Molecular characterization of genetic mutations with fitness loss in pulmonary tuberculosis patients associated with HIV co-infection in Northwest Amhara, Ethiopia

Objectives:Molecular approaches to identifying resistance-conferring mutations suggest a revolution in the field of tuberculosis. The aim of the study was to determine the association between resistance-conferring mutations with fitness loss in Mycobacterium tuberculosis clinical isolates and HIV co-infection in the Amhara region of Ethiopia.Methods:A laboratory-based cross-sectional study was conducted between September 2022 and June 2023. A line probe assay was performed on 146 culture-positive clinical isolates. Logistic regression analysis was used to measure the strength of the association between the drug-resistance-conferring mutations with fitness loss in M. tuberculosis isolates and tuberculosis/HIV co-infection. A p-value <= 0.05 was considered statistically significant.Results:A total of 11 distinct mutations at four genetic loci among 19 resistant isolates were detected. The frequency of rifampicin, isoniazid, and fluoroquinolones resistance-conferring mutations was identified in 12 (8.2%), 17 (11.6%), and 2 (1.4%) of the isolates, respectively. The most prominent specific mutations were S450L (5/9, 55.6%), S315T (11/11, 100%), C-15T (4/4, 100%), and D94G (1/1, 100%). Double mutations were observed in 10 (52.6%) multidrug-resistant tuberculosis isolates; the most common were detected in both the rpoB and katG genes (8/10, 80.0%). The HIV-co-infected tuberculosis patients carried a higher proportion of low fitness of non-rpoB S450L variants than those tuberculosis patients without HIV (80.0% vs 14.3%) and showed a significant association (cOR = 0.042, 95% CI: 0.002-0.877, p = 0.041), but not with the low fitness of non-katG S315T variants (cOR = 3.00, 95% CI: 0.348-25.870, p = 0.318).Conclusion:This study provides valuable information on the genetic variants with fitness loss associated with HIV co-infection, but requires further whole-genome-based mutation analysis.