Analysis of drug resistance using experimental evolution

Abstract

The emergence of drug-resistant bacteria is a growing concern for global public health. One possible strategy to deal with the problem of resistant bacteria is to understand the dynamics of adaptive evolution under antibiotics and then develop methods to suppress such adaptive evolution. For this purpose, we performed experimental evolution of Escherichia coli under various antibiotics and obtained resistant strains. The phenotypic changes in these resistant strains were quantified by transcriptome analysis, and the genomic changes were analyzed using next-generation sequencers. The results demonstrated that the resistance could be quantitatively predicted by changes in the expression of a small number of genes. Several candidate mutations contributing to the resistance were identified, while phenotype-genotype mapping was suggested to be complex and included various mutations that caused similar phenotypic changes. We also found that combinatorial use of appropriate pairs of antibiotics can suppress the emergence of resistant strains. In the presentation, I discussed how the integration of multi-omics data in experimentally obtained resistant strains enables us to develop methods to suppress the adaptive evolution of antibiotic resistance.