Evolutionary paths toward intrinsic antibiotic resistance

描述(申请人提供)：抗生素的广泛使用或误用刺激了进化适应，使细菌能够在我们许多最有效的药物中存活。虽然现有的抗菌剂正在失去作用，但近年来新药的开发急剧下降。如果这种趋势继续下去，我们对抗耐药微生物的药物将很快耗尽。由抗生素耐药感染引起的疾病和死亡的发生率不断上升，再加上社会必须为此付出的代价， 反映出我们迫切需要新的抗生素来阻断或规避耐药机制，或攻击新的目标。我们需要更好地了解抗药性形成的不同方式，以便我们能够开发新的方法来识别和消除抗药性。这里提出的研究将提供抗性进化的更广泛的图景，并剖析其发展的遗传机制。我们计划进行实验室进化实验，使大肠杆菌适应来自三个常用类别的抗生素，并使用高通量�测序和全基因组连锁分析来识别它们产生耐药性的机制。一旦识别和验证了影响基因座，我们将使用全球上位性分析和转录组分析，将它们置于特定信号和调控途径的更广泛的网络环境中。我们将对这些突变进行分类，以帮助简化和理清抗生素-�细菌动力学的规模和复杂性。以前的研究表明，临床上的抗生素耐药性水平可能是通过突变的顺序积累形成的，个体影响很小。了解不同突变发生的顺序将为我们提供信息，这些信息可能有助于开发更好的诊断方法，并延缓耐药性的发展。在整个研究过程中，将特别关注面对不断增加的药物剂量时的耐药性轨迹，以及在其他条件下微生物的相关适应成本。在没有治疗的情况下，微生物抵抗抗生素的能力往往会对它们的健康产生负面影响；然而，导致耐药性的突变往往很快就会出现在基因组其他地方的额外突变中，以弥补这些成本。了解不同突变发生的顺序将为我们提供关于菌株如何变得越来越耐药的信息。此外，由于适应成本决定了针对耐药细菌进行选择的强度，对这些成本的分析可能会为新的治疗方案提供依据。这项研究的目的是发现对抗耐药性的新靶点，发现与特定抗生素协同作用的新途径，并更广泛地加强和丰富为下一代新疗法、药物发现和该领域的诊断奠定基础的基本原理。

DESCRIPTION (provided by applicant): Widespread use or misuse of antibiotics has spurred evolutionarily adaptations that enable bacteria to survive many of our most powerful drugs. While existing antimicrobials are losing their effect, there has been in recent years a steep decline in the development of new drugs. If this trend continues, the drugs we have to combat resistant microorganisms will soon be depleted. The growing incidence of illness and death caused by antibiotic resistant infections, coupled with the cost that society has to pay for them, reflect our urgent need for new antibiotics that either block or circumvent resistance mechanisms, or attack new targets. We need a better understanding of the different ways in which resistance develops so that we can develop new methods to identify and counteract it. The research proposed here will provide a broader picture of the evolution of resistance and dissect the genetic mechanisms of its development. We plan to conduct laboratory evolution experiments in which we adapt E. coli to antibiotics from three commonly used classes and discern the mechanisms by which they develop resistance using high-�throughput sequencing and whole genome linkage analysis. Once contributing loci are identified and validated, we will use global epistasis assays and transcriptome analysis to place them in the broader network context of specific signaling and regulatory pathways. We will classify these mutations in order to help simplify and untangle the magnitude and complexity of antibiotic-�bacterial dynamics. Previous research suggests that clinical levels of antibiotic resistance may develop through the sequential accumulation of mutations of small individual effect. Understanding the order in which different mutations occur will give us information that may be useful for developing better diagnostics and in delaying the development of resistance. Throughout this research, special attention will be paid to the trajectory of resistance in the face of increasing drug dosage and also to associated fitness costs to the microorganism under other conditions. The ability of microbes to resist antibiotics often negatively impacts their fitness in the absence of treatment; however, mutations that confer resistance are often quickly followed by additional mutations elsewhere in the genome that compensate for these costs. Understanding the order in which different mutations occur will give us information about how strains become increasingly resistant. Also, since the fitness costs determine the strength of selection against resistant bacteria, analysis of these costs may inform novel treatment regimens. The aim of this research is to uncover new targets to combat resistance, new pathways that synergize with a particular antibiotic and, more broadly, to strengthen and enrich the underlying principles that will lay the foundation for the next generation of novel therapies, drug discovery, and diagnostics in the field.