Project 1: Dynamic Host Responses During Resolution of HAP

项目1：HAP缓解期间的动态宿主反应 医院获得性肺炎（HAP）是医院感染导致死亡的主要原因之一， 相关死亡率。由铜绿假单胞菌和不动杆菌属引起的HAP。基本上更 比其他病原体更难治疗，临床失败率高达50%。这些比率持续存在，尽管 抗生素方案的优化，表明抗生素耐药性以外的因素起作用。本课题 将检测宿主对肺炎的反应我们假设，持续性炎症的肺泡后， 适当的抗生素治疗有助于铜绿假单胞菌或不动杆菌属患者的临床失败。 肺炎我们将在三个相互关联的具体目标中检验这一假设。目标1.以确定是否 病原体相关的肺泡巨噬细胞和淋巴细胞亚群转录组特征的变化 随着时间的推移预测严重肺炎的结果。目标2.前瞻性验证预测性宿主应答 使用基于生态系统的建模方法在一个单独的重症肺炎患者队列中确定。 目标3.确定预测临床结局的经验证宿主应答是否与肺炎相关 小鼠模型中的终点。我们将联合收割机从电子健康记录的临床数据和整合这些 从流式分选的肺泡巨噬细胞和Treg中获得的转录组学和表观基因组学数据的临床数据 在患者的疾病过程中从NBBAL的系列样品中分离的细胞。通过发展 这些预测工具，该中心提供了潜在的发现新的途径， 推动肺炎病理生物学的治疗靶向，并有能力修改对肺炎的理解， 宿主、病原体和微生物组之间的复杂相互作用。

Project Summary Project 1: Dynamic Host Responses During Resolution of HAP   Hospital-acquired pneumonia (HAP) is one of the leading causes of death from nosocomial infections, with high rates of associated mortality. HAP due to Pseudomonas aeruginosa and Acinetobacter spp. is substantially more difficult to treat than other pathogens, with clinical failure rates as high as 50%. These rates persist despite optimization of antibiotic regimens, suggesting factors beyond antibiotic resistance contribute. In this Project, we will examine the host response to pneumonia. We hypothesize that persistent inflammation in the alveolus after appropriate antibiotic treatment contributes to clinical failure in patients with P. aeruginosa or Acinetobacter spp. pneumonia. We will test this hypothesis in three interrelated Specific Aims. Aim 1. To determine whether pathogen-associated changes in the transcriptomic signatures of alveolar macrophages and lymphocyte subsets over time predict outcome of severe pneumonia. Aim 2. To prospectively validate predictive host responses identified using an ecosystem-based modeling approach in a separate cohort of patients with severe pneumonia. Aim 3. To determine whether validated host responses predictive of clinical outcome are related to pneumonia endpoints in murine models. We will combine clinical data from the Electronic Health Record and integrate these clinical data with transcriptomic and epigenomic data obtained from flow sorted alveolar macrophages and Treg cells isolated from serial samples of NBBAL over the course of the patient's illness. Through the development of these predictive tools, the SCRIPT Systems Biology Center offers the potential to discover novel pathways that drive pneumonia pathobiology for therapeutic targeting, and the ability to revise understanding of pneumonia as a complex interaction between the host, pathogen, and microbiome.