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  • Review Article
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Non-canonical plant metabolism

Nature Plants volume 11, pages 696–708 (2025)Cite this article

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Abstract

Metabolism is essential for plant growth and has become a major target for crop improvement by enhancing nutrient use efficiency. Metabolic engineering is also the basis for producing high-value plant products such as pharmaceuticals, biofuels and industrial biochemicals. An inherent problem for such engineering endeavours is the tendency to view metabolism as a series of distinct metabolic pathways—glycolysis, the tricarboxylic acid cycle, the Calvin–Benson cycle and so on. While these canonical pathways may represent a dominant or frequently occurring flux mode, systematic analyses of metabolism via computational modelling have emphasized the inherent flexibility of the metabolic network to carry flux distributions that are distinct from the canonical pathways. Recent experimental estimates of metabolic network fluxes using 13C-labelling approaches have revealed numerous instances in which non-canonical pathways occur under different conditions and in different tissues. In this Review, we bring these non-canonical pathways to the fore, summarizing the evidence for their occurrence and the context in which they operate. We also emphasize the importance of non-canonical pathways for metabolic engineering. We argue that the introduction of a high-flux pathway to a desired metabolic product will, by necessity, require non-canonical supporting fluxes in central metabolism to provide the necessary carbon skeletons, energy and reducing power. We illustrate this using the overproduction of isoprenoids and fatty acids as case studies.

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Fig. 1: Different types of connection within a metabolic network.
Fig. 2: Predicted TCA cycle fluxes and associated exchange fluxes with the cytosol obtained by flux balance analysis for germinating soybean seedlings.
Fig. 3: Non-canonical supply of carbon to the Calvin–Benson cycle.
Fig. 4: Non-canonical involvement of Rubisco in fatty acid biosynthesis in green oilseeds.
Fig. 5: Potential routes to supply pyruvate in the chloroplast for the activity of an engineered high-flux MEP pathway in a leaf.
Fig. 6: Putative non-canonical metabolism to support an engineered high-flux MVA pathway in a leaf.

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References

  1. da Fonseca‐Pereira, P., de Cássia Monteiro-Batista, R., Araújo, W. L. & Nunes-Nesi, A. Harnessing enzyme cofactors and plant metabolism: an essential partnership. Plant J. 114, 1014–1036 (2023).

    Article  PubMed  Google Scholar 

  2. Mira, M. M. et al. Plant stem cells under low oxygen: metabolic rewiring by phytoglobin underlies stem cell functionality. Plant Physiol. 193, 1416–1432 (2023).

    Article  CAS  PubMed  Google Scholar 

  3. Simons, M., Misra, A. & Sriram, G. in Plant Metabolism: Methods and Protocols Vol. 1083 (ed. Sriram, G.) 213–230 (Humana, 2014).

  4. de Oliveira Dal’Molin, C. G. & Nielsen, L. K. Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses. Curr. Opin. Biotechnol. 49, 42–48 (2018).

    Article  Google Scholar 

  5. Gerlin, L., Frainay, C., Jourdan, F., Baroukh, C. & Prigent, S. Plant genome-scale metabolic networks. Adv. Bot. Res. 98, 237–270 (2021).

    Article  CAS  Google Scholar 

  6. Allen, D. K. & Young, J. D. Tracing metabolic flux through time and space with isotope labeling experiments. Curr. Opin. Biotechnol. 64, 92–100 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Clark, T. J., Guo, L., Morgan, J. & Schwender, J. Modeling plant metabolism: from network reconstruction to mechanistic models. Annu. Rev. Plant Biol. 71, 303–326 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Kaste, J. A. M. & Shachar-Hill, Y. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling. Bioinformatics 39, btad186 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ma, H. & Zeng, A. P. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19, 270–277 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Fabregas, N. & Fernie, A. R. The metabolic response to drought. J. Exp. Bot. 70, 1077–1085 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Botha, F. C. & Marquardt, A. Metabolic control of sugarcane internode elongation and sucrose accumulation. Agronomy 14, 1487 (2024).

    Article  CAS  Google Scholar 

  12. de Vries, S. & Feussner, I. Biotic interactions, evolutionary forces and the pan-plant specialized metabolism. Phil. Trans. R. Soc. B 379, 20230362 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kruger, N. J. & Ratcliffe, R. G. Pathways and fluxes: exploring the plant metabolic network. J. Exp. Bot. 63, 2243–2246 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. Judge, A. & Dodd, M. S. Metabolism. Essays Biochem. 64, 607–647 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dixon, R. A., Chen, F., Guo, D. & Parvathi, K. The biosynthesis of monolignols: a ‘metabolic grid’, or independent pathways to guaiacyl and syringyl units? Phytochemistry 57, 1069–1084 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Lanier, E. R., Andersen, T. B. & Hamberger, B. Plant terpene specialized metabolism: complex networks or simple linear pathways? Plant J. 114, 1178–1201 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bergman, M. E., Kortbeek, R. W., Gutensohn, M. & Dudareva, N. Plant terpenoid biosynthetic network and its multiple layers of regulation. Prog. Lipid Res. 95, 101287 (2024).

    Article  CAS  PubMed  Google Scholar 

  18. Palsson, B. Ø. Systems Biology: Constraint-Based Reconstruction and Analysis Ch. 12 (Cambridge Univ. Press, 2015).

  19. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Morandini, P. Rethinking metabolic control. Plant Sci. 176, 441–451 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. Saa, P. A. & Nielsen, L. K. Formulation, construction and analysis of kinetic models of metabolism: a review of modelling frameworks. Biotechnol. Adv. 35, 981–1003 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhu, X. G., Wang, Y., Ort, D. R. & Long, S. P. e-Photosynthesis: a comprehensive dynamic mechanistic model of C3 photosynthesis: from light capture to sucrose synthesis. Plant Cell Environ. 36, 1711–1727 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Feldman-Salit, A., Veith, N., Wirtz, M., Hell, R. & Kummer, U. Distribution of control in the sulfur assimilation in Arabidopsis thaliana depends on environmental conditions. New Phytol. 222, 1392–1404 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Guo, L. et al. Dynamic modeling of subcellular phenylpropanoid metabolism in Arabidopsis lignifying cells. Metab. Eng. 49, 36–46 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Wang, J. P. et al. Flux modelling for monolignol biosynthesis. Curr. Opin. Biotechnol. 56C, 187–192 (2019).

    Article  Google Scholar 

  27. Rao, X. & Barros, J. Modeling lignin biosynthesis: a pathway to renewable chemicals. Trends Plant Sci. 29, 546–559 (2024).

    Article  CAS  PubMed  Google Scholar 

  28. Töpfer, N., Braam, T., Shameer, S., Ratcliffe, R. G. & Sweetlove, L. J. Alternative Crassulacean acid metabolism modes provide environment-specific water-saving benefits in a leaf metabolic model. Plant Cell 32, 3689–3705 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Schuster, S., Pfeiffer, T. & Fell, D. A. Is maximization of molar yield in metabolic networks favoured by evolution? J. Theor. Biol. 252, 497–504 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Sarkar, D. & Kundu, S. Systems biology of plant metabolic interactions. J. Biosci. 49, 56 (2024).

    Article  PubMed  Google Scholar 

  31. Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Tong, H., Kuken, A., Razaghi-Moghadam, Z. & Nikoloski, Z. Characterisation of effects of genetic variants via genome-scale metabolic modelling. Cell. Mol. Life Sci. 78, 5123–5138 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Töpfer, N. Environment-coupled models of leaf metabolism. Biochem. Soc. Trans. 49, 119–129 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sampaio, M., Rocha, M. & Dias, O. Exploring synergies between plant metabolic modelling and machine learning. Comput. Struct. Biotechnol. J. 20, 1885–1900 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cheung, C. Y. M. et al. 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 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Sweetlove, L. J., Williams, T. C. R., Cheung, C. Y. M. & Ratcliffe, R. G. Modelling metabolic CO2 evolution—a fresh perspective on respiration. Plant Cell Environ. 36, 1631–1640 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Poolman, M. G., Miguet, L., Sweetlove, L. J. & Fell, D. A. A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol. 151, 1570–1581 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Poolman, M. G., Kundu, S., Shaw, R. & Fell, D. A. Responses to light intensity in a genome-scale model of rice metabolism. Plant Physiol. 162, 1060–1072 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cheung, C. Y. M., Poolman, M. G., Fell, D. A., Ratcliffe, R. G. & Sweetlove, L. J. 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 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yuan, H., Cheung, C. Y. M., Poolman, M. G., Hilbers, P. A. J. & van Riel, N. A. W. A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism. Plant J. 85, 289–304 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Moreira, T. B. et al. A genome-scale metabolic model of soybean (Glycine max) highlights metabolic fluxes in seedlings. Plant Physiol. 180, 1912–1929 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bender, M. L., Zhu, X. G., Falkowski, P., Ma, F. & Griffin, K. On the rate of phytoplankton respiration in the light. Plant Physiol. 190, 267–279 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tan, X. L. J. & Cheung, C. Y. M. A multiphase flux balance model reveals flexibility of central carbon metabolism in guard cells of C3 plants. Plant J. 104, 1648–1656 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Sprent, N. et al. Metabolic modeling reveals distinct roles of sugars and carboxylic acids in stomatal opening as well as unexpected carbon fluxes. Plant Cell 37, koae252 (2025).

    Article  Google Scholar 

  45. Hunt, H. et al. Analysis of companion cell and phloem metabolism using a transcriptome-guided model of Arabidopsis metabolism. Plant Physiol. 192, 1359–1377 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. de Oliveira Dal’Molin, C. G. et al. Metabolic reconstruction of Setaria italica: a systems biology approach for integrating tissue-specific omics and pathway analysis of bioenergy grasses. Front. Plant Sci. 7, 1138 (2016).

    PubMed  PubMed Central  Google Scholar 

  47. Moreno-Villena, J. J. et al. Spatial resolution of an integrated C4+CAM photosynthetic metabolism. Sci. Adv. 8, eabn2349 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. von Bismarck, T. et al. Growth in fluctuating light buffers plants against photorespiratory perturbations. Nat. Commun. 14, 7052 (2023).

    Article  Google Scholar 

  49. Fu, X., Schlüter, U., Smith, K., Weber, A. P. M. & Walker, B. J. Metabolomics of related C3 and C4 Flaveria species indicate differences in the operation of photorespiration under fluctuating light. Plant Direct 8, e70012 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Shaw, R. & Kundu, S. Metabolic plasticity and inter-compartmental interactions in rice metabolism: an analysis from reaction deletion study. PLoS ONE 10, e0133899 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Cheung, C. Y. M., Ratcliffe, R. G. & Sweetlove, L. J. A method of accounting for enzyme costs in flux balance analysis reveals alternative pathways and metabolite stores in an illuminated Arabidopsis leaf. Plant Physiol. 169, 1671–1682 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hay, J. & Schwender, J. 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 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Chatterjee, A. & Kundu, S. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica. Sci. Rep. 5, 14975 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Schuster, S., Dandekar, T. & Fell, D. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 17, 53–60 (1999).

    Article  CAS  PubMed  Google Scholar 

  55. Huma, B., Kundu, S., Poolman, M. G., Kruger, N. J. & Fell, D. A. Stoichiometric analysis of the energetics and metabolic impact of photorespiration in C3 plants. Plant J. 96, 1228–1241 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Hill, S. A. & ap Rees, T. Fluxes of carbohydrate metabolism in ripening bananas. Planta 192, 52–60 (1994).

    Article  CAS  Google Scholar 

  57. Ratcliffe, R. G. & Shachar-Hill, Y. Measuring multiple fluxes through plant metabolic networks. Plant J. 45, 490–511 (2006).

    Article  CAS  PubMed  Google Scholar 

  58. Kruger, N. J. & Ratcliffe, R. G. Fluxes through plant metabolic networks: measurements, predictions, insights and challenges. Biochem. J 465, 27–38 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Moreira, T. B., Lima, J. M., Coca, G. C. & Williams, T. C. R. Insights into the spatial and temporal organisation of plant metabolism from network flux analysis. Theor. Exp. Plant Physiol. 31, 215–226 (2019).

    Article  CAS  Google Scholar 

  60. Koley, S., Jyoti, P., Lingwan, M. & Allen, D. K. Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities. New Phytol. 242, 1911–1918 (2024).

    Article  PubMed  Google Scholar 

  61. Williams, T. C. R. et al. Metabolic network fluxes in heterotrophic Arabidopsis cells: stability of the flux distribution under different oxygenation conditions. Plant Physiol. 148, 704–718 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Masakapalli, S. K. et al. Subcellular flux analysis of central metabolism in a heterotrophic Arabidopsis cell suspension using steady-state stable isotope labeling. Plant Physiol. 152, 602–619 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Le, X. H. & Millar, A. H. The diversity of substrates for plant respiration and how to optimize their use. Plant Physiol. 191, 2133–2149 (2023).

    Article  CAS  PubMed  Google Scholar 

  64. Alonso, A. P., Goffman, F. D., Ohlrogge, J. B. & Shachar-Hill, Y. Carbon conversion efficiency and central metabolic fluxes in developing sunflower (Helianthus annuus L.) embryos. Plant J. 52, 296–308 (2007).

    Article  CAS  PubMed  Google Scholar 

  65. Allen, D. K., Ohlrogge, J. B. & Shachar-Hill, Y. The role of light in soybean seed filling metabolism. Plant J. 58, 220–234 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Alonso, A. P., Val, D. L. & Shachar-Hill, Y. Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering. Metab. Eng. 13, 96–107 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Lonien, J. & Schwender, J. Analysis of metabolic flux phenotypes for two Arabidopsis mutants with severe impairment in seed storage lipid synthesis. Plant Physiol. 151, 1617–1634 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Schwender, J., Shachar-Hill, Y. & Ohlrogge, J. B. Mitochondrial metabolism in developing embryos of Brassica napus. J. Biol. Chem. 281, 34040–34047 (2006).

    Article  CAS  PubMed  Google Scholar 

  69. Tcherkez, G. et al. In folio respiratory fluxomics revealed by 13C isotopic labeling and H/D isotope effects highlight the noncyclic nature of the tricarboxylic acid ‘cycle’ in illuminated leaves. Plant Physiol. 151, 620–630 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Gauthier, P. P. G. et al. In folio isotopic tracing demonstrates that nitrogen assimilation into glutamate is mostly independent from current CO2 assimilation in illuminated leaves of Brassica napus. New Phytol. 185, 988–999 (2010).

    Article  CAS  PubMed  Google Scholar 

  71. Shameer, S., Ratcliffe, R. G. & Sweetlove, L. J. Leaf energy balance requires mitochondrial respiration and export of chloroplast NADPH in the light. Plant Physiol. 180, 1947–1961 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lim, S. L. et al. In planta study of photosynthesis and photorespiration using NADPH and NADH/NAD+ fluorescent protein sensors. Nat. Commun. 11, 3238 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Igamberdiev, A. U. & Bykova, N. V. Mitochondria in photosynthetic cells: coordinating redox control and energy balance. Plant Physiol. 191, 2104–2119 (2023).

    Article  CAS  PubMed  Google Scholar 

  74. Szecowka, M. et al. Metabolic fluxes in an illuminated Arabidopsis rosette. Plant Cell 25, 694–714 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Arrivault, S. et al. Metabolite pools and carbon flow during C4 photosynthesis in maize: 13CO2 labeling kinetics and cell type fractionation. J. Exp. Bot. 68, 283–298 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Medeiros, D. B. et al. 13CO2 labeling kinetics in maize reveal impaired efficiency of C4 photosynthesis under low irradiance. Plant Physiol. 190, 280–304 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wieloch, T. & Sharkey, T. D. Compartment-specific energy requirements of photosynthetic carbon metabolism in Camelina sativa leaves. Planta 255, 103 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Xu, Y., Wieloch, T., Kaste, J. A. M. & Sharkey, T. D. Reimport of carbon from cytosolic and vacuolar sugar pools into the Calvin–Benson cycle explains photosynthesis labeling anomalies. Proc. Natl Acad. Sci. USA 119, e2121531119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Treves, H. et al. Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C3 and C4 plants. Nat. Plants 8, 78–91 (2022).

    Article  CAS  PubMed  Google Scholar 

  80. Hasunuma, T. et al. Metabolic turnover analysis by a combination of in vivo 13C-labelling from 13CO2 and metabolic profiling with CE-MS/MS reveals rate-limiting steps of the C3 photosynthetic pathway in Nicotiana tabacum leaves. J. Exp. Bot. 61, 1041–1051 (2010).

    Article  CAS  PubMed  Google Scholar 

  81. Ma, F., Jazmin, L. J., Young, J. D. & Allen, D. K. Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation. Proc. Natl Acad. Sci. USA 111, 16967–16972 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Young, J. D., Shastri, A. A., Stephanopoulos, G. & Morgan, J. A. Mapping photoautotrophic metabolism with isotopically nonstationary 13C flux analysis. Metab. Eng. 13, 656–665 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Sharkey, T. D. The end game(s) of photosynthetic carbon metabolism. Plant Physiol. 195, 67–78 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Makowka, A. et al. Glycolytic shunts replenish the Calvin–Benson–Bassham cycle as anaplerotic reactions in cyanobacteria. Mol. Plant 13, 471–482 (2020).

    Article  CAS  PubMed  Google Scholar 

  85. Wieloch, T., Augusti, A. & Schleucher, J. Anaplerotic flux into the Calvin–Benson cycle: hydrogen isotope evidence for in vivo occurrence in C3 metabolism. New Phytol. 234, 405–411 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Evans, S. E. et al. Rubisco supplies pyruvate for the 2-C-methyl-d-erythritol-4-phosphate pathway. Nat. Plants 10, 1453–1463 (2024).

    Article  CAS  PubMed  Google Scholar 

  87. Xu, Y., Schmiege, S. C. & Sharkey, T. D. The oxidative pentose phosphate pathway in photosynthesis: a tale of two shunts. New Phytol. 242, 2453–2463 (2024).

    Article  CAS  PubMed  Google Scholar 

  88. Wieloch, T., Augusti, A. & Schleucher, J. A model of photosynthetic CO2 assimilation in C3 leaves accounting for respiration and energy recycling by the plastidial oxidative pentose phosphate pathway. New Phytol. 239, 518–532 (2023).

    Article  CAS  PubMed  Google Scholar 

  89. Xu, Y., Fu, X., Sharkey, T. D., Shachar-Hill, Y. & Walker, A. B. The metabolic origins of non-photorespiratory CO2 release during photosynthesis: a metabolic flux analysis. Plant Physiol. 186, 297–314 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Schwender, J., Goffman, F., Ohlrogge, J. B. & Shachar-Hill, Y. Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature 432, 779–782 (2004).

    Article  CAS  PubMed  Google Scholar 

  91. Schwender, J. et al. Quantitative multilevel analysis of central metabolism in developing oilseeds of oilseed rape during in vitro culture. Plant Physiol. 168, 828–848 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Tsogtbaatar, E., Cocuron, J. C. & Alonso, A. P. Non-conventional pathways enable pennycress (Thlaspi arvense L.) embryos to achieve high efficiency of oil biosynthesis. J. Exp. Bot. 71, 3037–3051 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Deslandes-Hérold, G. et al. The PRK/Rubisco shunt strongly influences Arabidopsis seed metabolism and oil accumulation, affecting more than carbon recycling. Plant Cell 35, 808–826 (2023).

    Article  PubMed  Google Scholar 

  94. Bauwe, H. Photorespiration—Rubisco’s repair crew. J. Plant Physiol. 280, 153899 (2023).

    Article  CAS  PubMed  Google Scholar 

  95. Rachmilevitch, S., Cousins, A. B. & Bloom, A. J. Nitrate assimilation in plant shoots depends on photorespiration. Proc. Natl Acad. Sci. USA 101, 11506–11510 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Rosa-Téllez, S. et al. The serine–glycine–one-carbon metabolic network orchestrates changes in nitrogen and sulfur metabolism and shapes plant development. Plant Cell 36, 404–426 (2024).

    Article  PubMed  Google Scholar 

  97. Walker, B., Schmiege, S. C. & Sharkey, T. D. Re-evaluating the energy balance of the many routes of carbon flow through and from photorespiration. Plant Cell Environ. 47, 3365–3374 (2024).

    Article  CAS  PubMed  Google Scholar 

  98. Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    Article  CAS  PubMed  Google Scholar 

  99. Abadie, C., Boex-Fontvieille, E. R. A., Carroll, A. J. & Tcherkez, G. In vivo stoichiometry of photorespiratory metabolism. Nat. Plants 2, 15220 (2016).

    Article  CAS  PubMed  Google Scholar 

  100. Abadie, C. & Tcherkez, G. 13C isotope labelling to follow the flux of photorespiratory intermediates. Plants 10, 427 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Busch, F. A., Sage, R. F. & Farquhar, G. D. Plants increase CO2 uptake by assimilating nitrogen via the photorespiratory pathway. Nat. Plants 4, 46–54 (2018).

    Article  CAS  PubMed  Google Scholar 

  102. Fu, X., Gregory, L. M., Weise, S. E. & Walker, B. J. Integrated flux and pool size analysis in plant central metabolism reveals unique roles of glycine and serine during photorespiration. Nat. Plants 9, 169–178 (2023).

    Article  CAS  PubMed  Google Scholar 

  103. Timm, S. et al. Glycine decarboxylase controls photosynthesis and plant growth. FEBS Lett. 586, 3692–3697 (2012).

    Article  CAS  PubMed  Google Scholar 

  104. Timm, S. et al. Mitochondrial dihydrolipoyl dehydrogenase activity shapes photosynthesis and photorespiration of Arabidopsis thaliana. Plant Cell 27, 1968–1984 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Kebeish, R. et al. Chloroplastic photorespiratory bypass increases photosynthesis and biomass production in Arabidopsis thaliana. Nat. Biotechnol. 25, 593–599 (2007).

    Article  CAS  PubMed  Google Scholar 

  106. Maier, A. et al. Transgenic introduction of a glycolate oxidative cycle into A. thaliana chloroplasts leads to growth improvement. Front. Plant Sci. 3, 38 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. South, P. F., Cavanagh, A. P., Liu, H. W. & Ort, D. R. Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field. Science 363, eaat9077 (2019).

    Article  CAS  PubMed  Google Scholar 

  108. Eisenhut, M., Roell, M. S. & Weber, A. P. M. Mechanistic understanding of photorespiration paves the way to a new green revolution. New Phytol. 223, 1762–1769 (2019).

    Article  PubMed  Google Scholar 

  109. George, K. W., Alonso-Gutierrez, J., Keasling, J. D. & Lee, T. S. in Biotechnology of Isoprenoids (eds Schrader, J. & Bohlmann, J.) 355–389 (Springer, 2015).

  110. Yang, W. et al. Advances in pharmacological activities of terpenoids. Nat. Prod. Commun. 15, 1934578X20903555 (2020).

    CAS  Google Scholar 

  111. Joshi, S. & Mishra, S. Recent advances in biofuel production through metabolic engineering. Bioresour. Technol. 352, 127037 (2022).

    Article  CAS  PubMed  Google Scholar 

  112. Jiang, H. & Wang, X. Biosynthesis of monoterpenoid and sesquiterpenoid as natural flavors and fragrances. Biotechnol. Adv. 65, 108151 (2023).

    Article  CAS  PubMed  Google Scholar 

  113. O’Neill, E. C. & Kelly, S. Engineering biosynthesis of high-value compounds in photosynthetic organisms. Crit. Rev. Biotechnol. 37, 779–802 (2017).

    Article  PubMed  Google Scholar 

  114. Dai, Z., Cui, G., Zhou, S. F., Zhang, X. & Huang, L. Cloning and characterization of a novel 3-hydroxy-3-methylglutaryl coenzyme A reductase gene from Salvia miltiorrhiza involved in diterpenoid tanshinone accumulation. J. Plant Physiol. 168, 148–157 (2011).

    Article  CAS  PubMed  Google Scholar 

  115. Kai, G. et al. Metabolic engineering tanshinone biosynthetic pathway in Salvia miltiorrhiza hairy root cultures. Metab. Eng. 13, 319–327 (2011).

    Article  CAS  PubMed  Google Scholar 

  116. Li, Y. et al. Advanced metabolic engineering strategies for increasing artemisinin yield in Artemisia annua. L. Hortic. Res. 11, uhad292 (2024).

    Article  CAS  PubMed  Google Scholar 

  117. Masakapalli, S. K. et al. Metabolic flux phenotype of tobacco hairy roots engineered for increased geraniol production. Phytochemistry 99, 73–85 (2014).

    Article  CAS  PubMed  Google Scholar 

  118. Rodriguez, S. et al. ATP citrate lyase mediated cytosolic acetyl-CoA biosynthesis increases mevalonate production in Saccharomyces cerevisiae. Microb. Cell Fact. 15, 48 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Chapman, K. D., Dyer, J. M. & Mullen, R. T. Commentary: why don’t plant leaves get fat? Plant Sci. 207, 128–134 (2013).

    Article  CAS  PubMed  Google Scholar 

  120. Wang, Y. Q. et al. Proteomic analysis of chromoplasts from six crop species reveals insights into chromoplast function and development. J. Exp. Bot. 64, 949–961 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Andrews, T. J. & Kane, H. J. Pyruvate is a by-product of catalysis by ribulosebisphosphate carboxylase/oxygenase. J. Biol. Chem. 266, 9447–9452 (1991).

    Article  CAS  PubMed  Google Scholar 

  122. Chu, K. L. et al. Metabolic flux analysis of the non-transitory starch tradeoff for lipid production in mature tobacco leaves. Metab. Eng. 69, 231–248 (2022).

    Article  CAS  PubMed  Google Scholar 

  123. Eastmond, P. J., Dennis, D. T. & Rawsthorne, S. Evidence that a malate/inorganic phosphate exchange translocator imports carbon across the leucoplast envelope for fatty acid synthesis in developing castor seed endosperm. Plant Physiol. 114, 851–856 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Morley, S. A. et al. Expression of malic enzyme reveals subcellular carbon partitioning for storage reserve production in soybeans. New Phytol. 239, 1834–1851 (2023).

    Article  CAS  PubMed  Google Scholar 

  125. Schwender, J. Walking the ‘design–build–test–learn’ cycle: flux analysis and genetic engineering reveal the pliability of plant central metabolism. New Phytol. 239, 1539–1554 (2023).

    Article  PubMed  Google Scholar 

  126. Walker, B. J., Kramer, D. M., Fisher, N. & Fu, X. Flexibility in the energy balancing network of photosynthesis enables safe operation under changing environmental conditions. Plants 9, 301 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Vanhercke, T. et al. Step changes in leaf oil accumulation via iterative metabolic engineering. Metab. Eng. 39, 237–246 (2017).

    Article  CAS  PubMed  Google Scholar 

  128. Strand, D. D. & Walker, B. J. Energetic considerations for engineering novel biochemistries in photosynthetic organisms. Front. Plant Sci. 14, 1116812 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Brunk, E. et al. Characterizing strain variation in engineered E. coli using a multi-omics-based workflow. Cell Syst. 2, P335–P346 (2016).

    Article  Google Scholar 

  130. Brand, A. & Tissier, A. Control of resource allocation between primary and specialized metabolism in glandular trichomes. Curr. Opin. Plant Biol. 66, 102172 (2022).

    Article  CAS  PubMed  Google Scholar 

  131. Asadollahi, M. A. et al. Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab. Eng. 11, 328–334 (2009).

    Article  CAS  PubMed  Google Scholar 

  132. Assil-Companioni, L. et al. Engineering of NADPH supply boosts photosynthesis-driven biotransformations. ACS Catal. 10, 11864–11877 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Ding, N., Yuan, Z., Sun, L. & Yin, L. Dynamic and static regulation of nicotinamide adenine dinucleotide phosphate: strategies, challenges, and future directions in metabolic engineering. Molecules 29, 3687 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Chen, R. et al. Engineering cofactor supply and recycling to drive phenolic acid biosynthesis in yeast. Nat. Chem. Biol. 18, 520–529 (2022).

    Article  CAS  PubMed  Google Scholar 

  135. Yu, T. et al. Metabolic reconfiguration enables synthetic reductive metabolism in yeast. Nat. Metab. 4, 1551–1559 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Clomburg, J. M., Qian, S., Tan, Z., Cheong, S. & Gonzalez, R. The isoprenoid alcohol pathway, a synthetic route for isoprenoid biosynthesis. Proc. Natl Acad. Sci. USA 116, 12810–12815 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Smith, E. N. et al. Improving photosynthetic efficiency toward food security: strategies, advances, and perspectives. Mol. Plant 6, 1547–1563 (2023).

    Article  Google Scholar 

  138. Pasoreck, E. K. et al. Terpene metabolic engineering via nuclear or chloroplast genomes profoundly and globally impacts off‐target pathways through metabolite signalling. Plant Biotechnol. J. 14, 1862–1875 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Lynch, J. H., Huang, X. Q. & Dudareva, N. Silent constraints: the hidden challenges faced in plant metabolic engineering. Curr. Opin. Biotechnol. 69, 112–117 (2021).

    Article  CAS  PubMed  Google Scholar 

  140. Lynch, J. H. et al. Modulation of auxin formation by the cytosolic phenylalanine biosynthetic pathway. Nat. Chem. Biol. 16, 850–856 (2020).

    Article  CAS  PubMed  Google Scholar 

  141. Razaghi-Moghadam, Z. & Nikoloski, Z. GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics 37, 1717–1723 (2021).

    Article  CAS  PubMed  Google Scholar 

  142. Gurdo, N., Volke, D. C., McCloskey, D. & Nikel, P. I. Automating the design–build–test–learn cycle towards next-generation bacterial cell factories. New Biotechnol. 74, 1–15 (2023).

    Article  CAS  Google Scholar 

  143. Yilmaz, S., Nyerges, A., van der Oost, J., Church, G. M. & Claassens, N. J. Towards next-generation cell factories by rational genome-scale engineering. Nat. Catal. 5, 751–765 (2022).

    Article  Google Scholar 

  144. Sears, R. G., Lenaghan, S. C. & Stewart, C. N. AI to enable plant cell metabolic engineering. Trends Plant Sci. 29, 126–129 (2024).

    Article  CAS  PubMed  Google Scholar 

  145. Sahu, A., Blätke, M. A., Szymański, J. J. & Töpfer, N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput. Struct. Biotechnol. J. 19, 4626–4640 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Patané, A. et al. Multi-objective optimization of genome-scale metabolic models: the case of ethanol production. Ann. Oper. Res. 276, 211–227 (2019).

    Article  Google Scholar 

  147. Li, F. et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat. Catal. 5, 662–672 (2022).

    Article  CAS  Google Scholar 

  148. Gollub, M. G., Backes, T., Kaltenbach, H. M. & Stelling, J. ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties. Bioinformatics 40, btae652 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Wang, T. et al. DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures. Brief. Bioinform. 25, bbae409 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Kroll, A., Rousset, Y., Hu, X. P., Liebrand, N. A. & Lercher, M. J. Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nat. Commun. 14, 4139 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Kroll, A., Engqvist, M. K. M., Heckmann, D. & Lercher, M. J. Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biol. 19, e3001402 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Salas-Nuñez, L. F. et al. Machine learning to predict enzyme–substrate interactions in elucidation of synthesis pathways: a review. Metabolites 14, 154 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Erbe, R., Gore, J., Gemmill, K., Gaykalova, D. A. & Fertig, E. J. The use of machine learning to discover regulatory networks controlling biological systems. Mol. Cell 82, 260–273 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank T. C. R. Williams (Universidade de Brasília) for assistance with the data used for Fig. 2.

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Authors and Affiliations

  1. Department of Biology, University of Oxford, Oxford, UK

    Lee J. Sweetlove & R. George Ratcliffe

  2. Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany

    Alisdair R. Fernie

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  1. Lee J. Sweetlove
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  2. R. George Ratcliffe
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L.J.S., R.G.R. and A.R.F. conceived and wrote the review.

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Sweetlove, L.J., Ratcliffe, R.G. & Fernie, A.R. Non-canonical plant metabolism. Nat. Plants 11, 696–708 (2025). https://doi.org/10.1038/s41477-025-01965-3

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