Transcriptome meta-analysis of three follicular compartments and its correlation with ovarian follicle maturity and oocyte developmental competence in cows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.000 | 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 it