A genome catalogue of mercury‐methylating bacteria and archaea from sediments of a boreal river facing human disturbances
Bibliographic record
Abstract
Methyl mercury, a toxic compound, is produced by anaerobic microbes and magnifies in aquatic food webs, affecting the health of animals and humans. The exploration of mercury methylators based on genomes is still limited, especially in the context of river ecosystems. To address this knowledge gap, we developed a genome catalogue of potential mercury-methylating microorganisms. This was based on the presence of hgcAB from the sediments of a river affected by two run-of-river hydroelectric dams, logging activities and a wildfire. Through the use of genome-resolved metagenomics, we discovered a unique and diverse group of mercury methylators. These were dominated by members of the metabolically versatile Bacteroidota and were particularly rich in microbes that ferment butyrate. By comparing the diversity and abundance of mercury methylators between sites subjected to different disturbances, we found that ongoing disturbances, such as the input of organic matter related to logging activities, were particularly conducive to the establishment of a mercury-methylating niche. Finally, to gain a deeper understanding of the environmental factors that shape the diversity of mercury methylators, we compared the mercury-methylating genome catalogue with the broader microbial community. The results suggest that mercury methylators respond to environmental conditions in a manner similar to the overall microbial community. Therefore, it is crucial to interpret the diversity and abundance of mercury methylators within their specific ecological context.
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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 | 0.001 |
| 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 teacher head, 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".