Effects of Endogenous Growth Differentiation Factor 9 on Activin A-Induced Inhibin B Production in Human Granulosa-Lutein Cells
Bibliographic record
Abstract
BACKGROUND: We recently reported on the effects of exogenous growth differentiation factor 9 (GDF9) in enhancing activin A-induced inhibin beta(B)-subunit mRNA and inhibin B levels in human granulosa-lutein (hGL) cells by modulating key components of the activin signaling pathway. We undertook the following study to characterize the role of endogenous GDF9 in this regard. METHODS: We compared inhibin subunit (alpha, beta(A), and beta(B)) mRNA and inhibin B levels and activation of activin receptors (ACVRs) and Smad signaling pathway in hGL cells obtained from women undergoing in vitro fertilization and cultured with and without activin A treatment after GDF9-targeting small interfering RNA transfection. GDF9, inhibin subunits, ACVR2B/1B and Smad2/3/4/7 mRNA and/or protein levels, Smad phosphorylation, and inhibin B were assessed with RT-PCR, immunoblotting, and ELISA. Data were analyzed by ANOVA followed by Tukey's test. RESULTS: GDF9 was detected as mRNA and protein in hGL cells and protein in follicular fluid from all 11 patients tested. Reduced endogenous GDF9 expression after targeting small interfering RNA transfection was associated with decreased ACVR2B/1B and Smad2/3/4 but increased inhibitory Smad7 mRNA and protein levels and, consequently, reduced activin A-induced beta(B)-subunit mRNA and inhibin B levels. CONCLUSIONS: We report here for the first time autocrine roles for endogenous GDF9 in hGL cells in enhancing activin A-induced beta(B)-subunit mRNA and inhibin B levels via key components of the activin signaling pathway. However, the relative contributions of GDF9 in granulosa cells vs. oocyte as autocrine/paracrine regulators of beta(B)-subunit and inhibin B production in normal and abnormal human ovarian functions remain to be determined.
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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".