Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data

• Published in: Communications biology Vol. 2; no. 1; p. 214
• Main Authors: Toubiana, David, Puzis, Rami, Wen, Lingling, Sikron, Noga, Kurmanbayeva, Assylay, Soltabayeva, Aigerim, Del Mar Rubio Wilhelmi, Maria, Sade, Nir, Fait, Aaron, Sagi, Moshe, Blumwald, Eduardo, Elovici, Yuval
• Format: Journal Article
• Language: English
• Published: England 18.06.2019
• ISSN: 2399-3642

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected.