Zinc Finger Nuclease–Mediated Knockout of AHR or ARNT in Human Breast Cancer Cells Abolishes Basal and Ligand-Dependent Regulation of CYP1B1 and Differentially Affects Estrogen Receptor α Transactivation
Bibliographic record
Abstract
In this study, we used zinc finger nuclease-mediated knockout of the aryl hydrocarbon receptor (AHR) or AHR nuclear translocator (ARNT) in MCF7 and AHR knockout in MDA-MB-231 human breast cancer cells to investigate cross talk among AHR, ARNT, and estrogen receptor α (ERα). Knockout of AHR or ARNT prevented the 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-dependent induction of all AHR target genes examined. Knockout of AHR or ARNT also significantly reduced basal cytochrome P4501B1 (CYP1B1) expression levels, which were restored with overexpression of either protein but not with a DNA binding-deficient AHR mutant. Basal and TCDD-, 17β-estradiol (E2)-, or TCDD + E2-dependent recruitment of AHR, ARNT, ERα, NCoA3, and RNA polymerase II to CYP1B1 as well as CYP1B1 mRNA levels were abolished in MCF7-AHR((ko)) and MDA-MB-231 AHR(ko) cells. However, reduced but significant E2-dependent recruitment of ERα, NCoA3, and RNA polymerase II to CYP1B1 and weak increases in CYP1B1 mRNA levels were observed in MCF7 ARNT((ko)) cells. Interestingly, E2-dependent increases in trefoil factor 1, but not growth regulation by estrogen in breast cancer 1 (GREB1) mRNA levels, were dependent on ARNT expression. Moreover, the TCDD-dependent increases in the proteolytic degradation of ERα were prevented by the loss of AHR or ARNT. Our data show that AHR and ARNT play critical roles in the basal, TCDD, and E2-induced regulation of CYP1B1 but also reveal distinct roles for both proteins in ERα transactivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".