Multiple Pathways Are Co-regulated by the Protein Kinase Snf1 and the Transcription Factors Adr1 and Cat8*,

Elton T. Young {ddagger} §, Kenneth M. Dombek §, Chris Tachibana and Trey Ideker 

From the Department of Biochemistry, the University of Washington, Seattle, Washington 98195-7350

Received for publication, February 25, 2003 , and in revised form, March 31, 2003.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
ADR1 and CAT8 encode carbon source-responsive transcriptional regulators that cooperatively control expression of genes involved in ethanol utilization. These transcription factors are active only after the diauxic transition, when glucose is depleted and energy-generating metabolism has shifted to the aerobic oxidation of non-fermentable carbon sources. The Snf1 protein kinase complex is required for activation of their downstream target genes described previously. Using DNA microarrays, we determined the extent to which these three factors collaborate in regulating the expression of the yeast genome after glucose depletion. The expression of 108 genes is significantly decreased in the absence of ADR1. The importance of ADR1 during the diauxic transition is illustrated by the observation that expression of almost one-half of the 40 most highly glucose-repressed genes is ADR1-dependent. ADR1-dependent genes fall into a variety of functional classes with carbon metabolism containing the largest number of members. Most of the genes in this class are involved in the oxidation of different non-fermentable carbon sources. These microarray data show that ADR1 coordinates the biochemical pathways that generate acetyl-CoA and NADH from non-fermentable substrates. Only a small number of ADR1-dependent genes are also CAT8-dependent. However, nearly one-half of the ADR1-dependent genes are also dependent on the Snf1 protein kinase for derepression. Many more genes are SNF1-dependent than are either ADR1- or CAT8-dependent suggesting that SNF1 plays a broader role in gene expression than either ADR1 or CAT8. The largest class of SNF1-dependent genes encodes regulatory proteins that could extend SNF1 dependence to additional pathways.


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TABLE I
S. cerevisiae strains used in this study

 

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TABLE II
Primers used for PCR analysis of Adr1 ChIP DNA

 

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TABLE III
Functional classification of ADR1-dependent genes

 

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TABLE IV
Expression ratios of the 40 most highly glucose repressed genes Expression ratios in boldface type indicate a significantly higher level of expression based on a {lambda} statistic that is above the significance threshold.

 

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TABLE V
ADR1-dependent expression of IME1/lacZ in haploid and diploid

Haploid strains TYY201 and TYY202, ADR1 and adr1{Delta}1::LEU2, respectively, with pWL21 (IME1/lacZ, URA3) and diploid strains TGY25 and TGY27, adr1{Delta}1::LEU2/adr1{Delta}1::LEU2 and adr1{Delta}1::LEU2/ADR1, respectively, with pWL21 (IME1/lacZ, URA3), were grown in selective media (ura-) with 5% glucose (R) to about 2 x 107 cells/ml. A portion of the culture was assayed for {beta}-galactosidase activity (R) as described under "Experimental Procedures," and cells from another portion were collected by centrifugation and resuspended in selective media plus 0.05% glucose (DR) or 0.1% potassium acetate (SPM) and grown for 18 h at 30°C. A portion of each culture was removed and assayed for {beta}-galactosidase activity (DR and SPM, respectively). The {beta}-galactosidase activities are Miller units, and the standard deviations of the assays were about 20%.

 

    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
During the diauxic shift, when yeast cells deplete the glucose in the medium, the flow of metabolites changes dramatically to adapt to the use of alternative energy and carbon sources, primarily ethanol produced during fermentation. The changes in gene expression that are required for this metabolic reorganization were dramatically revealed by DNA microarray analysis (1). The expression of more than one-quarter of the yeast genome is altered when glucose is exhausted. These alterations allow the cell to utilize carbon sources other than glucose to channel metabolites into the tricarboxylic acid and glyoxylate cycles to generate energy and synthesize intermediates for gluconeogenesis. In addition, these changes allow the cell to alter its growth rate and prepare for growth arrest and stationary phase.

Numerous transcription factors and regulatory proteins are responsible for the altered transcriptional program that accompanies the depletion of glucose. Two transcription factors that play important roles during the diauxic transition are Adr11 and Cat8.

ADR1 was discovered as a regulatory gene that is required for expression of the glucose-repressed ADH22 gene (2, 3). ADH2 encodes an alcohol dehydrogenase isozyme that is required for the first step in ethanol oxidation. Expression of eight other genes is dependent on ADR1. These include other genes important for ethanol and glycerol utilization (46). Six of the eight genes encode components of the peroxisomal pathway for {beta}-oxidation of fatty acids (79).

CAT8 is an essential gene for growth on non-fermentable carbon sources (10). Most of the genes encoding enzymes of the glyoxylate cycle and two key enzymes of gluconeogenesis are well characterized targets of Cat8. DNA miniarray and proteomic analyses extended to 34 the number of CAT8-dependent genes (11). Many of the newly identified genes have roles in ethanol and lactate utilization and intracellular transport of glyoxylate and tricarboxylic acid cycle intermediates.

Many of the transcriptional changes occurring at the diauxic transition require an active Snf1 protein kinase complex (12). Snf1 is the yeast homolog of the AMP-activated protein kinase found in higher eukaryotes. Although Snf1 is present in glucose-grown cells, its activity is regulated, perhaps via phosphorylation by a Snf1 kinase kinase or by the accumulation of AMP that occurs in the cell when glucose is depleted. Its importance for aerobic growth is illustrated by the fact that snf1 mutant cells are unable to grow on any non-fermentable carbon source, while having apparently wild-type growth properties in the presence of glucose. SNF1 is also essential for growth on alternative sugars such as sucrose, galactose, and maltose.

Snf1-dependent phosphorylation regulates several glucose-responsive transcription factors. Phosphorylation of the repressor Mig1 leads to its export from the nucleus and derepression of glucose-repressed genes encoding enzymes required for metabolism of alternative sugars (13, 14). SNF1 regulates both the activity of the Cat8 and Sip4 transcription factors and the transcription of their genes (1518).

The interaction between Snf1- and ADR1-dependent gene activation is unclear. The expression of Adr1 is not SNF1-dependent (19), but Adr1 is unable to bind the ADH2 promoter in the absence of Snf1 (20). Snf1 could phosphorylate and activate Adr1 for binding, or it could act on ADR1-dependent promoters in some other manner to allow Adr1 to bind. For example, Snf1 phosphorylates Ser-10 on histone H3, leading to acetylation of Lys-14 and activation of the INO1 promoter (21). If Snf1 and Adr1 act in a concerted but independent manner on target genes, some ADR1-dependent genes might be activated independently of Snf1. Such genes could be identified by examining the overlap between ADR1- and SNF1-dependent genes using DNA microarrays.

Microarrays could help identify other transcription factors that play a role in the activation of Adr1 target genes. For example, two genes involved in ethanol metabolism, ADH2 and ACS1, are co-regulated by Adr1 and Cat8 in a synergistic manner (22). The genes of {beta}-oxidation that are ADR1-dependent are also regulated by oleate induction through the transcription factor Oaf1 ·Pip2 (8, 9). GUT1 is regulated by Ino2 and Ino4 as well as by Adr1 (5). Thus, Adr1 acts in concert with at least three other transcription factors.

To uncover the full repertoire of ADR1-dependent genes and to identify those that are also CAT8- and SNF1-dependent, we performed whole genome transcriptome profiling of derepressed cells using DNA microarrays. Strains carrying deletions of ADR1, CAT8, SNF1, and the double mutants adr1 cat8 and adr1 snf1 were compared with a wild-type strain using a two-color hybridization assay. One hundred eight genes are ADR1-dependent in the microarray assay. Additional genes in pathways already known to be ADR1-dependent as well as genes encoding enzymes and proteins performing cellular functions not previously suspected of being ADR1-dependent were identified. Many of these genes have potential Adr1-binding sites in their promoters, and chromatin immunoprecipitation showed nine of them to be direct targets of Adr1. The ADR1-dependent genes encode proteins having diverse cellular functions, ranging from formate metabolism to meiosis. In a related microarray experiment in which repressed and derepressed mRNAs were compared, most of the ADR1-dependent genes were highly glucose-repressed. Many, but not all, ADR1-dependent genes are dependent for expression on SNF1. Despite their diversity, the ADR1-dependent genes have several unifying features. They are all non-essential in rich glucose medium, and many are important for growth or survival when yeasts are grown on a non-fermentable carbon source.


    EXPERIMENTAL PROCEDURES
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Strains—Yeast strains are listed in Table I. To create isogenic strains that differ only at the ADR1 locus, we replaced the wild-type ADR1 gene with LEU2 (23) and introduced a low copy plasmid with or without ADR1. This was done because we observed that an adr1::LEU2 strain grows better on glucose minimal media plates than its ADR1 leu2 isogenic parent, suggesting that leucine biosynthesis may be limiting under these conditions. By using this strategy both the ADR1 wild-type and the adr1 mutant strains are Leu+ and will not differ in gene expression because of unknown indirect effects of leucine auxotrophy. The plasmid copy of ADR1 activates ADH2 expression to a level similar to the level activated by the endogenous ADR1 gene (24). Strain TYY390 was created from TYY202 by disrupting SNF1 with a snf1::URA3 fragment derived from pST70 (25). Strains TYY458 and TYY459 were created by disrupting CAT8 in strains TYY203 (ADR1) and TYY204 (adr1{triangleup}1::LEU2) with a PCR fragment generated from a KANR disruption cassette present in plasmid pUG6 (26).

Growth of Yeast Cultures—Yeast cultures were grown in synthetic (SM) or rich (YP) medium (27). To maintain selection for plasmids containing TRP1, 0.1% casamino acids was added rather than trp- drop-out solution. For repressed growth conditions, glucose was present at 2.5%. For growth in derepressing conditions, the cells were pelleted by centrifugation when they reached an A600 of 1.0 and resuspended in medium containing 0.05% glucose. The cells were harvested 6 h later by centrifugation and washed with 1/10 volume of cold H2O.

DNA Microarray Analyses—Cy3- and Cy5-labeled probes were prepared from poly(A) RNA and hybridized to yeast open reading frame (ORF) microarrays as described previously (28) with minor modifications. Total yeast RNA was isolated by hot phenol/SDS-glass bead breakage, followed by ethanol precipitation (24), and treated with DNase I that had been incubated with iodoacetate to inactivate RNase activity. Poly(A)-enriched RNA was isolated using an Oligotex mRNA Purification kit (Qiagen Corp., Valencia, CA). Forward (Cy3-wild-type/Cy5-mutant) and reverse labeling (Cy5-wild-type/Cy3-mutant) comparisons were performed for each experiment using 15 pmol of Cy3 and 25 pmol of Cy5-labeled cDNAs. Three biological replicates of the ADR1 wild-type to {triangleup}adr1 mutant comparison and one each of the wild-type to {triangleup}snf1, {triangleup}snf1{triangleup}adr1, {triangleup}cat8, and {triangleup}cat8{triangleup}adr1 comparisons were done. The first two {triangleup}adr1 experiments utilized low density yeast ORF microarrays prepared as described previously (28). PCR products from 4608 of the smaller ORFs were spotted in duplicate onto one slide and the remaining 1536 larger ORFs were spotted in duplicate onto a second slide. The third replicate of the {triangleup}adr1 experiment and all subsequent experiments utilized high density microarrays that contained 6129 yeast ORFs spotted in duplicate on each slide. These first generation high density microarrays were produced by the Center for Expression Arrays in the Department of Microbiology at the University of Washington (Seattle, WA, ra.microslu.washington.edu/aboutus/about_us.html). Hybridized microarray slides from the first wild-type to {triangleup}adr1 comparison experiment were scanned using a Generation II Scanner from Amersham Biosciences. Slides from all subsequent experiments were scanned using a Generation III Scanner (Amersham Biosciences) at the Center for Expression Arrays.

Raw spot intensities, local background, and spot quality estimates were extracted from microarray images using the image analysis program Dapple (29). The raw data were processed using the DNA Microarray Data Processing Pipeline Perl scripts and software developed at the Institute for Systems Biology (Seattle, WA, db.systemsbiology.net/software/ArrayProcess/index.html). Preprocessing of the data to normalize background-subtracted intensities and to calculate log10 ratios was performed as described previously (28). In brief, preprocessed data were subjected to several quality control checks before proceeding further with the analysis. First, the log10 ratio for each spot was plotted against its duplicate on the same array slide. Only data from slides having points clustered about a straight line with a positive slope of approximately 1 were further analyzed. Next, log10 ratios from forward and reverse labeling experiments were plotted against each other. Only data from experiments having points clustered about a straight line with a negative slope of approximately 1 were further analyzed. The data from each experiment were then merged and filtered using Dixon's outlier rejection test. Data from all three biological replicates of the wild-type to {triangleup}adr1 mutant comparison were merged into one data set.

To identify genes showing significantly different levels of mRNA, a maximum likelihood error model was produced for each data set using the program VERA, and this model was applied to that data set using the program SAM (www.systemsbiology.org/VERAandSAM). SAM uses the model produced by VERA to calculate a maximum likelihood ratio statistic ({lambda}) and a log10 ratio (muRatio) that is corrected for additive and multiplicative errors that arose during the course of the experiment. Each expression ratio presented in this paper is the anti-log of the muRatio. A muRatio of 0.3 or greater, representing a 2-fold or greater difference in mRNA level, was arbitrarily chosen as one criterion of significance. A {lambda} cutoff value of 44.0 for the low density microarrays and a cutoff value of 27.5 for the high density arrays were chosen as second criteria of significance. These {lambda} cutoff values were determined from the distribution of {lambda} values across all genes in a self-hybridization experiment for each type of microarray and represent the {lambda} value below which 95% of the genes fall in the control experiment. Higher {lambda} values eliminated many genes already known to be ADR1-dependent. For these control experiments, Cy3-labeled cDNA was prepared from the poly(A) RNA of derepressed wild-type cells and hybridized against Cy5-labeled cDNA prepared from the same poly(A) RNA.

Promoter-binding Site Analyses—Upstream sequences up to 600 bp from the translational start site of each gene were retrieved and analyzed using the Web-based RSA Tools (30) (embnet.cifn.unam.mx/rsa-tools). Transcription factor-binding sites were identified using the dna-pattern tool with the following sequence patterns: Adr1-binding site, DYGGRG, and exact matches to the UAS1-like sequence, CYCCRHN{2,36}DYGGRG (31); Cat8-binding site, CSRE, CCDNHN{3}CCG (11); Oaf1-Pip2-binding site, ORE, CGGN{15,18}CCG (32). The DNA-pattern tool was unable to search for UAS1-like sequences with random mismatches. Therefore, we developed the Perl script seekw and used it to search for UAS1-like sequences allowing for one mismatch anywhere in the consensus. Both strands of all upstream sequences were searched for matches to each of these patterns.

Construction of Reporter Plasmids—The base plasmids were pLG669Z (33) and pLG669Z(K) (34). Promoter fragments from the desired ORFs were amplified from genomic DNA using primers flanking the region of interest. The PCR products were either cloned as XhoI-XmaI or XhoI-KpnI restriction fragments directly into pLG669Z or pLG669Z(K), respectively.

Chromatin Immunoprecipitation Analysis—Chromatin immunoprecipitation was performed as described (20), except that cross-linking was with dimethyl adipimidate for 45 min (35) followed by formaldehyde treatment for 15 min. Primers used for PCR are listed in Table II.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
One Hundred and Eight Genes Are ADR1-dependent for Expression—Fig. 1 illustrates the functional categories of 108 genes whose expression is reduced 2-fold or more in the absence of ADR1 when cells are grown in depressing medium. The major categories of functionally annotated genes are non-fermentative carbon metabolism, peroxisome biogenesis and {beta}-oxidation, amino acid transport and metabolism, sporulation and meiosis, and transcriptional regulation and signal transduction. Thirteen genes are annotated but do not fall into any one of these categories. Thirty percent of the ADR1-dependent genes have unknown functions, similar to the fraction of genes of unknown function in the whole yeast genome. None of the ADR1-dependent genes are essential for growth in rich medium containing glucose as a carbon source, and none of the annotated genes are involved in cell cycle control. This was expected because adr1 mutants have no obvious growth defect in rich medium.



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FIG. 1.
Functional categories of ADR1-dependent genes. ADR1-dependent genes that were functionally annotated in the SGD data base were categorized based on their geneontology annotations (genome-www.stanford.edu/Saccharomyces/help/gotutorial.html). Genes falling into categories with three or fewer members were grouped into the category Other. Genes of unknown function were categorized based on homologies to genes of known function. Remaining genes were grouped together in the category Unknown. Each section of the pie chart represents a functional category, and the number next to it is the number of members of that category. The percentage of the total 108 ADR1-dependent genes present in each category follows the description of each category in the key next to the chart.

 

ADR1-dependent Genes Channel Metabolites into Acetyl-CoA and NADH Production—Fig. 2 illustrates the major pathways of carbon utilization in yeast. The genes that are expressed in an ADR1-dependent manner in derepressed conditions are shown in red boxes with their positive expression ratio, wildtype/adr1. Negative values in green boxes indicate genes whose expression is higher in the absence of ADR1 than in its presence.



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FIG. 2.
Influence of ADR1 on expression of genes involved in non-fermentable carbon metabolism. Pathways of non-fermentable carbon metabolism were constructed based on information taken from the literature (1, 37, 59, 60, 65). Located below each gene label is the fold change in gene expression. Positive values and red color indicate down-regulated expression in adr1 mutant cells. Negative values and green color indicate up-regulated expression. A question mark next to a value indicates that its associated {lambda} statistic is below the cutoff value. Most genes in black type showed no significant change in expression. No data were obtained for PDA2 and PYK1. ADH1 is uncolored because its expression ratios were considered unreliable due to cross-hybridization with ADH2 message. Genes marked by an asterisk have promoters that have been shown previously by ChIP analysis or in the current study to bind Adr1 in vivo.

 

ADR1-dependent genes figure prominently in pathways leading from ethanol, glycerol, lactate, and the oxidation of fatty acids to the formation of acetyl-CoA, generating NADH in the process. Formate metabolism is also apparently ADR1-dependent based on the strong dependence of FDH1 and FDH2 on ADR1 for expression. Formate and propionate, although unable to support yeast growth, can be co-metabolized with growth-limiting amounts of glucose to produce NADH, thus providing reducing equivalents that increase the growth potential of the cell (36, 37). Table III lists the ADR1-dependent genes of known function, their degree of ADR1 dependence, and their biochemical or physiological function based on information available on the SGD or the MIPS web sites. Regulation of the genes in Table III is described in more detail under "Discussion."

ADR1 and {beta}-Oxidation—The pathway of {beta}-oxidation in yeast is shown in Fig. 3. The entire pathway of {beta}-oxidation in yeast takes place in peroxisomes, membrane-bounded organelles whose size and number increase in response to the presence of fatty acids in the medium (38). The microarray data extend to at least 12, the number of genes encoding peroxisomal proteins that are dependent on ADR1 for expression in the absence of oleate (Table III). Many of the ADR1-dependent genes scored high in a genomic profile of genes involved in peroxisome assembly or function (39) and were also identified in a search for oleate-inducible, OAF1-PIP2-dependent genes (40, 41). In addition to {beta}-oxidation, uptake of fatty acyl-CoA by peroxisomes appears to be ADR1-dependent. PXA1 and PXA2 encode subunits of the ABC transporter responsible for uptake of long chain fatty acyl-CoA derivatives into peroxisomes (42). In the array data, PXA1 showed a 5.7-fold dependence on ADR1. Expression of PXA2 may also require ADR1, showing a 3-fold dependence. However, the significance of this dependence requires further testing because the {lambda} value for these data was 43, just below the cutoff of 44. Medium chain fatty acids are converted into their acyl-CoA derivative by a peroxisomal acyl-CoA ligase encoded by FAA2. Like PXA2, FAA2 may also require ADR1 for expression, showing a 9.8-fold dependence. However, the {lambda} statistic associated with this value was also below the established cutoff. An ORF of unknown function, YMR018W, encodes a protein that is homologous to PEX5, a pts1 receptor. Pts1 (peroxisomal targeting signal) receptors bind polypeptides that are translocated into peroxisomes (38). YMR018W is expressed in an ADR1-dependent fashion. Its role in peroxisome biogenesis has not been investigated. Another gene displaying ADR1 dependence in the array data is PEX11 which encodes a peroxin, a class of proteins that play a role in peroxisome proliferation (9).



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FIG. 3.
Influence of ADR1 on expression of genes involved in {beta}-oxidation of fatty acids. The {beta}-oxidation pathway was adapted from the review of fatty acid metabolism in S. cerevisiae by Trotter (66). Located below each gene label is the fold change in gene expression. Positive values and red color indicate down-regulated expression in adr1 mutant cells. A question mark next to a value indicates that its associated {lambda} statistic is below the cutoff value. All genes in black type showed no significant change in expression. Genes marked by an asterisk have promoters that have been shown previously by ChIP analysis or in the current study to bind Adr1 in vivo. The dotted arrow indicates that saturated acyl-CoA products of {beta}-oxidation become the substrates for another round of {beta}-oxidation.

 

Sporulation, Meiosis, and Replication/Recombination—An unexpected category of ADR1-dependent genes encodes proteins involved in meiosis and sporulation (Table III). This group includes two genes encoding transcription factors expressed early in meiosis, IME1 and SLZ1. Other ADR1-dependent genes affecting meiosis or sporulation are DMC1, a gene important for meiotic recombination, SPO20, SPR6, BNS1, CSM4, and ADY2. Finding sporulation-specific genes that are expressed in an ADR1-dependent fashion in haploid cells is surprising because most have no known function other than in the sporulation of diploid cells, and until now, ADR1 has not been implicated in meiosis or sporulation. Perhaps these genes have unknown functions in haploid cells during glucose starvation.

Amino Acid Metabolism—Another cohort of ADR1-dependent genes is involved in the transport or metabolism of amino acids (Table III). Five genes encode amino acid or peptide transporters. Two ORFs, YLR126C and YDR111C, encode putative transaminases that are identified only by homology to known glutamine amido-transferases and aspartate amino transferases, respectively, enzymes that are important for shuttling oxaloacetate between the mitochondrion, cytosol, and peroxisome. BAT1 and ARO9 encode branched-chain and aromatic amino acid transaminases, respectively. The expression of genes encoding enzymes that catabolize amino acids allows the cell to respond to starvation conditions by utilizing amino acids for gluconeogenesis and energy production.

Regulatory Genes—In addition to IME1 and SLZ1, two other genes, TEC1 and NRG1, encoding putative transcription factors, are expressed in an ADR1-dependent manner. Their molecular functions are unknown, but they have been implicated in pseudohyphal growth and Snf1-dependent glucose repression, respectively (43, 44). Two putative protein phosphatase regulatory subunits, GIP2 and RRD1, also appear to be expressed in an ADR1-dependent manner (Table III). GIP2 appears to play a role in regulating glycogen accumulation, and RRD1 appears to be involved in the response to osmotic stress (45, 46).

Genes Whose Expression Is Enhanced in the Absence of ADR1—A significant number of genes appear to be up-regulated in the absence of ADR1 in derepressed growth conditions (Table I, Supplemental Material). Interestingly, some of these genes encode enzymes that function in biosynthetic pathways that might be thought of as opposing the pathways dependent on ADR1 for expression. For example, two genes encoding enzymes required for fatty acid synthesis, OLE1 and FAS1, are up-regulated in the absence of ADR1, as is the gene, ACH1, encoding acetyl-CoA hydrolase. Several genes encoding glyoxylate cycle enzymes, CIT2, ICL1, and MLS1, are significantly up-regulated in the absence of ADR1 (Fig. 2), as is INO1, encoding the enzyme catalyzing the first step in phosphoinositide biosynthesis. Increased expression of these genes could reflect tight coordinate regulation of the opposing catabolic-biosynthetic pathways, or could be an indirect consequence of the loss of Adr1.

ADR1-dependent Genes Are Glucose-repressed—ADR1-dependent genes should be expressed only after glucose has been exhausted from the medium because Adr1 is unable to bind chromatin in repressed growth conditions (20, 47). To determine whether glucose repression is a common feature of the genes identified as ADR1-dependent for expression, we co-hybridized labeled cDNAs derived from cell populations growing in repressed and derepressed growth conditions.

In general there is good agreement between our data and the data derived from the diauxic shift experiment (1). The genes that show marked derepression in both sets of data include those encoding many of the enzymes of gluconeogenesis, the glyoxylate cycle, and ethanol, glycerol, lactate, formate, and fatty acid metabolism. The complete data set is given in Table I of the Supplemental Material.

Table IV shows the 40 genes that have the highest expression ratio when comparing derepressed versus repressed growth conditions. Among this group, 19 are at least 2-fold dependent on ADR1 for expression. Among a larger group of 100 genes that are highly derepressed, 38 are ADR1-dependent for expression. The genes that encode enzymes required for the metabolism of non-fermentable carbon compounds, especially ethanol and fatty acids, are the most prominent in this group. In the group of 108 ADR1-dependent genes, over 80% are at least 2-fold glucose-repressed.

ADR1 and SNF1 Dependence—Another characteristic of previously identified ADR1-dependent genes is dependence on SNF1 for derepression (2, 48, 49). To characterize the SNF1 dependence of the genes identified as ADR1-dependent on the microarrays, labeled cDNAs derived from isogenic wild-type and snf1 mutant strains grown in derepressing conditions were co-hybridized on DNA microarrays and analyzed. A related experiment was performed using labeled cDNAs from a wildtype strain and a double adr1snf1 mutant to confirm the degree of SNF1 dependence of the ADR1-dependent genes.

Many ADR1-dependent genes are also SNF1-dependent for expression (Table IV and Table I, Supplemental Material). Moreover, the expression ratios for these genes in derepressing conditions are very similar in the single snf1 mutant and the double snf1adr1 mutant, consistent with a requirement for SNF1 to act through ADR1 to activate ADR1-dependent gene expression. The exceptions to this generalization include several genes that are highly ADR1-dependent, such as POT1, POX1, and CIT3. Expression of POT1 was SNF1-dependent during oleate-inducing conditions in a previous study (49), but it did not appear to be SNF1-dependent in our microarrays. In summary, many of the genes identified by DNA microarrays as ADR1-dependent for expression have two additional properties that are expected based on previous studies of a small number of ADR1-dependent genes: they are glucose-repressed and dependent on SNF1 for derepression.

Properties of SNF1-dependent Genes—Over four hundred genes have a wild-type/snf1 expression ratio in depressing conditions of two or greater (Table I, Supplemental Material). Twenty nine of 40 of the most highly glucose-repressed genes are SNF1-dependent for derepression (Table IV).

The functional classification of the SNF1-dependent genes is shown in Fig. 4. The largest class of annotated SNF1-dependent genes functions in transcription or signal transduction pathways. The next largest class of SNF1-dependent genes is involved in non-fermentative carbon metabolism. Transition from growth in high glucose to growth in low glucose does not seem to trigger a major stress response because few of the SNF1-dependent genes are in the heat-shock protein family. Two particularly interesting categories of SNF1-dependent genes are transporters (Table II, Supplemental Material) and regulatory genes (Table III, Supplemental Material). The SNF1-dependent expression of these genes is described in more detail under "Discussion."



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FIG. 4.
Functional categories of SNF1-dependent genes. SNF1-dependent genes that were functionally annotated in the SGD data base were categorized based on geneontology annotations (genome-www.stanford.edu/Saccharomyces/help/gotutorial.html). Genes falling into categories with 10 or fewer members were grouped into the category Other. The remaining unannotated genes were grouped together in the category Unknown. Each section of the pie chart represents a functional category, and the number next to it is the number of members of that category. The percentage of the total 439 SNF1-dependent genes present in each category follows the description of each category in the color key next to the chart.

 

Although many glucose-repressed genes are Adr1- and Snf1-dependent for derepression, there is a significant number of SNF1-dependent genes that is not glucose-repressed. The best characterized of these genes is INO1 (Table I, Supplemental Material), encoding inositol-1-phosphate synthase, the first enzyme required for phosphoinositide biosynthesis. INO1 induction is dependent on the Snf1 protein kinase, and repression is dependent on Reg1/Glc7 (21, 5052). Other examples include six genes encoding histones: HTA1, HTA2, HTB1, HTB2, HHF1, and HHF2 (wild-type/mutant ~3–5; Table I, Supplemental Material) and many genes involved in metal ion homeostasis (Table II, Supplemental Material). These effects could be an indirect consequence of the growth-defective phenotype of the snf1 mutant after glucose depletion. However, a cat8 mutant is also growth-defective after glucose depletion, but it does not affect the expression of the same subset of yeast genes (see below and Table IV, Supplemental Material). This suggests that the effect of SNF1 is not caused by general growth defects.

Snf1 May Have a Negative Role in Gene Expression—The DNA microarray data suggest that Snf1 may play a role in down-regulating gene expression when glucose is exhausted. Many glycolytic genes are more highly represented in mRNA isolated from the snf1 mutant than in mRNA isolated from its wild-type parent (Table I, Supplemental Material). We confirmed that one glycolytic enzyme, ADHI, is present at higher than wild-type levels in the snf1 mutant by analyzing alcohol dehydrogenase activity using non-denaturing PAGE.3 Thus, the elevated ADH1 mRNA that is detected in the snf1 mutant by the microarray is functional in vivo. As described above, these effects could be an indirect consequence of the growth-defective phenotype of the snf1 mutant after glucose depletion, but they are not observed in a cat8 mutant strain in the same conditions.

CAT8-dependent Genes—Several of the genes encoding enzymes that metabolize ethanol and lactate share a dependence on ADR1 and CAT8 for derepression (Table III) (11). To identify additional genes that are co-regulated by ADR1 and CAT8, we co-hybridized cDNAs isolated from wild-type and cat8 mutant cells under derepressing conditions. To identify genes cooperatively regulated by both ADR1 and CAT8, a double adr1cat8 mutant was also examined.

Over 200 genes are at least 2-fold CAT8- or ADR1CAT8-dependent in the microarray experiments. Among the 40 most highly glucose-repressed genes, 17 are at least 2-fold dependent on CAT8 for expression (Table IV). Because the binding site or CSRE for CAT8 is known, we could determine which of these genes are likely to be direct targets of CAT8 regulation by examining their promoters for the CSRE motif. This analysis identified 70 genes that contained CSREs within 600 nucleotides (650 nucleotides in the case of JEN1) of the initiation codon. The CAT8-dependent genes that lack CSRE elements may be affected indirectly by the absence of CAT8 or they could contain non-consensus CSRE motifs in their promoters. Alternatively, they may contain a CSRE element further upstream of the ATG than 600 nucleotides.

Fifty new CAT8-dependent genes that contain CSREs within 600 nucleotides of the ATG are listed in the 1st column of Table IV of the Supplemental Material. We confirmed the CAT8 dependence reported earlier for 18 genes including all of the genes encoding major glyoxylate cycle enzymes (Table IV, Supplemental Material, 2nd column) (11, 15). Ten of the genes present in this list have been shown to have functional CSREs (15). Eleven genes that were reported to be CAT8-dependent (11) were not CAT8-dependent in our microarrays or did not appear in our data (Table IV, Supplemental Material, 3rd column). One of the former, CAT2, was just below the {lambda} cutoff value for significance in our data.

Thus, there is good agreement between the miniarray/proteomics and microarray data considering that the previous genomic study assayed only half of the existing yeast ORFs using miniarrays. ACS1, previously known to be both ADR1- and CAT8-dependent, behaves as expected in both microarrays. In the single mutants, its CAT8/cat8 and ADR1/adr1 expression ratios are 3 and 9, respectively. In the double mutant, its ADR1CAT8/adr1cat8 expression ratio is close to 10 (Tables I and IV of the Supplemental Material). Six of the CAT8-dependent genes in Table IV, Supplemental Material, are also ADR1-dependent (denoted with asterisks). The genes co-regulated by both ADR1 and CAT8 are not restricted to the pathway of ethanol oxidation (ADH2, ACS1). Lactate utilization (JEN1), meiosis (ADY2), and amino acid metabolism (PUT4, ALP1) are all potentially co-regulated by CAT8 and ADR1. In the total set of 200 CAT8-dependent genes, 30 are ADR1-dependent. Because most of these genes lack recognizable CSREs, they may be affected indirectly by the absence of CAT8.

Confirmation of ADR1 Dependence of Gene Expression—The ADR1 dependence of expression of several genes identified on the microarrays was confirmed by constructing promoter-lacZ gene fusions. The results in Fig. 5 confirm that expression of ADY2, FDH2, ALD4, and YIL057C is ADR1-dependent. The ADR1 dependence of IME1 expression was similarly confirmed. IME1 is a master regulator of meiotic-specific gene expression. It has a large and complex upstream regulatory region that controls its activity in response to ploidy and growth conditions (53). Its known activity is important only in diploids undergoing meiosis. Because ADR1-dependent IME1 expression in haploid cells is unexpected, we tested the activity of an IME1/lacZ gene fusion in adr1/adr1-homozygous diploids and ADR1/adr1 heterozygous diploids, as well as in haploids. IME1/lacZ activity increased about 10-fold in diploids relative to haploids but still showed significant ADR1 dependence (Table V). adr1/adr1-homozygous diploids are sporulation-competent, despite the fact that IME1/lacZ expression is reduced about 5-fold in adr1-null homozygous diploids.3



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FIG. 5.
Confirmation of ADR1 dependence using promoter-lacZ reporter genes. Promoter-lacZ reporter plasmids were constructed from three independent PCR products for each promoter region to be tested. Yeast strains TYY320 (ADR1) and TYY324 ({triangleup}adr1) were then transformed with each reporter plasmid. The promoter regions tested, numbered in base pairs relative to the ATG translational start site, are as follows: ADH2, -600 to -110 (54); FDH2, -468 to -182; ADY2, -445 to -187; YIL057C, -397 to -136; and ALD4, -280 to -91. These are demarcated on the transcription factor-binding site maps of each promoter by brackets. The pLGs/x control plasmid is the pLG669-Z plasmid lacking the UAS region of the CYC1 promoter (54). Transformants were grown at 30 °C in ura- trp- selective medium containing 5% glucose to a cell density of about 2 x 107 cells/ml. The cells from each culture were collected by centrifugation and resuspended in selective medium containing 0.05% glucose. These cells were incubated at 30 °C for 6 h, the same length of time cultures used to prepare poly(A) RNA for microarray analyses were allowed to derepress. Whole cells were permeabilized, and {beta}-galactosidase activity was measured as described under "Experimental Procedures." Each value represents the average {beta}-galactosidase activity of the three independent promoter-lacZ plasmids constructed for each promoter.

 

ADR1-dependent Genes Are Enriched in the Adr1 Consensus Binding Motif—ChIP experiments showed that Adr1 bound to the promoters of ADR1-dependent genes ADH2, ACS1, CTA1, POT1, and GUT1 only in derepressed cells (20), demonstrating that Adr1 acts directly on these genes. To find additional direct targets of Adr1, we analyzed the promoters of the 50 genes having the highest degree of Adr1 dependence using an algorithm that searches DNA sequences for common motifs. The Adr1-binding consensus motif, (T/G)(T/C)GGRG, is recovered as the most frequent 6-bp sequence in the promoter region located within 1000 bp upstream of the ATG codon of these genes, indicating that ADR1-dependent genes are enriched in the Adr1 consensus binding motif. In contrast, the promoters of genes that appeared to be repressed by Adr1 have a TATA-like sequence as their most frequent motif.4

The promoters of all genes displaying ADR1 dependence were also examined directly for the presence of Adr1-binding sites. Single Adr1-binding sites matching the consensus (31) perfectly were found in 90% of the promoters of the ADR1-dependent genes but in only 70% of the promoters in the total genome (Table V, Supplemental Material). In isolation, single Adr1-binding sites or direct repeats of a single site are not able to activate transcription (54). The activating sequence in the ADH2 promoter, UAS1, consists of an inverted repeat of Adr1-binding sites. The UAS1 consensus sequence was present in 18% of the ADR1-dependent promoters as compared with only 4% of all promoters from the whole genome. Similarly, imperfect UAS1 consensus sequences were found in 70% of the ADR1-dependent promoters as compared with only 51% of promoters from the whole genome. Surprisingly, some of the ADR1-dependent genes contained only direct repeats of the Adr1-binding motif and not UAS1 consensus sequences. In particular, YIL057C, which is one of the most strongly ADR1-dependent genes, has two Adr1-binding sites in tandem orientation (Fig. 5). ChIP experiments demonstrate that Adr1 binds directly to the YIL057C promoter indicating that Adr1 can bind and activate through direct repeats (see below, Fig. 7).



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FIG. 7.
Adr1 protein binds to the promoters of ADR1-dependent genes. Chromatin immunoprecipitation was used to enrich for Adr1-bound DNA from a strain with hemagglutinin-tagged Adr1 grown in depressing conditions as described in the legend to Fig. 5. Enriched DNA (1:1280 dilution) was used in PCRs with primer pairs for the promoters of genes (Table II) that were ADR1-dependent in the microarray experiments. Primers for ACT1 and ADH2 promoters were included as negative and positive controls, respectively.

 

Adr1-dependent Genes Have Promoters Enriched in OREs but Not CSREs—Because some ADR1-dependent genes are also dependent on either Cat8 or Oaf1/Pip2 for expression, we also searched for their binding sites, CSRE and ORE motifs, respectively. The promoters of ADR1-dependent genes are not enriched in CSRE sequences. Seventeen percent of both ADR1-dependent and total genomic promoter sequences have such sequences in their promoters (Table V, Supplemental Material). No discernible pattern of Adr1-binding sites or UAS1 positioning with respect to CSREs was observed. ADR1-dependent promoters, however, are enriched in ORE sequences. OREs were found in 25% of all ADR1-dependent genes compared with 11% of all genes in the entire genome. Fourteen of the 27 ADR1-dependent genes containing OREs have UAS1-like sequences adjacent to an ORE (Fig. 6). Work by Gurvitz et al. (9) suggests that a cooperative interaction between the Oaf1-Pip2 complex and Adr1 may play a role in controlling {beta}-oxidation and peroxisomal proliferation. Our findings support and extend their work.



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FIG. 6.
ADR1-dependent promoters that have adjacent Adr1-binding site or UAS1-ORE sequence elements. Transcription factor-binding site maps were produced as described under "Experimental Procedures" and are listed in order of ADR1 dependence. The value to the right of each map is the average expression ratio from the ADR1/{triangleup}adr1 microarray experiments.

 

Adr1 Binds to the Promoters of Many ADR1-dependent Genes—To determine whether Adr1 binds to the promoters of genes whose expression is ADR1-dependent in the microarray assay, a subset of the promoters was analyzed in ChIP experiments. As shown in Fig. 7, a hemagglutinin epitope-tagged version of Adr1 binds to the promoters of ADY2, ALD4, POX1, CIT3, GIP2, ICL2, ETR1, YIL057C, and FDH2 in derepressed growth conditions.

ADR1-dependent Growth—Yeast strains lacking ADR1 are unable to grow on glycerol or oleate as a sole carbon source (6, 49, 55), but our microarray data suggested that ADR1 may influence growth on a wider range of carbon sources. Fig. 8 shows that adr1 mutant cells grow poorly on medium containing glycerol, ethanol, citrate, or pyruvate as a carbon source. In addition, adr1 mutant strains grow poorly on low glucose in the presence of formate, presumably because formate cannot be metabolized in the absence of ADR1-dependent expression of formate dehydrogenase. However, adr1 mutant cells are able to grow on synthetic minimal medium containing the non-fermentable substrates acetate or lactate as the sole carbon source.



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FIG. 8.
ADR1 dependence of growth on different non-fermentable carbon sources. Strains TYY201 (ADR1) and TYY202 (adr1) were grown on YPD plates overnight. Several colonies were picked into 1 ml of H2O, and 5-fold serial dilutions were made and spotted onto plates containing synthetic medium with complete amino acid solution, uracil, adenine, and the indicated carbon sources. The concentrations of the carbon sources are indicated in parentheses. The spots shown contain about 1000 cells.

 


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Yeast growth in the absence of glucose relies on the breakdown of partially oxidized substrates that are derived from fermentative reactions. These metabolic products provide energy and intermediates for additional growth and cell division after glucose has been exhausted. The major source of metabolites and energy after the diauxic transition is the ethanol produced during fermentation.

Ethanol Metabolism—Genes encoding all of the enzymes required for the oxidation of ethanol, alcohol dehydrogenase, aldehyde dehydrogenase, and acetyl-CoA synthetase, show a high degree of ADR1 dependence. This group includes ADH2 and ACS1, two genes previously known to be ADR1-, CAT8-, and SNF1-dependent (3, 4, 11). Expression of ADH3, encoding a mitochondrial isozyme, and ADH5, encoding an alcohol dehydrogenase of unknown function, also appear to be ADR1-dependent. However, cDNAs derived from these two genes might co-hybridize with ADH2 cDNA on the array because they are about 74% identical. Because ADH2 is very highly expressed, if the cDNA derived from its abundant mRNA also hybridized to ADH3 and ADH5 on the array, they could be erroneously classified as ADR1-dependent. Two genes encoding aldehyde dehydrogenases, ALD4 and ALD5, are ADR1-dependent. A third aldehyde dehydrogenase, encoded by ALD6, is CAT8-dependent for expression (Table IV) (11). These genes share only 60% identity and would not be expected to cross-hybridize significantly under the microarray conditions. Thus, the ethanol-oxidation pathway utilizes multiple isozymes, many of which are regulated by Adr1 and/or Cat8, to generate acetyl-CoA during non-fermentative growth.

ADR1 and CAT8 are required for ethanol oxidation to occur efficiently because adr1 and cat8 mutants grow poorly on ethanol as a sole carbon source. Use of acetate as a sole carbon source, on the other hand, requires CAT8 but not ADR1. This observation suggests that at least one of the steps preceding acetate synthesis has an essential requirement for ADR1. Because ADHI should be able to substitute for ADHII in oxidizing ethanol, this result suggests that ALD4 is the major acetaldehyde dehydrogenase that is most important for ethanol utilization. The requirement for CAT8 for growth on both ethanol and acetate could reflect its importance for expression of ACS1 and genes of the glyoxylate cycle and gluconeogenesis, which are needed to generate essential intermediates for cell growth.

Glycerol Metabolism—Growth on glycerol as a carbon source requires the activity of two enzymes. Glycerol kinase, encoded by GUT1, catalyzes the first step in glycerol breakdown. Glycerol-3-phosphate dehydrogenase, encoded by GUT2, is an enzyme of the inner membrane of the mitochondria whose major role in glycerol metabolism is in the glycerophosphate shuttle. This process transfers reducing equivalents across the NADH-impermeable mitochondrial inner membrane. Both genes are regulated by INO2 and INO4, genes encoding transcription factors that are responsive to inositol signaling (56). Growth on glycerol is ADR1-dependent (6), presumably because of the dependence of GUT1 and GUT2 expression on ADR1 (5, 6). In the DNA array experiments (Fig. 2 and Table III) both GUT1 and GUT2 are 2-fold ADR1-dependent for expression in low glucose-containing medium. It would be surprising if a 2-fold dependence on ADR1 caused glycerol-deficient growth. Perhaps the full ADR1 dependence of these genes is only evident in cells growing on glycerol, or maybe the level of their mRNAs is not predictive of Gut1 and Gut2 enzyme levels. Alternatively, other reactions that are compromised in the absence of Adr1 could contribute to the glycerol-deficient growth.

Lactate Metabolism—Lactate metabolism is a third pathway utilized by yeast in the absence of glucose. Saccharomyces cerevisiae contains three genes encoding enzymes capable of converting lactate to pyruvate. CYB2 encodes an L-lactate dehydrogenase (cytochrome b2) that is ADR1-dependent for expression, confirming previous suggestions that ADR1 might play a role in CYB2 derepression (57). DLD1, encoding a D-lactate dehydrogenase isozyme, is CAT8-dependent, as is a lactate symporter encoded by JEN1 (11). Expression of JEN1 may also be ADR1-dependent. However, the significance of its expression ratio is unclear because it has an associated {lambda} value just below the established cutoff. A third lactate dehydrogenase is encoded by DLD3, expression of which is elevated in the absence of Adr1 (Table I, Supplemental Material). ORF YPL113C encodes a lactate dehydrogenase homolog of unknown function. This ORF is ADR1-dependent for expression. Thus, lactate metabolism, as with ethanol metabolism, utilizes multiple isozymes that are differentially regulated by ADR1 and CAT8. ADR1 appears to regulate genes involved in L-lactate utilization, whereas CAT8 appears to regulate D-lactate utilization.

The ability of adr1 mutants to grow on lactate is surprising because several genes in the pathway depend on ADR1 for expression. Lactate is metabolized to acetyl-CoA via pyruvate, and adr1 mutant cells cannot grow on pyruvate. An explanation for this apparent conundrum might be that while pyruvate can be utilized by adr1 mutants if it is generated internally, perhaps it cannot be transported into the cell, or into the mitochondria, in the absence of carriers that are dependent on ADR1 for expression. This scenario might also explain the inability of adr1 mutants to grow on citrate. This explanation seems reasonable because several mitochondrial carboxylate carriers, such as Ctp1, Dic1, and Oac1, are expressed in an ADR1-dependent fashion.

Formate Metabolism—Two genes encoding formate dehydrogenases FDH1 and FDH2 are among the most highly ADR1-dependent genes in yeast. These two recently diverged ORFs are 94% identical and are not distinguishable by the array hybridization conditions. In some strains, including W303 studied here, FDH2 is inactive due to a frameshift mutation (36). The primary role of formate dehydrogenase in yeast metabolism is unknown, but formate can be co-metabolized with growth-limiting amounts of glucose to increase the yield of yeast biomass. This is thought to result from oxidation of formate to CO2, which increases the amount of cytosolic NADH available for the production of ATP (36). A putative formate/nitrite transporter encoded by YHL008C (Table III) is also expressed in an ADR1-dependent manner.

Genes Encoding Mitochondrial Transport Proteins—ADR1 is involved in the expression of genes encoding mitochondrial inner membrane proteins that serve as transporters of intermediates in tricarboxylic acid and glyoxylate cycles. CTP1, encoding a citrate transport protein, and OAC1, encoding a transporter of oxaloacetate, are both ADR1-dependent for expression (Table III). Citrate transport is important because it allows citrate, formed from the reaction of acetyl-CoA and oxaloacetate in the mitochondrion, to be transported to the cytoplasm where it can be utilized for fatty acid biosynthesis and gluconeogenesis after being broken down to acetyl-CoA and oxaloacetate. This is the only mechanism known that allows acetyl-CoA produced in the mitochondria to be utilized in other cellular compartments. DIC1, encoding a dicarboxylic acid transporter in the mitochondrial inner membrane, is also expressed in an ADR1-dependent fashion.

{beta}-Oxidation—The utilization of exogenous fatty acids for yeast growth is dependent on ADR1, OAF1, and PIP2 (8, 9, 32). OAF1 is expressed constitutively, whereas expression of PIP2 is regulated (58). The heterodimeric Oaf1-Pip2 transcription factor is active only in the presence of an inducing fatty acid such as oleate. Thus, in the absence of oleate Adr1 appears to be sufficient to derepress all of the genes necessary for the {beta}-oxidation of fatty acids (Table III). However, some genes needed for use of exogenous fatty acids must require Oaf1-Pip2 for their expression because OAF1 and PIP2 are needed for growth on oleate. Genes that encode proteins that transport fatty acids across the cell and peroxisomal membranes are likely candidates, but the hypothesized transporter genes have not been identified.

A Methyl Citrate Cycle and Propionate Metabolism in S. cerevisiae—The {beta}-oxidation pathway can metabolize even- or odd-numbered fatty acids. However, if the acyl-CoA substrate contains an odd number of carbon atoms, propionyl-CoA rather than acetyl-CoA is the product of the last cycle of the reaction. Propionate is also derived from amino acid catabolism and can be converted to propionyl-CoA by acetyl-CoA synthase. Propionyl-CoA and oxaloacetate form 2-methyl citrate which is metabolized via the methyl citrate cycle to pyruvate and succinate (Fig. 2), thus generating pyruvate for mitochondrial oxidation and replenishing the tricarboxylic acid cycle with succinate. Based on their coordinate induction in cells with dysfunctional mitochondria, it was proposed that enzymes encoded by CIT3, ACO1, and ICL2 (59) catalyze the methyl citrate cycle in S. cerevisiae (60). PDH1, another co-induced gene, was hypothesized to carry out an essential function in propionate metabolism in S. cerevisiae because its deletion rendered cells sensitive to exogenous propionate.

CIT3 and ICL2 are highly ADR1-dependent suggesting that ADR1 might be involved in the response to dysfunctional mitochondria. PDH1 is a homolog of a bacterial dehydratase that preferentially utilizes 2-methyl citrate, rather than citrate, in the first step of the aconitase reaction, forming 2-methyl cis-aconitate (61, 62). All three genes are also highly glucose-repressed (Table IV). It is possible that CIT3, PDH1, and ICL2 perform the three steps of the methyl citrate cycle of propionate metabolism, rather than CIT3, ACO1, and ICL2, as suggested previously, and that the expression of the genes encoding the enzymes of the entire pathway is ADR1-dependent. If PDH1 is a methylcitrate dehydratase but not an aconitase, a fourth enzyme would be needed to form methyl isocitrate from 2-methyl cis-aconitate. Aconitase could carry out this function, or there could be an unidentified ORF encoding an enzyme that preferentially hydrates 2-methyl cis-aconitate. YJL200C encodes a protein with significant homology to ACO1 and could be the unidentified hydratase. As described below, the expression of BAT1, encoding a branched amino acid aminotransferase that makes propionate from isoleucine, is also ADR1-dependent, thus forging another link between ADR1 and the putative methyl citrate cycle.

ADR1 and Retrograde Regulation—Our data also suggest a connection between ADR1 and the sensing of mitochondrial function. Butow and colleagues (60) have characterized a yeast pathway called "retrograde regulation" that alters nuclear gene expression in response to changes in mitochondrial function. The genes that are regulated by this pathway and the genes that are regulated by Adr1 show considerable overlap. DNA microarray analysis identified 43 genes that are at least 2-fold induced in cells lacking mitochondrial DNA. Thirty five of these genes were present on our microarrays, and 17 of them are ADR1-dependent for expression. Thus, it is possible that ADR1 is involved in intracellular communication between the mitochondria and nucleus.

ADR1-dependent Promoters—Analysis of the promoters of many of the ADR1-dependent genes indicates that they contain putative binding sites for Adr1, suggesting that Adr1 could have a direct effect on the expression of these genes. Adr1 binding was demonstrated for an additional 9 genes, bringing to 14 the number of promoters known to bind Adr1 in vivo. Many of the genes of the {beta}-oxidation pathway contain binding sites for both Adr1 and Oaf1.Pip2, providing further support for the idea that these two transcription factors coordinately regulate the transcription of these genes in a direct manner.

SNF1-dependent Gene Expression—As expected SNF1 plays a major role in gene expression in the absence of glucose. Among the genes dependent on SNF1 for transcription are transcription factors and other regulatory proteins. Many of the effects of SNF1 on transcription could act through these genes (Table III, Supplemental Material). For example, CAT8 and SIP4 are two transcription factors that are known to be dependent on SNF1 for expression and activity (10, 15, 18). CAT8 expression is SNF1-dependent and glucose-repressed in our microarray data, as is expression of its functional homolog SIP4. This could explain the dependence of the genes encoding glyoxylate cycle and gluconeogenic enzymes on SNF1 for derepression (Table I, Supplemental Material).

SNF1-dependent expression of the transcription factors encoded by ZAP1 and CUP9 may explain the dependence of some of the metal- and peptide-transport proteins on SNF1 (Table II, Supplemental Material). CUP9-dependent effects on copper ion homeostasis are most pronounced in the absence of glucose (63), consistent with SNF1-dependent CUP9 expression. SNF1-dependent expression of sporulation-specific proteins may be transmitted through the transcription factor IME1, the early meiotic regulator.

Two ORFs encoding putative transcription factors are also expressed in a SNF1-dependent manner, YOX1 and YGR067C (Table III, Supplemental Material). YGR067C encodes a C2H2 zinc finger protein whose predicted DNA recognition properties are identical to those of ADR1, based on the amino acid sequence of its two zinc fingers. It is 30-fold derepressed in a SNF1-dependent manner, and its expression is also dependent on CAT8 (10) (see Table I, Supplemental Material). It could activate or repress many of the same genes that are regulated by ADR1 and be part of a feedback loop linking UAS1-containing genes to SNF1 and CAT8.

SNF1 controls expression of several genes encoding protein phosphatase and protein kinase regulatory subunits (Table III, Supplemental Material). REG2, a homolog of the type I protein phosphatase regulatory subunit encoded by REG1, is strongly derepressed in a SNF1-dependent fashion, as is another gene encoding a phosphatase-regulatory subunit, GAC1. A subunit of the Snf1 kinase complex, Sip2, is derepressed in an SNF1-dependent manner, suggesting a positive transcription feedback loop. Several other protein kinase regulatory subunits are also expressed in an SNF1-dependent fashion, including the two mitotic cyclins, CLB1 and CLB2. Thus, SNF1 has the potential for affecting transcription more globally by enhancing expression of upstream regulatory factors in addition to acting directly on specific transcription factors.

SNF1 does not seem to be important for expression of any components of the polII holoenzyme complex (Table I, Supplemental Material). No genes encoding polII subunits, general transcription factors, subunits of the mediator complex, or components of chromatin-modifying complexes are SNF1-dependent for expression. Thus, SNF1 must affect transcription in derepressed cells by altering the activity and expression of specific regulatory proteins or by altering the activity of the holoenzyme.

Our data are consistent with the regulatory network model of gene expression suggested by the recent genome-wide location analysis (64). Adr1, Cat8, and Snf1 play important roles in the expression of other transcriptional regulatory genes, parlaying an influence on direct target genes into a more global effect on genome expression. Equally important is the potential interaction between Adr1 and other transcription factors on the promoters of ADR1-dependent genes. These two aspects of Adr1 function contribute to its broad influence on gene regulation after glucose depletion. Adr1 is a key coordinator of pathways that metabolize non-fermentable carbon sources, allowing yeast to respond effectively to changes in nutrient conditions.


    FOOTNOTES
 
* This work was supported by NIGMS Research Grant GM26079 from the National Institutes of Health (to E. T. Y.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

The on-line version of this article (available at http://www.jbc.org) contains Tables I II III IV V and additional data. Back

§ Both authors contributed equally to this work. Back

Present address: Whitehead Institute, Nine Cambridge Center, Cambridge, MA 02142-1479. Back

{ddagger} To whom correspondence should be addressed: Dept. of Biochemistry, Box 357350, University of Washington, Seattle, WA 98195-7350. Tel.: 206-543-6517; Fax: 206-685-1792; E-mail: ety{at}u.washington.edu.

1 Proteins are indicated by gene names with a capital letter followed by lowercase letters (Adr1), and genotypes and gene names are capitalized in italics (ADR1). Back

2 The abbreviations used are: ADH, alcohol dehydrogenase; ChIP, chromatin immunoprecipitation; ORF, open reading frame; UAS, upstream activation sequence. Back

3 E. T. Young, unpublished data. Back

4 V. Voronkova, personal communication. Back


    ACKNOWLEDGMENTS
 
We thank Khoi Nguyen and Matthew Amster-Burton for constructing promoter-lacZ fusions, Khoi Nguyen and Ryan Hartmaier for performing {beta}-galactosidase assays, and Valentina Voronkova for allowing us to cite unpublished promoter analysis. We also thank Matthew Amster-Burton for the computer expertise in modifying and creating the Perl scripts for analyzing all of the microarray data and A. Mitchell for the IME1/lacZ reporter plasmid.



    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. DeRisi, J. L., Iyer, V. R., and Brown, P. O. (1997) Science 278, 680-686[Abstract/Free Full Text]
  2. Ciriacy, M. (1975) Mol. Gen. Genet. 138, 157-164[Medline] [Order article via Infotrieve]
  3. Ciriacy, M. (1979) Mol. Gen. Genet. 176, 427-431[Medline] [Order article via Infotrieve]
  4. Kratzer, S., and Schuller, H. J. (1997) Mol. Microbiol. 26, 631-641[Medline] [Order article via Infotrieve]
  5. Grauslund, M., Lopes, J. M., and Ronnow, B. (1999) Nucleic Acids Res. 27, 4391-4398[Abstract/Free Full Text]
  6. Pavlik, P., Simon, M., Schuster, T., and Ruis, H. (1993) Curr. Genet. 24, 21-25[Medline] [Order article via Infotrieve]
  7. Simon, M., Adam, G., Rapatz, W., Spevak, W., and Ruis, H. (1991) Mol. Cell. Biol. 11, 699-704[Medline] [Order article via Infotrieve]
  8. Gurvitz, A., Wabnegger, L., Rottensteiner, H., Dawes, I. W., Hartig, A., Ruis, H., and Hamilton, B. (2000) Mol. Cell. Biol. Res. Commun. 4, 81-89[CrossRef][Medline] [Order article via Infotrieve]
  9. Gurvitz, A., Hiltunen, J. K., Erdmann, R., Hamilton, B., Hartig, A., Ruis, H., and Rottensteiner, H. (2001) J. Biol. Chem. 276, 31825-31830[Abstract/Free Full Text]
  10. Hedges, D., Proft, M., and Entian, K. D. (1995) Mol. Cell. Biol. 15, 1915-1922[Abstract]
  11. Haurie, V., Perrot, M., Mini, T., Jeno, P., Sagliocco, F., and Boucherie, H. (2001) J. Biol. Chem. 276, 76-85[Abstract/Free Full Text]
  12. Carlson, M. (1999) Curr. Opin. Microbiol. 2, 202-207[CrossRef][Medline] [Order article via Infotrieve]
  13. DeVit, M. J., Waddle, J. A., and Johnston, M. (1997) Mol. Biol. Cell 8, 1603-1618[Abstract]
  14. Treitel, M. A., Kuchin, S., and Carlson, M. (1998) Mol. Cell. Biol. 18, 6273-6280[Abstract/Free Full Text]
  15. Hiesinger, M., Roth, S., Meissner, E., and Schuller, H. J. (2001) Curr. Genet. 39, 68-76[CrossRef][Medline] [Order article via Infotrieve]
  16. Lesage, P., Yang, X., and Carlson, M. (1996) Mol. Cell. Biol. 16, 1921-1928[Abstract]
  17. Vincent, O., and Carlson, M. (1998) EMBO J. 17, 7002-7008[Abstract/Free Full Text]
  18. Randez-Gil, F., Bojunga, N., Proft, M., and Entian, K. D. (1997) Mol. Cell. Biol. 17, 2502-2510[Abstract]
  19. Dombek, K. M., Camier, S., and Young, E. T. (1993) Mol. Cell. Biol. 13, 4391-4399[Abstract]
  20. Young, E. T., Kacherovsky, N., and Van Riper, K. (2002) J. Biol. Chem. 277, 38095-38103[Abstract/Free Full Text]
  21. Lo, W. S., Duggan, L., Tolga Emre, N. C., Belotserkovskya, R., Lane, W. S., Shiekhattar, R., and Berger, S. L. (2001) Science 293, 1142-1146[Abstract/Free Full Text]
  22. Walther, K., and Schuller, H. J. (2001) Microbiology 147, 2037-2044[Abstract/Free Full Text]
  23. Hartshorne, T. A., Blumberg, H., and Young, E. T. (1986) Nature 320, 283-287[Medline] [Order article via Infotrieve]
  24. Sloan, J. S., Dombek, K. M., and Young, E. T. (1999) J. Biol. Chem. 274, 37575-37582[Abstract/Free Full Text]
  25. Thompson-Jaeger, S., Francois, J., Gaughran, J. P., and Tatchell, K. (1991) Genetics 129, 697-706[Abstract/Free Full Text]
  26. Guldener, U., Heck, S., Fielder, T., Beinhauer, J., and Hegemann, J. H. (1996) Nucleic Acids Res. 24, 2519-2524[Abstract/Free Full Text]
  27. Guthrie, C., and Fink, J. R. (1991) Methods Enzymol. 194, 1-863[Medline] [Order article via Infotrieve]
  28. Ideker, T., Thorsson, V., Siegel, A. F., and Hood, L. E. (2000) J. Comput. Biol. 7, 805-817[CrossRef][Medline] [Order article via Infotrieve]
  29. Buhler, J., Ideker, T., and Haynor, D. (2000) University of Washington CSE Technical Report, pp. 1-12, University of Washington, Seattle
  30. van Helden, J., Andre, B., and Collado-Vides, J. (2000) Yeast 16, 177-187[CrossRef][Medline] [Order article via Infotrieve]
  31. Cheng, C., Kacherovsky, N., Dombek, K. M., Camier, S., Thukral, S. K., Rhim, E., and Young, E. T. (1994) Mol. Cell. Biol. 14, 3842-3852[Abstract]
  32. Rottensteiner, H., Kal, A. J., Filipits, M., Binder, M., Hamilton, B., Tabak, H. F., and Ruis, H. (1996) EMBO J. 15, 2924-2934[Abstract]
  33. Guarente, L. (1983) Methods Enzymol. 101, 181-191[Medline] [Order article via Infotrieve]
  34. Thukral, S. K., Morrison, M. L., and Young, E. T. (1992) Mol. Cell. Biol. 12, 2784-2792[Abstract]
  35. Kurdistani, S., Robyr, D., Tavozoie, S., and Grunstein, M. (2002) Nat. Genet. 31, 248-254[CrossRef][Medline] [Order article via Infotrieve]
  36. Overkamp, K. M., Kotter, P., van der Hoek, R., Schoondermark-Stolk, S., Luttik, M. A., van Dijken, J. P., and Pronk, J. T. (2002) Yeast 19, 509-520[CrossRef][Medline] [Order article via Infotrieve]
  37. Pronk, J. T., van der Linden-Beuman, A., Verduyn, C., Scheffers, W. A., and van Dijken, J. P. (1994) Microbiology 140, 717-722[Abstract]
  38. Kunau, W. H., and Hartig, A. (1992) Antonie Leeuwenhoek 62, 63-78[Medline] [Order article via Infotrieve]
  39. Smith, J. J., Marelli, M., Christmas, R. H., Vizeacoumar, F. J., Dilworth, D. J., Ideker, T., Galitski, T., Dimitrov, K., Rachubinski, R. A., and Aitchison, J. D. (2002) J. Cell Biol. 158, 259-271[Abstract/Free Full Text]
  40. Koerkamp, M. G., Rep, M., Bussemaker, H. J., Hardy, G. P., Mul, A., Piekarska, K., Szigyarto, C. A., De Mattos, J. M., and Tabak, H. F. (2002) Mol. Biol. Cell 13, 2783-2794[Abstract/Free Full Text]
  41. Kal, A. J., van Zonneveld, A. J., Benes, V., van den Berg, M., Koerkamp, M. G., Albermann, K., Strack, N., Ruijter, J. M., Richter, A., Dujon, B., Ansorge, W., and Tabak, H. F. (1999) Mol. Biol. Cell 10, 1859-1872[Abstract/Free Full Text]
  42. Shani, N., and Valle, D. (1996) Proc. Natl. Acad. Sci. U. S. A. 93, 11901-11906[Abstract/Free Full Text]
  43. Madhani, H. D., and Fink, G. R. (1997) Science 275, 1314-1317[Abstract/Free Full Text]
  44. Vyas, V. K., Kuchin, S., and Carlson, M. (2001) Genetics 158, 563-572[Abstract/Free Full Text]
  45. Rempola, B., Kaniak, A., Migdalski, A., Rytka, J., Slonimski, P. P., and di Rago, J. P. (2000) Mol. Gen. Genet. 262, 1081-1092[Medline] [Order article via Infotrieve]
  46. Williams-Hart, T., Wu, X., and Tatchell, K. (2002) Genetics 160, 1423-1437[Abstract/Free Full Text]
  47. Verdone, L., Wu, J., van Riper, K., Kacherovsky, N., Vogelauer, M., Young, E. T., Grunstein, M., Di Mauro, E., and Caserta, M. (2002) EMBO J. 21, 1101-1111[Abstract/Free Full Text]
  48. Denis, C. L. (1987) Mol. Gen. Genet. 208, 101-106[Medline] [Order article via Infotrieve]
  49. Simon, M., Binder, M., Adam, G., Hartig, A., and Ruis, H. (1992) Yeast 8, 303-309[Medline] [Order article via Infotrieve]
  50. Shirra, M. K., and Arndt, K. M. (1999) Genetics 152, 73-87[Abstract/Free Full Text]
  51. Shirra, M. K., Patton-Vogt, J., Ulrich, A., Liuta-Tehlivets, O., Kohlwein, S. D., Henry, S. A., and Arndt, K. M. (2001) Mol. Cell. Biol. 21, 5710-5722[Abstract/Free Full Text]
  52. Ouyang, Q., Ruiz-Noriega, M., and Henry, S. A. (1999) Genetics 152, 89-100[Abstract/Free Full Text]
  53. Sagee, S., Sherman, A., Shenhar, G., Robzyk, K., Ben-Doy, N., Simchen, G., and Kassir, Y. (1998) Mol. Cell. Biol. 18, 1985-1995[Abstract/Free Full Text]
  54. Yu, J., Donoviel, M. S., and Young, E. T. (1989) Mol. Cell. Biol. 9, 34-42[Medline] [Order article via Infotrieve]
  55. Young, E. T., Saario, J., Kacherovsky, N., Chao, A., Sloan, J. S., and Dombek, K. M. (1998) J. Biol. Chem. 273, 32080-32087[Abstract/Free Full Text]
  56. Grauslund, M., and Ronnow, B. (2000) Can. J. Microbiol. 46, 1096-1100[CrossRef][Medline] [Order article via Infotrieve]
  57. Ramil, E., Agrimonti, C., Shechter, E., Gervais, M., and Guiard, B. (2000) Mol. Microbiol. 37, 1116-1132[CrossRef][Medline] [Order article via Infotrieve]
  58. Rottensteiner, H., Kal, A. J., Hamilton, B., Ruis, H., and Tabak, H. F. (1997) Eur. J. Biochem. 247, 776-783[Abstract]
  59. Luttik, M. A., Kotter, P., Salomons, F. A., van der Klei, I. J., van Dijken, J. P., and Pronk, J. T. (2000) J. Bacteriol. 182, 7007-7013[Abstract/Free Full Text]
  60. Epstein, C. B., Waddle, J. A., Hale, W. T., Dave, V., Thornton, J., Macatee, T. L., Garner, H. R., and Butow, R. A. (2001) Mol. Biol. Cell 12, 297-308[Abstract/Free Full Text]
  61. Blank, L., Green, J., and Guest, J. R. (2002) Microbiology 148, 133-146[Abstract/Free Full Text]
  62. Bramer, C. O., and Steinbuchel, A. (2001) Microbiology 147, 2203-2214[Abstract/Free Full Text]
  63. Knight, S. A., Tamai, K. T., Kosman, D. J., and Thiele, D. J. (1994) Mol. Cell. Biol. 14, 7792-7804[Abstract]
  64. Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., Zeitlinger, J., Jennings, E. G., Murray, H. L., Gordon, D. B., Ren, B., Wyrick, J. J., Tagne, J. B., Volkert, T. L., Fraenkel, E., Gifford, D. K., and Young, R. A. (2002) Science 298, 799-804[Abstract/Free Full Text]
  65. Fraenkel, D. G. (1992) Annu. Rev. Genet. 26, 159-177[CrossRef][Medline] [Order article via Infotrieve]
  66. Trotter, P. J. (2001) Annu. Rev. Nutr. 21, 97-119[CrossRef][Medline] [Order article via Infotrieve]