1. Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.
- Author
-
Durán C, Ciucci S, Palladini A, Ijaz UZ, Zippo AG, Sterbini FP, Masucci L, Cammarota G, Ianiro G, Spuul P, Schroeder M, Grill SW, Parsons BN, Pritchard DM, Posteraro B, Sanguinetti M, Gasbarrini G, Gasbarrini A, and Cannistraci CV
- Subjects
- Bacteria classification, Bacteria genetics, Bacteria metabolism, Helicobacter Infections microbiology, Helicobacter pylori physiology, Humans, Population Dynamics, RNA, Ribosomal, 16S genetics, Stomach microbiology, Gastrointestinal Microbiome drug effects, Helicobacter Infections drug therapy, Helicobacter pylori drug effects, Machine Learning, Microbiota drug effects, Proton Pump Inhibitors therapeutic use
- 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.
- Published
- 2021
- Full Text
- View/download PDF