COPD Susceptibility, Heterogeneity, and Progression: Proteomics and Genetics

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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.