ArticleFrontiers in microbiology2024
16S rRNA sequencing-based evaluation of the protective effects of key gut microbiota on inhaled allergen-induced allergic rhinitis.
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Research progress on circRNAs in type 2 CRS.Frontiers in immunology · 2026Review
- Exploring putative links between gut microbiota and allergic rhinitis: insights from Mendelian randomization and multi-transcriptome integration.AMB Express · 2025Article
- Indolepropionic acid modulates the immune response in allergic rhinitis through the AKT/CEBPB/IL‑10 signaling pathway.Molecular medicine reports · 2025Article
- Decoding microbiome responses to quarantine potato wart disease: first insights into suppression and biocontrol by full-length 16S rRNA gene profiling and functional prediction.Frontiers in plant science · 2025Article
- Effects of chromium propionate supplementation on production performance, blood parameters, ruminal fermentation indices, and microbial diversity in heat-stressed Holstein dairy cows.Frontiers in veterinary science · 2025Article
- Integrated gut microbiome and metabolomics analysis reveals microbial-metabolic cross-talk in allergic rhinitis.Frontiers in microbiology · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Introduction: Allergic rhinitis (AR) is a common respiratory disorder influenced by various factors in its pathogenesis. Recent studies have begun to emphasize the significant role of gut microbiota in immune modulation and its potential association with the development of AR. This research aims to characterize the gut microbiota of patients with AR who are sensitized via inhalation, utilizing 16S rRNA sequencing to shed light on the pathogenesis of AR and identify potential therapeutic targets. Methods: To achieve the study's objectives, we compared the microbiota profiles between patients with AR and healthy controls. Microbial diversity was assessed using alpha and beta diversity indices, and differential microbiota populations were identified through Linear discriminant analysis Effect Size (LEfSe) analysis. A Least Absolute Shrinkage and Selection Operator (LASSO) regression model was employed to pinpoint key species. Additionally, PICRUSt2 was utilized to predict the functional pathways associated with these identified species. Results: The analysis identified a total of 1,122 common species, along with 1,803 species associated with AR and 1,739 species associated with healthy controls. LEfSe analysis revealed 20 significant discrepancies at the genus level. The LASSO regression model identified 8 key genera, including Discussion: This study identifies critical gut microbiota that could serve as potential biomarkers for allergic rhinitis, providing new insights into its pathogenesis and offering avenues for future therapeutic strategies. Further investigation into these microbiota may lead to enhanced understanding and management of AR.
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