Cargando…
Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts
The perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628233/ https://www.ncbi.nlm.nih.gov/pubmed/37932270 http://dx.doi.org/10.1038/s41467-023-42788-0 |
_version_ | 1785131711522144256 |
---|---|
author | Ning, Lijun Zhou, Yi-Lu Sun, Han Zhang, Youwei Shen, Chaoqin Wang, Zhenhua Xuan, Baoqin Zhao, Ying Ma, Yanru Yan, Yuqing Tong, Tianying Huang, Xiaowen Hu, Muni Zhu, Xiaoqiang Ding, Jinmei Zhang, Yue Cui, Zhe Fang, Jing-Yuan Chen, Haoyan Hong, Jie |
author_facet | Ning, Lijun Zhou, Yi-Lu Sun, Han Zhang, Youwei Shen, Chaoqin Wang, Zhenhua Xuan, Baoqin Zhao, Ying Ma, Yanru Yan, Yuqing Tong, Tianying Huang, Xiaowen Hu, Muni Zhu, Xiaoqiang Ding, Jinmei Zhang, Yue Cui, Zhe Fang, Jing-Yuan Chen, Haoyan Hong, Jie |
author_sort | Ning, Lijun |
collection | PubMed |
description | The perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To address this challenge, here we comprehensively analyze 9 metagenomic and 4 metabolomics cohorts of IBD from different populations. Through cross-cohort integrative analysis (CCIA), we identify a consistent characteristic of commensal gut microbiota. Especially, three bacteria, namely Asaccharobacter celatus, Gemmiger formicilis, and Erysipelatoclostridium ramosum, which are rarely reported in IBD. Metagenomic functional analysis reveals that essential gene of Two-component system pathway, linked to fecal calprotectin, are implicated in IBD. Metabolomics analysis shows 36 identified metabolites with significant differences, while the roles of these metabolites in IBD are still unknown. To further elucidate the relationship between gut microbiota and metabolites, we construct multi-omics biological correlation (MOBC) maps, which highlights gut microbial biotransformation deficiencies and significant alterations in aminoacyl-tRNA synthetases. Finally, we identify multi-omics biomarkers for IBD diagnosis, validated across multiple global cohorts (AUROC values ranging from 0.92 to 0.98). Our results offer valuable insights and a significant resource for developing mechanistic hypotheses on host-microbiome interactions in IBD. |
format | Online Article Text |
id | pubmed-10628233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106282332023-11-08 Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts Ning, Lijun Zhou, Yi-Lu Sun, Han Zhang, Youwei Shen, Chaoqin Wang, Zhenhua Xuan, Baoqin Zhao, Ying Ma, Yanru Yan, Yuqing Tong, Tianying Huang, Xiaowen Hu, Muni Zhu, Xiaoqiang Ding, Jinmei Zhang, Yue Cui, Zhe Fang, Jing-Yuan Chen, Haoyan Hong, Jie Nat Commun Article The perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To address this challenge, here we comprehensively analyze 9 metagenomic and 4 metabolomics cohorts of IBD from different populations. Through cross-cohort integrative analysis (CCIA), we identify a consistent characteristic of commensal gut microbiota. Especially, three bacteria, namely Asaccharobacter celatus, Gemmiger formicilis, and Erysipelatoclostridium ramosum, which are rarely reported in IBD. Metagenomic functional analysis reveals that essential gene of Two-component system pathway, linked to fecal calprotectin, are implicated in IBD. Metabolomics analysis shows 36 identified metabolites with significant differences, while the roles of these metabolites in IBD are still unknown. To further elucidate the relationship between gut microbiota and metabolites, we construct multi-omics biological correlation (MOBC) maps, which highlights gut microbial biotransformation deficiencies and significant alterations in aminoacyl-tRNA synthetases. Finally, we identify multi-omics biomarkers for IBD diagnosis, validated across multiple global cohorts (AUROC values ranging from 0.92 to 0.98). Our results offer valuable insights and a significant resource for developing mechanistic hypotheses on host-microbiome interactions in IBD. Nature Publishing Group UK 2023-11-06 /pmc/articles/PMC10628233/ /pubmed/37932270 http://dx.doi.org/10.1038/s41467-023-42788-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ning, Lijun Zhou, Yi-Lu Sun, Han Zhang, Youwei Shen, Chaoqin Wang, Zhenhua Xuan, Baoqin Zhao, Ying Ma, Yanru Yan, Yuqing Tong, Tianying Huang, Xiaowen Hu, Muni Zhu, Xiaoqiang Ding, Jinmei Zhang, Yue Cui, Zhe Fang, Jing-Yuan Chen, Haoyan Hong, Jie Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
title | Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
title_full | Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
title_fullStr | Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
title_full_unstemmed | Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
title_short | Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
title_sort | microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628233/ https://www.ncbi.nlm.nih.gov/pubmed/37932270 http://dx.doi.org/10.1038/s41467-023-42788-0 |
work_keys_str_mv | AT ninglijun microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT zhouyilu microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT sunhan microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT zhangyouwei microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT shenchaoqin microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT wangzhenhua microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT xuanbaoqin microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT zhaoying microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT mayanru microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT yanyuqing microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT tongtianying microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT huangxiaowen microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT humuni microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT zhuxiaoqiang microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT dingjinmei microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT zhangyue microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT cuizhe microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT fangjingyuan microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT chenhaoyan microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts AT hongjie microbiomeandmetabolomefeaturesininflammatoryboweldiseaseviamultiomicsintegrationanalysesacrosscohorts |