Unique Chemokine Profiles of Lung Tissues Distinguish Post-chemotherapeutic Persistent and Chronic Tuberculosis in a Mouse Model - Park, Soomin; Baek, Seung-Hun; Cho, Sang-Nae; Jang, Young-Saeng; Kim, Ahreum; Choi, In-Hong (Frontiers in Cellular and Infection Microbiology, 2017)
There is a substantial need for biomarkers to distinguish latent stage from active Mycobacterium tuberculosis infections, for predicting disease progression. To induce the reactivation of tuberculosis, we present a new experimental animal model modified based on the previous model established by our group. In the new model, the reactivation of tuberculosis is induced without administration of immunosuppressive agents, which might disturb immune responses. To identify the immunological status of the persistent and chronic stages, we analysed immunological genes in lung tissues from mice infected with M. tuberculosis. Gene expression was screened using cDNA microarray analysis and confirmed by quantitative RT-PCR. Based on the cDNA microarray results, 11 candidate cytokines genes, which were obviously up-regulated during the chronic stage compared with those during the persistent stage, were selected and clustered into three groups: 1) chemokine genes, except those of monocyte chemoattractant proteins (MCPs) (CXCL9, CXCL10, CXCL11, CCL5, CCL19); 2) MCP genes (CCL2, CCL7, CCL8, CCL12); and 3) TNF and IFN-gamma genes. Results from the cDNA microarray and quantitative RT-PCR analyses revealed that the mRNA expression of the selected cytokine genes was significantly higher in lung tissues of the chronic stage than of the persistent stage. Three chemokines (CCL5, CCL19 and CXCL9) and three MCPs (CCL7, CCL2 and CCL12) were noticeably increased in the chronic stage compared with the persistent stage by cDNA microarray (p<0.01, except CCL12) or RT-PCR (p<0.01). Therefore, these six significantly increased cytokines in lung tissue from the mouse tuberculosis model might be candidates for biomarkers to distinguish the two disease stages. This information can be combined with already reported potential biomarkers to construct a network of more efficient tuberculosis markers.