Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer

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Abstract

<p class="first" id="d5472384e445">In most cases of sporadic colorectal cancers, tumorigenesis is a multistep process, involving genomic alterations in parallel with morphologic changes. In addition, accumulating evidence suggests that the human gut microbiome is linked to the development of colorectal cancer. Here we performed fecal metagenomic and metabolomic studies on samples from a large cohort of 616 participants who underwent colonoscopy to assess taxonomic and functional characteristics of gut microbiota and metabolites. Microbiome and metabolome shifts were apparent in cases of multiple polypoid adenomas and intramucosal carcinomas, in addition to more advanced lesions. We found two distinct patterns of microbiome elevations. First, the relative abundance of Fusobacterium nucleatum spp. was significantly (P < 0.005) elevated continuously from intramucosal carcinoma to more advanced stages. Second, Atopobium parvulum and Actinomyces odontolyticus, which co-occurred in intramucosal carcinomas, were significantly (P < 0.005) increased only in multiple polypoid adenomas and/or intramucosal carcinomas. Metabolome analyses showed that branched-chain amino acids and phenylalanine were significantly (P < 0.005) increased in intramucosal carcinomas and bile acids, including deoxycholate, were significantly (P < 0.005) elevated in multiple polypoid adenomas and/or intramucosal carcinomas. We identified metagenomic and metabolomic markers to discriminate cases of intramucosal carcinoma from the healthy controls. Our large-cohort multi-omics data indicate that shifts in the microbiome and metabolome occur from the very early stages of the development of colorectal cancer, which is of possible etiological and diagnostic importance. </p>

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Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

Ian Holmes, Keith Harris, Christopher Quince (2012)

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.

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Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry.

Akiyoshi Hirayama, Kenjiro Kami, Masahiro Sugimoto … (2009)

Most cancer cells predominantly produce energy by glycolysis rather than oxidative phosphorylation via the tricarboxylic acid (TCA) cycle, even in the presence of an adequate oxygen supply (Warburg effect). However, little has been reported regarding the direct measurements of global metabolites in clinical tumor tissues. Here, we applied capillary electrophoresis time-of-flight mass spectrometry, which enables comprehensive and quantitative analysis of charged metabolites, to simultaneously measure their levels in tumor and grossly normal tissues obtained from 16 colon and 12 stomach cancer patients. Quantification of 94 metabolites in colon and 95 metabolites in stomach involved in glycolysis, the pentose phosphate pathway, the TCA and urea cycles, and amino acid and nucleotide metabolisms resulted in the identification of several cancer-specific metabolic traits. Extremely low glucose and high lactate and glycolytic intermediate concentrations were found in both colon and stomach tumor tissues, which indicated enhanced glycolysis and thus confirmed the Warburg effect. Significant accumulation of all amino acids except glutamine in the tumors implied autophagic degradation of proteins and active glutamine breakdown for energy production, i.e., glutaminolysis. In addition, significant organ-specific differences were found in the levels of TCA cycle intermediates, which reflected the dependency of each tissue on aerobic respiration according to oxygen availability. The results uncovered unexpectedly poor nutritional conditions in the actual tumor microenvironment and showed that capillary electrophoresis coupled to mass spectrometry-based metabolomics, which is capable of quantifying the levels of energy metabolites in tissues, could be a powerful tool for the development of novel anticancer agents that target cancer-specific metabolism.

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Comparative lesion sequencing provides insights into tumor evolution.

Siân Jones, Wei-Dong Chen, Giovanni Parmigiani … (2008)

We show that the times separating the birth of benign, invasive, and metastatic tumor cells can be determined by analysis of the mutations they have in common. When combined with prior clinical observations, these analyses suggest the following general conclusions about colorectal tumorigenesis: (i) It takes approximately 17 years for a large benign tumor to evolve into an advanced cancer but <2 years for cells within that cancer to acquire the ability to metastasize; (ii) it requires few, if any, selective events to transform a highly invasive cancer cell into one with the capacity to metastasize; (iii) the process of cell culture ex vivo does not introduce new clonal mutations into colorectal tumor cell populations; and (iv) the rates at which point mutations develop in advanced cancers are similar to those of normal cells. These results have important implications for understanding human tumor pathogenesis, particularly those associated with metastasis.