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

项目总结/摘要 抗微生物药物耐药性是一个主要的临床问题，耐药菌株的出现率 大大超过了新药的开发。传统上，对抗生素耐药性的研究 重点关注赋予耐药性的遗传变化，例如那些阻止药物的编码机制 靶向或修饰药物本身。然而，细菌也可以通过表达瞬时表达来逃避抗生素。 耐药机制，如多药外排泵，打开无论是stochemical或响应 抗生素应激研究表明，这些短暂的耐药机制在慢性、高脂血症中起着重要作用。 然而，最近的研究揭示了它们在增加感染方面也发挥关键作用的例子。 突变倾向目前还不清楚抗性基因表达的异质性和时间变异性如何影响耐药基因的表达。 导致突变，以及最终对人群耐药性进化的影响- 水平这项提案通过测量抗性基因随时间的表达直接解决了这一差距 和记者一起研究突变这些单细胞水平的研究与群体水平的实验相结合， 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 互补建模方法使用随机模型来描述基因表达的异质性， 突变率和生长率我们的中心假设是，瞬时耐药表达的异质性 基因可以导致单细胞水平的突变率差异，无论是通过诱导自发突变， 在不存在抗生素的情况下增加内源性应激，并在存在抗生素的情况下延长存活时间， 抗生素我们将测试这一假设使用定量的方法，整合单细胞时间推移， 显微镜，随机建模，全基因组测序，并行连续培养方法， 光遗传控制该项目围绕三个目标组织：（1）测量瞬态的表达历史 在自发突变之前细胞中的抗性基因。(2)定量单个细胞的死亡时间， 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性抗性水平和突变率。这项研究意义重大 因为它将动态的、单细胞水平的效应与瞬时抗性表达的异质性联系起来， 基因对群体水平抗性的出现增加。识别和消除成核 抗药性出现的时间点可以为评估和治疗方法提供信息。

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.