Characteristics of miRNAs Present in Bovine Sperm and Associations With Differences in Fertility
Bibliographic record
Abstract
Small non-coding RNAs have been linked to different phenotypes in bovine sperm, however attempts to identify sperm-borne molecular biomarkers of male fertility have thus far failed to identify a robust profile of expressed miRNAs related to fertility. We hypothesized that some differences in bull fertility may be reflected in the levels of different miRNAs in sperm. To explore such differences in fertility that are not due to differences in visible metrics of sperm quality, we employed Next Generation Sequencing to compare the miRNA populations in Bos taurus sperm from bulls with comparable motility and morphology but varying Sire Conception Rates. We identified the most abundant miRNAs in both populations (miRs -34b-3p; -100-5p; -191-5p; -30d-4p; -21-5p) and evaluated differences in the overall levels and specific patterns of isomiR expression. We also explored correlations between specific pairs of miRNAs in each population and identified 10 distinct pairs of miRNAs that were positively correlated in bulls with higher fertility and negatively correlated in comparatively less fertile individuals. Furthermore, 8 additional miRNA pairs demonstrated the opposite trend; negatively correlated in high fertility animals and positively correlated in less fertile bulls. Finally, we performed pathway analysis to identify potential roles of miRNAs present in bull sperm in the regulation of specific genes that impact spermatogenesis and embryo development. Together, these results present a comprehensive picture of the bovine sperm miRNAome that suggests multiple potential roles in fertility.
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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".