COPD Susceptibility, Heterogeneity, and Progression: Proteomics and Genetics

项目摘要 虽然吸烟是COPD的主要环境危险因素，但只有少数吸烟者会发生COPD。 具有临床意义的COPD;遗传因素影响这种变异性。COPD受试者具有广泛不同的 肺气肿和气道疾病的贡献，以及COPD异质性的生物学决定因素是 不明确。蛋白质生物标志物在生物学上接近遗传变异， 在定义COPD遗传学和异质性中的中间作用。我们的总体假设是，功能性遗传 变异导致异常的蛋白质组学状态，这将允许鉴定与蛋白质组学相关的蛋白质生物标志物。 COPD的发展和异质性。我们将使用质谱蛋白质组学提供全面的 1054例肺组织中可用蛋白质及其蛋白质型（包括翻译后修饰）的评估 来自肺组织研究联盟（LTRC）的组织样本，包括547例COPD病例和507例对照 科目Olink蛋白质组学数据（由TOPMed生成）将提供相同的正交蛋白质组学数据， 肺组织生物标本使用单细胞和批量RNA-Seq数据的细胞去卷积方法将在 用于确定蛋白质组学关联是否与肺细胞组成的变化有关。COPD 将根据临床/成像数据并通过使用基于网络的分层来定义亚型。 蛋白质组学数据。我们将通过测量COPD和COPD亚型的潜在血浆蛋白生物标志物， 来自相同LTRC COPD病例和对照的血浆样品中前100种肺组织COPD特异性蛋白 科目我们将利用现有的LTRC多组学数据（包括全基因组测序，RNA-Seq， DNA甲基化）结合新生成的质谱和基于亲和力的蛋白质组学数据， 确定COPD相关蛋白和COPD的罕见和常见遗传决定因素。机器学习和 网络分析将用于整合多组学数据，以深入了解COPD发病机制， 异质性几种主要COPD蛋白质生物标志物的网络关系将使用 原代肺细胞中的CRISPR-Cas9方法。COPD新蛋白的鉴定与鉴定 生物标志物可能为未来的临床试验提供深入了解COPD发病机制和工具。

PROJECT SUMMARY Although cigarette smoking is the major environmental risk factor for COPD, only a minority of smokers develops clinically significant COPD; genetic factors influence this variability. COPD subjects have widely varying contributions of emphysema and airway disease, and the biological determinants of COPD heterogeneity are not well-defined. Protein biomarkers, which are biologically proximate to genetic variants, could play a critical intermediate role in defining COPD genetics and heterogeneity. Our overall hypothesis is that functional genetic variants lead to abnormal proteomic states that will allow identification of protein biomarkers relevant for the development and heterogeneity of COPD. We will use mass spectrometry proteomics to provide comprehensive assessment of available proteins and their proteoforms (including post-translational modifications) in 1054 lung tissue samples from the Lung Tissue Research Consortium (LTRC), including 547 COPD cases and 507 control subjects. Olink proteomics data (generated by TOPMed) will provide orthogonal proteomics data on the same lung tissue biospecimens. Cellular deconvolution approaches using single cell and bulk RNA-Seq data will be used to determine whether proteomic associations relate to changes in lung cellular composition. COPD subtypes will be defined based on both clinical/imaging data and by using network-based stratification of the proteomics data. We will verify potential plasma protein biomarkers of COPD and COPD subtypes by measuring the top 100 lung tissue COPD-specific proteins in plasma samples from the same LTRC COPD cases and control subjects. We will leverage existing LTRC multi-Omics data (including whole genome sequencing, RNA-Seq, and DNA methylation) in conjunction with newly generated mass spectrometry and affinity-based proteomics data to identify rare and common genetic determinants of COPD-related proteins and COPD. Machine learning and network analysis will be used to integrate multi-Omics data to provide insight into COPD pathogenesis and heterogeneity. Network relationships for several top COPD protein biomarkers will be functionally validated using CRISPR-Cas9 approaches in primary lung cells. The identification and characterization of novel COPD protein biomarkers may provide insights into COPD pathogenesis and tools for future clinical trials.