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 大流行社区获得的肺炎（CAP）继发于严重急性呼吸道 综合征冠状病毒2（SARS-COV-2）将CAP的公共健康重要性置于焦点。 肺炎治疗（脚本）系统生物学中心成功临床反应的研究者 开发了强大的研究基础设施，从 595例严重帽和医院获得的肺炎患者（HAP）需要机械通气和 使用最先进的多词方法分析这些临床样本。我们利用这些数据 生成SARS-COV-2发病机理的系统模型，并将其应用于成功的临床试验 Auxora是一种钙释放的激活通道抑制剂，导致30天死亡率降低了53％ 第二阶段试验。在Super-Script（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，我们观察到患有严重的患者 在其过程中 与或多或少有利的结果相关的住院。这些过渡将作为 编码由BAL和血清分析生成的初步数据的模型的基础，其中包括 超过500,000个支气管肺泡灌洗细胞，细胞因子水平，蛋白质组学的单细胞RNA序列 细胞表观基因组和微生物组分析。我们将使用这些临床和分子数据来检验假设 应用于疾病发病机理潜在空间模型的机器学习方法可以识别 临床期间有利和不利的临床转变/结果的分子预测指标 帽子课程。推论假设是在受控临床试验中这些决定剂的扰动 药理学干预措施将允许迭代模型的预测能力。我们将解决这些 三个特定目标的假设： 目标1。确定有利和不利的临床转变/结果的临床预测因子 需要住院的患者的CAP进程。 目标2。确定预测有利或不利的不同宿主或病原体基因组特征 严重CAP患者的临床转变/结局。 目标3。确定可以针对现有或新开发的治疗的途径 疗法。 Script2借鉴了临床医生，生物学家和数据科学家之间的成功合作，以组织临床 数据，远端肺样本和集成的不同数据集中的过程中，以开发大型的空间模型 肺炎的比例模型可以快速转化为护理途径和新型疗法。