Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism
- Author(s)
- Hannes Doerfler, David Lyon, Thomas Nägele, Xiaoliang Sun, Lena Fragner, Franz Hadacek, Volker Egelhofer, Wolfram Weckwerth
- 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.
- Organisation(s)
- Journal
- Metabolomics
- Volume
- 9
- Pages
- 564-574
- No. of pages
- 11
- ISSN
- 1573-3882
- DOI
- https://doi.org/10.1007/s11306-012-0470-0
- Publication date
- 06-2013
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 106002 Biochemistry, 106007 Biostatistics, 106031 Plant physiology, 1040 Chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/granger-causality-in-integrated-gcms-and-lcms-metabolomics-data-reveals-the-interface-of-primary-and-secondary-metabolism(0787d72a-ebf6-40ec-a70a-1d5a653d4a0f).html