Uncovering resistance pathways to first- and last-line antibiotics in Mycobacterium tuberculosis populations

Tuberculosis (TB), caused by Mycobacterium tuberculosis , is one of the world’s deadliest diseases, currently responsible for ~1.5 million deaths per year and rising. Recently, rifampicin-resistant M. tuberculosis was designated as a critical priority pathogen status by the World Health Organization. However, few controlled laboratory studies are available that systematically assess the molecular drivers of antibiotic resistance development in TB. In this study, we paired laboratory-directed evolution and population-level deep-sequencing approaches to map the evolutionary pathways taken by M. tuberculosis to develop resistance to first- and last-line therapies (rifampicin and linezolid) and then characterized de novo resistance mutation occurrence over time. We demonstrated that the majority of M. tuberculosis populations readily acquire mutations in genes commonly found in rifampicin- and linezolid-resistant clinical isolates ( rpoB and rplC ). However, we also identified mutations in six genes, mostly present in subpopulations (17–41%) and not previously linked to rifampicin or linezolid resistance, including four associated with rifampicin resistance ( Rv0052 , ppsD , ppsE and mptC ) and two associated with linezolid resistance ( glpK and echA12 ). The ppsD , glpK and mptC mutations were also identified in published individual sequencing reads of antibiotic-resistant clinical isolates. Further investigation of the identified resistance determinants ppsD / E established that mutations in these genes appear to mediate resistance across multiple species, with an Escherichia coli mutant of the ortholog ( fabF ), representing a shared domain featured in PpsD and PpsE, phenotypically displaying increased antibiotic tolerance to low-level rifampicin. This study highlights the power of using controlled laboratory studies to uncover minority variants in populations of M. tuberculosis . These outcomes will lead to improved diagnosis of antibiotic resistance emergence in TB, to optimize management and treatment of TB infections, and ultimately to minimize patient deaths.