Sperm fertilizing ability in vitro influences bovine blastocyst miRNA content
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small highly conserved non-coding RNA molecules that orchestrate a wide range of biological processes through post-transcriptional regulation of gene expression. During development, miRNAs play a key role in driving embryo patterning and morphogenesis in a specific and stage-dependent manner. Here, we investigated whether sperm from bulls with different fertilizing ability in vitro influence blastocyst quality and miRNA content. Results demonstrate that blastocysts obtained using sperm from high fertility sires (H group) display significantly greater cleavage and blastocyst development as well as greater transcript abundance in blastocysts for the developmental competence markers CDX2, KRT8, NANOG, OCT4, PLAC8, PTGS2, SOX17, and SOX2, compared to blastocysts generated using sperm from low fertility sires (L group). In parallel, high throughput deep sequencing and differential expression studies revealed that H blastocysts exhibit a greater miRNA content compared to L blastocysts, with hsa-miR-4755-5p and hsa-miR-548d-3p uniquely detected in the H group, and greater abundance of hsa-miR-1225-3p in the H group. Gene ontology (GO) and KEGG pathway analyses indicated that the 3 differentially expressed miRNAs identified are involved in the regulation of many biological mechanisms with a key role in aspects of early embryo development, including transcriptional regulation, cellular biosynthesis, nucleic acid metabolism, cellular differentiation, apoptosis, cytoskeleton remodeling, cell-to-cell interactions, and endocytosis. Overall, our results indicate that sperm fertilizing ability influences blastocyst developmental ability and miRNA content. In addition, we demonstrate an association between blastocyst quality and miRNA content, thus suggesting the possibility to score miRNA expression as biomarkers for improved routine embryo selection technologies to support assisted reproductive efforts.
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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 | 0.000 |
| 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 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".