The following study was conducted by Scientists from Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL), Department of Physics, Technische Universität Dresden, Dresden, Germany; Paul Langerhans Institute Dresden, Helmholtz Zentrum Munchen, Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany; Department of Infrastructure and Environment University of Glasgow, School of Engineering, Glasgow, UK; Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Milan, Italy; Institute of Microbiology, Università Cattolica del Sacro Cuore, Rome, Italy; Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore, Rome, Italy; Department of Chemistry and Biotechnology, Division of Gene Technology, Tallinn University of Technology, Tallinn, 12618, Estonia; Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, Dresden, Germany; Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany; Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK; Department of Gastroenterology, Royal Liverpool and Broadgreen University Hospitals NHS Trust, Liverpool, UK; Center for Complex Network Intelligence (CCNI) at Tsinghua Laboratory of Brain and Intelligence (THBI), Department of Biomedical Engineering, Tsinghua University, Beijing, China. Study is published in Nature Communications Journal as detailed below.
Nature Communications volume 12, Article number: 1926 (2021)
Nonlinear Machine Learning Pattern Recognition and Bacteria-Metabolite Multilayer Network Analysis of Perturbed Gastric Microbiome
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.
Source:
Nature Communications
URL: https://www.nature.com/articles/s41467-021-22135-x
Citation:
Durán, C., Ciucci, S., Palladini, A. et al. Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome. Nat Commun 12, 1926 (2021). https://doi.org/10.1038/s41467-021-22135-x
