Inhibition of Liver X Receptor/Retinoid X Receptor–Mediated Transcription Contributes to the Proatherogenic Effects of Arsenic in Macrophages In Vitro
Bibliographic record
Abstract
OBJECTIVE: To determine whether arsenic inhibits transcriptional activation of the liver X receptor (LXR)/retinoid X receptor (RXR) heterodimers, thereby impairing cholesterol efflux from macrophages and potentially contributing to a proatherogenic phenotype. METHODS AND RESULTS: Arsenic is an important environmental contaminant and has been linked to an increased incidence of atherosclerosis. Previous findings showed that arsenic inhibits transcriptional activation of type 2 nuclear receptors, known to heterodimerize with RXR. Environmentally relevant arsenic doses decrease the LXR/RXR ligand-induced expression of the LXR target genes (ABCA1 and SREBP-1c). Arsenic failed to decrease cAMP-induced ABCA1 expression, suggesting a selective LXR/RXR effect. This selectivity correlated with the ability of arsenic to decrease LXR/RXR ligand-induced, but not cAMP-induced, cholesterol efflux. By using chromatin immunoprecipitation assays, we found that arsenic inhibits the ability of LXR/RXR ligands to induce activation markers on the ABCA1 and SREBP-1c promoters and blocks ligand-induced release of the nuclear receptor coexpressor (NCoR) from the promoter. Arsenic did not alter the ability of LXR to transrepress inflammatory gene transcription, further supporting our hypothesis that RXR is the target for arsenic inhibition. CONCLUSIONS: Exposure to arsenic enhances the risk of atherosclerosis. We present data that arsenic inhibits the transcriptional activity of the liver X receptor, resulting in decreased cholesterol-induced gene expression and efflux from macrophages. Therefore, arsenic may promote an athersclerotic environment by decreasing the ability of macrophages to efflux excess cholesterol, thereby favoring increased plaque formation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".