Colorectal cancer (CRC) is highly prevalent worldwide. In 2018, there were over 1.8 million new cases. Most sporadic CRC develop from polypoid adenomas and are preceded by intramucosal carcinoma ...(stage 0), which can progress into more malignant forms. This developmental process is known as the adenoma‐carcinoma sequence. Early detection and endoscopic removal are crucial for CRC management. Accumulating evidence suggests that the gut microbiota is associated with CRC development in humans. Comprehensive characterization of this microbiota is of great importance to assess its potential as a diagnostic marker in the very early stages of CRC. In this review, we summarized recent studies on CRC‐associated bacteria and their carcinogenic mechanisms in animal models, human cell lines and human cohorts. High‐throughput technologies have facilitated the identification of CRC‐associated bacteria in human samples. We have presented our metagenome and metabolome studies on fecal samples collected from a large Japanese cohort that revealed stage‐specific phenotypes of the microbiota in CRC. Furthermore, we have discussed the potential carcinogenic mechanisms of the gut microbiota, from which we can infer whether changes in the gut microbiota are a cause or effect in the multi‐step process of CRC carcinogenesis.
This review summarizes recent studies on colorectal cancer (CRC)‐associated bacteria and their carcinogenic mechanisms in animal models, human cell lines and human cohorts. In addition, we present our most recent metagenome and metabolome studies on fecal samples collected from a large Japanese cohort, which revealed stage‐specific phenotypes of the microbiota in CRC. Finally, we discuss the potential carcinogenic mechanisms of the gut microbiota and whether changes in the gut microbiota are a cause or effect in the multi‐step process of CRC carcinogenesis.
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.
Colorectal cancer (CRC) worldwide affects over a quarter of a million people each year. Most sporadic CRCs develop through formation of polypoid adenomas and are preceded by intramucosal carcinoma ...(Stage 0), which can progress into malignant forms. Detection of early cancers and their endoscopic removal are priorities for cancer control. Human gut microbiome has been associated to CRC development, and its comprehensive characterization is of a great importance to assess its potential as a diagnostic marker. We performed whole shotgun metagenomic sequencing and CE-TOFMS-based metabolomic studies on fecal samples collected from 616 participants undergoing colonoscopy to assess taxonomic and functional characteristics of gut microbiota and metabolites. As a result, microbiome and metabolite shifts were apparent in cases of multiple polypoid adenomas (MP) and Stage 0, in addition to more advanced lesions (Stage I/II and Stage III/IV). Two distinct patterns of microbiome elevations were found (P<0.005). First, CRC-associated species including Fusobacterium nucleatum were elevated continuously from Stage 0 to more advanced stages. Second, Atopobium parvulum and Actinomyces odontolyticus, which co-occurred in Stage 0, were elevated only in MP and/or in Stage 0. Metabolome analyses showed elevation of branched-chain amino acids and phenylalanine in Stage 0 and bile acids including deoxycholate in MP and/or Stage 0. Metagenomic functional analyses showed amino acid metabolism and sulfide producing pathways were associated with CRCs. Our study indicates possible etiological and diagnostic significance of fecal microbiota and metabolite in the very early stages of CRC (Yachida et al., Nature Medicine 2019).
Statistical methods, especially machine learning, learning(ML), are pivotal for the analyses of large data generated by multiomics human gut microbiota study. These analyses lead to the discovery of ...microbe-disease associations. Furthermore, recent efforts for more data transparency and accessible analytical tools improved data availability and study reproducibility. Our recent accumulated knowledge on microbe-disease associations brings light to the next questions: what is the role of microbes in disease progression and how can we apply our knowledge of microbiome in clinical settings? Here, we introduce recent studies that implemented ML to answer the questions of causal inference and clinical translation.
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•ML facilitates the detection of disease-associated microbial features.•Open data repository and tool development improves study reproducibility.•Recent methods integrate SNV and microbiome for microbe-disease causal inference.•Statistical methods can also be used to design microbiome-based clinical intervention.
Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to ...statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public.
Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a ...meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10
). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.
The microbial community during fermented vegetable production has a large impact on the quality of the final products. Lactic acid bacteria have been well-studied in such processes, but knowledge ...about the roles of non-lactic acid bacteria is limited. This study aimed to provide useful knowledge about the relationships between the microbiota, including non-lactic acid bacteria, and metabolites in commercial pickle production by investigating Japanese pickles fermented in rice-bran. The samples were provided by six manufacturers, divided into two groups depending on the production conditions. The microbiological content of these samples was investigated by high-throughput sequencing, and metabolites were assessed by liquid chromatography-mass spectrometry and enzymatic assay. The data suggest that Halomonas, halophilic Gram-negative bacteria, can increase glutamic acid content during the pickling process under selective conditions for bacterial growth. In contrast, in less selective conditions, the microbiota consumed glutamic acid. Our results indicate that the glutamic acid content in fermented pickle is influenced by the microbiota, rather than by externally added glutamic acid. Our data suggest that both lactic acid bacteria and non-lactic acid bacteria are positive key factors in the mechanism of commercial vegetable fermentation and affect the quality of pickles.
Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed ...a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.
Recent studies have demonstrated that gut microbiota development influences infants' health and subsequent host physiology. However, the factors shaping the development of the microbiota remain ...poorly understood, and the mechanisms through which these factors affect gut metabolite profiles have not been extensively investigated. Here we analyse gut microbiota development of 27 infants during the first month of life. We find three distinct clusters that transition towards Bifidobacteriaceae-dominant microbiota. We observe considerable differences in human milk oligosaccharide utilization among infant bifidobacteria. Colonization of fucosyllactose (FL)-utilizing bifidobacteria is associated with altered metabolite profiles and microbiota compositions, which have been previously shown to affect infant health. Genome analysis of infants' bifidobacteria reveals an ABC transporter as a key genetic factor for FL utilization. Thus, the ability of bifidobacteria to utilize FL and the presence of FL in breast milk may affect the development of the gut microbiota in infants, and might ultimately have therapeutic implications.
Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually ...proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.
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