Project 1: Dynamic Host Responses During Resolution of HAP

项目总结项目1：HAP解析过程中的动态宿主响应 -- 医院获得性肺炎(HAP)是导致医院感染死亡的主要原因之一。 相关死亡率。铜绿假单胞菌和不动杆菌引起的HAP大大超过了 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 优化抗生素方案，表明抗生素耐药性以外的因素起到了作用。在这个项目中，我们 将检查宿主对肺炎的反应。我们假设术后肺泡内持续发炎 适当的抗生素治疗会导致铜绿假单胞菌或不动杆菌感染患者的临床失败。 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.确定是否 病原体对肺泡巨噬细胞和淋巴细胞亚群转录特征的影响 随着时间的推移，可以预测严重肺炎的结果。目的2.前瞻性地验证宿主的预测性反应 在单独的重症肺炎患者队列中使用基于生态系统的建模方法确定。 目的3.确定预测临床结果的有效宿主反应是否与肺炎有关 小鼠模型中的终点。我们将结合电子健康记录中的临床数据，并将这些 来自流式分选的肺泡巨噬细胞和Treg的转录和表观基因组数据的临床数据 从患者病程期间的NBBAL系列样本中分离出细胞。通过发展…… 这些预测工具，脚本系统生物学中心提供了发现新路径的潜力 推动肺炎病理生物学的治疗目标，并有能力将对肺炎的理解修改为 寄主、病原体和微生物群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.