Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Grafahrend-Belau, Eva; Junker, Astrid; Eschenröder, André; Müller, Johannes; Schreiber, Falk; Junker, Björn H.

doi:10.1104/pp.113.224006

Cited by 126 publications

(110 citation statements)

References 66 publications

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“…Inhibition of SDH will disrupt the tricarboxylic acid cycle, which leads to a noncyclic reaction pathway that has actually been described earlier to occur in plants and green algae in response to various conditions, including low oxygen availability (Vanlerberghe et al, 1989(Vanlerberghe et al, , 1991Hanning and Heldt, 1993;Schwender et al, 2006;Steuer et al, 2007;Boyle and Morgan, 2009;Tcherkez et al, 2009;Sweetlove et al, 2010;Grafahrend-Belau et al, 2013;Ma et al, 2014). Interestingly, similar observations were made on the relative levels of the metabolites associated with the tricarboxylic acid cycle in antisense SDH tomato (Solanum lycopersicum) plants, which were deficient in the expression of the iron-sulfur subunit of SDH .…”

Section: Hypoxia Leads To the Inhibition Of Sdh And Concomitantly Of supporting

confidence: 63%

Regulation of Primary Metabolism in Response to Low Oxygen Availability as Revealed by Carbon and Nitrogen Isotope Redistribution

et al. 2015

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Based on enzyme activity assays and metabolic responses to waterlogging of the legume Lotus japonicus, it was previously suggested that, during hypoxia, the tricarboxylic acid cycle switches to a noncyclic operation mode. Hypotheses were postulated to explain the alternative metabolic pathways involved, but as yet, a direct analysis of the relative redistribution of label through the corresponding pathways was not made. Here, we describe the use of stable isotope-labeling experiments for studying metabolism under hypoxia using wild-type roots of the crop legume soybean (Glycine max). [ 13 C]Pyruvate labeling was performed to compare metabolism through the tricarboxylic acid cycle, fermentation, alanine metabolism, and the g-aminobutyric acid shunt, while [ 13 C]glutamate and [ 15 N]ammonium labeling were performed to address the metabolism via glutamate to succinate. Following these labelings, the time course for the redistribution of the 13 C/ 15 N label throughout the metabolic network was evaluated with gas chromatography-time of flight-mass spectrometry. Our combined labeling data suggest the inhibition of the tricarboxylic acid cycle enzyme succinate dehydrogenase, also known as complex II of the mitochondrial electron transport chain, providing support for the bifurcation of the cycle and the down-regulation of the rate of respiration measured during hypoxic stress. Moreover, up-regulation of the g-aminobutyric acid shunt and alanine metabolism explained the accumulation of succinate and alanine during hypoxia.

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Section: Hypoxia Leads To the Inhibition Of Sdh And Concomitantly Of supporting

confidence: 63%

Regulation of Primary Metabolism in Response to Low Oxygen Availability as Revealed by Carbon and Nitrogen Isotope Redistribution

et al. 2015

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“…A recently assembled Arabidopsis FBA model includes condition-specific biomass reactions for optimal (light-limited), nitrogen-limited, and carbonlimited conditions (Arnold and Nikoloski, 2014). A similar approach was taken in a multiscale model of barley (Hordeum vulgare) in which models of leaf, stem, seed, and root were interconnected via the phloem and environment to investigate sink-source relationships (Grafahrend-Belau et al, 2013). Time-specific biomass compositions in combination with minimization of the uptake of resources (e.g.…”

Section: Context-specific Models In Plantsmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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“…Being a member of the genus Brassicaceae, A. thaliana is a host of Xcc. Other genome-scale reconstructions are available for maize [239] and barley [240], a host of X. translucens pv. translucens [241,242], and it is likely that more plant models will become available with the increasing number of genomes being sequenced.…”

Section: Environmental Systems Biology As a Perspective For Analysis mentioning

confidence: 99%

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

et al. 2017

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Bacteria of the genus Xanthomonas are a major group of plant pathogens. They are hazardous to important crops and closely related to human pathogens. Being collectively a major focus of molecular phytopathology, an increasing number of diverse and intricate mechanisms are emerging by which they communicate, interfere with host signalling and keep competition at bay. Interestingly, they are also biotechnologically relevant polysaccharide producers. Systems biotechnology techniques have revealed their central metabolism and a growing number of remarkable features. Traditional analyses of Xanthomonas metabolism missed the Embden-Meyerhof-Parnas pathway (glycolysis) as being a route by which energy and molecular building blocks are derived from glucose. As a consequence of the emerging full picture of their metabolism process, xanthomonads were discovered to have three alternative catabolic pathways and they use an unusual and reversible phosphofructokinase as a key enzyme. In this review, we summarize the synthetic and systems biology methods and the bioinformatics tools applied to reconstruct their metabolic network and reveal the dynamic fluxes within their complex carbohydrate metabolism. This is based on insights from omics disciplines; in particular, genomics, transcriptomics, proteomics and metabolomics. Analysis of high-throughput omics data facilitates the reconstruction of organism-specific large-and genome-scale metabolic networks. Reconstructed metabolic networks are fundamental to the formulation of metabolic models that facilitate the simulation of actual metabolic activities under specific environmental conditions. SYSTEMS BIOLOGYIn recent years, systems and synthetic biology have substantially increased our understanding of many processes in life sciences at molecular and cellular levels [1][2][3]. Systems biology intends to understand the cell from a system-wide perspective. For over 100 years, life sciences have aimed at elucidating the details of molecular processes in organisms. In systems biology, scientists have started to inter-relate this data on a large scale in order to analyse the interactions of all elements [4,5], and thus initiate the decoding of entire systems, aiming at their quantitative understanding. This became possible with the development of high-throughput techniques associated with diverse computational methods and the availability of faster computing. Fundamental highthroughput methods are now routinely available in the fields of genomics, transcriptomics, metabolomics and proteomics, while additional specialized branches of omics disciplines have been developed, such as lipidomics [6][7][8], glycomics [9][10][11] and 13 C fluxomics [12,13].

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Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Cited by 126 publications

References 66 publications

Regulation of Primary Metabolism in Response to Low Oxygen Availability as Revealed by Carbon and Nitrogen Isotope Redistribution

Regulation of Primary Metabolism in Response to Low Oxygen Availability as Revealed by Carbon and Nitrogen Isotope Redistribution

Inference and prediction of metabolic network fluxes

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

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