Integrated microbiota–host–metabolome approaches reveal adaptive ruminal changes to prolonged high-grain feeding and phytogenic supplementation in cattle
Bibliographic record
Abstract
Diets rich in readily fermentable carbohydrates primarily impact microbial composition and activity, but can also impair the ruminal epithelium barrier function. By combining microbiota, metabolome, and gene expression analysis, we evaluated the impact of feeding a 65% concentrate diet for 4 weeks, with or without a phytogenic feed additive (PFA), on the rumen ecosystem of cattle. The breaking point for rumen health seemed to be the second week of high grain (HG) diet, with a dysbiosis characterized by reduced alpha diversity. While we did not find changes in histological evaluations, genes related with epithelial proliferation (IGF-1, IGF-1R, EGFR, and TBP) and ZO-1 were affected by the HG feeding. Integrative analyses allowed us to define the main drivers of difference for the rumen ecosystem in response to a HG diet, identified as ZO-1, MyD88, and genus Prevotella 1. PFA supplementation reduced the concentration of potentially harmful compounds in the rumen (e.g. dopamine and 5-aminovaleric acid) and increased the tolerance of the epithelium toward the microbiota by altering the expression of TLR-2, IL-6, and IL-10. The particle-associated rumen liquid microbiota showed a quicker adaptation potential to prolonged HG feeding compared to the other microenvironments investigated, especially by the end of the experiment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".