Transcriptome analysis of bovine oocytes from distinct follicle sizes: Insights from correlation network analysis
Bibliographic record
Abstract
Follicle size is recognized as a predictor of the potential for the enclosed oocyte to yield an embryo following in vitro maturation and in vitro fertilization. Oocytes from larger follicles are more likely to reach the blastocyst stage than those from smaller follicles. A growing oocyte accumulates all the transcripts needed to ensure development until the maternal embryonic transition, and this accumulation must be completed before the period of transcriptional arrest. Accordingly, the transcriptomes of bovine germinal-vesicle-stage oocytes collected from follicles of increasing sizes (<3, 3-5, >5-8, and >8 mm) were evaluated, using the EmbryoGENE bovine transcriptomic platform (custom Agilent 4 × 44 K), to better understand transcriptional modulation in the oocyte as the follicle becomes larger. Microarray analyses revealed very few differences between oocytes from small follicles (<3 vs. 3-5 mm), whereas an important number of differences were detected at the mRNA level between oocytes from larger follicles. Weighted gene correlation network analysis allowed for the identification of several hub genes involved in crucial functions such as transcriptional regulation (TAF2), chromatin remodeling (PPP1CB), energy production (SLC25A31), as well as transport of key molecules within the cell (NAGPA, CYHR1, and SLC3A12). The results presented here thus reinforce the hypothesis that developmental competence acquisition cannot be seen as a simple one-step process, especially in regards to the modulation of mRNA. Mol. Reprod. Dev. 83: 558-569, 2016. © 2016 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".