Feedback and Noise in a Multiple Antibiotic Resistance Circuit

产品说明： 抗生素耐药性是临床环境中日益严重的问题，并且对多种药物具有耐药性的细菌菌株以惊人的频率出现。虽然遗传变化传统上被研究为耐药性的来源，但细菌也可以通过短暂的、嘈杂的耐药机制表达来逃避抗生素。迄今为止，关于瞬时耐药性的研究集中在全或无耐受机制上，例如细菌持久性，其中细胞在药物耐受和药物敏感状态之间切换。在理解能够在人群中产生连续抗性水平的机制方面存在根本性差距。这是一个重要的问题，因为细菌可以使用这种策略作为垫脚石，以实现更高的，永久的耐药性水平。为了解决这个问题，我们将研究一种重要的调节蛋白，即多抗生素耐药激活因子（MarA），它控制着临床相关病原体中许多抗生素耐药基因的表达。我们的初步数据表明，在单细胞中，MarA的表达是嘈杂的，在群体中产生连续的表达水平。这些发现引发了一个问题，即这种噪音是否会导致耐药性的多样性，从而使细菌种群能够抵御突然出现的抗生素。我们的中心假设是，控制MarA的调节电路结构放大了噪音，导致耐药基因表达的变异性，并允许一部分细胞在抗生素治疗后存活。我们将测试这一假设使用的方法，集成了定量延时显微镜和随机数学建模，以确定机制和功能的噪声在马拉。该项目围绕三个目标：（1）通过比较调控网络和替代工程网络，确定MarA表型变异的遗传基础。(2)量化MarA中的噪音如何传播到其调节的多种下游抗生素耐药基因。(3)确定MarA的变异性如何影响时变抗生素治疗下的生存率。这项综合性研究意义重大，因为它有望提出对抗短暂抗生素耐药性的治疗策略，并将揭示有关短暂耐药性发展和持续时期的重要动态信息。此外，它还研究了一种新的机制，用于引入抗生素耐药基因表达的多样性，这可能与其他产生短暂抗生素耐药性的机制有关。

DESCRIPTION: Antibiotic resistance is an increasing problem in clinical settings and strains of bacteria that are resistant to multiple drugs are appearing with alarming frequency. While genetic changes have traditionally been studied as the source of drug resistance, bacteria can also evade antibiotics through transient, noisy expression of resistance mechanisms. Studies on transient resistance to date have focused on all-or-none tolerance mechanisms such as bacterial persistence where cells switch between a drug-tolerant and a drug-sensitive state. There is a fundamental gap in understanding of mechanisms that can generate a continuum of resistance levels within a population. This is a significant problem because bacteria can use such a strategy as a stepping stone to achieve higher, permanent levels of drug resistance. To address this, we will study an important regulatory protein, the multiple antibiotic resistance activator (MarA), which controls expression of many antibiotic resistance genes in clinically relevant pathogens. Our preliminary data show that expression of MarA is noisy in single cells, generating a continuum of expression levels within a population. These findings provoke the question of whether this noise leads to diversity in drug resistance, allowing populations of bacteria to hedge against the sudden appearance of an antibiotic. Our central hypothesis is that the regulatory circuit architecture controlling MarA amplifies noise, leading to variability in expression of resistance genes, and allowing a subset of cells to survive antibiotic treatment. We will test this hypothesis using an approach that integrates quantitative time-lapse microscopy and stochastic mathematical modeling to determine the mechanism and function of noise in MarA. The project is focused around three Aims: (1) Identify the genetic basis for phenotypic variability in MarA by comparing the regulatory network to alternative engineered networks. (2) Quantify how noise in MarA propagates to the diverse downstream antibiotic resistance genes it regulates. (3) Determine how variability in MarA impacts survival under time-varying antibiotic treatment. This integrative research is significant because it is expected to suggest treatment strategies for combating transient antibiotic resistance and will reveal important dynamic information about the period over which transient resistance develops and persists. Furthermore, it examines a novel mechanism for introducing diversity in antibiotic resistance gene expression, which is likely to be generally relevant to other mechanisms that generate transient antibiotic resistance.