<scp>ID</scp>3 mediates the <scp>TGF</scp>‐β1‐induced suppression of matrix metalloproteinase‐1 in human granulosa cells
Bibliographic record
Abstract
In the mammalian ovary, matrix metalloproteinase-1 (MMP1) is expressed in growing ovarian follicles, and MMP1-mediated extracellular matrix (ECM) remodeling plays a functional role in regulating the formation of corpus luteum. Transforming growth factor-β1 (TGF-β1) is an intraovarian growth factor that acts as a negative regulator of luteinization and progesterone production in human granulosa-lutein (hGL) cells. At present, whether TGF-β1 regulates the expression of MMP1 and thus affects ECM remodeling during corpus luteum formation remains largely unknown. The aim of this study was to investigate the effects of TGF-β1 and the molecular mechanisms by which it regulates the expression of MMP1 in immortalized human granulosa cells lines (SVOG) and primary hGL cells (obtained from consenting patients undergoing IVF treatment). We used inhibition approaches including a competitive antagonist for endogenous TGF-β type II receptor, pharmacological inhibitors (SB431542 and dorsomorphin), and specific small interfering RNA-targeted knockdown of ALK5 type I receptor and SMAD4 to demonstrate that TGF-β1 downregulates the expression and production of MMP1 via a TβRII/ALK5-mediated SMAD-dependent signaling pathway in hGL cells. Additionally, our results show that the suppressive effect of TGF-β1 on the expression of MMP1 is mediated by a transcription factor, the inhibitor of differentiation 3 (ID3) protein. Our findings provide insights into the molecular interactions and mechanisms of TGF-β1 and ID3 during the regulation of MMP1 in hGL cells.
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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".