Potential of fecal microbiota for early-stage detection of colorectal cancer

Proteomic indicators of oxidation and hydration state in colorectal cancer

New integrative approaches are needed to harness the potential of rapidly growing datasets of protein expression and microbial community composition in colorectal cancer. Chemical and thermodynamic models offer theoretical tools to describe populations of biomacromolecules and their relative potential for formation in different microenvironmental conditions. The average oxidation state of carbon (Z_C) can be calculated as an elemental ratio from the chemical formulas of proteins, and water demand per residue (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O) is computed by writing the overall formation reactions of proteins from basis species. Using results reported in proteomic studies of clinical samples, many datasets exhibit higher mean Z_C or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of proteins in carcinoma or adenoma compared to normal tissue. In contrast, average protein compositions in bacterial genomes often have lower Z_C for bacteria enriched in fecal samples from cancer patients compared to healthy donors. In thermodynamic calculations, the potential for formation of the cancer-related proteins is energetically favored by changes in the chemical activity of H₂O and fugacity of O₂ that reflect the compositional differences. The compositional analysis suggests that a systematic change in chemical composition is an essential feature of cancer proteomes, and the thermodynamic descriptions show that the observed proteomic transformations in host tissue could be promoted by relatively high microenvironmental oxidation and hydration states.

Toward the integration of Omics data in epidemiological studies: still a "long and winding road"

Primary and secondary prevention can highly benefit a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavor requires a precise characterization of the individual exposome, genome, and phenome. Massive molecular omics data representing the different layers of the biological processes of the host and the nonhost will enable to build more accurate risk prediction models. Epidemiologists aim to integrate omics data along with important information coming from other sources (questionnaires, candidate markers) that has been proved to be relevant in the discrimination risk assessment of complex diseases. However, the integrative models in large-scale epidemiologic research are still in their infancy and they face numerous challenges, some of them at the analytical stage. So far, there are a small number of studies that have integrated more than two omics data sets, and the inclusion of non-omics data in the same models is still missing in most of studies. In this contribution, we aim at approaching the omics and non-omics data integration from the epidemiology scope by considering the "massive" inclusion of variables in the risk assessment and predictive models. We also provide already available examples of integrative contributions in the field, propose analytical strategies that allow considering both omics and non-omics data in the models, and finally review the challenges imbedding this type of research.

Metagenome-wide association studies: fine-mining the microbiome

Metagenome-wide association studies (MWAS) have enabled the high-resolution investigation of associations between the human microbiome and several complex diseases, including type 2 diabetes, obesity, liver cirrhosis, colorectal cancer and rheumatoid arthritis. The associations that can be identified by MWAS are not limited to the identification of taxa that are more or less abundant, as is the case with taxonomic approaches, but additionally include the identification of microbial functions that are enriched or depleted. In this Review, we summarize recent findings from MWAS and discuss how these findings might inform the prevention, diagnosis and treatment of human disease in the future. Furthermore, we highlight the need to better characterize the biology of many of the bacteria that are found in the human microbiota as an essential step in understanding how bacterial strains that have been identified by MWAS are associated with disease.

Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights

Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the “healthy” microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly available at http://segatalab.cibio.unitn.it/tools/metaml.

The human microbiome–the entire set of microbial organisms associated with the human host–interacts closely with host immune and metabolic functions and is crucial for human health. Significant advances in the characterization of the microbiome associated with healthy and diseased individuals have been obtained through next-generation DNA sequencing technologies, which permit accurate estimation of microbial communities directly from uncultured human-associated samples (e.g., stool). In particular, shotgun metagenomics provide data at unprecedented species- and strain- levels of resolution. Several large-scale metagenomic disease-associated datasets are also becoming available, and disease-predictive models built on metagenomic signatures have been proposed. However, the generalization of resulting prediction models on different cohorts and diseases has not been validated. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of microbiome-phenotype associations. We consider 2424 samples from eight studies and six different diseases to assess the independent prediction accuracy of models built on shotgun metagenomic data and to compare strategies for practical use of the microbiome as a prediction tool.

Intestinal Microbiota in Animal Models of Inflammatory Diseases

The intestinal microbiota has long been known to play an important role in the maintenance of health. In addition, alterations of the intestinal microbiota have recently been associated with a range of immune-mediated and metabolic disorders. Characterizing the composition and functionality of the intestinal microbiota, unravelling relevant microbe-host interactions, and identifying disease-relevant microbes are therefore currently of major interest in scientific and medical communities. Experimental animal models for the respective diseases of interest are pivotal in order to address functional questions on microbe-host interaction and to clarify the clinical relevance of microbiome alterations associated with disease initiation and development. This review presents an overview of the outcomes of highly sophisticated experimental studies on microbe-host interaction in animal models of inflammatory diseases, with a focus on inflammatory bowel disease (IBD). We will address the advantages and drawbacks of analyzing microbe-host interaction in complex colonized animal models compared with gnotobiotic animal models using monoassociation, simplified microbial consortia (SMC), or microbial humanization.

Gut microbiota, inflammation and colorectal cancer

Although genes contribute to colorectal cancer, the gut microbiota are an important player. Accumulating evidence suggests that chronic infection and the ensuing inflammation contributes to tumor initiation and tumor progression. A variety of bacterial species and tumor-promoting virulence mechanisms have been investigated. Significant advances have been made in understanding the composition and functional capabilities of the gut microbiota and its roles in cancer. In the current review, we discuss the novel roles of microbiota in the progression of colon cancer. Although microbiota technically include organisms other than bacteria e.g., viruses and fungi, this review will primarily focus on bacteria. We summarize epidemiological studies of human microbiome and colon cancer. We discuss the progress in the scientific understanding of the interplay between the gut microbiota, barrier function, and host responses in experimental models. Further, we discuss the potential application in prevention, diagnosis, and therapy of colon cancer by targeting microbiota. We discuss the challenges lie ahead and the future direction in studying gut microbiome in colon cancer to close the gap between the basic sciences and clinical application.

Invasive Fusobacterium nucleatum may play a role in the carcinogenesis of proximal colon cancer through the serrated neoplasia pathway

The prevalence of invasive Fusobacterium nucleatum (Fn) within the serrated neoplasia pathway of the proximal colon has seldom been investigated. We examined the invasive Fn and bacterial biofilms in 35 proximal hyperplastic polyps (HPs), 33 sessile serrated adenomas (SSAs), 48 proximal colorectal cancers (CRCs) and 10 matched metastatic lymph nodes using 16S rRNA fluorescence in situ hybridization (FISH). Samples of normal mucosa, traditional adenomas (TAs), distal HPs, distal CRCs and matched lymph nodes with or without metastases were used as controls. The prevalence of invasive Fn within proximal HPs (65.7%) and SSAs (78.8%) were significantly higher than that of proximal TAs (28.9%) and distal TAs (24.4%; p < 0.05). Invasive Fn was detected in markedly more proximal CRCs (89.6%) than in distal CRCs (42.2%; p < 0.05). Moreover, invasive Fn was detected in a significantly higher proportion of matched metastatic lymph nodes (100%) than that within nonmetastatic lymph nodes (40.0%; p < 0.001). Bacterial biofilms were found on 52.1% of proximal CRCs, 55.6% of distal CRCs and 48.5% of SSAs. Biofilms were positive for Fn in 47.9% of proximal CRCs, 48.9% of distal CRCs and 27.3% of SSAs. However, the presence of Fn in biofilms was not related to invasive Fn within colorectal tissues (p = 0.415). Invasive Fn may play a role in the carcinogenesis of proximal colon developing via the serrated neoplasia pathway, but might have a less important role in the TA-carcinoma sequence. Bacterial biofilms may not contribute to the invasion of Fn into tumor tissues.

Colorectal Cancer and the Human Gut Microbiome: Reproducibility with Whole-Genome Shotgun Sequencing

Accumulating evidence indicates that the gut microbiota affects colorectal cancer development, but previous studies have varied in population, technical methods, and associations with cancer. Understanding these variations is needed for comparisons and for potential pooling across studies. Therefore, we performed whole-genome shotgun sequencing on fecal samples from 52 pre-treatment colorectal cancer cases and 52 matched controls from Washington, DC. We compared findings from a previously published 16S rRNA study to the metagenomics-derived taxonomy within the same population. In addition, metagenome-predicted genes, modules, and pathways in the Washington, DC cases and controls were compared to cases and controls recruited in France whose specimens were processed using the same platform. Associations between the presence of fecal Fusobacteria, Fusobacterium, and Porphyromonas with colorectal cancer detected by 16S rRNA were reproduced by metagenomics, whereas higher relative abundance of Clostridia in cancer cases based on 16S rRNA was merely borderline based on metagenomics. This demonstrated that within the same sample set, most, but not all taxonomic associations were seen with both methods. Considering significant cancer associations with the relative abundance of genes, modules, and pathways in a recently published French metagenomics dataset, statistically significant associations in the Washington, DC population were detected for four out of 10 genes, three out of nine modules, and seven out of 17 pathways. In total, colorectal cancer status in the Washington, DC study was associated with 39% of the metagenome-predicted genes, modules, and pathways identified in the French study. More within and between population comparisons are needed to identify sources of variation and disease associations that can be reproduced despite these variations. Future studies should have larger sample sizes or pool data across studies to have sufficient power to detect associations that are reproducible and significant after correction for multiple testing.

A microfluidics-based in vitro model of the gastrointestinal human-microbe interface

Changes in the human gastrointestinal microbiome are associated with several diseases. To infer causality, experiments in representative models are essential, but widely used animal models exhibit limitations. Here we present a modular, microfluidics-based model (HuMiX, human–microbial crosstalk), which allows co-culture of human and microbial cells under conditions representative of the gastrointestinal human–microbe interface. We demonstrate the ability of HuMiX to recapitulate in vivo transcriptional, metabolic and immunological responses in human intestinal epithelial cells following their co-culture with the commensal Lactobacillus rhamnosus GG (LGG) grown under anaerobic conditions. In addition, we show that the co-culture of human epithelial cells with the obligate anaerobe Bacteroides caccae and LGG results in a transcriptional response, which is distinct from that of a co-culture solely comprising LGG. HuMiX facilitates investigations of host–microbe molecular interactions and provides insights into a range of fundamental research questions linking the gastrointestinal microbiome to human health and disease.

Research on the interactions between the gut microbiota and human cells would greatly benefit from improved in vitro models. Here, Shah et al. present a modular microfluidics-based model that allows co-culture of human and microbial cells followed by 'omic' molecular analyses of the two cell contingents.

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.

Durable coexistence of donor and recipient strains after fecal microbiota transplantation

Fecal microbiota transplantation (FMT) has shown efficacy in treating recurrent Clostridium difficile infection and is increasingly being applied to other gastrointestinal disorders, yet the fate of native and introduced microbial strains remains largely unknown. To quantify the extent of donor microbiota colonization, we monitored strain populations in fecal samples from a recent FMT study on metabolic syndrome patients using single-nucleotide variants in metagenomes. We found extensive coexistence of donor and recipient strains, persisting 3 months after treatment. Colonization success was greater for conspecific strains than for new species, the latter falling within fluctuation levels observed in healthy individuals over a similar time frame. Furthermore, same-donor recipients displayed varying degrees of microbiota transfer, indicating individual patterns of microbiome resistance and donor-recipient compatibilities.

Functional Redundancy-Induced Stability of Gut Microbiota Subjected to Disturbance

The microbiota should be considered as just another component of the human epigenetic landscape. Thus, health is also a reflection of the diversity and composition of gut microbiota and its metabolic status. In defining host health, it remains unclear whether diversity is paramount, or whether greater weight is held by gut microbiota composition or mono- or multiple-functional capacity of the different taxa and the mechanisms involved. A network-biology approach may shed light on the key gut players acting to protect against, or promote, disorders or diseases. This could be achieved by integrating data on total and active species, proteins and molecules, and their association with host response. In this review, we discuss the utilization of top-down and bottom-up approaches, following a functional hierarchy perspective.

Oral microbiome and history of smoking and colorectal cancer

BACKGROUND

The equilibrium of oral microbiome may be altered by environmental factors, including cigarette smoking. Several recent studies also suggest that oral pathogens causing periodontal disease, such as Fusobacterium nucleatum, are involved in pathogenesis of colorectal cancer.

METHODS

For this study oral rinse DNA samples from 190 participants in a population-based case-control study for colorectal cancer were used to amplify a V3-V4 region of bacterial 16S rRNA gene. The amplicons were sequenced using Illumina MiSeq paired end chemistry on two runs, yielding approximately 35 million filtered reads which were assigned to bacterial phyla.

RESULTS

No association was found between Fusobacterium abundance or presence and colorectal cancer. However, adjusted for age and experimental batch, colorectal cancer history was associated with increased presence of genus Lactobacillus and increased relative abundance of Rothia by 28% and current smoking was associated with a 33% decrease in relative counts of Betaproteobacteria (primarily Neisseria) and 23% increase in relative abundance of Veillonellaceae family. We also found that smoking had significant effects on the 2^nd component scores and 2^nd coordinate distances in principal component and coordinate analyses.

CONCLUSIONS

It remains to be elucidated whether the observed differences can be translated into biochemical changes in oral environment, thus potentially affecting oral health.

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.

The gut microbiome of healthy Japanese and its microbial and functional uniqueness

The human gut microbiome has profound influences on the host's health largely through its interference with various intestinal functions. As recent studies have suggested diversity in the human gut microbiome among human populations, it will be interesting to analyse how gut microbiome is correlated with geographical, cultural, and traditional differences. The Japanese people are known to have several characteristic features such as eating a variety of traditional foods and exhibiting a low BMI and long life span. In this study, we analysed gut microbiomes of the Japanese by comparing the metagenomic data obtained from 106 Japanese individuals with those from 11 other nations. We found that the composition of the Japanese gut microbiome showed more abundant in the phylum Actinobacteria, in particular in the genus Bifidobacterium, than other nations. Regarding the microbial functions, those of carbohydrate metabolism were overrepresented with a concurrent decrease in those for replication and repair, and cell motility. The remarkable low prevalence of genes for methanogenesis with a significant depletion of the archaeon Methanobrevibacter smithii and enrichment of acetogenesis genes in the Japanese gut microbiome compared with others suggested a difference in the hydrogen metabolism pathway in the gut between them. It thus seems that the gut microbiome of the Japanese is considerably different from those of other populations, which cannot be simply explained by diet alone. We postulate possible existence of hitherto unknown factors contributing to the population-level diversity in human gut microbiomes.

Myeloid PTEN deficiency impairs tumor-immune surveillance via immune-checkpoint inhibition

Tumor-host interaction is determined by constant immune surveillance, characterized by tumor infiltration of myeloid and lymphoid cells. A malfunctioning or diverted immune response promotes tumor growth and metastasis. Recent advances had been made, by treating of certain tumor types, such as melanoma, with T-cell checkpoint inhibitors. This highlights the importance of understanding the molecular mechanisms underlying the crosstalk between tumors and their environment, in particular myeloid and lymphoid cells. Our aim was to study the contribution of the myeloid PI3K/PTEN-signaling pathway in the regulation of tumor-immune surveillance in murine models of cancer. We made use of conditional PTEN-deficient mice, which exhibit sustained activation of the PI3K-signaling axis in a variety of myeloid cell subsets such as macrophages and dendritic cells (DCs). In colitis-associated colon cancer (CAC), mice deficient in myeloid PTEN showed a markedly higher tumor burden and decreased survival. We attributed this observation to the increased presence of immune-modulatory conventional CD8α(+) DCs in the spleen, whereas other relevant myeloid cell subsets were largely unaffected. Notably, we detected enhanced surface expression of PD-L1 and PD-L2 on these DCs. As a consequence, tumoricidal T-cell responses were hampered or redirected. Taken together, our findings indicated an unanticipated role for the PI3K/PTEN-signaling axis in the functional regulation of splenic antigen-presenting cells (APCs). Our data pointed at potential, indirect, tumoricidal effects of subclass-specific PI3K inhibitors, which are currently under clinical investigation for treatment of tumors, via myeloid cell activation.

Fecal Microbiota, Fecal Metabolome, and Colorectal Cancer Interrelations

Background and Aims

Investigation of microbe-metabolite relationships in the gut is needed to understand and potentially reduce colorectal cancer (CRC) risk.

Methods

Microbiota and metabolomics profiling were performed on lyophilized feces from 42 CRC cases and 89 matched controls. Multivariable logistic regression was used to identify statistically independent associations with CRC. First principal coordinate-component pair (PCo1-PC1) and false discovery rate (0.05)-corrected P-values were calculated for 116,000 Pearson correlations between 530 metabolites and 220 microbes in a sex*case/control meta-analysis.

Results

Overall microbe-metabolite PCo1-PC1 was more strongly correlated in cases than in controls (Rho 0.606 vs 0.201, P = 0.01). CRC was independently associated with lower levels of Clostridia, Lachnospiraceae, p-aminobenzoate and conjugated linoleate, and with higher levels of Fusobacterium, Porphyromonas, p-hydroxy-benzaldehyde, and palmitoyl-sphingomyelin. Through postulated effects on cell shedding (palmitoyl-sphingomyelin), inflammation (conjugated linoleate), and innate immunity (p-aminobenzoate), metabolites mediated the CRC association with Fusobacterium and Porphyromonas by 29% and 34%, respectively. Overall, palmitoyl-sphingomyelin correlated directly with abundances of Enterobacteriaceae (Gammaproteobacteria), three Actinobacteria and five Firmicutes. Only Parabacteroides correlated inversely with palmitoyl-sphingomyelin. Other lipids correlated inversely with Alcaligenaceae (Betaproteobacteria). Six Bonferroni-significant correlations were found, including low indolepropionate and threnoylvaline with Actinobacteria and high erythronate and an uncharacterized metabolite with Enterobacteriaceae.

Conclusions

Feces from CRC cases had very strong microbe-metabolite correlations that were predominated by Enterobacteriaceae and Actinobacteria. Metabolites mediated a direct CRC association with Fusobacterium and Porphyromonas, but not an inverse association with Clostridia and Lachnospiraceae. This study identifies complex microbe-metabolite networks that may provide insights on neoplasia and targets for intervention.

Epidemiologic studies of the human microbiome and cancer

The human microbiome, which includes the collective genome of all bacteria, archaea, fungi, protists, and viruses found in and on the human body, is altered in many diseases and may substantially affect cancer risk. Previously detected associations of individual bacteria (e.g., Helicobacter pylori), periodontal disease, and inflammation with specific cancers have motivated studies considering the association between the human microbiome and cancer risk. This short review summarises microbiome research, focusing on published epidemiological associations with gastric, oesophageal, hepatobiliary, pancreatic, lung, colorectal, and other cancers. Large, prospective studies of the microbiome that employ multidisciplinary laboratory and analysis methods, as well as rigorous validation of case status, are likely to yield translational opportunities to reduce cancer morbidity and mortality by improving prevention, screening, and treatment.

Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies

BACKGROUND

The need to develop valid methods for sampling and analyzing fecal specimens for microbiome studies is increasingly important, especially for large population studies.

METHODS

Some of the most important attributes of any sampling method are reproducibility, stability, and accuracy. We compared seven fecal sampling methods [no additive, RNAlater, 70% ethanol, EDTA, dry swab, and pre/post development fecal occult blood test (FOBT)] using 16S rRNA microbiome profiling in two laboratories. We evaluated nine commonly used microbiome metrics: abundance of three phyla, two alpha-diversities, and four beta-diversities. We determined the technical reproducibility, stability at ambient temperature, and accuracy.

RESULTS

Although microbiome profiles showed systematic biases according to sample method and time at ambient temperature, the highest source of variation was between individuals. All collection methods showed high reproducibility. FOBT and RNAlater resulted in the highest stability without freezing for 4 days. In comparison with no-additive samples, swab, FOBT, and 70% ethanol exhibited the greatest accuracy when immediately frozen.

CONCLUSIONS

Overall, optimal stability and reproducibility were achieved using FOBT, making this a reasonable sample collection method for 16S analysis.

IMPACT

Having standardized method of collecting and storing stable fecal samples will allow future investigations into the role of gut microbiota in chronic disease etiology in large population studies.

Gut Microbiota Linked to Sexual Preference and HIV Infection

The precise effects of HIV-1 on the gut microbiome are unclear. Initial cross-sectional studies provided contradictory associations between microbial richness and HIV serostatus and suggested shifts from Bacteroides to Prevotella predominance following HIV-1 infection, which have not been found in animal models or in studies matched for HIV-1 transmission groups. In two independent cohorts of HIV-1-infected subjects and HIV-1-negative controls in Barcelona (n = 156) and Stockholm (n = 84), men who have sex with men (MSM) predominantly belonged to the Prevotella-rich enterotype whereas most non-MSM subjects were enriched in Bacteroides, independently of HIV-1 status, and with only a limited contribution of diet effects. Moreover, MSM had a significantly richer and more diverse fecal microbiota than non-MSM individuals. After stratifying for sexual orientation, there was no solid evidence of an HIV-specific dysbiosis. However, HIV-1 infection remained consistently associated with reduced bacterial richness, the lowest bacterial richness being observed in subjects with a virological-immune discordant response to antiretroviral therapy. Our findings indicate that HIV gut microbiome studies must control for HIV risk factors and suggest interventions on gut bacterial richness as possible novel avenues to improve HIV-1-associated immune dysfunction.

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The fecal microbiota of gay men in Europe is systematically richer and has a distinct composition.

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HIV-1 infection is independently associated with reduced gut bacterial richness, more so in immune discordant subjects.

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Interventions to increase gut bacterial richness might offer novel avenues to improve HIV-1-associated immune dysfunction.

The human intestinal microbiota is essential for human health and well-being and is driven by genetic, lifestyle and environmental factors. Here, we show in two independent cohorts of HIV-1-infected subjects and HIV-1-negative controls in Europe that gay men often have a distinct composition of the human fecal microbiota, with increased microbial richness and diversity and enrichment in the Prevotella enterotype. This is independent of HIV-1 status, and with only a limited contribution of diet effects. After accounting for sexual orientation, however, HIV-1 infection remains associated to reduced bacterial richness, more so in subjects with suboptimal CD4 + T-cell count recovery under antiretroviral therapy. Future studies should evaluate if interventions to increase gut bacterial richness could improve HIV-associated immune dysfunction.

Association of Streptococcus gallolyticus subspecies gallolyticus with colorectal cancer: Serological evidence

The colonic opportunist Streptococcus gallolyticus subspecies gallolyticus (SGG) is potentially associated with colorectal cancer (CRC). Large-scale seroepidemiological data for SGG antibodies and their possible association with CRC is currently missing. Associations between CRC and antibody responses to SGG were examined in 576 CRC cases and 576 controls matched by sex, age and province from a population-based multicase-control project (MCC-Spain). MCC-Spain was conducted between 2008 and 2013 in 12 Spanish provinces. Antibody responses to recombinant affinity-purified SGG pilus proteins Gallo1569, 2039, 2178 and 2179 were analysed by multiplex serology. Polyomavirus (PyV) JC VP1 and PyV 6 VP1 proteins served as disease-specificity controls. In the control population, antibody responses to pilus proteins were mostly weak. Antibody responses to individual pilus proteins Gallo2039 (OR: 1.58, 95% CI: 1.09-2.28), Gallo2178 (OR: 1.58, 95% CI: 1.09-2.30) and Gallo2179 (OR: 1.45, 95% CI: 1.00-2.11) were significantly associated with CRC risk. The association was stronger for positivity to two or more pilus proteins of Gallo1569, Gallo2178 and Gallo2179 (OR:1.93, 95% CI: 1.04-3.56) and for double-positivity to Gallo2178 and Gallo2179 (OR: 3.54, 95% CI: 1.49-8.44). The association between SGG infection and CRC risk was stronger among individuals younger than 65 years. For the first time we demonstrated a statistically significant association of exposure to SGG antigens and CRC in a large seroepidemiological study. These results should stimulate further studies on the role of SGG in CRC pathogenesis.

Gut mucosal microbiome across stages of colorectal carcinogenesis

Gut microbial dysbiosis contributes to the development of colorectal cancer (CRC). Here we catalogue the microbial communities in human gut mucosae at different stages of colorectal tumorigenesis. We analyse the gut mucosal microbiome of 47 paired samples of adenoma and adenoma-adjacent mucosae, 52 paired samples of carcinoma and carcinoma-adjacent mucosae and 61 healthy controls. Probabilistic partitioning of relative abundance profiles reveals that a metacommunity predominated by members of the oral microbiome is primarily associated with CRC. Analysis of paired samples shows differences in community configurations between lesions and the adjacent mucosae. Correlations of bacterial taxa indicate early signs of dysbiosis in adenoma, and co-exclusive relationships are subsequently more common in cancer. We validate these alterations in CRC-associated microbiome by comparison with two previously published data sets. Our results suggest that a taxonomically defined microbial consortium is implicated in the development of CRC.

Changes in gut microbial communities contribute to the development of colorectal cancer. Here, the authors analyse the gut mucosal microbiome of patients and healthy subjects and identify distinct microbial consortia associated with different stages of colorectal cancer tumorigenesis.

The Microbiome and Cancer: Is the 'Oncobiome' Mirage Real?

Investigations focused on the interplay between the human microbiome and cancer development, herein termed the 'oncobiome', have been growing at a rapid rate. However, these studies to date have primarily demonstrated associative relationships rather than causative ones. We pose the question of whether this emerging field of research is a 'mirage' without a clear picture, or truly represents a paradigm shift for cancer research. We propose the necessary steps needed to answer crucial questions and push the field forward to bring the mirage into a tangible reality.

Microbiology Meets Big Data: The Case of Gut Microbiota-Derived Trimethylamine

During the past decade, meta-omics approaches have revolutionized microbiology, allowing for a cultivation-free assessment of the composition and functional properties of entire microbial ecosystems. On the one hand, a phylogenetic and functional interpretation of such data relies on accumulated genetic, biochemical, metabolic, and phenotypic characterization of microbial variation. On the other hand, the increasing availability of extensive microbiome data sets and corresponding metadata provides a vast, underused resource for the microbiology field as a whole. To demonstrate the potential for integrating big data into a functional microbiology workflow, we review literature on trimethylamine (TMA), a microbiota-generated metabolite linked to atherosclerosis development. Translating recently elucidated microbial pathways resulting in TMA production into genomic orthologs, we demonstrate how to mine for their presence in public (meta-) genomic databases and link findings to associated metadata. Reviewing pathway abundance in public data sets shows that TMA production potential is associated with symptomatic atherosclerosis and allows identification of currently uncharacterized TMA-producing bacteria.

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Secrets from the microbiome: molecular biology meets microbiology meets histopathology...meets clinical biochemistry

The microbiome is the collective term used to describe the bacteria, viruses, fungi and archaea that reside on and in the human body. The majority of these organisms are found within the large bowel. Mounting evidence suggests that changes in the microbiome may be associated with the development of colorectal cancer, a disease which affects 1.3 million people a year worldwide. Using colorectal cancer as an example, this article presents the inter-specialty collaborative approach to microbiome research and discusses the key role that clinical biochemistry is likely to play.

Virulence genes are a signature of the microbiome in the colorectal tumor microenvironment

Background

The human gut microbiome is associated with the development of colon cancer, and recent studies have found changes in the microbiome in cancer patients compared to healthy controls. Studying the microbial communities in the tumor microenvironment may shed light on the role of host–bacteria interactions in colorectal cancer. Here, we highlight the major shifts in the colorectal tumor microbiome relative to that of matched normal colon tissue from the same individual, allowing us to survey the microbial communities in the tumor microenvironment and providing intrinsic control for environmental and host genetic effects on the microbiome.

Methods

We sequenced the microbiome in 44 primary tumor and 44 patient-matched normal colon tissue samples to determine differentially abundant microbial taxa These data were also used to functionally characterize the microbiome of the cancer and normal sample pairs and identify functional pathways enriched in the tumor-associated microbiota.

Results

We find that tumors harbor distinct microbial communities compared to nearby healthy tissue. Our results show increased microbial diversity in the tumor microenvironment, with changes in the abundances of commensal and pathogenic bacterial taxa, including Fusobacterium and Providencia. While Fusobacterium has previously been implicated in colorectal cancer, Providencia is a novel tumor-associated agent which has not been identified in previous studies. Additionally, we identified a clear, significant enrichment of predicted virulence-associated genes in the colorectal cancer microenvironment, likely dependent upon the genomes of Fusobacterium and Providencia.

Conclusions

This work identifies bacterial taxa significantly correlated with colorectal cancer, including a novel finding of an elevated abundance of Providencia in the tumor microenvironment. We also describe the predicted metabolic pathways and enzymes differentially present in the tumor-associated microbiome, and show an enrichment of virulence-associated bacterial genes in the tumor microenvironment. This predicted virulence enrichment supports the hypothesis that the microbiome plays an active role in colorectal cancer development and/or progression. Our results provide a starting point for future prognostic and therapeutic research with the potential to improve patient outcomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0177-8) contains supplementary material, which is available to authorized users.

From next-generation sequencing to systematic modeling of the gut microbiome

Changes in the human gut microbiome are associated with altered human metabolism and health, yet the mechanisms of interactions between microbial species and human metabolism have not been clearly elucidated. Next-generation sequencing has revolutionized the human gut microbiome research, but most current applications concentrate on studying the microbial diversity of communities and have at best provided associations between specific gut bacteria and human health. However, little is known about the inner metabolic mechanisms in the gut ecosystem. Here we review recent progress in modeling the metabolic interactions of gut microbiome, with special focus on the utilization of metabolic modeling to infer host–microbe interactions and microbial species interactions. The systematic modeling of metabolic interactions could provide a predictive understanding of gut microbiome, and pave the way to synthetic microbiota design and personalized-microbiome medicine and healthcare. Finally, we discuss the integration of metabolic modeling and gut microbiome engineering, which offer a new way to explore metabolic interactions across members of the gut microbiota.

Fecal Microbiota Characteristics of Patients with Colorectal Adenoma Detected by Screening: A Population-based Study

Background

Screening for colorectal cancer (CRC) and precancerous colorectal adenoma (CRA) can detect curable disease. However, participation in colonoscopy and sensitivity of fecal heme for CRA are low.

Methods

Microbiota metrics were determined by Illumina sequencing of 16S rRNA genes amplified from DNA extracted from feces self-collected in RNAlater. Among fecal immunochemical test-positive (FIT +) participants, colonoscopically-defined normal versus CRA patients were compared by regression, permutation, and random forest plus leave-one-out methods.

Findings

Of 95 FIT + participants, 61 had successful fecal microbiota profiling and colonoscopy, identifying 24 completely normal patients, 20 CRA patients, 2 CRC patients, and 15 with other conditions. Phylum-level fecal community composition differed significantly between CRA and normal patients (permutation P = 0.02). Rank phylum-level abundance distinguished CRA from normal patients (area under the curve = 0.767, permutation P = 0.006). CRA prevalence was 59% in phylum-level cluster B versus 20% in cluster A (exact P = 0.01). Most of the difference reflected 3-fold higher median relative abundance of Proteobacteria taxa (Wilcoxon signed-rank P = 0.03, positive predictive value = 67%). Antibiotic exposure and other potential confounders did not affect the associations.

Interpretation

If confirmed in larger, more diverse populations, fecal microbiota analysis might be employed to improve screening for CRA and ultimately to reduce mortality from CRC.

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Fecal microbiota composition differed for patients with colorectal adenoma compared to normals.

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Most of the difference reflected 3-fold higher abundance of Proteobacteria in patients with adenoma.

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Population-wide microbiota screening is feasible and, if validated, could complement established early-detection programs.

Temporal and technical variability of human gut metagenomes

BACKGROUND

Metagenomics has become a prominent approach for exploring the role of the gut microbiota in human health. However, the temporal variability of the healthy gut microbiome has not yet been studied in depth using metagenomics and little is known about the effects of different sampling and preservation approaches. We performed metagenomic analysis on fecal samples from seven subjects collected over a period of up to two years to investigate temporal variability and assess preservation-induced variation, specifically, fresh frozen compared to RNALater. We also monitored short-term disturbances caused by antibiotic treatment and bowel cleansing in one subject.

RESULTS

We find that the human gut microbiome is temporally stable and highly personalized at both taxonomic and functional levels. Over multiple time points, samples from the same subject clustered together, even in the context of a large dataset of 888 European and American fecal metagenomes. One exception was observed in an antibiotic intervention case where, more than one year after the treatment, samples did not resemble the pre-treatment state. Clustering was not affected by the preservation method. No species differed significantly in abundance, and only 0.36% of gene families were differentially abundant between preservation methods.

CONCLUSIONS

Technical variability is small compared to the temporal variability of an unperturbed gut microbiome, which in turn is much smaller than the observed between-subject variability. Thus, short-term preservation of fecal samples in RNALater is an appropriate and cost-effective alternative to freezing of fecal samples for metagenomic studies.