13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split ratio in Penicillium chrysogenum

Repression of mitochondrial metabolism for cytosolic pyruvate-derived chemical production in Saccharomyces cerevisiae

Background

Saccharomyces cerevisiae is a suitable host for the industrial production of pyruvate-derived chemicals such as ethanol and 2,3-butanediol (23BD). For the improvement of the productivity of these chemicals, it is essential to suppress the unnecessary pyruvate consumption in S. cerevisiae to redirect the metabolic flux toward the target chemical production. In this study, mitochondrial pyruvate transporter gene (MPC1) or the essential gene for mitophagy (ATG32) was knocked-out to repress the mitochondrial metabolism and improve the production of pyruvate-derived chemical in S. cerevisiae.

Results

The growth rates of both aforementioned strains were 1.6-fold higher than that of the control strain. ¹³C-metabolic flux analysis revealed that both strains presented similar flux distributions and successfully decreased the tricarboxylic acid cycle fluxes by 50% compared to the control strain. Nevertheless, the intracellular metabolite pool sizes were completely different, suggesting distinct metabolic effects of gene knockouts in both strains. This difference was also observed in the test-tube culture for 23BD production. Knockout of ATG32 revealed a 23.6-fold increase in 23BD titer (557.0 ± 20.6 mg/L) compared to the control strain (23.5 ± 12.8 mg/L), whereas the knockout of MPC1 revealed only 14.3-fold increase (336.4 ± 113.5 mg/L). Further investigation using the anaerobic high-density fermentation test revealed that the MPC1 knockout was more effective for ethanol production than the 23BD production.

Conclusion

These results suggest that the engineering of the mitochondrial transporters and membrane dynamics were effective in controlling the mitochondrial metabolism to improve the productivities of chemicals in yeast cytosol.

Metabolic reprogramming and redox adaptation in sorafenib-resistant leukemia cells: detected by untargeted metabolomics and stable isotope tracing analysis

BACKGROUND

Internal tandem duplications (ITD) within the juxtamembrane domain of FMS-like tyrosine kinase 3 (FLT3) represent a poor prognostic indicator in acute myeloid leukemia (AML). Therapeutic benefits of tyrosine kinase inhibitors, such as sorafenib, are limited due to the emergence of drug resistance. While investigations have been conducted to improve the understanding of the molecular mechanisms underlying the resistance to this FLT3 inhibitor, a profile of cell functioning at the metabolite level and crosstalk between metabolic pathways has yet to be created. This study aimed to elucidate the alteration of metabolomic profile of leukemia cells resistant to the FLT3 inhibitor.

METHODS

We established two sorafenib-resistant cell lines carrying FLT3/ITD mutations, namely the murine BaF3/ITD-R and the human MV4-11-R cell lines. We performed a global untargeted metabolomics and stable isotope-labeling mass spectrometry analysis to identify the metabolic alterations relevant to the therapeutic resistance.

RESULTS

The resistant cells displayed fundamentally rewired metabolic profiles, characterized by a higher demand for glucose, accompanied by a reduction in glucose flux into the pentose phosphate pathway (PPP); and by an increase in oxidative stress, accompanied by an enhanced glutathione synthesis. We demonstrated that the highest scoring network of altered metabolites in resistant cells was related to nucleotide degradation. A stable isotope tracing experiment was performed and the results indicated a decrease in the quantity of glucose entering the PPP in resistant cells. Further experiment suggested that the inhibition of major enzymes in the PPP consist of glucose-6-phosphate dehydrogenase deficiency (G6PD) in the oxidative arm and transketolase (TKT) in the non-oxidative arm. In addition, we observed that chronic treatment with sorafenib resulted in an increased oxidative stress in FLT3/ITD-positive leukemia cells, which was accompanied by decreased cell proliferation and an enhanced antioxidant response.

CONCLUSIONS

Our data regarding comparative metabolomics characterized a distinct metabolic and redox adaptation that may contribute to sorafenib resistance in FLT3/ITD-mutated leukemia cells.

Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics

Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation.

Transcriptomic analysis of Staphylococcus xylosus in the presence of nitrate and nitrite in meat reveals its response to nitrosative stress

Staphylococcus xylosus is one of the major starter cultures used for meat fermentation because of its crucial role in the reduction of nitrate to nitrite which contributes to color and flavor development. Despite longstanding use of these additives, their impact on the physiology of S. xylosus has not yet been explored. We present the first in situ global gene expression profile of S. xylosus in meat supplemented with nitrate and nitrite at the levels used in the meat industry. More than 600 genes of S. xylosus were differentially expressed at 24 or 72 h of incubation. They represent more than 20% of the total genes and let us to suppose that addition of nitrate and nitrite to meat leads to a global change in gene expression. This profile revealed that S. xylosus is subject to nitrosative stress caused by reactive nitrogen species (RNS) generated from nitrate and nitrite. To overcome this stress, S. xylosus has developed several oxidative stress resistance mechanisms, such as modulation of the expression of several genes involved in iron homeostasis and in antioxidant defense. Most of which belong to the Fur and PerR regulons, respectively. S. xylosus has also counteracted this stress by developing DNA and protein repair. Furthermore, it has adapted its metabolic response—carbon and nitrogen metabolism, energy production and cell wall biogenesis—to the alterations produced by nitrosative stress.

Continuous-time Markov chain-based flux analysis in metabolism

Metabolic flux analysis (MFA), a key technology in bioinformatics, is an effective way of analyzing the entire metabolic system by measuring fluxes. Many existing MFA approaches are based on differential equations, which are complicated to be solved mathematically. So MFA requires some simple approaches to investigate metabolism further. In this article, we applied continuous-time Markov chain to MFA, called MMFA approach, and transformed the MFA problem into a set of quadratic equations by analyzing the transition probability of each carbon atom in the entire metabolic system. Unlike the other methods, MMFA analyzes the metabolic model only through the transition probability. This approach is very generic and it could be applied to any metabolic system if all the reaction mechanisms in the system are known. The results of the MMFA approach were compared with several chemical reaction equilibrium constants from early experiments by taking pentose phosphate pathway as an example.

Sampling of intracellular metabolites for stationary and non-stationary (13)C metabolic flux analysis in Escherichia coli

The analysis of metabolic intermediates is a rich source of isotopic information for (13)C metabolic flux analysis ((13)C-MFA) and extends the range of its applications. The sampling of labeled metabolic intermediates is particularly important to obtain reliable isotopic information. The assessment of the different sampling procedures commonly used to generate such data, therefore, is crucial. In this work, we thoroughly evaluated several sampling procedures for stationary and non-stationary (13)C-MFA using Escherichia coli. We first analyzed the efficiency of these procedures for quenching metabolism and found that procedures based on cold or boiling solvents are reliable, in contrast to fast filtration, which is not. We also showed that separating the cells from the broth is not necessary in isotopic stationary state conditions. On the other hand, we demonstrated that the presence of metabolic intermediates outside the cells strongly affects the transient isotopic data monitored during non-stationary (13)C-labeling experiments. Meaningful isotopic data can be obtained by recovering intracellular labeled metabolites from pellets of cells centrifuged in cold solvent. We showed that if the intracellular pools are not separated from the extracellular ones, accurate flux maps can be established provided that the contribution of exogenous compounds is taken into account in the metabolic flux model.

Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids

¹³C metabolic flux analysis (MFA) is a tool of metabolic engineering for investigation of in vivo flux distribution. A direct ¹³C enrichment analysis of intracellular free amino acids (FAAs) is expected to reduce time for labeling experiments of the MFA. Measurable FAAs should, however, vary among the MFA experiments since the pool sizes of intracellular free metabolites depend on cellular metabolic conditions. In this study, minimal ¹³C enrichment data of FAAs was investigated to perform the FAAs-based MFA. An examination of a continuous culture of Escherichia coli using ¹³C-labeled glucose showed that the time required to reach an isotopically steady state for FAAs is rather faster than that for conventional method using proteinogenic amino acids (PAAs). Considering 95% confidence intervals, it was found that the metabolic flux distribution estimated using FAAs has a similar reliability to that of the PAAs-based method. The comparative analysis identified glutamate, aspartate, alanine and phenylalanine as the common amino acids observed in E. coli under different culture conditions. The results of MFA also demonstrated that the ¹³C enrichment data of the four amino acids is required for a reliable analysis of the flux distribution.

Publishing 13C metabolic flux analysis studies: a review and future perspectives

(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.

Simultaneous utilization of glucose and gluconate in Penicillium chrysogenum during overflow metabolism

The filamentous fungus Penicillium chrysogenum is one of the most important production organism for β-lactam antibiotics, especially penicillin. A specific feature of P. chrysogenum is the formation of gluconate as the primary overflow metabolite under non-limiting growth on glucose. Gluconate can be formed extracellularly by the enzyme glucose oxidase (GOD) that shows high activities under glucose excess conditions. Currently, it is assumed that under these conditions glucose is the preferred carbon substrate for P. chrysogenum and gluconate consumption first starts after glucose becomes limiting. Here, we specifically address this hypothesis by combining batch cultivation experiments on defined glucose media, time-dependent GOD activity measurements, and (13)C-tracer studies. Our data prove that both substrates are metabolized simultaneously independent from the actual glucose concentration and therefore suggest that no distinct mechanism of carbon catabolite repression exists for gluconate in P. chrysogenum. Moreover, gluconate consumption does not interfere with penicillin V production by repression of the penicillin genes. Finally, by following a model-driven approach the specific uptake rates for glucose and gluconate were quantified and found to be significantly higher for gluconate. In summary, our results show that P. chrysogenum metabolizes gluconate directly and at high rates making it an interesting alternative carbon source for production purposes.

Quantitative quenching evaluation and direct intracellular metabolite analysis in Penicillium chrysogenum

Sustained progress in metabolic engineering methodologies has stimulated new efforts toward optimizing fungal production strains such as through metabolite analysis of Penicillium chrysogenum industrial-scale processes. Accurate intracellular metabolite quantification requires sampling procedures that rapidly stop metabolism (quenching) and avoid metabolite loss via the cell membrane (leakage). When sampling protocols are validated, the quenching efficiency is generally not quantitatively assessed. For fungal metabolomics, quantitative biomass separation using centrifugation is a further challenge. In this study, P. chrysogenum intracellular metabolites were quantified directly from biomass extracts using automated sampling and fast filtration. A master/slave bioreactor concept was applied to provide industrial production conditions. Metabolic activity during sampling was monitored by 13C tracing. Enzyme activities were efficiently stopped and metabolite leakage was absent. This work provides a reliable method for P. chrysogenum metabolomics and will be an essential base for metabolic engineering of industrial processes.

Novel diagnostics of metabolic dysfunction detected in breath and plasma by selective isotope-assisted labeling

Metabolomics is the study of a unique fingerprint of small molecules present in biological systems under healthy and disease conditions. One of the major challenges in metabolomics is validation of fingerprint molecules to identify specifically perturbed pathways in metabolic aberrations. This step is crucial to the understanding of budding metabolic pathologies and the ability to identify early indicators of common diseases such as obesity, type 2 diabetes mellitus, metabolic syndrome, polycystic ovary syndrome, and cancer. We present a novel approach to diagnosing aberrations in glucose utilization including metabolic pathway switching in a disease state. We used a well-defined prenatally exposed glucocorticoid mouse model that results in adult females with metabolic dysfunction. We applied the complementary technologies of nuclear magnetic resonance spectroscopy and cavity ring-down spectroscopy to analyze serial plasma samples and real-time breath measurements following selective (13)C-isotope-assisted labeling. These platforms allowed us to trace metabolic markers in whole animals and identify key metabolic pathway switching in prenatally glucocorticoid-treated animals. Total glucose flux is significantly proportionally increased through the major oxidative pathways of glycolysis and the pentose phosphate pathway in the prenatally glucocorticoid-treated animals relative to the control animals. This novel diagnostics approach is fast, noninvasive, and sensitive for determining specific pathway utilization, and provides a direct translational application in the health care field.

Metabolic ensemble modeling for strain engineers

Previous mathematical modeling efforts have made significant contributions to the development of systems biology for predicting biological behavior quantitatively. However, dynamic metabolic model construction remains challenging due to uncertainties in mechanistic structures and parameters. In addition, parameter estimation and model validation often require designated experiments conducted only for purpose of modeling. Such difficulties have hampered the progress of modeling in biology and biotechnology. To circumvent these problems, ensemble approaches have been used to account for uncertainties in model structure and parameters. Specifically, this review focuses on approaches that utilize readily available fermentation data for parameter screening and model validation. Time course data for metabolite measurements, if available, can further calibrate the model. The basis for this approach is explained in non-mathematical terms accessible to experimentalists. Information gained from such an approach has been shown to be useful in designing Escherichia coli strains for metabolic engineering and synthetic biology.

Bridging the gap between fluxomics and industrial biotechnology

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. ¹³C-based metabolic flux analysis (¹³C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both ¹³C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

Cytosolic NADPH balancing in Penicillium chrysogenum cultivated on mixtures of glucose and ethanol

The in vivo flux through the oxidative branch of the pentose phosphate pathway (oxPPP) in Penicillium chrysogenum was determined during growth in glucose/ethanol carbon-limited chemostat cultures, at the same growth rate. Non-stationary (13)C flux analysis was used to measure the oxPPP flux. A nearly constant oxPPP flux was found for all glucose/ethanol ratios studied. This indicates that the cytosolic NADPH supply is independent of the amount of assimilated ethanol. The cofactor assignment in the model of van Gulik et al. (Biotechnol Bioeng 68(6):602-618, 2000) was supported using the published genome annotation of P. chrysogenum. Metabolic flux analysis showed that NADPH requirements in the cytosol remain nearly the same in these experiments due to constant biomass growth. Based on the cytosolic NADPH balance, it is known that the cytosolic aldehyde dehydrogenase in P. chrysogenum is NAD(+) dependent. Metabolic modeling shows that changing the NAD(+)-aldehyde dehydrogenase to NADP(+)-aldehyde dehydrogenase can increase the penicillin yield on substrate.

Dynamic flux responses in riboflavin overproducing Bacillus subtilis to increasing glucose limitation in fed-batch culture

How do intracellular fluxes respond to dynamically increasing glucose limitation when the physiology changes from strong overflow metabolism near to exclusively maintenance metabolism? Here we investigate this question in a typical industrial, glucose-limited fed-batch cultivation with a riboflavin overproducing Bacillus subtilis strain. To resolve dynamic flux changes, a novel approach to (13)C flux analysis was developed that is based on recording (13)C labeling patterns in free intracellular amino acids. Fluxes are then estimated with stationary flux ratio and iterative isotopomer balancing methods, for which a decomposition of the process into quasi-steady states and estimation of isotopic steady state (13)C labeling patterns was necessary. By this approach, we achieve a temporal resolution of 30-60 min that allows us to resolve the slow metabolic transients that typically occur in such cultivations. In the late process phase we found, most prominently, almost exclusive respiratory metabolism, significantly increased pentose phosphate pathway contribution and a strongly decreased futile cycle through the PEP carboxykinase. As a consequence, higher catabolic NADPH formation occurred than was necessary to satisfy the anabolic demands, suggesting a transhydrogenase-like mechanism to close the balance of reducing equivalents.

Systems biology of recombinant protein production in Bacillus megaterium

Over the last two decades the Gram-positive bacterium Bacillus megaterium was systematically developed to a useful alternative protein production host. Multiple vector systems for high yield intra- and extracellular protein production were constructed. Strong inducible promoters were combined with DNA sequences for optimised ribosome binding sites, various leader peptides for protein export and N- as well as C-terminal affinity tags for affinity chromatographic purification of the desired protein. High cell density cultivation and recombinant protein production were successfully tested. For further system biology based control and optimisation of the production process the genomes of two B. megaterium strains were completely elucidated, DNA arrays designed, proteome, fluxome and metabolome analyses performed and all data integrated using the bioinformatics platform MEGABAC. Now, solid theoretical and experimental bases for primary modeling attempts of the production process are available.

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity. This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.

(13)C-based metabolic flux analysis

Stable isotope, and in particular (13)C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on (13)C-labeled glucose and subsequent gas chromatography mass spectrometric detection of (13)C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on (13)C-pattern in free intracellular metabolites. The presented protocols take 5-10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

Dynamics of glycolytic regulation during adaptation of Saccharomyces cerevisiae to fermentative metabolism

The ability of baker's yeast (Saccharomyces cerevisiae) to rapidly increase its glycolytic flux upon a switch from respiratory to fermentative sugar metabolism is an important characteristic for many of its multiple industrial applications. An increased glycolytic flux can be achieved by an increase in the glycolytic enzyme capacities (V(max)) and/or by changes in the concentrations of low-molecular-weight substrates, products, and effectors. The goal of the present study was to understand the time-dependent, multilevel regulation of glycolytic enzymes during a switch from fully respiratory conditions to fully fermentative conditions. The switch from glucose-limited aerobic chemostat growth to full anaerobiosis and glucose excess resulted in rapid acceleration of fermentative metabolism. Although the capacities (V(max)) of the glycolytic enzymes did not change until 45 min after the switch, the intracellular levels of several substrates, products, and effectors involved in the regulation of glycolysis did change substantially during the initial 45 min (e.g., there was a buildup of the phosphofructokinase activator fructose-2,6-bisphosphate). This study revealed two distinct phases in the upregulation of glycolysis upon a switch to fermentative conditions: (i) an initial phase, in which regulation occurs completely through changes in metabolite levels; and (ii) a second phase, in which regulation is achieved through a combination of changes in V(max) and metabolite concentrations. This multilevel regulation study qualitatively explains the increase in flux through the glycolytic enzymes upon a switch of S. cerevisiae to fermentative conditions and provides a better understanding of the roles of different regulatory mechanisms that influence the dynamics of yeast glycolysis.

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

Background

Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³C isotopomer data are typically needed.

Results

We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³C isotopomer measurement techniques, substrates and substrate labelling patterns.

By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.

Conclusion

The core of ¹³C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.

Metabolic flux elucidation for large-scale models using 13C labeled isotopes

A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraint-based modeling framework with isotopic label tracing on a large scale. When cells are fed a growth substrate with certain carbon positions labeled with (13)C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the antimalarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-(13)C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.

13C-Labeled metabolic flux analysis of a fed-batch culture of elutriated Saccharomyces cerevisiae

This study addresses the question of whether observable changes in fluxes in the primary carbon metabolism of Saccharomyces cerevisiae occur between the different phases of the cell division cycle. To detect such changes by metabolic flux analysis, a 13C-labeling experiment was performed with a fed-batch culture inoculated with a partially synchronized cell population obtained through centrifugal elutriation. Such a culture exhibits dynamic changes in the fractions of cells in different cell cycle phases over time. The mass isotopomer distributions of free intracellular metabolites in central carbon metabolism were measured by liquid chromatography-mass spectrometry. For four time points during the culture, these distributions were used to obtain the best estimates for the metabolic fluxes. The obtained flux fits suggested that the optimally fitted split ratio for the pentose phosphate pathway changed by almost a factor of 2 up and down around a value of 0.27 during the experiment. Statistical analysis revealed that some of the fitted flux distributions for different time points were significantly different from each other, indicating that cell cycle-dependent variations in cytosolic metabolic fluxes indeed occurred.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for metabolic flux analyses using isotope-labeled ethanol

We describe a novel method for the determination of the concentration and labeling degree of ethanol originating from 1-13C-labeling experiments. This method is suitable for high-throughput metabolic flux analysis because of the possible parallel sample preparation and fast final analysis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). In a closed vial containing culture supernatant, ethanol is enzymatically oxidized to acetaldehyde. The acetaldehyde formed evaporates and is readily trapped in a second enclosed but open vial containing acidified 2,4-dinitrophenylhydrazine (DNPH). The 2,4-acetaldehyde dinitrophenylhydrazone (Ac-DNPH) that is formed is insoluble under these conditions. This leads to a constant conversion rate of the acetaldehyde produced from ethanol after 14 h minimum incubation time. MALDI-TOFMS was used to quantify the formed Ac-DNPH with [13C2]-ethanol as internal standard. The relative signal intensities of the unlabeled ethanol derivative as well as of [1-13C]-ethanol were linearly related to the ethanol concentration within a range of 1 to 50 mM with a limit of detection of 0.6 mM, a range which is sufficient for flux analysis in microtiter plate fermentation experiments. The method allows the estimation of the [1-13C]-ethanol originating from 1-13C-labeling experiments of Saccharomyces cerevisiae strains. In experiments where the expected flux range was exceeded, unlabeled ethanol was determined with a linear range from 30 to 500 mM. Ethanol quantification using this method was compared with enzymatic analysis and exhibited differences of less than 3.3% on average. Comparison of flux partitioning ratios between glycolysis and the pentose-phosphate pathway (PPP) based on MALDI-TOFMS and gas chromatography (GC)/MS methods showed good agreement, with differences for ethanol and alanine labeling of only 4.3%.

Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived C-labelling data

This study focuses on unravelling the carbon and redox metabolism of a previously developed glycerol-overproducing Saccharomyces cerevisiae strain with deletions in the structural genes encoding triosephosphate isomerase (TPI1), the external mitochondrial NADH dehydrogenases (NDE1 and NDE2) and the respiratory chain-linked glycerol-3-phosphate dehydrogenase (GUT2). Two methods were used for analysis of metabolic fluxes: metabolite balancing and (13)C-labelling-based metabolic flux analysis. The isotopic enrichment of intracellular primary metabolites was measured both directly (liquid chromatography-MS) and indirectly through proteinogenic amino acids (nuclear magnetic resonance and gas chromatography-MS). Because flux sensitivity around several important metabolic nodes proved to be dependent on the applied technique, the combination of the three (13)C quantification techniques generated the most accurate overall flux pattern. When combined, the measured conversion rates and (13)C-labelling data provided evidence that a combination of assimilatory metabolism and pentose phosphate pathway activity diverted some of the carbon away from glycerol formation. Metabolite balancing indicated that this results in excess cytosolic NADH, suggesting the presence of a cytosolic NADH sink in addition to those that were deleted. The exchange flux of four-carbon dicarboxylic acids across the mitochondrial membrane, as measured by the (13)C-labelling data, supports a possible role of a malate/aspartate or malate/oxaloacetate redox shuttle in the transfer of these redox equivalents from the cytosol to the mitochondrial matrix.

Cytosolic NADPH metabolism in penicillin-G producing and non-producing chemostat cultures of Penicillium chrysogenum

This study addresses the relation between NADPH supply and penicillin synthesis, by comparing the flux through the oxidative branch of the pentose phosphate pathway (PPP; the main source of cytosolic NADPH) in penicillin-G producing and non-producing chemostat cultures of Penicillium chrysogenum. The fluxes through the oxidative part of the PPP were determined using the recently introduced gluconate-tracer method. Significantly higher oxidative PPP fluxes were observed in penicillin-G producing chemostat cultures, indicating that penicillin production puts a major burden on the supply of cytosolic NADPH. To our knowledge this is the first time direct experimental proof is presented for the causal relationship between penicillin production and NADPH supply. Additional insight in the metabolism of P. chrysogenum was obtained by comparing the PPP fluxes from the gluconate-tracer experiment to oxidative PPP fluxes derived via metabolic flux analysis, using different assumptions for the stoichiometry of NADPH consumption and production.