Metabolite Profiling in Plants


Advances in analytical chemistry, computation and biotechnology have led to the recent development of methodologies for broad metabolite profiling. Metabolomics now plays a significant role in fundamental plant biology and applied biotechnology. The study of plant metabolism has been one of the first beneficiaries of these technologies, and applications have focused on plant metabolite engineering, functional genomics and physiology. Metabolomics has gained importance in biotechnology applications, as exemplified by quantitative loci analysis. Integration of different broad‐range metabolite profiling into genomics approaches (system biology) driven by metabolome data will bring a change in our understanding of the regulation of metabolic networks and, therefore, facilitate improvements in our understanding of plant breeding.

Key Concepts

  • Metabolomics is defined as the quantitative complement of low‐molecular‐weight metabolites present in a cell under a given set of physiological conditions.
  • Systems biology is the combination of molecular and system‐level studies applying a synergistic approach involving modelling, theory and experiment.
  • Metabolite marker is a metabolite that is objectively measured and evaluated as an indicator of normal biological processes or specific trait.
  • Metabolic phenotype is the products of interactions among a variety of factors such as environment or genetic perturbation.
  • Functional genomics is the field of molecular biology that uses many possible approaches (i.e. genomics, transcriptomics, proteomics and metabolomics) to understand the properties and function of the entirety of an organism's genes and gene products.

Keywords: metabolite profiling; metabolomics; metabolism; functional genomics; mass spectrometry

Figure 1. With current technologies, there is a trade‐off between the number of metabolites that can be measured and the quality of measurement. The term data quality is used to mean the accuracy, sensitivity and specificity of the measurement. Highest quality is achieved with dedicated single assays of individual metabolites. Targeted metabolite profiling methods provide data of reasonable quality on a subset of compounds with similar chemical properties but are limited in their scope. Broad metabolite profiling provides data on many hundreds of different metabolites, but the quality of the individual metabolite measurements can vary substantially. Finally, metabolite fingerprinting methods provide data derived from tens or hundreds of metabolites allowing samples to be classified; however, quantitative data on individual metabolites is not derived, and therefore the data quality is very low. Currently, only a fraction of the metabolome can be assessed with any one approach.
Figure 2. Image of a GC–TOF/MS metabolite profiling facility at the University of Málaga‐Spain. Owing to the robust and stable performance of the mature GC–MS technology, it is possible to run high‐throughput operations with many hundreds of samples processed per day.
Figure 3. Example Arabidopsis chromatogram from GC/GC–MS technology. A total ion chromatogram is visible at the back of the diagram and represents the first dimension of GC separation. The peaks that are formed in the orthagonal direction towards the front of the image represent the results of the second GC separation. The heights of these peaks represent the relative ion abundances for each analyte. These peaks, but not shown, are the unique mass spectra that allow precise determination and monitoring of metabolite identity.


Abdel‐Farid IB, Kim HK, Choi YH, et al. (2007) Metabolic characterization of Brassica rapa leaves by NMR spectroscopy. Journal of Agricultural and Food Chemistry 55: 7936–7943.

Allen DK, Libourel IG and Shachar‐Hill Y (2009) Metabolic flux analysis in plants: coping with complexity. Plant, Cell & Environment 32: 1241–1257.

Beckmann M, Enot DP, Overy D, et al. (2007) Representation, comparison and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato cultivars. Journal of Agricultural and Food Chemistry 55: 3444–3445.

Bielecka M, Watanabe M, Morcuende R, et al. (2015) Transcriptome and metabolome analysis of plant sulfate starvation and resupply provides novel information on transcriptional regulation of metabolism associated with sulfur, nitrogen and phosphorus nutritional responses in Arabidopsis. Frontiers in Plant Science 5: 805.

Caldana C, Degenkolbe T, Cuadros‐Inostroza A, et al. (2011) High‐density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions. Plant Journal 67: 869–884.

Carrari F, Baxter C, Usadel B, et al. (2006) Integrated analysis of metabolite and transcript levels reveals the metabolic shifts that underlie tomato fruit development and highlight regulatory aspects of metabolic network behavior. Plant Physiology 142: 1380–1396.

Chan EKF, Rowe HC and Kliebenstein DJ (2010) Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome‐wide association mapping. Genetics 185: 991–1007.

Chan EK, Rowe HC, Corwin JA, et al. (2011) Combining genome‐wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biology 9: e1001125.

Chauvin A, Caldelari D, Wolfender JL, et al. (2013) Four 13‐ lipoxygenases detection of metabolite induction in fungal co‐ cultures on solid media by high‐throughput differential ultra‐high pressure liquid chromatography‐time‐of‐flight mass spectrometry fingerprinting contribute to rapid jasmonate synthesis in wounded Arabidopsis thaliana leaves: a role for lipoxygenase 6 in responses to long‐distance wound signals. New Phytologist 197: 566–575.

Choi MY, Choi W, Park JH, et al. (2010) Determination of coffee origins by integrated metabolomic approach of combining multiple analytical data. Food Chemistry 121: 1260–1268.

Davey JW, Hohenlohe PA, Etter PD, et al. (2011) Genome‐wide genetic marker discovery and genotyping using next‐generation sequencing. Nature Reviews Genetics 12: 499–510.

Diels L, van der Lelie N, Bastiaens L (2002) New developments in treatment of heavy metal contaminated soils. Reviews in Environmental Science and Biotechnology 1: 75–82.

Fait A, Hanhineva K, Beleggia R, et al. (2008) Reconfiguration of the achene and receptacle metabolic network during strawberry fruit development. Plant Physiology 148: 730–750.

Fernie AR, Aharoni A, Willmitzer L, et al. (2011) Recommendations for reporting metabolite data. Plant Cell 23: 2477–2482.

Fiehn O (2001) Combining genomics, metabolome analysis, and biochemical modeling to understand metabolic networks. Comparative and Functional Genomics 2: 155–168.

Fiehn O, Sumner LW, Rhee KW, et al. (2007) Minimum reporting standards for plant biology context information in metabolomic studies. Metabolomics 3: 195–201.

Fraser PD, Enfissi EM, Goodfellow M, et al. (2007) Metabolite profiling of plant carotenoids using the matrix‐ assisted laser desorption ionization time‐of‐flight mass spectrometry. Plant Journal 49: 552–564.

Goodacre R, Vaidyanathan S, Dunn WB, et al. (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends in Biotechnology 22: 245–252.

Hawkesford AJ, Buchner P, Howarth JR, et al. (2005) Understanding the regulation of sulfur nutrition ‐ from sulfate transporter genes to the field. In: Sito LJ, De Kok I, Stulen MJ, Hawkesford E, Schnug A and Rennenberg H (eds) Sulfur Transport and Assimilation in Plants in the Post Genomic Era. Leiden: Backhuys Publishers.

Hernandez G, Ramirez M, Valdes‐Lopez O, et al. (2007) Phosphorus stress in common bean: root transcript and metabolic responses. Plant Physiology 144: 752–767.

Hirai MY, Yano M, Goodebowe DB, et al. (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proceedings of the National Academy of Sciences 101: 10205–10210.

Hirai MY, Klein M, Fujikawa Y, et al. (2005) Elucidation of gene‐to‐gene and metabolite‐to‐gene networks in arabidopsis by integration of metabolomics and transcriptomics. Journal of Biological Chemistry 280: 25590–25595.

Hirai MY, Sugiyama K, Sawada Y, et al. (2007) Omics‐based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proceedings of the National Academy of Sciences 104: 6478–6483.

Hoefgen R and Nikiforova VJ (2007) Metabolomics integrated with transcriptomics: assessing systems response to sulfur‐deficiency stress. Physiologia Plantarum 132: 190–198.

Joseph B, Corwin JA, Zust T, et al. (2013) Hierarchical nuclear and cytoplasmic genetic architectures for plant growth and defense within Arabidopsis. Plant Cell 25: 1929–1945.

Junot C, Fenaille F, Colsch B, et al. (2014) High resolution mass spectrometry based techniques at the crossroads of metabolomics pathways. Mass Spectrometry Reviews 33: 471–500.

Keurentjes JJB, Fu J, Vos CHR, et al. (2006) The genetics of plant metabolism. Nature Genetics 38: 842–849.

Kim HK, Choi YH and Verpoorte R (2009) NMR‐based metabolomic analysis of plants. Nature Protocols 5: 536–549.

Kim HK, Choi YH and Verpoorte R (2011) NMR‐based plant metabolomics: where do we stand, where do we go? Trends in Biotechnology 29: 267–275.

Kliebenstein DJ, Gershenzon J and Mitchell‐Olds T (2001) Comparative quantitative trait loci mapping of aliphatic, indolic and benzylic glucosinolate production in Arabidopsis thaliana leaves and seeds. Genetics 159: 359–370.

Kusano M, Fukushima A, Kobayashi M, et al. (2007) Application of a metabolomic method combining one‐dimensional and two‐dimensional gas chromatography‐time‐of‐flight/mass spec‐trometry to metabolic phenotyping of natural variants in rice. Journal of Chromatography B 855: 7179.

Li QQ, Zhao CC, Li YY, et al. (2010) Liquid chromatography/mass spectrometry‐based metabolic profiling to elucidate chemical differences of tobacco leaves between Zimbabwe and China. Journal of Separation Science 34: 119–126.

López‐Blanco MC, Reboreda‐Rodriguez B, Cancho‐Grande B, et al. (2002) Optimization of solid‐phase extraction and solid‐phase microextraction for the determination of alpha‐ and beta‐endosulfan in water by gas chromatography‐electron‐capture detection. Journal of Chromatography A 976: 293–299.

Menda N, Semel Y, Peled D, et al. (2004) In silico screening of a saturated mutation library of tomato. Plant Journal 38: 861–872.

Mishra BK, Meena KK, Dubey PN et al. (2016) Influence on yield and quality of fennel (Foeniculum vulgare Mill.) grown under semi‐arid saline soil, due to application of native phosphate solubilizing rhizobacterial isolates. Ecological Engineering 97: 327–333.

Nikiforova VJ, Kopka J, Tolstikov V, et al. (2005a) Systems rebalancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of Arabidopsis plants. Plant Physiology 138: 304–318.

Nikiforova VJ, Daub CO, Hesse H, et al. (2005b) Integrative gene‐metabolite network with implemented causality deciphers informational fluxes of sulphur stress response. Journal of Experimental Botany 56: 1887–1896.

Osorio S, Alba R, Damasceno CMB, et al. (2011) Systems biology of tomato fruit development: Combined transcript, protein, and metabolite analysis of tomato transcription factor (nor, rin) and ethylene receptor (Nr) mutant reveals novel regulatory interactions. Plant Physiology 157: 405–425.

Osorio S, Phuc TD and Fernie AR (2012) Profiling primary metabolism of tomato fruit with gas chromatography/mass spectrometry. Methods in Molecular Biology: 101–109.

Riedelsheimer C, Lisec J, Czedik‐Eysenberg A, et al. (2012) Genome‐wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proceedings of the National Academy of Sciences 109: 8872–8877.

Rivas‐Ubach A, Sardans J, Perez‐Trujillo M, et al. (2012) Strong relationship between elemental stoichiometry and metabolome in plants. Proceedings of the National Academy of Sciences 109: 4181–4186.

Rocca‐Serra P, Salek RM, Arita M, et al. (2016) Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics 12: 1–13.

Roessner U, Wagner C, Kopka J, et al. (2000) Simultaneous analysis of metabolites in potato tuber by gas chromatography ‐ mass spectrometry. Plant Journal 23: 131–142.

Roessner U, Willmitzer L and Fernie AR (2001) High‐resolution metabolic phenotyping of genetically and environmentally diverse potato tuber systems. Identification of phenocopies. Plant Physiology 127: 749–764.

Rohde A, Morreel K, Ralph J, et al. (2004) Molecular phenotyping of the pal1 and pal2 mutants of Arabidopsis thaliana reveals far‐reaching consequences on phenylpropanoid, amino acid, and carbohydrate metabolism. Plant Cell 16: 2749–2771.

Salek RM, Neumann S, Schober D, et al. (2015) Coordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics 11: 1587–1597.

Sato S, Soga T, Nishioka T and Tomita M (2004) Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection. Plant Journal 40: 151–163.

Sauter H, Lauer M and Fritsch H (1991) Metabolic profiling of plants – a new diagnostic technique. In: Baker DR, Fenyes JG and Moberg WK (eds) Synthesis and Chemistry of Agrochemicals II, pp. 288–299. Washington, DC: American Chemical Society.

Schauer N, Semel Y, Roessner U, et al. (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology 24: 447–454.

Sumner LW, Amberg A, Barret D, et al. (2007) Proposed minimum reporting standards for chemical analysis. (Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)). Metabolomics 3: 211–221.

Teng Q, Huang W, Collette T, et al. (2009) A direct cell quenching method for cell‐culture based metabolomics. Metabolomics 5: 199–208.

Tieman DM, Zeigler M, Schmelz EA, et al. (2005) Identification of loci affecting flavour volatile emissions in tomato fruits. Journal of Experimental Botany 57: 887–896.

Tieman DM, Taylor M, Schauer N, et al. (2006) Tomato aromatic amino acid decarboxylases participate in synthesis of the flavor volatiles 2‐phenylethanol and 2‐phenylacetaldehyde. Proceedings of the National Academy of Sciences 103: 8287–8292.

Tohge T, Nishiyama Y, Hirai MY, et al. (2005) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over‐expressing an MYB transcription factor. Plant Journal 42: 218–235.

Vallarino JG, Gainza‐Cortés F, Verdugo‐Alegría C, et al. (2014) Abiotic stresses differentially affect the expression of O‐methyltransferase genes related to methoxypyrazine biosynthesis in seeded and parthenocarpic fruits of Vitis vinífera (L.). Food Chemistry 154: 117–126.

Vansuyt G, Robin A, Briat JF, et al. (2007) Iron acquisition from Fe‐pyoverdine by Arabidopsis thaliana. Molecular Plant Microbe Interaction 20: 441–447.

Watanabe M, Balazadeh S, Tohge T, et al. (2013) Comprehensive dissection of spatiotemporal metabolic shift in primary, secondary, and lipid metabolism during developmental senescence in Arabidopsis. Plant Physiology 162: 1290–1310.

Weckworth W, Loureiro ME, Wenzel K, et al. (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proceedings of the National Academy of Sciences 101 (20): 7809–7814.

Yonekura‐Sakakibara K, Tohge T, Matsuda F, et al. (2008) Comprehensive flavonol profiling and transcriptome coexpression analysis leading to decoding gene‐metabolite correlations in Arabidopsis. Plant Cell 20: 2160–2176.

Zamboni A, Di Carli M, Guzzo F, et al. (2010) Identification of putative stage‐specific grapevine berry biomarkers and omics data integration into networks. Plant Physiology 154: 1439–1459.

Zanor MI, Osorio S, Nunes‐Nesi A, et al. (2009) RNA interference of LIN5 in tomato confirms its role in controlling Brix content, uncovers the influence of sugars on the levels of fruit hormones, and demonstrates the importance of sucrose cleavage for normal fruit development and fertility. Plant Physiology 150: 1204–1218.

Zheng P, Allen WB, Poesler K, et al. (2008) A phenylalanine in DGAT is a key determinant of oil content and composition in maize. Nature Genetics 40 (3): 367–372.

Zorrilla‐Fontanesi Y, Rambla JL, Cabeza J, et al. (2012) Genetic analysis of strawberry fruit aroma and identification of O‐methyltransferase FaOMT as the locus controlling natural variation in mesifurane content. Plant Physiology 159: 851–870.

Further Reading

Fernie AR, Trethewey RN, Krotzky AJ and Willmitzer L (2004) Metabolite profiling: from diagnostics to systems biology. Nature Reviews Molecular Cell Biology 5: 763–769.

Hall RD (2006) Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytologist 169: 453–468.

Johanningsmeier SD, Harris GK and Klevorn CM (2016) Metabolomic technologies for improving the quality of food: practice and promise. Annual Review of Food Science and Technology 7: 413–438.

Osorio S and Fernie AR (2012) Plant system biology. Reviews in Cell Biology and Molecular Medicine. DOI: 10.1002/3527600906.

Stitt M and Fernie AR (2003) From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon‐nitrogen interactions. Current Opinion in Biotechnology 14: 136–144.

Sumner LW, Mendes P and Dixon RA (2003) Plant metabolomics: large‐scale phytochemistry in the functional genomics era. Phytochemistry 62: 817–836.

Sweetlove LJ and Fernie AR (2005) Regulation of metabolic networks: understanding metabolic complexity in the systems biology era. New Phytologist 168: 9–24.

Tohge T and Fernie AR (2009) Web‐based resources for mass‐spectrometry‐based metabolomics: a user's guide. Phytochemistry 70 (4): 450–459.

Tohge T and Fernie AR (2015) Metabolomics‐inspired insight into developmental, environmental and genetic aspects of tomato fruit chemical composition and quality. Plant Cell Physiology 56 (9): 1681–1696.

Wurtzel ET and Kutchan TM (2016) Plant metabolism, the diverse chemistry set of the future. Science 353 (6305): 1232–1236.

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
Osorio, Sonia, and Vallarino, José G(Aug 2017) Metabolite Profiling in Plants. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0020105.pub2]