Follicle-Stimulating Hormone Regulates Oocyte Growth by Modulation of Expression of Oocyte and Granulosa Cell Factors
Bibliographic record
Abstract
Oocyte-granulosa cell communication is essential for oocyte development. The aims of this study were: 1) to determine the effect of FSH on expression of Kit ligand (KL), growth/differentiation factor-9, bone morphogenetic protein (BMP)-15, and Kit during growth of oocyte-granulosa cell complexes (OGCs) in vitro; 2) to investigate the role of BMP-15 in regulation of KL expression; and 3) to correlate mRNA expression with oocyte growth. OGCs from 12-d-old mice were cultured for up to 7 d in the presence of FSH [0.05 ng/ml (low), 5 ng/ml (high)] or BMP-15 (10 or 100 ng/ml). Transcripts were quantified using real-time RT-PCR, and oocyte and OGC diameters were measured. FSH regulated KL expression in a biphasic manner, with low FSH decreasing the KL-1/KL-2 ratio, and high FSH increasing the KL-1/KL-2 ratio, compared with controls (P < 0.05). The decrease in KL-1/KL-2 ratio with low FSH was due to increased KL-2 mRNA expression. Both FSH concentrations increased OGC diameter (P < 0.05), but only low FSH promoted oocyte growth (P < 0.05). High FSH also decreased BMP-15 expression (P < 0.05). FSH-stimulated oocyte growth was inhibited by Gleevec, an inhibitor of Kit activity. BMP-15 increased both KL-1 and KL-2 mRNA levels in a dose-dependent manner (P < 0.05) but did not alter the KL-1/KL-2 ratio or promote oocyte growth. When the KL-1/KL-2 ratio was increased by exogenous KL-1, FSH-stimulated oocyte growth was suppressed (P < 0.05), suggesting that lowered KL-1/KL-2 ratio is important for oocyte growth. In summary, the correct concentration of FSH is crucial for appropriate modulation of KL and BMP-15 to promote oocyte 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".