Mass spectrometry for metabolic flux analysis

Mass spectrometry in combination with tracer experiments based on 13C substrates can serve as a powerful tool for the modeling and analysis of intracellular fluxes and the investigation of biochemical networks. The theoretical background for the application of mass spectrometry to metabolic flux analysis is discussed. Mass spectrometry methods are especially useful to determine mass distribution of metabolites. Additional information gained from fragmentation of metabolites, e.g., by electron impact ionization, allows further localization of labeling positions, up to complete resolution of isotopomer pools. To effectively handle mass distributions in simulation experiments, a matrix based general methodology is formulated. The natural isotope distribution of carbon, oxygen, hydrogen and nitrogen in the target metabolites is considered by introduction of correction matrices. It is shown by simulation results for the central carbon metabolism that neglecting natural isotope distributions causes significant errors in intracellular flux distributions. By varying relative fluxes into pentosephosphate pathway and pyruvate carboxylation reaction, marked changes in the mass distributions of metabolites result, which are illustrated for pyruvate, oxaloacetate, and alpha-ketoglutarate. In addition mass distributions of metabolites are significantly influenced over a broad range by the degree of reversibility of transaldolase and transketolase reactions in the pentosephosphate pathway. The mass distribution of metabolites is very sensitive towards intracellular flux patterns and can be measured with high accuracy by routine mass spectrometry methods. Copyright 1999 John Wiley & Sons, Inc.     

Thermodynamics-based metabolic flux analysis

A new form of metabolic flux analysis (MFA) called thermodynamics-based metabolic flux analysis (TMFA) is introduced with the capability of generating thermodynamically feasible flux and metabolite activity profiles on a genome scale. TMFA involves the use of a set of linear thermodynamic constraints in addition to the mass balance constraints typically used in MFA. TMFA produces flux distributions that do not contain any thermodynamically infeasible reactions or pathways, and it provides information about the free energy change of reactions and the range of metabolite activities in addition to reaction fluxes. TMFA is applied to study the thermodynamically feasible ranges for the fluxes and the Gibbs free energy change, Delta(r)G', of the reactions and the activities of the metabolites in the genome-scale metabolic model of Escherichia coli developed by Palsson and co-workers. In the TMFA of the genome scale model, the metabolite activities and reaction Delta(r)G' are able to achieve a wide range of values at optimal growth. The reaction dihydroorotase is identified as a possible thermodynamic bottleneck in E. coli metabolism with a Delta(r)G' constrained close to zero while numerous reactions are identified throughout metabolism for which Delta(r)G' is always highly negative regardless of metabolite concentrations. As it has been proposed previously, these reactions with exclusively negative Delta(r)G' might be candidates for cell regulation, and we find that a significant number of these reactions appear to be the first steps in the linear portion of numerous biosynthesis pathways. The thermodynamically feasible ranges for the concentration ratios ATP/ADP, NAD(P)/NAD(P)H, and H(extracellular)(+)/H(intracellular)(+) are also determined and found to encompass the values observed experimentally in every case. Further, we find that the NAD/NADH and NADP/NADPH ratios maintained in the cell are close to the minimum feasible ratio and maximum feasible ratio, respectively.     

Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis

Background  

Cellular hypoxia is a component of many diseases, but mechanisms of global hypoxic adaptation and resistance are not completely understood. Previously, a population of Drosophila flies was experimentally selected over several generations to survive a chronically hypoxic environment. NMR-based metabolomics, combined with flux-balance simulations of genome-scale metabolic networks, can generate specific hypotheses for global reaction fluxes within the cell. We applied these techniques to compare metabolic activity during acute hypoxia in muscle tissue of adapted versus "na?ve" control flies.     

An improved method for statistical analysis of metabolic flux analysis using isotopomer mapping matrices with analytical expressions

Analytical expressions were derived for calculating the sensitivities of isotopomer distribution vectors, the weighted output matrix with respect to the fluxes, and the covariance matrix for the metabolic flux analysis based on isotopomers mapping matrices (IMM). These expressions allow us to implement efficient statistical analysis, avoiding the time-consuming Monte Carlo techniques for estimating the confidence interval of the fluxes. The analytical expressions are also useful in implementing a faster design of experiment, which requires repetitive computation of the covariance matrix that is not straightforward to make in practice with the numerical techniques based on the conventional IMM. The proposed method was applied for analyzing the central carbon metabolism of the mixotrophically cultivated Synechocystis sp. PCC6803, and the confidence intervals of all its fluxes were computed based on the isotopomer distribution measured using NMR and GC-MS. It was found that the best feasible mixture for labeling experiment is 70% unlabeled, 10% [U-13C] and 20% [1,2-13C2] labeled glucose to obtain the most reliable metabolic fluxes.     

Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes

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Stoichiometric model and metabolic flux analysis for Leptospirillum ferrooxidans

A metabolic model for Leptospirillum ferrooxidans was developed based on the genomic information of an analogous iron oxidizing bacteria and on the pathways of ferrous iron oxidation, nitrogen and CO₂ assimilation based on experimental evidence for L. ferrooxidans found in the literature. From this metabolic reconstruction, a stoichiometric model was built, which includes 86 reactions describing the main catabolic and anabolic aspects of its metabolism. The model obtained has 2 degrees of freedom, so two external fluxes were estimated to achieve a determined and observable system. By using the external oxygen consumption rate and the generation flux biomass as input data, a metabolic flux map with a distribution of internal fluxes was obtained. The results obtained were verified with experimental data from the literature, achieving a very good prediction of the metabolic behavior of this bacterium at steady state. Biotechnol. Bioeng. 2010;107:696–706. © 2010 Wiley Periodicals, Inc.     

A possibilistic framework for constraint-based metabolic flux analysis

Background  

Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements.     

Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.     

Metabolic flux analysis as a tool in metabolic engineering of plants

Methods of metabolic flux analysis (MFA) provide insights into the theoretical capabilities of metabolic networks and allow probing the in vivo performance of cellular metabolism. In recent years, an increasing awareness has developed that network analysis methods within the systems biology toolbox are serving to improve our understanding and ability to manipulate metabolism. In this minireview the potential of MFA to increase the chances of success in metabolic engineering of plants is presented, recent progress related to engineering and flux analysis in central metabolism of plants is discussed, and some recent advances in flux analysis methodology are highlighted.     

13C metabolic flux analysis.

Metabolic flux analysis using 13C-labeled substrates has become an important tool in metabolic engineering. It allows the detailed quantification of all intracellular fluxes in the central metabolism of a microorganism. The method has strongly evolved in recent years by the introduction of new experimental procedures, measurement techniques, and mathematical data evaluation methods. Many of these improvements require advanced skills in the application of nuclear magnetic resonance and mass spectrometry techniques on the one hand and computational and statistical experience on the other hand. This minireview summarizes these recent developments and sketches the major practical problems. An outlook to possible future developments concludes the text.     

Metabolic flux analysis and metabolic engineering of microorganisms

Recent advances in metabolic flux analysis including genome-scale constraints-based flux analysis and its applications in metabolic engineering are reviewed. Various computational aspects of constraints-based flux analysis including genome-scale stoichiometric models, additional constraints used for the improved accuracy, and several algorithms for identifying the target genes to be manipulated are described. Also, some of the successful applications of metabolic flux analysis in metabolic engineering are reviewed. Finally, we discuss the limitations that need to be overcome to make the results of genome-scale flux analysis more realistically represent the real cell metabolism.     

tetra: an improved program for population genetic analysis of allotetraploid microsatellite data

Population genetic analysis of allotetraploid microsatellite data has lagged far behind that of diploid data, largely because of an inability to determine allele copy number for partial heterozygotes. tetrasat developed by Markwith et al. (2006) uses an iterative substitution process to account for all probable combinations of allele copy numbers in populations with partial heterozygote samples. However, tetrasat cannot deal with microsatellite data containing more than 15 partial heterozygotes, because of an exponential increase in genotype combinations. tetra can handle the microsatellite data containing infinite partial heterozygotes. In the program tetra, the frequencies of alleles are measured as the probability with which the known alleles occur in unknown allele locations. The Hardy–Weinberg expected heterozygosity and Nei's coefficient of gene differentiation are calculated based on allele frequencies. The mean and standard error of expected heterozygosity are estimated through bootstrap method.     

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.     

Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments

Metabolic flux analysis (MFA) methods use external flux and isotopic measurements to quantify the magnitude of metabolic flows in metabolic networks. A key question in this analysis is choosing a set of measurements that is capable of yielding a unique flux distribution (identifiability). In this article, we introduce an optimization-based framework that uses incidence structure analysis to determine the smallest (or most cost-effective) set of measurements leading to complete flux elucidation. This approach relies on an integer linear programming formulation OptMeas that allows for the measurement of external fluxes and the complete (or partial) enumeration of the isotope forms of metabolites without requiring any of these to be chosen in advance. We subsequently query and refine the measurement sets suggested by OptMeas for identifiability and optimality. OptMeas is first tested on small to medium-size demonstration examples. It is subsequently applied to a large-scale E. coli isotopomer mapping model with more than 17,000 isotopomers. A number of additional measurements are identified leading to maximum flux elucidation in an amorphadiene producing E. coli strain.     

A universal framework for 13C metabolic flux analysis.

A general methodology is presented for the modeling, simulation, design, evaluation, and statistical analysis of (13)C-labeling experiments for metabolic flux analysis. The universal software framework 13C-FLUX was implemented to support all steps of this process. Guided by the example of anaplerotic flux determination in Corynebacterium glutamicum, the technical details of the model setup, experimental design, and data evaluation are discussed. It is shown how the network structure, the input substrate composition, the assumptions about fluxes, and the measurement configuration are specified within 13C-FLUX. Based on the network model, different experimental designs are computed depending on the goal of the investigations. Finally, a specific experiment is evaluated and the various statistical methods used to analyze the results are briefly explained. The appendix gives some details about the software implementation and availability.     

Strategies for metabolic flux analysis in plants using isotope labelling

Flux measurements through metabolic pathways generate insights into the integration of metabolism, and there is increasing interest in using such measurements to quantify the metabolic effects of mutation and genetic manipulation. Isotope labelling provides a powerful approach for measuring metabolic fluxes, and it gives rise to several distinct methods based on either dynamic or steady-state experiments. We discuss the application of these methods to photosynthetic and non-photosynthetic plant tissues, and we illustrate the different approaches with an analysis of the pathways interconverting hexose phosphates and triose phosphates. The complicating effects of the pentose phosphate pathway and the problems arising from the extensive compartmentation of plant cell metabolism are considered. The non-trivial nature of the analysis is emphasised by reference to invalid deductions in earlier work. It is concluded that steady-state isotopic labelling experiments can provide important information on the fluxes through primary metabolism in plants, and that the combination of stable isotope labelling with detection by nuclear magnetic resonance is particularly informative.     

293SF metabolic flux analysis during cell growth and infection with an adenoviral vector

Metabolic flux quantification of cell culture is becoming a crucial means to improve cell growth as well as protein and vector productions. The technique allows rapid determination of cell culture status, thus providing a tool for further feeding improvements. Herein, we report on key results of a metabolic investigation using 293 cells adapted to suspension and serum-free medium (293SF) during growth and infection with an adenoviral vector encoding the green fluorescence protein (GFP). The model developed contains 35 fluxes, which include the main fluxes of glycolysis, glutaminolysis, and amino acids pathways. It requires specific consumption and production rate measurements of amino acids, glucose, lactate, NH(3), and O(2), as well as DNA and total proteins biosynthesis rate measurements. Also, it was found that extracellular protein concentration measurement is important for flux calculation accuracy. With this model, we are able to describe the 293SF cell metabolism, grown under different culture conditions in a 3-L controlled bioreactor for batch and fed-batch with low glucose. The metabolism is also investigated during infection under two different feeding strategies: a fed-batch starting at the end of the growth phase and extending during infection without medium change and a fed-batch after infection following medium renewal. Differences in metabolism are observed between growth and infection, as well as between the different feeding strategies, thus providing a better understanding of the general metabolism.