Growth Differentiation Factor 9 Enhances Activin A-Induced Inhibin B Production in Human Granulosa Cells
Bibliographic record
Abstract
Activin A or growth differentiation factor 9 (GDF9) alone can increase beta(B)-mRNA level in human granulosa-lutein cells from women undergoing in vitro fertilization, but their potential interactions and related cell signaling pathways involved are unknown. We therefore compared inhibin subunit and inhibin levels and activation of activin receptors (ACVRs) and Smad signaling pathway in these human granulosa-lutein cells with and without GDF9 and/or activin A treatment. Inhibin subunit (alpha, beta(A), beta(B)), ACVR, and Smad2/3/4/7 mRNA levels, inhibin A and B production, and Smad phosphorylation were assessed by real-time RT-PCR, ELISA, and immunoblotting, respectively. Data were analyzed by ANOVA followed by Tukey's test. Activin A (1-50 ng/ml) or GDF9 (1-200 ng/ml) alone had only little stimulatory effects on alpha- and beta(A)-mRNA levels. In contrast, GDF9 could stimulate beta(B)-subunit levels but to a lesser degree than the dose- and time-dependent effects of activin A. Compared with untreated cells, GDF9 pretreatment for 24 h significantly enhanced activin A-induced beta(B)-mRNA levels, inhibin B secretion, and Smad2/3 phosphorylation (effects attenuated by bone morphogenetic protein receptor 2 extracellular domain, a GDF9 antagonist); and induced ACVR2B/1B and Smad2/3 but reduced Smad7 (an inhibitory Smad) mRNA levels. We report here for the first time that GDF9 enhances cell response to activin A by modulating key components of the activin signaling pathway in regulating inhibin subunits and hence inhibin B production 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 | 0.000 |
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".