Yuen Sze Chung, Zhu Hongmei, Leung Siu-Wai
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China.
School of Informatics, University of Edinburgh, Edinburgh, UK.
Methods Mol Biol. 2018;1762:179-197. doi: 10.1007/978-1-4939-7756-7_10.
Potential drug targets for the disease treatment can be identified from microarray studies on differential gene expression of patients and healthy participants. Here, we describe a method to use the information of differentially expressed (DE) genes obtained from microarray studies to build molecular interaction networks for identification of pivotal molecules as potential drug targets. The quality control and normalization of the microarray data are conducted with R packages simpleaffy and affy, respectively. The DE genes with adjusted P values less than 0.05 and log fold changes larger than 1 or less than -1 are identified by limma package to construct a molecular interaction network with InnateDB. The genes with significant connectivity are identified by the Cytoscape app jActiveModules. The interactions among the genes within a module are tested by psych package to determine their associations. The gene pairs with significant association and known protein structures according to the Protein Data Bank are selected as potential drug targets. As an example for drug target screening, we demonstrate how to identify potential drug targets from a molecular interaction network constructed with the DE genes of significant connectivity, using a microarray dataset of type 2 diabetes mellitus.
疾病治疗的潜在药物靶点可从对患者和健康参与者的差异基因表达进行的微阵列研究中确定。在此，我们描述一种方法，利用从微阵列研究中获得的差异表达（DE）基因信息构建分子相互作用网络，以识别作为潜在药物靶点的关键分子。微阵列数据的质量控制和标准化分别使用R包simpleaffy和affy进行。通过limma包识别调整后P值小于0.05且对数倍变化大于1或小于 -1的DE基因，以使用InnateDB构建分子相互作用网络。通过Cytoscape应用程序jActiveModules识别具有显著连通性的基因。用psych包测试模块内基因之间的相互作用，以确定它们的关联。根据蛋白质数据库选择具有显著关联且具有已知蛋白质结构的基因对作为潜在药物靶点。作为药物靶点筛选的一个例子，我们展示了如何使用2型糖尿病的微阵列数据集，从由具有显著连通性的DE基因构建的分子相互作用网络中识别潜在药物靶点。
