Fecal and serum metabolomic signatures and gut microbiota characteristics of allergic rhinitis mice model
Authors: Chen Zhen , He Shancai , Wei Yihan , Liu Yang , Xu Qingqing , Lin Xing , Chen Chenyu , Lin Wei , Wang Yingge , Li Li , Xu Yuanteng 
Published: April 2023

Background

The etiology of allergic rhinitis (AR) is complicated. Traditional therapy of AR still has challenges, such as low long-term treatment compliance, unsatisfactory therapeutic outcomes, and a high financial burden. It is urgent to investigate the pathophysiology of allergic rhinitis from different perspectives and explore brand-new possible preventative or treatment initiatives.

Objective

The aim is to apply a multi-group technique and correlation analysis to explore more about the pathogenesis of AR from the perspectives of gut microbiota, fecal metabolites, and serum metabolism.

Methods

Thirty BALB/c mice were randomly divided into the AR and Con(control) groups. 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 enzyme-linked immunosorbent assay (ELISA), evaluated the histological characteristics of the nasal tissues by the hematoxylin and eosin (H&amp;E) staining, and observed the nasal symptoms (rubs and sneezes) to evaluate the reliability of the AR mouse model. The colonic NF-κB protein was detected by Western Blot, and the colonic histological characteristics were observed by the H&amp;E staining to evaluate inflammation of colon tissue. We analyzed the V3 and V4 regions of the 16S ribosomal DNA (rDNA) gene from the feces (colon contents) through 16S rDNA sequencing technology. Untargeted metabolomics was used to examine fecal and serum samples to find differential metabolites. Finally, through comparison and correlation analysis of differential gut microbiota, fecal metabolites, and serum metabolites, we further explore the overall impact of AR on gut microbiota, fecal metabolites, and host serum metabolism and its correlation.

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 Con group, indicating the successful establishment of the AR model. No differences in diversity were detected between the AR and Con groups. However, there were modifications in the microbiota’s structure. At the phylum level, the proportion of Firmicutes and Proteobacteria in the AR group increased significantly, while the proportion of Bacteroides decreased significantly, and the ratio of Firmicutes/Bacteroides was higher. 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 Con 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. Interestingly, one of the significant difference metabolites, α-Linoleic acid (ALA), decreased consistently in feces and serum of AR. 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-κB 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 deeper understanding of AR’s pathogenesis, which may provide a theoretical basis for AR’s potential prevention and treatment strategies.