INTRODUCTION

Top Abstract Introduction Materials and Methods Results and Discussion Appendix References

Since the whole genome of Saccharomyces cerevisiae has been sequenced (15), several kinds of analyses have been applied in order to assign function to orphan genes. In many of these analyses, the aim has been to determine how the different genes (both the ones with known function and those open reading frames that have no assigned function) interact with each other, enabling the cells to take up nutrients, grow, divide, regulate their metabolism, release products to the environment, and respond to different stimuli (31).

The different approaches that have been used for this purpose can be classified as transcriptome, proteome, and metabolome analyses, depending on the type of compounds measured, i.e., transcripts, proteins, or metabolites (31). Besides these approaches, other types of phenotypic investigations have been performed, such as growth on synthetic or rich media, growth on different carbon sources, determination of the ability to consume oxygen, and measurement of enzyme activities in cell extracts. In recent work, Entian and coworkers (9) showed that the type and accuracy of the methods used are very important for the identification of phenotypes in single-deletion mutants.

A very prominent phenotypic investigation method that has been employed for the analysis of cells in different environments, as well as for the analysis of different mutants, is the quantification of metabolic fluxes in cells grown under balanced conditions, such as in a steady-state chemostat or during the exponential phase of a batch culture. This quantification can be carried out by metabolite balancing (30, 40, 41, 42), through which the intracellular fluxes are calculated from a few measured fluxes by using mass balances for the intracellular metabolites. For a more accurate quantification of the fluxes, one may use ¹³C-labeled substrates followed by measurements of the isotopomers using gas chromatography and mass spectrometry (GC-MS) or nuclear magnetic resonance (38). Thus, balances can be set up for the individual carbon atoms, and this gives a significant redundancy in the measurements, leading to more robust flux estimates. The use of ¹³C-labeled substrates enables both identification of the metabolic network structure and quantification of the metabolic fluxes and is therefore referred to as metabolic network analysis (5).

This kind of analysis can be applied to investigate the metabolic network of S. cerevisiae, which is known to be under carbon catabolite repression when grown on rapidly fermentable carbon sources (13). As glucose is often the carbon source present in laboratory media as well as in industrial processes, this type of transcriptional control is often termed glucose repression (3, 17). This phenomenon has been known for decades and refers to the repression of the transcription of genes involved in different cellular functions, such as the uptake and catabolism of alternate carbon sources, respiration, tricarboxylic acid (TCA) cycle enzymes, gluconeogenesis, and peroxisomal functions (3). However, the mechanism by which yeast cells sense glucose, and how the signal is transduced from this initial sensing to the actual repression of gene transcription at the end of the cascade, is not yet fully elucidated. There is evidence that glucose concentration, rather than glucose uptake, triggers the glucose-repression cascade (27). There is also evidence that hexokinase PII plays a major role in the early part of the cascade (10, 20), but it is not known how the signal is transduced from glucose to Snf1p, a protein kinase that plays a central role in the cascade (3). Snflp phosphorylates Mig1p (29, 32, 39), a protein that binds to the promoter region of glucose-repressible genes. When glucose levels are high, Snflp is not active and Mig1p is underphosphorylated and located in the nucleus, repressing the transcription of genes. When glucose levels are low, Snflp phosphorylates Mig1p, causing its migration to the cytoplasm, releasing repression (3).

Transcriptional repression via Mig1p is a dual-type regulation, as the protein can bind to structural genes as well as to their repressors or activators. A physiological role for Mig1p has been shown in the repression of genes involved in the catabolism of sugars such as galactose, maltose, and sucrose, as well as of the CAT8 gene, which codes for a derepressor of gluconeogenic genes (19). However, putative binding sites for Mig1p exist in the promoter region of several other genes which are involved in central metabolic functions, such as gluconeogenesis and respiration. Furthermore, TCA cycle genes, such as CIT1, CIT3, KGD1, KGD2, and LPD1, may be indirectly regulated by Mig1p, as it can bind to the HAP4 gene, which codes for the transcriptional activator of these TCA cycle genes (19). Yet, it is not clearly known to what extent glucose repression affects the central metabolism of S. cerevisiae.

In this work, we grew S. cerevisiae cells under different conditions of glucose repression. Different environmental conditions were applied both to a reference strain and to a mig1 disruption mutant. Metabolic network analysis was performed by combining labeling experiments with mathematical modelling (5).

	MATERIALS AND METHODS

Top Abstract Introduction Materials and Methods Results and Discussion Appendix References

Strains. Two strains were used throughout this work: the reference strain CEN.PK113-7D (MATa MAL2-8^c SUC2) and the mig1 disruption mutant T468 (MATa mig1::MELI MAL2-8c SUC2), which was constructed by Birgitte Rønnow, Danisco Cultor, as described previously (18).

Cultivations. A total of four cultivations were carried out in a bioreactor that was specially designed for labeling experiments. The temperature was controlled at 30°C, the pH was controlled at 5.00 ± 0.05 by addition of 0.5 N NaOH, and the dissolved oxygen concentration was kept above 60% saturation by introducing sterile air through a needle and agitating the medium with a magnetic stirrer at 700 min¹. The working volume was 200 ml in the batch cultures and 150 ml in the continuous cultures, and the volume was kept constant by withdrawing liquid through a continuously operating pump.

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Sample treatment. In all cases, 1.5-ml samples were taken from the cultivation for the determination of absorbance at 600 nm, which was carried out using a Shimadzu flow spectrophotometer (model CL-720; Kyoto, Japan). Thereafter, filtration of the remaining sample volume was carried out on 0.45-µm-pore-size acetate filters (Osmonics, Minnetonka, Minn.), and the filtrate was frozen at 20°C and later used for sugar and excreted metabolite analyses.

Measurement of sugar and excreted metabolite concentrations. Glucose, ethanol, glycerol, acetate, succinate, and pyruvate were separated on an Aminex HPX-87H ion-exclusion column (Bio-Rad, Hercules, Calif.) at 65°C, using 5 mM H₂SO₄ as the mobile phase at a flow rate of 0.6 ml/min. Glucose, ethanol, glycerol, and succinate were detected on a Waters 410 differential refractometer detector (Millipore, Milford, Mass.), whereas acetate and pyruvate were detected on a Waters 486 tunable absorbance detector set at 210 nm. The two detectors were connected in series.

Measurement of fractional labeling of intracellular metabolites. For the analysis of the fractional labeling of intracellular metabolites, ca. 20 mg of wet biomass at 80°C was transferred to 800 µl of 6 M HCl. The mixture was then separated into two fractions. One fraction, of ca. 700 µl, was used for determining the fractional labeling of amino acid fragments, whereas the other fraction, of ca. 100 µl, was used for determining the fractional labeling of intracellular glucose. The first fraction was hydrolyzed for 12 to 20 h at 105°C. After this, ca. 800 µl of distilled water was added to the sample, which was then centrifuged at 3,000 × g for 5 min for separation of the cell debris. The supernatant was then separated into 200-µl fractions, which were dried at 105°C. After this, the dried samples were separately derivatized by two different agents, ethylchloroformate (ECF) and (N,N)-dimethylformamide dimethyl acetal. In both cases, the protocol described by Christensen and Nielsen (6) was employed. The only difference was in the derivatization with ECF, in which trifluoroacetic anhydride was not added. The second fraction was hydrolyzed for only 20 to 30 min at 105°C, after which 100 µl of distilled water was added. Derivatization of glucose to glucose pentaacetate (GPA) was carried out according to the protocol described previously (7).

Mathematical modelling. The SFLs, as described above, were used as inputs to a mathematical routine that is used for quantifying the fluxes in the central carbon metabolism of S. cerevisiae. The mathematical framework has been described previously (7) and will not be shown in detail here. Briefly, in each iteration of the numerical procedure, a guess is made on the fluxes and is used for calculating the SFLs via the mathematical model. Subsequently, two errors are generated: one by comparing the calculated SFLs with the values measured experimentally (via GC-MS, as described above), and the other by comparing the guessed fluxes with the ones measured experimentally. (Alternatively, literature values for the biomass composition can be used as the measured fluxes). If the sum of these two errors is smaller then the stored error value, the guess on fluxes used in this iteration is considered the best one. Otherwise, this guess is disregarded. A new iteration is started and the process goes on until an acceptable error is achieved.

	RESULTS AND DISCUSSION

Top Abstract Introduction Materials and Methods Results and Discussion Appendix References

Method validation. In order to check the reproducibility of the methodology developed and validated previously for the measurement of SFLs of intracellular metabolite fragments of a filamentous fungus (6), which was here applied to S. cerevisiae, a so-called incorporation experiment was performed. This experiment consisted of a continuous cultivation in which samples were periodically taken from the reactor outlet during a period of 9 residence times after the feeding solution was switched to the medium containing labeled glucose. The whole content of the reactor, as described in Materials and Methods, was the final sample. The incorporation of label into some of the analyzed metabolite fragments is shown in Fig. 1 (only selected fragments are shown, for the sake of clarity). It can be seen that 4 or 5 residence times are enough for the complete incorporation of label into all fragments, which means that the isotopic steady state is achieved at this point. First-order kinetics can be used as an approximation for the incorporation phenomenon, as illustrated in Fig. 1 for all fragments. With first-order kinetics, the SFL is 98.2% of the isotopic steady-state value after 4 residence times. The results shown in Fig. 1 also demonstrate the reproducibility of the measurements, as can be observed from the standard deviations in Table 3, which were calculated based on independent measurements performed on at least six different samples obtained after the isotopic steady state had been achieved.

View larger version (30K): [in this window] [in a new window]   FIG. 1.   Label incorporation into fragments of selected intracellular metabolites of S. cerevisiae in a continuous cultivation. Time zero corresponds to the medium switch from naturally labeled glucose to [1-13C]glucose as the limiting substrate. The curves shown assume first-order incorporation kinetics, according to the equation SFL = SFLFINAL × (1  et).

Yield coefficients. In Table 4, the yield coefficients are presented for biomass formation on glucose, as well as for the main metabolites excreted during aerobic cultivation of S. cerevisiae. These coefficients provide an idea of the overall metabolic fluxes in the cell. It can be seen that cells cultivated in the chemostat present pure respiratory metabolism, as there is no excretion of metabolites to the medium. In contrast, cells grown in the batch cultures present respiro-fermentative metabolism, leading to excretion of metabolites to the medium, which results in a lower biomass yield on glucose. It can also be seen that there is an apparently smaller excretion of metabolites coupled with a slightly higher biomass yield on glucose in the mig1 mutant compared to that in the reference strain in the batch cultivation. However, in the chemostat it is difficult to draw any quantitative conclusions when the two strains are compared with each other in terms of metabolite yields, as the concentration range of metabolites for detection after high-performance liquid chromatography separation and the available sample volume for cell concentration measurements in terms of dry weight from the 200-ml reactors are both very low. Thus, it is not possible to affirm that dry weights of 0.52 and 0.46 g are necessarily different from each other.

Influence of Mig1p on network structure. From an analysis of the results presented in Table 3, the first remarkable observation is that almost no difference in the SFLs is found when the two strains are compared under the same cultivation condition. The observation that the data are the same in both chemostats might be explained by the fact that the cells are not under glucose-repressing conditions and thus there is no binding of Mig1p to the promoter of repressible genes. In this case, a different phenotype in terms of fluxes would not be expected in cells that do not express the MIG1 gene compared with the reference strain. On the other hand, it is known that Mig1p binds to glucose-repressible genes, at least in the case of SUC2, when cells are grown under glucose-repressing conditions (5, 12, 15, 19, 21), which is the case for the batch cultivations performed in this work (the high specific growth rate and the residual glucose concentration in the reactor should be enough for provoking repression). With the measurements performed in this work, it is not possible to affirm that Mig1p is binding to the central metabolic genes of S. cerevisiae, which present putative binding sites for this protein. However, the results from the batch cultivation with the mig1 mutant do indicate that the central metabolism is tightly regulated through pathways that operate in parallel to the Mig1p pathway, either by avoiding its binding to the genes or by eliminating the effect that this binding would have on the metabolic fluxes. In the case of gluconeogenic functions, it is known that the activation of gene expression, mediated by Sip4p and Cat8p, plays a more important role than that of repression by Mig1p (5, 35). Thus, gluconeogenic fluxes do not occur in mig1 cells. However, a relief in the repression of respiratory and TCA-cycle genes could be expected in the batch cultivation with the mig1 mutant, and this would have yielded a different SFL pattern for the internal metabolites than that of the reference strain.

Network identification: qualitative inspection of SFL data. It can be seen from Table 3 that the SFLs of a considerable number of amino acid fragments, as well as of glucose, could be measured by the GC-MS technique employed (6). With these measurements, it was possible to assess the SFLs of some key metabolites in the central metabolic pathways of S. cerevisiae, namely the EMP and PP pathways and the TCA cycle. Inspection of the SFLs allows identification of the network structure (in terms of the activity of different pathways), which is the first step in metabolic network analysis. In the discussion below, no separate analysis will be made with respect to the mig1 mutant strain, as the SFL data obtained for this strain were not significantly different from the data obtained for the reference strain.

(i) Glycine biosynthesis. In the case of glycine, it is supposed that its precursor is serine, which in turn is exclusively generated from 3-phosphoglycerate (46). However, the results from the chemostats show that the SFL of the fragment Gly175 (or Gly144) is higher than the value obtained for the fragment Ser175. This indicates that there is another route for the formation of glycine, which causes an increase in the SFL of this amino acid. Three genes from S. cerevisiae were cloned and characterized, the inactivation of which are required to generate auxotrophy for glycine (25). Two of these genes, denominated SHM1 and SHM2, encode the mitochondrial and cytosolic serine hydroxymethyltransferases (SHMs), respectively. The third gene, denominated GLY1, was assigned no function. Later, two independent investigations (24, 28) reported that the gene GLY1 of S. cerevisiae encodes a threonine aldolase that catalyzes the cleavage of threonine into glycine and acetaldehyde. Thus, when grown on glucose, two pathways exist for the biosynthesis of glycine: one starting at 3-phosphoglycerate via serine and another starting at oxaloacetate via threonine. When cells are grown on nonfermentable carbon sources, e.g., ethanol and acetate, a third pathway is the major source of glycine. In this case, glyoxylate is converted into glycine via the reaction catalyzed by the enzyme alanine-glyoxylate aminotransferase (28). However, the gene coding for this enzyme has not yet been found. As this pathway is not operative under growth on glucose, the analysis performed here points towards the activity of threonine aldolase, and therefore this reaction has been included in the mathematical model for flux calculations (see Appendix).

(ii) Compartmentation of pyruvate. It is known that valine is derived from mitochondrial pyruvate, as the first step in its biosynthetic route, catalyzed by acetolactate synthase, is mitochondrial (12). However, it is not yet known in which compartment alanine biosynthesis takes place, as there are putative alanine aminotransferases both in the cytosol and in the mitochondrion. Thus, it was decided to separate the pyruvate pool into two compartments in the model (see Appendix), although as already mentioned above, the SFLs of alanine and valine were not different from each other.

(iii) Compartmentation of acetyl-CoA. The SFLs of amino acids derived from more than one precursor, i.e., leucine, isoleucine, lysine, and phenylalanine, can be used to gain an insight into metabolism. Subtracting the SFL of the fragment Val127 from that of the fragment Leu158, it is possible to assess the SFL of the C-2 atom of acetyl-CoA (39.9% in the batch cultivation with the reference strain, 37.7% in the batch cultivation with the mig1 mutant, 33.0% in the chemostat with the reference strain, and 31.2% in the chemostat with the mig1 mutant). This probably represents the SFL of mitochondrial acetyl-CoA, as the biosynthetic step of leucine formation catalyzed by the enzyme -isopropylmalate synthase is located in the mitochondrion (36). Subtracting the SFL of the fragment Pro142 from that of the fragment Lys156, it is also possible to assess the SFL of the C-2 atom of acetyl-CoA (38.6% in the batch cultivation with the reference strain, 36.3% in the batch cultivation with the mig1 mutant, 31.7% in the chemostat with the reference strain, and 30.6% in the chemostat with the mig1 mutant). This probably represents the SFL of acetyl-CoA in the nucleus, as the biosynthetic step of lysine formation catalyzed by the two homocitrate synthase isoenzymes (Lys20p and Lys21p) occurs in the nucleus (4). In the model for flux calculations, this acetyl-CoA was included as cytosolic, as a means of differentiating it from the mitochondrial acetyl-CoA. In spite of the fact that the SFL values for mitochondrial and nonmitochondrial acetyl-CoA were not significantly different from each other, compartmentation of this metabolite proved to be important for minimizing the error in the calculations.

(iv) Malic enzyme. Concerning the SFL of phenylalanine, which is known to be synthesized from erythrose-4-phosphate and phosphoenolpyruvate, it is possible to see from the fragment Phe143 that the phosphoenolpyruvate C-1 and C-2 atoms have a lower SFL when compared to the SFL of pyruvate C-1 and C-2 atoms, as assessed from the fragment Val143. This observation points towards the activity of another pathway for pyruvate formation besides the pathway catalyzed by pyruvate kinase. Boles and coworkers (1) have shown that malic enzyme (Mae1p), the role of which still remains quite intriguing, is active when S. cerevisiae grows on glucose. This enzyme catalyzes the mitochondrial conversion of malate to pyruvate and may therefore explain the differences between the SFLs of pyruvate and phosphoenolpyruvate, as mentioned above. The reaction catalyzed by malic enzyme was also included in the model (see Appendix and Fig. 2).

(v) TCA cycle. When the batch cultivation is compared to the chemostat, significant differences are observed. In the former case, it becomes clear that respiro-fermentative metabolism occurs and that the TCA cycle is not operating as a cycle, as such, but rather as two branches. This can be deduced from the SFL of the C-2 atom of aspartate, which is equal to 2.0%, indicating that this carbon atom cannot originate from -ketoglutarate but is rather coming from pyruvate via the anaplerotic reaction catalyzed by pyruvate carboxylase, which is cytosolic (16, 45). The C-2 atom of aspartate (which reflects that of oxaloacetate) can only originate from the C-2 atom of pyruvate or from the C-3 and C-4 atoms of -ketoglutarate, considering the symmetric intermediates of the TCA cycle. As the SFLs of the proline and glutamate fragments were much higher than that of aspartate, it can be concluded that C-2 of oxaloacetate can only arise from pyruvate, which is reflected in the valine fragment and which in turn presents low labeling in the C-1 and C-2 atoms. On the other hand, in the chemostats cells grow with purely respiratory metabolism. In this case, the C-2 atom of aspartate has an SFL of 12.0%, indicating that it is at least in part formed from -ketoglutarate (based on the high label in the proline and glutamate fragments and the low label in the valine fragments).

(vi) PP pathway. Another observation is that the amino acid fragments originating from precursor metabolites in the EMP pathway, such as 3-phosphoglycerate and pyruvate, present lower SFLs in the chemostat than in the batch cultivation. This is due to the higher PP pathway flux in the former. A high PP pathway flux is required when the biomass yield is high, because a relatively larger amount of glucose has to be used for generation of NADPH. A high PP pathway flux gives lower SFLs for the lower glycolytic intermediates, since decarboxylation of glucose-6-phosphate carried out in the first steps of the PP pathway removes exactly the C-1 atom of this molecule, which is the one that receives the label from [1-¹³C]glucose.

View larger version (34K): [in this window] [in a new window]   FIG. 2.   Fluxes in the central metabolism of S. cerevisiae (reference strain). Values in bold correspond to respiro-fermentative metabolism (batch cultivation). Values in italics correspond to respiratory metabolism (chemostat). All the fluxes shown are net fluxes. For reversible reactions, the degree of reversibility is indicated in parentheses by the normalized exchange flux, calculated according to the formula vexch[0,1] = (vexch)/(vexch + vnet). All fluxes are relative to a glucose uptake of 100 (arbitrary units). Abbreviations: G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; G3P, glyceraldehyde-3-phosphate; PEP, phosphoenolpyruvate; PYR, pyruvate; ACA, acetaldehyde; ACE, acetate; AcCoA, acetyl-CoA; OAA, oxaloacetate; MAL, malate; FUM, fumarate; 2-KG, 2-ketoglutarate.

Metabolic fluxes. The analysis above, concerning the identification of the network structure, was made by inspecting the measured SFLs in a qualitative or semiquantitative manner. By combining these measurements with a suitable mathematical model, it was possible to quantify the fluxes in the central metabolism of S. cerevisiae. In this way, it was possible to compare cells growing in a chemostat at steady state, with a specific growth rate of 0.1 h¹, with cells growing in a batch cultivation, with µ_max = 0.37 h¹, in a quantitative fashion. The process of estimating fluxes is characterized by the process of finding the most suitable model that describes the metabolism under investigation, in a sort of trial-and-error process, which is based on biochemical knowledge and appropriate assumptions (with the aim of both fixing nondefined biochemical details and avoiding numerical problems in the calculations). The most suitable model for the experiments performed in this work is the one shown in the Appendix. The same model was suitable for both cultivation conditions investigated.

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Conclusions. Metabolic network analysis, as applied in the present work to cells of S. cerevisiae, is a tool that can be utilized for the quantitative inspection of metabolism. The first step in this analysis is the identification of the network structure, which is basically achieved by combining the inspection of the SFLs of intracellular metabolites, as measured by GC-MS, with biochemical knowledge. This step may bring insight into which enzymes are active and which ones are not active and if there is an unknown reaction taking place under the cultivation condition under investigation or in specific mutant cells. The second step in this analysis is a consequence of the first one. A mathematical model describing the metabolic network, as identified in the first step, combined with the measured SFLs with the aim of estimating the metabolic fluxes. These data can be used for the quantitative comparison of cells grown under different environmental conditions or for the comparison of different mutants. Besides yielding important physiological information for well-characterized species, such as the activities of the PP pathway, TCA cycle, threonine aldolase, and malic enzyme, as shown in this work with S. cerevisiae, metabolic network analysis is a promising tool for investigating other poorly characterized species with potential biotechnological applications.