A Genome-Scale Metabolic Model Accurately Predicts Fluxes in Central Carbon Metabolism under Stress Conditions    

Abstract

Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by 13 C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.As most of the uses of plants are intimately linked to their metabolic output or activity, there is a renewed interest in understanding the behavior and regulation of plant metabolic networks. The only direct measure of metabolic activity, and the facet most closely related to biological function, is flux through the metabolic network (Libourel and Shachar-Hill, 2008). There has been a considerable research effort in the last few years to develop and refine methods that allow fluxes in large metabolic networks to be determined. The best established of these methods, steady-state metabolic flux analysis (MFA), involves measuring the redistribution of a supplied stable isotope, usually

Metabolic Fluxes in an Illuminated Arabidopsis Rosette

“…Successful applications of 13 C-labeling and gas chromatographytime-of-flight-mass spectrometry (GC-TOF-MS) have been reported for a range of species and tissues, including Escherichia coli (Yuan et al, 2006;Haverkorn van Rijsewijk et al, 2011), Saccharomyces cerevisiae (Birkemeyer et al, 2005), photoautotrophic cyanobacteria, Synechocystis sp (Huege et al, 2011;Young et al, 2011), Arabidopsis thaliana (Huege et al, 2007;Williams et al, 2010), soybean (Glycine max) embryos (Sriram et al, 2004), Brassica napus (Schwender et al, 2004b(Schwender et al, , 2006, and potato (Solanum tuberosum) tubers (Roessner-Tunali et al, 2004). However, to date, there are relatively few reports that use 13 CO 2 to study metabolism in photosynthesizing plant tissue.…”

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf

“…4; Nogales et al, 2012;Poolman et al, 2014). More generally, while the efficiency of network structure, as assessed by the minimization of total flux through the network, has been shown to lead to realistic flux distributions in plant metabolic networks (Williams et al, 2010;Hay and Schwender, 2011;Cheung et al, 2013), costweighted flux minimization highlights the important caveat that greater predictive accuracy might be achievable with a knowledge of the unknown costs of supporting fluxes through different metabolic steps. This is to be expected, because lack of information, whether in the form of kinetic parameters for kinetic models or constraints for constraints-based models, is often encountered when predicting fluxes in complex networks.…”

“…FBA can generate accurate predictions of plant metabolic fluxes (Williams et al, 2010;Hay and Schwender, 2011;Cheung et al, 2013), but the analysis is complicated by the presence of alternative pathways that share the same function within the network. For example, mitochondria and chloroplasts have several potential mechanisms for maintaining energetic homeostasis, including alternative electron flow pathways, metabolite shuttles for the transfer of reducing power or ATP, and uncoupling mechanisms (Millar et al, 2011;Taniguchi and Miyake, 2012).…”

Predictive Modeling of Biomass Component Tradeoffs in Brassica napus Developing Oilseeds Based on in Silico Manipulation of Storage Metabolism

“…Several stoichiometric networks of plant and algae metabolism have been reported Grafahrend-Belau et al, 2009;Manichaikul et al, 2009;Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010a, 2010bRadrich et al, 2010;Williams et al, 2010;Chang et al, 2011;Schwender, 2011a, 2011b;Pilalis et al, 2011;Saha et al, 2011;Mintz-Oron et al, 2012). In conjunction with constraint-based computational methods like flux balance analysis (FBA; Sweetlove and Ratcliffe, 2011) and flux variability analysis (FVA; Schwender, 2011a, 2011b), these models are useful for predictive simulation of biomass formation from nutrient substrates and light.…”