Effect of heifer age on the granulosa cell transcriptome after ovarian stimulation
Bibliographic record
Abstract
Genomic selection is accelerating genetic gain in dairy cattle. Decreasing generation time by using younger gamete donors would further accelerate breed improvement programs. Although ovarian stimulation of peripubertal animals is possible and embryos produced in vitro from the resulting oocytes are viable, developmental competence is lower than when sexually mature cows are used. The aim of the present study was to shed light on how oocyte developmental competence is acquired as a heifer ages. Ten peripubertal Bos taurus Holstein heifers underwent ovarian stimulation cycles at the ages of 8, 11 (mean 10.8) and 14 (mean 13.7) months. Collected oocytes were fertilised in vitro with spermatozoa from the same adult male. Each heifer served as its own control. The transcriptomes of granulosa cells recovered with the oocytes were analysed using microarrays. Differential expression of certain genes was measured using polymerase chain reaction. Principal component analysis of microarray data revealed that the younger the animal, the more distinctive the gene expression pattern. Using ingenuity pathway analysis (IPA) and NetworkAnalyst (www.networkanalyst.ca), the main biological functions affected in younger donors were identified. The results suggest that cell differentiation, inflammation and apoptosis signalling are less apparent in peripubertal donors. Such physiological traits have been associated with a lower basal concentration of LH.
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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".