Connecting the Selection of Noisy Gene Expression Deviants to Genetic Evolution

DESCRIPTION (Provided by the applicant) 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.