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Scientific reports2024; 14(1); 12903; doi: 10.1038/s41598-024-63868-1

Dysbiosis not observed in Canadian horse with free fecal liquid (FFL) using 16S rRNA sequencing.

Abstract: Free Fecal Liquid (FFL), also termed Fecal Water Syndrome (FWS), is an ailment in horses characterized by variable solid and liquid (water) phases at defecation. The liquid phase can be excreted before, during, or after the solid defecation phase. While the underlying causes of FFL are unknown, hindgut dysbiosis is suggested to be associated with FFL. Three European studies investigated dysbiosis in horses with FFL using 16S rRNA sequencing and reported results that conflicted between each other. In the present study, we also used 16S rRNA sequencing to study the fecal microbial composition in 14 Canadian horses with FFL, and 11 healthy stable mate controls. We found no significant difference in fecal microbial composition between FFL and healthy horses, which further supports that dysbiosis is not associated with FFL.
Publication Date: 2024-06-05 PubMed ID: 38839848PubMed Central: PMC11153561DOI: 10.1038/s41598-024-63868-1Google Scholar: Lookup
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  • Journal Article

Summary

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The research article presents the investigation conducted on Canadian horses suffering from Free Fecal Liquid (FFL), a health condition characterized by erratic defecation with variable solid and liquid phases. The study, employing 16S rRNA sequencing methodology, discovered no significant difference in fecal microbial composition between healthy horses and those with FFL, suggesting that the condition isn’t associated with dysbiosis.

About the Research

  • The research seeks to resolve contradictory findings from previous European studies, investigating the link between dysbiosis — disturbance or imbalance in the gut microbiota — and FFL in horses. Using 16S rRNA sequencing, the study engages a sample size of 14 Canadian horses with FFL and contrasts their fecal microbial composition against 11 healthy horses.

Key Findings

  • The investigation did not identify any significant differences in the fecal microbial composition between unhealthy (with FFL condition) and healthy horses, suggesting that there is no association between FFL and dysbiosis.

Implications of the Research

  • The research contributes to the broader understanding of FFL, wherein current knowledge of this as yet incompletely understood ailment is primarily speculative.
  • It aids in debunking assumptions about the relation between dysbiosis and FFL, pointing towards a requirement for more comprehensive research to understand the actual causes of FFL condition in horses.
  • The findings could potentially aid in devising more effective treatment methods, as solutions targeted towards managing dysbiosis may not be beneficial for FFL health condition in horses.

Cite This Article

APA
Wester RJ, Baillie LL, McCarthy GC, Keever CC, Jeffery LE, Adams PJ. (2024). Dysbiosis not observed in Canadian horse with free fecal liquid (FFL) using 16S rRNA sequencing. Sci Rep, 14(1), 12903. https://doi.org/10.1038/s41598-024-63868-1

Publication

ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 14
Issue: 1
Pages: 12903

Researcher Affiliations

Wester, Robert J
  • Applied Genomics Centre, Kwantlen Polytechnic University, Surrey, BC, Canada.
Baillie, Lyndsey L
  • Applied Genomics Centre, Kwantlen Polytechnic University, Surrey, BC, Canada.
McCarthy, Garrett C
  • Applied Genomics Centre, Kwantlen Polytechnic University, Surrey, BC, Canada.
Keever, Carson C
  • Faculty of Science, Kwantlen Polytechnic University, Surrey, BC, Canada.
Jeffery, Lauren E
  • Agwest Veterinary Group Ltd., Abbotsford, BC, Canada.
Adams, Paul J
  • Applied Genomics Centre, Kwantlen Polytechnic University, Surrey, BC, Canada. paul.adams@kpu.ca.
  • Faculty of Science, Kwantlen Polytechnic University, Surrey, BC, Canada. paul.adams@kpu.ca.

MeSH Terms

  • Horses
  • Animals
  • Feces / microbiology
  • RNA, Ribosomal, 16S / genetics
  • Dysbiosis / microbiology
  • Dysbiosis / veterinary
  • Horse Diseases / microbiology
  • Male
  • Canada
  • Female
  • Gastrointestinal Microbiome / genetics

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 46 references
  1. Laustsen L. Free faecal water: Analysis of horse faecal microbiota and the impact of faecal microbial transplantation on symptom severity.. Animals 2021;11:2776.
    doi: 10.3390/ani11102776pmc: PMC8533009pubmed: 34679798google scholar: lookup
  2. Lindroth KM. Differential defecation of solid and liquid phases in horses—A descriptive survey.. Animals 2020;10(1):76.
    doi: 10.3390/ani10010076pmc: PMC7023164pubmed: 31906279google scholar: lookup
  3. Schoster A, Weese JS, Gerber V, Nicole Graubner C. Dysbiosis is not present in horses with fecal water syndrome when compared to controls in spring and autumn.. J. Vet. Intern. Med. 2020;34:1614–1621.
    doi: 10.1111/jvim.15778pmc: PMC7379055pubmed: 32588473google scholar: lookup
  4. Kienzle E. Field study on risk factors for free fecal water in pleasure horses.. J. Equine Vet. Sci. 2016;44:32–36.
  5. Lindroth KM, Dicksved J, Pelve E, Båverud V, Müller CE. Faecal bacterial composition in horses with and without free faecal liquid: a case control study.. Sci. Rep. 2021;11:4745.
    doi: 10.1038/s41598-021-83897-4pmc: PMC7910430pubmed: 33637818google scholar: lookup
  6. Garber A, Hastie P, Murray J-A. Factors influencing equine gut microbiota: Current knowledge.. J. Equine Vet. Sci. 2020;88:102943.
    doi: 10.1016/j.jevs.2020.102943pubmed: 32303307google scholar: lookup
  7. Chaucheyras-Durand F, Sacy A, Karges K, Apper E. Gastro-intestinal microbiota in equines and its role in health and disease: The black box opens.. Microorganisms 2022;10:2517.
  8. Faubladier C, Chaucheyras-Durand F, da Veiga L, Julliand V. Effect of transportation on fecal bacterial communities and fermentative activities in horses: Impact of Saccharomyces cerevisiae CNCM I-1077 supplementation1.. J. Animal Sci. 2013;91:1736–1744.
    doi: 10.2527/jas.2012-5720pubmed: 23408806google scholar: lookup
  9. Weese JS. Changes in the faecal microbiota of mares precede the development of post partum colic.. Equine Vet. J. 2015;47:641–649.
    doi: 10.1111/evj.12361pubmed: 25257320google scholar: lookup
  10. Costa MC. 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.
  11. Milinovich GJ. Fluorescence in situ hybridization analysis of hindgut bacteria associated with the development of equine laminitis.. Environ. Microbiol. 2007;9:2090–2100.
  12. Steelman SM, Chowdhary BP, Dowd S, Suchodolski J, Janečka JE. Pyrosequencing of 16S rRNA genes in fecal samples reveals high diversity of hindgut microflora in horses and potential links to chronic laminitis.. BMC Vet. Res. 2012;8:231.
    doi: 10.1186/1746-6148-8-231pmc: PMC3538718pubmed: 23186268google scholar: lookup
  13. Kuhl J. Changes in faecal bacteria and metabolic parameters in foals during the first six weeks of life.. Vet. Microbiol. 2011;151:321–328.
    doi: 10.1016/j.vetmic.2011.03.017pubmed: 21511405google scholar: lookup
  14. Schoster A, Staempfli HR, Guardabassi LG, Jalali M, Weese JS. Comparison of the fecal bacterial microbiota of healthy and diarrheic foals at two and four weeks of life.. BMC Vet. Res. 2017;13:1–10.
    doi: 10.1186/s12917-017-1064-xpmc: PMC5450145pubmed: 28558788google scholar: lookup
  15. du Sert NP. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.. PLOS Biol. 2020;18:3000410.
  16. Bolyen E. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.. Nat. Biotechnol. 2019;37:852–857.
    doi: 10.1038/s41587-019-0209-9pmc: PMC7015180pubmed: 31341288google scholar: lookup
  17. Bokulich NA. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing.. Nat. Methods. 2013;10:57–59.
    doi: 10.1038/nmeth.2276pmc: PMC3531572pubmed: 23202435google scholar: lookup
  18. 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
  19. Yilmaz P. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks.. Nucl. Acids Res. 2014;42:D643–D648.
    doi: 10.1093/nar/gkt1209pmc: PMC3965112pubmed: 24293649google scholar: lookup
  20. Quast C. 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
  21. Bokulich NA. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin.. Microbiome 2018;6:90.
    doi: 10.1186/s40168-018-0470-zpmc: PMC5956843pubmed: 29773078google scholar: lookup
  22. Katoh K, Standley DM. MAFFT Multiple sequence alignment software version 7: Improvements in performance and usability.. Mol. Biol. Evol. 2013;30:772–780.
    doi: 10.1093/molbev/mst010pmc: PMC3603318pubmed: 23329690google scholar: lookup
  23. Price MN, Dehal PS, Arkin AP. FastTree 2—Approximately maximum-likelihood trees for large alignments.. PLOS ONE 2010;5:e9490.
  24. R Core Team. R: A language and environment for statistical computing.. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2023).
  25. Posit team. Rstudio: Integrated development environment for R.. Posit Software, PBC, Boston, MA. URL http://www.posit.co/ (2023).
  26. McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data.. PLOS ONE 2013;8:e61217.
  27. Oksanen J. Vegan: Community Ecology Package.. R package Version 2.6–4. https://CRAN.R-project.org/package=vegan (2022).
  28. Wickham H. Welcome to the Tidyverse.. J. Open Source Softw. 2019;4:1686.
    doi: 10.21105/joss.01686google scholar: lookup
  29. Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples)†.. Biometrika 1965;52:591–611.
    doi: 10.1093/biomet/52.3-4.591google scholar: lookup
  30. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis.. J. Am. Stat. Assoc. 1952;47:583–621.
  31. Wilcoxon F. Individual comparisons by ranking methods.. Biom. Bull. 1945;1:80–83.
    doi: 10.2307/3001968google scholar: lookup
  32. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing.. J. R. Stat. Soc. Series B Methodol. 1995;57:289–300.
  33. Shannon CE, Weaver W. The Mathematical Theory of Communication.. The University of Illinois Press; 1949.
  34. Simpson EH. Measurement of diversity.. Nature 1949;163:688–688.
    doi: 10.1038/163688a0google scholar: lookup
  35. Chao A. Estimating the population size for capture-recapture data with unequal catchability.. Biometrics 1987;43:783–791.
    doi: 10.2307/2531532pubmed: 3427163google scholar: lookup
  36. Bray JR, Curtis JT. An ordination of the upland forest communities of southern Wisconsin.. Ecol. Monogr. 1957;27:326–349.
    doi: 10.2307/1942268google scholar: lookup
  37. McArdle BH, Anderson MJ. Fitting multivariate models to community data: A comment on distance-based redundancy analysis.. Ecology 2001;82:290–297.
  38. Clarke KR. Non-parametric multivariate analyses of changes in community structure.. Aust. J. Ecol. 1993;18:117–143.
  39. Nie K. Roseburia intestinalis: A beneficial gut organism from the discoveries in genus and species.. Front Cell Infect. Microbiol. 2021;11:757718.
    doi: 10.3389/fcimb.2021.757718pmc: PMC8647967pubmed: 34881193google scholar: lookup
  40. Hu D. Effect of gender bias on equine fecal microbiota.. J. Equine Vet. Sci. 2021;97:103355.
    doi: 10.1016/j.jevs.2020.103355pubmed: 33478764google scholar: lookup
  41. Ang L. Gut microbiome characteristics in feral and domesticated horses from different geographic locations.. Commun. Biol. 2022;5:172.
    doi: 10.1038/s42003-022-03116-2pmc: PMC8881449pubmed: 35217713google scholar: lookup
  42. Fernandes KA. Faecal microbiota of forage-fed horses in New Zealand and the population dynamics of microbial communities following dietary change.. PLOS ONE 2014;9:e112846.
  43. Ertelt A, Gehlen H. Free fecal water in the horse—An unsolved problem.. Pferdeheilkunde 2015;31:261–268.
    doi: 10.21836/PEM20150308google scholar: lookup
  44. Jandhyala SM. Role of the normal gut microbiota.. World J. Gastroenterol. 2015;21:8787–8803.
    doi: 10.3748/wjg.v21.i29.8787pmc: PMC4528021pubmed: 26269668google scholar: lookup
  45. Coleman MC. Non-invasive evaluation of the equine gastrointestinal mucosal transcriptome.. PLOS ONE 2020;15:e0229797.
  46. Mach N. Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes.. Commun. Biol. 2022;5:1–15.
    doi: 10.1038/s42003-022-03977-7pmc: PMC9529974pubmed: 36192523google scholar: lookup