Altered mucosal bacteria and metabolomics in patients with Peutz-Jeghers syndrome

Abstract

Background: Peutz-Jeghers syndrome (PJS) is a rare genetic disorder characterized by the development of pigmented spots, gastrointestinal polyps and increased susceptibility to cancers. Currently, most studies have investigated intestinal microbiota through fecal microbiota, and there are few reports about mucosa-associated microbiota. It remains valuable to search for the key intestinal microbiota or abnormal metabolic pathways linked to PJS.

Aim: This study aimed to assess the structure and composition of mucosa-associated microbiota in patients with PJS and to explore the potential influence of intestinal microbiota disorders and metabolite changes on PJS.

Methods: The bacterial composition was analyzed in 13 PJS patients and 12 controls using 16S rRNA gene sequencing (Illumina MiSeq) for bacteria. Differential analyses of the intestinal microbiota were performed from the phylum to species level. Liquid chromatography-tandem mass spectrometry (LC‒MS) was used to detect the differentially abundant metabolites of PJS patients and controls to identify different metabolites and metabolic biomarkers of small intestinal mucosa samples.

Results: High-throughput sequencing confirmed the special characteristics and biodiversity of the mucosa microflora in patients with PJS. They had lower bacterial biodiversity than controls. The abundance of intestinal mucosal microflora was significantly lower than that of fecal microflora. In addition, lipid metabolism, amino acid metabolism, carbohydrate metabolism, nucleotide metabolism and other pathways were significantly different from those of controls, which were associated with the development of the enteric nervous system, intestinal inflammation and development of tumors.

Conclusion: This is the first report on the mucosa-associated microbiota and metabolite profile of subjects with PJS, which may be meaningful to provide a structural basis for further research on intestinal microecology in PJS.

Keywords: Bacteria; LC‒MS; Metabolomics; Mucosa-associated microbiota; Peutz–Jeghers syndrome.

Fig. 1

Alpha diversity of the mucosa-associated bacteria in different groups at the OTU level. Rarefaction curve indicated that the sample was sufficient. The Simpson index is higher in Group P than Group H. However, Shannon (Student’s test, P = 0.05137), Simpson (Student’s test, P = 0.06055), Coverage (Student’s test, P = 0.4943) and other index analyses did not reach statistical significance. (P: PJS patient group; H: healthy control group)

Fig. 2

Venn diagram. In the Venn diagram, rare microbial OTUs were removed without subsampling. There was much overlap in the OTUs of each group. Group P: Peutz‒Jeghers syndrome (PJS) patients; Group H: non-PJS controls

Fig. 3

Community bar plot analysis of bacteria at the phylum and genus levels. The vertical coordinate in the figure represents the proportion of species in the samples of each group, and the species are represented by different colors in the figure (H: healthy control group; P: PJS patient group)

Fig. 4

Beta diversity of mucosa-associated bacteria. A The group ellipses of the 95% confidence interval overlapped significantly, which could not effectively distinguish the difference between groups (P = 0.4610), and the samples in the corresponding group of PJS patients were more discrete. B Based on OTU level analysis, the difference between groups was smaller than the difference within groups. C Bar graph of microflora relative abundance and hierarchical clustering of evolutionary relationships at the class level, revealing 5 clusters in the community branches of the two groups; (H: healthy control group, P: PJS patient group)

Fig. 5

Linear discriminant analysis of effect sizes (LEfSe) bar plot. The yellow dots in A represent species with no significant difference between the two groups, and the diameter is proportional to the LDA; the length of the bar in B is the influence of the different species; red represents the healthy control group, and blue represents the PJS patient group (LDA = 2.0)

Fig. 6

PLS-DA plot. A The separation between the two groups is obvious, the distance within the samples is close, and the classification effect is significant. B The two dotted lines represent regression lines of R2 and Q2. R2 is (0, 0.8842) and Q2 is (0, − 0.4197). The intercept between the regression line of Q2 and the Y-axis is less than 0, which means that the research model is robust

Fig. 7

Metabolite clustering tree and VIP bar plot. Each column represents a sample, and each row represents a metabolite. The denser the branch of the metabolite clustering tree, the more similar the expression patterns of all metabolites in the sample. In the histogram of VIP metabolites, the length of the blue bar indicates the contribution of metabolites to the difference between the two groups, the color of the VIP bar indicates the significance of the difference between the two groups of metabolites, and the lower the P value is, the darker the color (*P < 0.05, **P < 0.01, ***P < 0.001)

Fig. 8

Bar chart of KEGG functional pathway metabolism. The vertical coordinate is the secondary KEGG metabolic pathway, and the horizontal coordinate is the number of metabolites annotated to this pathway. Colors such as blue, red, yellow, green and purple represent the primary classification of the 5 major metabolic functional pathways in this study. They are metabolism, organismal systems, human diseases, environmental information processing and cellular processes

Fig. 9

KEGG pathway enrichment analysis. The P value is represented by the change in the color gradient on the right, the horizontal coordinate is the metabolic pathway name, and the vertical coordinate is the enrichment ratio (*P < 0.05, **P < 0.01, ***P < 0.001)