Systems Biology Modeling of Severe Hospital-Acquired Pneumonia

项目概要/摘要-项目2 机械通气挽救了无数呼吸衰竭患者的生命，但可能导致 呼吸机相关性肺炎（VAP）是一种可怕的并发症，是医院获得性肺炎（HAP）的一种。 HAP/VAP与特别高的死亡率相关，尽管给予适当的和高度的治疗。 强效抗生素最近的研究表明，宿主的炎症反应，特定的致病菌株 引起感染的病原体和肺部微生物组均参与HAP/VAP的发病机制。然而，在这方面， 据我们所知，这三个促成因素尚未得到全面审查。更好地 了解HAP/VAP后，我们建立了肺炎治疗的成功临床应答（SUCCESSING CLINICAL RESPONSE IN PNEumonia Therapy，CITT） 系统生物学中心招募肺炎患者，从他们那里收集支气管肺泡灌洗样本， 将多组学方法应用于这些样本。我们假设，这些方法将确定特定的主机 反应模式、病原体遗传生物特征和肺部微生物群落， HAP/VAP患者的临床轨迹，并确定这些感染的发病机制。为了验证这一 假设，我们将执行以下目标：（1）确定宿主反应生物签名，（2）细菌的特征， 病原体，和（3）告知发病机制和相关的肺微生物群落的模式 HAP/VAP临床改善或恶化。(4)整合宿主反应、病原体和微生物组 功能与临床元数据，以生成预测临床结局的综合计算模型 以及HAP/VAP的有利/不利转变。我们产生的数据将在临床上用于识别 那些需要积极管理和科学地提供更深入了解的患者， HAP/VAP的发病机制。

PROJECT SUMMARY/ABSTRACT – Project 2 Mechanical ventilation has saved the lives of countless patients with respiratory failure but may lead to the dreaded complication of ventilator-associated pneumonia (VAP), a form of hospital-acquired pneumonia (HAP). HAP/VAP are associated with particularly high mortality rates despite the administration of appropriate and highly potent antibiotics. Recent work suggests that the host inflammatory response, the specific pathogenic strain causing the infection, and the pulmonary microbiome all contribute to the pathogenesis of HAP/VAP. However, to our knowledge these three contributing factors have not been comprehensively examined together. To better understand HAP/VAP, we established the Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center to enroll pneumonia patients, collect bronchoalveolar lavage samples from them, and apply multi-omic approaches to these samples. We hypothesize that these approaches will identify specific host response patterns, pathogen genetic biosignatures, and pulmonary microbiome consortia that define and predict the clinical trajectory of patients with HAP/VAP and define the pathogenesis of these infections. To test this hypothesis, we will perform the following aims: (1) Identify host response biosignatures, (2) features of bacterial pathogens, and (3) patterns of pulmonary microbiome consortia that inform pathogenesis and are associated with clinical improvement or deterioration in HAP/VAP. (4) Integrate host response, pathogen, and microbiome features with clinical metadata to generate a comprehensive computational model that predicts clinical outcomes and favorable/unfavorable transitions in HAP/VAP. The data we generate will be useful clinically in identifying those patients who require aggressive management and scientifically in providing a deeper understanding of the pathogenesis of HAP/VAP.