Subpopulations in clinical samples of M. tuberculosis can give rise to rifampicin resistance and shed light on how resistance is acquired

The increasing threat of antimicrobial resistance necessitates accurate and rapid diagnostics. Whole genome sequencing (WGS) has become a key tool for diagnosing Mycobacterium tuberculosis infections, but discrepancies between genotypic and phenotypic drug susceptibility testing can hinder effective treatment and surveillance. This study investigates the impact of including resistant subpopulations and compensatory mutations in WGS-based rifampicin resistance prediction, based on a dataset of 35,538 M. tuberculosis samples. The sensitivity and specificity of resistance classification were evaluated with and without considering subpopulations and compensatory mutations. By lowering the fraction of reads required to identify a resistance-associated variant in a sample from 0.90 to 0.05, the sensitivity significantly increased from 94.3% to 96.4% with no significant impact on specificity. This indicates that a substantial fraction of false negative calls in WGS-based rifampicin resistance prediction can be explained by masked resistant subpopulations. Allowing compensatory mutations to predict resistance further lowered the false negative rate. Finally, we found that samples with resistant subpopulations were less likely to be compensated than homogeneous resistant samples, consistent with the recent evolution of resistance in the samples with subpopulations. Further analysis of these samples revealed distinct clusters with differing amounts of within-sample diversity, pointing towards different mechanisms of resistance acquisition, such as within-host evolution and secondary infections.