Omics and the Search for Blood Biomarkers in Chronic Obstructive Pulmonary Disease. Insights from COPDGene

Abstract

There is an unmet need for blood biomarkers in diagnosis and prognosis of chronic obstructive pulmonary disease (COPD). The search for these biomarkers has been revolutionized by high-throughput sequencing techniques and multiplex platforms that can measure thousands of gene transcripts, proteins, or metabolites. We review COPDGene (Genetic Epidemiology of COPD) project publications that include DNA methylation, transcriptomic, proteomic, and metabolomic blood biomarkers and discuss their impact on COPD. Key contributions from COPDGene include identification of DNA methylation effects from smoking and genetic variation, new transcriptomic signatures in the blood, identification of protein biomarkers associated with severity and progression (e.g., sRAGE [soluble receptor for advanced glycosylation end products], inflammatory cytokines IL-6 and IL-8), and identification of small molecules (ceramides and sphingomyelin) that may be pathogenic. COPDGene studies have revealed that some of the COPD genome-wide association study polymorphisms are strongly associated with blood biomarkers (e.g., rs2070600 in AGER is a pQTL [protein quantitative trait locus] for sRAGE), underscoring the importance of combining omics results. Investigators have developed molecular networks identifying lower CD4⁺ resting memory cells associated with COPD. Genes, proteins, and metabolite networks are particularly important because the explanatory value of any single molecule is small (1-10%) compared with panels of multiple markers. COPDGene has been a useful resource in the identification and validation of multiple biomarkers for COPD. These biomarkers, either combined in multiple biomarker panels or integrated with other omics data types, may lead to novel diagnostic and prognostic tests for COPD phenotypes and may be relevant for assessing novel therapies.

Keywords: biomarkers; chronic obstructive pulmonary disease; epigenetics; gene expression; metabolomics.

Figure 1.

Plasma sRAGE (soluble receptor for advanced glycosylation end products) is lower in COPDGene (Genetic Epidemiology of COPD) subjects with more severe emphysema (P < 0.0001) but is dependent on their genotype at rs2070600. COPD = chronic obstructive pulmonary disease. Adapted from Reference .

Figure 2.

Examples of biomarker pQTL (protein quantitative trait locus) SNPs. Plasma levels of (A) IL6R and (B) E-selectin are strongly influenced by pQTL SNPs (P = 10⁻¹⁹³ and P = 4 × 10⁻¹⁰⁴, respectively). Biomarkers were transformed to standard normal distribution, and statistical testing was performed using PLINK version 1.9 as described elsewhere (30). The pQTL SNP for IL6R (IL-6 receptor) is on chromosome 4, which is local (cis) to IL6R, the gene coding for its protein. The pQTL SNP for E-selectin protein is on chromosome 9, which is distant (trans) from SELE (chromosome 1), the gene coding for its protein. This pQTL SNP is in the ABO locus, which encodes α-1-3-N-acetylgalactosaminyltransferase. SPIROMICS = Subpopulations and Intermediate Outcomes in COPD Study.

Figure 3.

Metabolite correlations across BAL fluid (BALF) and plasma. Data from the mouse and human BALF and plasma samples were combined to identify metabolites that correlated across both biofluids using Spearman rank correlation. From among 298 annotated metabolites, a subset of 35 metabolites was selected on the basis of their detected high abundances in BALF and plasma. Red indicates a negative correlation (r = −1) across BALF and plasma; green indicates a positive correlation (r = +1) between BALF and plasma; and black indicates no correlation (r = 0) between BALF and plasma. CE = cholesterol ester; LysoPC = lysophosphatidycholine; LysoPE = lysophosphatidylethanolamine; MG = monoglycerides; PC = phosphotidylcholines; PE = phosphotidylethanolamines; PI = phosphotidylinositol; SM = sphingomyelins.