期刊
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY
卷 68, 期 6, 页码 651-663出版社
AMER THORACIC SOC
DOI: 10.1165/rcmb.2022-0302OC
关键词
chronic obstructive pulmonary disease; multi-omics analyses; quantitative trait locus; weighted gene co-expression network analysis
The integration of lung tissue transcriptomic and proteomic data with COPD-associated genetic variants provides insight into the biological mechanisms of COPD. Low correlations were observed between transcriptomics and proteomics, but higher correlations were found for COPD-associated proteins. Regulatory cis-QTLs were identified through the integration of COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins. Multiple COPD-associated biomarkers were found to be regulated by significant expression QTLs (eQTLs) and protein QTLs (pQTLs). Colocalization analysis, mediation analysis, and correlation-based network analysis identified key genes and proteins working together to influence COPD pathogenesis.
The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms of COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis-quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant pQTLs through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between COPD genome-wide association studies and eQTL and pQTL signals. Evidence was found for colocalization between COPD genome-wide association study signals and a pQTL for RHOB and an eQTL for DSP. We applied weighted gene co-expression network analysis to find consensus COPD-associated network modules. Two network modules generated by consensus weighted gene co-expression network analysis were associated with COPD with a false discovery rate lower than 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting determinants of transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple omics data may identify key genes and proteins that work together to influence COPD pathogenesis.
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