Mechanistic target of rapamycin is activated in bovine granulosa cells after <scp>LH</scp> surge but is not essential for ovulation
Bibliographic record
Abstract
The LH surge induces functional and morphological changes in granulosa cells. Mechanistic target of rapamycin (mTOR) is an integrator of signalling pathways in multiple cell types. We hypothesized that mTOR kinase activity integrates and modulates molecular pathways induced by LH in granulosa cells during the preovulatory period. Cows were ovariectomized and granulosa cells collected at 0, 3, 6, 12 and 24 hr after GnRH injection. While RHEB mRNA levels increased at 3 and 6 hr, returning to basal levels by 12 hr after GnRH treatment, RHOA mRNA levels increased at 6 hr and remained high thereafter. Western blot analyses revealed increased S6K phosphorylation at 3 and 6 hr after GnRH injection. Similarly, mRNA levels of ERK1/2, STAR and EGR-1 were higher 3 hr after GnRH treatment. Rapamycin treatment inhibited mTOR activity and increased AKT activity, but did not alter ERK1/2 phosphorylation and EGR1 protein levels in cultured bovine granulosa cells. Rapamycin also inhibited LH-induced increase in EREG mRNA abundance in granulosa cells in vitro. However, intrafollicular injection of rapamycin did not suppress ovulation. These findings suggest that mTOR is involved in the control of EREG expression in cattle, which may be triggered by LH surge stimulating RHEB and S6K activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".