Estrogen receptor α can selectively repress dioxin receptor-mediated gene expression by targeting DNA methylation
Bibliographic record
Abstract
Selective inhibitory crosstalk has been known to occur within the signaling pathways of the dioxin (AhR) and estrogen (ERα) receptors. More specifically, ERα represses a cytochrome P450-encoding gene (CYP1A1) that converts cellular estradiol into a metabolite that inhibits the cell cycle, while it has no effect on a P450-encoding gene (CYP1B1) that converts estrodiol into a genotoxic product. Here we show that ERα represses CYP1A1 by targeting the Dnmt3B DNA methyltransferase and concomitant DNA methylation of the promoter. We also find that histone H2A.Z can positively contribute to CYP1A1 gene expression, and its presence at that gene is inversely correlated with DNA methylation. Taken together, our results provide a framework for how ERα can repress transcription, and how that impinges on the production of an enzyme that generates genotoxic estradiol metabolites, and potential breast cancer progression. Finally, our results reveal a new mechanism for how H2A.Z can positively influence gene expression, which is by potentially competing with DNA methylation events in breast cancer cells.
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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) | 0.002 | 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".