ABSTRACT
Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.
ABSTRACT
In cattle, in vitro embryo production (IVEP) is an important reproductive biotechnology responsible for the rapid expansion of the Senepol breed in our country. This breed has shown important results when used in crossbreeding and estimate IVEP in Senepol based on seminal analysis would be valuable for the semen cryopreservation industry, research institutes and breeders. Combining the evaluation of sperm subpopulations with analysis of other sperm attributes may help to improve fertility predictions in cattle. Therefore, the objectives of the present study were to: 1) identify and characterize motile sperm subpopulations in cryopreserved Senepol semen following the washing process carried out before in vitro fertilization, and 2) to determine an model for estimate IVEP based on sperm subpopulations in conjunction with other sperm quality analyzes. Samples of 38 cryopreserved semen from 28 Senepol bulls, chosen based on retrospective data from 386 IVEP routines, underwent the semen washing and were evaluated by the computer-assisted sperm analysis system. Sperm morphology was evaluated by wet preparation technique, and plasma and acrosomal membranes integrity, mitochondrial potential, oxidative status and chromatin resistance were analyzed by flow cytometry. After multivariate analysis of principal components and grouping, three sperm subpopulations were identified: SBP1 (fast and progressive motility), SBP2 (hyperactivated motility) and SBP3 (slow non-progressive motility). After categorization of IVEP in high, medium and low embryo yield, logistic regression analysis was applied to associate the results of subpopulations and other sperm quality variables with IVEP. The SBP1 and SBP2 variables affected embryo production, and an IVEP estimation model was generated for Senepol bulls based on these two subpopulations: embryo yield = 0.1563 + 0.0328 (SBP1) + 0.0173 (SBP2). SBP1 and SBP2 represents the absolute value of the percentage of subpopulations in semen. If the calculated value (by this equation) is close to 1, the embryo yield will be low; if is close to 2, will be medium; if is close to 3, will be high. In conclusion, three subpopulations were found for Senepol semen and, despite all analyzed variables, only SBP1 and SBP2 were included in the model to estimate IVEP in this breed.
