A proteomics approach to identify COPD-related changes in lung fibroblasts

Lung fibroblasts are implicated in abnormal tissue repair in chronic obstructive pulmonary disease (COPD). The exact mechanisms are unknown and comprehensive analysis comparing COPD- and control fibroblasts is lacking. The aim of this study is to gain insight into the role of lung fibroblasts in COPD pathology using unbiased proteomic and transcriptomic analysis. Protein and RNA were isolated from cultured parenchymal lung fibroblasts of 17 patients with stage IV COPD and 16 non-COPD controls. Proteins were analyzed using LC-MS/MS and RNA through RNA sequencing. Differential protein and gene expression in COPD was assessed via linear regression, followed by pathway enrichment, correlation analysis, and immunohistological staining in lung tissue. Proteomic and transcriptomic data were compared to investigate the overlap and correlation between both levels of data. We identified 40 differentially expressed (DE) proteins and zero DE genes between COPD and control fibroblasts. The most significant DE proteins were HNRNPA2B1 and FHL1. Thirteen of the 40 proteins were previously associated with COPD, including FHL1 and GSTP1. Six of the 40 proteins were related to telomere maintenance pathways, and were positively correlated with the senescence marker LMNB1. No significant correlation between gene and protein expression was observed for the 40 proteins. We hereby describe 40 DE proteins in COPD fibroblasts including previously described COPD proteins (FHL1, GSTP1) and new COPD research targets like HNRNPA2B1. Lack of overlap and correlation between gene and protein data supports the use of unbiased proteomics analysis and indicates that different types of information are generated with both methods.

Automated summary

This study compared COPD and non-COPD fibroblasts using proteomic and transcriptomic analysis, and identified 40 differentially expressed proteins, including previously described COPD proteins and new COPD research targets. Lack of overlap and correlation between gene and protein data supports the use of unbiased proteomics analysis.