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

修改项目摘要/摘要部分 这种创新的综合系统生物学应用旨在描述在肺泡水平发生的复杂的宿主/病原体相互作用，这些相互作用导致严重肺炎（包括严重病毒性肺炎）对治疗的不成功反应。为了实现这一目标，我们将利用我们独特的获取肺泡液的途径，作为我们医疗重症监护室中疑似肺炎机械通气患者常规临床护理的一部分。支气管肺泡灌洗液将从具有良好特征的严重病毒性或假单胞菌属或不动杆菌属细菌性肺炎的机械通气患者中连续获得。这两种CDC指定的严重危害级别细菌病原体的临床失败率高达50%。稳健的临床定义将允许比较与治疗失败与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锚两个相互支持和迭代的研究项目。Project One将部署强大的工具，用于流式分选巨噬细胞和淋巴细胞亚群，从这些群体中分离RNA，并进行转录组学和表观基因组学分析，以比较成功和不成功的宿主反应。项目二将重点关注与不成功结果相关的特定病原体基因组图谱和差异微生物组反应。将由Technology Core在独特的人源化肺泡巨噬细胞小鼠模型中检测识别出的特定病原体基因组谱的因果关系。微生物群落的变化将通过鸟枪深度测序进行全面评估，以检测噬菌体，其他病毒和真菌DNA以及细菌。技术核心将进行BAL巨噬细胞和淋巴细胞亚群的细胞分选、RNA测序和全基因组甲基化，并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减少这些大型综合数据集的维度，包括临床表型组学，并将其提供给建模核心。然后，建模核心将使用基于生态系统的方法，结合独特的机器学习工具和神经网络，对这个复杂的自适应系统进行处理，以生成预测成功肺炎结果的宿主、病原体和/或微生物组模式的生物标志物。然后，在建模核心中开发的预测性生物标志物将在一个前瞻性确证性患者队列中进行验证，在该队列中将生成类似数据。还将检测生物标志物在小鼠模型中的因果关系。行政核心将执行教育和外展社区和赞助商的外向角色，以及定期与NIH赞助/批准的存储库站点交换数据集，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.