Gene Expression Analysis of Bovine Oocytes With High Developmental Competence Obtained From FSH‐Stimulated Animals
Bibliographic record
Abstract
Recent progress in the ovarian stimulation protocol used for bovine in vitro maturation and fertilization, especially through optimization of the follicle-stimulating hormone (FSH) withdrawal period ("coasting") after ovarian pre-treatment with FSH, has significantly improved blastocyst outcome. Despite this important success, the underlying factors leading to improved oocyte quality have not yet been identified. The aim of this project was to compare the transcriptome of germinal vesicle-stage oocytes collected from FSH-stimulated cows after various coasting periods (20, 44, 68, and 92 hr) to determine which transcripts were accumulated or depleted during the rise and fall of competence. Oocytes from each coasting period were compared to the three other times (optimal conditions, 44 and 68 hr; under-matured, 20 hr; and over-matured, 92 hr) per animal, allowing each cow to be its own control (24 collections). Microarray analysis revealed that between 5 and 338 transcripts were significantly different across the six comparisons, with an important longitudinal modulation in terms of gene expression profile. Not surprisingly, as the transcriptional activity decreased in these oocytes, several transcripts that are significantly modulated during coasting are related to RNA processing functions, as shown by functional analysis. Ingenuity Pathway Analysis also highlighted another important function: the control of chromosome segregation. The results presented here indicate that the quality gained with the optimal coasting time does not last, and also suggests a possible mechanism of control by transcript degradation that could be implicated if the oocyte is not ovulated at the right time.
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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".