Mechanisms of gold biomineralization in the bacterium <i>Cupriavidus metallidurans</i>
Bibliographic record
Abstract
While the role of microorganisms as main drivers of metal mobility and mineral formation under Earth surface conditions is now widely accepted, the formation of secondary gold (Au) is commonly attributed to abiotic processes. Here we report that the biomineralization of Au nanoparticles in the metallophillic bacterium Cupriavidus metallidurans CH34 is the result of Au-regulated gene expression leading to the energy-dependent reductive precipitation of toxic Au(III)-complexes. C. metallidurans, which forms biofilms on Au grains, rapidly accumulates Au(III)-complexes from solution. Bulk and microbeam synchrotron X-ray analyses revealed that cellular Au accumulation is coupled to the formation of Au(I)-S complexes. This process promotes Au toxicity and C. metallidurans reacts by inducing oxidative stress and metal resistances gene clusters (including a Au-specific operon) to promote cellular defense. As a result, Au detoxification is mediated by a combination of efflux, reduction, and possibly methylation of Au-complexes, leading to the formation of Au(I)-C-compounds and nanoparticulate Au(0). Similar particles were observed in bacterial biofilms on Au grains, suggesting that bacteria actively contribute to the formation of Au grains in surface environments. The recognition of specific genetic responses to Au opens the way for the development of bioexploration and bioprocessing tools.
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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".