Changes in the equine fecal microbiota associated with the use of systemic antimicrobial drugs.
Abstract: The intestinal tract is a rich and complex environment and its microbiota has been shown to have an important role in health and disease in the host. Several factors can cause disruption of the normal intestinal microbiota, including antimicrobial therapy, which is an important cause of diarrhea in horses. This study aimed to characterize changes in the fecal bacterial populations of healthy horses associated with the administration of frequently used antimicrobial drugs. Results: Twenty-four adult mares were assigned to receive procaine penicillin intramuscularly (IM), ceftiofur sodium IM, trimethoprim sulfadiazine (TMS) orally or to a control group. Treatment was given for 5 consecutive days and fecal samples were collected before drug administration (Day 1), at the end of treatment (Days 5), and on Days 14 and 30 of the trial. High throughput sequencing of the V4 region of the 16S rRNA gene was performed using an Illumina MiSeq sequencer. Significant changes of population structure and community membership were observed after the use of all drugs. TMS caused the most marked changes on fecal microbiota even at higher taxonomic levels including a significant decrease of richness and diversity. Those changes were mainly due to a drastic decrease of Verrucomicrobia, specifically the "5 genus incertae sedis". Changes in structure and membership caused by antimicrobial administration were specific for each drug and may be predictable. Twenty-five days after the end of treatment, bacterial profiles were more similar to pre-treatment patterns indicating a recovery from changes caused by antimicrobial administration, but differences were still evident, especially regarding community membership. Conclusions: The use of systemic antimicrobials leads to changes in the intestinal microbiota, with different and specific responses to different antimicrobials. All antimicrobials tested here had some impact on the microbiota, but TMS significantly reduced bacterial species richness and diversity and had the greatest apparent impact on population structure, specifically targeting members of the Verrucomicrobia phylum.
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.
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 study explores how systemic antimicrobial drugs affect the bacterial populations in the intestines of healthy horses, noting that such drugs can significantly disrupt the intestinal microbiota, potentially leading to health problems like diarrhea.
Research Design
In this study, the research team assigned 24 adult mares to receive one of three frequently used antimicrobial drugs – procaine penicillin, ceftiofur sodium, or trimethoprim sulfadiazine (TMS) – or to serve as part of a control group.
The horses received the assigned treatment for five consecutive days. Fecal samples were collected at various stages: before the drug administration, at the conclusion of the treatment, and then on the 14th and 30th day of the trial for analysis.
The analysis was performed using high throughput sequencing of a specific region (V4) of the 16S rRNA gene, using an Illumina MiSeq sequencer.
Key Findings
Significant changes were observed in the structure and membership of the bacterial populations in the horses’ feces following the use of all the drugs.
The most drastic changes were observed following the use of TMS, which caused a significant decrease in both the richness and diversity of the fecal microbiota.
In particular, TMS led to a drastic decrease of a type of bacteria known as Verrucomicrobia.
Interestingly, the changes induced by the antimicrobial drugs appeared to be specific to each drug, suggesting that the impacts on microbiota might be predictable based on the specific antimicrobial used.
Recovery and Persisting Differences
Towards the end of the trial, bacterial profiles seemed to recover and appeared to be more similar to pre-treatment patterns. However, differences were still evident, particularly in community membership.
Conclusions and Implications
The systemic use of antimicrobials can lead to significant changes in intestinal microbiota, with specific responses observed for different antimicrobials.
Although all tested antimicrobials had an impact on the microbiota, TMS showed the greatest effect by significantly reducing bacterial richness and diversity and notably affecting the Verrucomicrobia phylum.
This study gives veterinarians and those in equine care an important understanding of how these antimicrobials affect intestinal health. This could help with better planning of treatments and potential mitigation of side effects.
Cite This Article
APA
(2015).
Changes in the equine fecal microbiota associated with the use of systemic antimicrobial drugs.
BMC Vet Res, 11, 19.
https://doi.org/10.1186/s12917-015-0335-7
De La Cochetière MF, Durand T, Lalande V, Petit JC, Potel G, Beaugerie L. Effect of antibiotic therapy on human fecal microbiota and the relation to the development of Clostridium difficile.. Microb Ecol 2008 Oct;56(3):395-402.
Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation.. Proc Natl Acad Sci U S A 2011 Mar 15;108 Suppl 1(Suppl 1):4554-61.
Pérez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, Eismann K, Otto W, Rojo D, Bargiela R, von Bergen M, Neulinger SC, Däumer C, Heinsen FA, Latorre A, Barbas C, Seifert J, dos Santos VM, Ott SJ, Ferrer M, Moya A. Gut microbiota disturbance during antibiotic therapy: a multi-omic approach.. Gut 2013 Nov;62(11):1591-601.
Jakobsson HE, Jernberg C, Andersson AF, Sjölund-Karlsson M, Jansson JK, Engstrand L. Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome.. PLoS One 2010 Mar 24;5(3):e9836.
De La Cochetière MF, Durand T, Lepage P, Bourreille A, Galmiche JP, Doré J. Resilience of the dominant human fecal microbiota upon short-course antibiotic challenge.. J Clin Microbiol 2005 Nov;43(11):5588-92.
Ferran AA, Bibbal D, Pellet T, Laurentie M, Gicquel-Bruneau M, Sanders P, Schneider M, Toutain PL, Bousquet-Melou A. Pharmacokinetic/pharmacodynamic assessment of the effects of parenteral administration of a fluoroquinolone on the intestinal microbiota: comparison of bactericidal activity at the gut versus the systemic level in a pig model.. Int J Antimicrob Agents 2013 Nov;42(5):429-35.
Tanayama S, Yoshida K, Adachi K, Kondo T. Metabolic fate of SCE-1365, a new broad-spectrum cephalosporin, after parenteral administration to rats and dogs.. Antimicrob Agents Chemother 1980 Oct;18(4):511-8.
Peris-Bondia F, Latorre A, Artacho A, Moya A, D'Auria G. The active human gut microbiota differs from the total microbiota.. PLoS One 2011;6(7):e22448.
Robinson CJ, Young VB. Antibiotic administration alters the community structure of the gastrointestinal micobiota.. Gut Microbes 2010 Jul;1(4):279-284.
Morotomi N, Fukuda K, Nakano M, Ichihara S, Oono T, Yamazaki T, Kobayashi N, Suzuki T, Tanaka Y, Taniguchi H. Evaluation of intestinal microbiotas of healthy Japanese adults and effect of antibiotics using the 16S ribosomal RNA gene based clone library method.. Biol Pharm Bull 2011;34(7):1011-20.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glöckner FO. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies.. Nucleic Acids Res 2013 Jan 7;41(1):e1.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.. Appl Environ Microbiol 2013 Sep;79(17):5112-20.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection.. Bioinformatics 2011 Aug 15;27(16):2194-200.
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM. Ribosomal Database Project: data and tools for high throughput rRNA analysis.. Nucleic Acids Res 2014 Jan;42(Database issue):D633-42.