Unveiling the Bovine Epimural Microbiota Composition and Putative Function
Bibliographic record
Abstract
Numerous studies have used the 16S rRNA gene target in an attempt to characterize the structure and composition of the epimural microbiota in cattle. However, comparisons between studies are challenging, as the results show large variations associated with experimental protocols and bioinformatics methodologies. Here, we present a meta-analysis of the rumen epimural microbiota from 11 publicly available amplicon studies to assess key technical and biological sources of variation between experiments. Using the QIIME2 pipeline, 332 rumen epithelial microbiota samples were analyzed to investigate community structure, composition, and functional potential. Despite having a significant impact on microbial abundance, country of origin, farm, hypervariable region, primer set, animal variability, and biopsy location did not obscure the identification of a core microbiota. The bacterial genera Campylobacter, Christensenellaceae R-7 group, Defluviitaleaceae UCG-011, Lachnospiraceae UCG-010, Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-014, Succiniclasticum, Desulfobulbus, and Comamonas spp. were found in nearly all epithelium samples (>90%). Predictive analysis (PICRUSt) was used to assess the potential functions of the epithelial microbiota. Regularized canonical correlation analysis identified several pathways associated with the biosynthesis of precursor metabolites in Campylobacter, Comamonas, Desulfobulbus, and Ruminococcaceae NK4A214, highlighting key metabolic functions of these microbes within the epithelium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".