Association of luteinizing hormone receptor gene expression with cell cycle progression in granulosa cells
Bibliographic record
Abstract
During hormonally induced ovarian follicle growth, granulosa cell proliferation increases and returns to baseline prior to the administration of an ovulatory stimulus. Several key genes appear to follow a similar pattern, including the luteinizing hormone receptor (LHCGR), suggesting an association between cell cycle progression and gene expression. The expression of LHCGR mRNA in granulosa cells isolated from immature rats and treated in culture with FSH increased in a time-dependent manner, whereas administration of the cell cycle inhibitor mimosine completely suppressed expression. Although forskolin was able to induce luteinization in cells treated with mimosine, human chorionic gonadotropin had no effect, indicating the functional loss of LHCGR. The effects of mimosine on cell cycle progression and LHCGR mRNA expression were reversible within 24 h of mimosine removal. Cell cycle inhibition did not alter the stability of LHCGR mRNA, indicating that the primary effect was at the transcriptional level. To determine whether the relationship between LHCGR expression and cell cycle were relevant in vivo, immature rats were given a bolus of PMSG, followed by a second injection of either saline or PMSG 24 h later to augment levels of proliferation. The expression of LHCGR mRNA was elevated in the ovaries of animals receiving a supplement of PMSG. Mimosine also blocked cell cycle progression and LHCGR mRNA expression in macaque granulosa cells isolated following controlled ovarian stimulation cycles and in two different mouse Leydig tumor lines. These data collectively indicate that LHCGR mRNA is expressed as a function of the passage of cells across the G1-S phase boundary.
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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".