Gene Expression Analysis of Bovine Oocytes with High Developmental Competence in Super-Stimulation Protocol.
Bibliographic record
Abstract
In the last decades, super-stimulation protocols were improved and IVM-IVF became much more popular to treat infertile cows. The harvest of oocytes for such animals by ovum pick-up is often preceded by an ovarian pretreatment with FSH followed by a rest time (coasting) of 44 hours after the last FSH injection. This coasting period allows an increase in the number of transferable embryos produced in vitro. However, the mechanism of such changes during the coasting is not clearly defined. Therefore, the aim of this project is to obtain oocytes that are harvested earlier or later compared to the prescribed time (44h), in order to better understand why these oocytes are of lower or higher competence. Six milking cows (Holstein) received the same super-stimulation treatment followed by one of the four different coasting times (20, 44, 68, and 92 hours) during the luteal phase. Half of the oocytes recovered were put in IVF to observe the developmental competence and the other half was used to perform the transcriptomics analysis with an bovine embryo-specific 44K Agilent slide (EmbryoGene). The analysis of IVF data and blastocyst rate revealed that the best coasting period appears to be between 44 and 68 hours, with an average blastocyst rate of 69%. We then performed every comparison, for a total of 6 (20 hrs vs 44 hrs, 20 hrs vs 68 hrs, 44 hrs vs 68 hrs; etc.) using microarrays. The results showed that more than 15 000 probes (including reference genes, their 3' UTR alternative poly adenylation sites and splice variants) are presents among the four conditions (20; 44; 68 and 92 hours). Concerning the differentially expressed genes, we found between 53 and 477 up or down-regulated genes with a fold-change higher than 2 and p-value < 0.05. Looking at genes that follow a specific pattern, we observed that 365 and 544 genes followed the same direction (positive or negative, fold change ≠ 1, p<0.05), either in the comparison 20 hrs vs 44 hrs and in the 44 hrs vs 68 hrs comparison. When we compared the contrast 44 hrs vs 68 hrs and the 68 hrs vs 92 hrs, only 47 and 79 genes were up or down-regulated in both cases. This suggest that the major part of changes occurs at the beginning of the coasting period and that by the end of the coasting, the changes are finer and less important in term of number of genes differentially expressed. We are now studying more in details the groups of genes that follow those specific profiles to have a better understanding of what happens during this period. Research supported by NSERC and LAB Canada. (poster)
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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 | 0.001 |
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".