Genomics approaches to elucidating pathways to antibiotic resistance in Neisseria gonorrhoeae

项目摘要/摘要 抗药性细菌的崛起对公众健康构成了严重威胁。在竞争中击败易受影响的人 细菌和流行率的增加，耐药菌株必须能够补偿由 导致耐药性的突变和基因。然而，并不是每一种细菌都能补偿 同样好的是，在易感性和耐药性之间产生了一种复杂的进化图景。澄清 支持获得和维持抵抗的机制的性质和多样性将使我们能够 了解耐药菌株是如何出现和传播的，从而加速绝望的发展 需要新的战略来预防和治疗耐药感染。 我们使用临床上重要的病原体淋球菌(淋球菌)作为模型系统，给出了 它的高疾病负担(美国每年820,000例，全球每年近8,000万例)， 无法治愈的感染的迫在眉睫的威胁，以及实验操作的简便性。我们的目标是定义 支持获得和维持对临床上最常见的三种病毒的耐药性的遗传网络 治疗淋球菌的重要抗生素：广谱头孢菌素、阿奇霉素和 喹诺酮类药物。为此，我们将利用我们独特的流行病学和遗传学数据集&gt；1100 我们有完整基因组序列和抗生素敏感性的不同临床淋球菌分离株 配置文件。我们将使用基于种群的计算和实验方法，将多样性纳入其中 因此代表着从单一参考菌株的根本转变 学习。这些方法包括无偏见的统计工具，以确定亚群体的遗传差异； 高通量转座子突变筛选以确定影响抗性的基因座 遗传背景；以及基因组操作系统，以验证基因型和耐药性之间的联系 表型。 我们预计，这些研究的结果将确定导致抗药性的相互作用的基因座。 自然种群。这些结果可用于改进公共卫生监测工作和 治疗学的发展。此外，我们在这里建立的系统还可以用来进一步探索生物学 并为开发类似的系统提供了一个框架，以解剖基因 其他细菌病原体的耐药性网络。

Project Summary/Abstract The rise of antibiotic resistant bacteria poses a grave threat to public health. To outcompete susceptible bacteria and increase in prevalence, resistant strains must be able to compensate for fitness costs incurred by resistance-conferring mutations and genes. However, not every strain of a bacterial species can compensate equally well, yielding a complex evolutionary landscape between susceptibility and resistance. Elucidating the nature and diversity of the mechanisms that support acquisition and maintenance of resistance will allow us to understand how resistant strains emerge and spread and thereby accelerate development of desperately needed new strategies to prevent and treat resistant infections. We use the clinically important pathogen Neisseria gonorrhoeae (the gonococcus) as a model system, given its high burden of disease (820,000 cases in the US and nearly 80 million cases globally each year), the imminent threat of untreatable infection, and the ease of experimental manipulation. Our goal is to define the genetic networks that support acquisition and maintenance of resistance to three of the clinically most important antibiotics for treatment of gonococcus: the extended spectrum cephalosporins, azithromycin, and the quinolones. To do so, we will leverage our unique dataset of >1100 epidemiologically and genetically diverse clinical gonococcal isolates for which we have full genome sequences and antibiotic susceptibility profiles. We will use population-based computational and experimental methods that incorporate the diversity of susceptible and resistant populations and thus represent a fundamental shift from single reference strain studies. These methods include unbiased statistical tools to identify genetic differences in sub-populations; high-throughput transposon mutagenesis screens to define the loci that impact resistance as a function of genetic background; and a system for genome manipulation to validate links between genotype and resistance phenotype. We expect that the results from these studies will define the interacting loci that contribute to resistance in natural populations. These results can be applied to improving public health surveillance efforts and development of therapeutics. Moreover, the system we establish here can be used to further probe the biology of gonococcus and provides a framework for the development of similar systems to dissect of the genetic networks of resistance in other bacterial pathogens.