Discussion
This was the first study to identify bacterial compositional differences between patients with asthma and healthy controls using EBC-derived EVs. Patients with asthma exhibited significantly different diversity and richness indicators compared to the healthy controls. Five bacterial genera, including Sphingomonas , Akkermansia ,Methylophaga, Acidocella , and Marinobacter were significantly abundant in the EBC EVs of patients with asthma, whereasAcinetobacter , Staphylococcus , Bifidobacterium ,Blautia , and Collinsella were more abundant in the controls. No significant difference was observed between the EBC microbiota in eosinophilic and non-eosinophlic asthma. We suggested a diagnostic tool for asthma using AI modeling based on the distinctness of the bacterial community. Firmicutes and Proteobacteria were common important features at the phylum level in the GBM and ANN asthma models.
This metagenomic analysis of EBC-derevied EVs enhances the understanding of the bacterial communities residing in the airway. EVs are composed of a lipid bilayer that contains transmembrane proteins which affect host cells and other bacteria, even those located far from the colonized area17,18. Moreover, they participate in intercellular communication and have potential applications as biomarkers and therapeutic agents for several allergic diseases19,20. Airway studies focusing on the microbiota and EVs in nasal lavage and lung tissue samples reported that EVs have distinct characteristics associated with chronic rhinitis and chronic obstructive pulmonary diseases (COPD) 18,21.
Although the advances in metagenomics have elucidated the lung microbiota, sampling the lower respiratory tract is a challenge. Bronchoscopy enables investigation of the lower airway with greater precision, but is invasive. The use of bronchial brushes and bronchoalveolar lavage (BAL) fluid is disputed due to potential contamination from the oropharynx 22. The collection of induced sputum is noninvasive; however, it requires preliminary inhalation of an aerosol and is often impractical. The EBC collection technique is a safe that can be repeated easily, and the sample contains nonvolatile water-soluble compounds from the airway mucosa, which are likely to reflect the composition of the airway lining fluid23,24. Therefore, we used EBC-derived EVs to identify the bacterial composition of patients with asthma for the first time.
The relationship between asthma and lung bacterial community diversity is controversial. Previous studies in patients with asthma have documented increased bacterial burden and diversity using induced sputum and bronchial brushing compared with healthy controls2,25. Similarly, other studies reported an increase in the bacterial diversity in BAL samples obtained from 23 patients with eosinophil-low asthma 26. This study also showed greater bacterial diversity in patients with asthma based on Chao1, Shannon, and Simpson indices compared with controls using EVs derived from EBC. Conversely, other studies failed to replicate these findings and occasionally presented opposite results. A study reported that there were no significant differences in alpha diversity between patients with severe and non-severe asthmatics, and healthy controls27,28. This discrepancy may be attributed to the differences in the populations from which samples were obtained and in the sampling collection methods and sites.
In this study, EBC EV samples obtained from patients with asthma had a significantly higher proportion of Sphingomonas ,Akkermansia and Methylophaga at the genus level compared to the controls. Sphingomonas species have been associated with increased airway hyperresponsiveness to methacholine and eosinophil-high asthma 2,26. Firmicutes and Proteobacteria were phyla that were demonstrated to be increased in the patients with asthma in the GBM ad ANN asthma models. A previous study reported that relevant differences were found in Firmicutes and Proteobacteria between induced sputum samples obtained from asthmatics and healthy controls25, which is consistent with our results. Other studies using bronchial samples reported that Proteobacteria were present in higher proportions in patients with asthma compared to the controls 2,3. Elevated levels of the Firmicutes phylum have been found in the lung tissue-derived EVs of patients with COPD, which may be explained by differences in pH, oxygenation levels, and temperature of the lung environment related to esophageal reflux18. Patients with chronic lung diseases such as asthma and COPD share similar bacteria causing dysbiosis, such as Protebacteria and Firmicutes29. Although we can speculate on the causal relationship between the microbiota and airway inflammation, we cannot confirm its existence yet. Further studies are needed to investigate the specific causal relationship between the microbiota and airway inflammation mediated via the molecular pathways. Additionally, based on the result of the microbiome-based differences between patients with asthma and controls, we proposed a diagnostic AI model of asthma with good performance. This might be a biomarker for risk assessment and treatment of asthma through further studies.
As the scope of microbiome-focused research widens, numerous studies have reported distinct respiratory microbiota according to the various asthma phenotypes and endotypes, such as obesity, severity, airway hyperresponsiveness, asthma control, acute exacerbation, and response to steroids 30-34. In particular, the eosinophilic inflammatory phenotype and underlying T2-high endotype corresponded to lung microbial communities with comparatively lower bacterial loads and substantial diversity 35. Unfortunately, we did not demonstrate the distinction in the airway microbiota according to blood eosinophil counts in this study. Our results may be attributed to the relatively small number of EBC samples and variations in the participants, which may be potentially attributable to populations, environment, and antibiotic use history.
Recent immunomodulatory treatments for asthma mainly focused on the mechanisms of type 2 inflammation, such as biologics, i.e., antibodies against cytokine mediators (e.g., IL-5, IL-4, and IL-13)31. However, these treatments are not effective for patients with a T2-low endotype, which may limit the indications of biologics in them. The strong association between airway dysbiosis and pathogenesis of neutrophilic asthma has also been reported36,37; thus, the manipulation of the airway and gut microbiomes may be a novel therapeutic strategy for asthma, especially in patients with T2-low endotypes. Further investigation into the impact of microbiota therapies may be a useful tool for controlling asthma using a personalized medicine approaches.
This study has several limitations. Firstly, the sample size of both groups was relatively small. Larger studies are needed to confirm our finding. Second, baseline characteristics such as dietary patterns and use of antibiotics were not evaluated in controls and asthmatics, which might have affected the bacterial community as a confounding bias. Nevertheless, to the best our knowledge, this was the first study to analyze the microbiota using EBC-derived EVs in patients with asthma and compare it with healthy controls.
In conclusion, we have demonstrated differences between the lung microbiome of patients with asthma and controls by analyzing EVs derived from EBC. We also proposed an asthma diagnostic model using AI. Our findings suggest that asthma may be associated with dysbioisis of the bacterial community, and the microbiome obtained using EBC EVs may be a potential indicator for the diagnosis of asthma. Further studies are needed to verify the distinct patterns of the microbiome according to asthma phenotypes and endotypes, considering the heterogeneity of asthma.