Selenite Protection of Tellurite Toxicity Toward Escherichia coli
Bibliographic record
Abstract
In this work the influence of selenite on metal resistance in Escherichia coli was examined. Both synergistic and antagonistic resistance and toxicities were found upon co exposure with selenite. In wild type cells co-exposure to selenite had little effect on arsenic resistance, decreased resistance to cadmium and mercury but led to a dramatically increased resistance to tellurite of 32-fold. Due to the potential importance of thiol chemistry in metal biochemistry, deletion strains in γ-glutamylcysteine synthetase (key step in glutathione biosynthesis, encoded by gshA), thioredoxin (trxA), glutaredoxin (grxA), glutathione oxidoreductase (gor), and the periplasmic glutathione transporter (cydD) were also evaluated for resistance to various metals in the presence of selenite. The protective effect of selenite on tellurite toxicity was seen in several of the mutants and was pronounced in the gshA mutant were resistance to tellurite was increased up to 1000-fold relative to growth in the absence of selenite. Thiol oxidation studies revealed a faster rate of loss of reduced thiol content in the cell with selenite than with tellurite, indicating differential thiol reactivity. Selenite addition resulted in reactive oxygen species (ROS) production equivalent to levels associated with H2O2 addition. Tellurite addition resulted in considerably lower ROS generation while vanadate and chromate treatment did not increase ROS production above that of background. This work shows increased resistance toward most oxyanions in mutants of thiol redox suggesting that metalloid reaction with thiol components such as glutathione actually enhances toxicity of some metalloids.
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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".