Antimicrobial resistance (AMR) is a global health crisis and contributes to five million deaths per year. The ability to predict susceptibility data from genomic data is becoming vital to antimicrobial stewardship. One of the obstacles to tacking this problem is finding mutations responsible for resistance. Researchers from the University of Manchester (UK) reveal a mutant screening technique called Quantitative Mutational Scan Sequencing, or QMS-seq. This metagenomic sequencing technique allows investigators to see which genes are under antibiotic selection and captures how genetic background influences the evolution of resistance. They compared rifampicin-resistant E. coli strains exposed to three secondary antibiotics: Ciprofloxacin, Cycloserine, and Nitrofurantoin. These three secondary antibiotics were chosen because they target completely different cellular pathways: Ciprofloxacin targets DNA replication via DNA topoisomerase II (gyrase), Cycloserine targets cell wall biogenesis via disruption via peptidoglycan production, and Nitrofurantoin targets protein synthesis.
Starting with a genetically homogenous population growing in rich media without antibiotics, the heterogenous population then accumulates random mutations after 24 hours, with most variants having a single mutation. The population is then spread across selective agar plates containing the MIC (minimum inhibitory concentration) of an antibiotic. The MIC is defined as the lowest concentration of a given antibiotic that prevents colonies from forming on an agar plate. They then mix the colonies together and sequence collectively using whole genome sequencing.
Researchers identified 812 resistance mutations, across 251 genes, and 49 regulatory features. Surprisingly, nearly half of these mutations were found in genes and regulatory regions not normally associated with resistance. Many of the resistance genes with known functions were involved with membrane transport, translation, or cellular respiration. They found that multi-drug resistance (MDR) conferring resistance to all 3 antibiotics, in contrast to antibiotic-specific resistance (ASR) is acquired via different categories of mutations and their experimental design can tell them apart. They can then review which mutation grants resistance to a single (or multiple) antibiotics and also see how having resistance to one antibiotic could influence how the bacterium evolves to have resistance to another one. This technique can provide valuable information about resistance mechanisms and allows for rapid screening of resistance mutations. Similar studies in the field have focused on mutations that change the amino acid composition but growing evidence such as these findings suggests that mutations impacting gene expression can be significant.
This technique can provide insights into microbe biology, identify resistance mechanisms across an entire genome and allow researchers to untangle how the evolution of resistance depends on the strain genotype. It can also provide data to inform antimicrobial stewardship and microbial evolution studies.
Reference
Jago MG et al (2025) High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations. Nat. Commun. 16:780 Link.