Identifying Protein–metabolite Networks Associated with COPD Phenotypes

Abstract

Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein–metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein–metabolite network consisting of 13 proteins and 7 metabolites which had a −0.34 correlation (p-value = 2.5 × 10−28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a −0.27 correlation (p-value = 2.6 × 10−17) to percent emphysema. Protein–metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.

An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning

“…The Global Obstructive Lung Disease (GOLD) system was used to grade the severity of airflow limitation: GOLD 0 (controls) and GOLD 1-4 (COPD cases). Our study focuses on the 486 COPD cases (GOLD stage >0) in the Phase 2 study since the inherent protein networks between controls and COPD cases might be different (Mastej et al, 2020).…”

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

“…PC1 s is used to represent the data matrix X ′ as a single vector to facilitate comparison to the target variable. While there are other measures that can be used to represent correlation with a target variable, such as Spearman correlation or Kenall correlation, we chose Pearson correlation to match the measure used by Mastej et al to facilitate comparison between findings (Mastej et al, 2020).…”

“…Most biomarker studies have focused on single molecules, as they can facilitate prognosis and individualized treatment. However, as single biomarkers cannot fully explain the COPD cross-sectional and longitudinal outcomes, recent studies suggest multiple biomarkers may be more informative to predict severity, progression, and morality (Zemans et al, 2017 ; Mastej et al, 2020 ).…”

Integrating Genetics and Omics to Understand Chronic Obstructive Pulmonary Disease

“…Multiple Omics data can also be used to understand COPD pathogenesis. Recently, Mastej and Kechris 58 used SmCCNet with two Omics data types (metabolomics and proteomics of plasma) and quantitative COPD-related phenotypes (FEV 1 and CT quantitative emphysema) in 1008 COPDGene study participants. A network of seven metabolites and thirteen proteins was significantly correlated to lung function, and a network of ten metabolites and thirteen proteins was significantly correlated to emphysema.…”