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Updated: Jan 9, 2026

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray
Published on: April 25, 2014
Genomic landscape of Mycobacterium tuberculosis: Identifying mutation hotspots and stable regions for implications
Muhammad Tahir Khan1,2, Zeyu Luo3, Arwa Omar Al Khatib4
1State Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Department of Clinical Laboratory, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, China.
Abstract:
Whole-genome sequencing is the most promising approach for public health surveillance and antimicrobial drug resistance. The current study aimed to analyze base mutations across Mycobacterium tuberculosis genomes to assess mutation frequency and distribution across different regions. This study aimed to identify genomic regions of Mycobacterium tuberculosis with varying mutation frequencies to inform mechanisms of drug resistance and potential drug target discovery. The study analyzed base mutations across 209 whole genome sequences, which were aligned with reference H37Rv (NC_000962.3), using the PhyResSE pipeline. Based on the frequency of mutations, the regions have been classified into three main locations. High frequency mutation areas: around 2300 kb to 2400 kb, around 4100 kb to 4200 kb, around 1600 kb to 1700 kb, and 3700 kb to 3800 kb. These locations showed dense clusters linked to katG and inhA (Isoniazid resistance), Ethambutol resistance (embB), rifampicin resistance (compensatory role rpoA). Moderate frequency mutations were observed around 2000 kb to 2100 kb, around 1300 kb to 1400 kb, around 3800 kb to 3900 kb, and around 3400 kb to 3500 kb. These regions show mostly involved rrs mutations (amikacin, kanamycin, and capreomycin resistance), and rpoA has a compensatory role in rifampicin resistance. Regions exhibiting minimal mutation activity have been observed around 100 kb-300 kb, around 500 kb, and 2700 kb to 2800 kb. The most common nucleotide substitution was G to A (G→A) (16 %), followed by A to G (A→G) and T to C (T→C) (15 %). These findings collectively highlight novel genomic regions of stability and hypervariability, offering insights for refining WGS-based surveillance, resistance prediction, and future drug target prioritization.
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