bRapid 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.

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.

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.

BackgroundColorectal 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).MethodsWe 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.ResultsThe 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.ConclusionsThese findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0290-3) contains supplementary material, which is available to authorized users.

BackgroundA 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.ResultsWe 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.ConclusionOur 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.

Summary 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 IL18 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 germfree 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.

Understanding the nature of interpopulation interactions in hostassociated 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.microbiome | microbial ecology | culture independent | 454 sequencing | dynamical systems A nalysis of microbial communities is complicated by the communities' large size, diversity, and recalcitrance to culturing (1). Furthermore, even if a population can be cultured and studied under in vitro conditions, there is no guarantee that the observed phenotypes replicate an in vivo phenotype. An underused strategy for describing in vivo phenotypes is the use of quantitative models to describe temporal patterns of biodiversity. Generation of mathematical models can facilitate the inference of relative growth rates, mechanisms of interaction, and responses to perturbations (2-5).Mathematical models are powerful because they provide a method to explain past experiments and predict the outcomes of future experiments. A commonly used approach in microbial ecology literature is to develop correlation-based networks to describe the co-occurrence and dynamics of populations (6-8). These models are helpful for providing an initial description of the interaction network; however, they ignore the possibility that a relationship can be asymmetrical with one partner benefiting and the other being hindered. Furthermore, application of these methods to time-series data violates assumptions of independence between observations (9). A second approach that has received at...

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.