Changes in granulosa cells' gene expression associated with increased oocyte competence in bovine
Bibliographic record
Abstract
One of the challenges in mammalian reproduction is to understand the basic physiology of oocyte quality. It is believed that the follicle status is linked to developmental competence of the enclosed oocyte. To explore the link between follicles and competence in cows, previous research at our laboratory has developed an ovarian stimulation protocol that increases and then decreases oocyte quality according to the timing of oocyte recovery post-FSH withdrawal (coasting). Using this protocol, we have obtained the granulosa cells associated with oocytes of different qualities at selected times of coasting. Transcriptome analysis was done with Embryogene microarray slides and validation was performed by real-time PCR. Results show that the major changes in gene expression occurred from 20 to 44 h of coasting, when oocyte quality increases. Secondly, among upregulated genes (20-44 h), 25% were extracellular molecules, highlighting potential granulosa signaling cascades. Principal component analysis identified two patterns: one resembling the competence profile and another associated with follicle growth and atresia. Additionally, three major functional changes were identified: (i) the end of follicle growth (BMPR1B, IGF2, and RELN), involving interactions with the extracellular matrix (TFPI2); angiogenesis (NRP1), including early hypoxia, and potentially oxidative stress (GFPT2, TF, and VNN1) and (ii) apoptosis (KCNJ8) followed by iii) inflammation (ANKRD1). This unique window of analysis indicates a progressive hypoxia during coasting mixed with an increase in apoptosis and inflammation. Potential signaling pathways leading to competence have been identified and will require downstream testing. This preliminary analysis supports the potential role of the follicular differentiation in oocyte quality both during competence increase and decrease phases.
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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 | 0.001 | 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".