Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges

Abstract

Tuberculosis (TB) is a global infectious threat that is intensified by an increasing incidence of highly drug-resistant disease. Whole-genome sequencing (WGS) studies of Mycobacterium tuberculosis, the causative agent of TB, have greatly increased our understanding of this pathogen. Since the first M. tuberculosis genome was published in 1998, WGS has provided a more complete account of the genomic features that cause resistance in populations of M. tuberculosis, has helped to fill gaps in our knowledge of how both classical and new antitubercular drugs work, and has identified specific mutations that allow M. tuberculosis to escape the effects of these drugs. WGS studies have also revealed how resistance evolves both within an individual patient and within patient populations, including the important roles of de novo acquisition of resistance and clonal spread. These findings have informed decisions about which drug-resistance mutations should be included on extended diagnostic panels. From its origins as a basic science technique, WGS of M. tuberculosis is becoming part of the modern clinical microbiology laboratory, promising rapid and improved detection of drug resistance, and detailed and real-time epidemiology of TB outbreaks. We review the successes and highlight the challenges that remain in applying WGS to improve the control of drug-resistant TB through monitoring its evolution and spread, and to inform more rapid and effective diagnostic and therapeutic strategies.

Fig. 1

Challenges to predicting drug resistance accurately from clinical specimens using current culture-dependent molecular diagnostics. The left panel depicts an expectorated sputum sample, which may not accurately represent the microbiologic diversity within the source patient. Culturing this sample (center panel) introduces further biases between faster- and slower-growing strains, such that faster-growing strains are over-represented within the cultured sample. Genomic DNA that is isolated and sequenced is input to computer algorithms that determine the genomic content, including the identification of drug-resistance mutations. However, disambiguating samples that contain mixed strains or detecting heteroresistance remains a computational challenge. The left panel was adapted from Ford et al. [170], with permission from Elsevier