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Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models

Common intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data set...

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Autores principales: Jiang, Puzi, Wu, Sicheng, Luo, Qibin, Zhao, Xing-ming, Chen, Wei-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269207/
https://www.ncbi.nlm.nih.gov/pubmed/33947803
http://dx.doi.org/10.1128/mSystems.00112-21
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author Jiang, Puzi
Wu, Sicheng
Luo, Qibin
Zhao, Xing-ming
Chen, Wei-Hua
author_facet Jiang, Puzi
Wu, Sicheng
Luo, Qibin
Zhao, Xing-ming
Chen, Wei-Hua
author_sort Jiang, Puzi
collection PubMed
description Common intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis. IMPORTANCE Gut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis, and Crohn’s disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multidisease classifications. To the best of our knowledge, this is the first study to construct multiclass models for these common intestinal diseases.
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spelling pubmed-82692072021-08-02 Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models Jiang, Puzi Wu, Sicheng Luo, Qibin Zhao, Xing-ming Chen, Wei-Hua mSystems Research Article Common intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis. IMPORTANCE Gut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis, and Crohn’s disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multidisease classifications. To the best of our knowledge, this is the first study to construct multiclass models for these common intestinal diseases. American Society for Microbiology 2021-05-04 /pmc/articles/PMC8269207/ /pubmed/33947803 http://dx.doi.org/10.1128/mSystems.00112-21 Text en Copyright © 2021 Jiang et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Jiang, Puzi
Wu, Sicheng
Luo, Qibin
Zhao, Xing-ming
Chen, Wei-Hua
Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
title Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
title_full Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
title_fullStr Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
title_full_unstemmed Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
title_short Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
title_sort metagenomic analysis of common intestinal diseases reveals relationships among microbial signatures and powers multidisease diagnostic models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269207/
https://www.ncbi.nlm.nih.gov/pubmed/33947803
http://dx.doi.org/10.1128/mSystems.00112-21
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