The effects of glial cell line‐derived neurotrophic factor on the in vitro matured porcine oocyte transcriptome
Bibliographic record
Abstract
It is well documented that oocytes from small antral follicles are less competent than those derived from large follicles, and we have previously shown that glial cell line-derived neurotrophic factor (GDNF) enhances developmental competence in oocytes from antral follicles. Exactly how GDNF effects this change and if it depends on the stage of oocyte development is currently unknown. The objective of this study was to examine the transcriptomic effects of follicle size and GDNF on the in vitro maturation of porcine oocytes. Microarray analysis uncovered differentially expressed transcripts among in vitro-matured porcine oocytes from different-size antral follicles, in the absence or presence of GDNF. Oocytes isolated from small follicles showed a lower state of maturation than those from large follicles, with several transcripts associated with meiotic arrest. Addition of GDNF to the culture media had effects that depended on the stage of the follicle from which the oocyte was isolated, with those from small follicles showing decreased expression of genes associated with acetyltransferase activity while those from large follicles showed decreased metabolic activity. In summary, our results revealed considerable differences between the transcriptomes of small- and large-follicle-derived oocytes. Furthermore, GDNF affects the developmental competence of oocytes in follicle-stage dependent manner. Thus, improving our understanding of the requirements for successful in vitro maturation of porcine oocytes will inform current reproductive technologies, with implications for the future of animal and human health.
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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) | 0.000 | 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".