Abstract
Tuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries including India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network based nalysis of DEGs to identify the potential biomarker. We want to observe the transition of genes from the normal condition to different stages of the TB and identify, annotate those genes or pathways or processes that play key role in the progression of TB disease during its cyclic interventions in human body. We identified 319 genes that are ifferentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of the curated class of associated genes. These pathways importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are overexpressed as host responses against the pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to the pathogen for its progression. This study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.
