Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure.

backgroundProteomics and genomics studies have contributed to understanding the pathogenesis of chronic obstructive pulmonary disease (COPD), but previous studies have limitations. Here, using a machine learning (ML) algorithm, we attempted to identify pathways in cultured bronchial epithelial cells of COPD patients that were significantly affected when the cells were exposed to a cigarette smoke extract (CSE).

methodsSmall airway epithelial cells were collected from patients with COPD and those without COPD who underwent bronchoscopy. After expansion through primary cell culture, the cells were treated with or without CSEs, and the proteomics of the cells were analyzed by mass spectrometry. ML-based feature selection was used to determine the most distinctive patterns in the proteomes of COPD and non-COPD cells after exposure to smoke extract. Publicly available single-cell RNA sequencing data from patients with COPD (GSE136831) were used to analyze and validate our findings.

resultsFive patients with COPD and five without COPD were enrolled, and 7,953 proteins were detected. Ferroptosis was enriched in both COPD and non-COPD epithelial cells after their exposure to smoke extract. However, the ML-based analysis identified ferroptosis as the most dramatically different response between COPD and non-COPD epithelial cells, adjusted

conclusionOur ML-based feature selection from proteomic data reveals ferroptosis to be the most distinctive feature of cultured COPD epithelial cells compared to non-COPD epithelial cells upon exposure to smoke extract.