Gut microbial and metabolomics profiles reveal the potential mechanism of fecal microbiota transplantation in modulating the progression of colitis-associated colorectal cancer in mice

Abstract

Purpose: Intestinal flora promotes the pathogenesis of colorectal cancer (CRC) through microorganisms and their metabolites. This study aimed to investigate the composition of intestinal flora in different stages of CRC progression and the effect of fecal microbiota transplantation (FMT) on CRC mice.

Methods: The fecal microbiome from healthy volunteers (HC), colorectal adenoma (CRA), inflammatory bowel disease (IBD), and CRC patients were analyzed by 16s rRNA gene sequencing. In an azoxymethane (AOM)/dextran-sulfate-sodium (DSS)-induced CRC mouse, the effect of FMT from HC, CRA, CRC, and IBD patients on CRC mice was assessed by histological analysis. Expression of inflammation- EMT-associated proteins and Wnt/β-catenin pathway were assessed using qRT-PCR and western blot. The ratio of the fecal microorganisms and metabolomics alteration after FMT were also assessed.

Result: Prevotella, Faecalibacterium, Phascolarctobacterium, Veillonella, Alistipes, Fusobacterium, Oscillibacter, Blautia, and Ruminococcus abundance was different among HC, IBD, CRC, and CRA patients. HC-FMT alleviated disease progression and inflammatory response in CRC mice, inhibited splenic T help (Th)1 and Th17 cell numbers, and suppressed the EMT and Wnt/β-catenin pathways in tumor tissues of CRC mice. IBD-FMT, CRA-FMT, and CRC-FMT played deleterious roles; the CRC-FMT mice exhibited the most malignant phenotype. Compared with the non-FMT CRC mice, Muribaculaceae abundance was lower after FMT, especially lowest in the IBD-FMT group; while Lactobacillus abundance was higher after FMT and especially high in HC-FMT. Akkermansia and Ileibacterium abundance increased after FMT-HC compared to other groups. Metabolite correlation analysis revealed that Muribaculaceae abundance was significantly correlated with metabolites such as Betaine, LysoPC, and Soyasaponin III. Lactobacillus abundance was positively correlated with Taurocholic acid 3-sulfate, and Ileibacterium abundance was positively correlated with Linoleoyl ethanolamide.

Conclusion: The different intestinal microbiota communities of HC, IBD, CRA, and CRC patients may be attributed to the different modulation effects of FMT on CRC mice. CRC-FMT promoted, while HC-FMT inhibited the progress of CRC. Increased linoleoyl ethanolamide levels and abundance of Muribaculaceae, Akkermansia, and Ileibacterium and reduced Fusobacterium might participate in inhibiting CRC initiation and development. This study demonstrated that FMT intervention could restore the intestinal microbiota and metabolomics of CRC mice, suggesting FMT as a potential strategy for CRC therapy.

Keywords: 16s rRNA gene sequencing; Enteritis-associated colorectal cancer; Epithelial-mesenchymal transition (EMT); Fecal microbiota transplantation; Metabolomics; Th1/Th17 cells; Wnt/β-catenin pathway.

Fig. 1

Characterization of intestinal microorganisms in patients with IBD, CRA, CRC A: Alpha diversity consists of the following metrics: sobs, Chao1, and ace indices for assessing colony richness, Shannon and Simpson indices for assessing microbial diversity of samples, and coverage for assessing sequencing depth. B: Beta diversity was calculated using Unweighted UniFrac. C: Heatmap of the relative top20 abundance of the intestinal flora in each study group at the genus level * p < 0.05, ** p < 0.01, *** p < 0.001

Fig. 2

Differences in top 20 intestinal bacteria genus abundance between groups *p < 0.05, ** p < 0.01, t-test

Fig. 3

Metabolic prediction of intestinal microbiota in HC and patients with IBD, CRA, CRC A: Box plots demonstrating the levels of the predicted seven intestinal differential metabolites in HC and patients with IBD, CRA, and CRC. B: The correlations between intestinal bacterial genera and predicted differential metabolites were analyzed by Pearson correlation coefficients. * P < 0.05, ** P < 0.01, *** P < 0.001

Fig. 4

Intestinal microbiota can influence tumor growth in mice with AOM/DSS-induced colitis-associated carcinomas A: Animal experimental protocol. B: Representative gross images of mice colonic tissue samples from all treatment groups (top), histopathological alterations determined by H&E staining (middle & bottom, scale bar = 200 & 50 μm). C: IHC staining to detect Ki67 expression in the colonic tissues of mice from all groups (scale bar = 20 μm). D-E: Number & volume of tumors in colon tissues (n = 6) * P < 0.05, vs. the NC mice. ## P < 0.01, vs. HC-FMT mice

Fig. 5

Effect of intestinal flora on inflammatory factors and immune cell infiltration A-E: TNF-α, COX-2, MMP9 and CTNNB expression levels in tumor tissues of mice in each group (n = 3) were detected using qRT-PCR (A-D) and western blot (E). F: E-cadherin, N-cadherin, Vimentin, Snail and CD133 levels in intestinal tissues of mice in each group (n = 3) were detected using Western blot. G: Spleen/body weight ratio of mice in each group (n = 6). H-I: Flow cytometry to detect changes in Th1 (CD4 + IFN-γ) and Th17 (CD4 + IL-17 A) cell numbers in the spleens of mice in each group (n = 3) * P < 0.05, ** P < 0.01, vs. the NC mice. ## P < 0.01, vs. HC-FMT mice

Fig. 6

FMT alters the intestinal microbiota composition in CRC mice after FMT A-F: Pielou, Shannon, and Simpson to estimate microbial diversity in the samples, Observed features, Chao1, and ACE to assess the abundance of flora in the community. G: ANOSIM to calculate β-diversity. H: Top 20 dominant flora species at genus level among groups

Fig. 7

Box plots showed the relative abundance of top20 abundance intestinal microbiota after FMT. *P < 0.05, ** P < 0.01, *** P < 0.001

Fig. 8

Metabolomics of intestinal microorganisms in FMT-treated mice A-B: Heatmaps show intergroup differences in metabolites of intestinal content in NEG and POS modes in each group. C: KEGG enrichment to analyze the metabolic processes associated with differential metabolites in NEG and POS models

Fig. 9

Correlation analysis of intestinal microbiota and metabolites in FMT-treated mice A: Correlation analysis of top20 differential metabolites in NEG mode and differential microbiota. B: Correlation analysis of top20 differential metabolites in POS mode and differential microbiota. *P < 0.05, **P < 0.01