Theoretical aspects of 13C metabolic flux analysis with sole quantification of carbon dioxide labeling

The [13 C]octanoic acid breath test for gastric emptying quantification: A focus on nutrition and modeling

Gastric emptying (GE) is the process of food being processed by the stomach and delivered to the small intestine where nutrients such as lipids are absorbed into the blood circulation. The combination of an easy and inexpensive method to measure GE such as the CO₂ breath test using the stable isotope [¹³C]octanoic acid with semi‐mechanistic modeling could foster a wider application in nutritional studies to further understand the metabolic response to food. Here, we discuss the use of the [¹³C]octanoic acid breath test to label the solid phase of a meal, and the factors that influence GE to support mechanistic studies. Furthermore, we give an overview of existing mathematical models for the interpretation of the breath test data and how much nutritional studies could benefit from a physiological based pharmacokinetic model approach.

Computational Framework for Machine-Learning-Enabled 13C Fluxomics

¹³C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for ¹³C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical ¹³C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

Direct Comb Vernier Spectroscopy for Fractional Isotopic Ratio Determinations

Accurate isotopic composition analysis of the greenhouse-gasses emitted in the atmosphere is an important step to mitigate global climate warnings. Optical frequency comb-based spectroscopic techniques have shown ideal performance to accomplish the simultaneous monitoring of the different isotope substituted species of such gases. The capabilities of one such technique, namely, direct comb Vernier spectroscopy, to determine the fractional isotopic ratio composition are discussed. This technique combines interferometric filtering of the comb source in a Fabry-Perot that contains the sample gas, with a high resolution dispersion spectrometer to resolve the spectral content of each interacting frequency inside of the Fabry-Perot. Following this methodology, simultaneous spectra of ro-vibrational transitions of 12C16O2 and 13C16O2 molecules are recorded and analyzed with an accurate fitting procedure. Fractional isotopic ratio 13C/12C at 3% of precision is measured for a sample of CO2 gas, showing the potentialities of the technique for all isotopic-related applications of this important pollutant.

Measuring 13 C-enriched CO2 in air with a cavity ring-down spectroscopy gas analyser: Evaluation and calibration

RATIONALE

Cavity ring-down spectroscopy (CRDS) is becoming increasingly popular for δ¹³ C-CO₂ analysis of air. However, little is known about the effect of high ¹³ C abundances on the performance of CRDS. Overlap between ¹² CO₂ and ¹³ CO₂ spectral lines may adversely affect isotopic-CO₂ CRDS measurements of ¹³ C-enriched samples. Resolving this issue is important so that CRDS analysers can be used in CO₂ flux studies involving ¹³ C-labelled tracers.

METHODS

We tested a Picarro G2131-i CRDS isotopic-CO₂ gas analyser with specialty gravimetric standards of widely varying ¹³ C abundance (from natural to 20.1 atom%) and CO₂ mole fraction (xCO₂ : <0.1 to 2116 ppm) in synthetic air. The presence of spectroscopic interference between ¹² CO₂ and ¹³ CO₂ bands was assessed by analysing errors in measurements of the standards. A multi-component calibration strategy was adopted, incorporating isotope ratio and mole fraction data to ensure accuracy and consistency in corrected values of δ¹³ C-CO₂ , x¹² CO₂ , and x¹³ CO₂ .

RESULTS

CRDS measurements of x¹³ CO₂ were found to be accurate throughout the tested range (<0.005 to 100 ppm). On the other hand, spectral cross-talk in x¹² CO₂ measurements of standards containing elevated levels of ¹³ CO₂ led to inaccuracy in x¹² CO₂ , total-xCO₂ (x¹² CO₂  + x¹³ CO₂ ), and δ¹³ C-CO₂ data. An empirical relationship for x¹² CO₂ measurements that incorporated the ¹³ C/¹² C isotope ratio (i.e. ¹³ CO₂ /¹² CO₂ , RCO2) as a secondary (non-linear) variable was found to compensate for the perturbations, and enabled accurate instrument calibration for all CO₂ compositions covered by our standard gases.

CONCLUSIONS

¹³ C-enrichement in CO₂ leads to minor errors in CRDS measurements of x¹² CO₂ . We propose an empirical correction for measurements of ¹³ C-enriched CO₂ in air by CRDS instruments such as the Picarro G2131-i.

Assessing Metabolic Flux in Plants with Radiorespirometry

Carbohydrates are the dominant respiratory substrate in many plant cells. However, the route of carbohydrate oxidation varies depending on the relative cellular demands for energy, reductant, and precursors for biosynthesis. During these processes individual substrate carbon atoms are differentially released as carbon dioxide by specific reactions in the network, and this can be measured by monitoring the release of ¹⁴CO₂ from a range of positionally labeled forms of [¹⁴C]glucose. Although the relative amounts of carbon dioxide produced from different carbon positions do not allow precise determination of fluxes, they are indicative of the route of carbohydrate utilization. Such information can be used to determine whether a comprehensive metabolic flux analysis is merited, and also to facilitate independent verification of flux maps generated by other techniques. This chapter describes an approach to determine and interpret the pattern of oxidation of carbohydrates by monitoring ¹⁴CO₂ release during metabolism of exogenously supplied [1-¹⁴C]-, [2-¹⁴C]-, [3,4-¹⁴C]-, and [6-¹⁴C]glucose. The method is exemplified by studies on Arabidopsis cell suspension cultures, but the protocol can be easily adapted for the investigation of other plant materials.

Dynamic analysis of CO₂ labeling and cell respiration using membrane-inlet mass spectrometry

Here, we introduce a mass spectrometry-based analytical method and relevant technical details for dynamic cell respiration and CO2 labeling analysis. Such measurements can be utilized as additional information and constraints for model-based (13)C metabolic flux analysis. Dissolved dynamics of oxygen consumption and CO2 mass isotopomer evolution from (13)C-labeled tracer substrates through different cellular processes can be precisely measured on-line using a miniaturized reactor system equipped with a membrane-inlet mass spectrometer. The corresponding specific rates of physiologically relevant gases and CO2 mass isotopomers can be quantified within a short-term range based on the liquid-phase dynamics of dissolved fermentation gases.

An integrated biotechnology platform for developing sustainable chemical processes

Genomatica has established an integrated computational/experimental metabolic engineering platform to design, create, and optimize novel high performance organisms and bioprocesses. Here we present our platform and its use to develop E. coli strains for production of the industrial chemical 1,4-butanediol (BDO) from sugars. A series of examples are given to demonstrate how a rational approach to strain engineering, including carefully designed diagnostic experiments, provided critical insights about pathway bottlenecks, byproducts, expression balancing, and commercial robustness, leading to a superior BDO production strain and process.

Steady-state and instationary modeling of proteinogenic and free amino acid isotopomers for flux quantification

Metabolic flux analysis (MFA) is a powerful tool for exploring and quantifying carbon traffic in metabolic networks. Accurate flux quantification requires (1) high-quality isotopomer measurements, usually of biomass components including proteinogenic/free amino acids or central carbon metabolites, and (2) a mathematical model that relates the unknown fluxes to the measured isotopomers. Modeling requires a thorough knowledge of the structure of the underlying metabolic network, often available from many databases, as well as the ability to make reasonable assumptions that will enable simplification of the model. Here we describe a general methodology underlying computer-aided mathematical modeling of a flux-isotopomer relationship and some of the accompanying data-processing steps. One of two modeling strategies will need to be employed, depending on the type of isotope labeling experiment performed. These strategies-steady-state modeling and instationary modeling-have different experimental and computational demands. We discuss the concepts underlying these two types of modeling and demonstrate steady-state modeling in a step-by-step manner. Our methodology should be applicable to most isotope-assisted MFA applications and should serve as a general framework applicable to many realistic metabolic networks with little modification.

13C-based metabolic flux analysis: fundamentals and practice

Isotope-based metabolic flux analysis is one of the emerging technologies applied to system level metabolic phenotype characterization in metabolic engineering. Among the developed approaches, (13)C-based metabolic flux analysis has been established as a standard tool and has been widely applied to quantitative pathway characterization of diverse biological systems. To implement (13)C-based metabolic flux analysis in practice, comprehending the underlying mathematical and computational modeling fundamentals is of importance along with carefully conducted experiments and analytical measurements. Such knowledge is also crucial when designing (13)C-labeling experiments and properly acquiring key data sets essential for in vivo flux analysis implementation. In this regard, the modeling fundamentals of (13)C-labeling systems and analytical data processing are the main topics we will deal with in this chapter. Along with this, the relevant numerical optimization techniques are addressed to help implementation of the entire computational procedures aiming at (13)C-based metabolic flux analysis in vivo.

Rational design of ¹³C-labeling experiments for metabolic flux analysis in mammalian cells

Background

¹³C-Metabolic flux analysis (¹³C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. ¹³C-Tracer selection is an important step in conducting ¹³C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers.

Results

Recently, we introduced a new framework for optimal ¹³C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for ¹³C-MFA of mammalian cells, specifically [2,3,4,5,6-¹³C]glucose for elucidating oxPPP flux and [3,4-¹³C]glucose for elucidating PC flux. We demonstrate that ¹³C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers.

Conclusions

In this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal ¹³C-tracers for high-resolution flux elucidation in complex metabolic network models.

Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

Selection of tracers for 13C-metabolic flux analysis using elementary metabolite units (EMU) basis vector methodology

Metabolic flux analysis (MFA) is a powerful technique for elucidating in vivo fluxes in microbial and mammalian systems. A key step in (13)C-MFA is the selection of an appropriate isotopic tracer to observe fluxes in a proposed network model. Despite the importance of MFA in metabolic engineering and beyond, current approaches for tracer experiment design are still largely based on trial-and-error. The lack of a rational methodology for selecting isotopic tracers prevents MFA from achieving its full potential. Here, we introduce a new technique for tracer experiment design based on the concept of elementary metabolite unit (EMU) basis vectors. We demonstrate that any metabolite in a network model can be expressed as a linear combination of so-called EMU basis vectors, where the corresponding coefficients indicate the fractional contribution of the EMU basis vector to the product metabolite. The strength of this approach is the decoupling of substrate labeling, i.e. the EMU basis vectors, from the dependence on free fluxes, i.e. the coefficients. In this work, we demonstrate that flux observability inherently depends on the number of independent EMU basis vectors and the sensitivities of coefficients with respect to free fluxes. Specifically, the number of independent EMU basis vectors places hard limits on how many free fluxes can be determined in a model. This constraint is used as a guide for selecting feasible substrate labeling. In three example models, we demonstrate that by maximizing the number of independent EMU basis vectors the observability of a system is improved. Inspection of sensitivities of coefficients with respect to free fluxes provides additional constraints for proper selection of tracers. The present contribution provides a fresh perspective on an important topic in metabolic engineering, and gives practical guidelines and design principles for a priori selection of isotopic tracers for (13)C-MFA studies.

Eukaryotic metabolism: measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Fluxomers: a new approach for 13C metabolic flux analysis

Background

The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.

Results

This article addresses the mathematical and computational formulation of ¹³C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.

Conclusions

Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

Metabolic response of Geobacter sulfurreducens towards electron donor/acceptor variation

Background

Geobacter sulfurreducens is capable of coupling the complete oxidation of organic compounds to iron reduction. The metabolic response of G. sulfurreducens towards variations in electron donors (acetate, hydrogen) and acceptors (Fe(III), fumarate) was investigated via ¹³C-based metabolic flux analysis. We examined the ¹³C-labeling patterns of proteinogenic amino acids obtained from G. sulfurreducens cultured with ¹³C-acetate.

Results

Using ¹³C-based metabolic flux analysis, we observed that donor and acceptor variations gave rise to differences in gluconeogenetic initiation, tricarboxylic acid cycle activity, and amino acid biosynthesis pathways. Culturing G. sulfurreducens cells with Fe(III) as the electron acceptor and acetate as the electron donor resulted in pyruvate as the primary carbon source for gluconeogenesis. When fumarate was provided as the electron acceptor and acetate as the electron donor, the flux analysis suggested that fumarate served as both an electron acceptor and, in conjunction with acetate, a carbon source. Growth on fumarate and acetate resulted in the initiation of gluconeogenesis by phosphoenolpyruvate carboxykinase and a slightly elevated flux through the oxidative tricarboxylic acid cycle as compared to growth with Fe(III) as the electron acceptor. In addition, the direction of net flux between acetyl-CoA and pyruvate was reversed during growth on fumarate relative to Fe(III), while growth in the presence of Fe(III) and acetate which provided hydrogen as an electron donor, resulted in decreased flux through the tricarboxylic acid cycle.

Conclusions

We gained detailed insight into the metabolism of G. sulfurreducens cells under various electron donor/acceptor conditions using ¹³C-based metabolic flux analysis. Our results can be used for the development of G. sulfurreducens as a chassis for a variety of applications including bioremediation and renewable biofuel production.

Metabolic fluxes and beyond-systems biology understanding and engineering of microbial metabolism

The recent years have seen tremendous progress towards the understanding of microbial metabolism on a higher level of the entire functional system. Hereby, huge achievements including the sequencing of complete genomes and efficient post-genomic approaches provide the basis for a new, fascinating era of research-analysis of metabolic and regulatory properties on a global scale. Metabolic flux (fluxome) analysis displays the first systems oriented approach to unravel the physiology of microorganisms since it combines experimental data with metabolic network models and allows determining absolute fluxes through larger networks of central carbon metabolism. Hereby, fluxes are of central importance for systems level understanding because they fundamentally represent the cellular phenotype as integrated output of the cellular components, i.e. genes, transcripts, proteins, and metabolites. A currently emerging and promising area of research in systems biology and systems metabolic engineering is therefore the integration of fluxome data in multi-omics studies to unravel the multiple layers of control that superimpose the flux network and enable its optimal operation under different environmental conditions.

Analysis and engineering of metabolic pathway fluxes in Corynebacterium glutamicum

The Gram-positive soil bacterium Corynebacterium glutamicum was discovered as a natural overproducer of glutamate about 50 years ago. Linked to the steadily increasing economical importance of this microorganism for production of glutamate and other amino acids, the quest for efficient production strains has been an intense area of research during the past few decades. Efficient production strains were created by applying classical mutagenesis and selection and especially metabolic engineering strategies with the advent of recombinant DNA technology. Hereby experimental and computational approaches have provided fascinating insights into the metabolism of this microorganism and directed strain engineering. Today, C. glutamicum is applied to the industrial production of more than 2 million tons of amino acids per year. The huge achievements in recent years, including the sequencing of the complete genome and efficient post genomic approaches, now provide the basis for a new, fascinating era of research - analysis of metabolic and regulatory properties of C. glutamicum on a global scale towards novel and superior bioprocesses.

Ensemble modeling for aromatic production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.

Prospects for clinical cancer metabolomics using stable isotope tracers

Metabolomics provides a readout of the state of metabolism in cells or tissue and their responses to external perturbations. For this reason, the approach has great potential in clinical diagnostics. For more than two decades, we have been using stable isotope tracer approaches to probe cellular metabolism in greater detail. The ability to enrich common compounds with rare isotopes such as carbon ((13)C) and nitrogen ((15)N) is the only practical means by which metabolic pathways can be traced, which entails following the fate of individual atoms from the source molecule to products via metabolic transformation. Changes in regulation of pathways are therefore captured by this approach, which leads to deeper understanding of the fundamental biochemistry of cells. Using lessons learned from pathways tracing in cells and organs, we have been applying this methodology to human cancer patients in a clinical setting. Here we review the methodologies and approaches to stable isotope tracing in cells, animal models and in humans subjects.

Ensemble modeling of metabolic networks

Complete modeling of metabolic networks is desirable, but it is difficult to accomplish because of the lack of kinetics. As a step toward this goal, we have developed an approach to build an ensemble of dynamic models that reach the same steady state. The models in the ensemble are based on the same mechanistic framework at the elementary reaction level, including known regulations, and span the space of all kinetics allowable by thermodynamics. This ensemble allows for the examination of possible phenotypes of the network upon perturbations, such as changes in enzyme expression levels. The size of the ensemble is reduced by acquiring data for such perturbation phenotypes. If the mechanistic framework is approximately accurate, the ensemble converges to a smaller set of models and becomes more predictive. This approach bypasses the need for detailed characterization of kinetic parameters and arrives at a set of models that describes relevant phenotypes upon enzyme perturbations.

Hybrid optimization for 13C metabolic flux analysis using systems parametrized by compactification

Background

The importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. ¹³C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary.

Results

For flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of ¹³C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori.

Conclusion

This fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology.

Isotopomer-based metabolomic analysis by NMR and mass spectrometry

Nuclear magnetic resonance (NMR) and mass spectrometry (MS) together are synergistic in their ability to profile comprehensively the metabolome of cells and tissues. In addition to identification and quantification of metabolites, changes in metabolic pathways and fluxes in response to external perturbations can be reliably determined by using stable isotope tracer methodologies. NMR and MS together are able to define both positional isotopomer distribution in product metabolites that derive from a given stable isotope-labeled precursor molecule and the degree of enrichment at each site with good precision. Together with modeling tools, this information provides a rich functional biochemical readout of cellular activity and how it responds to external influences. In this chapter, we describe NMR- and MS-based methodologies for isotopomer analysis in metabolomics and its applications for different biological systems.

Metabolic flux engineering of l-lysine production in Corynebacterium glutamicum—over expression and modification of G6P dehydrogenase

In the present work, metabolic flux engineering of Corynebacterium glutamicum was carried out to increase lysine production. The strategy focused on engineering of the pentose phosphate pathway (PPP) flux by different genetic modifications. Over expression of the zwf gene, encoding G6P dehydrogenase, in the feedback-deregulated lysine-producing strain C. glutamicum ATCC 13032 lysC(fbr) resulted in increased lysine production on different carbon sources including the two major industrial sugars, glucose and sucrose. The additional introduction of the A243T mutation into the zwf gene and the over expression of fructose 1,6-bisphosphatase resulted in a further successive improvement of lysine production. Hereby the point mutation resulted in higher affinity of G6P dehydrogenase towards NADP and reduced sensitivity against inhibition by ATP, PEP and FBP. Overall, the lysine yield increased up to 70% through the combination of the different genetic modifications. Through strain engineering formation of trehalose was reduced by up to 70% due to reduced availability of its precursor G6P. Metabolic flux analysis revealed a 15% increase of PPP flux in response to over expression of the zwf gene. Overall a strong apparent NADPH excess resulted. Redox balancing indicated that this excess is completely oxidized by malic enzyme.

The topology of metabolic isotope labeling networks

Background

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.

Results

With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools.

Conclusion

Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

The thermodynamic meaning of metabolic exchange fluxes

Metabolic flux analysis (MFA) deals with the experimental determination of steady-state fluxes in metabolic networks. An important feature of the (13)C MFA method is its capability to generate information on both directions of bidirectional reaction steps given by exchange fluxes. The biological interpretation of these exchange fluxes and their relation to thermodynamic properties of the respective reaction steps has never been systematically investigated. As a central result, it is shown here that for a general class of enzyme reaction mechanisms the quotients of net and exchange fluxes measured by (13)C MFA are coupled to Gibbs energies of the reaction steps. To establish this relation the concept of apparent flux ratios of enzymatic isotope-labeling networks is introduced and some computing rules for these flux ratios are given. Application of these rules reveals a conceptional pitfall of (13)C MFA, which is the inherent dependency of measured exchange fluxes on the chosen tracer atom. However, it is shown that this effect can be neglected for typical biochemical reaction steps under physiological conditions. In this situation, the central result can be formulated as a two-sided inequality relating fluxes, pool sizes, and standard Gibbs energies. This relation has far-reaching consequences for metabolic flux analysis, quantitative metabolomics, and network thermodynamics.

Respirometric 13C flux analysis, Part I: design, construction and validation of a novel multiple reactor system using on-line membrane inlet mass spectrometry

A novel method for (13)C flux analysis based on on-line CO(2) labeling measurements is presented. This so-called respirometric (13)C flux analysis requires multiple parallel (13)C labeling experiments using differently labeled tracer substrates. In Part I of the work, a membrane-inlet mass spectrometry-based measurement system with 6 parallel reactors with each 12 ml liquid volume and associated experimental and computational methods for the respirometric (13)C data acquisition and evaluation are described. Signal dynamics after switching between membrane probes follow exactly first-order allowing extrapolation to steady state. Each measurement cycle involving 3 reactors takes about 2 min. After development of a dynamic calibration method, the suitability and reliability of the analysis was examined with a lysine-producing mutant of Corynebacterium glutamicum using [1-(13)C(1)], [6-(13)C(1)], [1,6-(13)C(2)] glucose. Specific rates of oxygen uptake and CO(2) production were estimated with an error less than +/-0.3 mmol g(-1) h(-1) and had +/-3% to +/-10% deviations between parallel reactors which is primarily caused by inaccuracies in initial biomass concentration. The respiratory quotient could be determined with an uncertainty less than +/-0.02 and varied only +/-3% between reactors. Fractional labeling of CO(2) was estimated with much higher precision of about +/-0.001 to +/-0.005. The detailed statistical analysis suggested that these data should be of sufficient quality to allow physiological interpretation and metabolic flux estimation. The obtained data were applied for the respirometric (13)C metabolic flux analysis in Part II.

Respirometric 13C flux analysis--Part II: in vivo flux estimation of lysine-producing Corynebacterium glutamicum

A novel method for metabolic flux studies of central metabolism which is based on respirometric (13)C flux analysis, i.e., parallel (13)C tracer studies with online CO(2) labeling measurements is applied to flux quantification of a lysine-producing mutant of Corynebacterium glutamicum. For this purpose, 3 respirometric (13)C labeling experiments with [1-(13)C(1)], [6-(13)C(1)] and [1,6-(13)C(2)] glucose were carried out in parallel. All fluxes comprising the reactions of glycolysis, of TCA cycle, of C3- and C4-metabolite interconversion and of lysine biosynthesis as well as the net reactions in the pentose phosphate pathway could be quantified solely using experimental data obtained from CO(2) labeling and extracellular rate measurements. At key branch points, 68+/-5% of glucose 6-phosphate were observed to be metabolized into pentose phosphate pathway and 48+/-1% of pyruvate into TCA cycle via pyruvate dehydrogenase. The results showed a good agreement with the previous studies using (13)C tracer cultivation and GC/MS analysis of proteinogenic amino acids. Also, respiratory quotient calculated from flux estimates using redox balance showed a high accordance with the value determined directly from the measured specific rates of O(2) consumption and CO(2) production. The results strongly support that the respirometric (13)C metabolic flux analysis is suited as an alternative to the conventional methods to study functional and regulatory activities of cells. The developed method is applicable to study growing or non-growing cells, primary and secondary metabolism and immobilized cells. Due to the non-accumulating nature of CO(2) labeling and instantaneous nature of the resulting fluxes, the method can also be used for dynamic profiling of metabolic activities. Therefore, it is complementary to conventional methods for metabolic flux analysis.