A Diel Flux Balance Model Captures Interactions between Light and Dark Metabolism during Day-Night Cycles in C3 and Crassulacean Acid Metabolism Leaves

Although leaves have to accommodate markedly different metabolic flux patterns in the light and the dark, models of leaf metabolism based on flux-balance analysis (FBA) have so far been confined to consideration of the network under continuous light. An FBA framework is presented that solves the two phases of the diel cycle as a single optimization problem and, thus, provides a more representative model of leaf metabolism. The requirement to support continued export of sugar and amino acids from the leaf during the night and to meet overnight cellular maintenance costs forces the model to set aside stores of both carbon and nitrogen during the day. With only minimal constraints, the model successfully captures many of the known features of C₃ leaf metabolism, including the recently discovered role of citrate synthesis and accumulation in the night as a precursor for the provision of carbon skeletons for amino acid synthesis during the day. The diel FBA model can be applied to other temporal separations, such as that which occurs in Crassulacean acid metabolism (CAM) photosynthesis, allowing a system-level analysis of the energetics of CAM. The diel model predicts that there is no overall energetic advantage to CAM, despite the potential for suppression of photorespiration through CO₂ concentration. Moreover, any savings in enzyme machinery costs through suppression of photorespiration are likely to be offset by the higher flux demand of the CAM cycle. It is concluded that energetic or nitrogen use considerations are unlikely to be evolutionary drivers for CAM photosynthesis.Photosynthetic metabolism continues to be studied intensively because of its importance for crop performance and the global carbon cycle in relation to climate change. The metabolic pathways and enzymes involved in carbon fixation and related metabolic processes, such as the synthesis of Suc and starch, have been well-characterized. However, it is apparent that full appreciation of leaf metabolism requires these metabolic processes to be placed in the context of the wider metabolic network (Szecowka et al., 2013). This is particularly important for predicting how strategies for engineering improved photosynthesis (Maurino and Weber, 2013) may affect network properties, such as redox and energy balancing (Kramer and Evans, 2011).Flux balance analysis (FBA) has emerged as the method of choice for predicting fluxes in large metabolic network models (Sweetlove and Ratcliffe, 2011), and several flux balance models have explicitly considered photosynthetic metabolism in a variety of plants species and microorganisms, including cyanobacteria (Synechocystis sp. PCC 6803; Knoop et al., 2010, 2013; Montagud et al., 2010; Nogales et al., 2012; Saha et al., 2012), Chlamydomonas reinhardtii (Boyle and Morgan, 2009; de Oliveira Dal''Molin et al., 2011), Arabidopsis (Arabidopsis thaliana; de Oliveira Dal’Molin et al., 2010a), rapeseed (Brassica napus) embryos (Hay and Schwender, 2011), rice (Oryza sativa; Poolman et al., 2013), maize (Zea mays; Saha et al., 2011), and several C₄ plants (de Oliveira Dal’Molin et al., 2010b). These models successfully predicted the metabolic routes involved in the fixation of CO₂ into different biomass components in the light. However, one major feature of metabolism of photosynthetic organisms, namely the interaction between light and dark metabolism, is neglected in most of these studies. Effectively, most models assume that the organism grows in constant light, which is rarely true in natural conditions.Apart from the obvious switch from photoautotrophic to heterotrophic metabolism between day and night, interactions between the two phases can occur through the temporal separation of storage compound synthesis and subsequent mobilization. For example, it has been shown that the carbon skeletons used for nitrogen assimilation during the day are largely provided by carboxylic acids that were synthesized and stored during the previous night (Gauthier et al., 2010). Such temporal shifts of carbon and nitrogen metabolism have substantial implications for fluxes in the central metabolic network of leaves in the light (Tcherkez et al., 2009). Interactions between temporally separated metabolic events are also a critical feature of Crassulacean acid metabolism (CAM) photosynthesis, in which CO₂ is initially fixed at night by phosphoenolpyruvate carboxylase (PEPC), leading to night storage of carboxylic acids (mainly malic acid) that are decarboxylated during the day to provide CO₂ for the conventional photosynthetic carbon assimilation cycle. Although this is principally an adaptation to arid environments, there are unresolved questions as to whether CAM photosynthesis is energetically more efficient than C₃ photosynthesis (Winter and Smith, 1996). Such questions are becoming more important in the light of the proposed use of CAM plants as a source of biofuel (Yan et al., 2011).One recent study used FBA to consider both light and dark metabolism in Synechocystis sp. PCC 6803 over a complete diel cycle divided into 192 time steps (Knoop et al., 2013). Time courses of metabolic flux predictions over a diel cycle were simulated by altering the constraints on metabolic outputs (biomass composition) depending on the time point and based on empirical rules. This simulation led to a highly constrained model and did not allow the range of potential interactions between the day and night phases to be fully explored. We have developed an alternative modeling framework for integrated day-night FBA, in which the metabolic fluxes in the light and dark phases were simulated simultaneously in a single optimization problem. A predefined list of storage compounds that can accumulate freely over the diurnal cycle was made available to the model. The model was then free to choose among these storage compounds, the choice being dictated by the need to satisfy the objective function within the applied constraints. This diurnal modeling framework was used to explore the interactions between light and dark metabolism and to predict the metabolic fluxes in the light in both C₃ and CAM photosynthesis. We show that accounting for day-night interactions leads to an altered pattern of fluxes during the day that provides a better match with experimental observations. We were also able to simulate network flux distributions in CAM metabolism. The model successfully predicts the classic CAM cycle in the different CAM subtypes and allows a comparison of the energetic efficiency and metabolic costs between CAM and C₃ photosynthetic metabolism.