| Background and Objective:In recent years, tuberculosis again showed the trend that was the regional outbreak. Now, there are as many as 8 million 560 thousand new cases of pulmonary tuberculosis each year, and 5-15% of them develop into active pulmonary tuberculosis. After the infection of Mycobacterium tuberculosis, the complex immune response mechanism of the body is very important to the occurrence and development of active pulmonary tuberculosis. At the same time, the genotype and expressed type of Mycobacterium tuberculosis have great changes. The traditional focus on the patterns of "reductionism" which was associated with single molecular changes has been unable to meet the early diagnosis of demand when current tuberculosis infection happened as well as the clinical application of tuberculosis diagnostic target. Therefore, we should integrate the advantages of multi subjects to build a gene network which is associated with active pulmonary tuberculosis immune response and to take high-throughput screening early diagnosis target which is infected by Mycobacterium tuberculosis, which will be of great importance in in-depth study of specific immune response mechanism that is associated with human infection with Mycobacterium tuberculosis. And it is significant to lay the foundation for the research of the early diagnosis of tuberculosis as well as the research and development of new drugs. Materials and methods:To screen out the eligible gene expression profile data sets, we collected them from GEO Datasets of NCBI. Then we applied method of linear model and empirical bayes statistics, which is from Limma bag, and combined traditional t-test to conduct a nonspecific filtering for the expression data. Controlling P<0.05, |Log2FC|>0.58(FC>1.5/FC<0.66) to eventually screen the differentially expressed genes. We made use of Database for Annotation, Visualization and Integrated Discovery(DAVID) and Kyoto Encyclopedia of Genes and Genomes(KEGG) to conduct function analysis(GO-Analysis)and pathway classification(Pathway-Analysis)of differential genes, respectively. Based on gene expression profile after microarray data analysis and through searching of Transcriptional Regulatory Element Database(TRED), we constructed a specific gene regulatory network for the presence of expression disorders in the body fluid of active pulmonary tuberculosis. Furthermore, we analyzed the correlation between the network genes and the diseases by DAVID. We used q PCR to assess the m RNA expression of SPI1, CEBPB, FCGR1 A and ICAM1.The expression of SPI1, CEBPB, FCGR1 A and ICAM1 was further analyzed by the receiver operating characteristic(ROC) curve in order to determine the sensitivity and specificity about the gene set consisting of SPI1, CEBPB, FCGR1 A and ICAM1. Results:Four gene expression profile data sets including 40 TB samples vs. 118 healthy control samples were collected in the study. A total of 1539 genes with altered expression were identified. These included 1041 genes that were up-regulated and 498 that were down-regulated. We got 68 significantly up-regulated items and 22 significantly down-regulated items by GO-Analysis(P<0.01); we got 17 significantly up-regulated items and 8 significantly down-regulated items by Pathway-Analysis(P<0.01). The TRED was used to construct a regulatory network consisting of 52 differentially expressed genes, which contained 6 transcription factors(SPI1, CEBPB, STAT1, STAT3 and STAT5A). The most of target genes were regulated by SPI1 and CEBPB. Further, the top four disease classes associated with these 52 differentially expressed genes were identified in the DAVID, which applied functional annotation to these genes. The most important class was IMMUNE(26 genes) followed by INFECTION(14 genes), CARDIOVASCULAR(15 genes) and CANCER(15 genes). The three genes that could be regarded as the hub-genes were FCGR1 A, ICAM1 and MUC1. We used q PCR to assess the m RNA expression of SPI1, CEBPB, FCGR1 A and ICAM1. The results showed that SPI1 m RNA expression was up-regulated by 1.99±0.09 fold(P<0.001), while that of CEBPB, FCGR1 A and ICAM1 was up-regulated by 2.57±0.32(P<0.01), 9.58±0.54(P<0.001), 1.86±0.15(P<0.01) times, respectively. And the expression level consistent with the results of the gene chip. The ROC curve analysis showed that the all AUC of SPI1, CEBPB, FCGR1 A and ICAM1 was 0.9781, which had good specificity and high sensitivity. Conclusion:1) A TF-gene network was constructed and its main transcription factors were SPI1 and CEBPB. The TF-gene network also presents the relationship and interaction among host genes in the development process of immune response when active pulmonary tuberculosis happened;2) The analysis of gene set(SPI1, CEBPB, FCGR1 A and ICAM1) had good specificity and high sensitivity, the gene set can be used as a method of active pulmonary tuberculosis humoral diagnostic candidate markers cluster. |
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