Mouse Arsenic (+3 Oxidation State) Methyltransferase Genotype Affects Metabolism and Tissue Dosimetry of Arsenicals after Arsenite Administration in Drinking Water
Bibliographic record
Abstract
Arsenic (+3 oxidation state) methyltransferase (As3mt) catalyzes methylation of inorganic arsenic (iAs) producing a number of methylated arsenic metabolites. Although methylation has been commonly considered a pathway for detoxification of arsenic, some highly reactive methylated arsenicals may contribute to toxicity associated with exposure to inorganic arsenic. Here, adult female wild-type (WT) C57BL/6 mice and female As3mt knockout (KO) mice received drinking water that contained 1, 10, or 25 ppm (mg/l) of arsenite for 33 days and blood, liver, kidney, and lung were taken for arsenic speciation. Genotype markedly affected concentrations of arsenicals in tissues. Summed concentrations of arsenicals in plasma were higher in WT than in KO mice; in red blood cells, summed concentrations of arsenicals were higher in KO than in WT mice. In liver, kidney, and lung, summed concentrations of arsenicals were greater in KO than in WT mice. Although capacity for arsenic methylation is much reduced in KO mice, some mono-, di-, and tri-methylated arsenicals were found in tissues of KO mice, likely reflecting the activity of other tissue methyltransferases or preabsorptive metabolism by the microbiota of the gastrointestinal tract. These results show that the genotype for arsenic methylation determines the phenotypes of arsenic retention and distribution and affects the dose- and organ-dependent toxicity associated with exposure to inorganic arsenic.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".