Connecting the selection of noisy gene expression deviants to genetic evolution

描述(由申请人提供) 摘要：致病细胞群的耐药性是导致治疗失败的主要原因，也是当今医学面临的主要挑战。早期的耐药性已被证明依赖于整个细胞群体中复杂的基因表达模式。然而，目前的基因表达调控技术(基因缺失、过表达、敲除等)的目的只是为了控制UlyU在整个细胞群体中的平均基因表达，因此不足以研究基因表达特性UlyU是如何影响耐药性的。这在知识上造成了越来越大的差距，因为我们和其他人最近证明，在药物治疗期间，除平均值以外的基因表达特征(如异常表达状态的方差或细胞记忆)对细胞群体存活的重要性与平均值一样重要。在这里，我们建议开发新的、通用的和模块化的基因结构来控制Uany GeneU在Uany OrganmU中的各种表达特征。基于负反馈的构建将允许对种群中每个细胞中的基因表达进行精确的、线性诱导依赖的控制。基于正反馈的结构将允许我们调整细胞记忆(随机表达波动率)。我们将使用这些构建体来控制耐药基因的不同表达特性，并研究这些表达特性如何在药物治疗期间影响细胞存活，并启动酵母细胞群体中遗传耐药的进化。我们将开发多尺度随机模型来解释实验观测背后的机制。我们现在可以直接在单细胞水平上可视化耐药基因的表达。UInnovationU包括在分子和细胞群体动力学之间架起桥梁，并在实验和模拟中将随机基因表达波动与遗传进化联系起来。这一结果可能会改变我们目前对耐药性的理解，并可能极大地改进未来的治疗策略。 公共卫生相关性：致病细胞群体的耐药性导致治疗失败，是当今医学的主要挑战。我们将开发方法，以非常规方式控制耐药基因的各种表达特征。这将使人们能够发现抗药性出现的未知机制，这可能会极大地改进未来抗击微生物感染和癌症的治疗策略。

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.