Moenchgesang, Susann and Strehmel, Nadine and Schmidt, Stephan and Westphal, Lore and Taruttis, Franziska and Mueller, Erik and Herklotz, Siska and Neumann, Steffen and Scheel, Dierk (2016) Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data. SCIENTIFIC REPORTS, 6: 29033. ISSN 2045-2322,
Full text not available from this repository. (Request a copy)Abstract
Many metabolomics studies focus on aboveground parts of the plant, while metabolism within roots and the chemical composition of the rhizosphere, as influenced by exudation, are not deeply investigated. In this study, we analysed exudate metabolic patterns of Arabidopsis thaliana and their variation in genetically diverse accessions. For this project, we used the 19 parental accessions of the Arabidopsis MAGIC collection. Plants were grown in a hydroponic system, their exudates were harvested before bolting and subjected to UPLC/ESI-QTOF-MS analysis. Metabolite profiles were analysed together with the genome sequence information. Our study uncovered distinct metabolite profiles for root exudates of the 19 accessions. Hierarchical clustering revealed similarities in the exudate metabolite profiles, which were partly reflected by the genetic distances. An association of metabolite absence with nonsense mutations was detected for the biosynthetic pathways of an indolic glucosinolate hydrolysis product, a hydroxycinnamic acid amine and a flavonoid triglycoside. Consequently, a direct link between metabolic phenotype and genotype was detected without using segregating populations. Moreover, genomics can help to identify biosynthetic enzymes in metabolomics experiments. Our study elucidates the chemical composition of the rhizosphere and its natural variation in A. thaliana, which is important for the attraction and shaping of microbial communities.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SECONDARY METABOLISM; MASS-SPECTROMETRY; GENETICS; ACCUMULATION; PLANTS; DIVERSITY; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 09 Apr 2019 12:00 |
| Last Modified: | 09 Apr 2019 12:00 |
| URI: | https://pred.uni-regensburg.de/id/eprint/3709 |
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