Genome-wide mapping and analysis of aryl hydrocarbon receptor (AHR)- and aryl hydrocarbon receptor repressor (AHRR)-binding sites in human breast cancer cells
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AHR) mediates the toxic actions of environmental contaminants, such as 2,3,7,8-tetrachlorodibenzo-ρ-dioxin (TCDD), and also plays roles in vascular development, the immune response, and cell cycle regulation. The AHR repressor (AHRR) is an AHR-regulated gene and a negative regulator of AHR; however, the mechanisms of AHRR-dependent repression of AHR are unclear. In this study, we compared the genome-wide binding profiles of AHR and AHRR in MCF-7 human breast cancer cells treated for 24 h with TCDD using chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq). We identified 3915 AHR- and 2811 AHRR-bound regions, of which 974 (35%) were common to both datasets. When these 24-h datasets were also compared with AHR-bound regions identified after 45 min of TCDD treatment, 67% (1884) of AHRR-bound regions overlapped with those of AHR. This analysis identified 994 unique AHRR-bound regions. AHRR-bound regions mapped closer to promoter regions when compared with AHR-bound regions. The AHRE was identified and overrepresented in AHR:AHRR-co-bound regions, AHR-only regions, and AHRR-only regions. Candidate unique AHR- and AHRR-bound regions were validated by ChIP-qPCR and their ability to regulate gene expression was confirmed by luciferase reporter gene assays. Overall, this study reveals that AHR and AHRR exhibit similar but also distinct genome-wide binding profiles, supporting the notion that AHRR is a context- and gene-specific repressor of AHR activity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".