Xylo-oligosaccharides and virginiamycin differentially modulate gut microbial composition in chickens
Bibliographic record
Abstract
BACKGROUND: The emergence and spread of antibiotic resistance in pathogens have led to a restriction on the use of antibiotic growth promoters (AGPs) in animal feed in some countries. The potential negative after-effects of a ban on AGPs could be mitigated by improving animal intestinal health with prebiotic dietary fibers such as xylo-oligosaccharides (XOS). However, the mechanism(s) by which an antibiotic or prebiotic contributes to the health and growth of animals are not well understood. Here, we evaluated XOS and virginiamycin (VIRG)-mediated changes in gut microbiota of broiler chickens using pyrosequencing of the 16S rRNA gene. RESULTS: There was a significant change in the relative abundance of certain bacteria, but the overall microbial diversity was not affected by treatment with either XOS or VIRG. Supplementation of HXOS (2 g XOS/kg diet) increased the proportion of Lactobacillus genus in the cecum, whereas Propionibacterium and Corynebacterium genera were enriched in the ileum of VIRG (16 mg/kg) treated birds. Furthermore, an increase in the cecal concentrations of acetate and propionate was observed in HXOS- and VIRG-fed chickens, respectively. These two groups of birds had better feed conversion efficiencies in comparison with the control group from day 7 to 21. In addition, temporal variations in the gut microbiota were evident in the chickens of different ages. CONCLUSIONS: Treatments with XOS or VIRG modified the relative abundance but not the presence or absence of specific microbial genus. The increase in both Lactobacillus spp. and acetate production in the cecum of HXOS-treated chickens may promote intestinal health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".