Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center

修改后的项目摘要/摘要部分 这一创新的集成系统生物学应用试图描绘在肺泡水平发生的复杂宿主/病原体相互作用，这些相互作用导致对包括严重病毒性肺炎在内的严重肺炎的治疗无效。为了实现这一目标，我们将利用我们独特的方式获得收集的肺泡液，作为我们医疗重症监护病房中机械通气疑似肺炎患者的常规临床护理的一部分。支气管肺泡灌洗液将从有严重病毒性肺炎或假单胞菌或不动杆菌肺炎的特征良好的机械通气患者身上连续获得。这两种CDC指定的严重危险级别细菌病原体的临床失败率都高达50%。一个可靠的临床定义将允许比较与治疗失败和成功相关的宿主和病原体特征。这些临床标本和广泛的患者表型组学将支撑两个相互支持和迭代的研究项目。项目一将部署强大的工具，用于流动分选巨噬细胞和淋巴细胞亚群，从这些群体中分离RNA，并执行转录和表观基因组分析，以比较成功和不成功的宿主反应。项目二将重点研究与不成功结局相关的特定病原体基因组图谱和不同的微生物组反应。已确定的特定病原体基因组图谱将由技术核心公司在一种独特的人源化肺泡巨噬细胞小鼠模型中进行因果关系测试。微生物群落的变化将通过鸟枪深度测序来综合评估，以检测噬菌体、其他病毒和真菌DNA，以及细菌。技术核心公司将进行BAL巨噬细胞和淋巴细胞亚群的细胞分类、RNA测序和全基因组甲基化，以及进行小鼠肺炎模型研究。数据管理和生物信息学核心将开发工具来降低这些大型综合数据集的维度，包括临床表现学，并将它们提供给建模核心。然后，建模核心将使用基于生态系统的方法来处理这一复杂的适应系统，并结合独特的机器学习工具和神经网络来生成预测肺炎成功结局的宿主、病原体和/或微生物组模式的生物标记物。然后，在建模核心中开发的预测性生物标记物将在将产生类似数据的患者的前瞻性确证队列中得到验证。生物标志物也将在小鼠模型中进行因果关系测试。行政核心将发挥面向外部的作用，向社区和赞助者进行教育和外联，并定期与NIH赞助/批准的储存库网站交换数据集、分析工具和标本。

Modified Project Summary/Abstract Section This innovative integrated systems biology application seeks to delineate the complex host/pathogen interactions occurring at the alveolar level that lead to unsuccessful response to therapy in serious pneumonia, including serious viral pneumonia. To achieve this objective, we will leverage our unique access to alveolar fluid collected as part of routine clinical care in mechanically ventilated patients with suspected pneumonia in our medical intensive care unit. Bronchoalveolar lavage fluid will be obtained serially from well characterized mechanically ventilated patients with serious viral or Pseudomonas or Acinetobacter bacterial pneumonias. Both of these CDC-designated serious hazard level bacterial pathogens have clinical failure rates as high as 50%. A robust clinical definition will allow comparison of both host and pathogen signatures associated with failure of therapy vs. success. These clinical specimens and extensive patient phenomics will anchor two mutually supportive and iterative research projects. Project One will deploy robust tools for flow sorting macrophage and lymphocyte subset populations, isolating RNA from these populations, and performing transcriptomic and epigenomic analysis to compare successful and unsuccessful host response. Project Two will focus on both specific pathogen genomic profiles associated with unsuccessful outcome and the differential microbiome response. Specific pathogen genomic profiles identified will be tested for causality in a unique humanized alveolar macrophage mouse model by the Technology Core. Changes in microbiome communities will be comprehensively assessed by shotgun deep sequencing to detect bacteriophage, other virus, and fungal DNA, in addition to bacterial. A Technical Core will perform cell sorting of BAL macrophage and lymphocyte subsets, RNA sequencing, and whole genome methylation, as well as perform the mouse pneumonia model studies. A Data Management and Bioinformatics Core will develop tools to reduce the dimensionality of these large comprehensive datasets, including the clinical phenomics, and provide them to the Modeling Core. The Modeling Core will then use an ecosystem-based approach to this complex adaptive system combined with unique machine learning tools and neural networks to generate biomarkers of host, pathogen and/or microbiome patterns predictive of successful pneumonia outcome. Predictive biomarkers developed in the Modeling Core will then be validated in a prospective confirmatory cohort of patients in whom analogous data will be generated. Biomarkers will also be tested for causality the mouse model. The Administrative Core will perform the outward- facing role of education and outreach to the community and sponsor, as well as regularly exchanging datasets, analytic tools, and specimens with NIH-sponsored/approved repository sites.