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

OBJECTIVES

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 investigated the impact of resistant subpopulations and compensatory mutations in WGS-based rifampicin resistance prediction.

METHODS

Based on a dataset of 35 538 clinical M. tuberculosis samples, the sensitivity and specificity of resistance classification were evaluated with and without considering subpopulations and compensatory mutations.

RESULTS

By lowering the fraction of reads required to identify a resistance-associated variant in a sample from 0.90 to 0.05, the sensitivity increased significantly from 94.3% to 96.4% without a significant impact on specificity. 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. 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.

CONCLUSIONS

Our results indicate that a substantial fraction of false negative calls in WGS-based rifampicin resistance prediction can be explained by masked resistant subpopulations. The genetic diversity within the heterogeneous samples is consistent with at least 28% of the rifampicin resistance arising from secondary infections.