Methyltransferase: An enzyme assay for microbial methylmercury formation in acidic soils and sediments
Bibliographic record
Abstract
Methylmercury (MeHg) is a highly toxic form of mercury that bioaccumulates in aquatic food chains. However, methods to reliably identify sites of MeHg formation or to quantify MeHg production require the use of isotopic tracers, which limits their use. In this paper, a method is presented to quantify the methylation of mercury by a methyltransferase pathway. This methyltransferase pathway is one of the biochemical pathways responsible for biological mercury methylation. Protein is extracted from environmental samples, and mercury methyltransferase (HgMT) activity of soil extracts is calculated by assessing increases in methyltransferase activity induced by Hg addition. In enzyme extracts from pure cultures or soil samples, HgMT activity correlated with net MeHg production and Hg consumption, suggesting that HgMT activity can be used to estimate MeHg production in field samples. Over the course of a three-month period in a freshwater wetland, HgMT activity correlated with net MeHg concentrations (r2 = 0.55; p < 0.057). Furthermore, HgMT activity predicted (r2 = 0.80; p < 0.01) gross MeHg formation in freshwater wetlands as well as in laboratory microcosms calculated using previously published rate constants. Our results show that a methyltransferase assay, in combination with demethylation estimates, accurately predicts MeHg formation under field and laboratory conditions. This assay does not require the use of mercury added to field samples to estimate activity but rather estimates the biological activity present in the soil by quantifying the amount of enzyme present in the soil. Such an assay is well suited for use in field surveillance programs assessing MeHg formation in a variety of environments.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".