Effects of Progestins on Progesterone Synthesis in a Stable Porcine Granulosa Cell Line: Control of Transcriptional Activity of the Cytochrome P450 Side-Chain Cleavage Gene1
Bibliographic record
Abstract
The purpose of the present study was to examine the effects of progestins on progesterone synthesis and expression of the cytochrome P450 cholesterol side-chain cleavage gene (P450(scc)) in a stable porcine granulosa cell line, the JC-410. Cells were incubated for 48 h with the synthetic progestogen-levornorgestrel with or without RU486 (progesterone and glucocorticoid receptor antagonist) or RWJ26819 (progesterone agonist without affinity to glucocorticoid receptors). Both levonorgestrel and RU486 enhanced progesterone accumulation in a dose-dependent manner. RU486 did not antagonize the effects of levonorgestrel, and RWJ26819 had no effect on progesterone production in cultured JC-410 cells. Progesterone and levonorgestrel increased steady state P450(scc) mRNA levels after 3-6 h of treatment. Progesterone and RU486 at 0.1, 1, and 10 microM increased the transcription rate of P450(scc) transiently expressed in JC-410 cells after 18 h of incubation; 30 microM had no effect, and 100 microM suppressed transcription. Levonorgestrel did not affect transcription of the P450(scc) gene, and RWJ26819 reduced its transcription. Progesterone and RU486 significantly decreased the number of cells and total protein content after 72 and 24 h of incubation, respectively. Levonorgestrel had no effect, whereas RWJ26819 increased (24 h) but subsequently reduced (72 h) cell number and protein content. The present results indicate that progestins are capable of directly modulating progesterone biosynthesis in porcine JC-410 granulosa cells. These effects may be exerted in part through the regulation of P450(scc) gene expression. Ostensible differences exist between progesterone and its synthetic analogues in the control of progesterone secretion in the stable porcine granulosa cell line in vitro.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".