Integration of Metabolomic and Proteomic Phenotypes

Abstract

Statistical mining and integration of complex molecular data including metabolites, proteins, and transcripts is one of the critical goals of systems biology (Ideker, T., Galitski, T., and Hood, L. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343-372). A number of studies have demonstrated the parallel analysis of metabolites and large scale transcript expression. Protein analysis has been ignored in these studies, although a clear correlation between transcript and protein levels is shown only in rare cases, necessitating that actual protein levels have to be determined for protein function analysis. Here, we present an approach to investigate the combined covariance structure of metabolite and protein dynamics in a systemic response to abiotic temperature stress in Arabidopsis thaliana wild-type and a corresponding starch-deficient mutant (phosphoglucomutase-deficient). Independent component analysis revealed phenotype classification resolving genotype-dependent response effects to temperature treatment and genotype-independent general temperature compensation mechanisms. An observation is the stress-induced increase of raffinose-family-oligosaccharide levels in the absence of transitory starch storage/mobilization in temperature-treated phosphoglucomutase plants indicating that sucrose synthesis and storage in these mutant plants is sufficient to bypass the typical starch storage/mobilization pathways under abiotic stress. Eventually, sample pattern recognition and correlation network topology analysis allowed for the detection of specific metabolite-protein co-regulation and assignment of a circadian output regulated RNA-binding protein to these processes. The whole concept of high-dimensional profiling data integration from many replicates, subsequent multivariate statistics for dimensionality reduction, and covariance structure analysis is proposed to be a major strategy for revealing central responses of the biological system under study. Molecular & Cellular Proteomics 7:1725-1736, 2008.Metabolomic technologies enable the very rapid non-targeted analysis of metabolites and provide a diagnostic tool for pattern recognition of biological samples (2-5). Typical pattern recognition methods are variance discrimination algorithms such as principal components analysis (PCA) 1 or independent component analysis (ICA) (2, 6 -9). Independent component analysis is an extension of covariance analysis by looking for kurtosis thresholds or high entropy (8, 10) and thus adds a further value for biological interpretation. Variance discrimination of samples relies strongly on a high biological variability of independent biological replicate analysis (4,11,12). Recently, we demonstrated that these covariance matrixes of experimentally determined metabolite levels are connected with the elasticities of pathway reaction networks (13). Consequently, changes in the structure of these covariance networks reveal biochemical regulations (4). This was confirmed by using topology studi...

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