Understanding the pathway to multidrug resistant bacterial pathogens

由革兰氏阴性细菌引起的感染是现代医疗保健中最重大的挑战之一。抗生素有效性的下降和新的耐药机制的出现加剧了这个问题。缓解这一不断升级的问题取决于对多药耐药(MDR)途径上发生的基因和表型变化的详细了解。广泛的下一代测序现在是病原体监测的常规组成部分，并已证实在全球大流行克隆中存在抗菌素耐药基因和多药耐药质粒。然而，人们对导致MDR出现的进化“垫脚石”知之甚少。了解这些有助于识别早期预警迹象和公共卫生干预措施。我们将研究导致细菌多药耐药的潜在进化机制。与其他研究不同的是，我们将确定支持MDR获得的一系列进化事件，这些研究侧重于众所周知的耐药基因的存在--例如那些与超广谱β-内酰胺酶(ESBL)和碳青霉烯酶相关的基因。具体地说，我们将研究：(I)赋予低水平耐药性的基因-MDR的垫脚石；(Ii)增强可能使某些菌株易于获得质粒的基因；(Iii)适应维持MDR质粒的遗传变化；(Iv)不同菌株是否具有趋同的进化步骤；(V)MDR出现的重复性。为了解决这些问题，我们将采取跨学科的方法，分析非常大的多样化的大肠杆菌分离株集合。将比较基因组学和功能基因组学方法与新的生物信息学方法和体外实验进化分析相结合，我们将表征支撑大流行耐多药大肠杆菌出现和传播的进化事件和基因变化-尿路感染和菌血症的最常见全球原因。首先，我们将确定与MDR相关的基因变化。其次，我们使用全基因组适合度分析来研究增强或适应AMR表型(耐受性、持久性等)的基因和网络。最后，我们将使用一个实验进化框架来研究进化的重复性，以测试临床分离株和参考菌株在多种遗传背景下进化的重复性。这将识别导致低水平耐药性的易感因素和增强突变，并促进多药耐药质粒的获取和维护。该项目将通过确定导致一些最常见和最严重的系统性细菌感染的病原体出现耐药性的基因变化，增加对MDR这一关键问题的认识。由于我们将表征当代(现实世界)感染分离株的基因变化，以及跨物种多样性，我们可以相信，我们的发现将与正在进行的抗击新出现的MDR细菌的斗争相关。特别是，帮助为监测、暴发风险模型和有针对性的干预设计新的实验室测试。

Infections caused by gram negative bacteria are among the most significant challenges in modern health care. The problem is exacerbated by a decline in antibiotic effectiveness and the emergence of new mechanisms of resistance. Mitigating this escalating problem depends upon detailed understanding of the genotypic and phenotypic changes that occur on the pathway to multidrug resistance (MDR). Widespread next-generation sequencing is now a routine component of pathogen surveillance and has confirmed the presence of antimicrobial resistance genes and MDR plasmids in global pandemic clones. However, little is known about the evolutionary 'stepping stones' that lead to the emergence of MDR. Understanding these could help with identifying early warning signs and public health interventions. We will investigate the underlying evolutionary mechanisms that give rise to MDR in bacteria. Unlike other studies, that focus on the presence of well known resistance genes - such as those linked to Extended-Spectrum Beta-Lactamase (ESBL) and Carbapenemase enzymes, we will identify the series of evolutionary events that underpin MDR acquisition. Specifically, we will investigate: (i) genes that confer low level resistance - the stepping stones to MDR; (ii) potentiating genes that may predispose certain strains to plasmid acquisition; (iii) accommodating genetic change that maintains MDR plasmids; (iv) if divergent strains have convergent evolutionary steps towards MDR; (v) the repeatability of MDR emergence. To address these question areas we will take an interdisciplinary approach, analysing very large diverse E. coli isolate collections. Combining comparative and functional genomics approaches with new bioinformatics methods and in vitro experimental evolution assays, we will characterize the evolutionary events and genetic changes that underpin the emergence and spread of pandemic MDR E. coli - the most common global cause of urinary tract infections and bacteraemia. First, we will identify genetic changes that covary with MDR. Second, we use genome-wide fitness profiling to investigate the genes and networks that potentiate or accommodate AMR phenotypes (tolerance, persistence etc.). Finally, we will investigate the reproducibility of evolution using an experimental evolution framework to test the repeatability of evolution in multiple genetic backgrounds of both clinical isolates and reference strains. This will identify predisposing factors and potentiating mutations that confer low level resistance and facilitating MDR plasmid acquisition and maintenance. This project will increase knowledge of the critical problem of MDR by identifying genetic changes underlying the emergence of resistance in pathogens responsible for some of the most common and serious systemic bacterial infections. Because we will characterize genetic changes in isolates from contemporary (real-world) infections, as well as across species diversity, we can be confident that our findings will be relevant to the ongoing battle against emergent MDR bacteria. In particular, helping design new laboratory testing for surveillance, outbreak risk models and targeted interventions.