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Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re...

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Autores principales: Durán, Claudio, Ciucci, Sara, Palladini, Alessandra, Ijaz, Umer Z., Zippo, Antonio G., Sterbini, Francesco Paroni, Masucci, Luca, Cammarota, Giovanni, Ianiro, Gianluca, Spuul, Pirjo, Schroeder, Michael, Grill, Stephan W., Parsons, Bryony N., Pritchard, D. Mark, Posteraro, Brunella, Sanguinetti, Maurizio, Gasbarrini, Giovanni, Gasbarrini, Antonio, Cannistraci, Carlo Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997970/
https://www.ncbi.nlm.nih.gov/pubmed/33771992
http://dx.doi.org/10.1038/s41467-021-22135-x
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author Durán, Claudio
Ciucci, Sara
Palladini, Alessandra
Ijaz, Umer Z.
Zippo, Antonio G.
Sterbini, Francesco Paroni
Masucci, Luca
Cammarota, Giovanni
Ianiro, Gianluca
Spuul, Pirjo
Schroeder, Michael
Grill, Stephan W.
Parsons, Bryony N.
Pritchard, D. Mark
Posteraro, Brunella
Sanguinetti, Maurizio
Gasbarrini, Giovanni
Gasbarrini, Antonio
Cannistraci, Carlo Vittorio
author_facet Durán, Claudio
Ciucci, Sara
Palladini, Alessandra
Ijaz, Umer Z.
Zippo, Antonio G.
Sterbini, Francesco Paroni
Masucci, Luca
Cammarota, Giovanni
Ianiro, Gianluca
Spuul, Pirjo
Schroeder, Michael
Grill, Stephan W.
Parsons, Bryony N.
Pritchard, D. Mark
Posteraro, Brunella
Sanguinetti, Maurizio
Gasbarrini, Giovanni
Gasbarrini, Antonio
Cannistraci, Carlo Vittorio
author_sort Durán, Claudio
collection PubMed
description The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities.
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spelling pubmed-79979702021-04-16 Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome Durán, Claudio Ciucci, Sara Palladini, Alessandra Ijaz, Umer Z. Zippo, Antonio G. Sterbini, Francesco Paroni Masucci, Luca Cammarota, Giovanni Ianiro, Gianluca Spuul, Pirjo Schroeder, Michael Grill, Stephan W. Parsons, Bryony N. Pritchard, D. Mark Posteraro, Brunella Sanguinetti, Maurizio Gasbarrini, Giovanni Gasbarrini, Antonio Cannistraci, Carlo Vittorio Nat Commun Article The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities. Nature Publishing Group UK 2021-03-26 /pmc/articles/PMC7997970/ /pubmed/33771992 http://dx.doi.org/10.1038/s41467-021-22135-x Text en © The Author(s) 2021 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/.
spellingShingle Article
Durán, Claudio
Ciucci, Sara
Palladini, Alessandra
Ijaz, Umer Z.
Zippo, Antonio G.
Sterbini, Francesco Paroni
Masucci, Luca
Cammarota, Giovanni
Ianiro, Gianluca
Spuul, Pirjo
Schroeder, Michael
Grill, Stephan W.
Parsons, Bryony N.
Pritchard, D. Mark
Posteraro, Brunella
Sanguinetti, Maurizio
Gasbarrini, Giovanni
Gasbarrini, Antonio
Cannistraci, Carlo Vittorio
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
title Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
title_full Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
title_fullStr Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
title_full_unstemmed Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
title_short Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
title_sort nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997970/
https://www.ncbi.nlm.nih.gov/pubmed/33771992
http://dx.doi.org/10.1038/s41467-021-22135-x
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