The Assessment of Fecal Volatile Organic Compounds in Healthy Infants: Electronic Nose Device Predicts Patient Demographics and Microbial Enterotype.

BACKGROUND: The assessment of fecal volatile organic compounds (VOCs) has emerged as a noninvasive biomarker in many different pathologies. Before assessing whether VOCs can be used to diagnose intestinal diseases, including necrotizing enterocolitis (NEC), it is necessary to measure the impact of variable infant demographic factors on VOC signals. MATERIALS AND METHODS: Stool samples were collected from term infants at four hospitals in a large metropolitan area. Samples were heated, and fecal VOCs assessed by the Cyranose 320 Electronic Nose. Twenty-eight sensors were combined into an overall smellprint and were also assessed individually. 16s rRNA gene sequencing was used to categorize infant microbiomes. Smellprints were correlated to feeding type (formula versus breastmilk), sex, hospital of birth, and microbial enterotype. Overall smellprints were assessed by PERMANOVA with Euclidean distances, and individual sensors from each smellprint were assessed by Mann-Whitney U-tests. P < 0.05 was significant. RESULTS: Overall smellprints were significantly different according to diet. Individual sensors were significantly different according to sex and hospital of birth, but overall smellprints were not significantly different. Using a decision tree model, two individual sensors could reliably predict microbial enterotype. CONCLUSIONS: Assessment of fecal VOCs with an electronic nose is impacted by several demographic characteristics of infants and can be used to predict microbiome composition. Further studies are needed to design appropriate algorithms that are able to predict NEC based on fecal VOC profiles.

Mathematical modeling of primary succession of murine intestinal microbiota.

Understanding the nature of interpopulation interactions in host-associated microbial communities is critical to understanding gut colonization, responses to perturbations, and transitions between health and disease. Characterizing these interactions is complicated by the complexity of these communities and the observation that even if populations can be cultured, their in vitro and in vivo phenotypes differ significantly. Dynamic models are the cornerstone of computational systems biology and a key objective of computational systems biologists is the reconstruction of biological networks (i.e., network inference) from high-throughput data. When such computational models reflect biology, they provide an opportunity to generate testable hypotheses as well as to perform experiments that are impractical or not feasible in vivo or in vitro. We modeled time-series data for murine microbial communities using statistical approaches and systems of ordinary differential equations. To obtain the dense time-series data, we sequenced the 16S ribosomal RNA (rRNA) gene from DNA isolated from the fecal material of germfree mice colonized with cecal contents of conventionally raised animals. The modeling results suggested a lack of mutualistic interactions within the community. Among the members of the Bacteroidetes, there was evidence for closely related pairs of populations to exhibit parasitic interactions. Among the Firmicutes, the interactions were all competitive. These results suggest future animal and in silico experiments. Our modeling approach can be applied to other systems to provide a greater understanding of the dynamics of communities associated with health and disease.

Intra- and interindividual variations mask interspecies variation in the microbiota of sympatric peromyscus populations.

Using populations of two sympatric Peromyscus species, we characterized the importance of the host species, physiology, environment, diet, and other factors in shaping the structure and dynamics of their gut microbiota. We performed a capture-mark-release experiment in which we obtained 16S rRNA gene sequence data from 49 animals at multiple time points. In addition, we performed 18S rRNA gene sequencing of the same samples to characterize the diet of each individual. Our analysis could not distinguish between the two species of Peromyscus on the basis of the structures of their microbiotas. However, we did observe a set of bacterial populations that were found in every animal. Most notable were abundant representatives of the genera Lactobacillus and Helicobacter. When we combined the 16S and 18S rRNA gene sequence analyses, we were unable to distinguish the communities on the basis of the animal's diet. Furthermore, there were no discernible differences in the structure of the gut communities based on the capture site or their developmental or physiological status. Finally, in contrast to humans, where each individual has a unique microbiota when sampled over years, among the animals captured in this study, the uniqueness of each microbiota was lost within a week of the original sampling. Wild populations provide an opportunity to study host-microbiota interactions as they originally evolved, and the ability to perform natural experiments will facilitate a greater understanding of the factors that shape the structure and function of the gut microbiota.

Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.

Rapid advances in sequencing technology have changed the experimental landscape of microbial ecology. In the last 10 years, the field has moved from sequencing hundreds of 16S rRNA gene fragments per study using clone libraries to the sequencing of millions of fragments per study using next-generation sequencing technologies from 454 and Illumina. As these technologies advance, it is critical to assess the strengths, weaknesses, and overall suitability of these platforms for the interrogation of microbial communities. Here, we present an improved method for sequencing variable regions within the 16S rRNA gene using Illumina's MiSeq platform, which is currently capable of producing paired 250-nucleotide reads. We evaluated three overlapping regions of the 16S rRNA gene that vary in length (i.e., V34, V4, and V45) by resequencing a mock community and natural samples from human feces, mouse feces, and soil. By titrating the concentration of 16S rRNA gene amplicons applied to the flow cell and using a quality score-based approach to correct discrepancies between reads used to construct contigs, we were able to reduce error rates by as much as two orders of magnitude. Finally, we reprocessed samples from a previous study to demonstrate that large numbers of samples could be multiplexed and sequenced in parallel with shotgun metagenomes. These analyses demonstrate that our approach can provide data that are at least as good as that generated by the 454 platform while providing considerably higher sequencing coverage for a fraction of the cost.

Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome.

Antimicrobial growth promoters (AGP) have played a decisive role in animal agriculture for over half a century. Despite mounting concerns about antimicrobial resistance and demand for antibiotic alternatives, a thorough understanding of how these compounds drive performance is missing. Here we investigate the functional footprint of microbial communities in the cecum of chickens fed four distinct AGP. We find relatively few taxa, metabolic or antimicrobial resistance genes similarly altered across treatments, with those changes often driven by the abundances of core microbiome members. Constraints-based modeling of 25 core bacterial genera associated increased performance with fewer metabolite demands for microbial growth, pointing to altered nitrogen utilization as a potential mechanism of narasin, the AGP with the largest performance increase in our study. Untargeted metabolomics of narasin treated birds aligned with model predictions, suggesting that the core cecum microbiome might be targeted for enhanced performance via its contribution to host-microbiota metabolic crosstalk.

The assessment of microbiome changes and fecal volatile organic compounds during experimental necrotizing enterocolitis.

INTRODUCTION: Necrotizing enterocolitis (NEC) remains a devastating disease that affects the gastrointestinal tract of the preterm infant. Volatile organic compounds (VOCs) have emerged as a non-invasive biomarker in many diseases. We hypothesized that fecal VOC profiles would be significantly different between control and NEC pups in a NEC mouse model. METHODS: Experimental NEC was induced in five-day-old mice. Breastfed and formula-fed control groups were also studied. After four days, pups were euthanized and intestines were H&E stained and blindly scored. Stool microbiome analysis was performed via 16S rRNA sequencing. VOC analysis was assessed by the CyranoseⓇ 320 eNose device and p<0.05 was significant. RESULTS: NEC pups had severe intestinal injury when compared to controls. Microbiome analysis showed that both control groups had significantly higher microbial diversity and relative abundance of Lactobacillus than NEC, and lower relative abundance of Escherichia. Fecal VOC profile for NEC pups was significantly different from controls. CONCLUSIONS: Experimental NEC was associated with intestinal dysbiosis. Fecal VOC analysis by the CyranoseⓇ 320 eNose device can discriminate NEC pups from both breastfed and formula-fed controls. Further research is warranted to establish whether fecal VOCs can be used as a biomarker or predictive algorithm to diagnose NEC.

The Glucoamylase Inhibitor Acarbose Has a Diet-Dependent and Reversible Effect on the Murine Gut Microbiome.

Acarbose is a safe and effective medication for type 2 diabetes that inhibits host glucoamylases to prevent starch digestion in the small intestines and thus decrease postprandial blood glucose levels. This results in an increase in dietary starch in the distal intestine, where it becomes food for the gut bacterial community. Here, we examined the effect of acarbose therapy on the gut community structure in mice fed either a high-starch (HS) or high-fiber diet rich in plant polysaccharides (PP). The fecal microbiota of animals consuming a low dose of acarbose (25 ppm) was not significantly different from that of control animals that did not receive acarbose. However, a high dose of acarbose (400 ppm) with the HS diet resulted in a substantial change to the microbiota structure. Most notably, the HS diet with a high dose of acarbose lead to an expansion of the Bacteroidaceae and Bifidobacteriaceae and a decrease in the Verrucomicrobiaceae (such as Akkermansia muciniphila) and the Bacteroidales S24-7. Once acarbose treatment ceased, the community composition quickly reverted to mirror that of the control group, suggesting that acarbose does not irreversibly alter the gut community. The high dose of acarbose in the PP diet resulted in a distinct community structure with increased representation of Bifidobacteriaceae and Lachnospiraceae Short-chain fatty acids (SCFAs) measured from stool samples were increased, especially butyrate, as a result of acarbose treatment in both diets. These data demonstrate the potential of acarbose to change the gut community structure and increase beneficial SCFA output in a diet-dependent manner.IMPORTANCE The gut microbial community has a profound influence on host physiology in both health and disease. In diabetic individuals, the gut microbiota can affect the course of disease, and some medications for diabetes, including metformin, seem to elicit some of their benefits via an interaction with the microbiota. Here, we report that acarbose, a glucoamylase inhibitor for type 2 diabetes, changes the murine gut bacterial community structure in a reversible and diet-dependent manner. In both high-starch and high-fiber diet backgrounds, acarbose treatment results in increased short-chain fatty acids, particularly butyrate, as measured in stool samples. As we learn more about how human disease is affected by the intestinal bacterial community, the interplay between medications such as acarbose and the diet will become increasingly important to evaluate.

Dynamics of Human Gut Microbiota and Short-Chain Fatty Acids in Response to Dietary Interventions with Three Fermentable Fibers.

Production of short-chain fatty acids (SCFAs), especially butyrate, in the gut microbiome is required for optimal health but is frequently limited by the lack of fermentable fiber in the diet. We attempted to increase butyrate production by supplementing the diets of 174 healthy young adults for 2 weeks with resistant starch from potatoes (RPS), resistant starch from maize (RMS), inulin from chicory root, or an accessible corn starch control. RPS resulted in the greatest increase in total SCFAs, including butyrate. Although the majority of microbiomes responded to RPS with increases in the relative abundance of bifidobacteria, those that responded with an increase in Ruminococcus bromii or Clostridium chartatabidum were more likely to yield higher butyrate concentrations, especially when their microbiota were replete with populations of the butyrate-producing species Eubacterium rectale RMS and inulin induced different changes in fecal communities, but they did not generate significant increases in fecal butyrate levels.IMPORTANCE These results reveal that not all fermentable fibers are equally capable of stimulating SCFA production, and they highlight the importance of the composition of an individual's microbiota in determining whether or not they respond to a specific dietary supplement. In particular, R. bromii or C. chartatabidum may be required for enhanced butyrate production in response to RS. Bifidobacteria, though proficient at degrading RS and inulin, may not contribute to the butyrogenic effect of those fermentable fibers in the short term.

Effects of Atypical Antipsychotic Treatment and Resistant Starch Supplementation on Gut Microbiome Composition in a Cohort of Patients with Bipolar Disorder or Schizophrenia.

STUDY OBJECTIVE: Previous studies identified shifts in gut microbiota associated with atypical antipsychotic (AAP) treatment that may link AAPs to metabolic burden. Dietary prebiotics such as resistant starch may be beneficial in obesity and glucose regulation, but little is known mechanistically about their ability to modify gut microbiota in AAP-treated individuals. This investigation was undertaken to delineate mechanistically the effects of AAP treatment and resistant starch supplementation on gut microbiota in a psychiatric population. DESIGN: Cross-sectional cohort study. SETTING: The study was performed in an outpatient setting. PATIENTS: A total of 37 adults with a diagnosis of bipolar disorder or schizophrenia who were treated with an AAP (clozapine, olanzapine, risperidone, quetiapine, or ziprasidone [21 patients]) or lithium and/or lamotrigine (16 patients) for at least 6 months. INTERVENTION: Patients in the AAP group received raw unmodified potato starch (resistant starch) daily for 14 days. MEASUREMENTS AND MAIN RESULTS: Of the 37 patients, the mean ± SD age was 52.2 ± 12.5 years, and 57% were male. The primary outcome was gut microbiome DNA composition. Microbiome DNA obtained from stool samples from all patients was subject to 16S ribosomal RNA (rRNA) gene sequencing before and during resistant starch supplementation. Inter- and intragroup microbial diversity measures were performed by permutational multivariate analysis of variance and the Inverse Simpson Diversity Index, respectively. Differentially abundant organisms were detected by using linear discriminant analysis effect size. Although no significant difference in overall microbiota composition was detected at baseline between AAP users and nonusers, non-AAP users showed increased fractional representation of Alistipes. AAP-treated women exhibited decreased diversity compared with non-AAP-treated women. Although the microbiome of AAP-treated patients varied with resistant starch administration, an increased abundance of the Actinobacteria phylum was observed. CONCLUSION: These data suggest that AAP treatment is associated with measurable differences in gut microbiota, particularly in female AAP-treated patients in whom reduced species richness was observed. Additionally, variable microbiome responses to resistant starch supplementation were seen, with a significant increase in starch degraders.

Normalization of the microbiota in patients after treatment for colonic lesions.

BACKGROUND: Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26). RESULTS: There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05). CONCLUSIONS: By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota.

NLRP6 Protects Il10-/- Mice from Colitis by Limiting Colonization of Akkermansia muciniphila.

Dysfunction in host immune responses and pathologic alterations in the gut microbiota, referred to as dysbiosis, can both contribute to the development of inflammatory bowel disease (IBD). However, it remains unclear how specific changes in host immunity or the microbiota cause disease. We previously demonstrated that the loss of the innate immune receptor NLRP6 in mice resulted in impaired production of interleukin-18 (IL-18) and increased susceptibility to epithelial-induced injury. Here, we show that NLRP6 is important for suppressing the development of spontaneous colitis in the Il10-/- mice model of IBD and that NLRP6 deficiency results in the enrichment of Akkermansia muciniphila. A. muciniphila was sufficient for promoting intestinal inflammation in both specific-pathogen-free and germ-free Il10-/- mice. Our results demonstrate that A. muciniphila can act as a pathobiont to promote colitis in a genetically susceptible host and that NLRP6 is a key regulator of its abundance.

Metabolic and Community Synergy of Oral Bacteria in Colorectal Cancer.

The oral periodontopathic bacterium Fusobacterium nucleatum has been repeatedly associated with colorectal tumors. Molecular analysis has identified specific virulence factors that promote tumorigenesis in the colon. However, other oral community members, such as members of the Porphyromonas spp., are also found with F. nucleatum on colonic tumors, and thus, narrow studies of individual pathogens do not take community-wide virulence properties into account. A broader view of oral bacterial physiology and pathogenesis identifies two factors that could promote colonization and persistence of oral bacterial communities in the colon. The polymicrobial nature of oral biofilms and the asaccharolytic metabolism of many of these species make them well suited to life in the microenvironment of colonic lesions. Consideration of these two factors offers a novel perspective on the role of oral microbiota in the initiation, development, and treatment of colorectal cancer.

Manipulation of the Gut Microbiota Reveals Role in Colon Tumorigenesis.

There is growing evidence that individuals with colonic adenomas and carcinomas harbor a distinct microbiota. Alterations to the gut microbiota may allow the outgrowth of bacterial populations that induce genomic mutations or exacerbate tumor-promoting inflammation. In addition, it is likely that the loss of key bacterial populations may result in the loss of protective functions that are normally provided by the microbiota. We explored the role of the gut microbiota in colon tumorigenesis by using an inflammation-based murine model. We observed that perturbing the microbiota with different combinations of antibiotics reduced the number of tumors at the end of the model. Using the random forest machine learning algorithm, we successfully modeled the number of tumors that developed over the course of the model on the basis of the initial composition of the microbiota. The timing of antibiotic treatment was an important determinant of tumor outcome, as colon tumorigenesis was arrested by the use of antibiotics during the early inflammation period of the murine model. Together, these results indicate that it is possible to predict colon tumorigenesis on the basis of the composition of the microbiota and that altering the gut microbiota can alter the course of tumorigenesis. IMPORTANCE Mounting evidence indicates that alterations to the gut microbiota, the complex community of bacteria that inhabits the gastrointestinal tract, are strongly associated with the development of colorectal cancer. We used antibiotic perturbations to a murine model of inflammation-driven colon cancer to generate eight starting communities that resulted in various severities of tumorigenesis. Furthermore, we were able to quantitatively predict the final number of tumors on the basis of the initial composition of the gut microbiota. These results further bolster the evidence that the gut microbiota is involved in mediating the development of colorectal cancer. As a final proof of principle, we showed that perturbing the gut microbiota in the midst of tumorigenesis could halt the formation of additional tumors. Together, alteration of the gut microbiota may be a useful therapeutic approach to preventing and altering the trajectory of colorectal cancer.

DNA from fecal immunochemical test can replace stool for detection of colonic lesions using a microbiota-based model.

BACKGROUND: There is a significant demand for colorectal cancer (CRC) screening methods that are noninvasive, inexpensive, and capable of accurately detecting early stage tumors. It has been shown that models based on the gut microbiota can complement the fecal occult blood test and fecal immunochemical test (FIT). However, a barrier to microbiota-based screening is the need to collect and store a patient's stool sample. RESULTS: Using stool samples collected from 404 patients, we tested whether the residual buffer containing resuspended feces in FIT cartridges could be used in place of intact stool samples. We found that the bacterial DNA isolated from FIT cartridges largely recapitulated the community structure and membership of patients' stool microbiota and that the abundance of bacteria associated with CRC were conserved. We also found that models for detecting CRC that were generated using bacterial abundances from FIT cartridges were equally predictive as models generated using bacterial abundances from stool. CONCLUSIONS: These findings demonstrate the potential for using residual buffer from FIT cartridges in place of stool for microbiota-based screening for CRC. This may reduce the need to collect and process separate stool samples and may facilitate combining FIT and microbiota-based biomarkers into a single test. Additionally, FIT cartridges could constitute a novel data source for studying the role of the microbiome in cancer and other diseases.

Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions.

BACKGROUND: Colorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90% chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have identified shifts in the composition of the gut microbiota associated with the progression of CRC, suggesting that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT). METHODS: We sequenced the 16S rRNA genes from the stool samples of 490 patients. We used the relative abundances of the bacterial populations within each sample to develop a random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool. RESULTS: The microbiota-based random forest model detected 91.7% of cancers and 45.5% of adenomas while FIT alone detected 75.0% and 15.7%, respectively. Of the colonic lesions missed by FIT, the model detected 70.0% of cancers and 37.7% of adenomas. We confirmed known associations of Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum with CRC. Yet, we found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT. CONCLUSIONS: These findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions.

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

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.

The gut microbiome modulates colon tumorigenesis.

UNLABELLED: 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.