Evidence that estrogen receptor β enhances MMP-13 promoter activity in HIG-82 cells and that this enhancement can be influenced by ligands and involves specific promoter sites
Bibliographic record
Abstract
Degradation of articular cartilage is characteristic of osteoarthritis, and matrix metalloproteinase-13 (MMP-13) has been implicated in this condition. Estrogen receptors (ERs) are present in connective tissues, indicating these tissues' potential responsiveness to estrogen. We based this study on the hypothesis that estrogen receptor beta (ERbeta) can modulate MMP-13 promoter activity. Transfection of cells with ERbeta constructs led to the induction of the endogenous MMP-13 gene, as evidenced by increased mRNA levels. The results also indicated that MMP-13 promoter construct activity in the HIG-82 cell line significantly increased when ERbeta was present, and that estrogen downregulated this response in a dose-dependent manner. ERbeta was shown to enhance MMP-13 expression somewhat more strongly than ERalpha, and the impact of a number of selective ER modulators (tamoxifen, raloxifene, and ICI 182,780) on ERbeta enhancement of promoter activity was found to be significantly less than that of estrogen. Furthermore, transcription regulatory sites in the MMP-13 promoter, specifically AP-1 and PEA-3, were shown to act in conjunction to mediate ERbeta effects. Thus, ERbeta likely influences MMP-13 promoter expression in normal and disease processes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".