BMP6 Downregulates GDNF Expression Through SMAD1/5 and ERK1/2 Signaling Pathways in Human Granulosa-Lutein Cells
Bibliographic record
Abstract
Bone morphogenetic protein (BMP) 6 is a critical regulator of follicular development that is expressed in mammalian oocytes and granulosa cells. Glial cell line‒derived neurotrophic factor (GDNF) is an intraovarian neurotrophic factor that plays an essential role in regulating mammalian oocyte maturation. The aim of this study was to investigate the effect of BMP6 on the regulation of GDNF expression and the potential underlying mechanisms. We used an established immortalized human granulosa cell line (SVOG cells) and primary human granulosa-lutein (hGL) cells as in vitro cell models. Our results showed that BMP6 significantly downregulated the expression of GDNF in both SVOG and primary hGL cells. With dual inhibition approaches (kinase receptor inhibitor and small interfering RNA knockdown), our results showed that both activin receptor kinase-like (ALK) 2 and ALK3 are involved in BMP6-induced downregulation of GDNF. In addition, BMP6 induced the phosphorylation of Sma- and Mad-related protein (SMAD)1/5/8 and ERK1/2 but not AKT or p38. Among three downstream mediators, both SMAD1 and SMAD5 are involved in BMP6-induced downregulation of GDNF. Moreover, concomitant knockdown of endogenous SMAD4 and inhibition of ERK1/2 activity completely reversed BMP6-induced downregulation of GDNF, indicating that both SMAD and ERK1/2 signaling pathways are required for the regulatory effect of BMP6 on GDNF expression. Our findings suggest an additional role for an intrafollicular growth factor in regulating follicular function through paracrine interactions in human granulosa 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".