Dioxin Increases the Interaction Between Aryl Hydrocarbon Receptor and Estrogen Receptor Alpha at Human Promoters
Bibliographic record
Abstract
Recent studies have shown that activated aryl hydrocarbon receptor (AHR) induced the recruitment of estrogen receptor-alpha (ERalpha) to AHR-regulated genes and that AHR is recruited to ERalpha-regulated genes. However, these findings were limited to a small number of well-characterized AHR- or ERalpha-responsive genes with little knowledge of what was occurring at other genomic regions. In this study, we showed using chromatin immunoprecipitation followed by hybridization to promoter focused microarrays (ChIP-chip) that 2,3,7,8-tetrachlorodibenzo-p-dioxin treatment significantly increased the overlap of genomic regions bound by both AHR and ERalpha. Conventional and sequential ChIPs confirmed the recruitment of AHR and ERalpha to many of the identified regions. Transcription factor binding site analysis revealed an overrepresentation of aryl hydrocarbon receptor response elements in regions bound by both AHR and ERalpha, suggesting that AHR was the important factor determining the recruitment of ERalpha to these regions. RNA interference-mediated knockdown of AHR confirmed its requirement for the recruitment of ERalpha to some, but not all, of the shared regions. Our findings demonstrate not only that dioxin induces the recruitment of ERalpha to AHR target genes but also that AHR is recruited to estrogen-responsive regions in a gene-specific manner, suggesting that AHR utilizes both of these mechanisms to modulate estrogen-dependent signaling.
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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 | 0.001 |
| 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) | 0.003 | 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".