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Computational analysis of the productivity potential of CAM

Nature Plants volume 4, pages 165–171 (2018)Cite this article

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Abstract

There is considerable interest in transferring crassulacean acid metabolism (CAM) to C3 crops to improve their water-use efficiency. However, because the CAM biochemical cycle is energetically costly, it is unclear what impact this would have on yield. Using diel flux balance analysis of the CAM and C3 leaf metabolic networks, we show that energy consumption is three-fold higher in CAM at night. However, this additional cost of CAM can be entirely offset by the carbon-concentrating effect of malate decarboxylation behind closed stomata during the day. Depending on the resultant rates of the carboxylase and oxygenase activities of rubisco, the productivity of the PEPCK-CAM subtype is 74–100% of the C3 network. We conclude that CAM does not impose a significant productivity penalty and that engineering CAM into C3 crops is likely to lead to a major increase in water-use efficiency without substantially affecting yield.

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Fig. 1: A simplified schematic of the CAM cycle in starch-storing leaves.
Fig. 2: A representation of major proton-consuming and proton-producing reactions in the CAM model.
Fig. 3: ATP production and consumption in C3 and CAM systems.
Fig. 4: Predicted productivity of CAM leaves with varying rubisco carboxylase/oxygenase (vC/vO) ratios.

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Acknowledgements

Funding from ERA-CAPS ('Simultaneous manipulation of source and sink metabolism for improved crop yield'; BO 1482/18-1 | FE 552/33-1 | RE 1351/2-1 | SW 122/2-1) is acknowledged. We would also like to thank A. Smith (Department of Plant Sciences, University of Oxford) for advice and discussions about CAM.

Author information

Authors and Affiliations

  1. Department of Plant Sciences, University of Oxford, Oxford, UK

    Sanu Shameer, Kambiz Baghalian, R. George Ratcliffe & Lee J. Sweetlove

  2. Yale-NUS College, Singapore, Singapore

    C. Y. Maurice Cheung

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  1. Sanu Shameer
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  2. Kambiz Baghalian
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  3. C. Y. Maurice Cheung
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  4. R. George Ratcliffe
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Contributions

L.J.S., R.G.R. and S.S. conceived the study and co-wrote the paper. S.S., C.Y.M.C. and K.B. constructed and curated the core model. S.S. did all subsequent computational analyses of the model and analysed the data generated.

Corresponding authors

Correspondence to R. George Ratcliffe or Lee J. Sweetlove.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figures 1–4, Supplementary Methods and Supplementary References.

Life Sciences Reporting Summary

Supplementary Data 1

The core diel leaf model in SBML format. Suffixes for metabolite and reaction names indicate cellular localization and whether they belong to the day or night metabolic network (suffix of 1 and 2 respectively). For example, ‘GLUCOKIN_RXN_c1’ represents a reaction occurring in the cytosol during the day. Similarly, ‘PYRUVATE_PROTON_mc2’ represents a reaction involving metabolites in night-time cytosol and mitochondria. A complete list of compartment identifiers is provided within the data file.

Supplementary Data 2

Phloem composition data used as output constraints for the diel FBA models.

Supplementary Data 3

Diel FBA flux predictions for C3 and CAM. Spreadsheet file listing all constraints applied to model C3 and CAM diel leaf metabolism and the resulting flux predictions.

Supplementary Data 4

C3 leaf diel model in SBML format, including all constraints used.

Supplementary Data 5

CAM leaf diel model in SMBL format, including all constraints used. Note that in this file the Rubisco vC/vO constraint is set.

Supplementary Data 6

Diel FBA flux predictions for different CAM subtypes. Spreadsheet file listing all constraints applied to model diel leaf metabolism in different CAM subtypes and the resulting flux predictions.

Supplementary Data 7

Manual curation log. A list of all changes made to the model as a result of manual curation. This includes modification or deletion of existing reactions and introduction of new reactions.

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Shameer, S., Baghalian, K., Cheung, C.Y.M. et al. Computational analysis of the productivity potential of CAM. Nature Plants 4, 165–171 (2018). https://doi.org/10.1038/s41477-018-0112-2

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