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Molecular networks underlying ovarian aging for clinical applications
- 언어ENG
- URLhttps://db.koreascholar.com/Article/Detail/354547
Due to modern trends with postponing child-bearing and getting worse living environment in women, an ovarian aging increased pregnancy failure and other complications with menopause or premature ovarian failure. Although several theories have been suggested such as mitochondrial malfunction, DNA damage/repair/methylation, caloric restriction, studies regarding ovarian aging-related molecular mechanisms for development of therapeutic methods are insufficient so far. Our objective is to determine molecular pathways of ovarian aging that result in pregnancy failure and other complications in women health to develop treatment strategies. This study is consisted of two parts: in Phase I stage, we analyzed distinct gene expression profile between young and aged mouse ovaries, and in Phase II stage several preferentially expressed genes in both ovaries were selected and analyzed their physiological functions and involved molecular networks related to ovarian aging for development of diagnostic markers and therapeutic methods. Ovaries from 10 week and 11 month-old FVB/NJ female mice with synchronized estrus cycle were collected for this study. A half of each ovary was used for RNA preparation and the other half for histological analysis. Using the Illumina HiSeq 2000 System, preferentially expressed genes were identified. Functional annotation database-based gene-set enrichment analyses and Pathway Studio® were employed to evaluate aging-related molecular networks. These findings were confirmed through qRT-PCR and immunohistochemistry. To validate RNA-Seq data, we examined expression patterns of marker genes (Amh, Bmp15 and Nobox) that were wellknown to be decreased in ovarian aging process. In young or aged ovary, preferentially expressed 876 genes were identified and extracellular matrix (ECM; p<0.001) and chromatin/nucleosome-related (p<0.001) protein-coded genes have the majority in these genes by GOTERM analysis. Amongthem, we selected several candidate genes and confirmed their expression profiles by qRT-PCR and immunohistochemistry followed by molecular network analysis. Regarding molecular interactions in these genes, PathwayStudio® was employed to predict aging-involved molecular networks in mouse ovary. Here we report a couple of candidate molecular networks and medicines (chemicals) for targeting these preferentially expressed genes/proteins. Further analyses are scheduled to produce transgenic animal models and with human ovarian tissues/cell lines.
