Dataset Information

The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or Salmonella infection on the fecal microbiome of horses.

ABSTRACT:

Background

The fecal microbiome of healthy horses may be influenced by signalment, diet, environmental factors, and disease.

Objectives

To assess the effects of age, breed, sex, geographic location, season, diet, and colitis caused by antibiotic use (antimicrobial-associated diarrhea [AAD]) and Salmonella infection on fecal microbiota.

Animals

Healthy horses (n = 80) were sampled from nonhospital environments across multiple geographical locations in the United States. Horses with AAD (n = 14) were defined as those that developed diarrhea secondary to antimicrobial use. Horses with Salmonella infection (n = 12) were presented with spontaneous onset of colitis and subsequently tested positive on Salmonella quantitative polymerase chain reaction. All horses were >1 year of age and stratified by a dietary scale that included forages (pasture and hay) and concentrates grouped by percentage of fiber and amount.

Methods

Illumina sequencing of 16S rRNA genes was performed on fecal DNA.

Results

Healthy horses fed higher amounts of grain clustered separately from those fed lower amounts of grain (analysis of similarities [ANOSIM], R = 0.356-0.385, Q = 0.002). Horses with AAD and Salmonella had decreased richness and evenness compared to healthy horses (P < .05). Univariable analysis of the 3 groups identified increases in Bacteroidetes (Q = 0.002) and Protebacteria (Q = 0.001) and decreases in Verrucomicrobia (Q = 0.001) in AAD horses whereas Salmonella horses had less Firmicutes (Q = 0.001) when compared to healthy horses.

Conclusions and clinical importance

Although the amount of grain in the diet had some impact on the fecal microbiome, colitis had a significantly larger influence. Horses with ADD have a more severe dysbiosis than do horses with Salmonella.

SUBMITTER: Arnold CE

PROVIDER: S-EPMC8478058 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or Salmonella infection on the fecal microbiome of horses.

Arnold Carolyn E CE Pilla Rachel R Chaffin M Keith MK Leatherwood Jessica L JL Wickersham Tryon A TA Callaway Todd R TR Lawhon Sara D SD Lidbury Jonathan A JA Steiner Joerg M JM Suchodolski Jan S JS

Journal of veterinary internal medicine 20210716 5

<h4>Background</h4>The fecal microbiome of healthy horses may be influenced by signalment, diet, environmental factors, and disease.<h4>Objectives</h4>To assess the effects of age, breed, sex, geographic location, season, diet, and colitis caused by antibiotic use (antimicrobial-associated diarrhea [AAD]) and Salmonella infection on fecal microbiota.<h4>Animals</h4>Healthy horses (n = 80) were sampled from nonhospital environments across multiple geographical locations in the United States. Hors ...[more]

PMID: 34268795

Similar Datasets

Project description:BackgroundDiet 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.ResultsRegardless 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.ConclusionsOverall, 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.

Dataset Information

The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or Salmonella infection on the fecal microbiome of horses.

Background

Objectives

Animals

Methods

Results

Conclusions and clinical importance

Publications

The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or Salmonella infection on the fecal microbiome of horses.

Similar Datasets

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