Microbial metal‐sulfide oxidation in inactive hydrothermal vent chimneys suggested by metagenomic and metaproteomic analyses
Bibliographic record
Abstract
Metal-sulfides are wide-spread in marine benthic habitats. At deep-sea hydrothermal vents, they occur as massive sulfide chimneys formed by mineral precipitation upon mixing of reduced vent fluids with cold oxygenated sea water. Although microorganisms inhabiting actively venting chimneys and utilizing compounds supplied by the venting fluids are well studied, only little is known about microorganisms inhabiting inactive chimneys. In this study, we combined 16S rRNA gene-based community profiling of sulfide chimneys from the Manus Basin (SW Pacific) with radiometric dating, metagenome (n = 4) and metaproteome (n = 1) analyses. Our results shed light on potential lifestyles of yet poorly characterized bacterial clades colonizing inactive chimneys. These include sulfate-reducing Nitrospirae and sulfide-oxidizing Gammaproteobacteria dominating most of the inactive chimney communities. Our phylogenetic analysis attributed the gammaproteobacterial clades to the recently described Woeseiaceae family and the SSr-clade found in marine sediments around the world. Metaproteomic data identified these Gammaproteobacteria as autotrophic sulfide-oxidizers potentially facilitating metal-sulfide dissolution via extracellular electron transfer. Considering the wide distribution of these gammaproteobacterial clades in marine environments such as hydrothermal vents and sediments, microbially accelerated neutrophilic mineral oxidation might be a globally relevant process in benthic element cycling and a considerable energy source for carbon fixation in marine benthic habitats.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".