Abstract
Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-012-0470-0) contains supplementary material, which is available to authorized users.
Highlights
The interaction of primary and secondary metabolism in plants and other organisms is probably one of the most active regulatory circuits balancing biotic and abiotic environmental pressures to the system
Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS)
We developed an approach for integrated analysis of primary and secondary metabolism in Arabidopsis thaliana during exposure to low temperature from the same sample by combined use of GC–MS and LC–MS techniques
Summary
The interaction of primary and secondary metabolism in plants and other organisms is probably one of the most active regulatory circuits balancing biotic and abiotic environmental pressures to the system. Secondary metabolites serve as important functional units to cope with these stresses and at the same time provide the richest resource of natural products in medicine and nutrition Besides their obvious interconnectivity, in most metabolomics studies either primary or secondary metabolites are analysed to reveal the metabolic response of the system to a specific perturbation. The Granger causality analysis, which is amongst other methods implemented in COVAIN, is a time-series correlation analysis, which allows for the identification of variables being controlled by time-lagged values of other variables This method originates from the investigation of causal relations within econometric models (Granger 1969), and recently it was applied in a study of yeast metabolism (Walther et al 2010). Granger causality analysis considers the time-series of variable X and Y, which can be expressed as follows (Eq 1): Xd
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