FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / PeerJ / Metagenomic investigation of the equine faecal microbiome re...
PeerJ2022; 10; e13084; doi: 10.7717/peerj.13084

Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity.

Abstract: The horse plays crucial roles across the globe, including in horseracing, as a working and companion animal and as a food animal. The horse hindgut microbiome makes a key contribution in turning a high fibre diet into body mass and horsepower. However, despite its importance, the horse hindgut microbiome remains largely undefined. Here, we applied culture-independent shotgun metagenomics to thoroughbred equine faecal samples to deliver novel insights into this complex microbial community. We performed metagenomic sequencing on five equine faecal samples to construct 123 high- or medium-quality metagenome-assembled genomes from Bacteria and Archaea. In addition, we recovered nearly 200 bacteriophage genomes. We document surprising taxonomic diversity, encompassing dozens of novel or unnamed bacterial genera and species, to which we have assigned new names. Many of these genera are conserved across a range of mammalian gut microbiomes. Our metagenomic analyses provide new insights into the bacterial, archaeal and bacteriophage components of the horse gut microbiome. The resulting datasets provide a key resource for future high-resolution taxonomic and functional studies on the equine gut microbiome.
© 2022 Gilroy et al.
Get Full Text
Publication Date: 2022-03-23 PubMed ID: 35345588PubMed Central: PMC8957277DOI: 10.7717/peerj.13084Google 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
  • 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.

This research is a comprehensive study into the microbial communities present in the horse gut, using non-culture based metagenomics techniques. The researchers assembled genome sequences from bacteria and archaea along with nearly 200 bacteriophage genomes and discovered an impressive itemization of new or previously unclassified bacteria genera and species, many of which are commonly found in the gut microbiomes of other mammals.

Overview of the Research

  • The study aimed to explore the diverse microbial population in the horse hindgut and understand its role in converting a fibre-rich diet into energy and mass.
  • This was accomplished using metagenomic sequencing on equine faecal samples from thoroughbred horses, a non-cultural method which allows for deeper examination of the hidden microbial ecosystem of the gut.

Findings of the Metagenomic Investigation

  • The researchers succeeded in constructing 123 high- or medium-quality metagenome-assembled genomes from Bacteria and Archaea – two significant domains of life that comprise the gut microbiome.
  • They also recovered nearly 200 bacteriophage genomes which are viral entities that infect and typically result in the death of bacteria.
  • There was profound taxonomic diversity with new or unknown bacterial genera and species being discovered. These were identified and given new names as part of this research work.
  • A surprising finding was that many of these newly identified genera are also present in the gut microbiomes of other mammals, suggesting a potentially fundamental role in mammalian digestion and metabolism.

Significance of the Study and Future Directions

  • The research provides valuable insights into the bacterial, archaeal, and bacteriophage components of the horse gut microbiome. This directly contributes to the existing knowledge about the equine gut microbiota, which is crucial for overall horse health.
  • The data assembled as part of this study also offer a robust resource for future research. This can involve high-resolution taxonomic and functional studies for a more refined understanding of the equine gut microbiome and its health implications.

Cite This Article

APA
Gilroy R, Leng J, Ravi A, Adriaenssens EM, Oren A, Baker D, La Ragione RM, Proudman C, Pallen MJ. (2022). Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity. PeerJ, 10, e13084. https://doi.org/10.7717/peerj.13084

Publication

PeerJ
ISSN: 2167-8359
NlmUniqueID: 101603425
Country: United States
Language: English
Volume: 10
Pages: e13084
PII: e13084

Researcher Affiliations

Gilroy, Rachel
  • Quadram Institute Bioscience, Norwich, United Kingdom.
Leng, Joy
  • School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Ravi, Anuradha
  • Quadram Institute Bioscience, Norwich, United Kingdom.
Adriaenssens, Evelien M
  • Quadram Institute Bioscience, Norwich, United Kingdom.
Oren, Aharon
  • The Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem, Israel.
Baker, Dave
  • Quadram Institute Bioscience, Norwich, United Kingdom.
La Ragione, Roberto M
  • School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Proudman, Christopher
  • School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
Pallen, Mark J
  • Quadram Institute Bioscience, Norwich, United Kingdom.
  • School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom.
  • University of East Anglia, Norwich, United Kingdom.

MeSH Terms

  • Animals
  • Horses
  • Metagenome / genetics
  • Metagenomics
  • Microbiota
  • Bacteria / genetics
  • Archaea / genetics
  • Mammals
  • Bacteriophages

Grant Funding

  • BB/R012504/1 / Biotechnology and Biological Sciences Research Council
  • BBS/E/F/000PR10351 / Biotechnology and Biological Sciences Research Council
  • BBS/E/F/000PR10353 / Biotechnology and Biological Sciences Research Council
  • BB/R012490/1 / Biotechnology and Biological Sciences Research Council
  • BBS/E/F/000PR10356 / Biotechnology and Biological Sciences Research Council

Conflict of Interest Statement

The authors declare that they have no competing interests.

References

This article includes 86 references
  1. Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, Lawley TD, Finn RD. A new genomic blueprint of the human gut microbiota.. Nature 2019 Apr;568(7753):499-504.
    doi: 10.1038/s41586-019-0965-1pmc: PMC6784870pubmed: 30745586google scholar: lookup
  2. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman NJ, Andersson AF, Quince C. Binning metagenomic contigs by coverage and composition.. Nat Methods 2014 Nov;11(11):1144-6.
    doi: 10.1038/nmeth.3103pubmed: 25218180google scholar: lookup
  3. Andrewes FW, Horder TJ. A study of the streptococci pathogenic for man. The Lancet 1906;168(4334):775–783.
    doi: 10.1016/S0140-6736(01)13797-9google scholar: lookup
  4. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2019. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
  5. Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, Zhu Q, Bolzan M, Cumbo F, May U, Sanders JG, Zolfo M, Kopylova E, Pasolli E, Knight R, Mirarab S, Huttenhower C, Segata N. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0.. Nat Commun 2020 May 19;11(1):2500.
    doi: 10.1038/s41467-020-16366-7pmc: PMC7237447pubmed: 32427907google scholar: lookup
  6. Babenko VV, Millard A, Kulikov EE, Spasskaya NN, Letarova MA, Konanov DN, Belalov IS, Letarov AV. The ecogenomics of dsDNA bacteriophages in feces of stabled and feral horses.. Comput Struct Biotechnol J 2020;18:3457-3467.
    doi: 10.1016/j.csbj.2020.10.036pmc: PMC7691681pubmed: 33294140google scholar: lookup
  7. British Equine Trade Association Market information. 2019. https://www.beta-uk.org/pages/industry-information/market-information.php https://www.beta-uk.org/pages/industry-information/market-information.php
  8. Belaunzaran X, Bessa RJ, Lavín P, Mantecón AR, Kramer JK, Aldai N. Horse-meat for human consumption - Current research and future opportunities.. Meat Sci 2015 Oct;108:74-81.
    doi: 10.1016/j.meatsci.2015.05.006pubmed: 26047980google scholar: lookup
  9. Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, Brister JR, Kropinski AM, Krupovic M, Lavigne R, Turner D, Sullivan MB. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks.. Nat Biotechnol 2019 Jun;37(6):632-639.
    doi: 10.1038/s41587-019-0100-8pubmed: 31061483google scholar: lookup
  10. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.. Bioinformatics 2014 Aug 1;30(15):2114-20.
    doi: 10.1093/bioinformatics/btu170pmc: PMC4103590pubmed: 24695404google scholar: lookup
  11. Breitwieser FP, Salzberg SL. Interactive analysis of metagenomics data for microbiomics and pathogen identification. BioRxiv 2016;6:19233.
    doi: 10.1101/084715google scholar: lookup
  12. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND.. Nat Methods 2015 Jan;12(1):59-60.
    doi: 10.1038/nmeth.3176pubmed: 25402007google scholar: lookup
  13. Calusinska M, Goux X, Fossépré M, Muller EEL, Wilmes P, Delfosse P. A year of monitoring 20 mesophilic full-scale bioreactors reveals the existence of stable but different core microbiomes in bio-waste and wastewater anaerobic digestion systems.. Biotechnol Biofuels 2018;11:196.
    doi: 10.1186/s13068-018-1195-8pmc: PMC6052691pubmed: 30038663google scholar: lookup
  14. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses.. Bioinformatics 2009 Aug 1;25(15):1972-3.
    doi: 10.1093/bioinformatics/btp348pmc: PMC2712344pubmed: 19505945google scholar: lookup
  15. Chan PP, Lowe TM. Gene prediction. New York, NY: Humana; 2019. pp. 1–14.
  16. 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.
    doi: 10.1093/bioinformatics/btz848pmc: PMC7703759pubmed: 31730192google scholar: lookup
  17. Clarkson N. World horse population likely to be over 60 million, figures suggest. 2017. https://www.horsetalk.co.nz/2017/07/10/world-horse-population-60-million/ https://www.horsetalk.co.nz/2017/07/10/world-horse-population-60-million/
  18. Connor TR, Loman NJ, Thompson S, Smith A, Southgate J, Poplawski R, Bull MJ, Richardson E, Ismail M, Thompson SE, Kitchen C, Guest M, Bakke M, Sheppard SK, Pallen MJ. CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community.. Microb Genom 2016 Sep;2(9):e000086.
    doi: 10.1099/mgen.0.000086pmc: PMC5537631pubmed: 28785418google scholar: lookup
  19. Costa MC, Arroyo LG, Allen-Vercoe E, Stämpfli HR, Kim PT, Sturgeon A, Weese JS. 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(7):e41484.
    doi: 10.1371/journal.pone.0041484pmc: PMC3409227pubmed: 22859989google scholar: lookup
  20. Costa MC, Weese JS. Understanding the Intestinal Microbiome in Health and Disease.. Vet Clin North Am Equine Pract 2018 Apr;34(1):1-12.
    doi: 10.1016/j.cveq.2017.11.005pubmed: 29402480google scholar: lookup
  21. Daly K, Stewart CS, Flint HJ, Shirazi-Beechey SP. Bacterial diversity within the equine large intestine as revealed by molecular analysis of cloned 16S rRNA genes. FEMS Microbiology Ecology 2001;38(2–3):141–151.
    doi: 10.1111/j.1574-6941.2001.tb00892.xgoogle scholar: lookup
  22. Delmont TO, Quince C, Shaiber A, Esen ÖC, Lee ST, Rappé MS, McLellan SL, Lücker S, Eren AM. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes.. Nat Microbiol 2018 Jul;3(7):804-813.
    doi: 10.1038/s41564-018-0176-9pmc: PMC6792437pubmed: 29891866google scholar: lookup
  23. Di Pietro R, Arroyo LG, Leclere M, Costa MC. Species-Level Gut Microbiota Analysis after Antibiotic-Induced Dysbiosis in Horses.. Animals (Basel) 2021 Sep 30;11(10).
    doi: 10.3390/ani11102859pmc: PMC8533001pubmed: 34679880google scholar: lookup
  24. Duerkop BA. Bacteriophages shift the focus of the mammalian microbiota.. PLoS Pathog 2018 Oct;14(10):e1007310.
    doi: 10.1371/journal.ppat.1007310pmc: PMC6201943pubmed: 30359456google scholar: lookup
  25. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput.. Nucleic Acids Res 2004;32(5):1792-7.
    doi: 10.1093/nar/gkh340pmc: PMC390337pubmed: 15034147google scholar: lookup
  26. Edwards JE, Shetty SA, van den Berg P, Burden F, van Doorn DA, Pellikaan WF, Dijkstra J, Smidt H. Multi-kingdom characterization of the core equine fecal microbiota based on multiple equine (sub)species.. Anim Microbiome 2020 Feb 12;2(1):6.
    doi: 10.1186/s42523-020-0023-1pmc: PMC7807809pubmed: 33499982google scholar: lookup
  27. Edwards JE, Shetty SA, van den Berg P, Burden F, van Doorn DA, Pellikaan WF, Dijkstra J, Smidt H. Multi-kingdom characterization of the core equine fecal microbiota based on multiple equine (sub)species.. Anim Microbiome 2020 Feb 12;2(1):6.
    doi: 10.1186/s42523-020-0023-1pmc: PMC7807809pubmed: 33499982google scholar: lookup
  28. Endo A, Roos S, Satoh E, Morita H, Okada S. Lactobacillus equigenerosi sp. nov., a coccoid species isolated from faeces of thoroughbred racehorses.. Int J Syst Evol Microbiol 2008 Apr;58(Pt 4):914-8.
    doi: 10.1099/ijs.0.65250-0pubmed: 18398194google scholar: lookup
  29. Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO. Anvi'o: an advanced analysis and visualization platform for 'omics data.. PeerJ 2015;3:e1319.
    doi: 10.7717/peerj.1319pmc: PMC4614810pubmed: 26500826google scholar: lookup
  30. Fages A, Hanghøj K, Khan N, Gaunitz C, Seguin-Orlando A, Leonardi M, McCrory Constantz C, Gamba C, Al-Rasheid KAS, Albizuri S, Alfarhan AH, Allentoft M, Alquraishi S, Anthony D, Baimukhanov N, Barrett JH, Bayarsaikhan J, Benecke N, Bernáldez-Sánchez E, Berrocal-Rangel L, Biglari F, Boessenkool S, Boldgiv B, Brem G, Brown D, Burger J, Crubézy E, Daugnora L, Davoudi H, de Barros Damgaard P, de Los Ángeles de Chorro Y de Villa-Ceballos M, Deschler-Erb S, Detry C, Dill N, do Mar Oom M, Dohr A, Ellingvåg S, Erdenebaatar D, Fathi H, Felkel S, Fernández-Rodríguez C, García-Viñas E, Germonpré M, Granado JD, Hallsson JH, Hemmer H, Hofreiter M, Kasparov A, Khasanov M, Khazaeli R, Kosintsev P, Kristiansen K, Kubatbek T, Kuderna L, Kuznetsov P, Laleh H, Leonard JA, Lhuillier J, Liesau von Lettow-Vorbeck C, Logvin A, Lõugas L, Ludwig A, Luis C, Arruda AM, Marques-Bonet T, Matoso Silva R, Merz V, Mijiddorj E, Miller BK, Monchalov O, Mohaseb FA, Morales A, Nieto-Espinet A, Nistelberger H, Onar V, Pálsdóttir AH, Pitulko V, Pitskhelauri K, Pruvost M, Rajic Sikanjic P, Rapan Papeša A, Roslyakova N, Sardari A, Sauer E, Schafberg R, Scheu A, Schibler J, Schlumbaum A, Serrand N, Serres-Armero A, Shapiro B, Sheikhi Seno S, Shevnina I, Shidrang S, Southon J, Star B, Sykes N, Taheri K, Taylor W, Teegen WR, Trbojević Vukičević T, Trixl S, Tumen D, Undrakhbold S, Usmanova E, Vahdati A, Valenzuela-Lamas S, Viegas C, Wallner B, Weinstock J, Zaibert V, Clavel B, Lepetz S, Mashkour M, Helgason A, Stefánsson K, Barrey E, Willerslev E, Outram AK, Librado P, Orlando L. Tracking Five Millennia of Horse Management with Extensive Ancient Genome Time Series.. Cell 2019 May 30;177(6):1419-1435.e31.
    doi: 10.1016/j.cell.2019.03.049pmc: PMC6547883pubmed: 31056281google scholar: lookup
  31. Forster SC, Kumar N, Anonye BO, Almeida A, Viciani E, Stares MD, Dunn M, Mkandawire TT, Zhu A, Shao Y, Pike LJ, Louie T, Browne HP, Mitchell AL, Neville BA, Finn RD, Lawley TD. A human gut bacterial genome and culture collection for improved metagenomic analyses.. Nat Biotechnol 2019 Feb;37(2):186-192.
    doi: 10.1038/s41587-018-0009-7pmc: PMC6785715pubmed: 30718869google scholar: lookup
  32. 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
  33. He B, Jin S, Cao J, Mi L, Wang J. Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases.. Biotechnol Biofuels 2019;12:153.
    doi: 10.1186/s13068-019-1498-4pmc: PMC6587244pubmed: 31249617google scholar: lookup
  34. Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, Butterfield CN, Hernsdorf AW, Amano Y, Ise K, Suzuki Y, Dudek N, Relman DA, Finstad KM, Amundson R, Thomas BC, Banfield JF. A new view of the tree of life.. Nat Microbiol 2016 Apr 11;1:16048.
    doi: 10.1038/nmicrobiol.2016.48pubmed: 27572647google scholar: lookup
  35. 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
  36. Julliand V, Grimm P. HORSE SPECIES SYMPOSIUM: The microbiome of the horse hindgut: History and current knowledge.. J Anim Sci 2016 Jun;94(6):2262-74.
    doi: 10.2527/jas.2015-0198pubmed: 27285903google scholar: lookup
  37. 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
  38. Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.. Nucleic Acids Res 2002 Jul 15;30(14):3059-66.
    doi: 10.1093/nar/gkf436pmc: PMC135756pubmed: 12136088google scholar: lookup
  39. Laconi A, Mughini-Gras L, Tolosi R, Grilli G, Trocino A, Carraro L, Di Cesare F, Cagnardi P, Piccirillo A. Microbial community composition and antimicrobial resistance in agricultural soils fertilized with livestock manure from conventional farming in Northern Italy.. Sci Total Environ 2021 Mar 15;760:143404.
    doi: 10.1016/j.scitotenv.2020.143404pubmed: 33199005google scholar: lookup
  40. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2.. Nat Methods 2012 Mar 4;9(4):357-9.
    doi: 10.1038/nmeth.1923pmc: PMC3322381pubmed: 22388286google scholar: lookup
  41. Leng J, Proudman C, Darby A, Blow F, Townsend N, Miller A, Swann J. Exploration of the Fecal Microbiota and Biomarker Discovery in Equine Grass Sickness.. J Proteome Res 2018 Mar 2;17(3):1120-1128.
    doi: 10.1021/acs.jproteome.7b00784pubmed: 29364680google scholar: lookup
  42. Leng J, Walton G, Swann J, Darby A, La Ragione R, Proudman C. "Bowel on the Bench": Proof of Concept of a Three-Stage, In Vitro Fermentation Model of the Equine Large Intestine.. Appl Environ Microbiol 2019 Dec 13;86(1).
    doi: 10.1128/AEM.02093-19pmc: PMC6912081pubmed: 31676474google scholar: lookup
  43. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools.. Bioinformatics 2009 Aug 15;25(16):2078-9.
    doi: 10.1093/bioinformatics/btp352pmc: PMC2723002pubmed: 19505943google scholar: lookup
  44. Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, Yamashita H, Lam TW. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices.. Methods 2016 Jun 1;102:3-11.
    doi: 10.1016/j.ymeth.2016.02.020pubmed: 27012178google scholar: lookup
  45. Limam RD, Chouari R, Mazéas L, Wu TD, Li T, Grossin-Debattista J, Guerquin-Kern JL, Saidi M, Landoulsi A, Sghir A, Bouchez T. Members of the uncultured bacterial candidate division WWE1 are implicated in anaerobic digestion of cellulose.. Microbiologyopen 2014 Apr;3(2):157-67.
    doi: 10.1002/mbo3.144pmc: PMC3996565pubmed: 24497501google scholar: lookup
  46. Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science 2017;3:e104.
    doi: 10.7717/peerj-cs.104google scholar: lookup
  47. Maddox TW, Clegg PD, Williams NJ, Pinchbeck GL. Antimicrobial resistance in bacteria from horses: Epidemiology of antimicrobial resistance.. Equine Vet J 2015 Nov;47(6):756-65.
    doi: 10.1111/evj.12471pubmed: 26084443google scholar: lookup
  48. Manrique P, Bolduc B, Walk ST, van der Oost J, de Vos WM, Young MJ. Healthy human gut phageome.. Proc Natl Acad Sci U S A 2016 Sep 13;113(37):10400-5.
    doi: 10.1073/pnas.1601060113pmc: PMC5027468pubmed: 27573828google scholar: lookup
  49. 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.
    doi: 10.1111/jbg.12441pubmed: 31523867google scholar: lookup
  50. Metcalf JL, Song SJ, Morton JT, Weiss S, Seguin-Orlando A, Joly F, Feh C, Taberlet P, Coissac E, Amir A, Willerslev E, Knight R, McKenzie V, Orlando L. Evaluating the impact of domestication and captivity on the horse gut microbiome.. Sci Rep 2017 Nov 14;7(1):15497.
    doi: 10.1038/s41598-017-15375-9pmc: PMC5686199pubmed: 29138485google scholar: lookup
  51. Molin G, Ternström A, Ursing J. Pseudomonas lundensis, a new bacterial species isolated from meat. International Journal of Systematic and Evolutionary Microbiology 1986;36:339–342.
    doi: 10.1099/00207713-36-2-339google scholar: lookup
  52. Morita H, Nakano A, Shimazu M, Toh H, Nakajima F, Nagayama M, Hisamatsu S, Kato Y, Takagi M, Takami H, Akita H, Matsumoto M, Masaoka T, Murakami M. Lactobacillus hayakitensis, L. equigenerosi and L. equi, predominant lactobacilli in the intestinal flora of healthy thoroughbreds.. Anim Sci J 2009 Jun;80(3):339-46.
    doi: 10.1111/j.1740-0929.2009.00633.xpubmed: 20163646google scholar: lookup
  53. Morita H, Shiratori C, Murakami M, Takami H, Kato Y, Endo A, Nakajima F, Takagi M, Akita H, Okada S, Masaoka T. Lactobacillus hayakitensis sp. nov., isolated from intestines of healthy thoroughbreds.. Int J Syst Evol Microbiol 2007 Dec;57(Pt 12):2836-2839.
    doi: 10.1099/ijs.0.65135-0pmc: PMC2884931pubmed: 18048734google scholar: lookup
  54. Moss EL, Maghini DG, Bhatt AS. Complete, closed bacterial genomes from microbiomes using nanopore sequencing.. Nat Biotechnol 2020 Jun;38(6):701-707.
    doi: 10.1038/s41587-020-0422-6pmc: PMC7283042pubmed: 32042169google scholar: lookup
  55. Murru F, Fliegerova K, Mura E, Mrázek J, Kopečný J, Moniello G. A comparison of methanogens of different regions of the equine hindgut.. Anaerobe 2018 Dec;54:104-110.
    doi: 10.1016/j.anaerobe.2018.08.009pubmed: 30142409google scholar: lookup
  56. Nicholls SM, Quick JC, Tang S, Loman NJ. Ultra-deep, long-read nanopore sequencing of mock microbial community standards.. Gigascience 2019 May 1;8(5).
    doi: 10.1093/gigascience/giz043pmc: PMC6520541pubmed: 31089679google scholar: lookup
  57. Ogilvie LA, Jones BV. The human gut virome: a multifaceted majority.. Front Microbiol 2015;6:918.
    doi: 10.3389/fmicb.2015.00918pmc: PMC4566309pubmed: 26441861google scholar: lookup
  58. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens H, Wagner HH. Vegan: community ecology package. 2019.
  59. 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
  60. 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
  61. 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
  62. Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, Hugenholtz P, Tyson GW. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life.. Nat Microbiol 2017 Nov;2(11):1533-1542.
    doi: 10.1038/s41564-017-0012-7pubmed: 28894102google scholar: lookup
  63. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments.. PLoS One 2010 Mar 10;5(3):e9490.
    doi: 10.1371/journal.pone.0009490pmc: PMC2835736pubmed: 20224823google scholar: lookup
  64. Proudman CJ, Hunter JO, Darby AC, Escalona EE, Batty C, Turner C. Characterisation of the faecal metabolome and microbiome of Thoroughbred racehorses.. Equine Vet J 2015 Sep;47(5):580-6.
    doi: 10.1111/evj.12324pubmed: 25041526google scholar: lookup
  65. Ravi A, Halstead FD, Bamford A, Casey A, Thomson NM, van Schaik W, Snelson C, Goulden R, Foster-Nyarko E, Savva GM, Whitehouse T, Pallen MJ, Oppenheim BA. Loss of microbial diversity and pathogen domination of the gut microbiota in critically ill patients.. Microb Genom 2019 Sep;5(9).
    doi: 10.1099/mgen.0.000293pmc: PMC6807385pubmed: 31526447google scholar: lookup
  66. Roberts A. Tamed: ten species that changed our world. New York: Random House; 2017.
  67. Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data.. PeerJ 2015;3:e985.
    doi: 10.7717/peerj.985pmc: PMC4451026pubmed: 26038737google scholar: lookup
  68. Roux S, Páez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, Reddy TBK, Nayfach S, Schulz F, Call L, Neches RY, Woyke T, Ivanova NN, Eloe-Fadrosh EA, Kyrpides NC. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses.. Nucleic Acids Res 2021 Jan 8;49(D1):D764-D775.
    doi: 10.1093/nar/gkaa946pmc: PMC7778971pubmed: 33137183google scholar: lookup
  69. Santos AS, Rodrigues MA, Bessa RJ, Ferreira LM, Martin-Rosset W. Understanding the equine cecum-colon ecosystem: current knowledge and future perspectives.. Animal 2011 Jan;5(1):48-56.
    doi: 10.1017/S1751731110001588pubmed: 22440701google scholar: lookup
  70. Scales BS, Erb-Downward JR, Falkowski NR, LiPuma JJ, Huffnagle GB. Genome Sequences of 12 Pseudomonas lundensis Strains Isolated from the Lungs of Humans.. Genome Announc 2018 Feb 15;6(7).
    doi: 10.1128/genomeA.01461-17pmc: PMC5814480pubmed: 29449399google scholar: lookup
  71. Schwengers O, Hain T, Chakraborty T, Goesmann A. ReferenceSeeker: rapid determination of appropriate reference genomes. Journal of Open Source Software 2020;5(46):1994.
    doi: 10.21105/joss.01994google scholar: lookup
  72. Seemann T. barrnap 0.9: rapid ribosomal RNA prediction. 2018.
  73. Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, Liu P, Narrowe AB, Rodríguez-Ramos J, Bolduc B, Gazitúa MC, Daly RA, Smith GJ, Vik DR, Pope PB, Sullivan MB, Roux S, Wrighton KC. DRAM for distilling microbial metabolism to automate the curation of microbiome function.. Nucleic Acids Res 2020 Sep 18;48(16):8883-8900.
    doi: 10.1093/nar/gkaa621pmc: PMC7498326pubmed: 32766782google scholar: lookup
  74. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks.. Genome Res 2003 Nov;13(11):2498-504.
    doi: 10.1101/gr.1239303pmc: PMC403769pubmed: 14597658google scholar: lookup
  75. 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
  76. SMITH DG, SHATTOCK PM. The serological grouping of Streptococcus equinus.. J Gen Microbiol 1962 Dec;29:731-6.
    doi: 10.1099/00221287-29-4-731pubmed: 13989375google scholar: lookup
  77. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies.. Bioinformatics 2014 May 1;30(9):1312-3.
    doi: 10.1093/bioinformatics/btu033pmc: PMC3998144pubmed: 24451623google scholar: lookup
  78. Stewart HL, Pitta D, Indugu N, Vecchiarelli B, Engiles JB, Southwood LL. Characterization of the fecal microbiota of healthy horses.. Am J Vet Res 2018 Aug;79(8):811-819.
    doi: 10.2460/ajvr.79.8.811pubmed: 30058849google scholar: lookup
  79. Tamaki H, Tanaka Y, Matsuzawa H, Muramatsu M, Meng XY, Hanada S, Mori K, Kamagata Y. Armatimonas rosea gen. nov., sp. nov., of a novel bacterial phylum, Armatimonadetes phyl. nov., formally called the candidate phylum OP10.. Int J Syst Evol Microbiol 2011 Jun;61(Pt 6):1442-1447.
    doi: 10.1099/ijs.0.025643-0pubmed: 20622056google scholar: lookup
  80. von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT.. Genome Biol 2019 Oct 22;20(1):217.
    doi: 10.1186/s13059-019-1817-xpmc: PMC6805573pubmed: 31640809google scholar: lookup
  81. Watanabe Y, Nagai F, Morotomi M. Characterization of Phascolarctobacterium succinatutens sp. nov., an asaccharolytic, succinate-utilizing bacterium isolated from human feces.. Appl Environ Microbiol 2012 Jan;78(2):511-8.
    doi: 10.1128/AEM.06035-11pmc: PMC3255759pubmed: 22081579google scholar: lookup
  82. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.
  83. Wittmann J, Turner D, Millard AD, Mahadevan P, Kropinski AM, Adriaenssens EM. From Orphan Phage to a Proposed New Family-the Diversity of N4-Like Viruses.. Antibiotics (Basel) 2020 Sep 30;9(10).
    doi: 10.3390/antibiotics9100663pmc: PMC7650795pubmed: 33008130google scholar: lookup
  84. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2.. Genome Biol 2019 Nov 28;20(1):257.
    doi: 10.1186/s13059-019-1891-0pmc: PMC6883579pubmed: 31779668google scholar: lookup
  85. 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
  86. Zhu W, Dong K, Yang J, Lu S, Lai XH, Pu J, Jin D, Huang Y, Zhang S, Zhou J, Huang Y, Xu J. Acinetobacter lanii sp. nov., Acinetobacter shaoyimingii sp. nov. and Acinetobacter wanghuae sp. nov., isolated from faeces of Equus kiang.. Int J Syst Evol Microbiol 2021 Jan;71(1).
    doi: 10.1099/ijsem.0.004567pubmed: 33196408google scholar: lookup

Citations

This article has been cited 18 times.
  1. Castillo-Fernandez J, Gilroy R, Jones RB, Honaker RW, Whittle MJ, Watson P, Amos GCA. Waltham catalogue for the canine gut microbiome: a complete taxonomic and functional catalogue of the canine gut microbiome through novel metagenomic based genome discovery. Microbiome 2026 Jan 17;14(1):25.
    doi: 10.1186/s40168-025-02265-wpubmed: 41547860google scholar: lookup
  2. Utkina I, Fan Y, Willing BP, Parkinson J. Metabolic modeling of microbial communities in the chicken ceca reveals a landscape of competition and co-operation. Microbiome 2025 Nov 27;13(1):248.
    doi: 10.1186/s40168-025-02241-4pubmed: 41310806google scholar: lookup
  3. Pallen MJ, Ponsero AJ, Telatin A, Moss CJ, Baker D, Heavens D, Davidson GL. Faecal metagenomes of great tits and blue tits provide insights into host, diet, pathogens and microbial biodiversity. Access Microbiol 2025;7(4).
    doi: 10.1099/acmi.0.000910.v3pubmed: 40302838google scholar: lookup
  4. Carter MM, Leatherwood JL, Paris BL, Moore GE, George JM, Martinez RE, Karges K, Cox JR, Arnold CE, Glass KG, Bradbery AN, Rodiles A, Wickersham TA. Influence of Saccharomyces cerevisiae CNCM I-1077 on the fecal pH, markers of gut permeability, fecal microbiota, and markers of systemic inflammation in sedentary horses fed a high-starch diet. J Anim Sci 2025 Jan 4;103.
    doi: 10.1093/jas/skaf005pubmed: 39803897google scholar: lookup
  5. Kang R, Song J, Park JK, Yun S, Lee JH, Ahn JS, Yu C, Kim G, Jeong J, Oh MG, Jo W, Lee W, Tilahun M, Park T. Impact of Forage Sources on Ruminal Bacteriome and Carcass Traits in Hanwoo Steers During the Late Fattening Stages. Microorganisms 2024 Oct 17;12(10).
    doi: 10.3390/microorganisms12102082pubmed: 39458391google scholar: lookup
  6. 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
  7. 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
  8. Álvarez Narváez S, Beaudry MS, Norris CG, Bartlett PB, Glenn TC, Sanchez S. Improved Equine Fecal Microbiome Characterization Using Target Enrichment by Hybridization Capture. Animals (Basel) 2024 Jan 29;14(3).
    doi: 10.3390/ani14030445pubmed: 38338088google scholar: lookup
  9. Gryaznova M, Smirnova Y, Burakova I, Morozova P, Nesterova E, Gladkikh M, Mikhaylov E, Syromyatnikov M. Characteristics of the Fecal Microbiome of Piglets with Diarrhea Identified Using Shotgun Metagenomics Sequencing. Animals (Basel) 2023 Jul 14;13(14).
    doi: 10.3390/ani13142303pubmed: 37508080google scholar: lookup
  10. Volmer JG, Soo RM, Evans PN, Hoedt EC, Astorga Alsina AL, Woodcroft BJ, Tyson GW, Hugenholtz P, Morrison M. Isolation and characterisation of novel Methanocorpusculum species indicates the genus is ancestrally host-associated. BMC Biol 2023 Mar 22;21(1):59.
    doi: 10.1186/s12915-023-01524-2pubmed: 36949471google scholar: lookup
  11. Ganda E, Chakrabarti A, Sardi MI, Tench M, Kozlowicz BK, Norton SA, Warren LK, Khafipour E. Saccharomyces cerevisiae fermentation product improves robustness of equine gut microbiome upon stress. Front Vet Sci 2023;10:1134092.
    doi: 10.3389/fvets.2023.1134092pubmed: 36908513google scholar: lookup
  12. 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
  13. Albright S, Louca S. Trait biases in microbial reference genomes. Sci Data 2023 Feb 9;10(1):84.
    doi: 10.1038/s41597-023-01994-7pubmed: 36759614google scholar: lookup
  14. 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
  15. Li C, Li X, Guo R, Ni W, Liu K, Liu Z, Dai J, Xu Y, Abduriyim S, Wu Z, Zeng Y, Lei B, Zhang Y, Wang Y, Zeng W, Zhang Q, Chen C, Qiao J, Liu C, Hu S. Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse. Microbiome 2023 Jan 12;11(1):7.
    doi: 10.1186/s40168-022-01448-zpubmed: 36631912google scholar: lookup
  16. Normand P, Pujic P, Abrouk D, Vemulapally S, Guerra T, Carlos-Shanley C, Hahn D. Draft Genomes of Frankia strains AiPa1 and AiPs1 Retrieved from Soil with Monocultures of Picea abies or Pinus sylvestris using Alnus incana as Capture Plant. J Genomics 2023;11:1-8.
    doi: 10.7150/jgen.77880pubmed: 36594039google scholar: lookup
  17. Cain JL, Norris JK, Ripley NE, Suri P, Finnerty CA, Gravatte HS, Nielsen MK. The microbial community associated with Parascaris spp. infecting juvenile horses. Parasit Vectors 2022 Nov 4;15(1):408.
    doi: 10.1186/s13071-022-05533-ypubmed: 36333754google scholar: lookup
  18. Mach N, Midoux C, Leclercq S, Pennarun S, Le Moyec L, Rué O, Robert C, Sallé G, Barrey E. Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes. Commun Biol 2022 Oct 3;5(1):1032.
    doi: 10.1038/s42003-022-03977-7pubmed: 36192523google scholar: lookup
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.