Gonadotropin-Dependent Regulation of Epiregulin in Equine Preovulatory Follicles During the Ovulatory Process.
Bibliographic record
Abstract
The mammalian ovulatory process is accompanied by a gonadotropin-dependent regulation of several genes. Little is known about ovarian regulation of epidermal growth factor (EGF)-like growth factors in non-primate species, including epiregulin (EREG). The objective of the study is to characterize the regulation of EREG during hCG-induced ovulation in mares. Follicles during estrus between 0 and 39 h post-hCG and corpora lutea on day 8 of the estrous cycle were collected, and RNA and proteins were extracted. Results from semi-quantitative RT-PCR/Southern blot analyses showed that levels of EREG mRNA were very low in follicles obtained at 0 h but markedly increased thereafter (P < 0.05). Tissue blot analyses revealed that levels of EREG transcripts were relatively high in muscle but low or undetectable in corpora lutea and other non-ovarian tissues tested. Analyses performed with individual preparations of theca and granulosa cells indicated that EREG was regulated significantly in both cell layers (P < 0.05), with a maximal induction obtained 33-39 h post-hCG. Immunohistochemistry and immunoblotting confirmed regulated-expression of EREG protein in both cell types after hCG. Results from primary granulosa cultures isolated from dominant follicles revealed that levels of ADAMTS1, PGR, and CTSL2 mRNA were initially low but markedly increased in response to the treatment with EGF. This treatment had no effect on PTGS2 and PTGER2 transcript expression. Thus, this study reports the gonadotropin-dependent regulation of EREG during ovulation in mares and shows for the first time the stimulation of genes required for the rupture of follicles after the treatment with EGF. (poster)
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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".