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
Bibliographic record
Abstract
The intestinal tract houses one of the richest and most complex microbial populations on the planet, and plays a critical role in health and a wide range of diseases. Limited studies using new sequencing technologies in horses are available. The objective of this study was to characterize the fecal microbiome of healthy horses and to compare the fecal microbiome of healthy horses to that of horses with undifferentiated colitis. A total of 195,748 sequences obtained from 6 healthy horses and 10 horses affected by undifferentiated colitis were analyzed. Firmicutes predominated (68%) among healthy horses followed by Bacteroidetes (14%) and Proteobacteria (10%). In contrast, Bacteroidetes (40%) was the most abundant phylum among horses with colitis, followed by Firmicutes (30%) and Proteobacteria (18%). Healthy horses had a significantly higher relative abundance of Actinobacteria and Spirochaetes while horses with colitis had significantly more Fusobacteria. Members of the Clostridia class were more abundant in healthy horses. Members of the Lachnospiraceae family were the most frequently shared among healthy individuals. The species richness reported here indicates the complexity of the equine intestinal microbiome. The predominance of Clostridia demonstrates the importance of this group of bacteria in healthy horses. The marked differences in the microbiome between healthy horses and horses with colitis indicate that colitis may be a disease of gut dysbiosis, rather than one that occurs simply through overgrowth of an individual pathogen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".