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Annual Review of Plant Biology

Volume 66, 2015

Moving Toward a Comprehensive Map of Central Plant Metabolism

Abstract

Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

Keyword(s): databases, flux, metabolism, perturbation, photosynthesis, systems biology
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/content/journals/10.1146/annurev-arplant-043014-114720
2015-04-29
2025-04-27
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Literature Cited

  1. Ainsworth EA, Beier C, Calfapietra C, Ceulemans R, Durand-Tardif M. 1.  et al. 2008. Next generation of elevated [CO2] experiments with crops: a critical investment for feeding the future world. Plant Cell Environ. 31:1317–24 [Google Scholar]
  2. Albinsky D, Kusano M, Higuchi M, Hayashi N, Kobayashi M. 2.  et al. 2010. Metabolomic screening applied to rice FOX Arabidopsis lines leads to the identification of a gene-changing nitrogen metabolism. Mol. Plant 3:125–42 [Google Scholar]
  3. Allwood JW, Parker D, Beckmann M, Draper J, Goodacre R. 3.  2012. Fourier transform ion cyclotron resonance mass spectrometry for plant metabolite profiling and metabolite identification. Plant Metabolomics: Methods and Protocols NW Hardy, RD Hall 157–76 Methods Mol. Biol 860 New York: Humana [Google Scholar]
  4. Alonso-Blanco C, Koornneef M. 4.  2000. Naturally occurring variation in Arabidopsis: an underexploited resource for plant genetics. Trends Plant Sci. 5:22–29 [Google Scholar]
  5. Amiour N, Imbaud S, Clément G, Agier N, Zivy M. 5.  et al. 2012. The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize. J. Exp. Bot. 63:5017–33 [Google Scholar]
  6. Araus JL, Cairns JE. 6.  2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19:52–61 [Google Scholar]
  7. Arrivault S, Guenther M, Florian A, Encke B, Feil R. 7.  et al. 2014. Dissecting the subcellular compartmentation of proteins and metabolites in Arabidopsis leaves using non-aqueous fractionation. Mol. Cell. Proteomics 13:2246–59 [Google Scholar]
  8. Baginsky S, Hennig L, Zimmermann P, Gruissem W. 8.  2010. Gene expression analysis, proteomics, and network discovery. Plant Physiol. 152:402–10 [Google Scholar]
  9. Barratt DHP, Derbyshire P, Findlay K, Pike M, Wellner N. 9.  et al. 2009. Normal growth of Arabidopsis requires cytosolic invertase but not sucrose synthase. PNAS 106:13124–29 [Google Scholar]
  10. Bedbrook JR. 10.  1980. Molecular cloning and sequencing of cDNA encoding the precursor to the small subunit of chloroplast ribulose-1,5-bisphosphate carboxylase. Nature 287:692–97 [Google Scholar]
  11. Belhaj K, Chaparro-Garcia A, Kamoun S, Nekrasov V. 11.  2013. Plant genome editing made easy: targeted mutagenesis in model and crop plants using the CRISPR/Cas system. Plant Methods 9:39 [Google Scholar]
  12. Blasing OE, Gibon Y, Günther M, Höhne M, Morcuende R. 12.  et al. 2005. Sugars and circadian regulation make major contributions to the global regulation of diurnal gene expression in Arabidopsis. Plant Cell 17:3257–81 [Google Scholar]
  13. Bourgis F, Kilaru A, Cao X, Ngando-Ebongue G-F, Drira N. 13.  et al. 2011. Comparative transcriptome and metabolite analysis of oil palm and date palm mesocarp that differ dramatically in carbon partitioning. PNAS 108:12527–32 [Google Scholar]
  14. Bradbury LMT, Niehaus TD, Hanson AD. 14.  2013. Comparative genomics approaches to understanding and manipulating plant metabolism. Curr. Opin. Biotechnol. 24:278–84 [Google Scholar]
  15. Bräutigam A, Kajala K, Wullenweber J, Sommer M, Gagneul D. 15.  et al. 2011. An mRNA blueprint for C4 photosynthesis derived from comparative transcriptomics of closely related C3 and C4 species. Plant Physiol. 155:142–56 [Google Scholar]
  16. Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS, Trotter MWB. 16.  2013. The effect of organelle discovery upon sub-cellular protein localisation. J. Proteomics 88:129–40 [Google Scholar]
  17. Chen L-Q, Hou B-H, Lalonde S, Takanaga H, Hartung ML. 17.  et al. 2010. Sugar transporters for intercellular exchange and nutrition of pathogens. Nature 468:527–32 [Google Scholar]
  18. Chen L-Q, Qu X-Q, Hou B-H, Sosso D, Osorio S. 18.  et al. 2012. Sucrose efflux mediated by SWEET proteins as a key step for phloem transport. Science 335:207–11 [Google Scholar]
  19. Chen W, Gao Y, Xie W, Gong L, Lu K. 19.  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]
  20. Chen W, Gong L, Guo Z, Wang W, Zhang H. 20.  et al. 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 6:1769–80 [Google Scholar]
  21. Cheung CYM, Poolman MG, Fell DA, Ratcliffe RG, Sweetlove LJ. 21.  2014. A diel flux balance model captures interactions between light and dark metabolism during day-night cycles in C3 and Crassulacean acid metabolism leaves. Plant Physiol. 165:917–29 [Google Scholar]
  22. Chia T, Thorneycroft D, Chapple A, Messerli G, Chen J. 22.  et al. 2004. A cytosolic glucosyltransferase is required for conversion of starch to sucrose in Arabidopsis leaves at night. Plant J. 37:853–63 [Google Scholar]
  23. Coleman HD, Yan J, Mansfield SD. 23.  2009. Sucrose synthase affects carbon partitioning to increase cellulose production and altered cell wall ultrastructure. PNAS 106:13118–23 [Google Scholar]
  24. Critchley JH, Zeeman SC, Takaha T, Smith AM, Smith SM. 24.  2001. A critical role for disproportionating enzyme in starch breakdown is revealed by a knock-out mutation in Arabidopsis. Plant J. 26:89–100 [Google Scholar]
  25. Cross JM, von Korff M, Altmann T, Bartzetko L, Sulpice R. 25.  et al. 2006. Variation of enzyme activities and metabolite levels in 24 Arabidopsis accessions growing in carbon-limited conditions. Plant Physiol. 142:1574–88 [Google Scholar]
  26. Cui H, Hao Y, Kong D. 26.  2012. SCARECROW has a SHORT-ROOT-independent role in modulating the sugar response. Plant Physiol. 158:1769–78 [Google Scholar]
  27. Curien G, Bastlen O, Robert-Genthon M, Cornish-Bowden A, Cárdenas ML, Dumas R. 27.  2009. Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters. Mol. Syst. Biol. 5:271 [Google Scholar]
  28. Curien G, Biou V, Mas-Droux C, Robert-Genthon M, Ferrer JL, Dumas R. 28.  2008. Amino acid biosynthesis: new architectures in allosteric enzymes. Plant Physiol. Biochem. 46:325–39 [Google Scholar]
  29. Curien G, Ravanel S, Robert M, Dumas R. 29.  2005. Identification of six novel allosteric effectors of Arabidopsis thaliana aspartate kinase-homoserine dehydrogenase isoforms: Physiological context sets the specificity. J. Biol. Chem. 280:41178–83 [Google Scholar]
  30. Dasgupta K, Khadilkar AS, Sulpice R, Pant B, Scheible W-R. 30.  et al. 2014. Expression of sucrose transporter cDNAs specifically in companion cells enhances phloem loading and long-distance transport of sucrose but leads to an inhibition of growth and the perception of a phosphate limitation. Plant Physiol. 165:715–31 [Google Scholar]
  31. de Oliveira Dal'Molin CG, Quek L-E, Palfreyman RW, Brumbley SM, Nielsen LK. 31.  2010. AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol. 152:579–89 [Google Scholar]
  32. de Oliveira Dal'Molin CG, Quek L-E, Palfreyman RW, Brumbley SM, Nielsen LK. 32.  2010. C4GEM, a genome-scale metabolic model to study C4 plant metabolism. Plant Physiol. 154:1871–85 [Google Scholar]
  33. Detarsio E, Alvarez CE, Saigo M, Andreo CS, Drincovich MF. 33.  2007. Identification of domains involved in tetramerization and malate inhibition of maize C4-NADP-malic enzyme. J. Biol. Chem. 282:6053–60 [Google Scholar]
  34. Dodd AN, Salathia N, Hall A, Kévei E, Tóth R. 34.  et al. 2005. Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science 309:630–33 [Google Scholar]
  35. Dreze M, Carvunis A-R, Charloteaux B, Galli M, Pevzner SJ. 35.  et al. 2011. Evidence for network evolution in an Arabidopsis interactome map. Science 333:601–7 [Google Scholar]
  36. Durek P, Walther D. 36.  2008. The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles. BMC Syst. Biol. 2:100 [Google Scholar]
  37. Fernie AR, Aharoni A, Willmitzer L, Stitt M, Tohge T. 37.  et al. 2011. Recommendations for reporting metabolite data. Plant Cell 23:2477–82 [Google Scholar]
  38. Fernie AR, Keurentjes JJ. 38.  2011. Genetics, genomics and metabolomics. Biology of Plant Metabolomics RD Hall 219–59 Annu. Plant Rev 43 Chichester, UK: Wiley-Blackwell [Google Scholar]
  39. Fernie AR, Klee HJ. 39.  2011. The use of natural genetic diversity in the understanding of metabolic organization and regulation. Front. Plant Sci. 2:59 [Google Scholar]
  40. Fernie AR, Stitt M. 40.  2012. On the discordance of metabolomics with proteomics and transcriptomics: coping with increasing complexity in logic, chemistry, and network interactions scientific correspondence. Plant Physiol. 158:1139–45 [Google Scholar]
  41. Fiehn O, Robertson D, Griffin J, van der Werf M, Nikolau B. 41.  et al. 2007. The metabolomics standards initiative (MSI). Metabolomics 3:175–78 [Google Scholar]
  42. Fiehn O, Wohlgemuth G, Scholz M, Kind T, Lee DY. 42.  et al. 2008. Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J. 53:691–704 [Google Scholar]
  43. Fiorani F, Schurr U. 43.  2013. Future scenarios for plant phenotyping. Annu. Rev. Plant Biol. 64:267–91 [Google Scholar]
  44. Fitzgerald TL, Kazan K, Manners JM. 44.  2012. The application of reverse genetics to polyploid plant species. Crit. Rev. Plant Sci. 31:181–200 [Google Scholar]
  45. Flood PJ, Harbinson J, Aarts MG. 45.  2011. Natural genetic variation in plant photosynthesis. Trends Plant Sci. 16:327–35 [Google Scholar]
  46. Foyer CH, Bloom AJ, Queval G, Noctor G. 46.  2009. Photorespiratory metabolism: genes, mutants, energetics, and redox signaling. Annu. Rev. Plant Biol. 60:455–84 [Google Scholar]
  47. Fukayama H, Ueguchi C, Nishikawa K, Katoh N, Ishikawa C. 47.  et al. 2012. Overexpression of Rubisco activase decreases the photosynthetic CO2 assimilation rate by reducing Rubisco content in rice leaves. Plant Cell Physiol. 53:976–86 [Google Scholar]
  48. Fukushima A, Kusano M. 48.  2013. Recent progress in the development of metabolome databases for plant systems biology. Front. Plant Sci. 4:73 [Google Scholar]
  49. Fukushima A, Kusano M, Mejia RF, Iwasa M, Kobayashi M. 49.  et al. 2014. Metabolomic characterization of knockout mutants in Arabidopsis: development of a metabolite profiling database for knockout mutants in Arabidopsis. Plant Physiol. 165:948–61 [Google Scholar]
  50. Fukushima A, Kusano M, Redestig H, Arita M, Saito K. 50.  2009. Integrated omics approaches in plant systems biology. Curr. Opin. Chem. Biol. 13:532–38 [Google Scholar]
  51. Fulton DC, Stettler M, Mettler T, Vaughan CK, Li J. 51.  et al. 2008. β-AMYLASE4, a noncatalytic protein required for starch breakdown, acts upstream of three active β-amylases in Arabidopsis chloroplasts. Plant Cell 20:1040–58 [Google Scholar]
  52. Génard M, Baldazzi V, Gibon Y. 52.  2014. Metabolic studies in plant organs: Don't forget dilution by growth. Front. Plant Sci. 5:85 [Google Scholar]
  53. Gibon Y, Blaesing OE, Palacios-Rojas N, Pankovic D, Hendriks JH. 53.  et al. 2004. Adjustment of diurnal starch turnover to short days: depletion of sugar during the night leads to a temporary inhibition of carbohydrate utilization, accumulation of sugars and post-translational activation of ADP-glucose pyrophosphorylase in the following light period. Plant J. 39:847–62 [Google Scholar]
  54. Gibon Y, Pyl E-T, Sulpice R, Lunn JE, Höhne M. 54.  et al. 2009. Adjustment of growth, starch turnover, protein content and central metabolism to a decrease of the carbon supply when Arabidopsis is grown in very short photoperiods. Plant Cell Environ. 32:859–74 [Google Scholar]
  55. Grafahrend-Belau E, Junker A, Eschenröder A, Müller J, Schreiber F, Junker BH. 55.  2013. Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale. Plant Physiol. 163:637–47 [Google Scholar]
  56. Graham JWA, Williams TCR, Morgan M, Fernie AR, Ratcliffe RG, Sweetlove LJ. 56.  2007. Glycolytic enzymes associate dynamically with mitochondria in response to respiratory demand and support substrate channeling. Plant Cell 19:3723–38 [Google Scholar]
  57. Hadrich N, Hendriks JHM, Kotting O, Arrivault S, Feil R. 57.  et al. 2012. Mutagenesis of cysteine 81 prevents dimerization of the APS1 subunit of ADP-glucose pyrophosphorylase and alters diurnal starch turnover in Arabidopsis thaliana leaves. Plant J. 70:231–42 [Google Scholar]
  58. Hanson A, Pribat A, Waller J. 58.  2010. “Unknown” proteins and “orphan” enzymes: the missing half of the engineering parts list—and how to find it. Biochem. J. 425:1–11 [Google Scholar]
  59. Harmer SL. 59.  2009. The circadian system in higher plants. Annu. Rev. Plant Biol. 60:357–77 [Google Scholar]
  60. Haydon MJ, Mielczarek O, Robertson FC, Hubbard KE, Webb AAR. 60.  2013. Photosynthetic entrainment of the Arabidopsis thaliana circadian clock. Nature 502:689 [Google Scholar]
  61. Heise R, Arrivault S, Szecowka M, Tohge T, Nunes-Nesi A. 61.  et al. 2014. Flux profiling of photosynthetic carbon metabolism in intact plants. Nat. Protoc. 9:1803–24 [Google Scholar]
  62. Heisel TJ, Li CY, Grey KM, Gibson SI. 62.  2013. Mutations in HISTONE ACETYLTRANSFERASE1 affect sugar response and gene expression in Arabidopsis. Front. Plant Sci. 4:245 [Google Scholar]
  63. Hotta CT, Gardner MJ, Hubbard KE, Baek SJ, Dalchau N. 63.  et al. 2007. Modulation of environmental responses of plants by circadian clocks. Plant Cell Environ. 30:333–49 [Google Scholar]
  64. Hummel I, Pantin F, Sulpice R, Piques M, Rolland G. 64.  et al. 2010. Arabidopsis plants acclimate to water deficit at low cost through changes of carbon usage: an integrated perspective using growth, metabolite, enzyme, and gene expression analysis. Plant Physiol. 154:357–72 [Google Scholar]
  65. Igamberdiev AU, Eprintsev AT, Fedorin DN, Popov VN. 65.  2014. Phytochrome-mediated regulation of plant respiration and photorespiration. Plant Cell Environ. 37:290–99 [Google Scholar]
  66. Ito J, Batth TS, Petzold CJ, Redding-Johanson AM, Mukhopadhyay A. 66.  et al. 2011. Analysis of the Arabidopsis cytosolic proteome highlights subcellular partitioning of central plant metabolism. J. Proteome Res. 10:1571–82 [Google Scholar]
  67. Jiang W, Zhou H, Bi H, Fromm M, Yang B, Weeks DP. 67.  2013. Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res. 41:e188 [Google Scholar]
  68. Jiang Y, Guo W, Zhu H, Ruan Y-L, Zhang T. 68.  2012. Overexpression of GhSusA1 increases plant biomass and improves cotton fiber yield and quality. Plant Biotechnol. J. 10:301–12 [Google Scholar]
  69. Jones AM, Grossmann G, Danielson JAH, Sosso D, Chen LQ. 69.  et al. 2013. In vivo biochemistry: applications for small molecule biosensors in plant biology. Curr. Opin. Plant Biol. 16:389–95 [Google Scholar]
  70. Juntawong P, Girke T, Bazin J, Bailey-Serres J. 70.  2014. Translational dynamics revealed by genome-wide profiling of ribosome footprints in Arabidopsis. PNAS 111:E203–12 [Google Scholar]
  71. Kacser H, Burns J. 71.  1979. Molecular democracy: Who shares the controls?. Biochem. Soc. Trans. 7:1149–60 [Google Scholar]
  72. Keurentjes JJ, Fu J, De Vos CR, Lommen A, Hall RD. 72.  et al. 2006. The genetics of plant metabolism. Nat. Genet. 38:842–49 [Google Scholar]
  73. Keurentjes JJ, Sulpice R, Gibon Y, Steinhauser M-C, Fu J. 73.  et al. 2008. Integrative analyses of genetic variation in enzyme activities of primary carbohydrate metabolism reveal distinct modes of regulation in Arabidopsis thaliana. Genome Biol. 9:R129 [Google Scholar]
  74. Kim HK, Choi YH, Verpoorte R. 74.  2010. NMR-based metabolomic analysis of plants. Nat. Protoc. 5:536–49 [Google Scholar]
  75. Kim HK, Choi YH, Verpoorte R. 75.  2011. NMR-based plant metabolomics: Where do we stand, where do we go?. Trends Biotechnol. 29:267–75 [Google Scholar]
  76. Kondou Y, Higuchi M, Matsui M. 76.  2010. High-throughput characterization of plant gene functions by using gain-of-function technology. Annu. Rev. Plant Biol. 61:373–93 [Google Scholar]
  77. Krouk G, Lingeman J, Colon AM, Coruzzi G, Shasha D. 77.  2013. Gene regulatory networks in plants: learning causality from time and perturbation. Genome Biol. 14:123 [Google Scholar]
  78. Krueger S, Giavalisco P, Krall L, Steinhauser M-C, Büssis D. 78.  et al. 2011. A topological map of the compartmentalized Arabidopsis thaliana leaf metabolome. PLOS ONE 6:e17806 [Google Scholar]
  79. Krueger S, Steinhauser D, Lisec J, Giavalisco P. 79.  2014. Analysis of subcellular metabolite distributions within Arabidopsis thaliana leaf tissue: a primer for subcellular metabolomics. Arabidopsis Protocols JJ Sanchez-Serrano, J Salinas 575–96 Methods Mol. Biol 1062 New York: Humana, 3rd ed.. [Google Scholar]
  80. Kueger S, Steinhauser D, Willmitzer L, Giavalisco P. 80.  2012. High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions. Plant J. 70:39–50 [Google Scholar]
  81. Lau W, Fischbach MA, Osbourn A, Sattely ES. 81.  2014. Key applications of plant metabolic engineering. PLOS Biol. 12:e1001879 [Google Scholar]
  82. Less H, Angelovici R, Tzin V, Galili G. 82.  2011. Coordinated gene networks regulating Arabidopsis plant metabolism in response to various stresses and nutritional cues. Plant Cell 23:1264–71 [Google Scholar]
  83. Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE. 83.  et al. 2012. RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res. 40:W622–W7 [Google Scholar]
  84. Marroni F, Pinosio S, Morgante M. 84.  2012. The quest for rare variants: pooled multiplexed next generation sequencing in plants. Front. Plant Sci. 3:133 [Google Scholar]
  85. Martins MC, Hejazi M, Fettke J, Steup M, Feil R. 85.  et al. 2013. Feedback inhibition of starch degradation in Arabidopsis leaves mediated by trehalose 6-phosphate. Plant Physiol. 163:1142–63 [Google Scholar]
  86. Massonnet C, Vile D, Fabre J, Hannah MA, Caldana C. 86.  et al. 2010. Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiol. 152:2142–57 [Google Scholar]
  87. Matros A, Kaspar S, Witzel K, Mock H-P. 87.  2011. Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics. Phytochemistry 72:963–74 [Google Scholar]
  88. Mayhew TM. 88.  2011. Quantifying immunogold localization on electron microscopic thin sections: a compendium of new approaches for plant cell biologists. J. Exp. Bot. 62:4101–13 [Google Scholar]
  89. Michelet L, Zaffagnini M, Morisse S, Sparla F, Perez-Perez ME. 89.  et al. 2013. Redox regulation of the Calvin-Benson cycle: something old, something new. Front. Plant Sci. 4:470 [Google Scholar]
  90. Mintz-Oron S, Meir S, Malitsky S, Ruppin E, Aharoni A, Shlomi T. 90.  2012. Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. PNAS 109:339–44 [Google Scholar]
  91. Mittler R, Blumwald E. 91.  2010. Genetic engineering for modern agriculture: challenges and perspectives. Annu. Rev. Plant Biol. 61:443–62 [Google Scholar]
  92. Moreno-Risueno MA, Busch W, Benfey PN. 92.  2010. Omics meet networks—using systems approaches to infer regulatory networks in plants. Curr. Opin. Plant Biol. 13:126–31 [Google Scholar]
  93. Muller B, Pantin F, Génard M, Turc O, Freixes S. 93.  et al. 2011. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J. Exp. Bot. 62:1715–29 [Google Scholar]
  94. Nakamura H, Hakata M, Amano K, Miyao A, Toki N. 94.  et al. 2007. A genome-wide gain-of-function analysis of rice genes using the FOX-hunting system. Plant Mol. Biol. 65:357–71 [Google Scholar]
  95. Nakamura H, Muramatsu M, Hakata M, Ueno O, Nagamura Y. 95.  et al. 2009. Ectopic overexpression of the transcription factor OsGLK1 induces chloroplast development in non-green rice cells. Plant Cell Physiol. 50:1933–49 [Google Scholar]
  96. Ng DW-K, Miller M, Helen HY, Huang T-Y, Kim E-D. 96.  et al. 2014. A role for CHH methylation in the parent-of-origin effect on altered circadian rhythms and biomass heterosis in Arabidopsis intraspecific hybrids. Plant Cell 26:2430–40 [Google Scholar]
  97. Niittyla T, Messerli G, Trevisan M, Chen J, Smith AM, Zeeman SC. 97.  2004. A previously unknown maltose transporter essential for starch degradation in leaves. Science 303:87–89 [Google Scholar]
  98. Osorio S, Alba R, Damasceno CM, Lopez-Casado G, Lohse M. 98.  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) mutants reveals novel regulatory interactions. Plant Physiol. 157:405–25 [Google Scholar]
  99. Pal SK, Liput M, Piques M, Ishihara H, Obata T. 99.  et al. 2013. Diurnal changes of polysome loading track sucrose content in the rosette of wild-type Arabidopsis and the starchless pgm mutant. Plant Physiol. 162:1246–65 [Google Scholar]
  100. Paparelli E, Gonzali S, Parlanti S, Novi G, Giorgi FM. 100.  et al. 2012. Misexpression of a chloroplast aspartyl protease leads to severe growth defects and alters carbohydrate metabolism in Arabidopsis. Plant Physiol. 160:1237–50 [Google Scholar]
  101. Pilalis E, Chatziioannou A, Thomasset B, Kolisis F. 101.  2011. An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism. Biotechnol. Bioeng. 108:1673–82 [Google Scholar]
  102. Poorter H, Bühler J, van Dusschoten D, Climent J, Postma JA. 102.  2012. Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Funct. Plant Biol. 39:839–50 [Google Scholar]
  103. Poorter H, Fiorani F, Stitt M, Schurr U, Finck A. 103.  et al. 2012. The art of growing plants for experimental purposes: a practical guide for the plant biologist. Funct. Plant Biol. 39:821–38 [Google Scholar]
  104. Rennenberg H, Herschbach C. 104.  2014. A detailed view on sulphur metabolism at the cellular and whole-plant level illustrates challenges in metabolite flux analyses. J. Exp. Bot. 65:5711–24 [Google Scholar]
  105. Rios-Estepa R, Lange BM. 105.  2007. Experimental and mathematical approaches to modeling plant metabolic networks. Phytochemistry 68:2351–74 [Google Scholar]
  106. Roessner-Tunali U, Hegemann B, Lytovchenko A, Carrari F, Bruedigam C. 106.  et al. 2003. Metabolic profiling of transgenic tomato plants overexpressing Hexokinase reveals that the influence of hexose phosphorylation diminishes during fruit development. Plant Physiol. 133:84–99 [Google Scholar]
  107. Rohwer JM. 107.  2012. Kinetic modelling of plant metabolic pathways. J. Exp. Bot. 63:2275–92 [Google Scholar]
  108. Sakurai T, Kondou Y, Akiyama K, Kurotani A, Higuchi M. 108.  et al. 2011. RiceFOX: a database of Arabidopsis mutant lines overexpressing rice full-length cDNA that contains a wide range of trait information to facilitate analysis of gene function. Plant Cell Physiol. 52:265–73 [Google Scholar]
  109. Sala A, Woodruff DR, Meinzer FC. 109.  2012. Carbon dynamics in trees: feast or famine?. Tree Physiol. 32:764–75 [Google Scholar]
  110. Santos PR, Saviani EE, Salgado I, Oliveira HC. 110.  2014. The effect of nitrate assimilation deficiency on the carbon and nitrogen status of Arabidopsis thaliana plants. Amino Acids 46:1121–29 [Google Scholar]
  111. Schliesky S, Gowik U, Weber APM, Brautigam A. 111.  2012. RNA-seq assembly—are we there yet?. Front. Plant Sci. 3:220 [Google Scholar]
  112. Schulze WX, Usadel B. 112.  2010. Quantitation in mass-spectrometry-based proteomics. Annu. Rev. Plant Biol. 61:491–516 [Google Scholar]
  113. Seo Y-S, Chern M, Bartley LE, Han M, Jung K-H. 113.  et al. 2011. Towards establishment of a rice stress response interactome. PLOS Genet. 7:e1002020 [Google Scholar]
  114. Shachar-Hill Y. 114.  2013. Metabolic network flux analysis for engineering plant systems. Curr. Opin. Biotechnol. 24:247–55 [Google Scholar]
  115. Sheth BP, Thaker VS. 115.  2014. Plant systems biology: insights, advances and challenges. Planta 240:33–54 [Google Scholar]
  116. Shuman JL, Cortes DF, Armenta JM, Pokrzywa RM, Mendes P, Shulaev V. 116.  2011. Plant metabolomics by GC-MS and differential analysis. Plant Reverse Genetics: Methods and Protocols A Pereira 229–46 Methods Mol. Biol 679 New York: Humana [Google Scholar]
  117. Skeffington AW, Graf A, Duxbury Z, Gruissem W, Smith AM. 117.  2014. Glucan, water dikinase exerts little control over starch degradation in Arabidopsis leaves at night. Plant Physiol. 165:866–79 [Google Scholar]
  118. Stettler M, Eicke S, Mettler T, Messerli G, Hortensteiner S, Zeeman SC. 118.  2009. Blocking the metabolism of starch breakdown products in Arabidopsis leaves triggers chloroplast degradation. Mol. Plant 2:1233–46 [Google Scholar]
  119. Stitt M. 119.  2013. Progress in understanding and engineering primary plant metabolism. Curr. Opin. Biotechnol. 24:229–38 [Google Scholar]
  120. Stitt M, Gibon Y. 120.  2014. Why measure enzyme activities in the era of systems biology?. Trends Plant Sci. 19:256–65 [Google Scholar]
  121. Stitt M, Sulpice R, Keurentjes J. 121.  2010. Metabolic networks: how to identify key components in the regulation of metabolism and growth. Plant Physiol. 152:428–44 [Google Scholar]
  122. Stitt M, Zeeman SC. 122.  2012. Starch turnover: pathways, regulation and role in growth. Curr. Opin. Plant Biol. 15:282–92 [Google Scholar]
  123. Streb S, Eicke S, Zeeman SC. 123.  2012. The simultaneous abolition of three starch hydrolases blocks transient starch breakdown in Arabidopsis. J. Biol. Chem. 287:41745–56 [Google Scholar]
  124. Sulpice R, Flis A, Ivakov AA, Apelt F, Krohn N. 124.  et al. 2014. Arabidopsis coordinates the diurnal regulation of carbon allocation and growth across a wide range of photoperiods. Mol. Plant 7:137–55 [Google Scholar]
  125. Sulpice R, Nikoloski Z, Tschoep H, Antonio C, Kleessen S. 125.  et al. 2013. Impact of the carbon and nitrogen supply on relationships and connectivity between metabolism and biomass in a broad panel of Arabidopsis accessions. Plant Physiol. 162:347–63 [Google Scholar]
  126. Sulpice R, Pyl E-T, Ishihara H, Trenkamp S, Steinfath M. 126.  et al. 2009. Starch as a major integrator in the regulation of plant growth. PNAS 106:10348–53 [Google Scholar]
  127. Sulpice R, Trenkamp S, Steinfath M, Usadel B, Gibon Y. 127.  et al. 2010. Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions. Plant Cell 22:2872–93 [Google Scholar]
  128. Sundberg M, Pfister B, Fulton D, Bischof S, Delatte T. 128.  et al. 2013. The heteromultimeric debranching enzyme involved in starch synthesis in Arabidopsis requires both isoamylase1 and isoamylase2 subunits for complex stability and activity. PLOS ONE 8:e75223 [Google Scholar]
  129. Sweetlove LJ, Beard KF, Nunes-Nesi A, Fernie AR, Ratcliffe RG. 129.  2010. Not just a circle: flux modes in the plant TCA cycle. Trends Plant Sci. 15:462–70 [Google Scholar]
  130. Sweetlove LJ, Fernie AR. 130.  2013. The spatial organization of metabolism within the plant cell. Annu. Rev. Plant Biol. 64:723–46 [Google Scholar]
  131. Sweetlove LJ, Obata T, Fernie AR. 131.  2014. Systems analysis of metabolic phenotypes: What have we learnt?. Trends Plant Sci. 19:222–30 [Google Scholar]
  132. Sweetlove LJ, Ratcliffe RG. 132.  2011. Flux-balance modeling of plant metabolism. Front. Plant Sci. 2:38 [Google Scholar]
  133. Szecowka M, Heise R, Tohge T, Nunes-Nesi A, Vosloh D. 133.  et al. 2013. Metabolic fluxes in an illuminated Arabidopsis rosette. Plant Cell 25:694–714 [Google Scholar]
  134. Takahashi H, Kopriva S, Giordano M, Saito K, Hell R. 134.  2011. Sulfur assimilation in photosynthetic organisms: molecular functions and regulations of transporters and assimilatory enzymes. Annu. Rev. Plant Biol. 62:157–84 [Google Scholar]
  135. Thorneycroft D, Sherson SM, Smith SM. 135.  2001. Using gene knockouts to investigate plant metabolism. J. Exp. Bot. 52:1593–601 [Google Scholar]
  136. Tohge T, Fernie AR. 136.  2010. Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function. Nat. Protoc. 5:1210–27 [Google Scholar]
  137. Trethewey RN, Aprees T. 137.  1994. A mutant of Arabidopsis thaliana lacking the ability to transport glucose across the chloroplast envelope. Biochem. J. 301:449–54 [Google Scholar]
  138. Tschoep H, Gibon Y, Carillo P, Armengaud P, Szecowka M. 138.  et al. 2009. Adjustment of growth and central metabolism to a mild but sustained nitrogen-limitation in Arabidopsis. Plant Cell Environ. 32:300–18 [Google Scholar]
  139. Van Daele I, Gonzalez N, Vercauteren I, de Smet L, Inzé D. 139.  et al. 2012. A comparative study of seed yield parameters in Arabidopsis thaliana mutants and transgenics. Plant Biotechnol. J. 10:488–500 [Google Scholar]
  140. Van der Kooij TAW, De Kok LJ. 140.  1996. Impact of elevated CO2 on growth and development of Arabidopsis thaliana L. Phyton 36:173–84 [Google Scholar]
  141. Vasseur F, Pantin F, Vile D. 141.  2011. Changes in light intensity reveal a major role for carbon balance in Arabidopsis responses to high temperature. Plant Cell Environ. 34:1563–76 [Google Scholar]
  142. Voss U, Larrieu A, Wells DM. 142.  2013. From jellyfish to biosensors: the use of fluorescent proteins in plants. Int. J. Dev. Biol. 57:525–33 [Google Scholar]
  143. Voytas DF. 143.  2013. Plant genome engineering with sequence-specific nucleases. Annu. Rev. Plant Biol. 64:327–50 [Google Scholar]
  144. Weber APM, Bräutigam A. 144.  2013. The role of membrane transport in metabolic engineering of plant primary metabolism. Curr. Opin. Biotechnol. 24:256–62 [Google Scholar]
  145. Weckwerth W. 145.  2011. Green systems biology—from single genomes, proteomes and metabolomes to ecosystems research and biotechnology. J. Proteomics 75:284–305 [Google Scholar]
  146. Whitaker WR, Dueber JE. 146.  2011. Metabolic pathway flux enhancement by synthetic protein scaffolding. Methods Enzymol. 497:447–68 [Google Scholar]
  147. Xing S, Poirier Y. 147.  2012. The protein acetylome and the regulation of metabolism. Trends Plant Sci. 17:423–30 [Google Scholar]
  148. Xu S-M, Brill E, Llewellyn DJ, Furbank RT, Ruan Y-L. 148.  2012. Overexpression of a potato sucrose synthase gene in cotton accelerates leaf expansion, reduces seed abortion, and enhances fiber production. Mol. Plant 5:430–41 [Google Scholar]
  149. Yonekura-Sakakibara K, Fukushima A, Saito K. 149.  2013. Transcriptome data modeling for targeted plant metabolic engineering. Curr. Opin. Biotechnol. 24:285–90 [Google Scholar]
  150. Zamir D. 150.  2013. Where have all the crop phenotypes gone?. PLOS Biol. 11:e1001595 [Google Scholar]
  151. Zeeman SC, Thorneycroft D, Schupp N, Chapple A, Weck M. 151.  et al. 2004. Plastidial α-glucan phosphorylase is not required for starch degradation in Arabidopsis leaves but has a role in the tolerance of abiotic stress. Plant Physiol. 135:849–58 [Google Scholar]
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Moving Toward a Comprehensive Map of Central Plant Metabolism
Annual Review of Plant Biology 66, 187 (2015); https://doi.org/10.1146/annurev-arplant-043014-114720
/content/journals/10.1146/annurev-arplant-043014-114720
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