Abstract: Drug resistance of pathogenic cell populations causes the failure of therapy and represents a major challenge for today's medicine. Early drug resistance has been shown to rely on intricate gene expression patterns across the cell population. However, current techniques of gene expression control (gene deletion, overexpression, knockdown, etc) are aimed to control UonlyU the average gene expression across the cell population, and are therefore insufficient to study how gene expression properties UotherU than the mean affect drug resistance. This is creating a widening gap in knowledge, as we and others have recently demonstrated that gene expression characteristics other than the mean (such as the variance or cellular memory of deviant expression states) are Ujust as importantU as the mean for cell population survival during drug treatment. Here we propose to develop novel, versatile and modular gene constructs to control various expression characteristics of Uany geneU in Uany organismU. Negative feedback-based constructs will permit precise, linear inducer-dependent control of gene expression in every cell of the population. Positive feedback-based constructs will allow us to adjust the cellular memory (rate of stochastic expression fluctuations). We will use these constructs to control diverse expression characteristics of a drug-resistance gene and study how these expression properties affect cell survival during drug treatment and initiate the evolution of genetic drug resistance in a yeast cell population. We will develop multi-scale stochastic models to explain the mechanisms underlying the experimental observations. We can now directly visualize the expression of a drug resistance gene at the single cell level. The UinnovationU consists in bridging molecular- and cell population dynamics, and in connecting stochastic gene expression fluctuations to genetic evolution in experiment and simulation. The results might transform our current understanding of drug resistance and might substantially improve future therapeutic strategies. Public Health Relevance: Drug resistance of pathogenic cell populations causes the failure of therapy and represents a major challenge for today's medicine. We will develop methods to control various expression characteristics of a drug-resistance gene in a non-conventional manner. This will enable the discovery of yet unknown mechanisms underlying the emergence of drug resistance, which might substantially improve future therapeutic strategies for combating microbial infections as well as cancer.

Agency
National Institute of Health (NIH)
Institute
Office of The Director, National Institutes of Health (OD)
Type
NIH Director’s New Innovator Awards (DP2)
Project #
1DP2OD006481-01
Application #
7848665
Study Section
Special Emphasis Panel (ZGM1-NDIA-O (02))
Program Officer
Basavappa, Ravi
Project Start
2009-09-30
Project End
2014-06-30
Budget Start
2009-09-30
Budget End
2014-06-30
Support Year
1
Fiscal Year
2009
Total Cost
$2,310,000
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Biology
Type
Other Domestic Higher Education
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
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Ray, J Christian J; Wickersheim, Michelle L; Jalihal, Ameya P et al. (2016) Cellular Growth Arrest and Persistence from Enzyme Saturation. PLoS Comput Biol 12:e1004825
Diao, Junchen; Charlebois, Daniel A; Nevozhay, Dmitry et al. (2016) Efflux Pump Control Alters Synthetic Gene Circuit Function. ACS Synth Biol 5:619-31
González, Caleb; Ray, Joe Christian J; Manhart, Michael et al. (2015) Stress-response balance drives the evolution of a network module and its host genome. Mol Syst Biol 11:827
Charlebois, Daniel A; Balázsi, Gábor; Kærn, Mads (2014) Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Phys Rev E Stat Nonlin Soft Matter Phys 89:052708
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Lee, Jiyoung; Lee, Jinho; Farquhar, Kevin S et al. (2014) Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc Natl Acad Sci U S A 111:E364-73
Nevozhay, Dmitry; Zal, Tomasz; Balazsi, Gabor (2013) Transferring a synthetic gene circuit from yeast to mammalian cells. Nat Commun 4:1451
Quan, Selwyn; Ray, J Christian J; Kwota, Zakari et al. (2012) Adaptive evolution of the lactose utilization network in experimentally evolved populations of Escherichia coli. PLoS Genet 8:e1002444

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