielding CL, Dechant JE. Colic in competing endurance horses presenting to referral centres: 36 cases.. Equine Vet J 2012 Jul;44(4):472-5.
  49. Nyholm L, Koziol A, Marcos S, Botnen AB, Aizpurua O, Gopalakrishnan S, Limborg MT, Gilbert MTP, Alberdi A. Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research.. iScience 2020 Aug 21;23(8):101414.
    doi: 10.1016/j.isci.2020.101414pmc: PMC7416341pubmed: 32777774google scholar: lookup
  50. Matsumoto M, Inoue R, Tsukahara T, Ushida K, Chiji H, Matsubara N, Hara H. Voluntary running exercise alters microbiota composition and increases n-butyrate concentration in the rat cecum.. Biosci Biotechnol Biochem 2008 Feb;72(2):572-6.
    doi: 10.1271/bbb.70474pubmed: 18256465google scholar: lookup
  51. Clarke SF, Murphy EF, O'Sullivan O, Lucey AJ, Humphreys M, Hogan A, Hayes P, O'Reilly M, Jeffery IB, Wood-Martin R, Kerins DM, Quigley E, Ross RP, O'Toole PW, Molloy MG, Falvey E, Shanahan F, Cotter PD. Exercise and associated dietary extremes impact on gut microbial diversity.. Gut 2014 Dec;63(12):1913-20.
    doi: 10.1136/gutjnl-2013-306541pubmed: 25021423google scholar: lookup
  52. Bressa C, Bailén-Andrino M, Pérez-Santiago J, González-Soltero R, Pérez M, Montalvo-Lominchar MG, Maté-Muñoz JL, Domínguez R, Moreno D, Larrosa M. Differences in gut microbiota profile between women with active lifestyle and sedentary women.. PLoS One 2017;12(2):e0171352.
  53. Munukka E, Ahtiainen JP, Puigbó P, Jalkanen S, Pahkala K, Keskitalo A, Kujala UM, Pietilä S, Hollmén M, Elo L, Huovinen P, D'Auria G, Pekkala S. Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women.. Front Microbiol 2018;9:2323.
    doi: 10.3389/fmicb.2018.02323pmc: PMC6178902pubmed: 30337914google scholar: lookup
  54. Petersen LM, Bautista EJ, Nguyen H, Hanson BM, Chen L, Lek SH, Sodergren E, Weinstock GM. Community characteristics of the gut microbiomes of competitive cyclists.. Microbiome 2017 Aug 10;5(1):98.
    doi: 10.1186/s40168-017-0320-4pmc: PMC5553673pubmed: 28797298google scholar: lookup
  55. Karl JP, Margolis LM, Madslien EH, Murphy NE, Castellani JW, Gundersen Y, Hoke AV, Levangie MW, Kumar R, Chakraborty N, Gautam A, Hammamieh R, Martini S, Montain SJ, Pasiakos SM. Changes in intestinal microbiota composition and metabolism coincide with increased intestinal permeability in young adults under prolonged physiological stress.. Am J Physiol Gastrointest Liver Physiol 2017 Jun 1;312(6):G559-G571.
    doi: 10.1152/ajpgi.00066.2017pubmed: 28336545google scholar: lookup
  56. Sorbara MT, Littmann ER, Fontana E, Moody TU, Kohout CE, Gjonbalaj M, Eaton V, Seok R, Leiner IM, Pamer EG. Functional and Genomic Variation between Human-Derived Isolates of Lachnospiraceae Reveals Inter- and Intra-Species Diversity.. Cell Host Microbe 2020 Jul 8;28(1):134-146.e4.
    doi: 10.1016/j.chom.2020.05.005pmc: PMC7351604pubmed: 32492369google scholar: lookup
  57. Steelman SM, Chowdhary BP, Dowd S, Suchodolski J, Janečka JE. Pyrosequencing of 16S rRNA genes in fecal samples reveals high diversity of hindgut microflora in horses and potential links to chronic laminitis.. BMC Vet Res 2012 Nov 27;8:231.
    doi: 10.1186/1746-6148-8-231pmc: PMC3538718pubmed: 23186268google scholar: lookup
  58. Venable EB. Effects of feeding management on the equine cecal microbiota. J. Equine Vet. Sci. 2017;49:113–121.
  59. Leitch EC, Walker AW, Duncan SH, Holtrop G, Flint HJ. Selective colonization of insoluble substrates by human faecal bacteria.. Environ Microbiol 2007 Mar;9(3):667-79.
  60. Xie F, Jin W, Si H, Yuan Y, Tao Y, Liu J, Wang X, Yang C, Li Q, Yan X, Lin L, Jiang Q, Zhang L, Guo C, Greening C, Heller R, Guan LL, Pope PB, Tan Z, Zhu W, Wang M, Qiu Q, Li Z, Mao S. An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants.. Microbiome 2021 Jun 12;9(1):137.
    doi: 10.1186/s40168-021-01078-xpmc: PMC8199421pubmed: 34118976google scholar: lookup
  61. Chen C, Zhou Y, Fu H, Xiong X, Fang S, Jiang H, Wu J, Yang H, Gao J, Huang L. Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome.. Nat Commun 2021 Feb 17;12(1):1106.
    pmc: PMC7889623pubmed: 33597514doi: 10.1038/s41467-021-21295-0google scholar: lookup
  62. Xiao L, Estellé J, Kiilerich P, Ramayo-Caldas Y, Xia Z, Feng Q, Liang S, Pedersen AØ, Kjeldsen NJ, Liu C, Maguin E, Doré J, Pons N, Le Chatelier E, Prifti E, Li J, Jia H, Liu X, Xu X, Ehrlich SD, Madsen L, Kristiansen K, Rogel-Gaillard C, Wang J. A reference gene catalogue of the pig gut microbiome.. Nat Microbiol 2016 Sep 19;1:16161.
    doi: 10.1038/nmicrobiol.2016.161pubmed: 27643971google scholar: lookup
  63. Clark A, Mach N. The Crosstalk between the Gut Microbiota and Mitochondria during Exercise.. Front Physiol 2017;8:319.
    doi: 10.3389/fphys.2017.00319pmc: PMC5437217pubmed: 28579962google scholar: lookup
  64. Bonora M, Wieckowski MR, Sinclair DA, Kroemer G, Pinton P, Galluzzi L. Targeting mitochondria for cardiovascular disorders: therapeutic potential and obstacles.. Nat Rev Cardiol 2019 Jan;16(1):33-55.
    doi: 10.1038/s41569-018-0074-0pmc: PMC6349394pubmed: 30177752google scholar: lookup
  65. Shabat SK, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, White BA, Shterzer N, Mizrahi I. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.. ISME J 2016 Dec;10(12):2958-2972.
    doi: 10.1038/ismej.2016.62pmc: PMC5148187pubmed: 27152936google scholar: lookup
  66. Boros M, Ghyczy M, Érces D, Varga G, Tőkés T, Kupai K, Torday C, Kaszaki J. The anti-inflammatory effects of methane.. Crit Care Med 2012 Apr;40(4):1269-78.
    doi: 10.1097/CCM.0b013e31823dae05pubmed: 22336723google scholar: lookup
  67. 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;13:43.
    doi: 10.1186/s12970-016-0155-6pmc: PMC5121944pubmed: 27924137google scholar: lookup
  68. Larsen N, Bussolo de Souza C, Krych L, Barbosa Cahú T, Wiese M, Kot W, Hansen KM, Blennow A, Venema K, Jespersen L. Potential of Pectins to Beneficially Modulate the Gut Microbiota Depends on Their Structural Properties.. Front Microbiol 2019;10:223.
    doi: 10.3389/fmicb.2019.00223pmc: PMC6384267pubmed: 30828323google scholar: lookup
  69. Vacca M, Celano G, Calabrese FM, Portincasa P, Gobbetti M, De Angelis M. The Controversial Role of Human Gut Lachnospiraceae.. Microorganisms 2020 Apr 15;8(4).
  70. Le Moyec L, Robert C, Triba MN, Bouchemal N, Mach N, Rivière J, Zalachas-Rebours E, Barrey E. A First Step Toward Unraveling the Energy Metabolism in Endurance Horses: Comparison of Plasma Nuclear Magnetic Resonance Metabolomic Profiles Before and After Different Endurance Race Distances.. Front Mol Biosci 2019;6:45.
    doi: 10.3389/fmolb.2019.00045pmc: PMC6581711pubmed: 31245385google scholar: lookup
  71. Younes M, Robert C, Cottin F, Barrey E. Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events.. PLoS One 2015;10(8):e0137013.
  72. Sinnwell JP, Therneau TM, Schaid DJ. The kinship2 R package for pedigree data.. Hum Hered 2014;78(2):91-3.
    doi: 10.1159/000363105pmc: PMC4154601pubmed: 25074474google scholar: lookup
  73. 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
  74. Mach N, Plancade S, Pacholewska A, Lecardonnel J, Rivière J, Moroldo M, Vaiman A, Morgenthaler C, Beinat M, Nevot A, Robert C, Barrey E. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.. Sci Rep 2016 Mar 10;6:22932.
    doi: 10.1038/srep22932pmc: PMC4785432pubmed: 26960911google scholar: lookup
  75. 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.
  76. Mach N, Foury A, Kittelmann S, Reigner F, Moroldo M, Ballester M, Esquerré D, Rivière J, Sallé G, Gérard P, Moisan MP, Lansade L. The Effects of Weaning Methods on Gut Microbiota Composition and Horse Physiology.. Front Physiol 2017;8:535.
    doi: 10.3389/fphys.2017.00535pmc: PMC5524898pubmed: 28790932google scholar: lookup
  77. Lan A, Bruneau A, Philippe C, Rochet V, Rouault A, Hervé C, Roland N, Rabot S, Jan G. Survival and metabolic activity of selected strains of Propionibacterium freudenreichii in the gastrointestinal tract of human microbiota-associated rats.. Br J Nutr 2007 Apr;97(4):714-24.
    doi: 10.1017/S0007114507433001pubmed: 17349084google scholar: lookup
  78. Clark A, Sallé G, Ballan V, Reigner F, Meynadier A, Cortet J, Koch C, Riou M, Blanchard A, Mach N. Strongyle Infection and Gut Microbiota: Profiling of Resistant and Susceptible Horses Over a Grazing Season.. Front Physiol 2018;9:272.
    doi: 10.3389/fphys.2018.00272pmc: PMC5871743pubmed: 29618989google scholar: lookup
  79. Massacci FR, Clark A, Ruet A, Lansade L, Costa M, Mach N. Inter-breed diversity and temporal dynamics of the faecal microbiota in healthy horses.. J Anim Breed Genet 2020 Jan;137(1):103-120.
    pubmed: 31523867doi: 10.1111/jbg.12441google scholar: lookup
  80. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data.. Nat Methods 2016 Jul;13(7):581-3.
    doi: 10.1038/nmeth.3869pmc: PMC4927377pubmed: 27214047google scholar: lookup
  81. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Gregory Caporaso J. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.. Microbiome 2018 May 17;6(1):90.
    doi: 10.1186/s40168-018-0470-zpmc: PMC5956843pubmed: 29773078google scholar: lookup
  82. McMurdie PJ, Holmes S. Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data.. Pac Symp Biocomput 2012;:235-46.
    pmc: PMC3357092pubmed: 22174279
  83. Dixon P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 2003;14:927.
  84. Kieser S, Brown J, Zdobnov EM, Trajkovski M, McCue LA. ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data.. BMC Bioinformatics 2020 Jun 22;21(1):257.
    doi: 10.1186/s12859-020-03585-4pmc: PMC7310028pubmed: 32571209google scholar: lookup
  85. Bushnell, B. BBMap. https://sourceforge.net/projects/bbmap/ (2015).
  86. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler.. Genome Res 2017 May;27(5):824-834.
    doi: 10.1101/gr.213959.116pmc: PMC5411777pubmed: 28298430google scholar: lookup
  87. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies.. Bioinformatics 2013 Apr 15;29(8):1072-5.
  88. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies.. PeerJ 2019;7:e7359.
    doi: 10.7717/peerj.7359pmc: PMC6662567pubmed: 31388474google scholar: lookup
  89. Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets.. Bioinformatics 2016 Feb 15;32(4):605-7.
    doi: 10.1093/bioinformatics/btv638pubmed: 26515820google scholar: lookup
  90. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, Banfield JF. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy.. Nat Microbiol 2018 Jul;3(7):836-843.
    doi: 10.1038/s41564-018-0171-1pmc: PMC6786971pubmed: 29807988google scholar: lookup
  91. Mach N, Midoux C, Rué O. A dataset of equine gut metagenome. V1 [Dataset] 10.15454/NGBSPC (2022).
    doi: 10.15454/ngbspcgoogle scholar: lookup
  92. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes.. Genome Res 2015 Jul;25(7):1043-55.
    doi: 10.1101/gr.186072.114pmc: PMC4484387pubmed: 25977477google scholar: lookup
  93. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication.. ISME J 2017 Dec;11(12):2864-2868.
    doi: 10.1038/ismej.2017.126pmc: PMC5702732pubmed: 28742071google scholar: lookup
  94. Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification.. BMC Bioinformatics 2010 Mar 8;11:119.
    doi: 10.1186/1471-2105-11-119pmc: PMC2848648pubmed: 20211023google scholar: lookup
  95. Steinegger M, Söding J. Clustering huge protein sequence sets in linear time.. Nat Commun 2018 Jun 29;9(1):2542.
    doi: 10.1038/s41467-018-04964-5pmc: PMC6026198pubmed: 29959318google scholar: lookup
  96. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data.. Bioinformatics 2012 Dec 1;28(23):3150-2.
  97. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks.. PLoS Comput Biol 2015 May;11(5):e1004226.
  98. Clarke KR, Ainsworth M. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. 1993;92:205–219.
    doi: 10.3354/meps092205google scholar: lookup
  99. Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, Lê Cao KA. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays.. Bioinformatics 2019 Sep 1;35(17):3055-3062.
  100. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.. Genome Biol 2014;15(12):550.
    doi: 10.1186/s13059-014-0550-8pmc: PMC4302049pubmed: 25516281google scholar: lookup
  101. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: An R package for 'omics feature selection and multiple data integration.. PLoS Comput Biol 2017 Nov;13(11):e1005752.

Citations

This article has been cited 20 times.
  1. Courtot É, Boisseau M, Dhorne-Pollet S, Serreau D, Gesbert A, Reigner F, Basiaga M, Kuzmina T, Lluch J, Annonay G, Kuchly C, Diekmann I, Krücken J, von Samson-Himmelstjerna G, Mach N, Sallé G. Comparison of two molecular barcodes for the study of equine strongylid communities with amplicon sequencing. PeerJ 2023;11:e15124.
    doi: 10.7717/peerj.15124pubmed: 37070089google scholar: lookup
  2. Boisseau M, Dhorne-Pollet S, Bars-Cortina D, Courtot É, Serreau D, Annonay G, Lluch J, Gesbert A, Reigner F, Sallé G, Mach N. Species interactions, stability, and resilience of the gut microbiota - Helminth assemblage in horses. iScience 2023 Feb 17;26(2):106044.
    doi: 10.1016/j.isci.2023.106044pubmed: 36818309google scholar: lookup
  3. Wunderlich G, Bull M, Ross T, Rose M, Chapman B. Understanding the microbial fibre degrading communities & processes in the equine gut. Anim Microbiome 2023 Jan 12;5(1):3.
    doi: 10.1186/s42523-022-00224-6pubmed: 36635784google scholar: lookup
  4. Sidibé O, Doublet B, Leclercq SO. IME_Rho_tet, a novel family of putative integrative mobilizable elements spreading tetracycline resistance genes tet(W) and tet(32) among human and animal gut bacteria. Microb Genom 2026 Feb;12(2).
    doi: 10.1099/mgen.0.001640pubmed: 41686688google scholar: lookup
  5. Kinkpe L, Solomon AI, Niu Y, Goswami N, Ikele CM, Hu D, Abdessan R, Zhigang H, Xia W. A guide to network analysis, multi-omics integration, and applications in livestock microbiome research. World J Microbiol Biotechnol 2025 Dec 31;42(1):17.
    doi: 10.1007/s11274-025-04755-3pubmed: 41474484google scholar: lookup
  6. Mach N, Monié-Ibanes M, Sikht FZ, Hygonenq MC, Pot G, Robert H, Bars D, Farizon Y, Richard E, Nouvel X, Citti C, Baranowski E, Ducatez M, Meyer G. Decoding the dynamics of calves' respiratory and gut microbiota: exploring stability, resistance, and individual patterns. Anim Microbiome 2025 Dec 18;7(1):126.
    doi: 10.1186/s42523-025-00494-wpubmed: 41413607google scholar: lookup
  7. Li F, Kong X, Khan MZ, Wei L, Wei J, Zhu M, Liu G, Huang B, Wang C, Zhang Z. Gut microbiome regulation in equine animals: current understanding and future perspectives. Front Microbiol 2025;16:1602258.
    doi: 10.3389/fmicb.2025.1602258pubmed: 41070119google scholar: lookup
  8. Mohr AE, Mach N, Pugh J, Grosicki GJ, Allen JM, Karl JP, Whisner CM. Mechanisms underlying alterations of the gut microbiota by exercise and their role in shaping ecological resilience. FEMS Microbiol Rev 2025 Jan 14;49.
    doi: 10.1093/femsre/fuaf037pubmed: 40796291google scholar: lookup
  9. Qin X, Xi L, Zhao L, Han J, Qu H, Xu Y, Weng W. Exploring the distinctive characteristics of gut microbiota across different horse breeds and ages using metataxonomics. Front Cell Infect Microbiol 2025;15:1590839.
    doi: 10.3389/fcimb.2025.1590839pubmed: 40692682google scholar: lookup
  10. Irving J, Pineau V, Shultz S, Ter Woort F, Julien F, Lambey S, van Erck-Westergren E. Impact of Low-Starch Dietary Modifications on Faecal Microbiota Composition and Gastric Disease Scores in Performance Horses. Animals (Basel) 2025 Jun 28;15(13).
    doi: 10.3390/ani15131908pubmed: 40646806google scholar: lookup
  11. Klimek D, Lage OM, Calusinska M. Phylogenetic diversity and community structure of Planctomycetota from plant biomass-rich environments. Front Microbiol 2025;16:1579219.
    doi: 10.3389/fmicb.2025.1579219pubmed: 40510668google scholar: lookup
  12. Sun YF, Han ZX, Yao XK, Meng J, Ren WL, Wang CK, Yuan XX, Zeng YQ, Wang YF, Sun ZW, Wang JW. Effects of Different Stages of Training on the Intestinal Microbes of Yili Horses Analyzed Using Metagenomics. Genes (Basel) 2025 Apr 27;16(5).
    doi: 10.3390/genes16050504pubmed: 40428326google scholar: lookup
  13. Li C, Li X, Liu K, Xu J, Yu J, Liu Z, Mach N, Ni W, Liu C, Zhou P, Wang L, Hu S. Multiomic analysis of different horse breeds reveals that gut microbial butyrate enhances racehorse athletic performance. NPJ Biofilms Microbiomes 2025 May 24;11(1):87.
    doi: 10.1038/s41522-025-00730-wpubmed: 40410196google scholar: lookup
  14. Curadi MC, Vallone F, Tenuzzo M, Gazzano A, Gazzano V, Macchioni F, Vannini C. Effect of Management System on Fecal Microbiota in Arabian Horses: Preliminary Results. Vet Sci 2025 Mar 28;12(4).
    doi: 10.3390/vetsci12040309pubmed: 40284811google scholar: lookup
  15. Vasseur M, Lepers R, Langevin N, Julliand S, Grimm P. Fibrolytic efficiency of the large intestine microbiota may benefit running speed in French trotters: A pilot study. Physiol Rep 2024 Nov;12(21):e70110.
    doi: 10.14814/phy2.70110pubmed: 39533164google scholar: lookup
  16. Sävilammi T, Alakangas RR, Häyrynen T, Uusi-Heikkilä S. Gut Microbiota Profiling as a Promising Tool to Detect Equine Inflammatory Bowel Disease (IBD). Animals (Basel) 2024 Aug 18;14(16).
    doi: 10.3390/ani14162396pubmed: 39199930google scholar: lookup
  17. Leng J, Moller-Levet C, Mansergh RI, O'Flaherty R, Cooke R, Sells P, Pinkham C, Pynn O, Smith C, Wise Z, Ellis R, Couto Alves A, La Ragione R, Proudman C. Early-life gut bacterial community structure predicts disease risk and athletic performance in horses bred for racing. Sci Rep 2024 Aug 7;14(1):17124.
    doi: 10.1038/s41598-024-64657-6pubmed: 39112552google scholar: lookup
  18. Wester RJ, Baillie LL, McCarthy GC, Keever CC, Jeffery LE, Adams PJ. Dysbiosis not observed in Canadian horse with free fecal liquid (FFL) using 16S rRNA sequencing. Sci Rep 2024 Jun 5;14(1):12903.
    doi: 10.1038/s41598-024-63868-1pubmed: 38839848google scholar: lookup
  19. Boucher L, Leduc L, Leclère M, Costa MC. Current Understanding of Equine Gut Dysbiosis and Microbiota Manipulation Techniques: Comparison with Current Knowledge in Other Species. Animals (Basel) 2024 Feb 28;14(5).
    doi: 10.3390/ani14050758pubmed: 38473143google scholar: lookup
  20. Clark A, Mach N. The gut mucin-microbiota interactions: a missing key to optimizing endurance performance. Front Physiol 2023;14:1284423.
    doi: 10.3389/fphys.2023.1284423pubmed: 38074323google scholar: lookup