Expression of atresia biomarkers in granulosa cells after ovarian stimulation in heifers
Bibliographic record
Abstract
The use of younger gamete donors in dairy cattle genetic selection programs significantly accelerates genetic gains by decreasing the interval between generations. Ovarian stimulation (OS) and the practice of follicle-stimulating hormone (FSH) withdrawal, also known as coasting, are intensively used in pre-pubertal heifers without detrimental effects on subsequent reproductive performance but generally with lower embryo yields. However, recent data from embryo transfer programs showed similar embryo yields in younger and sexually mature animals but with a significant difference in the coasting period. The aim of the present study was to identify a set of granulosa cell biomarkers capable of distinguishing optimal follicle differentiation from late differentiation and atresia in order to assess the differences in coasting dynamics between pre- and post-pubertal donors. We integrated transcriptomic data sets from a public depository and used vote counting meta-analysis in order to elucidate the molecular changes occurring in granulosa cells during late follicle differentiation and atresia. The meta-analysis revealed the gene expression associated with follicle demise, and most importantly, identified potential biomarkers of that status in bovine granulosa cells. The comparison of the expression of six biomarkers between pre- and post-pubertal donors revealed that younger donors had more signs of atresia after the same period of coasting. We found different follicular dynamics following coasting in younger donors. It is possible that younger donors are less capable to sustain follicular survival most likely due to insufficient luteinizing hormone signaling. In summary, the pre-pubertal status influences follicular dynamics and reduces the oocyte developmental competence curve following OS and FSH withdrawal in heifers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".