Dataset Information

Metabolomics and 16S rRNA sequencing of human colorectal cancers and adjacent mucosa.

ABSTRACT: Colorectal cancer (CRC) is ranked the third most common cancer in human worldwide. However, the exact mechanisms of CRC are not well established. Furthermore, there may be differences between mechanisms of CRC in the Asian and in the Western populations. In the present study, we utilized a liquid chromatography-mass spectrometry (LC-MS) metabolomic approach supported by the 16S rRNA next-generation sequencing to investigate the functional and taxonomical differences between paired tumor and unaffected (normal) surgical biopsy tissues from 17 Malaysian patients. Metabolomic differences associated with steroid biosynthesis, terpenoid biosynthesis and bile metabolism could be attributed to microbiome differences between normal and tumor sites. The relative abundances of Anaerotruncus, Intestinimonas and Oscillibacter displayed significant relationships with both steroid biosynthesis and terpenoid and triterpenoid biosynthesis pathways. Metabolites involved in serotonergic synapse/ tryptophan metabolism (Serotonin and 5-Hydroxy-3-indoleacetic acid [5-HIAA]) were only detected in normal tissue samples. On the other hand, S-Adenosyl-L-homocysteine (SAH), a metabolite involves in methionine metabolism and methylation, was frequently increased in tumor relative to normal tissues. In conclusion, this study suggests that local microbiome dysbiosis may contribute to functional changes at the cancer sites. Results from the current study also contributed to the list of metabolites that are found to differ between normal and tumor sites in CRC and supported our quest for understanding the mechanisms of carcinogenesis.

SUBMITTER: Loke MF

PROVIDER: S-EPMC6303059 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Metabolomics and 16S rRNA sequencing of human colorectal cancers and adjacent mucosa.

Loke Mun Fai MF Chua Eng Guan EG Gan Han Ming HM Thulasi Kumar K Wanyiri Jane W JW Thevambiga Iyadorai I Goh Khean Lee KL Wong Won Fen WF Vadivelu Jamuna J

PloS one 20181221 12

Colorectal cancer (CRC) is ranked the third most common cancer in human worldwide. However, the exact mechanisms of CRC are not well established. Furthermore, there may be differences between mechanisms of CRC in the Asian and in the Western populations. In the present study, we utilized a liquid chromatography-mass spectrometry (LC-MS) metabolomic approach supported by the 16S rRNA next-generation sequencing to investigate the functional and taxonomical differences between paired tumor and unaf ...[more]

PMID: 30576312

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Project description:As research focusing on the colorectal cancer fecal microbiome using shotgun sequencing continues, increasing evidence has supported correlations between colorectal carcinomas (CRCs) and fecal microbiome dysbiosis. However, large-scale on-site and off-site (surrounding adjacent) tissue microbiome characterization of CRC was underrepresented. Here, considering each taxon as a feature, we demonstrate a machine learning-based method to investigate tissue microbial differences among CRC, colorectal adenoma (CRA), and healthy control groups using 16S rRNA data sets retrieved from 15 studies. A total of 2,099 samples were included and analyzed in case-control comparisons. Multiple methods, including differential abundance analysis, random forest classification, cooccurrence network analysis, and Dirichlet multinomial mixture analysis, were conducted to investigate the microbial signatures. We showed that the dysbiosis of the off-site tissue of colonic cancer was distinctive and predictive. The AUCs (areas under the curve) were 80.7%, 96.0%, and 95.8% for CRC versus healthy control random forest models using stool, tissue, and adjacent tissue samples and 69.9%, 91.5%, and 89.5% for the corresponding CRA models, respectively. We also found that the microbiota ecologies of the surrounding adjacent tissues of CRC and CRA were similar to their on-site counterparts according to network analysis. Furthermore, based on the enterotyping of tissue samples, the cohort-specific microbial signature might be the crux in addressing classification generalization problems. Despite cohort heterogeneity, the dysbiosis of lesion-adjacent tissues might provide us with further perspectives in demonstrating the role of the microbiota in colorectal cancer tumorigenesis.IMPORTANCE Turbulent fecal and tissue microbiome dysbiosis of colorectal carcinoma and adenoma has been identified, and some taxa have been proven to be carcinogenic. However, the microbiomes of surrounding adjacent tissues of colonic cancerous tissues were seldom investigated uniformly on a large scale. Here, we characterize the microbiome signatures and dysbiosis of various colonic cancer sample groups. We found a high correlation between colorectal carcinoma adjacent tissue microbiomes and their on-site counterparts. We also discovered that the microbiome dysbiosis in adjacent tissues could discriminate colorectal carcinomas from healthy controls effectively. These results extend our knowledge on the microbial profile of colorectal cancer tissues and highlight microbiota dysbiosis in the surrounding tissues. They also suggest that microbial feature variations of cancerous lesion-adjacent tissues might help to reveal the microbial etiology of colonic cancer and could ultimately be applied for diagnostic and screening purposes.

Project description:Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME (Oxford Nanopore Technologies-ONT, Oxford, UK) and an in-house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 (Oxford Nanopore Technologies-ONT, Oxford, UK) and R9.2 (Oxford Nanopore Technologies-ONT, Oxford, UK) were compared. In conclusion, the current study shows that the nanopore sequencing platform is comparable with the Illumina platform in detection bacterial genera of the nasal microbiota, but the nanopore platform does have problems in detecting bacteria within the genus Corynebacterium. Although advances are being made, thorough validation of the nanopore platform is still recommendable.

Project description:BackgroundIn the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.ResultsIn this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim.ConclusionmetaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

Dataset Information

Metabolomics and 16S rRNA sequencing of human colorectal cancers and adjacent mucosa.

Publications

Metabolomics and 16S rRNA sequencing of human colorectal cancers and adjacent mucosa.

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