Divergence in the circulating small non-coding RNA transcriptome based on female pig fertility status

Area of Science:

• Animal Science
• Genetics
• Biotechnology

Background:

• Sow reproductive performance is crucial for pork industry productivity.
• Genetic selection for reproductive traits is hindered by low heritability and unfavorable genetic correlations.
• Small non-coding RNAs (sncRNAs) show potential as biomarkers to enhance selection for female pig reproductive potential.

Purpose of the Study:

• To compare circulating serum sncRNA profiles between infertile (INF) and high reproductive potential (HRP) female pigs.
• To identify candidate microRNA (miRNA) biomarkers associated with reproductive potential in sows.
• To evaluate the efficacy of a machine learning model for classifying sow reproductive potential using miRNA biomarkers.

Main Methods:

• Small RNA sequencing was performed on serum samples from INF (n=30) and HRP (n=50) female pigs.
• Differential expression analysis identified significant sncRNAs and miRNAs between the two groups.
• A support vector machine (SVM) model was trained using selected candidate miRNA biomarkers for classification.

Main Results:

• 19 significantly differentially expressed sncRNAs and 13 differentially expressed miRNAs were identified.
• Several DEmiRNAs have known roles in female reproduction and infertility.
• The SVM model achieved high accuracy (91%) in classifying reproductive potential, with high sensitivity for HRP (96%) and specificity for INF (83%).

Conclusions:

• Candidate miRNA biomarkers, including miR-140-3p, miR-215-5p, and miR-7142-3p, show promise for improving selection efficiency.
• The developed SVM model demonstrates potential for practical application in on-farm breeding herd selection.
• Further validation in a larger cohort is required to confirm generalizability and practical utility.