B-CELL EPITOPE PREDICTION IN CYSTIC FIBROSIS LUNG MICROBIAL COMMUNITY

Abstract

Human were considered as sterile for a long time period but, as the science advanced in Biology, a lot of techniques have been discovered that figured them out. Human body consisting of 100 trillion cells is inhabited by many bacteria and other microbes all over, for example, over skin, beneath the skin, gut, oral axis, and in lungs. All the microbial community is collectively called microbiome. They play role as the essential components of immunity, influence metabolism, and also modulate drug interactions. However, under disease conditions, as the normal physiology of body changes, dysbiosis of microbiota occurs and the advantageous normal microbes get replaced by the competent infectious ones. Human body thus acts as host to them. The present study aims at analysis of normal microbiota in human oral-gut axis and lungs, followed by the dysbiosis in lung microbiota in cystic fibrosis. This dysbiosis causes infection leading to a high mortality rate in cystic fibrosis patients. Most of these infectious agents in CF are drug resistant, so it has become difficult to treat them. Hence, a need has arisen to search for alternative strategies to treat cystic fibrosis. These infectious microbial community in lungs can be targeted on basis of their surface structures. This thesis aims to predict the B-cell epitope in three major microbes implicated in high mortality rate in CF. B-cell epitopes were predicted using major bioinformatics tool, IEDB, Uniprot, PDB, Bepipred, Discotope, Phyre^2 and Firstglance Jmol. Using data from the tools, antigen specific antibody can be synthesized. As, antibody binds the specific antigen, a higher chance is that the infection rate can be slowed or eliminated using other drugs along with the antibody cocktail.