Estrogen Receptor β Is Required for Optimal cAMP Production in Mouse Granulosa Cells
Bibliographic record
Abstract
Granulosa cells of preovulatory follicles differentiate in response to FSH, and this differentiation is augmented by estradiol. We have previously shown that FSH-mediated granulosa cell differentiation requires functional estrogen receptor-beta (ERbeta) by demonstrating that the granulosa cells of ERbeta(-/-) FSH-treated mice are unable to maximally induce expression of the LH receptor (an indicator of granulosa cell differentiation) compared with ERbeta(+/+) controls. As a result, FSH-primed ERbeta(-/-) granulosa cells exhibit a reduced response to a subsequent ovulatory dose of LH. In this study, we further characterized the attenuated response of ERbeta(-/-) granulosa cells to stimulation by LH and FSH using isolated mouse granulosa cells and primary granulosa cell cultures. We observed a 50% reduction in cAMP levels in cultured ERbeta(-/-) granulosa cells exposed to LH compared with ERbeta(+/+) controls. We also observed an attenuated genomic response in granulosa cells isolated from FSH-primed ERbeta(-/-) mice compared with ERbeta(+/+) controls. Our data indicate that this attenuated response may result from inadequate levels of cAMP, because cAMP levels in cultured ERbeta(-/-) granulosa cells exposed to forskolin were approximately 50% lower than in ERbeta(+/+) granulosa cells. Phosphorylation of cAMP regulatory element binding protein, an indicator of protein kinase A activity, was also reduced in FSH-treated ERbeta(-/-) granulosa cells compared with ERbeta(+/+) controls. These are the first data to indicate that ERbeta plays a role in the induction of the cAMP pathway in mouse granulosa cells and that disruption of proper ERbeta signaling associated with this pathway may cause negative effects on ovulation and fertility.
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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 | 0.001 |
| 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".