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. 2018 Nov 20;8(1):17132.
doi: 10.1038/s41598-018-35372-w.

Metabolomics and transcriptomics pathway approach reveals outcome-specific perturbations in COPD

Affiliations

Metabolomics and transcriptomics pathway approach reveals outcome-specific perturbations in COPD

Charmion I Cruickshank-Quinn et al. Sci Rep. .

Abstract

Chronic obstructive pulmonary disease (COPD) comprises multiple phenotypes such as airflow obstruction, emphysema, and frequent episodes of acute worsening of respiratory symptoms, known as exacerbations. The goal of this pilot study was to test the usefulness of unbiased metabolomics and transcriptomics approaches to delineate biological pathways associated with COPD phenotypes and outcomes. Blood was collected from 149 current or former smokers with or without COPD and separated into peripheral blood mononuclear cells (PBMC) and plasma. PBMCs and plasma were analyzed using microarray and liquid chromatography mass spectrometry, respectively. Statistically significant transcripts and compounds were mapped to pathways using IMPaLA. Results showed that glycerophospholipid metabolism was associated with worse airflow obstruction and more COPD exacerbations. Sphingolipid metabolism was associated with worse lung function outcomes and exacerbation severity requiring hospitalizations. The strongest associations between a pathway and a certain COPD outcome were: fat digestion and absorption and T cell receptor signaling with lung function outcomes; antigen processing with exacerbation frequency; arginine and proline metabolism with exacerbation severity; and oxidative phosphorylation with emphysema. Overlaying transcriptomic and metabolomics datasets across pathways enabled outcome and phenotypic differences to be determined. Findings are relevant for identifying molecular targets for animal intervention studies and early intervention markers in human cohorts.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow chart summarizing the omics methods and analysis. Peripheral blood mononuclear cells (PBMC) (n = 136 human subjects) and matched plasma (n = 131) were prepared and analyzed using functional genomics and metabolomics, respectively. Metabolomics samples were analyzed randomly in triplicate. Regression model fitting in R resulted in a list of statistically significant transcript probes and metabolites associated with each outcome. These significant transcript probes and metabolites were mapped to pathways using IMPaLA. Pathway p-values for metabolites and transcripts were combined by a meta-analysis using Fisher’s method to obtain a single p-value and FDR for each pathway. In the outcome tables, ‘# sig’ refers to the number of statistically significant transcripts probes and metabolites. BMI: body mass index, FEV1: forced expiratory volume in 1 second, FVC: forced vital capacity.
Figure 2
Figure 2
Venn diagrams. (A) Edwards’ Venn diagram showing the overlap of significant transcript probes (p ≤ 0.015, FDR ≤ 0.1) across COPD outcomes. (B) Edwards’ Venn diagram showing the overlap of statistically significant metabolites (p ≤ 0.05, FDR ≤ 0.15) across COPD outcomes. (C) Classic Venn diagram showing the overlap of statistically significant pathways (p ≤ 0.05, FDR ≤ 0.2, ≥3 hits). The overlapping pathways are shown in red arrows. The unique pathways are listed in Table 4. %Emphy: % emphysema, Exac Freq: exacerbation frequency, Exac Sever: exacerbation severity, BDR: bronchodilator response, FEV1: Forced expiratory volume in 1 second, FVC: Forced vital capacity, FEV1%Pred: FEV1% predicted.
Figure 3
Figure 3
Transcriptomics pathways. (A) Oxidative phosphorylation in emphysema. (B) Antigen processing and presentation for exacerbation frequency. Blue stars indicate: associated with a decrease with worsening outcome. Purple stars represent both an increase or decrease in expression if multiple transcripts are mapped to a single gene. Pathway image is modified from KEGG.
Figure 4
Figure 4
Metabolomics pathways. (A) Arginine and proline metabolism in exacerbation severity. (B) Glycine, serine and threonine metabolism in exacerbation severity. Red boxes indicate associated with an increase with worsening outcome and blue boxes indicate associated with a decrease with worsening outcome.
Figure 5
Figure 5
Integrated transcriptomics and metabolomics pathway diagrams. (A) Glycerophospholipid metabolism in FEV1% predicted. (B) Fat digestion and absorption in FEV1/FVC modified from KEGG. FA: fatty acid, BA: bile acid, PA: phosphatidic acid, PL: phospholipids, MAG: monoglycerides, DAG: diglycerides, TAG: triglycerides, CE: cholesterol ester, CL: cholesterol. Red font and red boxes indicate associated with an increase with worsening disease. Blue font and blue boxes indicate associated with a decrease with worsening disease. Black font or uncolored boxes indicate no statistical significance.
Figure 6
Figure 6
Overview of outcome perturbations and pathway relationships. The connections between the pathways were mapped using KEGG. White boxes indicate no statistical significance, while colored boxes indicate significance based on outcome as described in the Legend.

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