Protein Kinase A-Dependent Synergism between GATA Factors and the Nuclear Receptor, Liver Receptor Homolog-1, Regulates Human Aromatase (CYP19) PII Promoter Activity in Breast Cancer Cells
Bibliographic record
Abstract
Cancers, including that of the breast, are the result of multiple contributing factors including aberrant gene expression. Indeed, the CYP19 gene encoding P450 aromatase, the key enzyme for estrogen biosynthesis, is up-regulated in breast tumors predominantly via the cAMP-responsive gonad-type PII promoter, ultimately leading to increased intratumoral estrogen production and tumor growth. Thus, identifying the molecular factors involved in aromatase PII promoter regulation is essential for our understanding and treatment of the disease. Because we have previously shown activity of the murine aromatase PII promoter to be markedly up-regulated by GATA factors with respect to the gonads, we hypothesized that GATA factors are also key determinants of human PII promoter-driven aromatase transcription in breast tumors. We now show that GATA3 and GATA4 are indeed expressed in several breast cancer cells lines. Consistent with the cAMP dependence of the PII promoter, activation elicited by GATA3 or GATA4 alone and the striking synergism between GATA3 or GATA4 and the nuclear receptor liver receptor homolog (LRH)-1 was intimately linked to forskolin treatment or overexpression of protein kinase A (PKA) catalytic subunit. PKA-mediated phosphorylation increases the interaction between GATA3 and LRH-1 and the requirement for PKA in aromatase PII promoter stimulation involves at least three specific amino acid residues: GATA3 Ser308, GATA4 Ser261, and LRH-1 Ser469. Finally, we show that the human LRH-1 promoter is itself a target for GATA factors. Thus, taken together, our results suggest that GATA factors likely contribute to aberrant aromatase expression in breast tumors through two distinct, yet complementary mechanisms.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".