Global gene expression in the bovine corpus luteum is altered after stimulatory and superovulatory treatments
Bibliographic record
Abstract
Equine chorionic gonadotrophin (eCG) has been widely used in superovulation and artificial insemination programmes and usually promotes an increase in corpus luteum (CL) volume and stimulates progesterone production. Therefore, to identify eCG-regulated genes in the bovine CL, the transcriptome was evaluated by microarray analysis and the expression of selected genes was validated by qPCR and western blot. Eighteen Nelore crossbred cows were divided into control (n=5), stimulated (n=6) and superovulated groups (n=7). Ovulation was synchronised using a progesterone device-based protocol. Stimulated animals received 400 IU of eCG at device removal and superovulated animals received 2000 IU of eCG 4 days prior. Corpora lutea were collected 7 days after gonadotrophin-releasing hormone administration. Overall, 242 transcripts were upregulated and 111 transcripts were downregulated in stimulated cows (P ≤ 0.05) and 111 were upregulated and 113 downregulated in superovulated cows compared to the control animals (1.5-fold, P ≤ 0.05). Among the differentially expressed genes, many were involved in lipid biosynthesis and progesterone production, such as PPARG, STAR, prolactin receptors and follistatin. In conclusion, eCG modulates gene expression differently depending on the treatment, i.e. stimulatory or superovulatory. Our data contribute to the understanding of the pathways involved in increased progesterone levels observed after eCG treatment.
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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".