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Wounding caused by insects or abiotic factors such as wind and hail can cause severe stress for plants. Intrigued by the observation that wounding induces expression of genes involved in surface wax synthesis in a jasmonoyl-isoleucine (JA-Ile)-independent manner, the role of wax biosynthesis and respective genes upon wounding was investigated. Wax, a lipid-based barrier, protects plants both from environmental threats and from an uncontrolled loss of water. Its biosynthesis is described to be regulated by abscisic acid (ABA), whereas the main wound signal is the hormone JA-Ile. We show in this study that genes coding for enzymes of surface wax synthesis are induced upon wounding in Arabidopsis thaliana leaves in a JA-Ile-independent but an ABA-dependent manner. Furthermore, the ABA-dependent transcription factor MYB96 is a key regulator of wax biosynthesis upon wounding. On the metabolite level, wound-induced wax accumulation is strongly reduced in JA-Ile-deficient plants, but this induction is only slightly decreased in ABA-reduced plants. To further analyze the ABA-dependent wound response, we conducted wounding experiments in high humidity. They show that high humidity prevents the wound-induced wax accumulation in A. thaliana leaves. Together the data presented in this study show that wound-induced wax accumulation is JA-Ile-dependent on the metabolite level, but the expression of genes coding for enzymes of wax synthesis is regulated by ABA. [ABSTRACT FROM AUTHOR]

Plant oxylipins are a class of lipid-derived signaling molecules being involved in the regulation of various biotic and abiotic stress responses. A major class of oxylipins are the circular derivatives to which 12-oxo-phytodienoic acid (OPDA) and its metabolite jasmonic acid (JA) belong. While OPDA and its shorter chain homologue dinor-OPDA (dnOPDA) seem to be ubiquitously found in land plants ranging from bryophytes to angiosperms, the occurrence of JA and its derivatives is still under discussion. The bryophyte Physcomitrium patens has received increased scientific interest as a nonvascular plant model organism over the last decade. Therefore, we followed the metabolism upon wounding by metabolite fingerprinting with the aim to identify jasmonates as well as novel oxylipins in P. patens. A non-targeted metabolomics approach was used to reconstruct the metabolic pathways for the synthesis of oxylipins, derived from roughanic, linoleic, a-linolenic, and arachidonic acid in wild type, the oxylipin-deficient mutants of Ppaos1 and Ppaos2, the mutants of Ppdes being deficient in all fatty acids harboring a D6- double bond and the C20-fatty acid-deficient mutants of Ppelo. Beside of OPDA, iso-OPDA, dnOPDA, and iso-dnOPDA, three additional C18- compounds and a metabolite being isobaric to JA were identified to accumulate after wounding. These findings can now serve as foundation for future research in determining, which compound(s) will serve as native ligand(s) for the oxylipin-receptor COI1 in P. patens. [ABSTRACT FROM AUTHOR]

The biosynthesis of N -hydroxy pipecolic acid (NHP) has been intensively studied, though knowledge on its metabolic turnover is still scarce. To close this gap, we discovered three novel metabolites via metabolite fingerprinting in Arabidopsis thaliana leaves after Pseudomonas infection and UV-C treatment. Exact mass information and fragmentation by tandem mass spectrometry (MS/MS) suggest a methylated derivative of NHP (MeNHP), an NHP- O Glc-hexosyl conjugate (NHP- O Glc-Hex), and an additional NHP- O Glc-derivative. All three compounds were formed in wild-type leaves but were not present in the NHP-deficient mutant fmo1-1. The identification of these novel NHP-based molecules was possible by a dual-infiltration experiment using a mixture of authentic NHP and D9-NHP standards for leaf infiltration followed by UV-C treatment. Interestingly, the signal intensity of MeNHP and other NHP-derived metabolites increased in ugt76b1-1 mutant plants. For MeNHP, we unequivocally determined the site of methylation at the carboxylic acid moiety. MeNHP application by leaf infiltration leads to the detection of a MeNHP- O Glc as well as NHP, suggesting MeNHP hydrolysis to NHP. This is in line with the observation that MeNHP infiltration is able to rescue the fmo1-1 susceptible phenotype against Hyaloperonospora arabidopsidis Noco 2. Together, these data suggest MeNHP as an additional storage or transport form of NHP. [ABSTRACT FROM AUTHOR]

Maize smut caused by the fungus Ustilago maydis is a widespread disease characterized by the development of large plant tumours. U. maydisis a biotrophic pathogen that requires living plant tissue for its development and establishes an intimate interaction zone between fungal hyphae and the plant plasma membrane. U. maydis actively suppresses plant defence responses by secreted protein effectors (1,2). Its effector repertoire comprises at least 386 genes mostly encoding proteins of unknown function (1,3,4) and expressed exclusively during the biotrophic stage (3).The U. maydis secretome also contains about 150 proteins with probable roles in fungal nutrition, fungal cell wall modification and host penetration as well as proteins unlikely to act in the fungal-host interface (4) like a chorismate mutase. Chorismate mutases are key enzymes of the shikimate pathway and catalyse the conversion of chorismate to prephenate, the precursor for tyrosine and phenylalanine synthesis. Root-knot nematodes inject a secreted chorismate mutase into plant cells likely to affect development (5,6). Here we show that the chorismate mutase Cmu1 secreted by U. maydis is a virulence factor. The enzyme is taken up by plant cells, can spread to neighbouring cells and changes the metabolic status of these cells through metabolic priming. Secreted chorismate mutases are found in many plant-associated microbes and might serve as general tools for host manipulation., The U. maydis genome (http://mips.helmholtz-muenchen.de/genre/proj/ustilago) contains genes for both a cytosolic chorismate mutase, designated aro7 (um04220), and a putatively secreted chorismate mutase, cmul (um05731). Cmu1 belongs to the AroQ class [...]

Abstract Background A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters. Results We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data. Conclusion MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

Abstract Background One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters. Results We present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers. Conclusion Our specialized realization of self-organizing maps is well-suitable to gain insight into complex pattern variation in a large set of metabolite profiles. In comparison to other methods our visualization approach facilitates the identification of interesting groups of metabolites by means of a convenient overview on relevant intensity patterns. In particular, the visualization effectively supports researchers in analyzing many putative clusters when the true number of biologically meaningful groups is unknown.