Revealing metabolic phenotypes in plants: inputs from NMR analysis

Ratcliffe, George; Shachar‐Hill, Yair

doi:10.1017/s1464793104006530

Cited by 114 publications

(101 citation statements)

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“…Ultimately, this can be expected to establish the relative merits of composition and flux as the basis for defining metabolic phenotypes (Ratcliffe and Shachar-Hill, 2005).…”

Section: Network Stabilitymentioning

confidence: 99%

Metabolic Network Fluxes in Heterotrophic Arabidopsis Cells: Stability of the Flux Distribution under Different Oxygenation Conditions

et al. 2008

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Steady-state labeling experiments with [1-13 C]Glc were used to measure multiple metabolic fluxes through the pathways of central metabolism in a heterotrophic cell suspension culture of Arabidopsis (Arabidopsis thaliana). The protocol was based on in silico modeling to establish the optimal labeled precursor, validation of the isotopic and metabolic steady state, extensive nuclear magnetic resonance analysis of the redistribution of label into soluble metabolites, starch, and protein, and a comprehensive set of biomass measurements. Following a simple modification of the cell culture procedure, cells were grown at two oxygen concentrations, and flux maps of central metabolism were constructed on the basis of replicated experiments and rigorous statistical analysis. Increased growth rate at the higher O 2 concentration was associated with an increase in fluxes throughout the network, and this was achieved without any significant change in relative fluxes despite differences in the metabolite profile of organic acids, amino acids, and carbohydrates. The balance between biosynthesis and respiration within the tricarboxylic acid cycle was unchanged, with 38% 6 5% of carbon entering used for biosynthesis under standard O 2 conditions and 33% 6 2% under elevated O 2 . These results add to the emerging picture of the stability of the central metabolic network and its capacity to respond to physiological perturbations with the minimum of rearrangement. The lack of correlation between the change in metabolite profile, which implied significant disruption of the metabolic network following the alteration in the oxygen supply, and the unchanging flux distribution highlights a potential difficulty in the interpretation of metabolomic data.Although the complexity and plasticity of the metabolic network in plants allows them to adapt to fluctuating environmental conditions, the same properties also present a significant obstacle to metabolic engineering (Carrari et al., 2003a;Kruger and Ratcliffe, 2008;Sweetlove et al., 2008). The problem is particularly acute in primary metabolism, where there have been numerous instances of unsuccessful engineering, and reflects the current incomplete understanding of the way in which metabolic networks respond to environmental and genetic perturbations. Fluxes of central carbon metabolism are part of the missing information (Sweetlove et al., 2003), and although they are necessarily related to enzyme abundances, metabolite concentrations, and transcriptional responses (Carrari et al., 2006;Junker et al., 2007), their reliable prediction from the available data remains a nontrivial task . For this reason, the development and application of techniques for the measurement of flux in plants has become an important area of research (Schwender et al., 2004a;Fernie et al., 2005;Ratcliffe and Shachar-Hill, 2006). Steady-state metabolic flux analysis (MFA) has the capacity to resolve parallel, cyclic, and reversible fluxes, making it a useful technique for quantifying metabolic fluxes and investig...

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“…Ultimately, this can be expected to establish the relative merits of composition and flux as the basis for defining metabolic phenotypes (Ratcliffe and Shachar-Hill, 2005).…”

Section: Network Stabilitymentioning

confidence: 99%

Metabolic Network Fluxes in Heterotrophic Arabidopsis Cells: Stability of the Flux Distribution under Different Oxygenation Conditions

et al. 2008

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“…Thus, to understand the functioning of metabolism, it is necessary to know the fluxes of every active biochemical reaction at a given steady state. This information provides a precise quantification of the metabolic state as an integrated cellular phenotype (Ratcliffe and Shachar-Hill, 2005). More specifically, it allows us to identify which metabolic pathways are active, how active they are, and how their activity is coordinated with other metabolic pathways to form a balanced network.…”

mentioning

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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“…Compounds may also be measured directly without chromatographic separation. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and nuclear magnetic resonance spectroscopy (NMR) are two such examples (for review see [66]). The advantages and disadvantages of these technologies will be discussed in turn.…”

Section: Technologies For Metabolite Analysesmentioning

confidence: 99%

“…When combined with stable-isotopic labeling, NMR becomes very informative. Real-time in vivo flux of compounds can be monitored and even resolved between subcellular compartments [54,66,73]. For example, the location of 31 P in different plastids can be determined based on the chemical shift caused by the different pH of the subcellular compartments [73].…”

Section: Nmrmentioning

confidence: 99%