Cell-to-cell heterogeneity and the emergence of antibiotic resistance

项目摘要/摘要 抗菌素耐药性是一个主要的临床问题，耐药菌株的出现速度 这大大超过了新药的开发速度。传统上，对抗生素耐药性的研究 专注于赋予耐药性的基因变化，例如那些编码阻止药物的机制的基因变化 靶向或修饰药物本身。然而，细菌也可以通过表达瞬时基因来逃避抗生素。 耐药机制，如随机开启或响应的多药物外排泵 抗生素压力。研究表明，这些暂时性抵抗机制与慢性、顽固性 然而，最近的研究揭示了一些例子，在这些例子中，它们在增加 突变倾向。目前尚不清楚抗性基因表达的异质性和时间变异性 导致突变，以及最终对人群抗药性进化的影响- 水平。这一建议通过测量抗性基因随时间的表达直接解决了这一差距。 和记者一起进行突变。这些单细胞水平的研究与种群水平的实验相结合 调节瞬时抗性基因的表达，同时测量抗生素胁迫下的生长。一个 互补建模方法使用随机模型来描述基因表达的异质性， 突变率和生长。我们的中心假设是瞬时抗性表达的异质性 基因可以导致单细胞水平的突变率差异，这两种方式都是通过诱导自发突变来实现的 在没有抗生素的情况下增加内源性应激，并在存在的情况下通过延长生存时间来增加内源性压力 抗生素。我们将使用一种结合单细胞时间推移的定量方法来检验这一假设 显微镜、随机建模、全基因组测序、并行连续培养方法，以及 光遗传控制。该项目围绕三个目标进行组织：(1)测量瞬变的表情历史 在自发突变之前，细胞中的抗性基因。(2)量化单个细胞的死亡时间和 抗生素治疗下的耐药性演变。(3)控制AcrAB外排泵的时间变化 表达，以确定频率依赖的抗性水平和突变率。这项研究具有重要的意义 因为它连接了由于瞬时电阻表达的异质性而产生的动态、单细胞水平的效应 基因对种群水平的出现增加了抗性。识别和消除成核 出现耐药性的积分可以为评估和治疗方法提供依据。

Project Summary / Abstract Antimicrobial drug resistance is a major clinical problem, with resistant strains of bacteria emerging at a rate that dramatically outpaces development of new drugs. Traditionally, studies on antibiotic resistance have focused on genetic changes that confer resistance, such as those encoding mechanisms that block the drug target or modify the drug itself. However, bacteria can also evade antibiotics through expression of transient resistance mechanisms, such as multi-drug efflux pumps that turn on either stochastically or in response to antibiotic stress. Studies have implicated these transient resistance mechanisms in chronic, recalcitrant infections, however, recent research has revealed examples where they also play a critical role in increasing mutation propensity. It is unclear how heterogeneity and temporal variability in expression of resistance genes leads to mutations and what the ultimate implications are for the evolution of drug resistance at the population- level. This proposal addresses this gap directly by measuring expression of resistance genes over time alongside reporters for mutation. These single-cell level studies are joined by population-level experiments that modulate expression of the transient resistance genes while measuring growth under antibiotic stress. A complementary modeling approach uses stochastic models to describe heterogeneity in gene expression, mutation rate, and growth. Our central hypothesis is that heterogeneity in expression of transient resistance genes can lead to single-cell-level differences in mutation rate, both via inducing spontaneous mutations due to elevated endogenous stress in the absence of antibiotics and by extending survival times in the presence of antibiotics. We will test this hypothesis using a quantitative approach that integrates single-cell time-lapse microscopy, stochastic modeling, whole genome sequencing, parallelized continuous culture methods, and optogenetic control. The project is organized around three Aims: (1) Measure expression history of transient resistance genes in cells prior to spontaneous mutation. (2) Quantify time to death of single cells and the evolution of resistance under antibiotic treatment. (3) Control temporal variation of AcrAB efflux pump expression to determine frequency-dependent resistance levels and mutation rate. This research is significant because it links dynamic, single-cell-level effects due to heterogeneity in expression of transient resistance genes to the emergence of population-level increases in resistance. Identifying and eliminating nucleation points for the emergence of drug resistance can inform assessment and treatment approaches.