BMP4 and BMP7 Suppress StAR and Progesterone Production via ALK3 and SMAD1/5/8-SMAD4 in Human Granulosa-Lutein Cells
Bibliographic record
Abstract
Adequate production of progesterone by the corpus luteum is critical to the successful establishment of pregnancy. In animal models, bone morphogenetic protein (BMP) 4 and BMP7 have been shown to suppress either basal or gonadotropin-induced progesterone production, depending on the species examined. However, the effects of BMP4 and BMP7 on progesterone production in human granulosa cells are unknown. In the present study, we used immortalized (SVOG) and primary human granulosa-lutein cells to investigate the effects of BMP4 and BMP7 on steroidogenic acute regulatory protein (StAR) expression and progesterone production and to examine the underlying molecular mechanism. Treatment of primary and immortalized human granulosa cells with recombinant BMP4 or BMP7 decreased StAR expression and progesterone accumulation. In SVOG cells, the suppressive effects of BMP4 and BMP7 on StAR expression were blocked by pretreatment with inhibitors of activin receptor-like kinase (ALK)2/3/6 (dorsomorphin) or ALK2/3 (DMH1) but not ALK4/5/7 (SB-431542). Moreover, small interfering RNA-mediated depletion of ALK3, but not ALK2 or ALK6, reversed the effects of BMP4 and BMP7 on StAR expression. Likewise, BMP4- and BMP7-induced phosphorylation of SMAD 1/5/8 was reversed by treatment with DMH1 or small interfering RNA targeting ALK3. Knockdown of SMAD4, the essential common SMAD for BMP/TGF-β signaling, abolished the effects of BMP4 and BMP7 on StAR expression. Our results suggest that BMP4 and BMP7 down-regulate StAR and progesterone production via ALK3 and SMAD1/5/8-SMAD4 signaling in human granulosa-lutein 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 | 0.001 |
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".