Reduced sulphur sources favour Hg<sup>II</sup> reduction during anoxygenic photosynthesis by Heliobacteria
Bibliographic record
Abstract
Abstract The consumption of rice has become a global food safety issue because rice paddies support the production of high levels of the potent neurotoxin, methylmercury. The production of methylmercury is carried out by chemotrophic anaerobes that rely on a diversity of terminal electron acceptors, namely sulphate. Sulphur can be a limiting nutrient in rice paddies, and sulphate amendments are often used to stimulate crop production, which can increase methylmercury production. Mercury (Hg) redox cycling can affect Hg methylation by controlling the delivery of inorganic Hg substrates to methylators in anoxic habitats. Whereas sulphur is recognized as a key substrate controlling methylmercury production, the controls sulphur exerts on other microbe‐mediated Hg transformations remain poorly understood. To explore the potential coupling between sulphur assimilation and anaerobic Hg II reduction to Hg 0 , we studied Heliobacillus mobilis , a mesophilic anoxygenic phototroph representative from the Heliobacteriacea family originally isolated from a rice paddy. Here, we tested whether the redox state of the sulphur sources available to H. mobilis would affect its ability to reduce Hg II . By comparing Hg 0 production over a redox gradient of sulphur sources, we demonstrate that phototrophic Hg II reduction is favoured in the presence of reduced sulphur sources such as thiosulphate and cysteine. We also show that cysteine exerts dynamic control on Hg cycling by affecting not only Hg's bioavailability but also its abiotic photoreduction under low light conditions. Specifically, in the absence of cells we show that organic matter (as yeast extract) and cysteine are both required for photoreduction to occur. This study offers insights into how one of the most primitive forms of photosynthesis affects Hg redox transformations and frames Heliobacteria as key players in Hg cycling within paddy soils, forming a basis for management strategies to mitigate Hg accumulation in rice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 0.003 |
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; both teacher heads agree on what is shown here.
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".