Metagenomic analyses of the gut microbiota associated with colorectal adenoma

Saito, Keisuke; Koido, Shigeo; Odamaki, Toshitaka; Kajihara, Mikio; Kato, Koichi; Horiuchi, Sankichi; Adachi, Sei; Arakawa, Hiroshi; Yoshida, Sayumi; Akasu, Takafumi; Ito, Zensho; Uchiyama, Kan; Saruta, Masayuki; Xiao, Jin‐zhong; Sato, Nobuhiro; Ohkusa, Toshifumi

doi:10.1371/journal.pone.0212406

Cited by 42 publications

(39 citation statements)

References 31 publications

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“…Inhibition of immune cells would be desirable for tumor cells, since it will spread the tumor to other parts of the body [ 112 ]. Investigation of 16 s rRNA sequencing of increased F. nucleatum levels in mucosal or fecal samples of CRC patients has shown that F. nucleatum levels in CRC tissue is associated with the tumor site of right-sided proximal colorectum and CpG island methylator phenotype ( CIMP ) status, microsatellite instability ( MSI ) and mutations in BRAF, KRAS, CHD7, CHD8 and TP53 genes [ 131 ]. Increased inflammatory cytokines such as NF-KB, TNF-α, IL10, IL8, IL6, and increased levels of E. cadherin on epithelial cells activates B-catenin signaling, increases NF-κB , C-myc expression and proliferates tumor cells [ 70 ].…”

Section: Factors Affecting the Intestinal Microbial Populationmentioning

confidence: 99%

“…The direct association of Fusobacterium with recurrent CRC has even been postulated as a way to predict disease outcomes or change chemotherapy regimens such as inclusion of capecitabine and oxaliplatin for patients with a high burden of F. nucleatum [ 45 ]. These observations suggest further consideration of antimicrobial interventions as a potential treatment for patients with CRC related to Fusobacterium [ 131 ]. One concern is the negative effect of broad-spectrum antibiotics on the intestinal microbiome [ 20 ].…”

Section: Factors Affecting the Intestinal Microbial Populationmentioning

confidence: 99%

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The dysbiosis signature of Fusobacterium nucleatum in colorectal cancer-cause or consequences? A systematic review

et al. 2021

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Colorectal cancer (CRC) is the third most common cause of cancer globally and the fourth attributable cause of mortality and morbidity due to cancer. An emerging factor contributing to CRC is the gut microbiota and the cellular changes associated with it. Further insights on this may help in the prevention, diagnosis and new therapeutic approaches to colorectal cancer. In most cases of CRC, genetic factors appear to contribute less to its aetiology than environmental and epigenetic factors; therefore, it may be important to investigate these environmental factors, their effects, and the mechanisms that may contribute to this cancer. The gut microbiota has recently been highlighted as a potential risk factor that may affect the structural components of the tumor microenvironment, as well as free radical and enzymatic metabolites directly, or indirectly. Many studies have reported changes in the gut microbiota of patients with colorectal cancer. What is controversial is whether the cancer is the cause or consequence of the change in the microbiota. There is strong evidence supporting both possibilities. The presence of Fusobacterium nucleatum in human colorectal specimens has been demonstrated by RNA-sequencing. F. nucleatum has been shown to express high levels of virulence factors such as FadA, Fap2 and MORN2 proteins. Our review of the published data suggest that F. nucleatum may be a prognostic biomarker of CRC risk, and hence raises the potential of antibiotic treatment of F. nucleatum for the prevention of CRC.

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Section: Factors Affecting the Intestinal Microbial Populationmentioning

confidence: 99%

Section: Factors Affecting the Intestinal Microbial Populationmentioning

confidence: 99%

The dysbiosis signature of Fusobacterium nucleatum in colorectal cancer-cause or consequences? A systematic review

et al. 2021

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“…Using LEfSe, we found, for example, that Fusobacterium ( Fusobacteriaceae family), was more abundant at 10 days (LDA score > 3) in both metagenomics and metatranscriptomics data (Figure 3 ). The role of Fusobacterium on cockroaches’ gut microbiome deserve a detailed study due to these results and some interesting findings about this groups’ role in other organisms: Fusobacterium has been related to disease and stress situations in the human gut microbiota ( 48 ), but is has also been related to the infants gut microbiota ( 49 ). Conversely, an unidentified genus belonging to the family Ruminococcaceae , has been found more abundant in 20d than 10d condition (LDA score > 3) in metagenomics data (Figure 3A ), but no differences between conditions have been found in metatranscriptomics data (Figure 3B ).…”

Section: Resultsmentioning

confidence: 98%

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

Muñoz-Benavent

Hartkopf

Bossche

et al. 2020

NAR Genomics and Bioinformatics

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The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.

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“…Using LEfSe, we found, for example, that Fusobacterium (Fusobacteriaceae family), was more abundant at 10 days (LDA score > 3) in both metagenomics and metatranscriptomics data (Figure 3).. Fusobacterium has been related to disease and stress situations in the human gut microbiota [44], but is has also been related to the infants gut microbiota [45]. Conversely, an unidentified genus belonging to the family Ruminococcaceae, has been found more abundant in 20d than 10d condition (LDA score > 3) in metagenomics data (Figure 3a),, but no differences between conditions have been found in metatranscriptomics data (Figure 3b)..…”

Section: Comparison Of Metagenomics and Metatranscriptomics/metaprotementioning

confidence: 95%

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

Muñoz-Benavent

Hartkopf

Bossche

et al. 2019

Preprint

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Background The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared towards human microbiomes. Microbiota are better understood when analyzed in their biological context, which is together with their host or environment, but this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Results Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics, and metaproteomics in an integrative manner. The pipeline has been developed using the Snakemake framework in order to obtain an automated and reproducible workflow. One of the key features is the on-the-fly creation of a tailored proteogenomic database based on metagenomics and metatranscriptomics data, leading to improved protein identification, taxonomic and functional analysis. gNOMO combines meta-omics analysis of the host with its bacterial population and allows to investigate both host and microbiome of non-model organisms with commonly insufficiently complete reference databases. Conclusions Using experimental datasets of the German cockroach Blattella germanica , a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. gNOMO includes functional information of metagenomics, metatranscriptomics, and metaproteomics data of the microbiome in the same visualization facilitating the interpretation of the results. Moreover, host data can be analyzed in parallel to obtain an equivalent output that allows to study the metabolic situation of the whole symbiotic system. Finally, the metaproteomics identification and annotation are optimized using a tailored proteogenomics database automatically obtained within the gNOMO workflow. In conclusion, gNOMO is a fully automated pipeline, for integrating and analyzing multiple meta-omics data and for producing useful output visualizations. In addition, it is specifically designed for data from non-model organisms. The gNOMO pipeline is freely available under the Apache 2.0 open-source license and can be downloaded from https://gitlab.com/rki_bioinformatics/gnomo .

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Metagenomic analyses of the gut microbiota associated with colorectal adenoma

Cited by 42 publications

References 31 publications

The dysbiosis signature of Fusobacterium nucleatum in colorectal cancer-cause or consequences? A systematic review

The dysbiosis signature of Fusobacterium nucleatum in colorectal cancer-cause or consequences? A systematic review

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

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