ArticleNature communications2025
High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- Resistance potentiators: Evolutionary catalysts of antibiotic resistance.PLoS biology · 2026Article
- Beyond resistance and tolerance: rethinking evolutionary responses to antibiotics from the perspective of individual bacterial cells.Nature communications · 2026Review
- Epidemiology, Clinical Microbiology and Antimicrobial Therapy: A Shared Effort Against Infectious Diseases.Antibiotics (Basel, Switzerland) · 2026Article
- Antimutator and Mutational Spectrum Effects Can Combine to Reduce Evolutionary Potential in Escherichia coli ΔnudJ.Molecular biology and evolution · 2025Article
- Advancing antibiotic discovery with bacterial cytological profiling: a high-throughput solution to antimicrobial resistance.Frontiers in microbiology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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