Bioinformatics Advance Access originally published online on December 8, 2005
Bioinformatics 2006 22(4):392-399; doi:10.1093/bioinformatics/bti823
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Histone acetylation and transcriptional regulation in the genome of Saccharomyces cerevisiae
GlaxoSmithKline, Bioinformatics Division 709 Swedeland Road, King of Prussia, PA 19406, USA
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMaterials and methodsRESULTSDISCUSSIONREFERENCES |
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Motivation: In eukaryotic genomes, histone acetylation and thereafter departure from the chromatin are essential for gene transcription initiation. Because gene transcription is tightly regulated by transcription factors, there are some speculations on the cooperation of histone acetylation and transcription factor binding. However, systematic statistical analyses of this relationship on a genomic scale have not been reported.
Results: We apply several statistical methods to explore this relationship on two recent genomic datasets: acetylation levels on 11 histone lysines and binding activities of 203 transcription factors, both in promoter regions across the yeast genome. By canonical correlation analysis, we find that a histone acetylation pattern is correlated with a certain profile of transcription factor binding in the genome. Furthermore, after clustering the genes by their acetylation levels on the 11 histone lysines, the genes within clusters show distinct transcription factor binding profiles, as discovered by principle component analysis. Even after applying fairly stringent statistical measurement, most of these clusters have transcription factors with binding activities significantly deviated from the overall genome. We conclude that in the yeast genome, there is a strong correlation between histone acetylation and transcription factor binding in the promoter regions.
Contact: ron.2.liu{at}gsk.com
Supplementary information: Supplementary Data are available at Bioinformatics online.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMaterials and methodsRESULTSDISCUSSIONREFERENCES |
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A fundamental difference between eukaryotes and prokaryotes is that eukaryotic DNA is assembled into nucleosomes. The crystal structure of the nucleosome core particle shows that 146 bp of DNA wrap 1.65 turns around the histone octamer (Luger et al., 1997). This complex is the repeating unit of chromatin and structural basis of eukaryotic chromosomes. The histones cover the entire eukaryotic DNA and prevent the genetic information from being accessed by many biological machineries, such as transcription, replication and recombination. There are several mechanisms through which chromosomal histones are destabilized and DNA is exposed to various biological processes, such as acetylation, methylation, phosphorylation, and ubiquitination (Wu and Grunstein, 2000).
Among these histone modifications, acetylation is the best studied and most appreciated (Kurdistani and Grunstein, 2003). Nucleosomal core histones (including H2A, H2B, H3 and H4) have lysine-rich amino termini, which are positively charged and bind tightly with negatively charged DNA (Luger et al., 1997). These lysines undergo acetylation-deacetylation switch depending on different physiological conditions (Kornberg and Lorch, 1999). The balance of this modification is achieved by enzymes known as histone acetyltransferase and histone deacetylase. Histone acetylation on lysines neutralizes the charge on histones, therefore, facilitating their departure from DNA. As a result, DNA is exposed and many biological functions are carried out.
Since the discovery of histone acetylation over four decades ago (Allfrey et al., 1964), numerous studies have demonstrated that histone acetylation exists throughout the whole genome in eukaryotes (Kurdistani and Grunstein, 2003). Recently, genomic approaches are used to measure global histone acetylation in yeast Saccharomyces cerevisiae. One method is chromatin immunoprecipitation followed by multiplex PCR, which is used to measure the acetylation level in genomic region surrounding yeast gene PHO5 (Vogelauer et al., 2000). An even larger scale method is chromatin immunoprecipitation followed by DNA microarray measurements. It is capable of measuring histone acetylation levels across the entire yeast genome. Kurdistani et al. (2004) have applied this approach to investigate acetylation of 11 histone lysines in all the promoter regions and open reading frames of S.cerevisiae.
Histone acetylation may directly affect gene transcription. The initiation of gene transcription is triggered by the binding of transcription factors (TFs) to promoter regions (Freiman and Tjian, 2003; Goodrich et al., 1996). Many efforts have been undertaken to map the genome-wide transcription factor binding activities using chromatin immunoprecipitation followed by DNA microarrays in yeast. One of the early studies mapped binding activities for only two transcription factors (Gal4 and Ste12) in all the yeast promoter regions (Ren et al., 2000). In more recent studies, the binding activities of hundreds of transcription factors are being measured simultaneously (Lee et al., 2002; Harbison et al., 2004).
It is likely that histone acetylation and transcription factor binding are mutually dependent steps during gene transcription (Kurdistani and Grunstein, 2003). Observations have been made on transcription factors assisting histone acetylation in the promoters (Larschan and Winston, 2001). However, the relationship between histone acetylation and transcription factor binding has not been systematically studied. We take advantage of two recent genomic datasets in yeast: acetylation levels on 11 histone lysines (Kurdistani et al., 2004) and binding activities of 203 transcriptional regulators (Harbison et al., 2004), both on all the promoters. They are generated by studies with similar experimental settings. Both use yeast growing exponentially in rich medium and DNA microarrays as the measurement device. The similarity makes the two datasets ideal for exploring the correlation between yeast histone acetylation and transcription factor binding in the yeast genome.
In this study, the histone acetylation levels and transcription factor binding activities in yeast promoters are retrieved from the two datasets. We then perform statistical analyses on the correlation between acetylation and transcription factor binding in the yeast genome. By canonical correlation analysis, we find that there is a pattern in histone acetylation correlated with a specific profile of transcription factor binding on the promoters in the yeast genome. Furthermore, after clustering the genes by their acetylation levels on 11 histone lysines, the promoters in clusters show distinguished transcription factor binding profiles, as revealed by principle component analysis (PCA). Even after applying fairly stringent statistical criteria, most of these clusters have transcription factors with binding activity deviated from the overall genome. We conclude that in the yeast genome, there is a strong correlation between histone acetylation and transcription factor binding in promoter regions.
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Materials and methods |
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TOPABSTRACTINTRODUCTIONMaterials and methodsRESULTSDISCUSSIONREFERENCES |
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Datasets. The histone acetylation dataset is from Kurdistani et al. (2004). They have measured the acetylation levels of 11 histone lysines in yeast promoters (H2AK7—denotes lysine at position 7 on histone H2A; H2BK11, 16; H3K9, 14, 18, 23, 27; H4K8, 12, 61). After rigorous quality control measures, there were 1735 unique promoters having reliable acetylation levels on all 11 lysines (supplementary data of Kurdistani et al.). The transcription factor binding activity dataset is from Young's group (Harbison et al., 2004). They have determined the binding intensity on all the yeast promoters for 203 transcription factors. Combining these two datasets, we get 1735 promoters which have both acetylation level data on 11 lysines and transcription factor binding activity data for 203 transcription factors. In this study, the correlation between histone acetylation and transcription factor binding activity is analyzed based on these 1735 promoters.
Normalization and clustering. Because the amount of histones that are acetylated in any given promoter is affected by the rate of that region being occupied by nucleosomes (Hanlon and Lieb, 2004; van Leeuwen and van Steensel, 2005; Pokholok et al., 2005), we normalized the acetylation levels by the nucleosome occupancy measured by Shreiber's group (Bernstein et al., 2004). More specifically, the acetylation intensity is divided by the mean of H2A, H3 levels across all the duplicates. The acetylation levels after normalization on 11 histone lysines of the 1735 promoters are then clustered using the K-means clustering algorithm. We identified 53 clusters, with size ranging from 10 to 50 genes per cluster.
Canonical correlation and PCA. For the 1735 promoters, there are two sets of variables: acetylation levels on 11 lysines and binding activities of 203 transcription factors. We first want to see if there are any global correlations between these two sets of variables for the yeast promoters. Canonical correlation is used to measure the global correlation between histone acetylation and TF binding in the yeast promoters.
Another way of testing this relationship between TF binding activities and histone acetylation is by PCA. These 1735 promoters are clustered by their acetylation level on 11 lysines, with each cluster having a unique acetylation pattern. If PCA on the TF binding activities between these acetylation clusters can separate them, it suggests that the uniqueness in acetylation patterns corresponds to that uniqueness in TF binding profiles. This is a second proof of the relationship between histone acetylation and TF binding. We want to find out whether the TF binding activities are distinct within these clusters, even though they are clustered by histone acetylation levels.
t-Tests. Each of the 53 clusters was shown to have distinguished acetylation patterns on the 11 histone lysines. If the correlation between histone acetylation and TF binding activity is true, we shall see a unique TF binding profile in each cluster. Here we apply simple t-tests to see whether the mean binding activity of transcription factors in each cluster differs from that of the same TFs in the overall genome (genomic mean).
Multiple hypothesis tests. As we apply t-test in each cluster, we test all 203 transcription factors to assess their difference from genomic mean. For all 53 clusters, the number of hypothesis tests is 53 x 203. So the P-values of t-tests need to be corrected for multiple hypothesis testing. Because Bonferroni is too stringent for large number of tests (Storey and Tibshirani 2003), we use a less conserved method developed by Hommel (1988).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMaterials and methodsRESULTSDISCUSSIONREFERENCES |
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Global correlation of histone acetylation and TF binding
We have 1735 promoters with both acetylation levels on 11 lysines and binding activities of 203 transcription factors. They represent the promoters in the whole yeast genome reasonably well. Canonical correlation is used to explore the global (genomic) relationship between histone acetylation and transcription factor binding based on these 1735 promoters. We find several significant canonical correlations with Rc 0.72 (F = 2.4, P < 0.0001), 0.56 (F = 1.9, P < 0.0001), 0.44 (F = 1.7, P < 0.0001), respectively. However, the redundancy analysis (using histone acetylation levels to explain transcription factor binding activities) shows that the second/third canonical correlations only adds 19% of predictive power on top of the first canonical correlation. Furthermore, canonical coefficients are considered significant and reliable if they are greater than 0.3 (Tabachnick and Fidell, 2001). Only the first canonical correlation has coefficients over 0.3. So in the following analysis, we focus on the first canonical correlation.
The first canonical correlation (Rc = 0.72, P < 0.0001) reveals a strong correlation between histone acetylation levels and transcription factor binding activities in yeast promoters. Redundancy analysis suggests that histone acetylation levels can explain 58% of the variance in the transcription factor binding activities. The canonical coefficients (>0.3) of the first canonical correlation are listed in Table 1.
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Most of the 15 transcription factors (with correlation coefficients >0.3) are known to be related with histone acetylation. Transcription factor Gal4 interacts with its coactivator, histone acetyltransferase Gcn5, thereby affects histone acetylation (Larschan and Winston, 2001). Spt10 is a general transcription factor which is also a putative histone acetylase (Hess et al., 2004). Transcription factor Snf1 has a dual function of histone kinase, which works in concert with histone acetyltransferase Gcn5 (Lo et al., 2001). Rap1 works with NuA4 histone acetylase complex (Reid et al., 2000). Both Wtm1 and Hir2 contribute to gene silencing, which requires H3K4 hypomethylation and H4K12 hyperacetylation. In addition, from MIPS (Mewes et al., 1998), we know that Wtm1 and Hir2 interact with proteins involved in histone acetylation/deacetylation.
Several novel transcription factors also contribute to the correlation between acetylation and transcription factor binding. Though literature information on these genes are not available, they seem to interact with proteins involved in histone acetylation/deacetylation (MIP, Mewes et al., 1998). Our results suggest that they might be transcription factors that work together with proteins involved in the histone acetylation process.
In conclusion, canonical correlation analysis shows that, in the yeast genome, histone acetylation patterns have strong correlation with transcription factor activities. They are not two independent events. Instead, they may cooperate with each other in gene transcriptional regulation. In addition, we introduced the statistic method of canonical correlation into biological settings. This method should be fairly useful in analyzing global relationship of different patterns.
Distinguished TF binding profiles among histone acetylation clusters
The yeast genes are clustered into 53 clusters according to their acetylation levels on 11 histone lysines in the promoters. Each cluster has its unique acetylation pattern. If there is correlation between histone acetylation and transcription factor binding, the uniqueness of acetylation in clusters may correspond to the uniqueness of transcription factor binding profile. In the next step, we want to compare the transcription factor binding profiles across these acetylation clusters by PCA in SIMCA.
PCA separates the transcription factor binding profiles among different clusters reasonably well. Two examples are shown in Figure 1. The analyses indicate that cluster 14 has a transcription factor binding profile different from that of cluster 16 (Fig. 1A). Similarly, the transcription factor binding profiles of clusters 29 and 48 are also distinct (Fig. 1B). This suggests that the profiles of transcription factor binding are related with patterns of histone acetylation levels.
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By PCA, we find that the TF binding activities are distinct among these clusters, even though they are clustered by the histone acetylation levels. This further supports there is correlation between histone acetylation and transcription factor binding in the yeast genome.
TF binding profiles in histone acetylation clusters
By PCA, histone acetylation patterns seem correlated with transcription factor binding profiles in these clusters. Next, we analyze what is the specific transcription factor binding profile in each cluster. The size of clusters mostly ranged from 10 to 50 promoters, so canonical correlation analysis is not applicable (Tabachnick and Fidell, 2001). To investigate the unique transcription factor binding profile in a cluster, we use t-test to compare TF binding activities in the cluster with the mean binding activities of the same TFs in the genome. Because of multiple hypothesis testing, the P-values of t-test are corrected by the method developed by Hommel (1988).
From this analysis, we identify transcription factors with binding activity in a cluster significantly deviated from that of the overall genome. Many clusters have distinguished TF binding activity profiles detected by t-test (corrected P < 0.0001). The strict P-value cutoff was intended to pick up true transcription factors that are related with histone acetylation. These transcription factors together with functional annotations are listed in Table 2.
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Many of the transcription factors in Table 2 are related to histone acetylation in yeast. Nearly a dozen of them are known to affect the histone acetylation level by interacting with acetylation- or deacetylation-related proteins in the promoter regions. For example, Abf1 works with NuA4 histone acetylase complex (Reid et al., 2000), and Ume6 can interact with Sin3p-Rpd3p histone deacetylase (Aparicio et al., 2004). Ume1 represses gene transcription through its interaction with histone deacetylase Rpd3 (Mallory and Strich, 2003). We do not have literature information for a few of the other transcription factors, but they are known to interact with histone acetylation/deacetylation-related proteins, as indicated in MIPS database (Mewes et al., 1998).
The transcription factor Fhl1 appears in multiple clusters with binding activity significantly deviated from its genome mean. This transcription factor, together with its two cofactors Ifh1p and Crf1p, regulates rRNA processing and ribosomal protein synthesis (Martin et al., 2004). Fhl1 was also observed highly concentrated at yeast ribosomal protein gene promoters (Lee et al., 2002). On the other hand, histone acetylase Esa1 has been implicated in the activation of ribosomal protein genes (Reid et al., 2001). It is reasonable to believe that Fhl1 interacts with the histone acetylase Esa1 and activates transcription of ribosomal proteins.
Transcription factor binding profile in cluster 14
As shown in Figure 2, there are over a dozen transcription factors with binding activities significantly deviated from the genomic mean (for a complete list of TFs refer Table 2). Several of them are known to interact with proteins involved in histone acetylation or deacetylation from the literature. Abf1 works with NuA4 histone acetylase complex (Reid et al., 2000). SFL1 interact with histone deacetylase Hst1 and Hst2, there affect acetylation in the promoter regions (Halme et al., 2004).
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Four novel transcription factors (YBL054W, YBR267W, YDR520C and YDR049W) have binding activities significantly deviated from the genomic mean. Our analysis of MIPS data (Mewes et al., 1998) shows that they also interact with proteins involved in histone acetylation or deacetylation. The results suggest that these transcription factors may be involved in altering acetylation on histone lysines.
Transcription factor binding profile in cluster 16
Another cluster with an interesting transcription factor binding profile is cluster 3 (Fig. 3). Spt10, also functions as histone acetylase (Hess et al., 2004), Snf1 and histone acetyltransferase Gcn5 function in an obligate sequence to change the histone acetylation level (Lo et al., 2001). Transcription factor SPT10 has a dual function of histone acetylase (Hess et al., 2004). Two other significant transcription factors, Snf1 is also necessary for histone acetylation. Snf1 and histone acetyltransferase Gcn5 function in an obligate sequence to change the histone acetylation level (Lo et al., 2001).
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From MIPS (Mewes et al., 1998), we know that many of the rest of these significant transcription factors (Ask10, Dal80, Gal3, Mds3 and Wtm1/2) interact with proteins involved in changing histone acetylation level. Among the six transcription factors, Wtm1/2 are involved in gene silencing, which may be because they can interact with histone acetyltransferase (Pemberton and Blobel, 1997). The other four transcription factors might have a similar mechanism.
Based on MIPS (Mewes et al., 1998), we find that other three significant transcription factors (Kss1, Met18 and Ybr267w) interact with proteins involved in changing histone acetylation. So they may also be involved in histone acetylation process.
Here, we demonstrate that in a histone acetylation cluster, there is a transcription factor binding profile which is significantly deviated from the overall genome. Most of these transcription factors with significantly deviated binding activities are related with histone acetylation based on the literature information or MIPS interaction data. These results suggest that unique histone acetylation patterns may be related to specific transcription factor binding profiles.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMaterials and methodsRESULTSDISCUSSIONREFERENCES |
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Using two recent genomic datasets, we systematically analyze the relationship between histone acetylation and transcription factor binding in the yeast genome. We have identified close correlation of acetylation patterns with transcription factor binding profiles. By canonical correlation analysis, we find that a pattern in histone acetylation is correlated with a profile of transcription factor binding across the yeast genome. Furthermore, after clustering the genes by their promoter acetylation levels, the promoters within clusters show distinguished transcription factor binding profiles, as discovered by PCA. Even after applying fairly stringent statistical criteria, most of these clusters have transcription factors with binding activities significantly deviated from the overall genome. We believe that in the yeast genome, there is a strong correlation between histone acetylation and transcription factor binding in the promoters.
The biological mechanism behind this correlation is not entirely clear. There are two competing theories to explain this phenomenon. One theory is that transcription factors bind to chromosome first and then recruit HAT/HDAC to alter the acetylation level (Kuo and Allis, 1998). The reason is that HAT and HDAC do not have sequence specificity. They rely on transcription factors for directions to regulatory regions such as promoters. Data from the Workman laboratory and others have shown that the mosaic TF Gal4–VP16 (virion protein 16) can recruit yeast acetylation complex SAGA to an artificial promoter. Furthermore, the retention of SAGA on the targeted promoter is dependent on either the continued presence of the activator or acetylated histones (Syntichaki et al., 2000; Hassan et al., 2001, 2002). It was also observed that cell-cycle controlling transcription activators SWI/SNF recruit SAGA (Cosma et al., 1999).
Another theory is the so-called histone code theory: specific histone acetylation patterns (‘codes’) are recognized by specific transcription factors, thereby recruiting these transcription factors and directing gene transcription (Strahl and Allis, 2000; Edmondson et al., 1996; Dhalluin et al., 1999). Observations suggest that there are histone acetylation codes for transcription factor binding. For example, transcription factor Sir3 recognizes and only binds to deacetylated H4K16, thereby affects gene transcription (Kurdistani and Grunstein, 2003).
However, neither theory can be proved from our analysis. Both may have some truth in them. In the early stage of transcription initiation, when the entire DNA is coated with histone, histone acetylation must precede any transcription factor binding. It is conceivable that transcription factors may recognize this initial acetylated histones (‘codes’). In the later stage of transcription initiation, some transcription factors are already bound to DNA. It is likely that these transcription factors help bring in more proteins to acetylate histones of neighboring regions and accelerate the transcription initiation process. We believe both theories might be correct, though in different stages of gene transcription process.
Our analyses identify some transcription factors that are previously not known to be related with histone acetylation. Interestingly, a transcription factor Fhl1 appears significantly different from the genomic mean in multiple clusters. Fhl1p is highly concentrated at yeast ribosomal protein gene promoters and known to regulate ribosomal RNA processing and protein synthesis (Martin et al., 2004). It is also known that histone acetylase Esa1 plays a role in activation ribosomal protein genes. Our analysis suggests that Fhl1 may recruit the histone acetylase Esa1 and activate the transcription of ribosomal protein. Furthermore, four novel transcription factors (YBL054W, YBR267W, YDR520C and YDR049W) have binding activities deviated from genomic mean in certain clusters. Our analysis of MIPS data shows that they interact with proteins involved in histone acetylation/deacetylation. We propose that these four novel transcription factors can alter histone acetylation in promoters. It is worthwhile to further investigate the relationship between the histone acetylation pattern and the binding activities of these transcription factors.
We inevitably have some false positives in our t-test analysis, though our statistical cutoff is fairly stringent. Some of the transcription factors in Table 2 for which we could not find supporting evidence could be false positives. Nevertheless, our analysis strongly suggests correlation between histone acetylation and transcription factor binding in the promoters of yeast genome. It further suggests that histone acetylation is closely involved in gene transcriptional regulation.
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Acknowledgments |
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The authors sincerely appreciate their colleagues at GlaxoSmithKline, William Reisdorf, Vinod Kumar and Pankaj Agarwal, for critical reading of this manuscript. They also wish to thank David Searls, Mike Lutz and Pankaj Agarwal from GlaxoSmithKline for their continuing support.
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Alex Bateman
Received on August 20, 2005; revised on November 22, 2005; accepted on December 5, 2005
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