The combination of kinetic and flow cytometric semen parameters as a tool to predict fertility in cryopreserved bull semen. (11th April 2017)

• Record Type: Journal Article
• Authors: Gliozzi, T. M.; Turri, F.; Manes, S.; Cassinelli, C.; Pizzi, F.
• Is Part Of: Animal. Volume 11:Number 11(2017:Nov.)
• Page Start: 1975
• Page End: 1982
• Publication Date: 2017-04-11
• Subjects: cattle, -- sperm quality, -- fertility prediction, -- computer-assisted semen analysis, -- flow cytometry; Animal breeding -- Periodicals; Animal genetics -- Periodicals; Animal nutrition -- Periodicals; Animal physiology -- Periodicals; Environmental sciences -- Periodicals; 636.005
• DOI: 10.1017/S1751731117000684
• Languages: English
• ISSNs: 1751-7311

Abstract

Abstract : Within recent years, there has been growing interest in the prediction of bull fertility through in vitro assessment of semen quality. A model for fertility prediction based on early evaluation of semen quality parameters, to exclude sires with potentially low fertility from breeding programs, would therefore be useful. The aim of the present study was to identify the most suitable parameters that would provide reliable prediction of fertility. Frozen semen from 18 Italian Holstein-Friesian proven bulls was analyzed using computer-assisted semen analysis (CASA) (motility and kinetic parameters) and flow cytometry (FCM) (viability, acrosomal integrity, mitochondrial function, lipid peroxidation, plasma membrane stability and DNA integrity). Bulls were divided into two groups (low and high fertility) based on the estimated relative conception rate (ERCR). Significant differences were found between fertility groups for total motility, active cells, straightness, linearity, viability and percentage of DNA fragmented sperm. Correlations were observed between ERCR and some kinetic parameters, and membrane instability and some DNA integrity indicators. In order to define a model with high relation between semen quality parameters and ERCR, backward stepwise multiple regression analysis was applied. Thus, we obtained a prediction model that explained almost half ( R 2 =0.47, P <0.05) of the variation in the conception rate and included nine variables: five kinetic parameters measured by CASA (total motility, active cells, beat cross frequency, curvilinear velocity and amplitude of lateral head displacement) and four parameters related to DNA integrity evaluated by FCM (degree of chromatin structure abnormality Alpha-T, extent of chromatin structure abnormality (Alpha-T standard deviation), percentage of DNA fragmented sperm and percentage of sperm with high green fluorescence representative of immature cells). A significant relationship ( R 2 =0.84, P <0.05) was observed between real and predicted fertility. Once the accuracy of fertility prediction has been confirmed, the model developed in the present study could be used by artificial insemination centers for bull selection or for elimination of poor fertility ejaculates.