Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Research article

Transcriptome analysis of a respiratory Saccharomyces cerevisiae strain suggests the expression of its phenotype is glucose insensitive and predominantly controlled by Hap4, Cat8 and Mig1

Nicklas Bonander1, Cecilia Ferndahl2, Petter Mostad3, Martin DB Wilks4, Celia Chang5, Louise Showe5, Lena Gustafsson2, Christer Larsson2 and Roslyn M Bill1*

Author Affiliations

1 School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK

2 Department of Chemical and Biological Engineering/Molecular Biotechnology, Chalmers University of Technology, Box 462, 405 30, Göteborg, Sweden

3 Mathematical Sciences, Chalmers University of Technology and Göteborg University, SE-412 96, Göteborg, Sweden

4 Cameron International Ltd., Queen Street, Stourton, Leeds, LS10 1SB, UK

5 The Wistar Institute, 3601 Spruce Street, Philadelphia, Pennsylvania, 19104, USA

For all author emails, please log on.

BMC Genomics 2008, 9:365  doi:10.1186/1471-2164-9-365

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2164/9/365


Received:13 March 2008
Accepted:31 July 2008
Published:31 July 2008

© 2008 Bonander et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

We previously described the first respiratory Saccharomyces cerevisiae strain, KOY.TM6*P, by integrating the gene encoding a chimeric hexose transporter, Tm6*, into the genome of an hxt null yeast. Subsequently we transferred this respiratory phenotype in the presence of up to 50 g/L glucose to a yeast strain, V5 hxt1-7Δ, in which only HXT1-7 had been deleted. In this study, we compared the transcriptome of the resultant strain, V5.TM6*P, with that of its wild-type parent, V5, at different glucose concentrations.

Results

cDNA array analyses revealed that alterations in gene expression that occur when transitioning from a respiro-fermentative (V5) to a respiratory (V5.TM6*P) strain, are very similar to those in cells undergoing a diauxic shift. We also undertook an analysis of transcription factor binding sites in our dataset by examining previously-published biological data for Hap4 (in complex with Hap2, 3, 5), Cat8 and Mig1, and used this in combination with verified binding consensus sequences to identify genes likely to be regulated by one or more of these. Of the induced genes in our dataset, 77% had binding sites for the Hap complex, with 72% having at least two. In addition, 13% were found to have a binding site for Cat8 and 21% had a binding site for Mig1. Unexpectedly, both the up- and down-regulation of many of the genes in our dataset had a clear glucose dependence in the parent V5 strain that was not present in V5.TM6*P. This indicates that the relief of glucose repression is already operable at much higher glucose concentrations than is widely accepted and suggests that glucose sensing might occur inside the cell.

Conclusion

Our dataset gives a remarkably complete view of the involvement of genes in the TCA cycle, glyoxylate cycle and respiratory chain in the expression of the phenotype of V5.TM6*P. Furthermore, 88% of the transcriptional response of the induced genes in our dataset can be related to the potential activities of just three proteins: Hap4, Cat8 and Mig1. Overall, our data support genetic remodelling in V5.TM6*P consistent with a respiratory metabolism which is insensitive to external glucose concentrations.

Background

We engineered the first respiratory Saccharomyces cerevisiae strain, KOY.TM6*P, by integrating the gene encoding a chimeric hexose transporter, Tm6*, into the genome of an hxt null yeast [1]. Subsequently we demonstrated the transferability of this respiratory phenotype to a yeast strain, V5 hxt1-7Δ, in which only HXT1-7 had been deleted [2]. The resulting V5.TM6*P strain produced only minor amounts of ethanol when cultured on external glucose concentrations as high as 5% [2]. Despite the fact that the glucose flux was reduced to 30% in the V5.TM6*P strain compared with that of its parental V5 strain, the V5.TM6*P strain produced biomass at a specific rate as high as 85% of that produced by the V5 wild-type strain, the yield itself increasing by 50% in the mutant compared to its parental strain.

Having performed this initial physiological characterization of the V5.TM6*P strain, we wanted to examine more thoroughly the basis of its respiratory phenotype by comparing its transcriptome with that of its wild-type parent strain, V5. This approach has been widely used to obtain a global picture of the cellular responses of yeast to a wide range of physiological changes, including those experienced at the diauxic shift [3-5]. Recently Ohlmeier and colleagues [6] demonstrated that major changes at the transcriptional level are not reflected at the protein level. Indeed, focusing on the mitochondrial proteome following a diauxic shift, they showed that the levels of only 18 out of 253 identified proteins had changed (17 increased, and 1 decreased). Among them were proteins involved in the tricarboxylic acid (TCA) cycle (Sdh1, Sdh2, and Sdh4) and the respiratory chain (Cox4, Cyb2, and Qcr7). This seeming disagreement with their observations of heterogeneous changes in the transcriptome (where more than 4,000 changes were recorded) is also consistent with our own prior data where we showed that the protein patterns obtained from a 2D-PAGE analysis of the two strains (grown under the same conditions used in this study) were not substantially different [2]. We did note, however, that the levels of the upper-part glycolytic intermediates and ATP did differ between the two strains, and that Cdc19 is present in lower amounts in V5.TM6*P. This discrepancy between proteome and transcriptome regulation has also been observed by others [7] and is also consistent with the fact that there is an acknowledged lack of relationship between the levels of glycolytic proteins and the glycolytic flux [8-10]. Nonetheless, researchers continue to depend on mRNA as an indicator of cellular state, which is a situation that will continue while methods for the global analysis of protein expression are improved [7].

In this respect, Brauer and colleagues [3-5] have used transcriptome analysis to examine adjustments and remodelling of the metabolism of glucose-limited yeast cultures. In that study, the authors constructed metabolic models consistent with their cDNA array data. In this work, the V5.TM6*P strain was therefore characterized at the transcriptome level in comparison with the parental V5 strain in order to obtain a more global understanding of any genetic remodelling underlying the physiological differences between them. Both strains were cultured in 50 g/L glucose with six samples being taken at defined points between 38 and 6 g/L glucose for cDNA array analysis in two independent fermentations for each strain. Consequently, we generated a highly redundant dataset (n = 12) where 922 genes were either induced or repressed when comparing V5 and V5.TM6*P under all glucose concentrations. Using in vivo studies together with documented binding specificities of three transcription factors known to have a role in the regulation of respiratory genes (the Hap complex, Cat8 and Mig1), it was possible to correlate the up-regulation of all but 18 of the 146 genes induced by a factor ≥2.0 with the presence of one or more of their binding sites. This means that 75% of the transcriptional response of the genes in our dataset could be related to the activities of just three transcription factors, which in turn would account for 88% of the response of the induced set. Finally, we made the unexpected observation that both the induction and repression of many of the genes in our dataset had a clear glucose dependence in the parent V5 strain that was not present in V5.TM6*P. This important result indicates that the relief of glucose repression in the wild-type is already operable at much higher glucose concentrations than is currently widely accepted and that the phenotype of the respiratory strain is glucose insensitive by comparison.

Results

We compared the transcriptional profiles of the V5 and V5.TM6*P strains cultured in 50 g/L glucose, with samples extracted between 38 and 6 g/L residual glucose. We identified 190 genes with adjusted p-values < 0.05 that changed their expression levels either up by a factor of ≥2.0 (146 genes) or down by a factor of ≤0.5 (44 genes) when comparing the two strains at all three sampling points. These genes are presented by functional category of their gene products in Additional file 1 and are discussed below. Additional file 2 provides the information in Excel format and also includes other relevant literature data. A further 636 genes changed their expression levels either up by a factor of <2.0 (317 genes) or down by a factor > 0.5 (319 genes) and are presented as part of Additional file 3. The remaining 96 genes were either dubious, Ty-transposable elements, or were detected due to cross-hybridisation, as indicated in the legend to Additional file 1.

Additional file 1. Transcriptome analysis of V5.TM6*P compared with the parental V5 strain. Genes were tabulated if their expression was changed on going from the V5 parental strain to V5.TM6*P, as described in 'Materials and Methods'. All genes were then sorted according to their roles in yeast cellular physiology and alphabetically by gene name under each sub-heading. Note that HXT1-7 are deleted in the V5.TM6*P with a part of HXT1 and HXT7 reincorporated as the TM6* chimera. Due to overall high homology within the hexose transporter family we still observed changes on the cDNA array (HXT1 (YHR094C); factor 0.4;

    HXT4
(YHR092C); 0.2; HXT6 (YDR343C); 2.1; these are not listed in Additional file 2). As YLL053C is annotated as continuous with YLL052C (AQY2) in some strains, these ORFs are counted once here as YLL052C. Genes that were dubious were not included in Additional file 1 and neither were the Ty-transposable elements, YGR161C-C (encoding TyA gag protein) and YNL054W-B (encoding TyB gag protein). The top 30 genes with the largest numerical factor change (both up- and down-regulated) are underlined. If no functional information was available, phenotypic data from a deletion mutant was entered under 'Function'. In the 'Factor Change' column, change is expressed as a factor, where that factor is x when a gene expressed with intensity '1' in V5 is expressed with intensity 'x' in V5.TM6*P. All T-tests were jointly adjusted for multiple testing using the false discovery rate method (disregarding correlations between genes): p-values were adjusted so that when selecting all genes with p-values less than a threshold q, a proportion of q false positives would be expected amongst these genes. The genes shown have p-values < 0.05, and thus the expected false discovery rate is 5%. Transcription factors are listed if a binding site was predicted as described in the text. Data on 'Copies/cell' are from the yeast GFP fusion localization database and were collected from wild-type yeast grown on glucose. 'Protein Localisation' was extracted from the Saccharomyces Genome Database on 14th February 2008 and in each case has been manually curated by the site's curators unless the entry is underlined, indicating that the localization has been extracted from a genome wide study.

Format: PDF Size: 215KB Download file

This file can be viewed with: Adobe Acrobat ReaderOpen Data

Additional file 2. This table provides the information from Additional file 1, but in Excel format, with base expression values as well as SAGE [41] and other literature data also included. In the 'Factor Change' column, change is expressed as a factor, where that factor is x when a gene expressed with intensity '1' in V5 is expressed with intensity 'x' in V5.TM6*P. In Additional file 1 we have removed dubious genes and Ty-elements, but all genes that were originally spotted on the array are included in Additional file 2. As for Additional file 1, YLL052C and YLL053C are treated as one data point.

Format: XLS Size: 100KB Download file

This file can be viewed with: Microsoft Excel ViewerOpen Data

Additional file 3. A further 636 genes changed their expression levels either up by a factor of <2.0 (317 genes) or down by a factor > 0.5 (319 genes) and are presented in Excel format.

Format: XLS Size: 245KB Download file

This file can be viewed with: Microsoft Excel ViewerOpen Data

The majority of genes that have an altered expression in V5.TM6*P compared with its parental strain also have an altered expression following diauxic shift

We compared our dataset with three sets of expression data for yeast undergoing a diauxic shift, where the samples had been taken at least 2 hours after the glucose is depleted [3-5]. Of the 190 genes in our dataset that changed their expression levels either up or down by a factor of 2 or more, 154 of these could be identified as changing in the same direction in at least one of these three datasets, with 124 having at least two matches. Only 8 genes (ERG3, RPS27A, YHB1, TRX1, YER188w, YKL066w, YAR068w and Y0L150c) had changes that were in the opposite direction in at least two of the datasets. This significant overlap between our data set and the data sets of the three previous studies is entirely consistent with the fact that there is a transition from a respiro-fermentative metabolism in V5 to a respiratory metabolism in V5.TM6*P.

Glycolytic and gluconeogenic genes have substantially altered expression levels in V5.TM6*P

Four of the most strongly induced genes in Additional file 1 are the glycolytic genes, HXK1 (factor 9.0) and GLK1 (3.8), and the gluconeogenic genes, PCK1 (6.9) and FBP1 (5.3). Indeed, as shown in Figure 1, the induction in V5.TM6*P of several glycolytic and gluconeogenic genes could be consistent with these cells accommodating for a lack of glucose; the hypothesized mechanism behind the strain's reduced ethanol-producing, respiratory phenotype. The final step in glycolysis which produces pyruvate, is catalyzed by pyruvate kinase. In V5.TM6*P the pyruvate kinase genes, CDC19 and PYK2, respond in an opposing manner: CDC19 is repressed by a factor of 0.4 (consistent with our earlier proteome analysis where Cdc19 is present in lower amounts in V5.TM6*P than V5) while PYK2 is induced and is relieved of its glucose-repressed state as a result of the low glucose flux in the respiratory strain. Reduced ethanol production in V5.TM6*P compared to the parental strain is also consistent with the two most strongly repressed genes PDC1 (0.2) and PDC5 (0.2) encoding the main pyruvate decarboxylases.

thumbnailFigure 1. An overview of our dataset in the context of key yeast metabolic pathways. Genes with altered expression in V5.TM6*P compared with V5 are marked as follows: induced genes are denoted in large, bold font; down-regulated genes are crossed out. Genes that were previously found to be altered in studies of respiratory-state yeast [6], but which are not found in our dataset are denoted in small, grey text. The chimeric Tm6*p transporter, which comprises the amino-terminal half of Hxt1 and the carboxy-terminal half of Hxt7, is responsible for the respiratory phenotype of V5.TM6*P at high glucose concentrations and is present in the plasma membrane. The grey boundaries represent the plasma membrane (top) and the mitochondrial membrane (left). Note that ICL(1.5), NDE1 (1.8) and GPD1 (1.8) are induced in our study (see Additional file 3). LPD1 is a component of pyruvate dehydrogenase.

Alternative carbon source usage genes are induced in V5.TM6*P compared with V5, consistent with a reduced glucose flux

An adaptation to enable growth on alternative carbon sources is seen in the induction of the plasma membrane transporter genes MAL31 and STL1 as well as the genes SUC2, GUT1, GUT2, ALD6 and BDH1 encoding proteins for the utilization of sucrose, glycerol, acetaldehyde and butanediol, respectively. GUT1 in particular is strongly induced by a factor of 6.2, and STL1 is the most strongly induced of all genes on the cDNA array (21.7), suggesting that V5.TM6* has undergone a substantial genetic adjustment compared to its parent. Furthermore, CYB2 (10.1) and DLD3 (0.5), whose gene products are involved in the conversion of lactate to pyruvate, and in the case of CYB2 feeding the released electrons into the electron transport chain, are oppositely regulated. Biochemical data and sequence patterns for YGL157w (0.3) [11], YAL061w (2.9) and DSF1 (9.4, homologous to D-mannitol 2-dehydrogenase from Rhodobacter sphaeroides) suggest that these ORFs encode an oxidoreductase, a polyol dehydrogenase and a putative sugar dehydrogenase, respectively, which could be involved in channelling alternative carbon sources into the TCA cycle. The carbon-deficient state of V5.TM6*P [1] could also be consistent with the induction of CRC1 and CAT2 whose gene products are involved in β-oxidation and carnitine-dependent transport of acetyl-CoA from peroxisomes to mitochondria as a way of using carbon from sources other than glucose [12]. The induction of SFC1, JEN1, FBP1 and ATO2 seen in our study has previously been reported to be a specific identifier of carbon-limited growth [13].

All TCA- and glyoxylate cycle genes have increased expression in V5.TM6*P compared with V5

The genes encoding the enzymes of the TCA cycle (CIT1, ACO1, IDH1, KGD1, KGD2, LSC2, SDH1-4, FUM1, MDH1) all have a significant (adjusted p-value < 0.05) increase in expression when comparing V5 and V5.TM6*P. In addition, the gene encoding an isoform of aconitase (ACO2) has its expression level reduced by a factor of 0.5. Fermentation is almost completely abolished in the V5.TM6*P strain, and we have previously shown that the TCA cycle has a higher carbon flux [2] to harness the electron flow into the respiratory chain and generate a proton gradient using F0F1-ATPase for ATP synthesis (several respiratory chain genes are induced, as discussed below). Interestingly, all genes of the TCA-cycle are induced by greater than a factor of 3.5 except for CIT1/CIT2 and KGD1/KGD2 where the factor change is 2.2 – 3.5 for each gene of the pair, presumably yielding the same overall transcriptional response. Consistent with this, Ohlmeier and colleagues have shown that Sdh1, 2 and 4 all have increased expression after a diauxic shift [6].

The glyoxylate cycle is anabolic, yielding gluconeogenic precursors, and all its genes (CIT2, ACO1, ICL1 (induced by a factor of 1.5; Additional file 3), MLS1 and MDH2) are induced in V5.TM6*P compared with V5 (Additional file 1, Fig. 1). This could be consistent with the correspondingly increased biomass yield of the V5.TM6*P strain (Table 1). We note that V5.TM6*P has a lower biomass yield than expected from a totally respiratory phenotype. In chemostat culture under aerobic, fully respiratory conditions, a yield of 0.5 g/g is typical at low dilution rates, whereas the observed low yield in V5.TM6*P has already been published using a range of sugars and concentrations [2]. Furthermore, numerous experiments on KOY.TM6*P, our original respiratory strain based on CEN.PK2-1C, [1] have yielded identical results. These observations are in agreement with our findings in Table 1 of 0.32 g/g in V5.TM6*P and 0.21 g/g in V5 from batch cultures.

Table 1. Growth characteristics of V5 and V5.TM6*P at 30°C starting at 50 g/L glucose.

Genes encoding respiratory chain enzymes are induced in V5.TM6*P compared with V5

33 key genes encoding mitochondrial enzymes involved in respiration – as well as F0F1-ATPase and its chaperones – are all induced by a factor of 2 – 4 in V5.TM6*P (Additional file 1). The gene encoding Ndi1 (Fig. 2), which transfers electrons from NADH to ubiquinone (Q in Fig. 2) is induced the most (by a factor of 4.1), while all other components of the respiratory chain are induced by a factor of 2 – 2.5. Genes encoding heme synthesis and incorporation (HEM2, CYT2), complex assembly (MBA1) and heme degradation (HMX1) are also induced by a factor of 2 – 2.5. Cytochrome c oxidase also has different chaperones that are induced to form an active structure by proteolysis (COX20), as well as delivery of copper (COX17). In spite of the higher respiratory rate in V5.TM6*P, several genes involved in detoxification of oxygen radicals are repressed, e.g. genes encoding peroxiredoxin (AHP1) and thioredoxin (TRX2) are repressed by a factor of 0.4 – 0.5. Conversely, the capacity for nitrous oxide detoxification is increased by the induction of YHB1 by a factor of 2.4. It is possible that the respiratory phenotype of V5.TM6*P could induce a higher level of oxygen radical formation inside its mitochondria and thus oxidation of guanine could occur on the mitochondrial DNA. Such detrimental effects could be counteracted, via the observed induction of OGG1 by a factor of 2.2.

thumbnailFigure 2. An overview of our dataset in the context of the respiratory chain in yeast. Genes with altered expression in V5.TM6*P compared with V5 are marked as follows: genes induced by a factor ≥2.0 are denoted in large, bold font; genes induced by a factor <2.0 are denoted in smaller, grey font.

Changes of expression in genes encoding mitochondrial and plasma membrane transporters are consistent with a respiratory phenotype in V5.TM6*P

The genes encoding the hexose transporters, Hxt1 to Hxt7, have very high sequence identities [14] and thus cross reactivity between them is expected on the array. This explains the observed signal for HXT1, 4 and 6, which have been deleted in V5.TM6*P. HXT13, HXT15 and HXT16 are known to be repressed by high levels of glucose and are consequently induced as expected in V5.TM6*P. A carbon source deficiency triggers an up-regulation of genes encoding plasma membrane proteins responsible for the import of alternative carbon sources, as already noted: STL1 (21.7; glycerol); JEN1 (10.2; lactate), PUT4 (5.8; proline – the main nitrogen source in grape juice) and MAL31 (3.1; maltose). A gene associated with nutrient deficiency in yeast [15], YGP1, is induced by a factor of 2.6, again supporting the notion that V5.TM6*P has undergone a genetic adjustment compared to its parent.

It seems that V5.TM6*P also has an altered requirement for metal ions in that genes encoding three metal ion channels have altered expression: CTR3 (copper transport) is induced by a factor of 3.0, while ZRT1 (zinc transport) and ENB1 (iron transport) are repressed by a factor of 0.4 – 0.5. It is likely that copper import is increased so that copper can be transferred to cytochrome c oxidase, which is the main copper-requiring enzyme in Additional file 1, with 3 copper ions per complex and a copy number of 5,000–12,000 per cell. There are only 45 genes in Additional file 1 with greater than 5,000 copies per cell and none of these has copper as a prosthetic group except cytochrome c oxidase.

The amino acid uptake systems encoded by BAP3 and GNP1 are repressed while the proline permease encoded by PUT4 is induced. PUT4 has a carbon source responsive element (CSRE) [16] that is expected to be induced at the low glucose flux experienced in the V5.TM6*P strain, while BAP3 and GNP1 are regulated by a sensor of extracellular amino acid concentration, where Ssy5 [17] is a vital component. It is likely that in V5.TM6*P the repression of BAP3 and GNP1 is a result of the repression (by a factor 0.65; Additional file 3) of SSY5 seen on the cDNA array. Interestingly, induction of genes encoding the ammonia transport system in the V5.TM6*P strain (ATO2, MEP1) could suggest that the strain is experiencing general nutrient limitation. It has previously been reported for example that the induction of another MEP family member, MEP2, is associated with nitrogen limitation [13].

88% of genes induced in V5.TM6*P compared to its parent have transcription factor binding sites for one or more of the Hap complex, Cat8 or Mig1

Genome-wide studies have yielded a wealth of information on which promoters are bound by different transcription factors [18]. In order to rationalise these resources, we began by looking for biologically-verified data on transcription factors that might control the repression of the subset of 44 genes of Additional file 1. Since only limited information was publicly available, we did not pursue this part of the dataset further. In contrast, for the 146 induced genes in Additional file 1, our observation of the similarity between the transcriptional changes in the V5 to V5.TM6*P transition and those in the transition from glucose to ethanol growth lead us to analyse the roles of Hap4, Cat8 and Mig1 which have been previously been verified to be crucial to the expression of genes in the diauxic shift [16,19,20]: Hap4 (in complex with Hap2, 3 and 5) [19] and Cat8 (optionally in complex with Sip4 and Adr1) [21] are transcriptional activators, whereas Mig1 (optionally with Mig2/3) is a transcriptional repressor that works in concert with Tup1-Cyc8 [22].

The Hap complex is comprised of proteins Hap2, 3, 4 and 5, and has separate subunits for DNA binding and transcriptional activation. DNA binding is mediated by Hap2, 3 and 5 [23] while Hap4 functions as an activation domain. The expression of HAP4 is repressed by glucose and is responsible for regulation of Hap complex target genes. We began by scrutinising data for a HAP4 over-expression strain, in which 246 genes had their expression affected by at least a factor of 2 [19], which is 5.3% of the genome. 56 of these genes were found in our dataset and are listed in Table 2. Only three of these (GLK1, CTR3 and ZRT1) are down-regulated in the HAP4 over-expression strain, whereas GLK1 and CTR3 are induced in the V5.TM6*P strain. Interestingly, a typical Hap complex binding site was not seen in the 500 nucleotide region upstream of the GLK1, CTR3 and ZRT1 start codon (where the first A in ATG is position 1) while all other genes in Table 2 except AGX1, MNP1 and YNK1 were found to have at least one Hap complex binding site. These Hap complex-binding-site-containing genes comprise a significant subset (26% of our dataset of 190 genes in Additional file 1) with a verified biological dependence on Hap4 activation. Clearly, then, Hap4 has a statistically-significant role to play in the metabolism of V5.TM6*P as there is an association between Hap4 dependence and our dataset (χ2 = 182.75, degrees of freedom = 1, p = 0.00).

Table 2. Biologically-verified data on transcription factors that act on genes also found in Additional file 1: transcript dependence on HAP4 over-expression.

Since six of the genes from the HAP4 over-expression study did not contain Hap complex binding sites, we did a further in silico analysis of the induced genes of Additional file 1. We noted that the genes of Table 2, contained motifs that could be sub-divided into 2 distinct families: CCAATG (which we denoted as a Hap4_1 site) and (G/C)CAA(G/T)CAA (a Hap4_2 site). The bold sequences are the conserved sequences previously identified in the HAP4 over-expression strain study [19]. The genes of Additional file 1 were then examined 500 nucleotides upstream and 200 nucleotides downstream from the start codon using WebMOTIF [24] for these two biologically-relevant sites since it has been previously demonstrated that Hap complex binding sites are statistically over-represented up to 400 nucleotides upstream from the start codon [19]: we found that 112/146 (77%) induced genes and 127/190 (67%) of the complete dataset in Additional file 1 had one or more Hap complex binding site (Additional files 1 and 4). Our analysis included presumed binding sites such as CAAATC and CCAAAC which arose from the biologically-verified data on transcription factors (Table 2). For example, AQY2 is identified as Hap complex-dependent, and has two CCAAAC sites, but no CCAATNA sites. There are only two other genes that do not contain CCAATNA sites in Table 2, namely CRC1 and ALD6. In addition it was notable that Hap complex consensus sequences are predominantly found upstream of mitochondrial genes with 84/127 (66%) of such genes having at least a partial mitochondrial location. Furthermore, of the induced genes with a known cellular location and at least two Hap complex binding sites, 69/80 (86%) are localized to the mitochondria. It appears, then, that Hap4 activation is preferentially directed at mitochondrially-related functions especially within the TCA-cycle and respiratory chain, but cytosolic support via the glyoxylate cycle is also potentially regulated by Hap4 activation (Additional file 1). It was further noted that multiple Hap complex binding sites are associated with induction (Fig. 3A). A Fisher's exact test showed that there was a statistically-significant association between the change in gene expression and the number of Hap complex binding sites in the gene (p = 0.00). This supported the fact that genes with two or more Hap complex binding sites are likely to be induced, whereas those with one site may be either induced or repressed.

Additional file 4. Binding sites are listed for the Hap complex, Cat8 and Mig1. The file lists exact position and sequences of the relevant sites.

Format: XLS Size: 306KB Download file

This file can be viewed with: Microsoft Excel ViewerOpen Data

thumbnailFigure 3. (A) The influence of multiple Hap complex sites on the change in gene expression of the genes in Additional file 1. A Fisher's exact test was performed for genes containing predicted binding sites for the Hap complex and their association with induction or repression. Genes were grouped into either those with one Hap complex binding site or those with two or more according to Additional file 1. (B) The influence of Hap4, Cat8 and Mig1 on the magnitude of induction of the genes in Additional file 1. The average fold-induction of genes in Additional file 1, grouped according to the transcription factor binding sites they contain, was calculated. It was observed that those containing binding sites for each of the Hap complex (where Hap4 is the activator), Cat8 and Mig1, had the highest average fold induction with Mig1 being a dominant factor in high induction. A bar for Cat8 is not included as only one gene (YOR019W, factor change 2.1) has a Cat8 site alone. (C) A Venn diagram of the distribution of binding sites for the Hap complex, Cat8 and Mig1 in the genes in Additional file 1. 88% of the genes in Additional file 1 contain a binding site for one or more of the Hap complex, Cat8 and Mig1. The range of induction for the 190 genes from Additional file 1 is 2.0 to 21.7.

In order to adopt a similar approach for Cat8, we examined available biological data for Cat8 and Adr1 (as they are co-regulators [25]) from chromatin immunoprecipitations [25], and mRNA expression ratios in wt/Δcat8 strains [16]. We identified 20 genes out of 66 from these studies that were also in our dataset (Table 3). Only ATO2 was oppositely regulated when comparing our dataset. For all other genes, the change in expression followed the same trend of up-regulation (Table 3). As for the Hap4 analysis, we examined these 20 genes for Cat8 binding sites, and identified a typical Cat8 motif [21] in 13 of them, nine being of the Cat8-Sip4 type, TCCATTSRTCCGR (Additional file 4). The remaining six induced genes (CYB2, GUT1, GUT2, LSC2, SUE1 and ODC1) were further scrutinised, and it was apparent that all except ODC1 had been identified from immunoprecipitation data for Adr1 alone, consistent with their lack of Cat8-Sip4 motif. The induced genes of Additional file 1 were therefore searched from the start codon to 1,500 nucleotides upstream for the generic Cat8 motif, CC------CCG motif giving 54 genes (Additional file 4). The non-coding intergenic regions of these genes were then searched using WebMOTIF [24] and HXK1, COX4, ODC1, OM14, OM45 and YOR019w were found to share the consensus sequence GCCSSTSS(W/Y))CMS in common with IDP2 and are thus also listed in Additional file 4. We noted that genes with these Cat8 binding sites are induced in V5.TM6*P by an average factor of 6.2 while the average for induced genes in Additional file 1 is 3.3. The highly-induced, Cat8-site-containing genes include HXK1 (factor change 9.0), PCK1 (6.9), STL1 (21.7), JEN1 (10.3), ODC1 (6.3) and SFC1 (18.3).

Table 3. Biologically-verified data on transcription factors that act on genes also found in Additional file 1: transcript dependence on Cat8 and Adr1.

We observed that some strongly-induced genes in Additional file 1 did not have Hap complex or Cat8 binding sites. Indeed, the expression of many genes such as SUC2 is already known to be repressed by both Mig1 and Mig2 in glucose media, although Mig1 is the main factor in this response [20,26]. We therefore examined biological data on gene expression in Δmig1 and Δmig2 strains [20,26]. In these studies, 11 genes were presented as being Mig dependent, of which 5 were found in Additional file 1 (HXK1, EMI2, HXT13, HXT15 and DSF1) and 3 in Additional file 3 (REG2, DOG2 and YLR042C). We then examined protein binding microarray data [27] and tabulated the combined outputs as Table 4, which lists 15 Mig-dependent genes. When searching the induced set in Additional file 1 in silico for genes containing Mig1 binding sites, 31 were identified, predominantly containing consensus sequences of the SUC2B type (CCCCGGAT), but also in some cases sites that were more homologous to SUC2A (sharing the consensus motif CCCC(G/A)(G/C)AT [28]). We noted that the presence of a Mig1 site in a gene correlated with high induction (Fig. 3B) and that overall, our analysis provides a highly complete description of how three transcriptions factors might regulate the 146 induced genes of Additional file 1 (Fig. 3C).

Table 4. Biologically-verified data on transcription factors that act on genes also found in Additional file 1: transcript dependence on Mig1.

We noted that HAP4 (factor change 1.7, Additional file 3) is induced and CAT8 (0.5, adjusted p value < 0.06; Additional file 5), MIG2 (0.5, Additional file 1) and MIG3 (0.3, Additional file 1) are down-regulated in V5.TM6*P compared to V5. In our dataset, the specific change in array signal for MIG1 had an adjusted p-value > 0.05 over the complete experimental range. The result for CAT8 was unexpected as it contains an upstream Mig1 binding site similar to HAP4 (which is induced) and Cat8 target genes such as FBP1 and PCK1 are themselves induced. It is possible that the transcript was not probed accurately on the array either due to cross-hybridisation between closely related DNA sequences or on account of its low base expression value (0.24). A future real time Q-PCR experiment would probe the individual transcript.

Additional file 5. A computation in which the datasets with the two lowest glucose values have been removed (V5: 10.5, 7.4 g/L glucose and V5.TM6*P: 13.5, 5.6 g/L glucose). Genes are only listed if they are not present in Additional files 1, 2, or 3. Overall, the amplitudes of the factor changes varied by only 9.3% in this new calculation compared with the original one, which indicated that the glucose dependence of the V5 strain does not have a major influence on the way we generated the data presented in Additional file 1.

Format: XLS Size: 60KB Download file

This file can be viewed with: Microsoft Excel ViewerOpen Data

Transcripts dependent on external glucose concentration are not found in the respiratory V5.TM6*P strain, even though several glucose- or carbon-source-associated transcripts are glucose dependent in the parental wild-type strain

The transcriptome of the respiratory V5.TM6*P strain was not found to be responsive to changes in glucose concentration in the culture medium. However, in the wild-type parent strain (V5) the transcriptome was found to vary with glucose availability according to a least squares fit of the logarithmic array values for RNA extracted at the six different glucose concentrations. This dependence on external glucose concentrations is seen in the V5 strain for 20 genes (including those without functional annotations) with an adjusted p-value < 0.05 (Fig. 4). The genes with the strongest dependence on external glucose concentrations were found to be HXK1, RGS2, ADH7, CHA4 and GLK1 (Fig. 4). The sugar kinases HXK1 and GLK1 are induced as expected as they have their maximal expression during growth on other carbon sources [16]. The gene product of RGS2 inhibits Gpa2 in the PKA pathway, which is one of the pathways induced by glucose [29]. ADH7 appears to be induced on carbon sources other than glucose [30]. In a study by DeRisi and colleagues, batch yeast cultures were harvested for cDNA array analysis at gradually decreasing glucose concentration [5]. Our examination of the supplementary data provided by the authors showed that as glucose was consumed between 18.7 and 7.5 g/L, three of the genes in Figure 4 (GLK1, HXK1, and FAL1) were found in the top 22 genes with a changed expression from that study. Overall, the results in Figure 4 show that glucose repression is gradually relieved on going from 37 g/L to 9 g/L glucose. Furthermore relief of repression is already apparent at a surprisingly high glucose concentration, in contrast to the assumption that it is typically triggered over a range of low glucose concentrations [29], the trigger being very low for some genes [31].

thumbnailFigure 4. Transcript dependence on external glucose concentration for 20 genes in the V5 strain. A clear glucose-dependence (grey bars) was observed for all genes in the V5 strain with only a small variation in array signal (white bars) for the same genes in V5.TM6*P. Genes marked with a dot also have a significant change of expression on going from V5 to V5.TM6*P and as such are listed in Additional file 1. The factor change in gene expression was obtained as the quotient between the expression values at 37 and 9 g/L glucose given by the line fitted to the expression data for each gene. R2 values are for the linear fit for the V5 strain. The R2 values for V5.TM6*P were between 0.16 and 0.78 indicating no significant linear relationship.

In order to examine whether the glucose dependence of genes in V5 affected our data in Additional file 1, we excluded the data points at 13.5 and 5.6 g/L glucose for V5.TM6*P, and 10.5 and 7.4 g/L glucose for V5 (which gave the highest array signals) and compared the cDNA array data in this case with each other exactly as for the full data set. Following this procedure, only a small subset of genes that are highly induced had their factor changes increased substantially, but this did not affect the fact that they remain within the top 30 most induced genes in Additional file 1. They are HXK1 (9.0 in Additional file 1, 19.3 following re-calculation; the gene contains binding sites for the Hap complex, Cat8 and Mig1); STL1 (21.7, 27.5; Hap complex, Cat8); DSF1 (9.4, 15.1; Hap complex, Mig1); CYB2 (10.1, 13.4; Hap complex, Mig1) and FBP1 (5.3, 7.4; Cat8, Mig1). Fourteen of the seventeen genes that were most affected by this recalculation were found to contain a Mig1 binding site. It is therefore possible that Mig1 is also involved in relieving glucose repression in the 10 to 5 g/L range, and not only at the depletion of glucose at the diauxic shift. Overall, we found that the amplitudes of the factor changes varied by only 9.3% in this new calculation compared with the original one, which indicated that the glucose dependence of the V5 strain does not have a major influence on the way we generated the data presented in Additional file 1.

Discussion

The main physiological change in both the diauxic shift and the transition from a respiro-fermentative to a respiratory metabolism is that glycolysis is channelled to support respiration: this occurs upon glucose depletion during the diauxic shift and throughout the growth curve in glucose medium in V5.TM6*P. Although previously-published genomic analyses of yeast's response to the diauxic shift transition have all been sampled under different conditions – rich (YPD) medium with 2% glucose [5], minimal defined medium in chemostat cultures at different dilution rates [4], and minimal defined media in batch cultures sampled 4 h after the diauxic shift [3] – common themes emerge with changes on going from V5 to V5.TM6*P. Overall 81% of genes which are induced or repressed on going from V5 to V5.TM6*P in our study were found in at least one of these three datasets for the diauxic shift. Furthermore, we also find that for all proteins induced in a study of the diauxic shift by 2D-gel electrophoresis [6], the corresponding genes are induced on going from V5 to V5.TM6*P except for the alcohol dehydrogenase, Adh2. There is a hence close agreement between induction of the following genes and the levels of the corresponding proteins: FBP1, ICL1, PCK1, IDP2, MLS1, DLD1, ALD6, SDH1 and CIT2. This strong correlation with previously-published data and our own observed correlation of data for Cdc19 and CDC19 supports the phenotypic similarity between the V5 to V5.TM6*P transition and the diauxic shift. It is worth noting that there is no clear correlation of our data in Additional file 1 with any possible differences in growth rate between the two strains. In a study by Regenberg and colleagues [32], 180 genes changed in expression by more than a factor of 4 when comparing growth rates. Of these 180, 3% of all genes are found in Additional file 1 providing no significant overlap in data.

It is also possible to see parallels between our data set and a study of nitrogen deprivation and stationary phase growth for wild-type yeast by Gasch and colleagues [33]. This is supported by the fact that ten of the 15 genes that were found to be strongly induced upon nitrogen deprivation and growth in stationary phase in that study are also induced in the V5 to V5.TM6*P transition. Only two genes, YGR067c (likely to encode a transcription factor as it has high partial sequence homology with MIG3 and ADR1 and it fine-tunes the response to nutrient limitation) and ECM13, are not induced in the V5.TM6*P strain, but were highly induced in the Gasch study [33] upon nitrogen deprivation and stationary phase growth. In addition, we find that the genes encoding Mep1 and Ato2, which are involved in ammonia transport are also strongly induced in V5.TM6*P in our study. Such an induction is seen in both the diauxic shift studies performed in defined media [4,34], but not in rich medium [5], suggestive of nutrient limitation. In fact, V5.TM6*P also appears carbon limited, not least as a result of the fact that amongst the most strongly induced genes are FBP1 and PCK1, which encode gluconeogenic enzymes. STL1, MAL31 and SUC2 (encoding proteins involved in usage and/or transport of alternative carbon sources: glycerol, maltose and sucrose respectively) are induced in V5.TM6*P, also consistent with its low glycolytic flux and suggestive of an adaptation of metabolism ('discontinuous modelling) as seen by Brauer and colleagues [4,34] and supported by our previous work [1]. Several of these genes including FBP1 and STL1, as well as JEN1, ODC1, SFC1 and ATO2 have already been identified as being either induced under carbon limitation or being specific indicators of carbon limitation in yeast cultures [13], consistent with our own data.

The analysis of transcription factor binding sites present in the genes of Additional file 1 revealed that 127 genes had Hap complex binding sites, the majority of which encode mitochondrial proteins. Of genes that only had Hap complex binding sites present, thirteen had only one and of these only five were induced leading us to hypothesise that multiple Hap complex binding sites are correlated with induction of the Additional file 1 genes (Fig. 3A). Interestingly, in a microarray screen of a hap1Δ mutant for Hap1-dependent genes [35], 7/24 genes that were induced in the hap1Δ mutant are also induced in Additional file 1 (CYB2, MLS1, HMX1, PUT4, STL1, HPA2, PUT1), and in each case also have a Hap complex binding site. In addition, 19 genes containing a Cat8 site and 31 genes containing a Mig1 site were predicted, with many of these genes being experimentally-verified in previous in vivo studies. Our analysis of the regulation of 88% of the induced genes of our unique respiratory strain (Fig. 3) is further supported by a novel computational method [36] that predicts the Hap complex and Mig1 control the genes of Additional file 1; Cat8 is not included in that database.

A quite unexpected result from comparing the transcriptional profiles of V5 with V5.TM6*P is the data set in Figure 4, showing genes from the parent strain that are clearly glucose dependent and that the glucose dependence observed begins at concentrations (6 – 36 g/L glucose) higher than the range generally regarded as the glucose-repressed region. Strain DBY7286 [5], which is unrelated to V5, previously showed a gradual relief of glucose repression starting at 20 g/L glucose for GLK1, HXK1 and FAL1. These results for V5 and DBY7286 suggest that gradual relief is a general S. cerevisiae phenomenon. Future studies will show whether the gradient of the slope is different for different strains. That such glucose dependence requires an intact glucose uptake system is suggested by the fact that it is not seen in the V5.TM6*P strain: the array signal is not changed significantly for any of the genes in the V5.TM6*P strain at the different glucose concentrations sampled. This implies that in V5.TM6*P, glucose sensing is independent of external glucose concentration. The wild-type yeast, on the other hand, shows an extensive modulation of its expression profile at high glucose concentrations despite the fact that its physiological response is largely unaffected. The insensitivity of the V5.TM6*P strain therefore makes it an extremely valuable biotechnological tool as it can be cultured in a wide range of external glucose concentrations, whilst maintaining the same respiratory phenotype [2].

Conclusion

In this study, we have been able to characterize the transcriptome of a unique respiratory yeast strain. We have been able to identify highly complete collections of known genes in the TCA cycle, glyoxylate cycle and respiratory chain that are consistent with a respiratory metabolism. Our results suggest that there has been genetic remodelling predominantly through the activity of Hap4, Cat8 and Mig1, and that the gene expression profile of V5.TM6*P during growth on glucose resembles wild-type S. cerevisiae cells in the diauxic shift.

Methods

Yeast strains

The auxotrophic wine yeast strain V5 (MATa ura3 gal) and the V5 hxt1–hxt7Δ mutant strain (MATa ura3 gal hxt514Δ::loxP hxt367Δ::lox P hxt2Δ::loxP) were kindly provided by the Professor Bruno Blondin, Montpellier, France. The construction of V5.TM6*P was previously described by Henricsson and colleagues [2] where the expression cassette from KOY.TM6*P [1] was transferred into the genome of the V5 hxt1–hxt7Δ strain by using the primers PROHXT3 (TCAAATGGCGGTGTAGTTTGAAAAG) and TERHXT7 (TTAAGTGACGGGCGATGAGTAAGAA). Transformants were selected by growth on glucose. The resulting ura3 strain is referred to as V5.TM6*. The auxotrophic V5 wild type and V5.TM6* used in this study were made prototrophic by integration of URA3 [37] and are referred to as the V5 wild-type strain and V5.TM6*P, respectively.

Growth conditions

For both pre-cultures and main cultures, 5 × defined minimal medium [38] with 5% glucose as the sole carbon and energy source was used. A two-step pre-cultivation was performed on a rotary shaker. First a 10 mL culture was grown for 72 h, and used to inoculate 100 mL medium, which after 30 h at 30°C was inoculated to give a final OD610 of 0.05 in a bioreactor (BRO2, Belach Bioteknik AB, Sweden) in a volume of 2.5 L. Polypropylene glycol P2000 was added as antifoam (100 μL/L). The cultivation conditions were 30°C, 1,000 rpm, pH 5.0 with an airflow of 1.25 L/min. Gas evolution was monitored on-line (type CP460 O2/CO2, Belach Bioteknik AB). Cultivations were performed at least in duplicate.

RNA preparation

Yeast cells (2 – 20 mL) were directly transferred into twice their volume of ice-cold water. The cells were collected by centrifugation at 0°C for 5 min at 3000 × g, frozen in liquid nitrogen and stored at -80°C. Total RNA was then prepared using the RNeasy kit from Qiagen, following the manufacturer's instructions.

cDNA array production

PCR products for the yeast ORFs were generated from yeast DNA using the ORF-specific primer set from Research Genetics and then re-amplified with the Resgen universal forward and reverse primers (Catalogue number 40612). PCR products were prepared by Randy Strich (Fox Chase Cancer Centre, USA) as part of a collaboration with the Wistar Institute, Philadelphia, USA. PCR products were spotted with a BioRobotics Microgrid TASII (Genomic Solutions) by the Wistar Genomics Facility. Each array contained 6,319 individual yeast genes and 1,169 gene repeats. The complete list of arrayed genes on array YA04 is listed under GEO Series accession number GPL4423.

RNA amplification, hybridization, and scanning

Amplified RNA (aRNA) was prepared from 1 μg of total RNA for each sample tested using the RiboAmp kit (Arcturus). Labelled targets were prepared from 1.6 μg aRNA with Superscript II reverse transcriptase (Invitrogen), in the presence of 3,000–5,000 Ci/mM [α-33P] dCTP (Amersham Pharmacia Biotech), 1 mmol/L dATP, 1 mmol/L dTTP, 1 mmol/L dGTP, 67 ng/μg oligo-dT (Promega Biosciences), and 0.65 × random decamer primers (Ambion). Labelled targets were hybridized to individual arrays at 42°C for 18 h in 3 mL Microhyb buffer (Invitrogen). Arrays were washed twice in 2 × standard saline citrate (SSC)/1% SDS solution for 30 min at 50°C, once in 0.5 × SSC/1% SDS and once in 0.1 × SSC/0.5% SDS for 30 min at 55°C. The arrays were exposed to phosphor screens (Amersham Biosciences) for 6 days and scanned in a Storm 820 PhosphorImager (Molecular Dynamics). Quantitation of each spot was assessed by Imagene 5.0 software (BioDiscovery Incorporated) using manual spot alignment, measuring median pixel intensity for each spot and subtracting the local background. The data have been deposited in the NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE11799.

cDNA array data analysis

Observed values less than 0.1 (5% (8752 values) of the 176640 raw data values) were set to 0.1, in accordance with information from Wistar that variations in values below this threshold should be regarded as measurement noise. After converting to a logarithmic scale, average expression values were computed over spots with the same ORF, and arrays were scale normalized by subtracting the median for each of the 24 arrays. Averages were computed over repeated measurements with the same conditions producing 6 values at different glucose concentrations for each of the V5 (38.4, 35, 25.5, 24.4, 10.5, 7.4 g/L glucose) and V5.TM6*P (36.5, 34.8, 26.3, 25.1, 13.5, 5.6 g/L glucose) strains for each gene. T-tests were then performed, comparing the two strains, using Empirical Bayes robustification of variance estimates [39,40].

The T-tests were adjusted for multiple testing using the false discovery rate method (disregarding correlations between genes). Essentially, then, p-values were adjusted so that when selecting all genes with p-values less than a threshold q, a proportion of q false positives would be expected amongst these genes. The estimated effects are shown as factors in Additional file 1, as the linear modelling was performed on a log scale. The p-values in Additional file 1 are the adjusted values, so that selecting genes with p-values < 0.05 gives an expected false discovery rate of 5%. Calculations were performed using the BioConductor package limma [40]. To present the data, a tabular format was chosen over heatmaps in an attempt to make it as accessible as possible to the scientific community.

When producing data for Figure 4, a similar linear model was used, but with a different design matrix: Essentially, for each gene, 2 straight lines were simultaneously fitted, one for each of the strains, to plots of the 6 log-expression values versus glucose concentration. Empirical Bayes robustification and adjustment for multiple testing was included even in this analysis, using the limma tool [40]. Lines with slopes significantly different from zero were found for 20 genes for the V5 strain, but for no genes from the V5.TM6*P strain.

Authors' contributions

NB initiated the study, prepared the RNA and participated in the data analysis and interpretation, particularly of the transcription factor binding sites. CF carried out the fermentations. PM performed the array analysis. MW performed the statistical analyses of the array data. CC and LS generated the arrays and performed the arraying. LG and CL participated in the experimental design and helped to draft the manuscript. RB participated in the study design, coordinated the data analysis and interpretation, and drafted the manuscript. All authors contributed to the final version of the manuscript.

Acknowledgements

This work was supported by the European Commission via contracts LSHG-CT-2004-504601 (E-MeP) and LSHG-CT-2006-037793 (OptiCryst) to RMB, and QLG2-CT-2002-00988 (SPINE) to LG and RMB. The Wistar Genomics facility is supported in part by NCI P30 CA10815-34S3. The BBSRC supports bioreactors in the RMB laboratory through an REI award.

References

  1. Otterstedt K, Larsson C, Bill RM, Stahlberg A, Boles E, Hohmann S, Gustafsson L: Switching the mode of metabolism in the yeast Saccharomyces cerevisiae.

    EMBO Rep 2004, 5(5):532-537. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Henricsson C, de Jesus Ferreira MC, Hedfalk K, Elbing K, Larsson C, Bill RM, Norbeck J, Hohmann S, Gustafsson L: Engineering of a novel Saccharomyces cerevisiae wine strain with a respiratory phenotype at high external glucose concentrations.

    Appl Environ Microbiol 2005, 71(10):6185-6192. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Bonander N, Hedfalk K, Larsson C, Mostad P, Chang C, Gustafsson L, Bill RM: Design of improved membrane protein production experiments: quantitation of the host response.

    Protein Sci 2005, 14(7):1729-1740. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Brauer MJ, Saldanha AJ, Dolinski K, Botstein D: Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures.

    Mol Biol Cell 2005, 16(5):2503-2517. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale.

    Science 1997, 278(5338):680-686. PubMed Abstract | Publisher Full Text OpenURL

  6. Ohlmeier S, Kastaniotis AJ, Hiltunen JK, Bergmann U: The yeast mitochondrial proteome, a study of fermentative and respiratory growth.

    J Biol Chem 2004, 279(6):3956-3979. PubMed Abstract | Publisher Full Text OpenURL

  7. Hack CJ: Integrated transcriptome and proteome data: the challenges ahead.

    Brief Funct Genomic Proteomic 2004, 3(3):212-219. PubMed Abstract | Publisher Full Text OpenURL

  8. Davies SE, Brindle KM: Effects of overexpression of phosphofructokinase on glycolysis in the yeast Saccharomyces cerevisiae.

    Biochemistry 1992, 31(19):4729-4735. PubMed Abstract OpenURL

  9. Schaaff I, Heinisch J, Zimmermann FK: Overproduction of glycolytic enzymes in yeast.

    Yeast 1989, 5(4):285-290. PubMed Abstract OpenURL

  10. van Hoek P, van Dijken JP, Pronk JT: Regulation of fermentative capacity and levels of glycolytic enzymes in chemostat cultures of Saccharomyces cerevisiae.

    Enzyme Microb Technol 2000, 26(9-10):724-736. PubMed Abstract OpenURL

  11. Katz M, Frejd T, Hahn-Hagerdal B, Gorwa-Grauslund MF: Efficient anaerobic whole cell stereoselective bioreduction with recombinant Saccharomyces cerevisiae.

    Biotechnol Bioeng 2003, 84(5):573-582. PubMed Abstract | Publisher Full Text OpenURL

  12. Swiegers JH, Dippenaar N, Pretorius IS, Bauer FF: Carnitine-dependent metabolic activities in Saccharomyces cerevisiae: three carnitine acetyltransferases are essential in a carnitine-dependent strain.

    Yeast 2001, 18(7):585-595. PubMed Abstract OpenURL

  13. Boer VM, de Winde JH, Pronk JT, Piper MD: The genome-wide transcriptional responses of Saccharomyces cerevisiae grown on glucose in aerobic chemostat cultures limited for carbon, nitrogen, phosphorus, or sulfur.

    J Biol Chem 2003, 278(5):3265-3274. PubMed Abstract | Publisher Full Text OpenURL

  14. Kruckeberg AL: The hexose transporter family of Saccharomyces cerevisiae.

    Arch Microbiol 1996, 166(5):283-292. PubMed Abstract | Publisher Full Text OpenURL

  15. Destruelle M, Holzer H, Klionsky DJ: Identification and characterization of a novel yeast gene: the YGP1 gene product is a highly glycosylated secreted protein that is synthesized in response to nutrient limitation.

    Mol Cell Biol 1994, 14(4):2740-2754. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  16. Haurie V, Perrot M, Mini T, Jeno P, Sagliocco F, Boucherie H: The transcriptional activator Cat8p provides a major contribution to the reprogramming of carbon metabolism during the diauxic shift in Saccharomyces cerevisiae.

    J Biol Chem 2001, 276(1):76-85. PubMed Abstract | Publisher Full Text OpenURL

  17. Eckert-Boulet N, Regenberg B, Nielsen J: Grr1p is required for transcriptional induction of amino acid permease genes and proper transcriptional regulation of genes in carbon metabolism of Saccharomyces cerevisiae.

    Curr Genet 2005, 47(3):139-149. PubMed Abstract | Publisher Full Text OpenURL

  18. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok DK, Kellis M, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA: Transcriptional regulatory code of a eukaryotic genome.

    Nature 2004, 431(7004):99-104. PubMed Abstract | Publisher Full Text OpenURL

  19. Lascaris R, Bussemaker HJ, Boorsma A, Piper M, van der Spek H, Grivell L, Blom J: Hap4p overexpression in glucose-grown Saccharomyces cerevisiae induces cells to enter a novel metabolic state.

    Genome Biol 2003, 4(1):R3. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  20. Lutfiyya LL, Johnston M: Two zinc-finger-containing repressors are responsible for glucose repression of SUC2 expression.

    Mol Cell Biol 1996, 16(9):4790-4797. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  21. Roth S, Kumme J, Schuller HJ: Transcriptional activators Cat8 and Sip4 discriminate between sequence variants of the carbon source-responsive promoter element in the yeast Saccharomyces cerevisiae.

    Curr Genet 2004, 45(3):121-128. PubMed Abstract | Publisher Full Text OpenURL

  22. Treitel MA, Carlson M: Repression by SSN6-TUP1 is directed by MIG1, a repressor/activator protein.

    Proc Natl Acad Sci U S A 1995, 92(8):3132-3136. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  23. McNabb DS, Xing Y, Guarente L: Cloning of yeast HAP5: a novel subunit of a heterotrimeric complex required for CCAAT binding.

    Genes Dev 1995, 9(1):47-58. PubMed Abstract | Publisher Full Text OpenURL

  24. Romer KA, Kayombya GR, Fraenkel E: WebMOTIFS: automated discovery, filtering and scoring of DNA sequence motifs using multiple programs and Bayesian approaches.

    Nucleic Acids Res 2007, 35(Web Server issue):W217-20. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  25. Tachibana C, Yoo JY, Tagne JB, Kacherovsky N, Lee TI, Young ET: Combined global localization analysis and transcriptome data identify genes that are directly coregulated by Adr1 and Cat8.

    Mol Cell Biol 2005, 25(6):2138-2146. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  26. Lutfiyya LL, Iyer VR, DeRisi J, DeVit MJ, Brown PO, Johnston M: Characterization of three related glucose repressors and genes they regulate in Saccharomyces cerevisiae.

    Genetics 1998, 150(4):1377-1391. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  27. Mukherjee S, Berger MF, Jona G, Wang XS, Muzzey D, Snyder M, Young RA, Bulyk ML: Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays.

    Nat Genet 2004, 36(12):1331-1339. PubMed Abstract | Publisher Full Text OpenURL

  28. Lundin M, Nehlin JO, Ronne H: Importance of a flanking AT-rich region in target site recognition by the GC box-binding zinc finger protein MIG1.

    Mol Cell Biol 1994, 14(3):1979-1985. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  29. Yin Z, Wilson S, Hauser NC, Tournu H, Hoheisel JD, Brown AJ: Glucose triggers different global responses in yeast, depending on the strength of the signal, and transiently stabilizes ribosomal protein mRNAs.

    Mol Microbiol 2003, 48(3):713-724. PubMed Abstract | Publisher Full Text OpenURL

  30. Larroy C, Pares X, Biosca JA: Characterization of a Saccharomyces cerevisiae NADP(H)-dependent alcohol dehydrogenase (ADHVII), a member of the cinnamyl alcohol dehydrogenase family.

    Eur J Biochem 2002, 269(22):5738-5745. PubMed Abstract | Publisher Full Text OpenURL

  31. Yin Z, Smith RJ, Brown AJ: Multiple signalling pathways trigger the exquisite sensitivity of yeast gluconeogenic mRNAs to glucose.

    Mol Microbiol 1996, 20(4):751-764. PubMed Abstract OpenURL

  32. Regenberg B, Grotkjaer T, Winther O, Fausboll A, Akesson M, Bro C, Hansen LK, Brunak S, Nielsen J: Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae.

    Genome Biol 2006, 7(11):R107. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  33. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes.

    Mol Biol Cell 2000, 11(12):4241-4257. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  34. Brown AJ, Planta RJ, Restuhadi F, Bailey DA, Butler PR, Cadahia JL, Cerdan ME, De Jonge M, Gardner DC, Gent ME, Hayes A, Kolen CP, Lombardia LJ, Murad AM, Oliver RA, Sefton M, Thevelein JM, Tournu H, van Delft YJ, Verbart DJ, Winderickx J, Oliver SG: Transcript analysis of 1003 novel yeast genes using high-throughput northern hybridizations.

    EMBO J 2001, 20(12):3177-3186. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  35. Ter Linde JJ, Steensma HY: A microarray-assisted screen for potential Hap1 and Rox1 target genes in Saccharomyces cerevisiae.

    Yeast 2002, 19(10):825-840. PubMed Abstract | Publisher Full Text OpenURL

  36. Ernst J, Vainas O, Harbison CT, Simon I, Bar-Joseph Z: Reconstructing dynamic regulatory maps.

    Mol Syst Biol 2007, 3:74. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  37. Wieczorke R, Krampe S, Weierstall T, Freidel K, Hollenberg CP, Boles E: Concurrent knock-out of at least 20 transporter genes is required to block uptake of hexoses in Saccharomyces cerevisiae.

    FEBS Lett 1999, 464(3):123-128. PubMed Abstract | Publisher Full Text OpenURL

  38. Verduyn C, Postma E, Scheffers WA, Van Dijken JP: Effect of benzoic acid on metabolic fluxes in yeasts: a continuous-culture study on the regulation of respiration and alcoholic fermentation.

    Yeast 1992, 8(7):501-517. PubMed Abstract OpenURL

  39. Breitling R: Biological microarray interpretation: The rules of engagement.

    Biochim Biophys Acta 2006, 1759(7):319-327. PubMed Abstract | Publisher Full Text OpenURL

  40. Smyth GK: Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. New York , Springer; 2005:397-420. OpenURL

  41. Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE Jr., Hieter P, Vogelstein B, Kinzler KW: Characterization of the yeast transcriptome.

    Cell 1997, 88(2):243-251. PubMed Abstract | Publisher Full Text OpenURL