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

肺炎治疗 (SCRIPT) 系统生物学中心成功的临床反应

基本信息

批准号：
10322470
负责人：
RICHARD G WUNDERINK
金额：
$ 12万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-07 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10322470
关键词：
Acinetobacter Address Alveolar Alveolar Macrophages Alveolus Animal Model Antibiotic Therapy Antiviral Agents Bacterial Pneumonia Bacteriophages Bioinformatics Biological Markers Bronchoalveolar Lavage Bronchoalveolar Lavage Fluid COVID-19 Cause of Death Cell Separation Cells Centers for Disease Control and Prevention (U.S.)Characteristics Clinical Clinical Data Clinical Medicine Clinical Microbiology Clinical Trials Collection Communities Complex DNA Viruses DNA sequencing Data Data Set Development Dimensions Disease Ecosystem Education and Outreach Equilibrium Etiology Failure Fungal DNA Generations Genes Genetic Goals Human Immune response Infection Influenza Intensive Care Units Lead Link Liquid substance Lymphocyte Subset Lymphoid Cell Machine Learning Mechanical ventilation Medical Metagenomics Methylation Modeling Multiomic Data Mus Myeloid Cells Nosocomial Infections Nosocomial pneumonia Outcome Pathway interactions Patients Pattern Pneumonia Population Preparation Prospective cohort Protocols documentation Pseudomonas Pseudomonas aeruginosa Pseudomonas aeruginosa infection RNA Reproducibility Research Research Personnel Research Project Grants Role Sampling Science Secondary to Shotguns Site Sorting - Cell Movement Specimen Standardization Stream Study models System Systems Biology Technology Testing Treatment Failure United States National Institutes of Health Validation Viral Viral Pneumonia Virulence analytical tool base clinical care cohort data management data pipeline deep sequencing epigenomics experience genomic data genomic profiles hazard humanized mouse improved innovation macrophage microbiome microbiome composition mouse model multiple omics neural network new therapeutic target novel pathogen pathogen genomics pathogenic bacteria phenomics phenotypic data pneumonia model pneumonia treatment predictive marker predictive tools prospective public health relevance repository response success tool transcriptome sequencing transcriptomics viral DNA whole genome

项目摘要

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.

修改后的项目摘要/摘要部分这种创新的集成系统生物学应用旨在描述发生在肺泡水平的复杂宿主/病原体相互作用，这种相互作用导致严重肺炎（包括严重病毒性肺炎）的治疗反应不成功。为了实现这一目标，我们将利用我们独特的途径收集肺泡液，作为我们医疗重症监护病房疑似肺炎机械通气患者的常规临床护理的一部分。支气管肺泡灌洗液将从特征明确的患有严重病毒性或假单胞菌或不动杆菌细菌性肺炎的机械通气患者中连续获得。这两种CDC指定的严重危害级细菌病原体，临床失败率高达50%。强大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对巨噬细胞和淋巴细胞亚群进行流式分选，从这些群体中分离RNA，并进行转录组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。技术核心将在独特的人源化肺泡巨噬细胞小鼠模型中测试确定的特定病原体基因组图谱的因果关系。除了细菌之外，还将通过鸟枪法深度测序来检测噬菌体、其他病毒和真菌 DNA，全面评估微生物群落的变化。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，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（包括临床表组学）的维度，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来处理这个复杂的自适应系统，并结合独特的机器学习工具和神经网络，生成宿主、病原体和/或微生物组模式的生物标志物，预测肺炎的成功结果。然后，在建模核心中开发的预测生物标志物将在前瞻性验证患者队列中得到验证，在这些患者中将生成类似数据。生物标记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社区和赞助商的教育和外展作用，并定期与 NIH 赞助/批准的存储站点交换数据集、分析工具和样本。