FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Anim Microbiome / Fecal microbiome of horses transitioning between warm-season...
Animal microbiome2022; 4(1); 41; doi: 10.1186/s42523-022-00192-x

Fecal microbiome of horses transitioning between warm-season and cool-season grass pasture within integrated rotational grazing systems.

Abstract: Diet is a key driver of equine hindgut microbial community structure and composition. The aim of this study was to characterize shifts in the fecal microbiota of grazing horses during transitions between forage types within integrated warm- (WSG) and cool-season grass (CSG) rotational grazing systems (IRS). Eight mares were randomly assigned to two IRS containing mixed cool-season grass and one of two warm-season grasses: bermudagrass [Cynodon dactylon (L.) Pers.] or crabgrass [Digitaria sanguinalis (L.) Scop.]. Fecal samples were collected during transitions from CSG to WSG pasture sections (C-W) and WSG to CSG (W-C) on days 0, 2, 4, and 6 following pasture rotation and compared using 16S rRNA gene sequencing. Results: Regardless of IRS or transition (C-W vs. W-C), species richness was greater on day 4 and 6 in comparison to day 0 (P < 0.05). Evenness, however, did not differ by day. Weighted UniFrac also did not differ by day, and the most influential factor impacting β-diversity was the individual horse (R2 ≥ 0.24; P = 0.0001). Random forest modeling was unable to accurately predict days within C-W and W-C, but could predict the individual horse based on microbial composition (accuracy: 0.92 ± 0.05). Only three differentially abundant bacterial co-abundance groups (BCG) were identified across days within all C-W and W-C for both IRS (W ≥ 126). The BCG differing by day for all transitions included amplicon sequence variants (ASV) assigned to bacterial groups with known fibrolytic and butyrate-producing functions including members of Lachnospiraceae, Clostridium sensu stricto 1, Anaerovorax the NK4A214 group of Oscillospiraceae, and Sarcina maxima. In comparison, 38 BCG were identified as differentially abundant by horse (W ≥ 704). The ASV in these groups were most commonly assigned to genera associated with degradation of structural carbohydrates included Rikenellaceae RC9 gut group, Treponema, Christensenellaceae R-7 group, and the NK4A214 group of Oscillospiraceae. Fecal pH also did not differ by day. Conclusions: Overall, these results demonstrated a strong influence of individual horse on the fecal microbial community, particularly on the specific composition of fiber-degraders. The equine fecal microbiota were largely stable across transitions between forages within IRS suggesting that the equine gut microbiota adjusted at the individual level to the subtle dietary changes imposed by these transitions. This adaptive capacity indicates that horses can be managed in IRS without inducing gastrointestinal dysfunction.
© 2022. The Author(s).
Get Full Text
Publication Date: 2022-06-21 PubMed ID: 35729677PubMed Central: PMC9210719DOI: 10.1186/s42523-022-00192-xGoogle Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

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

The research article focuses on the impact of different types of grazing grass on the fecal microbiota of horses. The study finds that individual variations in horses have greater influence on the microbial communities than the grass type. Moreover, the study suggests that the equine gut microbiota can adapt to dietary changes imposed by different grass types, implying possible health stability in horses under different grazing systems.

Research Goals and Methodology

  • The study aims to understand how the fecal microbiota of horses changes when they switch between different types of grass within rotational grazing systems.
  • Eight mares were used in the study, rotating between mixed cool-season and warm-season grass in integrated grazing systems.
  • Fecal samples were collected during these transitions, specifically from cool-season grass to warm-season grass and vice versa.
  • The differences in fecal microbiota were studied using 16S rRNA gene sequencing, a popular method in molecular biology that enables the identification and comparison of bacteria from complex microbial communities.

Research Findings

  • The study observed an increase in species richness, the number of different species present in a particular area, on day 4 and 6 of transition in comparison to day 0.
  • However, species evenness, a measure of biodiversity that shows the relative abundance of different species in an area, did not show any significant change over the days.
  • The study found the individual horse to be the most influential factor affecting β-diversity, a measure of the total species diversity at a landscape level.
  • Three noticeably abundant bacterial co-abundance groups (BCG) were identified across all transitions, including members of Lachnospiraceae, Clostridium, Anaerovorax, and Sarcina maxima, which are known for their fibrolytic and butyrate-producing functions.
  • 38 BCG were found to be differentially abundant by horse, implicating significant individual variability in the gut microbiota of horses. The bacteria involved in these BCG primarily degrade structural carbohydrates.
  • The fecal pH of the horses remained unaffected by the type of grass they consumed.

Conclusion

  • The acquired results pointed towards a strong influence of individual horse over its fecal microbiota, particularly the specific composition of fiber-degraders.
  • The transition between forages within integrated rotational grazing systems (IRS) showed no major synchronic disturbance in the fecal microbial community, suggesting an adaptive response to subtle dietary changes.
  • This resilience and adaptive capacity indicate that horses can be managed in different grazing systems without causing any gastrointestinal disorders, providing essential insights for better horse nutrition management strategies.

Cite This Article

APA
Weinert-Nelson JR, Biddle AS, Williams CA. (2022). Fecal microbiome of horses transitioning between warm-season and cool-season grass pasture within integrated rotational grazing systems. Anim Microbiome, 4(1), 41. https://doi.org/10.1186/s42523-022-00192-x

Publication

Animal microbiome
ISSN: 2524-4671
NlmUniqueID: 101759457
Country: England
Language: English
Volume: 4
Issue: 1
Pages: 41

Researcher Affiliations

Weinert-Nelson, Jennifer R
  • Department of Animal Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08901, USA. jennifer.weinert@rutgers.edu.
Biddle, Amy S
  • Department of Animal and Food Sciences, College of Agriculture and Natural Resources, University of Delaware, Newark, DE, 19711, USA.
Williams, Carey A
  • Department of Animal Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08901, USA.

Grant Funding

  • gne17-162 / Northeast SARE
  • gne17-162 / Northeast SARE
  • 1003557 / National Institute of Food and Agriculture
  • NJ06170 / New Jersey Agricultural Experiment Station

Conflict of Interest Statement

No conflict of interest declared by any of the authors.

References

This article includes 122 references
  1. Williams CA, Kenny LB, Weinert JR, Sullivan K, Meyer W, Robson MG. Effects of 27 mo of rotational vs. continuous grazing on horse and pasture condition.. Transl Anim Sci 2020.
    doi: 10.1093/tas/txaa084pmc: PMC7370406pubmed: 32705075google scholar: lookup
  2. Taiz L, Zeiger E. Photosynthesis: carbon reactions.. Plant physiology 3. Sunderland: Sinauer Associates, Inc; 2002. pp. 111–143.
  3. Moore KJ, White TA, Hintz RL, Patrick PK, Brummer EC. Sequential grazing of cool-and warm-season pastures.. Agron J 2004;96(4):1103–1111.
    doi: 10.2134/agronj1991.00021962008300060027xgoogle scholar: lookup
  4. Tracy BF, Maughan M, Post N, Faulkner DB. Integrating annual and perennial warm-season grasses in a temperate grazing system.. Crop Sci 2010;50(5):2171–2177.
    doi: 10.2135/cropsci2010.02.0110google scholar: lookup
  5. DeBoer ML, Sheaffer CC, Grev AM, Catalano DN, Wells MS, Hathaway MR, Martinson KL. Yield, nutritive value, and preference of annual warm-season grasses grazed by horses.. Agron J 2017;109(5):2136–2148.
    doi: 10.2134/agronj2017.02.0099google scholar: lookup
  6. Ritz KE, Heins BJ, Moon R, Sheaffer C, Weyers SL. Forage yield and nutritive value of cool-season and warm-season forages for grazing organic dairy cattle.. Agronomy 2020;10(12):1963.
    doi: 10.3390/agronomy10121963google scholar: lookup
  7. Weinert-Nelson JR, Meyer WA, Williams CA. Yield, nutritive value, and horse condition in integrated crabgrass and cool-season grass rotational grazing pasture systems.. Transl Anim Sci 2021.
    doi: 10.1093/tas/txab208pmc: PMC8633121pubmed: 34859200google scholar: lookup
  8. Teutsch C. Warm-season annual grasses for summer forage.. Publication 418-004. Communication and marketing, College of Agriculture and Life Sciences, Virginia Polytechnic Inst. and State Univ.: Blacksburg; 2006.
  9. . Teff KS (Eragrostis teff (Zucc.)). Trotter. Promoting the Conservation and use of the under utilized crops.. vol. 12. Institute of Plant Genetics and Crop Plant Research, Garersleben/International Plant Genetic Resource Institute. Rome, Italy; 1997.
  10. Taliaferro CM. Breeding forage bermudagrass for the US Transition zone.. Proceedings 59th southern pasture and forage crop improvement conference, Philadelphia, MS; 2005. p. 11–13.
  11. Ditsch DC, Smith SR, Lacefield GD. Bermudagrass: a summer forage in Kentucky.. Publication #AGR-48. University of Kentucky College of Agriculture, Lexington, KY; 2011.
  12. Goodson J, Tyznik WJ, Cline JH, Dehority BA. Effects of an abrupt diet change from hay to concentrate on microbial numbers and physical environment in the cecum of the pony.. Appl Environ Microbiol 1988;54:1946–1950.
    doi: 10.1128/aem.54.8.1946-1950.1988pmc: PMC202784pubmed: 3178206google scholar: lookup
  13. Hudson JM, Cohen ND, Gibbs PG, Thompson JA. Feeding practices associated with colic in horses.. J Am Vet Med Assoc 2001;219(10):1419–1425.
    doi: 10.2460/javma.2001.219.1419pubmed: 11724182google scholar: lookup
  14. Garner HE, Moore JN, Johnson JH, ClarkL AJF, Tritschler LG, Coffmann JR, Sprouse RF, Hutcheson DP, Salem CA. Changes in the caecal flora associated with the onset of laminitis.. Equine Vet J 1978;10:249–252.
    doi: 10.1111/j.2042-3306.1978.tb02273.xpubmed: 738266google scholar: lookup
  15. Millinovich GJ, Burrell PC, Pollitt CC, Klieve AV, Blackall LL, Ouwerkerk D, Woodland E, Trott DJ. Microbial ecology of the equine hindgut during oliofructose-induced laminitis.. ISME J 2008;2:1089–1100.
    doi: 10.1038/ismej.2008.67pubmed: 18580970google scholar: lookup
  16. Tuniyazi M, He J, Guo J, Li S, Zhang N, Hu X, Fu Y. Changes of microbial and metabolome of the equine hindgut during oligofructose-induced laminitis.. BMC Vet Res 2021;17(1):1–13.
    doi: 10.1186/s12917-020-02686-9pmc: PMC7789226pubmed: 33407409google scholar: lookup
  17. Cohen ND, Matejka PL, Honnas CM, Hooper RN. Case-control study of the association between various management factors and development of colic in horses. Texas equine colic study group.. J Am Vet Med Assoc 1995;206(5):667–673.
    pubmed: 7744689
  18. Tinker MK, White NA, Lessard P, Thatcher CD, Pelzer KD, Davis B, Carmel DK. Prospective study of equine colic risk factors.. Equine Vet J 1997;29(6):454–458.
    doi: 10.1111/j.2042-3306.1997.tb03158.xpubmed: 9413718google scholar: lookup
  19. Venable E, Kerley MS, Raub R. Assessment of equine fecal microbial profiles during and after a colic episode using pyrosequencing.. J Equine Vet Sci 2013;33:347.
    doi: 10.1016/j.jevs.2013.03.066google scholar: lookup
  20. Weese JS, Holcombe SJ, Embertson RM, Kurtz KA, Roessner HA, Jalali M, Wismer SE. Changes in the faecal microbiota ofmares precede the development of post partum colic.. Equine Vet J 2015;47:641–649.
    doi: 10.1111/evj.12361pubmed: 25257320google scholar: lookup
  21. Stewart HL, Southwood LL, Indugu N, Vecchiarelli B, Engiles JB, Pitta D. Differences in the equine faecal microbiota between horses presenting to a tertiary referral hospital for colic compared with an elective surgical procedure.. Equine Vet J 2019;51(3):336–342.
    doi: 10.1111/evj.13010pubmed: 30153353google scholar: lookup
  22. United States Department of Agriculture. Lameness and laminitis in US horses.. USDA: APHIS: US, CEAH, National Animal Health Monitoring System. United States Department of Agriculture, Washington DC; 2000.
  23. United States Department of Agriculture. Baseline reference of equine health and management in the United States, 2015.. USDA: APHIS: US, CEAH, National Animal Health Monitoring System. US Department of Agriculture, Washington DC. 2016.
  24. Troya L, Blanco J, Romero I, Re M. Comparison of the colic incidence in a horse population with or without inclusion of germinated barley in the diet.. Equine Vet Educ 2020;32:28–32.
    doi: 10.1111/eve.13274google scholar: lookup
  25. Fernandes KA, Kittelmann S, Rogers CW, Gee EK, Bolwell CF, Thomas BEN, DG. Faecal microbiota of forage-fed horses in New Zealand and the population dynamics of microbial communities following dietary change.. PLoS ONE 2014;9(11):e112846.
    doi: 10.1371/journal.pone.0112846pmc: PMC4226576pubmed: 25383707google scholar: lookup
  26. Zhang C, Zhang M, Wang S, Han R, Cao Y, Hua W, Mao Y, Zhang X, Pang X, Wei C. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice.. ISME J 2010;4(2):232.
    doi: 10.1038/ismej.2009.112pubmed: 19865183google scholar: lookup
  27. Zhang C, Li S, Yang L, Huang P, Li W, Wang S, Zhao G, Zhang M, Pang X, Yan Z. Structural modulation of gut microbiota in life-long calorie-restricted mice.. Nat Commun 2013;4:2163.
    doi: 10.1038/ncomms3163pmc: PMC3717500pubmed: 23860099google scholar: lookup
  28. Dougal K, de la Fuente G, Harris PA, Girdwood SE, Pinloche E, Geor RJ, Nielsen BD, Schott HC II, Elzinga S, Newbold CJ. Characterisation of the faecal bacterial community in adult and elderly horses fed a high fibre, high oil or high starch diet using 454 pyrosequencing.. PLoS ONE 2014;9(2):e87424.
    doi: 10.1371/journal.pone.0087424pmc: PMC3913607pubmed: 24504261google scholar: lookup
  29. Chatterton NJ, Harrison PA, Bennett JH, Asay KH. Carbohydrate partitioning in 185 accessions of gramineae grown under warm and cool temperatures.. J Plant Physiol 1989;134(2):169–179.
    doi: 10.1016/S0176-1617(89)80051-3google scholar: lookup
  30. Jensen KB, Harrison P, Chatterton NJ, Bushman BS, Creech JE. Seasonal trends in nonstructural carbohydrates in cool-and warm-season grasses.. Crop Sci 2014;54(5):2328–2340.
    doi: 10.2135/cropsci2013.07.0465google scholar: lookup
  31. Hudson DJ, Leep RH, Dietz TS, Ragavendran A, Kravchenko A. Integrated warm-and cool-season grass and legume pastures: I. seasonal forage dynamics.. Agron J 2010;102(1):303–309.
    doi: 10.2134/agronj2009.0204google scholar: lookup
  32. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.. Nat Biotechnol 2019;37(8):852–857.
    doi: 10.1038/s41587-019-0209-9pmc: PMC7015180pubmed: 31341288google scholar: lookup
  33. Pelletier S, Tremblay GF, Bertrand A, Belanger G, Castonguay Y, Michaud R. Drying procedures affect non-structural carbohydrates and other nutritive value attributes in forage samples.. Anim Feed Sci Technol 2010;157:139–150.
    doi: 10.1016/j.anifeedsci.2010.02.010google scholar: lookup
  34. Garber A, Hastie P, McGuinness D, Malarange P, Murray JA. Abrupt dietary changes between grass and hay alter faecal microbiota of ponies.. PLoS ONE 2020;15(8):e0237869.
    doi: 10.1371/journal.pone.0237869pmc: PMC7446798pubmed: 32810164google scholar: lookup
  35. Muhonen S, Connysson M, Lindberg JE, Julliand V, Bertilsson J, Jansson A. Effects of crude protein intake from grass silage-only diets on the equine colon ecosystem after an abrupt feed change.. J Anim Sci 2008;86(12):3465–3472.
    doi: 10.2527/jas.2007-0374pubmed: 18676731google scholar: lookup
  36. Grimm P, Philippeau C, Julliand V. Faecal parameters as biomarkers of the equine hindgut microbial ecosystem under dietary change.. Animal 2017;11(7):1136–1145.
    doi: 10.1017/S1751731116002779pubmed: 28065211google scholar: lookup
  37. Fitzgerald DM, Spence RJ, Stewart ZK, Prentis PJ, Sillence MN, De Laat MA. The effect of diet change and insulin dysregulation on the faecal microbiome of ponies.. J Exper Biol 2020;223(7):jeb219154.
    doi: 10.1242/jeb.219154pubmed: 32098884google scholar: lookup
  38. Respondek F, Goachet A, Julliand RFV. Effects of short-chain fructo-oligosaccharides on the microbial and biochemical profile of different segments of the gastro-intestinal tract in horses.. Pferdeheilkunde 2008;23(2):146.
    doi: 10.21836/PEM20070206google scholar: lookup
  39. De Fombelle A, Julliand V, Drogoul C, Jacotot E. Feeding and microbial disorders in horses: 1-effects of an abrupt incorporation of two levels of barley in a hay diet on microbial profile and activities.. J Equine Vet Sci 2001;21:439–445.
    doi: 10.1016/S0737-0806(01)70018-4google scholar: lookup
  40. Warzecha CM, Coverdale JA, Janecka JE, Leatherwood JL, Pinchak WE, Wickersham TA, McCann JC. Influence of short-term dietary starch inclusion on the equine cecal microbiome.. J Anim Sci 2017;95(11):5077–5090.
    doi: 10.2527/jas2017.1754pmc: PMC6095290pubmed: 29293739google scholar: lookup
  41. Muhonen S, Julliand V, Lindberg JE, Bertilsson J, Jansson A. Effects on the equine colon ecosystem of grass silage and haylage diets after an abrupt change from hay.. J Anim Sci 2009;87(7):2291–2298.
    doi: 10.2527/jas.2008-1461pubmed: 19329474google scholar: lookup
  42. Zhang C, Zhao L. Strain-level dissection of the contribution of the gut microbiome to human metabolic disease.. Genome Med 2016;8(1):1–10.
    doi: 10.1186/s13073-016-0304-1pmc: PMC4839137pubmed: 27098841google scholar: lookup
  43. Pan F, Zhang L, Li M, Hu Y, Zeng B, Yuan H, Zhao L, Zhang C. Predominant gut Lactobacillus murinus strain mediates anti-inflammaging effects in calorie-restricted mice.. Microbiome 2018;6(1):1–17.
    doi: 10.1186/s40168-018-0440-5pmc: PMC5863386pubmed: 29562943google scholar: lookup
  44. Zhai R, Xue X, Zhang L, Yang X, Zhao L, Zhang C. Strain-specific anti-inflammatory properties of two Akkermansia muciniphila strains on chronic colitis in mice.. Front Cell Infect Microbiol 2019;9:239.
    doi: 10.3389/fcimb.2019.00239pmc: PMC6624636pubmed: 31334133google scholar: lookup
  45. Wu G, Zhao N, Zhang C, Lam YY, Zhao L. Guild-based analysis for understanding gut microbiome in human health and diseases.. Genome Med 2021;13(1):1–12.
    doi: 10.1186/s13073-021-00840-ypmc: PMC7874449pubmed: 33563315google scholar: lookup
  46. 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;10(1):1–19.
    doi: 10.1038/s41598-020-65444-9pmc: PMC7239938pubmed: 32433513google scholar: lookup
  47. Husso A, Jalanka J, Alipour MJ, Huhti P, Kareskoski M, Pessa-Morikawa T, Iivanainen A, Niku M. The composition of the perinatal intestinal microbiota in horse.. Sci Rep 2020;10(1):1–12.
    doi: 10.1038/s41598-019-57003-8pmc: PMC6965133pubmed: 31949191google scholar: lookup
  48. Gomez A, Sharma AK, Grev A, Sheaffer C, Martinson K. The horse gut microbiome responds in a highly individualized manner to forage lignification.. J Equine Vet Sci 2021;96:103306.
    doi: 10.1016/j.jevs.2020.103306pubmed: 33349409google scholar: lookup
  49. Theelen MJ, Luiken RE, Wagenaar JA, Sloet van Oldruitenborgh-Oosterbaan MM, Rossen JW, Zomer AL. The equine faecal microbiota of healthy horses and ponies in The Netherlands: impact of host and environmental factors.. Animals 2021;11(6):1762.
    doi: 10.3390/ani11061762pmc: PMC8231505pubmed: 34204691google scholar: lookup
  50. Zhang C, Yin A, Li H, Wang R, Wu G, Shen J, Zhang M, Wang L, Hou Y, Ouyang H, Zhang Y. Dietary modulation of gut microbiota contributes to alleviation of both genetic and simple obesity in children.. EBioMedicine 2015;2(8):968–984.
    doi: 10.1016/j.ebiom.2015.07.007pmc: PMC4563136pubmed: 26425705google scholar: lookup
  51. Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, Fu H, Xue X, Lu C, Ma J, Yu L. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes.. Science 2018;359(6380):1151–1156.
    doi: 10.1126/science.aao5774pubmed: 29590046google scholar: lookup
  52. Chen T, Liu AB, Sun S, Ajami NJ, Ross MC, Wang H, Zhang L, Reuhl K, Kobayashi K, Onishi JC, Zhao L, Yang CS. Green tea polyphenols modify the gut microbiome in db/db mice as co-abundance grouips correlating with the blood glucose lowering effect.. Mol Nutr Food Res 2019;63(8):180164.
    doi: 10.1002/mnfr.201801064pmc: PMC6494111pubmed: 30667580google scholar: lookup
  53. Blackmore TM, Dugdale A, Argo CM, Curtis G, Pinloche E, Harris PA, Worgan HJ, Girwood SE, Dougal K, Newbold CJ, McEwan NR. Strong stability and host specific bacterial community in faeces of ponies.. PLoS ONE 2013;8(9):e75079.
    doi: 10.1371/journal.pone.0075079pmc: PMC3770578pubmed: 24040388google scholar: lookup
  54. Costa MC, Weese JS. The equine intestinal microbiome.. Anim Health Res Rev 2012;13(1):121–128.
    doi: 10.1017/S1466252312000035pubmed: 22626511google scholar: lookup
  55. Proudman A, Darby C, Escalona E. Faecal microbiome of the Thoroughbred racehorse and its response to dietary amylase supplementation.. Equine Vet J 2014;46(S46):35.
    doi: 10.1111/evj.12267_107google scholar: lookup
  56. Salem SE, Maddox TW, Berg A, Antczak P, Ketley JM, Williams NJ, Archer DC. Variation in faecal microbiota in a group of horses managed at pasture over a 12-month period.. Sci Rep 2018;8(1):8510.
    doi: 10.1038/s41598-018-26930-3pmc: PMC5981443pubmed: 29855517google scholar: lookup
  57. Johnson AJ, Vangay P, Al-Ghalith GA, Hillman BM, Ward TL, Shields-Cutler RR, Kim AD, Shmagel AK, Syed AN, Personalized Microbiome Students, Walter J. Daily sampling reveals personalized diet-microbiome associations in humans.. Cell Host Microbe 2019;25(6):789–802.
    doi: 10.1016/j.chom.2019.05.005pubmed: 31194939google scholar: lookup
  58. Smits SA, Marcobal A, Higginbottom S, Sonnenburg JL, Kashyap PC. Individualized responses of gut microbiota to dietary intervention modeled in humanized mice.. mSystems 2016;1(5):e00098.
    doi: 10.1128/mSystems.00098-16pmc: PMC5069738pubmed: 27822551google scholar: lookup
  59. Ericsson AC, Johnson PJ, Gieche LM, Zobrist C, Bucy K, Townsend KS, Martin LM, LaCarrubba AM. The influence of diet change and oral metformin on blood glucose regulation and the fecal microbiota of healthy horses.. Animals 2021;11(4):976.
    doi: 10.3390/ani11040976pmc: PMC8065426pubmed: 33915682google scholar: lookup
  60. Zhu Y, Wang X, Deng L, Chen S, Zhu C, Li J. Effects of pasture grass, silage, and hay diet on equine fecal microbiota.. Animals 2021;11(5):1330.
    doi: 10.3390/ani11051330pmc: PMC8148540pubmed: 34066969google scholar: lookup
  61. Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, Beaumont M, Van Treuren W, Knight R, Bell JT, Spector TD, Clark AG, Ley RE. Human genetics shape the gut microbiome.. Cell 2014;159(4):789–799.
    doi: 10.1016/j.cell.2014.09.053pmc: PMC4255478pubmed: 25417156google scholar: lookup
  62. Svartström O, Alneberg J, Terrapon N, Lombard V, de Bruijn I, Malmsten J, Dalin A, Muller EEL, Shah P, Wilmes P, Henrissat B, Aspeborg H, Andersson AF. Ninety-nine de novo assembled genomes from the moose (Alces alces) rumen microbiome provide new insights into microbial plant biomass degradation.. ISME J 2017;11:2538–2551.
    doi: 10.1038/ismej.2017.108pmc: PMC5648042pubmed: 28731473google scholar: lookup
  63. La Reau AJ, Suen G. The Ruminocci: key symbionts of the gut ecosystem.. J Microbiol 2018;56(3):199–208.
    doi: 10.1007/s12275-018-8024-4pubmed: 29492877google scholar: lookup
  64. Tokuda G, Mikaelyan A, Fukui C, Watanabe H, Funishima M, Brune A. Fiber-associated spirochetes are major agents of hemicellulose degradation in the hindgut of wood-feeding higher termites.. PNAS 2018;115(51):E11996–E12004.
    doi: 10.1073/pnas.1810550115pmc: PMC6304966pubmed: 30504145google scholar: lookup
  65. Ren Q, Si H, Yan X, Liu C, Ding L, Long R, Li Z, Qiu Q. Bacterial communities in the solid, liquid, dorsal, and ventral epithelium fractions of yak (Bos grunniens) rumen.. Microbiologyopen 2020;9(2):e963.
    doi: 10.1002/mbo3.963pmc: PMC7002109pubmed: 31701637google scholar: lookup
  66. Vital M, Jairong G, Rizzo R, Harrison T, Tiedje JM. Diet is a major factor governing the fecal butyrate-producing community structure across Mammalia, Aves and Reptilia.. ISME J 2015;9:832–843.
    doi: 10.1038/ismej.2014.179pmc: PMC4817703pubmed: 25343515google scholar: lookup
  67. Perea K, Perz K, Olivo SK, Williams A, Lachman M, Ishaq SL, Thomson J, Yeoman CJ. J Anim Sci. 2017;95(6):2585–2592. doi: 10.2527/jas.2016.1222.
    doi: 10.2527/jas.2016.1222pubmed: 28727071google scholar: lookup
  68. Gharechahi J, Vahidi MF, Ding X-Z, Han J-L, Salekdeh GH. Temporal changes in microbial communities attached to forages with different lignocellulosic compositions in cattle rumen.. FEMS Microbiol Ecol 2020.
    doi: 10.1093/femsec/fiaa069pubmed: 32304321google scholar: lookup
  69. Goodrich JK, Davenport ER, Waters JL, Clark AG, Ley RE. Cross-species comparisons of host genetic associations with the microbiome.. Science 2016;352:532–535.
    doi: 10.1126/science.aad9379pmc: PMC5116907pubmed: 27126034google scholar: lookup
  70. Lim MY, You HJ, Yoon HS, Kwon B, Lee JY, Lee S, Song Y, Lee K, Sung J, Ko G. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome.. Gut 2017;66:1031–1038.
    doi: 10.1136/gutjnl-2015-311326pubmed: 27053630google scholar: lookup
  71. Waters JL, Ley RE. The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health.. BMC Biol 2019;17:83.
    doi: 10.1186/s12915-019-0699-4pmc: PMC6819567pubmed: 31660948google scholar: lookup
  72. Ilmberger N, Güllert S, Dannenberg J, Rabausch U, Torres J, Wemheuer B, Alawi M, Poehlein A, Chow J, Turaev D, Rattei T. A comparative metagenome survey of the fecal microbiota of a breast- an a plant-fed Asian elephant reveals an unexpectedly high diversity of glycoside hydrolase family enzymes.. PLoS ONE 2014;9(9):e106707.
    doi: 10.1371/journal.pone.0106707pmc: PMC4160196pubmed: 25208077google scholar: lookup
  73. Li Y, Hu X, Yang S, Zhou J, Zhang T, Qi L, Sun X, Fan M, Xu S, Cha M, Zhang M. Comparative analysis of the gut microbiota composition between captive and wild forest musk deer.. Front Microbiol 2017;8:1705.
    doi: 10.3389/fmicb.2017.01705pmc: PMC5591822pubmed: 28928728google scholar: lookup
  74. Huang Q, Holman BD, Alexander T, Hu T, Jin L, Xu Z, McAllister TA, Acharya S, Zhao G, Wang Y. Fecal microbiota of lambs fed purple prairie clover (Dalea purpurea Vent) and alfalfa (Medicago sativa). Arch Microbiol 2018;200(1):137–145.
    doi: 10.1007/s00203-017-1427-5pubmed: 28864945google scholar: lookup
  75. Rodriquez C, Taminiau B, Brévers B, Avesani V, Van Broeck J, Leroux A, Gallot M, Bruwier A, Amory H, Delmée M, Daube G. Faecal microbiota characterisation of horses using 16 rdna barcoded pyrosequencing, and carriage rate of clostridium difficile at hospital admission.. BMC Microbiol 2015;15(1):1–14.
    doi: 10.1186/s12866-015-0514-5pmc: PMC4573688pubmed: 26377067google scholar: lookup
  76. Li Y, Zhang K, Yang L, Kai L, Defu H, Wronski T. Community composition and diversity of intestinal microbiota in captive and re-introduced Prezwalski's Horse (Equus ferus prezwalskii). Front Microbiol 2019;10:1821.
    doi: 10.3389/fmicb.2019.01821pmc: PMC6693443pubmed: 31440229google scholar: lookup
  77. Graf J. The family Rikenellaceae.. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes. Berlin: Springer Berlin Heidelberg; 2014. pp. 857–859.
  78. Asma Z, Sylvie C, Laurent C, Jérôme M, Christophe K, Oliver B, Annabelle TM, Francis E. Microbial ecology of the rumen evaluated by 454 GS FLX pyrosequencing is affected by starch and oil supplementation of diets.. FEMS Microbio Ecol 2013;83(2):504–514.
    doi: 10.1111/1574-6941.12011pubmed: 22974422google scholar: lookup
  79. Bomar I, Malz M, Colston S, Graf J. Directed culturing of microorganisms using metatranscriptomics.. MBio 2011;2(2):e00012–e111.
    doi: 10.1128/mBio.00012-11pmc: PMC3069634pubmed: 21467263google scholar: lookup
  80. Maurice CF, Knowles SC, Ladau J, Pollard KS, Fenton A, Pedersen AB, Turnbaugh PJ. Marked seasonal variation in the wild mouse gut microbiota.. ISME J 2015;9(11):2423–2434.
    doi: 10.1038/ismej.2015.53pmc: PMC4611506pubmed: 26023870google scholar: lookup
  81. Amato KR, Leigh SR, Kent A, Mackie RI, Yeoman CJ, Stumpf RM, Wilson BA, Nelson KE, White BA, Garber PA. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb Ecol 2015;69(2):434–443.
    doi: 10.1007/s00248-014-0554-7pubmed: 25524570google scholar: lookup
  82. Parfrey LW, Knight R. Spatial and temporal variability of the human microbiota.. Clin Microbiol Infect 2012;18(S4):5–7.
    doi: 10.1111/j.1469-0691.2012.03861.xpubmed: 22647040google scholar: lookup
  83. Williams CA, Kenny LB, Burk AO. Effects of grazing system, season, and forage carbohydrates on glucose and insulin dynamics of the grazing horse.. J Anim Sci 2019;97(6):2541–2554.
    doi: 10.1093/jas/skz103pmc: PMC6541809pubmed: 30911753google scholar: lookup
  84. Kagan IA, Kirch BH, Thatcher CD, Strickland JR, Teutsch CD, Elvinger F, Pleasant RS. Seasonal and diurnal variation in simple sugar and fructan composition of orchardgrass pasture and hay in the Piedmont region of the United States.. J Equine Vet Sci 2011;31(8):488–497.
    doi: 10.1016/j.jevs.2011.03.004google scholar: lookup
  85. Kagan IA, Kirch BH, Thatcher CD, Teutsch CD, Elvinger F, Shepherd DM, Pleasant S. Seasonal and diurnal changes in starch content and sugar profiles of Bermudagrass in the Piedmont region of the United States.. J Equine Veterinary Sci 2011;31(9):521–529.
    doi: 10.1016/j.jevs.2011.08.010google scholar: lookup
  86. Weinert-Nelson JR, Meyer WA, Williams CA. Diurnal variation in forage nutrient composition of mixed cool-season grass, crabgrass, and bermudagrass pastures.. J Equine Vet Sci 2022;110:103836.
    doi: 10.1016/j.jevs.2021.103836pubmed: 34921981google scholar: lookup
  87. Berg EL, Fu CJ, Porter JH, Kerley MS. Fructooligosaccharide supplementation in the yearling horse: effects on fecal pH, microbial content, and volatile fatty acid concentrations.. J Anim Sci 2005;83(7):1549–1553.
    doi: 10.2527/2005.8371549xpubmed: 15956463google scholar: lookup
  88. Biddle AS, Stewart L, Blanchard J, Leschine S. Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in Diverse Gut Communities.. Diversity 2013;5(3):627–640.
    doi: 10.3390/d5030627google scholar: lookup
  89. Lawson PA, Rainey FA. Proposal to restrict the genus Clostridium Prazmowski to Clostridium butyricum and related species.. Int J Syst Evol 2016;66(2):1009–1016.
    doi: 10.1099/ijsem.0.000824pubmed: 26643615google scholar: lookup
  90. La Reau AJ, Suen G. The Ruminococci: key symbionts of the gut ecosystem.. J Microbiol 2018;56(3):199–208.
    doi: 10.1007/s12275-018-8024-4pubmed: 29492877google scholar: lookup
  91. Willing B, Vörös A, Roos S, Jones C, Jansson A, Lindberg J. Changes in faecal bacteria associated with concentrate and forage-only diets fed to horses in training.. Equine Vet J 2009;41:908–914.
    doi: 10.2746/042516409X447806pubmed: 20383990google scholar: lookup
  92. Sorensen RJ, Drouillard JS, Douthit TL, Ran Q, Marthaler DG, Kang Q, Vahl CI, Lattimer JM. Effect of hay type on cecal and fecal microbiome and fermentation parameters in horses.. J Anim Sci 2021.
    doi: 10.1093/jas/skaa407pmc: PMC7846146pubmed: 33515482google scholar: lookup
  93. Office of the New Jersey State climatologist at Rutgers University: Rutgers New Jersey weather network. https://www.njweather.org/data (2021). Accessed 12 Jul 2021.
  94. Henneke DR, Potter GD, Kreider JL, Yeates BF. Relationship between condition score, physical measurements and body fat percentage in mares.. Equine Vet J 1983;15(4):371–372.
    doi: 10.1111/j.2042-3306.1983.tb01826.xpubmed: 6641685google scholar: lookup
  95. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens M, Betley J, Fraser L, Bauer M, Gormley N. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.. ISME J 2012;6:1621–1624.
    doi: 10.1038/ismej.2012.8pmc: PMC3400413pubmed: 22402401google scholar: lookup
  96. R Development Core Team. R: A language and environment for statistical computing.. 2010. http://cran.r-project.org.
  97. McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J, Wendel D, Wilke A, Huse S, Hufnagle J, Meyer F, Knight R. The Biological observation matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome.. Gigascience 2012;1(1):2047–2217.
    doi: 10.1186/2047-217X-1-7pmc: PMC3626512pubmed: 23587224google scholar: lookup
  98. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data.. Nat Methods 2016;13(7):581–583.
    doi: 10.1038/nmeth.3869pmc: PMC4927377pubmed: 27214047google scholar: lookup
  99. Lane DJ. 16S/23S rRNA Sequencing.. In: Stakebrandt E, Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. New York City: John Wiley and Sons; 1991. pp. 115–175.
  100. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments.. PLoS ONE 2010;5(3):e9490.
    doi: 10.1371/journal.pone.0009490pmc: PMC2835736pubmed: 20224823google scholar: lookup
  101. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability.. Mol Biol Evol 2013;30(4):772–780.
    doi: 10.1093/molbev/mst010pmc: PMC3603318pubmed: 23329690google scholar: lookup
  102. Shannon CE. A mathematical theory of communication.. Bell Sys Tech J 1948;27(3):379–423.
    doi: 10.1002/j.1538-7305.1948.tb01338.xgoogle scholar: lookup
  103. Pielou EC. The measurement of diversity in different types of biological collections.. J Theor Biol 1966;13:131–144.
    doi: 10.1016/0022-5193(66)90013-0google scholar: lookup
  104. Faith D. Conservation evaluation and phylogenetic diversity.. Biol Conserv 1992;61(1):1–10.
    doi: 10.1016/0006-3207(92)91201-3google scholar: lookup
  105. McKinney W. Data structures for statistical computing in python.. In: van der Walt S, Millman J, editors. Proceedings of the 9th python in science conference; 2010. p. 51–6.
  106. Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, Lozupone C, Zaneveld JR, Vázquez-Baeza Y, Birmingham A, Hyde ER. Normalization and microbial differential abundance strategies depend upon data characteristics.. Microbiome 2017;5(1):1–18.
    doi: 10.1186/s40168-017-0237-ypmc: PMC5335496pubmed: 28253908google scholar: lookup
  107. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities.. Appl Envir Microbiol 2005;71(12):8228–8235.
    doi: 10.1128/AEM.71.12.8228-8235.2005pmc: PMC1317376pubmed: 16332807google scholar: lookup
  108. Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and uqalitative β diversity measures lead to different insights into factors that structure microbial communities.. Appl Environ Microbiol 2007;73(5):1576–1585.
    doi: 10.1128/AEM.01996-06pmc: PMC1828774pubmed: 17220268google scholar: lookup
  109. Hamady M, Lozupone C, Knight R. Fast unifrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequening and PhyloChip data.. ISME J 2010;4(1):17–27.
    doi: 10.1038/ismej.2009.97pmc: PMC2797552pubmed: 19710709google scholar: lookup
  110. Chang Q, Luan Y, Sun F. Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny.. BMC Bioinform 2011.
    doi: 10.1186/1471-2105-12-118pmc: PMC3108311pubmed: 21518444google scholar: lookup
  111. Chen J, Bittinger K, Charlson ES, Hofmann C, Lewis J, Wu GD, Collman G, Bushman FD, Li H. Associating microbiome composition with environmental covariates using generalized UniFrac distances.. Bioinformatics 2012;28(16):2106–2113.
    doi: 10.1093/bioinformatics/bts342pmc: PMC3413390pubmed: 22711789google scholar: lookup
  112. McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Zhenjiang X, Gonzalez A, Knight R. Striped UniFrac: enabling microbiome analysis at unprecedented scale.. Nat Methods 2018;15(11):847–848.
    doi: 10.1038/s41592-018-0187-8pmc: PMC7250580pubmed: 30377368google scholar: lookup
  113. Anderson MJ. A new method for non-parametric multivariate analysis of variance.. Austral Ecol 2001;26(1):32–46.
    doi: 10.1111/j.1442-9993.2001.01070.pp.xgoogle scholar: lookup
  114. Hagberg AA, Shult DA, Swart PJ. Exploring network structure, dynamics, and function using NetworkX.. In: Varoquaux G, Vaught T, Millman J, editors. Proceedings of the 7th Python in Science Conference; 2008. p. 11–15.
  115. Shaffer M, Thurimella K, Lozupone CA. SCNIC: Sparse correlation network investigation for compositional data.. bioRxiv 2020.
    doi: 10.1101/2020.11.13.380733pmc: PMC9744196pubmed: 36001047google scholar: lookup
  116. Bokulich N, Dillon M, Bolyen E, Kaehler BD, Huttley GA, Caporaso JG. q2-sample-classifier: machine-learning tools for microbiome classification and regression.. J Open Source Softw 2018;3(30):934.
    doi: 10.21105/joss.00934pmc: PMC6759219pubmed: 31552137google scholar: lookup
  117. Pedregosa F, Varoquaux G, Gramfort A, Michel B, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in Python.. J Mach Learn Res 2011;12:2825–2830.
  118. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Microb Ecol Health Dis. 2015;26(1):27663.
    doi: 10.3402/mehd.v26.27663pmc: PMC4450248pubmed: 26028277google scholar: lookup
  119. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig K, Peplies J, Glockner FO. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB.. Nucl Acids Res 2007;35:7188–7196.
    doi: 10.1093/nar/gkm864pmc: PMC2175337pubmed: 17947321google scholar: lookup
  120. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Pablo J, Glockner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools.. Nucl Acids Res 2013;41:D590–D596.
    doi: 10.1093/nar/gks1219pmc: PMC3531112pubmed: 23193283google scholar: lookup
  121. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics.. PeerJ 2016;4:e2584.
    doi: 10.7717/peerj.2584pmc: PMC5075697pubmed: 27781170google scholar: lookup
  122. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Caparaso JG. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-eature-classifier plugin.. Microbiome 2018;1(6):90.
    doi: 10.1186/s40168-018-0470-zpmc: PMC5956843pubmed: 29773078google scholar: lookup

Citations

This article has been cited 15 times.
  1. Moaby I, Aitken A, Varga S. Scientific Evidence and Common Perceptions of Factors Affecting Sugar Content in Pasture Grass: Is There a Link With Pre-existing Horse-Related Experience?. Vet Med Sci 2026 Jan;12(1):e70778.
    doi: 10.1002/vms3.70778pubmed: 41548206google scholar: lookup
  2. Yano R, Moriyama T, Arai H, Scheftgen AJ, Suen G, Nishida T, Handa M, Fukuma N. Correlation of hindgut microbiome and fermentation properties with a history of gas and/or impaction colic in Japanese draft horses. J Equine Sci 2025;36(3):93-102.
    doi: 10.1294/jes.36.93pubmed: 40980339google scholar: lookup
  3. 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
  4. Dunay E, Hirji I, Owens LA, Marah K, Anderson N, Ruiz M, Atencia R, Rukundo J, Rosati AG, Cole MF, Emery Thompson M, Negrey JD, Angedakin S, Elfenbein JR, Goldberg TL. Distribution and prevalence of Sarcina troglodytae in chimpanzees and the environment throughout Africa. J Med Microbiol 2025 Jul;74(7).
    doi: 10.1099/jmm.0.002044pubmed: 40680089google scholar: lookup
  5. Deng J, Wang X, Yan C, Huang Z, Luo H, Dai C, Huang X, Huang Y, Fu Q. Dietary purslane modulates gut microbiota and fecal metabolites in aging rats. Front Microbiol 2025;16:1549853.
    doi: 10.3389/fmicb.2025.1549853pubmed: 40177479google scholar: lookup
  6. François AC, Cesarini C, Taminiau B, Renaud B, Kruse CJ, Boemer F, van Loon G, Palmers K, Daube G, Wouters CP, Lecoq L, Gustin P, Votion DM. Unravelling Faecal Microbiota Variations in Equine Atypical Myopathy: Correlation with Blood Markers and Contribution of Microbiome. Animals (Basel) 2025 Jan 26;15(3).
    doi: 10.3390/ani15030354pubmed: 39943124google scholar: lookup
  7. Brandi LA, Nunes AT, Faleiros CA, Poleti MD, Oliveira ECM, Schmidt NT, Sousa RLM, Fukumasu H, Balieiro JCC, Brandi RA. Dietary Energy Sources Affect Cecal and Fecal Microbiota of Healthy Horses. Animals (Basel) 2024 Dec 3;14(23).
    doi: 10.3390/ani14233494pubmed: 39682460google scholar: lookup
  8. Bishop RC, Kemper AM, Clark LV, Wilkins PA, McCoy AM. Stability of Gastric Fluid and Fecal Microbial Populations in Healthy Horses under Pasture and Stable Conditions. Animals (Basel) 2024 Oct 16;14(20).
    doi: 10.3390/ani14202979pubmed: 39457909google scholar: lookup
  9. Tang BB, Su CX, Wen N, Zhang Q, Chen JH, Liu BB, Wang YQ, Huang CQ, Hu YL. FMT and TCM to treat diarrhoeal irritable bowel syndrome with induced spleen deficiency syndrome- microbiomic and metabolomic insights. BMC Microbiol 2024 Oct 26;24(1):433.
    doi: 10.1186/s12866-024-03592-ypubmed: 39455910google scholar: lookup
  10. Sha Y, Liu X, Li X, Wang Z, Shao P, Jiao T, He Y, Zhao S. Succession of rumen microbiota and metabolites across different reproductive periods in different sheep breeds and their impact on the growth and development of offspring lambs. Microbiome 2024 Sep 12;12(1):172.
    doi: 10.1186/s40168-024-01892-zpubmed: 39267132google scholar: lookup
  11. Wang Y, Guo H, Li X, Chen X, Peng L, Zhu T, Sun P, Liu Y. Peracetic acid (PAA)-based pretreatment effectively improves medium-chain fatty acids (MCFAs) production from sewage sludge. Environ Sci Ecotechnol 2024 Jul;20:100355.
    doi: 10.1016/j.ese.2023.100355pubmed: 38192428google scholar: lookup
  12. Wang D, Chen L, Tang G, Yu J, Chen J, Li Z, Cao Y, Lei X, Deng L, Wu S, Guan LL, Yao J. Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats. Microbiome 2023 Sep 29;11(1):215.
    doi: 10.1186/s40168-023-01652-5pubmed: 37773207google scholar: lookup
  13. Beckers KF, Gomes VCL, Crissman KR, Liu CC, Schulz CJ, Childers GW, Sones JL. Metagenetic Analysis of the Pregnant Microbiome in Horses. Animals (Basel) 2023 Jun 15;13(12).
    doi: 10.3390/ani13121999pubmed: 37370509google scholar: lookup
  14. Weinert-Nelson JR, Biddle AS, Sampath H, Williams CA. Fecal Microbiota, Forage Nutrients, and Metabolic Responses of Horses Grazing Warm- and Cool-Season Grass Pastures. Animals (Basel) 2023 Feb 22;13(5).
    doi: 10.3390/ani13050790pubmed: 36899650google scholar: lookup
  15. Liu S, Wang K, Lin S, Zhang Z, Cheng M, Hu S, Hu H, Xiang J, Chen F, Li G, Si H. Comparison of the Effects between Tannins Extracted from Different Natural Plants on Growth Performance, Antioxidant Capacity, Immunity, and Intestinal Flora of Broiler Chickens. Antioxidants (Basel) 2023 Feb 10;12(2).
    doi: 10.3390/antiox12020441pubmed: 36829999google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.