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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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