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Tre6P synthesis by TPS1 is essential for embryogenesis and postembryonic growth in Arabidopsis, and appropriate Suc signaling by Tre6P is dependent on the noncatalytic domains of TPS1. In Arabidopsis (Arabidopsis thaliana), TREHALOSE-6-PHOSPHATE SYNTHASE1 (TPS1) catalyzes the synthesis of the sucrose-signaling metabolite trehalose 6-phosphate (Tre6P) and is essential for embryogenesis and normal postembryonic growth and development. To understand its molecular functions, we transformed the embryo-lethal tps1-1 null mutant with various forms of TPS1 and with a heterologous TPS (OtsA) from Escherichia coli, under the control of the TPS1 promoter, and tested for complementation. TPS1 protein localized predominantly in the phloem-loading zone and guard cells in leaves, root vasculature, and shoot apical meristem, implicating it in both local and systemic signaling of Suc status. The protein is targeted mainly to the nucleus. Restoring Tre6P synthesis was both necessary and sufficient to rescue the tps1-1 mutant through embryogenesis. However, postembryonic growth and the sucrose-Tre6P relationship were disrupted in some complementation lines. A point mutation (A119W) in the catalytic domain or truncating the C-terminal domain of TPS1 severely compromised growth. Despite having high Tre6P levels, these plants never flowered, possibly because Tre6P signaling was disrupted by two unidentified disaccharide-monophosphates that appeared in these plants. The noncatalytic domains of TPS1 ensure its targeting to the correct subcellular compartment and its catalytic fidelity and are required for appropriate signaling of Suc status by Tre6P.
Tre6P synthesis by TPS1 is essential for embryogenesis and postembryonic growth in Arabidopsis, and appropriate Suc signaling by Tre6P is dependent on the noncatalytic domains of TPS1. In Arabidopsis (Arabidopsis thaliana), TREHALOSE-6-PHOSPHATE SYNTHASE1 (TPS1) catalyzes the synthesis of the sucrose-signaling metabolite trehalose 6-phosphate (Tre6P) and is essential for embryogenesis and normal postembryonic growth and development. To understand its molecular functions, we transformed the embryo-lethal tps1-1 null mutant with various forms of TPS1 and with a heterologous TPS (OtsA) from Escherichia coli, under the control of the TPS1 promoter, and tested for complementation. TPS1 protein localized predominantly in the phloem-loading zone and guard cells in leaves, root vasculature, and shoot apical meristem, implicating it in both local and systemic signaling of Suc status. The protein is targeted mainly to the nucleus. Restoring Tre6P synthesis was both necessary and sufficient to rescue the tps1-1 mutant through embryogenesis. However, postembryonic growth and the sucrose-Tre6P relationship were disrupted in some complementation lines. A point mutation (A119W) in the catalytic domain or truncating the C-terminal domain of TPS1 severely compromised growth. Despite having high Tre6P levels, these plants never flowered, possibly because Tre6P signaling was disrupted by two unidentified disaccharide-monophosphates that appeared in these plants. The noncatalytic domains of TPS1 ensure its targeting to the correct subcellular compartment and its catalytic fidelity and are required for appropriate signaling of Suc status by Tre6P.
The metabolic state of an organism reflects the entire phenotype that is jointly affected by genetic and environmental changes. Due to the complexity of metabolism, system-level modelling approaches have become indispensable tools to obtain new insights into biological functions. In particular, simulation and analysis of metabolic networks using constraint-based modelling approaches have helped the analysis of metabolic fluxes. However, despite ongoing improvements in prediction of reaction flux through a system, approaches to directly predict metabolite concentrations from large-scale metabolic networks remain elusive. In this thesis, we present a computational approach for inferring concentration ranges from genome-scale metabolic models endowed with mass action kinetics. The findings specify a molecular mechanism underling facile control of concentration ranges for components in large-scale metabolic networks. Most importantly, an extended version of the approach can be used to predict concentration ranges without knowledge of kinetic parameters, provided measurements of concentrations in a reference state. We show that the approach is applicable with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life. By challenging the predictions of concentration ranges in the genome-scale metabolic network of Escherichia coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach. To predict concentration ranges in other species, e.g. model plant species Arabidopsis thaliana, we would rely on estimates of kinetic parameters (i.e. enzyme catalytic rates) since plant-specific enzyme catalytic rates are poorly documented. Using the constraint-based approach of Davidi et al. for estimation of enzyme catalytic rates, we obtain values for 168 plant enzymes. The approach depends on quantitative proteomics data and flux estimates obtained from constraint-based model of plant leaf metabolism integrating maximal rates of selected enzymes, plant-specific constraints on fluxes through canonical pathways, and growth measurements from Arabidopsis thaliana rosette under ten conditions. We demonstrate a low degree of plant enzyme saturation, supported by the agreement between concentrations of nicotinamide adenine dinucleotide, adenosine triphosphate, and glyceraldehyde 3-phosphate, based on our maximal in vivo catalytic rates, and available quantitative metabolomics data. Hence, our results show genome-wide estimation for plant-specific enzyme catalytic rates is feasible. These can now be readily employed to study resource allocation, to predict enzyme and metabolite concentrations using recent constrained-based modelling approaches. Constraint-based methods do not directly account for kinetic mechanisms and corresponding parameters. Therefore, a number of workflows have already been proposed to approximate reaction kinetics and to parameterize genome-scale kinetic models. We present a systems biology strategy to build a fully parameterized large-scale model of Chlamydomonas reinhardtii accounting for microcompartmentalization in the chloroplast stroma. Eukaryotic algae comprise a microcompartment, the pyrenoid, essential for the carbon concentrating mechanism (CCM) that improves their photosynthetic performance. Since the experimental study of the effects of microcompartmentation on metabolic pathways is challenging, we employ our model to investigate compartmentation of fluxes through the Calvin-Benson cycle between pyrenoid and stroma. Our model predicts that ribulose-1,5-bisphosphate, the substrate of Rubisco, and 3-phosphoglycerate, its product, diffuse in and out of the pyrenoid. We also find that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Therefore, our computational approach represents a stepping stone towards understanding of microcompartmentalized CCM in other organisms. This thesis provides novel strategies to use genome-scale metabolic networks to predict and integrate metabolite concentrations. Therefore, the presented approaches represent an important step in broadening the applicability of large-scale metabolic models to a range of biotechnological and medical applications.