Mammalian Hyaluronidase Induces Ovarian Granulosa Cell Apoptosis and Is Involved in Follicular Atresia
Bibliographic record
Abstract
During ovarian folliculogenesis, the vast majority of follicles will undergo atresia by apoptosis, allowing a few dominant follicles to mature. Mammalian hyaluronidases comprise a family of six to seven enzymes sharing the same catalytic domain responsible for hyaluronan hydrolysis. Interestingly, some of these enzymes have been shown to induce apoptosis. In the ovary, expression of three hyaluronidases (Hyal-1, Hyal-2, and Hyal-3) has been documented. However, their precise cellular localization and role in ovarian regulation have not yet been defined. We herein investigated the possible involvement of these enzymes in ovarian atresia. First, we established a mouse model for ovarian atresia (gonadotropin withdrawal by anti-equine chorionic gonadotropin treatment) and showed that the mRNA levels of Hyal-1, Hyal-2, and Hyal-3 were significantly increased in apoptotic granulosa cells as well as in atretic follicles. Second, using ovaries of normally cycling mice, we demonstrated the correlation of Hyal-1 mRNA and protein expression with cleavage of caspase-3. In addition, we showed that expression of all three hyaluronidases induced apoptosis in transfected granulosa cells. Significantly, the induction of apoptosis by hyaluronidases was independent of catalytic activity, because enzymatically inactive Hyal-1 mutant (D157A/E159A) was as efficient as the wild-type enzyme in apoptosis induction. The activation of the extrinsic apoptotic signaling pathway was involved in this induction, because increased levels of cleaved caspase-8, caspase-3, and poly-ADP-ribose polymerase (PARP) were observed upon hyaluronidase ectopic expression. Our present findings provide a better understanding of the role of hyaluronidases in ovarian functions, showing for the first time their involvement in follicular atresia.
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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".