Increased homocysteine regulated by androgen activates autophagy by suppressing the mammalian target of rapamycin pathway in the granulosa cells of polycystic ovary syndrome mice
Bibliographic record
Abstract
The purpose of this study was to explore the potential molecular mechanisms of excess homocysteine in relation to autophagic activity in the ovarian tissue of polycystic ovarian syndrome (PCOS) with hyperandrogenism.A PCOS model was constructed using ICR mice. ELISA was used to detect the Hcy levels in the serum and ovarian tissues of PCOS model. The expression level of key enzymes (Methionine synthase and Betaine-homocysteine methyltransferase, MTR and BHMT) in homocysteine metabolism and autophagy-related proteins were detected in ovarian tissues and mouse granulosa cells (mGCs) that were treated with homocysteine, androgen, autophagy inhibitors or BHMT-expressing plasmid by western blot and immunohistochemistry. Electron microscope experiments were used to evaluate autophagosomes in Hcy-treated mGCs. The prenatally androgenized (PNA) PCOS mouse model showed hyperhomocysteinemia and hyperandrogenism. Homocysteine levels displayed a significant increase, while its metabolic enzymes levels were significantly decreased in ovarian tissues of PCOS mice and dihydrotestosterone (DHT)-stimulated mGCs. The LC3II and Beclin1 expression levels were increased and the P62 and p-mTOR levels were decreased in vivo in ovarian tissue from the PCOS mice. The in vitro data were similarly with the in vivo by stimulation of mGCs with DHT or homocysteine. These effects could be diminished by the autophagy inhibitor (MHY1485), androgen receptor antagonists (ARN509) or BHMT-expressing plasmid. Androgen increases homocysteine concentration by downregulating the key enzymes in homocysteine metabolism. And then Hcy promotes GCs autophagy via the mTOR signal pathway.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".