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Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics

BACKGROUND: Gut microbiome plays a crucial role in the formation and progression of colorectal cancer (CRC). To better identify the underlying gene-level pathogenic mechanisms of microbiome-associated CRC, we applied our newly developed Reverse Microbiomics (RM) to predict potential pathogenic facto...

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Autores principales: Wang, Haihe, Zhang, Kaibo, Wu, Lin, Qin, Qian, He, Yongqun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091335/
https://www.ncbi.nlm.nih.gov/pubmed/35574378
http://dx.doi.org/10.3389/fonc.2022.882874
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author Wang, Haihe
Zhang, Kaibo
Wu, Lin
Qin, Qian
He, Yongqun
author_facet Wang, Haihe
Zhang, Kaibo
Wu, Lin
Qin, Qian
He, Yongqun
author_sort Wang, Haihe
collection PubMed
description BACKGROUND: Gut microbiome plays a crucial role in the formation and progression of colorectal cancer (CRC). To better identify the underlying gene-level pathogenic mechanisms of microbiome-associated CRC, we applied our newly developed Reverse Microbiomics (RM) to predict potential pathogenic factors using the data of microbiomes in CRC patients. RESULTS: Our literature search first identified 40 bacterial species enriched and 23 species depleted in the guts of CRC patients. These bacteria were systematically modeled and analyzed using the NCBI Taxonomy ontology. Ten species, including 6 enriched species (e.g., Bacteroides fragilis, Fusobacterium nucleatum and Streptococcus equinus) and 4 depleted species (e.g., Bacteroides uniformis and Streptococcus thermophilus) were chosen for follow-up comparative genomics analysis. Vaxign was used to comparatively analyze 47 genome sequences of these ten species. In total 18 autoantigens were predicted to contribute to CRC formation, six of which were reported with experimental evidence to be correlated with drug resistance and/or cell invasiveness of CRC. Interestingly, four human homology proteins (EDK89078.1, EDK87700.1, EDK89777.1, and EDK89145.1) are conserved among all enriched strains. Furthermore, we predicted 76 potential virulence factors without homology to human proteins, including two riboflavin synthase proteins, three ATP-binding cassettes (ABC) transporter protein family proteins, and 12 outer membrane proteins (OMPs). Riboflavin synthase is present in all the enriched strains but not in depleted species. The critical role of riboflavin synthase in CRC development was further identified from its hub role in our STRING-based protein−protein interaction (PPI) network analysis and from the finding of the riboflavin metabolism as the most significantly enriched pathway in our KEGG pathway analysis. A novel model of the CRC pathogenesis involving riboflavin synthase and other related proteins including TpiA and GrxC was further proposed. CONCLUSIONS: The RM strategy was used to predict 18 autoantigens and 76 potential virulence factors from CRC-associated microbiome data. In addition to many of these autoantigens and virulence factors experimentally verified as reported in the literature, our study predicted many new pathogenetic factors and developed a new model of CRC pathogenesis involving the riboflavin synthase from the enriched colorectal bacteria and other associated proteins.
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spelling pubmed-90913352022-05-12 Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics Wang, Haihe Zhang, Kaibo Wu, Lin Qin, Qian He, Yongqun Front Oncol Oncology BACKGROUND: Gut microbiome plays a crucial role in the formation and progression of colorectal cancer (CRC). To better identify the underlying gene-level pathogenic mechanisms of microbiome-associated CRC, we applied our newly developed Reverse Microbiomics (RM) to predict potential pathogenic factors using the data of microbiomes in CRC patients. RESULTS: Our literature search first identified 40 bacterial species enriched and 23 species depleted in the guts of CRC patients. These bacteria were systematically modeled and analyzed using the NCBI Taxonomy ontology. Ten species, including 6 enriched species (e.g., Bacteroides fragilis, Fusobacterium nucleatum and Streptococcus equinus) and 4 depleted species (e.g., Bacteroides uniformis and Streptococcus thermophilus) were chosen for follow-up comparative genomics analysis. Vaxign was used to comparatively analyze 47 genome sequences of these ten species. In total 18 autoantigens were predicted to contribute to CRC formation, six of which were reported with experimental evidence to be correlated with drug resistance and/or cell invasiveness of CRC. Interestingly, four human homology proteins (EDK89078.1, EDK87700.1, EDK89777.1, and EDK89145.1) are conserved among all enriched strains. Furthermore, we predicted 76 potential virulence factors without homology to human proteins, including two riboflavin synthase proteins, three ATP-binding cassettes (ABC) transporter protein family proteins, and 12 outer membrane proteins (OMPs). Riboflavin synthase is present in all the enriched strains but not in depleted species. The critical role of riboflavin synthase in CRC development was further identified from its hub role in our STRING-based protein−protein interaction (PPI) network analysis and from the finding of the riboflavin metabolism as the most significantly enriched pathway in our KEGG pathway analysis. A novel model of the CRC pathogenesis involving riboflavin synthase and other related proteins including TpiA and GrxC was further proposed. CONCLUSIONS: The RM strategy was used to predict 18 autoantigens and 76 potential virulence factors from CRC-associated microbiome data. In addition to many of these autoantigens and virulence factors experimentally verified as reported in the literature, our study predicted many new pathogenetic factors and developed a new model of CRC pathogenesis involving the riboflavin synthase from the enriched colorectal bacteria and other associated proteins. Frontiers Media S.A. 2022-04-27 /pmc/articles/PMC9091335/ /pubmed/35574378 http://dx.doi.org/10.3389/fonc.2022.882874 Text en Copyright © 2022 Wang, Zhang, Wu, Qin and He https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wang, Haihe
Zhang, Kaibo
Wu, Lin
Qin, Qian
He, Yongqun
Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics
title Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics
title_full Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics
title_fullStr Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics
title_full_unstemmed Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics
title_short Prediction of Pathogenic Factors in Dysbiotic Gut Microbiomes of Colorectal Cancer Patients Using Reverse Microbiomics
title_sort prediction of pathogenic factors in dysbiotic gut microbiomes of colorectal cancer patients using reverse microbiomics
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091335/
https://www.ncbi.nlm.nih.gov/pubmed/35574378
http://dx.doi.org/10.3389/fonc.2022.882874
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