Identification of sulfate-reducing bacteria in methylmercury-contaminated mine tailings by analysis of SSU rRNA genes
Bibliographic record
Abstract
Sulfate-reducing bacteria (SRB) are often used in bioremediation of acid mine drainage because microbial sulfate reduction increases pH and produces sulfide that binds with metals. Mercury methylation has also been linked with sulfate reduction. Previous geochemical analysis indicated the occurrence of sulfate reduction in mine tailings, but no molecular characterization of the mine tailings-associated microbial community has determined which SRB are present. This study characterizes the bacterial communities of two geochemically contrasting, high-methylmercury mine tailing environments, with emphasis on SRB, by analyzing small subunit (SSU) rRNA genes present in the tailings sediments and in enrichment cultures inoculated with tailings. Novel Deltaproteobacteria and Firmicutes-related sequences were detected in both the pH-neutral gold mine tailings and the acidic high-sulfide base-metal tailings. At the subphylum level, the SRB communities differed between sites, suggesting that the community structure was dependent on local geochemistry. Clones obtained from the gold tailings and enrichment cultures were more similar to previously cultured isolates whereas clones from acidic tailings were more closely related to uncultured lineages identified from other acidic sediments worldwide. This study provides new insights into the novelty and diversity of bacteria colonizing mine tailings, and identifies specific organisms that warrant further investigation with regard to their roles in mercury methylation and sulfur cycling in these environments.
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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 | 0.001 | 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) | 0.000 | 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".