Metabolic Flux Analysis in Plants: From Intelligent Design to Rational Engineering

Libourel, Igor G. L.; Shachar‐Hill, Yair

doi:10.1146/annurev.arplant.58.032806.103822

Cited by 95 publications

(77 citation statements)

References 125 publications

(147 reference statements)

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“…First, the WT flux distribution was predicted by integrating the measured fluxes with the seed model through a standard optimization approach (Materials and Methods). Then, the effect of the PK knockdown was predicted via two computational approaches: minimization of metabolic adjustment (MOMA) (36) and regulatory on/off minimization (ROOM) (37), the potential use of which in metabolic engineering in plants was recently highlighted (38). With both methods, the predicted fluxes following the knockdown were found to be significantly correlated with the measured flux following the knockdown, with a Spearman correlation of 0.57 (P value = 0.006) for MOMA and 0.52 (P value = 0.005) for ROOM (Table S5).…”

Section: Validating the Tissue Model's Ability To Correctly Predict Fluxmentioning

confidence: 99%

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity

Mintz-Oron

Meir

Malitsky

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

233

197

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Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.C urrent challenges in using plants as factories for bio-energy and nutraceuticals require predesigned and efficient strategies for metabolic engineering (1). Currently, plant metabolic engineering mostly involves trial-and-error approaches, without the utilization of computational modeling procedures to rationally design genetic modifications. The marginal contribution played by metabolic modeling in plants until now stands in marked contrast to its prominent success in microbial metabolic engineering (2, 3). Metabolic network reconstructions were manually reconstructed for dozens of bacterial species (3), and automated approaches recently generated draft models for a total of 130 bacteria (4). A modeling approach, called constraintbased modeling (CBM), serves to analyze the function of such large-scale metabolic networks by solely relying on simple physical-chemical constraints, overcoming the problem of missing enzyme kinetic data (3). Applications of CBM for large-scale microbial networks has proven to be highly successful in predicting metabolic phenotypes in metabolic engineering and many other applications (3).The reconstruction of metabolic network models for multicellular eukaryotes is significantly more challenging than that for bacteria, because of the larger size of the networks, the subcellular compartmentalization of me...

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Section: Validating the Tissue Model's Ability To Correctly Predict Fluxmentioning

confidence: 99%

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity

Mintz-Oron

Meir

Malitsky

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

233

197

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“…When 13 C-labeled substrates are supplied, steady-state labeling of the metabolites reflects the relative fluxes through different metabolic pathways (Libourel and Shachar-Hill, 2008). After labeling, phosphorylated Table I).…”

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“…Kinetic modeling of datasets such as these (RiosEstepa and Lange, 2007;Libourel and Shachar-Hill, 2008) allows the construction and validation of mechanistic predictive models.…”

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Quantifying the Labeling and the Levels of Plant Cell Wall Precursors Using Ion Chromatography Tandem Mass Spectrometry

et al. 2010

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The biosynthesis of cell wall polymers involves enormous fluxes through central metabolism that are not fully delineated and whose regulation is poorly understood. We have established and validated a liquid chromatography tandem mass spectrometry method using multiple reaction monitoring mode to separate and quantify the levels of plant cell wall precursors. Target analytes were identified by their parent/daughter ions and retention times. The method allows the quantification of precursors at low picomole quantities with linear responses up to the nanomole quantity range. When applying the technique to Arabidopsis (Arabidopsis thaliana) T87 cell cultures, 16 hexose-phosphates (hexose-Ps) and nucleotide-sugars (NDP-sugars) involved in cell wall biosynthesis were separately quantified. Using hexose-P and NDPsugar standards, we have shown that hot water extraction allows good recovery of the target metabolites (over 86%). This method is applicable to quantifying the levels of hexose-Ps and NDP-sugars in different plant tissues, such as Arabidopsis T87 cells in culture and fenugreek (Trigonella foenum-graecum) endosperm tissue, showing higher levels of galacto-mannan precursors in fenugreek endosperm. In Arabidopsis cells incubated with [U-13 C Fru ]sucrose, the method was used to track the labeling pattern in cell wall precursors. As the fragmentation of hexose-Ps and NDP-sugars results in high yields of [PO 3 ] 2 and/or [H 2 PO 4 ] 2 ions, mass isotopomers can be quantified directly from the intensity of selected tandem mass spectrometry transitions. The ability to directly measure 13 C labeling in cell wall precursors makes possible metabolic flux analysis of cell wall biosynthesis based on dynamic labeling experiments.Plant cell walls are the most abundant renewable resources (Pauly and Keegstra, 2008a). Much of the current biotechnological research on plant cell wall synthesis involves manipulating these biosynthetic processes to obtain higher concentrations of starches or oil, which show much promise in biofuel production, or to alter cell wall composition for easier breakdown. A detailed knowledge of these processes is essential to understanding and utilizing plant cell wall materials as well as for progress in understanding plant growth and structural development (Pauly and Keegstra, 2008b). However, research into cell wall biosynthesis has been hindered by our limited understanding of the metabolic processes that produce cell walls and particularly their regulation. Progress in this area is limited by the difficulty of differentiating among the compounds involved and of analyzing the fluxes through the biochemical network of wall biosynthesis. Many of the metabolic steps involve isomeric sugars, including hexose-Ps and nucleotidesugars (NDP-sugars) that serve as direct precursors to plant cell wall biosynthesis. Separate quantification of these sugars has been difficult to achieve.Much of the current research on identifying and differentiating among different metabolic pathways involves the use of chromato...

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“…This is a particularly useful approach to determine flux distributions and the systemic characteristics of metabolic networks (for review, see Llaneras and Picó, 2008). When experimental designs supporting metabolic and isotopic steady state are employed, isotope labeling data can be utilized for the development of quantitative flux maps of metabolic pathways (for review, see Libourel and Shachar-Hill, 2008). For dynamic systems, kinetic modeling is regarded as the generally most suitable method (McNeil et al, 2000;Poolman et al, 2004;Bruggeman and Westerhoff, 2006;Rios-Estepa and Lange, 2007;Mendes et al, 2009).…”

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Mathematical Modeling-Guided Evaluation of Biochemical, Developmental, Environmental, and Genotypic Determinants of Essential Oil Composition and Yield in Peppermint Leaves

et al. 2010

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We have previously reported the use of a combination of computational simulations and targeted experiments to build a first generation mathematical model of peppermint (Mentha 3 piperita) essential oil biosynthesis. Here, we report on the expansion of this approach to identify the key factors controlling monoterpenoid essential oil biosynthesis under adverse environmental conditions. We also investigated determinants of essential oil biosynthesis in transgenic peppermint lines with modulated essential oil profiles. A computational perturbation analysis, which was implemented to identify the variables that exert prominent control over the outputs of the model, indicated that the essential oil composition should be highly dependent on certain biosynthetic enzyme concentrations [(+)-pulegone reductase and (+)-menthofuran synthase], whereas oil yield should be particularly sensitive to the density and/or distribution of leaf glandular trichomes, the specialized anatomical structures responsible for the synthesis and storage of essential oils. A microscopic evaluation of leaf surfaces demonstrated that the final mature size of glandular trichomes was the same across all experiments. However, as predicted by the perturbation analysis, differences in the size distribution and the total number of glandular trichomes strongly correlated with differences in monoterpenoid essential oil yield. Building on various experimental data sets, appropriate mathematical functions were selected to approximate the dynamics of glandular trichome distribution/density and enzyme concentrations in our kinetic model. Based on a x 2 statistical analysis, simulated and measured essential oil profiles were in very good agreement, indicating that modeling is a valuable tool for guiding metabolic engineering efforts aimed at improving essential oil quality and quantity.

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Metabolic Flux Analysis in Plants: From Intelligent Design to Rational Engineering

Cited by 95 publications

References 125 publications

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity

Quantifying the Labeling and the Levels of Plant Cell Wall Precursors Using Ion Chromatography Tandem Mass Spectrometry

Mathematical Modeling-Guided Evaluation of Biochemical, Developmental, Environmental, and Genotypic Determinants of Essential Oil Composition and Yield in Peppermint Leaves

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