Increasing the copper sensitivity of microorganisms by restricting iron supply, a strategy for bio‐management practices
Bibliographic record
Abstract
Summary Pollution by copper (Cu 2+ ) extensively used as antimicrobial in agriculture and farming represents a threat to the environment and human health. Finding ways to make microorganisms sensitive to lower metal concentrations could help decreasing the use of Cu 2 + in agriculture. In this respect, we showed that limiting iron (Fe) uptake makes bacteria much more susceptible to Cu 2 + or Cd 2+ poisoning. Using efflux mutants of the purple bacterium Rubrivivax gelatinosus, we showed that Cu + and Cd 2+ resistance relies on the expression of the Fur‐regulated FbpABC and Ftr iron transporters. To support this conclusion, inactivation of these Fe‐importers in the Cu + or Cd 2+ ‐ATPase efflux mutants gave rise to hypersensitivity towards these ions. Moreover, in metal overloaded cells the expression of FbpA, the periplasmic iron‐binding component of the ferric ion transport FbpABC system was induced, suggesting that cells perceived an ‘iron‐starvation’ situation and responded to it by inducing Fe‐importers. In this context, the Fe‐Sod activity increased in response to Fe homoeostasis dysregulation. Similar results were obtained for Vibrio cholerae and Escherichia coli, suggesting that perturbation of Fe‐homoeostasis by metal excess appeared as an adaptive response commonly used by a variety of bacteria. The presented data support a model in which metal excess induces Fe‐uptake to support [4Fe‐4S] synthesis and thereby induce ROS detoxification system.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".