Wouter A. van Winden

Malic Acid Production by Saccharomyces cerevisiae: Engineering of Pyruvate Carboxylation, Oxaloacetate Reduction, and Malate Export

ABSTRACT Malic acid is a potential biomass-derivable “building block” for chemical synthesis. Since wild-type Saccharomyces cerevisiae strains produce only low levels of malate, metabolic engineering is required to achieve efficient malate production with this yeast. A promising pathway for malate production from glucose proceeds via carboxylation of pyruvate, followed by reduction of oxaloacetate to malate. This redox- and ATP-neutral, CO2-fixing pathway has a theoretical maximum yield of 2 mol malate (mol glucose)−1. A previously engineered glucose-tolerant, C2-independent pyruvate decarboxylase-negative S. cerevisiae strain was used as the platform to evaluate the impact of individual and combined introduction of three genetic modifications: (i) overexpression of the native pyruvate carboxylase encoded by PYC2, (ii) high-level expression of an allele of the MDH3 gene, of which the encoded malate dehydrogenase was retargeted to the cytosol by deletion of the C-terminal peroxisomal targeting sequence, and (iii) functional expression of the Schizosaccharomyces pombe malate transporter gene SpMAE1. While single or double modifications improved malate production, the highest malate yields and titers were obtained with the simultaneous introduction of all three modifications. In glucose-grown batch cultures, the resulting engineered strain produced malate at titers of up to 59 g liter−1 at a malate yield of 0.42 mol (mol glucose)−1. Metabolic flux analysis showed that metabolite labeling patterns observed upon nuclear magnetic resonance analyses of cultures grown on 13C-labeled glucose were consistent with the envisaged nonoxidative, fermentative pathway for malate production. The engineered strains still produced substantial amounts of pyruvate, indicating that the pathway efficiency can be further improved.

Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics.

In this work, we present a time-scale analysis based model reduction and parameter identifiability analysis method for metabolic reaction networks. The method uses the information obtained from short term chemostat perturbation experiments. We approximate the time constant of each metabolite pool by their turn-over time and classify the pools accordingly into two groups: fast and slow pools. We performed a priori model reduction, neglecting the dynamic term of the fast pools. By making use of the linlog approximative kinetics, we obtained a general explicit solution for the fast pools in terms of the slow pools by elaborating the degenerate algebraic system resulting from model reduction. The obtained relations yielded also analytical relations between a subset of kinetic parameters. These relations also allow to realize an analytical model reduction using lumped reaction kinetics. After solving these theoretical identifiability problems and performing model reduction, we carried out a Monte Carlo approach to study the practical identifiability problems. We illustrated the methodology on model reduction and theoretical/practical identifiability analysis on an example system representing the glycolysis in Saccharomyces cerevisiae cells.

Fast dynamic response of the fermentative metabolism of Escherichia coli to aerobic and anaerobic glucose pulses.

The response of Escherichia coli cells to transient exposure (step increase) in substrate concentration and anaerobiosis leading to mixed‐acid fermentation metabolism was studied in a two‐compartment bioreactor system consisting of a stirred tank reactor (STR) connected to a mini‐plug‐flow reactor (PFR: BioScope, 3.5 mL volume). Such a system can mimic the situation often encountered in large‐scale, fed‐batch bioreactors. The STR represented the zones of a large‐scale bioreactor that are far from the point of substrate addition and that can be considered as glucose limited, whereas the PFR simulated the region close to the point of substrate addition, where glucose concentration is much higher than in the rest of the bioreactor. In addition, oxygen‐poor and glucose‐rich regions can occur in large‐scale bioreactors. The response of E. coli to these large‐scale conditions was simulated by continuously pumping E. coli cells from a well stirred, glucose limited, aerated chemostat (D = 0.1 h−1) into the mini‐PFR. A glucose pulse was added at the entrance of the PFR. In the PFR, a total of 11 samples were taken in a time frame of 92 s. In one case aerobicity in the PFR was maintained in order to evaluate the effects of glucose overflow independently of oxygen limitation. Accumulation of acetate and formate was detected after E. coli cells had been exposed for only 2 s to the glucose‐rich (aerobic) region in the PFR. In the other case, the glucose pulse was also combined with anaerobiosis in the PFR. Glucose overflow combined with anaerobiosis caused the accumulation of formate, acetate, lactate, ethanol, and succinate, which were also detected as soon as 2 s after of exposure of E. coli cells to the glucose and O2 gradients. This approach (STR‐mini‐PFR) is useful for a better understanding of the fast dynamic phenomena occurring in large‐scale bioreactors and for the design of modified strains with an improved behavior under large‐scale conditions. Biotechnol. Bioeng. 2009; 104: 1153–1161.

Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

ABSTRACT The in vivo kinetics in Saccharomyces cerevisiae CEN.PK 113-7D was evaluated during a 300-second transient period after applying a glucose pulse to an aerobic, carbon-limited chemostat culture. We quantified the responses of extracellular metabolites, intracellular intermediates in primary metabolism, intracellular free amino acids, and in vivo rates of O2 uptake and CO2 evolution. With these measurements, dynamic carbon, electron, and ATP balances were set up to identify major carbon, electron, and energy sinks during the postpulse period. There were three distinct metabolic phases during this time. In phase I (0 to 50 seconds after the pulse), the carbon/electron balances closed up to 85%. The accumulation of glycolytic and storage compounds accounted for 60% of the consumed glucose, caused an energy depletion, and may have led to a temporary decrease in the anabolic flux. In phase II (50 to 150 seconds), the fermentative metabolism gradually became the most important carbon/electron sink. In phase III (150 to 300 seconds), 29% of the carbon uptake was not identified in the measurements, and the ATP balance had a large surplus. These results indicate an increase in the anabolic flux, which is consistent with macroscopic balances of extracellular fluxes and the observed increase in CO2 evolution associated with nonfermentative metabolism. The identified metabolic processes involving major carbon, electron, and energy sinks must be taken into account in in vivo kinetic models based on short-term dynamic metabolome responses.

Revisiting the 13C‐label distribution of the non‐oxidative branch of the pentose phosphate pathway based upon kinetic and genetic evidence

The currently applied reaction structure in stoichiometric flux balance models for the nonoxidative branch of the pentose phosphate pathway is not in accordance with the established ping‐pong kinetic mechanism of the enzymes transketolase (EC 2.2.1.1) and transaldolase (EC 2.2.1.2). Based upon the ping‐pong mechanism, the traditional reactions of the nonoxidative branch of the pentose phosphate pathway are replaced by metabolite specific, reversible, glycolaldehyde moiety (C2) and dihydroxyacetone moiety (C3) fragments producing and consuming half‐reactions. It is shown that a stoichiometric model based upon these half‐reactions is fundamentally different from the currently applied stoichiometric models with respect to the number of independent C2 and C3 fragment pools in the pentose phosphate pathway and can lead to different label distributions for 13C‐tracer experiments. To investigate the actual impact of the new reaction structure on the estimated flux patterns within a cell, mass isotopomer measurements from a previously published 13C‐based metabolic flux analysis of Saccharomyces cerevisiae were used. Different flux patterns were found. From a genetic point of view, it is well known that several micro‐organisms, including Escherichia coli and S. cerevisiae, contain multiple genes encoding isoenzymes of transketolase and transaldolase. However, the extent to which these gene products are also actively expressed remains unknown. It is shown that the newly proposed stoichiometric model allows study of the effect of isoenzymes on the 13C‐label distribution in the nonoxidative branch of the pentose phosphate pathway by extending the half‐reaction based stoichiometric model with two distinct transketolase enzymes instead of one. Results show that the inclusion of isoenzymes affects the ensuing flux estimates.

A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

BackgroundDynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog) format have been proposed as a suitable alternative with fewer parameters.ResultsIn this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data generated with a dynamic model of the glycolytic pathway of Saccharomyces cerevisiae based on mechanistic rate equations. Elasticities are estimated from the generated data, which define the complete linlog kinetic model of the glycolysis. The effect of data noise on the accuracy of the estimated elasticities is presented. Finally, identifiable subset of parameters is determined using information on the standard deviations of the estimated elasticities through Monte Carlo (MC) simulations.ConclusionThe parameter estimation within the linlog kinetic framework as presented here allows the determination of the elasticities directly from experimental data from typical dynamic and/or steady state experiments. These elasticities allow the reconstruction of the full kinetic model of Saccharomyces cerevisiae, and the determination of the control coefficients. MC simulations revealed that certain elasticities are potentially unidentifiable from dynamic data only. Addition of steady state perturbation of enzyme activities solved this problem.

13C-Labeled Gluconate Tracing as a Direct and Accurate Method for Determining the Pentose Phosphate Pathway Split Ratio in Penicillium chrysogenum

ABSTRACT In this study we developed a new method for accurately determining the pentose phosphate pathway (PPP) split ratio, an important metabolic parameter in the primary metabolism of a cell. This method is based on simultaneous feeding of unlabeled glucose and trace amounts of [U-13C]gluconate, followed by measurement of the mass isotopomers of the intracellular metabolites surrounding the 6-phosphogluconate node. The gluconate tracer method was used with a penicillin G-producing chemostat culture of the filamentous fungus Penicillium chrysogenum. For comparison, a 13C-labeling-based metabolic flux analysis (MFA) was performed for glycolysis and the PPP of P. chrysogenum. For the first time mass isotopomer measurements of 13C-labeled primary metabolites are reported for P. chrysogenum and used for a 13C-based MFA. Estimation of the PPP split ratio of P. chrysogenum at a growth rate of 0.02 h−1 yielded comparable values for the gluconate tracer method and the 13C-based MFA method, 51.8% and 51.1%, respectively. A sensitivity analysis of the estimated PPP split ratios showed that the 95% confidence interval was almost threefold smaller for the gluconate tracer method than for the 13C-based MFA method (40.0 to 63.5% and 46.0 to 56.5%, respectively). From these results we concluded that the gluconate tracer method permits accurate determination of the PPP split ratio but provides no information about the remaining cellular metabolism, while the 13C-based MFA method permits estimation of multiple fluxes but provides a less accurate estimate of the PPP split ratio.

Isotopic non-stationary 13C gluconate tracer method for accurate determination of the pentose phosphate pathway split-ratio in Penicillium chrysogenum.

Current (13)C labeling experiments for metabolic flux analysis (MFA) are mostly limited by either the requirement of isotopic steady state or the extremely high computational effort due to the size and complexity of large metabolic networks. The presented novel approach circumvents these limitations by applying the isotopic non-stationary approach to a local metabolic network. The procedure is demonstrated in a study of the pentose phosphate pathway (PPP) split-ratio of Penicillium chrysogenum in a penicillin-G producing chemostat-culture grown aerobically at a dilution rate of 0.06h(-1) on glucose, using a tracer amount of uniformly labeled [U-(13)C(6)] gluconate. The rate of labeling inflow can be controlled by using different cell densities and/or different fractions of the labeled tracer in the feed. Due to the simplicity of the local metabolic network structure around the 6-phosphogluconate (6pg) node, only three metabolites need to be measured for the pool size and isotopomer distribution. Furthermore, the mathematical modeling of isotopomer distributions for the flux estimation has been reduced from large scale differential equations to algebraic equations. Under the studied cultivation condition, the estimated split-ratio (41.2+/-0.6%) using the novel approach, shows statistically no difference with the split-ratio obtained from the originally proposed isotopic stationary gluconate tracing method.

Proliferating bloodstream-form Trypanosoma brucei use a negligible part of consumed glucose for anabolic processes.

Our quantitative knowledge of carbon fluxes in the long slender bloodstream form (BSF) Trypanosoma brucei is mainly based on non-proliferating parasites, isolated from laboratory animals and kept in buffers. In this paper we present a carbon balance for exponentially growing bloodstream form trypanosomes. The cells grew with a doubling time of 5.3h, contained 46 μ mol of carbon (10(8) cells)(-1) and had a glucose consumption flux of 160 nmol min(-1) (10(8) cells)(-1). The molar ratio of pyruvate excreted versus glucose consumed was 2.1. Furthermore, analysis of the (13)C label distribution in pyruvate in (13)C-glucose incubations of exponentially growing trypanosomes showed that glucose was the sole substrate for pyruvate production. We conclude that the glucose metabolised in glycolysis was hardly, if at all, used for biosynthetic processes. Carbon flux through glycolysis in exponentially growing trypanosomes was 10 times higher than the incorporation of carbon into biomass. This biosynthetic carbon is derived from other precursors present in the nutrient rich growth medium. Furthermore, we found that the glycolytic flux was unaltered when the culture went into stationary phase, suggesting that most of the ATP produced in glycolysis is used for processes other than growth.

Metabolic Flux Analysis, Modeling, and Engineering Solutions

Microorganisms have been used for many decades to produce valuable chemicals for the food, pharmaceutical, and bulk industries. These include amino acids, vitamins, antibiotics, alcohols, and organic acids. Random classical mutation techniques have led to improvements in production properties.