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      Analysis of the Compartmentalized Metabolome – A Validation of the Non-Aqueous Fractionation Technique

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          Abstract

          With the development of high-throughput metabolic technologies, a plethora of primary and secondary compounds have been detected in the plant cell. However, there are still major gaps in our understanding of the plant metabolome. This is especially true with regards to the compartmental localization of these identified metabolites. Non-aqueous fractionation (NAF) is a powerful technique for the determination of subcellular metabolite distributions in eukaryotic cells, and it has become the method of choice to analyze the distribution of a large number of metabolites concurrently. However, the NAF technique produces a continuous gradient of metabolite distributions, not discrete assignments. Resolution of these distributions requires computational analyses based on marker molecules to resolve compartmental localizations. In this article we focus on expanding the computational analysis of data derived from NAF. Along with an experimental workflow, we describe the critical steps in NAF experiments and how computational approaches can aid in assessing the quality and robustness of the derived data. For this, we have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Furthermore, using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected. Finally, we discuss caveats and benefits of NAF analysis in the context of the compartmentalized metabolome.

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          Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

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            Sugar transporters for intercellular exchange and nutrition of pathogens.

            Sugar efflux transporters are essential for the maintenance of animal blood glucose levels, plant nectar production, and plant seed and pollen development. Despite broad biological importance, the identity of sugar efflux transporters has remained elusive. Using optical glucose sensors, we identified a new class of sugar transporters, named SWEETs, and show that at least six out of seventeen Arabidopsis, two out of over twenty rice and two out of seven homologues in Caenorhabditis elegans, and the single copy human protein, mediate glucose transport. Arabidopsis SWEET8 is essential for pollen viability, and the rice homologues SWEET11 and SWEET14 are specifically exploited by bacterial pathogens for virulence by means of direct binding of a bacterial effector to the SWEET promoter. Bacterial symbionts and fungal and bacterial pathogens induce the expression of different SWEET genes, indicating that the sugar efflux function of SWEET transporters is probably targeted by pathogens and symbionts for nutritional gain. The metazoan homologues may be involved in sugar efflux from intestinal, liver, epididymis and mammary cells.
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              A glucosinolate metabolism pathway in living plant cells mediates broad-spectrum antifungal defense.

              Selection pressure exerted by insects and microorganisms shapes the diversity of plant secondary metabolites. We identified a metabolic pathway for glucosinolates, known insect deterrents, that differs from the pathway activated by chewing insects. This pathway is active in living plant cells, may contribute to glucosinolate turnover, and has been recruited for broad-spectrum antifungal defense responses. The Arabidopsis CYP81F2 gene encodes a P450 monooxygenase that is essential for the pathogen-induced accumulation of 4-methoxyindol-3-ylmethylglucosinolate, which in turn is activated by the atypical PEN2 myrosinase (a type of beta-thioglucoside glucohydrolase) for antifungal defense. We propose that reiterated enzymatic cycles, controlling the generation of toxic molecules and their detoxification, enable the recruitment of glucosinolates in defense responses.
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                Author and article information

                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in plant science
                Frontiers Research Foundation
                1664-462X
                22 September 2011
                2011
                : 2
                : 55
                Affiliations
                [1] 1simpleDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology Potsdam-Golm, Germany
                [2] 2simpleBotanical Institute II, University of Cologne Cologne, Germany
                Author notes

                Edited by: Alisdair Fernie, Max Planck Institute for Plant Physiology, Germany

                Reviewed by: Alisdair Fernie, Max Planck Institute for Plant Physiology, Germany; Lee Sweetlove, University of Oxford, UK

                *Correspondence: Dirk Steinhauser, Department of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany. e-mail: steinhauser@ 123456mpimp-golm.mpg.de

                Sebastian Klie, Stephan Krueger and Dirk Steinhauser have contributed equally to this work.

                This article was submitted to Frontiers in Plant Physiology, a specialty of Frontiers in Plant Science.

                Article
                10.3389/fpls.2011.00055
                3355776
                22645541
                a24ff700-4c78-4711-9327-df78a5f8d4f0
                Copyright © 2011 Klie, Krueger, Krall, Giavalisco, Flügge, Willmitzer and Steinhauser.

                This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

                History
                : 01 May 2011
                : 05 September 2011
                Page count
                Figures: 20, Tables: 0, Equations: 4, References: 96, Pages: 27, Words: 13894
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                computational simulations,least squares algorithms,analysis workflow,subcellular metabolomics,visualization,bestfit tool

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