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

David Toubiana, Rami Puzis, Lingling Wen, Noga Sikron, Assylay Kurmanbayeva, Aigerim Soltabayeva, Maria del Mar Rubio Wilhelmi, Nir Sade, Aaron Fait, Moshe Sagi, Eduardo Blumwald, Yuval Elovici

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

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.

Original languageEnglish
Article number214
JournalCommunications Biology
Volume2
Issue number1
DOIs
StatePublished - 1 Dec 2019

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