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

Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system

¹³C-Metabolic flux analysis (¹³C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by ¹³C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for ¹³C-MFA. The best single tracers were doubly ¹³C-labeled glucose tracers, including [1,6-¹³C]glucose, [5,6-¹³C]glucose and [1,2-¹³C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-¹³C]glucose and [1,2-¹³C]glucose. Combined analysis of [1,6-¹³C]glucose and [1,2-¹³C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-¹³C]glucose +20% [U-¹³C]glucose.

Cutting the Gordian Knot: Identifiability of anaplerotic reactions in Corynebacterium glutamicum by means of (13) C-metabolic flux analysis

Corynebacterium glutamicum is the major workhorse for the microbial production of several amino and organic acids. As long as these derive from tricarboxylic acid cycle intermediates, the activity of anaplerotic reactions is pivotal for a high biosynthetic yield. To determine single anaplerotic activities (13) C-Metabolic Flux Analysis ((13) C-MFA) has been extensively used for C. glutamicum, however with different network topologies, inconsistent or poorly determined anaplerotic reaction rates. Therefore, in this study we set out to investigate whether a focused isotopomer model of the anaplerotic node can at all admit a unique solution for all fluxes. By analyzing different scenarios of active anaplerotic reactions, we show in full generality that for C. glutamicum only certain anaplerotic deletion mutants allow to uniquely determine the anaplerotic fluxes from (13) C-isotopomer data. We stress that the result of this analysis for different assumptions on active enzymes is directly transferable to other compartment-free organisms. Our results demonstrate that there exist biologically relevant metabolic network topologies for which the flux distribution cannot be inferred by classical (13) C-MFA.

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.

Modelling metabolic CO₂ evolution--a fresh perspective on respiration

Respiration is a major contributor to net exchange of CO₂ between plants and the atmosphere and thus an important aspect of the vegetation component of global climate change models. However, a mechanistic model of respiration is lacking, and so here we explore the potential for flux balance analysis (FBA) to predict cellular CO₂ evolution rates. Metabolic flux analysis reveals that respiration is not always the dominant source of CO₂, and that metabolic processes such as the oxidative pentose phosphate pathway (OPPP) and lipid synthesis can be quantitatively important. Moreover, there is considerable variation in the metabolic origin of evolved CO₂ between tissues, species and conditions. Comparison of FBA-predicted CO₂ evolution profiles with those determined from flux measurements reveals that FBA is able to predict the metabolic origin of evolved CO₂ in different tissues/species and under different conditions. However, FBA is poor at predicting flux through certain metabolic processes such as the OPPP and we identify the way in which maintenance costs are accounted for as a major area of improvement for future FBA studies. We conclude that FBA, in its standard form, can be used to predict CO₂ evolution in a range of plant tissues and in response to environment.

Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.

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.

Automated measurement and monitoring of bioprocesses: key elements of the M(3)C strategy

The state-of-routine monitoring items established in the bioprocess industry as well as some important state-of-the-art methods are briefly described and the potential pitfalls discussed. Among those are physical and chemical variables such as temperature, pressure, weight, volume, mass and volumetric flow rates, pH, redox potential, gas partial pressures in the liquid and molar fractions in the gas phase, infrared spectral analysis of the liquid phase, and calorimetry over an entire reactor. Classical as well as new optical versions are addressed. Biomass and bio-activity monitoring (as opposed to "measurement") via turbidity, permittivity, in situ microscopy, and fluorescence are critically analyzed. Some new(er) instrumental analytical tools, interfaced to bioprocesses, are explained. Among those are chromatographic methods, mass spectrometry, flow and sequential injection analyses, field flow fractionation, capillary electrophoresis, and flow cytometry. This chapter surveys the principles of monitoring rather than compiling instruments.

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.

Metabolic flux analysis in systems biology of mammalian cells

Reaction rates or metabolic fluxes reflect the integrated phenotype of genome, transcriptome and proteome interactions, including regulation at all levels of the cellular hierarchy. Different methods have been developed in the past to analyse intracellular fluxes. However, compartmentation of mammalian cells, varying utilisation of multiple substrates, reversibility of metabolite uptake and production, unbalanced growth behaviour and adaptation of cells to changing environment during cultivation are just some reasons that make metabolic flux analysis (MFA) in mammalian cell culture more challenging compared to microorganisms. In this article MFA using the metabolite balancing methodology and the advantages and disadvantages of (13)C MFA in mammalian cell systems are reviewed. Application examples of MFA in the optimisation of cell culture processes for the production of biopharmaceuticals are presented with a focus on the metabolism of the main industrial workhorse. Another area in which mammalian cell culture plays a key role is in medical and toxicological research. It is shown that MFA can be used to understand pathophysiological mechanisms and can assist in understanding effects of drugs or other compounds on cellular metabolism.

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.

Data integration and analysis of biological networks

During the past decade, bottom-up and top-down approaches of network reconstruction have greatly facilitated integration and analysis of biological networks, including transcriptional, protein interaction, and metabolic networks. As increasing amounts of multidimensional high-throughput data become available, biological networks have also been upgraded, allowing more accurate understanding of whole cellular characteristics. The network size is constantly expanding as larger volume of information and omics data are further integrated into the biological networks previously built upon a single type of data. Such effort more recently led to the modeling of human metabolic network and prediction of its tissue-specific metabolism, reconstruction of consensus yeast metabolic network, and simulation of mutual interactions among multiple microorganisms. It is expected that this trend will continue, the outcomes of which will allow development of more sophisticated networks integrating diverse omics data, and enhance our understanding of biological systems.

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.

Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example

Background

The rapid progress of post-genomic analyses, such as transcriptomics, proteomics, and metabolomics has resulted in the generation of large amounts of quantitative data covering and connecting the complete cascade from genotype to phenotype for individual organisms. Various benefits can be achieved when these "Omics" data are integrated, such as the identification of unknown gene functions or the elucidation of regulatory networks of whole organisms. In order to be able to obtain deeper insights in the generated datasets, it is of utmost importance to present the data to the researcher in an intuitive, integrated, and knowledge-based environment. Therefore, various visualization paradigms have been established during the last years. The visualization of "Omics" data using metabolic pathway maps is intuitive and has been applied in various software tools. It has become obvious that the application of web-based and user driven software tools has great potential and benefits from the use of open and standardized formats for the description of pathways.

Results

In order to combine datasets from heterogeneous "Omics" sources, we present the web-based ProMeTra system that visualizes and combines datasets from transcriptomics, proteomics, and metabolomics on user defined metabolic pathway maps. Therefore, structured exchange of data with our "Omics" applications Emma 2, Qupe and MeltDB is employed. Enriched SVG images or animations are generated and can be obtained via the user friendly web interface.

To demonstrate the functionality of ProMeTra, we use quantitative data obtained during a fermentation experiment of the L-lysine producing strain Corynebacterium glutamicum DM1730. During fermentation, oxygen supply was switched off in order to perturb the system and observe its reaction. At six different time points, transcript abundances, intracellular metabolite pools, as well as extracellular glucose, lactate, and L-lysine levels were determined.

Conclusion

The interpretation and visualization of the results of this complex experiment was facilitated by the ProMeTra software. Both transcriptome and metabolome data were visualized on a metabolic pathway map. Visual inspection of the combined data confirmed existing knowledge but also delivered novel correlations that are of potential biotechnological importance.

Numerical bias estimation for mass spectrometric mass isotopomer analysis

Mass spectrometric (MS) isotopomer analysis has become a standard tool for investigating biological systems using stable isotopes. In particular, metabolic flux analysis uses mass isotopomers of metabolic products typically formed from (13)C-labeled substrates to quantitate intracellular pathway fluxes. In the current work, we describe a model-driven method of numerical bias estimation regarding MS isotopomer analysis. Correct bias estimation is crucial for measuring statistical qualities of measurements and obtaining reliable fluxes. The model we developed for bias estimation corrects a priori unknown systematic errors unique for each individual mass isotopomer peak. For validation, we carried out both computational simulations and experimental measurements. From stochastic simulations, it was observed that carbon mass isotopomer distributions and measurement noise can be determined much more precisely only if signals are corrected for possible systematic errors. By removing the estimated background signals, the residuals resulting from experimental measurement and model expectation became consistent with normality, experimental variability was reduced, and data consistency was improved. The method is useful for obtaining systematic error-free data from (13)C tracer experiments and can also be extended to other stable isotopes. As a result, the reliability of metabolic fluxes that are typically computed from mass isotopomer measurements is increased.

Advances in analysis of microbial metabolic fluxes via (13)C isotopic labeling

Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields.

Fueling industrial biotechnology growth with bioethanol

Industrial biotechnology is the conversion of biomass via biocatalysis, microbial fermentation, or cell culture to produce chemicals, materials, and/or energy. Industrial biotechnology processes aim to be cost-competitive, environmentally favorable, and self-sustaining compared to their petrochemical equivalents. Common to all processes for the production of energy, commodity, added value, or fine chemicals is that raw materials comprise the most significant cost fraction, particularly as operating efficiencies increase through practice and improving technologies. Today, crude petroleum represents the dominant raw material for the energy and chemical sectors worldwide. Within the last 5 years petroleum prices, stability, and supply have increased, decreased, and been threatened, respectively, driving a renewed interest across academic, government, and corporate centers to utilize biomass as an alternative raw material. Specifically, bio-based ethanol as an alternative biofuel has emerged as the single largest biotechnology commodity, with close to 46 billion L produced worldwide in 2005. Bioethanol is a leading example of how systems biology tools have significantly enhanced metabolic engineering, inverse metabolic engineering, and protein and enzyme engineering strategies. This enhancement stems from method development for measurement, analysis, and data integration of functional genomics, including the transcriptome, proteome, metabolome, and fluxome. This review will show that future industrial biotechnology process development will benefit tremendously from the precedent set by bioethanol - that enabling technologies (e.g., systems biology tools) coupled with favorable economic and socio-political driving forces do yield profitable, sustainable, and environmentally responsible processes. Biofuel will continue to be the keystone of any industrial biotechnology-based economy whereby biorefineries leverage common raw materials and unit operations to integrate diverse processes to produce demand-driven product portfolios.

Metabolic flux analysis in plants using dynamic labeling technique: application to tryptophan biosynthesis in cultured rice cells

The concept and methodology of using dynamic labeling for the MFA of plant metabolic pathways are described, based on a case study to develop a method for the MFA of the tryptophan biosynthetic pathway in cultured rice cells. Dynamic labeling traces the change in the labeling level of a metabolite in a metabolic pathway after the application of a stable isotope-labeled compound. In this study, [1-(13)C] l-serine was fed as a labeling precursor and the labeling level of Trp was determined by using the LC-MS/MS. The value of metabolic flux is determined by fitting a model describing the labeling dynamics of the pathway to the observed labeling data. The biosynthetic flux of Trp in rice suspension cultured cell was determined to be 6.0+/-1.1 nmol (gFWh)(-1). It is also demonstrated that an approximately sixfold increase in the biosynthetic flux of Trp in transgenic rice cells expressing the feedback-insensitive version of anthranilate synthase alpha-subunit gene (OASA1D) resulted in a 45-fold increase in the level of Trp. In this article, the basic workflow for the experiment is introduced and the details of the actual experimental procedures are explained. Future perspectives are also discussed by referring recent advances in the dynamic labeling approach.