Structure of the gut microbiome following colonization with human feces determines colonic tumor burden

Abstract

Background: A growing body of evidence indicates that the gut microbiome plays a role in the development of colorectal cancer (CRC). Patients with CRC harbor gut microbiomes that are structurally distinct from those of healthy individuals; however, without the ability to track individuals during disease progression, it has not been possible to observe changes in the microbiome over the course of tumorigenesis. Mouse models have demonstrated that these changes can further promote colonic tumorigenesis. However, these models have relied upon mouse-adapted bacterial populations and so it remains unclear which human-adapted bacterial populations are responsible for modulating tumorigenesis.

Results: We transplanted fecal microbiota from three CRC patients and three healthy individuals into germ-free mice, resulting in six structurally distinct microbial communities. Subjecting these mice to a chemically induced model of CRC resulted in different levels of tumorigenesis between mice. Differences in the number of tumors were strongly associated with the baseline microbiome structure in mice, but not with the cancer status of the human donors. Partitioning of baseline communities into enterotypes by Dirichlet multinomial mixture modeling resulted in three enterotypes that corresponded with tumor burden. The taxa most strongly positively correlated with increased tumor burden were members of the Bacteroides, Parabacteroides, Alistipes, and Akkermansia, all of which are Gram-negative. Members of the Gram-positive Clostridiales, including multiple members of Clostridium Group XIVa, were strongly negatively correlated with tumors. Analysis of the inferred metagenome of each community revealed a negative correlation between tumor count and the potential for butyrate production, and a positive correlation between tumor count and the capacity for host glycan degradation. Despite harboring distinct gut communities, all mice underwent conserved structural changes over the course of the model. The extent of these changes was also correlated with tumor incidence.

Conclusion: Our results suggest that the initial structure of the microbiome determines susceptibility to colonic tumorigenesis. There appear to be opposing roles for certain Gram-negative (Bacteroidales and Verrucomicrobia) and Gram-positive (Clostridiales) bacteria in tumor susceptibility. Thus, the impact of community structure is potentially mediated by the balance between protective, butyrate-producing populations and inflammatory, mucin-degrading populations.

Keywords: Colorectal cancer; Community structure; Germ-free; Gut microbiome; Humanized mice; Microbiota.

Figure 1

Experimental design. Germ-free mice were inoculated by oral gavage with one of six human inocula. Twenty-one days later (day 0), they received a single intraperitoneal injection of AOM (10 mg/kg). Mice were subsequently administered three five-day rounds of 2% DSS in the drinking water, with 16 days of rest in between. Mice were euthanized 73 days after the AOM injection for enumeration of colonic tumors. The inocula and samples collected on day 0 and day 73 were used for 16S rRNA gene sequencing. AOM, azoxymethane; DSS, dextran sulfate sodium.

Figure 2

Taxonomic composition and beta diversity across treatment groups and time. (A) Phylum level relative abundance of the fecal microbiome of each group on days 0 and 73 and in its inoculum. (B) Average Θ_YC distances (±standard error in the mean) within and between groups at various time points; between each group and its inoculum, within each group at day 0, each group compared with others at day 0, between day 0 and day 73 for each group, each group compared with others at day 73, and between the inoculum and day 73.

Figure 3

Correlation of tumor incidence with initial gut community structure. (A) Strip chart of tumor counts (with line at median) for each group. (B) NMDS plot based on Θ_YC distances between samples at day 0 with biplot of the 15 OTUs most strongly correlated with the NMDS axes (stress = 0.21). Median tumor counts for each group are adjacent to their corresponding dots. NMDS, nonmetric dimensional scaling; OTU, operational taxonomic unit.

Figure 4

Correlation of enterotypes with tumor incidence. (A) NMDS plot based on genus level abundances with median tumor counts for each group (stress = 0.13). Samples are circled based on their DMM enterotype. (B) Tumor counts for the mice in each DMM enterotype (* P < 0.05, **P < 0.01, Wilcoxon rank-sum test). (C) Relative abundance of the genera with the largest differences between enterotypes. NMDS, nonmetric dimensional scaling.

Figure 5

Temporal changes in the microbiome are conserved between groups. Strip chart showing the relative abundances of the ten OTUs with the highest importance for distinguishing between baseline (day 0) and endpoint (day 73) communities by Random Forest as measured by the MDA when the OTU was removed from the model. Each dot represents a single mouse. The black lines represent the mean relative abundance for all mice. MDA, mean decrease accuracy; OTU, operational taxonomic unit.