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.

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

BackgroundPlant adaptation to limited phosphate availability comprises a wide range of responses to conserve and remobilize internal phosphate sources and to enhance phosphate acquisition. Vigorous restructuring of root system architecture provides a developmental strategy for topsoil exploration and phosphate scavenging. Changes in external phosphate availability are locally sensed at root tips and adjust root growth by modulating cell expansion and cell division. The functionally interacting Arabidopsis genes, LOW PHOSPHATE RESPONSE 1 and 2 (LPR1/LPR2) and PHOSPHATE DEFICIENCY RESPONSE 2 (PDR2), are key components of root phosphate sensing. We recently demonstrated that the LOW PHOSPHATE RESPONSE 1 - PHOSPHATE DEFICIENCY RESPONSE 2 (LPR1-PDR2) module mediates apoplastic deposition of ferric iron (Fe3+) in the growing root tip during phosphate limitation. Iron deposition coincides with sites of reactive oxygen species generation and triggers cell wall thickening and callose accumulation, which interfere with cell-to-cell communication and inhibit root growth.ResultsWe took advantage of the opposite phosphate-conditional root phenotype of the phosphate deficiency response 2 mutant (hypersensitive) and low phosphate response 1 and 2 double mutant (insensitive) to investigate the phosphate dependent regulation of gene and protein expression in roots using genome-wide transcriptome and proteome analysis. We observed an overrepresentation of genes and proteins that are involved in the regulation of iron homeostasis, cell wall remodeling and reactive oxygen species formation, and we highlight a number of candidate genes with a potential function in root adaptation to limited phosphate availability. Our experiments reveal that FERRIC REDUCTASE DEFECTIVE 3 mediated, apoplastic iron redistribution, but not intracellular iron uptake and iron storage, triggers phosphate-dependent root growth modulation. We further highlight expressional changes of several cell wall-modifying enzymes and provide evidence for adjustment of the pectin network at sites of iron accumulation in the root.ConclusionOur study reveals new aspects of the elaborate interplay between phosphate starvation responses and changes in iron homeostasis. The results emphasize the importance of apoplastic iron redistribution to mediate phosphate-dependent root growth adjustment and suggest an important role for citrate in phosphate-dependent apoplastic iron transport. We further demonstrate that root growth modulation correlates with an altered expression of cell wall modifying enzymes and changes in the pectin network of the phosphate-deprived root tip, supporting the hypothesis that pectins are involved in iron binding and/or phosphate mobilization.

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.

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.

Piriformospora indica is a root-colonizing fungus, which interacts with a variety of plants including Arabidopsis thaliana. This interaction has been considered as mutualistic leading to growth promotion of the host. So far, only indolic glucosinolates and phytohormones have been identified as key players. In a comprehensive non-targeted metabolite profiling study, we analyzed Arabidopsis thaliana’s roots, root exudates, and leaves of inoculated and non-inoculated plants by ultra performance liquid chromatography/electrospray ionization quadrupole-time-of-flight mass spectrometry (UPLC/(ESI)-QTOFMS) and gas chromatography/electron ionization quadrupole mass spectrometry (GC/EI-QMS), and identified further biomarkers. Among them, the concentration of nucleosides, dipeptides, oligolignols, and glucosinolate degradation products was affected in the exudates. In the root profiles, nearly all metabolite levels increased upon co-cultivation, like carbohydrates, organic acids, amino acids, glucosinolates, oligolignols, and flavonoids. In the leaf profiles, we detected by far less significant changes. We only observed an increased concentration of organic acids, carbohydrates, ascorbate, glucosinolates and hydroxycinnamic acids, and a decreased concentration of nitrogen-rich amino acids in inoculated plants. These findings contribute to the understanding of symbiotic interactions between plant roots and fungi of the order of Sebacinales and are a valid source for follow-up mechanistic studies, because these symbioses are particular and clearly different from interactions of roots with mycorrhizal fungi or dark septate endophytes

Most of the time, graduate students spend long days between books and in the laboratory and risk getting stuck in the microbubble of their research topic. Student conferences are a good way to meet fellow PhD students, share experiences, and exchange scientific ideas to think out of the box. The arising discussions usually boost further research.

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.