Institute of Biotechnology, ETH Zürich, Zürich, Switzerland
Correspondence
Lars M. Blank
blank{at}biotech.biol.ethz.ch
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ABSTRACT |
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INTRODUCTION |
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Here, we extend METAFoR analysis by GC-MS from E. coli (Fischer & Sauer, 2003a) to the compartmentalized S. cerevisiae metabolism. The particular focus of this study was to investigate the impact of different environmental conditions such as pH, osmolarity and temperature on the central carbon metabolism of S. cerevisiae during growth on glucose.
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METHODS |
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13C-labelling experiments.
All labelling experiments were done in batch cultures assuming pseudo-steady-state conditions during the exponential growth phase (Fischer & Sauer, 2003a; Sauer et al., 1999
; Wittmann & Heinzle, 2001
). 13C-labelling of proteinogenic amino acids was achieved by growth on 5 g glucose l1 as a mixture of 80 % (w/w) unlabelled and 20 % (w/w) uniformly labelled [U-13C]glucose (13C, >98 %; Isotech). Cells from an overnight minimal medium culture were washed and used for inoculation below an OD600 of 0·03. 13C-labelled biomass aliquots were harvested during the mid-exponential growth phase at an OD600 of
1. At this point the residual glucose concentration was between 1 and 3 g l1, thus clearly above the reported 0·5 g l1 threshold of invertase repression in S. cerevisiae strain CEN.PK 113-7D (Herwig et al., 2001
). The cells were harvested by centrifugation, washed once with sterile water, and hydrolysed in 500 µl 6 M HCl at 105 °C for 24 h. The hydrolysate was dried in a heating block at 80 °C under a constant airflow. The free amino acids were derivatized at 85 °C for 1 h using 25 µl dimethylformamide and 25 µl N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (Dauner & Sauer, 2000
; Wittmann et al., 2002a
).
GC-MS analysis was carried out as reported recently (Fischer & Sauer, 2003a) using a previously described biochemical reaction network (Maaheimo et al., 2001
). The recently described cytosolic alanine synthesis in glucose/acetate co-metabolism experiments was not seen in our glucose experiments (Dos Santos et al., 2003
). The labelling pattern of phosphoenolpyruvate (PEP) derived from tyrosine and phenylalanine was different from the labelling pattern of mitochondrial pyruvate referred from valine. The labelling patterns of alanine and valine, however, were highly similar, suggesting that alanine is indeed synthesized from mitochondrial pyruvate in the experimental conditions used in this study.
METAFoR analysis using amino acids mass isotopomer data.
The GC-MS data represent sets of ion clusters, each showing the distribution of mass isotopomers of a given amino acid fragment. For each fragment , one mass isotopomer distribution vector (MDV) was assigned,
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A second independent equation for calculating the fraction of OAAmit through the TCA cycle can be formulated by assuming that OGA15 corresponds to OAA24mit+acetyl-CoA12. Thus, OGA25 equals OAA23mit+acetyl-CoA12 and OGA12 equals OAA34mit. The relative contribution of the TCA cycle to OAA synthesis can then also be quantified by:
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The relative contribution of different synthesis pathways to mitochondrial and cytosolic acetyl-CoA could not be quantified, because the MDVAA of LEU12 was not accessible with the derivatizing agent used and LYS12 was not accessible with the procedure employed. Consequently, the MDVM of cytosolic acetyl-CoA was not available for comparison with the calculated mitochondrial acetyl-CoA MDVM from OGA.
From the MDVs of oxaloacetate one can calculate the cytosolic OAAcyt derived from cytosolic pyruvate:
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RESULTS |
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In contrast to the above two flux ratios, however, none of the 10 other detected flux ratios exhibited a distinct correlation with the growth rate (data not shown). The malic enzyme for example was more active at acidic pH, but was inactive at pH 7·5, although the growth rate was higher (0·19 h1) than at pH 3·0 (0·07 h1). The reverse in vivo activity pattern was determined for the gluconeogenic reaction catalysed by PEP carboxykinase, which was only detected at pH values 7·0 and 7·5.
To elucidate whether the growth rate was also correlated with any other physiological property, we quantified the growth physiology over a wide range of pH values (Fig. 5). For standard conditions (high glucose concentration, 30 °C, aerated batch culture, pH 56), these parameters (Fig. 5
) were in good agreement with previous reports for S. cerevisiae CEN.PK strains (Smits et al., 2000
; van Dijken et al., 2000
; van Maris et al., 2001
). Akin to the maximum growth rate (Fig. 5a
), the specific glucose uptake rate decreased significantly outside the optimal pH range of 4·06·0 (Fig. 5b
). The biomass yield, in contrast, was rather constant and exhibited no correlation with the growth rate (Fig. 5c
). The specific glycerol production rate showed a generally positive correlation with the culture pH, i.e. resulted in the highest production rate at pH 7·5 (Fig. 5d
). This positive correlation may reflect a similar response as was reported for high osmolarity, where production of the main osmolyte glycerol was increased in yeast (Hohmann, 2002
). It should be noted that significant ethanol production was not reported because the high evaporation rate in the shake flasks compromised a thorough quantitative analysis.
Glucose sensing is not required for the increase of relative TCA cycle flux during slow growth
Although the initial glucose concentrations were identical in all the above experiments, we cannot exclude that glucose sensing was relevant for modulating the relative TCA cycle flux. We therefore constructed isogenic mutants of the two glucose sensors Snf3p and Rgt2p (Ozcan et al., 1996). If extracellular glucose sensing played a significant role in modulating the TCA cycle flux, one would expect no increase in flux upon an environmentally reduced growth rate in both mutants. However, the TCA cycle activity increased in both mutants at lower growth rates (Fig. 6
). To further exclude the possibility of a partial functional overlap of the high and low glucose concentration sensors, Rgt2p and Snf3p, respectively, we used two further mutants. Strain MMB4 cannot sense extracellular glucose at all due to deletions in SNF3, RGT2, and the plasma membrane G-protein coupled sensor GPR1. To enable growth on glucose, this strain also carries the MTH1 deletion. The second strain was deleted in GRR1, which is essential for glucose sensing from the Snf3p and Rgt2p signal transduction pathway. Although both strains already exhibit high respiratory TCA cycle flux under standard conditions at growth rates of 0·39 and 0·26 h1 for MMB4 and the grr1 mutant, respectively, they still increased the relative TCA cycle flux upon an environmental modulation of the specific growth rate with high osmolarity (Fig. 6
). These results strongly suggest that extracellular glucose sensing is not involved in the relative TCA cycle flux increase at slow growth rates in batch cultures. Nevertheless, the higher TCA cycle flux in the grr1 and MMB4 mutants, when compared to the wild-type at the same growth rate, shows that glucose sensing has an additional repressive effect.
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DISCUSSION |
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The correlation observed here between the rates of growth and/or glucose uptake and relative respiratory TCA cycle flux contrasts with the generally held view of a catabolite-repressed TCA cycle in glucose-excess batch cultures of S. cerevisiae (Gancedo, 1998; Rolland et al., 2002
). Under standard batch conditions, the TCA cycle operates as a bifurcated pathway to sustain biomass precursor requirements (Gombert et al., 2001
). Although expression of the majority of the TCA cycle genes is subject to glucose repression (DeRisi et al., 1997
) at extracellular glucose concentrations that may be as low 0·1 g l1 (Yin et al., 2003
), we show here that the relative in vivo respiratory activity of the TCA cycle may increase even at high glucose concentrations, provided the growth rate or the glucose uptake rate are impaired by other environmental parameters.
While the major regulation pathways of glucose repression are known (Rolland et al., 2002), the molecular mechanisms that initiate repression are still elusive and several metabolism-derived triggers have been discussed (Carlson, 1999
; Gancedo, 1998
; Rolland et al., 2002
). Our glucose sensor mutant results exclude that glucose repression of the TCA cycle is exclusively mediated by sensing of extracellular glucose concentrations, which is known to repress several HXT genes (Rolland et al., 2002
). The relative TCA cycle flux increase in four different mutants impaired in glucose sensing strongly suggests that the metabolic trigger for TCA cycle repression must be an intracellular, metabolism-derived signal. This view is consistent with increasing oxygen consumption rates upon genetic reduction of growth rate and glucose uptake rate (Ye et al., 1999
). In addition, there is the concentration-dependent repression because mutants completely devoid of glucose sensing (e.g. grr1 and MMB4) exhibited higher TCA cycle fluxes than would be expected from their growth rates.
Generally, one would expect the repression signal to be related to the glucose uptake rate rather than to the growth rate, but the two were coupled in all environmental modulations. In the hxk2 mutant, however, the two parameters were decoupled and the relative TCA-cycle flux correlated better with uptake than with growth rate, thus providing some evidence for a flux-related signal of glucose repression of the TCA cycle. The imperfect uptakeTCA cycle correlation in the hxk2 mutant, with a weaker repression of the TCA cycle than expected from pH experiments with similar growth rates, could be related to the regulatory role of Hxk2p in glucose repression, which would also influence the flux (Carlson, 1999; Gancedo, 1998
; Rolland et al., 2002
). Apparently, glucose repression of the TCA cycle exhibits a different pattern and probably also uses different signals than the paradigm glucose repression gene SUC2 (Meijer et al., 1998
; Rolland et al., 2002
). The general dependence of relative TCA cycle fluxes on the exact environmental conditions may also explain minor differences in TCA cycle activity obtained from previous 13C-labelling experiments (Christensen et al., 2002
; Fiaux et al., 2003
; Gombert et al., 2001
; Maaheimo et al., 2001
).
The methodology described for metabolic flux profiling based on GC-MS data of proteinogenic amino acids in yeast is robust, rapid, and also applicable to mini-cultivation systems. Most of the reported flux ratios have varied only within a rather narrow range. Importantly, the fluxes through the PP pathway, the malic enzyme and the gluconeogenic reaction catalysed by PEP carboxykinase are low and change little when considering the severe physiological impacts of the environmental conditions used. The sole exceptions were the respiratory TCA cycle flux and the mitochondrial exchange flux between oxaloacetate and fumarate. This suggests a general robustness of intracellular metabolism to the environmental conditions on a given substrate. While the rate at which glucose enters the cell may vary over a wide range, the relative distribution of carbon fluxes within the cell remains rather stable.
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ACKNOWLEDGEMENTS |
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Received 16 October 2003;
revised 19 December 2003;
accepted 22 December 2003.
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