AUTHOR=Chen Zhen , He Shancai , Wei Yihan , Liu Yang , Xu Qingqing , Lin Xing , Chen Chenyu , Lin Wei , Wang Yingge , Li Li , Xu Yuanteng 

TITLE=Fecal and serum metabolomic signatures and gut microbiota characteristics of allergic rhinitis mice model

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2023.1150043

DOI=10.3389/fcimb.2023.1150043

ISSN=2235-2988

ABSTRACT=Objective: The aim is to apply a multi-group technique and correlation analysis to explore more about the pathogenesis of AR (Allergic rhinitis) from the perspectives of gut microbiota, fecal metabolites, and serum metabolism.
Methods: A standardized Ovalbumin (OVA)-induced AR mouse model was established by intraperitoneal OVA injection followed by nasal excitation. We detected the serum IL-4, IL-5, and IgE by ELISA, evaluated the histological characteristics of the nasal tissues by H&E staining and observed the nasal symptoms to evaluate the reliability of the AR mouse model. We analyzed the V3 and V4 regions of the 16S rDNA gene from the fecal sample through 16S rDNA sequencing. Untargeted metabolomics was used to examine fecal and serum samples to find differential metabolites. Finally, through comparison and correlation analysis of differential gut microbiota, differential fecal metabolites, and differential serum metabolites.
Results: In the AR group, the IL-4, IL-5, IgE, eosinophil infiltration, and the times of rubs and sneezes were significantly higher than those in the control group, indicating the successful establishment of the AR model. There were modifications in the microbiota's structure of AR. The key differential genera, such as Ruminococcus, were increased significantly in the AR group, while the other key differential genera, such as Lactobacillus, Bacteroides, and Prevotella, were significantly decreased in the control group. Untargeted metabolomics analysis identified 28 upregulated and 4 downregulated differential metabolites in feces and 11 upregulated and 16 downregulated differential metabolites in serum under AR conditions. KEGG functional enrichment analysis and correlation analysis showed a close relationship between differential serum metabolites and fecal metabolites, and changes in fecal and serum metabolic patterns are associated with altered gut microbiota in AR. The NF-B protein and inflammatory infiltration of
the colon increased considerably in the AR group.
Conclusion: Our study reveals that AR alters fecal and serum metabolomic signatures and gut microbiota characteristics, and there is a striking correlation between the three. The correlation analysis of the microbiome and metabolome provides a deeperunderstanding of AR's pathogenesis, which may provide a theoretical basis for AR's potential prevention and treatment strategies.