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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM097356-03
Application #
8538463
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Lyster, Peter
Project Start
2011-09-15
Project End
2016-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
3
Fiscal Year
2013
Total Cost
$221,454
Indirect Cost
$60,787
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
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