Microarray and network-based identification of functional modules and pathways of active tuberculosis

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.

Differential expression network analysis for diabetes mellitus type 2 based on expressed level of islet cells

OBJECTIVE

Diabetes mellitus type 2 (T2DM) is a metabolic disease that has become a pressing issue, with potential adverse impact on mental health. We aimed to explore the potential molecular mechanism of T2DM.

MATERIAL AND METHODS

GSE38642 microarray data downloaded from gene expression omnibus was used to identify the differentially expressed genes (DEGs). Profiling of complex functionality (ProfCom) was used to analyze the complex function and mine T2DM signature genes. Finally, the differential expression network (DEN) was constructed.

RESULTS

We identified 147 DEGs including 59 up- and 88 down-regulated genes. With increasing of degree, the specificity of functional description of DEGs was higher. GO term of "integral to membrane and immune response (not receptor activity) not regulation of immune response" in degree 4 was enriched by 6 DEGs, while the GO term of "immune response" in degree 1 was enriched by 12 DEGs. Two complex functions of integral to membrane an immune response and response to glucose stimulus were enriched by 11 T2DM signature genes including ARG2, GLP1R, PFKFB2, PTPRN, ACSL5, CCR7, IL2RA, IL7R, IL1R2, IL1RL1 and CHST4. Finally, DEN including 11 signature genes and 491 edges was obtained.

CONCLUSION

The identified DEGs especially 11 signature genes such as PTPRN, GLP1R, CCR7 and IL2RA may play important roles in the pathogenesis of T2DM.