Combining gene network, metabolic, and leaf-level models show means to future-proof soybean photosynthesis under rising CO2.

Abstract

Global 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 modeling processes from gene expression through photosynthetic metabolism to predict leaf physiology in evaluating acclimation of photosynthesis to rising [CO2]. Model integration with the yggdrasil interface enabled asynchronous message passing between models. The multiscale model of soybean 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 GRN through protein concentration to the metabolic model it was possible to identify transcription factors (TF) that matched the up- and down-regulation of genes needed to improve photosynthesis. Most striking was TF GmGATA2, 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 modeling predicted as optimal for the future elevated [CO2] world.