Genomics approaches to elucidating pathways to antibiotic resistance in Neisseria gonorrhoeae

项目总结/摘要 抗生素耐药性细菌的增加对公众健康构成严重威胁。在竞争中战胜易受影响的 细菌和增加的流行，耐药菌株必须能够补偿健身成本所产生的 赋予抗性的突变和基因。然而，并非细菌物种的每一种菌株都可以补偿 同样好，产生了易感性和抗性之间的复杂进化景观。阐明 支持获得和维持抵抗力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， 需要新的策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病奈瑟菌（淋球菌）作为模型系统， 其高疾病负担（美国每年有82万例，全球每年近8000万例）， 无法治愈的感染的迫在眉睫的威胁，以及实验操作的容易性。我们的目标是定义 支持获得和维持对三种临床上最常见的 治疗淋球菌的重要抗生素：超广谱头孢菌素、阿奇霉素和 喹诺酮类为了做到这一点，我们将利用我们独特的数据集， 我们拥有全基因组序列和抗生素敏感性的各种临床淋球菌分离株 数据区.我们将使用基于人口的计算和实验方法， 敏感和耐药群体，因此代表了从单一参考菌株 问题研究这些方法包括无偏的统计工具，以确定亚群体的遗传差异; 高通量转座子诱变筛选以确定影响抗性的基因座， 遗传背景;以及一个基因组操作系统，以验证基因型和抗性之间的联系 表型。 我们希望这些研究的结果将确定有助于耐药性的相互作用位点， 自然人口。这些结果可用于改善公共卫生监测工作， 治疗学的发展。此外，我们在这里建立的系统可以用于进一步探索生物学 并为类似系统的发展提供了一个框架， 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.