Transcriptome meta-analysis of three follicular compartments and its correlation with ovarian follicle maturity and oocyte developmental competence in cows
Bibliographic record
Abstract
Oocyte developmental competence in superstimulated cows is dependent in part on the duration of the FSH coasting. FSH coasting refers to superstimulation with FSH (2 days of endogenous FSH following follicle ablation and 3 days of FSH injections) followed by no FSH for a specific duration. The optimal duration varies among individuals. FSH coasting appears to modulate the transcriptome of different follicular compartments, which cooperate as a single functional unit. However, the integrative effects of FSH coasting on different follicular compartments remain ambiguous. Meta-analysis of three independent transcriptome studies, each focused on a single cell type (granulosa, cumulus, and oocyte) during FSH coasting, allowed the identification of 12 gene clusters with similar time-course expression patterns in all three compartments. Network analysis identified HNF4A (involved in metabolic functions) and ELAVL1 (an RNA-binding protein) as hub genes regulated respectively upward and downward in the clusters enriched at the optimal coasting time, and APP (involved in mitochondrial functions) and COPS5 (a member of the COP9 signalosome) as hub genes regulated respectively upwards and downwards in the clusters enriched progressively throughout the coasting period. We confirmed the effects on HNF4A downstream targets (TTR, PPL) and other hub genes (ELAVL1, APP, MYC, and PGR) in 30 cows with RT-quantitative PCR. The correlation of hub gene expression levels with FSH coasting indicated that a combination of these genes could predict oocyte competence with 83% sensitivity, suggesting that they are potential biomarkers of follicle differentiation. These findings could be used to optimize FSH coasting on an individual basis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".