Katharina Nöh

A roadmap for interpreting (13)C metabolite labeling patterns from cells.

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

13CFLUX2—high-performance software suite for 13C-metabolic flux analysis

Summary: 13C-based metabolic flux analysis (13C-MFA) is the state-of-the-art method to quantitatively determine in vivo metabolic reaction rates in microorganisms. 13CFLUX2 contains all tools for composing flexible computational 13C-MFA workflows to design and evaluate carbon labeling experiments. A specially developed XML language, FluxML, highly efficient data structures and simulation algorithms achieve a maximum of performance and effectiveness. Support of multicore CPUs, as well as compute clusters, enables scalable investigations. 13CFLUX2 outperforms existing tools in terms of universality, flexibility and built-in features. Therewith, 13CFLUX2 paves the way for next-generation high-resolution 13C-MFA applications on the large scale. Availability and implementation: 13CFLUX2 is implemented in C++ (ISO/IEC 14882 standard) with Java and Python add-ons to run under Linux/Unix. A demo version and binaries are available at www.13cflux.net. Contact: info@13cflux.net or k.noeh@fz-juelich.de Supplementary information: Supplementary data are available at Bioinformatics online.

C metabolic flux analysis identifies an unusual route for pyruvate dissimilation in mycobacteria which requires isocitrate lyase and carbon dioxide fixation.

Mycobacterium tuberculosis requires the enzyme isocitrate lyase (ICL) for growth and virulence in vivo. The demonstration that M. tuberculosis also requires ICL for survival during nutrient starvation and has a role during steady state growth in a glycerol limited chemostat indicates a function for this enzyme which extends beyond fat metabolism. As isocitrate lyase is a potential drug target elucidating the role of this enzyme is of importance; however, the role of isocitrate lyase has never been investigated at the level of in vivo fluxes. Here we show that deletion of one of the two icl genes impairs the replication of Mycobacterium bovis BCG at slow growth rate in a carbon limited chemostat. In order to further understand the role of isocitrate lyase in the central metabolism of mycobacteria the effect of growth rate on the in vivo fluxes was studied for the first time using 13C-metabolic flux analysis (MFA). Tracer experiments were performed with steady state chemostat cultures of BCG or M. tuberculosis supplied with 13C labeled glycerol or sodium bicarbonate. Through measurements of the 13C isotopomer labeling patterns in protein-derived amino acids and enzymatic activity assays we have identified the activity of a novel pathway for pyruvate dissimilation. We named this the GAS pathway because it utilizes the Glyoxylate shunt and Anapleurotic reactions for oxidation of pyruvate, and Succinyl CoA synthetase for the generation of succinyl CoA combined with a very low flux through the succinate – oxaloacetate segment of the tricarboxylic acid cycle. We confirm that M. tuberculosis can fix carbon from CO2 into biomass. As the human host is abundant in CO2 this finding requires further investigation in vivo as CO2 fixation may provide a point of vulnerability that could be targeted with novel drugs. This study also provides a platform for further studies into the metabolism of M. tuberculosis using 13C-MFA.

13C-flux spectral analysis of host-pathogen metabolism reveals a mixed diet for intracellular Mycobacterium tuberculosis.

Summary Whereas intracellular carbon metabolism has emerged as an attractive drug target, the carbon sources of intracellularly replicating pathogens, such as the tuberculosis bacillus Mycobacterium tuberculosis, which causes long-term infections in one-third of the world’s population, remain mostly unknown. We used a systems-based approach—13C-flux spectral analysis (FSA) complemented with manual analysis—to measure the metabolic interaction between M. tuberculosis and its macrophage host cell. 13C-FSA analysis of experimental data showed that M. tuberculosis obtains a mixture of amino acids, C1 and C2 substrates from its host cell. We experimentally confirmed that the C1 substrate was derived from CO2. 13C labeling experiments performed on a phosphoenolpyruvate carboxykinase mutant revealed that intracellular M. tuberculosis has access to glycolytic C3 substrates. These findings provide constraints for developing novel chemotherapeutics.

Collisional fragmentation of central carbon metabolites in LC‐MS/MS increases precision of 13C metabolic flux analysis

Experimental determination of fluxes by 13C‐tracers relies on detection of 13C‐patterns in metabolites or by‐products. In the field of 13C metabolic flux analysis, the most recent developments point toward recording labeling patterns by liquid chromatography (LC)‐mass spectrometry (MS)/MS directly in intermediates in central carbon metabolism (CCM) to increase temporal resolution. Surprisingly, the flux studies published so far with LC‐MS measurements were based on intact metabolic intermediates—thus neglected the potential benefits of using positional information to improve flux estimation. For the first time, we exploit collisional fragmentation to obtain more fine‐grained 13C‐data on intermediates of CCM and investigate their impact in 13C metabolic flux analysis. For the case study of Bacillus subtilis grown in mineral medium with 13C‐labeled glucose, we compare the flux estimates obtained by iterative isotopologue balancing of 13C‐data obtained either by LC‐MS/MS for solely intact intermediates or LC‐MS/MS for intact and fragmented intermediates of CCM. We show that with LC‐MS/MS data, fragment information leads to more precise estimates of fluxes in pentose phosphate pathway, glycolysis, and to the tricarboxylic acid cycle. Additionally, we present an efficient analytical strategy to rapidly acquire large sets of 13C‐patterns by tandem MS, and an in‐depth analysis of the collisional fragmentation of primary intermediates. In the future, this catalogue will enable comprehensive in silico calculability analyses to identify the most sensitive measurements and direct experimental design. Biotechnol. Bioeng. 2012; 109:763–771.

Isotopically non-stationary metabolic flux analysis: complex yet highly informative

Metabolic flux analysis (MFA) using isotopic tracers aims at the experimental determination of in vivo reaction rates (fluxes). In recent years, the well-established 13C-MFA method based on metabolic and isotopic steady state was extended to INST-MFA (isotopically non-stationary MFA), which is performed in a transient labeling state. INST-MFA offers short-time experiments with a maximal information gain, and can moreover be applied to a wider range of growth conditions or organisms. Some of these conditions are not accessible by conventional methods. This comes at the price of significant methodological complexity involving high-frequency sampling and quenching, precise analysis of many samples and an extraordinary computational effort. This review gives a brief overview of basic principles, experimental workflows, and recent progress in this field. Special emphasis is laid on the trade-off between total effort and information gain, particularly on the suitability of INST-MFA for certain types of biological questions. In order to integrate INST-MFA as a viable method into the toolbox of MFA, some major challenges must be addressed in the coming years. These are discussed in the outlook.

The benefits of being transient: isotope-based metabolic flux analysis at the short time scale

Metabolic fluxes are the manifestations of the co-operating actions in a complex network of genes, transcripts, proteins, and metabolites. As a final quantitative endpoint of all cellular interactions, the intracellular fluxes are of immense interest in fundamental as well as applied research. Unlike the quantities of interest in most omics levels, in vivo fluxes are, however, not directly measureable. In the last decade, 13C-based metabolic flux analysis emerged as the state-of-the-art technique to infer steady-state fluxes by data from labeling experiments and the use of mathematical models. A very promising new area in systems metabolic engineering research is non-stationary 13C-metabolic flux analysis at metabolic steady-state conditions. Several studies have demonstrated an information surplus contained in transient labeling data compared to those taken at the isotopic equilibrium, as it is classically done. Enabled by recent, fairly multi-disciplinary progress, the new method opens several attractive options to (1) generate new insights, e.g., in cellular storage metabolism or the dilution of tracer by endogenous pools and (2) shift limits, inherent in the classical approach, towards enhanced applicability with respect to cultivation conditions and biological systems. We review the new developments in metabolome-based non-stationary 13C flux analysis and outline future prospects for accurate in vivo flux measurement.

13C labeling experiments at metabolic nonstationary conditions : an exploratory study

BackgroundStimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of 13C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.ResultsIn this contribution, the idea of increasing the information content of the dynamic experiment by adding 13C labeling is analyzed. For this purpose a small example network is studied by simulation and statistical methods. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis revealed a specific influence of the kinetic parameters on the labeling measurements. Statistical methods based on parameter sensitivities and different measurement models are applied to assess the information gain of the labeled stimulus response experiment.ConclusionIt was found that the use of a (specifically) labeled substrate will significantly increase the parameter estimation accuracy. An overall information gain of about a factor of six is observed for the example network. The information gain is achieved from the specific influence of the kinetic parameters towards the labeling measurements. This also leads to a significant decrease in correlation of the kinetic parameters compared to an experiment without 13C-labeled substrate.

Visualizing multi-omics data in metabolic networks with the software Omix—A case study

Systems Biology is a multi-disciplinary research field with the aim of understanding the function of complex processes in living organisms. These intracellular processes are described by biochemical networks. Experimental studies in alliance with computer simulation lead to a continually increasing amount of data in liaison with different layers of biochemical networks. Thus, visualization is very important for getting an overview of data in association with the network components. Omix is a software for the visualization of any data in biochemical networks. The unique feature of Omix is: the software is programmable by a scripting language called Omix Visualization Language (OVL). In Omix, the visualization of data coming from experiment or simulation is completely performed by the software user realized in concise OVL scripts. By this, visualization becomes most flexible and adaptable to the requirements of the user and can be adapted to new application fields. We present four case studies of visualizing data of diverse kind in biochemical networks on metabolic level by using Omix and the OVL scripting language. These worked examples demonstrate the power of OVL in conjunction with pleasing visualization, an important requirement for successful interdisciplinary communication in the interface between more experimental and more theoretical researchers.

Comparative 13C Metabolic Flux Analysis of Pyruvate Dehydrogenase Complex-Deficient, l-Valine-Producing Corynebacterium glutamicum

ABSTRACT l-Valine can be formed successfully using C. glutamicum strains missing an active pyruvate dehydrogenase enzyme complex (PDHC). Wild-type C. glutamicum and four PDHC-deficient strains were compared by 13C metabolic flux analysis, especially focusing on the split ratio between glycolysis and the pentose phosphate pathway (PPP). Compared to the wild type, showing a carbon flux of 69% ± 14% through the PPP, a strong increase in the PPP flux was observed in PDHC-deficient strains with a maximum of 113% ± 22%. The shift in the split ratio can be explained by an increased demand of NADPH for l-valine formation. In accordance, the introduction of the Escherichia coli transhydrogenase PntAB, catalyzing the reversible conversion of NADH to NADPH, into an l-valine-producing C. glutamicum strain caused the PPP flux to decrease to 57% ± 6%, which is below the wild-type split ratio. Hence, transhydrogenase activity offers an alternative perspective for sufficient NADPH supply, which is relevant for most amino acid production systems. Moreover, as demonstrated for l-valine, this bypass leads to a significant increase of product yield due to a concurrent reduction in carbon dioxide formation via the PPP.