Whole-genome sequencing as a tool for studying the microevolution of drug-resistant serial Mycobacterium tuberculosis isolates. (December 2021)

• Record Type: Journal Article
• Authors: de Lourdes do Carmo Guimarães Diniz, Jaciara; von Groll, Andrea; Unis, Gisela; Dalla-Costa, Elis Regina; Rosa Rossetti, Maria Lúcia; Vianna, Júlia Silveira; Ramos, Daniela Fernandes; Reis, Ana Júlia; Bartolomeu Halicki, Priscila Cristina; Rheingantz Scaini, João Luis; Castillos de Ibrahim das Neves, Yasmin; Phelan, Jody; Gomes, Ana Rita; Campino, Susana; Machado, Karina dos Santos; Werhli, Adriano Velasque; Pain, Arnab; Clark, Taane Gregory; Perdigão, João; Viveiros, Miguel; Portugal, Isabel; Almeida Silva, Pedro Eduardo
• Is Part Of: Tuberculosis. Volume 131(2021)
• Publication Date: 2021-12
• Subjects: Catalase assay -- Exogenous reinfection -- Microevolution -- Resistance -- Whole-genome sequencing; 616.995
• DOI: 10.1016/j.tube.2021.102137
• Languages: English
• ISSNs: 1472-9792

Abstract

Abstract: Treatment of drug-resistant tuberculosis requires extended use of more toxic and less effective drugs and may result in retreatment cases due to failure, abandonment or disease recurrence. It is therefore important to understand the evolutionary process of drug resistance in Mycobacterium tuberculosis . We here in describe the microevolution of drug resistance in serial isolates from six previously treated patients. Drug resistance was initially investigated through phenotypic methods, followed by genotypic approaches. The use of whole-genome sequencing allowed the identification of mutations in the katG, rpsL and rpoB genes associated with drug resistance, including the detection of rare mutations in katG and mixed populations of strains. Molecular docking simulation studies of the impact of observed mutations on isoniazid binding were also performed. Whole-genome sequencing detected 266 single nucleotide polymorphisms between two isolates obtained from one patient, suggesting a case of exogenous reinfection. In conclusion, sequencing technologies can detect rare mutations related to drug resistance, identify subpopulations of resistant strains, and identify diverse populations of strains due to exogenous reinfection, thus improving tuberculosis control by guiding early implementation of appropriate clinical and therapeutic interventions.