Systems Biology Modeling of Severe Community-Acquired Pneumonia

严重社区获得性肺炎的系统生物学模型

• 批准号: 10551466
• 负责人: RICHARD G WUNDERINK
• 金额: $ 58.24万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-17 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551466
• 关键词: 2019-nCoV; Biological Markers; Biological Models; Blood specimen; Bronchoalveolar Lavage; COVID-19 pneumonia; COVID-19 treatment; Calcium; Caring; Cells; Clinical; Clinical Data; Clinical Trials; Collaborations; Controlled Clinical Trials; Curettage procedure; Data; Data Scientist; Data Set; Disease model; Disparate; Distal; Electronic Health Record; Enrollment; Epidemic; Foundations; Frequencies; Hospitalization; Hospitals; Immune response; Infection; Influenza A virus; Intervention; Lung; Machine Learning; Mechanical ventilation; Middle East Respiratory Syndrome Coronavirus; Modality; Modeling; Molecular; Molecular Profiling; Multiomic Data; Nasopharynx; Nose; Nosocomial pneumonia; Outcome; Pathogenesis; Pathway interactions; Patients; Phase II Clinical Trials; Phenotype; Pneumonia; Population; Prediction of Response to Therapy; Process; Proteomics; Public Health; Publishing; Research Infrastructure; Research Personnel; SARS coronavirus; SARS-CoV-2 pathogenesis; Sampling; Secondary to; Serum; Space Models; Steroids; Systems Biology; T-Lymphocyte; Testing; Therapeutic; Translating; Update; Variant; Viral; Virus; Zoonoses; cell type; climate change; clinical predictors; community acquired pneumonia; cytokine; emerging pathogen; epigenomics; experimental study; genomic data; improved; inhibitor; insight; lung microbiome; microbiome analysis; mortality; mouse model; multiple omics; novel; novel therapeutics; pandemic disease; pandemic potential; pathogen; pathogen genomics; pharmacologic; phase II trial; pneumonia model; pneumonia treatment; predictive modeling; prospective; prototype; respiratory; response; severe COVID-19; single-cell RNA sequencing; specific biomarkers; targeted treatment; tocilizumab; tool; treatment response

Project Summary/Abstract – Project 1 Pandemic community-acquired pneumonia (CAP) secondary to infection with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) brought the public health importance of CAP into sharp focus. Investigators in the Successful Clinical Response in Pneumonia Therapy (SCRIPT) systems biology center developed a robust research infrastructure to prospectively collect 1,567 serial distal respiratory samples from 595 patients with severe CAP and hospital acquired pneumonia (HAP) requiring mechanical ventilation and analyze these clinical samples using state-of-the art multi-omics approaches. We leveraged these data to generate a systems model of SARS-CoV-2 pathogenesis and applied it toward a successful clinical trial of Auxora, a calcium release activated channel inhibitor, that resulted in a 53% reduction in 30-day mortality in a phase II trial. In Super-SCRIPT (SCRIPT2), we propose to leverage and expand the longitudinal clinical and molecular data in SCRIPT. By applying machine learning to clinical data, we observe that patients with severe pneumonia undergo transitions between distinct, clinically recognizable states over the course of their hospitalization that are associated with more or less favorable outcomes. These transitions will serve as the foundation for a model incorporating preliminary data generated from BAL and serum analysis that includes single-cell RNA-sequencing of more than 500,000 bronchoalveolar lavage cells, cytokine levels, proteomic, T cell epigenomic, and microbiome analyses. We will use these clinical and molecular data to test the hypothesis that machine learning approaches applied to a latent space model of disease pathogenesis can identify molecular predictors of favorable and unfavorable clinical transitions/outcomes during the clinical course of CAP. A corollary hypothesis is that perturbations of these determinants during controlled clinical trials of pharmacologic interventions will allow iteration of the models’ predictive capabilities. We will address these hypotheses in three Specific Aims: Aim 1. To identify clinical predictors of favorable and unfavorable clinical transitions/outcomes over the course of CAP in patients requiring hospitalization. Aim 2. To determine distinct host or pathogen genomic features that predict favorable or unfavorable clinical transitions/outcomes in patients with severe CAP. Aim 3. To identify pathways that can be targeted for therapy with existing or newly developed therapeutics. SCRIPT2 draws on successful collaborations between clinicians, biologists and data scientists to organize clinical data, process distal lung samples and integrate disparate datasets into latent space models to develop large scale models of pneumonia that can be rapidly translated into care pathways and novel therapies.

项目摘要/摘要-项目1 严重急性呼吸道感染继发的大流行性社区获得性肺炎 冠状病毒-2综合征(SARS-CoV-2)使CAP对公共卫生的重要性成为人们关注的焦点。 肺炎治疗的成功临床反应(SCRIPT)系统生物学中心的研究人员 开发了强大的研究基础设施，以前瞻性地从以下地点收集1,567个系列远端呼吸道样本 595例重症CAP和医院获得性肺炎(HAP)患者需要机械通气和 使用最先进的多组学方法分析这些临床样本。我们利用这些数据 建立SARS-CoV-2致病机制的系统模型，并将其应用于成功的临床试验 Auxora，一种钙释放激活的通道抑制剂，使患者30天的死亡率降低53% 第二阶段试验。在超级脚本(SCRIPT2)中，我们建议利用和扩展纵向临床和 脚本中的分子数据。通过将机器学习应用于临床数据，我们观察到重症患者 肺炎在其病程中经历不同的、临床可识别的状态之间的转换 与或多或少有利结果相关的住院治疗。这些过渡将作为 将BAL和血清分析产生的初步数据整合在一起的模型的基础，包括 500,000多个支气管肺泡灌洗细胞、细胞因子水平、蛋白质组、T细胞的单细胞RNA测序 细胞表观基因组学和微生物组分析。我们将使用这些临床和分子数据来检验这一假说 将机器学习方法应用于疾病发病机制的潜在空间模型可以识别 临床过程中有利和不利临床过渡/结果的分子预测因子 CAP的疗程。一个推论是，在受控临床试验期间，这些决定因素的扰动 药理学干预的结果将使模型的预测能力得以迭代。我们将解决这些问题 三个具体目标中的假设： 目的1.确定有利和不利的临床过渡/结果的临床预测因子 需要住院的患者的CAP病程。 目的2.确定预测有利或不利的不同寄主或病原体基因组特征 重度CAP患者的临床转归/转归。 目标3.确定可针对现有的或新开发的治疗的途径 治疗学。 SCRIPT2利用临床医生、生物学家和数据科学家之间的成功合作来组织临床 数据，处理远端肺样本，并将不同的数据集集成到潜在空间模型中，以开发大型 肺炎的规模模型，可以迅速转化为护理途径和新的治疗方法。