Introduction
Asthma is a complex inflammatory disease resulting from the interaction between genetic susceptibility and environmental exposures over the course of the individuals life 1. The bronchial microbiome of patients with asthma differs from that of the healthy controls 2,3. Researchers have studied the relationships between the respiratory microbiota and heterogeneity of asthma, and suggested that the microbiome may be a factor determining asthma progression 4,5. The potential role of the airway microbiota has been the focus of research on the pathogenesis of asthma.
Metagenomic analysis has been conducted using 16S rDNA sequencing from extracellular vesicles (EVs) to identify microbiomes and provide culture-independent estimation of microbial diversity6. EVs, which are nanoparticles secreted by bacteria, have an important biological function as intercellular signaling mediators via enclosed proteins, nucleic acids, and lipids, and as biomarkers for the diagnosis and prognosis of diseases7,8. They also contain fragments of bacterial genomic DNA and induce the host immune response, and may reflect the microbiota of the host9. Thus, EVs are valuable diagnostic tool for metagenomics.
There is no consensus regarding to the best sample procedure for the metagenomics analysis of respiratory disease. Studies of the lower airways can be challenging because of the limitations associated with sampling the bronchial airway, which involves invasive bronchoscopy. Exhaled breath condensate (EBC) contains aerosol and volatile compounds that can be analyzed to noninvasively understand the physiologic and pathologic processes in the lung10.
This study performed metagenomic analysis using EBC-dereived EVs to determine and compare the characteristics of the microbiome in patients with asthma with those in healthy controls, and to investigate the microbiome differences between eosinophilic and non-eosinophilic asthma. Moreover, we suggested a diagnostic tool for asthma using artificial intelligence (AI) modeling based on the results of the microbiota composition in patients with asthma.