Identification of immune cell-cell communication networks and inflammatory pulmonary microenvironments associated with the progression of COPD

识别与 COPD 进展相关的免疫细胞间通讯网络和炎症性肺部微环境

• 批准号: 10352395
• 负责人: Kelly Arnold
• 金额: $ 31.98万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10352395
• 关键词: Accounting; Archives; Asthma; Biological Assay; Biological Markers; Blood; Blood specimen; Bronchoalveolar Lavage; Bronchoscopy; Caring; Cause of Death; Cell Communication; Cells; Chronic; Chronic Obstructive Pulmonary Disease; Clinical; Clinical Data; Clinical Trials; Cohort Studies; Complex; Data; Death Records; Development; Diagnostic; Disease; Disease Progression; Enrollment; Epithelial; Event; Flow Cytometry; Frequencies; Functional disorder; Funding; Future; Gene Proteins; Goals; Health Care Costs; Healthcare; Heterogeneity; Human; Immune; Immunologic Factors; In Vitro; Individual; Inflammation; Inflammatory; Irrigation; Longitudinal Studies; Lung; Lung Transplantation; Lung diseases; Measurement; Measures; Methods; Mucous Membrane; Outcome; Outcome Measure; Palliative Care; Participant; Pathogenesis; Patients; Physiological; Population Heterogeneity; Positioning Attribute; Process; Proteome; Pulmonary Inflammation; Radiology Specialty; Research; Research Personnel; Risk; Sampling; Severities; Smoker; Spirometry; System; Testing; Tissues; Vision; Visit; X-Ray Computed Tomography; adjudicate; clinically relevant; cohort; combinatorial; cost; cytokine; data-driven model; design; detector; diagnostic assay; diagnostic biomarker; disease phenotype; disorder risk; experience; follow-up; idiopathic pulmonary fibrosis; insight; machine learning model; never smoking; new therapeutic target; noninvasive diagnosis; novel therapeutic intervention; peripheral blood; personalized medicine; prognostic; prognostic value; prospective; transcriptome sequencing

PROJECT SUMMARY Chronic obstructive pulmonary disease (COPD) is a fatal lung disease that is the fourth leading cause of death in the U.S. Despite an estimated $50 billion in yearly healthcare costs, it has no current cure and only palliat- ive treatments. COPD is clearly characterized by chronic lung inflammation that likely arises from dysregulat- ion of complex networks of immune factors and cells across multiple tissue compartments. Although multiple individual genes and proteins have been associated with COPD risk and progression, global mechanistic understanding of its pathophysiology is lacking, particularly regarding the marked heterogeneity in COPD phenotypes. The overall objective of our study is to gain systems-level insight into complex inflammatory and immune mechanisms underlying COPD, by applying data-driven (also called ‘machine learning’) modeling approaches to clinical samples collected from human pulmonary microenvironments and matched immune cell networks from peripheral blood. Our central hypothesis is that immune networks will be more predictive of COPD phenotype, progression, and exacerbation than individual factors. We will test this hypothesis in three Specific Aims, using matched brochoalveolar lavage (BAL) and blood samples collected in SPIROMICS I and II clinical trials. Aim 1 will identify changes in immune cell-cell communication networks, by high-throughput cytokine measurements from stimulated systems of peripheral blood immune cells from smokers with and without COPD and never-smoking controls (collected from the upcoming SPIROMICS II visit; n=150). Aim 2 will determine systems-level changes that occur in the inflamed lung microenvironment, using high-throughput cytokine measurements in BAL samples (both archival from SPIROMICS I, n=200, and collected during upcoming SPIROMICS II bronchoscopies) from smokers with and without COPD and never-smoking controls. We will identify networks associated with longitudinal clinical progression and exacerbation frequency. Aim 3 will integrate measurements across lung and blood tissue compartments to define key combinatorial relationships associated with progression and exacerbation events. Overall, this project will provide systems- level insight into COPD pathogenesis and progression, and create a new paradigm for the study of other pulmonary conditions involving chronic inflammation, including idiopathic pulmonary fibrosis, asthma, and lung transplant. Results will aid in the future development of new non-invasive diagnostic assays and will guide systems-level mechanistic studies that could result in new combinatorial therapies.

项目概要 慢性阻塞性肺病（COPD）是一种致命的肺部疾病，是第四大死因 在美国，尽管每年的医疗费用估计高达 500 亿美元，但目前还没有治愈方法，只能缓解病情。 积极治疗。慢性阻塞性肺病 (COPD) 的明显特征是慢性肺部炎症，可能是由于调节失调引起的。 跨多个组织隔室的免疫因子和细胞的复杂网络的离子。虽然多 个别基因和蛋白质与慢性阻塞性肺病的风险和进展、整体机制有关 缺乏对其病理生理学的了解，特别是慢性阻塞性肺病的显着异质性 表型。我们研究的总体目标是获得对复杂炎症和疾病的系统级洞察。 通过应用数据驱动（也称为“机器学习”）建模，COPD 背后的免疫机制 从人体肺部微环境和匹配的免疫细胞收集临床样本的方法 来自外周血的网络。我们的中心假设是免疫网络将更能预测 COPD 表型、进展和恶化比个体因素更重要。我们将分三步检验这个假设 具体目标，使用匹配的支气管肺泡灌洗液 (BAL) 和在 SPIROMICS I 和 SPIROMICS 中收集的血液样本 II 临床试验。目标 1 将通过高通量识别免疫细胞间通讯网络的变化 吸烟者外周血免疫细胞刺激系统的细胞因子测量 没有 COPD 且不吸烟的对照（从即将到来的 SPIROMICS II 访视收集；n=150）。目标2 将使用高通量确定发炎的肺部微环境中发生的系统级变化 BAL 样本中的细胞因子测量（均来自 SPIROMICS I，n=200，并在 即将推出的 SPIROMICS II 支气管镜）来自患有或不患有慢性阻塞性肺病的吸烟者以及从不吸烟的对照。 我们将确定与纵向临床进展和恶化频率相关的网络。目标 3 将整合肺和血液组织隔室的测量值来定义关键组合 与进展和恶化事件相关的关系。总体而言，该项目将提供系统- 深入了解 COPD 发病机制和进展，并为其他疾病的研究创建新范式 涉及慢性炎症的肺部疾病，包括特发性肺纤维化、哮喘和肺病 移植。结果将有助于未来新的非侵入性诊断分析的开发，并将指导 系统级机制研究可能会产生新的组合疗法。