Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study

Abstract

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

Keywords: biomarkers; interstitial lung disease; proteomics; pulmonary emphysema.

Figure 1.

Volcano plot of proteins significantly associated with quantitative interstitial abnormalities independently in COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) on the left and CARDIA (Coronary Artery Disease Risk in Young Adults) on the right. AFM = afamin; APOM = apolipoprotein M; ATP1B1 = Sodium/potassium-transporting ATPase subunit β-1; B2M = β-2-microglobulin; BAGE3 = B melanoma antigen 3; CAPG = macrophage-capping protein; CCDC126 = coiled-coil domain-containing protein 126; CCL18 = C-C motif chemokine 18; CDCP1 = CUB domain-containing protein 1; CDH11 = cadherin-11: extracellular domain; COPDGene = Genetic Epidemiology of Chronic Obstructive Pulmonary Disease; CXCL16 = C-X-C motif chemokine 16; EGFR = epidermal growth factor receptor; F2 = thrombin; FDR = false discovery rate; GDF15 = growth differentiation factor 15; LUM = lumican; MXRA8 = matrix remodeling–associated protein 8: extracellular domain; NPS = neuropeptide S; PLA2G12B = group XIIB secretory phospholipase A2-like protein; PROC = vitamin K–dependent protein C; RFU = relative fluorescent units; SVEP1 = sushi, von Willebrand factor type A, EGF and pentraxin domain-containing protein 1: EGF-like domains 4–6; THBS2 = thrombospondin-2; WFDC2 = WAP four-disulfide core domain protein 2; ZG16 = zymogen granule membrane protein 16.

Figure 2.

Proteins significantly associated with quantitative interstitial abnormalities in CARDIA (Coronary Artery Disease Risk in Young Adults) and COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease), with β coefficients of associations in CARDIA on the x-axis and in COPDGene on the y-axis. ADAMTS13 = A disintegrin and metalloproteinase with thrombospondin motifs 13; AFM = afamin; AGER = receptor for advanced glycation end-products; APOM = apolipoprotein M; BAGE3 = B melanoma antigen 3; CCDC126 = coiled-coil domain-containing protein 126; CCL18 = C-C motif chemokine 18; CHI3L1 = chitin-3-like protein 1; CNTFR = ciliary neurotrophic factor receptor subunit α; CNTN1 = contactin-1; COL13A1 = collagen α-1(XIII) chain; CXCL16 = C-X-C motif chemokine 16; EGFR = epidermal growth factor receptor; F2 = thrombin; IL6R = IL-6 receptor subunit α; LILRA5 = leukocyte immunoglobulin-like receptor subfamily A member 5; LUM = lumican; MXRA8 = matrix remodeling–associated protein 8: extracellular domain; NPS = neuropeptide S; PLA2G12B = group XIIB secretory phospholipase A2-like protein; PROC = vitamin K–dependent protein C; SPON1 = spondin-1; WFDC2 = WAP four-disulfide core domain protein 2; ZG16 = zymogen granule membrane protein 16.

Figure 3.

Pathway enrichment analysis of proteins associated with quantitative interstitial abnormalities shared in COPDGene and CARDIA participants. CARDIA = Coronary Artery Disease Risk in Young Adults; COPDGene = Genetic Epidemiology of Chronic Obstructive Pulmonary Disease.

Figure 4.

Results of the tissue-specific deconvolution of the significant proteins using the Genotype-Tissue Expression database. The gene expression activities of the proteins positively significantly associated with quantitative interstitial abnormalities were scored using the R package singscore for each tissue, which were used to rank the 37 tissues in COPDGene (left) and CARDIA (right). CARDIA = Coronary Artery Disease Risk in Young Adults; COPDGene = Genetic Epidemiology of Chronic Obstructive Pulmonary Disease.