Abstract
Typically, metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individual plants grown under strictly controlled conditions. Although several researches have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways. In this study, we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 Arabidopsis genotypes, Col-0 wild-type, methionine overaccumulation 1 (mto1), and transparent testa4 (tt4) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to detect densely connected metabolites and evaluated the clusters by KEGG pathway enrichment analysis. This study demonstrated that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway.
