Serine protease inhibitor‐E2 (SERPINE2) is differentially expressed in granulosa cells of dominant follicle in cattle
Bibliographic record
Abstract
Abstract The objective was to analyze gene expression in bovine granulosa cells of the dominant follicle by mRNA differential display. Total RNA was extracted from granulosa cells of ≤4 mm follicles, day 5 (D5) dominant follicles, and hCG‐induced preovulatory follicles. A differentially expressed cDNA observed in the dominant follicle group was used to screen a granulosa cell cDNA library, which resulted in the cloning of a 2,096 bp cDNA. Amino acid comparison showed identity level of 91.4, 83.9, and 83.1% when compared to human, rat, and mouse serine protease inhibitor E2, SERPINE2, also called Glia‐derived nexin or protease Nexin‐1. A single transcript of 2.4 kb was shown to be differentially expressed in different bovine tissues. Immunoblotting with a specific antibody raised against a fragment of SERPINE2 (S12‐R196) showed that SERPINE2 migrated at 47.5 kDa in support of glycosylation. Primordial, primary, and secondary pre‐antral follicles showed immunostaining associated with granulosa cells and oocytes, and strong labeling in large antral follicles was located with granulosa cells and follicular fluid. Heterogeneity of SERPINE2 labeling was observed in CL. Semi‐quantitative real‐time fluorescent RT‐PCR showed a six‐fold increase (P = 0.0002) in mRNA level of SERPINE2 in granulosa cells of D5 dominant follicle compared to granulosa cells collected from the ≤4 mm or preovulatory hCG‐induced follicles. This report demonstrates that SERPINE2 mRNA is regulated in a spatio–temporal pattern with highest levels in granulosa cells of growing dominant bovine follicles, and support the hypothesis that a high expression of SERPINE2 may contribute to follicular growth whereas a decrease following hCG injection may contribute to ovulation. Mol. Reprod. Dev. 64: 152–165, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".