Anaerobic HgII reduction is driven by cellular HgII-thiol interactions
Bibliographic record
Abstract
Redox reactions play a critical role in determining the availability of mercury species, Hg II and Hg 0 , to anaerobic microbes responsible for methylating inorganic mercury into toxic monomethylmercury. Some anaerobes also contribute to Hg cycling in methylation hotspots by reducing Hg II to its gaseous elemental form, Hg 0 . However, their contributions remain poorly quantified due to limited mechanistic insights and the absence of genetic targets. In this study, we investigated the mechanisms of anaerobic Hg II reduction in the versatile anoxygenic photoheterotroph and fermenter Heliomicrobium modesticaldum Ice1. Given Hg II strong electrophilic affinity for thiol groups, we hypothesized that cellular thiols are key interaction sites mediating Hg II reduction. Exposure of H. modesticaldum to the thiol-alkylating agent N -ethylmaleimide (NEM), which irreversibly binds thiols, resulted in a concentration-dependent inhibition of Hg 0 production during both photoheterotrophy and fermentation. Hg partitioning assays with Escherichia coli cells revealed no significant differences in Hg-cell partitioning in the presence or absence of NEM, suggesting that Hg II reduction is dependent on intracellular thiol interactions. These findings highlight the importance of thiol-mediated pathways in Heliobacterial Hg II reduction. Although the exact cellular components remain unidentified, we discuss potential thiol-containing coupling sites that warrant further investigation.
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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".