Specific response pathways, or responsive switches, in bacteria constitute a prevalent survival strategy that involves sensing environmental fluctuations and up-regulating appropriate response genes. Such pathways are implicated in many stress responses, including classical drug resistance such as the tetracycline- resistance operon. Bacteria also possess a diverse class of alternative survival mechanisms, known as stochastic switches, which allow single cells to spontaneously alter their phenotypic state, without sensing and responding to changes in the environment. Stochastic switching mechanisms are prevalent in pathogenic bacteria, maintaining subpopulations of cells in pre-adapted states that are prepared for future environmental stresses, including transfer between different hosts. Experiments at the single-cell level have recently demonstrated that antibiotic persistence is mediated by stochastic switching in several bacteria, including Escherichia coli and Mycobacterium smegmatis. This grant will develop a method to detect stochastic switching behavior of bacteria in many different types of fluctuation conditions. The method relies on a coordinated combination of theory, simulation, and experiments, and is applicable to a large range of bacterial species, including species for which genetic tools do not exist. The experimental approach involves creating a fluctuating condition of interest in a microfluidic device that allows single-cell lineage tracking to be observed continuously over several days. The theoretical approach takes this lineage data, and using simulations and modeling deduces the switching rates that characterize the bacterium's behavior in the given fluctuation. The approach is able to cleanly distinguish between stochastic and responsive switching under diverse fluctuation regimes. The approach will be applied to clinically relevant strains of Escherichia coli, Pseudomonas aeruginosa, and Mycobacterium smegmatis, to reveal unknown stochastic switching modalities. In particular cases, the methodology will be applied to reveal the underlying genetic loci that control the rates of stochastic switching. The goal of the research is to provide a comprehensive picture of the stochastic switching repertoire of these three species, and to develop a general approach for their detection in any species of interest. This will provide a significant advance in ability to detect this important class of bacterial survival mechanisms in diverse species, and to identify genetic loci that constitute key drug targets for combating bacterial persistence.

Public Health Relevance

This grant will develop a method to detect a class of bacterial survival mechanisms, known as stochastic switches, in an automatic way in diverse species of bacteria. Such detection is critical for identifying key drug targets to combat bacterial persistence, yet currently no general detection approach exists. The new method will be applied to reveal stochastic switches in clinically relevant strains of Escherichia coli, Pseudomonas aeruginosa, and Mycobacterium smegmatis.

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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Hagan, Ann A
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New York University
Schools of Arts and Sciences
New York
United States
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Nozoe, Takashi; Kussell, Edo; Wakamoto, Yuichi (2017) Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data. PLoS Genet 13:e1006653
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
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Lambert, Guillaume; Kussell, Edo; Kussel, Edo (2014) Memory and fitness optimization of bacteria under fluctuating environments. PLoS Genet 10:e1004556
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