Abstract

Key distinguishing characteristics of yeast glucose metabolism are the relative proportions of fermentation and respiration. Crabtree-positive yeast species exhibit a respirofermentative metabolism, whereas aerobic species respire fully without secretion of fermentation byproducts. Physiological data suggest a gradual transition in different species between these two states. Here, we investigate whether this gradual transition also occurs at the intracellular level by quantifying the intracellular metabolism of Saccharomyces cerevisiae, Saccharomyces bayanus, Saccharomyces exiguus, Kluyveromyces thermotolerans, Yarrowia lipolytica, Pichia angusta and Candida rugosa by 13C-flux analysis and metabolomics. Different from the extracellular physiology, the intracellular fluxes through the tricarboxylic acid cycle fall into two classes where the aerobic species exhibit much higher respiratory fluxes at otherwise similar glycolytic fluxes. More generally, we found the intracellular metabolite concentrations to be primarily species-specific. The sole exception of a metabolite-flux correlation in a species-overarching manner was found for fructose-1,6-bisphosphate and dihydroxyacetone-phosphate, indicating a conservation of the functional properties around these two metabolites.

Introduction

Although the reaction network topology of the central carbon metabolism is conserved among different yeasts, magnitude and distribution of flux through these pathways varies among them (Flores., 2000; Fiaux., 2003; Cannizzaro., 2004; Blank., 2005). This is particularly true for aerobic catabolism of glucose, the preferred carbon source for yeast and many other organisms. Aerobic glucose catabolism is classified into the groups of fermentative, respirofermentative, respiratory and obligate aerobic metabolism (van Dijken., 1993; Pronk., 1996). Respirofermentative yeasts, such as Saccharomyces cerevisiae, are characterized by the occurrence of the long-term Crabtree effect (Zimmermann & Entian, 1997); i.e. they exhibit high glucose uptake rates that are routed to ethanol under aerobic, glucose excess conditions. In contrast, respiratory and obligate respiratory yeasts, summarized as aerobic yeasts, exhibit low glucose uptake rates that are fully channelled into respiration without secretion of fermentation byproducts.

The metabolic trait of Crabtree-positive metabolism evolved to a large extent by horizontal gene transfer and whole genome duplication, through which metabolism acquired the ability for anaerobic growth, including high glycolytic and fermentation rates (Seoighe & Wolfe, 1999; Piskur & Langkjaer, 2004; Conant & Wolfe, 2007; Merico., 2007; van Hoek & Hogeweg, 2009). In present-day yeasts, metabolism is subject to additional levels of regulation. These include glucose repression of the tricarboxylic acid (TCA) cycle and respiration (De Deken, 1966; Gancedo, 2008) and overflow metabolism at the pyruvate decarboxylase branch point due to its high capacity and limited flux through pyruvate dehydrogenase (van Urk., 1989; Pronk., 1996). Additionally different metabolites with regulatory roles affect respiration and other cellular functions. This regulation might occur either directly, via the redox or energy state of the cell, or via glucose signalling (Muller., 1995; Tisi., 2002; Vemuri., 2007; Diaz-Ruiz., 2008; Gancedo, 2008).

Here, we ask whether the evolution of metabolism and its regulation led to a gradual transition between Crabtree-positive and aerobic steady-state metabolism in modern yeasts. Physiological studies of S. cerevisiae and other yeasts revealed a gradual transition from respiratory to fermentative metabolism with increasing glucose uptake rates (van Urk., 1989; Blank., 2005), which would suggest the existence of intermediate metabolic states. At present, it remains unclear whether the intracellular distribution of fluxes reflects such a gradual transition between respirofermentative and fully respiratory, aerobic states. Additionally, we ask whether absolute metabolite concentrations are species-specific or rather correlate with fluxes in a species-overarching manner, as one might conclude from the good flux correlations of glycolytic intermediate concentrations in S. cerevisiae under different conditions (Elbing., 2004; Tai., 2007; Bosch., 2008; Kresnowati., 2008a, b). Furthermore, it was shown qualitatively that the metabolite levels in different organisms respond similarly to environmental perturbations (Brauer., 2006). Whether or not such circumstantial observations are more generally true is investigated here.

To address these questions, we chose seven yeast species, including S. cerevisiae, the biotechnologically relevant Yarrowia lipolytica (Gellissen., 2005; Vakhlu & Kour, 2006; Abbott., 2009; Beopoulos., 2009), and the less-characterized biotechnologically emerging Candida rugosa (Dominguez de Maria., 2006; Lee & Park, 2009). The other four species were chosen for a gradual coverage of extracellular rates between the Crabtree-positive S. cerevisiae and the obligate aerobic Y. lipolytica (Blank., 2005; Merico., 2007). Specifically, we quantified their glucose excess metabolism in batch culture by 13C flux analysis (Sauer, 2006) and metabolomics (Roessner & Bowne, 2009).

Materials and methods

Strain medium and cultivation conditions

The investigated yeasts are listed in Table 1. All liquid cultivations were carried out in minimal medium with 10 g L−1 glucose (Blank & Sauer, 2004). After precultivation overnight in glucose minimal medium, 25–50 mL cultures were inoculated to a starting OD600 nm of about 0.05 and grown in 500-mL shake flasks at 30 °C and 250 r.p.m. Aliquots were withdrawn during the exponential growth phase on glucose. For flux analysis experiments, natural abundance glucose was replaced by either 100% of the 1-13C glucose or a mixture of 20% of the U-13C isotopologue and 80% natural abundance glucose (13C-enrichment ≥99%, Cambridge Usotope Laboratories, Andover).

1

Yeast species used in this study

Species Genotype Source 
S. cerevisiae FY4 Mata – wild type Winston. (1995) 
S. bayanus var. uvarum Wild type CLIB 251 
S. exiguous Wild type CLIB 179 
K. thermotolerans Wild type CLIB 292 
Y. lipolytica Wild type H222 
P. angusta Wild type CLIB 421 
C. rugosa Wild type IFO 0750 
Species Genotype Source 
S. cerevisiae FY4 Mata – wild type Winston. (1995) 
S. bayanus var. uvarum Wild type CLIB 251 
S. exiguous Wild type CLIB 179 
K. thermotolerans Wild type CLIB 292 
Y. lipolytica Wild type H222 
P. angusta Wild type CLIB 421 
C. rugosa Wild type IFO 0750 
*

CLIB, Collection de levures d'intérêt biotechnologique (http://www.inra.fr/Internet/Produits/clib/).

Biomass and extracellular metabolite concentrations

Biomass concentrations were determined by recording OD600 nm with a spectrophotometer (Novaspec II, Pharmacia Biotech, Uppsala, Sweden). For each species, we determined mass to OD600 nm conversion factors by determining cellular dry weight from 5–10 mL filtrate with predried and preweighed membranes (0.45 μM, Sartorius, Goettingen, DE), followed by three wash steps with 4 °C ddH2O. These membranes were dried overnight at 85 °C and the weight difference was measured.

Extracellular metabolite concentrations were determined with an HPX-87C Aminex, ion-exclusion column (Biorad, Munich, Germany) as described in Heer & Sauer (2008) on an HPLC HP1100 system (Agilent Technologies, Santa Clara). The column temperature was 60 °C and a flow rate of 0.6 mL min−1 of 5 mM H2SO4 as the eluant was used.

Biomass yields were obtained from a linear fit of substrate or byproduct concentrations during exponential growth as a function of corresponding biomass concentrations. Multiplication with the growth rate then yielded specific glucose uptake and byproduct secretion rates. The physiological parameters were determined from at least two independent biological replicates.

13 C-flux analysis

For labelling experiments, at least two replicate cultures were inoculated to an OD600 nm of 0.05 or less. During sampling, 1 mL of culture was harvested during exponential growth followed by two wash steps with ddH2O and stored at −20 °C for further analysis. The processing for GC-MS analysis was performed as described previously (Zamboni., 2009). The pellets were hydrolyzed with 6 M HCl overnight at 105 °C and then dried at 95 °C under a constant air stream. We dissolved the hydrolysates in 30 μL of the solvent DMF (Sigma-Aldrich, Buchs, Switzerland) and added 30 μL of the derivatization agent N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide with 1% tert-butyldimethyl-chlorosilane (Sigma-Aldrich). Upon incubation at 85 °C for 1 h, mass isotopomer distributions of the protein-bound amino acids were determined on a 6890N GC system (Agilent Technologies) combined with a 5875 Inert XL MS system (Agilent Technologies).

The mass isotopomer distribution of the amino acid fragments was corrected for the amount of naturally occurring stable isotopes and unlabeled biomass. From the corrected mass isotopomer distribution of the amino acids, ratios of converging fluxes were calculated with the analytical equations described in Blank & Sauer (2004) and Zamboni. (2005). These ratios together with the extracellular fluxes and a general stoichiometric network were then used as constraints for the netto subprogram of the fiatflux software to iteratively identify an absolute flux solution that best described the data (Zamboni., 2005, 2009).

Intracellular metabolite concentrations

For rapid quenching of metabolism (Buscher., 2009; Ewald., 2009), a 1-mL culture aliquot was transferred to 4 mL of −40 °C 60% methanol and 10 mM ammonium acetate (pH 7.5) within 10 s. The quenching was followed by 3 min of centrifugation with a swing-out rotor at 4500 g and −9 °C (Centrifuge 5804R, Eppendorf, Germany). Pellets were stored at −80 °C until extraction. The extraction was performed at 80 °C in 75% boiling ethanol and 10 mM ammonium acetate (pH 7.5). At this step, 100 μL of fully labelled 13C-biomass was added as an internal standard (Wu., 2005). The extracts were dried using a vacuum centrifuge (Christ-RVC 2–33 CD plus, Kuehner AG, Birsfeld, Switzerland). The dried extracts were dissolved in 50–100 μL ddH2O before being separated by ion pairing-reverse phase liquid chromatography coupled to a ultrahigh-performance system.

We used a Waters Acquity UPLC (Waters Corporation, Milford, MA) with a Waters Acquity T3 end-capped reverse-phase column with dimensions 150 mm × 2.1 mm × 1.8 μm (Waters Corporation) for metabolite separation as described in detail in Buscher. (2009). The chromatography was coupled to a Thermo TSQ Quantum Ultra triple quadrupole mass spectrometer (Thermo Fisher Scientific, Waltham, MA) with a heated electrospray ionization source (Thermo Fisher Scientific) in negative mode with multiple reaction monitoring. Acquisition and peak integration was performed with an in-house software (B. Begemann & N. Zamboni, unpublished data) and the peak areas were further normalized to fully 13C-labeled internal standards and the amount of biomass.

The metabolome of all species was measured with at least four separately quenched replicates. To exclude artifacts, we performed an outlier detection on the raw data. With four data-points, the outlier detection discards the data point furthest away from the mean. With more data points, we calculated the 0.2 and 0.8 quantiles. From these data points, we determined the mean and the SD. The points being higher/lower than the average ±2 times the SD were discarded.

Results

Physiology and growth

To verify the presence and to quantify the degree of the respirofermentative metabolism in the seven selected yeast species, we characterized their physiology by determining specific growth, uptake and secretion rates in aerobic batch cultures with 10 g L−1 glucose (Table 2). The seven species covered a wide range of glucose uptake and ethanol secretion rates. As expected, the Crabtree-positive species S. cerevisiae, Saccharomyces bayanus, Saccharomyces exiguus and Kluyveromyces thermotolerans exhibit high glucose uptake rates between 6.3 and 15.4 mmol g−1 h−1, coupled with ethanol secretion and to a minor extent with acetate and glycerol secretion. The aerobic species Pichia angusta and Y. lipolytica exhibited much lower glucose uptake rates, ranging from 2.8 to 4.4 mmol g−1 h−1, without detectable secretion of any metabolic byproducts. Consequently, the aerobic species grew more efficiently with a yield of at least 0.5 gbiomass gglucose−1, whereas the Crabtree-positive species achieved yields between 0.13 and 0.27 gbiomass gglucose−1 during exponential growth on glucose. Despite their slower overall metabolism, the aerobic species P. angusta, Y. lipolytica and C. rugosa grew faster than the Crabtree-positive species.

2

Growth parameters of the different yeast species in glucose minimal medium batch

Species Growth rate (h−1Glucose uptake (mmol g−1 h−1Glycerol secretion (mmol g−1 h−1Acetate secretion (mmol g−1 h−1Ethanol secretion (mmol g−1 h−1Yield (g g−1
S. cerevisiae 0.35 ± 0.01 15.4 ± 1.1 1.26 ± 0.15 0.69 ± 0.16 22.2 ± 6.8 0.13 ± 0.01 
S. bayanus var uvarum 0.22 ± 0.04 8.8 ± 2.1 0.5 ± 0.1 0.04 ± 0.02 12 ± 2.4 0.13 ± 0.03 
S. exiguus 0.28 ± 0.04 7.2 ± 0.1 0.3 ± 0.02 0.37 ± 0.05 10.2 ± 1 0.18 ± 0.02 
K. thermotolerans 0.30 ± 0.05 6.3 ± 1.1 0.1 ± 0.02 0.41 ± 0.18 6.2 ± 0.2 0.27 ± 0.04 
Y. lipolytica 0.46 ± 0.03 4.2 ± 1.2 0.51 ± 0.02 
P. angusta 0.42 ± 0.05 4.4 ± 0.8 0.52 ± 0.08 
C. rugosa 0.45 ± 0.09 2.8 ± 0.4 0.79 ± 0.02 
Species Growth rate (h−1Glucose uptake (mmol g−1 h−1Glycerol secretion (mmol g−1 h−1Acetate secretion (mmol g−1 h−1Ethanol secretion (mmol g−1 h−1Yield (g g−1
S. cerevisiae 0.35 ± 0.01 15.4 ± 1.1 1.26 ± 0.15 0.69 ± 0.16 22.2 ± 6.8 0.13 ± 0.01 
S. bayanus var uvarum 0.22 ± 0.04 8.8 ± 2.1 0.5 ± 0.1 0.04 ± 0.02 12 ± 2.4 0.13 ± 0.03 
S. exiguus 0.28 ± 0.04 7.2 ± 0.1 0.3 ± 0.02 0.37 ± 0.05 10.2 ± 1 0.18 ± 0.02 
K. thermotolerans 0.30 ± 0.05 6.3 ± 1.1 0.1 ± 0.02 0.41 ± 0.18 6.2 ± 0.2 0.27 ± 0.04 
Y. lipolytica 0.46 ± 0.03 4.2 ± 1.2 0.51 ± 0.02 
P. angusta 0.42 ± 0.05 4.4 ± 0.8 0.52 ± 0.08 
C. rugosa 0.45 ± 0.09 2.8 ± 0.4 0.79 ± 0.02 

Intracellular flux distribution

Given the differences in macroscopic physiological rates, we ask whether the intracellular distribution of fluxes relied on the same pathways within each group of aerobic glucose metabolism. For this purpose, separate isotopic tracer experiments with [U-13C]- and [1-13C]-labelled glucose were performed in at least duplicate batch cultures for each case. Specifically, we determined the mass isotopomer distributions of proteinogenic amino acids to calculate ratios of converging fluxes at key branch points in central metabolism (Blank., 2005; Zamboni., 2009). The largest variability between species was in the pentose phosphate pathway and the TCA cycle, indicated by large differences in the split ratios serine derived through glycolysis and mitochondrial oxaloacetate originating from anaplerosis, respectively (Fig. 1a).

1

Flux distribution in the seven yeast species on glucose minimal medium. (a) Ratios of converging fluxes at branch points of the central carbon metabolism. The ratios are given by the division of the flux indicated by the solid line over the flux indicated by the dotted line. (b) The quantitative flux distribution of Crabtree-positive (left) and aerobic (right) yeasts in mmol g−1 h−1. The thickness of the arrows indicates the glucose uptake normalized flux distribution with a variability that is depicted in grey.

1

Flux distribution in the seven yeast species on glucose minimal medium. (a) Ratios of converging fluxes at branch points of the central carbon metabolism. The ratios are given by the division of the flux indicated by the solid line over the flux indicated by the dotted line. (b) The quantitative flux distribution of Crabtree-positive (left) and aerobic (right) yeasts in mmol g−1 h−1. The thickness of the arrows indicates the glucose uptake normalized flux distribution with a variability that is depicted in grey.

For a more detailed insight, we estimated network-wide absolute fluxes using these intracellular flux ratios (Fig. 1a) and secretion rates (Table 2) as input values for net flux analysis using the fiatflux software (Zamboni., 2005, 2009). Specifically, this method estimates the best flux distribution by iteratively fitting intracellular fluxes to the experimental data within a model of yeast metabolism (Supporting Information, Appendix S1). These absolute flux values clearly differed between Crabtree-positive and -negative metabolism (Fig. 1b). In the Crabtree-positive species, glucose is almost exclusively catabolized through glycolysis and finally to the fermentative product ethanol, while the respiratory TCA cycle flux was negligible. This single-pathway metabolism was essentially independent of the overall flux rate that increased about threefold from K. thermotolerans to S. cerevisiae (Fig. 1b). In aerobic species, in contrast, the pentose phosphate pathway and TCA cycle became major pathways that equal the glycolytic flux. However, because the overall flux was much lower in the aerobic species, the pentose and NADPH-producing pentose phosphate pathway remained largely constant in all species at 1–1.7 mmol g−1 h−1, and contributed between 50% and 80% to the overall NADPH required for biomass production. Because we considered only the NAD-dependent isoform of the isocitrate dehydrogenase to be active, we could not further distinguish the isocitrate dehydrogenase or acetaldehyde dehydrogenase contribution to NADPH regeneration.

To elucidate whether there was a gradual shift between respiratory and fermentative metabolism, we correlated key fluxes from all species. We also included previously published S. cerevisiae fluxes during growth on the respiratory substrates galactose and pyruvate (Fendt & Sauer, 2010). On these substrates, the glycolytic flux was low and the TCA cycle flux high, representing an in-between state of metabolism. Consistent with their role as the key pathways, substrate uptake rate and yield correlated with both glycolytic flux and ethanol secretion, independently of whether respiratory metabolism was triggered genetically or environmentally (Fig. 2a). Including the data of S. cerevisiae on galactose and pyruvate further underlines the key role of the pentose phosphate pathway in supplying the anabolic cofactor NADPH for biomass formation in yeasts (Fig. 2b), which differs from its primarily catabolic role in some bacteria (Fischer & Sauer, 2005). These monotonous changes in various fluxes across genetic and environmental changes suggest similar underlying regulatory mechanisms. A similarly good correlation was found between the substrate uptake rate or glycolytic flux with the respiratory TCA cycle flux among all Crabtree-positive yeast on glucose and S. cerevisiae on respiratory substrates. The aerobic yeasts, however, fall completely out of these correlations; i.e. they exhibited several-fold higher TCA cycle fluxes than would be expected for the same glycolytic flux in galactose-grown S. cerevisieae (Fig. 2c). This suggests a different mode of regulation in the aerobic species relative to the environmentally triggered regulation of respiratory metabolism in S. cerevisiae. Generally, the increased TCA cycle flux of the Crabtree-positive yeasts is a function of higher biomass yield as their metabolism becomes more efficient with reduced ethanol secretion. In aerobic species, in contrast, the biomass yield is not increased any further.

2

Inter-relationship between fluxes of the central carbon metabolism in Crabtree-positive species (●), aerobic species (○) and Saccharomyces cerevisiae under galactose and pyruvate conditions (+). The fitted values from the net flux analysis were used. (a) Correlations to glucose uptake and glycolysis. (b) Correlations to growth rate. (c) Correlations to the TCA cycle flux.

2

Inter-relationship between fluxes of the central carbon metabolism in Crabtree-positive species (●), aerobic species (○) and Saccharomyces cerevisiae under galactose and pyruvate conditions (+). The fitted values from the net flux analysis were used. (a) Correlations to glucose uptake and glycolysis. (b) Correlations to growth rate. (c) Correlations to the TCA cycle flux.

Relations between fluxes and metabolites

Finally, we asked whether intracellular metabolite concentrations are species-specific or generally related to the flux through their pathway. For this purpose, we quantified 26 intermediates of the central metabolism plus 10 amino acids by LC-MS/MS (Buscher., 2009) from mid-exponential growing aerobic batch cultures of the seven species (Fig. 3). Generally our metabolite concentrations were in good agreement with published data (Bolten & Wittmann, 2008; Canelas., 2009; Fendt., 2010). The sole exception was a systematically higher AMP concentration in the Crabtree-positive strains that resulted in a slightly lower energy charge of about 0.7 compared with 0.9 in the other yeasts (Wiebe & Bancroft, 1975). Our data do not differentiate whether this difference was biological or a putative artifact from imperfect quenching by cold methanol. None of the species featured systematically lower or higher concentrations than the others, indicating a comparable quality of extraction by the hot ethanol procedure used. Generally, the intracellular pool of amino acids accounted for about 90% of all determined metabolite concentrations, with glutamate having concentrations up to 150 nmol mg−1 (Fig. 4). In Y. lipolytica and C. rugosa, glutamate was the dominating metabolite, accounting for about 50% of the total determined metabolite pool. In the two Saccharomyces species S. bayanus and S. cerevisiae, three amino acids each contributed about one-quarter to the total pool; i.e. glutamate, glutamine and alanine or arginine (Fig. 4). All other concentrations varied significantly between species at much lower concentrations between 0.02 and about 10 nmol mg−1 (Fig. 4).

3

Intracellular metabolite concentrations of the seven yeast species. CR, Candida rugosa; PA, Pichia angusta; YL, Yarrowia lipolytica; KT, Kluyveromyces thermotolerans; SE, Saccharomyces exiguus; SB, Saccharomyces bayanus; SC, Saccharomyces cerevisiae. The species are sorted according to their glucose uptake rate, and hence the Crabtree effect. The concentrations are given in nmol mg−1 or arbitrary units (AU).

3

Intracellular metabolite concentrations of the seven yeast species. CR, Candida rugosa; PA, Pichia angusta; YL, Yarrowia lipolytica; KT, Kluyveromyces thermotolerans; SE, Saccharomyces exiguus; SB, Saccharomyces bayanus; SC, Saccharomyces cerevisiae. The species are sorted according to their glucose uptake rate, and hence the Crabtree effect. The concentrations are given in nmol mg−1 or arbitrary units (AU).

4

Fractionation of the measured metabolome into the different metabolites. The amino acids make up 90% of the total metabolite concentrations measured. Nevertheless, the portioning of this pool between the different amino acids is quite diverse.

4

Fractionation of the measured metabolome into the different metabolites. The amino acids make up 90% of the total metabolite concentrations measured. Nevertheless, the portioning of this pool between the different amino acids is quite diverse.

To assess the relationship of intracellular metabolite concentrations with fluxes or species, we correlated the determined pathway fluxes with all metabolite concentrations (Fig. 5). There was clearly no general correlation between metabolite levels and fluxes in a species-overarching manner; hence, metabolite levels are rather species-specific. The exceptions were the good correlations of fructose-1,6-bisphosphate and dihydroxyacetone phosphate with glycolysis and ethanol secretion (R2>0.9; P<10−9). Because both metabolites span a relatively large range of concentrations of about one order of magnitude in either case, they could potentially function as general glycolytic flux indicators, a mechanism that then would be conserved across species.

5

Correlation between fluxes and metabolites in the seven yeast species. In the heat map, the correlation coefficients (R2) between all metabolites (left) and the representative fluxes (top) are illustrated. Fructose-1,6-bisphosphate and dihydroxyacetone-phosphate showed a high correlation with glycolysis and ethanol secretion in a species-overarching manner (right).

5

Correlation between fluxes and metabolites in the seven yeast species. In the heat map, the correlation coefficients (R2) between all metabolites (left) and the representative fluxes (top) are illustrated. Fructose-1,6-bisphosphate and dihydroxyacetone-phosphate showed a high correlation with glycolysis and ethanol secretion in a species-overarching manner (right).

Discussion

We quantified metabolic fluxes in seven yeast species with different strengths of the Crabtree effect by 13C flux analysis and metabolomics. For six species, the physiological metabolic states of aerobic glucose metabolism were in agreement with published data (Blank., 2005; Merico., 2007; Fendt & Sauer, 2010). As expected, decreasing glucose uptake rates concur with gradually decreasing ethanol secretion until complete absence of fermentation. Despite this gradual physiological transition, the corresponding intracellular flux distributions fall into two clearly different modes of Crabtree-positive and aerobic metabolism. Specifically, the aerobic species featured about threefold higher respiratory TCA cycle fluxes than respiring Crabtree-positive S. cerevisiae at otherwise equal rates of overall metabolism on galactose and pyruvate. This difference in respiratory rates at equal glycolytic flux are potentially explained by the stronger repression of the TCA cycle and respiration, with concomitant activation of overflow metabolism at the pyruvate decarboxylase in the Crabtree-positive species (Pronk., 1996; Gancedo, 2008; Zaman., 2008). In this case, the fermentation route is the only possibility to reoxidize NADH, hence to maintain redox homeostasis. For the aerobic species Kluyveromyces lactis and Pichia stipitis, in contrast, it was shown that glucose repression of respiratory genes is absent and overflow metabolism at the pyruvate decarboxylase branchpoint is negatively regulated in an oxygen-dependent manner (Passoth., 1996; Kiers., 1998). Thus, aerobic species channel their glycolytic flux into the TCA cycle for complete oxidation and NAD+ regeneration via respiration (Snoek & Steensma, 2007). Interestingly, aerobic species rely primarily on energy-dependent glucose transport (van Urk., 1989; van Dijken., 1993), which might further require an energy-efficient mode of metabolism.

Previous studies with S. cerevisiae revealed glycolytic intermediate concentrations to vary with differentially repressive substrates and thus with glycolytic fluxes (Elbing., 2004; Bosch., 2008). If this was indeed a general feature of glycolysis, one would expect a correlation between flux and the respective pathway intermediate concentrations across all species. Here, we show that intracellular metabolites do not generally correlate with flux and are therefore species-specific. Most strikingly, up to 90% of the total metabolite pool was made up of amino acids. Different from Escherichia coli (Bennett., 2009), the total amino acid pool in yeast species was not only dominated by glutamate but rather by a combination of glutamate, glutamine, arginine, alanine and aspartate. The large variability of other metabolite concentrations was not correlated with fluxes, which indicates diverse kinetic properties and expression levels of enzymes in the different yeast species. The exceptions were the good correlations between fructose-1,6-bisphosphate and dihydroxyacetone-phosphate concentrations with glycolytic flux, which indicates that the functional properties of enzymes around these two metabolites are conserved among the different species. Because fructose-1,6-bisphosphate inhibition of the respiratory complex III in Crabtree-positive species was proposed earlier (Diaz-Ruiz., 2008), it is tempting to speculate that this inhibition causes the negative correlation between the TCA cycle and the glycolytic flux. The almost equally good correlation of dihydroxyacetone-phosphate might then be explained by the near equilibrium with fructose-1,6-bisphosphate via aldolase.

Acknowledgements

We thank the Degussa AG and the Swiss initiative for systems biology (SystemsX.ch) project YeastX for financial support.

References

Abbott
DA
Zelle
RM
Pronk
JT
van Maris
AJ
(
2009
)
Metabolic engineering of Saccharomyces cerevisiae for production of carboxylic acids: current status and challenges
.
FEMS Yeast Res
 
9
:
1123
1136
.
Bennett
BD
Kimball
EH
Gao
M
Osterhout
R
Van Dien
SJ
Rabinowitz
JD
(
2009
)
Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli
.
Nat Chem Biol
 
5
:
593
599
.
Beopoulos
A
Cescut
J
Haddouche
R
Uribelarrea
JL
Molina-Jouve
C
Nicaud
JM
(
2009
)
Yarrowia lipolytica as a model for bio-oil production
.
Prog Lipid Res
 
48
:
375
387
.
Blank
LM
Sauer
U
(
2004
)
TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined specific growth and glucose uptake rates
.
Microbiology
 
150
:
1085
1093
.
Blank
LM
Lehmbeck
F
Sauer
U
(
2005
)
Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts
.
FEMS Yeast Res
 
5
:
545
558
.
Bolten
CJ
Wittmann
C
(
2008
)
Appropriate sampling for intracellular amino acid analysis in five phylogenetically different yeasts
.
Biotechnol Lett
 
30
:
1993
2000
.
Bosch
D
Johansson
M
Ferndahl
C
Franzen
CJ
Larsson
C
Gustafsson
L
(
2008
)
Characterization of glucose transport mutants of Saccharomyces cerevisiae during a nutritional upshift reveals a correlation between metabolite levels and glycolytic flux
.
FEMS Yeast Res
 
8
:
10
25
.
Brauer
MJ
Yuan
J
Bennett
BD
Lu
WY
Kimball
E
Botstein
D
Rabinowitz
JD
(
2006
)
Conservation of the metabolomic response to starvation across two divergent microbes
.
P Natl Acad Sci USA
 
103
:
19302
19307
.
Buscher
JM
Czernik
D
Ewald
JC
Sauer
U
Zamboni
N
(
2009
)
Cross-platform comparison of methods for quantitative metabolomics of primary metabolism
.
Anal Chem
 
81
:
2135
2143
.
Canelas
AB
ten Pierick
A
Ras
C
Seifar
RM
van Dam
JC
van Gulik
WM
Heijnen
JJ
(
2009
)
Quantitative evaluation of intracellular metabolite extraction techniques for yeast metabolomics
.
Anal Chem
 
81
:
7379
7389
.
Cannizzaro
C
Christensen
B
Nielsen
J
von Stockar
U
(
2004
)
Metabolic network analysis on Phaffia rhodozyma yeast using C-13-labeled glucose and gas chromatography-mass spectrometry
.
Metab Eng
 
6
:
340
351
.
Conant
GC
Wolfe
KH
(
2007
)
Increased glycolytic flux as an outcome of whole-genome duplication in yeast
.
Mol Syst Biol
 
3
:
129
.
De Deken
RH
(
1966
)
The Crabtree effect: a regulatory system in yeast
.
J Gen Microbiol
 
44
:
149
156
.
Diaz-Ruiz
R
Averet
N
Araiza
D
Pinson
B
Uribe-Carvajal
S
Devin
A
Rigoulet
M
(
2008
)
Mitochondrial oxidative phosphorylation is regulated by fructose 1,6-bisphosphate. A possible role in Crabtree effect induction?
J Biol Chem
 
283
:
26948
26955
.
Dominguez de Maria
P
Sanchez-Montero
JM
Sinisterra
JV
Alcantara
AR
(
2006
)
Understanding Candida rugosa lipases: an overview
.
Biotechnol Adv
 
24
:
180
196
.
Elbing
K
Larsson
C
Bill
RM
Albers
E
Snoep
JL
Boles
E
Hohmann
S
Gustafsson
L
(
2004
)
Role of hexose transport in control of glycolytic flux in Saccharomyces cerevisiae
.
Appl Environ Microb
 
70
:
5323
5330
.
Ewald
JC
Heux
S
Zamboni
N
(
2009
)
High-throughput quantitative metabolomics: workflow for cultivation, quenching, and analysis of yeast in a multiwell format
.
Anal Chem
 
81
:
3623
3629
.
Fendt
SM
Sauer
U
(
2010
)
Transcriptional regulation of respiration in yeast metabolizing differently repressive carbon substrates
.
BMC Syst Biol
 
4
:
12
.
Fendt
SM
Buescher
JM
Rudroff
F
Picotti
P
Zamboni
N
Sauer
U
(
2010
)
Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity
.
Mol Syst Biol
 
6
:
356
.
Fiaux
J
Cakar
ZP
Sonderegger
M
Wuthrich
K
Szyperski
T
Sauer
U
(
2003
)
Metabolic-flux profiling of the yeasts Saccharomyces cerevisiae and Pichia stipitis
.
Eukaryot Cell
 
2
:
170
180
.
Fischer
E
Sauer
U
(
2005
)
Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism
.
Nat Genet
 
37
:
636
640
.
Flores
CL
Rodriguez
C
Petit
T
Gancedo
C
(
2000
)
Carbohydrate and energy-yielding metabolism in non-conventional yeasts
.
FEMS Microbiol Rev
 
24
:
507
529
.
Gancedo
JM
(
2008
)
The early steps of glucose signalling in yeast
.
FEMS Microbiol Rev
 
32
:
673
704
.
Gellissen
G
Kunze
G
Gaillardin
C
Cregg
JM
Berardi
E
Veenhuis
M
van der Klei
I
(
2005
)
New yeast expression platforms based on methylotrophic Hansenula polymorpha and Pichia pastoris and on dimorphic Arxula adeninivorans and Yarrowia lipolytica– a comparison
.
FEMS Yeast Res
 
5
:
1079
1096
.
Heer
D
Sauer
U
(
2008
)
Identification of furfural as a key toxin in lignocellulosic hydrolysates and evolution of a tolerant yeast strain
.
Microb Biotechnol
 
1
:
497
506
.
Kiers
J
Zeeman
AM
Luttik
M
Thiele
C
Castrillo
JI
Steensma
HY
van Dijken
JP
Pronk
JT
(
1998
)
Regulation of alcoholic fermentation in batch and chemostat cultures of Kluyveromyces lactis CBS 2359
.
Yeast
 
14
:
459
469
.
Kresnowati
MT
van Winden
WA
van Gulik
WM
Heijnen
JJ
(
2008a
)
Dynamic in vivo metabolome response of Saccharomyces cerevisiae to a stepwise perturbation of the ATP requirement for benzoate export
.
Biotechnol Bioeng
 
99
:
421
441
.
Kresnowati
MT
van Winden
WA
van Gulik
WM
Heijnen
JJ
(
2008b
)
Energetic and metabolic transient response of Saccharomyces cerevisiae to benzoic acid
.
FEBS J
 
275
:
5527
5541
.
Lee
SH
Park
OJ
(
2009
)
Uses and production of chiral 3-hydroxy-gamma-butyrolactones and structurally related chemicals
.
Appl Microbiol Biot
 
84
:
817
828
.
Merico
A
Sulo
P
Piskur
J
Compagno
C
(
2007
)
Fermentative lifestyle in yeasts belonging to the Saccharomyces complex
.
FEBS J
 
274
:
976
989
.
Muller
S
Boles
E
May
M
Zimmermann
FK
(
1995
)
Different internal metabolites trigger the induction of glycolytic gene-expression in Saccharomyces cerevisiae
.
J Bacteriol
 
177
:
4517
4519
.
Passoth
V
Zimmermann
M
Klinner
U
(
1996
)
Peculiarities of the regulation of fermentation and respiration in the crabtree-negative, xylose-fermenting yeast Pichia stipitis
.
Appl Biochem Biotech
 
57–58
:
201
212
.
Piskur
J
Langkjaer
RB
(
2004
)
Yeast genome sequencing: the power of comparative genomics
.
Mol Microbiol
 
53
:
381
389
.
Pronk
JT
Yde Steensma
H
Van Dijken
JP
(
1996
)
Pyruvate metabolism in Saccharomyces cerevisiae
.
Yeast
 
12
:
1607
1633
.
Roessner
U
Bowne
J
(
2009
)
What is metabolomics all about?
Biotechniques
 
46
:
363
365
.
Sauer
U
(
2006
)
Metabolic networks in motion: 13C-based flux analysis
.
Mol Syst Biol
 
2
:
62
.
Seoighe
C
Wolfe
KH
(
1999
)
Yeast genome evolution in the post-genome era
.
Curr Opin Microbiol
 
2
:
548
554
.
Snoek
ISI
Steensma
HY
(
2007
)
Factors involved in anaerobic growth of Saccharomyces cerevisiae
.
Yeast
 
24
:
1
10
.
Tai
SL
Daran-Lapujade
P
Luttik
MA
Walsh
MC
Diderich
JA
Krijger
GC
van Gulik
WM
Pronk
JT
Daran
JM
(
2007
)
Control of the glycolytic flux in Saccharomyces cerevisiae grown at low temperature: a multi-level analysis in anaerobic chemostat cultures
. J Biol Chem
282
:
10243
10251
.
Tisi
R
Baldassa
S
Belotti
F
Martegani
E
(
2002
)
Phospholipase C is required for glucose-induced calcium influx in budding yeast
.
FEBS Lett
 
520
:
133
138
.
Vakhlu
J
Kour
A
(
2006
)
Yeast lipases: enzyme purification, biochemical properties and gene cloning
.
Elect Biotechnol
 
9
:
69
85
.
van Dijken
JP
Weusthuis
RA
Pronk
JT
(
1993
)
Kinetics of growth and sugar consumption in yeasts
.
Anton Leeuw Int J G
 
63
:
343
352
.
van Hoek
MJ
Hogeweg
P
(
2009
)
Metabolic adaptation after whole genome duplication
.
Mol Biol Evol
 
26
:
2441
2453
.
van Urk
H
Postma
E
Scheffers
WA
van Dijken
JP
(
1989
)
Glucose transport in crabtree-positive and crabtree-negative yeasts
.
J Gen Microbiol
 
135
:
2399
2406
.
Vemuri
GN
Eiteman
MA
McEwen
JE
Olsson
L
Nielsen
J
(
2007
)
Increasing NADH oxidation reduces overflow metabolism in Saccharomyces cerevisiae
.
P Natl Acad Sci USA
 
104
:
2402
2407
.
Wiebe
WJ
Bancroft
K
(
1975
)
Use of the adenylate energy charge ratio to measure growth state of natural microbial communities
.
P Natl Acad Sci USA
 
72
:
2112
2115
.
Winston
F
Dollard
C
Ricuperohovasse
SL
(
1995
)
Construction of a set of convenient Saccharomyces cerevisiae strains that are isogenic to S288C
.
Yeast
 
11
:
53
55
.
Wu
L
Mashego
MR
van Dam
JC
Proell
AM
Vinke
JL
Ras
C
van Winden
WA
van Gulik
WM
Heijnen
JJ
(
2005
)
Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards
.
Anal Biochem
 
336
:
164
171
.
Zaman
S
Lippman
SI
Zhao
X
Broach
JR
(
2008
)
How Saccharomyces responds to nutrients
.
Annu Rev Genet
 
42
:
27
81
.
Zamboni
N
Fischer
E
Sauer
U
(
2005
)
FiatFlux – a software for metabolic flux analysis from 13C-glucose experiments
.
BMC Bioinf
 
6
:
209
.
Zamboni
N
Fendt
SM
Ruhl
M
Sauer
U
(
2009
)
C-13-based metabolic flux analysis
.
Nat Protocol
 
4
:
878
892
.
Zimmermann
FK
Entian
K-D
(
1997
)
Yeast Sugar Metabolism: Biochemistry, Genetics, Biotechnology, and Applications
 .
Technomic Publishing Company Inc.
,
Basel
.

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