- Open Access
Hap4p overexpression in glucose-grown Saccharomyces cerevisiae induces cells to enter a novel metabolic state
© Lascaris et al.; licensee BioMed Central Ltd. 2002
- Received: 8 August 2002
- Accepted: 29 October 2002
- Published: 17 December 2002
Metabolic and regulatory gene networks generally tend to be stable. However, we have recently shown that overexpression of the transcriptional activator Hap4p in yeast causes cells to move to a state characterized by increased respiratory activity. To understand why overexpression of HAP4 is able to override the signals that normally result in glucose repression of mitochondrial function, we analyzed in detail the changes that occur in these cells.
Whole-genome expression profiling and fingerprinting of the regulatory activity network show that HAP4 overexpression provokes changes that also occur during the diauxic shift. Overexpression of HAP4, however, primarily acts on mitochondrial function and biogenesis. In fact, a number of nuclear genes encoding mitochondrial proteins are induced to a greater extent than in cells that have passed through a normal diauxic shift: in addition to genes required for mitochondrial energy conservation they include genes encoding mitochondrial ribosomal proteins.
We show that overproduction of a single nuclear transcription factor enables cells to move to a novel state that displays features typical of, but clearly not identical to, other derepressed states.
- Additional Data File
- Mitochondrial Biogenesis
- Zinc Metabolism
- Upstream Activation Sequence
- Diauxic Shift
Unraveling and understanding the complex regulatory networks in a cell is an important step in genomic research, since it should enable us to understand how the cell modifies its behavior in response to both internal and external signals.
A diauxic shift occurs when yeast cells have consumed all glucose fermentatively and convert their metabolism to oxidative catabolism of the ethanol remaining in the medium. This shift has been extensively studied  and is a good example of how cells move from one 'equilibrium' state, for example, fermentative physiology, to another 'equilibrium' state, for example, respiratory physiology. The transition from fermentative physiology to respiratory physiology, in the wild or in the laboratory, involves many changes in gene expression that contribute to the final phenotype. The HAP4 overexpression system on the other hand is relatively simple: one factor that is known to be required for induction of respiratory activity can indeed induce a physiological change that resembles that of the diauxic shift .
Metabolic and regulatory gene networks have generally evolved to allow physiological and developmental processes to compensate for the effects of potentially deleterious mutations, termed robustness . Important questions are how equilibrium states are maintained and how the transition between states is implemented by the cell. Depending on where a particular regulatory protein is located in the hierarchical global regulatory network of the cell, the response to a change in its activity may be either localized or pleiotropic. To identify the various components of the pleiotropic response to HAP4 overexpression, that is, to 'fingerprint' the changes in the regulatory network, we use two different methods. First, we use the algorithm REDUCE  to infer the regulatory activity of transcription factors from the mRNA expression of their target genes. Second, we use a new and related algorithm, named Quontology, to identify classes of genes with similar function that are significant induced or repressed. Both methods have the property that they can detect small-amplitude but coordinated changes in the average expression level of a set of genes, even if the expression of individual genes is not changing significantly.
In this paper we report detailed analysis of the changes that occur as a result of overexpression of HAP4. Using a number of techniques it could be shown that overexpression of HAP4 enhances transcription of a large set of mitochondrial protein genes, leading to increased mitochondrial biogenesis and enabling cells to move to a distinct and novel state.
Cells overexpressing HAP4upregulate mitochondrial biogenesis and activity even in the presence of glucose
To determine the effect of overexpression of HAP4 on mitochondria, several different experimental approaches were used to quantify mitochondrial components and mitochondrial structures in the cell.
The cellular content of cytochromes c + c1 and b is also markedly increased, 50% and 65% respectively, when the HAP4 gene is overexpressed in glucose-grown cells (Figure 2b). The cytochrome aa3 content is also increased in the HAP4 strain, although the corresponding part of the difference spectra does not allow proper quantification.
The strong increase in OXPHOS complex abundance and in mitochondrial protein and cytochrome content per cell, together with the evidence that shows that HAP4 cells contain more mitochondrial structures per cell, conclusively shows that overexpression of HAP4 in glucose-growth conditions results in increased mitochondrial biogenesis.
Fingerprinting the regulatory network of HAP4overexpressing cells
Genome-wide expression was monitored with Affymetrix GeneChips using RNA from wild-type and HAP4 cells grown aerobically on minimal glucose medium (YNB) in a batch fermenter. For each strain, two separate hybridizations were carried out and the log2-expression ratios or fold-changes were calculated for HAP4 overexpression versus wild type.
From Figure 5 it is evident that overexpression of HAP4 results in alterations in the regulatory network that resembles the time points of the diauxic shift with respect to several important functional categories (see categories marked with a filled circle).
The alterations in the regulatory activity network of the diauxic shift mainly occur in three phases, as indicated in Figure 5: high glucose concentrations; strongly decreasing glucose concentration; and glucose depletion where cells grow in respiratory mode on the remaining ethanol. The HAP4 overexpression profile is similar in a number of respects to the profiles of cells growing in decreasing glucose concentrations (13.5 and 15.5 hours; see Figure 5). However, in this case also, the changes that occur in the diauxic shift are much more extensive, in particular with respect to the categories 'metabolism of energy reserves' and 'rRNA transcription'. Hence, the regulatory activity network of the HAP4-overexpressing strain has moved to a state that resembles one of the transition states of the diauxic shift, but at the same time differs in a number of important aspects.
Hap4pis a global regulator that coordinately and specifically upregulates mitochondrial function and mitochondrial biogenesis
The two clusters of genes in Figure 6 fall into different functional categories. The group of genes that is responsive to changes in the respiro-fermentative growth mode consists mainly of genes encoding proteins that are directly involved in respiration (see genes listed in the left part of Figure 6). The second, larger and non-responsive group mainly consists of genes that encode proteins involved in mitochondrial protein synthesis (see genes listed in the right part of Figure 6). Apparently, overexpression of HAP4 not only results in increased expression of genes encoding proteins required for mitochondrial energy conservation (or generation) but also of genes that are required for the synthesis of functional mitochondria.
Overexpression of HAP4negatively affects transcription of a small set of genes involved in zinc metabolism
In addition to the above-mentioned genes, which are upregulated and lead to increased mitochondrial biogenesis, it should be noted that the expression of 23 genes is reduced by overexpression of HAP4. These genes are RPR1 (-2.3), EUG1 (-2.3), ZRT1(-17.0), ADH4(-7.9), YGL258W(-3.9), YHR048W (-2.5), HXT4 (-2.7), TDH1 (-3.0), ZAP1(-6.8), CWP1 (-3.0), ZRT3(-3.4), ZRT2(-2.9), TFS1 (-2.4), THI7 (-2.9), CAR2 (-2.7), SNO1 (-3.8), SNZ1 (-2.9), FET4(-2.7), YNL254C(-5.3), FRE4 (-3.0), COS10 (-2.4), YOL154W(-27.3), YOR387C(-18.4). Interestingly, a large number of these genes are involved in zinc metabolism. Genes indicated in bold are most strongly affected by overexpression of HAP4 (<-3) and have been proposed to be targeted by zinc-specific transcription factor Zap1p . In fact, the expression of ZAP1 itself is strongly repressed by overexpression of HAP4 and, furthermore, the repressive motif ACC-5-GGT, the DNA-binding site of Zap1p , was identified by the REDUCE algorithm with high significance, p < 10-12. Hence, we conclude that the main repressive effect of overexpression of HAP4 is mediated through decreased expression of the Zap1p transcription factor. Genes that are typically not altered in expression on zinc depletion or zap1-deletion  are given in non-italic type above, and are suggested to be repressed through a different mechanism, possibly through the same mechanism that initially represses the ZAP1 gene.
In this report we show that overexpression of the gene encoding the activation moiety of the Hap2/3/4/5p transcription complex causes glucose-grown cells to move to a novel state that is characterized by a shift to higher respiratory activity. HAP4-overexpressing cells contain more mitochondrial structures, increased amounts of mitochondrial proteins/cytochromes and enhanced transcription of the mitochondrial genome. This suggests that overexpression of HAP4 results in a general increase in mitochondrial biogenesis. Consistently, whole-genome expression profiling and subsequent computational analyses do indeed show that overexpression of HAP4 specifically alters transcription of a very large set of mitochondrial protein genes. HAP4-overexpressing cells resemble cells that undergo a physiological diauxic shift, but at the same time differ from these cells in a number of important respects. First, in the HAP4 overexpressor no increase occurs in the functional category 'metabolism of energy reserves' and in functional categories that are involved in transcription, mainly 'rRNA transcription'. Second, genes involved in zinc metabolism are downregulated, and third, genes involved in protein synthesis in mitochondria are strongly upregulated.
With respect to the latter it should be noted that the effects we show in Figure 6 might to some extent be affected by differences in the set-up of the experiments, that is, differences in the genetic background of the strains and/or of the array platforms used. However, other previously published expression profiles show induction of this non-responsive group of genes, which contains mainly mitochondrial ribosomal protein genes (MRP genes). These studies looked at the effects of oxidative stress , addition of rapamycin , mitochondrial dysfunction [13,14], a 'pulse'-like induction at the diauxic shift (see lanes 2 in Figure 6), and at the diauxic shift that precedes the stationary phase . Furthermore, if the Z-scores are calculated for the genes of the non-responsive and the responsive group using the expression profiles of 300 null mutants from Hughes et al. , we find that the expression of the non-responsive group alters more strongly in mutants that have a decreased growth rate (Figure 7b). As the growth rate of the HAP4 overproducer is not affected, we suggest that the transcriptional response of MRP genes is not only dependent on the growth rate itself, but also on whether cells respire more and/or ferment less.
The fact that genes involved in zinc metabolism are primarily downregulated by overexpression of HAP4 is surprising. Complex (auto)regulation of the zinc regulator Zap1p and the central role of mitochondria in metal metabolism are likely to be important in this downregulation. However, as zinc is an essential component of more than 300 enzymes - including, paradoxically, cytochrome oxidase - particular zinc-dependent factors may be involved. Candidate factors that require zinc are: the mitochondrial metalloproteases Afg3p, Rca1p and Yem1p; the transcription factors Cat8, Mig1p, Mig2p, Mal63 and Hap1p; and alcohol dehydrogenases.
We have shown here that increased expression of a single transcription factor enables the alteration of a particular branch of the yeast regulatory activity network, leading to remarkably specific alterations in expression of specific functional sets of genes and revealing only minor cross-talk with other parts of the regulatory activity network. Hence, the regulatory activity network that determines mitochondrial function appears to be highly modular. This is an important notion, as the regulatory activity network may consist of similar more-or-less isolated modules, which may enable the construction of a simple but realistic computational model of the in silico cell.
Strains and growth conditions
The wild-type strain CEN.PK113-7D and its HAP4-overproducing counterpart 436 GH , in which the HAP4 gene is overexpressed more than fivefold in glucose-growth conditions, were used. The latter contains a genomically integrated HAP4-overproduction cassette in which the TDH3 promoter regulates the HAP4 gene. Cells were grown aerobically in YPD (1% yeast extract, 1% Bacto-peptone and 2 or 3% D-glucose) or YPEG (YP containing 2% ethanol and 2% glycerol). In all experiments cells were harvested at early logarithmic phase.
Isolation of mitochondria and blue native PAGE
Yeast cells were grown in 100 ml YPD or YPEG medium until OD600 of 0.7-1.0 and mitochondria were isolated according to . Protein concentration was determined using Bradford. Two-dimensional gel electrophoresis was carried out according to : the first dimension consisted of blue native, 5-13% gradient polyacrylamide gel electrophoresis (PAGE), and the second of denaturing PAGE on gels containing 10% SDS. Protein was visualized by Coomassie staining of the gels. The identity of a constitutive spot at the upper-left part of the gels was determined using mass spectrometry .
Spectral analysis and fluorescence microscopy
Cells were grown to OD600 = 1.0-1.2, frozen in liquid nitrogen and stored in -20°C. The cell pellet was resuspended in a final volume of 2 ml using a buffer containing 100 mM KPO4 pH 7.3, 250 mM sucrose and 0.5% Na-cholate. Difference spectra were derived from dithionite-reduced cells minus ferricyanide-oxidized cells. Spectral measurements were carried out at room temperature in a DW2000 dual-beam spectrophotometer. Concentrations of cytochromes were determined essentially as in .
Mitochondrial biogenesis in living cells was analyzed by DASPMI staining of cells grown logarithmically in liquid medium (YPD). YPD culture samples of 5 ml were incubated at 28°C in the presence of 10 μl of 1 mg/ml CalcoFluorWhite , centrifuged, resuspended in 20 μl YPD. CFW stained cells (2 μl) and 2 μl unstained cells were mixed, 1 μl of 1 mg/ml DASPMI and 15 μl 0.1 M Tris-Cl pH 8.0 were added. After 5 min, cells were washed in 0.1 M Tris-Cl pH 8.0, centrifuged and resuspended in 0.1 M Tris-Cl pH 8.0. After addition of 1 volume YPD, cells were aerated by pipeting and analyzed microscopically.
Northern analysis and microarray experiments
RNA isolation, northern blotting and hybridizations were carried out essentially as in . Probes for mitochondrial transcripts COX1, COX2 and SCEI and the loading control PDA1 were obtained by [32P]ATP labeling of PCR-generated DNA fragments. For microarray analyses, a pre-culture was grown overnight in YNB medium (0.67% yeast nitrogen base) containing 2% raffinose. The main culture was inoculated at OD600 = 0.1 in a batch fermenter containing 500 ml YNB medium buffered at pH 5.0 with 100 mM sodium phthalate and supplemented with 2% glucose, stirred at 500 rpm and aerated at 1 vv (vessel volume per minute). Both strains grew equally well, having growth rates of μ = 0.36/h and 0.34/h for wild-type and HAP4, respectively. The respiratory quotient (RQ), decreased from RQ = 3.5 in wild-type cells to 2.6 in the HAP4-overexpressing strain. This shows that the latter strain grows in a more respiratory mode, similar to , which is accompanied by a decrease in the glucose consumption and ethanol production rates: from qglucose = 15.6 mmol/g/h and qethanol = 19.8 mmol/g/h for the wild-type strain, to qglucose = 11.1 mmol/g/h and qethanol = 15.6 mmol/g/h for the HAP4-overexpressing strain. The cultures were grown until OD600 = 1.0 (approximately 8 h), cells were chilled rapidly by addition of ice, centrifuged and stored at -70°C. RNA was extracted using hot phenol according to  and poly(A)+ mRNA was isolated using Oligotex (Qiagen). Labeling and hybridization of GeneChip Yeast Genome S98 Arrays were carried out according to Affymetrix.
The average ratio was calculated from log2 expression ratios that derive from two independent experiments of the HAP4 overexpressor relative to two independent wild-type experiments. ORFs for which the expression ratios diverged twofold in duplicate experiments were excluded from the analysis. Using interpolated variance analysis, 110 genes were calculated to be regulated significantly (p ≤ 0.01; data not shown). In this analysis genes are selected that show a twofold change, k-means clustering was carried out using Cluster , corrected for a minor bug that inappropriately displays the gene of the second cluster as the final gene of the first cluster. Matrix searches were carried out using the Hap2/3/4/5/p nucleotide-distribution matrix from the TransFac database  and the MatInspector program . Matches to the Hap2/3/4/5/p matrix were analyzed in random-sequence DNA (A/T 31%, C/G 19% ) that contained the same number of promoters of the non-responsive gene cluster.
Several publications have used genome-wide expression patterns to score functional gene categories, by considering the overlap between each category and the set of genes induced or repressed above a certain threshold. We take a very different approach that is essentially identical to the REDUCE algorithm for scoring promoter elements  but with manually defined gene categories from an ontology replacing the set of genes whose promoter contains a particular DNA motif. Again, a Z-score can be calculated for each category that measures the deviation of the average log-ratio for genes in the category from the genome-wide average, in units of the standard deviation. A similar method has recently been discussed by Pavlidis et al. .
The original data used to carry out this analysis is available as an Excel file. This contains the following columns: Affymetrix identifier, ORF names, four columns containing the Affymetrix cross-experiment comparison (log2) of HAP4 versus wild type, and one column containing the average log ratio of four data points (log2).
We thank Leo Nijtmans, Marta Artal, Monique van Galen, Jan Berden, Conrad Woldringh, Matthew Piper, Jack Pronk, Betsie Voetdijk, Elzo de Wit and Joost Teixeira de Mattos for help, advice and technical support. This work was financially supported by the Dutch Ministry of Economic Affairs (EET program EETK-99020).
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