Comparative Microbiome Analysis Reveals the Ecological Relationships Between Rumen Methanogens, Acetogens, and Their Hosts
Bibliographic record
Abstract
Ruminant methane, which is generated by methanogens through the consumption of hydrogen and supports the normal function of the rumen ecosystem, is a major source of greenhouse gases. Reductive acetogenesis by acetogens is a possible alternative sink that can dispose of hydrogen for acetate production. However, the distribution of rumen methanogens and acetogens along with the relationships among methanogens, acetogens, and their host are poorly understood. Therefore, we investigated the rumen methanogen and acetogen communities of 97 individual animals representing 14 ruminant species within three ruminant families Cervidae (deer), Bovidae (bovid), and Moschidae (musk deer). The results showed that Methanobrevibacter spp. and acetogens associated with Eubacteriaceae were the most widespread methanogens and acetogens, respectively. However, other methanogens and acetogens exhibited host specificity in rumen of reindeer and Chinese muntjac deer. Acetogen and methanogen communities were not correlated in these species, and the phylosymbiosis signature between host phylogeny and the composition of both communities was lacking. The abundance of Methanobrevibacter gottschalkii was negatively correlated with the degree of papillation of the rumen wall. Finally, co-occurrence analysis showed that the variation of the predicted methane yields was characterized by the interactive patterns between methanogens, acetogens, and concentrations of rumen metabolites. Our results show that rumen methanogen and acetogen communities have low compositional interdependence and do not exhibit parallel host evolution, which suggests the strategies for mitigating methane production should be based on a species-specific rumen microbiota analysis.
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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".