Global transcriptional expression in ovarian follicles from Tsaiya ducks (Anas platyrhynchos) with a high-fertilization rate

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• 作者：Shyh-Jong Wu^a ; Yu-Shin Cheng^b ; Hsiao-Lung Liu^b ; Hsing-He Wang^c ; Hsiu-Lin Huang^c ; ^{hlhuang@mdu.edu.tw" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fertilization rate ; Differentially expressed transcript ; Ovarian follicle ; Duck
• 刊名：Theriogenology
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：85
• 期：8
• 页码：1439-1445.e1
• 全文大小：764 K

文摘

Novel candidates for biomarkers of a high-fertilization rate were identified here through global transcriptional profiling of ovarian follicles. Some other differentially expressed candidate genes were first noted to influence animal reproduction in our previous cDNA microarray analysis and are now recognized as markers for marker-assisted selection. In the present study, we compared gene expression in ovarian follicles from animals with high- and low-fertilization rates using an oligonucleotide array. On the basis of a fold change of greater than 1.2 and less than −1.2, a difference of >100 Affymetrix arbitrary units between the two groups, and a P value of less than 0.05, 47 genes were found to be associated with fertilization rate. GOEAST and MetaCore software were further used to identify the functional categories of genes that were differentially expressed. Then, we focused on three interesting genes associated with a high-fertilization rate: one of these genes was discovered to participate in signaling pathways of fertilization, and two genes take roles in lipid metabolism. An oligonucleotide array showed that the levels of orthodenticle homeobox 2 (OTX2) and lecithin:cholesterol acyltransferase (LCAT) gene expression were 1.62-fold and 1.95-fold higher in the high-fertilization rate group than in the low-fertilization rate group, respectively (P < 0.05). The level of apolipoprotein A-I (APOA1) gene expression was also higher in the high-fertilization rate group, with a difference of 2.31-fold (P < 0.05). The data were validated through quantitative polymerase chain reaction analysis. These results confirm the usefulness of the array technique and data mining methods in the discovery of new biomarkers and add knowledge to our understanding of the factors affecting fertilization rates in ovarian follicles.