Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach

In our study of metabolite profiles 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 over-accumulation 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 extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways.We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.Combining and integrating different 'omics' data such as transcript-, protein-, and metabolite levels and enzyme activities is essential for a full understanding of the nature of the cellular metabolism as a system [1-4]. With respect to transcript levels, a large amount of microarray data is publicly available for Arabidopsis thaliana, a model plant. Such large datasets facilitate the construction of gene co-expression databases [5] and the survey of transcriptome organization [6-8]. Integrating transcript- and metabolite data by, for example, studying the correlation relationships among profiled data, facilitates the characterization of unknown gene functions, and furthers our understanding of plant cellular systems [9-11].The correlation between variables (e.g. genes and metabolites) is also important for multivariate statistical analyses such as principal component analysis (PCA) and hierarchical clust