432303 Phenotypic and Gene Expression Variability Underlies Adaptive Resistance in Heterogeneous Bacterial Populations

Monday, November 9, 2015: 12:30 PM
155A (Salt Palace Convention Center)
Keesha Erickson, Peter Otoupal and Anushree Chatterjee, Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO

The rapid rise of drug-resistant superbugs and the declining antibiotic pipeline are serious challenges to global health. The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve tolerance to antibiotics and other environmental toxins. Bacteria exhibit extensive intra- and inter- population heterogeneity, which provides an advantage in adaptation to diverse environmental pressures, but renders identification of underlying mechanisms of adaption challenging. Here, we used RNA-Sequencing to locate key regulators of adaptive resistance by investigating transcriptomes of bacterial populations upon prolonged exposure to diverse chemical toxins and comparing to gene expression profiles in unadapted populations. Our lab-evolution experiments show that initially antibiotic-sensitive isogenic bacteria adapted to the same toxin exhibit rapid population-level phenotypic divergence. To investigate the mechanisms underlying heterogeneity across populations, we evolved clinically derived multi-drug pathogens and lab strains of Escherichia coli to a range of dissimilar toxins, including antibiotics and biofuels. We find a set of genes that are differentially expressed across multiple adaptation conditions. Further, we observe the presence of significant shifts in gene expression variability across unadapted and adapted populations. A more focused quantification of mRNA expression of stress-response genes corresponding to the multiple antibiotic resistance (mar) regulon, the general stress response, and the SOS response, all known to regulate adaptation, confirms the underlying gene expression heterogeneity. Hierarchical clustering and principal component analysis reveal that adapted populations exhibit variability in transcript levels that is distinct from unadapted populations; certain stress response genes are observed to increase or decrease expression variability with respect to wild type, suggesting the presence of dissimilar regulation of gene expression during adaptation. By propagating strains with corresponding stress response genes either knocked out or perturbed via CRISPR-interference mechanism, we identify unique relationships between gene expression variability and impact on adaptation that differentiate adapted or unadapted populations. This study provides insight into the dynamic nature of the bacterial adaptive transcriptome and evidence that gene expression variability can be used to identify bacterial populations undergoing adaptive resistance. The identification of genes contributing to adaptive resistance may be an avenue towards the development of antimicrobial agents that hinder the onset of drug-resistance.

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