Network analysis provides insight into tomato lipid metabolism

Anastasiya Kuhalskaya, Micha Wijesingha Ahchige, Leonardo Perez de Souza, José Vallarino, Yariv Brotman, Saleh Alseekh

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Metabolic correlation networks have been used in several instances to obtain a deeper insight into the complexity of plant metabolism as a whole. In tomato (Solanum lycopersicum), metabolites have a major influence on taste and overall fruit quality traits. Previously a broad spectrum of metabolic and phenotypic traits has been described using a Solanum pennellii introgression-lines (ILs) population. To obtain insights into tomato fruit metabolism, we performed metabolic network analysis from existing data, covering a wide range of metabolic traits, including lipophilic and volatile compounds, for the first time. We provide a comprehensive fruit correlation network and show how primary, secondary, lipophilic, and volatile compounds connect to each other and how the individual metabolic classes are linked to yield-related phenotypic traits. Results revealed a high connectivity within and between different classes of lipophilic compounds, as well as between lipophilic and secondary metabolites. We focused on lipid metabolism and generated a gene-expression network with lipophilic metabolites to identify new putative lipid-related genes. Metabolite–transcript correlation analysis revealed key putative genes involved in lipid biosynthesis pathways. The overall results will help to deepen our understanding of tomato metabolism and provide candidate genes for transgenic approaches toward improving nutritional qualities in tomato.

Original languageEnglish
Article number152
JournalMetabolites
Volume10
Issue number4
DOIs
StatePublished - 1 Apr 2020

Keywords

  • Lipid metabolism
  • Lipid-related genes
  • Lipophilic compounds

Fingerprint

Dive into the research topics of 'Network analysis provides insight into tomato lipid metabolism'. Together they form a unique fingerprint.

Cite this