Metagenomic Anaysis of Nasal Microbiota-Derived Extracellular Vesicle in Patients with Allergic Rhinitis [post]

Tsai-Yeh Chiang, Yu-Ru Yang, Ming-Ying Zhuo, Feng Yang, Ying-Fei Zhang, Chia-Hsiang Fu, Ta-Jen Lee, Wen-Hung Chung, Liang Chen, Chih-Jung Chang
2021 unpublished
Nasal Microbiota is crucial for the pathogenesis of allergic rhinitis (AR). However, never study investigates the microbiota in nasal extracellular vesicles (EVs). Objective: We aim to compare the microbiome composition and diversity in EVs between AR and health controls (HCs), and reveal the potential metabolic mechanisms in AR. Eosinophil count and serum immunoglobulin E (IgE) were measured in AR patients (n=20) and HCs (n=19). Nasal EVs were identified by transmission electron microscopy and
more » ... flow cytometry. 16S rRNA sequencing was used to profile microbial communities. Alpha and beta diversity were analyzed to reflect the microbial diversity. Taxonomic abundance was analyzed based on linear discriminant analysis effect size (LEfSe). Microbial metabolic pathways were characterized using PICRUSt and KEGG analyses. Eosinophils, total serum IgE, and specific IgE to Dermatophagoides were increased in AR patients. Alpha diversity in nasal EVs from AR patients were lower than that in HCs. Beta diversity showed the microbiome differences between AR and HCs. Microbial abundance was distinct between AR and HCs at different taxonomic levels. The significant higher level of genera Acetobacter, Mycoplasma, Escherichia and Halomonas in AR patients than those in HCs. Conversely, the genera Zoogloea, Streptococcus, Burkholderia as well as Pseudomonas were more abundant in the HCs group. Moreover, 35 microbial metabolic pathways were different between AR and HCs, and 25 pathways were more abundant in AR. AR patients had distinguished microbiota characteristics in nasal EVs compared with HCs. The metabolic mechanisms of microbiota regulating AR development also altered. The nasal fluid may reflect the specific pattern of microbiome EVs in patients with AR.
doi:10.21203/ fatcat:qljs4q77n5hudngth4sh7clq2i