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

Please always quote using this URN: urn:nbn:de:bvb:20-opus-189560
  • 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 desirableExperimental 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.show moreshow less

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Metadaten
Author: Matthias Nagler, Thomas Nägele, Christian Gilli, Lena Fragner, Arthur Korte, Alexander Platzer, Ashley Farlow, Magnus Nordborg, Wolfram Weckwerth
URN:urn:nbn:de:bvb:20-opus-189560
Document Type:Journal article
Faculties:Fakultät für Biologie / Center for Computational and Theoretical Biology
Language:English
Parent Title (English):Frontiers in Plant Science
ISSN:1664-462X
Year of Completion:2018
Volume:9
Issue:1556
Source:Frontiers in Plant Science 2019 9:1556.doi: 10.3389/fpls.2018.01556
DOI:https://doi.org/10.3389/fpls.2018.01556
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:Jacobian matrix; SNP; eco-metabolomics; green systems biology; in situ analysis; metabolic modeling; metabolomics; natural variation
Release Date:2019/11/25
Date of first Publication:2018/11/06
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International