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

  • 1Institute of MD Healthcare Inc., Seoul, Korea
  • 2Department of Health and Safety Convergence Science, Korea University, Seoul, Korea
  • 3Department of Internal Medicine, Dankook University College of Medicine, Cheonan, Korea
  • 4Cell Therapy Research Center, GCLabCell, Yongin, Korea
  • 5Department of Pulmonary and Critical Care Medicine, and Clinical Research Center for Chronic Obstructive Airway Disease, Asan Medical Center, Seoul, Korea
  • 6Department of Allergy and Clinical Immunology, Asan Medical Center, Seoul, Korea
  • 7Department of Allergy and Clinical Immunology, Ajou University Medical Center, Suwon, Korea
  • 8Professor Emeritus of Seoul National University, Honorary President of Korea Asthma Allergy Foundation, Seoul, Korea


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.
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.
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.
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.


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