Quinone extraction drives atmospheric carbon monoxide oxidation in bacteria
Bibliographic record
Abstract
Abstract Diverse bacteria and archaea use atmospheric carbon monoxide (CO) as an energy source during long-term survival. This process enhances the biodiversity of soil and marine ecosystems globally and removes 250 million tonnes of a toxic, climate-relevant pollutant from the atmosphere each year. Bacteria use [MoCu]-carbon monoxide dehydrogenases (Mo-CODH) to convert CO to carbon dioxide, then transfer the liberated high-energy electrons to the aerobic respiratory chain. However, given no high-affinity Mo-CODH has been purified, it is unknown how these enzymes oxidise CO at low concentrations and interact with the respiratory chain. Here we resolve these knowledge gaps by analysing Mo-CODH (CoxSML) and its hypothetical partner CoxG from Mycobacterium smegmatis . Kinetic and electrochemical analyses show purified Mo-CODH is a highly active high-affinity enzyme ( K m = 139 nM, k cat = 54.2 s -1 ). Based on its 1.85 Å resolution cryoEM structure, Mo-CODH forms a CoxSML homodimer similar to characterised low-affinity homologs, but has distinct active site coordination and narrower gas channels that may modulate affinity. We provide structural, biochemical, and genetic evidence that Mo-CODH transfers CO-derived electrons to the aerobic respiratory chain via the membrane-bound menaquinone-binding protein CoxG. Consistently, CoxG is required for CO-driven respiration, extracts menaquinone from mycobacterial membranes, and binds quinones in a hydrophobic pocket. Finally, we show that Mo-CODH and CoxG genetically and structurally associate in diverse bacteria and archaea. These findings reveal the basis of a biogeochemically and ecologically important process, while demonstrating that the newly discovered process of long-range quinone transport is a general mechanism of energy conservation, which convergently evolved on multiple occasions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".