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Title |
Classification of Mycobacterium tuberculosis DR, MDR, XDR Isolates and Identification of Signature Mutation Pattern of Drug Resistance
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Authors |
Akshatha Prasanna & Vidya Niranjan*
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Affiliation |
Department of Biotechnology, Rashtreeya Vidyalaya College of Engineering;
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Akshatha Prasanna - E-mail: akshathap@rvce.edu.in; Vidya Niranjan - E-mail: vidya.n@rvce.edu.in; *Corresponding author
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Article Type |
Research Article
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Date |
Received February 28, 2019; Revised on March 15, 2019; Accepted March 15, 2019; Published April 15, 2019
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Abstract |
Mycobacterium tuberculosis - a global threat, the recent breakout in MDR-TB and XDR-TB has challenged researchers in diagnosis to provide effective treatment. The main objective to combat drug resistance is to provide rapid, reliable and sensitive diagnostic methods in health care centres. This study focuses on development of an effective pipeline to identify drug resistance mutations in whole genome data of Mycobacterium tuberculosis utilizing the Next Generation Sequencing approach and classification of drug resistance strains based on genetic markers obtained from TGS-TB, tbvar and TBDReamDB. 74 isolates are characterized into 20 DR-TB, 16 MDR-TB, 16 XDR-TB and 6 nonresistant strains based on known drug resistance genetic markers. Results provide mutation pattern for each of the classified strains and profiling of drug resistance to the group of anti-TB drugs. The presence of specific mutation causing resistance to a drug will help set the dosage levels which play an important role in the treatment. Findings on amino acid changes and its respective codon positions in candidate genes will provide insights in drug sensitivity and a way for discovery of potent drugs. The implementation of these approaches in clinical setting provides rapid and sensitive diagnostics to combat the emerging drug resistance.
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Keywords |
Mycobacterium tuberculosis, Next Generation Sequencing (NGS), Antimicrobial Resistance (AMR) Prediction
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Citation |
Prasanna & Niranjan, Bioinformation 15(4): 261-268 (2019)
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Edited by |
P Kangueane
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ISSN |
0973-2063
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Publisher |
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License |
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
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