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Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods

The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism...

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Autores principales: Balzerani, Francesco, Hinojosa-Nogueira, Daniel, Cendoya, Xabier, Blasco, Telmo, Pérez-Burillo, Sergio, Apaolaza, Iñigo, Francino, M. Pilar, Rufián-Henares, José Ángel, Planes, Francisco J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279433/
https://www.ncbi.nlm.nih.gov/pubmed/35831427
http://dx.doi.org/10.1038/s41540-022-00234-9
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author Balzerani, Francesco
Hinojosa-Nogueira, Daniel
Cendoya, Xabier
Blasco, Telmo
Pérez-Burillo, Sergio
Apaolaza, Iñigo
Francino, M. Pilar
Rufián-Henares, José Ángel
Planes, Francisco J.
author_facet Balzerani, Francesco
Hinojosa-Nogueira, Daniel
Cendoya, Xabier
Blasco, Telmo
Pérez-Burillo, Sergio
Apaolaza, Iñigo
Francino, M. Pilar
Rufián-Henares, José Ángel
Planes, Francisco J.
author_sort Balzerani, Francesco
collection PubMed
description The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.
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spelling pubmed-92794332022-07-15 Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods Balzerani, Francesco Hinojosa-Nogueira, Daniel Cendoya, Xabier Blasco, Telmo Pérez-Burillo, Sergio Apaolaza, Iñigo Francino, M. Pilar Rufián-Henares, José Ángel Planes, Francisco J. NPJ Syst Biol Appl Article The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions. Nature Publishing Group UK 2022-07-12 /pmc/articles/PMC9279433/ /pubmed/35831427 http://dx.doi.org/10.1038/s41540-022-00234-9 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Balzerani, Francesco
Hinojosa-Nogueira, Daniel
Cendoya, Xabier
Blasco, Telmo
Pérez-Burillo, Sergio
Apaolaza, Iñigo
Francino, M. Pilar
Rufián-Henares, José Ángel
Planes, Francisco J.
Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
title Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
title_full Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
title_fullStr Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
title_full_unstemmed Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
title_short Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
title_sort prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279433/
https://www.ncbi.nlm.nih.gov/pubmed/35831427
http://dx.doi.org/10.1038/s41540-022-00234-9
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