Abstract
AbstractGlobal population increase coupled with rising urbanization underlies the predicted need for 60% more food by 2050, but produced on the same amount of land as today. Improving photosynthetic efficiency is a largely untapped approach to addressing this problem. Here, we scale modelling processes from gene expression through photosynthetic metabolism to predict leaf physiology in evaluating acclimation of photosynthesis to rising atmospheric concentrations of CO2 ([CO2]). Model integration with the yggdrasil interface enabled asynchronous message passing between models. The multiscale model of soybean (Glycine max) photosynthesis calibrated to physiological measures at ambient [CO2] successfully predicted the acclimatory changes in the photosynthetic apparatus that were observed at 550 ppm [CO2] in the field. We hypothesized that genetic alteration is necessary to achieve optimal photosynthetic efficiency under global change. Flux control analysis in the metabolic system under elevated [CO2] identified enzymes requiring the greatest change to adapt optimally to the new conditions. This predicted that Rubisco was less limiting under elevated [CO2] and should be down-regulated allowing re-allocation of resource to enzymes controlling the rate of regeneration of ribulose-1,5-bisphosphate (RuBP). By linking the Gene Regulatory Network through protein concentration to the metabolic model, it was possible to identify transcription factors (TFs) that matched the up- and down-regulation of genes needed to improve photosynthesis. Most striking was TF Gm-GATA2, which down-regulated genes for Rubisco synthesis while up-regulating key genes controlling RuBP regeneration and starch synthesis. The changes predicted for this TF most closely matched the physiological ideotype that the modelling predicted as optimal for the future elevated [CO2] world.
Highlights
As the world’s most important seed legume and most widely grown dicotyledonous crop, the future-proofing of photosynthesis in soybean (Glycine max (L.) Merr.) under rising atmospheric concentrations of CO2 ([CO2]) is of importance
As [CO2] continues to rise, it follows from the steady-state biochemical model of photosynthesis of (Farquhar et al, 1980) and its subsequent modifications (Von Caemmerer, 2000) that control will shift from Rubisco to RubP regeneration (Long et al, 2004), which is represented by the maximum in vivo rate of whole chain electron transport (Jmax)
We have previously developed complete mechanistic metabolic models of photosynthetic carbon metabolism that successfully predict dynamic responses of leaf chlorophyll fluorescence and fluxes of CO2 and O2 to changes in light, [CO2] and [O2] (Zhu et al, 2007, Zhu et al, 2013)
Summary
As the world’s most important seed legume and most widely grown dicotyledonous crop, the future-proofing of photosynthesis in soybean (Glycine max (L.) Merr.) under rising atmospheric concentrations of CO2 ([CO2]) is of importance. While described by electron transport, most evidence points to this being limited by the metabolic steps of carbon metabolism leading to RubP regeneration (Raines, 2003, Stitt and Sonnewald, 1995). This shift from Rubisco- to RubP- limited photosynthesis permits a reduction in leaf Rubisco content without a loss in Asat (Woodrow, 1994, Long et al, 2004, Ainsworth and Long, 2005).
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