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酿酒酵母体内的三羧酸循环途径

TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined speci?c growth and glucose uptake rates

Lars M.Blank and Uwe Sauer

Correspondence Lars M.Blank

blank@biotech.biol.ethz.ch

Institute of Biotechnology,ETH Zu ¨rich,Zu ¨rich,Switzerland

Received 16October 2003Revised 19December 2003

Accepted 22December 2003

Metabolic responses of Saccharomyces cerevisiae to different physical and chemical environmental conditions were investigated in glucose batch culture by GC-MS-detected mass isotopomer distributions in proteinogenic amino acids from 13C-labelling experiments.For this purpose,GC-MS-based metabolic ?ux ratio analysis was extended from bacteria to the compartmentalized metabolism of S.cerevisiae .Generally,S.cerevisiae was shown to have low catabolic ?uxes through the pentose phosphate pathway and the tricarboxylic acid (TCA)cycle.Notably,respiratory TCA cycle ?uxes exhibited a strong correlation with the maximum speci?c growth rate that was attained under different environmental conditions,including a wide range of pH,osmolarity,decoupler and salt concentrations,but not temperature.At pH values of 4?0to 6?0with near-maximum growth rates,the TCA cycle operated as a bifurcated pathway to ful?l exclusively biosynthetic functions.Increasing or decreasing the pH beyond this physiologically optimal range,however,reduced growth and glucose uptake rates but increased the ‘cyclic’respiratory mode of TCA cycle operation for catabolism.Thus,the results indicate that glucose repression of the TCA cycle is regulated by the rates of growth or glucose uptake,or signals derived from these.While sensing of extracellular glucose concentrations has a general in?uence on the in vivo TCA cycle activity,the growth-rate-dependent increase in respiratory TCA cycle activity was independent of glucose sensing.

INTRODUCTION

Genome-wide mRNA responses of the baker’s yeast Saccharomyces cerevisiae to changes in pH,osmolarity or temperature revealed differential expression of more than 1000transcripts during adaptation (Causton et al .,2001;Gasch et al .,2000).Since transcriptome or proteome changes do not directly reveal cellular phenotypes,one would like to connect these inventory data with the appar-ent cellular physiology (Bailey,1999).One such approach is metabolic ?ux analysis,which estimates material ?ow through biochemical reaction networks,and thus provides a direct link to the physiological phenotype (Hellerstein,2003).

Different approaches for metabolic ?ux analysis based on 13

C-labelling experiments have been developed,allowing precise quanti?cation of central carbon metabolism (Sauer,2004;Wiechert,2001).Recent applications include the bacteria Bacillus subtilis (Dauner et al .,2001;Zamboni &Sauer,2003),Corynebacterium glutamicum (Klapa et al .,2003;Petersen et al .,2000;Wittmann &Heinzle,2002)and

Escherichia coli (Emmerling et al .,2002;Fischer &Sauer,2003b;Jiao et al .,2003;Sauer et al .,2004).Although conceptually more dif?cult,?ux analysis has also been applied successfully to compartmentalized microbes such as S.cerevisiae (Christensen et al .,2002;Dos Santos et al .,

2003),Saccharomyces kluyveri (Mo

¨ller et al .,2002),Kluyvero-myces marxianus (Wittmann et al .,2002b)and Penicillium chrysogenum (Van Winden et al .,2003).Often,metabolic ?ux analysis combines 13C-labelling data with quantitative physiology data to obtain a best-?t ?ux solution.A somewhat different methodology is metabolic ?ux ratio (METAFoR)analysis,which quanti?es the relative contribution of con-verging pathways or reactions to a given intracellular meta-bolite (Fischer &Sauer,2003a).Without data ?tting,this biochemical approach relies exclusively on 13C data and has been used successfully with NMR data in the yeasts S.cerevisiae (Maaheimo et al .,2001)and Pichia stipitis (Fiaux et al .,2003).

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.

Abbreviations:MDV,mass distribution vector;METAFoR,metabolic ?ux ratio;PP,pentose phosphate;TCA,tricarboxylic acid (for other abbreviations see the legend of Fig.1).

0002-6845G 2004SGM Printed in Great Britain

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Microbiology (2004),150,1085–1093DOI 10.1099/mic.0.26845-0

METHODS

Yeast strains and growth conditions.The haploid S.cerevisiae strains used in this study are listed in Table1.The hxk2,rgt2and

snf3mutants were constructed by using existing kanMX4cassettes (Winzeler et al.,1999).The cassettes were ampli?ed using primers located~500bp upstream and downstream of the start and stop codon of the corresponding gene,respectively.Batch cultures of

20ml were performed in500ml baf?ed Erlenmeyer?asks on a rotary shaker at30u C and225r.p.m.,ensuring fully aerated condi-tions,in yeast minimal medium(Verduyn et al.,1992)containing (per litre)5g(NH4)2SO4,3g KH2PO4,0?5g MgSO4.7H2O,

4?5mg ZnSO4.7H2O,0?3mg CoCl2.6H2O,1?0mg MnCl2.4H2O, 0?3mg CuSO4.5H2O,4?5mg CaCl2.2H2O,3?0mg FeSO4.7H2O, 0?4mg NaMoO4.2H2O,1?0mg H3BO3,0?1mg KI,15mg EDTA,

0?05mg biotin,1?0mg calcium pantothenate,1?0mg nicotinic acid,25mg inositol,1?0mg pyridoxine,0?2mg p-aminobenzoic acid and1?0mg thiamin.To avoid pH changes due to ammonia uptake and acetate production the medium was supplemented with

100mM potassium hydrogen phthalate.For all experiments at pH values of6or higher,100mM MOPS was used as the buffering agent instead.The?nal pH of the cultures was in all experiments within0?05of the pre-inoculation value.Filter-sterilized glucose was

added prior to the experiment at a?nal concentration of5g l21. Analytical procedures and physiological parameters.Cell

growth was followed by monitoring the OD600.Samples for extra-cellular metabolite determination were centrifuged at14000r.p.m. in an Eppendorf tabletop centrifuge to remove cells.Glucose and glycerol concentrations in the supernatant were determined with

commercial enzymic kits[Triglyceride337-40A,Sigma;D-Glucose Test(E0716251),R-Biopharm AG].The physiological parameters maximum speci?c growth rate,biomass yield on glucose,and speci-

?c glucose consumption rate were calculated during the exponential growth phase(Sauer et al.,1999).

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 on5g glucose l21as a mixture of80%(w/w)unlabelled and20%(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 OD600of0?03.13C-labelled biomass aliquots were har-vested during the mid-exponential growth phase at an OD600of?1. At this point the residual glucose concentration was between1 and3g l21,thus clearly above the reported0?5g l21threshold of invertase repression in S.cerevisiae strain CEN.PK113-7D(Herwig et al.,2001).The cells were harvested by centrifugation,washed once with sterile water,and hydrolysed in500m l6M HCl at105u C for 24h.The hydrolysate was dried in a heating block at80u C under a constant air?ow.The free amino acids were derivatized at85u C for1h using25m l dimethylformamide and25m l N-(tert-butyldimethylsilyl)-N-methyltri?uoroacetamide(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 phospho enol pyruvate(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 frag-ment.For each fragment a,one mass isotopomer distribution vector (MDV)was assigned,

MDV a~

(m0)

(m1)

(m2)

...

(m n)

with

m i~1e1T

with m0being the fractional abundance of the lowest mass and m i>0the abundances of molecules with higher masses.To obtain the exclusive mass isotope distribution of the carbon skeleton, corrections for naturally occurring isotopes in the derivatization reagent and the amino acids were performed as described previously (Fischer&Sauer,2003a),followed by the calculations of the mass distribution vectors for amino acids(MDV AA)and metabolites (MDV M).Metabolic?ux ratios were calculated from the MDV M as described for E.coli(Fischer&Sauer,2003a)with the following exceptions,which account for the compartmentalized biochemical reaction network of S.cerevisiae.Since the mitochondrial aspartate aminotransferase is probably inactive under the conditions used (Maaheimo et al.,2001),the mass distribution of mitochondrial oxaloacetate was not directly accessible(Fig.1).Instead,the MDV of the four-carbon C-1–C-2–C-3–C-4fragment of mitochondrial oxaloacetate(OAA14mit)(see Fig.1for abbreviations)was accessed by assuming that only anaplerosis and the TCA cycle contribute to its labelling pattern.Thus,the MDV M of OAA14mit can be calculated

Table1.Yeast strains used in this work

Strain Genotype Source or reference CEN.PK113-7D MAT a MAL2-8c SUC2Euroscarf*

LMB45CEN.PK113-7D hxk2::kanMX4This study

LMB179CEN.PK113-7D snf3::kanMX4This study

LMB243CEN.PK113-7D rgt2::kanMX4This study

CEN.PK513-3A CEN.PK113-7D grr1::loxP-kanMX4-loxP P.Ko¨tter,https://www.wendangku.net/doc/053809069.html,m.

MMB4W303-1A MAT a ade2-1can1-100his3-11,15leu2-3,112trp1-1

ura3-1snf3::HIS3rgt2::leu2mth1::trp1gpr1::kanMX4

J.M.Gancedo,https://www.wendangku.net/doc/053809069.html,m. *European Saccharomyces cerevisiae Archive for Functional Analysis(euroscarf@em.uni-frankfurt.de).

1086Microbiology150 L.M.Blank and U.Sauer

with the following equation:

OAA14mit~

1{OAA mit

through TCA cycle

.(PEP13|MDV

CO2

)z OAA mit

through TCA cycle

.OGA25

e2T

The fraction of OAA mit derived through the TCA cycle is obtained from:

OAA mit through TCA cycle~

OGA25{(GLU2U|GLU1U|GLU1U)

(GLU2U|GLU2U){(GLU2U|GLU1U|GLU1U)

e3T

where GLU1U and GLU2U are uniformly13C-labelled1-and2-carbon glucose fragments,respectively.This approach assumes that the bond between carbon atoms2and3of every oxaloacetate is broken per round through the TCA cycle.

A second independent equation for calculating the fraction of OAA mit through the TCA cycle can be formulated by assuming that OGA15 corresponds to OAA24mit+acetyl-CoA12.Thus,OGA25equals OAA23mit+acetyl-CoA12and OGA12equals OAA34mit.The relative contribution of the TCA cycle to OAA synthesis can then also be quanti?ed by:

OAA mit

through TCA cycle

~1{

OAA23mit{(GLU1U|GLU1U)

U U

e4T

Since the independent equations3and4gave very similar results in all analyses described here,only those obtained from equation4are reported in the following.

The relative contribution of different synthesis pathways to mitochon-drial and cytosolic acetyl-CoA could not be quanti?ed,because the MDV AA of LEU12was not accessible with the derivatizing agent used and LYS12was not accessible with the procedure employed.Con-sequently,the MDV M of cytosolic acetyl-CoA was not available for comparison with the calculated mitochondrial acetyl-CoA MDV M from OGA.

From the MDVs of oxaloacetate one can calculate the cytosolic OAA cyt derived from cytosolic pyruvate:

OAA cyt from pyruvate cyt~

OAA24cyt{OAA24mit

PEP13{OAA24

e5T

Furthermore one can determine the activities of the reversible exchange ?uxes between oxaloacetate and fumarate or succinate:

OAA mit rev:from fumarate mit~

OAA24mit{pyruvate23mit|MDV CO

(0:5pyruvate13mit z0:5(pyruvate23mit|MDV CO

){pyruvate23mit|MDV CO

e6TOAA cyt rev:from fumarate cyt~

OAA24cyt{PEP23|MDV CO

(0:5PEP13z0:5(PEP23|MDV CO

){PEP23|MDV CO

e7T

Since the malic enzyme is located in the mitochondria(Boles et al., 1998),the mitochondrial pools of pyruvate and oxaloacetate are used to calculate lower and upper bounds of malic enzyme activity: Pyruvate mit from malate

(lower bound)

pyruvate23mit{PEP23

(GLU1U|GLU1U){PEP23

e8TPyruvate mit from malate(upper bound)~

pyruvate mit from malate(lower bound)

OAA mit through TCA cycle

e9T

The contribution of the non-oxidative pentose phosphate(PP) pathway via transketolase to the synthesis of the triose pool can be estimated from the MDV of PEP12,revealing the fraction of trioses rearranged between carbon C-1and C-2by the action of the transketolase.The remaining PEP12molecules originate from an unbroken C2unit of glucose derived through glycolysis.

PEP through transketolase~

PEP12{GLU2U

GLU1U|GLU1U{GLU2U

e10T

An upper bound for the contribution of the PP pathway to trioses synthesis can than be calculated by assuming that?ve trioses are produced from three pentoses and that at least two of these trioses are rearranged by a transketolase:

PEP through PP pathway~5=2?PEP through transketolasee11TSince the contribution of the PP pathway to PEP synthesis is an upper bound,we present the results from equation11by including the error

interval.

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TCA cycle activity is a function of growth rate

RESULTS

METAFoR analysis by GC-MS of S.cerevisiae

in glucose batch culture

To establish a rapid and robust methodology for?ux analysis,we extended METAFoR analysis by GC-MS from bacterial metabolism(Fischer&Sauer,2003a;Fischer et al., 2004;Zamboni&Sauer,2003)to the compartmentalized

metabolism of S.cerevisiae.Firstly,we found the mass isotopomer distributions in the GC-MS-analysed protein-ogenic amino acids to be generally consistent with previous network analysis(Christensen et al.,2002;Maaheimo et al., 2001;Van Winden,2002)of amino acid biosynthesis in S.cerevisiae(data not shown).Next,we compared GC-MS-based METAFoR analysis of a shake-?ask-grown S.cerevisiae batch culture on glucose at30u C and pH5?5 to NMR-based METAFoR analysis under almost identical conditions(Maaheimo et al.,2001).Indeed,very similar results such as low PP pathway and TCA cycle activities

were found(Fig.2).Since the interpretation of13C-labelling pattern in amino acids by MS and NMR analyses is only connected by the assumed biochemical network, this consistency provides evidence for faithful and reliable quanti?cation of metabolic?ux ratios by either method. Minor differences in?ux ratios may be attributed to the different minimal medium composition and to the some-what lower growth rate of the NMR-analysed culture (Maaheimo et al.,2001).

Impact of pH and temperature on the metabolic ?ux pro?le

To investigate the in?uence of physical and chemical environmental parameters on central carbon metabolism under aerobic fermentation conditions,we grew S.cerevisiae at25,30and37u C and at pH values of3?5,5?0and6?0. The16determined metabolic?ux ratios were surprisingly stable under all conditions,and the relative?uxes through the TCA cycle and PP pathway remained rather low(Fig.3). Generally,low TCA cycle activity and concomitant ethanol production were expected for the respiro-fermentative S.cerevisiae(Alexander&Jeffries,1990).Previous estimates for the relative catabolic PP pathway?ux to trioses vary between0and4%(Fiaux et al.,2003;Maaheimo et al., 2001)and14%(Gombert et al.,2001).In the experiments described here,we observe an upper bound of7%on the fraction of PEP derived through the PP pathway from uniformly labelled glucose experiments(Fig.3).Thus,we conclude that the catabolic PP pathway activity of S. cerevisiae in batch cultures is indeed lower than seen for example in E.coli(Fischer&Sauer,2003a).

The growth rate of S.cerevisiae correlates with the relative respiratory TCA cycle?ux in batch culture

The increased fraction of OAA mit derived through the TCA cycle in the pH3?5culture indicates signi?cantly increased respiratory TCA cycle activity,when compared to the biosynthetic TCA cycle activity(Fig.3b).Since this low pH also reduced the maximum speci?c growth rate by10%, we could not distinguish between a growth rate and an environmental condition effect on the TCA cycle activity. To quantify the in?uence of environmental conditions on the maximal speci?c growth rate and intracellular?ux response,we grew batch cultures under aerobic fermenta-tion conditions,with extensive ethanol formation at different pH values,osmolarities,temperatures,salt and decoupler concentrations.As shown in Fig.4,the contribu-tion of the TCA cycle to the synthesis of OAA mit generally increased with decreasing growth rate.This observation appeared to be independent of the wide variety of environ-mental conditions that were used to reduce the growth rate.The sole exception was temperature,which had only a modest effect on TCA cycle?uxes(Figs3and4),although the growth rate was signi?cantly reduced.Since the speci?c glucose uptake rate is closely correlated with the speci?c growth rate(Diderich et al.,1999;Van Hoek et al.,1998) (Fig.5a,b),it is not surprising that the glucose uptake rate was likewise correlated with the relative TCA cycle ?ux(data not shown).While we cannot exclude that the TCA cycle activity was directly in?uenced by the environ-mental conditions chosen,the wide range of conditions makes it rather unlikely that all of them have a similar and speci?c effect on the TCA cycle?ux.Moreover,it was shown recently that the TCA cycle activity of C.glutamicum does not change with the osmolarity of the medium(Varela et al.,2003).

The OAA cyt pool that is required for anaplerosis of the TCA cycle and biosynthesis of amino acids of the aspartate family was synthesized exclusively via pyruvate carboxylase (OAA cyt from pyruvate cyt)in most experiments.Only at

ND ND

Fig.2.Origin of metabolic intermediates in S.cerevisiae

during growth in batch cultures at306C and pH5?5deter-

mined by NMR(grey bars)and GC-MS(white bars).The

NMR-based data were taken from Maaheimo et al.(2001).The

standard deviations were estimated from redundant mass distri-

butions.ND,Not determined.

1088Microbiology150 L.M.Blank and U.Sauer

growth rates below 0?2h 21may a signi?cant fraction of 7–22%originate from the OAA mit pool,probably catalysed by the action of the mitochondrial OAA transporter Oac1p,since transport was shown to be bidirectional (Palmieri et al .,

1999).This ?ux ratio change was also independent of the environmental condition that reduced the growth rate.In contrast to the above two ?ux ratios,however,none of the 10other detected ?ux ratios exhibited a distinct correla-tion 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?19h 21)than at pH 3?0(0?07h 21).The reverse in vivo activity pattern was determined for the gluconeogenic reaction catalysed by PEP carboxykinase,which was only detected at pH values 7?0and 7?5.

To elucidate whether the growth rate was also correlated with any other physiological property,we quanti?ed the growth physiology over a wide range of pH values (Fig.5).For standard conditions (high glucose concentration,30u C,aerated batch culture,pH 5–6),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 speci?c glucose uptake rate decreased signi?cantly outside the optimal pH range of 4?0–6?0(Fig.5b).The biomass yield,in contrast,was rather constant and exhibited no correlation with the growth rate (Fig.5c).The speci?c 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 re?ect a similar response as was reported for high osmolarity,where production of the main osmolyte glycerol was increased in yeast (Hohmann,

0.50.40.30.20.1

0.1

0.20.30.40.5

pecific growth rate (h _1)

O A A m i t t h r o u g h T C A c y c l e

Fig.4.The fraction of OAA mit derived through the TCA cycle as a function of the maximum speci?c growth rate of glucose-grown S.cerevisiae under different environmental conditions in batch culture.The standard deviation of the TCA cycle activity was estimated from redundant mass distributions and the growth rate error was estimated using SigmaPlot 8.0(SPSS).DNP,2,4-dinitrophenol.ND ND

ND ND

(a)

(b)

Fig.3.Origin of metabolic intermediates in S.cerevisiae during growth in batch cul-tures.(a)At temperatures of 256C (black bars),306C (white bars)and 376C (grey bars),at pH 5?0;(b)at pH values of 3?5(black bars),5?0(white bars)and 6?0(grey bars),at 306C.The standard deviations were estimated from redundant mass distri-butions.Malic enzyme activity could only be determined in cultures with less than 5%TCA cycle activity,and was labelled not determined (ND )in all other cases.

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TCA cycle activity is a function of growth rate

2002).It should be noted that signi?cant ethanol produc-tion was not reported because the high evaporation rate in the shake ?asks compromised a thorough quantitative analysis.

Glucose sensing is not required for the increase of relative TCA cycle ?ux 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 ?ux.We therefore constructed isogenic mutants of the two glucose sensors Snf3p and Rgt2p (Ozcan et al .,1996).If extracellular glucose sensing played a signi?cant role in modulating the TCA cycle ?ux,one would expect no increase in ?ux 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 concentra-tion sensors,Rgt2p and Snf3p,respectively,we used two further mutants.Strain MMB4cannot 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 MTH1deletion.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 ?ux under standard conditions at growth rates of 0?39and 0?26h 21for MMB4and the grr1mutant,respectively,they still increased the

relative TCA cycle ?ux upon an environmental modulation of the speci?c growth rate with high osmolarity (Fig.6).These results strongly suggest that extracellular glucose sensing is not involved in the relative TCA cycle ?ux increase at slow growth rates in batch cultures.Nevertheless,the higher TCA cycle ?ux in the grr1and MMB4mutants,when

(a)(b)

(c)

(d)

Fig.5.In?uence of extracellular pH on maximum speci?c growth rate (a),speci?c glucose uptake rate (b),biomass yield (c)and speci?c glycerol production rate (d)in batch cultures of S.cerevisiae .Standard deviations were estimated using SigmaPlot 8.0(SPSS)and the Gaussian law of error

propagation.

Fig.6.The fraction of OAA mit derived through the TCA cycle in S.cerevisiae mutants impaired in glucose sensing.The values were determined under standard batch conditions (white bars)and at 0?75M NaCl (grey bars),which reduced the maximal speci?c growth rate by 40–60%.The experimental error of the TCA cycle activity was estimated from redundant mass distributions.

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L.M.Blank and U.Sauer

compared to the wild-type at the same growth rate,shows that glucose sensing has an additional repressive effect.

Is the TCA cycle?ux correlated with the rate of growth or glucose uptake?

The speci?c rates of growth and glucose uptake were coupled in the batch experiments performed;thus we cannot distinguish if one or both rates were correlated with the relative TCA cycle?ux.To address this question,we used a hexokinase II-de?cient mutant that exhibits allevi-ated glucose repression in glucose batch cultures(Diderich et al.,2001).When compared to the wild-type,the speci?c growth rate of the hxk2mutant was only modestly lower, but the speci?c glucose uptake rate was about45%lower. The relative TCA cycle?ux in this mutant exhibits no correlation with the growth rate but a much better,albeit weak,correlation with the glucose uptake rate(Fig.7).This would suggest that it is the speci?c glucose uptake rate rather than the speci?c growth rate that correlates with the relative TCA cycle?ux in wild-type S.cerevisiae. DISCUSSION

Using the newly developed GC-MS-based METAFoR analy-sis,we quanti?ed intracellular?ux responses of S.cerevisiae to a wide range of environmental conditions in batch culture.In contrast to all other monitored?ux ratios,the relative respiratory activity of the TCA cycle increased with decreasing growth rate and/or glucose uptake rate at extra-cellular glucose concentrations between1and5g l21.This correlation was independent of the four different chemical parameters that were used to modulate the growth rate. As the sole exception,temperature-induced growth rate changes gave a much less pronounced TCA cycle response. The correlation observed here between the rates of growth and/or glucose uptake and relative respiratory TCA cycle ?ux 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 require-ments(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 low0?1g l21(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 repres-sion 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 repres-sion 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?ux 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 con-sistent 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.grr1and MMB4)exhibited higher TCA cycle?uxes 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 hxk2mutant,however,the two parameters were decoupled and the relative TCA-cycle ?ux correlated better with uptake than with growth rate, thus providing some evidence for a?ux-related signal of glucose repression of the TCA cycle.The imperfect uptake–TCA cycle correlation in the hxk2mutant,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 in?uence the?ux(Carlson,1999;Gancedo,

0.1 0.2 0.3 0.4 0.5

0.6

0.5

0.4

0.3

0.2

0.1

0 2 4 6 8 10 12 14 16 18

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1998;Rolland et al.,2002).Apparently,glucose repression of the TCA cycle exhibits a different pattern and prob-ably 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?uxes on the exact environmental conditions may also explain minor differences in TCA cycle activity obtained from previous13C-labelling experiments(Christensen et al., 2002;Fiaux et al.,2003;Gombert et al.,2001;Maaheimo et al.,2001).

The methodology described for metabolic?ux pro?ling 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?ux ratios have varied only within a rather narrow range.Importantly,the?uxes 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 ?ux and the mitochondrial exchange?ux between oxalo-acetate 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?uxes within the cell remains rather stable. ACKNOWLEDGEMENTS

The authors wish to thank Juana Maria Gancedo and Peter Ko¨tter for providing yeast https://www.wendangku.net/doc/053809069.html,rs M.Blank gratefully acknowledges ?nancial support by the Deutsche Akademie der Naturforscher Leopoldina(BMBF-LPD/8-78).

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TCA cycle activity is a function of growth rate