Unraveling the phylogenomic diversity of Methanomassiliicoccales and implications for mitigating ruminant methane emissions
Bibliographic record
Abstract
Abstract Background Methanomassiliicoccales are a recently identified order of methanogens that are diverse across global environments particularly the gastrointestinal tracts of animals; however, their metabolic capacities are defined via a limited number of cultured strains. Results Here, we profile and analyze 243 Methanomassiliicoccales genomes assembled from cultured representatives and uncultured metagenomes recovered from various biomes, including the gastrointestinal tracts of different animal species. Our analyses reveal the presence of numerous undefined genera and genetic variability in metabolic capabilities within Methanomassiliicoccales lineages, which is essential for adaptation to their ecological niches. In particular, gastrointestinal tract Methanomassiliicoccales demonstrate the presence of co-diversified members with their hosts over evolutionary timescales and likely originated in the natural environment. We highlight the presence of diverse clades of vitamin transporter BtuC proteins that distinguish Methanomassiliicoccales from other archaeal orders and likely provide a competitive advantage in efficiently handling B12. Furthermore, genome-centric metatranscriptomic analysis of ruminants with varying methane yields reveal elevated expression of select Methanomassiliicoccales genera in low methane animals and suggest that B12exchanges could enable them to occupy ecological niches that possibly alter the direction of H2utilization. Conclusions We provide a comprehensive and updated account of divergent Methanomassiliicoccales lineages, drawing from numerous uncultured genomes obtained from various habitats. We also highlight their unique metabolic capabilities involving B12, which could serve as promising targets for mitigating ruminant methane emissions by altering H2flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".