Sperm subpopulations influence the pregnancy rates in cattle

Abstract

This study aimed to study the characteristics and subpopulations of spermatozoa from bulls with low and high reproductive performance based on pregnancy rates.Based on historical records of pregnancy rate from four farms, 24 bulls were selected. Two groups were established, with low pregnancy rates (n = 12; LOW), including bulls that presented pregnancy rates <52.27% (33.33% to 51.81%); and a group with high pregnancy rates (n = 12; HIGH), with pregnancy rates >52.27% (52.27% to 69.64%), after fixed-time artificial insemination (FTAI). The thawed sperm straws were analysed to sperm kinetics, morphology, plasma membrane integrity and sperm subpopulations. The LOW group exhibited a higher proportion of static cells (p < .05).In contrast, the HIGH group showed greater percentages for membrane integrity and total and progressive motility, and cells with fast and medium velocity (p < .05). In the cluster procedures, four sperm subpopulations were established. The low-fertility bulls presented the highest percentage of subpopulation 2 (41.46%), characterized by slow and progressive spermatozoa. The high-fertility bulls exhibited the highest percentage of subpopulation 3 (37.17%), characterized by fast and nonlinear spermatozoa. Results from this study indicated that bulls with greater percentages of fast and nonlinear spermatozoa seem to have greater fertilization capacity and the subpopulations analysis can be considered a tool to identify ejaculates with high fertility.

A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin

“…In our work, we found subpopulations characterized by the presence of trajectories sinuous (Subpopulations 2 and 4) and nonlinear. This is in line with the fact that more significant percentages of fast and nonlinear sperm seem to have greater fertilization capacity [27].…”

“…The statistical management of the data impacts on the prediction of fertility. For instance, in bulls' subpopulation analysis associated with the clustering method can be considered effective tin predicting higher fertility [27]. However, in boars, the clustering of the ejaculates using the means per ejaculate of all kinematic variables has a limited predictive capacity for litter size and fertility variables [28].…”