Requirement of Ras-Dependent Pathways for Activation of the Transforming Growth Factor β3 Promoter by Estradiol**This work was supported by the Canadian Institutes of Health Research and the National Cancer Institute of Canada through the Canadian Breast Cancer Research Initiative program.
Bibliographic record
Abstract
It has been previously observed that the transforming growth factor beta3 (TGFbeta3) gene can be activated by both estradiol (E(2)) and selective estrogen receptor modulators (SERMs) in vivo but that only SERMs have a potent stimulatory effect on the TGFbeta3 promoter in cultured cells. We demonstrate in this report that E(2) can act also as a potent inducer of the TGFbeta3 promoter via a novel and specific estrogen receptor (ER)alpha-mediated mechanism. Our results show that treatment with epidermal growth factor or transfection of a constitutively active Ras mutant allows E(2) to induce the TGFbeta3 promoter via ERalpha in cotransfected HeLa and osteosarcoma MG63 cells. Both protein kinase C (PKC) and mitogen-activated protein kinase (MAPK) inhibitors can block the combined stimulatory effect of E(2) and epidermal growth factor/Ras. However, E(2) induction of the TGFbeta3 promoter was found to be unaffected by mutation of ERalpha serine 118, a well-characterized target of MAPK. Progressive deletion analysis of the ERalpha amino-terminal region delineated three separate domains modulating the E(2)/activated Ras response, revealing a complex functional organization of the ERalpha A/B domain required for regulation of the TGFbeta3 promoter. In addition, PKC and MAPK inhibitors had no effect on the induction of TGFbeta3 promoter activity by the SERM EM-652. These results indicate that induction of the TGFbeta3 promoter by the E(2)/ERalpha complex requires the concomitant activation of PKC and MAPK signaling and provide a novel framework for the design of more effective estrogen-based therapeutic strategies.
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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) | 0.000 | 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) | 0.002 | 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".