Molecular insights of drug-resistant tuberculosis: genetic mutations and their profile - Singh, Amresh Kumar; Singh, Nandini; Kumar, Sushil; Mishra, Ashwini Kumar; Singh, Narendra Pratap (Frontiers in Microbiology, 2025)
IntroductionDrug-resistant tuberculosis (DR-TB) poses a significant public health threat, with molecular diagnostics playing a pivotal role in understanding the genetic mechanisms of resistance. This study focuses on the patterns of genetic mutations observed in DR-TB cases, with the aim to identify key mutations associated with resistance to rifampicin (RIF) and isoniazid (INH).MethodologyA total of 6,954 non-duplicate clinical samples were obtained from individuals of all age groups, categorized as TB and DR-TB, from seven linked districts between June 2022 and May 2024. The samples were transported under cold chain conditions to an intermediate reference laboratory. TB was confirmed using fluorescence microscopy, and 1,998 sputum-positive samples were analyzed using line probe assay for characterization of genetic mutations.ResultsAmong the analyzed cases, a total of 136 cases of DR-TB were identified. This included 57 cases (41.92%) of multidrug-resistant TB (MDR-TB), 73 cases (53.68%) of INH monoresistance, and 6 cases (4.4%) of RIF monoresistance. The analysis revealed a high prevalence of rpoB MUT3 (S531L) mutations in 52 cases (82.25%), which is associated with RIF resistance. In high-level INH (katG gene mutation) resistance noted in 83 (63.35%) cases, katG MUT1 (S315T1) was predominant, while low-level INH resistance (inhA gene mutation), inhA MUT1 (C-15T) mutation, was found in 29 (22.13%) cases. Maharajganj and Deoria reported the highest prevalence of rpoB MUT3 (S531L) mutations, while Kushinagar and Sant Kabir Nagar exhibited higher rates of katG MUT1 (S315T1) mutations. Other regions showed notable distribution of rpoB, katG, and inhA gene mutations.ConclusionThe high prevalence of mutations such as rpoB MUT3 (S531L) and katG MUT1 (S315T1) highlights the need for integrating molecular tools into routine workflows to identify genetic mutations. District-specific mutations emphasize the influence of local epidemiological factors on resistance patterns, necessitating region-specific interventions. Continuing research into regional resistance trends are vital to addressing the global DR-TB burden effectively.