Biotransformation of arsenic by a Yellowstone thermoacidophilic eukaryotic alga
Bibliographic record
Abstract
Arsenic is the most common toxic substance in the environment, ranking first on the Superfund list of hazardous substances. It is introduced primarily from geochemical sources and is acted on biologically, creating an arsenic biogeocycle. Geothermal environments are known for their elevated arsenic content and thus provide an excellent setting in which to study microbial redox transformations of arsenic. To date, most studies of microbial communities in geothermal environments have focused on Bacteria and Archaea, with little attention to eukaryotic microorganisms. Here, we show the potential of an extremophilic eukaryotic alga of the order Cyanidiales to influence arsenic cycling at elevated temperatures. Cyanidioschyzon sp. isolate 5508 oxidized arsenite [As(III)] to arsenate [As(V)], reduced As(V) to As(III), and methylated As(III) to form trimethylarsine oxide (TMAO) and dimethylarsenate [DMAs(V)]. Two arsenic methyltransferase genes, CmarsM7 and CmarsM8, were cloned from this organism and demonstrated to confer resistance to As(III) in an arsenite hypersensitive strain of Escherichia coli. The 2 recombinant CmArsMs were purified and shown to transform As(III) into monomethylarsenite, DMAs(V), TMAO, and trimethylarsine gas, with a T(opt) of 60-70 degrees C. These studies illustrate the importance of eukaryotic microorganisms to the biogeochemical cycling of arsenic in geothermal systems, offer a molecular explanation for how these algae tolerate arsenic in their environment, and provide the characterization of algal methyltransferases.
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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 | 0.000 | 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) | 0.000 | 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".