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Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models
Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive pr...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814625/ https://www.ncbi.nlm.nih.gov/pubmed/36604447 http://dx.doi.org/10.1038/s41598-022-26816-5 |
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author | Fernandes, Philip Sharma, Yash Zulqarnain, Fatima McGrew, Brooklyn Shrivastava, Aman Ehsan, Lubaina Payne, Dawson Dillard, Lillian Powers, Deborah Aldridge, Isabelle Matthews, Jason Kugathasan, Subra Fernández, Facundo M. Gaul, David Papin, Jason A. Syed, Sana |
author_facet | Fernandes, Philip Sharma, Yash Zulqarnain, Fatima McGrew, Brooklyn Shrivastava, Aman Ehsan, Lubaina Payne, Dawson Dillard, Lillian Powers, Deborah Aldridge, Isabelle Matthews, Jason Kugathasan, Subra Fernández, Facundo M. Gaul, David Papin, Jason A. Syed, Sana |
author_sort | Fernandes, Philip |
collection | PubMed |
description | Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets. |
format | Online Article Text |
id | pubmed-9814625 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98146252023-01-06 Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models Fernandes, Philip Sharma, Yash Zulqarnain, Fatima McGrew, Brooklyn Shrivastava, Aman Ehsan, Lubaina Payne, Dawson Dillard, Lillian Powers, Deborah Aldridge, Isabelle Matthews, Jason Kugathasan, Subra Fernández, Facundo M. Gaul, David Papin, Jason A. Syed, Sana Sci Rep Article Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets. Nature Publishing Group UK 2023-01-05 /pmc/articles/PMC9814625/ /pubmed/36604447 http://dx.doi.org/10.1038/s41598-022-26816-5 Text en © The Author(s) 2022 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 Fernandes, Philip Sharma, Yash Zulqarnain, Fatima McGrew, Brooklyn Shrivastava, Aman Ehsan, Lubaina Payne, Dawson Dillard, Lillian Powers, Deborah Aldridge, Isabelle Matthews, Jason Kugathasan, Subra Fernández, Facundo M. Gaul, David Papin, Jason A. Syed, Sana Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models |
title | Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models |
title_full | Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models |
title_fullStr | Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models |
title_full_unstemmed | Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models |
title_short | Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models |
title_sort | identifying metabolic shifts in crohn's disease using 'omics-driven contextualized computational metabolic network models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814625/ https://www.ncbi.nlm.nih.gov/pubmed/36604447 http://dx.doi.org/10.1038/s41598-022-26816-5 |
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