Biphasic Effects of Leptin in Porcine Granulosa Cells1
Bibliographic record
Abstract
The direct effects of recombinant porcine leptin on porcine granulosa cells were studied to test the hypothesis that leptin, acting through the nuclear transcription factor signal transducer and activator of transcription 3 (STAT-3), modulates sterol regulatory element-binding protein 1 (SREBP1) thereby increasing steroidogenesis. In porcine granulosa cells in culture over 48 h, leptin at 10 ng/ml increased progesterone accumulation 3-fold while it was reduced by leptin at 1000 ng/ml. Leptin had no effect on progression of granulosa cells through the cell cycle nor on the frequency of cell death. Leptin treatment at 24 or 48 h of culture resulted in dose-dependent 2- to 4-fold increases in tyrosine phosphorylation of STAT-3. Leptin had a biphasic effect on the abundance of membrane-bound and transcriptionally active forms of SREBP1. In transient transfection of primary porcine granulosa cells, the plasmid expressing the transcriptionally active form of SREPB-1 induced transcription of the key regulator of steroidogenesis, the steroidogenic acute regulatory protein (StAR). StAR transcription was also increased by the low dose of leptin and was further upregulated in the presence of the SREBP plasmid. Leptin at 1000 ng/ml inhibited SREBP1-induced StAR expression. Thus, leptin, acting through STAT-3, modulates steroidogenesis in a biphasic and dose-dependent manner, and SREBP1 induction of StAR expression may be in the cascade of regulatory events.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".