Estrogen receptor‐β modulates MMP‐13 promoter activity differently from ER‐α in HIG‐82 cells
Bibliographic record
Abstract
Objectives Matrix metalloproteinase 13 (MMP-13) has been implicated in connective tissue remodeling and repair. The presence of estrogen receptors (ER) in connective tissues is also well established & hormones have been implicated in regulating joint function. As such tissues may vary in levels of ER-α and ER-β and the relative levels of the two isoforms may be key determinants of ER regulatory influence on MMP-13 expression, this study was aimed at comparing the impact of ER-α and ER-β on the activity of a series of MMP-13 promoter constructs +/− estrogen agonists or antagonists. Methods ER-β & ER-α were sub-cloned into the same expression vector. These constructs were used to co-transfect a rabbit synoviocyte cell line (HIG-82) along with a series of deletion or mutated MMP-13 promoter-luciferase promoter constructs & the results were evaluated using luciferase assays. Results Of the two isoforms, ER-β proved to be a significantly more potent modulator of MMP-13 promoter activity in HIG-82 cells. The results indicate that specific transcription factor binding sites in the MMP-13 promoter act in conjunction, in modulating the influence of ER-β. The impact of estrogen agonists & antagonists on the influence of ER-βon the promoter also differed significantly from ER-α. Conclusions ER-β is a more potent activator of the MMP-13 promoter than ER-α & the impact of ER on MMP-13 may depend on the relative ratio of isoforms in specific tissues, as well as availability of ligands. Understanding MMP-13 regulation by ER isoforms and ligands in joint tissues may explain some aspects of gender differences in tissue regulation, as well as menstrual cycle and menopause influences.
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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) | 0.002 | 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".