High diversity, abundance and expression of hydrogenases in groundwater
Bibliographic record
Abstract
Abstract Hydrogen may be the most important electron donor available in the subsurface. Here we analyze the diversity, abundance and expression of hydrogenases in 5 proteomes, 25 metagenomes and 265 amplicon datasets of groundwaters with diverse geochemistry. A total of 1,772 new [NiFe]-hydrogenase gene sequences were recovered, which almost doubled the number of sequences in a widely used database. [NiFe]-hydrogenases were highly abundant, almost as abundant as the DNA-directed RNA polymerase. The abundance of hydrogenase genes increased with depth from 0 to 129 m. Hydrogenases were present in 502 out of 1,245 metagenome-assembled-genomes. The populations with hydrogenases accounted for ∼50% of all populations. Hydrogenases were actively expressed, making up as much as 5.9% of methanogen proteomes. Most of the newly discovered diversity of hydrogenases was in “Group 3b”, which was linked to sulfur metabolism. “Group 3d” was the most abundant, which was previously linked to fermentation, but we observed this group mainly in methanotrophs and chemoautotrophs. “Group 3a”, associated with methanogenesis, was the most active in proteomes. Two newly discovered groups of [NiFe]-hydrogenases further expanded the biodiversity. Our results highlight the vast diversity, abundance and expression of hydrogenases in the sampled groundwaters, suggesting a high potential for hydrogen oxidation in subsurface 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) | 0.000 | 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".