Editor: Andrew Alspaugh
Physiological and transcriptional characterization of Saccharomyces cerevisiae strains with modified expression of catabolic regulators
Article first published online: 24 SEP 2007
FEMS Yeast Research
Volume 8, Issue 1, pages 26–34, February 2008
How to Cite
Schuurmans, J. M., Boorsma, A., Lascaris, R., Hellingwerf, K. J. and Teixeira de Mattos, M. J. (2008), Physiological and transcriptional characterization of Saccharomyces cerevisiae strains with modified expression of catabolic regulators. FEMS Yeast Research, 8: 26–34. doi: 10.1111/j.1567-1364.2007.00309.x
- Issue published online: 24 SEP 2007
- Article first published online: 24 SEP 2007
- Received 15 March 2007; revised 29 June 2007; accepted 2 July 2007.First published online 24 September 2007.
- glucose repression;
- fermento–respirative metabolism;
A comparative physiological and transcriptional study is presented on wild-type Saccharomyces cerevisiae and mutants with altered levels of catabolic regulators: hxk2Δ lacking hexokinase2, HAP4↑ overproducing hap4p and hxk2ΔHAP4↑. Relative to the wild-type, HAP4↑ showed the same growth rate with some increased yield on glucose, and hxk2Δ grew 28% slower but with a dramatically improved yield. Hxk2ΔHAP4↑ grew 14% slower but showed fully oxidative growth. A higher yield correlated with increased respiration. For both hxk2Δ strains, glucose repression was suppressed (upregulation of high-affinity sugar transporters, invertase and oxidative phosphorylation). T-profiler analysis showed that genes under control of the hap2/3/4/5-binding motif were significantly altered in expression in all strains. HAP4 overexpression, directly or in hxk2 knockouts, led to repression of the genes containing the Zap1p motif including ZAP1 itself, indicating altered zinc metabolism. Whereas HAP4 overexpression resulted in a shift towards oxidative metabolism only, deletion of HXK2 resulted in a strain that, in addition to being oxidative, almost completely lacked the ability to sense glucose. As the double mutant had an energy efficiency close to the maximum even with excess glucose and was derepressed to a larger extent and over a broader range, the functioning of the two regulators is in general considered to be additive.
Saccharomyces cerevisiae is a unicellular fungus, which, in its natural habitat, encounters rapid changes in the environment. In its original habitat, either ripening or decomposing fruits, the concentration of various sugars changes dramatically. It is therefore not surprising that to cope with these changes effectively, S. cerevisiae has developed a very complex regulatory network to control their uptake and utilization of various carbon and energy sources. Saccharomyces cerevisiae favors glucose (or fructose) for the latter purpose above other sources, and the presence of these sugars will repress the expression of genes encoding enzymes needed to utilize other sugars, such as maltose, sucrose and galactose. Additionally, the concentration of glucose per se is important, as at high concentrations (>30 mM), the organism also represses genes encoding enzymes needed for respiration (Gancedo, 1998). Thus, at high glucose concentrations, S. cerevisiae partly ferments glucose to ethanol even in the presence of an excess of oxygen, the so-called Crabtree effect (De Deken, 1966).
In some industrial processes, fermentation is undesirable as it significantly reduces the biomass yield on glucose, and ethanol at high concentrations can become a growth inhibitor. Hence, the catabolic regulatory network, also known as the glucose repression pathway, has been studied quite extensively. Many of the proteins involved in this regulation network have been identified and characterized (Gancedo, 1998). Signaling pathways can be divided into three, namely the G-coupled protein Gpr1p (Kraakman et al., 1999), the Snf3p and Rgt2p glucose sensors (Ozcan et al., 1996) and the glucose transport systems plus hexokinase II (Teusink et al., 1998). Gpr1p senses glucose and can activate the Ras-cAMP pathway (Rolland et al., 2002). As this pathway also controls many genes involved in nitrogen source utilization, Gpr1p links glucose availability to the nitrogen status of the cell. Snf3p and Rgt2p are involved in the control of the expression of various hexose transporter (HXT) genes (Ozcan et al., 1998), thereby directly exerting control on the glucose influx rate, as each individual hexose transporter has a different affinity for glucose transport (Diderich et al., 1999). With respect to glucose transport itself and hexokinase II (Moreno & Herrero, 2002; Elbing et al., 2004), the exact signal received and transmitted is not exactly known. Note that the above three systems are intricately linked and interact with each other at various levels. For instance, changes in HXT expression mediated by Snf3p or Rgt2p may change the glucose uptake rate, which again will affect the glycolytic flux and Hxk2p activity and this may subsequently affect the expression of HXT genes again.
Hexokinase II is not only the primary glucose-phosphorylating enzyme; it also has a regulatory function in glucose repression (Moreno & Herrero, 2002). It has been shown that in the presence of high glucose concentrations (>30 mM), hexokinase II migrates to the nucleus to exert its regulatory function, together with Mig1p (Ahuatzi et al., 2004). It has also been shown that a strain lacking the HXK2 gene is largely shifted towards respiratory metabolism (Diderich et al., 2001) and shows coconsumption of various sugars and even ethanol (Raamsdonk et al., 2001). The genes needed for utilization of these alternative sugars are normally repressed most predominantly by Mig1p and to a lesser extent by Mig2p. These latter two proteins also repress genes involved in respiration. A mig1Δmig2Δ strain indeed shows increased respiration (Klein et al., 1999), although not as profound as might have been expected, given the absence of the main repressor genes. For a fully functional respiratory chain and tricarboxylic acid (TCA) cycle, genes in the corresponding genes involved in these processes also need to be properly induced and activated. The transcriptional activator Hap4p mediates this process. It has in fact been shown that overproduction of this protein results in a considerably higher respiration rate and an increased biomass yield for S. cerevisiae grown on glucose (Blom et al., 2000; van Maris et al., 2001).
Clearly, the regulatory mechanisms that result in the net glycolytic, fermentative and respiratory activity of the yeast cell are complex, and the contributions of the various components of the network to achieve the final output remain largely unknown. Each of the regulators has its own characteristics, often studied in isolation (see also Gancedo, 1998; Santangelo, 2006). The question that arises is to what extent do the various regulatory proteins communicate with each other within the regulatory network. It may be that each of them affects gene expression and hence the physiology of the cell for some specific adaptive response or they may act in a concerted manner to provide the cell with a means to cope with a variety of conditions. In this study, a quantitative analysis is carried out of the separate contribution of two of the key regulators (Hap4p and Hxk2p) to the metabolic activity of baker's yeast as well their synergistic functioning by a comparative study of the wild-type strain, and single and double mutants.
Materials and methods
Strains and cultivation
The strains used were CEN.PK113-7D (MATa URA3 LEU2 HIS3 TRP1 SUC2 MAL MEL GAL), a strain overexpressing HAP4 (van Maris et al., 2001), an hxk2Δ:KanMX4 mutant (Diderich et al., 2001) and a strain overexpressing hxk2Δ::KanMX4 HAP4 (Lascaris et al., 2004). All mutants were derived from the CEN.PK113 background.
The strains were precultured overnight at 30 °C in 0.67% YNB (yeast nitrogen base), 2% glucose and 0.1 M phtalic acid (pH adjusted to 5.0 with KOH). Cells were inoculated to an OD600 nm of 0.2 in the same medium in batch fermentors with a 0.5 L working volume, aerated with 1 v/v min−1 and stirred at 500 r.p.m. Growth was followed in time both by measuring OD600 nm and biomass. All results are typical results representing a series of experiments.
Metabolite and flux analysis
Extracellular metabolites were measured every hour by HPLC. One milliliter of culture was mixed with 100 μL 35% (v/v) cold PCA. After 10 min, the PCA (perchloric acid) was precipitated by adding 55 μL 7 M KOH. After centrifugation, the supernatant was filtered and analyzed for glucose, ethanol, glycerol, acetate, succinate, phosphate and pyruvate by HPLC (Phenomenex type Rezex Organic Acid column; eluent, 7.2 mM H2SO4 at 45 °C). From the data on changes in dry weight and on metabolites, the specific flux of compound x during a given time span could be calculated, using the following equation:
expressed in mmol g dry weight−1 h−1. Biomass yield on glucose (Yglc g dry weight g glucose−1) was calculated from Y=1000(μqglc−1)/180 and is presented as a percentage.
The YATP value was calculated assuming that net 1 mole ATP per mole ethanol produced was synthesized and that 2.4 mole of ATP per mole of O2 reduced (Verduijn et al., 1991) was synthesized. The following relationships were used:
Preparation of total RNA and labeled cRNA
Samples for RNA isolation were collected every hour from a batch fermentor culture at appropriate time points, flash-frozen in liquid nitrogen and stored at −80 °C. Total RNA was extracted using the method of Llinas, in the same way as was done by Zakrzewska et al. (2005). The concentration and quality of RNA was determined by measuring A260 nm, A280 nm and A230 nm on a Nanodrop spectrophotometer. The purity and integrity of the RNA samples were further validated with an RNA LabChip on an Agilent Technologies 2100 Bioanalyzer.
Total RNA was labeled according to the manufacturer's protocol (Affymetrix). Twenty micrograms of total RNA was used for first-strand cDNA synthesis. This was followed by synthesis of second-strand cDNA. cDNA was purified using the GeneChip Sample CleanUp Module (Qiagen). The cDNA was used for the synthesis of biotin-labeled cRNA, which was performed with the ENZO BioArray HighYield RNA Transcript Labeling Kit (Affymetrix). The synthesized cRNA was purified with the GeneChip Sample CleanUp Module (Qiagen). The concentration and quality of labeled cRNA was tested using a Nanodrop spectrophotometer. Subsequently, the cRNA fragmentation reaction was carried out according to the manufacturer's protocol. The degree of fragmentation was confirmed with an RNA LabChip on an Agilent Technologies 2100 Bioanalyzer. The samples were stored at −20 °C before hybridization.
Hybridization and scanning of the DNA microarrays
The biotin-labeled cRNA samples were hybridized to the Affymetrix GeneChip® Yeast Genome S98 Array according to Affymetrix protocols (http://www.affymetrix.com). This chip contains 25-mer oligonucleotide probes for c. 6400 S. cerevisiae ORFs. Each ORF is represented by c. 16 probes, covering different parts of its sequence. Every probe has a neighboring probe that is identical, except for one nucleotide in the middle of its sequence. This probe is called the ‘mismatch’ probe (MM), as opposed to the ‘perfect match’ probe (PM). The arrays were scanned with the GeneArray Scanner System on standard settings at 3 mm resolution. The data were extracted from the scanned images with MAS 5.0 (Microarray Suite 5.0).
dchip was used to normalize the raw data. dchip is a software package implementing model-based expression analysis of oligonucleotide arrays and several high-level analysis procedures (Li & Wong, 2001). The model-based approach allows probe-level analysis on multiple arrays. The arrays were normalized by adjusting the overall brightness of the arrays to a similar level. Background subtraction was performed before calculating the expression values. Expression values were calculated using the perfect match model only, as this is unaffected by adverse effects of mismatch probes. A more detailed description of the procedure used is given in Zakrzewska et al. (2005).
T-profiler analysis of DNA microarray data
To assess the contribution of the expression of genes from specific gene classes to the total gene expression, T-profiler was used (Boorsma & Bussemaker, 2005). This algorithm uses an unpaired t-test to classify a difference between the mean of a set of a specific class of genes and the mean of the remaining genes of the total gene expression profile. Classes of genes containing a similar promoter element were studied. The elements used were within 600 bp of the start codon and were based on the literature and computational analysis results. GO: Ontology (http://www.yeastgenome.org) has been used to classify genes into specific categories. Further analysis was performed to see whether specific GO categories are overrepresented within the arrays.
The experiments were separately analyzed and no arbitrary cutoffs were applied before T-profiler analysis. T-profiler and additional information can be found on http://www.t-profiler.org.
In order to characterize the effects of mutations in the glycolytic regulatory network on the physiological and transcriptional level, wild-type and mutant cells were grown in batch fermentors and growth was followed from the early to the mid-log phase. During the growth phase, samples were withdrawn and quickly quenched to determine metabolite concentrations. Off-gas was analyzed for CO2 and O2 just before sampling for microarrays. From these data the physiological parameters as given in Table 1 were calculated. At an OD600 nm of c. 1, the cells were harvested for microarray analysis. The wild-type strain was found to grow on the medium used at a maximal specific growth rate (μ) of 0.36 h−1, with a respiratory quotient (RQ, defined as the ratio and indicative of the fermentative catabolism over the oxidative catabolism ratio; a value of c. 1 represents fully oxidative metabolism) of 3.4, indicating both respiration and fermentation. A biomass yield on glucose of 18% was calculated. The data obtained for the HAP4↑ strain are in accordance with a previous report (Blom et al., 2000): it showed a growth rate similar to that of as the wild type, with a slight reduction in ethanol production, glucose consumption and RQ, resulting in a biomass yield value on glucose that was 30% higher than the value found for the wild-type one. The absence of the other major global glycolytic regulator, HXK2, resulted in a significantly lower growth rate (0.26 h−1) but a much higher catabolic efficiency, i.e. catabolism was shifted towards respiration as can be deduced from the lower RQ ratio. Indeed, under the conditions tested, this strain showed nearly full respiration, concomitant with a doubling of the biomass yield on glucose. Virtually maximal energetic efficiency, i.e. a completely oxidative catabolism resulting in the highest energy conservation rate (Bruinenberg, 1986), with little decrease in growth rate capacity, was obtained with the hxk2ΔHAP4↑ strain. This strain had a growth rate of 0.31 h−1 and an insignificant ethanol flux and hence an even higher biomass yield of 47%, comparable with the values found for fully respiring wild-type cultures, e.g. as found in glucose-limited chemostat cultures (Bruinenberg, 1986).
|Strain||μ (h−1)||qetoh (mmol g dw−1 h−1)||qO2 (mmol g dw−1 h−1)||RQ||Yglc (%)|
For all strains, a YATP value of 11.5±1.0 was calculated assuming a P/O ratio of 1.2 (Verduijn et al., 1991). Carbon balances were consistently found to be 110±1% and therefore suggest a technical error. Even if this is due to a systematic overestimation of the gas flow analysis, none of the calculations can be affected to any significant extent.
Transcript profiles were made of samples taken from the mid-log phase at an OD between 0.8 and 1.0. A comparative analysis of these profiles of the mutants and the wild-type strain was carried out. With respect to the central carbon metabolism (sugar transport, glycolysis, the pentose phosphate pathway, the TCA and the glyoxylate cycle, shown in Fig. 1), the following interesting points were noted. In strains lacking HXK2, a strong upregulation of expression of the high-affinity glucose transporters HXT6 and HXT7, and a concomitant strong decrease in expression of the low-affinity transporter HXT1 was seen. Expression of HXT4, a glucose transporter with a rather high affinity, increased as well, whereas HXT3 (medium to low affinity) the mRNA levels remained unchanged. It appeared that the modification of the regulatory network resulted in a shift from low-affinity glucose transport to high-affinity glucose transport. In addition, HXT5 was upregulated in the hxk2 strains, whereas under these conditions HXT5 was completely shut off in the wild type.
In distinct contrast to the significant changes in the glucose transport machinery, the expression of most glycolytic genes was hardly changed by modifications to the level of the general regulators. The only exception seemed to be the expression levels of the sugar phosphorylating enzymes HXK1 and (to a minor extent) GLK1, both being strongly upregulated when HXK2 was absent, but not when only HAP4 was overproduced. Further noteworthy changes in the early part of glycolysis included upregulation of PFK1 and PFK2 but downregulation of ENO1, both in the hxk2ΔHAP4↑ strain. More dramatic effects of altered levels of HAP4p and/or hxk2p were observed for the later part of glycolysis; here, the change in the expression of ADH2 in the hxk2ΔHAP4↑ strain was the most notable alteration. It should be noted that ADH2 is the alcohol dehydrogenase that is expressed in the wild type only after glucose depletion and is associated with gluconeogenic growth. As exemplified by ADH2 and also FBP1, there was an upregulation of genes associated with gluconeogenic growth in strains lacking hxk2p but not in the strain only overproducing HAP4p. PDC5, ADH4 and ADH5 were also downregulated in the hxk2Δ strains. ALD4 was strongly upregulated in the latter cells, presumably to facilitate reshuttling of acetate into the TCA cycle. The other ALD genes remained generally constant, with the exception of a twofold downregulation of ALD3 in the hxk2Δ strain.
In line with the physiological data, there was a huge increase in mRNA levels of genes encoding TCA-cycle enzymes. In each mutant, many of these genes were strongly upregulated. In addition, in most cases, it was a gene encoding a single isoenzyme that was strongly increased, whereas the other remained unchanged, due to different repressors and/or inductors interacting with a specific isoenzyme only. In addition, a strong upregulation of almost all those genes that are involved in oxidative phosphorylation and expressed from the nucleus was observed. As shown in Fig. 2, although some specific genes remained expressed at the same level, the total expression levels of genes related to each individual component of the respiratory chain (NADH dehydrogenase, complex II, III and IV) and the ATP synthetase were increased. This correlates well with the increased respiration and to the lowering of ethanol production. Also, genes in the glyoxylate cycle were upregulated primarily in the hxk2Δ strains, most likely enabling the use of fermentation products to re-enter the respiratory chain.
Regulatory motifs (T-profiler)
A T-profiler analysis was carried out to score the activity of gene groups based on shared regulatory motifs. Essentially, this analysis quantifies the correlation between changes in expression of genes sharing common DNA motifs in their promoter region. Statistical analysis yields so-called t-scores to be assigned to such a group of genes, with a positive value indicating that these genes are upregulated on average. A P-value is assigned by comparing the amount of genes in a group against the total amount of genes. A P-value below 0.05 is considered to be significant. A detailed description of the analysis can be found in Boorsma & Bussemaker (2005).
Using T-profiler, it was found that the hxk2Δ and HAP4↑ mutant strains had increased levels of transcription of genes containing the general transcriptional activator motifs, known to be bound by PAC (RNA polymerase A and C box). The largest contribution to this increase was found to be due to genes required for mitochondrial biogenesis and function. As expected, the HAP4 (ccaatca) motif was found in all the mutants and the MIG1 (cggggta) motif was found in all strains, except in the HAP4↑ strain (see Table 2). Furthermore, strong contributions of the CAT8 and oleate responsive element (ORE) motifs were found in the hxk2ΔHAP4↑ strain, but surprisingly these motifs were not significantly present in either the hxk2Δ or the HAP4↑ strains. These two motifs are associated with a derepressed status, as found during growth on poor carbon sources or after the diauxic shift. Most surprisingly, the transcriptomes of the tested mutants showed a very strong negative contribution of the transcription factor of ZAP1, a transcription factor controlling zinc transport and homeostasis.
Analysis of nonredundant functional categories
A further analysis was carried out using the GO Ontology categories in the T-profiler. This methodology analyzes the extent to which genes within assigned functional categories are coregulated. Assignments may be based on the GO, MIPS or KEGG databases. A T-profiler analysis is presented based on the GO Ontology database in Table 3.
|GO Ontology category||t||ORFs|
|Mitochondrial electron transport chain||+4.68||21|
|Cellular component unknown||−6.21||743|
|Main pathways of carbohydrate metabolism||+4.86||65|
|Hydrolase activity, hydrolyzing O-glycosyl compounds||+4.48||31|
|Amino acid and derivative metabolism||−6.42||186|
|Cytoplasm organization and biogenesis||+5.09||670|
|Transition metal ion transport||−4.96||36|
A strong positive upregulation for both the mitochondrial category and oxidative phosphorylation for all the strains was observed, again showing the direct involvement of HAP4 in this process. Similarly, a strong upregulation of the hexose transporter category in all the strains (except HAP4↑) was seen, mainly due to the induction of high-affinity hexose transporters (HXT6-7). Furthermore, glucose catabolism was significantly downregulated as a whole in the hxk2Δ strains, but not in the other strains. This suggests that HAP4↑ does not interfere with glucose catabolism as a functional group and that it is dependent on other regulators under the control of Hxk2p. Finally, it should be noted that the finding of downregulation of genes under the control of Zap1p and Rcs1p is supported by a significant downregulation of the categories involved in transition metal ion transport and siderochrome transport, respectively, in the hxk2Δ and in the hxk2Δ HAP↑ mutants.
The rather subtle regulation of glucose catabolism in S. cerevisiae is brought about by a complex regulatory network that remains a challenge to understand, despite the fact that it has been the subject of many studies. Here the effects of two important catabolic regulators, Hap4p and Hxk2p, on the physiology of S. cerevisiae have been quantified. Their overproduction and absence, respectively, results in a major redistribution of the carbon flux over fermentative and respiratory pathways. Moreover, their effects are to some extent additive: in both the hxk2Δ and the HAP4↑ strain, a shift to respiratory catabolism is seen under excess glucose conditions and this is the case even more in the hxk2ΔHAP↑ mutant. Accordingly, the increased energy efficiency due to respiration results in considerable gain in biomass yield on glucose (up to threefold). Increased yield values on the energy source may indicate either a more efficient mode of energy conservation or a lowered energetic demand for biosynthesis and maintenance. As there is no reason to assume the latter (the cell composition presumably does not change and growth conditions being identical), the same YATP value for the various strains should be obtained. Assuming a P/O ratio of 1.2 (Verduijn et al., 1991), for all strains a value of 11.5±1 was indeed calculated.
Although under the conditions tested here, the growth rate in the hxk2Δ strain was significantly affected, it has also been shown that under other conditions (glucose-limited chemostat cultures with simple salt medium), this mutant can grow at a similar rate as the wild type. Apparently, the mechanisms to maintain a high μmax are less robust in this strain and it has been shown that the μmax of the hxk2Δ strain depends on the external glucose concentration (Diderich et al., 2002). In addition, HAP4 overexpression appears to restore the growth rate to almost that of the wild type. The HAP4↑ strain expresses an increased amount of respiratory genes, although this increase is not as large under these conditions as in the hxk2Δ strain. The additive effect of overexpression of HAP4 in the hxk2Δ background suggests that in the latter background, the genes needed for mitochondrial biogenesis and function are not induced to their fullest extent, and thus can be increased even more by addition of more active HAP4. This increased respiratory capacity may cause μmax to increase.
Many of the physiological effects observed in the various strains are in good agreement with the analysis of the transcriptome profiles. Thus, the upregulation of gene expression related to the TCA cycle and oxidative phosphorylation corresponds with the increased respiration. More complex relationships are seen with respect to the large changes in the glucose transport step, with a clear shift from low-affinity transport to high-affinity transport. The fact that this shift is absent in the HAP4↑ strain could be explained by induction of the high-affinity transport system(s) being brought about by relieving the repression due to increased phosphorylation levels of Mig1p and Mig2p (indirectly caused by the absence of Hxk2p) (Treitel et al., 1998). Downregulation of low-affinity transport could be interpreted as a response to the increased expression of high-affinity transporters. It cannot be excluded, however, that control of the glucose sensors Rgt2p/Snf3p may be altered or absent due to this mutation, resulting in a low glucose signal regardless of the extracellular glucose concentration.
It should be emphasized that the observed changes in expression levels are not necessarily due to a direct (absence of) interaction of the regulator per se: if a regulator acts upstream in the catabolic pathways, the resulting changes in metabolite concentrations may in turn affect the expression of catabolic genes encoding enzymes anywhere in the pathway. For example, increased respiration may cause glycolysis to be somewhat downregulated in all mutants by the accompanying changes in the redox state (or the NADH pool) and the energy state (or the ATP and ADP pool).
Interestingly, both the absence of hxk2p and the overexpression of hap4p results in a very significant increase in genes associated with growth on nonfermentable carbon sources, e.g. ADH2 and FBP1. This suggests that in the hxk2 mutants, glycolysis and gluconeogenetic enzymes, the glyoxylate cycle and the TCA cycle are all present and may be active, given proper intermediate levels. In this regard, it should be mentioned that coconsumption of ethanol and glucose in the hxk2 strain has been reported (Raamsdonk et al., 2001). The occurrence of the simultaneous expression of genes involved in processes with opposing functions but considered to be in the same categories illustrates the need for a proper definition of functional categories (Koerkamp et al., 2002).
With respect to the PP (pentose phosphate) pathway, only minor changes in expression were observed. Mass flux analyses showed that the flux through the PP pathway is stoichiometrically related to the growth rates owing to the need for NADPH as a biosynthetic redox carrier (Maaheimo et al., 2001). As there are minor changes in the growth rate for the various strains tested, PP fluxes should have changed according to this relationship; however, gene expression of the PP pathway has not significantly changed, suggesting that the changes necessary are either too small to be noted experimentally or they are not regulated at the transcriptional level. More drastic changes were seen for the glyoxylate cycle. Whereas in the wild type this cycle seems to be almost nonactive during growth on glucose, the hxk2Δ strains show an increased expression level of most of the genes involved. Moreover, only in the hxk2ΔHAP4↑ strain is there a strong induction of ACS1, which is required for the formation of acetyl-coenzyme A to be used either in the TCA cycle or in the glyoxylate cycle, which also suggests that this cycle is active in both strains and is actually both producing and consuming ethanol simultaneously, as has been reported previously (Raamsdonk et al., 2001).
On a more global scale, it was found that many DNA motifs contained in the promoter regions of genes changed in expression according to expectation. For instance, genes with the ccaatca (Hap2/3/4/5p) motif were mostly upregulated in all the mutants. By contrast, genes containing the Mig1-specific cggggta motif were mostly upregulated in all the strains, except the HAP4↑ strain. These findings are in close agreement with the proposed regulatory network involving mig1p and hap4p.
A surprisingly strong negative contribution was seen for the motif known to be bound by the Zap1p regulator, which controls zinc transport and zinc homeostasis. This suggests that there is a much lower demand for Zn in strains that overexpress HAP4. In accordance, it is observed that the zinc-dependent alcohol dehydrogenase ADH4 is downregulated in this strain. In addition, the manganese-dependent superoxide dismutase SOD2 was upregulated, thereby diminishing the need for the zinc-dependent SOD1, although it should be mentioned that the latter was not found to be downregulated. In general, the results point towards an additive effect of the two regulators studied.
Again specifically for the hxk2Δ and hxk2ΔHAP4↑ strains, categories associated with growth on alternative carbon sources, such as galactose and also maltose were found.
All the above suggests that glucose repression, and preference for glucose as the carbon and energy source are completely abolished in strains lacking Hxk2p. In other words, the HXK2 gene is crucial in maintaining glucose repression and its activity forms the basis for the Crabtree-positive phenotype. The absence of repression, respiratory activity and coconsumption of carbon sources other than glucose, combined with the maintenance of high-affinity glucose transport characteristics under excess glucose conditions, justifies the conclusion that a lack of Hxk2p renders S. cerevisiae relatively unable to respond appropriately to extracellular glucose and thus becomes a nonadaptive yeast. The data indicate that the mechanism of control by Hap4p is quite different. Changed levels of this regulator do decrease the Crabtree-positive behavior but do not affect glucose sensitivity. The effect of increased energetic efficiency seems to be solely due to increased expression levels, and hence capacity, of oxidative catabolism.
- 2004) The glucose-regulated nuclear localization of hexokinase 2 in Saccharomyces cerevisiae is Mig1-dependent. J Biol Chem 279: 14440–14446. , , & (
- 2000) Redirection of the respiro-fermentative flux distribution in Saccharomyces cerevisiae by overexpression of the transcription factor Hap4p. Appl Environ Microbiol 66: 1970–1973. , & (
- 2005) T-profiler: a web-tool to infer transcriptional module activity from gene expression data. Nucleic Acids Res 33: W592–W595. & (
- 1986) The NADP(H) redox couple in yeast metabolism. Antonie Van Leeuwenhoek 52: 411–429. (
- 1966) The crabtree effect: a regulatory system in yeast. J Gen Microbiol 44: 149–156. (
- 1999) Glucose uptake kinetics and transcription of HXT genes in chemostat cultures of Saccharomyces cerevisiae. J Biol Chem 274: 15350–15359. , & et al. (
- 2001) Physiological properties of Saccharomyces cerevisiae from which hexokinase II has been deleted. Appl Environ Microbiol 67: 1587–1593. , , , & (
- 2002) Effects of a hexokinase II deletion on the dynamics of glycolysis in continuous cultures of Saccharomyces cerevisiae. FEMS Yeast Res 2: 165–172. , , , , , & (
- 2004) Role of hexose transport in control of glycolytic flux in Saccharomyces cerevisiae. Appl Environ Microbiol 70: 5323–5330. , , , , , , & (
- 1998) Yeast carbon catabolite repression. Microbiol Mol Biol Rev 62: 334–361. (
- 1999) Investigation of the impact of MIG1 and MIG2 on the physiology of Saccharomyces cerevisiae. J Biotechnol 68: 197–212. , , , & (
- 2002) Dissection of transient oxidative stress response in Saccharomyces cerevisiae by using DNA microarrays. Mol Biol Cell 13: 2783–2794. , , , , , , , & (
- 1999) A Saccharomyces cerevisiae G-protein coupled receptor, Gpr1, is specifically required for glucose activation of the cAMP pathway during the transition to growth on glucose. Mol Microbiol 32: 1002–1012. , , , , , , , & (
- 2004) Overexpression of HAP4 in glucose-derepressed yeast cells reveals respiratory control of glucose-regulated genes. Microbiology 150: 929–934. , , , , & (
- 2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA 98: 31–36. & (
- 2001) Central carbon metabolism of Saccharomyces cerevisiae explored by biosynthetic fractional (13)C labeling of common amino acids. Eur J Biochem 268: 2464–2479. , , , , & (
- 2002) The hexokinase 2-dependent glucose signal transduction pathway of Saccharomyces cerevisiae. FEMS Microbiol Rev 26: 83–90. & (
- 1996) Two glucose transporters in Saccharomyces cerevisiae are glucose sensors that generate a signal for induction of gene expression. Proc Natl Acad Sci USA 93: 12428–12432. , , , & (
- 1998) Glucose sensing and signaling by two glucose receptors in the yeast Saccharomyces cerevisiae. EMBO J 17: 2566–2573. , & (
- 2001) Co-consumption of sugars or ethanol and glucose in a Saccharomyces cerevisiae strain deleted in the HXK2 gene. Yeast 18: 1023–1033. , , , , , , & (
- 2002) Glucose-sensing and -signalling mechanisms in yeast. FEMS Yeast Res 2: 183–201. , & (
- 2006) Glucose signaling in S. cerevisiae. Microbiol Mol Biol Rev 70: 253–282. (
- 1998) Intracellular glucose concentration in derepressed yeast cells consuming glucose is high enough to reduce the glucose transport rate by 50%. J Bacteriol 180: 556–562. , , , & (
- 1998) Snf1 protein kinase regulates phosphorylation of the Mig1 repressor in Saccharomyces cerevisiae. Mol Cell Biol 18: 6273–6280. , & (
- 2001) Modulating the distribution of fluxes among respiration and fermentation by overexpression of HAP4 in Saccharomyces cerevisiae. FEMS Yeast Res 1: 139–149. , , , , , , & (
- 1992) Effect of benzoic acid on metabolic fluxes in yeasts: a continuous-culture study on the regulation of respiration and alcoholic fermentation. Yeast 8: 501–517. , , & (
- 2005) Transcriptional response of Saccharomyces cerevisiae to the plasma membrane-perturbing compound chitosan. Eukaryot Cell 4: 703–715. , , , & (