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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM097356-07
Application #
9406188
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Resat, Haluk
Project Start
2011-09-15
Project End
2020-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
7
Fiscal Year
2018
Total Cost
Indirect Cost
Name
New York University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
041968306
City
New York
State
NY
Country
United States
Zip Code
10012
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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
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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:
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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