Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice

Abstract

The beneficial roles of probiotics in lowering the gastrointestinal inflammation and preventing colorectal cancer have been frequently demonstrated, but their immunomodulatory effects and mechanism in suppressing the growth of extraintestinal tumors remain unexplored. Here, we adopted a mouse model and metagenome sequencing to investigate the efficacy of probiotic feeding in controlling s.c. hepatocellular carcinoma (HCC) and the underlying mechanism suppressing the tumor progression. Our result demonstrated that Prohep, a novel probiotic mixture, slows down the tumor growth significantly and reduces the tumor size and weight by 40% compared with the control. From a mechanistic point of view the down-regulated IL-17 cytokine and its major producer Th17 cells, whose levels decreased drastically, played critical roles in tumor reduction upon probiotics feeding. Cell staining illustrated that the reduced Th17 cells in the tumor of the probiotic-treated group is mainly caused by the reduced frequency of migratory Th17 cells from the intestine and peripheral blood. In addition, shotgun-metagenome sequencing revealed the crosstalk between gut microbial metabolites and the HCC development. Probiotics shifted the gut microbial community toward certain beneficial bacteria, including Prevotella and Oscillibacter, that are known producers of antiinflammatory metabolites, which subsequently reduced the Th17 polarization and promoted the differentiation of antiinflammatory Treg/Tr1 cells in the gut. Overall, our study offers novel insights into the mechanism by which probiotic treatment modulates the microbiota and influences the regulation of the T-cell differentiation in the gut, which in turn alters the level of the proinflammatory cytokines in the extraintestinal tumor microenvironment.

Keywords: IL-17; Th17; hepatocellular carcinoma; metagenome; probiotics.

Conflict of interest statement

Conflict of interest statement: C.Y.J.S., N.L., and H.E.-N. are holders of a patent under the publication number US 2015/0164964A1, “Method and compositions for treating cancer using probiotics.”

Figures

Fig. S1.

Different efficacy of the probiotics in retarding the tumor progression. (A) Temporal variation of the tumor size in different antibiotic groups. (B) Distribution of tumor weight/body weight in different antibiotic groups. The male 5–6 wk C57BL6/N were fed with probiotics daily starting 1 wk before s.c. injection of mouse hepatoma cell line Hepa1-6. The animals were killed 38 d after tumor injection to determine tumor: body weight ratio. Representative images of tumor from each group are shown. Tumor burden of probiotics groups were significantly smaller than control by Tukey’s multiple comparison test, but were indifferent between heat-inactivated and viable bacteria. *P < 0.05; **P < 0.01; ***P < 0.001. C, negative control; ed, heat-inactivated EcN (108 CFU/d); eh, viable high dose EcN (108 CFU/d); el, viable low-dose EcN (106 CFU/d); ld, heat-inactivated LGG (108 CFU/d); lh, viable high dose LGG (108 CFU/d); ll, viable low-dose LGG (106 CFU/d); vd, heat-inactivated VSL#3 (1,010 CFU/d); vh, viable high dose VSL#3 (1010 CFU/d); vl, viable low-dose VSL#3 (108 CFU/d).

Fig. 1.

Probiotics reduced the tumor size and increased hypoxia in the tumor. (A) Study design: Male 5–6 wk C57BL6/N mice (n = 8 in each group) were fed with Prohep daily starting 1 wk before or at the same day of s.c. injection of mouse hepatoma cell line Hepa1-6. Two extra groups, control (normal diet) and cisplatin, were also included for comparison. The animals were killed 38 d after tumor injection to quantify the tumor size. (B) Tumor size variation during 38 d of monitoring. (C) Distribution of tumor weight at the end of the experiment. (D) Immunostaining for representative tumor sections for the GLUT-1 (blue) hypoxic marker and CD31 (red) angiogenesis marker. (E) Images of 3D models obtained by confocal Z stacks, after superimposition of multiple confocal planes (section thickness, 25 μm). (F) Distribution of the relative vascular area in four groups at the end of experiments. (G) Distribution of vessel sprout in four groups at the end of the experiments. All of the statistical tests were performed using t test between each treatment group and control group. *0.01 < P value < 0.05; **0.001 < P value < 0.01; ***P value < 0.001.

Fig. S2.

Immunostaining for representative tumor sections for Ki67 (cell proliferation; A) and caspase-3 (apoptosis; B) in 38-d samples. (C) Distribution of Ki67+ frequency in four groups. (D) Distribution of caspase-3+ frequency in four groups.

Fig. S3.

Area of hypoxic regions in 38-d samples. **0.001 P value < 0.01 compared with the control.

Fig. 2.

Probiotics retarded the tumor growth and its association with Th17 and IL-17. (A) Down-regulated IL-17 and other angiogenic factors, and up-regulated IL-10 in the two Prohep groups in 38-d samples. (B) Correspondence analysis of the qRT-PCR results of 38-d samples in four groups. (C) Tumor size variation during 38 d of monitoring with anti-IL-17 antibody. (D) Confocal images of tumor sections with IL-17 staining (blue), costained (red) with CD3 T cells (Left), CD11b macrophage (Center), and MPO neutrophils (Right). (E) Percentage of cell expressing IL-17 in CD3+, CD11b+ and MPO+ cells. (F) Frequency distribution of subpopulation of CD3+ cells in three groups. (G) Distribution of IL-17 production among different cell types. (H–L) Frequency of subpopulation of T cells in tumor: Th1 (H), TH17 (I), Th2 (J), Treg (K), and Tr1 (L). (M) Positive correlation between the Th17 proportion and tumor volume. (N) Frequency of migratory Th17 cells in the tumor section. (O) Th17 frequency in various organs measured by flowcytometry. All of the statistical tests were performed using t test between each treatment group and control group. *0.01 < P value < 0.05; **0.001 < P value < 0.01; ***P value < 0.001.

Fig. S4.

The qPCR results for some functionally important genes, including (A) IL-27, (B) IL-13, (C) IFNG, (D) IP10, (E) STAT4, (F) TBET, and (G) HIF-1.

Fig. S5.

Prohep lowers microvessel density in tumor in an IL-17-dependent manner based on the comparisons of 38-d samples. **0.001 P value < 0.01; ***P value < 0.001 compared with the control.

Fig. S6.

Frequency distribution of Tr1 after excluding two extremely large values in the ProPre group. *0.01 P value < 0.05 compared with the control.

Fig. S7.

Hierarchical clustering and taxonomy profiling of 8 samples at phylum level.

Fig. 3.

The effect of probiotics feeding on the composition and diversity of gut microbiota. (A) Taxonomy distribution at genus level in different samples. (Left) The phylogenetic relationships and the affiliated phylum for each genus. (B) Simpson diversity (Upper), pairwised Unifrac distance (Lower Triangle), and Bray-Curtis dissimilarity (Upper Triangle) between samples. The yellow square frame highlighted the within group beta-diversity. (C) Significantly enriched genera in the ProPre group. The fold change (38-d vs. baseline) of these genera in all four groups were displayed. (D) Significantly enriched or depleted species in the ProPre group.

Fig. S8.

MDS based on the Unifrac distance among samples.

Fig. 4.

Significantly enriched pathways in the ProPre group. (A) Significantly enriched MetaCyc pathways classes (I and II). The stars highlighted the pathways related to SCFAs synthesis. (B) Top-15 significantly enriched pathways; If no significant difference was detected between the tested and control group, the fold-change would be set to 1. (C) Enriched pathways related to pyruvate fermentation and SCFAs.

Fig. S9.

Distribution of mice body weight at day 38. ***P value < 0.001 compared with the control.