Pathogen and Microbiome Temporal Changes During Resolution of HAP

项目总结项目2：HAP分解过程中病原菌和微生物组的时间变化 重症肺炎是机械通气患者的一种可怕的并发症，与 高死亡率。为了更好地了解这些具有挑战性的感染，我们建议开发成功的 肺炎治疗(SCRIPT)系统生物学中心的临床反应。剧本的总体目标 研究项目2是创建一个基于微生物生物签名的计算模型，用于预测临床 呼吸机相关性肺炎患者治疗失败。铜绿假单胞菌等特定病原体 和鲍曼不动杆菌在呼吸机相关性肺炎中尤其有问题，并与 临床失败率高达50%，即使在接受适当抗生素治疗的患者中也是如此。为了这个 原因，我们将重点关注由这些病原体引起的肺炎。我们小组和其他人的工作表明 这些细菌的菌株在引起严重感染的能力上有很大的差异。此外，新兴的 证据表明，由病原体或使用的抗生素引起的肺部微生物群的变化 治疗它们可能会导致较差的临床结果。我们推测，P. 铜绿假单胞菌和鲍曼不动杆菌等。以及肺部微生物群的特殊变化 与HAP患者的临床失败有关。为了验证这一假设，我们将执行以下操作 目的：目的：1.鉴定与铜绿假单胞菌和鲍曼不动杆菌相关的遗传生物特征 重症肺炎患者临床反应差。目标2.我们将鉴定肺部微生物群 与临床不良相关的成分(细菌、病毒和真菌)和纵向微生物组模式 重症肺炎患者的反应。目标3.生成包含病原体的计算模型 基因组、病原体转录组和微生物组成分对重症患者临床反应的预测 由铜绿假单胞菌或鲍曼不动杆菌引起的肺炎。我们生成的数据将以迭代的方式使用 创建和优化一个计算模型，根据以下数据确定临床失败的风险 他们肺炎的微生物学。临床失败的高度歧视性微生物生物特征将是 进一步检查以确定它们是否在肺炎的发展中起因果作用。

Project Summary Project 2: Pathogen and Microbiome Temporal Changes During Resolution of HAP Severe pneumonia is a dreaded complication among mechanically ventilated patients and is associated with high rates of mortality. To better understand these challenging infections, we propose to develop the Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center. The overall goal of SCRIPT Research Project 2 is to create a computational model based on microbial biosignatures that predicts clinical failure in patients with ventilator-associated pneumonia. Specific pathogens such as Pseudomonas aeruginosa and Acinetobacter baumannii are particularly problematic in ventilator-associated pneumonia and are associated with clinical failure rates as high as 50%, even in patients treated with appropriate antibiotic therapy. For this reason, we will focus on pneumonia caused by these pathogens. Work from our group and others has shown that strains of these bacteria differ dramatically in their ability to cause severe infections. Furthermore, emerging evidence indicates that alterations in the pulmonary microbiome induced by pathogens or by the antibiotics used to treat them may contribute to poor clinical outcomes. We hypothesize that specific genetic biosignatures of P. aeruginosa and Acinetobacter baumannii and other spp. and particular alterations to the pulmonary microbiome are associated with clinical failure in patients with HAP. To test this hypothesis, we will perform the following aims: Aim 1. We will identify genetic biosignatures of P. aeruginosa and A. baumannii strains associated with poor clinical responses in patients with severe pneumonia. Aim 2. We will identify pulmonary microbiome constituents (bacteria, viruses, and fungi) and longitudinal microbiome patterns associated with poor clinical responses in patients with severe pneumonia. Aim 3. Generate a computational model that integrates pathogen genome, pathogen transcriptome, and microbiome components to predict the clinical response in severe pneumonia caused by P. aeruginosa or A. baumannii. The data we generate will be used in an iterative manner to create and optimize a computational model that identifies patients at risk for clinical failure based upon the microbiology of their pneumonia. Highly discriminatory microbiological biosignatures for clinical failure will be further examined to determine whether they play a causal role in the progression of pneumonia.