Warzych Ewelina, Cieslak Adam, Madeja Zofia E, Pawlak Piotr, Wolc Anna, Lechniak Dorota
Department of Genetics and Animal Breeding, Poznan University of Life Sciences, 60-637 Poznan, Poland.
J Reprod Dev. 2014 Mar 7;60(1):1-8. doi: 10.1262/jrd.2013-086. Epub 2013 Nov 21.
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 Δ(9)-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.
最近，人们为寻找一种可靠、快速且无创的检测方法以筛选适合体外胚胎生产的卵母细胞进行了大量尝试。卵泡中的潜在标志物已被描述，如卵泡液（FF）或卵丘细胞（CCs）。然而，报道的结果相互矛盾，这可能反映了卵巢卵泡代谢的复杂性。在本实验中，获得了来自已知直径单个卵泡的数据集：卵丘 - 卵母细胞复合体（COC）形态、FF中的脂肪酸组成和葡萄糖浓度以及CCs中的凋亡指数。对获得的数据进行单独（单变量分析）或同时（多变量分析）统计分析，以检验其在牛COC形态评估中的预测价值。尽管单变量分析得出了所选参数的复杂关系系统，但未能确定明确的结果。在多变量分析中，选择了四种脂肪酸（C16:0、C16:1、C18:1cis9、C22:5n3）的浓度以及Δ(9)-去饱和酶（16）和延长酶活性作为协变量。这使得能够以72%的准确率预测COC的形态，这是该实验最有趣的发现。本研究表明，由与卵泡相关的所选参数组成的多因素模型在预测牛COC的形态方面似乎更有效，这可能会提高体外生产系统的效率。
