194 EFFECT OF MATERNAL AGE ON THE GRANULOSA CELL TRANSCRIPTOME OF PREOVULATORY FOLLICLES IN CATTLE
Bibliographic record
Abstract
The objective was to determine how maternal age influences the transcriptome of the dominant follicle during the preovulatory period. We tested the hypotheses that delayed ovulation in aged cows is associated with 1) altered gene expression of granulosa cells of the preovulatory follicle and 2) decreased synthesis of progesterone by granulosa cells of the preovulatory follicle. Granulosa cells of preovulatory follicles were obtained 24 h after LH treatment from aged Hereford cows (17.0 ± 2.5 years; n = 6) and their daughters (9.0 ± 0.6 years; n = 6), and compared using bovine specific microarrays (EMBV3, EmbryoGENE, Québec, QC, Canada). Results were confirmed by RT-qPCR. A total of 1340 genes or gene isoforms were expressed differentially (=2-fold change; P = 0.05) in aged cows v. their younger daughters. Differentially expressed up- and down-regulated genes were related to 1) LH response (?RGS2, ?SERPINE2, ?PTGS2), 2) cellular differentiation and luteinization (?TNFAIP6, ?GADD45B, ?VNN1), and 3) progesterone synthesis (?STAR, ?HSD3B2, ?NR5A2, ?NR4A1). Intra-follicular concentration of progesterone was lower (P < 0.05) in aged v. young cows. Pathway analysis of the dataset revealed that mechanisms of delayed ovulation in aged cows may involve 1) post-receptor desensitization of G-coupled protein receptors, 2) inactivation of tissue plasminogen activator, and 3) delayed production of prostaglandin E2. In conclusion, transcriptome analysis of granulosa cells from aged cows revealed a delayed or suboptimal response to the preovulatory LH stimulus, represented by delayed cellular differentiation, luteinization, and progesterone synthesis. This study was supported by grants from the National Science and Engineering Research Council of Canada, and the EmbryoGene Network, Canada. M.I.R. Khan was supported by graduate assistant scholarship from the Higher Education Commission of Pakistan.
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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 | 0.000 |
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".