Multifactorial Analysis of the Follicular Environment is Predictive of Oocyte Morphology in Cattle

Abstract

Numerous attempts have been recently made in the search for a reliable, fast and noninvasive assay for selection of oocytes suitable for in vitro embryo production. Potential markers have been described in the follicle such as follicular fluid (FF) or cumulus cells (CCs). However, the reported findings are contradictory, which may reflect the complexity of metabolism of the ovarian follicle. In the present experiment, a data set from individual follicles of known diameter was obtained: cumulus-oocyte complex (COC) morphology, fatty acid composition and glucose concentration in FF as well as apoptotic index in CCs. The obtained data was statistically analyzed either separately (univariate analysis) or simultaneously (multivariate analysis) to examine its predictive value in morphology assessment of bovine COCs. Although the univariate analysis yielded a complex relation system of the selected parameters, no clear outcome could be established. In multivariate analysis, the concentration of the four fatty acids (C16:0, C16:1, C18:1cis9, C22:5n3) and Δ9-desaturase (16) as well as elongase activities were selected as covariates. This allowed prediction of the morphology of a COC with an accuracy of 72%, which is the most interesting finding of the experiment. The present study indicates that the multifactorial model comprising of selected parameters related to the follicle appeared more effective in predicting the morphology of a bovine COC, which may improve the effectiveness of in vitro production systems.