Estrogen Receptor Subtype– and Promoter-Specific Modulation of Aryl Hydrocarbon Receptor–Dependent Transcription
Bibliographic record
Abstract
In this study, we examined the role of estrogen receptors (ER) in aryl hydrocarbon receptor (AHR)-dependent transactivation. Chromatin immunoprecipitation assays showed that AHR agonists differentially induced recruitment of ERalpha to the AHR target genes CYP1A1 and CYP1B1. Cotreatment with 17beta-estradiol significantly increased beta-naphthoflavone (BNF)- and 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced recruitment of ERalpha to CYP1A1, whereas 3,3'-diindolylmethane induced promoter occupancy of ERalpha at CYP1A1 that was unaffected by cotreatment with 17beta-estradiol. Cyclical recruitment of AHR and ERalpha to CYP1A1 was only observed in cells treated with BNF. Stable and subtype-specific knockdown of ERalpha or ERbeta using shRNA showed that suppression of ERalpha significantly reduced, whereas knockdown of ERbeta significantly enhanced, AHR agonist-induced Cyp1a1 expression in HC11 mouse mammary epithelial cells. AHR agonist-induced Cyp1b1 expression was reduced by ERbeta knockdown but unaffected by ERalpha knockdown. The siRNA-mediated knockdown of ERalpha in MCF-7 human breast cancer cells did not affect 2,3,7,8-tetrachlorodibenzo-p-dioxin-dependent regulation of CYP1A1 and CYP1B1 mRNA expression. In agreement with our in vitro findings in the HC11 cells, ERalpha knockout mice exhibit reduced BNF-dependent induction of Cyp1a1 mRNA. These results establish ligand- and promoter-specific influences on the cyclical recruitment patterns for AHR and show ER species-, subtype-, and promoter-specific modulation of AHR-dependent transcription.
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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 | 0.001 |
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".