Effects of Inorganic Arsenic, Methylated Arsenicals, and Arsenobetaine on Atherosclerosis in the apoE−/− Mouse Model and the Role of As3mt-Mediated Methylation
Bibliographic record
Abstract
Background: Arsenic is metabolized through a series of oxidative methylation reactions by arsenic (3) methyltransferase (As3MT) to yield methylated intermediates. Although arsenic exposure is known to increase the risk of atherosclerosis, the contribution of arsenic methylation and As3MT remains undefined. Objectives: Our objective was to define whether methylated arsenic intermediates were proatherogenic and whether arsenic biotransformation by As3MT was required for arsenic-enhanced atherosclerosis. Methods: We utilized the apoE−/− mouse model to compare atherosclerotic plaque size and composition after inorganic arsenic, methylated arsenical, or arsenobetaine exposure in drinking water. We also generated apoE−/−/As3mt−/− double knockout mice to test whether As3MT-mediated biotransformation was required for the proatherogenic effects of inorganic arsenite. Furthermore, As3MT expression and function were assessed in in vitro cultures of plaque-resident cells. Finally, bone marrow transplantation studies were performed to define the contribution of As3MT-mediated methylation in different cell types to the development of atherosclerosis after inorganic arsenic exposure. Results: We found that methylated arsenicals, but not arsenobetaine, are proatherogenic and that As3MT is required for arsenic to induce reactive oxygen species and promote atherosclerosis. Importantly, As3MT was expressed and functional in multiple plaque-resident cell types, and transplant studies indicated that As3MT is required in extrahepatic tissues to promote atherosclerosis. Conclusion: Taken together, our findings indicate that As3MT acts to promote cardiovascular toxicity of arsenic and suggest that human AS3MT SNPs that correlate with enzyme function could predict those most at risk to develop atherosclerosis among the millions that are exposed to arsenic. https://doi.org/10.1289/EHP806
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".