Additive effects of metal excess and superoxide, a highly toxic mixture in bacteria
Bibliographic record
Abstract
Summary Heavy metal contamination is a serious environmental problem. Understanding the toxicity mechanisms may allow to lower concentration of metals in the metal‐based antimicrobial treatments of crops, and reduce metal content in soil and groundwater. Here, we investigate the interplay between metal efflux systems and the superoxide dismutase (SOD) in the purple bacterium Rubrivivax gelatinosus and other bacteria through analysis of the impact of metal accumulation. Exposure of the Cd 2+ ‐efflux mutant Δ cadA to Cd 2+ caused an increase in the amount and activity of the cytosolic Fe‐Sod SodB, thereby suggesting a role of SodB in the protection against Cd 2+ . In support of this conclusion, inactivation of sodB gene in the Δ cadA cells alleviated detoxification of superoxide and enhanced Cd 2+ toxicity. Similar findings were described in the Cu + ‐efflux mutant with Cu + . Induction of the Mn‐Sod or Fe‐Sod in response to metals in other bacteria, including Escherichia coli , Pseudomonas aeruginosa , Pseudomonas putida , Vibrio cholera and Bacillus subtilis , was also shown. Both excess Cd 2+ or Cu + and superoxide can damage [4Fe‐4S] clusters. The additive effect of metal and superoxide on the [4Fe‐4S] could therefore explain the hypersensitive phenotype in mutants lacking SOD and the efflux ATPase. These findings underscore that ROS defence system becomes decisive for bacterial survival under metal excess.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".