Landscape transcriptomic analysis of bovine follicular cells during key phases of ovarian follicular development
Bibliographic record
Abstract
BACKGROUND: There are many gaps in our understanding of the mechanisms involved in ovarian follicular development in cattle, particularly regarding follicular deviation, acquisition of ovulatory capacity, and preovulatory changes. Molecular evaluations of ovarian follicular cells during follicular development in cattle, especially serial transcriptomic analyses across key growth phases, have not been reported. This study aims to address this gap by analyzing gene expression using RNA-seq in granulosa and antral cells recovered from ovarian follicular fluid during critical phases of ovarian follicular development in Holstein cows. RESULTS: Integrated analysis of gene ontology (GO), gene set enrichment (GSEA), protein-protein interaction (PPI), and gene topology identified that differentially expressed genes (DEGs) in the largest ovarian follicles at deviation (Dev) were primarily involved in FSH-negative feedback, steroidogenesis, cell proliferation, apoptosis, and the prevention of early follicle rupture. In contrast, DEGs in the second largest follicles (DevF2) were mainly related to loss of cell viability, apoptosis, and immune cell invasion. In the dominant (PostDev) and preovulatory (PreOv) follicles, DEGs were associated with vascular changes and inflammatory responses. CONCLUSIONS: The transcriptome of ovarian follicular fluid cells had a predominance of granulosa cells in the dominant follicle at deviation, with upregulation of genes involved in cell viability, steroidogenesis, and apoptosis prevention, whereas in the non-selected follicle there was upregulation of cell death-related transcripts. Immune cell transcripts increased significantly after deviation, particularly in preovulatory follicles, indicating strong intrafollicular chemotactic activity. We inferred that immune cell invasion occurred despite an intact basal lamina, contributing to follicular maturation.
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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".