Comparative analysis of functional diversity of rumen microbiome in bison and beef heifers
Bibliographic record
Abstract
ABSTRACT Enhancing crop residue digestibility can boost the available energy for growth and milk yield and improve the sustainability of ruminant production systems. As the natural diet of bison is high in lignocellulose, bison have developed a microbiome that efficiently digests cellulose and hemicellulose. This study compared the rumen metatranscriptomes of bison and beef heifers and investigated the effects of inoculating heifers with bison rumen contents. Gene Ontology, molecular function, and Kyoto Encyclopedia of Genes and Genomes orthology terms identified through gene set enrichment analysis successfully captured differences in gene expression of rumen microbiome between heifers and bison fed different diets. Specifically, differences in nitrogen metabolism was detected between heifers and bison rumen microbiomes. Heifer rumen microbiomes demonstrated a higher dissimilatory nitrate reduction, while bison microbiomes tended to suppress this pathway. In contrast, bison microbiomes expressed higher levels of glutamate dehydrogenase 2 (GDH2) genes. However, glutamate dehydrogenase gene expression was observed to be strictly regulated in heifers, as inoculation with bison rumen contents had no effect on expression. Moreover, the transfer of bison rumen contents led to a persistent downregulation of microbial nitrogen metabolism in heifers after 27 days of transfer. In addition, gene set enrichment analysis also identified the crucial regulatory role of serine/threonine kinase in heifer rumen microbial metabolism and differences in ion transport between heifers and bison. These findings provide valuable insights into the complex interplay between diet and the rumen microbiome. IMPORTANCE Ruminants play a key role in the conversion of cellulolytic plant material into high-quality meat and milk protein for humans. The rumen microbiome is the driver of this conversion, yet there is little information on how gene expression within the microbiome impacts the efficiency of this conversion process. The current study investigates gene expression in the rumen microbiome of beef heifers and bison and how transplantation of ruminal contents from bison to heifers alters gene expression. Understanding interactions between the host and the rumen microbiome is the key to developing informed approaches to rumen programming that will enhance production efficiency in ruminants.
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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 | 0.000 | 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".