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.