Population Analysis of Pseudomonas aeruginosa Virulence

铜绿假单胞菌（PA）在住院患者中引起频繁和严重的感染。此外，本发明还提供了一种方法， 耐多药PA的流行率正在增加，目前在许多地区为15-30%。照经上所 毫不奇怪，IDSA、WHO和CDC都将PA列为严重的公共卫生优先需求 新的治疗剂。关于PA的一个古老的问题是，为什么有些菌株会导致 比其他人更积极的感染。下一代测序平台的最新应用 一个问题已经开始提供一个解释，证明PA基因组与 一个接一个在典型的PA菌株中，大约10-15%的基因是“辅助性的”，这意味着它们 存在于某些菌株中，而不是其他菌株。同样，“核心”或保守的基因组包含许多 单核苷酸变体（SNV）和小插入-缺失（indel）。虽然这些配件中有一些 基因和核心等位基因已经被表征并显示出调节毒力，它们代表了“毒力的尖端”。 冰山。“对PA菌株间差异的系统性检查可能会发现大量新的 影响毒力的基因和等位基因。确定这些因素将产生若干重要后果： 它们将极大地增强我们对PA毒力的理解，以及这种毒力的机制。 细菌引起严重的疾病;和（ii）他们将允许人们预测PA菌株的毒力， 其基因组中存在的辅助和核心毒力基因/等位基因的互补。 我们假设，应用比较基因组学方法对大量PA分离株进行研究， 识别新的毒力基因/等位基因，并允许生成机器学习模型来预测 PA分离株的毒力基于它们的基因组。我们将执行以下具体目标来测试这些 假设：（1）使用机器学习模型来预测PA分离株的毒力， 基因组含量(2)鉴定在基因突变中起因果作用的辅助基因和核心基因组SNV/indel。 毒性(3)开发基于基因组的模型，预测PA患者的临床结局 血液感染这项建议的影响是双重的。首先，它将为未来奠定基础 为临床医生治疗PA感染患者提供有价值的预后信息的模型。二是会 鉴定介导新型致病感染机制的新PA毒力因子。

Pseudomonas aeruginosa (PA) causes frequent and severe infections in hospitalized patients. In addition, the prevalence of multidrug-resistant PA is increasing and is now between 15-30% in many areas. Thus, it is not surprising that the IDSA, WHO, and the CDC have each listed PA as serious public health priority in need of new therapeutic agents. An age-old question concerning PA is why some strains cause substantially more aggressive infections than others. The recent application of next generation sequencing platforms to this problem has begun to provide an explanation by demonstrating that PA genomes differ substantially from strain to strain. Approximately 10-15% of the genes in a typical PA strain are "accessory," meaning that they are present in some strains but not others. Likewise, the "core" or conserved genome contains numerous single nucleotide variants (SNVs) and small insertion-deletions (indels). Although a few of these accessory genes and core alleles have been characterized and shown to modulate virulence, they represent the "tip of the iceberg." A systematic examination of strain-to-strain differences in PA is likely to uncover a wealth of novel virulence-impacting genes and alleles. Identification of these would have several important consequences: (i) They would dramatically enhance our understanding of PA virulence and the mechanisms by which this bacterium causes severe disease; and (ii) they would allow one to predict the virulence of PA strains based on the complement of accessory and core virulence genes/alleles that were present in their genomes. We hypothesize that application of comparative genomic approaches to large numbers of PA isolates will identify novel virulence genes/alleles and allow the generation of machine learning models to predict the virulence of PA isolates based on their genomes. We will perform the following specific aims to test these hypotheses: (1) Use machine learning models to predict the virulence of PA isolates based upon their genomic content. (2) Identify accessory genes and core genome SNVs/indels that play a causal role in virulence. (3) Develop a genome-based model that predicts clinical outcomes in patients with PA bloodstream infections. The impact of this proposal is twofold. First, it will lay the foundation for future models that provide valuable prognostic information to clinicians treating PA-infected patients. Second, it will identify new PA virulence factors that mediate novel pathogenic mechanisms of infection.