Dietary benzoic acid and supplemental enzymes alter fiber-fermenting taxa and metabolites in the cecum of weaned pigs
Bibliographic record
Abstract
Inclusion of enzymes and organic acids in pig diets is an important strategy supporting decreased antibiotic usage in pork production. However, limited knowledge exists about how these additives impact intestinal microbes and their metabolites. To examine the effects of benzoic acid and enzymes on gut microbiota and metabolome, 160 pigs were assigned to one of four diets 7 days after weaning: a control diet or the addition of 0.5% benzoic acid, 0.045% dietary enzymes (phytase, β-glucanase, xylanase, and α-amylase), or both and fed ad libitum for 21 to 22 d. Individual growth performance and group diarrhea incidence data were collected throughout the experimental period. A decrease of 20% in pen-level diarrhea incidence from days 8 to 14 in pigs-fed both benzoic acid and enzymes compared to the control diet (P = 0.047). Cecal digesta samples were collected at the end of the experimental period from 40 piglets (n = 10 per group) and evaluated for differences using 16S rRNA sequencing and two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS). Analysis of cecal microbiota diversity revealed that benzoic acid altered microbiota composition (Unweighted Unifrac, P = 0.047, r2 = 0.07) and decreased α-diversity (Shannon, P = 0.041; Faith's Phylogenetic Diversity, P = 0.041). Dietary enzymes increased fiber-fermenting bacterial taxa such as Prevotellaceae. Two-step feature selection identified 17 cecal metabolites that differed among diets, including increased microbial cross-feeding product 1,2-propanediol in pigs-fed benzoic acid-containing diets. In conclusion, dietary benzoic acid and enzymes affected the gut microbiota and metabolome of weaned pigs and may support the health and resolution of postweaning diarrhea.
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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".