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

• Published in: Nature communications 2025-01, Vol.16 (1), p.780-13, Article 780
• Main Authors: Jago, Matthew J., Soley, Jake K., Denisov, Stepan, Walsh, Calum J., Gifford, Danna R., Howden, Benjamin P., Lagator, Mato
• Format: Article
• Language: English
• Subjects: 49/23; 631/1647/2217/2218; 631/181/2475; 631/326/22/1434; Anti-Bacterial Agents - pharmacology; Antibiotic resistance; Antibiotics; Antimicrobial resistance; Ciprofloxacin; Ciprofloxacin - pharmacology; Cycloserine; Cycloserine - pharmacology; Drug Resistance, Bacterial - genetics; E coli; Escherichia coli - drug effects; Escherichia coli - genetics; Evolutionary genetics; Gene frequency; Genes; High-Throughput Nucleotide Sequencing - methods; Humanities and Social Sciences; Impact resistance; Microbial Sensitivity Tests; multidisciplinary; Mutation; Mutation hot spots; Nitrofurantoin; Nitrofurantoin - pharmacology; Regulatory sequences; Resistance factors; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-025-56050-2

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. Resistance mutations are challenging to characterize because their effects are highly context dependent. Here, authors present a quantitative mutant screening technique that deconstructs how factors like antibiotic mechanism and genetic background interact to govern resistance evolution.