Epithelium-derived cystatin SN inhibits house dust mite protease activity in allergic asthma.

BACKGROUND: Allergen source-derived proteases are a critical factor in the formation and development of asthma. The cysteine protease activity of house dust mite (HDM) disrupts the epithelial barrier function. The expression of cystatin SN (CST1) is elevated in asthma epithelium. CST1 inhibits the cysteine protease activity. We aimed to elucidate the role of epithelium-derived CST1 in the development of asthma caused by HDM. METHODS: CST1 protein levels in sputum supernatants and serum of patients with asthma and healthy volunteers were measured by ELISA. The ability of CST1 protein to suppress HDM-induced bronchial epithelial barrier function was examined in vitro. The effects of exogenous CST1 protein on abrogating HDM-induced epithelial barrier function and inflammation were examined in mice in vivo. RESULTS: CST1 protein levels were higher in sputum supernatants (142.4 ± 8.95 vs 38.87 ± 6.85 ng/mL, P < 0.0001) and serum (1129 ± 73.82 vs 703.1 ± 57.02 pg/mL, P = 0.0035) in patients with asthma than in healthy subjects. The levels were significantly higher in patients with not well- and very poorly controlled asthma than those with well-controlled asthma. Sputum and serum CST1 protein levels were negatively correlated with lung function in asthma. CST1 protein levels were significantly lower in the serum of HDM-specific IgE (sIgE)-positive asthmatics than in sIgE-negative asthmatics. The HDM-induced epithelial barrier function disruption was suppressed by recombinant human CST1 protein (rhCST1) in vitro and in vivo. CONCLUSION: Our data indicated that human CST1 protein suppresses asthma symptoms by protecting the asthmatic bronchial epithelial barrier through inhibiting allergenic protease activity. CST1 protein may serve as a potential biomarker for asthma control.

A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach.

Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic value has not yet been fully established in COPD. The purpose of this study was to develop an effective model for the diagnosis of COPD based on circulating miRNAs. We included circulating miRNA expression profiles of two independent cohorts consisting of 63 COPD and 110 normal samples, and then we constructed a miRNA pair-based matrix. Diagnostic models were developed using several machine learning algorithms. The predictive performance of the optimal model was validated in our external cohort. In this study, the diagnostic values of miRNAs based on the expression levels were unsatisfactory. We identified five key miRNA pairs and further developed seven machine learning models. The classifier based on LightGBM was selected as the final model with the area under the curve (AUC) values of 0.883 and 0.794 in test and validation datasets, respectively. We also built a web tool to assist diagnosis for clinicians. Enriched signaling pathways indicated the potential biological functions of the model. Collectively, we developed a robust machine learning model based on circulating miRNAs for COPD screening.