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The Gut Microbiome Modulates Colon Tumorigenesis

Zackular, Joseph P., Baxter, Nielson T., Iverson, Kathryn D., Sadler, William D., Petrosino, Joseph F., Chen, Grace Y., Schloss, Patrick D.·2013·mBio
DOI 10.1128/mbio.00692-13PMC PMC3892781
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Abstract

Recent studies have shown that individuals with colorectal cancer have an altered gut microbiome compared to healthy controls. It remains unclear whether these differences are a response to tumorigenesis or actively drive tumorigenesis. To determine the role of the gut microbiome in the development of colorectal cancer, we characterized the gut microbiome in a murine model of inflammation-associated colorectal cancer that mirrors what is seen in humans. We followed the development of an abnormal microbial community structure associated with inflammation and tumorigenesis in the colon. Tumor-bearing mice showed enrichment in operational taxonomic units (OTUs) affiliated with members of the Bacteroides , Odoribacter , and Akkermansia genera and decreases in OTUs affiliated with members of the Prevotellaceae and Porphyromonadaceae families. Conventionalization of germfree mice with microbiota from tumor-bearing mice significantly increased tumorigenesis in the colon compared to that for animals colonized with a healthy gut microbiome from untreated mice. Furthermore, at the end of the model, germfree mice colonized with microbiota from tumor-bearing mice harbored a higher relative abundance of populations associated with tumor formation in conventional animals. Manipulation of the gut microbiome with antibiotics resulted in a dramatic decrease in both the number and size of tumors. Our results demonstrate that changes in the gut microbiome associated with inflammation and tumorigenesis directly contribute to tumorigenesis and suggest that interventions affecting the composition of the microbiome may be a strategy to prevent the development of colon cancer. IMPORTANCE The trillions of bacteria that live in the gut, known collectively as the gut microbiome, are important for normal functioning of the intestine. There is now growing evidence that disruptive changes in the gut microbiome are strongly associated with the development colorectal cancer. However, how the gut microbiome changes with time during tumorigenesis and whether these changes directly contribute to disease have not been determined. We demonstrate using a mouse model of inflammation-driven colon cancer that there are dramatic, continual alterations in the microbiome during the development of tumors, which are directly responsible for tumor development. Our results suggest that interventions that target these changes in the microbiome may be an effective strategy for preventing the development of colorectal cancer.

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FIG 1 Inflammation-induced tumorigenesis is commensal dependent. (A) Mice were injected with azoxymethane (AOM) on day 1, followed by 3 subsequent rounds of water-administered 2% DSS. Colons were harvested 73 days after AOM, and tumors were grossly counted. Black wedges indicate fecal samples used for gut microbiome analysis (n = 12). (B) Representative mice were euthanized following each round of DSS to identify macroscopic tumors (n = 5 for each time point). An antibiotic cocktail of metronidazole, streptomycin, and vancomycin was administered in the drinking water of a separate cohort of mice for the duration of the model (n = 9). Statistical analysis was performed using a two-tailed Student’s t test. *, P < 0.01. (C) Representative images of tumors in the distal colon of conventional mice treated with AOM/DSS (n = 12) and mice treated with an antibiotic cocktail and AOM/DSS (n = 9). Error bars represent ±SEM.
FIG 1 Inflammation-induced tumorigenesis is commensal dependent. (A) Mice were injected with azoxymethane (AOM) on day 1, followed by 3 subsequent rounds of water-administered 2% DSS. Colons were harvested 73 days after AOM, and tumors were grossly counted. Black wedges indicate fecal samples used for gut microbiome analysis (n = 12). (B) Representative mice were euthanized following each round of DSS to identify macroscopic tumors (n = 5 for each time point). An antibiotic cocktail of metronidazole, streptomycin, and vancomycin was administered in the drinking water of a separate cohort of mice for the duration of the model (n = 9). Statistical analysis was performed using a two-tailed Student’s t test. *, P < 0.01. (C) Representative images of tumors in the distal colon of conventional mice treated with AOM/DSS (n = 12) and mice treated with an antibiotic cocktail and AOM/DSS (n = 9). Error bars represent ±SEM.CC BY-NC-SA · PubMed Central
FIG 2 Development of a dysbiotic gut microbiome during colon tumorigenesis. Microbiome analysis was performed with fecal samples from 10 representative mice; color coding is as indicated in Fig. 1A. (A) Inverse Simpson’s diversity index. (B) Observed community richness estimate. Statistical analysis was performed using repeated-measures paired group analysis of variance. (C) Nonmetric multidimensional scaling (NMDS) ordination based on θyc distances for all 10 mice during the AOM/DSS model. (D) Average θYC distance within (black) and between (gray) phases of the model. Error bars represent ±SEM.
FIG 2 Development of a dysbiotic gut microbiome during colon tumorigenesis. Microbiome analysis was performed with fecal samples from 10 representative mice; color coding is as indicated in Fig. 1A. (A) Inverse Simpson’s diversity index. (B) Observed community richness estimate. Statistical analysis was performed using repeated-measures paired group analysis of variance. (C) Nonmetric multidimensional scaling (NMDS) ordination based on θyc distances for all 10 mice during the AOM/DSS model. (D) Average θYC distance within (black) and between (gray) phases of the model. Error bars represent ±SEM.CC BY-NC-SA · PubMed Central
FIG 3 Heat map of OTUs with relative abundances that are significantly different from their relative abundances at the time of AOM administration. The average OTU abundance between mice for each OTU was calculated for each time point. The timeline is colored for the following groups: baseline samples (prior to AOM), black; following the first round of DSS, blue; following the second round of DSS, green; following the third round of DSS, red. The OTU number and taxonomic group based on RDP classification are represented for each row. Repeated-measures paired group analysis of variance was used to identify significantly altered OTUs.
FIG 3 Heat map of OTUs with relative abundances that are significantly different from their relative abundances at the time of AOM administration. The average OTU abundance between mice for each OTU was calculated for each time point. The timeline is colored for the following groups: baseline samples (prior to AOM), black; following the first round of DSS, blue; following the second round of DSS, green; following the third round of DSS, red. The OTU number and taxonomic group based on RDP classification are represented for each row. Repeated-measures paired group analysis of variance was used to identify significantly altered OTUs.CC BY-NC-SA · PubMed Central
FIG 4 Tumor-associated gut microbiome alterations exacerbate tumorigenesis in germfree mice. (A) Number of tumors observed at the end of the model when germfree mice were colonized using bedding from healthy mice (Healthy community) or mice with tumors (Dysbiotic community). (B) Representative images of tumors in the distal colon of mice conventionalized with a healthy microbiome (n = 10) or the microbiome of tumor-bearing mice (n = 9). (C) NMDS ordination based on θyc distances for all 19 mice following conventionalization with a healthy microbiome (Healthy community) or the microbiome of tumor-bearing mice (Dysbiotic community). Error bars represent ±SEM.
FIG 4 Tumor-associated gut microbiome alterations exacerbate tumorigenesis in germfree mice. (A) Number of tumors observed at the end of the model when germfree mice were colonized using bedding from healthy mice (Healthy community) or mice with tumors (Dysbiotic community). (B) Representative images of tumors in the distal colon of mice conventionalized with a healthy microbiome (n = 10) or the microbiome of tumor-bearing mice (n = 9). (C) NMDS ordination based on θyc distances for all 19 mice following conventionalization with a healthy microbiome (Healthy community) or the microbiome of tumor-bearing mice (Dysbiotic community). Error bars represent ±SEM.CC BY-NC-SA · PubMed Central

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Metadata

Authors
Zackular, J. P., Baxter, N. T., Iverson, K. D., Sadler, W. D., Petrosino, J. F., Chen, G. Y., & Schloss, P. D.
Venue
mBio
Publisher
American Society for Microbiology
Published
2013-12-31
Volume
4
Issue
6
ISSN
  • 2161-2129
  • 2150-7511
DOI
10.1128/mbio.00692-13
PMC
PMC3892781
Cited by
629
References
9
Language
en
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