Altered Nasal Microbiota-Metabolome Interactions in Allergic Rhinitis: Implications for Inflammatory Dysregulation

Abstract

Objective: The aim of this study was to investigate the correlation between nasal microbiome, metabolites, and their potential contribution to the pathogenesis of allergic rhinitis (AR), a widespread chronic inflammatory disorder that poses a considerable healthcare burden worldwide. Immune dysregulation and environmental factors are key in the development of allergic responses, but the importance of host-microbiota interactions in influencing these responses is gaining recognition.

Methods: 32 AR patients and 20 healthy controls underwent 16S rDNA sequencing and untargeted metabolomics analysis. Microbial diversity, composition, and functional pathways were compared between groups. Metabolomic alterations were evaluated using LC-MS/MS, and correlations between microbiota and metabolites were analyzed.

Results: While α-diversity did not differ significantly between groups, β-diversity analysis revealed distinct microbial community shifts in AR patients. Specifically, Actinobacteria and Bacteroidetes abundances were increased, and genera Vibrio and Aeromonas were significantly enriched. Metabolomic profiling identified 528 differential metabolites, including altered levels of LPC, and pathway analysis highlighted disrupted linoleic acid metabolism, arachidonic acid metabolism, and tryptophan metabolism. Correlation analysis revealed significant associations between specific microbial taxa (eg, Aeromonas, Vibrio) and metabolites (eg, LPC, arachidonic acid), suggesting a potential link between microbiota-derived metabolic shifts and inflammatory responses in AR.

Conclusion: The perturbation of nasal microbiota-metabolite interactions may play a role in the pathogenesis of AR, emphasizing the need for future investigations into potential pathophysiological mechanisms.

Keywords: allergic rhinitis; inflammation; metabolomics; nasal microbiota.

Figure 1

Comparison of bacterial alpha diversity indices, including the Chao1, Shannon, and observed_otus indices (A). Principal component analysis (PCA) (B). Principal coordinate analysis (PCoA). Dots of the same color represent each biological repetition in the group, and the distribution state of dots reflects the difference between and within the group (C). Venn diagrams were generated according to the OTUs (D).

Figure 2

Distribution of taxa at the phylum, genus and species levels in the AR and control groups (A, D and G). Significantly different bacteria between the AR and control groups at the phylum, genus and species levels (B, C, E, F, H and I).

Figure 3

LDA score map and cladogram (LDA fold=3 and P <0.05). The red nodes in the LDA value distribution histogram represent the microbial groups that play important roles in the AR group, and the green nodes represent the microbial groups that play important roles in the Con group. Only species with an LDA score > 3.5 are shown in the figure. (A) LDA score map. The histogram’s length represents the LDA value’s size. (B) Cladogram. The circles represent the phylum, class, order, family, and genus from the inside to the outside. Each small circle at a different classification level represents a classification at that level. The diameter of the small circle is proportional to the relative abundance.

Figure 4

Correlation analysis between nasal microbiota and environmental factors in the AR and control groups (A–C).

Figure 5

Statistically significant differences in nasal microbiota function between the AR and control groups (A and B).

Figure 6

Nasal metabolomic profiles of the different groups. PCA score plots of the serum metabolic profiles of the allergic rhinitis and control groups (A and B). OPLS-DA score plots of the serum metabolic profiles of the AR and control groups (C and D). The permutation plots of the OPLS-DA models (E and F).

Figure 7

Volcano plot of differentially abundant metabolites between the AR and control groups in the ESI+ mode (A). Volcano plot of differentially abundant metabolites between the AR and control groups in ESI mode (B). Hierarchical clustering heatmap of differentially abundant metabolites between the AR and control groups in ESI+ mode (C). Hierarchical clustering heatmap of differentially abundant metabolites between the AR and control groups in ESI mode (D). Matchstick plot of the top 20 metabolites in terms of their up- and downranking in the ESI+ mode (E). Matchstick plot of the top 20 metabolites in terms of their up- and downranking in ESI mode (F). Red and blue shading represent high and low metabolite levels, respectively.

Figure 8

Correlation analyses of differentially abundant metabolites ((A) ESI+ mode; (B) ESI- mode).

Figure 9

Differential metabolic pathways identified via KEGG metabolic pathway enrichment analysis ((A) ESI+ mode; (B) ESI- mode). Changes in differentially abundant metabolites related to metabolic pathways (C–H).

Figure 10

Correlation heatmap of differentially abundant metabolites and altered microbial species. Horizontal represents different bacteria, vertical represents different metabolites, the right side of the legend represents the correlation coefficient, red represents a positive correlation, and blue represents a negative correlation ((A) ESI+ mode; (C) ESI- mode). Correlation chord diagram. Nodes represent differential bacterial genera and differential metabolites. String width indicates the strength of correlation; The string border color indicates correlation, with red indicating positive correlation and blue indicating negative correlation ((B) ESI+ mode; (D) ESI- mode).