Integrative RNA-sequencing analysis of COPD-related genes in association with individual PM_2.5 exposure

Airborne fine particulate matter (PM_2.5) is associated with chronic obstructive pulmonary disease (COPD); however, the precise mechanism remains unclear. Here, we examined distinct gene and pathway characteristics across varying personal and ambient PM_2.5 exposure durations within a prospective COPD cohort and the associations between differentially expressed genes (DEGs) and clinical phenotypes.

Blood samples for RNA-sequencing were collected from 50 patients with COPD who underwent spirometry and quantitative computed tomography. We estimated personal and ambient PM_2.5 exposure levels using hybrid and land use regression models. Associations between DEGs and PM_2.5 exposure were examined in relation to lung function indicators (FEV₁, FVC, and FEV₁/FVC ratio) using Pearson correlation analysis adjusted for factors such as hospitalization, age, sex, season, Charlson Comorbidity Index score, and smoking status.

We analyzed DEGs across three cumulative PM_2.5 exposure periods using personal and ambient exposure assessments. Gene ontology annotation and biological pathway analysis of the identified DEGs using the individual air pollution exposure prediction model revealed significant associations with gas transport, cellular processes related to cell cycle, cell proliferations, and neuron projection morphogenesis. The ambient air pollution prediction model revealed significant biological responses related to purine metabolism and antigen processing and presentation. EDAR, NKILA, HSD11B2, LOC100130027, LOC105378367, SENCR, CAMP, CEACAM6, CHIT1, EREG, HSD17B3, NPPA-AS1, and TRPV4 showed increased expression with higher PM_2.5, correlating with reduced lung function.

Our findings offer insights into the role of gene expression in patients with COPD in response to personal and ambient PM_2.5 exposure, suggesting strategies to enhance respiratory conditions linked to air pollution.