Abstract
Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA–mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA–mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA–mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA–mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.
Highlights
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide (Celli and Wedzicha, 2019) and is primarily attributable to the effects of cigarette smoking
The samples in this miRNA–mRNA network study covered a range of spirometry profiles including normal (183), COPD with all four grades of Global Obstructive Lung disease (GOLD) airflow limitation severity (GOLD 1: 47; GOLD 2: 68; GOLD 3: 37; GOLD 4: 17), and Preserved Ratio Impaired Spirometry category (PRISm: 52)
We found three miRNA–mRNA networks associated with FEV1pp and three miRNA–mRNA networks associated with percent emphysema (Table 3)
Summary
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide (Celli and Wedzicha, 2019) and is primarily attributable to the effects of cigarette smoking. Smoke exposure drives COPD, we still have a poor understanding of the molecular traits and biologic pathways that are associated with specific COPD-related traits (Carolan et al, 2014). Different COPD-related phenotypes might be attributable to different molecular mechanisms, such as miRNA–mRNA networks. MicroRNAs (miRNAs) are a type of small non-coding RNAs that are approximately 21–25 nucleotides long and play important roles in regulating both gene and protein levels by binding to mRNAs to contribute to either transcript degradation or inhibition of protein translation. A single miRNA may regulate tens to hundreds of genes simultaneously due to the redundancy of complementary sequences between miRNAs and target sequences in the 3′UTR of mRNA(s). Many studies have implicated miRNAs in the pathogenesis of COPD (Osei et al, 2015; Salimian et al, 2018; Keller et al, 2019)
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