Identifying active rumen epithelial associated bacteria and archaea in beef cattle divergent in feed efficiency using total RNA-seq
Bibliographic record
Abstract
To date, the role of ruminal epithelial attached microbiota in cattle feed efficiency is undefined. In this study, we aimed to characterize transcriptionally active bacteria and archaea attached to the rumen epithelial wall and to determine whether they differ in cattle with varied feed efficiency. RNA-sequencing was performed to obtain the rumen epithelial transcriptomes from 9 of the most efficient (low RFI) and 9 of the most inefficient (high RFI) animals. The bacteria and archaea 16S rRNA transcripts were identified using an in-house developed pipeline, enriched from filtered reads that did not map to the bovine genome. Archaea from unclassified genera belonging to the Euryarchaeota phylum showed the most activity on the rumen epithelium of low RFI (81.3 ± 1.9%) and high RFI (76.4 ± 3.0%) steers. Bacteria from the Succinivibrionaceae family showed the greatest activity of bacteria on the low RFI (28.7 ± 9.0%) and high RFI (33.9± 8.8%) epithelium. Of the bacterial families, Campylobacteraceae and Neisseriaceae had significantly greater activity on the low RFI epithelium (p < 0.05) and are known to play a role in oxygen scavenging. Greater activity of rumen epithelial attached oxygen scavenging bacteria may provide more optimal feed fermentation conditions, which contributes to high fermentation efficiency in the rumen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".