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

Claudio Durán, Sara Ciucci, Alessandra Palladini, Umer Z. Ijaz, Antonio G. Zippo, Francesco Paroni Sterbini, Luca Masucci, Giovanni Cammarota, Gianluca Ianiro, Pirjo Spuul, Michael Schroeder, Stephan W. Grill, Bryony N. Parsons, D. Mark Pritchard, Brunella Posteraro, Maurizio Sanguinetti, Giovanni Gasbarrini, Antonio Gasbarrini and Carlo Vittorio Cannistraci ()
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Claudio Durán: Technische Universität Dresden
Sara Ciucci: Technische Universität Dresden
Alessandra Palladini: Technische Universität Dresden
Umer Z. Ijaz: Department of Infrastructure and Environment University of Glasgow, School of Engineering
Antonio G. Zippo: Consiglio Nazionale delle Ricerche
Francesco Paroni Sterbini: Università Cattolica del Sacro Cuore
Luca Masucci: Università Cattolica del Sacro Cuore
Giovanni Cammarota: Università Cattolica del Sacro Cuore
Gianluca Ianiro: Università Cattolica del Sacro Cuore
Pirjo Spuul: Tallinn University of Technology
Michael Schroeder: Technische Universität Dresden
Stephan W. Grill: Technische Universität Dresden
Bryony N. Parsons: University of Liverpool
D. Mark Pritchard: University of Liverpool
Brunella Posteraro: Università Cattolica del Sacro Cuore
Maurizio Sanguinetti: Università Cattolica del Sacro Cuore
Giovanni Gasbarrini: Università Cattolica del Sacro Cuore
Antonio Gasbarrini: Università Cattolica del Sacro Cuore
Carlo Vittorio Cannistraci: Technische Universität Dresden

Nature Communications, 2021, vol. 12, issue 1, 1-22

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-22135-x

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