Identification of sperm proteins as biomarkers of field fertility in Holstein-Friesian bulls used for artificial insemination

Despite passing stringent quality control, bulls used in artificial insemination can vary significantly in their fertility, emphasizing the need for reliable markers of sperm quality. This study aimed to identify sperm proteins acting as biomarkers of fertility in 2 different populations of dairy bulls classified based on their field fertility. Semen was collected and cryopreserved from: 54 Holstein bulls located in Ireland, classified according to fertility indexes as low fertility (LF, n = 23), medium fertility (n = 14), or high fertility (HF, n = 17); and 18 Holstein bulls located in Denmark, classified as LF (n = 8) or HF (n = 10). The proteome was measured through liquid chromatography-mass spectrometry and data were analyzed with the R software. Differentially abundant proteins between HF and LF bulls and biomarker proteins were determined through a modified t-test and random forest, respectively, selecting 301 differentially abundant proteins and 34 biomarker proteins. The predictive ability of the 34 biomarkers was evaluated employing support vector machine as the classifier, using their abundance levels in the Irish bulls to train the model and in the Danish bulls for validation. The prediction accuracy was 94.4%, with only one HF bull misclassified, corresponding to the lowest fertility index bull in the HF group. The biomarkers more abundant in sperm of HF bulls enriched axoneme assembly and sperm motility (false discovery rate <0.05), according to functional analysis. In conclusion, a robust model coupled with the application of appropriate bioinformatic tools allowed the identification of functionally relevant sperm proteins predictive of the fertility of Holstein bulls used in artificial insemination.

Automated summary

This study aimed to identify sperm proteins acting as biomarkers of fertility in dairy bulls. Through the analysis of proteome, 301 differentially abundant proteins and 34 biomarker proteins were determined. The predictive ability of the biomarkers was evaluated, achieving a prediction accuracy of 94.4%.