Stochastic switches are a broad class of genetic mechanisms that enable single cells to switch certain genes on and off randomly, without responding to their environment. Such switches are prevalent in pathogenic bacteria, where they are often involved in generating diverse surface protein repertoires across the bacterial population, which enables a subset of cells to avoid detection by the immune system. In general, stochastic switches provide a strategy for survival in fluctuating environments, by maintaining subpopulations of cells in pre-adapted states that are prepared for future, possibly unpredictable, environmental stresses. In particular, these strategies are known to be important in antibiotic persistence, a bacterial phenotypic state consisting of slow growth and enhanced tolerance for antibiotics. This grant applies highly sensitive single-cell measurements combined with mathematical models to study three major facets of stochastic switching. We use synthetic stochastic switches to drive antibiotic resistance genes, and by measuring the population dynamics under antibiotic pulses over multi-day experiments, we quantify and model the emergence of resistance, a process of major clinical importance. We use stochastic switches as a model to study the evolutionary pressures that populations experience when transferred from one environment to another through population bottlenecks, a key component of disease transmission. And, we study antibiotic persistence in Escherichia coli, where a continuum of growth states across a bacterial population can confer varying degrees of antibiotic tolerance. By using novel methods for analysis of single cell population data mapped with phenotypic information, we investigate the genetic network that underlies bacterial persistence. The proposed research will substantially advance understanding of the role of stochasticity in bacterial adaptation. Through its emphasis on predictive mathematical modeling, the research will provide the ability to predict the impact of treatment protocols on the emergence of antibiotic resistance and on levels of persistence, and to identify new ways of slowing down or reversing these complex, biomedically relevant processes.

Public Health Relevance

This grant applies a combination of microfluidics, microscopy, synthetic biology, and mathematical modeling to study a broad class of bacterial survival mechanisms known as stochastic switches. The research will reveal how antibiotic resistance emerges in populations from single cells to fixation, how population bottlenecks can affect the basic parameters of adaptation, and how a continuum of growth states within a bacterial population leads to antibiotic persistence phenotypes. The project will yield clinically important findings including the ability to predict how treatment protocols impact the evolution of resistance and persistence, and identify new ways to slow down or reverse these critical processes.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Resat, Haluk
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New York University
Schools of Arts and Sciences
New York
United States
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Moxon, Richard; Kussell, Edo (2017) The impact of bottlenecks on microbial survival, adaptation, and phenotypic switching in host-pathogen interactions. Evolution 71:2803-2816
Nozoe, Takashi; Kussell, Edo; Wakamoto, Yuichi (2017) Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data. PLoS Genet 13:e1006653
Hashimoto, Mikihiro; Nozoe, Takashi; Nakaoka, Hidenori et al. (2016) Noise-driven growth rate gain in clonal cellular populations. Proc Natl Acad Sci U S A 113:3251-6
Lin, Wei-Hsiang; Kussell, Edo (2016) Complex Interplay of Physiology and Selection in the Emergence of Antibiotic Resistance. Curr Biol 26:1486-93
Skanata, Antun; Kussell, Edo (2016) Evolutionary Phase Transitions in Random Environments. Phys Rev Lett 117:038104
Lambert, Guillaume; Kussell, Edo (2015) Quantifying selective pressures driving bacterial evolution using lineage analysis. Phys Rev X 5:
Lin, Wei-Hsiang; Rocco, Mark J; Bertozzi-Villa, Amelia et al. (2015) Populations adapt to fluctuating selection using derived and ancestral allelic diversity. Evolution 69:1448-1460
Lambert, Guillaume; Kussell, Edo; Kussel, Edo (2014) Memory and fitness optimization of bacteria under fluctuating environments. PLoS Genet 10:e1004556
Kussell, Edo (2013) Evolution in microbes. Annu Rev Biophys 42:493-514
Lin, Wei-Hsiang; Kussell, Edo (2012) Evolutionary pressures on simple sequence repeats in prokaryotic coding regions. Nucleic Acids Res 40:2399-413