Flux analysis in central carbon metabolism in plants: 13C NMR experiments and analysis

Metabolic flux analysis is crucial in metabolic engineering. This research concentrated on improvements in 13C labeling-based flux analysis, a powerful flux quantification method, particularly oriented toward application to plants. Furthermore, systemic 13C flux analyses were performed on two model plant systems: Glycine max (soybean) embryos, and Catharanthus roseus hairy roots.;The concepts \u27bond integrity\u27, \u27bondomer\u27 and the algorithm \u27Boolean function mapping\u27 were introduced, to facilitate efficient flux evaluation from carbon bond labeling experiments, and easier flux identifiability analysis.;13C labeling experiments were performed on developing soybean (Glycine max) embryos and C. roseus hairy roots. A computer program, NMR2Flux, was developed to automatically calculate fluxes from the labeling data. This program accepts a user-defined metabolic network model, and incorporates recent mathematical advances toward accurate and efficient evaluation of fluxes and their standard deviations. Several physiological insights were obtained from the flux results. For instance, in soybean embryos, the reductive pentose phosphate pathway was active in the plastid and negligible in the cytosol. Also, unknown fluxes (such as plastidic fructose-1,6-bisphosphatase) could be identified and quantified. To the best of the author\u27s knowledge, this is the most comprehensive flux analysis of a plant system to date.;Investigations on flux identifiability were carried out for the soybean embryo system. Using these, optimal labeling experiments were designed, that utilize judicious combinations of labeled varieties of two substrates (sucrose and glutamine), to maximize the statistical quality of the evaluated fluxes.;The identity of four intense peaks observed in the 2-D [13C, 1H] spectra of protein isolated from soybean embryos, was investigated. These peaks were identified as levulinic acid and 5-hydroxymethyl furfural, and were degradation products of glycosylating sugars associated with soybean embryo protein. A 2-D NMR study was conducted on them, and it was shown that the metabolic information in the degradation products can be used toward metabolic flux or pathway analysis.;In addition, the elemental make-up and composition of the biomass of C. roseus hairy roots (crucial toward flux analysis) is reported. 89.2% (+/-9.7%) of the biomass was accounted for.*;*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat

FiatFlux – a software for metabolic flux analysis from (13)C-glucose experiments

BACKGROUND: Quantitative knowledge of intracellular fluxes is important for a comprehensive characterization of metabolic networks and their functional operation. In contrast to direct assessment of metabolite concentrations, in vivo metabolite fluxes must be inferred indirectly from measurable quantities in (13)C experiments. The required experience, the complicated network models, large and heterogeneous data sets, and the time-consuming set-up of highly controlled experimental conditions largely restricted metabolic flux analysis to few expert groups. A conceptual simplification of flux analysis is the analytical determination of metabolic flux ratios exclusively from MS data, which can then be used in a second step to estimate absolute in vivo fluxes. RESULTS: Here we describe the user-friendly software package FiatFlux that supports flux analysis for non-expert users. In the first module, ratios of converging fluxes are automatically calculated from GC-MS-detected (13)C-pattern in protein-bound amino acids. Predefined fragmentation patterns are automatically identified and appropriate statistical data treatment is based on the comparison of redundant information in the MS spectra. In the second module, absolute intracellular fluxes may be calculated by a (13)C-constrained flux balancing procedure that combines experimentally determined fluxes in and out of the cell and the above flux ratios. The software is preconfigured to derive flux ratios and absolute in vivo fluxes from [1-(13)C] and [U-(13)C]glucose experiments and GC-MS analysis of amino acids for a variety of microorganisms. CONCLUSION: FiatFlux is an intuitive tool for quantitative investigations of intracellular metabolism by users that are not familiar with numerical methods or isotopic tracer experiments. The aim of this open source software is to enable non-specialists to adapt the software to their specific scientific interests, including other (13)C-substrates, labeling mixtures, and organisms

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

<p>Abstract</p> <p>Background</p> <p>Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³<it>C </it>isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³<it>C </it>isotopomer data are typically needed.</p> <p>Results</p> <p>We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³<it>C </it>isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³<it>C </it>isotopomer measurement techniques, substrates and substrate labelling patterns.</p> <p>By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms <it>Bacillus subtilis </it>and <it>Saccharomyces cerevisiae </it>we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by <it>in silico </it>calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.</p> <p>Conclusion</p> <p>The core of ¹³<it>C </it>metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.</p

OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis

Background: The quantitative analysis of metabolic fluxes, i. e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on C-13 substrates, (ii) C-13 labelling analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis

Quantitative Metabolomics and Instationary 13C-Metabolic Flux Analysis Reveals Impact of Recombinant Protein Production on Trehalose and Energy Metabolism in Pichia pastoris

Pichia pastoris has been recognized as an effective host for recombinant protein production. In this work, we combine metabolomics and instationary 13C metabolic flux analysis (INST 13C-MFA) using GC-MS and LC-MS/MS to evaluate the potential impact of the production of a Rhizopus oryzae lipase (Rol) on P. pastoris central carbon metabolism. Higher oxygen uptake and CO2 production rates and slightly reduced biomass yield suggest an increased energy demand for the producing strain. This observation is further confirmed by 13C-based metabolic flux analysis. In particular, the flux through the methanol oxidation pathway and the TCA cycle was increased in the Rol-producing strain compared to the reference strain. Next to changes in the flux distribution, significant variations in intracellular metabolite concentrations were observed. Most notably, the pools of trehalose, which is related to cellular stress response, and xylose, which is linked to methanol assimilation, were significantly increased in the recombinant strain

Metabolic engineering: Use of system-level approaches and application to fuel production in Escherichia coli

Metabolic engineering was formally defined more than two decades ago (Bailey, 1991) and it is now an established discipline. Metabolic engineering is generally defined as the directed improvement of product formation or cellular properties through the modification of specific biochemical reactions or the introduction of new ones with the use of recombinant DNA technology (Bailey, 1991; Stephanopoulos et al. 1998). Therefore, the analysis and engineering/synthesis of metabolic pathways is of central importance to metabolic engineering. The analytical part uses a number of experimental and modeling techniques for the systematic study of cellular responses (in terms of RNA, protein and metabolite levels, metabolic fluxes, etc.) to genetic and environmental perturbations. This facilitates a rational design of metabolic modifications, which are implemented using recombinant DNA technology. Both, the analysis and the synthesis of metabolic pathways will be covered in this review. Recent efforts on the engineering of fermentative and biosynthetic pathways for biofuel production in Escherichia coli, as well as those enabling the utilization of novel feedstocks, will be highlighted

Analysis of metabolic flux using dynamic labeling and metabolic modeling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms which control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches having been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences are reviewed and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed