Population Analysis of Pseudomonas aeruginosa Virulence

铜绿假单胞菌(PA)在住院患者中引起频繁而严重的感染。此外, 耐多药PA的患病率正在上升，目前在许多地区在15-30%之间。因此，它是 毫不奇怪，IDSA、WHO和CDC都将PA列为需要优先考虑的严重公共卫生问题 新的治疗药物。关于PA的一个古老的问题是，为什么一些菌株引起的 比其他人更具侵略性的感染。新一代测序平台在这方面的最新应用 问题已经开始提供一种解释，通过证明PA基因组与 努力工作，努力工作。在一个典型的PA菌株中，大约10%-15%的基因是“附属的”，这意味着他们 在某些菌株中存在，但在其他菌株中不存在。同样，“核心”或保守的基因组包含许多 单核苷酸变异(SNV)和小插入-缺失(INDELs)。尽管这些配件中有一些 基因和核心等位基因已经被表征并被证明可以调节毒力，它们代表了 对PA中品系间差异的系统研究可能会发现大量的小说 影响毒力的基因和等位基因。查明这些因素将产生几个重要后果：(I) 它们将极大地增强我们对PA毒力的理解，以及通过这些机制 细菌会导致严重的疾病；以及(Ii)它们将使人们能够根据以下条件预测PA菌株的毒力 基因组中存在的副毒力和核心毒力基因/等位基因的互补。 我们假设，对大量PA分离株应用比较基因组学方法将 识别新的毒力基因/等位基因，并允许机器学习模型的生成来预测 基于其基因组的PA分离物的毒力。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 基因组内容。(2)确定辅助基因和核心基因组SNV/INDELs在 致命性。(3)开发一种基于基因组的模型来预测PA患者的临床结果 血液感染。这项提议的影响是双重的。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。其次，它将 确定新的PA毒力因子，以介导新的感染致病机制。

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.