Metabolic profiling identifies fertility markers in bull sperm†

Early and accurate assessment of bull fertility is critical for the success of artificial insemination (AI) programs in cattle production. However, current selection tools, including genomic predictions and standard semen evaluations, offer limited reliability in forecasting field fertility outcomes. To address this limitation, we explored the sperm metabolome as a potential source of novel fertility-associated biomarkers. Using high-throughput untargeted metabolomics, we profiled frozen-thawed sperm from Holstein-Friesian bulls with high (n = 12) and low (n = 12) adjusted fertility scores, each with a minimum of 500 AI service records (range from 519 to 99,953 per bull). Raw peak intensities for 615 metabolites were normalized to the total protein concentration of each sample, and, following data filtration, 547 metabolites were retained for downstream analyses. Unpaired t-tests combined with fold-change thresholding identified 18 differentially abundant metabolites between high fertility and low fertility groups (P < 0.1, absolute fold change >1.5), with significant enrichment in pathways relating to lipid and energy metabolism. Further interrogation of these differentially abundant metabolites in the literature revealed possible metabolic differences associated with calcium channel inhibition and reactive oxygen species production in the low fertility bulls. Machine learning-based biomarker discovery further identified a subset of five metabolites-3-phosphoglycerate, phenylalanine, ceramide, citrate, and citrulline-capable of distinguishing fertility status with high predictive accuracy (AUROC = 0.877; P = 0.02). Overall, these data support sperm metabolomics as a promising omics-based approach to enhance bull fertility evaluation and improve selection strategies in AI programs.