- Open Access
HDAC inhibitors cause site-specific chromatin remodeling at PU.1-bound enhancers in K562 cells
© Frank et al. 2016
Received: 6 January 2016
Accepted: 5 April 2016
Published: 16 April 2016
Small molecule inhibitors of histone deacetylases (HDACi) hold promise as anticancer agents for particular malignancies. However, clinical use is often confounded by toxicity, perhaps due to indiscriminate hyperacetylation of cellular proteins. Therefore, elucidating the mechanisms by which HDACi trigger differentiation, cell cycle arrest, or apoptosis of cancer cells could inform development of more targeted therapies. We used the myelogenous leukemia line K562 as a model of HDACi-induced differentiation to investigate chromatin accessibility (DNase-seq) and expression (RNA-seq) changes associated with this process.
We identified several thousand specific regulatory elements [~10 % of total DNase I-hypersensitive (DHS) sites] that become significantly more or less accessible with sodium butyrate or suberanilohydroxamic acid treatment. Most of the differential DHS sites display hallmarks of enhancers, including being enriched for non-promoter regions, associating with nearby gene expression changes, and increasing luciferase reporter expression in K562 cells. Differential DHS sites were enriched for key hematopoietic lineage transcription factor motifs, including SPI1 (PU.1), a known pioneer factor. We found PU.1 increases binding at opened DHS sites with HDACi treatment by ChIP-seq, but PU.1 knockdown by shRNA fails to block the chromatin accessibility and expression changes. A machine-learning approach indicates H3K27me3 initially marks PU.1-bound sites that open with HDACi treatment, suggesting these sites are epigenetically poised.
We find HDACi treatment of K562 cells results in site-specific chromatin remodeling at epigenetically poised regulatory elements. PU.1 shows evidence of a pioneer role in this process by marking poised enhancers but is not required for transcriptional activation.
Large cancer genotyping efforts such as The Cancer Genome Atlas (TCGA) have found epigenetic regulators are frequent targets of oncogenic mutation and translocation. For instance, 76 % of urothelial carcinomas analyzed by TCGA were found to carry at least one inactivating mutation in a chromatin regulatory gene . These types of mutations are thought to lead to aberrant epigenetic states at many loci in the genome that together contribute to expression patterns and signaling cascades that facilitate oncogenic phenotypes. Alongside these discoveries, chromatin regulator-targeted pharmacological therapies have shown the potential to reverse aberrant epigenetic states driving cancer cell proliferation . Histone deacetylase inhibitors (HDACi) represent one class of compounds with this potential.
The majority of HDACi investigated for anticancer application act to block removal of acetyl groups from protein lysines by inhibiting the active sites of zinc-dependent class I, II, and IV histone deacetylases [3, 4]. HDACi are capable of inducing cell cycle arrest, differentiation, and apoptosis in a variety of preclinical cancer models, and thus far three HDACi have been FDA cleared for anticancer applications [3, 4]. In clinical trials, multiple HDACi have been shown to have severe dose-limiting toxicities , indicating that a more nuanced understanding of HDACi anticancer effects could be beneficial to the development of more specific and well-tolerated therapies.
Exposure to HDACi is thought to increase chromatin acetylation levels genome wide [6, 7] but in many cases the exact mechanisms by which HDACi stop cancer cell proliferation remain poorly defined. Increased histone tail acetylation levels have classically been associated with relaxation of local chromatin and greater accessibility for transcription factor binding [3, 8]; however, more recent studies show that specific histone acetylation marks demarcate different gene regulatory element functions and are dynamically maintained and read by trans-acting “writer,” “eraser,” and “reader” factors [9, 10]. Furthermore, previous studies report only a fraction (~10 %) of genes respond to HDACi treatment with expression changes, indicating these compounds are more specific than would be expected by global histone hyperacetylation [11, 12]. Therefore, a better understanding of how these drugs impact gene expression is necessary.
The ability of HDACi to induce differentiation responses analogous to the highly effective all-trans retinoic acid therapy used in acute promyelocytic leukemia  is a particularly interesting application of these compounds that could be useful for resensitizing cancer cells to other chemotherapeutics or eliminating cancer stem cells. For over 30 years, it has been noted that HDACi treatment of the myelogenous leukemia line K562 results in differentiation along an erythrocytic lineage [14, 15], providing a well-characterized system for HDACi-initiated differentiation.
To investigate the relationship between chromatin changes and the transcriptional response to HDACi treatment in the context of induced cancer cell differentiation, we measured genome-wide chromatin accessibility (DNase-seq) and expression (RNA-seq) changes resulting from sublethal HDACi treatment of K562 cells. As cell proliferation slowed, we detected several thousand gene regulatory elements where chromatin accessibility increased or decreased. These changes coincide with nearby gene expression changes and likely represent enhancer element activation or deactivation events. Motif enrichment analysis indicated that the pioneer factor PU.1 was bound to many of the newly opened DHS sites, which we confirmed by ChIP-seq. Since PU.1 is known to be involved in hematopoietic cell differentiation [16, 17], we tested whether overexpression and knockdown of PU.1 could explain the HDACi observed changes in chromatin and expression. Overexpression of PU.1 modestly opened the DHS sites shown to be opened by HDACi treatment, and shRNA-mediated knockdown of PU.1 failed to block the chromatin accessibility and gene expression changes associated with HDACi treatment. Together, this suggests that while PU.1 is present at sites of HDACi-induced chromatin changes, this factor is not the primary driver of these changes. Instead, a machine-learning approach suggests that enrichment of H3K27me3 specifically marks HDACi-responsive DHS sites. These findings add to our mechanistic knowledge of how HDACi alter chromatin and gene expression patterns, induce differentiation, and ultimately block cancer cell proliferation.
HDACi drive site-specific chromatin accessibility changes in K562 cells
Although the two drug treatments resulted in approximately tenfold different totals of differential DHS sites, we found the direction of signal changes was remarkably similar between drugs across these sites (Additional file 2: Fig. S1A–D). Additionally, increasing NaBut concentration fourfold to 2 mM over the same 72-h treatment resulted in a 15,599 additional differential DHS sites at FDR < 0.05 (Additional file 2: Fig. S1E). This suggests the two HDACi are capable of inducing similar chromatin accessibility changes and that the differences in the number of differential DNase sites are likely due to inhibitor potency. Indeed, a principle components analysis separates vehicle controls from both drug-treated populations along the first principal component (Fig. 1f), implying the greatest source of variation in these data is an HDACi response in common to NaBut and SAHA. Clustering samples by Euclidean distance between regularized log-transformed DHS signal (Additional file 2: Fig. S1F) further supports the notion these two drug treatments result in similar global chromatin accessibility changes in K562 cells.
HDACi-opened and HDACi-closed DHS sites associate with directional gene expression changes
To further test whether HDACi-opened DHS sites function as enhancers in K562 cells, we cloned eight ~800-bp regions encompassing HDACi-opened DHS sites in front of a minimal promoter driving luciferase reporter expression. At 24 h post-transfection into K562 cells, we treated cells with NaBut, SAHA, or their respective vehicle controls for 24 h and then measured luciferase activity. Normalizing signal to co-transfected Renilla luciferase, we detected luciferase activity elevated above the minimal promoter construct alone for five of the eight elements in untreated K562 cells (Fig. 2e, f). Importantly, the addition of either NaBut or SAHA resulted in seven of the eight elements significantly increasing luciferase activity. This effect was not observed in a known strong enhancer that does not increase in accessibility following HDACi treatment (Fig. 2e, f). Together, these results suggest many opened DHS sites indeed function as enhancer elements and that these elements become more active upon HDACi treatment.
PU.1 binds preferentially at opened DHS sites
We first tested PU.1 binding by chromatin immunoprecipitation qPCR (ChIP-qPCR) at six DHS sites that both open with HDACi treatment and contain at least one canonical 5′-GGAA-3′ PU.1-binding motif. Enrichment of PU.1 occupancy was detected for each of the six tested sites (Additional file 6: Fig. S3A, B), and substantial increases in PU.1 occupancy were observed following either SAHA (Fig. 3b) or NaBut (Fig. 3c) 72-h treatments. PU.1 binding and establishment of functional enhancers has been previously reported to coincide with increased local deposition of the histone enhancer mark H3K4me1 . We detected H3K4me1 enrichment at each of the six sites prior to HDACi treatment (Additional file 6: Fig. S3C) that modestly decreased at five of the six sites following treatment (Fig. 3d), possibly reflecting nucleosome repositioning.
To characterize genome-wide PU.1-binding changes with HDACi treatment, ChIP-seq was performed on vehicle and SAHA-treated K562 cells in triplicate, which identified 31977 PU.1-binding sites in total. We observed hundreds of sites with increased PU.1 binding following HDACi treatment similar to those flanking the CDKN1A gene (p21) (Fig. 3e), a well-studied tumor suppressor that mediates p53-dependent G1 growth arrest and becomes up-regulated following HDACi treatment . As predicted, PU.1 binding was highly enriched in opened DHS sites relative to all DHS sites in K562 cells or closed DHS sites (Fig. 3f). Interestingly, PU.1 ChIP-seq signal increased globally at the vast majority of peaks following SAHA treatment (Fig. 3g) while maintaining a similar distribution of total binding sites. This result was detected in each of the three replicates processed as pairs of HDACi-treated and vehicle control cells. The global increase in PU.1 binding also matches our ChIP-qPCR results (Fig. 3b, c).
PU.1 overexpression modestly increases accessibility
To characterize the impact of overexpression of PU.1 on chromatin accessibility, we analyzed the original set of 7962 SAHA-opened DHS sites and divided them by those bound by PU.1 (n = 2137) versus the remaining that did not bind PU.1 (n = 5825). For the 2137 sites bound by PU.1, we observed a reproducible increase in mean accessibility in cells that overexpress PU.1 (Fig. 4b). This increase in accessibility was not detected at the 5825 SAHA-opened DHS sites that do not bind PU.1 (Fig. 4b). Furthermore, the distribution of fold-changes in accessibility found at SAHA-opened DHS sites with PU.1 binding was significantly greater than that of the SAHA-opened DHS sites without PU.1 binding (Mann–Whitney test, P < 2.9 × 10−59) or PU.1-bound DHS sites that did not open further with SAHA treatment (P < 6.7 × 10−39) (Fig. 4c). These patterns are exemplified by two DHS sites found in the same intron of the TMEM51 gene; both sites open with SAHA treatment, but only the site with PU.1 bound displays increased hypersensitivity following PU.1 overexpression (Fig. 4d). The difference between PU.1-bound DHS sites that open and those that do not increase accessibility with SAHA treatment cannot be explained by differing baseline levels of PU.1 binding (Additional file 7: Fig. S4C). Together these analyses suggest a subset of PU.1 bound DHS sites become more accessible in response to elevated PU.1 expression and that this subset overlaps many of the same DHS sites that open up in response to HDACi treatment.
Depletion of PU.1 fails to block HDACi-induced chromatin accessibility and expression changes
Surprisingly, depletion of PU.1 displayed no significant differences in chromatin accessibility from control untreated K562 at an adjusted P value cutoff of 0.10 (Fig. 5c; Additional file 8: Fig. S5B). These results indicate that normal PU.1 levels are not required for maintaining chromatin accessibility patterns in K562 cells, though we cannot rule out that residual low PU.1 protein levels are sufficient for the chromatin maintenance. Even more surprisingly, we found no significant chromatin accessibility differences in PU.1-depleted versus control cells when treated with SAHA for 72 h (Fig. 5d; Additional file 8: Fig. S5C). We also detected no substantial differences in DNase signal in the subset of PU.1-bound DHS sites that open upon SAHA treatment (Fig. 5e, f; Additional file 8: Fig. S5D, E). This indicates that reduced levels of PU.1 do not prevent the altered chromatin accessibility patterns observed after SAHA treatment.
To determine whether PU.1 is required for the gene expression changes that occur with HDACi treatment, we performed RNA-seq on control and PU.1-depleted cells treated with or without SAHA. We identified just 45 genes in untreated cells and 115 genes in SAHA-treated cells significantly differential (FDR < 0.05) with PU.1 knockdown (Additional file 9: Table S4). Of these, 75.6 % (34/45) and 67.8 % (78/115) were also genes differential as a result of SAHA treatment in our original RNA-seq analysis. This suggests that PU.1 knockdown impacts a fraction of SAHA transcriptional changes in K562. However, the distribution of changes in expression for genes differential upon SAHA treatment in vector control-transfected cells (999 genes at FDR < 0.05) was remarkably similar to cells with PU.1 depleted (Additional file 10: Fig. S6A). Furthermore, a heatmap of these significantly differential genes shows a clear separation between all SAHA-treated samples and all vehicle controls (Additional file 10: Fig. S6B). These results indicate that PU.1 is not required for the vast majority of HDACi-induced expression changes and further suggest other cofactors may be responsible for activation of opened enhancer elements.
Histone marks and transcription factor binding are predictive of HDACi response at PU.1-bound DHS sites
Heatmaps of H3K27me3, GATA1, and TAL1 ChIP signal over the DHS sites used for classification show these three features are indeed enriched in PU.1-bound DHS sites that open with HDACi treatment (Fig. 6c–e). RNA-seq shows TAL1 and GATA1 significantly increase in expression following SAHA treatment (Additional file 3: Table S2) and the enriched binding of these hematopoietic factors implicates them as regulators of the HDACi-induced enhancers. Interestingly, the presence of elevated H3K27me3 suggests PU.1-bound sites that open with HDACi may be poised in chromatin status for this response , while sites stable in accessibility may represent mainly already active enhancers, as evidenced by greater H3K36me3, H3K4me2, RNA polymerase II (Pol2), and H3K27ac signal (Fig. 6b) [25–27]. While not informative for distinguishing between opened and unchanged DHS sites, H3K4me1 signal also appears enriched in all PU.1-bound DHS sites ahead of HDACi treatment (Additional file 11: Fig. S7), consistent with our ChIP-qPCR data (Additional file 6: Fig. S3C).
HDAC inhibitors have the ability to halt proliferation, induce cell death, or resensitize many cancers, but the pleiotropic effects of global hyperacetylation may limit their therapeutic potential. Isolating the processes underlying anticancer efficacy of HDACi from those that are harmful to normal cells is paramount to developing safer, more targeted therapies. We investigated the process of HDACi-induced cancer cell differentiation using K562 cells that slow in growth and display markers of erythroid lineage commitment with treatment [14, 28]. While histone acetylation levels increase genome wide following HDACi treatment [6, 7], our data showed only about 10 % of DHS sites change in K562 cells in response to treatment, with both opening and closing events. This implies that global hyperacetylation of histone tails is not sufficient to appreciably open local chromatin structure. These results are compatible with our RNA-seq data and previous expression studies identifying limited sets of differentially expressed genes following HDACi exposure [11, 12]. We postulate that the chromatin accessibility changes we observed were largely due to programmed enhancer element activation or repression as K562 cells undergo differentiation. The specificity of enhancer activity changes likely results from an exchange of specific transcription factors that either activate and increase accessibility or deactivate and decrease accessibility. While it appears HDACi treatment is capable of gene repression, it may be the case that the DHS sites that close following HDACi treatment represent secondary effects of increased repressive transcription factor expression.
The reprogramming of K562 by HDACi-induced differentiation may be very similar to the mechanism by which all-trans retinoic acid is used to treat acute promyelocytic leukemia (APL). In APL, all-trans retinoic acid induces differentiation of leukemic promyelocytes into mature granulocytes . Chromosomal rearrangements in APL most commonly fuse the retinoic acid receptor-alpha (RARA) gene to the promyelocytic leukemia (PML) gene, generating a hybrid protein that binds to genes involved in normal granulocyte differentiation and recruits the nuclear corepressor (NCoR) complex . NCoR represses gene expression by recruiting HDACs, and all-trans retinoic acid alleviates this repression by effectively displacing NCoR. Indeed, this connection between HDAC-mediated gene repression in APL and the mechanism of HDACi treatments has been suggested before . Our evidence of specific chromatin and gene expression changes driving K562 differentiation in the presence of HDACi support the idea that similar complexes may repress tumor suppressor genes, and this repression is alleviated after treatment.
Our motif enrichment analysis and expression data implicated the pioneer factor PU.1 in the HDACi-induced opening of DHS sites. ChIP-seq for PU.1 before and after treatment demonstrated that this transcription factor binds many of the opened DHS sites and increases binding following HDACi treatment. PU.1 is known to be critical for enhancer activation and chromatin changes in B cell and macrophage differentiation [16, 17]. However, we found evidence that PU.1 is not a major driver of HDACi enhancer activation in K562 cells. Forced overexpression of PU.1 only produced modest chromatin accessibility increases at a subset of bound loci, and reduction of PU.1 levels by shRNA knockdown did not prevent the chromatin accessibility increases or gene expression changes with HDACi treatment. However, we cannot rule out PU.1 may be still acting as a pioneer factor earlier during the development of the K562 epigenome and more important for poising future enhancer sites. It is also possible that low residual PU.1 levels are enough to initiate the process of chromatin remodeling and that increased binding at opened DHS sites is not important for this process. Creating a PU.1 null cell line may help address this possibility. Lastly, it may be the case that other factors, including ETS family proteins, are functionally redundant in the chromatin remodeling process and capable of assuming PU.1’s enhancer activation role in K562.
Using a random forest classifier, we identified the histone mark H3K27me3 as the strongest positive predictor for PU.1-bound DHS sites to respond to HDACi treatment with chromatin accessibility increases. Additionally, we found that the chromobox proteins CBX2 and CBX8, which are components of Polycomb repressor complexes that maintain H3K27me3 marks , are enriched at the responsive DHS sites (Fig. 6b; Additional file 11: Fig. S7B, C). The dual presence of H3K27me3 and H3K4me3 has been described as “bivalent domains” with competing activating and repressive modifications that together mark poised promoter regions during development . As cells differentiate, they can potentially activate or repress marked genes by changing histone modifications and chromatin accessibility status. Similarly, H3K4me1 in combination with H3K27me3 marks poised distal enhancer elements . The presence of the H3K4me1 and H3K27me3 marks, together with the low levels of PU.1 binding prior to HDACi treatment, suggests the HDACi-opened DHS sites we observed might be initially poised for enhancer activity and a chromatin remodeling response in K562 cells. In this model, once de-repressed by removal of HDAC function, these enhancers become activated and drive gene expression patterns that coordinate the erythroid differentiation process. The PU.1-bound DHS sites that do not significantly increase accessibility with HDACi treatment likely include more sites that are already active enhancers, as evidenced by enriched H3K36me3, Pol2, H3K4me2, and H3K27ac signal (Fig. 6b) [25–27].
We also found a moderate enrichment of GATA1 and TAL1 binding at opening PU.1-bound DHS sites. The GATA family motif was enriched in our sets of HDACi-opened and HDACi-closed DHS sites in general (Additional file 4: Fig. S2). PU.1 and GATA1 are capable of inhibiting each other’s function by protein–protein interactions that block DNA binding [33, 34], but it is unclear how that mechanism may be involved in this process. TAL1 has been shown to be required for chromatin looping between the β-globin locus control region and the γ-globin gene to enhance its expression in K562 cells, demonstrating a role it plays in this system to establish functional enhancers . The extent to which PU.1, GATA1, and TAL1 cooperatively interact to control enhancer activation following HDACi treatment is currently unknown and will be the target of future studies. As the role of lineage-committing transcription factors and chromatin changes in induced cancer cell differentiation are better defined, strategies can be further refined to inform more targeted cancer treatments.
We found that despite widespread hyperacetylation, HDACi cause site-specific chromatin remodeling in the genome of K562 cells with roughly equal numbers of DHS sites gaining or losing accessibility. Opening DHS sites often reflect gain of enhancer activity at sites marked by PU.1 binding that increases with HDACi treatment. PU.1-bound DHS sites with the largest accessibility gains appear to be epigenetically poised by a combination of repressive H3K27me3 and activating H3K4me1 histone marks. Together, these results help explain the ability of HDACi to drive differentiation of K562 cells at sublethal concentrations by activating and deactivating particular enhancer elements to regulate gene expression in a pre-programmed manner. We postulate a similar directed differentiation process underlies HDACi efficacy against various leukemias and contributes to the ability of HDACi to sensitive cancer cells to other drugs in combination therapies.
K562 cell culture and HDACi treatments
K562 cells were obtained from ATCC (CCL-243) and maintained in 1× RPMI1640 media supplemented with 10 % FBS and 1× antibiotic–antimycotic (Life Technologies). Initial titrations of the HDACi NaBut and SAHA (Sigma-Aldrich) were performed to determine the maximum concentration with which <10 % of cells are killed at 72 h by Trypan blue staining. Addition of NaBut dissolved in 1× PBS to 0.5 mM or SAHA dissolved in DMSO to 1 μM final concentration versus an equal volume of vehicle was used for all 72-h exposures presented with the exception of 2 mM NaBut presented in Additional file 1: Fig. S1. Additionally, 1 mM NaBut was used for the shorter 24-h treatments with luciferase reporter constructs.
15–50 million K562 nuclei were spun down, washed twice with 1× PBS, divided, and digested with a range of recombinant DNase I enzyme (Roche) concentrations (between 0.12 and 12 U) for 16 min at 37 °C in 120 μL 1× DNase buffer. DNase-seq libraries were constructed as previously described . Briefly, each set of digestions was checked by pulsed-field gel electrophoresis, and material was pooled in equimolar amounts following blunt-ending reactions from three different DNase concentrations to match extent of digestion (0.36, 1.2, and 3.6 U used in all experiments). Following ligation to adapters, MmeI digestion, streptavidin bead-based enrichment, and 14 cycles of linker-mediated PCR, each library was sequenced for 50 cycles on the Illumina HiSeq2000 platform, with the exception of the first replicate of NaBut treatments, which were sequenced for 36 cycles on a GAIIx machine. Three replicates (beginning with a new K562 frozen stock) were processed per condition with HDACi-treated and corresponding vehicle control samples co-processed throughout each replicate.
DNase-seq reads were trimmed to the first 20 bp (fixed insert length generated by MmeI digest) and aligned to the UCSC hg19 reference genome using Bowtie  allowing up to 1 mismatch and requiring unique alignments. Potential PCR artifacts were filtered from alignment by removing reads where greater than 70 % map to a single bp within a 31-bp window . Reads mapping to ENCODE blacklisted regions , mitochondria, and chrY were removed from alignment. MACS2  was used to call peaks (FDR < 0.05) and generate genome coverage files by artificially extending DNase reads to 200 bp in length and centering on 5′ read ends. bigWig files for browser images were normalized to millions of reads aligned per sample.
For differential signal tests, raw read counts were tabulated for the union of all peak calls across replicates as input for DESeq2 . Principle components analysis and clustering by Euclidean distance were executed using DESeq2 following regularized log transformation of normalized read counts of DNase signal found in all peaks. DHS sites were associated with the single nearest hg19 reference gene for expression level comparisons. DHS sites mapping within 2 kb of an annotated TSS were considered promoter-located, and all other DHS sites were considered distal. Enriched motifs in differential DHS sites were identified using MEME-ChIP  with alignments to JASPAR and UniPROBE databases.
ChIP was performed essentially as previously described . Briefly, 20 million K562 cells per ChIP were cross-linked with 1 % formaldehyde for 10 min, quenched with 0.125 M glycine, and lysed. DNA was sonicated by 45 min with 30-s on/off cycles on a Bioruptor (Diagenode) in RIPA buffer. An aliquot of sonicated material was reserved for agarose gel sizing and for an input chromatin control sample with each treatment. After resuspending chromatin in 1 % Triton X-100, 2 mM EDTA, 20 mM TrisCl, 150 mM NaCl buffer, and pre-clearing material with Protein A Dynabeads (Life Technologies) for 2 h at 4 °C, 6 μg of the following antibodies was incubated with the sonicated chromatin overnight at 4 °C: rabbit anti-PU.1 Santa Cruz Biotech sc-22,805 and rabbit anti-H3K4me1 Abcam ab8895. Protein A beads were bound to antibody for 3 h at 4 °C and then washed five times with LiCl wash buffer (500 mM LiCl, 100 mM TrisCl, 1 % NP40, 1 % sodium deoxycholate) and once with 1× TE. Cross-linking was reversed with overnight 65 °C incubation in 1 % SDS and 0.1 M sodium bicarbonate buffer, and DNA was eluted with a PCR cleanup kit (Qiagen). Following TruSeq adapter ligations, DNA fragments were separated from free adapters by AmpureXL bead size selection steps, and PCR amplification was performed for 18 cycles. ChIP-seq libraries were sequenced on the Illumina HiSeq2000 machine with 50-bp SE reads. Three replicates (beginning with a new K562 frozen stock) were processed per condition with HDACi-treated and corresponding vehicle control samples co-processed throughout each replicate to mitigate batch effects.
ChIP-seq reads were aligned to the hg19 reference genome using Bowtie  allowing up to 1 mismatch and requiring unique alignments. Putative PCR artifacts and reads mapping to blacklisted regions, mitochondrial, or chrY sequences were removed exactly as with DNase-seq alignments. Peak calls were generated by MACS2  with default settings at FDR < 0.05 over K562 input background samples with matching vehicle or HDACi treatment where appropriate. Genome browser visualizations were normalized by total mapped reads in each sample. Genomic regions tested for enrichment by qPCR were amplified with the following primers: S1, F: ACTTCCCCTTTCCCTTGCT, R: GGGCTGGGAGGACTACTGTG; S2, F: GCCTTGGGCAGATGTACAAA, R: TTCCACTTCCTCTTTCTGTGC; S3, F: ACCAGAGGTCCCTGGAGTG, R: CTGGGAGGACAGCTGCTAAG; S4, F: TCAAGTCGTGGTTTGGATGA, R: GGGGTACCTCTCACCACTCA; S5, F: GATCTTGGGGGTGGCTTG, R: AAGTGAGAAGGGGCTGTTGT; S6, F: GACTGGGGGAAGGACCTCT, R: CCAGCACGAAGCTGACTGAT; C1, F: TCTCTGGGGAGATGGATTACA, R: CGTGAATCCTTTATTCTTGGAA; C2, F: CCAATAACAGAAGCATTAAAATTCA, R: TTCAAGCACAGGCATACAGG.
RNA was isolated from 5 to 10 million K562 cells with an RNeasy Mini kit (Qiagen) with on-column DNase digestion. Two microgram of total RNA was used for TruSeq library preparation with polyA selection. Libraries for initial NaBut and SAHA versus vehicle control experiments were subjected to 50-bp paired-end Illumina HiSeq2000 sequencing to an average depth of 40.2 million read pairs. Libraries for SAHA versus vehicle control with PU.1 depleted by shRNA were sequenced as 50-bp SE on Illumina HiSeq with average depth of 36.4 million reads. Following quality-score-based trimming, reads were aligned to hg19 UCSC genes with Tophat v2.0.12  allowing up to 2 mismatches. FPKM estimates for individual samples were obtained with Cufflinks v2.2.1, and differential genes across conditions were identified with Cuffdiff v2.2.1 . Three (for NaBut treatment) or two (for SAHA treatments) independent replicates (beginning with new K562 frozen stock) were processed with HDACi-treated and corresponding vehicle control samples in parallel throughout.
Classification of opened versus stable sites
In order to identify transcription factors and epigenetic marks associated with increased accessibility of DHS sites bound by PU.1, we implemented a random forest classifier to distinguish sites that open from those that do not significantly change accessibility following HDACi treatment. We used all ChIP-seq data available through ENCODE (genome assembly hg19) for the untreated K562 cell line (“treatment = None”) with the exception of experiments labeled with “revoked” status. A total of 112 transcription factor, 15 histone modification, and 18 chromatin-modifying factor ChIP-seq alignments were downloaded and processed with MACS2 (v2.10)  to generate fragment pileup scores (bedGraph format) over input control. Before classification, we removed all DHS sites localizing to known gene promoters or exons (based on UCSC hg19 known genes). Next, to control for the DNase-seq and PU.1-binding signal in the sets of opened versus stable sites used for classification, for each opened DHS site we randomly selected a stable DHS site with close to equal baseline DNase-seq and PU.1 ChIP-seq signal in untreated K562 cells. DHS center ±200 bp was used for DNase signal, and DHS center ±150 bp was used for PU.1 ChIP signal matching. This resulted in ~930 opened and stable sites used for classification. For each selected DHS site, transcription factor features were computed as the maximum ChIP-seq pileup signal over 200-bp windows with 100-bp overlap in the DHS site (defined as DHS site center ±300 bp). For chromatin-modifying factors, we used a larger region of DHS center ±700 bp. For histone modifications, we summed the total ChIP-seq signal in the DHS center ±700 bp. The random forest classifier was run through R package “randomForest” with “mtry” set to 10 and “ntree” set to 500. 75 % of the input data were used to train the model and 25 % reserved for testing. The random splitting of training and testing samples followed by classification was repeated 10 times to assess the stability of the top features and classification accuracy. The importance score of each feature was computed as the Gini index value and the mean decrease in accuracy when its class labels are randomly permuted. Heatmap and summary plots were generated using deepTools software .
~800 bp of each DHS site was cloned upstream of the minimal promoter in a pGL4.23 luciferase reporter vector (Promega) using amplicons generated with the following primers (hg19): chr1:228639823–228640570, F: GATGACTGGGGGAAGTCTCA, R: GACAGCAGGTGTCTGGTGAA; chr11: 76847652–76848380, F: AGTTAACCATGGCTGGCACT, R: GCAACGAAAAAGACGGTGAT; chr19: 8641896–8642579, F: TTGTCCCTGCAGACTCTGTG, R: CTGAATCGCCCTGAAAAGAG; chr5: 95168365–95169111, F: CCATTTCTGCCTCCCTCATA, R: GCGCTTTCCGACTACTCATC; chr6: 41690943–41691723, F: CCTTCTTCCCCATTCTTTCC, R: AGGAAGATGGTGAGCTGTGG; chr19: 6778173–6778776, F: CCTGACCTTAAGTGACCCACA, R: CAGGCAAAATTGGGTTTTGT; chr7: 100839167–100839788, F: AGTGGGTGGAGGTCTCAGTG, R: TGAATGACCCTGGGAGGTAG; chr1: 35384552–35385452, F: TCCTCGTGTCTGCTGTGATG, R: TTTTTGGTCTGGCAGTAGGG; chr9: 131900863–131902480, F: ATCCCTCCTCAGTCCCTTTC, R: AGGGGAATCGTGTGAGTCAG. Inserts were verified by Sanger sequencing. K562 cells were transferred to antibiotic-free media 24-h preceding transfection. 15,000 K562 cells were added per well to 96-well plates and transfected with the pGL4.23 construct and pGL4.73 Renilla control plasmid using Lipofectamine LTX with Plus reagent (Thermo Fisher Scientific) following manufacturer’s protocol. 24 h after transfection, 1 mM NaBut, 1 μM SAHA, or an equal volume of 1× PBS or DMSO vehicle was added to each well. 24 h after HDACi addition, cells were simultaneously lysed and exposed to substrate following manufacturer’s instructions for the dual-luciferase reporter system (Promega). Plate emissions were read on a VICTOR2 multilabel counter plate reader (Perkin Elmer). Measurements were performed in technical replicates of six wells, normalized per well by Renilla output, and normalized per plate by average empty pGL4.23 signal. Each construct was tested with at least two separate sets of transfections on different days. The positive control region (chr9: 131900863–131902480) overlaps an alternative promoter of PPP2R4 that we observed exhibits strong enhancer activity with luciferase reporters in K562 cells, but does not significantly open with HDACi treatment.
PU.1 shRNA knockdown
We purchased shRNAs targeting human PU.1 mRNA that were cloned in the lentiviral vector pLKO.1 (Sigma-Aldrich) and obtained a non-hairpin control pLKO.1 (Addgene plasmid #10879 from David Root). Five shRNAs targeting PU.1 were tested for knockdown efficiency, and the two most effective, TRCN0000426240 and TRCN0000417534, were selected for subsequent use. shRNA vectors were introduced by transfection of 10 million K562 cells with Lipofectamine LTX with Plus reagent (Thermo Fisher Scientific) following manufacturer’s protocol. 24 h after transfection, puromycin (Gibco) was added to media to 2 μg/mL for selection over 10–12 days. Puromycin was reduced to 1 μg/mL during 72-h SAHA treatments. Knockdown efficiency was evaluated by qPCR or western blot on days cells were subjected to DNase-seq.
PU.1 cDNA was obtained by reverse transcription from K562 total RNA extracts and amplification with following primers (IDT): F: TGACGGATCC GCCGCCACCATGTTACAGGCGTGCAAAATG, R: TGACGCGGCCGCTCAGTGGGGCGGGTGG that amplify from start to stop codons of PU.1 cDNA with addition of a Kozak sequence, and BamHI and NotI recognition sites. BamHI and NotI double digest (NEB) was used to clone the amplified cDNA into the pTargeT mammalian expression vector (Promega), and the insert was verified with Sanger sequencing. The PU.1 overexpression construct or empty pTarget was introduced to 0.5 million K562 cells by transfection with Lipofectamine LTX with Plus reagent (Thermo Fisher Scientific) following manufacturer’s protocol. 24 h following transfection, media were supplemented with 400 μg/mL G418 disulfate (Sigma-Aldrich) for 12–14 days of selection. Following selection, G418 concentration was reduced to 250 μg/mL and cells were expanded for 3–4 days. Overexpression was verified by qPCR or western blot on days cells were subjected to DNase-seq.
1–2 μg of total RNA was used for reverse transcription with oligo dT primers and Superscript II (Invitrogen). Quantitative SYBR green PCR was performed on an ABI 7500 real-time PCR machine (Applied Biosystems) using the following primers (IDT): PU.1, F: TGTTACAGGCGTGCAAAATG, R: TCATAGGGCACCAGGTCTTC; β-actin, F: GCCGGGACCTGACTGACTAC, R: TTCTCCTTAATGTCACGCACGAT; CDKN1A, F: GACTCTCAGGGTCGAAAACG, R: GGATTAGGGCTTCCTCTTGG. Each sample was measured in technical triplicate and normalized to β-actin.
5–10 million K562 cells were lysed in RIPA buffer and spun down at 10,000 RPM for 15 min to remove cell debris. Samples were disrupted by brief sonication, a BCA assay (Pierce) was used to measure protein concentrations, and 10 μg of each lysate was loaded per lane for SDS-PAGE. Gels were transferred to nitrocellulose for blotting. Primary antibodies used were rabbit anti-PU.1 Abcam ab76543 and mouse anti-β-actin Santa Cruz Biotech sc-81178. Bands were visualized by supplying substrate for the following HRP-conjugated secondary antibodies: goat anti-rabbit Pierce #31462 and goat anti-mouse Santa Cruz Biotech sc-2005.
All sequencing data presented here (with the exception of ENCODE ChIP-seq datasets referenced above) have been deposited as GEO entry GSE74999.
GEC, CLF, and RG designed the study. CLF performed experiments. CLF and DM performed bioinformatic analyses. CLF and GEC wrote the paper with input from all authors. All authors read and approved the final manuscript.
We thank Shaio-Wen David Hsu and Jen-Tsan Ashley Chi for helpful discussions and the Duke Sequencing Core for library preparation and sequencing assistance. This work was partially supported by NIH/NHGRI ENCODE Consortium grant U54 HG004563 to GEC and NIH/NIGMS grant R01-GM117106 to RG.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature. 2014;507:315–22.View ArticleGoogle Scholar
- Yoo CB, Jones PA. Epigenetic therapy of cancer: past, present and future. Nat Rev Drug Discov. 2006;5:37–50.View ArticlePubMedGoogle Scholar
- West AC, Johnstone RW. New and emerging HDAC inhibitors for cancer treatment. J Clin Invest. 2014;124:30–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Xu WS, Parmigiani RB, Marks PA. Histone deacetylase inhibitors: molecular mechanisms of action. Oncogene. 2007;26:5541–52.View ArticlePubMedGoogle Scholar
- Guha M. HDAC inhibitors still need a home run, despite recent approval. Nat Rev Drug Discov. 2015;14:225–6.View ArticlePubMedGoogle Scholar
- Hezroni H, Sailaja BS, Meshorer E. Pluripotency-related, valproic acid (VPA)-induced genome-wide histone H3 lysine 9 (H3K9) acetylation patterns in embryonic stem cells. J Biol Chem. 2011;286:35977–88.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang Z, Zang C, Cui K, Schones DE, Barski A, Peng W, Zhao K. Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell. 2009;138:1019–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Grunstein M. Histone acetylation in chromatin structure and transcription. Nature. 1997;389:349–52.View ArticlePubMedGoogle Scholar
- Falkenberg KJ, Johnstone RW. Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders. Nat Rev Drug Discov. 2014;13:673–91.View ArticlePubMedGoogle Scholar
- Jenuwein T, Allis CD. Translating the histone code. Science. 2001;293:1074–80.View ArticlePubMedGoogle Scholar
- Glaser KB, Staver MJ, Waring JF, Stender J, Ulrich RG, Davidsen SK. Gene expression profiling of multiple histone deacetylase (HDAC) inhibitors: defining a common gene set produced by HDAC inhibition in T24 and MDA carcinoma cell lines. Mol Cancer Ther. 2003;2:151–63.PubMedGoogle Scholar
- Mitsiades CS, Mitsiades NS, McMullan CJ, Poulaki V, Shringarpure R, Hideshima T, Akiyama M, Chauhan D, Munshi N, Gu X, Bailey C, Joseph M, Libermann TA, Richon VM, Marks PA, Anderson KC. Transcriptional signature of histone deacetylase inhibition in multiple myeloma: biological and clinical implications. Proc Natl Acad Sci USA. 2004;101:540–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Coombs CC, Tavakkoli M, Tallman MS. Acute promyelocytic leukemia: where did we start, where are we now, and the future. Blood Cancer J. 2015;5:e304.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersson LC, Jokinen M, Gahmberg CG. Induction of erythroid differentiation in the human leukaemia cell line K562. Nature. 1979;278:364–5.View ArticlePubMedGoogle Scholar
- Kruh J. Effects of sodium butyrate, a new pharmacological agent, on cells in culture. Mol Cell Biochem. 1982;42:65–82.PubMedGoogle Scholar
- Ghisletti S, Barozzi I, Mietton F, Polletti S, De Santa F, Venturini E, Gregory L, Lonie L, Chew A, Wei C-L, Ragoussis J, Natoli G. Identification and characterization of enhancers controlling the inflammatory gene expression program in macrophages. Immunity. 2010;32:317–28.View ArticlePubMedGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38:576–89.View ArticlePubMedPubMed CentralGoogle Scholar
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.View ArticlePubMedPubMed CentralGoogle Scholar
- Wei G-H, Badis G, Berger MF, Kivioja T, Palin K, Enge M, Bonke M, Jolma A, Varjosalo M, Gehrke AR, Yan J, Talukder S, Turunen M, Taipale M, Stunnenberg HG, Ukkonen E, Hughes TR, Bulyk ML, Taipale J. Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo. EMBO J. 2010;29:2147–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Abbas T, Dutta A. p21 in cancer: intricate networks and multiple activities. Nat Rev Cancer. 2009;9:400–14.View ArticlePubMedPubMed CentralGoogle Scholar
- DeKoter RP, Singh H. Regulation of B lymphocyte and macrophage development by graded expression of PU.1. Science. 2000;288:1439–41.View ArticlePubMedGoogle Scholar
- Tenen DG. Disruption of differentiation in human cancer: AML shows the way. Nat Rev Cancer. 2003;3:89–101.View ArticlePubMedGoogle Scholar
- ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.View ArticleGoogle Scholar
- Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, Jaenisch R, Wagschal A, Feil R, Schreiber SL, Lander ES. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell. 2006;125:315–26.View ArticlePubMedGoogle Scholar
- Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, Hanna J, Lodato MA, Frampton GM, Sharp PA, Boyer LA, Young RA, Jaenisch R. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci USA. 2010;107:21931–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Liang G, Lin JCY, Wei V, Yoo C, Cheng JC, Nguyen CT, Weisenberger DJ, Egger G, Takai D, Gonzales FA, Jones PA. Distinct localization of histone H3 acetylation and H3-K4 methylation to the transcription start sites in the human genome. Proc Natl Acad Sci USA. 2004;101:7357–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Zentner GE, Tesar PJ, Scacheri PC. Epigenetic signatures distinguish multiple classes of enhancers with distinct cellular functions. Genome Res. 2011;21:1273–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Witt O, Sand K, Pekrun A. Butyrate-induced erythroid differentiation of human K562 leukemia cells involves inhibition of ERK and activation of p38 MAP kinase pathways. Blood. 2000;95:2391–6.PubMedGoogle Scholar
- Grignani F, De Matteis S, Nervi C, Tomassoni L, Gelmetti V, Cioce M, Fanelli M, Ruthardt M, Ferrara FF, Zamir I, Seiser C, Grignani F, Lazar MA, Minucci S, Pelicci PG. Fusion proteins of the retinoic acid receptor-alpha recruit histone deacetylase in promyelocytic leukaemia. Nature. 1998;391:815–8.View ArticlePubMedGoogle Scholar
- Minucci S, Nervi C, Lo Coco F, Pelicci PG. Histone deacetylases: a common molecular target for differentiation treatment of acute myeloid leukemias? Oncogene. 2001;20:3110–5.View ArticlePubMedGoogle Scholar
- Morey L, Helin K. Polycomb group protein-mediated repression of transcription. Trends Biochem Sci. 2010;35:323–32.View ArticlePubMedGoogle Scholar
- Rada-Iglesias A, Bajpai R, Swigut T, Brugmann SA, Flynn RA, Wysocka J. A unique chromatin signature uncovers early developmental enhancers in humans. Nature. 2011;470:279–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Nerlov C, Querfurth E, Kulessa H, Graf T. GATA-1 interacts with the myeloid PU.1 transcription factor and represses PU.1-dependent transcription. Blood. 2000;95:2543–51.PubMedGoogle Scholar
- Zhang P, Zhang X, Iwama A, Yu C, Smith KA, Mueller BU, Narravula S, Torbett BE, Orkin SH, Tenen DG. PU.1 inhibits GATA-1 function and erythroid differentiation by blocking GATA-1 DNA binding. Blood. 2000;96:2641–8.PubMedGoogle Scholar
- Yun WJ, Kim YW, Kang Y, Lee J, Dean A, Kim A. The hematopoietic regulator TAL1 is required for chromatin looping between the β-globin LCR and human γ-globin genes to activate transcription. Nucleic Acids Res. 2014;42:4283–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Song L, Crawford GE. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb Protoc. 2010;2010:pdb.prot5384.Google Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.View ArticlePubMedPubMed CentralGoogle Scholar
- Boyle AP, Song L, Lee B-K, London D, Keefe D, Birney E, Iyer VR, Crawford GE, Furey TS. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res. 2011;21:456–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.View ArticlePubMedPubMed CentralGoogle Scholar
- Machanick P, Bailey TL. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics. 2011;27:1696–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Johnson DS, Mortazavi A, Myers RM, Wold B. Genome-wide mapping of in vivo protein-DNA interactions. Science. 2007;316:1497–502.View ArticlePubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Ramírez F, Dündar F, Diehl S, Grüning BA, Manke T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 2014;42(Web Server issue):W187–91.View ArticlePubMedPubMed CentralGoogle Scholar