Systematic expression profiling analysis mines dys-regulated modules in active tuberculosis based on re-weighted protein-protein interaction network and attract algorithm.
About 90% of tuberculosis (TB) patients latently infected with Mycobacterium tuberculosis (Mtb) show no symptoms, yet have a 10% chance in lifetime to progress active TB. Nevertheless, current diagnosis approaches need improvement in efficiency and sensitivity. The objective of this work was to detect potential signatures for active TB to further improve the understanding of the biological roles of functional modules involved in this disease. First, targeted networks of active TB and control groups were established via re-weighting protein-protein interaction (PPI) networks using Pearson's correlation coefficient (PCC). Candidate modules were detected from the targeted networks, and the modules with Jaccard score >0.7 were defined as attractors. After that, identification of dys-regulated modules was conducted from the attractors using attract method, Subsequently, gene oncology (GO) enrichment analyses were implemented for genes in the dys-regulated modules. We obtained 33 and 65 candidate modules from the targeted networks of control and active TB groups, respectively. Overall, 13 attractors were identified. Using the cut-off criteria of false discovery rate <0.05, there were 4 dys-regulated modules (Module 1, 2, 3, and 4). Based on the GO annotation results, genes in Modules 1, 2 and 4 were only involved in translation. Most genes in Module 1, 2 and 4 were associated with ribosomes. Accordingly, these dys-regulated modules might serve as potential biomarkers of active TB, facilitating the development for a more efficient, and sensitive diagnostic assay for active TB.
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Ying
Y
Sun
Department of Gastroenterology, China Meitan General Hospital, Beijing 100028, China. Electronic address: wengyansnow@126.com.
Yan
Y
Weng
Central Supply Service Department, Jilin Hospital of Integrated Traditional Chinese and Western Medicine, Jilin 132400, Jilin Province, China.
Ying
Y
Zhang
Department of Anesthesiology, No 65334 Hospital of PLA, Yanji 133000, Jilin Province, China.
Xiang
X
Yan
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Lei
L
Guo
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Jia
J
Wang
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Xin
X
Song
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Ying
Y
Yuan
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Fu-Ye
FY
Chang
Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.
Chun-Ling
CL
Wang
D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D020869:Gene Expression Profiling / Q000379:methods; D015964:Gene Expression Regulation, Bacterial; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D009169:Mycobacterium tuberculosis / Q000235:genetics* / Q000472:pathogenicity; D060066:Protein Interaction Maps; D015398:Signal Transduction; D014376:Tuberculosis / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism*
D015415:Biomarkers
Active tuberculosis; Attractors; Dys-regulated modules; Protein-protein interaction network