Chromosomal antioxidant genes have metal ion‐specific roles as determinants of bacterial metal tolerance
Bibliographic record
Abstract
Microbiological metal toxicity involves redox reactions between metal species and cellular molecules, and therefore, we hypothesized that antioxidant systems might be chromosomal determinants affecting the susceptibility of bacteria to metal toxicity. Here, survival was quantified in metal ion-exposed planktonic cultures of several Escherichia coli strains, each bearing a mutation in a gene important for redox homeostasis. This characterized approximately 250 gene-metal combinations and identified that sodA, sodB, gor, trxA, gshA, grxA and marR have distinct roles in safeguarding or sensitizing cells to different toxic metal ions (Cr(2)O(7)(2-), Co(2+), Cu(2+), Ag(+), Zn(2+), AsO(2)(-), SeO(3)(2-) or TeO(3)(2-)). To shed light on these observations, fluorescent sensors for reactive oxygen species (ROS) and reduced thiol (RSH) quantification were used to ascertain that different metal ions exert oxidative toxicity through disparate modes-of-action. These oxidative mechanisms of metal toxicity were categorized as involving ROS and thiol-disulfide chemistry together (AsO(2)(-), SeO(3)(2-)), ROS predominantly (Cu(2+), Cr(2)O(7)(2-)) or thiol-disulfide chemistry predominantly (Ag(+), Co(2+), Zn(2+), TeO(3)(2-)). Corresponding to this, promoter-luxCDABE fusions showed that toxic doses of different metal ions up- or downregulate the transcription of gene sets marking distinct pathways of cellular oxidative stress. Altogether, our findings suggest that different metal ions are lethal to cells through discrete pathways of oxidative biochemistry, and moreover, indicate that chromosomally encoded antioxidant systems may have metal ion-specific physiological roles as determinants of bacterial metal tolerance.
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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) | 0.001 | 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".