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

Area of Science:

• Microbiology
• Genetics
• Evolutionary Biology

Background:

• Antimicrobial resistance (AMR) is a global health crisis.
• Identifying resistance mutations in specific genetic and environmental contexts is challenging.

Purpose of the Study:

• To introduce Quantitative Mutational Scan sequencing (QMS-seq), a high-throughput method for identifying antibiotic resistance mutations.
• To quantitatively assess how genetic background influences the evolution of antibiotic resistance.

Main Methods:

• Exposed four E. coli strains to ciprofloxacin, cycloserine, or nitrofurantoin.
• Utilized QMS-seq to quantitatively compare gene selection and mutation frequency with single base pair resolution.

Main Results:

• Identified 812 resistance mutations, including many in previously unrecognized genes and regulatory regions.
• Demonstrated that multi-drug and antibiotic-specific resistance arise from distinct mutation types.
• Showed that minor genetic variations significantly alter evolutionary paths to resistance.

Conclusions:

• QMS-seq provides a powerful tool for rapid screening of resistance mutations.
• The method elucidates resistance mechanisms and identifies mutational hotspots.
• Understanding genetic background's role is crucial for predicting and combating AMR evolution.