1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Genetics
  4. Volume 51, 2017
  5. Article

Annual Review of Genetics

Volume 51, 2017

The Genetics of Plant Metabolism

Abstract

Plant metabolic studies have traditionally focused on the role and regulation of the enzymes catalyzing key reactions within specific pathways. Within the past 20 years, reverse genetic approaches have allowed direct determination of the effects of the deficiency, or surplus, of a given protein on the biochemistry of a plant. In parallel, top-down approaches have also been taken, which rely on screening broad, natural genetic diversity for metabolic diversity. Here, we compare and contrast the various strategies that have been adopted to enhance our understanding of the natural diversity of metabolism. We also detail how these approaches have enhanced our understanding of both specific and global aspects of the genetic regulation of metabolism. Finally, we discuss how such approaches are providing important insights into the evolution of plant secondary metabolism.

Keyword(s): evolution of metabolism, genetic architecture, metabolic quantitative trait loci, plant metabolism
Loading

Article metrics loading...

/content/journals/10.1146/annurev-genet-120116-024640
2017-11-27
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/genet/51/1/annurev-genet-120116-024640.html?itemId=/content/journals/10.1146/annurev-genet-120116-024640&mimeType=html&fmt=ahah

Literature Cited

  1. Alexandrov N, Tai SS, Wang WS, Mansueto L, Palis K. 1.  et al. 2015. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res 43:D1023–27 [Google Scholar]
  2. Alonso JM, Stepanova AN, Leisse TJ, Kim CJ, Chen HM. 2.  et al. 2003. Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301:653–57 [Google Scholar]
  3. Alseekh S, Ofner I, Pleban T, Tripodi P, Di Dato F. 3.  et al. 2013. Resolution by recombination: breaking up Solanum pennellii introgressions. Trends Plant Sci 18:536–38 [Google Scholar]
  4. Alseekh S, Tohge T, Wendenberg R, Scossa F, Omranian N. 4.  et al. 2015. Identification and mode of inheritance of quantitative trait loci for secondary metabolite abundance in tomato. Plant Cell 27:485–512Identification of secondary metabolite QTL allowed definition of heritability, mode of inheritance, and network linkage of secondary metabolites in the tomato. [Google Scholar]
  5. Angelovici R, Batushansky A, Deason N, Gonzalez-Jorge S, Gore MA. 5.  et al. 2017. Network-guided GWAS improves identification of genes affecting free amino acids. Plant Physiol 173:872–86 [Google Scholar]
  6. Angelovici R, Lipka AE, Deason N, Gonzalez-Jorge S, Lin HN. 6.  et al. 2013. Genome-wide analysis of branched-chain amino acid levels in Arabidopsis seeds. Plant Cell 25:4827–43 [Google Scholar]
  7. Araujo WL, Ishizaki K, Nunes-Nesi A, Larson TR, Tohge T. 7.  et al. 2010. Identification of the 2-hydroxyglutarate and isovaleryl-CoA dehydrogenases as alternative electron donors linking lysine catabolism to the electron transport chain of Arabidopsis mitochondria. Plant Cell 22:1549–63 [Google Scholar]
  8. Ballester AR, Tikunov Y, Molthoff J, Grandillo S, Viquez-Zamora M. 8.  et al. 2016. Identification of loci affecting accumulation of secondary metabolites in tomato fruit of a Solanum lycopersicum×Solanum chmielewskii introgression line population. Front. Plant Sci. 7:1428 [Google Scholar]
  9. Barrantes W, Lopez-Casado G, Garcia-Martinez S, Alonso A, Rubio F. 9.  et al. 2016. Exploring new alleles involved in tomato fruit quality in an introgression line library of Solanum pimpinellifolium. Front. Plant Sci. 7:1172 [Google Scholar]
  10. Beleggia R, Rau D, Laido G, Platani C, Nigro F. 10.  et al. 2016. Evolutionary metabolomics reveals domestication-associated changes in tetraploid wheat kernels. Mol. Biol. Evol. 33:1740–53Evaluation of primary metabolite levels in wheat and progenitors revealed domestication-related changes in metabolism. [Google Scholar]
  11. Bentsink L, Alonso-Blanco C, Vreugdenhil D, Tesnier K, Groot SPC, Koornneef M. 11.  2000. Genetic analysis of seed-soluble oligosaccharides in relation to seed storability of Arabidopsis. Plant Physiol 124:1595–604 [Google Scholar]
  12. Bhattacharyya MK, Smith AM, Ellis THN, Hedley C, Martin C. 12.  1990. The wrinkled-seed character of pea described by Mendel is caused by a transposon-like insertion in a gene encoding starch-branching enzyme. Cell 60:115–22This article describes the seminal molecular cloning of the wrinkled mutation in pea—one of a handful of mutants leading to the foundation of genetics. [Google Scholar]
  13. Bolger A, Scossa F, Bolger ME, Lanz C, Maumus F. 13.  et al. 2014. The genome of the stress-tolerant wild tomato species Solanum pennellii. Nat. Genet. 46:1034–38 [Google Scholar]
  14. Bordych C, Eisenhut M, Pick TR, Kuelahoglu C, Weber APM. 14.  2013. Co-expression analysis as tool for the discovery of transport proteins in photorespiration. Plant Biol 15:686–93 [Google Scholar]
  15. Borevitz JO, Hazen SP, Michael TP, Morris GP, Baxter IR. 15.  et al. 2007. Genome-wide patterns of single-feature polymorphism in Arabidopsis thaliana. PNAS 104:12057–62 [Google Scholar]
  16. Borevitz JO, Nordborg M. 16.  2003. The impact of genomics on the study of natural variation in Arabidopsis. Plant Physiol 132:718–25 [Google Scholar]
  17. Broman KW. 17.  2001. Review of statistical methods for QTL mapping in experimental crosses. Lab. Anim. 30:44–52 [Google Scholar]
  18. Carreno-Quintero N, Acharjee A, Maliepaard C, Bachem CWB, Mumm R. 18.  et al. 2012. Untargeted metabolic quantitative trait loci analyses reveal a relationship between primary metabolism and potato tuber quality. Plant Physiol 158:1306–18 [Google Scholar]
  19. Chaib J, Lecomte L, Buret M, Causse M. 19.  2006. Stability over genetic backgrounds, generations and years of quantitative trait locus (QTLs) for organoleptic quality in tomato. Theor. Appl. Genet. 112:934–44 [Google Scholar]
  20. Chan EKF, Rowe HC, Corwin JA, Joseph B, Kliebenstein DJ. 20.  2011. Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLOS Biol 9:e1001125 [Google Scholar]
  21. Chen LQ, Qu XQ, Hou BH, Sosso D, Osorio S. 21.  et al. 2012. Sucrose efflux mediated by SWEET proteins as a key step for phloem transport. Science 335:207–11 [Google Scholar]
  22. Chen W, Gao Y, Xie W, Gong L, Lu K. 22.  et al. 2014. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat. Genet. 46:714–21 [Google Scholar]
  23. Chen W, Wang WS, Peng M, Gong L, Gao YQ. 23.  et al. 2016. Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat. Commun. 7:12767 [Google Scholar]
  24. Chitwood DH, Kumar R, Headland LR, Ranjan A, Covington MF. 24.  et al. 2013. A quantitative genetic basis for leaf morphology in a set of precisely defined tomato introgression lines. Plant Cell 25:2465–81 [Google Scholar]
  25. D'Auria JC. 25.  2006. Acyltransferases in plants: a good time to be BAHD. Curr. Opin. Plant Biol. 9:331–40 [Google Scholar]
  26. Dixon RA, Strack D. 26.  2003. Phytochemistry meets genome analysis, and beyond. Phytochemistry 62:815–16 [Google Scholar]
  27. Do PT, Prudent M, Sulpice R, Causse M, Fernie AR. 27.  2010. The influence of fruit load on the tomato pericarp metabolome in a Solanum chmielewskii introgression line population. Plant Physiol 154:1128–42 [Google Scholar]
  28. Dormann P, Balbo I, Benning C. 28.  1999. Arabidopsis galactolipid biosynthesis and lipid trafficking mediated by DGD1. Science 284:2181–84 [Google Scholar]
  29. Eckert AJ, Wegrzyn JL, Cumbie WP, Goldfarb B, Huber DA. 29.  et al. 2012. Association genetics of the loblolly pine (Pinus taeda, Pinaceae) metabolome. New Phytol 193:890–902 [Google Scholar]
  30. Emms DM, Covshoff S, Hibberd JM, Kelly S. 30.  2016. Independent and parallel evolution of new genes by gene duplication in two origins of C4 photosynthesis provides new insight into the mechanism of phloem loading in C4 species. Mol. Biol. Evol. 33:1796–806 [Google Scholar]
  31. Eshed Y, Abu-Abied M, Saranga Y, Zamir D. 31.  1992. Lycopersicon esculentum lines containing small overlapping introgressions from L. pennellii. Theor. Appl. Genet. 83:1027–34 [Google Scholar]
  32. Estelle MA, Somerville CR. 32.  1986. The mutants of Arabidopsis. Trends Genet 2:89–93 [Google Scholar]
  33. Fan P, Miller AM, Schilmiller AL, Liu X, Ofner I. 33.  et al. 2016. In vitro reconstruction and analysis of evolutionary variation of the tomato acylsucrose metabolic network. PNAS 113:E239–48 [Google Scholar]
  34. Fernie AR. 34.  2007. The future of metabolic phytochemistry: larger numbers of metabolites, higher resolution, greater understanding. Phytochemistry 68:2861–80 [Google Scholar]
  35. Fernie AR, Klee HJ. 35.  2011. The use of natural genetic diversity in the understanding of metabolic organization and regulation. Front. Plant Sci. 2:59 [Google Scholar]
  36. Fernie AR, Pichersky E. 36.  2015. Focus issue on metabolism: metabolites, metabolites everywhere. Plant Physiol 169:1421–23 [Google Scholar]
  37. Fernie AR, Schauer N. 37.  2009. Metabolomics-assisted breeding: a viable option for crop improvement?. Trends Genet 25:39–48 [Google Scholar]
  38. Fernie AR, Tadmor Y, Zamir D. 38.  2006. Natural genetic variation for improving crop quality. Curr. Opin. Plant Biol. 9:196–202 [Google Scholar]
  39. Fernie AR, Tohge T. 39.  2015. Location, location, location—no more! The unravelling of chromatin remodeling regulatory aspects of plant metabolic gene clusters. New Phytol 205:458–60 [Google Scholar]
  40. Fernie AR, Trethewey RN, Krotzky AJ, Willmitzer L. 40.  2004. Metabolite profiling: from diagnostics to systems biology. Nat. Rev. Mol. Cell Biol. 5:763–69 [Google Scholar]
  41. Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey RN, Willmitzer L. 41.  2000. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18:1157–61 [Google Scholar]
  42. Field B, Osbourn AE. 42.  2008. Metabolic diversification—independent assembly of operon-like gene clusters in different plants. Science 320:543–47This research described the independent formation of multiple gene clusters in plants, thus identifying the leitmotif that set the path for multiple contemporary studies. [Google Scholar]
  43. Fraser CM, Rider LW, Chapple C. 43.  2005. An expression and bioinformatics analysis of the Arabidopsis serine carboxypeptidase-like gene family. Plant Physiol 138:1136–48 [Google Scholar]
  44. Fridman E, Carrari F, Liu YS, Fernie AR, Zamir D. 44.  2004. Zooming in on a quantitative trait for tomato yield using interspecific introgressions. Science 305:1786–89This represents the first description of a quantitative trait nucleotide that exerts a metabolically mediated effect on crop yield. [Google Scholar]
  45. Fridman E, Pleban T, Zamir D. 45.  2000. A recombination hotspot delimits a wild-species quantitative trait locus for tomato sugar content to 484 bp within an invertase gene. PNAS 97:4718–23 [Google Scholar]
  46. Futuyma DJ, Agrawal AA. 46.  2009. Macroevolution and the biological diversity of plants and herbivores. PNAS 106:18054–61 [Google Scholar]
  47. Galili G, Amir R, Fernie AR. 47.  2016. The regulation of essential amino acid synthesis and accumulation in plants. Annu. Rev. Plant Biol. 67:153–178 [Google Scholar]
  48. Garcia-Seco D, Zhang Y, Gutierrez-Manoro FJ, Martin C, Ramos-Solano B. 48.  2015. RNA-Seq analysis and transcriptome assembly for blackberry (Rubus sp Var. Lochness) fruit. BMC Genom 16:5 [Google Scholar]
  49. Giavalisco P, Hummel J, Lisec J, Inostroza AC, Catchpole G, Willmitzer L. 49.  2008. High-resolution direct infusion-based mass spectrometry in combination with whole 13C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Anal. Chem. 80:9417–25 [Google Scholar]
  50. Hansey CN, Vaillancourt B, Sekhon RS, de Leon N, Kaeppler SM, Buell CR. 50.  2012. Maize (Zea mays L.) genome diversity as revealed by RNA-sequencing. PLOS ONE 7:e33071 [Google Scholar]
  51. Harjes CE, Rocheford TR, Bai L, Brutnell TP, Kandianis CB. 51.  et al. 2008. Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319:330–33 [Google Scholar]
  52. Hill CB, Taylor JD, Edwards J, Mather D, Bacic A. 52.  et al. 2013. Whole-genome mapping of agronomic and metabolic traits to identify novel quantitative trait loci in bread wheat grown in a water-limited environment. Plant Physiol 162:1266–81 [Google Scholar]
  53. Holtgrawe D, Sorensen TR, Viehover P, Schneider J, Schulz B. 53.  et al. 2014. Reliable in silico identification of sequence polymorphisms and their application for extending the genetic map of sugar beet (Beta vulgaris). PLOS ONE 9:e110113 [Google Scholar]
  54. Hooker WJ. 54.  1837. Petunia violacea; hybrida. Purple petunia; hybrid var. Curtis Bot. Mag. 64:3446 [Google Scholar]
  55. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T. 55.  et al. 2010. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45:703–14 [Google Scholar]
  56. Hu CY, Shi JX, Quan S, Cui B, Kleessen S. 56.  et al. 2014. Metabolic variation between japonica and indica rice cultivars as revealed by non-targeted metabolomics. Sci. Rep. 4:5067 [Google Scholar]
  57. Huang X, Han B. 57.  2014. Natural variations and genome-wide association studies in crop plants. Annu. Rev. Plant Biol. 65:531–51 [Google Scholar]
  58. Hufford MB, Xu X, van Heerwaarden J, Pyhajarvi T, Chia JM. 58.  et al. 2012. Comparative population genomics of maize domestication and improvement. Nat. Genet. 44:808–11 [Google Scholar]
  59. 59. Int. Wheat Genome Seq. Consort. 2014. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345:1251788 [Google Scholar]
  60. Ishihara H, Tohge T, Viehover P, Fernie AR, Weisshaar B, Stracke R. 60.  2016. Natural variation in flavonol accumulation in Arabidopsis is determined by the flavonol glucosyltransferase BGLU6. J. Exp. Bot. 67:1505–17 [Google Scholar]
  61. Ishii N, Nakahigashi K, Baba T, Robert M, Soga T. 61.  et al. 2007. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316:593–97 [Google Scholar]
  62. Itkin M, Heinig U, Tzfadia O, Bhide AJ, Shinde B. 62.  et al. 2013. Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes. Science 341:175–79 [Google Scholar]
  63. Joseph B, Corwin JA, Kliebenstein DJ. 63.  2015. Genetic variation in the nuclear and organellar genomes modulates stochastic variation in the metabolome, growth, and defense. PLOS Genet 11:e1004779 [Google Scholar]
  64. Joseph B, Corwin JA, Li B, Atwell S, Kliebenstein DJ. 64.  2013. Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome. eLife 2:e00776A thought-provoking evaluation of the influence of non-nuclear genetics on variation in the metabolome and the consequences on the interpretation of mQTL studies. [Google Scholar]
  65. Kang JH, Gonzales-Vigil E, Matsuba Y, Pichersky E, Barry CS. 65.  2014. Determination of residues responsible for substrate and product specificity of Solanum habrochaites short-chain cis-prenyltransferases. Plant Physiol 164:80–91 [Google Scholar]
  66. Kaul S, Koo HL, Jenkins J, Rizzo M, Rooney T. 66.  et al. 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815 [Google Scholar]
  67. Keurentjes JJB, Bentsink L, Alonso-Blanco C, Hanhart CJ, Vries HBD. 67.  et al. 2007. Development of a near-isogenic line population of Arabidopsis thaliana and comparison of mapping power with a recombinant inbred line population. Genetics 175:891–905 [Google Scholar]
  68. Keurentjes JJB, Fernie AR. 68.  2011. Genetics, genomics and metabolomics. Annual Plant Reviews RD Hall 239–52 London: Wiley [Google Scholar]
  69. Keurentjes JJB, Fu JY, de Vos CHR, Lommen A, Hall RD. 69.  et al. 2006. The genetics of plant metabolism. Nat. Genet. 38:842–49The first nonbiased metabolomics analysis of metabolite QTLs re-found known QTLs, as well as a large number of novel QTLs. [Google Scholar]
  70. Kim J, Buell CR. 70.  2015. A revolution in plant metabolism: genome-enabled pathway discovery. Plant Physiol 169:1532–39 [Google Scholar]
  71. Kim J, Matsuba Y, Ning J, Schilmiller AL, Hammar D. 71.  et al. 2014. Analysis of natural and induced variation in tomato glandular trichome flavonoids identifies a gene not present in the reference genome. Plant Cell 26:3272–85 [Google Scholar]
  72. Kim S, Park M, Yeom S-I, Kim Y-M, Lee JM. 72.  et al. 2014. Genome sequence of the hot pepper provides insights into the evolution of pungency in Capsicum species. Nat. Genet. 46:270–78 [Google Scholar]
  73. Kim S, Plagnol V, Hu TT, Toomajian C, Clark RM. 73.  et al. 2007. Recombination and linkage disequilibrium in Arabidopsis thaliana. Nat. Genet. 39:1151–55 [Google Scholar]
  74. Kleessen S, Antonio C, Sulpice R, Laitinen R, Fernie AR. 74.  et al. 2012. Structured patterns in geographic variability of metabolic phenotypes in Arabidopsis thaliana. Nat. Commun. 3:1319 [Google Scholar]
  75. Kleessen S, Laitinen R, Fusari CM, Antonio C, Sulpice R. 75.  et al. 2014. Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana. Nat. Commun. 5:3537 [Google Scholar]
  76. Kliebenstein DJ, Kroymann J, Brown P, Figuth A, Pedersen D. 76.  et al. 2001. Genetic control of natural variation in Arabidopsis glucosinolate accumulation. Plant Physiol 126:811–25 [Google Scholar]
  77. Kliebenstein DJ, Lambrix VM, Reichelt M, Gershenzon J, Mitchell-Olds T. 77.  2001. Gene duplication in the diversification of secondary metabolism: tandem 2-oxoglutarate-dependent dioxygenases control glucosinolate biosynthesis in Arabidopsis. Plant Cell 13:681–93 [Google Scholar]
  78. Kliebenstein DJ, Osbourn A. 78.  2012. Making new molecules—evolution of pathways for novel metabolites in plants. Curr. Opin. Plant Biol. 15:415–23 [Google Scholar]
  79. Knapp S, Peralta I. 79.  2016. The tomato (Solanum lycopersicum L. Solanaceae) and its botanical relatives. In The Tomato Genome M Causse, J Giovannoni, M Bouyazen, M Zouine 7–21 Berlin: Springer [Google Scholar]
  80. Koenig D, Jimenez-Gomez JM, Kimura S, Fulop D, Chitwood DH. 80.  et al. 2013. Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato. PNAS 110:E2655–62 [Google Scholar]
  81. Koornneef M, Meinke D. 81.  2010. The development of Arabidopsis as a model plant. Plant J 61:909–21 [Google Scholar]
  82. Koornneef M, Reuling G, Karssen CM. 82.  1984. The isolation and characterization of abscisic-acid insensitive mutants of Arabidopsis thaliana. Physiol. Plant. 61:377–83 [Google Scholar]
  83. Kruger NJ, Troncoso-Ponce MA, Ratcliffe RG. 83.  2008. 1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues. Nat. Protoc. 3:1001–12 [Google Scholar]
  84. Kusano M, Yang Z, Okazaki Y, Nakabayashi R, Fukushima A, Saito K. 84.  2015. Using metabolomic approaches to explore chemical diversity in rice. Mol. Plant 8:58–67 [Google Scholar]
  85. Lam H-M, Xu X, Liu X, Chen W, Yang G. 85.  et al. 2010. Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat. Genet. 42:1053–41 [Google Scholar]
  86. Larsson SJ, Lipka AE, Buckler ES. 86.  2013. Lessons from Dwarf8 on the strengths and weaknesses of structured association mapping. PLOS Genet 9:e1003246 [Google Scholar]
  87. Li DP, Baldwin IT, Gaquerel E. 87.  2015. Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis. PNAS 112:E4147–55 [Google Scholar]
  88. Li H, Peng Z, Yang X, Wang W, Fu J. 88.  et al. 2013. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat. Genet. 45:43–72This early metabolic genome-wide association study highlights the key genes in maize kernel oil biosynthesis. [Google Scholar]
  89. Li X, Bergelson J, Chapple C. 89.  2010. The ARABIDOPSIS Accession Pna-10 is a naturally occurring sng1 deletion mutant. Mol. Plant 3:91–100 [Google Scholar]
  90. Lin T, Zhu G, Zhang J, Xu X, Yu Q. 90.  et al. 2014. Genomic analyses provide insights into the history of tomato breeding. Nat. Genet. 46:1220–26 [Google Scholar]
  91. Lisec J, Meyer RC, Steinfath M, Redestig H, Becher M. 91.  et al. 2008. Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. Plant J 53:960–72 [Google Scholar]
  92. Lisec J, Roemisch-Margl L, Nikoloski Z, Piepho H-P, Giavalisco P. 92.  et al. 2011. Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns. Plant J 68:326–36 [Google Scholar]
  93. Lisec J, Steinfath M, Meyer RC, Selbig J, Melchinger AE. 93.  et al. 2009. Identification of heterotic metabolite QTL in Arabidopsis thaliana RIL and IL populations. Plant J 59:777–88 [Google Scholar]
  94. Liu YS, Gur A, Ronen G, Causse M, Damidaux R. 94.  et al. 2003. There is more to tomato fruit colour than candidate carotenoid genes. Plant Biotechnol. J. 1:195–207 [Google Scholar]
  95. Loudet O, Chaillou S, Merigout P, Talbotec J, Daniel-Vedele F. 95.  2003. Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol 131:345–58 [Google Scholar]
  96. Luo J. 96.  2015. Metabolite-based genome-wide association studies in plants. Curr. Opin. Plant Biol. 24:31–38 [Google Scholar]
  97. Ma FF, Jazmin LJ, Young JD, Allen DK. 97.  2014. Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation. PNAS 111:16967–72 [Google Scholar]
  98. Matsuba Y, Nguyen TTH, Wiegert K, Falara V, Gonzales-Vigil E. 98.  et al. 2013. Evolution of a complex locus for terpene biosynthesis in Solanum. Plant Cell 25:2022–36 [Google Scholar]
  99. Matsuda F, Nakabayashi R, Yang ZG, Okazaki Y, Yonemaru J. 99.  et al. 2015. Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism. Plant J 81:13–23 [Google Scholar]
  100. Matsuda F, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R. 100.  et al. 2012. Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J 70:624–36 [Google Scholar]
  101. McClintock B. 101.  1950. The origin and behavior of mutable loci in maize. PNAS 36:344–55 [Google Scholar]
  102. Mendel G. 102.  1865. Versuche über Pflanzen-Hybriden. Verh. Naturforschenden Ver. Brünn 4:3–47 [Google Scholar]
  103. Meyer RC, Steinfath M, Lisec J, Becher M, Witucka-Wall H. 103.  et al. 2007. The metabolic signature related to high plant growth rate in Arabidopsis thaliana. PNAS 104:4759–64 [Google Scholar]
  104. Michael TP, VanBuren R. 104.  2015. Progress, challenges and the future of crop genomes. Curr. Opin. Plant Biol. 24:71–81 [Google Scholar]
  105. Milo R, Last RL. 105.  2012. Achieving diversity in the face of constraints: lessons from metabolism. Science 336:1663–67 [Google Scholar]
  106. Mitchell-Olds T, Pedersen D. 106.  1998. The molecular basis of quantitative genetic variation in central and secondary metabolism in Arabidopsis. Genetics 149:739–47 [Google Scholar]
  107. Miyamoto K, Fujita M, Shenton MR, Akashi S, Sugawara C. 107.  et al. 2016. Evolutionary trajectory of phytoalexin biosynthetic gene clusters in rice. Plant J 87:293–304 [Google Scholar]
  108. Moose SP, Dudley JW, Rocheford TR. 108.  2004. Maize selection passes the century mark: a unique resource for 21st century genomics. Trends Plant Sci 9:358–64This is a fantastic review of the century-old Illinois maize breeding program. [Google Scholar]
  109. Nakabayashi R, Kusano M, Kobayashi M, Tohge T, Yonekura-Sakakibara K. 109.  et al. 2009. Metabolomics-oriented isolation and structure elucidation of 37 compounds including two anthocyanins from Arabidopsis thaliana. Phytochemistry 70:1017–29 [Google Scholar]
  110. Ning J, Moghe GD, Leong B, Kim J, Ofner I. 110.  et al. 2015. A feedback-insensitive isopropylmalate synthase affects acylsugar composition in cultivated and wild tomato. Plant Physiol 169:1821–35 [Google Scholar]
  111. Obata T, Fernie AR. 111.  2012. The use of metabolomics to dissect plant responses to abiotic stresses. Cell. Mol. Life Sci. 69:3225–43 [Google Scholar]
  112. Ofner I, Lashbrooke J, Pleban T, Aharoni A, Zamir D. 112.  2016. Solanum pennellii backcross inbred lines (BILs) link small genomic bins with tomato traits. Plant J 87:151–60 [Google Scholar]
  113. 113. 1001 Genomes Consort. 2016. 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166:481–91 [Google Scholar]
  114. Paris CD, Haney WJ. 114.  1958. Genetic studies in Petunia I. Nine genes for flower color. J. Am. Soc. Hortic. Sci. 72:462–72 [Google Scholar]
  115. Peng M, Gao YQ, Chen W, Wang WS, Shen SQ. 115.  et al. 2016. Evolutionarily distinct BAHD N-acyltransferases are responsible for natural variation of aromatic amine conjugates in rice. Plant Cell 28:1533–50 [Google Scholar]
  116. Pick TR, Brautigam A, Schulz MA, Obata T, Fernie AR, Weber APM. 116.  2013. PLGG1, a plastidic glycolate glycerate transporter, is required for photorespiration and defines a unique class of metabolite transporters. PNAS 110:3185–90 [Google Scholar]
  117. Puchta H, Fauser F. 117.  2014. Synthetic nucleases for genome engineering in plants: prospects for a bright future. Plant J 78:727–41 [Google Scholar]
  118. Quadrana L, Almeida J, Asis R, Duffy T, Dominguez PG. 118.  et al. 2014. Natural occurring epialleles determine vitamin E accumulation in tomato fruits. Nat. Commun. 5:4027This illustrates the role of epigenetics in metabolite QTLs using the nutritionally crucial vitamin E as an example. [Google Scholar]
  119. Quattrocchio F, Wing JF, Leppen HTC, Mol JNM, Koes RE. 119.  1993. Regulatory genes controlling anthocyanin pigmentation are functionally conserved among plant-species and have distinct sets of target genes. Plant Cell 5:1497–512 [Google Scholar]
  120. Radwanski ER, Last RL. 120.  1995. Tryptophan biosynthesis and metabolism—biochemical and molecular genetics. Plant Cell 7:921–34 [Google Scholar]
  121. Riedelsheimer C, Lisec J, Czedik-Eysenberg A, Sulpice R, Flis A. 121.  et al. 2012. Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. PNAS 109:8872–77 [Google Scholar]
  122. Rontein D, Dieuade-Noubhani M, Dufourc EJ, Raymond P, Rolin D. 122.  2002. The metabolic architecture of plant cells: stability of central metabolism and flexibility of anabolic pathways during the growth cycle of tomato cells. J. Biol. Chem. 277:43948–60 [Google Scholar]
  123. Routaboul JM, Dubos C, Beck G, Marquis C, Bidzinski P. 123.  et al. 2012. Metabolite profiling and quantitative genetics of natural variation for flavonoids in Arabidopsis. J. Exp. Bot. 63:3749–64 [Google Scholar]
  124. Rowe HC, Hansen BG, Halkier BA, Kliebenstein DJ. 124.  2008. Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20:1199–216A thorough network analysis identified metabolites interlinked in a manner currently not described in textbooks. [Google Scholar]
  125. Sauvage C, Segura V, Bauchet G, Stevens R, Do PT. 125.  et al. 2014. Genome-wide association in tomato reveals 44 candidate loci for fruit metabolic traits. Plant Physiol 165:1120–32 [Google Scholar]
  126. Schatz MC, Maron LG, Stein JC, Hernandez Wences A, Gurtowski J. 126.  et al. 2014. Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica. Genome Biol. 15:506 [Google Scholar]
  127. Schauer N, Semel Y, Balbo I, Steinfath M, Repsilber D. 127.  et al. 2008. Mode of inheritance of primary metabolic traits in tomato. Plant Cell 20:509–23 [Google Scholar]
  128. Schauer N, Semel Y, Roessner U, Gur A, Balbo I. 128.  et al. 2006. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat. Biotechnol. 24:447–54 [Google Scholar]
  129. Schilmiller AL, Moghe GD, Fan PX, Ghosh B, Ning J. 129.  et al. 2015. Functionally divergent alleles and duplicated loci encoding an acyltransferase contribute to acylsugar metabolite diversity in Solanum trichomes. Plant Cell 27:1002–17 [Google Scholar]
  130. Schilmiller AL, Shi F, Kim J, Charbonneau AL, Holmes D. 130.  et al. 2010. Mass spectrometry screening reveals widespread diversity in trichome specialized metabolites of tomato chromosomal substitution lines. Plant J 62:391–403 [Google Scholar]
  131. Schwahn K, de Souza LP, Fernie AR, Tohge T. 131.  2014. Metabolomics-assisted refinement of the pathways of steroidal glycoalkaloid biosynthesis in the tomato clade. J. Integr. Plant Biol. 56:864–75 [Google Scholar]
  132. Schwarte S, Wegner F, Havenstein K, Groth D, Steup M, Tiedemann R. 132.  2015. Sequence variation, differential expression, and divergent evolution in starch-related genes among accessions of Arabidopsis thaliana. Plant Mol. Biol. 87:489–519 [Google Scholar]
  133. Schwartz D. 133.  1971. Genetic control of alcohol dehydrogenase—competition model for regulation of gene action. Genetics 67:411–25 [Google Scholar]
  134. Schwartz D, Laughner WJ. 134.  1969. A molecular basis for heterosis. Science 166:626–27 [Google Scholar]
  135. Scossa F, Brotman Y, de Abreu e Lima F, Willmitzer L, Nikoloski Z. 135.  et al. 2016. Genomics-based strategies for the use of natural variation in the improvement of crop metabolism. Plant Sci 242:47–64 [Google Scholar]
  136. Shahaf N, Rogachev I, Heinig U, Meir S, Malitsky S. 136.  et al. 2016. The WEIZMASS spectral library for high-confidence metabolite identification. Nat. Commun. 7:12423 [Google Scholar]
  137. Shirley BW, Hanley S, Goodman HM. 137.  1992. Effects of ionizing-radiation on a plant genome—analysis of 2 Arabidopsis transparent testa mutations. Plant Cell 4:333–47 [Google Scholar]
  138. Smith AM. 138.  1988. Major differences in isoforms of starch-branching enzyme between developing embryos of round-seeded and wrinkled-seeded peas (Pisum sativum L). Planta 175:270–9 [Google Scholar]
  139. Somerville CR, Ogren WL. 139.  1980. Photorespiration mutants of Arabidopsis thaliana deficient in serine-glyoxylate aminotransferase activity. PNAS 77:2684–87 [Google Scholar]
  140. Stehle F, Brandt W, Stubbs MT, Milkowski C, Strack D. 140.  2009. Sinapoyltransferases in the light of molecular evolution. Phytochemistry 70:1652–62 [Google Scholar]
  141. Stitt M, Sonnewald U. 141.  1995. Regulation of metabolism in transgenic plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 46:341–68 [Google Scholar]
  142. Strauch RC, Svedin E, Dilkes B, Chapple C, Li X. 142.  2015. Discovery of a novel amino acid racemase through exploration of natural variation in Arabidopsis thaliana. PNAS 112:11726–31 [Google Scholar]
  143. Sweetlove LJ, Fernie AR. 143.  2013. The spatial organization of metabolism within the plant cell. Annu. Rev. Plant Biol. 64:723–46 [Google Scholar]
  144. Szecowka M, Heise R, Tohge T, Nunes-Nesi A, Vosloh D. 144.  et al. 2013. Metabolic fluxes in an illuminated Arabidopsis rosette. Plant Cell 25:694–714 [Google Scholar]
  145. Tieman DM, Taylor M, Schauer N, Fernie AR, Hanson AD, Klee HJ. 145.  2006. Tomato aromatic amino acid decarboxylases participate in synthesis of the flavor volatiles 2-phenylethanol and 2-phenylacetaldehyde. PNAS 103:8287–92 [Google Scholar]
  146. Tieman DM, Zeigler M, Schmelz EA, Taylor MG, Bliss P. 146.  et al. 2006. Identification of loci affecting flavour volatile emissions in tomato fruits. J. Exp. Bot. 57:887–96 [Google Scholar]
  147. Tieman DM, Zhu G, Resende MF Jr., Lin T, Nguyen C. 147.  et al. 2017. A chemical genetic roadmap to improved tomato flavor. Science 355:391–94This iGWAS in tomato identified genes associated with taste. [Google Scholar]
  148. Tohge T, Watanabe M, Hoefgen R, Fernie AR. 148.  2013. The evolution of phenylpropanoid metabolism in the green lineage. Crit. Rev. Biochem. Mol. Biol. 48:123–52 [Google Scholar]
  149. Tohge T, Wendenburg R, Ishihara H, Nakabayashi R, Watanabe M. 149.  et al. 2016. Characterization of a recently evolved flavonol–phenylacyltransferase gene provides signatures of natural light selection in Brassicaceae. Nat. Commun. 7:12399 [Google Scholar]
  150. Tripathi LP, Sowdhamini R. 150.  2006. Cross genome comparisons of serine proteases in Arabidopsis and rice. BMC Genom 7:200 [Google Scholar]
  151. Verslues PE, Lasky JR, Juenger TE, Liu TW, Kumar MN. 151.  2014. Genome-wide association mapping combined with reverse genetics identifies new effectors of low water potential-induced proline accumulation in Arabidopsis. Plant Physiol 164:144–59 [Google Scholar]
  152. von Korff M, Wang H, Leon J, Pillen K. 152.  2004. Development of candidate introgression lines using an exotic barley accession (Hordeum vulgare ssp spontaneum) as donor. Theor. Appl. Genet. 109:1736–45 [Google Scholar]
  153. Waddington CH. 153.  1942. Canalization of development and the inheritance of acquired characters. Nature 150:563–65 [Google Scholar]
  154. Wei X, Liu KY, Zhang YX, Feng Q, Wang LH. 154.  et al. 2015. Genetic discovery for oil production and quality in sesame. Nat. Commun. 6:8609 [Google Scholar]
  155. Wen WW, Brotman Y, Willmitzer L, Yan JB, Fernie AR. 155.  2016. Broadening our portfolio in the genetic improvement of maize chemical composition. Trends Genet 32:459–69 [Google Scholar]
  156. Wen WW, Li D, Li X, Gao YQ, Li WQ. 156.  et al. 2014. Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat. Commun. 5:3438 [Google Scholar]
  157. Wen WW, Li K, Alseekh S, Omranian N, Zhao LJ. 157.  et al. 2015. Genetic determinants of the network of primary metabolism and their relationships to plant performance in a maize recombinant inbred line population. Plant Cell 27:1839–56 [Google Scholar]
  158. Weng J-K, Philippe RN, Noel JP. 158.  2012. The rise of chemodiversity in plants. Science 336:1667–70 [Google Scholar]
  159. White O. 159.  1917. Studies of inheritance in Pisum. II. The present state of knowledge of heredity and variation in peas. Proc. Am. Philos. Soc. 56:487–588 [Google Scholar]
  160. Wu S, Alseekh S, Cuadros-Inostroza A, Fusari CM, Mutwil M. 160.  et al. 2016. Combined use of genome-wide association data and correlation networks unravels key regulators of primary metabolism in Arabidopsis thaliana. PLOS Genet 12:1006363 [Google Scholar]
  161. Xu Q, Chen L-L, Ruan X, Chen D, Zhu A. 161.  et al. 2013. The draft genome of sweet orange (Citrus sinensis). Nat. Genet. 45:59–66 [Google Scholar]
  162. Yang ZG, Nakabayashi R, Okazaki Y, Mori T, Takamatsu S. 162.  et al. 2014. Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses. Metabolomics 10:543–55 [Google Scholar]
  163. Yonekura-Sakakibara K, Hanada K. 163.  2011. An evolutionary view of functional diversity in family 1 glycosyltransferases. Plant J 66:182–93 [Google Scholar]
  164. Yonekura-Sakakibara K, Nakabayashi R, Sugawara S, Tohge T, Ito T. 164.  et al. 2014. A flavonoid 3-O-glucoside:2′′-O-glucosyltransferase responsible for terminal modification of pollen-specific flavonols in Arabidopsis thaliana. Plant J 79:769–82 [Google Scholar]
  165. Zamir D. 165.  2013. Where have all the crop phenotypes gone?. PLOS Biol 11:e1001595 [Google Scholar]
  166. Zanor MI, Rambla JL, Chaib J, Steppa A, Medina A. 166.  et al. 2009. Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents. J. Exp. Bot. 60:2139–54 [Google Scholar]
  167. Zeng P, Zhao Y, Qian C, Zhang L, Zhang R. 167.  et al. 2015. Statistical analysis for genome-wide association study. J. Biomed. Res 29285–97 [Google Scholar]
  168. Zhang B, Tieman DM, Jiao C, Xu YM, Chen KS. 168.  et al. 2016. Chilling-induced tomato flavor loss is associated with altered volatile synthesis and transient changes in DNA methylation. PNAS 113:12580–85 [Google Scholar]
  169. Zhang JZ. 169.  2003. Evolution by gene duplication: an update. Trends Ecol. Evol. 18:292–98 [Google Scholar]
/content/journals/10.1146/annurev-genet-120116-024640
Loading
The Genetics of Plant Metabolism
Annual Review of Genetics 51, 287 (2017); https://doi.org/10.1146/annurev-genet-120116-024640
/content/journals/10.1146/annurev-genet-120116-024640
/content/journals/10.1146/annurev-genet-120116-024640
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error