Eco-metabolomics and metabolic modeling

Matthias Nagler, Thomas Nägele, Christian Gilli, Lena Fragner, Arthur Korte, Alexander Platzer, Ashley Farlow, Magnus Nordborg, Wolfram Weckwerth

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.

Research Platform Vienna Metabolomics Center, Large-Instrument Facility for Mass Spectrometry in Life Sciences
External organisation(s)
Julius-Maximilians-Universität Würzburg, Österreichische Akademie der Wissenschaften (ÖAW), Ludwig-Maximilians-Universität München
Frontiers in Plant Science
Publication date
Peer reviewed
Austrian Fields of Science 2012
106057 Metabolomics, 106044 Systems biology, 106023 Molecular biology, 106014 Genomics
ASJC Scopus subject areas
Control and Systems Engineering, Computer Science(all)
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