DNMT inhibitors reverse a specific signature of aberrant promoter DNA methylation and associated gene silencing in AML
© Lund et al.; licensee BioMed Central 2014
Received: 12 March 2014
Accepted: 9 July 2014
Published: 30 August 2014
Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are neoplastic disorders of hematopoietic stem cells. DNA methyltransferase inhibitors, 5-azacytidine and 5-aza-2′-deoxycytidine (decitabine), benefit some MDS/AML patients. However, the role of DNA methyltransferase inhibitor-induced DNA hypomethylation in regulation of gene expression in AML is unclear.
We compared the effects of 5-azacytidine on DNA methylation and gene expression using whole-genome single-nucleotide bisulfite-sequencing and RNA-sequencing in OCI-AML3 cells. For data analysis, we used an approach recently developed for discovery of differential patterns of DNA methylation associated with changes in gene expression, that is tailored to single-nucleotide bisulfite-sequencing data (Washington University Interpolated Methylation Signatures). Using this approach, we find that a subset of genes upregulated by 5-azacytidine are characterized by 5-azacytidine-induced signature methylation loss flanking the transcription start site. Many of these genes show increased methylation and decreased expression in OCI-AML3 cells compared to normal hematopoietic stem and progenitor cells. Moreover, these genes are preferentially upregulated by decitabine in human primary AML blasts, and control cell proliferation, death, and development.
Our approach identifies a set of genes whose methylation and silencing in AML is reversed by DNA methyltransferase inhibitors. These genes are good candidates for direct regulation by DNA methyltransferase inhibitors, and their reactivation by DNA methyltransferase inhibitors may contribute to therapeutic activity.
Myelodysplastic syndrome (MDS) is a collection of neoplastic disorders of hematopoietic stem cells (HSCs) characterized by inefficient hematopoiesis, peripheral blood cytopenia, morphologic dysplasia, and susceptibility to acute myeloid leukemia (AML). AML is characterized by accumulation of immature myeloid ‘blasts’ in the bone marrow and peripheral blood . Accrual of epigenetic abnormalities likely contributes to development of MDS and AML. For example, promoter DNA hypermethylation and associated silencing of tumor suppressor gene CDKN2b, encoding p15INK4b, has been reported in up to 80% of AML . Accordingly, there has been substantial interest in application of so-called epigenetic therapies to combat MDS and AML, most notably, DNA methylation inhibitors and histone deacetylase inhibitors .
In the US, two DNA methyltransferase inhibitors (DNMTi), 5-Azacitidine (AzaC) and 5-aza-2'-deoxycytidine (decitabine), are licenced for therapeutic use in MDS/AML . In the UK, AzaC is approved for use in some adults with MDS, chronic myelomonocytic leukemia or AML. Decitabine is not approved for use in the UK. These drugs act as ‘fraudulent bases’ mimicking cytosine, and once incorporated into DNA in S phase are able to trap DNMTs. Trapped DNMTs are degraded by the proteasome resulting in passive hypomethylation of the DNA during subsequent replication cycles .
Initial studies focused on the DNA hypomethylating activity of DNMT inhibitors as being the basis of their therapeutic effects. Approximately 60% of human gene promoters are associated with CpG rich regions termed CpG islands . CpG islands are typically maintained free of DNA methylation and this is permissive for gene expression. However, in many human cancers, a proportion of CpG islands is hypermethylated and this is linked to silencing of some tumor suppressor genes . Hypermethylation of regions of lower CpG density adjacent to islands, termed CpG island shores, are also linked to silencing .
Accordingly, it has been proposed that DNMT inhibitors cause hypomethylation of promoter regulatory regions of tumor suppressor genes silenced by DNA methylation, thereby reactivating cell growth arrest and differentiation. For example, treatment of AML cell lines and patient blasts with decitabine induced hypomethylation and reactivation of expression of p15INK4b . However, other studies have failed to confirm a strong correlation between promoter and CpG island hypomethylation and activation of gene expression ,. Indeed, in addition to causing DNA hypomethylation, DNMTi cause damage to DNA, and AzaC is also incorporated into RNA, and these activities might also contribute to their biological effects .
To date, studies investigating the relationship between AzaC- and decitabine-induced DNA hypomethylation and gene expression have employed analysis methods that fail to survey methylation across the entire epigenome –. For example, frequently used Illumina 27 K and 450 K arrays sample only a small number of CpGs per CpG island, and only 27,000 and 450,000 respectively of the approximately 56 million cytosines in CpG context in the genome (that is, approximately 28 million dyad CpGs). To date, no study has compared methylation changes across all CpGs with changes in gene expression. Therefore, we set out to investigate the effects of AzaC in a model AML cell line, using more comprehensive whole genome bisulfite sequencing (WGBS) to map the DNA methylation landscape in AzaC untreated and treated cells, and employing a sophisticated computational approach tailored to whole genome data to unveil relationships between altered methylation and altered gene expression (Washington University Interpolated Methylation Signatures (WIMSi)). By this approach, we identified a set of genes whose is expression is aberrantly repressed by DNA methylation in AML and reversed by DNMTi, perhaps contributing to therapeutic effects of DNMTi.
Accordingly, AML3 cells were treated three times at 24-h intervals with 0.5 μM AzaC in triplicate and harvested 96 h after the first treatment. Genomic DNA was purified from two replicates and subjected to whole genome bisulfite sequencing (in excess of 15× coverage of each replicate), yielding a total of 237Gb of sequence data (Additional file 2: Table S1). In parallel, RNA was purified from three replicates and analyzed by RNA seq of poly (A) RNA. Analysis of the DNA methylation data confirmed that individual replicates of untreated and treated cells were highly concordant (Additional file 2: Tables S2 and S3), with paired Spearman coefficients in the range of 0.79 to 0.94 between like samples (Additional file 2: Table S4). Importantly, in untreated cells there was also a strong correlation in promoter CpG methylation and gene expression between AML3 and primary AML cells (data from TCGA); Spearman correlation coefficient of 0.79 and 0.85 for CpG methylation and gene expression, respectively (Additional file 3: Figure S2). Absolute levels and changes (between untreated and treated) in methylation at non-CpG sites, CHG, and CHH (defined in Material and Methods), were negligible (untreated to treated, 0.44% to 0.40% (CHG) and 0.43% to 0.39% (CHH)) (Additional file 2: Table S5 and S6), compared to the frequency of failed bisulfite conversion of unmethylated C to U (Additional file 2: Table S7). Of 56,328,604 cytosines in a CpG context in the hg18 reference genome, 6,679,526 showed lower methylation (hypomethylated) in AzaC treated cells, compared to untreated cells (FDR corrected P value level of 0.05) (Figure 1g) (Additional file 2: Table S8). One hundred and ninety-two individual CpGs gained DNA methylation (hypermethylated) in AzaC treated cells (FDR corrected P value level of 0.05) (Figure 1g, Additional file 2: Table S8). As expected, analysis revealed an overall decrease in cytosine methylation in AzaC treated cells (Figure 1h), from 66.97% to 32.32% methylcytosine basecalls at reference CpG sites.
Although AzaC induced a relatively uniform approximately two-fold decrease in methylation across the whole genome, the absolute difference in percent methylation varied quite widely, depending on the level of methylation in untreated cells (Figure 5c). However, a dot plot of absolute difference in percent methylation at CpG islands versus difference in gene expression between untreated and treated cells failed to show a correlation between altered methylation and altered expression (Figure 5d and Additional file 6: Figure S4a). Similarly, there was not a strong link between absolute difference in percent methylation at CpG island shores and altered expression (Figure 5e and Additional file 6: Figure S4b). This is the case whether changes in expression are assessed by fold change (Additional file 6: Figure S4a, b) or absolute change in expression (Figure 5d, e). Finally, genes whose expression increased significantly after AzaC did not show a greater decrease in promoter methylation than all other genes (Figure 5f). Together, these results do not support the hypothesis that CpG island and/or shore hypomethylation is primarily responsible for activation of gene expression by AzaC.
To further interrogate the significance of this gene set, we repeated the same differential methylation/expression analysis, but compared the same 1,147 genes to the difference in methylation between AML3 cells and normal human hematopoietic stem and progenitor cells (HSPC) previously reported by Hannon and co-workers . This directly tested whether there are methylation changes from HSPC to AML3 cells that correlate with gene expression changes in AML3 cells upon treatment with 5-AzaC. This analysis identified 336 genes that exhibited a similar methylation difference between AML3 and HSPC (Figure 6e and Additional file 5: Dataset 4, ‘Split WIMSi_336’). Specifically, these show more methylation in AML3 compared to HSPC, most marked 5′ and 3′ to the TSS (Figure 6e). Remarkably, all of these genes were upregulated on AzaC treatment of AML3 (Additional file 5: Dataset 4). Of the 246 genes showing decreased methylation on AzaC treatment of AML3 and the 336 genes showing increased methylation between HSPC and AML3, 157 were in common, a significant (P <1 × 10-37) 2.18-fold enrichment over random (Figure 6f and Additional file 5: Dataset 5, ‘Overlap_157’). Thus, genes in the AML3_AzaC_WIMSi_246 gene set tend to show increased promoter methylation in AML3 compared to HSPC.
Accordingly, we reasoned that genes in the AML3_AzaC_WIMSi_246 gene set might tend to be silenced in AML3 compared to HSPC. To test this, we identified all those genes whose expression is downregulated in AML3 compared to HSPC, based on two previously published HSPC gene expression datasets ,, and whose expression is significantly regulated by AzaC treatment of AML3 (Additional file 5: Dataset 6, ‘HSPC_AML_Expr both Down_259’). Conversely, we identified all those genes whose expression is upregulated in AML3 compared to HSPC and whose expression is significantly regulated by AzaC treatment of AML3 (Additional file 5: Dataset 8, ‘HSPC_AML_Expr both Up_410’). We then assessed overlap between the AML3_AzaC_WIMSi_246 gene set and these two gene sets. A substantial proportion of the 246 gene set were silenced in AML3 cells compared to normal HSPC (Additional file 5: Dataset 7, ‘Overlap_84’, 84 gene overlap represents a 1.5-fold enrichment over random (P < 3 × 10-6)), but a much smaller proportion was activated in AML3 cells compared to HSPC (Additional file 5: Dataset 9, ‘Overlap_56’, 56 gene overlap represents a 1.57-fold depletion over random (P <1 × 10-6)). Consistent with this, the AML3_AzaC_WIMSi_246 genes were expressed at relatively lower levels in AML3 than in HSPC (Figure 6i). In sum, the AML3_AzaC_WIMSi_246 gene set tends to be silenced in AML3 compared to HSPC.
Results of IPA analysis of 246 genes identified by WIMSi in AzaC-treated AML3 cells (AML3_AzaC WIMSi (246))
Molecular and cellular function
Cell death and survival
Cellular function and maintenance
Cellular growth and proliferation
Cell-to-cell signaling and interaction
To date, most WGBS analyses of human cancers have been performed on solid tumors –. DNA methylation analyses in AML have tended to employ less comprehensive methods, such as Illumina arrays, reduced representation bisulfite sequencing (RRBS) and methylated DNA immunoprecipitation (MeDIP)-seq. While these studies obviously have their own important strengths, such as throughput of multiple primary samples from patients ,–, no previous study has performed WGBS on primary AML blasts or cell lines. Nor have previous studies examined the effects of DNMTi on DNA methylation and gene expression, employing such comprehensive methods as WGBS and RNA-seq. This is important because DNMTi are used in the clinic, yet the relationship between their effects on DNA methylation and gene expression is unclear.
Accordingly, we report here the first WGBS analysis of DNA methylation in an AML cell line. We also report the effects of AzaC treatment on DNA methylation and gene expression. Based on simple quantitative analyses of methylation at promoters, CpG islands, and shores, there was no significant correlation between loss of DNA methylation and change in gene expression. However, a more sophisticated search algorithm identified a subset of upregulated genes with a signature loss of methylation flanking the TSS. Remarkably, many of these same genes gained methylation in AML3 cells compared to normal hematopoietic stem and progenitor cells and this was typically accompanied by their downregulation in AML3 cells. These genes have functions in cell movement, cell death and survival, and cell growth and proliferation and are preferentially upregulated on decitabine treatment of patient-derived primary AML blasts. Hence, these genes are candidates for genes whose expression is aberrantly repressed by DNA methylation in AML and reversed by DNMTi treatment.
Globally, the DNA methylation landscape of proliferating AML cells, without AzaC treatment, is reminiscent of other solid tumor epigenomes analyzed by WGBS –; large regions of near complete DNA methylation are interspersed with regions of partial methylation and much more focal regions that are largely depleted of DNA methylation. As in normal genomes, regions lacking DNA methylation are predominantly at promoters containing CpG islands . However, as is typical of cancer genomes, a proportion of CpG islands is methylated ; in AML3 cells, about 12% of CpG islands overlapping gene TSS are methylated. Consistent with the link between CpG island hypermethylation and gene silencing , genes with methylated CpG islands tend to be expressed at a lower level than genes with unmethylated CpG islands. As shown previously in other cell types, at gene bodies there is general trend towards increasing gene body methylation with increasing expression, although this relationship breaks down at the most highly expressed genes ,,,,.
Treatment of AML3 cells with AzaC resulted in a near-uniform approxiamte 50% decrease in methylation across the whole genome. Only promoters, CpG islands and 5′ UTRs underwent a slightly more modest decrease, presumably because many of these regions are unmethylated or barely methylated even prior to AzaC treatment. In contrast, previous reports have suggested that AzaC and decitabine cause preferential loss of DNA methylation at some regions of the genome ,,. While differences between cell lines, primary blasts, and DNMTi treatment protocols might account for some differences, the 15× genome-wide coverage achieved in our study unambiguously reveals a uniform decrease across the genome in this study. Of course, a 50% decrease in methylation across the whole genome results in a greater absolute loss of methylation at highly methylated regions, compared to lowly methylated regions. In fact, from this perspective our data appear consistent with those of Ley and coworkers . In sum, at least in AML3 cells, there is no locus-specific preferential loss or retention of DNA methylation.
In contrast to the uniform loss of methylation across the genome, AzaC caused highly targeted gene-specific changes in gene expression. Specifically, 792 genes were significantly upregulated, and 426 downregulated. Since about 12% of genes with a CpG island overlapping the TSS harbor a methylated CpG island in AzaC-untreated cells, and since methylation is associated with decreased expression in these cells, we initially asked whether upregulation of gene expression was associated with CpG island hypomethylation. However, based on simple quantitative analyses of methylation at promoters, CpG islands, and shores, there was no significant correlation between loss of DNA methylation and change in gene expression. Previous studies, for example employing Illumina 450 K arrays or Sequenom technology targeted to selected genes, similarly failed to observe a strong link in this regard ,,–. Conceivably, failure to observe widespread upregulation of hypomethylated genes in in vitro studies depends, in part, on lack of appropriate in vivo signals and environmental factors. Obviously, this issue can only be addressed in humans in the context of clinical studies.
Regardless, a major advantage of WGBS data lies in the ability to perform unbiased searches for patterns of methylation (at the single nucleotide level) that correlate with expression ,. Indeed, a more sophisticated search algorithm, WIMSi , identified a subset of 246 upregulated genes with a shared signature loss of methylation flanking the TSS. Increased expression of these genes after AzaC, associated with a common methylation loss signature, tentatively suggests that these genes might be directly regulated by DNA methylation. In further support of this idea, many of these same genes gained methylation in AML3 cells compared to normal hematopoietic stem and progenitor cells and this was typically accompanied by their downregulation. Conceivably, these are genes whose is expression is aberrantly repressed by DNA methylation in AML and reversed by AzaC treatment of AML; the remainder of the genes regulated by AzaC in AML3 might be regulated as a secondary consequence of these candidate primary targets, or might be regulated via effects of AzaC on RNA or DNA damage. Consistent with these 246 genes being directly regulated by DNA methylation, these genes were also significantly and preferentially upregulated in decitabine-treated human primary AML blasts, compared to all genes and even genes regulated by AzaC in AML3 but lacking the loss of methylation signature characteristic of the 246 genes. Since AzaC and decitabine share the ability to induce DNA hypomethylation, but differ in some other respects such as AzaC’s preferential incorporation into RNA, this points to the 246 genes being regulated by DNA methylation. Underscoring the power of the WGBS and WIMSi analysis approach, the original authors of this decitabine study reported a limited correlation between change in DNA methylation and expression , likely because the array-based approach had insufficient coverage of promoter CpGs. The 246 AzaC-regulated genes are involved in processes the can reasonably drive anti-neoplastic activity, cell death, cell movement, and cell proliferation, supporting the view that reversal of silencing of these genes by DNMTi contributes to therapeutic activity. If so, methylation and/or expression status of these genes might have utility as biomarkers to predict and/or monitor patient response to DNMTi.
In sum, our WGBS and WIMSi data analysis approach has identified a set of genes whose methylation and silencing in AML is reversed by DNMTi. Known genetic determinants of altered genome methylation in AML, namely DNMT3A, IDH1, and IDH2 mutations, also preferentially impact methylation of this group of 246 genes in normal karyotype primary AML. These genes are good candidates for direct regulation by DNMTi, and their reactivation by DNMTi may contribute to therapeutic activity. Consequently, regulation of these genes by DNMTi might serve as a biomarker to monitor on-target activity of DNMTi in patients, and perhaps to predict therapeutic response. This study also demonstrates the ability of WIMSi to reveal relationships between DNA methylation and gene expression, based on single-nucleotide bisulfite-sequencing and RNA-seq data.
Materials and methods
Antibodies to the following targets were used in this study: actin (A1978, Sigma); DNMT1 (AB19905, Abcam); γH2AX (05-636, Millipore), PARP (9542P, Cell Signaling), and 5′-BrdU (347580, Becton Dickinson).
Cell culture, AzaC treatment, and cell viability assays
OCI-AML3 cells were obtained from DSMZ  and authenticated using Applied Biosystems AMPF/STR identifier kit (short tandem repeat multiplex assay). Cells were cultured in suspension in RPMI media supplemented with FBS 20%, Penicillin 5%, and L-Glutamine 5%, incubated at 37°C in humidified conditions and 5% CO2. To passage, cells were counted by hemocytometer and either centrifuged to a pellet and resuspended in fresh media or resuspended in 50% fresh media at a concentration of 0.5 × 106/mL every 2 to 3 days. For long-term storage cells were centrifuged to a pellet and resuspended in 1 mL freezing media (70% RPMI/20% FBS/10% DMSO) in batches of between 4 and 10 × 106/mL and stored in cryovials at -80°C.
AzaC was dissolved from lyophilised powder into culture-sterile DMSO to produce a 20 mM stock solution and stored as 15 μL aliquots at -80°C. For each new experiment a fresh aliquot of AzaC was diluted in RPMI/20% FBS to produce a 2 mM working stock which was diluted directly onto cells in culture.
Viable cells were counted using vital dye (trypan blue) to assess membrane integrity. All counts were performed in duplicate or triplicate. Where indicated, cell viability was also assessed using the indicator dye Rezasaurin to measure metabolic capacity of the cells, according to the manufacturer’s instructions (CellTiter-Blue, Promega).
Cell lysates were prepared by resuspending cells in freshly-boiled 1x Laemmli SDS sample buffer. Protein quantitation was performed by Bradford assay (Bio-Rad). Western blotting was performed as described previously .
Activated caspase assays
NucView 488 Caspase 3 substrate was used to detect caspase 3 activity as a reflection of apoptosis, according to manufacturer’s instructions (Biotium).
Cell cycle analysis
DNA content was measured by FACS in fixed, permeabilized 7-AAD-stained cells, as described previously . Cell cycle distribution was modelled from DNA content using FlowJo . Two color 7-AAD and 5-BrdU cell cycle analysis was performed as described previously .
CFSE staining was used to track cumulative cell divisions, as described previously .
Bisulfite sequencing of duplicate samples of genomic DNA from untreated and AzaC-treated AML3 cells was performed by BGI Tech.
DNA methylation data analysis/statistics
Processing and alignment of Bisulfite sequencing reads
Sequence reads are transformed in silico to fully bisulfite converted forward (C- > T) and reverse (G- > A) reads. The converted sequences are aligned against a converted human reference genome (hg18) in each combination: (1) forward (C- > T) reads aligned to forward (C- > T) genome, (2) reverse (G- > A) reads aligned to reverse (G- > A) genome, (3) forward (C- > T) reads aligned to reverse (G- > A) genome, (4) reverse (G- > A) reads aligned to forward (C- > T) genome. During the library preparation process genomic fragments representing alignments (3) and (4) are generated in the PCR step however they are not sequenced and only fragments corresponding to alignments (1) and (2) are read. As a result only uniquely matching alignments from (1) and (2) are retained. Alignment was performed using bismark  (version 0.5.1), based on the Bowtie  aligner (version 0.12.7). Unaligned reads resulting from the initial alignments from these libraries were trimmed 15 bp from the 5′ end in order to remove the adapter sequences, as some libraries contained these sequences and realigned.
For each aligned sequence tag, the original unconverted sequence is compared against the original unconverted reference genome and the methylation status is inferred. Sequences aligned from (1) and (2) give information on cytosines on the forward and reverse strands, respectively.
To remove PCR bias a deduplication step removes potential duplicate reads, where both ends of the fragment align to the same genomic positions on the same strand, only one of the reads is retained. To control for potential incomplete bisulfite treatment any reads with more than three methylated cytosines in non-CpG contexts are discarded. Additional file 2: Table S1 details the sequence yields at each stage of this process. Additional file 2: Tables S2, S5, and S6 detail the number of methylated and unmethylated bases sequenced within CpG, CHG, and CHH contexts (H = A, C, or T). Additionally, reads are mapped against the unmethylated lambda genome which was added to bisulfite sequencing reactions, giving the number of methylated bases allowing combined error rate resulting from sequencing errors and incomplete bisulfite conversion (Additional file 2: Table S7) to be determined.
Identification of methylated cytosines
Processed reads are aggregated on a per CpG basis (number of bases read supporting methylated/unmethylated status). At each reference cytosine the binomial distribution was used to identify whether a subset of the genomes within the sample were methylated at this location. Methylcytosines were identified while keeping the number of false positives below 1%. The probability of sequencing an observed number of cytosines given the identified error rates from the lambda alignment was determined using the binomial distribution. At each reference cytosine the number of trials in the binomial test was the read depth and the number of successes in the test was equal to the number of cytosines sequenced at that base. The probability was then corrected using the BH-FDR function and the list of CpG sites was thresholded at the 0.01 FDR level. See Additional file 2: Table S3.
A two-tailed Fisher’s exact test was used to identify CpGs that were differentially methylated. Only CpG determined using the binomial distribution in at least one sample, with at least three reads in at least one condition and with at least one read in the other condition, were considered for testing. P values were corrected using the BH-FDR function to control false positives at a rate of 5%. At this stage replicates were pooled for subsequent analysis.
Percentage CpG methylation in genomic windows
The percentage CpG methylation for a given window was calculated as the total number of methylated cytosines sequenced (at CpG sites for that window), divided by the total number of cytosines sequenced (at CpG sites for that window), multiplied by 100.
Difference and relative difference in CpG methylation
Difference in CpG methylation was defined as the difference between the treated and untreated percentage methylation. Relative difference in CpG methylation was defined as the difference in CpG methylation divided by the untreated percentage methylation.
The whole genome was split into non-overlapping 2 kb windows. For each window the start position was the nearest upstream reference CpG site to the end of the previous window. The percentage CpG methylation (AzaC untreated and treated), and relative difference in CpG methylation was determined for each window as previously described. Windows with no reads at all CpG sites, in either dataset were omitted.
The genomic features genic, exonic, 5′ UTR, and 3′ UTR were downloaded from Biomart (Ensembl genes version 54), and CpG Islands, SINEs, LINEs, STRs, LTRs, low complexity DNA, DNA transposons, and satellite repeats were downloaded from UCSC (hg18). Promoter regions were defined as TSS +/- 2 kb, and intronic regions as genic and non-exonic (overlapping exons were merged). CpG island shores were defined as the 2 kb flanking the CpG island, and CpG island shelves as the 2 kb flanking the shores .
The observed-to-expected CpG Ratio was calculated as: (CpGs in window * window length)/(Number of Gs in Window * Number of Cs in window) .
Determination of observed-to-expected overlaps
Overlaps were computed on a per base pair basis between two datasets (A and B). For every region within A the number of base pairs that were occupied by a region within B was computed. A permutation test was performed in order to determine the background genomic average expected overlap. One thousand sets of regions with properties (length distribution and chromosome distribution) equal to set B were generated. Randomly generated regions of B were prevented from being generated within unsequenced regions of the genome (as defined by UCSC mapping and sequencing track - gap). The overlap of A and B was repeated for each randomly generated set of B to determine the average expected random overlap. P values were estimated empirically from the observed overlaps of the randomly generated sets.
Methylation difference plots
Data bins were created for the integer values 0 to 100 and initialized as empty lists. For each CpG site within the pooled data the methylation percentage for AzaC untreated and treated samples was calculated, considering only CpGs with minimum coverage of 10 reads in at least one sample and three reads in both samples. Methylation percentage of the AzaC untreated sample was truncated to an integer value and used to select an appropriate data bin into which the treated sample methylation percentage was appended and the average of each bin was computed giving the corresponding final methylation percentage in treated cells. Difference in methylation was defined as the difference between final and starting average methylation percentages, and relative difference in methylation was defined as the difference in methylation divided by the average starting methylation percentages.
Smoothed methylation plots
The pooled whole genome methylation data were processed using the BSmooth algorithm from the bsseq (v0.8.0) package within Bioconductor as described in . A modified version of the bsseq plotSmoothData function was created to plot the smoothed data with individual CpGs shown (addPoints = True) but while suppressing the vertical ablines.
Composite plot generation
To generate composite gene profiles the Ensembl gene annotation (version 54) was used to identify the outermost transcript start and end sites (TSS and TES) for each of the Ensembl genes in the expression dataset. The area between these sites, for each gene, was classified as the gene body and split into 50 windows of equal size (each corresponding to 2% of the total gene body). Ten additional 1 kb windows were prepended (appended) to the start (end) of the gene to provide genomic context. The AzaC untreated percentage methylated CpGs for each window was determined.
Genes by decile plot
Expressed Genes (FPKM > 0) were ranked by expression and placed into appropriate ranked decile bins. For each composite plot window, the percentage methylation was averaged for all genes in each decile.
Gene body heatmap
Heatmaps were generated where the y-axis corresponds to Ensembl genes ordered by expression value in AzaC untreated RNA-seq dataset and the x-axis to percentage CpG methylation in the composite plot windows described previously and their position along genes. Genes for which greater than 50% of windows contained no reference CpGs were omitted.
TSS centered heatmaps
The area around each TSS was split into 200 bp windows spanning 5 kb upstream and downstream of the TSS. The percentage CpG methylation and observed-to-expected CpG ratio for each window was determined. Heatmaps were generated where the y-axis was as the gene body heatmap, and the x-axis to the described windows and their position relative to the TSS. Genes for which greater than 50% of windows contained no reference CpGs were omitted.
Samples were prepared for RNA sequencing (RNA-seq) according to the Illumina manufacturer’s instructions. Samples were sequenced using the Illumina GAIIX sequencer. For analysis, RNA-seq paired-end reads are aligned to the human genome (hg18) using a splicing-aware aligner (tophat). Reference splice junctions are provided by a reference transcriptome (Ensembl build 54), and novel splicing junctions are determined by detecting reads that span exons that are not in the reference annotation. Aligned reads are processed to assemble transcript isoforms, and abundance is estimated using the maximum likelihood estimate function (cuffdiff) from which differential expression and splicing can be derived. See Additional file 2: Table S9. RNA-seq gene expression data from HSPC cells ,, was processed using edgeR and an FDR cutoff of 0.1.
Comparison of gene expression and DNA methylation data
Comparison of AML and AML3 methylation
The Cancer Genome Atlas (TCGA) Illumina Human Methylation 450 k Array data for AML was downloaded from the TCGA data portal . The hg18 genomic coordinates for each CpG probe was identified using the Illumina Human Methylation 450 k Array annotation file obtained from GEO . To allow reasonable comparison, the TCGA methylation data were filtered to remove all CpGs where there were fewer than 10 reads in our pooled AML3 BS-Seq dataset. Next, for each gene (NCBI gene annotation 36.1): the mean beta value across all samples, for all CpGs within 2 kb of the TSS, was calculated. Finally the mean beta values were plotted against the mean% CpG methylation at the equivalent CpGs in the pooled AML3 data. To generate smoothed scatter plots the R (v3.0.2) function smoothscatter was used, using the transformation function x^1. Spearman correlations were calculated using the R function cor.test method = ‘spearman’.
Comparison of AML and AML3 expression
The Cancer Genome Atlas (TCGA) RNA-seq V2 data for AML was downloaded from the TCGA data portal . For each gene, the mean read count across all TCGA samples was calculated. For each gene, the mean read count across all AML3 RNA-seq samples was calculated using HTSeq . Finally expression values (log2) for AML were plotted against the equivalent values (log2) in AML3, using R (v3.0.2) as above.
Comparison of TCGA AML subtypes data preparation
The Cancer Genome Atlas (TCGA) Patient and Mutation data for AML were downloaded from the TCGA data portal . Mutation subtypes (number of samples) were identified as: WT/WT (120 genes), WT/mut (23 genes), mut/WT (26 genes), and mut/mut (28 genes) for DNMT3A and NPM1, respectively. Where WT corresponds to no somatic mutations identified, and mut corresponds to at least one somatic mutation identified in the sample. Additional mutation subtypes (number of samples) were identified as: NK DNMT3A R882+ (20 samples), NK DNMT3A R882- (82 samples), NK IDH1 R132+ (10 samples), NK IDH1 R132- (92 samples), NK IDH2 R140+ (12 samples), and IDH2 R140- (90 samples). Where NK indicates normal karyotype and +/- indicates the presence/absence of the specific mutation in the sample.
Using the TCGA illumina human methylation 450 k array data for AML
For each gene (NCBI gene annotation 37), the mean beta value across all samples of each mutation subtype, for all CpGs within 2 kb of the TSS, was calculated.
Using the RNA-seq V2 data for AML
For each gene, the mean read count across all samples of each mutation subtype was calculated.
Comparison of TCGA AML subtypes, scatter, and boxplots
To generate the TCGA AML mutation subtype scatter plots, the expression and methylation data described above were filtered to include only the 246 upregulated genes identified by WIMSi between AML3 and AML3 + AzaC. Scatter and boxplots were then generated comparing each mutation subset to its appropriate control, for both methylation and expression. For expression a log2 scale was used. Pearson Correlation Coefficients were calculated using Libre Office (v184.108.40.206). Wilcoxon tests were performed using the R (v3.0.2) function wilcox.test. To generate control random gene lists, the python (v2.7.4) library random.py was used.
Identification of altered methylation associated with altered gene expression by WIMSi
This was performed as described in . For this approach, methylation signatures were created for each gene by interpolating the differential methylation scores over a fixed window relative to the gene’s transcription start site (TSS). The geneset was significant changed genes (Additional file 5: Dataset 1, not including 71 small RNAs). A curve similarity metric, Dynamic Time Warping, was used to cluster genes together based on the shape of the differential methylation data in the window. We then identified clusters of genes with similar patterns that have coordinated differential expression. To generate a gene list, we executed this procedure over many 5 kb windows around the TSS and selectively combine the results.
Duplicates from AzaC treated and untreated were averaged and cross-referenced with Ensembl to determine the TSS. Genes with no sites having a differential methylation level of at least 0.2 within the window were removed. Genes for which methylation could not be interpolated due to a low number of sites in the region were removed. Signatures were clustered for 23 5 kb windows overlapping every 500 bp, covering the area from 8 kb upstream of the TSS to 8 kb downstream. Clusters that were significantly up- or downregulated compared to the overall set of expression values were then identified (Kolmogorov-Smirnov test; FDR <0.05 using Benajmini-Hochberg). The Fréchet distance was computed using a scaling factor between the x-axis (bp) and y-axis (differential methylation score) of 2,500 bp to 1 unit. A minimum cluster size of 10 and a minimum cluster purity of 0.85 were used. Gaussian smoothing (σ = 50) was applied to the signatures before clustering. A gene was included in the final list if at least two of its replicates were present in a significant cluster for at least three of the 5 kb windows.
Analysis of decitabine-treated AML cells
Processed expression values were downloaded from GEO (GSE40442). Expression values were averaged across probes for each Refseq gene. For each gene differential expression was computed as log2(decitabine treated expression/Mock treated expression). Differential expression values for each sample from the 246 AzaC responsive genes (AML3_AzaC_WIMSi_246) were compared to all genes using Fisher’s Exact test. To identify genes likely not regulated by methylation, we ran the WIMSi gene list tool and found 662 genes (out of 1,147 genes differentially expressed in AzaC treated AML3 cells) that were not flagged as significant in any window.
RNA seq data from human CD34+ HSPC (UNSW) GEO accession, GSM1097887 .
RNA seq data from human CD34+ HSPC (NIH) GEO accession, GSM651554 .
DNA methylation data from human HSPCs GEO accession, GSE31971 .
Microarray data from DAC treated AML cells, GEO accession GSE40442 .
TCGA data were from the TCGA data portal ().
New datasets in this MS:
RNA-seq supplementary data: .
BS-seq supplementary data: .
We thank Dr. Alice Baudot for p53 inducible SAOS2 cells. Whole genome bisulfite sequencing was performed by BGI Tech. Work in the lab of PDA is supported by CRUK program grant A10250 (renewal A16566). Work in the lab of JRE is supported by grants NIH 4-R00-CA127360-02, NIH 1R21LM011199-01, and DOD BC101296P1. MC was supported by the Scottish Funding Council SSCF scheme (SCD/04).
- Weinberg OK, Seetharam M, Ren L, Seo K, Ma L, Merker JD, Gotlib J, Zehnder JL, Arber DA: Clinical characterization of acute myeloid leukemia with myelodysplasia-related changes as defined by the 2008 WHO classification system. Blood. 2009, 113: 1906-1908. 10.1182/blood-2008-10-182782.PubMedView ArticleGoogle Scholar
- Herman JG, Civin CI, Issa JP, Collector MI, Sharkis SJ, Baylin SB: Distinct patterns of inactivation of p15INK4B and p16INK4A characterize the major types of hematological malignancies. Cancer Res. 1997, 57: 837-841.PubMedGoogle Scholar
- Kelly TK, De Carvalho DD, Jones PA: Epigenetic modifications as therapeutic targets. Nat Biotechnol. 2010, 28: 1069-1078. 10.1038/nbt.1678.PubMedPubMed CentralView ArticleGoogle Scholar
- Kihslinger JE, Godley LA: The use of hypomethylating agents in the treatment of hematologic malignancies. Leuk Lymphoma. 2007, 48: 1676-1695. 10.1080/10428190701493910.PubMedView ArticleGoogle Scholar
- Takai D, Jones PA: Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci U S A. 2002, 99: 3740-3745. 10.1073/pnas.052410099.PubMedPubMed CentralView ArticleGoogle Scholar
- Baylin SB, Jones PA: A decade of exploring the cancer epigenome - biological and translational implications. Nat Rev Cancer. 2011, 11: 726-734. 10.1038/nrc3130.PubMedPubMed CentralView ArticleGoogle Scholar
- Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, Ji H, Potash JB, Sabunciyan S, Feinberg AP: The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet. 2009, 41: 178-186. 10.1038/ng.298.PubMedPubMed CentralView ArticleGoogle Scholar
- Paul TA, Bies J, Small D, Wolff L: Signatures of polycomb repression and reduced H3K4 trimethylation are associated with p15INK4b DNA methylation in AML. Blood. 2010, 115: 3098-3108. 10.1182/blood-2009-07-233858.PubMedPubMed CentralView ArticleGoogle Scholar
- Klco JM, Spencer DH, Lamprecht TL, Sarkaria SM, Wylie T, Magrini V, Hundal J, Walker J, Varghese N, Erdmann-Gilmore P, Lichti CF, Meyer MR, Townsend RR, Wilson RK, Mardis ER, Ley TJ: Genomic impact of transient low-dose decitabine treatment on primary AML cells. Blood. 2013, 121: 1633-1643. 10.1182/blood-2012-09-459313.PubMedPubMed CentralView ArticleGoogle Scholar
- Claus R, Pfeifer D, Almstedt M, Zucknick M, Hackanson B, Plass C, Lübbert M: Decitabine induces very early in vivo DNA methylation changes in blasts from patients with acute myeloid leukemia. Leuk Res. 2013, 37: 190-196. 10.1016/j.leukres.2012.10.015.PubMedView ArticleGoogle Scholar
- Quentmeier H, Martelli MP, Dirks WG, Bolli N, Liso A, Macleod RA, Nicoletti I, Mannucci R, Pucciarini A, Bigerna B, Martelli MF, Mecucci C, Drexler HG, Falini B: Cell line OCI/AML3 bears exon-12 NPM gene mutation-A and cytoplasmic expression of nucleophosmin. Leukemia. 2005, 19: 1760-1767. 10.1038/sj.leu.2403899.PubMedView ArticleGoogle Scholar
- Tiacci E, Spanhol-Rosseto A, Martelli MP, Pasqualucci L, Quentmeier H, Grossmann V, Drexler HG, Falini B: The NPM1 wild-type OCI-AML2 and the NPM1-mutated OCI-AML3 cell lines carry DNMT3A mutations. Leukemia. 2012, 26: 554-557. 10.1038/leu.2011.238.PubMedView ArticleGoogle Scholar
- Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE, Kandoth C, Payton JE, Baty J, Welch J, Harris CC, Lichti CF, Townsend RR, Fulton RS, Dooling DJ, Koboldt DC, Schmidt H, Zhang Q, Osborne JR, Lin L, O'Laughlin M, McMichael JF, Delehaunty KD, McGrath SD, Fulton LA, Magrini VJ, Vickery TL, Hundal J, Cook LL, Conyers JJ, et al: DNMT3A mutations in acute myeloid leukemia. N Engl J Med. 2010, 363: 2424-2433. 10.1056/NEJMoa1005143.PubMedPubMed CentralView ArticleGoogle Scholar
- Falini B, Mecucci C, Tiacci E, Alcalay M, Rosati R, Pasqualucci L, La Starza R, Diverio D, Colombo E, Santucci A, Bigerna B, Pacini R, Pucciarini A, Liso A, Vignetti M, Fazi P, Meani N, Pettirossi V, Saglio G, Mandelli F, Lo-Coco F, Pelicci PG, Martelli MF: Cytoplasmic nucleophosmin in acute myelogenous leukemia with a normal karyotype. N Engl J Med. 2005, 352: 254-266. 10.1056/NEJMoa041974.PubMedView ArticleGoogle Scholar
- Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, Wen B, Wu H, Liu Y, Diep D, Briem E, Zhang K, Irizarry RA, Feinberg AP: Increased methylation variation in epigenetic domains across cancer types. Nat Genet. 2011, 43: 768-775. 10.1038/ng.865.PubMedPubMed CentralView ArticleGoogle Scholar
- Hon GC, Hawkins RD, Caballero OL, Lo C, Lister R, Pelizzola M, Valsesia A, Ye Z, Kuan S, Edsall LE, Camargo AA, Stevenson BJ, Ecker JR, Bafna V, Strausberg RL, Simpson AJ, Ren B: Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012, 22: 246-258. 10.1101/gr.125872.111.PubMedPubMed CentralView ArticleGoogle Scholar
- Berman BP, Weisenberger DJ, Aman JF, Hinoue T, Ramjan Z, Liu Y, Noushmehr H, Lange CP, van Dijk CM, Tollenaar RA, Van Den Berg D, Laird PW: Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina-associated domains. Nat Genet. 2012, 44: 40-46. 10.1038/ng.969.PubMed CentralView ArticleGoogle Scholar
- Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR: Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009, 462: 315-322. 10.1038/nature08514.PubMedPubMed CentralView ArticleGoogle Scholar
- Edwards JR, O’Donnell AH, Rollins RA, Peckham HE, Lee C, Milekic MH, Chanrion B, Fu Y, Su T, Hibshoosh H, Gingrich JA, Haghighi F, Nutter R, Bestor TH: Chromatin and sequence features that define the fine and gross structure of genomic methylation patterns. Genome Res. 2010, 20: 972-980. 10.1101/gr.101535.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Cruickshanks HA, McBryan T, Nelson DM, Vanderkraats ND, Shah PP, van Tuyn J, Singh Rai T, Brock C, Donahue G, Dunican DS, Drotar ME, Meehan RR, Edwards JR, Berger SL, Adams PD: Senescent cells harbour features of the cancer epigenome. Nat Cell Biol. 2013, 15: 1495-1506. 10.1038/ncb2879.PubMedPubMed CentralView ArticleGoogle Scholar
- Rauch TA, Wu X, Zhong X, Riggs AD, Pfeifer GP: A human B cell methylome at 100-base pair resolution. Proc Natl Acad Sci U S A. 2009, 106: 671-678. 10.1073/pnas.0812399106.PubMedPubMed CentralView ArticleGoogle Scholar
- Hellman A, Chess A: Gene body-specific methylation on the active X chromosome. Science. 2007, 315: 1141-1143. 10.1126/science.1136352.PubMedView ArticleGoogle Scholar
- Aran D, Toperoff G, Rosenberg M, Hellman A: Replication timing-related and gene body-specific methylation of active human genes. Hum Mol Genet. 2011, 20: 670-680. 10.1093/hmg/ddq513.PubMedView ArticleGoogle Scholar
- Jjingo D, Conley AB, Yi SV, Lunyak VV, Jordan IK: On the presence and role of human gene-body DNA methylation. Oncotarget. 2012, 3: 462-474.PubMedPubMed CentralView ArticleGoogle Scholar
- Ball MP, Li JB, Gao Y, Lee JH, LeProust EM, Park IH, Xie B, Daley GQ, Church GM: Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol. 2009, 27: 361-368. 10.1038/nbt.1533.PubMedPubMed CentralView ArticleGoogle Scholar
- Sproul D, Meehan RR: Genomic insights into cancer-associated aberrant CpG island hypermethylation. Brief Funct Genomics. 2013, 12: 174-190. 10.1093/bfgp/els063.PubMedPubMed CentralView ArticleGoogle Scholar
- Vanderkraats ND, Hiken JF, Decker KF, Edwards JR: Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes. Nucleic Acids Res. 2013, 41: 6816-6827. 10.1093/nar/gkt482.PubMedPubMed CentralView ArticleGoogle Scholar
- Hodges E, Molaro A, Dos Santos CO, Thekkat P, Song Q, Uren PJ, Park J, Butler J, Rafii S, McCombie WR, Smith AD, Hannon GJ: Directional DNA methylation changes and complex intermediate states accompany lineage specificity in the adult hematopoietic compartment. Mol Cell. 2011, 44: 17-28. 10.1016/j.molcel.2011.08.026.PubMedPubMed CentralView ArticleGoogle Scholar
- Cui K, Zang C, Roh TY, Schones DE, Childs RW, Peng W, Zhao K: Chromatin signatures in multipotent human hematopoietic stem cells indicate the fate of bivalent genes during differentiation. Cell Stem Cell. 2009, 4: 80-93. 10.1016/j.stem.2008.11.011.PubMedPubMed CentralView ArticleGoogle Scholar
- Beck D, Thoms JA, Perera D, Schütte J, Unnikrishnan A, Knezevic K, Kinston SJ, Wilson NK, O'Brien TA, Göttgens B, Wong JW, Pimanda JE: Genome-wide analysis of transcriptional regulators in human HSPCs reveals a densely interconnected network of coding and noncoding genes. Blood. 2013, 122: e12-e22. 10.1182/blood-2013-03-490425.PubMedView ArticleGoogle Scholar
- Russler-Germain DA, Spencer DH, Young MA, Lamprecht TL, Miller CA, Fulton R, Meyer MR, Erdmann-Gilmore P: The R882H DNMT3A mutation associated with AML dominantly inhibits wild-type DNMT3A by blocking its ability to form active tetramers. Cancer Cell. 2014, 25: 442-454. 10.1016/j.ccr.2014.02.010.PubMedPubMed CentralView ArticleGoogle Scholar
- Figueroa ME, Abdel-Wahab O, Lu C, Ward PS, Patel J, Shih A, Li Y, Bhagwat N, Vasanthakumar A, Fernandez HF, Tallman MS, Sun Z, Wolniak K, Peeters JK, Liu W, Choe SE, Fantin VR, Paietta E, Löwenberg B, Licht JD, Godley LA, Delwel R, Valk PJ, Thompson CB, Levine RL, Melnick A: Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell. 2010, 18: 553-567. 10.1016/j.ccr.2010.11.015.PubMedPubMed CentralView ArticleGoogle Scholar
- Wasserman WW, Sandelin A: Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet. 2004, 5: 276-287. 10.1038/nrg1315.PubMedView ArticleGoogle Scholar
- Yan P, Frankhouser D, Murphy M, Tam HH, Rodriguez B, Curfman J, Trimarchi M, Geyer S, Wu YZ, Whitman SP, Metzeler K, Walker A, Klisovic R, Jacob S, Grever MR, Byrd JC, Bloomfield CD, Garzon R, Blum W, Caligiuri MA, Bundschuh R, Marcucci G: Genome-wide methylation profiling in decitabine-treated patients with acute myeloid leukemia. Blood. 2012, 120: 2466-2474. 10.1182/blood-2012-05-429175.PubMedPubMed CentralView ArticleGoogle Scholar
- Akalin A, Garrett-Bakelman FE, Kormaksson M, Busuttil J, Zhang L, Khrebtukova I, Milne TA, Huang Y, Biswas D, Hess JL, Allis CD, Roeder RG, Valk PJ, Löwenberg B, Delwel R, Fernandez HF, Paietta E, Tallman MS, Schroth GP, Mason CE, Melnick A, Figueroa ME: Base-pair resolution DNA methylation sequencing reveals profoundly divergent epigenetic landscapes in acute myeloid leukemia. PLoS Genet. 2012, 8: e1002781-10.1371/journal.pgen.1002781.PubMedPubMed CentralView ArticleGoogle Scholar
- Saied MH, Marzec J, Khalid S, Smith P, Down TA, Rakyan VK, Molloy G, Raghavan M, Debernardi S, Young BD: Genome wide analysis of acute myeloid leukemia reveal leukemia specific methylome and subtype specific hypomethylation of repeats. PLoS One. 2012, 7: e33213-10.1371/journal.pone.0033213.PubMedPubMed CentralView ArticleGoogle Scholar
- Hagemann S, Heil O, Lyko F, Brueckner B: Azacytidine and decitabine induce gene-specific and non-random DNA demethylation in human cancer cell lines. PLoS One. 2011, 6: e17388-10.1371/journal.pone.0017388.PubMedPubMed CentralView ArticleGoogle Scholar
- Flotho C, Claus R, Batz C, Schneider M, Sandrock I, Ihde S, Plass C, Niemeyer CM, Lübbert M: The DNA methyltransferase inhibitors azacitidine, decitabine and zebularine exert differential effects on cancer gene expression in acute myeloid leukemia cells. Leukemia. 2009, 23: 1019-1028. 10.1038/leu.2008.397.PubMedView ArticleGoogle Scholar
- Tsai HC, Li H, Van Neste L, Cai Y, Robert C, Rassool FV, Shin JJ, Harbom KM, Beaty R, Pappou E, Harris J, Yen RW, Ahuja N, Brock MV, Stearns V, Feller-Kopman D, Yarmus LB, Lin YC, Welm AL, Issa JP, Minn I, Matsui W, Jang YY, Sharkis SJ, Baylin SB, Zahnow CA: Transient low doses of DNA-demethylating agents exert durable antitumor effects on hematological and epithelial tumor cells. Cancer Cell. 2012, 21: 430-446. 10.1016/j.ccr.2011.12.029.PubMedPubMed CentralView ArticleGoogle Scholar
- Mossman D, Kim KT, Scott RJ: Demethylation by 5-aza-2′-deoxycytidine in colorectal cancer cells targets genomic DNA whilst promoter CpG island methylation persists. BMC Cancer. 2010, 10: 366-10.1186/1471-2407-10-366.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmelz K, Sattler N, Wagner M, Lubbert M, Dorken B, Tamm I: Induction of gene expression by 5-Aza-2′-deoxycytidine in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) but not epithelial cells by DNA-methylation-dependent and -independent mechanisms. Leukemia. 2005, 19: 103-111.PubMedGoogle Scholar
- Weber M, Hellmann I, Stadler MB, Ramos L, Pääbo S, Rebhan M, Schübeler D: Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet. 2007, 39: 457-466. 10.1038/ng1990.PubMedView ArticleGoogle Scholar
- Qiu X, Hother C, Ralfkiær UM, Søgaard A, Lu Q, Workman CT, Liang G, Jones PA, Grønbæk K: Equitoxic doses of 5-azacytidine and 5-aza-2′deoxycytidine induce diverse immediate and overlapping heritable changes in the transcriptome. PLoS One. 2010, 5: e12994-10.1371/journal.pone.0012994.PubMedPubMed CentralView ArticleGoogle Scholar
- Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures. ,, [http://www.dsmz.de/home.html]
- Harlow E, Lane D: Antibodies: A laboratory manual. 1988, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NYGoogle Scholar
- Crissman HA, Steinkamp JA: Rapid, simultaneous measurement of DNA, protein, and cell volume in single cells from large mammalian cell populations. J Cell Biol. 1973, 59: 766-771. 10.1083/jcb.59.3.766.PubMedPubMed CentralView ArticleGoogle Scholar
- Flowjo., , [http://www.flowjo.com]
- Dolbeare F, Gratzner H, Pallavicini MG, Gray JW: Flow cytometric measurement of total DNA content and incorporated bromodeoxyuridine. Proc Natl Acad Sci U S A. 1983, 80: 5573-5577. 10.1073/pnas.80.18.5573.PubMedPubMed CentralView ArticleGoogle Scholar
- Lyons AB, Parish CR: Determination of lymphocyte division by flow cytometry. J Immunol Methods. 1994, 171: 131-137. 10.1016/0022-1759(94)90236-4.PubMedView ArticleGoogle Scholar
- Krueger F, Andrews SR: Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011, 27: 1571-1572. 10.1093/bioinformatics/btr167.PubMedPubMed CentralView ArticleGoogle 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: R25-10.1186/gb-2009-10-3-r25.PubMedPubMed CentralView ArticleGoogle Scholar
- Hansen KD, Langmead B, Irizarry RA: BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012, 13: R83-10.1186/gb-2012-13-10-r83.PubMedPubMed CentralView ArticleGoogle Scholar
- TCGA public data., , [https://tcga-data.nci.nih.gov/tcga/dataAccessMatrix.htm?]
- GEO public data., , [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL13534]
- Anders S, Pyl PT, Huber W: HTSeq — A Python framework to work with high-throughput sequencing data.; 2014. bioRxiv preprint.Google Scholar
- GEO public data., , [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=khcfkyoonngfbof&acc=GSE55123]
- GEO public data., : , [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=erczewmadpsnrep&acc=GSE55124]