Uncovering hidden phylo- and ecogenomic diversity of the widespread methanotrophic genus <i>Methylobacter</i>
Bibliographic record
Abstract
ABSTRACT The globally distributed genus Methylobacter plays a crucial role in mitigating methane emissions from diverse ecosystems, including freshwater and marine habitats, wetlands, soils, sediments, groundwater, and landfills. Despite their frequent presence and abundance in these systems, we still know little about the genomic adaptations that they exhibit. Here, we used a collection of 97 genomes and metagenome-assembled genomes to ecogenomically characterise the genus. Our analyses suggest that the genus Methylobacter may contain more species than previously thought, with >30 putative species clusters. Some species clusters shared >98.65% sequence identity of the full-length 16S rRNA gene, demonstrating the need for genome-resolved species delineation. The ecogenomic differences between Methylobacter spp. include various combinations of methane monooxygenases, multigene loci for alternative dissimilatory metabolisms related to hydrogen, sulphur cycling, and denitrification, as well as other lifestyle-associated functions. Additionally, we describe and tentatively name the two new Methylobacter species, which we recently cultured from sediment of a temperate eutrophic fishpond, as Methylobacter methanoversatilis , sp. nov. and Methylobacter spei , sp. nov. Overall, our study highlights previously unrecognised species diversity within the Methylobacter genus, their diverse metabolic potential, versatility, as well as the presence of distinct genomic adaptations for thriving in various environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".