Growth Differentiation Factor 9 (GDF9) Suppresses Follistatin and Follistatin-Like 3 Production in Human Granulosa-Lutein Cells
Bibliographic record
Abstract
BACKGROUND: We have demonstrated that growth differentiation factor 9 (GDF9) enhances activin A-induced inhibin β(B)-subunit mRNA levels in human granulosa-lutein (hGL) cells by regulating receptors and key intracellular components of the activin signaling pathway. However, we could not exclude its effects on follistatin (FST) and follistatin-like 3 (FSTL3), well recognized extracellular inhibitors of activin A. METHODOLOGY: hGL cells from women undergoing in vitro fertilization (IVF) treatment were cultured with and without siRNA transfection of FST, FSTL3 or GDF9 and then treated with GDF9, activin A, FST, FSTL3 or combinations. FST, FSTL3 and inhibin β(B)-subunit mRNA, and FST, FSTL3 and inhibin B protein levels were assessed with real-time RT-PCR and ELISA, respectively. Data were log transformed before ANOVA followed by Tukey's test. PRINCIPAL FINDINGS: GDF9 suppressed basal FST and FSTL3 mRNA and protein levels in a time- and dose-dependent manner and inhibited activin A-induced FST and FSTL3 mRNA and protein expression, effects attenuated by BMPR2 extracellular domain (BMPR2 ECD), a GDF9 antagonist. After GDF9 siRNA transfection, basal and activin A-induced FST and FSTL3 mRNA and protein levels increased, but changes were reversed by adding GDF9. Reduced endogenous FST or FSTL3 expression with corresponding siRNA transfection augmented activin A-induced inhibin β(B)-subunit mRNA levels as well as inhibin B levels (P values all <0.05). Furthermore, the enhancing effects of GDF9 in activin A-induced inhibin β(B)-subunit mRNA and inhibin B production were attenuated by adding FST. CONCLUSION: GDF9 decreases basal and activin A-induced FST and FSTL3 expression, and this explains, in part, its enhancing effects on activin A-induced inhibin β(B)-subunit mRNA expression and inhibin B production 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".