Volatile Arsenic Species Released from <i>Escherichia coli</i> Expressing the AsIII S-adenosylmethionine Methyltransferase Gene
Bibliographic record
Abstract
Biological systems, ranging from bacteria and fungi to humans, can methylate arsenic. Recent studies have suggested that the AsIII S-adenosylmethionine methyltransferase (arsM) gene in bacteria was responsible for the removal of arsenic as the volatile arsines from the bacteria. However, there has been no direct measure of the arsines released from bacteria cultures. We describe here an integrated system incorporating the bacterial incubation and volatile arsenic species analysis, and we demonstrate its application to the identification of the volatile arsines produced in bacterial cultures. The headspace of the bacterial cultures was purged with helium, and the volatile arsenic species were trapped in a chromatographic column immersed in liquid nitrogen. The cryogenically trapped arsines [AsH3, (CH3)AsH2, (CH3)2AsH, and (CH3)3As] were separated by gas chromatography and were detected by inductively coupled plasma mass spectrometry. A hydride generation system was coupled to the bacterial culture system, allowing for spiking standards and for generating calibration arsines necessary for quantitative analysis. Both bacteria containing the arsM gene or its variant arsMC2 gene were able to produce 400-500 ng of trimethylarsine. No trimethylarsine was detectable in bacteria lacking the arsM gene (containing the vector plasmid as negative control). These results confirm that arsM is responsible for releasing arsenic as volatile species from the arsenic-resistant bacteria. Our results also show traces of AsH3, CH3AsH2, and (CH3)2AsH in cultures of bacteria expressing arsM. The method detection limits for AsH3, CH3AsH2, (CH3)2AsH, and (CH3)3As were 0.5, 0.5, 0.7, and 0.6 pg, respectively. The ability to quantify trace levels of these volatile arsenic species makes it possible to study the biotransformation and biochemical roles of the evolution of these volatile arsenic species by biological systems.
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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".