Effect of pH on Intracellular Accumulation of Trace Concentrations of Hg(II) in <i>Escherichia coli</i> under Anaerobic Conditions, as Measured Using a <i>mer-lux</i> Bioreporter
Bibliographic record
Abstract
The effects of pH on the uptake and accumulation of Hg(II) by Escherichia coli were determined at trace, environmentally relevant, concentrations of Hg and under anaerobic conditions. Hg(II) accumulation was measured using inducible light production from E. coli HMS174 harboring a mer-lux bioreporter plasmid (pRB28). The effect of pH on the toxicity of higher concentrations of Hg(II) was measured using a constitutive lux plasmid (pRB27) in the same bacterial host. In this study, intracellular accumulation and toxicity of Hg(II) under anaerobic conditions were both significantly enhanced with decreasing pH over the pH range of 8 to 5. The pH effect on Hg(II) accumulation was most pronounced at pHs of <6, which substantially enhanced the Hg(II)-dependent light response. This enhanced response did not appear to be due to pH stress, as similar results were obtained whether cells were grown at the same pH as the assay or at a different pH. The enhanced accumulation of Hg(II) was also not related to differences in the chemical speciation of Hg(II) in the external medium resulting from the changes in pH. Experiments with Cd(II), also detectable by the mer-lux bioreporter system, showed that Cd(II) accumulation responded differently to pH changes than the net accumulation of Hg(II). Potential implications of these findings for our understanding of bacterial accumulation of Hg(II) under anaerobic conditions and for bacteria-mediated cycling of Hg(II) in aquatic ecosystems are discussed. Arguments are provided suggesting that this differential accumulation is due to changes in uptake of mercury.
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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 | 0.000 | 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".