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Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics
The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a cl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928190/ https://www.ncbi.nlm.nih.gov/pubmed/35308387 http://dx.doi.org/10.3389/fmicb.2022.846555 |
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author | Koduru, Lokanand Lakshmanan, Meiyappan Hoon, Shawn Lee, Dong-Yup Lee, Yuan Kun Ow, Dave Siak-Wei |
author_facet | Koduru, Lokanand Lakshmanan, Meiyappan Hoon, Shawn Lee, Dong-Yup Lee, Yuan Kun Ow, Dave Siak-Wei |
author_sort | Koduru, Lokanand |
collection | PubMed |
description | The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics. |
format | Online Article Text |
id | pubmed-8928190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89281902022-03-18 Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics Koduru, Lokanand Lakshmanan, Meiyappan Hoon, Shawn Lee, Dong-Yup Lee, Yuan Kun Ow, Dave Siak-Wei Front Microbiol Microbiology The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics. Frontiers Media S.A. 2022-03-03 /pmc/articles/PMC8928190/ /pubmed/35308387 http://dx.doi.org/10.3389/fmicb.2022.846555 Text en Copyright © 2022 Koduru, Lakshmanan, Hoon, Lee, Lee and Ow. 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 | Microbiology Koduru, Lokanand Lakshmanan, Meiyappan Hoon, Shawn Lee, Dong-Yup Lee, Yuan Kun Ow, Dave Siak-Wei Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics |
title | Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics |
title_full | Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics |
title_fullStr | Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics |
title_full_unstemmed | Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics |
title_short | Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics |
title_sort | systems biology of gut microbiota-human receptor interactions: toward anti-inflammatory probiotics |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928190/ https://www.ncbi.nlm.nih.gov/pubmed/35308387 http://dx.doi.org/10.3389/fmicb.2022.846555 |
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