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Center for Nonlinear Studies |
Antimicrobial resistance is a worldwide healthcare issue of increasing concern, as pathogens have evolved immunity to all currently available treatments. By decoding the intrinsic mechanisms that bacteria use to gain tolerance, it may be possible to avert the continued emergence of new resistances. Landmark bacterial evolution studies have eloquently uncovered trends in genetic diversity during adaptation; incremental genomic changes undoubtedly promote heritable resistance, and studies that focus on the genome elucidate what specific mutations enable resistance to a stress. Contrastingly, less is known about how resistance is achieved – namely, the regulatory responses promoting heterogeneity and underlying stepwise increases in resistance. Emerging evidence suggests that cellular phenotype has a strong non-genetic basis, influenced by shifts in gene expression, which provide immense flexibility when cells are exposed to a stressful environment. Here, we probe the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotic and biofuels. First, we examine gene expression trends in a focused set of stress-response genes. We find shifts in gene expression variability that are sufficient to distinguish unadapted and adapted populations. We present a mathematical model that examines the basis for the observed variability shifts. Next, we analyze transcriptome data for both traditional differential gene expression as well as more unconventional analysis focusing on gene expression variability. Our results highlight the occurrence of extensive gene expression heterogeneity across adapted populations, including shifts in both gene expression level and gene expression variability, then filters that information to arrive at a small set of genes with conserved signatures across diverse adapted bacterial populations. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using CRISPR-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes greater heterogeneity in MIC and an increased degree of antibiotic tolerance, with no observed changes in mutation rate, which suggests that these genes may provide a useful set of targets for novel therapeutics that hinder adaptation mechanisms. Overall, this work provides evidence for a complex non-genetic response, encompassing shifts in gene expression and gene expression variability, which contributes to adaptive resistance.
Host: William S. Hlavacek (T-6)