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      Root Exudation of Phytochemicals in Arabidopsis Follows Specific Patterns That Are Developmentally Programmed and Correlate with Soil Microbial Functions

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          Abstract

          Plant roots constantly secrete compounds into the soil to interact with neighboring organisms presumably to gain certain functional advantages at different stages of development. Accordingly, it has been hypothesized that the phytochemical composition present in the root exudates changes over the course of the lifespan of a plant. Here, root exudates of in vitro grown Arabidopsis plants were collected at different developmental stages and analyzed using GC-MS. Principle component analysis revealed that the composition of root exudates varied at each developmental stage. Cumulative secretion levels of sugars and sugar alcohols were higher in early time points and decreased through development. In contrast, the cumulative secretion levels of amino acids and phenolics increased over time. The expression in roots of genes involved in biosynthesis and transportation of compounds represented in the root exudates were consistent with patterns of root exudation. Correlation analyses were performed of the in vitro root exudation patterns with the functional capacity of the rhizosphere microbiome to metabolize these compounds at different developmental stages of Arabidopsis grown in natural soils. Pyrosequencing of rhizosphere mRNA revealed strong correlations (p<0.05) between microbial functional genes involved in the metabolism of carbohydrates, amino acids and secondary metabolites with the corresponding compounds released by the roots at particular stages of plant development. In summary, our results suggest that the root exudation process of phytochemicals follows a developmental pattern that is genetically programmed.

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          MetaboAnalyst: a web server for metabolomic data analysis and interpretation

          Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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            Plant-microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture.

            Plant-associated microorganisms fulfill important functions for plant growth and health. Direct plant growth promotion by microbes is based on improved nutrient acquisition and hormonal stimulation. Diverse mechanisms are involved in the suppression of plant pathogens, which is often indirectly connected with plant growth. Whereas members of the bacterial genera Azospirillum and Rhizobium are well-studied examples for plant growth promotion, Bacillus, Pseudomonas, Serratia, Stenotrophomonas, and Streptomyces and the fungal genera Ampelomyces, Coniothyrium, and Trichoderma are model organisms to demonstrate influence on plant health. Based on these beneficial plant-microbe interactions, it is possible to develop microbial inoculants for use in agricultural biotechnology. Dependent on their mode of action and effects, these products can be used as biofertilizers, plant strengtheners, phytostimulators, and biopesticides. There is a strong growing market for microbial inoculants worldwide with an annual growth rate of approximately 10%. The use of genomic technologies leads to products with more predictable and consistent effects. The future success of the biological control industry will benefit from interdisciplinary research, e.g., on mass production, formulation, interactions, and signaling with the environment, as well as on innovative business management, product marketing, and education. Altogether, the use of microorganisms and the exploitation of beneficial plant-microbe interactions offer promising and environmentally friendly strategies for conventional and organic agriculture worldwide.
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              Quality control for plant metabolomics: reporting MSI-compliant studies.

              The Metabolomics Standards Initiative (MSI) has recently released documents describing minimum parameters for reporting metabolomics experiments, in order to validate metabolomic studies and to facilitate data exchange. The reporting parameters encompassed by MSI include the biological study design, sample preparation, data acquisition, data processing, data analysis and interpretation relative to the biological hypotheses being evaluated. Herein we exemplify how such metadata can be reported by using a small case study - the metabolite profiling by GC-TOF mass spectrometry of Arabidopsis thaliana leaves from a knockout allele of the gene At1g08510 in the Wassilewskija ecotype. Pitfalls in quality control are highlighted that can invalidate results even if MSI reporting standards are fulfilled, including reliable compound identification and integration of unknown metabolites. Standardized data processing methods are proposed for consistent data storage and dissemination via databases.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                1 February 2013
                : 8
                : 2
                : e55731
                Affiliations
                [1 ]Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado, United States of America
                [2 ]Research Institute for Sustainable Humanosphere, Kyoto University, Uji, Kyoto, Japan
                [3 ]United States Department of Agriculture - Agricultural Research Service, Soil-Plant-Nutrient Research Unit, Fort Collins, Colorado, United States of America
                National Taiwan University, Taiwan
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JMC DVB MGB JMV. Performed the experiments: JMC AS DVB. Analyzed the data: JMC DVB MGB JMV. Contributed reagents/materials/analysis tools: JMV DKM JMC. Wrote the paper: JMC DVB MGB DKM JMV.

                Article
                PONE-D-12-35392
                10.1371/journal.pone.0055731
                3562227
                23383346
                19650c98-11e3-4f42-92dd-24cfabacea8e
                Copyright @ 2013

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 6 November 2012
                : 29 December 2012
                Page count
                Pages: 10
                Funding
                Work was supported by a grant from the National Science Foundation to J.M.V. (MCB-0950857). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Agriculture
                Soil Science
                Biology
                Biochemistry
                Metabolism
                Ecology
                Plant Ecology
                Genetics
                Gene Expression
                Microbiology
                Plant Microbiology
                Plant Science
                Botany
                Plant Anatomy
                Plant Ecology
                Plant-Environment Interactions
                Plant Growth and Development
                Plants

                Uncategorized
                Uncategorized

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