Predictive indicators of cryotolerance and fertility in bovine sperm: evaluating fresh semen quality to improve AI outcomes with frozen-thawed sperm

Abstract

The success of artificial insemination (AI) with frozen-thawed semen in cattle is influenced by both female factors and sperm quality. In terms of sperm quality, prior studies indicate that the ability of frozen-thawed bovine sperm to fertilise an oocyte is dependent on their quality and resilience to cryopreservation. Cryopreservation induces oxidative stress, leading to ultrastructural damage in the sperm. This study aimed to determine whether the quality of fresh semen can identify bulls with good and poor sperm freezability. This difference between fresh and frozen semen from the same bull allows us to predict fertility. Motility and kinetic parameters were assessed using computer-assisted sperm analysis (CASA), while six functional variables were evaluated through flow cytometry, both before and after the freeze-thaw process on the sperm from 13 bulls. In vivo fertility was measured using 90-day non-return rates. The principal component analysis (PCA) of eight sperm variables post-thaw identified one principal component explaining 81.19% of the total variance and classified the bulls into two groups: Poor freezability bulls (progressive motility: 48.12% ± 8.41%; viability: 77.51% ± 7.61%) and good freezability bulls (progressive motility: 58.64% ± 6.64%; viability: 88.12% ± 2.52%). Bulls with higher freezability showed better sperm viability and motility, as well as lower levels of ROS, superoxides and intracellular calcium before cryopreservation that were significantly correlated with higher non-return rates (NRR). The results underscore the importance of assessing the quality and functionality of fresh semen to predict the fertility potential of cryopreserved sperm. This approach can aid in selecting ejaculates with the best potential for successful artificial insemination, ultimately improving reproductive performance in dairy cattle

Ministry of Science and Innovation, Spain. Grant Number: AGL2017-88329-Rand PID2020-113320RB-I0