Pathogen and Microbiome Temporal Changes During Resolution of HAP

项目2：HAP缓解期间病原体和微生物组的时间变化 严重肺炎是机械通气患者中可怕的并发症， 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 肺炎治疗临床反应（Clinical Response In Pneumonia Therapy，CNOT）系统生物学中心CBT的总体目标 研究项目2是建立一个基于微生物生物特征的计算模型， 呼吸机相关性肺炎患者的失败。特定病原体，如铜绿假单胞菌 和鲍曼不动杆菌在呼吸机相关性肺炎中尤其成问题， 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。为此 因此，我们将重点关注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不同。此外，新兴 有证据表明，病原体或所用抗生素引起的肺部微生物组的改变 治疗它们可能会导致不良的临床结果。我们假设，特定的遗传生物签名的P。 铜绿假单胞菌和鲍曼不动杆菌以及其他物种。特别是肺部微生物组的改变 与HAP患者的临床失败有关。为了检验这个假设，我们将执行以下操作 目标：目标1。我们将鉴定铜绿假单胞菌和A.鲍曼不动杆菌相关株 严重肺炎患者的临床反应较差。目标2.我们将识别肺部微生物组 成分（细菌，病毒和真菌）和纵向微生物组模式与不良临床 严重肺炎患者的反应。目标3。生成一个计算模型， 基因组、病原体转录组和微生物组组分预测重度 铜绿假单胞菌或A.鲍曼不动杆菌。我们生成的数据将以迭代的方式使用 创建并优化计算模型，该计算模型基于 肺炎的微生物学临床失败的高度区分性微生物生物特征将 进一步检查，以确定它们是否在肺炎的进展中发挥因果作用。

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.