Control of oestradiol secretion and of cytochrome P450 aromatase messenger ribonucleic acid accumulation by FSH involves different intracellular pathways in oestrogenic bovine granulosa cells <i>in vitro</i>
Bibliographic record
Abstract
The objective of this study was to determine the major intracellular signalling pathways used by FSH and insulin to stimulate cytochrome P450 aromatase (Cyp19) mRNA and oestradiol accumulation in oestrogenic bovine granulosa cells in vitro. Bovine granulosa cells from small follicles (2-4 mm diameter) were cultured for 6 days under non-luteinizing conditions in the presence of insulin at 100 ng/ml, or insulin (10 ng/ml) and FSH (1 ng/ml). On day 4 of culture, specific inhibitors of phosphatidylinositol 3-kinase (PI3K; LY-294002), protein kinase C (PKC; GF-109203X), protein kinase A (PKA; H-89) or mitogen-activated protein (MAP) kinase activation (PD-98059) were added. The addition of PI3K and PKC inhibitors, but not of PKA inhibitor, significantly decreased insulin-stimulated Cyp19 mRNA levels and oestradiol accumulation (P < 0.001). The PKA inhibitor significantly decreased FSH-stimulated Cyp19 mRNA abundance and oestradiol secretion, whereas PI3K and PKC inhibitors decreased oestradiol secretion without affecting Cyp19 mRNA accumulation. Inhibition of MAP kinase pathway significantly increased Cyp19 mRNA abundance in insulin- and FSH-stimulated cells. P450scc mRNA levels and progesterone secretion were not affected by any inhibitor in either experiment. Although FSH stimulates Cyp19 expression predominantly through PKA, oestradiol secretion is altered by PI3K and PKC pathways independently of Cyp19 mRNA levels. In addition, we suggest that Cyp19 is under tonic inhibition mediated through a MAP kinase pathway.
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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".