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Insights into the spatial and temporal organisation of plant metabolism from network flux analysis

  • Thiago Batista Moreira
  • Janderson Moraes Lima
  • Guilherme Carvalho Coca
  • Thomas Christopher Rhys Williams
Article
  • 39 Downloads

Abstract

The great complexity of plant metabolism, with multiple subcellular compartments, cell types, tissues and organs, greatly complicates its analysis. This problem is compounded by the extensive changes that occur in metabolism over time, both during day/night cycles and throughout the course of an entire life cycle. In this context in silico network flux analysis, including both isotope labelling and stoichiometric modelling approaches, represent an important tool to study plant metabolism, providing a framework for the integration of data from a number of experimental strategies. Together, these methods have provided insight into the subcellular distribution of metabolic flux, the interactions between different cell types, source and sink relationships between organs, and how metabolic flux alters over time both at the cellular and whole plant scale. In this review we discuss how network flux analysis has contributed to our understanding of the spatial and temporal organisation of plant metabolism, looking in detail at key studies that address questions surrounding plant metabolism at different scales, from subcellular to whole plant.

Keywords

Metabolic flux analysis Flux balance analysis Compartmentation Metabolic modelling Spatiotemporal organisation 

Notes

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The authors would also like to thank the Fundação de Apoio a Pesquisa do Distrito Federal (FAPDF) for support in the form of a doctoral grant for TBM and research funding for TCRW (Processo 193.000.193/2014).

References

  1. Allen DK (2016) Quantifying plant phenotypes with isotopic labeling & metabolic flux analysis. Curr Opin Biotechnol 37:45–52.  https://doi.org/10.1016/j.copbio.2015.10.002 CrossRefPubMedGoogle Scholar
  2. Allen DK, Young JD (2013) Carbon and nitrogen provisions alter the metabolic flux in developing soybean embryos. Plant Physiol 161:1458–1475.  https://doi.org/10.1104/pp.112.203299 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Allen DK, Shachar-Hill Y, Ohlrogge JB (2007) Compartment-specific labeling information in 13C metabolic flux analysis of plants. Phytochemistry 68:2197–2210.  https://doi.org/10.1016/j.phytochem.2007.04.010 CrossRefPubMedGoogle Scholar
  4. Allen DK, Ohlrogge JB, Shachar-Hill Y (2009) The role of light in soybean seed filling metabolism. Plant J 58:220–234.  https://doi.org/10.1111/j.1365-313X.2008.03771.x CrossRefPubMedGoogle Scholar
  5. Alonso AP, Goffman FD, Ohlrogge JB, Shachar-Hill Y (2007a) Carbon conversion efficiency and central metabolic fluxes in developing sunflower (Helianthus annuus L.) embryos. Plant J 52:296–308.  https://doi.org/10.1111/j.1365-313X.2007.03235.x CrossRefPubMedGoogle Scholar
  6. Alonso AP, Raymond P, Hernould M, Rondeau-Mouro C, de Graaf AA, Chourey P, Lahaye M, Shachar-Hill Y, Rolin D, Dieuaide-Noubhani M (2007b) A metabolic flux analysis to study the role of sucrose synthase in the regulation of the carbon partitioning in central metabolism in maize root tips. Metab Eng 9:419–432.  https://doi.org/10.1016/j.ymben.2007.06.002 CrossRefPubMedGoogle Scholar
  7. Alonso AP, Dale VL, Shachar-Hill Y (2010) Understanding fatty acid synthesis in developing maize embryos using metabolic flux analysis. Metab Eng 12:488–497.  https://doi.org/10.1016/j.ymben.2010.04.002 CrossRefPubMedGoogle Scholar
  8. Alonso AP, Val DL, Shachar-Hill Y (2011) Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering. Metab Eng 13:96–107.  https://doi.org/10.1016/j.ymben.2010.10.002 CrossRefPubMedGoogle Scholar
  9. Arrivault S, Obata T, Szecówka M, Mengin V, Guenther M, Hoehne M, Fernie AR, Stitt M (2017) Metabolite pools and carbon flow during C4 photosynthesis in maize: 13CO2 labeling kinetics and cell type fractionation. J Exp Bot 68:283–298.  https://doi.org/10.1093/jxb/erw414 CrossRefPubMedGoogle Scholar
  10. Benfey PN, Scheres B (2000) Root development. Curr Biol 10:813–815CrossRefGoogle Scholar
  11. Biais B, Benard C, Beauvoit B, Colombié S, Prodhomme D, Menard G, Bernillon S, Gehl B, Gautier H, Ballias P, Mazat J-P, Sweetlove LJ, Genard M, Gibon Y (2014) Remarkable reproducibility of enzyme activity profiles in tomato fruits grown under contrasting environments provides a roadmap for studies of fruit metabolism. Plant Physiol 164:1204–1221.  https://doi.org/10.1104/pp.113.231241 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Bogart E, Myers CR (2016) Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves. PLoS ONE 11:e0151722.  https://doi.org/10.1371/journal.pone.0151722 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Borisjuk L, Neuberger T, Schwender J, Heinzel N, Sunderhaus S, Fuchs J, Hay JO, Tschiersch H, Braun H-P, Denolf P, Lambert B, Jakob PM, Rolletschek H (2013) Seed architecture shapes embryo metabolism in oilseed rape. Plant Cell 25:1625–1640.  https://doi.org/10.1105/tpc.113.111740 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Boyle NR, Sengupta N, Morgan JA (2017) Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii. PLoS ONE 12:1–23.  https://doi.org/10.1371/journal.pone.0177292 CrossRefGoogle Scholar
  15. Brady SM, Orlando DA, Lee J-Y, Wang JY, Koch J, Dinneny JR, Mace D, Ohler U, Benfey PN (2007) A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318:801–806.  https://doi.org/10.1126/science.1146265 CrossRefPubMedGoogle Scholar
  16. Chang T-G, Zhu X-G (2017) Source-sink interaction: a century old concept under the light of modern molecular systems biology. J Exp Bot 68:4417–4431.  https://doi.org/10.1093/jxb/erx002 CrossRefPubMedGoogle Scholar
  17. Cheung CYM, Williams TCR, Poolman MG, Fell DA, Ratcliffe RG, Sweetlove LJ (2013) A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions. Plant J 75:1050–1061.  https://doi.org/10.1111/tpj.12252 CrossRefPubMedGoogle Scholar
  18. Cheung CYM, Poolman MG, Fell DA, Ratcliffe RG, Sweetlove LJ (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 165:917–929.  https://doi.org/10.1104/pp.113.234468 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Cheung CYM, Ratcliffe RG, Sweetlove LJ (2015) A method of accounting for enzyme costs in flux balance analysis reveals alternative pathways and metabolite stores in an illuminated Arabidopsis leaf. Plant Physiol.  https://doi.org/10.1104/pp.15.00880 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Colombié S, Nazaret C, Bénard C, Biais B, Mengin V, Solé M, Fouillen L, Dieuaide-Noubhani M, Mazat J-P, Beauvoit B, Gibon Y (2015) Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit. Plant J 81:24–39.  https://doi.org/10.1111/tpj.12685 CrossRefPubMedGoogle Scholar
  21. Colombié S, Beauvoit B, Nazaret C, Bénard C, Vercambre G, Le Gall S, Biais B, Cabasson C, Maucourt M, Bernillon S, Moing A, Dieuaide-Noubhani M, Mazat J-P, Gibon Y (2017) Respiration climacteric in tomato fruits elucidated by constraint-based modelling. New Phytol 213:1726–1739CrossRefPubMedGoogle Scholar
  22. Dal’Molin CGO, Nielsen LK (2018) Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses. Curr Opin Biotechnol 49:42–48CrossRefGoogle Scholar
  23. Dal’Molin CGO, Quek L-E, Palfreyman RW, Brumbley SM, Nielsen LK (2010) C4GEM, a genome-scale metabolic model to study C4 plant metabolism. Plant Physiol 154:1871–1885.  https://doi.org/10.1104/pp.110.166488 CrossRefPubMedGoogle Scholar
  24. Dal’Molin CGO, Quek L-E, Saa PA, Nielsen LK (2015) A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. Front Plant Sci.  https://doi.org/10.3389/fpls.2015.00004 CrossRefGoogle Scholar
  25. Dal’Molin CGO, Orellana C, Gebbie L, Steen J, Hodson MP, Chrysanthopoulos P, Plan MR, McQualter R, Palfreyman RW, Nielsen LK (2016) Metabolic reconstruction of Setaria italica: A systems biology approach for integrating tissue-specific omics and pathway analysis of bioenergy grasses. Front Plant Sci 7:1–18.  https://doi.org/10.3389/fpls.2016.01138 CrossRefGoogle Scholar
  26. Daloso DM, Antunes WC, Pinheiro DP, Waquim JP, Araújo WL, Loureiro ME, Fernie AR, Williams TCR (2015) Tobacco guard cells fix CO2 by both Rubisco and PEPcase while sucrose acts as a substrate during light-induced stomatal opening. Plant Cell Environ 38:2353–2371.  https://doi.org/10.1111/pce.12555 CrossRefPubMedGoogle Scholar
  27. Focks N, Benning C (1998) wrinkled1: a novel, low-seed-oil mutant of Arabidopsis with a deficiency in the seed-specific regulation of carbohydrate metabolism. Plant Physiol 118:91–101.  https://doi.org/10.1104/pp.118.1.91 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Fujii T, Matsuda S, Tejedor ML, Esaki T, Sakane I, Mizuno H, Tsuyama N, Masujima T (2015) Direct metabolomics for plant cells by live single-cell mass spectrometry. Nat Protoc 10:1445–1456.  https://doi.org/10.1038/nprot.2015.084 CrossRefPubMedGoogle Scholar
  29. Giegé P, Heazlewood JL, Roessner-Tunali U, Millar AH, Fernie AR, Leaver CJ, Sweetlove LJ (2003) Enzymes of glycolysis are functionally associated with the mitochondrion in Arabidopsis cells. Plant Cell 15:2140–2151.  https://doi.org/10.1105/tpc.012500 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Grafahrend-Belau E, Schreiber F, Koschutzki D, Junker BH (2009) Flux balance analysis of Barley seeds: a computational approach to study systemic properties of central metabolism. Plant Physiol 149:585–598.  https://doi.org/10.1104/pp.108.129635 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Grafahrend-Belau E, Junker A, Eschenroder A, Muller J, Schreiber F, Junker BH (2013) Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale. Plant Physiol 163:637–647.  https://doi.org/10.1104/pp.113.224006 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Graham JWA, Williams TCR, Morgan M, Fernie AR, Ratcliffe RG, Sweetlove LJ (2007) Glycolytic enzymes associate dynamically with mitochondria in response to respiratory demand and support substrate channeling. Plant Cell 19:3723–3738.  https://doi.org/10.1105/tpc.107.053371 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Hay JO, Schwender J (2011) Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to 13C metabolic flux analysis. Plant J 67:513–525.  https://doi.org/10.1111/j.1365-313X.2011.04613.x CrossRefPubMedGoogle Scholar
  34. Iyer VV, Sriram G, Fulton DB, Zhou R, Westgate ME, Shanks JV (2008) Metabolic flux maps comparing the effect of temperature on protein and oil biosynthesis in developing soybean cotyledons. Plant Cell Environ 31:506–517.  https://doi.org/10.1111/j.1365-3040.2008.01781.x CrossRefPubMedGoogle Scholar
  35. Kruger NJ, Ratcliffe RG (2015) Fluxes through plant metabolic networks: measurements, predictions, insights. Biochem J 38:27–38.  https://doi.org/10.1042/BJ20140984 CrossRefGoogle Scholar
  36. Kruger NJ, Von Schaewen A (2003) The oxidative pentose phosphate pathway: structure and organisation. Curr Opin Plant Biol 6:236–246.  https://doi.org/10.1016/S1369-5266(03)00039-6 CrossRefPubMedGoogle Scholar
  37. Kruger NJ, Masakapalli SK, Ratcliffe RG (2012) Strategies for investigating the plant metabolic network with steady-state metabolic flux analysis: lessons from an Arabidopsis cell culture and other systems. J Exp Bot 63:2309–2323.  https://doi.org/10.1093/jxb/err382 CrossRefPubMedGoogle Scholar
  38. Libourel IGL, Shachar-Hill Y (2008) Metabolic flux analysis in plants: From intelligent design to rational engineering. Annu Rev Plant Biol 59:625–650.  https://doi.org/10.1146/annurev.arplant.58.032806.103822 CrossRefPubMedGoogle Scholar
  39. Lonien J, Schwender J (2009) Analysis of metabolic flux phenotypes for two Arabidopsis mutants with severe impairment in seed storage lipid synthesis. Plant Physiol 151:1617–1634.  https://doi.org/10.1104/pp.109.144121 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Ma F, Jazmin LJ, Young JD, Allen DK (2014) Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation. Proc Natl Acad Sci 111:16967–16972.  https://doi.org/10.1073/pnas.1319485111 CrossRefPubMedGoogle Scholar
  41. Masakapalli SK, Le Lay P, Huddleston JE, Pollock NL, Kruger NJ, Ratcliffe RG (2010) Subcellular fluxanalysis of central metabolism in a heterotrophic Arabidopsis cell suspension using steady-state stable isotope labeling. Plant Physiol 152:602–619.  https://doi.org/10.1104/pp.109.151316 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Masakapalli SK, Kruger NJ, Ratcliffe RG (2013) The metabolic flux phenotype of heterotrophic Arabidopsis cells reveals a complex response to changes in nitrogen supply. Plant J 74:569–582.  https://doi.org/10.1111/tpj.12142 CrossRefPubMedGoogle Scholar
  43. Masakapalli SK, Bryant FM, Kruger NJ, Ratcliffe RG (2014a) The metabolic flux phenotype of heterotrophic Arabidopsis cells reveals a flexible balance between the cytosolic and plastidic contributions to carbohydrate oxidation in response to phosphate limitation. Plant J 78:964–977.  https://doi.org/10.1111/tpj.12522 CrossRefPubMedGoogle Scholar
  44. Masakapalli SK, Ritala A, Dong L, Van Der Krol AR, Oksman-Caldentey KM, Ratcliffe RG, Sweetlove LJ (2014b) Metabolic flux phenotype of tobacco hairy roots engineered for increased geraniol production. Phytochemistry 99:73–85.  https://doi.org/10.1016/j.phytochem.2013.12.007 CrossRefPubMedGoogle Scholar
  45. Masclaux-Daubresse C, Daniel-Vedele F, Dechorgnat J, Chardon F, Gaufichon L, Suzuki A (2010) Nitrogen uptake, assimilation and remobilization in plants: challenges for sustainable and productive agriculture. Ann Bot 105:1141–1157.  https://doi.org/10.1093/aob/mcq028 CrossRefPubMedPubMedCentralGoogle Scholar
  46. Medeiros DB, Perez Souza L, Antunes WC, Araújo WL, Daloso DM, Fernie AR (2018) Sucrose breakdown within guard cells provides substrates for glycolysis and glutamine biosynthesis during light-induced stomatal opening. Plant J 94:583–594.  https://doi.org/10.1111/tpj.13889 CrossRefPubMedGoogle Scholar
  47. Mintz-Oron S, Meir S, Malitsky S, Ruppin E, Aharoni A, Shlomi T (2012) Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc Natl Acad Sci 109:339–344.  https://doi.org/10.1073/pnas.1100358109 CrossRefPubMedGoogle Scholar
  48. Moussaieff A, Rogachev I, Brodsky L, Malitsky S, Toal TW, Belcher H, Yativ M, Brady SM, Benfey PN, Aharoni A (2013) High-resolution metabolic mapping of cell types in plant roots. Proc Natl Acad Sci 110:E1232–E1241.  https://doi.org/10.1073/pnas.1302019110 CrossRefPubMedGoogle Scholar
  49. Nikoloski Z, Perez-Storey R, Sweetlove LJ (2015) Inference and prediction of metabolic network fluxes. Plant Physiol.  https://doi.org/10.1104/pp.15.01082 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Pfau T, Christian N, Masakapalli SK, Sweetlove LJ, Poolman MG, Ebenhöh O (2018) The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling. Sci Rep 8:1–11.  https://doi.org/10.1038/s41598-018-30884-x CrossRefGoogle Scholar
  51. Poolman MG, Miguet L, Sweetlove LJ, Fell DA (2009) A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol 151:1570–1581.  https://doi.org/10.1104/pp.109.141267 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Ratcliffe RG, Shachar-Hill Y (2005) Revealing metabolic phenotypes in plants: Inputs from NMR analysis. Biol Rev Camb Philos Soc 80:27–43.  https://doi.org/10.1017/S1464793104006530 CrossRefPubMedGoogle Scholar
  53. Ratcliffe RG, Shachar-Hill Y (2006) Measuring multiple fluxes through plant metabolic networks. Plant J 45:490–511.  https://doi.org/10.1111/j.1365-313X.2005.02649.x CrossRefPubMedGoogle Scholar
  54. Robaina-Estévez S, Daloso DM, Zhang Y, Fernie AR, Nikoloski Z (2017) Resolving the central metabolism of Arabidopsis guard cells. Sci Rep 7:1–13.  https://doi.org/10.1038/s41598-017-07132-9 CrossRefGoogle Scholar
  55. Rolletschek H, Grafahrend-Belau E, Munz E, Radchuk V, Kartäusch R, Tschiersch H, Melkus G, Schreiber F, Jakob PM, Borisjuk L (2015) Metabolic architecture of the cereal grain and its relevance to maximize carbon use efficiency. Plant Physiol.  https://doi.org/10.1104/pp.15.00981 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Rossi MT, Kalde M, Srisakvarakul C, Kruger NJ, Ratcliffe RG (2017) Cell-type specific metabolic flux analysis: a challenge for metabolic phenotyping and a potential solution in plants. Metabolites 7:1–16.  https://doi.org/10.3390/metabo7040059 CrossRefGoogle Scholar
  57. Saha R, Suthers PF, Maranas CD (2011) Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. PLoS ONE.  https://doi.org/10.1371/journal.pone.0021784 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Salon C, Avice JC, Colombié S, Dieuaide-Noubhani M, Gallardo K, Jeudy C, Ourry A, Prudent M, Voisin AS, Rolin D (2017) Fluxomics links cellular functional analyses to whole-plant phenotyping. J Exp Bot 68:2083–2098.  https://doi.org/10.1093/jxb/erx126 CrossRefPubMedGoogle Scholar
  59. Schnyder H (1993) The role of carbohydrate storage and redistribution in the source-sink relations of wheat and barley during grain filling: a review. New Phytol 123:233–245.  https://doi.org/10.1111/j.1469-8137.1993.tb03731.x CrossRefGoogle Scholar
  60. Schwender J, Goffman FD, Ohlrogge JB, Shachar-Hill Y (2004) Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature 432:779–782CrossRefPubMedGoogle Scholar
  61. Schwender J, Shachar-Hill Y, Ohlrogge JB (2006) Mitochondrial metabolism in developing embryos of Brassica napus. J Biol Chem 281:34040–34047.  https://doi.org/10.1074/jbc.M606266200 CrossRefPubMedGoogle Scholar
  62. Schwender J, Hebbelmann I, Heinzel N, Hildebrandt TM, Rogers A, Naik D, Klapperstück M, Braun H-P, Schreiber F, Denolf P, Borisjuk L, Rolletschek H (2015) Quantitative multilevel analysis of central metabolism in developing oilseeds of oilseed rape during in vitro culture. Plant Physiol 168:828–848.  https://doi.org/10.1104/pp.15.00385 CrossRefPubMedPubMedCentralGoogle Scholar
  63. Shameer S, Baghalian K, Cheung CYM, Ratcliffe RG, Sweetlove LJ (2018) Computational analysis of the productivity potential of CAM. Nat Plants 4:165–171.  https://doi.org/10.1038/s41477-018-0112-2 CrossRefPubMedGoogle Scholar
  64. Shaw R, Cheung CYM (2018) A dynamic multi-tissue flux balance model captures carbon and nitrogen metabolism and optimal resource partitioning during Arabidopsis growth. Front Plant Sci 9:1–15.  https://doi.org/10.3389/fpls.2018.00884 CrossRefGoogle Scholar
  65. Shrestha B, Vertes A (2009) In situ metabolic profiling of single cells by laser ablation electrospray ionization mass spectrometry. Anal Chem 81:8265–8271.  https://doi.org/10.1021/ac901525g CrossRefPubMedGoogle Scholar
  66. Sriram G, Fulton DB, Iyer VV, Peterson JM, Zhou R, Westgate ME, Spalding MH, Shanks JV (2004) Quantification of compartmented metabolic fluxes in developing soybean embryos by employing biosynthetically directed fractional 13C labeling, two-dimensional [13C, 1H] nuclear magnetic resonance, and comprehensive isotopomer balancing. Plant Physiol 136:3043–3057.  https://doi.org/10.1104/pp.104.050625.plant CrossRefPubMedPubMedCentralGoogle Scholar
  67. Sriram G, Fulton DB, Shanks JV (2007) Flux quantification in central carbon metabolism of Catharanthus roseus hairy roots by 13C labeling and comprehensive bondomer balancing. Phytochemistry 68:2243–2257.  https://doi.org/10.1016/j.phytochem.2007.04.009 CrossRefPubMedGoogle Scholar
  68. Stitt M, Müller C, Matt P, Gibon Y, Carillo P, Morcuende R, Scheible W-R, Krapp A (2002) Steps towards an integrated view of nitrogen metabolism. J Exp Bot 53:959–970.  https://doi.org/10.1093/jexbot/53.370.959 CrossRefPubMedGoogle Scholar
  69. Sweetlove LJ, Fernie AR (2013) The spatial organization of metabolism within the plant cell. Annu Rev Plant Biol 64:723–746.  https://doi.org/10.1146/annurev-arplant-050312-120233 CrossRefPubMedGoogle Scholar
  70. Sweetlove LJ, Beard KFM, Nunes-Nesi A, Fernie AR, Ratcliffe RG (2010) Not just a circle: flux modes in the plant TCA cycle. Trends Plant Sci 15:462–470.  https://doi.org/10.1016/j.tplants.2010.05.006 CrossRefPubMedGoogle Scholar
  71. Sweetlove LJ, Williams TCR, Cheung CYM, Ratcliffe RG (2013) Modelling metabolic CO2 evolution: a fresh perspective on respiration. Plant Cell Environ 36:1631–1640.  https://doi.org/10.1111/pce.12105 CrossRefPubMedGoogle Scholar
  72. Szecówka M, Heise R, Tohge T, Nunes-Nesi A, Vosloh D, Huege J, Feil R, Lunn J, Nikoloski Z, Stitt M, Fernie AR, Arrivault S (2013) Metabolic fluxes in an illuminated Arabidopsis rosette. Plant Cell 25:694–714.  https://doi.org/10.1105/tpc.112.106989 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Tausta SL, Li P, Si Y, Gandotra N, Liu P, Sun Q, Brutnell TP, Nelson T (2014) Developmental dynamics of Kranz cell transcriptional specificity in maize leaf reveals early onset of C4-related processes. J Exp Bot 65:3543–3555.  https://doi.org/10.1093/jxb/eru152 CrossRefPubMedPubMedCentralGoogle Scholar
  74. Tcherkez G, Boex-Fontvieille E, Mahé A, Hodges M (2012) Respiratory carbon fluxes in leaves. Curr Opin Plant Biol 15:308–314.  https://doi.org/10.1016/j.pbi.2011.12.003 CrossRefPubMedGoogle Scholar
  75. Wahrheit J, Nicolae A, Heinzle E (2011) Eukaryotic metabolism: measuring compartment fluxes. Biotechnol J 6:1071–1085.  https://doi.org/10.1002/biot.201100032 CrossRefPubMedGoogle Scholar
  76. Wang L, Czedik-Eysenberg A, Mertz RA, Si Y, Tohge T et al (2014) Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice. Nat Biotechnol 32:1158–1164.  https://doi.org/10.1038/nbt.3019 CrossRefPubMedGoogle Scholar
  77. Wiechert W, Möllney M, Petersen S, de Graaf AA (2001) A universal framework for 13C metaboli flux analysis. Metab Eng 3:265–283.  https://doi.org/10.1006/mben.2001.0188 CrossRefPubMedGoogle Scholar
  78. Williams TCR, Miguet L, Masakapalli SK, Kruger NJ, Sweetlove LJ, Ratcliffe RG (2008) Metabolic network fluxes in heterotrophic Arabidopsis cells: stability of the flux distribution under different oxygenation conditions. Plant Physiol 148:704–718.  https://doi.org/10.1104/pp.108.125195 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Williams TCR, Sweetlove LJ, Ratcliffe RG (2011) Capturing metabolite channeling in metabolic flux phenotypes. Plant Physiol 157:981–984.  https://doi.org/10.1104/pp.111.184887 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Winkel BSJ (2004) Metabolic channeling in plants. Annu Rev Plant Biol 55:85–107.  https://doi.org/10.1146/annurev.arplant.55.031903.141714 CrossRefPubMedGoogle Scholar
  81. Xiong W, Liu L, Wu C, Yang C, Wu Q (2010) 13C-tracer and gas chromatography-mass spectrometry analyses reveal metabolic flux distribution in the oleaginous microalga Chlorella protothecoides. Plant Physiol 154:1001–1011.  https://doi.org/10.1104/pp.110.158956 CrossRefPubMedPubMedCentralGoogle Scholar
  82. Zhang Y, Beard KFM, Swart C, Bergmann S, Krahnert I, Nikoloski Z, Graf A, Ratcliffe RG, Sweetlove LJ, Fernie AR, Obata T (2017) Protein–protein interactions and metabolite channelling in the plant tricarboxylic acid cycle. Nat Commun.  https://doi.org/10.1038/ncomms15212 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Brazilian Society of Plant Physiology 2018

Authors and Affiliations

  1. 1.Department of Botany, Institute of Biological SciencesUniversity of BrasíliaBrasíliaBrazil

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