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

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

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.

Details

Original languageEnglish
Article number1926
JournalNature communications
Volume12
Issue number1
Publication statusPublished - 26 Mar 2021
Peer-reviewedYes

External IDs

PubMedCentral PMC7997970
Scopus 85103532911
ORCID /0000-0003-2848-6949/work/141543357

Keywords

Keywords

  • Bacteria/classification, Gastrointestinal Microbiome/drug effects, Helicobacter Infections/drug therapy, Helicobacter pylori/drug effects, Humans, Machine Learning, Microbiota/drug effects, Population Dynamics, Proton Pump Inhibitors/therapeutic use, RNA, Ribosomal, 16S/genetics, Stomach/microbiology

Library keywords