Stable-isotope probing implicates <i>Methylophaga</i> spp and novel <i>Gammaproteobacteria</i> in marine methanol and methylamine metabolism
Bibliographic record
Abstract
The metabolism of one-carbon (C(1)) compounds in the marine environment affects global warming, seawater ecology and atmospheric chemistry. Despite their global significance, marine microorganisms that consume C(1) compounds in situ remain poorly characterized. Stable-isotope probing (SIP) is an ideal tool for linking the function and phylogeny of methylotrophic organisms by the metabolism and incorporation of stable-isotope-labelled substrates into nucleic acids. By combining DNA-SIP and time-series sampling, we characterized the organisms involved in the assimilation of methanol and methylamine in coastal sea water (Plymouth, UK). Labelled nucleic acids were analysed by denaturing gradient gel electrophoresis (DGGE) and clone libraries of 16S rRNA genes. In addition, we characterized the functional gene complement of labelled nucleic acids with an improved primer set targeting methanol dehydrogenase (mxaF) and newly designed primers for methylamine dehydrogenase (mauA). Predominant DGGE phylotypes, 16S rRNA, methanol and methylamine dehydrogenase gene sequences, and cultured isolates all implicated Methylophaga spp, moderately halophilic marine methylotrophs, in the consumption of both methanol and methylamine. Additionally, an mxaF sequence obtained from DNA extracted from sea water clustered with those detected in (13)C-DNA, suggesting a predominance of Methylophaga spp among marine methylotrophs. Unexpectedly, most predominant 16S rRNA and functional gene sequences from (13)C-DNA were clustered in distinct substrate-specific clades, with 16S rRNA genes clustering with sequences from the Gammaproteobacteria. These clades have no cultured representatives and reveal an ecological adaptation of particular uncultured methylotrophs to specific C(1) compounds in the coastal marine environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".