High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations

Matthew J. Jago, Jake K. Soley, Stepan Denisov, Calum J. Walsh, Danna R. Gifford, Benjamin P. Howden and Mato Lagator ()
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Matthew J. Jago: University of Manchester
Jake K. Soley: University of Manchester
Stepan Denisov: University of Manchester
Calum J. Walsh: University of Melbourne, at the Peter Doherty Institute for Infection and Immunity
Danna R. Gifford: University of Manchester
Benjamin P. Howden: University of Melbourne, at the Peter Doherty Institute for Infection and Immunity
Mato Lagator: University of Manchester

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract A fundamental obstacle to tackling the antimicrobial resistance crisis is identifying mutations that lead to resistance in a given genomic background and environment. We present a high-throughput technique – Quantitative Mutational Scan sequencing (QMS-seq) – that enables quantitative comparison of which genes are under antibiotic selection and captures how genetic background influences resistance evolution. We compare four E. coli strains exposed to ciprofloxacin, cycloserine, or nitrofurantoin and identify 812 resistance mutations, many in genes and regulatory regions not previously associated with resistance. We find that multi-drug and antibiotic-specific resistance are acquired through categorically different types of mutations, and that minor genotypic differences significantly influence evolutionary routes to resistance. By quantifying mutation frequency with single base pair resolution, QMS-seq informs about the underlying mechanisms of resistance and identifies mutational hotspots within genes. Our method provides a way to rapidly screen for resistance mutations while assessing the impact of multiple confounding factors.

Date: 2025
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DOI: 10.1038/s41467-025-56050-2

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