Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field.

Abstract

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.

Plant phenotyping relevance

植物集団の代謝表現型を対象に、メタボロミクス、データマイニング、代謝経路再構築を統合して分類・予測する手法の適用可能性を検証しており、単なる生物学的測定ではない。

Code and data availability

The paper's supplementary material, publicly hosted at the Frontiers article page, contains paper-specific phenotyping assets: example plant images of the sampled Arabidopsis populations (Data Sheet S1), metabolomics analysis outputs (PCA loadings of GC-MS/LC-MS metabolites, Jacobian entry tables), and SNP-enriched-gen