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Publications - Stress and Develop Biology

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Publications

Strehmel, N.; Hoehenwarter, W.; Mönchgesang, S.; Majovsky, P.; Krüger, S.; Scheel, D.; Lee, J.; Stress-Related Mitogen-Activated Protein Kinases Stimulate the Accumulation of Small Molecules and Proteins in Arabidopsis thaliana Root Exudates Front. Plant Sci. 8, 1292, (2017) DOI: 10.3389/fpls.2017.01292

A delicate balance in cellular signaling is required for plants to respond to microorganisms or to changes in their environment. Mitogen-activated protein kinase (MAPK) cascades are one of the signaling modules that mediate transduction of extracellular microbial signals into appropriate cellular responses. Here, we employ a transgenic system that simulates activation of two pathogen/stress-responsive MAPKs to study release of metabolites and proteins into root exudates. The premise is based on our previous proteomics study that suggests upregulation of secretory processes in this transgenic system. An advantage of this experimental set-up is the direct focus on MAPK-regulated processes without the confounding complications of other signaling pathways activated by exposure to microbes or microbial molecules. Using non-targeted metabolomics and proteomics studies, we show that MAPK activation can indeed drive the appearance of dipeptides, defense-related metabolites and proteins in root apoplastic fluid. However, the relative levels of other compounds in the exudates were decreased. This points to a bidirectional control of metabolite and protein release into the apoplast. The putative roles for some of the identified apoplastic metabolites and proteins are discussed with respect to possible antimicrobial/defense or allelopathic properties. Overall, our findings demonstrate that sustained activation of MAPKs alters the composition of apoplastic root metabolites and proteins, presumably to influence the plant-microbe interactions in the rhizosphere. The reported metabolomics and proteomics data are available via Metabolights (Identifier: MTBLS441) and ProteomeXchange (Identifier: PXD006328), respectively.
Publications

Mönchgesang, S.; Strehmel, N.; Trutschel, D.; Westphal, L.; Neumann, S.; Scheel, D.; Plant-to-Plant Variability in Root Metabolite Profiles of 19 Arabidopsis thaliana Accessions Is Substance-Class-Dependent Int. J. Mol. Sci. 17, 1565, (2016) DOI: 10.3390/ijms17091565

Natural variation of secondary metabolism between different accessions of Arabidopsis thaliana (A. thaliana) has been studied extensively. In this study, we extended the natural variation approach by including biological variability (plant-to-plant variability) and analysed root metabolic patterns as well as their variability between plants and naturally occurring accessions. To screen 19 accessions of A. thaliana, comprehensive non-targeted metabolite profiling of single plant root extracts was performed using ultra performance liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) and gas chromatography/electron ionization quadrupole mass spectrometry (GC/EI-QMS). Linear mixed models were applied to dissect the total observed variance. All metabolic profiles pointed towards a larger plant-to-plant variability than natural variation between accessions and variance of experimental batches. Ratios of plant-to-plant to total variability were high and distinct for certain secondary metabolites. None of the investigated accessions displayed a specifically high or low biological variability for these substance classes. This study provides recommendations for future natural variation analyses of glucosinolates, flavonoids, and phenylpropanoids and also reference data for additional substance classes.
Publications

Mönchgesang, S.; Strehmel, N.; Schmidt, S.; Westphal, L.; Taruttis, F.; Müller, E.; Herklotz, S.; Neumann, S.; Scheel, D.; Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data Sci. Rep. 6, 29033, (2016) DOI: 10.1038/srep29033

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.
Preprints

Thum, A.; Mönchgesang, S.; Westphal, L.; Lübken, T.; Rosahl, S.; Neumann, S.; Posch, S.; Supervised Penalized Canonical Correlation Analysis arXiv (2014)

The canonical correlation analysis (CCA) is commonly used to analyze data sets with paired data, e.g. measurements of gene expression and metabolomic intensities of the same experiments. This allows to find interesting relationships between the data sets, e.g. they can be assigned to biological processes. However, it can be difficult to interpret the processes and often the relationships observed are not related to the experimental design but to some unknown parameters.Here we present an extension of the penalized CCA, the supervised penalized approach (spCCA), where the experimental design is used as a third data set and the correlation of the biological data sets with the design data set is maximized to find interpretable and meaningful canonical variables. The spCCA was successfully tested on a data set of Arabidopsis thaliana with gene expression and metabolite intensity measurements and resulted in eight significant canonical variables and their interpretation. We provide an R-package under the GPL license.
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