Combined analysis of expression data and transcription factor binding sites in the yeast genome

Development of a search algorithm for targets of the a1-α 2 complex

To generate an algorithm that combines microarray expression data and mutational analysis of binding sites, we first defined a scoring method that ranks gene expression data. We utilized the microarray expression data from Galitski and coworkers for gene expression in the a and α haploid and a/ α diploid cells, as well as various polyploids [11]. Since the a 1-α 2 complex should be absent in any of the homozygous a or α type polyploids (a, aa, ..., α,αα,..., etc.) we expect the expression of haploid-specific genes in these cells to be much higher than in cells that are heterozygous for the MAT locus (a α, aa α, a αα, aa αα, etc.). Thus, one term in the scoring function rewards lower expression in heterozygous cell types compared to the homozygous cell types (see the Methods section for details). We expect that most of the haploid-specific genes will be expressed equally in both of a and α cell types. Consequently, we have introduced a second term in the scoring function that penalizes such differences in expression in the two haploid cell types. This scoring function would identify haploid-specific genes that are repressed in diploid cells, but would not indicate if these genes are direct targets of the a 1-α 2 repressor complex.

To identify genes from this ranking that are directly repressed by the a 1-α 2 complex we used the available mutational data on the a 1-α 2-binding site [9]. In these experiments, the effects of single base pair mutations of the a 1-α 2 consensus binding site were measured by assaying their ability to repress transcription of a heterologous promoter and by electrophoretic mobility shift DNA-binding assays (EMSA). Under the assumption that the level of expression is proportional to how often that site is unoccupied, we used the effects of the single base mutations to estimate the parameters for the binding energy of sites with different bases at each position. We then used this information to search for potentially strong binding sites in the promoter regions (in practice, 800 bp upstream of the translation start site) of every gene in the genome. This search provides us with a list of genes with putative a 1-α 2 binding sites in the promoter, irrespective of functionality.

a 1-α 2 binding to the promoter regions of genes identified in the search

To evaluate the success of our computational algorithm for identifying direct targets of the a 1-α 2 repressor complex, we assayed binding by the complex to the identified promoters using chromatin-immunoprecipitation (ChIP) assays with polyclonal antibody directed against the α 2 protein. In α haploid and a/ α-diploid cells the α 2 protein combines with the MADS-box transcription factor Mcm1 to bind to elements in the promoters of a-specific genes to repress their transcription [13, 14]. We therefore included in each PCR a primer set for the promoter region of the a-specific gene STE6 to serve as a positive control for the ability to ChIP α 2 in both haploid α and diploid a/ α cells. This primer set also allowed us to rule out the possibility that the predicted a 1-α 2 target genes were immunoprecipitating because of binding by the α 2-Mcm1 complex. The primer set for the YDL223C promoter, a gene not bound or repressed by the a 1-α 2 or α 2-Mcm1 complexes, was included in the reaction as a negative control for non-specific immunoprecipitation of the DNA. A gel displaying the results for a few of the promoters that were assayed is shown in Figure 1 and the data summarized for all the promoters that were predicted to be directly repressed by the a 1-α 2 complex is listed in Table 1. In general, there is a very good correlation between the experimental data with the predictions based on our computational algorithm. Almost all of the high scoring genes were bound by the a 1-α 2 complex in vivo. Genes that had a combined p-value higher than the threshold of 1.5 × 10^-4 (which corresponds to choosing the top 15 of the list in Table 1) do not appear to be strongly bound by the complex. This p-value corresponds to roughly a probability of one in six thousand, indicating it is possible to get such combination by chance.

In comparison to higher eukaryotes, most yeast promoters are relatively small and contain activator or repressor binding sites within several hundred base pairs of the start site of the open reading frame (ORF) of the gene. However, there are a few genes, like HO, whose regulation is controlled by a region several Kb long. Consequently, we did a separate search looking for additional a 1-α 2 binding sites that are within a region 1.5 Kb upstream of the ORF. Most of the sites identified in this search were well above the threshold value and were not bound by the a 1-α 2 complex in ChIP assays (data not shown). However, the search identified one site upstream of the AMN1/CST13 gene that was a potential target site. The ChIP analysis verified this as a functional target site for the a 1-α 2 complex in vivo (Figure 1, Table 1).

Analysis of haploid-specific genes predicted not to be bound by a 1-α 2

Among the top 35 genes in the list ranked by haploid-specific expression score, more than half do not appear to contain an identifiable a 1-α 2 site by our analysis (lack of a significant binding site being defined as binding p-value greater than 2 × 10^-4) (Table 2). Although there were no apparent strong affinity a 1-α 2-binding sites in the promoters of these genes, it is possible that there are several weak affinity sites in the promoter that were not identified in the search. If present, these sites may work cooperatively to increase binding by the a 1-α 2 complex to the promoter and therefore repress transcription. In support of this model we have found that under some conditions weak affinity a 1-α 2 sites in the HO promoter have a role in repression of the promoter (Mathias and Vershon, unpublished). Promoters with a number of weak affinity sites may therefore be directly regulated by the a 1-α 2 complex. However, it is also possible that these genes are indirectly repressed by a 1-α 2, through its ability to repress expression of another transcription factor that is required for expression of the genes identified in the microarray. To distinguish between these possibilities we assayed for a 1-α 2 binding to these promoters by ChIP (Figure 2 and Table 2). As predicted from the site identification analysis, the a 1-α 2 complex did not appear to bind to most of these promoters. This result suggests that these genes are not directly repressed by the complex. The one exception to our predictions was that the a 1-α 2 complex appeared to weakly bind to the NEM1 promoter in the ChIP assays (Figure 2). Interestingly, NEM1 is downstream of GPA1, a gene that is strongly repressed by the a 1-α 2 complex (Fig 1). Binding and repression by a 1-α 2 at GPA1 may help binding to weak sites in the NEM1 promoter. Alternatively, although we sheared the DNA used in the ChIP to an average of less than 500 bp, it is possible that there may have been some fragments that spanned the ~2 kb between the genes, thereby giving a positive result in the ChIP assay.

ORF	Gene	Expression p-vala	Binding p-valb	Combined p-val	a1-α 2 ChIPc
YIL117C	PRM5	0.0011	0.3690	0.0004	-
YLR159W		0.0015	0.4050	0.0006	-
YBR051W		0.0022	0.4390	0.0010	-
YLR080W	EMP46	0.0024	0.6757	0.0016	-
YIR039C	YPS6	0.0025	0.7843	0.0020	-
YCL014W	BUD3	0.0027	0.3475	0.0009	-
YPL189W	GUP2	0.0030	0.3708	0.0011	-
YJL077C	ICS3	0.0032	0.9540	0.0030	-
YML042W	CAT2	0.0033	0.7564	0.0025	-
YHR004C	NEM1	0.0035	0.6396	0.0022	+
YFR046C	CNN1	0.0036	0.5712	0.0020	-
YDR220C		0.0038	0.5919	0.0022	-
YPL025C		0.0041	0.6844	0.0028	-
YBR006W	UGA2	0.0043	0.7684	0.0033	-
YBR108W		0.0044	0.4390	0.0019	-
YFL034W		0.0046	0.3287	0.0015	-
YCL027W	FUS1	0.0047	0.7170	0.0034	-
YLR308W	CDA2	0.0049	0.3424	0.0017
YGR014W	MSB2	0.0050	0.6554	0.0033
YJL202C		0.0052	0.6416	0.0033

^a The ranking of haploid-specific gene expression determined by analysis of microarray data [11].

^b The ranking of potential a 1-α 2 target of haploid-specific genes.

^c A + indicates that the a 1-α 2 binds to the promoter by ChIP assay.

Comparison with binding site identification by the weight matrix method

We compare the performance of our algorithm with that of the weight matrix method [15–18]. In our study, we derived our parameters from a set of artificial sequences. Usually, the weight matrix has to be constructed from a set of known sites. We calculate the weight matrix for a 1-α 2 from regulatory elements upstream several known target genes: HO, GPA1, FUS3, AXL1, STE5, RME1 and MAT α 1. As usual, one is faced with a choice of threshold weight matrix score for selecting putative sites in the yeast genome. For a stringent threshold that corresponds to the top 16 targets, we recovered all the genes, other than RME1, used in construction of the weight matrix. However, we did not recover most of the other genuine targets identified, and verified, in this study. If we set the threshold to be lax enough to include RME1, we obtained 55 candidate genes, including STE18 and RDH54, but still miss targets like STE4. It is likely that most of the 55 putative targets are false positives, as evidenced by lack of haploid-specific regulation in the corresponding gene expression data.

Overall, we find our method to be more successful than the weight matrix method. The use of mutational data as opposed to literature based data for sequence preference possibly accounts for part of the success (an advantage we may not have for some other transcription factors). However, much of our success has to do with cutting down of false positive rates by using microarray data judiciously.

Analysis of all potential a 1-α 2 target sites in the genome

Among the genes identified in the computational analysis, there is a good correlation between the presence of strong a 1-α 2-binding sites in their promoter region and repression in diploid cells. This raises the question of whether all a 1-α 2-binding sites function as repressor sites. To address this question we searched for all potential binding sites in the yeast genome. As expected, many of the best sites are in the promoters of known or previously identified haploid-specific genes (Table 1). However, we also identified a number of putative a 1-α 2-binding sites within ORFs (Table 3). To test if the a 1-α 2 complex is able to bind to these sites we performed electrophoretic mobility shift assays (EMSAs) with purified α 2 and a 1 proteins and radiolabeled oligonucleotides containing these sites (Fig 3A). The a 1-α 2 complex bound to sites from the YKL162C, CDC25, PRM8, PRM9, and URB1 ORFs with weaker affinity than to a strong binding site from the HO promoter, HO(10). However, these sites did have slightly better binding affinity than to the HO(8) site, which we have shown is unable to repress transcription on its own (Mathias and Vershon, unpublished).

ORF	Gene	Expression p-vala	Binding p-valb	Combined p-val	a1-α 2 ChIPc	EMSAd
YKL014C	URB1	0.621	0.042	0.025	-	25×
YGL053W	PRM8	0.291	0.011	0.002	-	5×
YAR031W	PRM9	0.891	0.013	0.012	-	10×
YKL162C		0.782	0.017	0.014	-	10×
YLR310C	CDC25	0.102	0.018	0.002	-	5×
YPL061W	ALD6	0.597	0.018	0.009	-
YBR028C		0.793	0.046	0.036	-
YOL022C		0.563	0.023	0.013	-
YJR016C	ILV3	0.248	0.025	0.006	-
YBR218C	PYC2	0.324	0.027	0.008	-
YFR040W	SAP155	0.234	0.165	0.038	-
YMR269W		0.332	0.049	0.016	-
YJL129C	TRK1	0.297	0.083	0.024	-
YCR053W	THR4	0.607	0.073	0.044	-

^a The ranking of haploid-specific gene expression determined by analysis of microarray data [11].

^b The ranking of potential a 1-α 2 target of haploid-specific genes.

^c A + indicates that the a 1-α 2 binds to the promoter by ChIP assay.

^d Relative binding affinity in fold decrease in affinity compared to the HO(10) binding site.

Since the a 1-α 2 complex was able to bind to these sites with weak to moderate affinity in vitro, it is possible these sites may partially repress transcription on their own. To test this model, we cloned these sites into the context of the CYC1 promoter driving expression of a lacZ gene and measured the ability of the sites to repress transcription of the reporter in diploid cells [9]. The sites from the CDC25 and URB1 ORFs did not repress transcription of the reporter promoter in diploid cells (Fig 3A). However, the site from PRM8 ORF, which showed the highest binding affinity among the sites found in ORF regions, weakly (2.8-fold) repressed the reporter promoter. This result indicates that this site can function as a repressor site in vivo if placed in the proper context. We next tested whether a 1-α 2 bound to these sites in the normal genomic context in vivo by ChIP assays. None of the sites in the ORF regions were bound by the a 1-α 2 complex (Fig 3B and Table 3). This result indicates that while they are competent for weak binding and repression in a heterologous promoter, they are unable to repress transcription in their normal genomic context.

Our search also identified several potential a 1-α 2 binding sites in the promoter regions of genes that do not appear to be repressed in diploid cells (Table 4). Only the COX13 site had moderate binding affinity for the a 1-α 2 complex in the EMSAs (Fig 4A). However, despite the relatively weak binding affinity of these sites, they were able to partially repress transcription of the reporter in diploid cells (Fig 4A). In particular, the sites from the COX13 and REX2 promoters showed significant levels of repression. Interestingly, although these sites functioned as repressor sites in the context of the heterologous reporter, except for the COX13 promoter, most of these sites were not bound by the a 1-α 2 complex at their genomic locations by ChIP assays (Fig 4B). These results suggest that the genomic context of most of these a 1-α 2 sites prevents binding by the complex.

ORF	Gene	Expressiona p-val	Binding p-val	Combined p-val	a1-α 2 ChIPb
YGL191W	COX13	0.4111	0.0053	0.0021	+
YPL099C	FMP14	0.1622	0.0075	0.0012
YLR059C	REX2	0.1532	0.0098	0.0015	+/-
YDR212W	TCP1	0.5694	0.0017	0.0010
YJL124C	LSM1	0.0830	0.0120	0.0010	+/-
YAR033W	MST28	0.7419	0.0133	0.0099	-
YMR015C	ERG5	0.6021	0.0138	0.0083	-
YMR291W		0.2659	0.0218	0.0058	-
YPL188W	POS5	0.2648	0.0230	0.0061	+/-
YDR101C	ARX1	0.3694	0.0298	0.0110	+
YHR058C	MED6	0.4507	0.0350	0.0157
YGL117W		0.5577	0.0370	0.0206
YIL027C	KRE27	0.5762	0.0381	0.0220

^a The ranking of haploid-specific gene expression determined by analysis of microarray data [11].

^b A + indicates that the a 1-α 2 binds to the promoter by ChIP assay. A +/- indicates weak (2-fold) enhancement of the band in a/α cells by ChIP assay.

Combined scoring of genes from microarray data and mutational analysis

We define a scoring algorithm that ranks gene expression patterns. For gene g, the score is given in terms of the expression in different types of cells (a, α and a/α)

Score(g) = sgn((X _a (g) + X _α (g))/2 - X _a/α (g)) [(X _a (g)) + X _α (g))/2 - X _a/α (g)]² - A(X _a (g) - X _α (g))².

We initially used the logarithm of expression level of the gene g for the three cell types for the variables X _t(g) with t indicating the type. We have since found that using the complete expression data for the polyploids is a better strategy. In the final results, shown in the paper, X _a (g) is the average of the log expression for a, aa and aaa. Likewise, X _α (g) is the average from α, αα and ααα. X _a/α (g) comes from averaging log expression over aa α, a αα and aa αα. The polyploid averaged quantities tend to be less noisy (demonstrated, for example, by the quantities X _a and X _α being close to each other for the generic gene, which is not regulated by cell type. This, in turn, allows easier detection of genuine haploid-specific targets.

An explanation of the purpose served by different terms in the overall score is described below. The first term scores well when expression in diploids is lower than the average expression in haploids. The second term penalizes the gene if the expressions in different types of haploids are very different. A is chosen to be large enough so that known a-specific genes and α-specific genes score worse than known haploid-specific genes, but not large enough to overwhelm the first term. The optimal A is about 10. Comparison of the performance of our algorithm for A = 1 and A = 10, shows that the biologically known sites almost always stay near the top but further down in the list the second choice is better. The exception is a special gene: MATα 1. Since MATα 1 is not present in the MAT a type cell, there is a penalty for expression patterns. Cumulative probability for any gene to have higher score than a gene g is P _exprn (g), namely, fraction of genes with score higher than g.

This scoring function would rank haploid-specific genes high but may not select out genes that are directly regulated by the a 1-α 2 repressor complex. In order to select for genes with an upstream region with a strong a 1-α 2 repressor, we used the binding site mutational data available [9]. In these experiments, repression of a heterologous promoter, incorporating single site mutations of a consensus binding site of the a 1-α 2 repressor, was measured. Under the assumption that the degree of repression is inversely proportional to how often that site is occupied, we derived the expression: 1/Repression ∝ [1 - 1/(1 + e ^βE(S)/z)] ≈ e ^βE(S)/z assuming near saturation of binding. The symbol z represents the fugacity and β is inverse of k _B T, k _B being the boltzman constant. The binding (free) energy is given by E(S) = Σ _ib ε _ib S _ib , within the single base model [15, 16]. The index i runs over the positions in the motif and b runs over the bases A, C, G, T.S _ib is 1 or 0 depending upon whether the i- th base is b or not. The parameters ε _ib represent effects of single base changes on the binding (free) energy. They are related to the weight matrix parameters [17, 18] widely used to characterized variable motifs. Note that e ^βE(S)/z would more commonly be represented as (K/ [Protein])•exp(ΔG(S)/RT) in the biochemistry literature [18, 39]. The independent base model is only an approximation and mutations in nearest neighbor sites could produce effects that we cannot estimate from the existing data. There is a better separation between well-known sites and generic sequences if an extra penalty is added to the score for neighboring base pairs which both differ from the consensus. In this way every base different from consensus and neighboring another base different from consensus draws an additional penalty to the binding energy score. This parameter was set to be ln(2), by experience. Although this method prevented many false positives, it also penalized a few genuine candidates, such as the binding sites in the promoters of FAR1 and MATα 1. Thus, from the effect of the single base mutations, we estimated the parameters ε _ib . Armed with these parameters, we found the probability P(E|ε, L) that a random sequence of a certain length, L, would have a subsequence of binding energy greater than E. For gene g, the strongest site in the upstream region of length L would have binding energy E _g . Low values of P _binding (g) = P(E _g |ε,L), indicated the presence of a good binding site.

The genes were ordered according to the lowest value of a combined p-value, P _exprn (g) P _binding (g), and then ranked as candidates for haploid-specific genes that are directly repressed by the a 1-α 2 complex. One of the issues in such studies is how to decide on how many of the top candidates are significant. This problem occurs even in solely, sequence based analysis as well [40]. In our study, we generated random combinations of expression p-values with scrambled binding p-values, so that we could choose a cutoff threshold by comparing the ordered p-values with the ordered "scrambled" p-values. Figure 5 plots these sorted p-values against average (log) sorted p-values for the random combinations. The comparison suggests that only about 10–15 top candidates in the list are significant.

Weight matrix based search

A weight matrix [15–18] search for binding sites was performed using a set of known sites [9] to construct the matrix. Each matrix entry, w _ia , was set to log((f _ia + δ)/(P _a + δ)), where f _ia is the frequency with which base a appears in the i th position in the known sites, P _a is the frequency with which base a appears in the promoters of genes and δ is a small number added to ensure that the weight matrix score is finite even when f _ia = 0. Each subsequence, S _ia , of length 20 in each promoter was assigned a weight matrix score Σ _ia S _ia w _ia . After a threshold score is chosen, sites scoring above that threshold are declared to be binding sites.

Automated primer generation

An automated procedure for generating primers flanking a specified site in the genome sequence, σ, was implemented. To each pair of numbers, d _u , and d _d , representing primer distances upstream and downstream of the candidate binding site respectively, and primer lengths l _u , and l _d , a score is assigned via

S(d _u , d _d , l _u , l _d ,σ) = - Σ _a k _a (P _a (d _u , d _d , l _u , l _d ,σ) - )²

where the P _a are functions including the melting temperatures, distances of upstream and downstream primers from the candidate site, total length of the bound region, and the difference between the primer melting temperatures. The s are the preferred values of those functions. The k _a are adjusted to reflect the relative importance of the parameters; for example it is more important that the difference in melting temperatures be close to zero than it is that the distance to the upstream primer match the distance to the downstream primer.

Values of d _u , d _d , l _u and l _d are restricted to those whose corresponding primers have GG, GC, CG, or CC at the end nearest the candidate site. Primers are identified by selecting the values of d _u , d _d , l _u and l _d which maximize S.

[A web-based interface to this algorithm is available at http://hill-226-174.rutgers.edu/]

Chromatin immunoprecipitation

Chromatin immunoprecipitation (ChIP) was carried out as described previously [41] with the following modifications. One liter of JRY103 (MAT α /MAT a ade2-1/ADE2 HIS3/his3-11,15 leu2-3,112/leu2-3,112 trp1-1/trp1-1 ura3-1/ura3-1 ash1Δ::LEU2/ash1Δ::LEU2) and JRY118 (MAT α /mat a Δ::TRP1 ade2-1/ADE2 HIS3/his3-11,15 leu2-3,112/leu2-3,112 trp1-1/trp1-1 ura3-1/ura3-1 ash1Δ::LEU2/ash1Δ::LEU2) cultures were grown to an A600 of 0.5 and treated with 1% formaldehyde for 20 min at RT on a rotating shaker at low speed. Cells were collected, washed 2X with cold 1XTBS. Equal volumes of cells were aliquoted into ten 1.5 ml microfuge tubes, washed once with 1.5 ml of cold 1X TBS. The pellets in each tube were resuspended with 400 μ l of lysis buffer (50 mM HEPES, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% Na-Deoxycholate) plus 1 mM PMSF, 1 mM benzamidine, and 1X Protease inhibitor cocktail from Roche (Cat No. 1873580) and also manufacturer recommended concentration of protease inhibitor cocktail from SIGMA (Cat No., P 8215). To this 200 μ l of glass beads were added to each tube and lysed using a multitube vortexer at full speed for 30 min at 4°C. The lysate was transferred in a new tube and 400 μ l of lysis buffer was added and vortexed briefly. The lysates were centrifuged at 12,000 g for 10 min at 4°C and the supernatants were sonicated at 30% output for four 10 sec cycles with intermittent cooling on ice.

The lysates were cleared by centrifugation at 12,000 g for 10 min and 1 mM PMSF was added to the samples. A 1/10^th volume aliquot was removed and frozen to be used as total chromatin control. The remaining sample was precleared by the addition of 25 μ l recombinant protein G-agarose beads, incubated while nutating for 30 min and the supernatant was collected after centrifugation at 12,000 for 5 min. 1 μ l of rabbit anti-α 2 antiserum (a gift from A. Johnson, UCSF) was added to each supernatant of the samples and incubated 12 h on a nutator at 4°C. To immunopreciptiate α 2 50 μ l of recombinant protein G agarose beads (Roche) was added to the samples and nutated for 90 minutes at 4°C. The protein G beads were pelleted, washed once in low salt buffer (0.1%SDS, 1% Triton X-100, 20 mM Tris pH8.0, 2 mM EDTA and 150 mM NaCl), once in high salt (composition same as lowsalt + 500 mM NaCl), once in LiCl buffer (0.25 M LiCl, 1% IGEPAL, 1XTE and 1% Na-Deoxycholate) and twice with 1XTE (pH8.0). The immunoprecipitated DNA was eluted twice with 250 μ l of elution buffer (1%SDS and 0.1 M NaHCO3) and the eluates were pooled (500 μ l final volume). To this 20 μ l of 5 M NaCl was added and incubated 12 h at 65°C. To remove the crosslinks, 10 μ l of 0.5 M EDTA, 20 μ l of 1 M Tris-HCl, pH 7.5 and 2 μ l of proteinase K (10 mg/ml) was added and incubated for 45 minutes at 45°C. The DNA samples were extracted once with Phenol:chloroform:Isoamylalcohol and the DNA was ethanol precipitated, washed once with 70% ethanol and resuspended in 50 μ l (IP) or 500 μ l (TC) TE.

Purified DNA from the immunoprecipitated samples was subjected to multiplex PCR amplification with primers specific for the STE6 promoter as a positive control for the immunoprecipitation of α 2 and the YDL223C ORF as a negative control for nonspecific immunoprecipitation, along with the specific primers for candidate α 2-a 1 target sites. PCRs were carried out in 50 μ l containing 10 pmols of each primer, 0.2 mM dNTPs, 2 mM MgCl2, 1X Eppendorf Taq buffer, 0.5X Taq Master buffer and 2.5 U of Eppendorf Taq polymerase. The amplifications were carried out at 94°C for 1 min and 30 secs, followed by 25 cycles of 94°C for 30 secs, 52°C for 1 min, and 72°C for 30 secs and a final extension step of 7 min at 72°C. The PCR products were separated on 2.5% agarose gels.

Electrophoretic mobility shift assays

Oligonucleotides containing the predicted a 1-α 2 binding sites from within the ORFs of URB1, PRM8, PRM9, YKL162C and CDC25 and the promoters of COX13, REX2, LSM1, and FMP14 were synthesized, one strand was end-labeled with [γ-³²P]-ATP, and then annealed with excess cold complementary oligonucleotide. The HO(10) and HO(8) a 1-α 2 sites within Upstream Regulatory Sequence 1 (URS1) of the HO promoter were used as strong and weak binding sites respectively. The EMSA was performed as described previously [42], using a constant 1.4 μ M a 1 and five-fold titrations of α 2 starting at 82 nM in protein dilution buffer (50 mM Tris pH 7.6. 1 mM EDTA, 500 mM NaCl, 10 mM 2-mercaptoethanol, 10 mg/ml bovine serum albumin).

β-galactosidase assays

Oligonucleotides containing a 1-α 2 binding sites were synthesized with 5' overhangs to allow cloning into the Xho I site of pTBA23 (2μ URA3 Amp^r), a reporter plasmid containing a CYC1-lacZ fusion [43]. Reporter constructs were transformed into JRY103 and JRY118 and the β-galactosidase activity was measured on three independent transformants, as described previously [14].