Tuberculosis (TB) is the second largest infectious airborne disease that mostly affects the lungs and leads to damage to the respiratory system. There are mainly four factors susceptibility, exposure, infectiousness, and environment which determine the probability of transmission through Mycobacterium tuberculosis (MTB), a TB-causing pathogenic bacteria. To develop the antibiotic for the treatment of TB, two-component systems (TCSs) were mostly considered important targets. The mostly known TCS for MTB is MtrAB, which is present in all mycobacterial species. In this study, we applied network theory-based methods, using MtrAB protein-protein interactions, and identified potential drug targets for MTB. The constructed network showed hierarchical behavior having modules organization that performed a particular function. During our analysis, we found Rv1364c, regX3, gltB, dosT, and devS as five key regulators. The regX3 was found to be an important key regulator and showed interaction with other hubs. In addition, we also identified regX3, MtrA, MtrB, and Rv1364c formed four nodes motif in which regX3 regulates MtrA protein through MtrB and Rv1364c, which can be crucial for the network. The hub removal analysis showed that gltB is a key regulator mainly in communicating the signals. Furthermore, MtrA was not present in any of the identified modules, which suggested it is indirectly related to the modules function and also, possesses the potential to cross-talk between the modules through hubs. In the future, the development of a common drug target against regX3 and Rv1364c proteins could be a potential therapeutic cure for TB.
Key words: MtrAB, regX3, Rv1364c, Mycobacterium tuberculosis, Protein-Protein interaction network
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