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A novel dataset of 2,362 equine fecal microbiomes from veterinary teaching hospitals across three countries reveals effects of geography and disease.
Abstract: Horses and other equids are reliant on the gut microbiome for health, and studies have reported associations between certain clinical conditions and features of the fecal microbiome. However, research to date on the equine fecal microbiome has often relied on small sample sizes collected from single and relatively localized geographic regions. Previous work also largely employs single timepoint analyses, or horses selected based on limited health criteria. Results: To address these limitations and expand our understanding of the core microbiome in health, and the changes associated with adverse outcomes, the Equine Gut Group (EGG) has collected and performed 16S rRNA sequencing on 2,362 fecal samples from 1,190 healthy and affected horses. This resource of 16S rRNA sequencing data with accompanying demographic and clinical metadata represent a diverse equine population in health and disease. We identified features making up the core microbiome of healthy equids and metadata factors influencing the relative abundance of those features. We then identified microbial markers of acute gastrointestinal disease at the community and taxonomic levels. Conclusions: Here we present the EGG database and demonstrate its utility in characterizing the equine microbiome in health and acute gastrointestinal disease. The EGG 16S rRNA database is a valuable resource to study the equine microbiome and its role in equine health.
© 2025. The Author(s).
Publication Date: 2025-12-03 PubMed ID: 41339959PubMed Central: PMC12676801DOI: 10.1186/s42523-025-00493-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.
- Journal Article
Summary
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Overview
- This study presents a large dataset of equine fecal microbiomes from over two thousand samples collected across three countries.
- The research investigates how geographic location and disease affect the gut microbial community in horses, providing insights into core microbiome components in health as well as markers associated with disease.
Background and Motivation
- Horses and other equids depend heavily on their gut microbiome for maintaining health.
- Prior studies found links between certain clinical conditions and fecal microbiome features but were limited by small sample sizes, localized sampling, and focus on single time points or narrow health criteria.
- This limited scope reduced the ability to generalize findings across diverse equine populations and diseases.
Objectives of the Study
- To overcome prior limitations by compiling a large, geographically diverse dataset of equine fecal microbiomes.
- To characterize the core microbiome components in healthy horses across different regions.
- To identify microbial markers associated with acute gastrointestinal disease across a broad equine population.
Methods
- The Equine Gut Group (EGG) collected 2,362 fecal samples from 1,190 horses, representing both healthy and diseased individuals.
- The samples were sourced from veterinary teaching hospitals across three different countries, enabling geographic diversity.
- Researchers performed 16S rRNA gene sequencing on the fecal samples to profile bacterial communities.
- Comprehensive demographic and clinical metadata were also gathered to correlate microbiome profiles with health status and geography.
Key Findings
- The study successfully identified a “core microbiome” — a set of microbial taxa consistently present in healthy horses regardless of location.
- Various metadata factors, such as geography, influenced the relative abundance of these core microbes but did not fundamentally change the core composition.
- Distinct microbial signatures indicative of acute gastrointestinal disease were characterized at both community and taxonomic levels.
- These microbial markers can aid in understanding disease mechanisms and potentially serve as diagnostic indicators in clinical contexts.
Significance and Applications
- The EGG database is the largest and most geographically diverse collection of equine fecal microbiome data to date.
- This resource enables more robust studies of microbiome variation due to geography, health, and disease status in horses.
- Veterinarians and researchers can utilize these findings to improve equine health management through microbiome-informed diagnostics and treatments.
- The study sets a foundation for future longitudinal investigations and targeted therapeutic development.
Cite This Article
APA
McAdams ZL, Campbell EJ, Dorfmeyer RA, Turner G, Shaffer S, Ford T, Lawson J, Terry J, Raju M, Coghill L, Cresci L, Lascola K, Pridgen T, Blikslager A, Barrell E, Banse H, Paul L, Gillen A, Nott S, VandeCandelaere M, van Galen G, Townsend KS, Martin LM, Johnson PJ, Ericsson AC.
(2025).
A novel dataset of 2,362 equine fecal microbiomes from veterinary teaching hospitals across three countries reveals effects of geography and disease.
Anim Microbiome, 7(1), 124.
https://doi.org/10.1186/s42523-025-00493-x Publication
Researcher Affiliations
- Molecular Pathogenesis and Therapeutics Program, University of Missouri, Columbia, Mo, 65201, USA.
- Department of Pathobiology and Integrative Biomedical Sciences, University of Missouri Metagenomics Center, Columbia, MO, 65201, USA.
- University of Missouri, Columbia, MO, 65201, USA.
- Department of Pathobiology and Integrative Biomedical Sciences, University of Missouri Metagenomics Center, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- Department of Pathobiology and Integrative Biomedical Sciences, University of Missouri Metagenomics Center, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- University of Missouri, Columbia, MO, 65201, USA.
- Hickman High School, Columbia, MO, 65203, USA.
- Bond Life Sciences Center, University of Missouri Bioinformatics and Analytics Core, Columbia, MO, 65211, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- Bond Life Sciences Center, University of Missouri Bioinformatics and Analytics Core, Columbia, MO, 65211, USA.
- College of Veterinary Medicine, Auburn University, Auburn, AL, 36849, USA.
- College of Veterinary Medicine, Auburn University, Auburn, AL, 36849, USA.
- College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA.
- College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA.
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Minneapolis, MN, 55108, USA.
- School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
- School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
- Department of Equine Clinical Science, Philip Leverhulme Equine Hospital, University of Liverpool, Chester High Road, Neston, Wirral, CH64 7TE, UK.
- UQ VETS Equine Specialist Hospital, University of Queensland, Outer Ring Road, UQ Gatton Campus, Lawes, QLD, 4343, Australia.
- Sydney School of Veterinary Science, The University of Sydney, Camperdown, NSW, 2006, Australia.
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- Sydney School of Veterinary Science, The University of Sydney, Camperdown, NSW, 2006, Australia.
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA.
- Molecular Pathogenesis and Therapeutics Program, University of Missouri, Columbia, Mo, 65201, USA. ericssona@missouri.edu.
- Department of Pathobiology and Integrative Biomedical Sciences, University of Missouri Metagenomics Center, Columbia, MO, 65201, USA. ericssona@missouri.edu.
- College of Veterinary Medicine, University of Missouri, Columbia, MO, 65201, USA. ericssona@missouri.edu.
Conflict of Interest Statement
Declarations. Ethical approval: Not applicable. Conflict of interest: The authors declare no competing interests.
References
This article includes 48 references
- Kauter A, Epping L, Semmler T, Antao E-M, Kannapin D, Stoeckle SD. The gut microbiome of horses: current research on equine enteral microbiota and future perspectives.. Anim Microbiome 2019;1:14.
- Singh V, Lee G, Son H, Koh H, Kim ES, Unno T. Butyrate producers, “The Sentinel of Gut”: their intestinal significance with and beyond butyrate, and prospective use as microbial therapeutics.. Front Microbiol 2023;13:1103836.
- O’Reilly GC, Holman DB, Muscat K, Muscatello G, Parra MC, Meale SJ. Characterisation of the faecal microbiome of foals from 0–5 months of age and their respective mares across five geographic locations.. Front Biosci-Elite 2022;14:22.
- Ang L, Vinderola G, Endo A, Kantanen J, Jingfeng C, Binetti A. Gut microbiome characteristics in feral and domesticated horses from different geographic locations.. Commun Biol 2022;5:172.
- Arnold CE, Pilla R, Chaffin MK, Leatherwood JL, Wickersham TA, Callaway TR. The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or salmonella infection on the fecal microbiome of horses.. J Vet Intern Med 2021;35:2437–48.
- Curtis L, Burford JH, England GCW, Freeman SL. Risk factors for acute abdominal pain (Colic) in the adult horse: a scoping review of risk factors, and a systematic review of the effect of management-related changes.. PLoS One 2019;14:e0219307.
- Costa MC, Arroyo LG, Allen-Vercoe E, Stampfli HR, Kim PT, Sturgeon A. Comparison of the fecal microbiota of healthy horses and horses with colitis by high throughput sequencing of the V3-V5 region of the 16S rRNA gene.. PLoS One 2012;7:e41484.
- 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;51:336–42.
- Weese JS, Holcombe SJ, Embertson RM, Kurtz KA, Roessner HA, Jalali M. Changes in the faecal microbiota of mares precede the development of post partum colic.. Equine Vet J 2015;47:641–49.
- Stewart HL, Pitta D, Indugu N, Vecchiarelli B, Hennessy ML, Engiles JB. Changes in the faecal bacterial microbiota during hospitalisation of horses with colic and the effect of different causes of colic.. Equine Vet J 2021;53:1119–31.
- Elzinga SE, Weese JS, Adams AA. Comparison of the fecal microbiota in horses with equine metabolic syndrome and metabolically normal controls fed a similar all-forage diet.. J Eq Vet Sci 2016;44:9–16.
- Milinovich GJ, Burrell PC, Pollitt CC, Klieve AV, Blackall LL, Ouwerkerk D. Microbial ecology of the equine hindgut during oligofructose-induced laminitis.. The ISME J 2008;2:1089–100.
- Morrison PK, Newbold CJ, Jones E, Worgan HJ, Grove-White DH, Dugdale AH. The equine gastrointestinal microbiome: impacts of age and obesity.. Front Microbiol 2018;9:3017.
- Coleman MC, Whitfield-Cargile CM, Madrigal RG, Cohen ND. Comparison of the microbiome, metabolome, and lipidome of obese and non-obese horses.. PLoS One 2019;14:e0215918.
- Biddle AS, Tomb JF, Fan Z. Microbiome and blood analyte differences point to community and metabolic signatures in lean and obese horses.. Front Vet Sci 2018;5:225.
- Walters WA, Caporaso JG, Lauber CL, Berg-Lyons D, Fierer N, Knight R. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers.. Bioinformatics 2011;27:1159–61.
- Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.. Proc Natl Acad Sci 2011;108:4516–22.
- Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.. Nat Biotechnol 2019;37:852–57.
- Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads.. EMBnetJ 2011;17:10–12.
- Callahan BJ, Pj M, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data.. Nat Methods 2016;13:581–83.
- Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P. The silva ribosomal rna gene database project: improved data processing and web-based tools.. Nucleic Acids Res 2013;41:D590–6.
- Team RDC R. A language and environment for statistical computing.. 2022.
- Ggplot2 WH. Elegant graphics for data analysis.. 2016;11–31.
- Kassambara A. Rstatix: pipe-friendly framework for basic statistical tests.. 2022.
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.. J R Stat Soc Ser B Methodol 1995;57:289–300.
- Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4.. J Stat Softw 2015;67.
- Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest package: tests in linear mixed effects models.. J Stat Softw 2017;82.
- Lenth R. Emmeans: estimated marginal means, aka least-squares means. 2023.
- Dixon P. Vegan, a package of R functions for community ecology.. J Veg Sci 2003;14:927–30.
- Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR. Vegan Community Ecol Package.. 2022.
- Arbizu MP. pairwiseAdonis: pairwise multilevel comparison using adonis. 2020.
- Paradis E, Schliep K. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2019;35:526–28.
- McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of Microbiome census data. PLoS One 2013;8:e61217.
- Ben-Shachar M, Lüdecke D, Makowski D. Effectsize: estimation of effect size indices and standardized parameters. J Open Source Softw 2020;5:2815.
- Liaw A, Wiener M. Classification and regression by randomForest. R News 2002.
- Topçuoğlu BD, Lapp Z, Sovacool KL, Snitkin E, Wiens J, Schloss PM. User-friendly R package for supervised machine learning pipelines. J Open Source Softw 2021;6:3073.
- Edwards JE, Shetty SA, van den BP, Burden F, van DD, Pellikaan WF. Multi-kingdom characterization of the core equine fecal microbiota based on multiple equine (sub)species. Anim Microbiome 2020;2:6.
- McKinney CA, Bedenice D, Pacheco AP, Oliveira BCM, Paradis M-R, Mazan M. Assessment of clinical and microbiota responses to fecal microbial transplantation in adult horses with diarrhea. PLoS One 2021;16:e0244381.
- Donnell MMO, Harris HMB, Jeffery IB, Claesson MJ, Younge B, Toole PWO. The core faecal bacterial microbiome of Irish thoroughbred racehorses. Lett Appl Microbiol 2013;57:492–501.
- Costa MC, Silva G, Ramos RV, Staempfli HR, Arroyo LG, Kim P. Characterization and comparison of the bacterial microbiota in different gastrointestinal tract compartments in horses. Vet J 2015;205:74–80.
- Dougal K, de la FG, 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:e77660.
- Jassim RAMA, Andrews FM. The bacterial community of the horse gastrointestinal tract and its relation to fermentative acidosis, laminitis, colic, and stomach ulcers. Vet Clin N Am: Equine Pr 2009;25:199–215.
- Wunderlich G, Bull M, Ross T, Rose M, Chapman B. Understanding the microbial fibre degrading communities & processes in the equine gut. Anim Microbiome 2023;5:3.
- Loublier C, Costa M, Taminiau B, Lecoq L, Daube G, Amory H. Longitudinal changes in fecal microbiota during hospitalization in horses with different types of colic. J Vet Intern Med 2025;39:e70039.
- Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT. Structure, function and diversity of the healthy human microbiome. Nature 2012;486:207–14.
- Bulmer LS, Murray J-A, Burns NM, Garber A, Wemelsfelder F, McEwan NR. High-starch diets alter equine faecal microbiota and increase behavioural reactivity. Sci Rep 2019;9:18621.
- de BM, Plummer C, MacNicol J, Gomez D. Impact of ambient temperature sample storage on the equine fecal microbiota. Animals 2021;11:819.
- Beckers KF, Schulz CJ, Childers GW. Rapid regrowth and detection of microbial contaminants in equine fecal microbiome samples. PLoS One 2017;12:e0187044.
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