Comprehensive antibiotic-linked mutation assessment by Resistance Mutation Sequencing (RM-seq)

Abstract

Acquired mutations are a major mechanism of bacterial antibiotic resistance generation and dissemination, and can arise during treatment of infections. Early detection of sub-populations of resistant bacteria harbouring defined resistance mutations could prevent inappropriate antibiotic prescription. Here we present RM-seq, a new amplicon-based DNA sequencing workflow based on single molecule barcoding coupled with deep-sequencing that enables the high-throughput characterisation and sensitive detection of resistance mutations from complex mixed populations of bacteria. We show that RM-seq reduces both background sequencing noise and PCR amplification bias and allows highly sensitive identification and accurate quantification of antibiotic resistant sub-populations, with relative allele frequencies as low as 10-4. We applied RM-seq to identify and quantify rifampicin resistance mutations in Staphylococcus aureus using pools of 10,000 in vitro selected clones and identified a large number of previously unknown resistance-associated mutations. Targeted mutagenesis and phenotypic resistance testing was used to validate the technique and demonstrate that RM-seq can be used to link subsets of mutations with clinical resistance breakpoints at high-throughput using large pools of in vitro selected resistant clones. Differential analysis of the abundance of resistance mutations after a selection bottleneck detected antimicrobial cross-resistance and collateral sensitivity-conferring mutations. Using a mouse infection model and human clinical samples, we also demonstrate that RM-seq can be effectively applied in vivo to track complex mixed populations of S. aureus and another major human pathogen, Mycobacterium tuberculosis during infections. RM-seq is a powerful new tool to both detect and functionally characterise mutational antibiotic resistance.