Engineering of Penicillium chrysogenum for fermentative production of a novel carbamoylated cephem antibiotic precursor.

Penicillium chrysogenum was successfully engineered to produce a novel carbamoylated cephalosporin that can be used as a synthon for semi-synthetic cephalosporins. To this end, genes for Acremonium chrysogenum expandase/hydroxylase and Streptomyces clavuligerus carbamoyltransferase were expressed in a penicillinG high-producing strain of P.chrysogenum. Growth of the engineered strain in the presence of adipic acid resulted in production of adipoyl-7-amino-3-carbamoyloxymethyl-3-cephem-4-carboxylic acid (ad7-ACCCA) and of several adipoylated pathway intermediates. A combinatorial chemostat-based transcriptome study, in which the ad7-ACCCA-producing strain and a strain lacking key genes in beta-lactam synthesis were grown in the presence and absence of adipic acid, enabled the dissection of transcriptional responses to adipic acid per se and to ad7-ACCCA production. Transcriptome analysis revealed that adipate catabolism in P.chrysogenum occurs via beta-oxidation and enabled the identification of putative genes for enzymes involved in mitochondrial and peroxisomal beta-oxidation pathways. Several of the genes that showed a specifically altered transcript level in ad7-ACCCA-producing cultures were previously implicated in oxidative stress responses.

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.

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

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.

Short-term metabolome dynamics and carbon, electron, and ATP balances in chemostat-grown Saccharomyces cerevisiae CEN.PK 113-7D following a glucose pulse.

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 O(2) uptake and CO(2) 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 CO(2) 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.

Metabolic engineering of the E. coli L-phenylalanine pathway for the production of D-phenylglycine (D-Phg).

D-phenylglycine (D-Phg) is an important side chain building block for semi-synthetic penicillins and cephalosporins such as ampicillin and cephalexin. To produce d-Phg ultimately from glucose, metabolic engineering was applied. Starting from phenylpyruvate, which is the direct precursor of L-phenylalanine, an artificial D-Phg biosynthesis pathway was created. This three-step route is composed of the enzymes hydroxymandelate synthase (HmaS), hydroxymandelate oxidase (Hmo), and the stereoinverting hydroxyphenylglycine aminotransferase (HpgAT). Together they catalyse the conversion of phenylpyruvate via mandelate and phenylglyoxylate to D-Phg. The corresponding genes were obtained from Amycolatopsis orientalis, Streptomyces coelicolor, and Pseudomonas putida. Combined expression of these activities in E. coli strains optimized for the production of L-phenylalanine resulted in the first completely fermentative production of D-Phg.

Physiological characterization of the ARO10-dependent, broad-substrate-specificity 2-oxo acid decarboxylase activity of Saccharomyces cerevisiae.

Aerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae CEN.PK113-7D were grown with different nitrogen sources. Cultures grown with phenylalanine, leucine, or methionine as a nitrogen source contained high levels of the corresponding fusel alcohols and organic acids, indicating activity of the Ehrlich pathway. Also, fusel alcohols derived from the other two amino acids were detected in the supernatant, suggesting the involvement of a common enzyme activity. Transcript level analysis revealed that among the five thiamine-pyrophospate-dependent decarboxylases (PDC1, PDC5, PDC6, ARO10, and THI3), only ARO10 was transcriptionally up-regulated when phenylalanine, leucine, or methionine was used as a nitrogen source compared to growth on ammonia, proline, and asparagine. Moreover, 2-oxo acid decarboxylase activity measured in cell extract from CEN.PK113-7D grown with phenylalanine, methionine, or leucine displayed similar broad-substrate 2-oxo acid decarboxylase activity. Constitutive expression of ARO10 in ethanol-limited chemostat cultures in a strain lacking the five thiamine-pyrophosphate-dependent decarboxylases, grown with ammonia as a nitrogen source, led to a measurable decarboxylase activity with phenylalanine-, leucine-, and methionine-derived 2-oxo acids. Moreover, even with ammonia as the nitrogen source, these cultures produced significant amounts of the corresponding fusel alcohols. Nonetheless, the constitutive expression of ARO10 in an isogenic wild-type strain grown in a glucose-limited chemostat with ammonia did not lead to any 2-oxo acid decarboxylase activity. Furthermore, even when ARO10 was constitutively expressed, growth with phenylalanine as the nitrogen source led to increased decarboxylase activities in cell extracts. The results reported here indicate the involvement of posttranscriptional regulation and/or a second protein in the ARO10-dependent, broad-substrate-specificity decarboxylase activity.

Serial 13C-based flux analysis of an L-phenylalanine-producing E. coli strain using the sensor reactor.

With the aid of the recently developed Sensor reactor system, a series of three subsequent (13)C labeling experiments was performed mirroring the l-phenylalanine (l-Phe) production phase of a recombinant E. coli strain that was cultivated under industry-like conditions in a 300 L bioreactor. On the basis of the data from NMR labeling analysis, three subsequent flux patterns were successfully derived monitoring the l-Phe formation during an observation window from 14 to 23.3 h process time. Linear programming was performed to identify optimal flux patterns for l-Phe formation. Additionally, flux sensitivity analysis was used to identify the most promising metabolic engineering target. As a result, high rates of phosphoenolpyruvate (PEP) to pyruvate (PYR) conversion were identified as the most important reason for deterioration of the l-Phe/glucose yield from 20 to finally 11 mol %. Considering the characteristics of the enzyme kinetics involved, the working hypothesis was formulated that phosphoenolpyruvate synthase activity was increasingly hampered by rising oxaloacetate and 2-oxoglutarate concentrations, while at the same time pyruvate kinase activity arose due to activation by fructose 1,6-diphosphate. Hence, pps overexpression should be performed to optimize the existing production strain.

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation.

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model.