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Background

Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.

Results

We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.

Conclusion

Our proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.

Epigenetics is the study of all mechanisms that regulate gene transcription and genome stability that are maintained throughout the cell division, but do not include the DNA sequence itself. The best-studied epigenetic mechanism to date is DNA methylation, where methyl groups are added to the cytosine base within cytosine–guanine dinucleotides (CpG sites). CpGs are frequently clustered in high density (CpG islands (CGIs)) at the promoter of over half of all genes. Current knowledge of transcriptional regulation by DNA methylation centres on its role at the promoter where unmethylated CGIs are present at most actively transcribed genes, whereas hypermethylation of the promoter results in gene repression. Over the last 5 years, research has gradually incorporated a broader understanding that methylation patterns across the gene (so-called intragenic or gene body methylation) may have a role in transcriptional regulation and efficiency. Numerous genome-wide DNA methylation profiling studies now support this notion, although whether DNA methylation patterns are a cause or consequence of other regulatory mechanisms is not yet clear. This review will examine the evidence for the function of intragenic methylation in gene transcription, and discuss the significance of this in carcinogenesis and for the future use of therapies targeted against DNA methylation.

It is now understood that virtually all human cancer types are the result of the accumulation of both genetic and epigenetic changes. DNA methylation is a molecular modification of DNA that is crucial for normal development. Genes that are rich in CpG dinucleotides are usually not methylated in normal tissues, but are frequently hypermethylated in cancer. With the advent of high-throughput platforms, large-scale structure of genomic methylation patterns is available through genome-wide scans and tremendous amount of DNA methylation data have been recently generated. However, sophisticated statistical methods to handle complex DNA methylation data are very limited. Here we developed a likelihood based Uniform-Normal-mixture model to select differentially methylated loci between case and control groups using Illumina arrays. The idea is to model the data as three types of methylation loci, one unmethylated, one completely methylated, and one partially methylated. A three-component mixture model with two Uniform distributions and one truncated normal distribution was used to model the three types. The mixture probabilities and the mean of the normal distribution were used to make inference about differentially methylated loci. Through extensive simulation studies, we demonstrated the feasibility and power of the proposed method. An application to a recently published study on ovarian cancer identified several methylation loci that are missed by the existing method.

Aberrant DNA methylation often occurs in colorectal cancer (CRC). In our study we applied a genome-wide DNA methylation analysis approach, MethylCap-seq, to map the differentially methylated regions (DMRs) in 24 tumors and matched normal colon samples. In total, 2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified, which include potential biomarkers for CRC diagnosis. Hypermethylation in the tumor samples was enriched at CpG islands and gene promoters, while hypomethylation was distributed throughout the genome. Using epigenetic data from human embryonic stem cells, we show that frequently hypermethylated regions coincide with bivalent loci in human embryonic stem cells. DNA methylation is commonly thought to lead to gene silencing; however, integration of publically available gene expression data indicates that 75% of the frequently hypermethylated genes were most likely already lowly or not expressed in normal tissue. Collectively, our study provides genome-wide DNA methylation maps of CRC, comprehensive lists of DMRs, and gives insights into the role of aberrant DNA methylation in CRC formation.

DNA methylation profiles can be used to define molecular cancer subtypes that may better inform disease etiology and clinical decision-making. This investigation aimed to create DNA methylation profiles of bladder cancer based on CpG methylation from almost 800 cancer-related genes and to then examine the relationship of those profiles with exposures related to risk and clinical characteristics. DNA, derived from formalin-fixed paraffin-embedded tumor samples obtained from incident cases involved in a population-based case-control study of bladder cancer in New Hampshire, was used for methylation profiling on the Illumina GoldenGate Methylation Bead Array. Unsupervised clustering of those loci with the greatest change in methylation between tumor and non-diseased tissue was performed to defined molecular subgroups of disease, and univariate tests of association followed by multinomial logistic regression was used to examine the association between these classes, bladder cancer risk factors and clinical phenotypes. Membership in the two most methylated classes was significantly associated with invasive disease (P < 0.001 for both class 3 and 4). Male gender (P = 0.04) and age >70 years (P = 0.05) was associated with membership in one of the most methylated classes. Finally, average water arsenic levels in the highest percentile predicted membership in an intermediately methylated class of tumors (P = 0.02 for both classes). Exposures and demographic associated with increased risk of bladder cancer specifically associate with particular subgroups of tumors defined by DNA methylation profiling and these subgroups may define more aggressive disease.

DNA methylation, consisting of the addition of a methyl group at the fifth-position of cytosine in a CpG dinucleotide, is one of the most well-studied epigenetic mechanisms in mammals with important functions in normal and disease biology. Disease-specific aberrant DNA methylation is a well-recognized hallmark of many complex diseases. Accordingly, various studies have focused on characterizing unique DNA methylation marks associated with distinct stages of disease development as they may serve as useful biomarkers for diagnosis, prognosis, prediction of response to therapy, or disease monitoring. Recently, novel CpG dinucleotide modifications with potential regulatory roles such as 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine have been described. These potential epigenetic marks cannot be distinguished from 5-methylcytosine by many current strategies and may potentially compromise assessment and interpretation of methylation data. A large number of strategies have been described for the discovery and validation of DNA methylation-based biomarkers, each with its own advantages and limitations. These strategies can be classified into three main categories: restriction enzyme digestion, affinity-based analysis, and bisulfite modification. In general, candidate biomarkers are discovered using large-scale, genome-wide, methylation sequencing, and/or microarray-based profiling strategies. Following discovery, biomarker performance is validated in large independent cohorts using highly targeted locus-specific assays. There are still many challenges to the effective implementation of DNA methylation-based biomarkers. Emerging innovative methylation and hydroxymethylation detection strategies are focused on addressing these gaps in the field of epigenetics. The development of DNA methylation- and hydroxymethylation-based biomarkers is an exciting and rapidly evolving area of research that holds promise for potential applications in diverse clinical settings.

BACKGROUND

DNA methylation is a fundamental epigenetic event associated with physiologic and pathologic conditions including cancer. Hypermethylation of CpG islands at active gene promoters lead to transcriptional repression while hypomethylation is associated with gene overexpression. The aim of this study was to identify genes in adenoid cystic carcinoma (ACC) of salivary gland strongly deregulated by epigenetic CpG island methylation, to validate selected genes by conventional techniques, and to correlate the findings with clinico-pathologic factors.

METHODS

We analyzed 16 matched normal and tumor tissues for aberrant DNA methylation using the methylated CpG island amplification and microarray (MCAM) method, and the pyrosequencing technique.

RESULTS

Microarray analysis showed hypomethylation in seven, and hypermethylation in 32 CpG islands. Hypomethylation was identified in CpG islands near FBXO17, PHKG1, LOXL1, DOCK1 and PARVG. Hypermethylation was identified near genes encoding predominantly transcription factors (EN1, FOXE1, GBX2, FOXL2, TBX4, MEIS1, LBX2, NR2F2, POU3F3, IRX3, TFAP2C, NKX2-4, PITX1, NKX2-5), and 13 genes with different functions (MT1H, EPHX3, AQPEP, BCL2L11, SLC35D3, S1PR5, PNLIPRP1, CLIC6, RASAL, XRN2, GSTM5, FNDC1, INSRR). Four CpG islands by EN1, FOXE1, TBX4, and PITX1 were validated by pyrosequencing.

CONCLUSION

The highly methylated genes in tumor versus normal are linked to developmental, apoptotic and other fundamental cellular pathways, suggesting that downregulation of these genes is associated with ACC development and progression. With EN1 hypermethylation showing potential as possible biomarker for ACC in salivary gland, the biological and therapeutic implications of our findings require further preclinical investigations.

It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation (“methylome”), i.e., predominant hypomethylation and localized hypermethylation, within “CpG islands” (CGIs). Moreover, although cancer cells have reduced methylation “fidelity” and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance remains largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper- and hypo-methylated CGIs. Empirical Bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high- methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable “random” methylation (582 loci). These results support a “stochastic model” of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p<2.2×10−5) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having “random” methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs.

DNA methylation is an epigenetic modification of the mammalian genome that occurs predominantly at cytosine residues of the CpG dinucleotide. Following formaldehyde activation, pixantrone alkylates DNA and particularly favours the CpG motif. Aberrations in CpG methylation patterns are a feature of most cancer types, a characteristic that may determine their susceptibility to specific drug treatments. Given their common target, DNA methylation may modulate the DNA damage induced by formaldehyde-activated pixantrone. In vitro transcription, mass spectrometry and oligonucleotide band shift assays were utilized to establish that pixantrone–DNA adduct formation was consistently enhanced 2–5-fold at discrete methylated CpG doublets. The methylation-mediated enhancement was exquisitely sensitive to the position of the methyl substituent since methylation at neighboring cytosine residues failed to confer an increase in pixantrone–DNA alkylation. Covalent modification of DNA by formaldehyde-activated doxorubicin, but not cisplatin, was augmented by neighbouring CpG methylation, indicating that modulation of binding by CpG methylation is not a general feature of all alkylators. HCT116 colon cancer cells vastly deficient in CpG methylation were 12- and 10-fold more resistant to pixantrone and doxorubicin relative to the wild-type line, suggesting that these drugs may selectively recognize the aberrant CpG methylation profiles characteristic of most tumour types.

DNA methylation is a major epigenetic modification in the genomes of higher eukaryotes. In vertebrates, DNA methylation occurs predominantly on the CpG dinucleotide, and approximately 60% to 90% of these dinucleotides are modified. Distinct DNA methylation patterns, which can vary between different tissues and developmental stages, exist on specific loci. Sites of DNA methylation are occupied by various proteins, including methyl-CpG binding domain (MBD) proteins which recruit the enzymatic machinery to establish silent chromatin. Mutations in the MBD family member MeCP2 are the cause of Rett syndrome, a severe neurodevelopmental disorder, whereas other MBDs are known to bind sites of hypermethylation in human cancer cell lines. Here, we review the advances in our understanding of the function of DNA methylation, DNA methyltransferases, and methyl-CpG binding proteins in vertebrate embryonic development. MBDs function in transcriptional repression and long-range interactions in chromatin and also appear to play a role in genomic stability, neural signaling, and transcriptional activation. DNA methylation makes an essential and versatile epigenetic contribution to genome integrity and function.

The combined effects of genetic and epigenetic aberrations are well recognized as causal in tumorigenesis. Here, we defined profiles of DNA methylation in primary renal cell carcinomas (RCC) and assessed the association of these profiles with the expression of genes required for the establishment and maintenance of epigenetic marks. A bead-based methylation array platform was used to measure methylation of 1,413 CpG loci in ∼800 cancer-associated genes and three methylation classes were derived by unsupervised clustering of tumors using recursively partitioned mixture modeling (RPMM). Quantitative RT-PCR was performed on all tumor samples to determine the expression of DNMT1, DNMT3B, VEZF1 and EZH2. Additionally, methylation at LINE-1 and AluYb8 repetitive elements was measured using bisulfite pyrosequencing. Associations between methylation class and tumor stage (p = 0.05), LINE-1 (p < 0.0001) and AluYb8 (p < 0.0001) methylation, as well as EZH2 expression (p < 0.0001) were noted following univariate analyses. A multinomial logistic regression model controlling for potential confounders revealed that AluYb8 (p < 0.003) methylation and EZH2 expression (p < 0.008) were significantly associated with methylation class membership. Because EZH2 is a member of the Polycomb repressive complex 2 (PRC2), we next analyzed the distribution of Polycomb group (PcG) targets among methylation classes derived by clustering the 1,413 array CpG loci using RPMM. PcG target genes were significantly enriched (p < 0.0001) in methylation classes with greater differential methylation between RCC and non-diseased kidney tissue. This work contributes to our understanding of how repressive marks on DNA and chromatin are dysregulated in carcinogenesis, knowledge that might aid the development of therapies or preventive strategies for human malignancies.

Diet is associated with cancer prognosis, including head and neck cancer (HNC), and has been hypothesized to influence epigenetic state by determining the availability of functional groups involved in the modification of DNA and histone proteins. The goal of this study was to describe the association between pretreatment diet and HNC tumor DNA methylation. Information on usual pretreatment food and nutrient intake was estimated via food frequency questionnaire (FFQ) on 49 HNC cases. Tumor DNA methylation patterns were assessed using the Illumina Goldengate Methylation Cancer Panel. First, a methylation score, the sum of individual hypermethylated tumor suppressor associated CpG sites, was calculated and associated with dietary intake of micronutrients involved in one-carbon metabolism and antioxidant activity, and food groups abundant in these nutrients. Second, gene specific analyses using linear modeling with empirical Bayesian variance estimation were conducted to identify if methylation at individual CpG sites was associated with diet. All models were controlled for age, sex, smoking, alcohol and HPV status. Individuals reporting in the highest quartile of folate, vitamin B12 and vitamin A intake, compared with those in the lowest quartile, showed significantly less tumor suppressor gene methylation, as did patients reporting the highest cruciferous vegetable intake. Gene specific analyses identified differential associations between DNA methylation and vitamin B12 and vitamin A intake when stratifying by HPV status. These preliminary results suggest that intake of folate, vitamin A and vitamin B12 may be associated with the tumor DNA methylation profile in HNC and enhance tumor suppression.

Background

Most previous studies of the CpG island methylator phenotype (CIMP) in colorectal cancer (CRC) have been conducted on a relatively small numbers of CpG sites. In the present study we performed comprehensive DNA methylation profiling of CRC with the aim of characterizing CIMP subgroups.

Methods

DNA methylation at 1,505 CpG sites in 807 cancer-related genes was evaluated using the Illumina GoldenGate® methylation array in 28 normal colonic mucosa and 91 consecutive CRC samples. Methylation data was analyzed using unsupervised hierarchical clustering. CIMP subgroups were compared for various clinicopathological and molecular features including patient age, tumor site, microsatellite instability (MSI), methylation at a consensus panel of CpG islands and mutations in BRAF and KRAS.

Results

A total of 202 CpG sites were differentially methylated between tumor and normal tissue. Unsupervised hierarchical clustering of methylation data from these sites revealed the existence of three CRC subgroups referred to as CIMP-low (CIMP-L, 21% of cases), CIMP-mid (CIMP-M, 14%) and CIMP-high (CIMP-H, 65%). In comparison to CIMP-L tumors, CIMP-H tumors were more often located in the proximal colon and showed more frequent mutation of KRAS and BRAF (P < 0.001).

Conclusions

Comprehensive DNA methylation profiling identified three CRC subgroups with distinctive clinicopathological and molecular features. This study suggests that both KRAS and BRAF mutations are involved with the CIMP-H pathway of CRC rather than with distinct CIMP subgroups.

Background

DNA methylation is essential for normal development and differentiation and plays a crucial role in the development of nearly all types of cancer. Aberrant DNA methylation patterns, including genome-wide hypomethylation and region-specific hypermethylation, are frequently observed and contribute to the malignant phenotype. A number of studies have recently identified distinct features of genomic sequences that can be used for modeling specific DNA sequences that may be susceptible to aberrant CpG methylation in both cancer and normal cells. Although it is now possible, using next generation sequencing technologies, to assess human methylomes at base resolution, no reports currently exist on modeling cell type-specific DNA methylation susceptibility. Thus, we conducted a comprehensive modeling study of cell type-specific DNA methylation susceptibility at three different resolutions: CpG dinucleotides, CpG segments, and individual gene promoter regions.

Results

Using a k-mer mixture logistic regression model, we effectively modeled DNA methylation susceptibility across five different cell types. Further, at the segment level, we achieved up to 0.75 in AUC prediction accuracy in a 10-fold cross validation study using a mixture of k-mers.

Conclusions

The significance of these results is three fold: 1) this is the first report to indicate that CpG methylation susceptible "segments" exist; 2) our model demonstrates the significance of certain k-mers for the mixture model, potentially highlighting DNA sequence features (k-mers) of differentially methylated, promoter CpG island sequences across different tissue types; 3) as only 3 or 4 bp patterns had previously been used for modeling DNA methylation susceptibility, ours is the first demonstration that 6-mer modeling can be performed without loss of accuracy.

Background

Alterations in DNA methylation in cancer include global hypomethylation and gene-specific hypermethylation. It is not clear whether these two epigenetic errors are mechanistically linked or occur independently. This study was performed to determine the relationship between DNA hypomethylation, hypermethylation and microsatellite instability in cancer.

Methodology/Principal Findings

We examined 61 cancer cell lines and 60 colorectal carcinomas and their adjacent tissues using LINE-1 bisulfite-PCR as a surrogate for global demethylation. Colorectal carcinomas with sporadic microsatellite instability (MSI), most of which are due to a CpG island methylation phenotype (CIMP) and associated MLH1 promoter methylation, showed in average no difference in LINE-1 methylation between normal adjacent and cancer tissues. Interestingly, some tumor samples in this group showed increase in LINE-1 methylation. In contrast, MSI-showed a significant decrease in LINE-1 methylation between normal adjacent and cancer tissues (P<0.001). Microarray analysis of repetitive element methylation confirmed this observation and showed a high degree of variability in hypomethylation between samples. Additionally, unsupervised hierarchical clustering identified a group of highly hypomethylated tumors, composed mostly of tumors without microsatellite instability. We extended LINE-1 analysis to cancer cell lines from different tissues and found that 50/61 were hypomethylated compared to peripheral blood lymphocytes and normal colon mucosa. Interestingly, these cancer cell lines also exhibited a large variation in demethylation, which was tissue-specific and thus unlikely to be resultant from a stochastic process.

Conclusion/Significance

Global hypomethylation is partially reversed in cancers with microsatellite instability and also shows high variability in cancer, which may reflect alternative progression pathways in cancer.

Background

It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical methods are desirable but very limited in the literature.

Results

In this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions for Illumina Array Methylation data. We compare the new method with other methods using simulated and real data. Our study shows that the proposed one outperforms other methods considered in this paper.

Conclusions

Due to the unique feature of the Illumina Array Methylation data, commonly used statistical tests will lose power or give misleading results. Therefore, appropriate statistical methods are crucial for this type of data. Powerful statistical approaches remain to be developed.

Availability

R codes are available upon request.

DNA methylation plays an important role in development and disease. The primary sites of DNA methylation in vertebrates are cytosines in the CpG dinucleotide context, which account for roughly three quarters of the total DNA methylation content in human and mouse cells. While the genomic distribution, inter-individual stability, and functional role of CpG methylation are reasonably well understood, little is known about DNA methylation targeting CpA, CpT, and CpC (non-CpG) dinucleotides. Here we report a comprehensive analysis of non-CpG methylation in 76 genome-scale DNA methylation maps across pluripotent and differentiated human cell types. We confirm non-CpG methylation to be predominantly present in pluripotent cell types and observe a decrease upon differentiation and near complete absence in various somatic cell types. Although no function has been assigned to it in pluripotency, our data highlight that non-CpG methylation patterns reappear upon iPS cell reprogramming. Intriguingly, the patterns are highly variable and show little conservation between different pluripotent cell lines. We find a strong correlation of non-CpG methylation and DNMT3 expression levels while showing statistical independence of non-CpG methylation from pluripotency associated gene expression. In line with these findings, we show that knockdown of DNMTA and DNMT3B in hESCs results in a global reduction of non-CpG methylation. Finally, non-CpG methylation appears to be spatially correlated with CpG methylation. In summary these results contribute further to our understanding of cytosine methylation patterns in human cells using a large representative sample set.

Author Summary

Epigenetic modifications including DNA methylation at the position 5 of the cytosine base provide regulatory information to the genome sequence. The primary target of cytosine methylation in mammals is the CpG dinucleotide. However, previous studies in the mouse and more recent work in humans have highlighted the presence of non-CpG methylation in pluripotent cells. Currently, little is known about the role of this type of DNA methylation. We sought to further characterize non-CpG methylation by employing a comprehensive data set of genome-scale methylation maps across various human cell types. Our analysis reveals that non-CpG methylation varies dramatically between pluripotent cells and is closely linked to CpG methylation. Moreover, we show that depletion of the de novo DNA methyltransferases results in a global reduction of non-CpG methylation levels. Taken together, these findings further advance our understanding of cytosine methylation and describe its distribution among a large number of human cell types.

Background

Abberant DNA methylation at CpG dinucleotides represents a common mechanism of transcriptional silencing in cancer. Since CpG methylation is a reversible event, tumor supressor genes that have undergone silencing through this mechanism represent promising targets for epigenetically active anti-cancer therapy. The cytosine analog 5-aza-2'-deoxycytidine (decitabine) induces genomic hypomethylation by inhibiting DNA methyltransferase, and is an example of an epigenetic agent that is thought to act by up-regulating silenced genes.

Methods

It is unclear why decitabine causes some silenced loci to re-express, while others remain inactive. By applying data-mining techniques to large-scale datasets, we attempted to elucidate the qualities of promoter regions that define susceptibility to the drug's action. Our experimental data, derived from melanoma cell strains, consist of genome-wide gene expression data before and after treatment with decitabine, as well as genome-wide data on un-treated promoter methylation status, and validation of specific genes by bisulfite sequencing.

Results

We show that the combination of promoter CpG content and methylation level informs the ability of decitabine treatment to up-regulate gene expression. Promoters with high methylation levels and intermediate CpG content appear most susceptible to up-regulation by decitabine, whereas few of those highly methylated promoters with high CpG content are up-regulated. For promoters with low methylation levels, those with high CpG content are more likely to be up-regulated, whereas those with low CpG content are underrepresented among up-regulated genes.

Conclusions

Clinically, elucidating the patterns of action of decitabine could aid in predicting the likelihood of up-regulating epigenetically silenced tumor suppressor genes and others from pathways involved with tumor biology. As a first step toward an eventual translational application, we build a classifier to predict gene up-regulation based on promoter methylation and CpG content, which achieves a performance of 0.77 AUC.

DNA methylation is the best-studied epigenetic modification and describes the conversion of cytosine to 5-methylcytosine. The importance of this phenomenon is that aberrant promoter hypermethylation is a common occurrence in cancer and is frequently associated with gene silencing. Various techniques are currently available for the analysis of DNA methylation. However, accurate and reproducible quantification of DNA methylation remains challenging. In this report, we describe Bio-COBRA (combined bisulfite restriction analysis coupled with the Agilent 2100 Bioanalyzer platform), as a novel approach to quantitative DNA methylation analysis. The combination of a well-established method, COBRA, which interrogates DNA methylation via the restriction enzyme analysis of PCR-amplified bisulfite treated DNAs, with the Bioanalyzer platform allows for the rapid and quantitative assessment of DNA methylation patterns in large sample sets. The sensitivity and reproducibility of Bio-COBRA make it a valuable tool for the analysis of DNA methylation in clinical samples, which could aid in the development of diagnostic and prognostic parameters with respect to disease detection and management.

Summary

Epigenetic alterations of DNA play key roles in determining gene structure and expression. Methylation of the 5-position of cytosine is thought to be the most common modification of the genome in mammals. Studies have generally shown that hypermethylation in gene regulatory regions is associated with inactivation and reduced transcription and that alteration in established methylation patterns during development can affect embryonic viability. Changes in methylation have also been associated with aging and cellular senescence as well as tumorogenesis. DNA methyltransferase 1 (DNMT1) is thought to play an important role in maintaining already established methylation patterns during DNA replication and catalyzes the transfer of a methyl moiety from S-adenosyl-l-methionine (SAM) to the 5-position of cytosines in the CpG dinucleotide. Several studies illustrate changes in activity and transcription of DNMT1 during aging and here we show a comprehensive method of detection of DNMT1 mRNA transcription from senescing cells in culture.

Background

Both gastric and colorectal cancers (CRC) are the most frequently occurring malignancies worldwide with the overall survival of these patients remains unsatisfied. Identification of tumor suppressor genes (TSG) silenced by promoter CpG methylation uncovers mechanisms of tumorigenesis and identifies new epigenetic biomarkers for early cancer detection and prognosis assessment. Cystathionine-beta-synthase (CBS) functions in the folate metabolism pathway, which is intricately linked to methylation of genomic DNA. Dysregulation of DNA methylation contributes substantially to cancer development.

Methodology/Principal Findings

To identify potential TSGs silenced by aberrant promoter methylation in CRC, we analyzed tumor and adjacent tissues from CRC cases using the Illumina Human Methylation45 BeadChip. We identified hypermethylation of the CBS gene in CRC samples, compared to adjacent tissues. Methylation and decreased mRNA expression of CBS were detected in most CRC cell lines by methylation-specific PCR and semiquantitative RT-PCR, as well as in gastric cancer. Treatment with 5-aza-2'-deoxycytidine and/or trichostatin A reversed methylation and restored CBS mRNA expression indicating a direct effect. Aberrant methylation was further detected in 31% of primary CRCs (29 of 96) and 55% of gastric tumors (11 of 20). In contrast, methylation was seldom found in normal tissues adjacent to the tumor. CBS methylation was associated with KRAS mutations in primary CRCs (P = 0.04, by χ2-test). However, no association was found between CBS methylation or KRAS mutations with cancer relapse/metastasis in Stage II CRC patients.

Conclusion

A novel finding from this study is that the folate metabolism enzyme CBS mRNA levels are frequently downregulated through CpG methylation of the CBS gene in gastric cancer and CRC, suggesting that CBS functions as a tumor suppressor gene. These findings warrant further study of CBS as an epigenetic biomarker for molecular diagnosis of gastrointestinal cancers.

Background

Epigenetic alteration of gene expression is a common event in human cancer. DNA methylation is a well-known epigenetic process, but verifying the exact nature of epigenetic changes associated with cancer remains difficult.

Methods

We profiled the methylome of human gastric cancer tissue at 50-bp resolution using a methylated DNA enrichment technique (methylated CpG island recovery assay) in combination with a genome analyzer and a new normalization algorithm.

Results

We were able to gain a comprehensive view of promoters with various CpG densities, including CpG Islands (CGIs), transcript bodies, and various repeat classes. We found that gastric cancer was associated with hypermethylation of 5' CGIs and the 5'-end of coding exons as well as hypomethylation of repeat elements, such as short interspersed nuclear elements and the composite element SVA. Hypermethylation of 5' CGIs was significantly correlated with downregulation of associated genes, such as those in the HOX and histone gene families. We also discovered long-range epigenetic silencing (LRES) regions in gastric cancer tissue and identified several hypermethylated genes (MDM2, DYRK2, and LYZ) within these regions. The methylation status of CGIs and gene annotation elements in metastatic lymph nodes was intermediate between normal and cancerous tissue, indicating that methylation of specific genes is gradually increased in cancerous tissue.

Conclusions

Our findings will provide valuable data for future analysis of CpG methylation patterns, useful markers for the diagnosis of stomach cancer, as well as a new analysis method for clinical epigenomics investigations.

Background

Epigenetics, particularly DNA methylation, has recently been elucidated as important in gastric cancer (GC) initiation and progression. We investigated the clinical and prognostic importance of whole blood global and site-specific DNA methylation in GC.

Methods

Genomic DNA was extracted from the peripheral blood of 105 Omani GC patients at diagnosis. DNA methylation was quantified by pyrosequencing of global DNA and specific gene promoter regions at 5 CpG sites for CDH1, 7 CpG sites for p16, 4 CpG sites for p53, and 3 CpG sites for RUNX3. DNA methylation levels in patients were categorized into low, medium, and high tertiles. Associations between methylation level category and clinicopathological features were evaluated using χ2 tests. Survival analyses were carried out using the Kaplan-Meier method and log rank test. A backward conditional Cox proportional hazards regression model was used to identify independent predictors of survival.

Results

Older GC patients had increased methylation levels at specific CpG sites within the CDH1, p53, and RUNX-3 promoters. Male gender was significantly associated with reduced global and increased site-specific DNA methylation levels in CDH1, p16, and p53 promoters. Global DNA low methylation level was associated with better survival on univariate analysis. Patients with high and medium methylation vs. low methylation levels across p16 promoter CpG sites, site 2 in particular, had better survival. Multivariate analysis showed that global DNA hypermethylation was a significant independent predictor of worse survival (hazard ratio (HR) = 2.0, 95% CI: 1.1–3.8; p = 0.02) and high methylation mean values across p16 promoter sites 1–7 were associated with better survival with HR of 0.3 (95% CI, 0.1–0.8; p = 0.02) respectively.

Conclusions

Analysis of global and site-specific DNA methylation in peripheral blood by pyrosequencing provides quantitative DNA methylation values that may serve as important prognostic indicators.

Background

Many groups, including our own, have proposed the use of DNA methylation profiles as biomarkers for various disease states. While much research has been done identifying DNA methylation signatures in cancer vs. normal etc., we still lack sufficient knowledge of the role that differential methylation plays during normal cellular differentiation and tissue specification. We also need thorough, genome level studies to determine the meaning of methylation of individual CpG dinucleotides in terms of gene expression.

Results

In this study, we have used (insert statistical method here) to compile unique DNA methylation signatures from normal human heart, lung, and kidney using the Illumina Infinium 27 K methylation arraysand compared those to gene expression by RNA sequencing. We have identified unique signatures of global DNA methylation for human heart, kidney and liver, and showed that DNA methylation data can be used to correctly classify various tissues. It indicates that DNA methylation reflects tissue specificity and may play an important role in tissue differentiation. The integrative analysis of methylation and RNA-Seq data showed that gene methylation and its transcriptional levels were comprehensively correlated. The location of methylation markers in terms of distance to transcription start site and CpG island showed no effects on the regulation of gene expression by DNA methylation in normal tissues.

Conclusions

This study showed that an integrative analysis of methylation array and RNA-Seq data can be utilized to discover the global regulation of gene expression by DNA methylation and suggests that DNA methylation plays an important role in normal tissue differentiation via modulation of gene expression.

Background

Methylation of CpG dinucleotides is a fundamental mechanism of epigenetic regulation in eukaryotic genomes. Development of methods for rapid genome wide methylation profiling will greatly facilitate both hypothesis and discovery driven research in the field of epigenetics. In this regard, a single molecule approach to methylation profiling offers several unique advantages that include elimination of chemical DNA modification steps and PCR amplification.

Results

A single molecule approach is presented for the discernment of methylation profiles, based on optical mapping. We report results from a series of pilot studies demonstrating the capabilities of optical mapping as a platform for methylation profiling of whole genomes. Optical mapping was used to discern the methylation profile from both an engineered and wild type Escherichia coli. Furthermore, the methylation status of selected loci within the genome of human embryonic stem cells was profiled using optical mapping.

Conclusion

The optical mapping platform effectively detects DNA methylation patterns. Due to single molecule detection, optical mapping offers significant advantages over other technologies. This advantage stems from obviation of DNA modification steps, such as bisulfite treatment, and the ability of the platform to assay repeat dense regions within mammalian genomes inaccessible to techniques using array-hybridization technologies.