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Quantitative analysis of disease-related metabolic dysregulation of human microbiota

The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with t...

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Detalles Bibliográficos
Autores principales: Fumagalli, Maria Rita, Saro, Stella Maria, Tajana, Matteo, Zapperi, Stefano, La Porta, Caterina A.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823209/
https://www.ncbi.nlm.nih.gov/pubmed/36624837
http://dx.doi.org/10.1016/j.isci.2022.105868
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author Fumagalli, Maria Rita
Saro, Stella Maria
Tajana, Matteo
Zapperi, Stefano
La Porta, Caterina A.M.
author_facet Fumagalli, Maria Rita
Saro, Stella Maria
Tajana, Matteo
Zapperi, Stefano
La Porta, Caterina A.M.
author_sort Fumagalli, Maria Rita
collection PubMed
description The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.
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spelling pubmed-98232092023-01-08 Quantitative analysis of disease-related metabolic dysregulation of human microbiota Fumagalli, Maria Rita Saro, Stella Maria Tajana, Matteo Zapperi, Stefano La Porta, Caterina A.M. iScience Article The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis. Elsevier 2022-12-23 /pmc/articles/PMC9823209/ /pubmed/36624837 http://dx.doi.org/10.1016/j.isci.2022.105868 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Fumagalli, Maria Rita
Saro, Stella Maria
Tajana, Matteo
Zapperi, Stefano
La Porta, Caterina A.M.
Quantitative analysis of disease-related metabolic dysregulation of human microbiota
title Quantitative analysis of disease-related metabolic dysregulation of human microbiota
title_full Quantitative analysis of disease-related metabolic dysregulation of human microbiota
title_fullStr Quantitative analysis of disease-related metabolic dysregulation of human microbiota
title_full_unstemmed Quantitative analysis of disease-related metabolic dysregulation of human microbiota
title_short Quantitative analysis of disease-related metabolic dysregulation of human microbiota
title_sort quantitative analysis of disease-related metabolic dysregulation of human microbiota
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823209/
https://www.ncbi.nlm.nih.gov/pubmed/36624837
http://dx.doi.org/10.1016/j.isci.2022.105868
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