Enrichment of peat yields novel methanogens: approaches for obtaining uncultured organisms in the age of rapid sequencing
Bibliographic record
Abstract
Methanogens are among the oldest forms of life on Earth and are detectable in a wide range of environments, but our knowledge of their overall diversity and functioning is limited. Peatlands in particular host a broad range of methanogens that contribute large amounts of methane to the atmosphere, but are largely under-represented in pure cultures. Here, we anaerobically enriched peat with common growth substrates, supplements and antibiotics to identifying novel methanogen sequences and potential growth conditions. Over 3 years, we obtained 28 new mcrA sequences from taxa that have remained previously uncultured and undescribed beyond distantly related clones or sequences detected in environmental samples. Evidence suggests that the novel methanogens, representing five of the seven known orders, were capable of growing on H2 as well as acetate and at temperatures ranging from 6 to ca. 22°C. Methods involving the use of ampicillin proved useful, although obtaining high methane production in the absence of H2 was difficult. Our results also indicate that many methanogens may rely on bacterial symbionts (commonly Clostridium spp.). Such enrichment approaches represent a useful intermediary between maker-gene detection and isolation, allowing us to broaden our understanding of methanogen physiological ecology while potentially providing valuable sequence data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".