دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.
المؤلفون: Durán C; 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., Ciucci S; 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., Palladini A; 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., Ijaz UZ; Department of Infrastructure and Environment University of Glasgow, School of Engineering, Glasgow, UK., Zippo AG; Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Milan, Italy., Sterbini FP; Institute of Microbiology, Università Cattolica del Sacro Cuore, Rome, Italy., Masucci L; Institute of Microbiology, Università Cattolica del Sacro Cuore, Rome, Italy., Cammarota G; Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore, Rome, Italy., Ianiro G; Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore, Rome, Italy., Spuul P; Department of Chemistry and Biotechnology, Division of Gene Technology, Tallinn University of Technology, Tallinn, 12618, Estonia., Schroeder M; Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, Dresden, Germany., Grill SW; 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., Parsons BN; Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK., Pritchard DM; 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., Posteraro B; Institute of Microbiology, Università Cattolica del Sacro Cuore, Rome, Italy., Sanguinetti M; Institute of Microbiology, Università Cattolica del Sacro Cuore, Rome, Italy., Gasbarrini G; Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore, Rome, Italy., Gasbarrini A; Internal Medicine and Gastroenterology Unit, Università Cattolica del Sacro Cuore, Rome, Italy., Cannistraci CV; 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. kalokagathos.agon@gmail.com.; Center for Complex Network Intelligence (CCNI) at Tsinghua Laboratory of Brain and Intelligence (THBI), Department of Biomedical Engineering, Tsinghua University, Beijing, China. kalokagathos.agon@gmail.com.
المصدر: Nature communications [Nat Commun] 2021 Mar 26; Vol. 12 (1), pp. 1926. Date of Electronic Publication: 2021 Mar 26.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مواضيع طبية MeSH: Machine Learning*, Gastrointestinal Microbiome/*drug effects , Helicobacter Infections/*drug therapy , Helicobacter pylori/*drug effects , Microbiota/*drug effects , Proton Pump Inhibitors/*therapeutic use, Bacteria/classification ; Bacteria/genetics ; Bacteria/metabolism ; Helicobacter Infections/microbiology ; Helicobacter pylori/physiology ; Humans ; Population Dynamics ; RNA, Ribosomal, 16S/genetics ; Stomach/microbiology
مستخلص: 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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المشرفين على المادة: 0 (Proton Pump Inhibitors)
0 (RNA, Ribosomal, 16S)
تواريخ الأحداث: Date Created: 20210327 Date Completed: 20210412 Latest Revision: 20231111
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC7997970
DOI: 10.1038/s41467-021-22135-x
PMID: 33771992
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-021-22135-x