Flux-Balance Modeling of Plant Metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to 13C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement

MSL1 is a mechanosensitive ion channel that dissipates mitochondrial membrane potential and maintains redox homeostasis in mitochondria during abiotic stress

Mitochondria must maintain tight control over the electrochemical gradient across their inner membrane to allow ATP synthesis while maintaining a redox-balanced electron transport chain and avoiding excessive reactive oxygen species production. However, there is a scarcity of knowledge about the ion transporters in the inner mitochondrial membrane that contribute to control of membrane potential. We show that loss of MSL1, a member of a family of mechanosensitive ion channels related to the bacterial channel MscS, leads to increased membrane potential of Arabidopsis mitochondria under specific bioenergetic states. We demonstrate that MSL1 localises to the inner mitochondrial membrane. When expressed in Escherichia coli, MSL1 forms a stretch-activated ion channel with a slight preference for anions and provides protection against hypo-osmotic shock. In contrast, loss of MSL1 in Arabidopsis did not prevent swelling of isolated mitochondria in hypo-osmotic conditions. Instead, our data suggest that ion transport by MSL1 leads to dissipation of mitochondrial membrane potential when it becomes too high. The importance of MSL1 function was demonstrated by the observation of a higher oxidation state of the mitochondrial glutathione pool in msl1-1 mutants under moderate heat- and heavy-metal-stress. Furthermore, we show that MSL1 function is not directly implicated in mitochondrial membrane potential pulsing, but is complementary and appears to be important under similar conditions

The intertwined metabolism during symbiotic nitrogen fxation elucidated by metabolic modelling

Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed fux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the efects of the symbiosis on metabolic fuxes and plant growth and could demonstrate how oxygen availability infuences metabolic exchanges between plant and symbiont, thus elucidating potential benefts of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective

Synchronization of developmental, molecular and metabolic aspects of source–sink interactions

Plants have evolved a multitude of strategies to adjust their growth according to external and internal signals. Interconnected metabolic and phytohormonal signalling networks allow adaption to changing environmental and developmental conditions and ensure the survival of species in fluctuating environments. In agricultural ecosystems, many of these adaptive responses are not required or may even limit crop yield, as they prevent plants from realizing their fullest potential. By lifting source and sink activities to their maximum, massive yield increases can be foreseen, potentially closing the future yield gap resulting from an increasing world population and the transition to a carbon-neutral economy. To do so, a better understanding of the interplay between metabolic and developmental processes is required. In the past, these processes have been tackled independently from each other, but coordinated efforts are required to understand the fine mechanics of source–sink relations and thus optimize crop yield. Here, we describe approaches to design high-yielding crop plants utilizing strategies derived from current metabolic concepts and our understanding of the molecular processes determining sink development.Research in the authors’ laboratories was supported by the following grants: the cassava source–sink (CASS) project of the Bill and Melinda Gates Foundation (to A.R.F., H.E.N., M.S. and U.S.); the ERA-CAPs project SourSi (to A.R.F. and L.J.S.); the BIO2015-3019-EXP grant from the Spanish Ministry of Economy, Industry and Competitiveness and the PCIN-2017-032 CONCERT-JAPAN project financed by the Ministry of Science, Innovation and Universities (to S.P.); Australian Research Council DP180103834 (to Y.L.R.); the US National Science Foundation (grant no. IOS-1457183); the Agriculture and Food Research Initiative (AFRI; grant no. 2017-67013-26158) from the USDA National Institute of Food and Agriculture (to M.T.); the Finnish Centre of Excellence in Molecular Biology of Primary Producers (Academy of Finland CoE program 2014–2019; grant no. 271832); the Gatsby Foundation (grant no. GAT3395/PR3); the University of Helsinki (grant no. 799992091); the European Research Council Advanced Investigator Grant SYMDEV (grant no. 323052; to Y.H.); the BMBF (grant no. 031B0191); the DFG (SPP1530: WA3639/1-2, 2-1); and the Max-Planck-Society (to V.W.). We additionally thank D. Ko and R. Ruonala for their comments on the manuscript