Gut Microbiome Alterations and Functional Prediction in Chronic Spontaneous Urticaria Patients

Abstract

The effects of the gut microbiome on both allergy and autoimmunity in dermatological diseases have been indicated in several recent studies. Chronic spontaneous urticaria (CSU) is a disease involving allergy and autoimmunity, and there is no report detailing the role of microbiota alterations in its development. This study was performed to identify the fecal microbial composition of CSU patients and investigate the different compositions and potential genetic functions on the fecal microbiota between CSU patients and normal controls. The gut microbiota of CSU patients and healthy individuals were obtained by 16s rRNA massive sequencing. Gut microbiota diversity and composition were compared, and bioinformatics analysis of the differences was performed. The gut microbiota composition results showed that Firmicutes, Bacteroidetes, Proteobacteria, and Verrucomicrobia were dominant microbiota in CSU patients. The differential analysis showed that relative abundance of the Proteobacteria (p = 0.03), Bacilli (p = 0.04), Enterobacterales (p = 0.03), Enterobacteriaceae (p = 0.03) was significantly increased in CSU patients. In contrast, the relative abundance of Megamonas, Megasphaera, and Dialister (all p < 0.05) in these patients significantly decreased compared with healthy controls. The different microbiological compositions impacted normal gastrointestinal functions based on function prediction, resulting in abnormal pathways, including transport and metabolism. We found CSU patients exhibited gut microbiota dysbiosis compared with healthy controls. Our results indicated CSU is associated with gut microbiota dysbiosis and pointed out that the bacterial taxa increased in CSU patients, which might be involved in the pathogenesis of CSU. These results provided clues for future microbial-based therapies on CSU.

Keywords: Gut microbiome; bacterial diversity; dysbiosis; urticaria.

Fig. 1

Venn diagram showing the unique and shared operational taxonomic units (OTUs) in the healthy control group (N) in green colored and the CSU patient group (P) in red colored.

Fig. 2. The composition of gut microbiota in CSU patients group (P) and healthy controls group (N).

The top 10 taxa in relative abundance at different levels.

Fig. 3. Analysis of beta diversity revealed significant differences between CSU patients group (P) and healthy controls group (N) performed on the unweighted UniFrac (p < 0.001).

The results revealed a significant separation in the bacterial community composition between CSU patients and healthy individuals. PCoA scatter diagram based on unweighted Unifrac data between samples. In the analysis results, the more similar the samples are, the closer the distances reflected in the PCoA diagram are. Red dots stand for patients group (P) and blue dots stand for controls group (N). It revealed significant clustering of patients group (P) and controls group (N).

Fig. 4. Relative abundance at different bacterial levels between CSU patients (P) and controls (N) groups was compared.

The taxa with significant differences (p < 0.05) were shown including Proteobacteria at phylum level, Bacilli at class level, Enterobacterales at order level, Enterobacteriaceae at family level, Megamonas, Dialister and Megasphaera at genus level.

Fig. 5. Differential pathway functional analysis between patients group (P) in red and controls group (N) in blue.

Left side: the ordinate is the different KEGG pathway, and the abscissa represents the abundance of the different pathway. Right side: the abscissa is the confidence interval range of the abundance difference between groups, and the ordinate is the p-value.