Abstract
BackgroundPrevious studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in COPD. Yet, it is not clear whether the lung pathobiology is mirrored by these surrogate tissues. The aim of this study was to explore this question.MethodsWe used Weighted Gene Co-expression Network Analysis (WGCNA) to identify shared pathological mechanisms across four COPD gene-expression datasets: two sets of lung tissues (L1 n = 70; L2 n = 124), and one each of induced sputum (S; n = 121) and peripheral blood (B; n = 121).ResultsWGCNA analysis identified twenty-one gene co-expression modules in L1. A robust module preservation between the two L datasets was observed (86%), with less preservation in S (33%) and even less in B (23%). Three modules preserved across lung tissues and sputum (not blood) were associated with the severity of airflow limitation. Ontology enrichment analysis showed that these modules included genes related to mitochondrial function, ion-homeostasis, T cells and RNA processing. These findings were largely reproduced using the consensus WGCNA network approach.ConclusionsThese observations indicate that major differences in lung tissue transcriptomics in patients with COPD are poorly mirrored in sputum and are unrelated to those determined in blood, suggesting that the systemic component in COPD is independently regulated. Finally, the fact that one of the preserved modules associated with FEV1 was enriched in mitochondria-related genes supports a role for mitochondrial dysfunction in the pathobiology of COPD.
Highlights
Previous studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in Chronic Obstructive Pulmonary Disease (COPD)
We used Weighted gene co-expression network correlationbased analysis (WGCNA) to meta-analyse four previously published COPD transcriptomic datasets determined in lung tissue (L), blood (B) and sputum (S) [3, 16,17,18] (1) to identify L modules related with FEV 1, (2) to investigate if these L modules are preserved in S and/or B data sets, and (3) to investigate the biological processes associated with these modules
Core of co-expressed genes and related ontologies We found that, out of the 185 genes shared by the yellow WGCNA module and the brown consensus module, 60 genes were nominally associated with forced expiratory volume in 1 s (FEV1)% predicted at p < 0.1 in lung tissue and sputum (Additional file 8: Table S4); Gene Ontology analysis showed enrichment in mitochondrial-related ontologies (Fig. 5a, Additional file 9: Table S5)
Summary
Previous studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in COPD. Since the lung parenchyma is difficult to access, the pathobiology of COPD is often studied in surrogate tissue samples, such as sputum or circulating blood In these surrogate tissues, different mRNAs whose expression is associated with the severity of airflow limitation have been identified. Weighted gene co-expression network correlationbased analysis (WGCNA) is a particular type of network analysis that allows the identification of modules of co-expressed genes in a given transcriptomic dataset, the investigation of the degree of module preservation in other datasets, and the study of their relationship with clinical features of interest [13,14,15] This network based comparison can be performed using data from different technological platforms [13,14,15]. We used WGCNA to meta-analyse four previously published COPD transcriptomic datasets determined in lung tissue (L), blood (B) and sputum (S) [3, 16,17,18] (1) to identify L modules related with FEV 1, (2) to investigate if these L modules are preserved in S and/or B data sets, and (3) to investigate the biological processes associated with these modules
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