Assessment of in vitro sperm characteristics and their importance in the prediction of conception rate in a bovine timed-AI program

The aims of this study were to assess in vivo fertility and in vitro sperm characteristics of different sires and to identify sperm variables important for the prediction of conception rate. Multiparous Nelore cows (n = 191) from a commercial farm underwent the same timed artificial insemination (timed-AI) protocol. Three batches of frozen semen from three Angus bulls were used (n = 9). A routine semen thawing protocol was performed in the laboratory to mimic field conditions. The following in vitro sperm analyses were performed: Computer Assisted Semen Analysis (CASA), Thermal Resistance Test (TRT), Hyposmotic Swelling Test (HOST), assessment of plasma and acrosomal membrane integrity, assessment of sperm plasma membrane stability and of lipid peroxidation by flow cytometry and assessment of sperm morphometry and chromatin structure by Toluidine Blue staining. For statistical analyses, Partial Least Squares (PLS) regression was used to explore the importance of various sperm variables in the prediction of conception rate. The following in vitro sperm variables were determined to be important predictors of conception rate: total motility (TM), progressive motility (PM), TM after 2 h of thermal incubation (TM_2 h), PM after 2 h of thermal incubation (PM_2 h), Beat Cross Frequency after 2 h of thermal incubation (BCF_2 h), percentage of rapidly moving cells after 2 h of thermal incubation (RAP_2 h), intact plasma membrane evaluated by HOST, intact plasma and acrosomal membranes evaluated by flow cytometry, intact plasma membrane suffering lipid peroxidation, major defects, total defects, morphometric width/length ratio, Fourier_0 and Fourier_2 and Chromatin Heterogeneity. We concluded that PLS regression is a suitable statistical method to identify in vitro sperm characteristics that have an important relationship with in vivo bull fertility.