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Science
18 January 2025

New High-Throughput Method Unveils Antibiotic Resistance Mutations

Researchers identify over 800 unknown mutations, shedding light on bacterial resistance mechanisms.

Antimicrobial resistance (AMR) has become one of the most pressing health crises of our time, leading to approximately five million deaths annually. The challenge lies in identifying the mutations responsible for resistance as they can vary significantly within different genomic backgrounds. Researchers at the University of Manchester have developed a high-throughput technique called Quantitative Mutational Scan sequencing (QMS-seq). This method allows for accurate identification of resistance mutations often overlooked by traditional approaches. By analyzing four strains of Escherichia coli exposed to multiple antibiotics, the team uncovered over 800 previously unidentified mutations linked to antibiotic resistance.

AMR often results from chromosomal mutations, complicing the ability of healthcare professionals to predict which drugs will effectively treat infections. While existing methods are useful, they tend to focus on high-level resistance mutations, resulting in the neglect of smaller-effect mutations. This gap is significant, as these minor mutations often play pivotal roles during the evolution of resistance. The QMS-seq technique offers researchers the prospect of more efficient screening for resistance mutations, allowing them to evaluate various environmental and genomic factors simultaneously. This is particularly important when treatment guidelines are based primarily on genomic data.

The QMS-seq process begins with the accumulation of random mutations under minimal selective pressure. The experimental setup involves growing E. coli strains for 24 hours without antibiotics, permitting the emergence of diverse mutants. The altered strains are then screened against selective conditions provided by agar plates containing the minimum inhibitory concentration (MIC) of chosen antibiotics.

Through this method, researchers identified 812 mutations across 251 genes and 49 regulatory features. Many of these mutations were located within genes and regions not previously associated with resistance, thereby indicating the broad mutational spectrum involved. Notably, researchers discovered significant differences between mutations leading to multi-drug resistance (MDR) and those resulting in antibiotic-specific resistance (ASR). Investigations revealed distinctive patterns of mutations: whereas MDR mutations tended to cluster within specific regions of genes, ASR mutations often spanned the entire gene length, indicating variances between their cellular impacts.

A remarkable finding was the identification of mutations within intergenic regions—those between genes—which accounted for 37% of observed mutations, much higher than the roughly 13% found within E. coli’s overall genome. This suggests regulatory mutations, previously thought to play minimal roles, could have significant contributions to resistance evolution. For example, mutations affecting known persistence genes—responsible for temporary resistance to antibiotics—raise questions on the overlaps between resistance and persistence mechanisms.

The inclusion of environmental E. coli isolates from extensive databases for comparative analysis illustrated the relevance of the revealed mutations. This comprehensive mapping of mutations to resistance phenotypes adds practicality to the QMS-seq findings, demonstrating the technique's strength compared to conventional methods. The incorporation of previously unexplored genes provides fresh avenues for antibiotic research.

Another interesting aspect unveiled by this study is how slight variations among E. coli strains impact their evolutionary trajectories under antibiotic selection. Such genetic differences significantly modify how these bacteria adapt to resistents, linking mutations to distinct adaptive pathways shaped by genetic backgrounds.

QMS-seq marks a substantial advancement toward elucidation of antibiotic resistance mechanisms, providing researchers with an efficient and powerful tool to map and understand the complex evolutionary interactions at play. This could lead to the development of effective prevention strategies against AMR, shifting the response from reactive to proactive as researchers closely monitor the evolution of resistance genes and their pathways.

Moving forward, this innovative sequencing technique could empower scientists to tap new knowledge surrounding the genetics of bacteria, paving ways for developing targeted therapies and improving antibiotic stewardship overall. QMS-seq is not merely about identifying resistance, but rather about paving the way to smarter use of antibiotics to combat the ominous rise of antimicrobial resistance.