Distinct roles of the Gcn5 histone acetyltransferase revealed during transient stress-induced reprogramming of the genome
© Xue-Franzén et al.; licensee BioMed Central Ltd. 2013
Received: 14 February 2013
Accepted: 15 July 2013
Published: 16 July 2013
Gcn5 belongs to a family of histone acetyltransferases (HATs) that regulate protein function by acetylation. Gcn5 plays several different roles in gene transcription throughout the genome but their characterisation by classical mutation approaches is hampered by the high degree of apparent functional redundancy between HAT proteins.
Here we utilise the reduced redundancy associated with the transiently high levels of genomic reprogramming during stress adaptation as a complementary approach to understand the functions of redundant protein families like HATs. We show genome-wide evidence for two functionally distinct roles of Gcn5. First, Gcn5 transiently re-localises to the ORFs of long genes during stress adaptation. Taken together with earlier mechanistic studies, our data suggests that Gcn5 plays a genome- wide role in specifically increasing the transcriptional elongation of long genes, thus increasing the production efficiency of complete long transcripts. Second, we suggest that Gcn5 transiently interacts with histones close to the transcription start site of the many genes that it activates during stress adaptation by acetylation of histone H3K18, leading to histone depletion, probably as a result of nucleosome loss as has been described previously.
We show that stress adaptation can be used to elucidate the functions of otherwise redundant proteins, like Gcn5, in gene transcription. Further, we show that normalization of chromatin-associated protein levels in ChIP experiments in relation to the histone levels may provide a useful complement to standard approaches. In the present study analysis of data in this way provides an alternative explanation for previously indicated repressive role of Gcn5 in gene transcription.
KeywordsGcn5 Gene length Transcription elongation Histone acetyltransferase Stress Genome-wide association study
Changes in epigenetic marks, such as histone acetylation, are critical for normal biological function and defects in epigenetic programming are associated with cancer [1, 2]. For example, a general reduction of acetylated histone H4K16 (H4K16ac) and tri-methylated histone H4K20 has been reported for many cancer types . Further, reduced levels of histone H3K4 di-methylation and acetylated histone H3K18 (H3K18ac) have been associated with a higher risk of prostate cancer recurrence, as well as poor survival rates in both lung and kidney cancer patients . Histone modification enzymes, such as histone deacetylases (HDACs) and histone acetyltransferases (HATs), have thus been suggested as promising drug targets with potential for therapeutic applications . It is therefore important to understand the specific roles played by different enzymes involved in epigenetic programming.
The Gcn5 protein is one of the best characterised HATs. Gcn5 performs both global and locus-specific histone acetylation, as well as acetylation of non-histone proteins such as transcriptional factors . Gcn5 is the catalytic subunit of several related HAT complexes, notably the SAGA complex . The structure and function of Gcn5 is evolutionarily conserved. The protein is found throughout eukaryotic organisms and the human HAT domain can functionally replace the equivalent domain in the yeast protein . Gcn5 is also involved in a conserved sub-set of stress responses in evolutionarily divergent yeast species . Yeast is thus a useful tool for understanding the basic functions of eukaryotic Gcn5 proteins.
Gcn5 has been reported to play a range of different functions associated with transcription. The protein was originally characterised as a transcriptional co-activator, which is thought to be recruited to regulatory gene regions where it contributes to gene activation by acetylating key lysines on histones, notably histones H3 and 2B [5, 9]. Enrichment of Gcn5 has also been associated with genes that are repressed during stress [7, 10, 11], but no mechanism for how Gcn5 contributes to gene repression has been characterised. Genome-wide studies showed that Gcn5 is also present throughout the transcribed regions of genes and there is evidence that the Gcn5 protein is important for transcriptional elongation [12–14]. Further work is needed in order to gain an overall picture of how Gcn5 contributes to gene transcription and in particular to understand which aspects of Gcn5 function contribute to gene regulation.
Functional studies of HATs are hampered by the considerable levels of redundancy that are seen between different HAT proteins under many of the conditions that have been studied [15–17]. Conventional approaches studying the loss of functionality associated with gene mutations do not reveal complete information about redundant functions since the proteins that remain adapt their function to compensate for absent protein(s). Thus only a relatively small number of genes require Gcn5 for their expression even though Gcn5 is widely spread throughout the genome [7, 10]. It has previously been shown that the level of inter-HAT redundancy is strongly reduced during stress adaptation in yeast . During stress adaptation in the budding yeast, Saccharomyces cerevisiae, there is a redistribution of Gcn5 from short genes to the ORFs of long genes . In this work we use a stress and adaptation growth regime to further investigate this phenomenon as well as other aspects of Gcn5 function in gene transcription.
Measurement of changes in the genome-wide localisation of Gcn5 and histone acetylation during stress adaptation and recovery
To select histone acetylation marks for the study we used published microarray data from Kurtistani et al., which contains acetylation patterns for 11 lysine residues in histones, to find sites that correlate with previously published Gcn5 ChIP-on-chip tiling array data . We have previously shown that Gcn5 localisation patterns on genes can be defined by the patterns found in five main groups of genes. Two classes show Gcn5 enrichment in the 5′ or 3′ inter-genic sequence (IGR) respectively, while a further two classes show high or low Gcn5 levels in the open reading frame (ORF) region of genes. In the fifth class Gcn5 is equally distributed throughout genes. By comparing average acetylation levels for different acetylation sits and Gcn5 enrichment for the ORFs of the 5 gene classes (Figure 1B), we found that most acetylation sites show a similar trend to Gcn5 (yellow), with H3K18ac (black) and H3K27ac (red) being the clearest examples. H4K16ac (purple) shows the opposite trend to Gcn5. Within IGR regions the majority of acetylation sites show the opposite pattern compared to Gcn5. The exceptions are H3K18ac (black), H3K27ac (red) and H3K9ac (orange) (Figure 1C). We chose to study H3K18ac as an example of the modifications that are well correlated with Gcn5 localisation in both the ORFs and IGRs of genes. H4K16ac, which tends to be negatively correlated with Gcn5 localisation in both ORFs and IGRs, was chosen as a control mark that we would expect to be less well correlated with Gcn5 in the study. The overall level of histone H3 (H3) was measured to give an indication of the density of histones and nucleosomes in different chromosomal regions.
Gcn5 in the ORF region of genes transiently relocalises from short to long genes during stress adaptation
Reduced Gcn5 occupancy on the ORFs of many short genes during stress adaptation is associated with reduced histone H3K18 acetylation
Next we tested whether transient Gcn5 re-localisation is associated with similar changes in histone acetylation. Figure 2B and C show that the acetylation levels in ORFs of both H3K18 and H4K16 reduce as gene length increases, similar to the pattern observed for Gcn5. This gene length dependent reduction in the average levels of histone acetylation in ORF regions appears to be general for many acetylation sites (Additional file 2). Conversely, H3 levels tend to be higher on longer genes (Figure 2D) and so the length dependent change in histone acetylation per histone is less than for overall acetylation levels even though the trend is still clear. Most importantly, we did not observe a measurable gene length dependent difference in H3K18ac and H4K16ac between cells sampled at different points of the stress and recovery growth regime either without normalisation in relation to H3 levels (Figure 2B and C) or with H3 normalisation (Additional file 3). We conclude that either there is no change in the histone acetylation marks studied that correspond to the changes in Gcn5 or that we have not been able to detect such changes for any of a number of possible reasons.
To increase the chance of observing changes in histone acetylation that correlate with changes in Gcn5 localisation we used non-parametric ANOVA to identify the sets of gene ORFs that were most significantly (p<0.05) changed between conditions in
Regulated genes under stress adaptation do not show gene-length bias
In order to group the 854 genes into sub-groups characterised by their expression pattern through the stress and recovery regime we performed K-means clustering. Figure 4B shows that the genes cluster into three main groups and that the composition of the clusters is strongly influenced by gene expression patterns during stress adaptation (Sample B). Cluster 1 and Cluster 2 contain genes that are respectively induced or repressed during stress adaptation (Sample B). However, Cluster 3 contains genes that are induced both by stress adaptation (Sample B) and recovery from stress (Sample D). Genes in clusters 1 and 3 are over-represented in over-lapping but distinct sets of gene ontologies categories (Additional file 4). The cluster 1 categories tend to be more focused on stress response and metabolic processes while cluster 3 contains many categories related to stress response and ion transport. Cluster 2 genes, on the other hand, tend to be over-represented in categories related to the protein synthesis capacity of cells. The down-regulation of these genes may thus account for the pause in growth that occurs during stress adaptation.
Transiently altered levels of Gcn5 and histone H3K18ac at promoter regions correlate with gene regulation during stress adaptation
H3K18ac has previously been regarded as a histone modification associated with highly expressed genes . This is true for the present study (Figure 7B), where the level and significance of the correlation is similar to that shown for H3K18ac with regulated genes. There is also a significant correlation between transcript abundance and both Gcn5 and H4K16ac levels in the promoter peak. Figure 7C shows that there is no observable correlation between transcript abundance and the level of gene regulation (rs = −0.004, p = 0.77) and thus Gcn5 and H3K18ac appear to function together both to maintain the high expression level of highly expressed genes and to activate transcription of up-regulated genes. The molecular mechanism involved could be the same in each case (see Discussion).
Here we show that stress adaptation is a useful approach for studying the functions of HATs, which often show high levels of functional redundancy, making them difficult to study using mutant analysis in steady-state growth conditions. During stress adaptation when gene regulation activity is highly elevated, two clearly distinct genome-wide functions of the Gcn5 HAT are revealed. First, Gcn5 plays a gene regulatory role at promoters. Transient increases in Gcn5 and H3K18ac are well correlated with transiently reduced histone density in promoters and changes in Gcn5 and H3K18ac levels are also significantly correlated with the levels of gene regulation. Second, Gcn5 plays a transient role on the ORFs of long genes during stress adaptation, which is not generally correlated with gene regulation. This role probably involves the HAT activity of Gcn5, which is required for transcriptional elongation.
H318ac has mainly been regarded as a histone modification associated with highly expressed genes . We show that a peak of H3K18ac in promoters is correlated with a similar peak in Gcn5, suggesting that Gcn5 is at least in part responsible for maintaining the high H3K18ac levels on highly expressed genes. However, highly expressed genes tend to be constitutively expressed and do not overlap significantly with the group of genes that are transiently regulated during stress adaptation. We observed that the co-localisation of Gcn5 and H3K18ac at the promoter region is correlated with the transient gene regulation during stress adaptation. The results suggest that similar mechanisms involving Gcn5 and H3K18ac may be involved in both maintaining constitutive high expression and activation of increased gene expression. The mechanism is likely to involve de-stabilisation of key promoter nucleosomes as has been observed for individual genes previously . Our findings suggest that Gcn5 and H3K18ac levels are involved in gene regulation activity at a genome-wide level. This conclusion is consistent with bioinformatics findings that show high connectivity between H3K18ac and transcription factors . Other highly connected modifications are H3K27ac, H2BK16ac, H3K23ac and H3K9ac. Thus the results we show H3K18 may reflect changes occurring at other acetylation sites that occur simultaneously. Consistent with our findings H4K16ac was very poorly connected to transcription factors  perhaps due to the important general role it plays in establishing boundaries between euchromatin and heterochromatin [23, 24].
An interesting aspect of our results is that the stress adaptation associated changes in the promoter levels of Gcn5 are only evident if we measure Gcn5 levels in relation to histone levels. This is because the transient increase in Gcn5 and H3K18ac during stress adaption is accompanied by a simultaneous transient reduction in histone levels within the promoter, as measured by H3. This global trend is consistent with mechanistic studies of individual genes where acetylation of H3K18 has been shown to precede nucleosome loss at the transcription start site . It is possible that activators recruit Gcn5 to promoters, as has been demonstrated in vitro [25, 26], but that the residence time of Gcn5 on promoters is controlled by interactions with histones, either via active-site interactions with substrates during acetylation or more likely via interactions between the Gcn5 bromodomain and acetylated lysines. Interestingly, the Gcn5 bromodomain interacts with H4K16ac , the levels of which are much more mildly affected during stress adaptation than H3K18ac.
Measurement of Gcn5 levels in relation to histone levels may be significant in another context. Several reports have shown that Gcn5 levels (not corrected for histone levels) are elevated on a subset of repressed genes under different stress conditions [7, 10, 11, 28] and a similar tendency is seen for the down-regulated genes in this study. This observation is somewhat unexpected given the documented role of Gcn5 as a transcriptional activator and to our knowledge no mechanism for Gcn5 mediated gene repression has been reported. Interestingly, histone density increases transiently in the class of repressed genes during stress adaptation and when this is taken account there is a clear transient reduction in the amount of Gcn5 in relation to histones within the promoter peak, which correlates well with the transiently reduced levels of H3K18ac observed for the same genes. We conclude that studying the chromatin associated levels of Gcn5 and perhaps other histone-associating proteins in relation to histone levels, as has been widely accepted for covalent modifications of histones , may provide a useful complement to existing approaches in functional studies of histone associating proteins.
Our data show that histone density tends to be higher on gene ORFs than on promoters and that H3K18ac and H4K16ac tend to be lower for the longer genes. Nucleosomes cause a considerable obstacle to elongating RNA polymerase  and their presence in ORFs has been shown to be important for suppressing aberrant intra-genic transcripts, which might be expected to interfere with the transcription of bona fide gene transcripts [30, 31]. The density of histones and their degree of hypoacetylation on ORFs increase as a function of gene length. This might be expected, since the density of elongating RNA Polymerase on ORFs that is required to produce a given number of transcripts should be lower for longer genes. Consequently, the proportion of ORF chromatin that is subject to elongation-related hyperacetylation and nucleosome eviction will be lower for long genes than for short genes. Our results show that the HAT activity of Gcn5 is important specifically under conditions of nucleotide depletion (induced by MPA treatment) that limit transcriptional elongation. This result builds further on previous observations suggesting a role of Gcn5 in transcriptional elongation  and is consistent with the role that has been suggested for Gcn5 and other HATs in transiently opposing the inhibitory nature of ORF chromatin during transcriptional elongation [32, 33].
The transient role of Gcn5 on long genes is unclear. Interestingly, mechanistic studies using individual artificial genes have shown a specific role of Gcn5 in the efficient transcription of long genes and that RNA Polymerase II density in gcn5Δ mutants is reduced at the 3′ end of long genes but that there is no difference at the 5′ end of the same genes nor in short genes [14, 19]. Taken together with our results, this suggests that long genes are more susceptible to pausing and loss of elongating RNA Pol II prior to transcriptional termination and that Gcn5 is particularly well adapted to prevent this. The fact that Gcn5 localisation is not particularly correlated with gene length in normally growing cells suggests that there is significant redundancy between HATs with respect to this function, and that this special Gcn5 role is only revealed under conditions of highly elevated transcriptional reprogramming such as those seen during stress adaptation. In conclusion, by studying cells during stress adaptation we have been able to unveil two independent functions of Gcn5 that are not evident in cells growing under steady- state conditions. This represents an approach to studying redundant protein functions that is complementary to classical approaches using mutations individually or in combination.
We show that reduced levels of redundancy during stress adaptation provide an opportunity for characterizing the genome-wide roles of redundant protein families in gene transcription. Gcn5 plays a gene regulatory role at many activated gene promoters, which is correlated to increased levels of H3K18ac. Gcn5 plays an unrelated genome-wide role on long gene ORFs, which is not correlated to gene regulation and probably involves acetylation of a target distinct from H3K18. Interpretation of the results shows that the levels of chromatin associated proteins can be considered in relation to histone levels as a valuable complementary approach to classical analysis methods. Analysis of data in this way appears to dispel previous indications that Gcn5 plays a role in transcriptional repression.
Strains, plasmid and growth conditions
The Gcn5-myc tagged strain (By4742, MATα, his3-1, leu2-0, lys2-0, ura3-0 Gcn5- MYC13-KanMX6) is from . Cells were cultivated at 30°C in YPD medium (1% yeast extract, 2% bacto peptone and 2% glucose) to a log phase density of 1 × 107 cells/ml (sample A collection), then and diluted to 5 × 106 cells/ml and subjected to stress by adding a equal volume of 30°C pre-warmed YPD medium containing 2M KCl. Sample B was collected 1hr after dilution. Growth in KCl- containing medium was continued until cells reached a density of 1 × 107 cells/ml (sample C collection). Cells were pelleted and re-suspended in 30°C pre-warmed YPD without KCl at a density of about 2.5 × 106 cells/ml. Sample D was collected 1hr after the medium change. After 2–3 generations of growth sample E was collected. Cell number was counted every hour. 100ml of Sample A-E with around 5×108 cells were collected, one third of each sample was pelleted and frozen immediately in liquid nitrogen for RNA extraction to be used in gene expression profiling; the other two third of the each sample was pelleted and immediately processed by 1% paraformaldehyde fixation and frozen for ChIP-chip experiment. Two replicate cultures were grown on different occasions.
Details of S. cerevisiae strains lacking GCN5 or expressing Gcn5 derivatives containing substitution mutations in the HAT domain are described elsewhere . The strain lacking the TFIIS transcriptional elongation factor (BY4742; Mat a; his3D1; leu2D0; lys2D0; ura3D0; YGL043w::kanMX4) was obtained from Euroscarf  Spotting assays were performed by spotting 5-fold serial dilutions of cultures with a minimum cell density of 106/ml. Cells were cultivated on synthetic complete medium (Bacto- yeast nitrogen base 0.67%, glucose 2%, bacto-agar 2%, amino acid mix 0.2%), with or without mycophenolic acid (MPA) at a final concentration of 30 μg/ml, or with MPA plus Guanine (final concentration 100 μg/ml).
Gene expression profiling and ChIP-on-chip microarray experiments
Affymetrix GeneChip® Yeast Genome 2.0 Arrays were used for expression profiling. RNA was prepared by the hot phenol method as described previously . Probe labeling and hybridization were performed according to the Affymetrix manufacture’s protocol.
Affymetrix GeneChip S. cerevisiae Tiling 1.0R arrays were used for ChIP-on-chip experiments. Antibodies directed against H3K18ac (ab1191), H4K16ac (ab61240) and Histone H3 (ab1191) were obtained from Abcam and were used at a 1:100 dilution for Chromatin Immuno-precipitation experiments. The antibody directed against Gcn5-myc was obtained from Sigma (M5546) and used at a 1:50 dilution in ChIP experiments. The ChIP-on-chip procedure was as described previously . Probe labeling and hybridization were performed according to Affymetrix manufacture’s protocol.
Gene expression profiling data analysis was processed with the RMA method using Affymetrix Expression Console software. Fold changes were obtained by comparing expression level to that under normal growth conditions (Sample A). K-means clustering  was used to identify different gene expression groups. Cluster dendrogram of conditions is performed by the core function in R packages (http://www.R-project.org). GO ontology analysis was performed using GOminer software  as described previously .
Raw ChIP-on-chip tiling array data from Affymetrix tiling arrays (CEL format) were analyzed using Tiling Analysis Software (TAS) from Affymetrix to obtain genome- wide ChIP signal (bar file). The data for the 5 samples (A-E) were normalized to the have the same median value. Average gene analysis was done using a procedure modified from . Briefly, the signals corresponding to the ORF region of each gene in S. cerevisiae were divided into 20 equally sized bins. Signals from both the 5′IGR and 3′IGR were divided into 9 equal-sized bins. The 5′IGR was defined as a region starting at the middle of upstream intergenic regions and ending at the nucleotide preceding the initiation codon. The 3′IGR was defined as the region starting immediately after the stop codon and ending at the middle of the intergenic region. The Java code for average gene analysis is available by request. Correlation analysis was performed by calculating the Spearman rank correlation coefficient and associated p-value using the basic function in R package (http://www.R-project.org).
Since the modification of histones can only occur in chromosomal regions in which relevant histones are present, it is common practice to view levels of histone modifications in relation to histone levels, as used in this study . While this approach is useful, it also has limitations since in the extreme case, where no histones are present in a region, the denominator in the normalization would be zero, leading to the interpretation that modification level would be infinitely high. The approach should thus be used with caution and as a complement to classical microarray normalization methods that do not involve correction for histone density, which are also used in this study.
Bio-Rad iQ™ SYBR green super mix (Cat. No. 170–8880) was used for ChIP-qPCR reactions, PCR reactions were conducted using a Bio-Rad “i cycler” Thermal cycler with the following settings: 95°C for 3min, then 40 cycles at 95°C for 15 sec, 57°C for 30 sec and 72°C for 30 sec. Internal control genes for which Gcn5 levels were not expected to change were chosen based on the ChIP-chip data. The primer sequences for control genes and long genes are described in Additional file 6.
The project data is available at Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/projects/geo under accession number: SuperSeriesGSE 36601. Subset series GSE36599 is the expression profiling array data and subset series GSE36600 is the tiling array data.
Gene Expression Omnibus
Tiling Analysis Software
Transcription start site
We thank Helmi Siltala for technical assistance. We thank David Brodin, Marika Rönnholm, and Fredrik Fagerström-Billai at the Bioinformatics and expression Analysis Core facility at Karolinska Institute for helping with microarray experiments and data analysis. AW is supported by grants from the Swedish Research Council, the Swedish Cancer Society and the Baltic Sea Foundation. TB is supported by the Swedish Research Council, Center for Biosciences, and Baltic Sea Foundation.
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