bioRxivMicrobiologyPreprint
Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis.
Abstract
Mutations in Mycobacterium tuberculosis associated with resistance to antibiotics often come with a fitness cost for the bacteria. Resistance to the first-line drug rifampicin leads to lower competitive fitness of M. tuberculosis populations when compared to susceptible populations. This fitness cost, introduced by resistance mutations in the RNA polymerase, can be alleviated by compensatory mutations (CMs) in other regions of the affected protein. CMs are of particular interest clinically since they could lock in resistance mutations, encouraging the spread of resistant strains worldwide. Here, we report the statistical inference of a comprehensive set of CMs in the RNA polymerase of M. tuberculosis, using over 70,000 M. tuberculosis genomes that were collated as part of the CRyPTIC project. The unprecedented size of this data set gave the statistical tests to investigate the association of putative CMs with resistance-conferring mutations much more power. Overall, we propose 51 high-confidence CMs by means of statistical association testing and suggest hypotheses for how they exert their compensatory mechanism by mapping them onto the protein structure. In addition, we were able to show an association of CMs with higher in vitro growth densities, and hence presumably with higher fitness, in resistant samples in the more virulent M. tuberculosis Lineages 2 and 3. In Lineage 2, our results even suggest the association of CM presence with significantly higher in vitro growth than for wild-type samples, although this association is confounded with lineage and sub-lineage affiliation. Our findings emphasise the integral role of CMs and lineage affiliation in resistance spread and increases the urgency for antibiotic stewardship, which implies accurate, cheap and widely accessible diagnostics for M. tuberculosis infections to not only improve patient outcomes but also to prevent the spread of resistant strains.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
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