Molecular evidence for novel mercury methylating microorganisms in sulfate-impacted lakes
Bibliographic record
Abstract
Methylmercury (MeHg) is a bioaccumulative neurotoxin that is produced by certain anaerobic microorganisms, but the abundance and importance of different methylating populations in the environment is not well understood. We combined mercury geochemistry, hgcA gene cloning, rRNA methods, and metagenomics to compare microbial communities associated with MeHg production in two sulfate-impacted lakes on Minnesota's Mesabi Iron Range. The two lakes represent regional endmembers among sulfate-impacted sites in terms of their dissolved sulfide concentrations and MeHg production potential. rRNA amplicon sequencing indicates that sediments and anoxic bottom waters from both lakes contained diverse communities with multiple clades of sulfate reducing Deltaproteobacteria and Clostridia. In hgcA gene clone libraries, however, hgcA sequences were from taxa associated with methanogenesis and iron reduction in addition to sulfate reduction, and the most abundant clones were from unknown groups. We therefore applied metagenomics to identify the unknown populations in the lakes with the capability to methylate mercury, and reconstructed 27 genomic bins with hgcA. Some of the most abundant potential methylating populations were from phyla that are not typically associated with MeHg production, including a relative of the Aminicenantes (formerly candidate phylum OP8) and members of the Kiritimatiellaeota (PVC superphylum) and Spirochaetes that, together, were more than 50% of the potential methylators in some samples. These populations do not have genes for sulfate reduction, and likely degrade organic compounds by fermentation or other anaerobic processes. Our results indicate that previously unrecognized populations with hgcAB are abundant and may be important for MeHg production in some freshwater ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".