Bian Zhong-Rui, Yin Juan, Sun Wen, Lin Dian-Jie
Department of Cardiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China.
Beijing Spirallink Medical Research Institute, Beijing 100054, China.
Microb Pathog. 2017 Apr;105:68-73. doi: 10.1016/j.micpath.2017.02.012. Epub 2017 Feb 8.
Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB.
活动性结核病（TB）的诊断具有挑战性，治疗反应也难以有效监测。本研究的目的是对公开可用数据集中的样本采用微阵列和基于网络的综合分析方法，以获得活动性结核病的诊断模块集和通路。为实现这一目标，基于全局蛋白质 - 蛋白质相互作用（PPI）信息和基因表达数据生成背景PPI网络，随后从背景PPI网络中识别差异表达网络（DEN）。然后，根据DEN中的度特征提取自我基因。接下来，基于EgoNet算法通过自我基因扩展进行模块收集。之后，使用随机排列检验评估活动性结核病与对照之间模块的差异表达。最后，基于Reactome数据库通过通路富集分析检测差异模块的生物学意义，并采用Fisher精确检验提取活动性结核病的差异通路。总共从DEN中鉴定出47个自我基因和47个候选模块。通过设置基因大小>5和分类准确率≥0.9的截止标准，提取了7个自我模块（模块4、模块7、模块9、模块19、模块25、模块38和模块43），并且它们在活动性结核病与对照之间均具有统计学意义。然后，进行Fisher精确检验以捕获活动性结核病的差异通路。有趣的是，模块4、模块25、模块38和模块43中的基因富集于同一通路，即游离40S亚基池的形成。模块7和模块9的显著通路是真核生物翻译终止，模块19的显著通路是外显子连接复合体（EJC）增强的无义介导衰变。因此，差异模块和通路可能是治疗活动性结核病的潜在生物标志物，并为更好地理解活动性结核病的分子机制提供有价值的线索。
