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.
References
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).
ArticlePubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Simons, M., Misra, A. & Sriram, G. in Plant Metabolism: Methods and Protocols Vol. 1083 (ed. Sriram, G.) 213–230 (Humana, 2014).
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).
ArticleGoogle Scholar
Gerlin, L., Frainay, C., Jourdan, F., Baroukh, C. & Prigent, S. Plant genome-scale metabolic networks. Adv. Bot. Res. 98, 237–270 (2021).
ArticleCASGoogle Scholar
Allen, D. K. & Young, J. D. Tracing metabolic flux through time and space with isotope labeling experiments. Curr. Opin. Biotechnol. 64, 92–100 (2020).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Kaste, J. A. M. & Shachar-Hill, Y. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling. Bioinformatics 39, btad186 (2023).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Fabregas, N. & Fernie, A. R. The metabolic response to drought. J. Exp. Bot. 70, 1077–1085 (2019).
ArticleCASPubMedGoogle Scholar
Botha, F. C. & Marquardt, A. Metabolic control of sugarcane internode elongation and sucrose accumulation. Agronomy 14, 1487 (2024).
ArticleCASGoogle Scholar
de Vries, S. & Feussner, I. Biotic interactions, evolutionary forces and the pan-plant specialized metabolism. Phil. Trans. R. Soc. B 379, 20230362 (2024).
ArticlePubMedPubMed CentralGoogle Scholar
Kruger, N. J. & Ratcliffe, R. G. Pathways and fluxes: exploring the plant metabolic network. J. Exp. Bot. 63, 2243–2246 (2012).
ArticleCASPubMedGoogle Scholar
Judge, A. & Dodd, M. S. Metabolism. Essays Biochem. 64, 607–647 (2020).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Lanier, E. R., Andersen, T. B. & Hamberger, B. Plant terpene specialized metabolism: complex networks or simple linear pathways? Plant J. 114, 1178–1201 (2023).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Palsson, B. Ø. Systems Biology: Constraint-Based Reconstruction and Analysis Ch. 12 (Cambridge Univ. Press, 2015).
Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).
ArticlePubMedPubMed CentralGoogle Scholar
Morandini, P. Rethinking metabolic control. Plant Sci. 176, 441–451 (2009).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Guo, L. et al. Dynamic modeling of subcellular phenylpropanoid metabolism in Arabidopsis lignifying cells. Metab. Eng. 49, 36–46 (2018).
ArticleCASPubMedGoogle Scholar
Wang, J. P. et al. Flux modelling for monolignol biosynthesis. Curr. Opin. Biotechnol. 56C, 187–192 (2019).
ArticleGoogle Scholar
Rao, X. & Barros, J. Modeling lignin biosynthesis: a pathway to renewable chemicals. Trends Plant Sci. 29, 546–559 (2024).
ArticleCASPubMedGoogle Scholar
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).
ArticlePubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Sarkar, D. & Kundu, S. Systems biology of plant metabolic interactions. J. Biosci. 49, 56 (2024).
ArticlePubMedGoogle Scholar
Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Töpfer, N. Environment-coupled models of leaf metabolism. Biochem. Soc. Trans. 49, 119–129 (2021).
ArticlePubMedPubMed CentralGoogle Scholar
Sampaio, M., Rocha, M. & Dias, O. Exploring synergies between plant metabolic modelling and machine learning. Comput. Struct. Biotechnol. J. 20, 1885–1900 (2022).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
PubMedPubMed CentralGoogle Scholar
Moreno-Villena, J. J. et al. Spatial resolution of an integrated C4+CAM photosynthetic metabolism. Sci. Adv. 8, eabn2349 (2022).
ArticleCASPubMedPubMed CentralGoogle Scholar
von Bismarck, T. et al. Growth in fluctuating light buffers plants against photorespiratory perturbations. Nat. Commun. 14, 7052 (2023).
ArticleGoogle Scholar
Fu, X., Schlüter, U., Smith, K., Weber, A. P. M. & Walker, B. J. Metabolomics of related C3 and C4Flaveria species indicate differences in the operation of photorespiration under fluctuating light. Plant Direct 8, e70012 (2024).
ArticleCASPubMedPubMed CentralGoogle Scholar
Shaw, R. & Kundu, S. Metabolic plasticity and inter-compartmental interactions in rice metabolism: an analysis from reaction deletion study. PLoS ONE 10, e0133899 (2015).
ArticlePubMedPubMed CentralGoogle Scholar
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).
ArticlePubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Chatterjee, A. & Kundu, S. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica. Sci. Rep. 5, 14975 (2015).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Hill, S. A. & ap Rees, T. Fluxes of carbohydrate metabolism in ripening bananas. Planta 192, 52–60 (1994).
ArticleCASGoogle Scholar
Ratcliffe, R. G. & Shachar-Hill, Y. Measuring multiple fluxes through plant metabolic networks. Plant J. 45, 490–511 (2006).
ArticleCASPubMedGoogle Scholar
Kruger, N. J. & Ratcliffe, R. G. Fluxes through plant metabolic networks: measurements, predictions, insights and challenges. Biochem. J 465, 27–38 (2015).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASGoogle Scholar
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).
ArticlePubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Allen, D. K., Ohlrogge, J. B. & Shachar-Hill, Y. The role of light in soybean seed filling metabolism. Plant J. 58, 220–234 (2009).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Schwender, J., Shachar-Hill, Y. & Ohlrogge, J. B. Mitochondrial metabolism in developing embryos of Brassica napus. J. Biol. Chem. 281, 34040–34047 (2006).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Igamberdiev, A. U. & Bykova, N. V. Mitochondria in photosynthetic cells: coordinating redox control and energy balance. Plant Physiol. 191, 2104–2119 (2023).
ArticleCASPubMedGoogle Scholar
Szecowka, M. et al. Metabolic fluxes in an illuminated Arabidopsis rosette. Plant Cell 25, 694–714 (2013).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Wieloch, T. & Sharkey, T. D. Compartment-specific energy requirements of photosynthetic carbon metabolism in Camelina sativa leaves. Planta 255, 103 (2022).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Sharkey, T. D. The end game(s) of photosynthetic carbon metabolism. Plant Physiol. 195, 67–78 (2024).
ArticleCASPubMedPubMed CentralGoogle Scholar
Makowka, A. et al. Glycolytic shunts replenish the Calvin–Benson–Bassham cycle as anaplerotic reactions in cyanobacteria. Mol. Plant 13, 471–482 (2020).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Evans, S. E. et al. Rubisco supplies pyruvate for the 2-C-methyl-d-erythritol-4-phosphate pathway. Nat. Plants 10, 1453–1463 (2024).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticlePubMedGoogle Scholar
Bauwe, H. Photorespiration—Rubisco’s repair crew. J. Plant Physiol. 280, 153899 (2023).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticlePubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Abadie, C., Boex-Fontvieille, E. R. A., Carroll, A. J. & Tcherkez, G. In vivo stoichiometry of photorespiratory metabolism. Nat. Plants 2, 15220 (2016).
ArticleCASPubMedGoogle Scholar
Abadie, C. & Tcherkez, G. 13C isotope labelling to follow the flux of photorespiratory intermediates. Plants 10, 427 (2021).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Timm, S. et al. Glycine decarboxylase controls photosynthesis and plant growth. FEBS Lett. 586, 3692–3697 (2012).
ArticleCASPubMedGoogle Scholar
Timm, S. et al. Mitochondrial dihydrolipoyl dehydrogenase activity shapes photosynthesis and photorespiration of Arabidopsis thaliana. Plant Cell 27, 1968–1984 (2015).
ArticleCASPubMedPubMed CentralGoogle Scholar
Kebeish, R. et al. Chloroplastic photorespiratory bypass increases photosynthesis and biomass production in Arabidopsis thaliana. Nat. Biotechnol. 25, 593–599 (2007).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticlePubMedGoogle Scholar
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).
Yang, W. et al. Advances in pharmacological activities of terpenoids. Nat. Prod. Commun. 15, 1934578X20903555 (2020).
CASGoogle Scholar
Joshi, S. & Mishra, S. Recent advances in biofuel production through metabolic engineering. Bioresour. Technol. 352, 127037 (2022).
ArticleCASPubMedGoogle Scholar
Jiang, H. & Wang, X. Biosynthesis of monoterpenoid and sesquiterpenoid as natural flavors and fragrances. Biotechnol. Adv. 65, 108151 (2023).
ArticleCASPubMedGoogle Scholar
O’Neill, E. C. & Kelly, S. Engineering biosynthesis of high-value compounds in photosynthetic organisms. Crit. Rev. Biotechnol. 37, 779–802 (2017).
ArticlePubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Kai, G. et al. Metabolic engineering tanshinone biosynthetic pathway in Salvia miltiorrhiza hairy root cultures. Metab. Eng. 13, 319–327 (2011).
ArticleCASPubMedGoogle Scholar
Li, Y. et al. Advanced metabolic engineering strategies for increasing artemisinin yield in Artemisia annua. L. Hortic. Res. 11, uhad292 (2024).
ArticleCASPubMedGoogle Scholar
Masakapalli, S. K. et al. Metabolic flux phenotype of tobacco hairy roots engineered for increased geraniol production. Phytochemistry 99, 73–85 (2014).
ArticleCASPubMedGoogle Scholar
Rodriguez, S. et al. ATP citrate lyase mediated cytosolic acetyl-CoA biosynthesis increases mevalonate production in Saccharomyces cerevisiae. Microb. Cell Fact. 15, 48 (2016).
ArticlePubMedPubMed CentralGoogle Scholar
Chapman, K. D., Dyer, J. M. & Mullen, R. T. Commentary: why don’t plant leaves get fat? Plant Sci. 207, 128–134 (2013).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Andrews, T. J. & Kane, H. J. Pyruvate is a by-product of catalysis by ribulosebisphosphate carboxylase/oxygenase. J. Biol. Chem. 266, 9447–9452 (1991).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticlePubMedGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Vanhercke, T. et al. Step changes in leaf oil accumulation via iterative metabolic engineering. Metab. Eng. 39, 237–246 (2017).
ArticleCASPubMedGoogle Scholar
Strand, D. D. & Walker, B. J. Energetic considerations for engineering novel biochemistries in photosynthetic organisms. Front. Plant Sci. 14, 1116812 (2023).
ArticlePubMedPubMed CentralGoogle Scholar
Brunk, E. et al. Characterizing strain variation in engineered E. coli using a multi-omics-based workflow. Cell Syst. 2, P335–P346 (2016).
ArticleGoogle Scholar
Brand, A. & Tissier, A. Control of resource allocation between primary and specialized metabolism in glandular trichomes. Curr. Opin. Plant Biol. 66, 102172 (2022).
ArticleCASPubMedGoogle Scholar
Asadollahi, M. A. et al. Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab. Eng. 11, 328–334 (2009).
ArticleCASPubMedGoogle Scholar
Assil-Companioni, L. et al. Engineering of NADPH supply boosts photosynthesis-driven biotransformations. ACS Catal. 10, 11864–11877 (2020).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Chen, R. et al. Engineering cofactor supply and recycling to drive phenolic acid biosynthesis in yeast. Nat. Chem. Biol. 18, 520–529 (2022).
ArticleCASPubMedGoogle Scholar
Yu, T. et al. Metabolic reconfiguration enables synthetic reductive metabolism in yeast. Nat. Metab. 4, 1551–1559 (2022).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Smith, E. N. et al. Improving photosynthetic efficiency toward food security: strategies, advances, and perspectives. Mol. Plant 6, 1547–1563 (2023).
ArticleGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
Lynch, J. H. et al. Modulation of auxin formation by the cytosolic phenylalanine biosynthetic pathway. Nat. Chem. Biol. 16, 850–856 (2020).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASPubMedGoogle Scholar
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).
ArticleCASGoogle Scholar
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).
ArticleGoogle Scholar
Sears, R. G., Lenaghan, S. C. & Stewart, C. N. AI to enable plant cell metabolic engineering. Trends Plant Sci. 29, 126–129 (2024).
ArticleCASPubMedGoogle Scholar
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).
ArticlePubMedPubMed CentralGoogle Scholar
Patané, A. et al. Multi-objective optimization of genome-scale metabolic models: the case of ethanol production. Ann. Oper. Res. 276, 211–227 (2019).
ArticleGoogle Scholar
Li, F. et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat. Catal. 5, 662–672 (2022).
ArticleCASGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle Scholar
Salas-Nuñez, L. F. et al. Machine learning to predict enzyme–substrate interactions in elucidation of synthesis pathways: a review. Metabolites 14, 154 (2024).
ArticlePubMedPubMed CentralGoogle Scholar
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).
ArticleCASPubMedPubMed CentralGoogle 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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Department of Biology, University of Oxford, Oxford, UK
Lee J. Sweetlove & R. George Ratcliffe
Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
Alisdair R. Fernie
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Lee J. Sweetlove
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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 (2025). https://doi.org/10.1038/s41477-025-01965-3
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Received:23 October 2024
Accepted:01 March 2025
Published:31 March 2025
DOI:https://doi.org/10.1038/s41477-025-01965-3
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