Estrogen Receptor‐Alpha (+/− Ligands) Modulates MMP‐13 Promoter Activity
Bibliographic record
Abstract
Objective Females are at increased risk for joint injuries/diseases, and the response of joint tissues to the hormonal milieu has been implicated. While the presence of ER-α has been well established in connective tissues, its functional role & potential for contributing to this risk is largely unknown. Matrix metalloproteinase 13 (MMP-13) is well recognized for its role in connective tissue repair and remodeling. The present studies were aimed at understanding the possible influence on the MMP-13 promoter by ER-α +/− specific ligands. Methods A rabbit synoviocyte cell line (HIG-82) was transiently transfected with expression constructs for ER-α + a series of MMP-13 promoter constructs (deletions, specific mutations), & the transfected cells were either untreated or were treated with ligands (17-β Estradiol, Tamoxifen, Raloxifene; 10−6 to 10−14 M). MMP-13 promoter activity was determined via luciferase assays. Results Preliminary studies showed that HIG-82 cells were negative for ER-α prior to transfection. The expression of MMP-13 promoter constructs was positively influenced by co-transfection with ER-α & the AP-1 regulatory site in the MMP-13 promoter was vital for its regulation. Addition of 17-β estradiol led to a dose-dependent decrease in the enhancement of promoter expression by ER-α, while other agonists/antagonists of ER-α exhibited differential affects on the activity of the MMP-13 promoter. Conclusions ER-α influences MMP-13 promoter activity differently in the presence or absence of specific agonists/ antagonists. These findings may have relevance to the functioning of tissues in joints such as the knee during the menstrual cycle or following menopause.
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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) | 0.003 | 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".