Dietary butyrate glycerides modulate intestinal microbiota composition and serum metabolites in broilers
Bibliographic record
Abstract
Butyrate can modulate the immune response and energy expenditure of animals and enhance intestinal health. The present study investigated changes in the intestinal microbiota composition and serum metabolites of young broilers in response to 3,000 ppm butyrate in the form of butyrate glycerides (BG) via pyrosequencing of bacterial 16S rRNA genes and nuclear magnetic resonance (NMR). The dietary treatment did not affect the alpha diversity of intestinal microbiota, but altered its composition. Thirty-nine key operational taxonomic units (OTUs) in differentiating cecal microbiota community structures between BG treated and untreated chickens were also identified. Bifidobacterium was, in particular, affected by the dietary treatment significantly, showing an increase in not only the abundance (approximately 3 fold, P ≤ 0.05) but also the species diversity. The (NMR)-based analysis revealed an increase in serum concentrations of alanine, low-density and very low-density lipoproteins, and lipids (P ≤ 0.05) by BG. More interestingly, the dietary treatment also boosted (P ≤ 0.05) serum concentrations of bacterial metabolites, including choline, glycerophosphorylcholine, dimethylamine, trimethylamine, trimethylamine-N-oxide, lactate, and succinate. In conclusion, the data suggest the modulation of intestinal microbiota and serum metabolites by BG dietary treatment and potential contribution of intestinal bacteria to lipid metabolism/energy homeostasis in broilers.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".