Regulation of Cyclin D2 Expression and Degradation by Follicle-Stimulating Hormone During Rat Granulosa Cell Proliferation In Vitro1
Bibliographic record
Abstract
Cyclin D2 (CCND2, encoded by Ccnd2) plays an important role in the induction of early-to-mid G1 phase transition and is required for granulosa cell proliferation during ovarian folliculogenesis. In the present study, we investigated the role of follicle-stimulating hormone (FSH) in the regulation of cyclin D2 expression and degradation during rat granulosa cell proliferation in vitro. FSH acutely increased granulosa cell Ccnd2 mRNA abundance and CCND2 protein content as well as proliferation. FSH-induced granulosa cell CCND2 protein content and proliferation were mimicked by forskolin and attenuated by inhibitors of protein kinase A (PKA; H89) and phosphatidylinositol 3-kinase (PI3K; LY294002) as well as PKA catalytic subunit (PRKACA) small interfering RNA (siRNA) and dominant-negative Akt (dn-Akt) but were not affected by mitogen-activated protein kinase kinase 1/2 (MEK1/2; U0126). Interestingly, FSH also enhanced CCND2 protein degradation in granulosa cells, a process involving a PKA-mediated ubiquitin-proteasome degradation pathway. Taken together, these results demonstrate that FSH acutely regulated CCND2 expression through both PKA and PI3K signaling pathways during granulosa cell proliferation and also accelerated its ubiquitination-proteasomal degradation, which may prevent overstimulation of granulosa cell proliferation and follicular growth.
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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".