title: Microarray and network-based identification of functional modules and pathways of active tuberculosis.
abstract: 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.
authors.affiliation: Department of Cardiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China.
authors.forename: Zhong-Rui
authors.initials: ZR
authors.lastname: Bian
authors.affiliation: Beijing Spirallink Medical Research Institute, Beijing 100054, China.
authors.forename: Juan
authors.initials: J
authors.lastname: Yin
authors.affiliation: Beijing Spirallink Medical Research Institute, Beijing 100054, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Sun
authors.affiliation: Department of Respiratory Medicine, Shandong Provincial Hospital, Jinan 250021, Shandong Province, China. Electronic address: linmountain_power@126.com.
authors.forename: Dian-Jie
authors.initials: DJ
authors.lastname: Lin
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D053263:Gene Regulatory Networks; D006801:Humans; D060066:Protein Interaction Maps; D014376:Tuberculosis / Q000175:diagnosis* / Q000473:pathology*
chemical_list: D015415:Biomarkers
keywords: Active tuberculosis; Differential expression network; Differential modules; Ego genes; Reactome