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      Comparative Metabolomics and Molecular Phylogenetics of Melon ( Cucumis melo, Cucurbitaceae) Biodiversity

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      1 , 2 , 3 , 4 , 4 , 3 , 1 , 5 , 6 , 7 , 8 , 1 , 5 , 8 , 9 , 10 , 11 , 1 , 9 , 5 , 9 , 1 , 3 , 4 , 12 , 9 , 9 , 3 , 1 , 9 , 11 , 9 , 9 , 12 , 4 , 8 , 12 , 13 , 8 , *
      Metabolites
      MDPI
      genetic resources, melon, genotype by sequencing, elemental analysis, metabolome, Cucumis melo

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          Abstract

          The broad variability of Cucumis melo (melon, Cucurbitaceae) presents a challenge to conventional classification and organization within the species. To shed further light on the infraspecific relationships within C. melo, we compared genotypic and metabolomic similarities among 44 accessions representative of most of the cultivar-groups. Genotyping-by-sequencing (GBS) provided over 20,000 single-nucleotide polymorphisms (SNPs). Metabolomics data of the mature fruit flesh and rind provided over 80,000 metabolomic and elemental features via an orchestra of six complementary metabolomic platforms. These technologies probed polar, semi-polar, and non-polar metabolite fractions as well as a set of mineral elements and included both flavor- and taste-relevant volatile and non-volatile metabolites. Together these results enabled an estimate of “metabolomic/elemental distance” and its correlation with the genetic GBS distance of melon accessions. This study indicates that extensive and non-targeted metabolomics/elemental characterization produced classifications that strongly, but not completely, reflect the current and extensive genetic classification. Certain melon Groups, such as Inodorous, clustered in parallel with the genetic classifications while other genome to metabolome/element associations proved less clear. We suggest that the combined genomic, metabolic, and element data reflect the extensive sexual compatibility among melon accessions and the breeding history that has, for example, targeted metabolic quality traits, such as taste and flavor.

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          Most cited references68

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          The genome of melon (Cucumis melo L.).

          We report the genome sequence of melon, an important horticultural crop worldwide. We assembled 375 Mb of the double-haploid line DHL92, representing 83.3% of the estimated melon genome. We predicted 27,427 protein-coding genes, which we analyzed by reconstructing 22,218 phylogenetic trees, allowing mapping of the orthology and paralogy relationships of sequenced plant genomes. We observed the absence of recent whole-genome duplications in the melon lineage since the ancient eudicot triplication, and our data suggest that transposon amplification may in part explain the increased size of the melon genome compared with the close relative cucumber. A low number of nucleotide-binding site-leucine-rich repeat disease resistance genes were annotated, suggesting the existence of specific defense mechanisms in this species. The DHL92 genome was compared with that of its parental lines allowing the quantification of sequence variability in the species. The use of the genome sequence in future investigations will facilitate the understanding of evolution of cucurbits and the improvement of breeding strategies.
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            MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing.

            Hyphenated full-scan MS technology creates large amounts of data. A versatile easy to handle automation tool aiding in the data analysis is very important in handling such a data stream. MetAlign softwareas described in this manuscripthandles a broad range of accurate mass and nominal mass GC/MS and LC/MS data. It is capable of automatic format conversions, accurate mass calculations, baseline corrections, peak-picking, saturation and mass-peak artifact filtering, as well as alignment of up to 1000 data sets. A 100 to 1000-fold data reduction is achieved. MetAlign software output is compatible with most multivariate statistics programs.
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              TagFinder for the quantitative analysis of gas chromatography--mass spectrometry (GC-MS)-based metabolite profiling experiments.

              Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap. TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling. Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.
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                Author and article information

                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                24 March 2020
                March 2020
                : 10
                : 3
                : 121
                Affiliations
                [1 ]INRAE, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Bordeaux Metabolome Facility MetaboHUB, Centre INRAE de Nouvelle Aquitaine - Bordeaux, 33140 Villenave d’Ornon, France; annick.moing@ 123456inrae.fr (A.M.); bbiais@ 123456free.fr (B.B.); catherine.deborde@ 123456inrae.fr (C.D.); daniel.jacob@ 123456inrae.fr (D.J.); mickael.maucourt@ 123456inrae.fr (M.M.); dominique.rolin@ 123456inrae.fr (D.R.)
                [2 ]The James Hutton Institute, Environmental & Biochemical Sciences, Invergowrie, Dundee, DD2 5DA Scotland, UK; william.allwood@ 123456hutton.ac.uk
                [3 ]Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel; asaph.aharoni@ 123456weizmann.ac.il (A.A.); sagit.meir@ 123456weizmann.ac.il (S.M.); Shifra.ben-dor@ 123456weizmann.ac.il (S.B.-D.)
                [4 ]Rothamsted Research, Harpenden, Herts AL5 2JQ, UK; john.baker@ 123456pfizer.com (J.B.); mike.beale@ 123456rothamsted.ac.uk (M.H.B.); sonia.miller83@ 123456yahoo.co.uk (S.M.); jane.ward@ 123456rothamsted.ac.uk (J.L.W.)
                [5 ]Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany; federicobrigante@ 123456outlook.com (F.B.); Erban@ 123456mpimp-golm.mpg.de (A.E.); Kopka@ 123456mpimp-golm.mpg.de (J.K.)
                [6 ]Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Dto. Química Orgánica, Córdoba 5000, Argentina
                [7 ]CONICET, ICYTAC (Instituto de Ciencia y Tecnologia de Alimentos Córdoba), Córdoba 5000, Argentina
                [8 ]Institute of Plant Science, Agricultural Research Organization—Volcani Center, Rishon LeZiyyon 7515101, Israel; burgery@ 123456agri.gov.il (Y.B.); adif@ 123456agri.gov.il (A.F.); elenae@ 123456agri.gov.il (E.Y.)
                [9 ]Newe Ya‘ar Research Center, Agricultural Research Organization, P. O. Box 1021, Ramat Yishay 3009500, Israel; amitgur@ 123456volcani.agri.gov.il (A.G.); katzirn@ 123456agri.gov.il (N.K.); twefraim@ 123456agri.gov.il (E.L.); elad.oren@ 123456mail.huji.ac.il (E.O.); hsparis@ 123456agri.gov.il (H.S.P.); yarden@ 123456agri.gov.il (U.S.); tadmory@ 123456agri.gov.il (Y.T.); galilt@ 123456agri.gov.il (G.T.)
                [10 ]Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK; roy.goodacre@ 123456liverpool.ac.uk
                [11 ]Department of Plant and Environmental Sciences & Copenhagen Plant Science Center, Faculty of Science, University of Copenhagen, DK-1871 Frederiksberg C, Denmark; thh@ 123456plen.ku.dk (T.H.H.); jks@ 123456plen.ku.dk (J.K.S.)
                [12 ]Business Unit Bioscience, Wageningen University & Research, Post Box 16, 6700AA, Wageningen, Netherlands; roland.mumm@ 123456wur.nl (R.M.); robert.hall@ 123456wur.nl (R.D.H.)
                [13 ]Department of Plant Physiology, Wageningen University & Research, Laboratory of Plant Physiology, Post Box 16, 6700AA, Wageningen, Netherlands
                Author notes
                [* ]Correspondence: vcaris@ 123456volcani.agri.gov.il ; Tel.: + 972(3)9683646
                [†]

                These authors have contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-1144-3600
                https://orcid.org/0000-0001-6433-2694
                https://orcid.org/0000-0001-9604-1939
                https://orcid.org/0000-0001-5687-9059
                https://orcid.org/0000-0003-2230-645X
                https://orcid.org/0000-0001-8260-880X
                https://orcid.org/0000-0001-7102-3152
                https://orcid.org/0000-0002-1281-5624
                https://orcid.org/0000-0002-5786-768X
                Article
                metabolites-10-00121
                10.3390/metabo10030121
                7143154
                32213984
                763daa3c-0a81-47d1-a1b7-98b3dfd56b1b
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 February 2020
                : 20 March 2020
                Categories
                Article

                genetic resources,melon,genotype by sequencing,elemental analysis,metabolome,cucumis melo

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