five

Gene expression analysis of EBV-encoded dUTPase in hDCs. Homo sapiens

收藏
NIAID Data Ecosystem2026-03-07 收录
下载链接:
https://www.ncbi.nlm.nih.gov/bioproject/PRJNA200721
下载链接
链接失效反馈
官方服务:
资源简介:
Gene expression patterns provide insight into complex biological networks in which viruses and host cells interact. Dendritic cells are the most potent antigen presenting cells of the immune system and are crucial for the initiation of T-cell responses to viral pathogens. To better understand the biological effects of the EBV-encoded dUTPase on gene modulation in cells that play a central role in innate and adaptive immune responses, microarray gene expression profiling studies were performed in untreated or EBV-encoded dUTPase treated hDCs using the human genome U133 Plus 2.0 GeneChip. The results of this study identified over 800 differentially expressed genes between control and EBV-encoded dUTPase treated samples for each normalized data set using the criteria fold change >2 and p<0.05. Overall design: The effect of the EBV-encoded dUTPase on gene expression in human dendritic Langerhan cells (hDCs), derived from human umbilical cord blood (HUCB) cells, was determined by treating hDCs with purified EBV-encoded dUTPase (10 µg/ml), for 4 h and then compared the gene expression in these cells to untreated controls. A total of 5 samples (2 controls and 3 treated) were analyzed.

基因表达谱可为解析病毒与宿主细胞相互作用的复杂生物网络提供重要见解。树突状细胞(Dendritic Cells, DC)是免疫系统中功能最强的抗原呈递细胞,对于启动T细胞针对病毒病原体的免疫应答至关重要。为深入阐释EB病毒(Epstein-Barr Virus, EBV)编码的脱氧尿苷三磷酸酶(dUTPase)对先天免疫与适应性免疫核心细胞的基因调控生物学效应,本研究采用人类基因组U133 Plus 2.0基因芯片(Human Genome U133 Plus 2.0 GeneChip),对未经处理及经EBV编码dUTPase处理的人类树突状细胞(human dendritic cells, hDCs)开展基因表达谱分析。本研究结果显示,以倍数变化(fold change)>2且p值<0.05作为筛选标准,对每个标准化数据集进行分析后,对照组与EBV编码dUTPase处理组样本间共鉴定得到800余个差异表达基因。 实验整体设计:本研究以人类脐带血(Human Umbilical Cord Blood, HUCB)来源的朗格汉斯树突状细胞(human dendritic Langerhan cells, hDCs)为研究对象,将纯化的EBV编码dUTPase以10 µg/ml的浓度处理细胞4小时,随后将处理组细胞的基因表达水平与未处理对照组进行比较,以此明确EBV编码dUTPase对hDCs基因表达的调控作用。本研究共分析了5份样本,其中对照组2份,处理组3份。
创建时间:
2013-04-30
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作