Recent Progress in the Development of Metabolome Databases for Plant Systems Biology

Abstract

Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.

Introduction

Metabolomics, i.e., the measurement of the full suite of metabolites in a living tissue, has expanded greatly over the last decade, especially in the context of biochemical phenotyping. Specifically, in plant science, metabolomic approaches are increasingly used for understanding regulatory networks involved in genotype comparison (Roessner et al., 2001; Weckwerth et al., 2004), measurement of diurnal/circadian rhythms (Urbanczyk-Wochniak et al., 2005; Gibon et al., 2006; Fukushima et al., 2009a; Espinoza et al., 2010), evaluation of genetically modified plants (Catchpole et al., 2005; Baker et al., 2006; Kusano et al., 2011a; Ricroch et al., 2011), uncovering relationships between metabolites associated with carbon and nitrogen metabolism (Stitt and Fernie, 2003; Sato et al., 2008; Kusano et al., 2011b), stress responses (Kaplan et al., 2004; Urano et al., 2009; Caldana et al., 2011; Kusano et al., 2011c; Obata and Fernie, 2012), characterization of many bioresources (Meyer et al., 2007; Rowe et al., 2008; Sulpice et al., 2009), and identifying metabolite quantitative trait loci (mQTLs) (Morreel et al., 2006; Schauer et al., 2006; Lisec et al., 2008; Carreno-Quintero et al., 2012; Matsuda et al., 2012).

It is estimated that approximately 200,000 metabolites are produced in the plant kingdom (Fiehn, 2002). There is no single technique suitable for measurement of all metabolites because of the chemical diversity of cellular metabolites and their broad dynamic range, particularly as this pertains to plants (Hall, 2006; Fukushima et al., 2009b; Saito and Matsuda, 2010; Lei et al., 2011; Weckwerth, 2011). Because of this, an array of analytical methods and extraction procedures has been developed for the detection of a broad spectrum of metabolites. Most procedures are based on either mass spectrometry (MS) or nuclear magnetic resonance (NMR). Metabolite data are typically generated through the following processes (Figure 1): (1) Sample preparation, (2) Data acquisition, and (3) Data pre-processing (the first half of the cycle in Figure 1). The resultant data are then subjected to multi-step downstream processes, including (4) Statistical data analysis approaches such as principal component analysis (PCA) and (5) Data interpretation using methods such as pathway analysis, which facilitate (6) The generation of testable hypotheses and the construction of models that best represent the biological phenomenon (the second half of the cycle in Figure 1). Experimental validation (7) of hypotheses and models [(7) in Figure 1] is necessary for closing the systems biology research cycle (Kitano, 2002; Fernie, 2012).

Figure 1

Traditionally, the elucidation of the structure of an unknown natural chemical compound has typically required the study of physicochemical properties, including the accurate mass and chemical shifts in ¹H- and ¹³C-NMR spectra when the compound was first isolated. In contrast, metabolite identification in metabolomics using gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) is not unambiguous. There are two venues to the identification of a chromatographic peak: (1) purification and NMR analysis or (2) interpretation of the spectra yielding a putative structure, followed by synthesizing or buying the compound and spiking. Detectable peaks using GC-MS and LC-MS are thought to be abundant and often authentic standards exist to identify them by spiking. Nonetheless, researchers commonly attempt to provisionally identify these peaks by comparing their mass spectra and/or the retention time or retention indices (RIs) with those present in a database build from the data of authentic standards. To validate the metabolite identification rigorously, the Metabolomics Standard Initiative (MSI)(Fiehn et al., 2007; Sansone et al., 2007) recommends different levels of identification (Sumner et al., 2007). Fernie et al. (2011) have also stated additional practical recommendations for reporting large-scale metabolite data.

It is quite evident that databases cataloging mass spectra and compounds give great support to metabolomic studies (for example, see Tohge and Fernie, 2009; Scalbert et al., 2011). Here, we highlight a wide range of metabolome databases, especially those that are widely used in MS-based metabolite profiling for rapid, but accurate, quantification, and identification of metabolites (Fiehn, 2002). We also discuss further steps to develop future databases facilitating metabolomic analyses and to improve bioinformatics tools in plant systems biology.

Mass Spectrum-Oriented Information

Since non-targeted metabolite profiling using GC-MS for plant extracts was established in the early 2000s (Fiehn, 2001; Lisec et al., 2006), many software packages and databases for electron impact (EI) mass spectra and RIs of compounds analyzed by GC-MS have been created (Table 1). The NIST/EPA/NIH mass spectral database represents the largest database commercially available for metabolite identification, containing mainly EI mass spectra RIs (Stein, 1999). Recently, the database also stores a set of MS/MS spectra of metabolites, drugs, peptides, and other compounds which are obtained by using ion trap-as well as tandem-MS instruments. The Golm Metabolome Database (GMD) provides GC-EI-MS mass spectral and RI (MSRI) libraries (Kopka et al., 2005; Schauer et al., 2005). It also contains mass spectral tags (MSTs) (Schauer et al., 2005), i.e., MS spectra of putative biological molecules which remain largely unidentified due to the lack of authentic standard compounds. GMD uses both alkanes and fatty acid methyl esters (FAMEs) for RI calculation whereas FiehnLib (Kind et al., 2009), a commercial MSRI library, uses FAMEs rather than alkanes. The Spectral Database for Organic Compounds¹ (SDBS) includes a wide range of mass spectra for organic compounds, such as polysaccharides. MassBase² is a mass spectral archive for LC-, GC-, and Capillary electrophoresis–MS (CE-MS). SetupX³ and BinBase⁴ are a Laboratory Information Management System (LIMS)/database system for automated metabolite annotation and mass spectra, respectively. The Adams library (Adams, 2007), Terpenoids Library⁵, and VocBinBase (Skogerson et al., 2011) are GC-specific MSRI libraries for volatile compounds. The former two are commercially available, while the VocBinBase database is freely available for their provisional identification (Skogerson et al., 2011). For MS data management and data sharing, MetabolomeExpress (Carroll et al., 2010) and MetaboLights (Haug et al., 2012; Steinbeck et al., 2012) were developed. The former is an ftp server that acts as a public data repository and web application for online data pre-processing and meta-analysis of publicly available metabolomic datasets analyzed by GC-MS. The latter is a general metabolomics repository; users can browse publicly available metabolomic datasets, search and see experimental meta-data, and re-use associated data files.

It has been demonstrated that metabolite profiling using LC-MS has the potential to reveal secondary metabolites produced by plants, but most of the detected peaks in LC-MS profile data are largely unknown (Moco et al., 2006; De Vos et al., 2007; Iijima et al., 2008; Matsuda et al., 2010). Compared to EI, LC-MS ionization methods such as electrospray ionization (ESI) does hardly fragment the molecular ions. Even if authentic standards do not exist, putative metabolite identification can be done via MS/MS fragmentation and recording of accurate masses using ultra-high resolution MS such as Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) (Lenz et al., 2004; Nakabayashi et al., 2013). Different collision energy/analytical conditions cause different fragment patterns in mass spectra and should also be noted. Several databases have been developed for the sharing of ESI mass spectral information. METLIN stores high-resolution MS/MS spectra at four different collision energies (Tautenhahn et al., 2012). MassBank is a publicly available database of ESI-MS/MS spectra of authentic metabolite standards obtained under five collision energies as well as EI spectra (Horai et al., 2010). Bio-MassBank catalogs those obtained from biological samples⁶. In analogy with the MST spectra obtained from GC-MS data, the MS/MS spectral tag (MS2T) of detectable metabolites using LC-ESI-quadrupole-time-of-flight/MS is also available (Matsuda et al., 2009, 2010; Sakurai et al., 2013). This library contains MS2T obtained from species such as Arabidopsis thaliana, rice, soybean, and wheat. Compatible LC-MS settings with those used for the MS2T recording can be used for annotating detected peaks. Based on literature surveys, a plant-specific MS/MS spectra database was constructed by the same group (Sawada et al., 2012). MetaboSearch can be used to simultaneously retrieve mass-based metabolite data from multiple metabolite databases (Zhou et al., 2012). This contains tools to query and/or comprehensively analyze LC-MS-based metabolomic data. Together, these mass spectral databases make an important contribution to metabolite identification and also facilitate the development of bioinformatic tools (e.g., mining unknown metabolites) in metabolomics.

Compound-Oriented Information and Structure Characterization

Compound databases

There are also compound databases (Table 1), such as Chemical Abstract Service (CAS)⁷. CAS is the oldest database of chemical information (e.g., journal abstracts); substances in the CAS registry database are each assigned a unique ID number. The PubChem database in NCBI (Wang et al., 2009), ChEBI (Degtyarenko et al., 2008), and ChemSpider (Pence and Williams, 2010) are freely available and can be used to retrieve chemical structures of small molecules. Well-curated chemical information including compounds and pathways are available in KEGG database (Kanehisa et al., 2012), KNApSAcK database (Afendi et al., 2012), and Metabolomics.jp⁸ (Arita and Suwa, 2008). The Plant Metabolome Database (PMDB) is a freely available database of secondary metabolites in plants (Udayakumar et al., 2012). For bioactive lipids, LipidBank (Watanabe et al., 2000) and LipidMap (Fahy et al., 2007) are available. The Manchester Metabolomics Database (MMD) has been developed to simultaneously utilize genome-scale data from the Human Metabolome Database (HMDB)(Wishart et al., 2009), KEGG, and LipidMaps. Other well-organized databases of biochemical knowledge are also available, such as BKM-react (Lang et al., 2011) and MetRxn (Kumar et al., 2012).

Structural characterization

One of the known bottlenecks in metabolomics is in the identification process of unknown metabolites, which can be classified as either “known unknowns” or “unknown unknowns” (Wishart, 2009). The former corresponds to a metabolite that has been previously detected but has not yet been identified, while the latter corresponds to a truly novel metabolite that has never been formally identified. Schymanski et al. (2012) have shown that consensus structure elucidation using a combination of GC-EI-MS, structure generation, and physicochemical properties calculated from unknown compounds may be applicable to the characterization of unknown metabolites. Kumari et al. (2011) evaluated a novel de novo workflow for the annotation of unknown metabolites using accurate mass data, PubChem queries, RI matching, and structure constraints. To predict elemental compositions from accurate mass data collected from high-resolution mass spectrometers, “Seven Golden Rules” (Kind and Fiehn, 2007) and MFSearcher⁹ are available. Krumsiek et al. (2012) demonstrated that the integration of metabolite profiling with genome-wide association studies (GWAS) on metabolic quantitative traits is very useful for deriving biochemical pathways for unknown metabolites. In addition, several groups have attempted to classify unidentified MSTs using supervised machine learning approaches, including decision tree (Hummel et al., 2010) and soft independent modeling of class analogies (SIMCA)(Tsugawa et al., 2011). For structural characterization, there are recent powerful approaches by comparing mass spectral fragmentation trees (Rasche et al., 2011; Hufsky et al., 2012; Rojas-Cherto et al., 2012)(see also the review by Xiao et al., 2012). To evaluate whether detected peaks are biochemically produced by organisms, an in vivo¹³C-labeling system has been used with ¹³CO₂ in metabolite profiling using both GC-MS (Huege et al., 2007) and LC-MS (Giavalisco et al., 2009). The method allows for the rejection of non-biological peaks and improved annotation of elemental composition. Because artificial biological gradients developed by Redestig et al. (2011) can evaluate actual concentration differences of metabolite peaks detected in two different types of samples (e.g., leaves and fruits), this allows to filter out all unavoidable artifacts in MST/MS2T data. Such a method will make it possible to reject analytical artifacts and prioritize unknown candidate metabolites for further characterization.

Statistical Models, Pathway Information, and Data Interpretation

Uni- and multivariate analysis

To perform extensive data analysis such as PCA, MetaGeneAlyse (Daub et al., 2003) and MetaboAnalyst (Xia et al., 2012) are available. Conceptually, these are very similar web-based applications for the analysis of high-throughput omics data. MetaGeneAlyse implements standard normalization/clustering methods, e.g., k-means, and independent component analysis (ICA). MetaboAnalyst provides many statistical methods, including t-tests, partial least square discriminant analysis (PLSDA), pathway enrichment analysis, and additional machine learning methods. Please note that several tools and databases presented in this review have multiple functions. Furthermore, several web-based applications for metabolomic data are available (Table 1), such as MetaMapp (Barupal et al., 2012), metaP-Server (Kastenmuller et al., 2011), MeltDB (Neuweger et al., 2008), and MetiTree (Rojas-Cherto et al., 2012). They cover multiple steps from data pre-processing to biological interpretation.

Metabolite pathway analysis

Metabolite data, which contain information about metabolite name and changes of metabolite levels/relationships, can be described in pathways or networks. For example, the nodes represent metabolites and the edges represent biochemical reactions. Well-curated database for metabolic pathways in plants are available, such as KEGG (Kanehisa et al., 2012) and AraCyc (Zhang et al., 2005). MetaCrop stores well-curated information for 60 major metabolic pathways in eight crop plants, as well as Arabidopsis (Schreiber et al., 2012). UniPathway (Morgat et al., 2012) and SMPDB (Frolkis et al., 2010) also provide well-curated information about metabolic pathways. Tools involving pathway analysis and enrichment analysis are also available, such as AraPath (Lai et al., 2012), Kappa-view (Tokimatsu et al., 2005; Sakurai et al., 2011), and MapMan (Usadel et al., 2009) (Table 1). For detailed information about these tools, see the excellent review by Chagoyen and Pazos (2012).

Mathematical Model Information and Other Tool

Genome-scale metabolism reconstruction

Over the past few decades, a significant number of metabolic reconstructions have been performed in many organisms, for example, SEED servers (Aziz et al., 2012). Currently, several genome-scale metabolic models in plants are available for evaluating metabolic behavior based on the alteration of metabolic pathways (Table 1) (Collakova et al., 2012; De Oliveira Dal’molin and Nielsen, 2012; Seaver et al., 2012). Poolman et al. (2009) constructed such a metabolism model in Arabidopsis to characterize possible flux behaviors using flux balance analysis (FBA) (Orth et al., 2010;Sweetlove and Ratcliffe, 2011). Instead of using metabolic flux analysis (MFA)(for example, see the reviews by Libourel and Shachar-Hill, 2008; Allen et al., 2009), this analysis can predict steady-state flux distribution by using a linear programing. AraGEM is also another metabolic reconstruction of Arabidopsis metabolism (De Oliveira Dal’molin et al., 2010). Radrich et al. (2010) semi-automatically integrated multiple databases involving metabolic pathways to reconstruct Arabidopsis metabolism. A compartmentalized, reconstructed metabolic model of Arabidopsis is also currently available (Mintz-Oron et al., 2012). Combinations of theoretical and experimental approaches will pave the way for robust interpretation of metabolomic data and practical metabolic engineering in plants.

Tools for metabolite identifiers

Managing compound identifiers in metabolomic data analysis is important. MSI also proposed the use of database identifiers for peer-reviewed papers, for example, the most common compound identifiers, including CAS, KEGG COMPOUND, CHEBI, and HMDB. The Chemical Translation Service (CTS) (Wohlgemuth et al., 2010) and MetMask (Redestig et al., 2010) are a conversion tool for chemical identifiers (Table 1). The former is a web-based tool for performing batch conversions of compound identifiers, while the latter is a stand-alone command line program for integrating the most common compound identifiers. Metab2MeSH (Sartor et al., 2012) is a web application for annotating compounds with Medical Subject Headings (MeSH), which is a controlled vocabulary. Controlled vocabulary means well defined index term is used for indexing journal articles. Metab2MeSH links from metabolites to the biomedical research literature, PubChem, and HMDB. These tools in this subsection are helpful for reporting metabolomic data.

Metabolite-Profiling-Oriented Information

In addition to mass spectrum and compound databases, several metabolite-profiling databases have also been developed in the past few years (Table 1). Among these, PlantMetabolomics.org (Bais et al., 2010, 2012; Quanbeck et al., 2012) and Medicinal Plant Metabolomics Resource (MPMR) (Wurtele et al., 2012) are one of the most important databases. These contain metabolomic information for >140 Arabidopsis mutants and 14 medicinal plants based on MS data from multiple laboratories (Bais et al., 2012). Their profiling broadly covers a wide range of metabolites relating to amino and fatty acids, organic acids, phytosterols, isoprenoids, lipids, and secondary metabolites. PlantMetabolomics.org and MPMR also provide multiple data analysis tools including data normalization and visualization. Using these tools investigators can generate testable hypotheses with respect to gene functions in Arabidopsis (Quanbeck et al., 2012). Another example is Chloroplast 2010, which contains data related to large-scale phenotypic screening of Arabidopsis chloroplast mutants (Lu et al., 2011), based on assays of amino acids and fatty acids in leaves and seeds using GC-MS and LC-MS (Gu et al., 2007; Bell et al., 2012). Recently, we constructed the MeKO database¹⁰ (Fukushima et al., submitted), which is similar in concept to PlantMetabolomics.org. MeKO contains metabolomic information on 50 Arabidopsis mutants, including plants with uncharacterized gene functions. The website also provides MSI-compliant data, experimental meta-data, and the results of statistical data analyses such as differential accumulation compared with wild-type plants (Columbia ecotype). These databases are very useful for functional genomics and make it possible to develop additional bioinformatic tools for pre-processing of metabolomics raw data, extraction of biologically meaningful mass spectra, and reduction/correction of unwanted variation in large-scale metabolomic data.

Conclusion

In this review, we have highlighted an extensive list of databases that incorporate both MS-based metabolomics, as well as data analysis tools. Clearly, a small, but significant, number of integrated databases, including the full annotation of metabolites, metabolite profiling, and data analysis tools are emerging, such as PlantMetabolomics.org (Quanbeck et al., 2012). In addition to those for plants, metabolome databases for bacteria and animals also exist (see Table 1). Increases in metabolomic data sharing and the improvement of technological capabilities, such as database integration, are likely to play important roles in the future development of plant metabolomics, and facilitate advances plant systems biology.

References

• 1

  AdamsR. P. (2007). Identification of Essential Oil Components by Gas Chromatography-Quadrupole Mass Spectroscopy, 4th Edn.Carol Stream, IL: Allured Pub Corp.

  • Google Scholar
  • View reference in article

• 2

  AfendiF. M.OkadaT.YamazakiM.Hirai-MoritaA.NakamuraY.NakamuraK.et al (2012). KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research. Plant Cell Physiol.53, e1.10.1093/pcp/pcr165

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  AllenD. K.LibourelI. G.Shachar-HillY. (2009). Metabolic flux analysis in plants: coping with complexity. Plant Cell Environ.32, 1241–1257.10.1111/j.1365-3040.2009.01992.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  AritaM.SuwaK. (2008). Search extension transforms wiki into a relational system: a case for flavonoid metabolite database. BioData Min.1, 7.10.1186/1756-0381-1-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  AzizR. K.DevoidS.DiszT.EdwardsR. A.HenryC. S.OlsenG. J.et al (2012). SEED servers: high-performance access to the seed genomes, annotations, and metabolic models. PLoS ONE7:e48053.10.1371/journal.pone.0048053

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  BaisP.MoonS. M.HeK.LeitaoR.DreherK.WalkT.et al (2010). PlantMetabolomics.org: a web portal for plant metabolomics experiments. Plant Physiol.152, 1807–1816.10.1104/pp.109.151027

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  BaisP.Moon-QuanbeckS. M.NikolauB. J.DickersonJ. A. (2012). Plantmetabolomics.org: mass spectrometry-based Arabidopsis metabolomics – database and tools update. Nucleic Acids Res.40, D1216–D1220.10.1093/nar/gkr969

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  BakerJ. M.HawkinsN. D.WardJ. L.LovegroveA.NapierJ. A.ShewryP. R.et al (2006). A metabolomic study of substantial equivalence of field-grown genetically modified wheat. Plant Biotechnol. J.4, 381–392.10.1111/j.1467-7652.2006.00197.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  BarupalD. K.HaldiyaP. K.WohlgemuthG.KindT.KothariS. L.PinkertonK. E.et al (2012). MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity. BMC Bioinformatics13:99.10.1186/1471-2105-13-99

  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  BellS. M.BurgoonL. D.LastR. L. (2012). MIPHENO: data normalization for high throughput metabolite analysis. BMC Bioinformatics13:10.10.1186/1471-2105- 13-10

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  CaldanaC.DegenkolbeT.Cuadros-InostrozaA.KlieS.SulpiceR.LeisseA.et al (2011). High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions. Plant J.67, 869–884.10.1111/j.1365-313X.2011.04640.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  Carreno-QuinteroN.AcharjeeA.MaliepaardC.BachemC. W.MummR.BouwmeesterH.et al (2012). Untargeted metabolic quantitative trait loci analyses reveal a relationship between primary metabolism and potato tuber quality. Plant Physiol.158, 1306–1318.10.1104/pp.111.188441

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  CarrollA. J.BadgerM. R.Harvey MillarA. (2010). The MetabolomeExpress project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets. BMC Bioinformatics11:376.10.1186/1471-2105-11-376

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  CatchpoleG. S.BeckmannM.EnotD. P.MondheM.ZywickiB.TaylorJ.et al (2005). Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc. Natl. Acad. Sci. U.S.A.102, 14458–14462.10.1073/pnas.0503955102

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  ChagoyenM.PazosF. (2012). Tools for the functional interpretation of metabolomic experiments. Brief. Bioinformatics (in press).

  • Pubmed Abstract
  • Google Scholar
  • View reference in article

• 16

  CollakovaE.YenJ. Y.SengerR. S. (2012). Are we ready for genome-scale modeling in plants?Plant Sci.19, 53–70.10.1016/j.plantsci.2012.04.010

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  DaubC. O.KloskaS.SelbigJ. (2003). MetaGeneAlyse: analysis of integrated transcriptional and metabolite data. Bioinformatics19, 2332–2333.10.1093/bioinformatics/btg321

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  De Oliveira Dal’molinC. G.NielsenL. K. (2012). Plant genome-scale metabolic reconstruction and modelling. Curr. Opin. Biotechnol.24, 271–277.10.1016/j.copbio.2012.08.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  De Oliveira Dal’molinC. G.QuekL. E.PalfreymanR. W.BrumbleyS. M.NielsenL. K. (2010). AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol.152, 579–589.10.1104/pp.109.148817

  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  De VosR. C.MocoS.LommenA.KeurentjesJ. J.BinoR. J.HallR. D. (2007). Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc.2, 778–791.10.1038/nprot.2007.95

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  DegtyarenkoK.De MatosP.EnnisM.HastingsJ.ZbindenM.McnaughtA.et al (2008). ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res.36, D344–D350.10.1093/nar/gkm791

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  EspinozaC.DegenkolbeT.CaldanaC.ZutherE.LeisseA.WillmitzerL.et al (2010). Interaction with diurnal and circadian regulation results in dynamic metabolic and transcriptional changes during cold acclimation in Arabidopsis. PLoS ONE5:e14101.10.1371/journal.pone.0014101

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  FahyE.SudM.CotterD.SubramaniamS. (2007). LIPID MAPS online tools for lipid research. Nucleic Acids Res.35, W606–W612.10.1093/nar/gkm324

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  FernieA. R. (2012). Grand challenges in plant systems biology: closing the circle(s). Front. Plant Sci.3:35.10.3389/fpls.2012.00035

  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  FernieA. R.AharoniA.WillmitzerL.StittM.TohgeT.KopkaJ.et al (2011). Recommendations for reporting metabolite data. Plant Cell23, 2477–2482.10.1105/tpc.111.086272

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  FiehnO. (2001). Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genomics2, 155–168.10.1002/cfg.82

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  FiehnO. (2002). Metabolomics – the link between genotypes and phenotypes. Plant Mol. Biol.48, 155–171.10.1023/A:1013713905833

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  FiehnO.RobertsonD.GriffinJ. L.Van Der WerfM.NikolauB.MorrisonN.et al (2007). The metabolomics standards initiative (MSI). Metabolomics3, 175–178.10.1007/s11306-007-0068-0

  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  FrolkisA.KnoxC.LimE.JewisonT.LawV.HauD. D.et al (2010). SMPDB: the small molecule pathway database. Nucleic Acids Res.38, D480–D487.10.1093/nar/gkp1002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 30

  FukushimaA.KusanoM.NakamichiN.KobayashiM.HayashiN.SakakibaraH.et al (2009a). Impact of clock-associated Arabidopsis pseudo-response regulators in metabolic coordination. Proc. Natl. Acad. Sci. U.S.A.106, 7251–7256.10.1073/pnas.0900952106

  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  FukushimaA.KusanoM.RedestigH.AritaM.SaitoK. (2009b). Integrated omics approaches in plant systems biology. Curr. Opin. Chem. Biol.13, 532–538.10.1016/j.cbpa.2009.09.022

  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  GiavaliscoP.KohlK.HummelJ.SeiwertB.WillmitzerL. (2009). 13C isotope-labeled metabolomes allowing for improved compound annotation and relative quantification in liquid chromatography-mass spectrometry-based metabolomic research. Anal. Chem.81, 6546–6551.10.1021/ac900979e

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 33

  GibonY.UsadelB.BlaesingO. E.KamlageB.HoehneM.TretheweyR.et al (2006). Integration of metabolite with transcript and enzyme activity profiling during diurnal cycles in Arabidopsis rosettes. Genome Biol.7, R76.10.1186/gb-2006-7-8-r76

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  GuL.JonesA. D.LastR. L. (2007). LC-MS/MS assay for protein amino acids and metabolically related compounds for large-scale screening of metabolic phenotypes. Anal. Chem.79, 8067–8075.10.1021/ac070938b

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  HallR. D. (2006). Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytol.169, 453–468.10.1111/j.1469-8137.2005.01632.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  HaugK.SalekR. M.ConesaP.HastingsJ.De MatosP.RijnbeekM.et al (2012). MetaboLights – an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res.41, D781–D786.10.1093/nar/gks1004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  HoraiH.AritaM.KanayaS.NiheiY.IkedaT.SuwaK.et al (2010). MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass. Spectrom.45, 703–714.10.1002/jms.1777

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  HuegeJ.SulpiceR.GibonY.LisecJ.KoehlK.KopkaJ. (2007). GC-EI-TOF-MS analysis of in vivo carbon-partitioning into soluble metabolite pools of higher plants by monitoring isotope dilution after ¹³CO₂ labelling. Phytochemistry68, 2258–2272.10.1016/j.phytochem.2007.03.026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  HufskyF.RemptM.RascheF.PohnertG.BockerS. (2012). De novo analysis of electron impact mass spectra using fragmentation trees. Anal. Chim. Acta739, 67–76.10.1016/j.aca.2012.06.021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  HummelJ.StrehmelN.SelbigJ.WaltherD.KopkaJ. (2010). Decision tree supported substructure prediction of metabolites from GC-MS profiles. Metabolomics6, 322–333.10.1007/s11306-010-0198-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 41

  IijimaY.NakamuraY.OgataY.TanakaK.SakuraiN.SudaK.et al (2008). Metabolite annotations based on the integration of mass spectral information. Plant J.54, 949–962.10.1111/j.1365-313X.2008.03434.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 42

  KanehisaM.GotoS.SatoY.FurumichiM.TanabeM. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res.40, D109–D114.10.1093/nar/gks316

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 43

  KaplanF.KopkaJ.HaskellD. W.ZhaoW.SchillerK. C.GatzkeN.et al (2004). Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiol.136, 4159–4168.10.1104/pp.104.052142

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 44

  KastenmullerG.Romisch-MarglW.WageleB.AltmaierE.SuhreK. (2011). metaP-server: a web-based metabolomics data analysis tool. J. Biomed. Biotechnol.2011:839862.10.1155/2011/839862

  • CrossRef
  • Google Scholar
  • View reference in article

• 45

  KindT.FiehnO. (2007). Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics8:105.10.1186/1471-2105-8-105

  • CrossRef
  • Google Scholar
  • View reference in article

• 46

  KindT.WohlgemuthG.DoY.LuY.PalazogluM.ShahbazS.et al (2009). FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem.81, 10038–10048.10.1021/ac9019522

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 47

  KitanoH. (2002). Systems biology: a brief overview. Science295, 1662–1664.10.1126/science.1069492

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 48

  KopkaJ.SchauerN.KruegerS.BirkemeyerC.UsadelB.BergmullerE.et al (2005). GMD@CSB.DB: the Golm metabolome database. Bioinformatics21, 1635–1638.10.1093/bioinformatics/bti236

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 49

  KrumsiekJ.SuhreK.EvansA. M.MitchellM. W.MohneyR. P.MilburnM. V.et al (2012). Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet.8:e1003005.10.1371/journal.pgen.1003005

  • CrossRef
  • Google Scholar
  • View reference in article

• 50

  KumarA.SuthersP. F.MaranasC. D. (2012). MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. BMC Bioinformatics13:6.10.1186/1471-2105-13-6

  • CrossRef
  • Google Scholar
  • View reference in article

• 51

  KumariS.StevensD.KindT.DenkertC.FiehnO. (2011). Applying in-silico retention index and mass spectra matching for identification of unknown metabolites in accurate mass GC-TOF mass spectrometry. Anal. Chem.83, 5895–5902.10.1021/ac2006137

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 52

  KusanoM.RedestigH.HiraiT.OikawaA.MatsudaF.FukushimaA.et al (2011a). Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment. PLoS ONE6:e16989.10.1371/journal.pone.0016989

  • CrossRef
  • Google Scholar
  • View reference in article

• 53

  KusanoM.TabuchiM.FukushimaA.FunayamaK.DiazC.KobayashiM.et al (2011b). Metabolomics data reveal a crucial role of cytosolic glutamine synthetase 1;1 in coordinating metabolic balance in rice. Plant J.66, 456–466.10.1111/j.1365-313X.2011.04506.x

  • CrossRef
  • Google Scholar
  • View reference in article

• 54

  KusanoM.TohgeT.FukushimaA.KobayashiM.HayashiN.OtsukiH.et al (2011c). Metabolomics reveals comprehensive reprogramming involving two independent metabolic responses of Arabidopsis to UV-B light. Plant J.67, 354–369.10.1111/j.1365-313X.2011.04599.x

  • CrossRef
  • Google Scholar
  • View reference in article

• 55

  LaiL.LiberzonA.HennesseyJ.JiangG.QiJ.MesirovJ. P.et al (2012). AraPath: a knowledgebase for pathway analysis in Arabidopsis. Bioinformatics28, 2291–2292.10.1093/bioinformatics/bts421

  • CrossRef
  • Google Scholar
  • View reference in article

• 56

  LangM.StelzerM.SchomburgD. (2011). BKM-react, an integrated biochemical reaction database. BMC Biochem.12:42.10.1186/1471-2091-12-42

  • CrossRef
  • Google Scholar
  • View reference in article

• 57

  LeiZ.HuhmanD. V.SumnerL. W. (2011). Mass spectrometry strategies in metabolomics. J. Biol. Chem.286, 25435–25442.10.1074/jbc.M111.273482

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  LenzE. M.BrightJ.KnightR.WilsonI. D.MajorH. (2004). Cyclosporin A-induced changes in endogenous metabolites in rat urine: a metabonomic investigation using high field 1H NMR spectroscopy. J. Pharm. Biomed. Anal.35, 599–608.10.1016/j.jpba.2004.02.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 59

  LibourelI. G.Shachar-HillY. (2008). Metabolic flux analysis in plants: from intelligent design to rational engineering. Annu. Rev. Plant Biol.59, 625–650.10.1146/annurev.arplant.58.032806.103822

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 60

  LisecJ.MeyerR. C.SteinfathM.RedestigH.BecherM.Witucka-WallH.et al (2008). Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. Plant J.53, 960–972.10.1111/j.1365-313X.2007.03383.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 61

  LisecJ.SchauerN.KopkaJ.WillmitzerL.FernieA. R. (2006). Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat. Protoc.1, 387–396.10.1038/nprot.2006.59

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 62

  LuY.SavageL. J.LarsonM. D.WilkersonC. G.LastR. L. (2011). Chloroplast 2010: a database for large-scale phenotypic screening of Arabidopsis mutants. Plant Physiol.155, 1589–1600.10.1104/pp.110.167015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 63

  MatsudaF.HiraiM. Y.SasakiE.AkiyamaK.Yonekura-SakakibaraK.ProvartN. J.et al (2010). AtMetExpress development: a phytochemical atlas of Arabidopsis development. Plant Physiol.152, 566–578.10.1104/pp.109.148031

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 64

  MatsudaF.OkazakiY.OikawaA.KusanoM.NakabayashiR.KikuchiJ.et al (2012). Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J.70, 624–636.10.1111/j.1365-313X.2012.04903.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 65

  MatsudaF.Yonekura-SakakibaraK.NiidaR.KuromoriT.ShinozakiK.SaitoK. (2009). MS/MS spectral tag-based annotation of non-targeted profile of plant secondary metabolites. Plant J.57, 555–577.10.1111/j.1365-313X.2008.03663.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 66

  MeyerR. C.SteinfathM.LisecJ.BecherM.Witucka-WallH.TorjekO.et al (2007). The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A.104, 4759–4764.10.1073/pnas.0609709104

  • CrossRef
  • Google Scholar

• 67

  Mintz-OronS.MeirS.MalitskyS.RuppinE.AharoniA.ShlomiT. (2012). Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc. Natl. Acad. Sci. U.S.A.109, 339–344.10.1073/pnas.1100358109

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  MocoS.BinoR. J.VorstO.VerhoevenH. A.De GrootJ.Van BeekT. A.et al (2006). A liquid chromatography-mass spectrometry-based metabolome database for tomato. Plant Physiol.141, 1205–1218.10.1104/pp.106.078428

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  MorgatA.CoissacE.CoudertE.AxelsenK. B.KellerG.BairochA.et al (2012). UniPathway: a resource for the exploration and annotation of metabolic pathways. Nucleic Acids Res.40, D761–D769.10.1093/nar/gkr730

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  MorreelK.GoeminneG.StormeV.SterckL.RalphJ.CoppietersW.et al (2006). Genetical metabolomics of flavonoid biosynthesis in Populus: a case study. Plant J.47, 224–237.10.1111/j.1365-313X.2006.02786.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  NakabayashiR.SawadaY.YamadaY.SuzukiM.HiraiM. Y.SakuraiT.et al (2013). Combination of liquid chromatography-fourier transform ion cyclotron resonance-mass spectrometry with (13)c-labeling for chemical assignment of sulfur-containing metabolites in onion bulbs. Anal. Chem.85, 1310–1315.10.1021/ac302733c

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  NeuwegerH.AlbaumS. P.DondrupM.PersickeM.WattT.NiehausK.et al (2008). MeltDB: a software platform for the analysis and integration of metabolomics experiment data. Bioinformatics24, 2726–2732.10.1093/bioinformatics/btn452

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73

  ObataT.FernieA. R. (2012). The use of metabolomics to dissect plant responses to abiotic stresses. Cell. Mol. Life Sci.69, 3225–3243.10.1007/s00018-012-1091-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  OrthJ. D.ThieleI.PalssonB. O. (2010). What is flux balance analysis?Nat. Biotechnol.28, 245–248.10.1038/nbt.1614

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  PenceH. E.WilliamsA. (2010). ChemSpider: an online chemical information resource. J. Chem. Educ.87, 1123–1124.10.1021/ed800105f

  • CrossRef
  • Google Scholar

• 76

  PoolmanM. G.MiguetL.SweetloveL. J.FellD. A. (2009). A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol.151, 1570–1581.10.1104/pp.109.141267

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77

  QuanbeckS. M.BrachovaL.CampbellA. A.GuanX.PereraA.HeK.et al (2012). Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function.”Front. Plant Sci.3:15.10.3389/fpls.2012.00015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78

  RadrichK.TsuruokaY.DobsonP.GevorgyanA.SwainstonN.BaartG.et al (2010). Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst. Biol.4:114.10.1186/1752-0509-4-114

  • CrossRef
  • Google Scholar

• 79

  RascheF.SvatosA.MaddulaR. K.BottcherC.BockerS. (2011). Computing fragmentation trees from tandem mass spectrometry data. Anal. Chem.83, 1243–1251.10.1021/ac101825k

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  RedestigH.KobayashiM.SaitoK.KusanoM. (2011). Exploring matrix effects and quantification performance in metabolomics experiments using artificial biological gradients. Anal. Chem.83, 5645–5651.10.1021/ac200786y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  RedestigH.KusanoM.FukushimaA.MatsudaF.SaitoK.AritaM. (2010). Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis. BMC Bioinformatics11:214.10.1186/1471-2105-11-214

  • CrossRef
  • Google Scholar

• 82

  RicrochA. E.BergeJ. B.KuntzM. (2011). Evaluation of genetically engineered crops using transcriptomic, proteomic, and metabolomic profiling techniques. Plant Physiol.155, 1752–1761.10.1104/pp.111.173609

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  RoessnerU.LuedemannA.BrustD.FiehnO.LinkeT.WillmitzerL.et al (2001). Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell13, 11–29.10.1105/tpc.13.1.11

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  Rojas-ChertoM.PeironcelyJ. E.KasperP. T.Van Der HooftJ. J.De VosR. C.VreekenR.et al (2012). Metabolite identification using automated comparison of high-resolution multistage mass spectral trees. Anal. Chem.84, 5524–5534.10.1021/ac2034216

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85

  RoweH. C.HansenB. G.HalkierB. A.KliebensteinD. J. (2008). Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell20, 1199–1216.10.1105/tpc.108.058131

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  SaitoK.MatsudaF. (2010). Metabolomics for functional genomics, systems biology, and biotechnology. Annu. Rev. Plant Biol.61, 463–489.10.1146/annurev.arplant.043008.092035

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  SakuraiN.AraT.OgataY.SanoR.OhnoT.SugiyamaK.et al (2011). KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data. Nucleic Acids Res.39, D677–D684.10.1093/nar/gkq846

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  SakuraiT.YamadaY.SawadaY.MatsudaF.AkiyamaK.ShinozakiK.et al (2013). PRIMe update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation. Plant Cell Physiol.54, e5.10.1093/pcp/pcs184

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  SansoneS. A.FanT.GoodacreR.GriffinJ. L.HardyN. W.Kaddurah-DaoukR.et al (2007). The metabolomics standards initiative. Nat. Biotechnol.25, 846–848.10.1038/nbt0807-846a

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  SartorM. A.AdeA.WrightZ.StatesD.OmennG. S.AtheyB.et al (2012). Metab2MeSH: annotating compounds with medical subject headings. Bioinformatics28, 1408–1410.10.1093/bioinformatics/bts156

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  SatoS.AritaM.SogaT.NishiokaT.TomitaM. (2008). Time-resolved metabolomics reveals metabolic modulation in rice foliage. BMC Syst. Biol.2:51.10.1186/1752-0509-2-51

  • CrossRef
  • Google Scholar

• 92

  SawadaY.NakabayashiR.YamadaY.SuzukiM.SatoM.SakataA.et al (2012). RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database. Phytochemistry82, 38–45.10.1016/j.phytochem.2012.07.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  ScalbertA.Andres-LacuevaC.AritaM.KroonP.ManachC.Urpi-SardaM.et al (2011). Databases on food phytochemicals and their health-promoting effects. J. Agric. Food Chem.59, 4331–4348.10.1021/jf200591d

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  SchauerN.SemelY.RoessnerU.GurA.BalboI.CarrariF.et al (2006). Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat. Biotechnol.24, 447–454.10.1038/nbt1192

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  SchauerN.SteinhauserD.StrelkovS.SchomburgD.AllisonG.MoritzT.et al (2005). GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett.579, 1332–1337.10.1016/j.febslet.2005.01.029

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  SchreiberF.ColmseeC.CzaudernaT.Grafahrend-BelauE.HartmannA.JunkerA.et al (2012). MetaCrop 2.0: managing and exploring information about crop plant metabolism. Nucleic Acids Res.40, D1173–D1177.10.1093/nar/gkr1004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  SchymanskiE. L.GallampoisC. M.KraussM.MeringerM.NeumannS.SchulzeT.et al (2012). Consensus structure elucidation combining GC/EI-MS, structure generation, and calculated properties. Anal. Chem.84, 3287–3295.10.1021/ac203471y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  SeaverS. M.HenryC. S.HansonA. D. (2012). Frontiers in metabolic reconstruction and modeling of plant genomes. J. Exp. Bot.63, 2247–2258.10.1093/jxb/err371

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  SkogersonK.WohlgemuthG.BarupalD. K.FiehnO. (2011). The volatile compound BinBase mass spectral database. BMC Bioinformatics12:321.10.1186/1471-2105-12-321

  • CrossRef
  • Google Scholar

• 100

  SteinS. E. (1999). An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom.10, 770–781.10.1016/S1044-0305(99)00047-1

  • CrossRef
  • Google Scholar

• 101

  SteinbeckC.ConesaP.HaugK.MahendrakerT.WilliamsM.MaguireE.et al (2012). MetaboLights: towards a new COSMOS of metabolomics data management. Metabolomics8, 757–760.10.1007/s11306-012-0462-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  StittM.FernieA. R. (2003). From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon-nitrogen interactions. Curr. Opin. Biotechnol.14, 136–144.10.1016/S0958-1669(03)00023-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  SulpiceR.PylE. T.IshiharaH.TrenkampS.SteinfathM.Witucka-WallH.et al (2009). Starch as a major integrator in the regulation of plant growth. Proc. Natl. Acad. Sci. U.S.A.106, 10348–10353.10.1073/pnas.0903478106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  SumnerL. W.AmbergA.BarrettD.BealeM. H.BegerR.DaykinC. A.et al (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics3, 211–221.10.1007/s11306-007-0082-2

  • CrossRef
  • Google Scholar

• 105

  SweetloveL. J.RatcliffeR. G. (2011). Flux-balance modeling of plant metabolism. Front. Plant Sci.2:38.10.3389/fpls.2011.00038

  • CrossRef
  • Google Scholar

• 106

  TautenhahnR.ChoK.UritboonthaiW.ZhuZ.PattiG. J.SiuzdakG. (2012). An accelerated workflow for untargeted metabolomics using the METLIN database. Nat. Biotechnol.30, 826–828.10.1038/nbt.2348

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107

  TohgeT.FernieA. R. (2009). Web-based resources for mass-spectrometry-based metabolomics: a user’s guide. Phytochemistry70, 450–456.10.1016/j.phytochem.2009.02.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108

  TokimatsuT.SakuraiN.SuzukiH.OhtaH.NishitaniK.KoyamaT.et al (2005). KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol.138, 1289–1300.10.1104/pp.105.060525

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109

  TsugawaH.TsujimotoY.AritaM.BambaT.FukusakiE. (2011). GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA). BMC Bioinformatics12:131.10.1186/1471-2105-12-131

  • CrossRef
  • Google Scholar

• 110

  UdayakumarM.ChandarD. P.ArunN.MathangiJ.HemavathiK.SeenivasagamR. (2012). PMDB: plant f database – a metabolomic approach. Med. Chem. Res.21, 47–52.10.1007/s00044-010-9506-z

  • CrossRef
  • Google Scholar

• 111

  UranoK.MaruyamaK.OgataY.MorishitaY.TakedaM.SakuraiN.et al (2009). Characterization of the ABA-regulated global responses to dehydration in Arabidopsis by metabolomics. Plant J.57, 1065–1078.10.1111/j.1365-313X.2008.03748.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112

  Urbanczyk-WochniakE.BaxterC.KolbeA.KopkaJ.SweetloveL. J.FernieA. R. (2005). Profiling of diurnal patterns of metabolite and transcript abundance in potato (Solanum tuberosum) leaves. Planta221, 891–903.10.1007/s00425-005-1483-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  UsadelB.PoreeF.NagelA.LohseM.Czedik-EysenbergA.StittM. (2009). A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species. Maize. Plant Cell Environ.32, 1211–1229.10.1111/j.1365-3040.2009.01978.x

  • CrossRef
  • Google Scholar

• 114

  WangY.XiaoJ.SuzekT. O.ZhangJ.WangJ.BryantS. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res.37, W623–W633.10.1093/nar/gkp212

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  WatanabeK.YasugiE.OshimaM. (2000). How to search the glycolipid data in LIPIDBANK for Web: the newly developed lipid database. Jpn. Trend Glycosci. Glycotechnol.12, 175–184.10.4052/tigg.12.175

  • CrossRef
  • Google Scholar

• 116

  WeckwerthW. (2011). Unpredictability of metabolism – the key role of metabolomics science in combination with next-generation genome sequencing. Anal. Bioanal. Chem.400, 1967–1978.10.1007/s00216-011-4948-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  WeckwerthW.LoureiroM. E.WenzelK.FiehnO. (2004). Differential metabolic networks unravel the effects of silent plant phenotypes. Proc. Natl. Acad. Sci. U.S.A.101, 7809–7814.10.1073/pnas.0303415101

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  WishartD. S. (2009). Computational strategies for metabolite identification in metabolomics. Bioanalysis1, 1579–1596.10.4155/bio.09.138

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  WishartD. S.KnoxC.GuoA. C.EisnerR.YoungN.GautamB.et al (2009). HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res.37, D603–D610.10.1093/nar/gkn810

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  WohlgemuthG.HaldiyaP. K.WillighagenE.KindT.FiehnO. (2010). The chemical translation service – a web-based tool to improve standardization of metabolomic reports. Bioinformatics26, 2647–2648.10.1093/bioinformatics/btq476

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121

  WurteleE. S.ChappellJ.JonesA. D.CelizM. D.RansomN.HurM.et al (2012). Medicinal plants: a public resource for metabolomics and hypothesis development. Metabolites2, 1031–1059.10.3390/metabo2041031

  • CrossRef
  • Google Scholar

• 122

  XiaJ.MandalR.SinelnikovI. V.BroadhurstD.WishartD. S. (2012). MetaboAnalyst 2.0 – a comprehensive server for metabolomic data analysis. Nucleic Acids Res.40, W127–W133.10.1093/nar/gks374

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  XiaoJ. F.ZhouB.RessomH. W. (2012). Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends. Analyt. Chem.32, 1–14.10.1016/j.trac.2011.08.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  ZhangP.FoersterH.TissierC. P.MuellerL.PaleyS.KarpP. D.et al (2005). MetaCyc and AraCyc. Plant Physiol.138, 27–37.10.1104/pp.104.057869

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125

  ZhouB.WangJ.RessomH. W. (2012). MetaboSearch: tool for mass-based metabolite identification using multiple databases. PLoS ONE7:e40096.10.1371/journal.pone.0040096

  • Pubmed Abstract
  • CrossRef
  • Google Scholar