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

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 beta-alaninedegradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the p-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.