Prediction of COPD acute exacerbation in response to air pollution using exosomal circRNA profile and Machine learning

Abstract

Ambient fine particulate matter (PM2.5) is linked to an increased risk of chronic obstructive pulmonary disease (COPD) exacerbations, which significantly increase the risk of mortality in COPD patients. Identifying the subtype of COPD patients who are sensitive to environmental aggressions is necessary. Using in vitro and in vivo PM2.5 exposure models, we demonstrate that exosomal hsa_circ_0005045 is upregulated by PM2.5 and binds to the protein cargo peroxiredoxin2, which functionally aggravates hallmarks of COPD by recruiting neutrophil elastase and triggering in situ release of tumor necrosis factor (TNF)-α by inflammatory cells. The biological function of hsa_circ_0005045 associated with aggravation of COPD is validated using exosome-transplantation and conditional circRNA-knockdown murine models. By sorting the major components of PM2.5, we find that PM2.5-bound heavy metals, which are distinguishable from the components of cigarette smoke, trigger the elevation of exosomal hsa_circ_0005045. Finally, using machine learning models in a cohort with 327 COPD patients, the PM2.5 exposure-sensitive COPD patients are characterized by relatively high hsa_circ_0005045 expression, non-smoking, and group C (mMRC 0–1 (or CAT < 10) and ≥ 2 exacerbations (or ≥ 1 exacerbation leading to hospital admission) in the past year). Thus, our results suggest that environmental reduction in PM2.5 emission provides a targeted approach to protecting non-smoking COPD patients against air pollution-related disease exacerbation.

Keywords

• COPD
• circRNA
• PM2.5
• Air pollution
• Machine learning