Changes in the Rumen Epimural Bacterial Diversity of Beef Cattle as Affected by Diet and Induced Ruminal Acidosis
Bibliographic record
Abstract
Little is known about the nature of the rumen epithelial adherent (epimural) microbiome in cattle fed different diets. Using denaturing gradient gel electrophoresis (DGGE), quantitative real-time PCR (qPCR), and pyrosequencing of the V3 hypervariable coding region of 16S rRNA, epimural bacterial communities of 8 cattle were profiled during the transition from a forage to a high-concentrate diet, during acidosis, and after recovery. A total of 153,621 high-quality gene sequences were obtained, with populations exhibiting less taxonomic variability among individuals than across diets. The bacterial community composition exhibited clustering (P < 0.03) by diet, with only 14 genera, representing >1% of the rumen epimural population, differing (P ≤ 0.05) among diets. During acidosis, levels of Atopobium, Desulfocurvus, Fervidicola, Lactobacillus, and Olsenella increased, while during the recovery, Desulfocurvus, Lactobacillus, and Olsenella reverted to levels similar to those with the high-grain diet and Sharpea and Succinivibrio reverted to levels similar to those with the forage diet. The relative abundances of bacterial populations changed during diet transition for all qPCR targets except Streptococcus spp. Less than 5% of total operational taxonomic units (OTUs) identified exhibited significant variability across diets. Based on DGGE, the community structures of epithelial populations differed (P ≤ 0.10); segregation was most prominent for the mixed forage diet versus the grain, acidotic challenge, and recovery diets. Atopobium, cc142, Lactobacillus, Olsenella, RC39, Sharpea, Solobacterium, Succiniclasticum, and Syntrophococcus were particularly prevalent during acidosis. Determining the metabolic roles of these key genera in the rumens of cattle fed high-grain diets could define a clinical microbial profile associated with ruminal acidosis.
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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) | 0.001 | 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) | 0.000 | 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".