MicroRNA in sperm from Duroc, Landrace and Yorkshire boars
Bibliographic record
Abstract
Sperm contain microRNAs (miRNAs), which may have roles in epigenetic control. Regarding phylogenetic relationships among various swine breeds, Yorkshire and Landrace, are considered phenotypically and genetically very similar, but distinctly different from Duroc. The objective of the present study was to compare abundance of boar sperm miRNAs in these three breeds. Overall, 252 prioritized miRNAs were investigated using real-time PCR; relative expression of miRNAs in sperm was similar in Yorkshire and Landrace boars, but significantly different compared to Duroc. Seventeen miRNAs (hsa-miR-196a-5p, hsa-miR-514a-3p, hsa-miR-938, hsa-miR-372-3p, hsa-miR-558, hsa-miR-579-3p, hsa-miR-595, hsa-miR-648, hsa-miR-524-3p, hsa-miR-512-3p, hsa-miR-429, hsa-miR-639, hsa-miR-551a, hsa-miR-624-5p, hsa-miR-585-3p, hsa-miR-508-3p and hsa-miR-626) were down-regulated (P < 0.05; fold regulation ≤-2) in Yorkshire and Landrace sperm, compared to Duroc sperm. Furthermore, three miRNAs (hsa-miR-9-5p, hsa-miR-150-5p, and hsa-miR-99a-5p) were significantly up-regulated in Yorkshire and Landrace sperm compared to Duroc sperm, However, 240 miRNAs were not significantly different (within + 2 fold) between Yorkshire and Landrace sperm. We concluded that miRNAs in sperm were not significantly different between Yorkshire and Landrace boars, but there were significant differences between those two breeds and Duroc boars. Furthermore, integrated target genes for selected down-regulated miRNAs (identified via an in-silico method) appeared to participate in spermatogenesis and sperm functions.
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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".