Embryo response to different progesterone concentrations during superovulation of Holstein heifers: a transcriptomic analysis
Bibliographic record
Abstract
This study aimed to evaluate the impact of high and low progesterone (P4) concentrations during superovulation on the transcriptome profile of early bovine embryos. A total of 63 post-pubertal Holstein heifers were randomly assigned to two experimental groups: High P4 (n = 32) and Low P4 (n = 31). Heifers underwent a pre-synchronization protocol followed by a protocol of superovulation that included the allocated P4 treatment. Embryos were collected 7d post-artificial insemination (AI) and assessed for developmental stage and quality grade. Embryos classified as good quality (High P4: n = 27; Low P4: n = 27 embryos) were randomly allocated in three biological replicates per treatment, with replicates balanced for stage of embryonic development. Total RNA was extracted from each replicate, and libraries were prepared and sequenced using the NovaSeq 6000 platform. Differential gene expression between treatment groups was determined through pairwise comparisons, with adjusted P-values calculated using the Benjamini-Hochberg correction. Ingenuity pathway analysis was used to identify upstream regulators, molecules, and networks influenced by the P4 treatment. Transcriptome analysis suggested that exposure to high or low concentrations of P4 during superovulation affects gene expression of 7d old embryos. These modifications were associated with downregulation of beta-estradiol and the WNT signaling pathway, as well as upregulation of trophoblast-related genes in High P4 embryos. Additionally, downregulation of genes related to neurological processes and differentiation was observed in High P4 embryos compared to Low P4. In conclusion, these transcriptional differences may be associated with distinct developmental competence of embryos following transfer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".