DNA methylation “code” and transcriptional regulation

The correlation between promoter methylation and transcriptional repression of downstream gene(s) has been established by many studies based on a limited set of loci (Bird and Wolffe 1999; Eckhardt et al. 2006; Suzuki and Bird 2008). Recently, Weber et al. (2007) reported that promoters with weak CpG islands unbound to RNA polymerase II (Pol II) were frequently methylated. However, the relevance of specific promoter features of DNA methylation, such as the distance of DNA methylation relative to the transcription start site (TSS), or the role of absolute and relative levels of mCpG to the transcriptional repression of downstream genes(s) remain poorly understood.

Profiling genome-wide promoters is expected to produce different patterns of DNA methylation. In order to classify these patterns, we divided each promoter into three regions relative to TSS: proximal (−200 to +500 bp), intermediate (−200 to −1000 bp), and distal (−1000 to −2200 bp) as presented in Figure 1A. Each region was then defined as highly methylated or unmethylated based on the average probe-level RMS (higher or lower than 0.5, respectively). Promoters were classified into groups according to their methylation profiles (for example, 111: fully methylated, 000: extensively unmethylated, and 001: proximally methylated). We also took into account the overall promoter CpG content as described (Weber et al. 2007). Promoters were divided into three groups based on their CpG ratio: low CpG (LCPs), intermediate CpG (ICPs), and high CpG content promoters (HCPs). The number of promoters with a specific methylation profile within each group is shown in Figure 1B–D. In general, almost 90% of ICPs and HCPs were either unmethylated (000 profile) or exclusively distally methylated (100 profile), while LCPs showed heterogeneous methylation patterns. LCPs in melanoma cells were hypomethylated compared with those in normal melanocytes, as shown by a 29% decrease in the 111 profile (fully methylated promoter; χ² P = 3 × 10⁻²⁹) and a 62% increase in the 000 profile (fully demethylated promoter; P = 1 × 10⁻³¹). Conversely, ICPs and HCPs methylated in intermediate and/or proximal regions (X10, X01, and X11 profiles) were consistently more represented in melanoma cells than in normal melanocytes (+66% and +613%, on average, in ICPs and HCPs, respectively; 7 × 10⁻² < P < 1 × 10⁻³⁶). Taken together, the results suggested that differential methylation of promoters in melanoma cells is dependent on overall CpG content and is enriched in specific regions relative to the TSS.

Open in a separate window

Figure 1.

Distribution of promoter methylation in normal melanocytes and melanoma cells. (A) Diagram showing the three promoter regions relative to the TSS: Proximal (−200 to +500 bp), Intermediate (−200 to −1000 bp), and Distal (−1000 to −2200 bp). Each region is labeled as highly methylated (1) or unmethylated (0) if its average probe-level RMS is higher or lower than 0.5, respectively. (B) Number of LCPs for each cell type (NBMEL, ADMEL, and melanomas) and for each methylation profile. (e.g., 111 fully methylated, 001 proximally methylated, and combination thereof). (C,D) The same as B, for ICPs and HCPs.

Gene expression was evaluated for each methylation profile in order to assess the site of promoter methylation relevant to transcriptional repression. Figure 2A and Supplemental Figure S2 show the expression of each gene according to its promoter methylation profile and CpG content for normal melanocytes and melanoma cells. The results indicated that the expression of genes under the control of LCPs is not dependent on the promoter methylation profile in both cell types (trend P-values 0.01 and 0.3 in normal melanocytes and melanoma cells, respectively). ICPs with proximal methylation seem to be less transcriptionally active than those without proximal methylation (NBMEL P = 0.003, and melanomas P = 0.005). Finally, the location of methylation relative to the TSS in HCPs significantly correlated with transcriptional repression of downstream genes (NBMEL P = 0.004, and melanomas P = 0.005). These findings suggest that transcriptional repression driven by promoter methylation is inversely related to the distance of mCpGs from the TSS and increases with promoter CpG content. Interestingly, this pattern is slightly more prominent in normal melanocytes than in melanoma cells, suggesting a possible degeneration of the epigenetic “code” and/or transcriptional machinery in malignant cells.

Open in a separate window

Figure 2.

Promoter methylation and transcriptional repression. (A) Gene expression in normal melanocytes (NBMEL) under the control of promoters with each methylation profile in each promoter category; red and blue groups indicate promoters with and without proximal methylation (XX1 and XX0 profiles, respectively); yellow indicates unmethylated promoters; the groups range from fully methylated to unmethylated from left to right, and the order is based on the progressive absence of methylation from distal to proximal regions; smoothing over the median for each group is shown. (B) Gene expression in normal melanocytes (NBMEL) and five melanoma cell strains as a function of the probe-level AMS of proximal promoters; smoothing over the median for each group is shown. (C) Gene expression of WW165 primary melanoma cells for each promoter category as a function of the probe-level AMS of proximal promoters; smoothing over the median for each group is shown. For each panel the trend P-value is indicated (see Methods).

We also compared the absolute levels of DNA methylation with transcriptional intensity to evaluate the possibility of any quantitative relationship between the two. All cell strains showed a clear correlation between AMS and transcriptional repression (Fig. 2B). The same analysis was repeated, segregating the promoters on the basis of their CpG content to further confirm the relevance of the level of promoter methylation in the setting of overall CpG content. Again, only ICPs and HCPs demonstrated association with methylation, whereas genes under the control of LCPs were expressed almost independently of the methylation status (Fig. 2C). Taken together, localization and density of mCpG, as well as overall promoter CpG content, were highly predictive of the degree of transcriptional repression. Notably, we did not find the lack of association between HCPs methylation and transcriptional repression reported by Weber et al. (2007) based on Poll II binding data.

Methylation profiling by MeDIP

The MeDIP assay was performed as described (Weber et al. 2005). Briefly, genomic DNA was extracted by DNeasy Blood & Tissue Kit (QIAGEN), sheared into 300–1200-bp fragments by sonication, and aliquots (10 μg) were incubated with 20 μL of anti-5-methylcytidine mAb (Eurogentec) for 12 h at 4°C. Antibody-bound DNA was precipitated with 50 μL of Dynabeads (M-280 sheep antibodies to mouse IgG, Dynal Biotech) at 4°C for 2 h on a rotating wheel. Bound DNA was eluted two times with 200 μL of TE containing 1.0% and 0.67% SDS, respectively, and DNA was purified by standard proteinase K/phenol-chloroform procedure. The eluted DNA fractions and sonicated input DNA were differentially labeled with fluorescent dyes (Cy3 and Cy5, respectively) and hybridized to genomic promoter tiling arrays.

The methylation level of each cell strain was evaluated by one array hybridization. We decided to invest more in the number of cell strains rather than replicates because of the expected heterogeneity in the DNA methylation levels. Also, in order to obtain replicated hybridizations we would have needed to collect higher amounts of genomic DNA, likely increasing the number of passages in culture. This, in turn, would have been problematic because of the altered methylation patterns determined by cell culture (Meissner et al. 2008). Application of the same methodology for the selection of markers to other cancer types could be less problematic and take advantage of an increased replication level.

Array design, data processing, and probe annotation of the arrays for detection of genome-wide promoter methylation

We used NimbleGen C4226-00-01 promoter-tiling arrays that were designed based on the HG18 genome release. The array contains 390,000 probes with an average length of 60 bp tiled in 110-bp steps along the upstream promoter regions. We applied standard normalization methods for two-channel microarrays: within (Loess-based) and between (Quantile-based) normalization, available in the Limma Bioconductor library (Ihaka and Gentleman 1996; Smyth and Speed 2003; Gentleman et al. 2004; Smyth 2005). The probe-level log ratio was determined as the log₂ of the cy3/cy5 channels and used as a measure of MeDIP enrichment. The position of the center of each probe on the array was compared with the transcription start site (TSS) of known RefSeqs retrieved from UCSC human genome annotations (hg18). Multiple annotations of a probe in association with different RefSeq IDs were allowed.

Array design, data processing, and probe annotation of the arrays for detection of genome-wide gene expression

We used NimbleGen 2005-04-20_Human_60mer_1in2 genome-wide human expression arrays that were designed based on the HG17 genome release. A total of 380,000 probes for almost 30,000 transcripts and 20,000 known genes are represented on this array. NimbleGen provided design and probe annotation. The QSPLINE nonlinear normalization method was applied on a data set of single-channel absolute values (Workman et al. 2002).

Determination of absolute and relative methylation estimates

Absolute and relative methylation scores (AMS and RMS, respectively) were determined using the MEDME Bioconductor library (Pelizzola et al. 2008; http://espresso.med.yale.edu/medme/ and Bioconductor repository http://www.bioconductor.org). MEDME was calibrated on the data as described before (Pelizzola et al. 2008) using a window size of 1 kb and linear weighting for smoothing of log ratios and determination of the probe CpG content.

Determination of trend P-value

Trend P-values in Figure 2 were determined based on the box plot medians, using the SAGx R library (http://www.r-project.org/).

Identification of biomarkers

Intermediate and proximal promoter regions of each RefSeq were divided into six regions based on the distance from the TSS (−500 to −350, −200 to −50, −50 to +100, +100 to +250, and +250 to +500 bp, with respect to the TSS). The AMS for a given region was determined as the average of the AMS for the probes in that genomic area. A linear mixed effects model was used in order to select promoters that are differentially methylated between melanoma cells and normal melanocytes. The cell type (melanoma vs. normal melanocytes) was included as a fixed effect; the individual melanoma strains were considered as random samples from the type populations and, thus, included as random effects. Quadratic and cubic terms for distance were included in the model due to the nonlinear relationship between the distance of the promoter regions from the TSS and AMS. The P-value was determined for the normal newborn and adult melanocytes in comparison to each possible group of seven out of eight melanoma cell strains, and the best P-value was retained for subsequent steps. Only promoters whose P-values were less than α = 0.05 were considered for further evaluation.

Finally, selected promoters were screened based on three filters designed to increase the likelihood of differential methylation and differential expression being causally related: (1) promoters with proximal RMS higher than 0.5 were retained; (2) promoters with low CpG content (as defined in Weber et al. 2007) were discarded; (3) only promoters whose downstream gene was differentially expressed at least 1.5-fold in at least six out of eight melanoma cell strains were considered. For the last filter, methylation markers in melanoma cells were required to display inverse relationship to gene expression, i.e., hyper- and hypomethylated being down- and up-regulated, respectively.

Sequencing of bisulfite modified DNA

Genomic DNA was extracted from normal melanocytes and melanoma cells and subjected to bisulfite (BS) modification (Epitect Bisulfite Kit, QIAGEN), followed by Sanger sequencing as described (Jacobsen et al. 2000). The target regions of the relevant genes and the primers used for amplification are listed in Supplemental Table S3. The cell strain methylation levels displayed in Figures 4 and ​and55 were calculated from the amplitude of cytosine and thymine within each CpG dinucleotide, C/(C+T), using the phred software (http://www.phrap.com/). Considering the likelihood of contamination from normal cells in clinical samples, the methylation level of snap-frozen tissues indicated in Table 2 was determined based on three CpGs that were highly discriminative between normal melanocytes and melanoma cells. Each promoter was then defined as methylated or unmethylated based on the presence of significant amplitude of cytosine or thymine, respectively, consistently on the three CpGs.

Treatment with Decitabine

Decitabine (5-Aza-2′-deoxycytidine, Sigma Chemical [Aza]) was dissolved in methanol as 10 mM stock solution, aliquoted and kept at −20°C. Melanoma cells were seeded in Petri dishes (~5000 cells/cm²) in regular medium untreated or treated with Aza (0.2 μM) for 2 d, with fresh drug-supplemented medium on the second day. The cells were harvested after 1-d recovery in drug-free medium.

We thank the Cell Culture Core facility of the Yale Skin Disease Research Core Center (YSDRCC) supported by NIAMS grant 5P30 AR 041942-12 (Robert Tigelaar) for providing normal human melanocytes and melanoma cells, to Antonella Bacchiocchi for collecting the tissues and growing the cells, and to Harriet Kluger for staging the melanoma tumors. This work was supported by the National Cancer Institute grant Yale SPORE in Skin Cancer number 1 P50 CA121974 (R.H.). M.K. was supported by the National Library of Medicine K22LM009255, and A.M.M. was supported by a National Cancer Institute grant K22CA123146-2.

Authors' contributions: Y.K. and M.P. designed the study. Y.K. and E.C. performed the experiments. M.P. and Y.K. analyzed the data and interpreted the results. M.K. and A.M.M. contributed bioinformatics and statistical analysis. R.H. and S.W. provided scientific leadership. S.A. and D.N. excised the tissues. M.S. is the oncologist who took care of the patients. Y.K., M.P., R.H., and S.W. wrote the manuscript.