Regulation of rumen development in neonatal ruminants through microbial metagenomes and host transcriptomes
Bibliographic record
Abstract
BACKGROUND: In ruminants, early rumen development is vital for efficient fermentation that converts plant materials to human edible food such as milk and meat. Here, we investigate the extent and functional basis of host-microbial interactions regulating rumen development during the first 6 weeks of life. RESULTS: The use of microbial metagenomics, together with quantification of volatile fatty acids (VFAs) and qPCR, reveals the colonization of an active bacterial community in the rumen at birth. Colonization of active complex carbohydrate fermenters and archaea with methyl-coenzyme M reductase activity was also observed from the first week of life in the absence of a solid diet. Integrating microbial metagenomics and host transcriptomics reveals only 26.3% of mRNA transcripts, and 46.4% of miRNAs were responsive to VFAs, while others were ontogenic. Among these, one host gene module was positively associated with VFAs, while two other host gene modules and one miRNA module were negatively associated with VFAs. Eight host genes and five miRNAs involved in zinc ion binding-related transcriptional regulation were associated with a rumen bacterial cluster consisting of Prevotella, Bacteroides, and Ruminococcus. CONCLUSION: This three-way interaction suggests a potential role of bacteria-driven transcriptional regulation in early rumen development via miRNAs. Our results reveal a highly active early microbiome that regulates rumen development of neonatal calves at the cellular level, and miRNAs may coordinate these host-microbial interactions.
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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 | 0.000 |
| 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".