COPD Susceptibility, Heterogeneity, and Progression: Proteomics and Genetics

项目总结 尽管吸烟是慢性阻塞性肺疾病的主要环境危险因素，但只有少数吸烟者会患上这种疾病。 临床上显著的慢性阻塞性肺疾病；遗传因素影响这种变异性。慢性阻塞性肺疾病的对象有很大的不同 肺气肿和呼吸道疾病的贡献，以及COPD异质性的生物决定因素是 定义不明确。蛋白质生物标记物在生物学上与基因变异很接近，它可能起到关键作用 在确定慢性阻塞性肺疾病遗传学和异质性方面的中间作用。我们的总体假设是功能性遗传 变异导致异常的蛋白质组状态，这将允许识别与 慢性阻塞性肺疾病的发展和异质性。我们将利用质谱学蛋白质组学提供全面的 1054个肺中可用蛋白质及其蛋白形式(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患者和507名对照 研究对象。Olink蛋白质组学数据(由TOPMed生成)将在同一数据库上提供正交蛋白质组学数据 肺组织生物显微镜。使用单细胞和批量RNA-Seq数据的细胞去卷积方法将是 用于确定蛋白质组关联是否与肺细胞成分的变化有关。慢性阻塞性肺疾病（慢阻肺） 将根据临床/影像数据并使用基于网络的分层来定义亚型 蛋白质组学数据。我们将通过检测来验证潜在的COPD和COPD亚型的血浆蛋白生物标志物 同一LTRC COPD患者和对照组血浆中前100个肺组织COPD特异性蛋白 研究对象。我们将利用现有的LTRC多OMICS数据(包括全基因组测序、RNA-Seq和 DNA甲基化)与新产生的质谱学和基于亲和力的蛋白质组学数据相结合 确定罕见和常见的COPD相关蛋白和COPD的遗传决定因素。机器学习和 网络分析将用于整合多个OMICS数据，以深入了解COPD的发病机制和 异质性。几个顶级COPD蛋白生物标记物的网络关系将通过以下方式进行功能验证 CRISPR-Cas9在原代肺细胞中的表达。慢性阻塞性肺疾病新蛋白的鉴定与鉴定 生物标志物可能为COPD的发病机制提供洞察力，并为未来的临床试验提供工具。

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.