Genome-wide mapping and analysis of aryl hydrocarbon receptor (AHR)- and aryl hydrocarbon receptor repressor (AHRR)-binding sites in human breast cancer cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it