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Doxorubicin is an anthracycline DNA intercalator that is among the most commonly used anti-cancer drugs . Doxorubicin causes DNA double-strand breaks in rapidly dividing cells, although whether it also affects general chromatin properties is unknown. Here, we use a metabolic labeling strategy to directly measure nucleosome turnover  to examine the effect of doxorubicin on chromatin dynamics in squamous cell carcinoma cell lines derived from genetically defined mice. We find that doxorubicin enhances nucleosome turnover around gene promoters, and turnover correlates with gene expression level. Consistent with a direct action of doxorubicin, enhancement of nucleosome turnover around promoters gradually increases with time of exposure to the drug. Interestingly, enhancement occurs both in wild-type cells and in cells lacking either the p53 tumor suppressor gene or the master regulator of the DNA damage response, Atm, suggesting that doxorubicin action on nucleosome dynamics is independent of the DNA damage checkpoint. In addition, another anthracycline drug, aclarubicin, shows similar effects on enhancing nucleosome turnover around promoters. Our results suggest that anthracycline intercalation promotes nucleosome turnover around promoters by its effect on DNA topology, with possible implications for mechanisms of cell killing during cancer chemotherapy.
In order to directly measure nucleosome turnover, our lab developed the CATCH-IT (Covalent Attachment to Tags to Capture Histones and Identify Turnover) method , whereby newly synthesized proteins are labeled with a methionine analog, azidohomoalanine (Aha), which allows coupling to biotin for affinity capture of purified chromatin. Extraction of DNA from the resulting newly synthesized nucleosomes is followed by genome-wide mapping using tiling microarrays or short-read massively parallel sequencing . We previously applied CATCH-IT to investigate nucleosome changes during the heat-shock response in Drosophila cells, and discovered that global changes in turnover were associated with transcription without corresponding changes in nucleosome occupancy . The high sensitivity of the CATCH-IT assay to changes in genome-wide chromatin dynamics encouraged us to examine other possible chromatin perturbations using CATCH-IT, such as those that are induced by chemotherapeutic agents. As doxorubicin (also called adriamycin) is a widely used anti-cancer drug that interacts directly with DNA, we wondered whether chromatin dynamics are affected by doxorubicin in cancer cells. Accordingly, we applied CATCH-IT to genetically defined mouse squamous cell carcinoma (SCC) cell lines before and after doxorubicin treatment. These cell lines were derived from SCCs induced by the two step 7,12-dimethylbenz[α]anthracene (DMBA) and 12-O-tetradecanoylphorbol-13-acetate (TPA) carcinogenesis protocol applied to the dorsal skin of mice [5, 6]. Using this protocol, tumors consistently harbor activating mutations in the oncogene Hras1.
We first applied CATCH-IT to mouse SCC cells . Sequencing of exons 2-11 of the p53 gene revealed no mutations, and these cells are designated p53 wild-type MSCC-CK1 cells. CATCH-IT and input DNAs were labeled with Cy5 and Cy3, respectively, and were hybridized together to high-density mouse promoter arrays. Gene 5’ or 3’ ends analysis was performed by aligning all annotated mouse genes at transcriptional start sites (TSSs) or transcriptional end sites (TESs) and calculating average nucleosome turnover in 50-bp intervals over a range of 3 kb upstream and 3 kb downstream, using a 200-bp sliding window. As shown in Figure 1A, nucleosome turnover is most rapid around TSSs, decreasing towards gene bodies, consistent with results in Drosophila S2 cells . Additionally, strong enrichment was observed upstream of TSSs (Figure 1A). This double-peak pattern is in a good agreement with profiles of H3.3, H2A.Z, and many histone modifications associated with active genes in both mouse and human cells [8-11], possibly driven by bi-directional transcription of mammalian promoters. Examples of CATCH-IT profiles are shown for two transcriptionally active genes (p21 and Mdm2), where strong CATCH-IT signals were observed around TSSs in the Aha sample but not in the Met control (Figure 1B).
We had previously shown that nucleosome turnover is correlated with gene expression in Drosophila cells [2, 4]. To ascertain whether there is a similar relationship between turnover and gene expression in SCC cells, we used microarrays to obtain gene expression profiles. We first compared untreated SCC cells with cells treated with 0.34 μM doxorubicin for 18 hours . Of the total 18,233 genes, only 192 genes were upregulated more than two-fold with doxorubicin treatment, and 61 genes were downregulated more than two-fold. Although a minor effect on gene expression was detected upon doxorubicin treatment, Gene Ontology (GO) analysis revealed that genes related to the DNA damage response and cell cycle arrest were significantly upregulated (Figure S1).
To examine the relationship between nucleosome turnover and gene expression, genes were grouped into five quintiles according to their expression levels and 5’ ends analysis was done for the genes in each quintile. In addition, we generated a heat map of nucleosome turnover over the ±3-kb region surrounding the TSSs. We found that nucleosome turnover is correlated with gene expression level before and after doxorubicin treatment, consistent with previous findings in Drosophila cells [2, 4] (Figures 2A and 2B). Therefore, nucleosome turnover is likely to be coupled to transcription in mammalian cells, as it is in Drosophila.
To determine whether doxorubicin treatment affects chromatin dynamics, the difference in nucleosome turnover between treated and untreated cells around 5’ end and 3’ end was displayed as a heat map, ordered by decreasing gene expression. Interestingly, doxorubicin treatment resulted in increased nucleosome turnover almost exclusively at active genes, with the highest levels seen on both sides of the most active gene promoters but not around 3’ ends (Figures 3A and S2A). To identify the genes showing enhancement or reduction in turnover in an unbiased manner, unsupervised k-means clustering was performed to separate genes into two groups. About 43% of all mouse annotated genes showed enhancement of nucleosome turnover after doxorubicin treatment (Group 1) (Figure 3A). Because only ~1% of all genes showed more than a twofold increase in gene expression with doxorubicin treatment, we conclude that this global enhancement in nucleosome turnover around active gene promoters is not a consequence of doxorubicin-induced transcriptional upregulation, but rather is a general feature of transcriptional activity in doxorubicin-treated cells.
It is thought that intercalation of doxorubicin into DNA traps topoisomerase II when it is covalently bound to DNA during double-strand (ds) cleavage, and this results in ds breaks that must be repaired to avoid chromosome breakage . These breaks induce a strong DNA damage response leading to upregulation of the p53 transcription factor and induction of cell cycle arrest or apoptosis [12, 14]. Cells lacking p53 are defective in cell cycle arrest or apoptosis following DNA damage. As p53 is frequently mutated in human squamous cell cancer [15, 16] and it is a central player in the cellular response to doxorubicin [17-19], we asked whether doxorubicin-induced increases in nucleosome turnover are dependent on the p53 protein. For this, we developed an SCC cell line derived from a mouse carrying conditional deletion in the p53 gene [7, 20] designated MSCC-CK4 Trp53 -/- (Cre + Trp53 lox/lox) referred to as p53 -/-. The cre/lox mediated deletion of p53 was confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) (Figure S2B). In addition, induction of a well-known p53 transcriptional target, p21/WAF1 , was observed in p53 WT but not in p53 -/- SCC cells after 18 hours of doxorubicin treatment (Figure S2C), further confirming the absence of p53 in p53 -/- SCC cells. Furthermore, doxorubicin treatment caused a cell cycle arrest in the p53 WT, demonstrating that p53 is active in these cells (Figure S2D). We performed CATCH-IT on p53 -/- SCC cells and generated gene expression profiles before and after doxorubicin treatment for 18 hours as we had done for p53 WT SCC cells. Similar to the results of expression profiling for p53 WT cells, only 119 of 18,233 genes were upregulated more than two-fold and only 97 genes were downregulated more than two-fold. As expected for p53-/- cells, GO analysis revealed a lack of DNA-damage response genes among those significantly upregulated (Figure S1). Gene 5’ ends analysis for the genes in each expression quintile, and heat maps ordered by gene expression level, were plotted for doxorubicin-treated and untreated p53 -/- SCC cells. These plots revealed that in p53 -/- SCC cells, nucleosome turnover correlates with gene expression level whether or not the cells were treated with doxorubicin (Figure 2C and 2D).
Similar to what we had observed for p53 WT SCC cells, nucleosome turnover increased around active gene promoters, but not around 3’ ends, after doxorubicin treatment in about 53% of all mouse annotated genes (Group 1) in p53 -/- SCC cells (Figure 3B). Therefore, p53 is not required for elevation of nucleosome turnover by doxorubicin.
In the above experiments, enhancement of nucleosome turnover was assayed 18 hours after doxorubicin treatment, and it is possible that effects on nucleosome turnover were indirect consequences of intermediate events, such as a global DNA damage response or induction of genes that themselves enhance nucleosome turnover. If instead, doxorubicin intercalation directly causes nucleosome turnover, then we would expect to see turnover changes gradual increases with time of exposure to the drug. Therefore, we treated WT SCC cells with doxorubicin and performed CATCH-IT on samples at 0, 1, 4, 8, and 24 hours later. As expected for a direct effect of doxorubicin on nucleosome turnover, we found that doxorubicin enhanced nucleosome turnover gradually over the time course (Figures 4A, 4B and S3).
The DNA ds breaks induced by doxorubicin treatment activate the Atm kinase that is the master regulator of the DNA damage response . Among other targets, ATM phosphorylates p53  and H2A.X  at DNA ds breaks and recruits repair proteins to religate the breaks [25-27]. Therefore, we wondered whether Atm is required for the genome-wide enhancement of nucleosome turnover upon doxorubicin treatment. We derived an Atm -/- SCC cell line, MSCC-CK104, from a DMBA/TPA induced SCC originating in a mouse with a germline mutation in Atm . CATCH-IT was performed at 0, 1, 4, 8, and 24 hours after doxorubicin treatment. As shown in Figures 4C and 4D, nucleosome turnover increased gradually upon doxorubicin treatment, indicating that doxorubicin-induced elevation of nucleosome turnover is independent of Atm. We conclude that the DNA damage response is not required for the increase in nucleosome turnover induced by doxorubicin.
Doxorubicin is thought to trap covalently bound topoisomerase II at DNA ds breakage sites to prevent religation especially when low doses (< 1 μM) are used . Breakage by this mechanism is thought to rapidly induce the DNA damage response . To investigate whether topoisomerase II mediated DNA ds breakage is required for the enhancement of nucleosome turnover around promoters, we used another anthracycline drug, aclarubicin (also called aclacinomycin A), which is a DNA intercalator that inhibits topoisomerase activity prior to cleavage, thus not directly resulting in DNA breakage . We first treated WT SCC cells with a series of doses (0.1 μM, 0.2 μM, 0.4 μM, 0.8 μM, 1.6 μM, 3.2 μM, and 6.4 μM) for 24 hours to test for toxicity of aclarubicin. Cell death was observed when treated with 0.8 μM or higher doses (data not shown), consistent with the toxicity seen in human small cell lung cancer cell lines . CATCH-IT was performed on WT SCC cells after 24 hours aclarubicin treatment at three different doses (0.1 μM, 0.2 μM and 0.4 μM). Similar to what we had observed for doxorubicin treatment, aclarubicin enhanced nucleosome turnover around promoters of active genes, especially downstream of promoters (Figures 4E and 4F). We conclude that direct topoisomerase II mediated DNA breakage is not required for the observed enhancement of nucleosome turnover.
In summary, we have identified a novel role of widely used chemotherapeutic anthracycline drugs: enhancement of nucleosome turnover around promoters of active genes. This enhancement occurs despite almost undetectable increases in gene expression, and is seen in cells that lack both p53 and Atm, which have both been linked to doxorubicin sensitivity . What is the mechanism by which doxorubicin or aclarubicin induces nucleosome turnover around active gene promoters? As DNA intercalators, anthracycline drugs untwist DNA, which in a topologically constrained situation results in positive supercoiling. During transcription initiation, melting of DNA by RNA polymerase would cause the propagation of positive supercoiling on both sides of the promoter, resulting in nucleosome unwrapping . Melting would facilitate intercalation of anthracyclines, thus enhancing positive supercoil propagation and nucleosome unwrapping. In addition, topoisomerases are able to relax positively supercoiled DNA supercoils, thus inhibition of topoisomerase II by doxorubicin or aclarubicin may also elevate positive torsional stress generated by RNA polymerase II movement [31, 32]. In either case, enhancement of unwrapping should increase nucleosome eviction and replacement that normally occurs during active transcription. In this way, the intercalation of anthracyclines will increase turnover independent of changes in gene expression or the DNA damage response.
Based on our new findings, we speculate that enhancement of nucleosome turnover at promoters by anthracycline drugs can result in increased DNA fragility around promoters. Loss of nucleosomes at human telomeres is thought to be responsible for DNA damage and genome instability associated with ALT (alternative lengthening of telomeres) caused by mutations in the H3.3/DAXX/ATRX nucleosome assembly pathway . Considering that the doses we used in our experiments (0.1 to 0.4 μM) are much lower than doses used in cancer chemotherapy (~5 μM ), DNA fragility and breakage caused by nucleosome turnover around promoters might contribute to cancer cell killing. Our model might also account for the observation that aclarubicin causes DNA damage in a Drosophila in vivo assay , despite its inhibition of topoisomerase II activity prior to DNA cleavage. Understanding the molecular effects of anthracycline drug treatment on nucleosome dynamics may provide new insights into the design of anti-cancer therapies that combine new classes of drugs that target chromatin regulators with traditional chemotherapeutic agents.
Squamous cell carcinomas were induced by treatment with carcinogens DMBA/TPA on the dorsal skin of mice carrying germline mutations in Atm, or p53 as well as wild type mice . Tumors arising from this protocol uniformly harbor activating mutations in the oncogene Hras1. When tumors underwent malignant papilloma to carcinoma conversion, tumors were explanted into culture. Carcinoma cells from the above genotypes were cultured and frozen down at early passage.
Mouse squamous cell carcinoma cell lines (MSCC-CK1, WT; MSCC-CK4, p53 -/-; MSCC-CK-04, Atm -/-) were cultured in Dulbecco's Modified Eagle Medium (DMEM) media (Cat# 11965-092, Invitrogen) supplemented with 10% FBS and 1% penicillin-streptomycin at 37 °C.
WT SCC cells after 18 hours doxorubicin treatment were washed in 1X PBS and fixed in 70% ethanol for about 2 h. After fixation, cells were washed in 1X PBS, resuspended in 1 ml of propidium iodide (PI) staining solution (1X PBS, 0.1% Triton X-100, 20 ug/ml PI, 100 mg/mL DNase-free RNase A), and incubated at room temperature for 30 min. Flow cytometric analysis was performed utilizing a BD FACS Canto II (Becton Dickinson). Cell cycle analysis was performed with CellQuest software (Becton Dickinson).
CATCH-IT was performed as previously described  with some modifications. Briefly, tissue culture medium was replaced with DMEM media without methionine (Cat# 21013-024, Invitrogen) supplemented with 1X glutamine, 0.2 mM L-cystine, 10% FBS, and 1% penicillin-streptomycin and cells were grown at 37 °C for starvation for 30 minutes, followed by 30 minutes incubation at 37 °C with 4 mM azidohomoalanine (Cat# 63669, Anaspec) added to the media. Cells were then harvested, washed with 1X PBS, and resuspended in ice-cold TM2 buffer (10 mM Tris, pH=7.5, 2 mM MgCl2). Cells were lysed using 0.2% of NP-40 and nuclei were isolated, followed by digestion of micrococcal nuclease for 10 minutes to produce mostly mononucleosomes. Biotin coupling, chromatin extraction, streptavidin pulldown, urea wash, and DNA isolation were then performed as previously described .
Input and CATCH-IT DNAs were amplified using whole genome amplification kit (Cat# WGA2, Sigma), followed by labeling using Cy3 and Cy5 heptamers according to the Roche Nimblegen labeling protocol. Labeled samples were hybridized together to mouse 2.1 million probe promoter arrays (Roche Nimblegen). For data analysis, arrays were corrected for dye bias  and ends analysis, k-mean clustering, and heat maps were performed as previously described .
Total RNA was isolated using the Qiagen RNeasy kit. For RT-PCR analysis, first strand cDNA was synthesized using Superscript II reverse transcriptase (Invitrogen), followed by PCR by using a set of p53 primer (Forward: 5’ GCTTCTCCGAAGACTGGATGACT, Reverse: 5’ GATTGTGTCTCAGCCCTGAAGTCA). For expression array analysis, first strand and second strand cDNAs were synthesized and double-stranded cDNAs were labeled according to the NimbleGen protocol for expression arrays. Labeled samples were hybridized to NimbleGen mouse 385K expression arrays. All samples were normalized together using the Robust Multichip Average (RMA) analysis function provided by DEVA software (NimbleGen). Gene ontology analysis was performed by using GeneCodis [38-40].
We thank Kay E. Gurley for isolating mouse SCC cells; Russell Moser for help with cell culture and cell cycle analysis; Neil G. Shafer and Andy Marty for processing arrays; Jorja Henikoff for bioinformatics. This work was supported by NIH Grant R01 ES020116 (S.H. and C.J.K.), the Howard Hughes Medical Institute (S.H.), and NIH Grant U54 CA143862 (S.H.).
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Supplemental information with this article is available online. The microarray data from this study have been submitted to the NCBI Gene Expression Omnibus under accession no. GSE43753.