bioRxivMicrobiologyPreprint
Antimicrobial resistance in a warmer world: selection and transfer of kanamycin resistance is temperature dependent in E. coli.
Zoltán ErdősiD, Tatiana DimitriuiD, Ben RaymondiD, Angus G. J. BucklingiD, Daniel PadfieldiD
Abstract
Climate warming consistently correlates with increased antimicrobial resistance (AMR). Understanding the biological mechanisms underpinning this relationship is important for both prediction and mitigation of the future burden of antibiotic resistance in a warmer world, especially given AMR can spread through both selection and horizontal gene transfer. Here, we experimentally measure how temperature alters susceptibility to kanamycin, selection and spread of resistance in Escherichia coli MG1655 carrying the natural plasmids R1 and RP4. We predict that susceptibility should be lowest - and plasmid transfer highest - at maximal growth rate. Thermal performance of bacterial growth peaked at 40{degrees}C and we detected a small plasmid cost - for RP4, but not R1 - in the absence of kanamycin. The thermal performance of plasmid transfer did not mirror that of bacterial growth, instead, R1 transfer peaked at 35{degrees}C with a much narrower thermal tolerance. Susceptibility to kanamycin decreased with increasing growth rate until 31{degrees}C, before increasing again as temperatures approached and exceeded optimal growth temperature. Increasing susceptibility coupled with high growth rates meant that selection was strongest at the highest measured temperature, but the shape of the relationship changed with kanamycin concentration. In mixed populations of resistant and susceptible bacteria, we found that AMR spread most where selection was strongest, irrespective of plasmid transfer. Our findings suggest increased AMR at higher temperatures is predominantly driven by higher growth rates and increased susceptibility, resulting in stronger selection. Our study provides a framework to uncover generalities across antibiotic classes, strains, species, and plasmids, laying the foundation for improved prediction of AMR selection in the environment.
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