Microbial characterization of the nasal cavity in patients with allergic rhinitis and non-allergic rhinitis - Che, Yanlu; Wang, Nan; Ma, Qianzi; Liu, Junjie; Xu, Zhaonan; Li, Qiuying; Wang, Jingting; Sun, Yanan (Frontiers in Cellular and Infection Microbiology, 2023)
Introduction Although recent studies have shown that the human microbiome is involved in the pathogenesis of allergic diseases, the impact of microbiota on allergic rhinitis (AR) and non-allergic rhinitis (nAR) has not been elucidated. The aim of this study was to investigate the differences in the composition of the nasal flora in patients with AR and nAR and their role in the pathogenesis. Method From February to September 2022, 35 AR patients and 35 nAR patients admitted to Harbin Medical University’s Second Affiliated Hospital, as well as 20 healthy subjects who underwent physical examination during the same period, were subjected to 16SrDNA and metagenomic sequencing of nasal flora. Results The microbiota composition of the three groups of study subjects differs significantly. The relative abundance of Vibrio vulnificus and Acinetobacter baumanni in the nasal cavity of AR patients was significantly higher when compared to nAR patients, while the relative abundance of Lactobacillus murinus, Lactobacillus iners, Proteobacteria, Pseudomonadales, and Escherichia coli was lower. In addition, Lactobacillus murinus and Lacttobacillus kunkeei were also negatively correlated with IgE, while Lacttobacillus kunkeei was positively correlated with age. The relative distribution of Faecalibacterium was higher in moderate than in severe AR patients. According to KEGG functional enrichment annotation, ICMT(protein-S-isoprenylcysteine O-methyltransferase,ICMT) is an AR microbiota-specific enzyme that plays a role, while glycan biosynthesis and metabolism are more active in AR microbiota. For AR, the model containing Parabacteroides goldstemii, Sutterella-SP-6FBBBBH3, Pseudoalteromonas luteoviolacea, Lachnospiraceae bacterium-615, and Bacteroides coprocola had the highest the area under the curve (AUC), which was 0.9733(95%CI:0.926-1.000) in the constructed random forest prediction model. The largest AUC for nAR is 0.984(95%CI:0.949−1.000) for the model containing Pseudomonas-SP-LTJR-52, Lachnospiraceae bacterium-615, Prevotella corporis, Anaerococcus vaginalis, and Roseburia inulinivorans. Conclusion In conclusion, patients with AR and nAR had significantly different microbiota profiles compared to healthy controls. The results suggest that the nasal microbiota may play a key role in the pathogenesis and symptoms of AR and nAR, providing us with new ideas for the treatment of AR and nAR.