Understanding the function and regulation of plant secondary metabolism through metabolomics approaches

  • Jay C. Delfin
  • Mutsumi Watanabe
  • Takayuki Tohge


Plant secondary metabolism consists of highly complex pathways by the fact that its structures and levels are largely divergent between different tissues, growth stages, species and environmental conditions. The metabolomics of plant secondary metabolism has been developed for both functional genomics and analysis of physiological processes via multi-platform metabolite profiling as well as integration analysis with other omics data. Whilst recent efforts and significant technological advances of mass spectrometry have solved common analytical problems of metabolite detection at higher sensitivity, the challenge of improving (i) the coverage of detected compounds and (ii) peak annotation to enable functional genomics approach still persists. Here, we review progress made following this approach with research examples and strategies of gene functional analyses in plant species. Taken together, these examples prove that the current strategy of metabolomics focusing on plant secondary metabolism via integration with genetics and transcriptomics is a highly effective tool to understand the function and regulation of metabolic complexity in plants.


Plant secondary metabolism Metabolomic analysis Functional genomics Metabolic regulation 



We gratefully acknowledge the funding support of the Nara Institute of Science and Technology (NAIST) and Japanese Government (MEXT) scholarships for JCD.


  1. Aarabi F, Kusajima M, Tohge T, Konishi T, Gigolashvili T, Takamune M, Sasazaki Y, Watanabe M, Nakashita H, Fernie AR, Saito K, Takahashi H, Hubberten HM, Hoefgen R, Maruyama-Nakashita A (2016) Sulfur deficiency-induced repressor proteins optimize glucosinolate biosynthesis in plants. Sci Adv 2:e1601087PubMedPubMedCentralCrossRefGoogle Scholar
  2. Achnine L, Huhman DV, Farag MA, Sumner LW, Blount JW, Dixon RA (2005) Genomics-based selection and functional characterization of triterpene glycosyltransferases from the model legume Medicago truncatula. Plant J 41:875–887PubMedCrossRefGoogle Scholar
  3. Aharoni A, Keizer LC, Bouwmeester HJ, Sun Z, Alvarez-Huerta M, Verhoeven HA, Blaas J, van Houwelingen AM, De Vos RC, van der Voet H, Jansen RC, Guis M, Mol J, Davis RW, Schena M, van Tunen AJ, O’Connell AP (2000) Identification of the SAAT gene involved in strawberry flavor biogenesis by use of DNA microarrays. Plant Cell 12:647–662PubMedPubMedCentralCrossRefGoogle Scholar
  4. Aharoni A, Ric de Vos CH, Verhoeven HA, Maliepaard CA, Kruppa G, Bino R, Goodenowe DB (2002) Nontargeted metabolome analysis by use of Fourier Transform Ion Cyclotron Mass Spectrometry. OMICS 6:217–234PubMedCrossRefGoogle Scholar
  5. Alejandro S, Lee Y, Tohge T, Sudre D, Osorio S, Park J, Bovet L, Lee Y, Geldner N, Fernie AR, Martinoia E (2012) AtABCG29 is a monolignol transporter involved in lignin biosynthesis. Curr Biol 22(13):1207–1212PubMedCrossRefGoogle Scholar
  6. Alseekh S, Fernie AR (2018) Metabolomics 20 years on: what have we learned and what hurdles remain? Plant J 94:933–942PubMedCrossRefGoogle Scholar
  7. Alseekh S, Tohge T, Wendenberg R, Scossa F, Omranian N, Li J, Kleessen S, Giavalisco P, Pleban T, Mueller-Roeber B, Zamir D, Nikoloski Z, Fernie AR (2015) Identification and mode of inheritance of quantitative trait loci for secondary metabolite abundance in tomato. Plant Cell. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Amaral MN, Souza GM (2017) The challenge to translate OMICS data to whole plant physiology: the context matters. Front Plant Sci 8:2146. CrossRefPubMedPubMedCentralGoogle Scholar
  9. Atwell S, Huang YS, Vilhjalmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT et al (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465(7298):627–631PubMedPubMedCentralCrossRefGoogle Scholar
  10. Bielecka M, Watanabe M, Morcuende R, Scheible WR, Hawkesford MJ, Hesse H, Hoefgen R (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. Front Plant Sci 5:805PubMedPubMedCentralCrossRefGoogle Scholar
  11. Brunetti C, George RM, Tattini M, Field K, Davey MP (2013) Metabolomics in plant environmental physiology. J Exp Bot 64:4011–4020PubMedCrossRefGoogle Scholar
  12. Caldana C, Degenkolbe T, CuadrosInostroza A, Klie S, Sulpice R, Leisse A et al (2011) High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions. Plant J 67:869–884PubMedCrossRefGoogle Scholar
  13. Chen W, Gong L, Guo Z, Wang W, Zhang H, Liu X, Yu S, Xiong L, Luo J (2013) A novel integrated method for large-scale detection, identification, and quantification of widely targeted metabolites: application in the study of rice metabolomics. Mol Plant 6(6):1769–1780PubMedCrossRefGoogle Scholar
  14. Chen W, Wang W, Peng M, Gong L, Gao Y, Wan J, Wang S, Shi L, Zhou B, Li Z, Peng X, Yang C, Qu L, Liu X, Luo J (2016) Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat Commun 7:12767. CrossRefPubMedPubMedCentralGoogle Scholar
  15. Davey MP, Woodward FI, Quick WP (2009) Intraspecfic variation in cold-temperature metabolic phenotypes of Arabidopsis lyrata ssp. petraea. Metabolomics 5:138–149CrossRefGoogle Scholar
  16. Dixon RA, Strack D (2003) Phytochemistry meets genome analysis, and beyond. Phytochemistry 62:815–816PubMedCrossRefGoogle Scholar
  17. Dong X, Gao Y, Chen W, Wang W, Gong L, Liu X, Luo J (2015) Spatiotemporal distribution of phenolamides and the genetics of natural variation of hydroxycinnamoyl spermidine in rice. Mol Plant 8(1):111–121PubMedCrossRefGoogle Scholar
  18. Farag MA, Huhman DV, Dixon RA, Sumner LW (2008) Metabolomics reveals novel pathways and differential mechanistic and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago trunculata cell cultures. Plant Physiol 146:387–402PubMedPubMedCentralCrossRefGoogle Scholar
  19. Farre EM, Tech S, Trethewey RN, Fernie AR, Willmitzer L (2006) Subcellular pyrphosphate metabolism in developing tubers of potato (Solanum tuberosum). Planta Mol Biol 62:165–179CrossRefGoogle Scholar
  20. Feng J, Long Y, Shi L, Shi J, Barker G, Meng J (2012) Characterization of metabolite quantitative trait loci and metabolic networks that control glucosinolate concentration in the seeds and leaves of Brassica napus. New Phytol 193:96–108. CrossRefPubMedGoogle Scholar
  21. Fernie AR, Schauer N (2008) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet 25:39–48PubMedCrossRefGoogle Scholar
  22. Fernie AR, Stitt M (2012) On the discordance of metabolomics with proteomics and transcriptomics: coping with increasing complexity in logic, chemistry, and network interactions. Plant Physiol 158:1139–1145PubMedPubMedCentralCrossRefGoogle Scholar
  23. Fernie AR, Tohge T (2017) The genetics of plant metabolism. Annu Rev Genet 52:287–310CrossRefGoogle Scholar
  24. Fernie AR, Geigenberger P, Stitt M (2005) Flux an important, but neglected component of functional genomics. Curr Opin Plant Biol 8:174–182PubMedCrossRefGoogle Scholar
  25. Fernie AR, Aharoni A, Willmitzer L, Stitt M, Tohge T, Kopka J, Carroll AJ, Saito K, Fraser PD, DeLuca V (2011) Recommendations for reporting metabolite data. Plant Cell 23:2477–2482PubMedPubMedCentralCrossRefGoogle Scholar
  26. Fiehn O (2002) Metabolomics—the link between genotype and phenotype. In: Town C (ed) Functional genomics. Springer, DordrechtGoogle Scholar
  27. Fiehn O, Robertson D, Griffin J, Van Der Werf M, Nikolau B, Morrison N, Sumner LW, Goodacre R, Hardy NW, Taylor C, Fostel J, Kristal B, Kaddurah-Daouk R, Mendes P, van Ommen B, Lindon JC, Sansone SA (2007) The metabolomics standards initiative (MSI). Metabolomics 3(3):175–178CrossRefGoogle Scholar
  28. Fiehn O, Wohlgemuth G, Scholz M, Kind T, Lee DY, Lu Y, Moon S, Nikolau B (2008) Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J 53(4):691–704PubMedCrossRefGoogle Scholar
  29. Fridman E, Wang J, Iijima Y, Froehlich JE, Gang DR, Ohlrogge J, Pichersky E (2005) Metabolic, genomic, and biochemical analyses of glandular trichomes from the wild tomato species Lycopersicon hirsutum identify a key enzyme in the biosynthesis of methylketones. Plant Cell 17(4):1252–1267PubMedPubMedCentralCrossRefGoogle Scholar
  30. Gago J, Fernie AR, Nikoloski Z, Tohge T, Martorell S, Escalona JM, Ribas-Carbó M, Flexas J, Medrano H (2017) Integrative field scale phenotyping for investigating metabolic components of water stress within a vineyard. Plant Methods 13:90PubMedPubMedCentralCrossRefGoogle Scholar
  31. Gong L, Chen W, Gao Y, Liu X, Zhang H, Xu C, Yu S, Zhang Q, Luo J (2013) Genetic analysis oft he metabolome exemplified using a rice population. Proc Nat Acad Sci USA 110(50):20320–20325PubMedCrossRefGoogle Scholar
  32. Goossens A, Hakkinen ST, Laakso I, Seppanen-Laakso T, Biondi S, De Sutter V, Lammertyn F, Nuutila AM, Soderlund H, Zabeau M, Inze D, Oksman-Caldentey KM (2003) A functional genomics approach toward the understanding of secondary metabolism in plant cells. Proc Natl Acad Sci USA 100:8595–8600PubMedCrossRefGoogle Scholar
  33. Hall R, Beale M, Fiehn O, Hardy N, Sumner L, Bino R (2002) Plant metabolomics: the missing link in functional genomic strategies. Plant Cell 14:1437–1440PubMedPubMedCentralCrossRefGoogle Scholar
  34. Heinzle E, Matsuda F, Miyagawa H, Wakasa K, Nishioka T (2007) Estimation of metabollic fluxes, expression levels and metabolite dynamics of a secondary metabolic pathway in potato using label pulse-feeding experiments combined with kinetic network modelling and simulation. Plant J 50:176–187PubMedCrossRefGoogle Scholar
  35. Hill CB, Taylor JD, Edwards J, Mather D, Langridge P, Bacic A, Roessner U (2015) Detection of QTL for metabolic and agronomic traits in wheat with adjustments for variation at genetic loci that affect plant phenology. Plant Sci 233:143–154PubMedCrossRefGoogle Scholar
  36. Hirai MY, Sugiyama K, Sawada Y, Tohge T, Obayashi T, Suzuki A, Araki R, Sakurai N, Suzuki H, Aoki K, Goda H, Nishizawa OI, Shibata D, Saito K (2007) Omics-based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proc Natl Acad Sci USA 104(15):6478–6483PubMedCrossRefGoogle Scholar
  37. Hirayama T, Shinozaki K (2010) Research on plant abiotic stress responses in the post-genome era: past, present and future. Plant J 61:1041–1052PubMedCrossRefGoogle Scholar
  38. Hu C, Shi J, Quan S, Cui B, Kleessen S, Nikoloski Z, Tohge T, Alexander D, Guo L, Lin H, Wang J, Cui X, Rao J, Luo Q, Fernie AR, Zhang D (2014) Metabolic variation between japonica and indica rice cultivars as revealed by non-targeted metabolomics. Sci Rep 4:5067PubMedPubMedCentralCrossRefGoogle Scholar
  39. Ishihara H, Tohge T, Viehoever P, Fernie AR, Weisshaar B, Stracke R (2016) Natural variation in flavonol 3-O-gentiobioside 7-O-rhamnoside content in A. thaliana is determined by a glycoside hydrolase type flavonol glucosyltransferase BGLU6. J Exp Bot 67:1505–1517PubMedCrossRefPubMedCentralGoogle Scholar
  40. Keurentjes JJB (2009) Genetical metabolomics: closing in on phenotypes. Curr Opin Plant Biol 12:223–230PubMedCrossRefGoogle Scholar
  41. Kliebenstein D, Lambrix V, Reichelt M, Gershenzon J, Mitchell-Olds T (2001a) Gene duplication and the diversification of secondary metabolism: side chain modification of glucosinolates in Arabidopsis thaliana. Plant Cell 13:681–693PubMedPubMedCentralCrossRefGoogle Scholar
  42. Kliebenstein DJ, Gershenzon J, Mitchell-Olds T (2001b) Comparative quantitative trait loci mapping of aliphatic, indolic and benzylic glucosinolate production in Arabidopsis thaliana leaves and seeds. Genetics 159:359–370PubMedPubMedCentralGoogle Scholar
  43. Kruger NJ, Ratcliffe RG (2009) Insights into plant metabolic networks from steady-state metabolic flux analysis. Biochimie 91:697–702PubMedPubMedCentralCrossRefGoogle Scholar
  44. Kumar A, Mosa KA, Ji L, Kage U, Dhokane D, Karre S, Madalageri D, Pathania N (2018) Metabolomics-assisted biotechnological interventions for developing plant-based functional foods and nutraceuticals. Crit Rev Food Sci Nutr 58:1791–1807PubMedCrossRefGoogle Scholar
  45. Kusano M, Fukushima A, Kobayashi M, Hayashi N, Jonsson P et al (2007) Application of a metabolomics method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. J Chromatogr B 855:71–79CrossRefGoogle Scholar
  46. Kusano M, Tohge T, Fukushima A, Kobayashi M, Hayashi N, Otsuki H, Kondou Y, Goto H, Kawashima M, Matsuda F, Niida R, Matsui M, Saito K, Fernie AR (2011) Metabolomics reveals comprehensive reprogramming involving two independent metabolic responses of Arabidopsis to UV-B light. Plant J 67:354–369. CrossRefPubMedGoogle Scholar
  47. Libourel IG, Shachar-Hill Y (2008) Metabolic flux analysis in plants: from intelligent design to rational engineering. Annu Rev Plant Biol 59:625–650PubMedCrossRefGoogle Scholar
  48. Matsuda F, Shinbo Y, Oikawa A, Hirai MY, Fiehn O et al (2009a) Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches. PLoS ONE 4:e7490PubMedPubMedCentralCrossRefGoogle Scholar
  49. Matsuda F, Yonekura-Sakakibara K, Niida R, Kuromori T, Shinozaki K, Saito K (2009b) MS/MS spectal tag-based annotation of non-targeted profile of plant secondary metabolites. Plant J 57:555–577. CrossRefPubMedGoogle Scholar
  50. Matsuda F, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R, Kikuchi J, Yonemaru JI, Ebana K, Yano M, Saito K (2012) Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J 70:624–636PubMedCrossRefGoogle Scholar
  51. Matsuda F, Nakabayashi R, Yang Z, Okazaki Y, Yonemaru JI, Ebana K, Yano M, Saito K (2015) Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism. Plant J 81:13–23PubMedCrossRefGoogle Scholar
  52. Miyagi A, Takahara K, Takahashi H, Kawai-Yamada M, Uchimiya H (2010) Targeted metabolomics in an intrusive weed, Rumex obtusifolius L., grown under different environmental conditions reveals alterations of organ related metabolite pathway. Metabolomics 6:497–510CrossRefGoogle Scholar
  53. Moreno-Risueno MA, Busch W, Benfey PN (2009) Omics meet networks—using systems approaches to infer regulatory networks in plants. Curr Opin Plant Biol 13:1–6CrossRefGoogle Scholar
  54. Morreel K, Saeys Y, Dima O, Lu F, Van de Peer Y, Vanholme R, Ralph J, Vanholme B, Boerjan W (2014) Systematic structural characterization of metabolites in Arabidopsis via candidate substrate-product pair networks. Plant Cell. CrossRefPubMedPubMedCentralGoogle Scholar
  55. Obata T, Fernie AR (2012) The use of metabolomics to dissect plant responses to abiotic stresses. Cell Mol Life Sci 69:3225–3243PubMedPubMedCentralCrossRefGoogle Scholar
  56. Ohnishi M, Mimura T, Tsujimura T, Mitsuhashi N, Washitani-Nemoto S et al (2007) Inorganic phosphate uptake in intact vacuoles isolated from suspension-cultured cells of Catharanthus roseus (L.) G. Don under varying Pi status. Planta 225:711–718PubMedCrossRefGoogle Scholar
  57. Okazaki Y, Saito K (2012) Recent advances of metabolomics in plant biotechnology. Plant Biotechnol Rep 6:1–15PubMedCrossRefGoogle Scholar
  58. Owens BF, Lipka AE, Lundback MM, Tiede T, Diepenbrock CH, Kandianis CB, Kim E, Cepela J, Hernandez MM, Buell CR, Buckler ES, DellaPenna D, Gore MA, Rocheford T (2014) A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels. Genetics 198(4):1699–1716PubMedPubMedCentralCrossRefGoogle Scholar
  59. Peng M, Shahzad R, Gul A, Subthain H, Shen S, Lei L, Zheng Z, Zhou J, Lu D, Wang S, Nishawy E, Liu X, Tohge T, Fernie AR, Luo J (2017) Differentially evolved glucosyltransferases determine natural variation of rice flavone accumulation and UV-tolerance. Nat Commun 8:1975PubMedPubMedCentralCrossRefGoogle Scholar
  60. Perez de Souza L, Naake T, Tohge T, Fernie AR (2017) From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web-resources for mass spectral plant metabolomics. Gigascience 6:1–20PubMedPubMedCentralCrossRefGoogle Scholar
  61. Piasecka A, Sawikowska A, Kuczynska A, Ogrodowicz P, Mikolajczak K, Krystkowiak K, Gudys K, Guzy-Wrobelska J, Krajewski P, Kachlicki P (2017) Drought-related econdary metabolites of barley (Hordeum vulgare L.) leaves and their metabolomic quantitative trait loci. Plant J 89:898–913. CrossRefPubMedGoogle Scholar
  62. Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, Sauer U, Picotti P (2018) A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172:358–372.e23PubMedCrossRefGoogle Scholar
  63. Riedelsheimer C, Lisec J, Eysenberg AC, Sulpice R, Flis A, Grieder C, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Nat Acad Sci USA 109(23):8872–8877PubMedCrossRefGoogle Scholar
  64. Roessner-Tunali U, Liu JL, Leisse A, Balbo I, Perez-Melis A, Willmitzer L, Fernie AR (2004) Kinetics of labelling of organic and amino acids in potato tubers by gas chromatography-mass spectrometry following incubation in (13)C labelled isotopes. Plant J 39(4):668–679PubMedCrossRefGoogle Scholar
  65. Rohrmann J, Tohge T, Alba R, Osorio S, Caldana C, McQuinn R, Arvidsson S, van der Merwe MJ, Riaño-Pachón DM, Mueller-Roeber B, Fei Z, Nesi AN, Giovannoni JJ, Fernie AR (2011) Combined transcription factor profiling, microarray analysis and metabolite profiling reveals the transcriptional control of metabolic shifts occurring during tomato fruit development. Plant J 68(6):999–1013PubMedCrossRefGoogle Scholar
  66. Routaboul JM, Dubos C, Beck G, Marquis C, Bidzinski P, Loudet O, Lepiniec L (2012) Metabolite profiling and quantitative genetics of natural variation for flavonoids in Arabidopsis. J Exp Bot 63(10):3749–3764PubMedPubMedCentralCrossRefGoogle Scholar
  67. Ryan D, Robards K (2006) Metabolomics: the greatest omics of them all? Anal Chem 78:7954–7958PubMedCrossRefGoogle Scholar
  68. Saito K, Matsuda F (2010) Metabolomics for functional genomics, systems biology, and biotechnology. Annu Rev Plant Biol 61:463–489PubMedCrossRefGoogle Scholar
  69. Sampaio BL, Edrada-Ebel R, Da Costa FB (2016) Effect of the environment on the secondary metabolic profile of Tithonia diversifolia: a model for environmental metabolomics of plants. Sci Rep 6:29265. CrossRefPubMedPubMedCentralGoogle Scholar
  70. Sawada Y, Akiyama K, Sakata A, Kuwahara A, Otsuki H, Sakurai T, Saito K, Hirai MY (2009) Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants. Plant Cell Physiol 50(1):37–47. CrossRefPubMedGoogle Scholar
  71. Schauer N, Fernie AR (2006) Plant metabolomics: towards biological function and mechanism. Trend Plant Sci. CrossRefGoogle Scholar
  72. Schwab W (2003) Metabolome diversity: too few genes, too many metabolites? Phytochemistry 62:837–849PubMedCrossRefGoogle Scholar
  73. Schwahn K, Perez de Souza L, Fernie AR, Tohge T (2014) Metabolomics-assisted refinement of the pathways of steroidal glycoalkaloid biosynthesis in the tomato clade. J Integr Plant Biol 56:864–875PubMedCrossRefGoogle Scholar
  74. Scossa F, Benina M, Alseekh S, Zhang Y, Fernie AR (2018) The integration of metabolomics and next-generation sequencing data to elucidate the pathways of natural product metabolism in medicinal plants. Planta Med 84:855–873PubMedCrossRefGoogle Scholar
  75. Shimizu T, Watanabe M, Fernie AR, Tohge T (2018) Targeted LC-MS analysis for plant secondary metabolites. Methods Mol Biol 1778:171–181PubMedCrossRefGoogle Scholar
  76. Shimma S, Nagao H, Giannakopulos AE, Hayakawa S, Awazu K, Toyoda M (2008) High-energy collision-induced dissociation of phosphopeptides using a multi-turn tandem time-of-flight mass spectrometer ‘MULTUM-TOF/TOF’. J Mass Spectrom 43:535–537PubMedCrossRefGoogle Scholar
  77. Shirai K, Matsuda F, Nakabayashi R, Okamoto M, Tanaka M, Fujimoto A, Shimizu M, Shinozaki K, Seki M, Saito K, Hanada K (2017) A highly specific genome-wide association study integrated with transcriptome data reveals the contribution of copy number variations to specialized metabolites in Arabidopsis thaliana accessions. Mol Biol Evol. CrossRefPubMedGoogle Scholar
  78. Sulpice R, Pyl ET, Ishihara H, Trenkamp S, Steinfath M, Witucka-Wall H et al (2009) Starch as a major integrator in the regulation of plant growth. Proc Natl Acad Sci USA 106:10348–10353PubMedCrossRefGoogle Scholar
  79. Sweetlove LJ, Fernie AR (2013) The spatial organization of metabolism within the plant cell. Annu Rev Plant Biol 64:723–746PubMedCrossRefGoogle Scholar
  80. Swender J (2011) Experimental flux measurements on a network scale. Front Plant Sci 2:63Google Scholar
  81. Szecowka M, Heisse R, Tohge T, Nunes-Nesi A, Vorsloh D, Nikoloski Z, Stitt M, Fernie AR, Arrivault S (2013) Metabolic fluxes of an illuminated Arabidopsis thaliana rosette. Plant Cell 25:694–714PubMedPubMedCentralCrossRefGoogle Scholar
  82. Tohge T, Fernie AR (2009) Web-based resources for mass-spectrometry-based metabolomics: a user’s guide. Phytochemistry 70:450–456PubMedCrossRefGoogle Scholar
  83. Tohge T, Fernie AR (2010) Combining genetic diversity, informatics, and metabolomics to facilitate annotation of plant gene function. Nat Protoc 5:1210–1227PubMedCrossRefGoogle Scholar
  84. Tohge T, Fernie AR (2014) Lignin, mitochondrial family, and photorespiratory transporter classification as case studies in using co-expression, co-response, and protein locations to aid in identifying transport functions. Front Plant Sci 5:75. CrossRefPubMedPubMedCentralGoogle Scholar
  85. Tohge T, Fernie AR (2017) An Overview of compounds derived from the shikimate and phenylpropanoid pathways and their medicinal importance. Mini Rev Med Chem 17:1013–1027PubMedCrossRefGoogle Scholar
  86. Tohge T, Nishiyama Y, Hirai MY, Yano M, Nakajima J, Awazuhara M, Inoue E, Takahashi H, Goodenowe DB, Kitayama M, Noji M, Yamazaki M, Saito K (2005a) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing a MYB transcription factor. Plant J 42:218–235PubMedCrossRefGoogle Scholar
  87. Tohge T, Nishiyama Y, Hirai MY, Yano M, Nakajima JI, Awazuhara M, Inoue E, Takahashi H, Goodenowe DB, Kitayama M, Noji M, Yamazaki M, Saito K (2005b) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J 42(2):218–235PubMedCrossRefGoogle Scholar
  88. Tohge T, Yonekura-Sakakibara K, Niida R, Watanabe-Takahashi A, Saito K (2007) Phytochemical genomics in Arabidopsis thaliana: a case study for functional identification of flavonoid biosynthesis genes. Pure Appl Chem 79:811–823CrossRefGoogle Scholar
  89. Tohge T, Watanabe M, Hoefgen R, Fernie AR (2013) The evolution of phenylpropanoid metabolism in the green lineage. Crit Rev Biochem Mol Biol 48:123–152PubMedCrossRefGoogle Scholar
  90. Tohge T, Souza LP, Fernie AR (2014) Genome-enabled plant metabolomics. J Chromatogr B 966:7–20Google Scholar
  91. Tohge T, Scossa F, Fernie AR (2015) Integrative approaches to enhance understanding of plant metabolic pathway structure and regulation. Plant Physiol 169:1499–1511PubMedPubMedCentralGoogle Scholar
  92. Tohge T, Wendenburg R, Ishihara H, Nakabayashi R, Watanabe M, Sulpice R, Hoefgen R, Takayama H, Saito K, Stitt M, Fernie AR (2016) Characterization of a recently-evolved flavonol-phenylacyltransferase gene provides signatures of natural light selection in Brassicaceae. Nat Commun 7:12399. CrossRefPubMedPubMedCentralGoogle Scholar
  93. Tohge T, Borghi M, Fernie AR (2018) The natural variance of the Arabidopsis floral secondary metabolites. Sci Data 5:180051PubMedPubMedCentralCrossRefGoogle Scholar
  94. Udomsom N, Rai A, Suzuki H, Okuyama J, Imai R, Mori T, Nakabayashi R, Saito K, Yamazaki M (2016) Function of AP2/ERF Transcription Factors Involved in the Regulation of Specialized Metabolism in Ophiorrhiza pumila Revealed by Transcriptomics and Metabolomics. Front Plant Sci 7:1861PubMedPubMedCentralCrossRefGoogle Scholar
  95. Urano K, Maruyama K, Ogata Y, Morishita Y, Takeda M, Sakurai N, Suzuki H, Saito K, Shibata D, Kobayashi M, Yamaguchi-Shinozaki K, Shinozaki K (2009) Characterization of the ABA-regulated global responses to dehydration in Arabidopsis by metabolomics. Plant J 57:1065–1078PubMedCrossRefGoogle Scholar
  96. Urano K, Kurihara Y, Seki M, Shinozaki K (2010) ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Curr Opin Plant Biol 13:132–138PubMedCrossRefGoogle Scholar
  97. Urbanczyk-Wochniak E, Luedemann A, Kopka J, Selbig J, Roessner-Tunali U, Willmitzer L, Fernie AR (2003) Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep 4(10):989–993PubMedPubMedCentralCrossRefGoogle Scholar
  98. Veyel D, Kierszniowska S, Kosmacz M, Sokolowska EM, Michaelis A, Luzarowski M, Szlachetko J, Willmitzer L, Skirycz A (2017) System-wide detection of protein-small molecule complexes suggests extensive metabolite regulation in plants. Sci Rep 7:42387PubMedPubMedCentralCrossRefGoogle Scholar
  99. Wagner C, Sefkow M, Kopka J (2003) Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochemistry 62:887–900PubMedCrossRefGoogle Scholar
  100. Wang K, Yin XR, Zhang B, Grierson D, Xu CJ, Chen KS (2017) Transcriptomic and metabolic analyses provide new insights into chilling injury in peach fruit. Plant Cell Environ 40:1531–1551PubMedCrossRefGoogle Scholar
  101. Watanabe M, Balazadeh S, Tohge T, Erban A, Giavalisco P, Kopka J, Fernie AR, Mueller-Roeber B, Hoefgen R (2013) Comprehensive dissection of spatiotemporal metabolic shifts in primary, secondary, and lipid metabolism during developmental senescence in Arabidopsis. Plant Physiol 162:1290–1310PubMedPubMedCentralCrossRefGoogle Scholar
  102. Wen W, Li D, Li X, Gao Y, Li W, Li H, Liu J, Liu H, Chen W, Luo J, Yan J (2014) Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun 5:3484. CrossRefGoogle Scholar
  103. Wink M (1988) Plant breeding: importance of plant secondary metabolites for protection against pathogens and herbivores. Theor Appl Genet 75:225–233. CrossRefGoogle Scholar
  104. Wisecaver JH, Borowsky AT, Tzin V, Jander G, Kliebenstein DJ, Rokas A (2017) A global co-expression network approach for connecting genes to specialized metabolic pathways in plants. Plant Cell. CrossRefPubMedPubMedCentralGoogle Scholar
  105. Wu S, Tohge T, Cuadros-Inostroza A, Tong H, Tenenboim H, Kooke R, Meret M, Keurentjes JB, Nikoloski Z, Fernie AR, Willmitzer L, Brotman Y (2018) Mapping the Arabidopsis metabolic landscape by untargeted metabolomics at different environmental conditions. Mol Plant 11:118–134. CrossRefPubMedGoogle Scholar
  106. Yamazaki M, Shibata M, Nishiyama Y, Springob K, Kitayama M, Shimada N, Aoki T, Ayabe S, Saito K (2008) Differential gene expression profiles of red and green forms of Perilla frutescens leading to comprehensive identification of anthocyanin biosynthetic genes. FEBS J 275:3494–3502PubMedCrossRefGoogle Scholar
  107. Yonekura-Sakakibara K, Tohge T, Niida R, Saito K (2007) Identification of a flavonol 7-O-rhamnosyltransferase gene determining flavonoid pattern in Arabidopsis by transcriptome coexpression analysis and reverse genetics. J Biol Chem 282:14932–14941PubMedCrossRefGoogle Scholar
  108. Yonekura-Sakakibara K, Tohge T, Matsuda F, Nakabayashi R, Takayama H, Niida R, Watanabe-Takahashi A, Inoue E, Saito K (2008) Comprehensive flavonol profiling and transcriptome coexpression analysis leading to decoding gene-metabolite correlations in Arabidopsis. Plant Cell 20:2160–2176PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Brazilian Society of Plant Physiology 2018

Authors and Affiliations

  • Jay C. Delfin
    • 1
  • Mutsumi Watanabe
    • 1
  • Takayuki Tohge
    • 1
  1. 1.Graduate School of Science and TechnologyNara Institute of Science and TechnologyIkomaJapan

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