Allergy Asthma Immunol Res.  2020 Jul;12(4):669-683. 10.4168/aair.2020.12.4.669.

Lung Disease Diagnostic Model Through IgG Sensitization to Microbial Extracellular Vesicles

• Yang J
• Hong G
• Kim YS
• Seo H
• Kim S
• McDowell A
• Lee WH
• Kim YS
• Oh YM
• Cho YS
• Choi YW
• Kim YY
• Jee YK
• Kim YK

• KMID: 2500746
• DOI: http://doi.org/10.4168/aair.2020.12.4.669

Abstract

Purpose
Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease.
Methods
We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis.
Results
Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81.
Conclusions
The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Keyword

Extracellular vesicles; dust; diagnosis; asthma; chronic obstructive pulmonary disease; lung neoplasms, microbiome; bacteria