Wong David J, Chang Howard Y
Program in Epithelial Biology, Stanford University School of Medicine, Stanford, California 94305, USA.
J Invest Dermatol. 2005 Aug;125(2):175-82. doi: 10.1111/j.0022-202X.2005.23827.x.
Global gene expression patterns can provide comprehensive molecular portraits of biologic diversity and complex disease states, but understanding the physiologic meaning and genetic basis of the myriad gene expression changes have been a challenge. Several new analytic strategies have now been developed to improve the interpretation of microarray data. Because genes work together in groups to carry out specific functions, defining the unit of analysis by coherent changes in biologically meaningful sets of genes, termed modules, improves our understanding of the biological processes underlying the gene expression changes. The gene module approach has been used in exploratory discovery of defective oxidative phosphorylation in diabetes mellitus and also has allowed definitive hypothesis testing on a genomic scale for the relationship between wound healing and cancer and for the oncogenic mechanism of cyclin D. To understand the genetic basis of global gene expression patterns, computational modeling of regulatory networks can highlight key regulators of the gene expression changes, and many of these predictions can now be experimentally validated using global chromatin-immunoprecipitation analysis.
全球基因表达模式能够提供生物多样性和复杂疾病状态的全面分子图谱，但理解众多基因表达变化的生理意义和遗传基础一直是一项挑战。目前已开发出几种新的分析策略来改进对微阵列数据的解读。由于基因是协同发挥作用以执行特定功能的，通过具有生物学意义的基因集（称为模块）中的连贯变化来定义分析单元，能增进我们对基因表达变化背后生物学过程的理解。基因模块方法已被用于探索性发现糖尿病中氧化磷酸化缺陷，并且还能在基因组规模上对伤口愈合与癌症之间的关系以及细胞周期蛋白D的致癌机制进行明确的假设检验。为了理解全球基因表达模式的遗传基础，调控网络的计算建模可以突出基因表达变化的关键调节因子，现在许多这些预测都可以通过全基因组染色质免疫沉淀分析进行实验验证。
