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Published online before print
January 2, 2007, 10.1101/gr.5875007 Genome Res. 17:136-144, 2007 ©2007 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/07 $5.00
Letter Serum response factor binding sites differ in three human cell types1 Department of Genetics, Stanford University School of Medicine, Stanford, California 94305-5120, USA
The serum response factor (SRF) is essential for embryonic development and maintenance of muscle cells and neurons. The mechanism by which this factor controls these divergent pathways is unclear. Here we present a genome-wide view of occupancy of SRF at its binding sites with a focus on those that vary with cell type. We used chromatin immunoprecipitation (ChIP) in combination with human promoter microarrays to identify 216 putative SRF binding sites in the human genome. We performed independent quantitative PCR validation at over half of these sites that resulted in 146 sites we assert to be true binding sites at over 90% confidence. Nearly half of the sites are bound by SRF in only one of the three cell types we tested, providing strong evidence for the diverse roles for SRF in different cell types. We also explore possible mechanisms controlling differential binding of SRF in these cell types by assaying cofactor binding, DNA methylation, histone methylation, and histone acetylation at a subset of sites bound preferentially in smooth muscle cells. Although we did not see a strong correlation between SRF binding and epigenetics modifications, at these sites, we propose that SRF cofactors may play an important role in determining cell-dependent SRF binding sites. ELK4 (previously known as SAP-1 [SRF-associated protein-1]) is ubiquitously expressed. Therefore, we expected it to occupy sites where SRF binding is common in all cell types. Indeed, 90% of SRF sites also bound by ELK4 were common to all three cell types. Together, our data provide a more complete understanding of the regulatory network controlled by SRF.
The serum response factor (SRF) is an essential gene, expressed throughout the human body and throughout development (Gauthier-Rouviere et al. 1991  ; Belaguli et al. 1997). It plays important roles in human disease and development, specifically in muscle cells and neurons. Because of the essential roles of SRF throughout the body, identifying the genes it regulates provides key information to understanding its functions.
SRF is a MADS-box transcription factor first identified for its role in the response of tissue culture cells to serum (Treisman et al. 1992
Given its well-described role in cell proliferation, the link between SRF and cancer is not surprising; however, recent studies have shown that it is also essential for development and maintenance of smooth muscle cells and neurons. SRF is necessary for mesoderm formation (Arsenian et al. 1998
The distinct roles for SRF in cell proliferation and cell-type specific maintenance suggest the genes it regulates may differ between cell types and environments. Studies to date have focused on identifying SRF binding sites in a single tissue or analyzing the gross phenotypes of SRF knockouts without identifying the downstream effectors. SRF is highly expressed in muscle but detectable throughout the body, leaving the unanswered question of how SRF effects its changes in divergent pathways (Treisman 1986 The study we describe significantly improves our knowledge of SRF binding sites and demonstrates the variability of binding in diverse cell types. By using chromatin immunoprecipitation (ChIP) in combination with human promoter microarrays, we identified more than 200 binding sites of SRF in three human cell lines. We confirmed binding at known sites but also identified many new sites. Our study is also unique in its extensive validation by quantitative PCR (qPCR), resulting in positive validation for 86 of 116 tested sites. These validation data allow us to assert that an additional 60 binding sites are real at a 90% confidence level. In an effort to understand the mechanism of tissue-specific SRF binding observed in our data, we characterized binding of the ELK4 cofactor as well as epigenetic modifications at validated SRF binding sites. Together, these data provide the most comprehensive view of gene regulation by SRF to date.
Identification of over 200 genome-wide SRF binding sites By using ChIP and human promoter microarrays, we characterized genome-wide binding sites of SRF in three cell lines: Jurkat cells (a transformed lymphoblast cell line), T/G HA-VSMC (primary aortic smooth muscle cells), and Be(2)-C (a neuroblastoma cell line). In Jurkat cells, we assayed binding before and after induction with serum, although we did not observe many differences between the two treatments. To detect SRF binding sites, we applied each ChIP sample to arrays tiling 1.3 kb each (1 kb upstream of the transcription start site [TSS] and 0.3 kb downstream) of  19,000 human promoters. Based on the average DNA shearing size, we expected to detect binding sites ranging from 2 kb upstream of the TSS to 1 kb downstream. Because of random shearing of genomic DNA, we observed the greatest DNA enrichment at the SRF binding sites with reduced but detectable enrichment by using probes up to a few kilobases away from the binding site. Our ability to detect SRF occupancy is proportional to the distance between the probes and the binding site (Trinklein et al. 2004). On the basis of the ratio of ChIP-enriched sample to a control mock immunoprecipitation, we determined a Z-score resulting in a score and confidence for SRF binding at each promoter (see Methods).
We identified a total of 216 putative targets of SRF in the cell lines and conditions described (Fig. 1). These sites all have Z-scores >2 in multiple cell lines or a Z-score >2.5, to improve confidence, if they appear in only one cell line. These thresholds were chosen to maximize true positives and minimize false negatives based on the validation study described (Methods). (An additional 144 putative sites were identified in only one cell type with a Z-score between 2 and 2.5.) In two phases, we independently measured the ChIP enrichment at SRF binding sites as determined by the array. In the first phase, we tested 163 amplicons identified in Jurkat cells with Z-scores ranging from 0 to >3, allowing a careful evaluation of the accuracy of the array. Classifying putative sites by Z-score, we observed 92% validation of binding sites with a Z-score >3 and a cumulative validation of 64% for sites with a Z-score >2 (Table 1). In the second phase of validation, we tested an additional 64 sites by qPCR, emphasizing validation of binding sites identified in only one cell type or condition. Between the two rounds of validation, 86 binding sites were successfully validated. Of the untested putative binding sites, 47 have a Z-score >3. We expect 92% of these to validate based on the validation rate of other sites with similar Z-scores (see Methods and Table 1). An additional 13 untested sites are inferred to be true with high confidence based on conservation of human and mouse SRF consensus sites in the region (see below). Only 29 of the binding sites we identified were common to all cell types and conditions of cell growth. Common binding sites include those regulating genes involved in cell proliferation such as early growth response 1 (EGR1), early growth response 2 (EGR2), FOS, FOSB, and SRF itself, where SRF is known to bind constitutively (Herrera et al. 1989
SRF occupancy varies by cell type Previous work characterizing binding sites of SRF focused on a single cell type, such as rat or mouse cardiomyocytes (Manabe and Owens 2001 ; Nelson et al. 2005; Zhang et al. 2005) or a single human tissue culture cell line (Rene et al. 2005). We found that many SRF binding sites depend on tissue type. Over half of the targets we identified were unique to a particular cell type or condition (Fig. 1). Because of the implication for disease and development, binding sites specific to smooth muscle cells and neurons were especially interesting. We used qPCR to test binding at 65 tissue-dependent SRF binding sites with a Z-score >2.5 on the array. Of the 65 tested, 43 promoters validated (enrichment greater than threefold by qPCR) as tissue-dependent SRF-binding sites. Because of the array false-negative rate, we independently confirmed by qPCR that these sites were not bound by SRF in the remaining cell types for 12 cell-type–dependent targets (Supplemental Fig. 1).
We identified 27 targets specific to smooth muscle cells (tested and validated 19 of 19 successful amplicons) (Table 2). These data confirmed known binding sites in promoters of genes involved in muscle development and cytoskeleton including transgelin (TAGLN), caldesmon (CALD1), vinculin (VCL), smooth muscle myosin heavy chain 11 (MYH11), and melusin (ITGB1BP2). We observed SRF binding at promoters of genes encoding additional cytoskeletal proteins such as myosin 1e (MYO1E) and beta tubulin (TUBB). Most interestingly, we identified binding sites in genes not previously known to be regulated by SRF. For example, adenylyl cyclase-associated protein 1 (CAP1) is involved in the cyclic AMP cycle, is expressed in smooth muscle and cardiomyocytes, and binds actin (Diehn et al. 2003
Another class of promoters is bound by SRF only in the neuronal cell line. These sites include the promoter of another cytoskeletal protein, non-muscle myosin (MYH9), and the EGR4 promoter. The Egr family is partially redundant (Tourtellotte et al. 2000 ) and previous work demonstrates a role for Egr1 in synaptic plasticity (Li et al. 2005). Egr4 may compensate for or complement the role of Egr1 in the brain. We also show SRF binding in the nuclear export factor (NPAS4) promoter. This gene directly regulates expression of drebrin, a protein involved in dendritic-cytoskeleton modulation at synapses (Ooe et al. 2004). The FILIP1L promoter was also identified as a brain-specific binding site. Although this protein is normally associated with cell cycle transition, recent data implicate the complex containing FILIP1L in axonal growth and synaptic function (for review, see Teng and Tang 2005). Identification of neuron-specific binding sites of SRF is particularly important since the role of SRF in the brain is poorly understood.
SRF binding sites contain CArG boxes
Next, we hoped to determine whether the binding sites for SRF differed between the two classes of promoters. To do this, we first characterized the presence of the known consensus site near SRF binding sites. The consensus binding site for SRF, often called the CArG box, is well established from a large number of biochemical and mutagenesis experiments: CC(A/T)TATA(A/T)GG (Vlieghe et al. 2006
While almost all previously published SRF binding sites are within 1 kb of the TSS, Sun and colleagues (2006)
Because functionally important CArG boxes should be evolutionarily conserved, we searched the orthologous mouse promoters for the consensus binding site. In many cases, the mouse promoters also contain a CArG box. We identified orthologous promoters for 96 of the 133 human promoters containing a CArG box. Of these, 56 (58%) contain a sequence matching the CArG consensus site in the mouse genome. We have plotted the position of each predicted CArG box relative to the predicted start site and see a significant correlation between the positions in human and mouse (Fig. 2). This suggests that the orientation relative to the TSS may be important. Interestingly, the presence of a CArG box in both human and mouse promoters is a strong indication of SRF binding. Seventy-three percent of validated SRF-bound promoters with a CArG box also contained an orthologous consensus site in mouse. While the presence of the CArG box in mouse is not required, in the cases in which an orthologous CArG box existed, validation rate was very high. For 32 of 33 promoters with orthologous human and mouse CArG boxes, we successfully validated SRF binding indicated by arrays. On the basis of these analyses, we predict that the 23 binding sites with a conserved CArG box that were not validated by qPCR are also true SRF binding sites with high confidence (>95%). In addition, 10 of these sites also have a Z-score >3. Indeed, the untested promoters include genes previously identified to be regulated by SRF, such as smooth muscle
Correlating cell-type–dependent SRF binding with epigenetic modifications Because SRF binding is often dependent on cell type, we wished to explore the mechanism determining SRF binding sites in vivo. We proposed that differences in chromatin structure between cell types could control SRF binding. We assayed three modifications affecting chromatin structure and asked whether differences in these modifications correlated with differences in SRF binding. First, we assayed DNA methylation of the six promoters bound by SRF in smooth muscle cells but not Jurkat cells that overlapped with CpG islands (Fig. 3). By using methyl-sensitive enzymes, we digested unmethylated DNA and removed the regions cut into small pieces by the six enzymes using size selection. We assayed depletion of these regions, which represent unmethylated DNA by qPCR, normalizing to an uncut sample prepared in parallel (see Methods). In 5 of 6 cases, we found that promoters were unmethylated in both cell types indicating that differential methylation of these promoters does not account for the difference we observed in SRF binding. In the case of the caldesmon gene (CALD1), there is a difference in depletion between the two cell types, suggesting that while this is not the typical mechanism for regulation, DNA methylation may play a role in some cases.
Histone modifications provide another indication of chromatin structure and are generally considered to be more dynamic than DNA methylation. We assayed acetylation of histone H3 and trimethylation of histone H3 at lysine 4 (H3K4), both modifications present in active chromatin (Santos-Rosa et al. 2002 ), by using ChIP and qPCR in smooth muscle and Jurkat cells. As expected, we detected both of these modifications at active genes such as GAPDH and RPL30; however, we only observed significant enrichment due to these modifications at three of the nine promoters bound by SRF in smooth muscle cells but not Jurkat cells (data not shown). In cases where we observed acetylation and trimethyl modification of histone H3, it was present in both cell types. It remains a possibility that other histone modifications, specifically those resulting in silencing, would correlate with the lack of SRF binding in Jurkat cells compared with smooth muscle cells, but we have not identified a correlation with the tests described here.
Tissue-independent SRF binding sites are bound by ELK4
These data and analyses provide a significant improvement in knowledge of previously determined roles for SRF. We identified a total of 216 SRF binding sites and completed one of the most comprehensive ChIP–chip validation studies for any transcription factor to date. The result was the identification of 86 validated binding sites and 60 additional high-confidence binding sites for a total of 146 sites at a 90% or higher confidence level (see Supplemental Table 4). (High-confidence binding sites were not tested by qPCR but have an array Z-score >3 or a Z-score >2 with CArG box conserved in human and mouse.) The total number of sites identified in the genome is relatively small compared with other cell proliferation transcription factors such as MYC; however, SRF may regulate this process by controlling a smaller number of genes, whereas MYC is known to act generally to promote transcription. Compared to previous work, our study included an extensive validation study absent in many other data sets, leaving the possibility that other groups may have high false-positive rates. Previous studies have used ChIP sequencing and ChIP with semi-quantitative PCR to identify over 100 in vivo SRF binding sites; however, these binding sites were identified almost exclusively in cardiomyocytes (Philippar et al. 2004 ; Zhang et al. 2005; Balza Jr. and Misra 2006; Sun et al. 2006). While over 90 of these genes were present on the arrays, our experiments identified only 30 previously known SRF binding sites. Nearly half of the overlap is from sites we found in smooth muscle that were previously found in cardiomyocytes, a similar cell type (Zhang et al. 2005). Twenty sites we did not identify are more than 10 kb upstream of the TSS in mouse and a few are located in the first intron. Many of these would not be identified by our method. Additional binding sites may have been missed because of false-negative array results; however, the evidence we have presented supporting the degree of cell-type dependent binding suggests these binding sites may be specific to the cell types tested in previous experiments. Indeed, we tested cell types not previously assayed and were able to verify binding at these sites. These new targets were identified in cell lines where other groups have not searched. Future studies including additional tissues and cell lines will provide interesting insight into SRF function throughout the body.
We identified 84 (60 qPCR validated, 24 untested) SRF binding sites that changed with cell type. These genes are key to understanding the role that SRF plays in determining cell fate during development and maintaining a chosen cell fate. A more complete list of downstream molecules that control muscle and neuronal development will be valuable for a more detailed understanding of these processes. In neurons, for example, SRF is necessary for migration and circuitry formation. SRF was previously thought to exert its control on this pathway primarily through regulation of cytoskeletal proteins; however, we have uncovered a connection between SRF and FILIP1L, a key component of the APC complex and a molecule not previously known to be under the control of SRF but vital to normal neuronal function. Likewise, in smooth muscle cells, where the target genes of SRF have been more fully explored, we have identified a number of previously unknown binding sites, including one in the promoter of CAP1. The CAP1 protein is involved in regulating actin dynamics and binds cofilin, another target gene of SRF (Bertling et al. 2004
Here we explored two potential mechanisms by which SRF binding sites are influenced: chromatin structure and cofactor presence. In the cases we explored, the main factor correlated to SRF binding was the presence of SRF cofactors. Our data provide one example in which CpG island methylation seems to affect SRF binding. In each of the other five cases we tested, the promoters of genes differentially bound were unmethylated in both cell types. We also assayed trimethylation at histone H3K4 and acetylation of histone H3, and we did not observe a difference between Jurkat cells and smooth muscle cells (SMC) at promoters bound by SRF in SMC but not Jurkat cells. Our favored hypothesis explaining the ability of SRF to bind differentially is the presence of SRF cofactors. GATA4, for example, is expressed only in muscle and is known to bind with SRF at a few smooth muscle-specific targets (Small and Krieg 2003 The goal of our study was to gain a more complete understanding of elements that regulate genes in this network and ultimately predict their effects, individually and cumulatively, on gene expression. The work presented here provides, first and foremost, a more comprehensive and accurate catalog of genes that are regulated by SRF: 216 total binding sites, the vast majority of which were previously unknown. Our data include 86 validated and 60 high-confidence sites. These data, in combination with studies characterizing chromatin structure and cofactor occupancy at SRF binding sites, provide the beginnings of a network that will ultimately lead to a more complete understanding of the roles of SRF. As technology improves and large-scale studies become increasingly feasible, data sets identifying binding sites in different tissues, binding sites of SRF cofactors, and expression levels of downstream genes will allow us to complete the regulatory network involving SRF. Understanding the regulation of these genes will provide insight into processes as diverse as embryonic development, heart disease, cancer, and learning.
Cell culture We obtained each of the cell lines from American Type Culture Collection (www.atcc.org). Jurkat (TIB-152), BE(2)-C (CRL-2268), and T/G HA-VSMC (CRL-1999) were grown as directed. To induce the serum response, we grew Jurkat cells in media with 0.1% FBS for 48 h and then induced the serum response by replacing the low-serum media with media containing 10% FBS for 24 h.
Chromatin immunoprecipitation
Human promoter microarrays
Microarray analysis
Microarray validation
GO term analysis and gene annotation
Quantitative PCR
Preparation of genomic DNA
Methylation assay
We thank members of the Myers Laboratory for helpful discussion and encouragement. We thank Chris Hopkins for his advice and discussions as we established the Agilent array technology in our laboratory. S.J.C. is supported by the Stanford Genome Training Program (Training Grant NIH 5 T32 HG00044). This work was supported by NIH Grant 1 U01 HG 03162-01 (to R.M.M) from the National Human Genome Research Institute.
2 Present addresses: Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; 
3 Switchgear Genomics, Menlo Park, California 94025, USA.
E-mail myers{at}shgc.stanford.edu; fax (650) 725-9689. [Supplemental material is available online at www.genome.org.] Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.5875007
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ABSTRACT
Results
2 = 7.68, P = 0.053). On the basis of this observation, it is not surprising that promoters bound by SRF under only a single condition are less likely to overlap a CpG island (
- and 
-actins (ACTA2 and ACTG2), connectin tissue growth factor (CTGF), and smooth muscle myosin heavy chain (MYH11) (for review, see Miano 2003
, where x is the experimental ratio, xbar is the mean ratio for all probes, and 
