TGF-β1 Downregulates StAR Expression and Decreases Progesterone Production Through Smad3 and ERK1/2 Signaling Pathways in Human Granulosa Cells
Bibliographic record
Abstract
CONTEXT: Regulation of progesterone production in granulosa cells is important for normal reproductive functions. Steroidogenic acute regulatory protein (StAR) is recognized as the key regulatory protein involved in the rate-limiting step of steroidogenesis. TGF-β1 protein is detected in human follicular fluid, and TGF-β1 and its receptors are expressed in human granulosa cells. However, the functional role of TGF-β1 in the regulation of StAR expression and progesterone production in human granulosa cells remains unknown. OBJECTIVE: Our objective was to investigate the effects of TGF-β1 on StAR expression and progesterone production in human granulosa cells. DESIGN AND SETTING: SVOG cells are human granulosa cells that were obtained from women undergoing in vitro fertilization and immortalized with SV40 large T antigen. SVOG cells were used to investigate the effects of TGF-β1 on StAR expression and progesterone production at an academic research center. MAIN OUTCOME MEASURES: Levels of mRNA and protein were examined by RT-qPCR and western blotting, respectively. The accumulation levels of progesterone were measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: TGF-β1 treatment downregulated StAR expression and decreased progesterone production. The suppressive effects of TGF-β1 on StAR expression and progesterone production were abolished by the inhibition of TGF-β type I receptor. In addition, treatment with TGF-β1 activated the Smad2/3 and ERK1/2 signaling pathways. The inhibition of the Smad3 and ERK1/2 signaling pathways attenuated the TGF-β1-induced downregulation of StAR expression and progesterone production. CONCLUSION: TGF-β1 downregulated StAR expression and decreased progesterone production by activating the Smad3 and ERK1/2 signaling pathways in human granulosa cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".