Using gut microbiota and non-targeted metabolomics techniques to study the effect of xylitol on alleviating DSS-induced inflammatory bowel disease in mice

Abstract

Background: Inflammatory bowel disease (IBD) has become a global healthcare issue, with its incidence continuing to rise, but currently there is no complete cure. Xylitol is a widely used sweetener in various foods and beverages, but there is limited research on the effects of xylitol on IBD symptoms.

Aim: Study on the effect of oral xylitol in improving intestinal inflammation and damage in IBD mice, further explore the mechanism of xylitol in alleviating IBD symptoms using intestinal microbiota and non-targeted metabolomics techniques.

Methods: An IBD mouse model was induced using sodium dextran sulfate (DSS). After 30 days of oral administration of xylitol, we assessed the disease activity index (DAI) scores of mice in each group. The expression levels of inflammatory factors in the colon tissues were measured using qPCR. Additionally, we examined the damage to the intestinal mucosa and tight junction structures through HE staining and immunohistochemical staining. Finally, the alterations in the gut microbiota of the mice were analyzed using 16S rDNA sequencing technology.The production of three main short-chain fatty acids (SCFAs, including acetate, propionic acid and butyric acid) in feces and the changes of serum metabolomics were measured by non-targeted metabolomics techniques.

Results: The findings indicated that xylitol effectively mitigated weight loss and improved the DAI score in mice with IBD. Moreover, xylitol reduced the expressions of Caspase-1, IL-1β, and TNF-α in the colon tissue of the mice, and increased the expressions of ZO-1 and occludin in intestinal mucosal. Xylitol could enhance the variety of intestinal bacteria in IBD mice and influenced the abundance of different bacterial species. Additionally, metabolomic analysis revealed that oral xylitol increased the levels of three main SCFAs in the feces of IBD mice, while also impacting serum metabolites.

Conclusions: Our findings suggest that xylitol can help improve IBD symptoms. Xylitol can improve the intestinal flora of IBD mice and increase the production of SCFAs to play an anti-inflammatory role and protect the mucosal tight junction barrier. These discoveries present a fresh prophylactic treatment of IBD.

Clinical trial number: Not applicable.

Keywords: Gut microbiota; Inflammation; Inflammatory bowel disease; Metabolomics; Tight junction; Xylitol.

Fig. 1

Changes in mouse body weight and DAI score during the experiment. A: body weight changes; B: DAI score changes. a: compared with the NC group, P<0.05

Fig. 2

The results of mouse intestinal mucosa HE staining and tight junction proteins ZO-1 and Occludin immunohistochemical staining (n = 3). A: NC group HE staining; B: Xylitol Control group HE staining results; C: DSS group HE staining results; D: Xylitol group HE staining results; E: NC group ZO-1 immunohistochemical staining; F: Xylitol Control ZO-1 immunohistochemical staining; G: DSS group ZO-1 immunohistochemical staining; H: Xylitol group ZO-1 immunohistochemical staining; I: NC group Occludin immunohistochemical staining; J: Xylitol Control group Occludin immunohistochemical staining; K: DSS group Occludin immunohistochemical staining; L: Xylitol group Occludin immunohistochemical staining; M: ZO-1 protein expression level; N: Occludin protein expression level

Fig. 3

The mRNA expressions of proinflammatory cytokines in mouse colon tissue (n = 6). A: NLRP3; B: Caspase-1; C: IL-1β; D: TNF-α. a: compared with the NC group, P<0.05; b: compared with the DSS group, P<0.05

Fig. 4

Concentrations of acetic acid, propionic acid, and butyric acid in the mouse colonic contents (n = 6). A: acetic concentration; B: propionic acid concentration; C: butyric acid concentration. a: compared with the NC group, P<0.05; b: compared with the DSS group, P<0.05

Fig. 5

Results of intestinal flora detection in mice (n = 6). A: α diversity (Shannon index). B: α diversity (Simpson index). C: α diversity (Chao1 index). D: α diversity (ACE index). E: PCA scatter plot. F: PCoA scatter plot. G: phylum level clustering heat map; H: genus level clustering heat map. a: compared with the NC group, P<0.05

Fig. 6

Changes in the abundance of important bacteria in the intestinal flora of mice at phylum and genus levels (n = 6). A: Fimicutes; B: Gemmatimonadetes; C: Akkesmansia; D: Allobaculum; E: Bilophila; F: Odoribacter; G: Oscillospira; H: Prevotrllaceae; I: Parabacteroides; J: Streptococcus. a: compared with the NC group, P<0.05; b: compared with the DSS group, P<0.05

Fig. 7

Mouse serum metabolites PCA plot and OPLS-DA plot. A: PCA plot; B: OPLS-DA plot of the NC and DSS groups; C: OPLS-DA plot of the NC and xylitol groups; D: OPLS-DA plot of the DSS and xylitol groups

Fig. 8

Differential metabolites and related metabolic pathways in mouse serum. A: Map of differential metabolite percentage accumulation in mouse serum; B: Clustering heat map of differential metabolites in mouse serum; C: Analysis of metabolic pathways of differential metabolites in the NC and DSS groups mouse serum. 1: Tryptophan metabolism. 2: Primary bile acid biosynthesis. 3: D-glutamine and D-glutamate metabolism. 4: Ammonia metabolism. 5: Purine metabolism. 6: Sphingolipid metabolism. D: Analysis of metabolic pathways of differential metabolites in the DSS and xylitol groups mouse serum. 1: D-glutamine and D-glutamate metabolism. 2: Ammonia metabolism. 3: Pyrimidine metabolism. 4: Tryptophan metabolism. 5: Primary bile acid biosynthesis. 6: Purine metabolism. 7: Glycolysis/gluconeogenesis