Deciphering Gut Microbiota Dysbiosis and Corresponding Genetic and Metabolic Dysregulation in Psoriasis Patients Using Metagenomics Sequencing

Abstract

Background: Increasing evidence has shown that alterations in the intestinal microbiota play an important role in the pathogenesis of psoriasis. The existing relevant studies focus on 16S rRNA gene sequencing, but in-depth research on gene functions and comprehensive identification of microbiota is lacking.

Objectives: To comprehensively identify characteristic gut microbial compositions, genetic functions and relative metabolites of patients with psoriasis and to reveal the potential pathogenesis of psoriasis.

Methods: DNA was extracted from the faecal microbiota of 30 psoriatic patients and 15 healthy subjects, and metagenomics sequencing and bioinformatic analyses were performed. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database, cluster of orthologous groups (COG) annotations, and metabolic analyses were used to indicate relative target genes and pathways to reveal the pathogenesis of psoriasis.

Results: Compared with healthy individuals, the gut microbiota of psoriasis patients displayed an alteration in microbial taxa distribution, but no significant difference in microbial diversity. A distinct gut microbial composition in patients with psoriasis was observed, with an increased abundance of the phyla Firmicutes, Actinobacteria and Verrucomicrobia and genera Faecalibacterium, Bacteroides, Bifidobacterium, Megamonas and Roseburia and a decreased abundance of the phyla Bacteroidetes, Euryarchaeota and Proteobacteria and genera Prevotella, Alistipes, and Eubacterium. A total of 134 COGs were predicted with functional analysis, and 15 KEGG pathways, including lipopolysaccharide (LPS) biosynthesis, WNT signaling, apoptosis, bacterial secretion system, and phosphotransferase system, were significantly enriched in psoriasis patients. Five metabolites, hydrogen sulfide (H₂S), isovalerate, isobutyrate, hyaluronan and hemicellulose, were significantly dysregulated in the psoriatic cohort. The dysbiosis of gut microbiota, enriched pathways and dysregulated metabolites are relevant to immune and inflammatory response, apoptosis, the vascular endothelial growth factor (VEGF) signaling pathway, gut-brain axis and brain-skin axis that play important roles in the pathogenesis of psoriasis.

Conclusions: A clear dysbiosis was displayed in the gut microbiota profile, genetic functions and relative metabolites of psoriasis patients. This study is beneficial for further understanding the inflammatory pathogenesis of psoriasis and could be used to develop microbiome-based predictions and therapeutic approaches.

Keywords: genetic functions; gut microbiota; metabolites; metagenomics sequencing; psoriasis.

Figure 1

Species accumulation curves. The species accumulation curve of each group tended to plateau, indicating that sample biodiversity was adequately covered with the applied sequencing depth.

Figure 2

α-Diversity based on Shannon indices displayed no significant differences in the intestinal microbiota between the two groups.

Figure 3

β-Diversity represented as a two-dimensional PCoA showed no differential clustering between the two groups. (The red dots showed the samples from psoriasis patients, whereas the green dots showed the samples from healthy individuals. The closer the dots in one group, the more similar in gut microbiota. The gut microbiota compositions are indicated with red and green circles, respectively. The smaller the overlap of two circles, the more different in the gut microbiota compositions of two groups).

Figure 4

Relative abundance (%) of the intestinal microbiota determined at the phylum (A, B) genus (C, D) and species (E) levels. The columns represent individuals in the psoriasis or control group (A, C, E).

Figure 5

Intestinal microbiota profiles in psoriasis patients. (A) Cladogram generated from the LEfSe analysis indicating the phylogenetic distribution of the microbiota of psoriasis and control groups from phylum to genus. (B) Histogram of LDA scores to identify differentially abundant bacteria between patients and controls (LDA score > 2.0).

Figure 6

Faecal metabolic heatmap of samples from psoriasis patients. Hierarchical clustering showing the 5 significantly differentially abundant metabolites between the 2 groups (increased and decreased levels of metabolites are indicated by red and blue, respectively). The lines indicate specific metabolites, and the columns represent the individuals.