Global transcriptional responses following a glucose pulse
In glucose-limited cultures of S. cerevisiae
where metabolism is fully respiratory, the very low residual glucose concentration (0.15 mM) was instantaneously increased to 5.6 mM by pulsing a concentrated glucose solution (). Three independent cultures were pulsed with glucose and samples for transcriptome analysis were taken at various time points up to 330 s after glucose addition. These three independent pulses were highly reproducible and the average coefficient of variation for transcript levels measured at replicate time points was below 19% (Supplementary information 1
Multiclass statistical analysis yielded a set of 1154 genes that displayed significant changes in transcription between at least two time points. Analysis of this set of genes by K
-means clustering identified five glucose-responsive gene clusters (). Clusters A, B and C (589 genes) grouped genes the expression of which was increased after glucose addition, whereas clusters D and E (565 genes) showed the opposite trend () (Supplementary information 2
). Significant changes in genes transcription only started between 120 and 210 s after the glucose pulse (), thus providing an exact quantification of the time required for glucose signal transduction and activation of transcription.
Glucose-responsive transcripts were subsequently analyzed to assess the enrichment of functional categories ( and Supplementary information 3
). The gene clusters that were transcriptionally upregulated after the glucose pulse showed a significant enrichment of metabolic functions and more specifically of amino-acid, purine ribonucleotide and nucleotide metabolism. Other significantly overrepresented categories among the upregulated transcripts were involved in the transcription, synthesis and processing of ribosomal RNA (). The gene clusters downregulated after the glucose pulse exhibited a significant enrichment in the ‘energy and metabolism' functional categories (). This global analysis revealed that drastic metabolic rearrangements are set in motion in the first minutes after release from glucose limitation.
Figure 2 Interpretation of transcriptome data. (A) The 1154 differentially expressed genes were distributed over MIPS functional categories as a function of time (s). The number mentioned in brackets indicates the total number of genes found in the categories. (more ...)
In order to identify the regulatory networks responsible for the transcriptional response to the glucose pulse, our dataset was combined with the genome-wide yeast location analysis datasets for 102 transcription factors from Harbison et al (2004)
. Thus, 12 transcription factors could be assigned to the clusters of upregulated genes with high confidence () (Supplementary information 4
). The function of these transcription factors obviously overlapped with the enriched functional categories. The most overrepresented factor was Bas1p, which corroborated the enrichment in purines and nucleotides metabolism categories. In addition to Bas1p, several sets of transcription factors could be distinguished and assigned to specific cellular functions (). A first coherent set including Met4p, Met31p, Met32p and Cbf1p,
all members of a transcriptional complex, revealed a major transcriptional reprogramming of sulfur metabolism (Rouillon et al, 2000
). Gcn4p and Leu3p are involved in amino-acid metabolism and biosynthesis. Fhl1p, Rap1p and Abf1p could be intuitively connected to ribosome biogenesis transcriptional control (Lascaris et al, 2000
; Martin et al, 2004
; Rudra et al, 2005
). However, the involvement of Ash1p (involved in filamentous growth; Pan and Heitman, 2000
) and Swi4p (cell cycle; Nasmyth and Dirick, 1991
) could not be predicted from the enriched functional categories ().
The 12 transcription factors found significantly linked to the clusters of downregulated genes were in good agreement with the transcriptional network involved in glucose catabolite repression (), such as the Cyc8p-Tup1p-associated factors Nrg1p and Sko1p, and general regulator such as Ume6p (Williams et al, 2002
) and the activator of the gluconeogenic regulon Sip4 (Schuller, 2003
), known to be repressed in the presence of excess glucose. Additionally, overrepresentation of Msn2p and Msn4p, STRE (st
lement) transcription factors, which are part of Gpr1p/Gpa2p glucose-sensing pathway (Gelade et al, 2003
), was observed, completing this regulatory network.
Addition of glucose to carbon-limited chemostat cultures results in a drain of the adenine nucleotides
The 5.6-mM glucose pulse to aerobic, carbon-limited cultures resulted in an immediate increase in the rate of glucose consumption. As described previously, the acceleration of glucose consumption was accompanied by switching to respiro-fermentative metabolism (Visser et al, 2004
), evidenced by the accumulation of ethanol and, to a lesser extent, acetate and pyruvate in the cultures (). Intracellular metabolites were analyzed with a particular emphasis on mono-, di- and triphosphate nucleotides (NXP). As previously shown (Theobald et al, 1993
), an immediate dramatic decrease of intracellular ATP concentration and a concomitant increase in AMP were observed, followed by slow recovery (). However, contrary to earlier assumption, this drop in ATP could neither be entirely attributed to the hydrolysis of ATP for energy transfer process such as glucose phosphorylation nor to the increase in RNA synthesis (Theobald et al, 1997
). First of all, the net increase in AMP and ADP did not balance the ATP loss. The adenine moiety pool (ATP, ADP plus AMP) was not conserved over time: after a clear drop within the first 60 s, the sum AXP rose (). Secondly, the profiles of the UXP, CXP and GXP showed similar initial decreases compared to the AXP profiles, albeit in different absolute level, and the amplitude of the GXP drop was 20-fold lower than for the AXP pool (). The U, G and C nucleotide pools returned to their initial concentrations or increased beyond those within the first 200 s after the glucose pulse (see Supplementary information 5
) (). As RNA biosynthesis consumes all four nucleotides (ATP, UTP, GTP and CTP) in a 0.254:0.246:0.226:0.274 molar ratio (Herbert et al, 1971
), AXP consumption for RNA biosynthesis can be calculated from the lowest drop in nucleotide pools, that is, CXP, assuming that the biosynthesis of the nucleotides does not immediately increase to the demand. This calculation reveals that RNA biosynthesis would only account for 5% of the decreased AXP pool (). Estimation of the other ATP-consuming processes compatible with an increase in the growth rate to its maximum (CEN PK 113-7D
=0.45/h; van Dijken et al, 2000
) in DNA, histidine and cofactor biosynthesis is far from sufficient to explain the observed drop in sum AXP ( and Supplementary information 6
). This clearly indicated that the AXP nucleotide pool was involved in unknown processes during the first 60 s after the addition of glucose. Quantitative determination of other possible adenine moiety sinks, free adenosine, adenine, hypoxanthine, nicotinamide adenine dinucleotide (NAD/NADH) and other adenosine-containing molecules such as S
-adenosylhomocysteine, or even activated sugars (ADP- and UDP-glucose), is of primary importance to understand this still unsolved phenomenon. The LC-MS/MS methods for the quantification of these metabolites are still under development.
Figure 3 Intracellular concentrations of mono-, di- and triphosphate nucleotides (AXP, CXP, GXP, UXP) in micromoles per gram of dry weight (μmol/g DW) following 5.6 mM glucose pulse. (A) ATP, (B) ADP, (C) AMP, (D) e-charge defined as the ratio (ATP+0.5 (more ...)
Following its early drop, the AXP pool recovered at a rate of approximately 0.01 μmol/g DW/s (calculated from the total nucleotide pool slope), whereas at steady state the net adenine nucleotide synthesis rate was only 0.0001 μmol /g DW/s (calculated from AXP concentration at steady state at a growth rate of 0.05/h; see Supplementary information 6
), that is, about two orders of magnitude lower than the observed recovery rate. This implies a strong increase in the adenine biosynthesis rate and an important role of the salvage pathway.
Metabolic inter-relations explain transcriptome co-responses: the adenine nucleotide pool drain is accompanied by upregulation of purine biosynthesis, C1 and sulfur metabolism
Consistent with the drop in adenosine nucleotide pool that has been previously discussed, the genes of the de novo
purine biosynthesis pathway, by which the AXP pool is synthesized, were found significantly overrepresented among the upregulated genes (). All but one of the 13 genes composing that pathway were upregulated (); only ADE16
, encoding a bifunctional IMP cyclohydrolase—phosphoribosyl-amino imidazole carboxamide formyltransferase, was expressed constitutively. The expression of genes encoding one-carbon (C1
) metabolism such as SHM2
was concurrently upregulated (). In addition to purine biosynthesis, the C1
metabolism, using folate coenzymes, is essential for glycine, methionine and methyl group biogenesis. Genes encoding mitochondrial glycine cleavage pathway (GCV1
), genes encoding methionine biosynthesis (MET3
) and S
-adenosylmethionine (methyl donor) biosynthesis (SAM1
) were also upregulated accordingly (). Piper et al (2000)
proposed a model in which cytosolic 5,10 methylene-THF is mainly directed to methionine biosynthesis for methylation reaction and mitochondrial one-carbon units derived from glycine are directed to purine biosynthesis. The simultaneous upregulation of the GCV
genes encoding mitochondrial glycine decarboxylase and SHM2
encoding the cytosolic serine-hydroxymethyltransferase clearly suggested that not only purine but also generation of S
-adenosylmethionine was important for the cell upon glucose exposure. Furthermore, the co-regulation of both folate metabolism branches revealed the fast utilization and recycling of 5,10 methylene-THF, as the upregulation of the GCV
genes is an indicator of a low level of 5,10 methylene-THF (Piper et al, 2000
). To further support the hypothesis that C1
metabolism is central in the transition described here, the genes encoding serine biosynthesis pathway (SER1
), which converts 3-phosphoglycerate to serine, were all significantly upregulated. Serine is indeed a co-substrate with THF for glycine and 10-formyl THF biosynthesis ().
Figure 4 Coordinated upregulation of the purine biosynthesis, sulfur assimilation and methionine and adenine salvage pathways. Metabolic pathways: The numbers indicated represent the fold change calculated between the expression values obtained at 330 s and the (more ...)
The transcriptional regulation of the purine biosynthesis and part of the 10-formyl THF (SHM2
) pathways has been shown to be under the control of Bas1p, a myb-like transcriptional activator (Denis et al, 1998
; Denis and Daignan-Fornier, 1998
). In agreement with this, the transcript level of BAS1
itself was coordinately upregulated more than two-fold. Integration of the data presented in this study and the supporting Bas1p location analysis by chromatin immunoprecipitation data (Harbison et al, 2004
) agreed on the regulation of the glycine cleavage pathway (ADE3
) by Bas1p as well. These results were also supported by the presence of TGACTC Bas1p binding site in the promoter of the latter genes. Altogether, these data would confirm the regulation by Bas1p of both purine and C1
metabolism derived from glycine.
On the other hand, a complex including differentially expressed MET28, MET31 and MET32 transcriptionally regulated the sulfur metabolism in a time-dependent manner (). Genes encoding methionine uptake transport system (MUP1, MUP3), sulfate assimilation to methionine (MET3, MET14, MET16, MET2, MET6) and formation of methyl donor AdoMet (SAM1, SAM2) were all upregulated. Adomet, a sulfur-containing compound that functions as a methylating agent, may reflect an increase in methylation processes, as will be discussed in the following section.
Finally, the methyl transfer converts Adomet to S-adenosylhomocysteine, which can be recycled to methionine via a few steps, in which an adenosine moiety is released. The gene involved in this pathway, SAH1, was also found to be significantly upregulated (). The released adenosine can be recycled to the adenosine nucleotide pool via the purine salvage pathway (involving upregulated AAH1, HTP1) reducing the cost of AMP synthesis via de novo purine biosynthesis pathway (which requires five ATP and one GTP to form one AMP molecule from PRPP).
Although the metabolic crosstalks are quite apparent from a biochemical network, the regulatory network that coordinates the upregulation of genes involved in de novo purine biosynthesis, serine biosynthesis, THF metabolism, sulfur metabolism and purine salvage pathway is not trivial. Alternately, upregulation of purine and THF metabolism on the one hand and sulfur metabolism on the other can be explained as discussed above. However, no available reports can relate serine biosynthesis gene regulation to THF metabolism, as current transcriptome and metabolome data seem to relate them to one another.
Although some phenotypic evidences relate the serine biosynthetic pathway to purine metabolism, as a mutation in SER1
(initially named ADE9
) leads to an adenine requirement, no molecular basis had been demonstrated so far (Buc and Rolfes, 1999
). While the search for BAS1
binding motif (TGACTC) in the promoter sequences of the SER1
identified this binding motif in SER2
, location analysis data for BAS1
failed to report any binding activity on SER
gene promoters. However, ChIP on chip data revealed that the SER33
promoter sequence was bound by Cbf1 (Harbison et al, 2004
), member of the Cbf1/Met30/Met4/Met28 complex (Thomas and Surdin-Kerjan, 1997
; Blaiseau and Thomas, 1998
) that regulates sulfur metabolism. Furthermore, genome-wide transcriptome analysis of S. cerevisiae
grown in chemostat revealed that SER33
was specifically upregulated under sulfur limitation (Tai et al, 2005
). These experimental facts suggest that cytosolic processes leading to C1
transfer for methionine and Adomet biosynthesis (serine biosynthesis, 5-methyl-THF synthesis) are coordinately controlled by central sulfur metabolism regulation.
Ribosome biogenesis is upregulated after relief from glucose limitation
The higher requirement of methylation substrate as deduced from the data mentioned above could be sustained as many genes involved in ribosomal RNA synthesis, processing and modification were upregulated following glucose addition (). This major induction of rRNA synthesis and ribosome biogenesis is indicative of a rapid synthesis and recruitment of the translational machinery. Among the 565 genes displaying a continuous increase in expression after pulsing glucose, 180 were related to transcription and protein synthesis of which 145 were involved in the assembly and activity of the translation machinery (). Although the microarrays used in this study cannot provide quantitative information on rRNA, the upregulation of the components of the machinery involved in their transcription suggested an increased transcription of the genes encoding for rRNA. Indeed five subunits (RPA12, RPA135, RPA34, RPA43, RPA49) and two essential initiation factors (RRN7 and RRN11) of the RNA polymerase I (RNA-pol I) involved in the transcription of the rDNA were upregulated. Four additional genes (RPB10, RPB8, RPB5, RPO26) encoding subunits shared by RNA-pol I, II and III and RPC19 encoding a shared subunit of RNA-pol I and III were also upregulated. Besides, seven genes encoding subunits of either RNA-pol II (RPB9, RPB11, ROX3) or RNA-pol III (RPC11, RPC31, RPC37, RPC82) displayed increasing transcription profiles. These data clearly illustrated the concerted upregulation of all three RNA polymerases. In conjunction with an upregulation of the RNA-pol I subunits, 23 genes coding for ribosomal proteins and 121 genes encoding proteins involved in processing, maturation, export, modification and transcription of rRNA and ribosome components shared a similar increase in expression ().
Figure 5 Genes upregulated during the glucose pulse involved in transcription and translation functions according to MIPS categories. The independent replicate transcriptome datasetss for each time point were averaged and then compared. Blue (relatively low expression) (more ...)
The ribosomes undergo modifications such as conversion of uridines into pseudouridines and addition of methyl group to specific nucleotides with a majority at the 2′-O
position of the ribose (Bachellerie and Cavaille, 1997
). Consistently, five genes participating in Adomet-dependent methylation activity were upregulated (NOP1
+2.0). In good agreement with literature, FHL1
(transcription factors involved in transcriptional control of ribosome biogenesis) targets were significantly overrepresented within the set of upregulated genes (). The significant upregulation of genes encoding specialized methyltransferases involved in translation initiation (GCD10
+2.0 and GCD14
+1.9) and tRNA modifications (NCL1
+1.8) indicated the importance of Adomet role in the metabolic circumstance described in this study ().
The role of methylation reactions using Adomet should be taken into consideration in explaining a part of the drain of the AXP pools in the first minute following the addition of glucose (). As shown here, this hypothesis would be in line with the upregulation of the purine and the methionine salvage pathways in response to the increase of S-adenosylhomocysteine when Adomet is used as methyl donor.
New insight into central carbon metabolism by integration of metabolite and transcript levels
The transcriptome analysis of the response of S. cerevisiae
to a sudden relief from glucose limitation classified 565 genes with downregulated transcription (clusters D and E; ). These clusters showed a specific enrichment for genes involved in energy generation and metabolism (). In previous chemostat-based studies (Boer et al, 2003
; Tai et al, 2005
), 19 genes exhibited consistent repression at high glucose concentration, irrespective of the limiting macronutrient (nitrogen, sulfur or phosphorus). In the current study, which applied dynamic glucose perturbation, 15 of these genes were found downregulated (JEN1, CSR2, HXK1, SUC2, SUC4, ISF1, GAL4, SOL1, MRK1,
YER067W, YGR243W, YIL057C, YMR206W) confirming the occurrence of glucose repression even within the short time interval of 330 s.
In addition, the integration of the central carbon metabolism metabolite data with transcript analysis allows better understanding of the very early metabolic response of the cell facing a sudden increase of environmental glucose concentration. As previously reported (Visser et al, 2004
), a rapid and transient increase of the metabolites of the top part of the glycolysis () was observed, whereas the metabolites of the lower part followed the opposite trend () (Theobald et al, 1993
). This metabolite distribution was regarded as a direct consequence of the rate-limiting phosphofructokinase activity (Theobald et al, 1997
). However, the constant increase of the F1,6-P2/F6P concentration ratio (as calculated from ) contradicts this initial hypothesis and instead supports the hypothesis that the increase of the glyceraldehyde-3-phosphate dehydrogenase reaction rate and the delayed increase in ethanol formation () affect the redox status of the cell, as shown by the large increase of NADH/NAD ratio (). This increase likely inhibits glyceraldehyde-3-phosphate dehydrogenase, which explains the observed reduction of metabolite concentrations of lower part of the glycolysis ().
Figure 6 Intracellular concentration of glycolytic and TCA cycle intermediates following 5.6 mM glucose pulse, expressed in micromoles per gram of dry weight (μmol/g DW) except mentioned otherwise. (A) Glucose-6-phosphate (G6P), (B) fructose-6-phosphate (more ...)
To restore redox homeostasis, yeast produces ethanol and glycerol () and fine-tunes the tricarboxylic acid (TCA) cycle, which is a source of reduced cofactor. In contrast to regulation of glycolysis in steady-state cultures, which predominantly takes place at the post-transcriptional level (Daran-Lapujade et al, 2004
), TCA cycle regulation was visible at metabolome and transcriptome levels. The concentration of TCA cycle intermediates such as malate, fumarate and α-ketoglutarate increased to reach a new pseudo-steady-state level, whereas the citrate concentration was constant throughout the pulse experiment (), which indicates flux discontinuation from α-ketoglutarate to C4
pool (metabolite concentrations are provided in Supplementary information 7
). This complies with the previous observation that under respiro-fermentative condition, the TCA cycle is not performing as a cycle but as two separate branches: an oxidative branch from pyruvate to α-ketoglutarate and a reductive branch from pyruvate to malate and fumarate (Gombert et al, 2001
). Moreover, the transcriptome data clearly illustrate rapid transcriptional responses of the structural genes encoding TCA cycle enzymes (). Eleven genes (KGD2, MDH1, SDH3, SDH1, ACO1, IDP3, MDH2, IDH2, LSC1,
YMR118C, YLR164W) involved in the TCA cycle were immediately downregulated, whereas CIT2
were upregulated (). Transcription of HAP4
, which encodes the activator of the Hap2p/3p/4p/5p complex involved in the transcriptional regulation of TCA cycle genes (Lascaris et al, 2003
), was concomitantly downregulated more than eight-fold.
Figure 7 Addition of glucose to a steady-state culture triggers an acceleration of the mRNA turnover. Effect of glucose pulse on expression of genes of the glycolytic, storage carbohydrate and TCA cycle metabolic pathways. The numbers represent the fold change (more ...)
Our results are consistent with the notion that trehalose-6-phosphate (T6P) inhibition of glucose phosphorylation is required to avoid excessive phosphorylation and ‘glucose-accelerated death' (Blazquez et al, 1993
; Francois and Parrou, 2001
). The concentration of T6P increased by 15-fold within the first 180 s following the addition of glucose to reach a concentration (4.8 mM) () that suffices for the complete in vitro
inhibition of both hexokinase I (Ki
=40 μM) and hexokinase II (Ki
=200 μM) () (Blazquez et al, 1993
). In the meantime, the genes GLK1
encoding gluco- and hexokinases were also downregulated, thus reinforcing the notion that the cell limits glucose phosphorylation in response to a sudden increase in glucose availability.
The response of the metabolites of the upper part of the glycolysis was extremely rapid (within the first 30 s) and preceded all detectable transcriptional control. However, we also measured a significant increase in fructose-2,6-biphosphate (F2,6P2) about 120 s after the perturbation (). This rise was accompanied by a concomitant transcriptional upregulation of PFK27 (encoding a 6-phosphofructo-2-kinase (+2.74)) and a downregulation of PFK26 (encoding the second form of the 6-phosphofructose-2-kinase (−3.43)) and FBP26 (encoding the fructose-2,6-bisphosphatase (−1.8)), which is involved in the degradation of F2,6P2 (). The accumulation of F2,6P2 fitted with its role in activating the phosphofructokinase activity rate, whereas the substrates levels (F6P and ATP) were low, maintaining a high product (F1,6P2) concentration. This correlation between metabolite and related transcripts levels illustrates how complex and synergistic metabolic and transcriptional control are in fine-tuning metabolic pathway regulations to master the large changes in metabolite concentration.
Fast decay of downregulated transcripts indicates active mRNA degradation
The average half-life of yeast poly(A)+
mRNA in S. cerevisiae
has previously been estimated around 30 min using a temperature-sensitive RNA-pol II mutant (Wang et al, 2002
). shows a comparison of mRNA half-lives observed by Wang et al (2002)
with those calculated from the present study (see Supplementary information 8a
for calculation). In our experiments, simultaneous transcription and degradation may occur, which should lead to an underestimation of the presented mRNA decay constant. Nevertheless, the set of 565 downregulated transcripts displayed an order of magnitude faster decay with an average half-life of 4 min (). This suggests that active mRNA degradation, which has previously been described for SDH1
, affects large sets of genes involved in processes such as the TCA cycle and storage carbohydrate metabolism. For example, 18 genes involved in the latter process (TPS1, TPS2, TSL1, ATH1, NTH2, GSY1, GSY2, GLG1, GLC3, GAC1, GPH1, GDB1, PGM2, UGP1, GIP2 FSP2, PIG2, PIG1
) showed a much faster decay than expected based on previous data on mRNA half-lives (Wang et al, 2002)
(Supplementary information 8
Figure 8 (A) Scatter plot comparing mRNA half-life measured by Wang et al (2002) and the mRNA half-life calculated from the data of this study. The dashed line represents the average value of half-life. (B) Motifs identified in the 3′ untranslated regions (more ...)
In S. cerevisiae
and higher eukaryotes, mRNA degradation can be initiated by poly(A) tail shortening (van Hoof and Parker, 2002
). After poly(A) tail removal, mRNA degradation involves the decapping enzyme Dcp1p (LaGrandeur and Parker, 1998
) and the 5′-to-3′ exonuclease Xrn1p (Heyer et al, 1995
). This mechanism was indeed proposed for the faster decay of SHD1, SHD2
genes (Prieto et al, 2000
). Additionally, 3′ degradation may occur, which involves the exosome, a complex of 3′-to-5′ exonucleases. In addition to the mRNA degradation, the exosome is involved in the processing of several RNA species. In yeast, the exosome is recruited via the fixation of Puf3p on AU-rich motif located in the 3′UTR of a gene (Olivas and Parker, 2000
; Jackson et al, 2004
Possible involvement of 3′ degradation was investigated by a systematic analysis of the 250 base pairs downstream of the stop codon of 163 downregulated genes belonging to the significantly overrepresented functional categories (). Four consensus motifs were found statistically overrepresented compared to their respective genome representation by binomial probability (). Three of them were close variations of the already described Puf3p motif (UGUANAUA). A fourth motif was found in a small subset of 19 genes. Out of the 163 genes tested, 116 genes harbored at least one of the four motifs, and 80 genes carried two or more elements (Supplementary information 9
). The observed correlation between fast mRNA decay and the presence of conserved 3′UTR sequences supported a widespread involvement of active mRNA degradation in the fast response of S. cerevisiae
to glucose. This mechanistic synergy results in an accelerated disappearance of translational substrate, which might be because of energy saving and optimizing the translational efficiency of newly transcribed mRNA. However, in the metabolic context described in this study, this mechanism could also be considered as a nucleotides salvage pathway. The RNA degradation recovery might be of importance regarding the fitness of a strain to adapt to rapid change in environment.
With the exception of the responses in purine and sulfur metabolism, many of the transcriptional events after the relief from glucose limitation have previously been linked to the TOR signal transduction pathway. In particular, the TOR pathway has been implicated in the regulation of mRNA turnover in S. cerevisiae
(Albig and Decker, 2001
) and in the expression of genes for ribosomal RNA and ribosomal proteins (Martin et al, 2004
; Schawalder et al, 2004
; Rudra et al, 2005
). In mammalian cells, mTOR has been proposed to be a homeostatic ATP sensor (Dennis et al, 2001
). Based on the transcript levels alone, this would have offered an attractive explanation for the observed upregulation of TOR targets after relief from glucose limitation. However, the metabolite data revealed that, in fact, intracellular ATP concentrations decreased after the glucose pulse. This observation underlines how simultaneous analysis at different information levels (transcriptome, metabolome) can improve interpretation of biological phenomena.