Gerling Ivan C, Singh Sudhir, Lenchik Nataliya I, Marshall Dana R, Wu Jian
Department of Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee 28104, USA.
Mol Cell Proteomics. 2006 Feb;5(2):293-305. doi: 10.1074/mcp.M500197-MCP200. Epub 2005 Oct 16.
Non-obese diabetic (NOD) mice spontaneously develop autoimmunity to the insulin producing beta cells leading to insulin-dependent diabetes. In this study we developed and used new data analysis and mining approaches on combined proteome and transcriptome (molecular phenotype) data to define pathways affected by abnormalities in peripheral leukocytes of young NOD female mice. Cells were collected before mice show signs of autoimmunity (age, 2-4 weeks). We extracted both protein and RNA from NOD and C57BL/6 control mice to conduct both proteome analysis by two-dimensional gel electrophoresis and transcriptome analysis on Affymetrix expression arrays. We developed a new approach to analyze the two-dimensional gel proteome data that included two-way analysis of variance, cluster analysis, and principal component analysis. Lists of differentially expressed proteins and transcripts were subjected to pathway analysis using a commercial service. From the list of 24 proteins differentially expressed between strains we identified two highly significant and interconnected networks centered around oncogenes (Myc and Mycn) and apoptosis-related genes (Bcl2 and Casp3). The 273 genes with significant strain differences in RNA expression levels created six interconnected networks with a significant over-representation of genes related to cancer, cell cycle, and cell death. They contained many of the same genes found in the proteome networks (including Myc and Mycn). The combination of the eight, highly significant networks created one large network of 272 genes of which 82 had differential expression between strains either at the protein or the RNA level. We conclude that new proteome data analysis strategies and combined information from proteome and transcriptome can enhance the insights gained from either type of data alone. The overall systems biology of prediabetic NOD mice points toward abnormalities in regulation of the opposing processes of cell renewal and cell death even before there are any clear signatures of immune system activation.
非肥胖型糖尿病(NOD)小鼠会自发地对产生胰岛素的β细胞产生自身免疫,进而导致胰岛素依赖型糖尿病。在本研究中,我们针对蛋白质组和转录组(分子表型)的组合数据开发并使用了新的数据分析和挖掘方法,以确定年轻雌性NOD小鼠外周白细胞异常所影响的通路。在小鼠出现自身免疫迹象之前(年龄为2 - 4周)收集细胞。我们从NOD小鼠和C57BL/6对照小鼠中提取蛋白质和RNA,通过二维凝胶电泳进行蛋白质组分析,并在Affymetrix表达阵列上进行转录组分析。我们开发了一种新方法来分析二维凝胶蛋白质组数据,该方法包括双向方差分析、聚类分析和主成分分析。使用商业服务对差异表达的蛋白质和转录本列表进行通路分析。从两品系间差异表达的24种蛋白质列表中,我们鉴定出两个高度显著且相互关联的网络,其围绕癌基因(Myc和Mycn)以及凋亡相关基因(Bcl2和Casp3)。在RNA表达水平上具有显著品系差异的273个基因形成了六个相互关联的网络,其中与癌症、细胞周期和细胞死亡相关的基因显著富集。它们包含了蛋白质组网络中发现的许多相同基因(包括Myc和Mycn)。这八个高度显著的网络组合形成了一个由272个基因组成的大型网络,其中82个基因在蛋白质或RNA水平上存在品系间差异表达。我们得出结论,新的蛋白质组数据分析策略以及蛋白质组和转录组的联合信息能够增强仅从单一类型数据中获得的见解。糖尿病前期NOD小鼠的整体系统生物学表明,甚至在免疫系统激活的任何明显特征出现之前,细胞更新和细胞死亡这两个相反过程的调节就存在异常。