Identification of Potential Markers of Oocyte Competence Expressed in Bovine Cumulus Cells Matured with Follicle-Stimulating Hormone and/or Phorbol Myristate Acetate In Vitro
Bibliographic record
Abstract
Oocyte competence is the ability of the oocyte to complete maturation, undergo successful fertilization, and reach the blastocyst stage. Cumulus cells are indispensable for this process. Their removal significantly affects the blastocyst rates. Moreover, the properties and functions of cumulus cells are regulated by the oocyte. They also reflect the oocyte's degree of maturation. Our study was aimed at identifying markers of oocyte competence that are expressed in bovine cumulus cells. In a previous study in our laboratory, the blastocyst yield following FSH or phorbol myristate acetate (PMA) treatment was 45%%. Therefore, we tested four sets of conditions during the first 6 h of in vitro maturation (IVM): FSH (0.1 microg/ml), PMA (0.1 microM), FSH ++ PMA, and negative control. Extracts from each IVM treatment were hybridized against the same negative control on a microarray containing a partial library of differentially expressed transcripts in the cumulus of competent oocytes collected at 6 h after LH in vivo. Common positive clones between diffrentially treated cells were selected, and 15 candidates were validated by real-time PCR. Based on this, the main candidates expressed in cumulus cells and that could be valuable and indirect markers of oocyte competence are hyaluronan synthase 2 (HAS2), inhibin betaA (INHBA), epidermal growth factor receptor (EGFR), gremlin 1 (GREM1), betacellulin (BTC), CD44, tumor necrosis factor-induced protein 6 (TNFAIP6), and prostaglandin-endoperoxide synthase 2 (PTGS2). These biomarkers could be potential candidates to predict oocyte competence and to select higher-quality embryos for transfer. Additionally, these indirect predictors of oocyte competence and follicular health could improve our knowledge of gene expression patterns in the cumulus and yield insights into the molecular pathways controlling oocyte competence.
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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".