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      Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea mays

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          Abstract

          To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H 2 tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H 2 scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2′ is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

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          Most cited references31

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          Finding and evaluating community structure in networks

          We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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            fw2.2: a quantitative trait locus key to the evolution of tomato fruit size.

            Domestication of many plants has correlated with dramatic increases in fruit size. In tomato, one quantitative trait locus (QTL), fw2.2, was responsible for a large step in this process. When transformed into large-fruited cultivars, a cosmid derived from the fw2.2 region of a small-fruited wild species reduced fruit size by the predicted amount and had the gene action expected for fw2.2. The cause of the QTL effect is a single gene, ORFX, that is expressed early in floral development, controls carpel cell number, and has a sequence suggesting structural similarity to the human oncogene c-H-ras p21. Alterations in fruit size, imparted by fw2.2 alleles, are most likely due to changes in regulation rather than in the sequence and structure of the encoded protein.
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              Not just a circle: flux modes in the plant TCA cycle.

              The tricarboxylic acid (TCA) cycle is one of the iconic pathways in metabolism. The cycle is commonly thought of in terms of energy metabolism, being responsible for the oxidation of respiratory substrates to drive ATP synthesis. However, the reactions of carboxylic acid metabolism are embedded in a larger metabolic network and the conventional TCA cycle is only one way in which the component reactions can be organised. Recent evidence from labelling studies and metabolic network models suggest that the organisation of carboxylic acid metabolism in plants is highly dependent on the metabolic and physiological demands of the cell. Thus, alternative, non-cyclic flux modes occur in leaves in the light, in some developing oilseeds, and under specific physiological circumstances such as anoxia. 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                12 July 2016
                2016
                : 7
                : 1022
                Affiliations
                [1] 1Institute of Dryland Biotechnology and Agriculture, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev Midreshet Ben-Gurion, Israel
                [2] 2Institute for Genomic Diversity, Cornell University Ithaca, NY, USA
                [3] 3Mitrani Department of Desert Ecology, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev Midreshet Ben-Gurion, Israel
                [4] 4Max Planck Institute of Molecular Plant Physiology Golm, Germany
                Author notes

                Edited by: Atsushi Fukushima, RIKEN, Japan

                Reviewed by: Kris Morreel, University Ghent, Belgium; Jedrzej Jakub Szymanski, Weizmann Institute of Science, Israel

                *Correspondence: Edward S. Buckler esb33@ 123456cornell.edu

                This article was submitted to Plant Systems and Synthetic Biology, a section of the journal Frontiers in Plant Science

                †Present Address: Wentao Xue, College of Life Sciences, Guizhou University, Guiyang, Guizhou, China

                Amit Gur, Department of Vegetable Crops and Plant Genetics, Israeli Agricultural Research Organization, Newe Yaár Research Center, Ramat Yishay, Israel

                Shai Pilosof, Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA

                Yves Gibon, Institut National de la Recherche Agronomique, UMR 1332 Biologie du Fruit et Pathologie, Université de Bordeaux, Bordeaux Cedex, France

                Article
                10.3389/fpls.2016.01022
                4940414
                27462343
                e6333f09-9c8c-448e-9378-6c2ee2274a08
                Copyright © 2016 Toubiana, Xue, Zhang, Kremling, Gur, Pilosof, Gibon, Stitt, Buckler, Fernie and Fait.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 February 2016
                : 28 June 2016
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 47, Pages: 10, Words: 6856
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                zea mays,correlation-based network analysis,metabolic networks and pathways,enzymatic processes,metabolism,tca cycle

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