Applications of CpG island microarrays for high-throughput analysis of DNA methylation

Detection of DNA Methylation by MeDIP and MBDCap Assays: An Overview of Techniques

DNA methylation has been characterized as the representative example of epigenetic modifications and implicated in numerous biological processes, such as genomic imprinting and X chromosome inactivation. It primarily occurs at CpG dinucleotides in mammals and plays a critical role in transcriptional regulations. Examination of DNA methylation patterns in gene(s) or across a genome is vital to understand the role of epigenetic modulation in the progress of development and tumorigenesis. Currently, lots of approaches have been developed to investigate DNA methylation patterns for either limited regions or genome-scale studies, but some of them rely on using restriction enzymes. In this chapter, we describe two commonly used protocols to detect enrichment of methylated DNA regions, namely methylated immunoprecipitation (MeDIP) and capture of methylated DNA by methyl-CpG binding domain-based (MBD) proteins (MBDCap). They are the most economical and effective methods to study DNA methylation in either single locus or genome-wide scale.

Towards DNA methylation detection using biosensors

DNA methylation, a stable and heritable covalent modification which mostly occurs in the context of a CpG dinucleotide, has great potential as a biomarker to detect disease, provide prognoses and predict therapeutic responses. It can be detected in a quantitative manner by many different approaches both genome-wide and at specific gene loci, in various biological fluids such as urine, plasma, and serum, which can be obtained without invasive procedures. The current, classical methods are effective in studying DNA methylation patterns, however, for the most part; they have major drawbacks such as expensive instruments, complicated and time consuming protocols as well as relatively low sensitivity, and high false positive rates. To overcome these obstacles, great efforts have been made toward the development of reliable sensor devices to solve these limitations, providing sensitive, fast and cost-effective measurements. The use of biosensors for DNA methylation biomarkers has increased in recent years, because they are portable, simple, rapid, and inexpensive which offers a straightforward way to detect methylated biomarkers. In this review, we give an overview of the conventional techniques for the detection of DNA methylation and then will focus on recent advances in biosensor based methylation detection that eliminate bisulfite conversion and PCR amplification.

Integrative analysis of gene expression and methylation data for breast cancer cell lines

Background

The deadly costs of cancer and necessity for an accurate method of early cancer detection have demanded the identification of genetic and epigenetic factors associated with cancer. DNA methylation, an epigenetic event, plays an important role in cancer susceptibility. In this paper, we use DNA methylation and gene expression data integration and pathway analysis to further explore and understand the complex relationship between methylation and gene expression.

Results

Through linear modeling and analysis of variance, we obtain genes that show a significant correlation between methylation and gene expression. We then examine the functions and relationships of these genes using bioinformatic tools and databases. In particular, using ConsensusPathDB, we analyze the networks of statistically significant genes to identify hub genes, genes with a large number of links to other genes. We identify eight major hub genes, all in strong association with cancer susceptibility. Through further analysis of the function, gene expression level, and methylation level of these hub genes, we conclude that they are novel potential biomarkers for breast cancer.

Conclusions

Our findings have various implications for cancer screening, early detection methods, and potential novel treatments for cancer. Researchers can also use our results to develop more effective methods for cancer study.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0174-8) contains supplementary material, which is available to authorized users.

Gamma-Normal-Gamma Mixture Model for Detecting Differentially Methylated Loci in Three Breast Cancer Cell Lines

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data.

Epigenetic regulation of cognition: A circumscribed review of the field

The last decade has been marked by an increased interest in relating epigenetic mechanisms to complex human behaviors, although this interest has not been balanced, accentuating various types of affective and primarily ignoring cognitive functioning. Recent animal model data support the view that epigenetic processes play a role in learning and memory consolidation and help transmit acquired memories even across generations. In this review, we provide an overview of various types of epigenetic mechanisms in the brain (DNA methylation, histone modification, and noncoding RNA action) and discuss their impact proximally on gene transcription, protein synthesis, and synaptic plasticity and distally on learning, memory, and other cognitive functions. Of particular importance are observations that neuronal activation regulates the dynamics of the epigenome's functioning under precise timing, with subsequent alterations in the gene expression profile. In turn, epigenetic regulation impacts neuronal action, closing the circle and substantiating the signaling pathways that underlie, at least partially, learning, memory, and other cognitive processes.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential

BACKGROUND

Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics.

RESULTS

These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses.

CONCLUSION

This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

Integrated analysis of epigenomic and genomic changes by DNA methylation dependent mechanisms provides potential novel biomarkers for prostate cancer

Epigenetic silencing mediated by CpG methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with tumor progression may identify potential prognostic markers for prostate cancer (PCa). We treated two PCa cell lines, 22Rv1 and DU-145 with the demethylating agent 5-Aza 2’–deoxycitidine (DAC) and global methylation status was analyzed by performing methylation-sensitive restriction enzyme based differential methylation hybridization strategy followed by genome-wide CpG methylation array profiling. In addition, we examined gene expression changes using a custom microarray. Gene Set Enrichment Analysis (GSEA) identified the most significantly dysregulated pathways. In addition, we assessed methylation status of candidate genes that showed reduced CpG methylation and increased gene expression after DAC treatment, in Gleason score (GS) 8 vs. GS6 patients using three independent cohorts of patients; the publically available The Cancer Genome Atlas (TCGA) dataset, and two separate patient cohorts. Our analysis, by integrating methylation and gene expression in PCa cell lines, combined with patient tumor data, identified novel potential biomarkers for PCa patients. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.

Methylation-associated gene silencing of RARB in areca carcinogens induced mouse oral squamous cell carcinoma

Regarding oral squamous cell carcinoma (OSCC) development, chewing areca is known to be a strong risk factor in many Asian cultures. Therefore, we established an OSCC induced mouse model by 4-nitroquinoline-1-oxide (4-NQO), or arecoline, or both treatments, respectively. These are the main two components of the areca nut that could increase the occurrence of OSCC. We examined the effects with the noncommercial MCGI (mouse CpG islands) microarray for genome-wide screening the DNA methylation aberrant in induced OSCC mice. The microarray results showed 34 hypermethylated genes in 4-NQO plus arecoline induced OSCC mice tongue tissues. The examinations also used methylation-specific polymerase chain reaction (MS-PCR) and bisulfite sequencing to realize the methylation pattern in collected mouse tongue tissues and human OSCC cell lines of different grades, respectively. These results showed that retinoic acid receptor β (RARB) was indicated in hypermethylation at the promoter region and the loss of expression during cancer development. According to the results of real-time PCR, it was shown that de novo DNA methyltransferases were involved in gene epigenetic alternations of OSCC. Collectively, our results showed that RARB hypermethylation was involved in the areca-associated oral carcinogenesis.

Deciphering the epigenetic code: an overview of DNA methylation analysis methods

SIGNIFICANCE

Methylation of cytosine in DNA is linked with gene regulation, and this has profound implications in development, normal biology, and disease conditions in many eukaryotic organisms. A wide range of methods and approaches exist for its identification, quantification, and mapping within the genome. While the earliest approaches were nonspecific and were at best useful for quantification of total methylated cytosines in the chunk of DNA, this field has seen considerable progress and development over the past decades.

RECENT ADVANCES

Methods for DNA methylation analysis differ in their coverage and sensitivity, and the method of choice depends on the intended application and desired level of information. Potential results include global methyl cytosine content, degree of methylation at specific loci, or genome-wide methylation maps. Introduction of more advanced approaches to DNA methylation analysis, such as microarray platforms and massively parallel sequencing, has brought us closer to unveiling the whole methylome.

CRITICAL ISSUES

Sensitive quantification of DNA methylation from degraded and minute quantities of DNA and high-throughput DNA methylation mapping of single cells still remain a challenge.

FUTURE DIRECTIONS

Developments in DNA sequencing technologies as well as the methods for identification and mapping of 5-hydroxymethylcytosine are expected to augment our current understanding of epigenomics. Here we present an overview of methodologies available for DNA methylation analysis with special focus on recent developments in genome-wide and high-throughput methods. While the application focus relates to cancer research, the methods are equally relevant to broader issues of epigenetics and redox science in this special forum.

Putting the 'epi' into epigenetics research in psychiatry

During the past two decades, research concerned with the aetiology of psychopathology has generally progressed along two separate paths: investigations that have characterised the roles played by environmental determinants such as childhood adversity in the development of psychopathology, and those that have focused on neurobiological processes involving genetic and intracellular pathways. Epigenetic modifications, functionally relevant changes to gene expression that do not reflect changes in gene sequence, may explain how environmental exposures 'get under the skin' to modify the expression of genes and produce phenotypic variability. The potential of epigenetic research to unify two disparate strands of inquiry has contributed to substantial, and growing, interest in epigenetics in mental health research. However, there are several challenges with which investigators must contend in studies considering the role of epigenetic modifications in psychopathology. These include the development of causal models in study design, considerations about sample size and generalisability, and robust measurement of epigenetic modification. We employ an epidemiological lens to discuss these challenges and to provide recommendations for future studies in this area.

Empirical bayes model comparisons for differential methylation analysis

A number of empirical Bayes models (each with different statistical distribution assumptions) have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs)), the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division—(1, 3, and 5) and drug-treatment-(cisplatin) dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.

Epigenetic modifications associated with suicide and common mood and anxiety disorders: a systematic review of the literature

Epigenetic modifications are those reversible, mitotically heritable alterations in genomic expression that occur independent of changes in gene sequence. Epigenetic studies have the potential to improve our understanding of the etiology of mood and anxiety disorders and suicide by bridging the gap in knowledge between the exogenous environmental exposures and pathophysiology that produce common mood and anxiety disorders and suicide. We systematically reviewed the English-language peer-reviewed literature about epigenetic regulation in these disorders between 2001–2011, summarizing and synthesizing this literature with respect to directions for future work. Twenty-one articles met our inclusion criteria. Twelve studies were concerned with epigenetic changes among suicide completers; other studies considered epigenetic regulation in depression, post-traumatic stress disorder, and panic disorder. Several studies focused on epigenetic regulation of amine, glucocorticoid, and serotonin metabolism in the production of common mood and anxiety disorders and suicide. The literature is nascent and has yet to reach consensus about the roles of particular epigenetic modifications in the etiology of these outcomes. Future studies require larger sample sizes and measurements of environmental exposures antecedent to epigenetic modification. Further work is also needed to clarify the link between epigenetic modifications in the brain and peripheral tissues and to establish ‘gold standard’ epigenetic assays.

Hypermethylation of the gene LARP2 for noninvasive prenatal diagnosis of β-thalassemia based on DNA methylation profile

In order to identify epigenetic markers of β-thalassemia, a genome-wide profiling method named differential methylation hybridization was used to search these differentially methylated genes. Unsupervised hierarchical clustering and molecular annotation system were used to analyze the data, and methylation-specific PCR and real-time PCR were used to confirm the differentially methylated genes. This system was validated by detecting 13 cases, 10 of which were homo-zygous β-thalassaemia. Totally 113 genes were identified as methlyation-enriched genes (ratio ≥ 2.0, P < 0.05) and 96 genes were identified as hypomethylated genes in both groups (ratio ≤ 0.5, P < 0.05). The promoter of the gene of La ribonucleoprotein domain family (LARP2) was significantly hypermethylated in β-thalassemia, and the expression of LARP2 was significantly lower in β-thalassemia. Hypermethylation of the LARP2 promoter was correlated with its lower expression in β-thalassemia and our chip-based DNA methylation detection system can provide earlier diagnosis of β-thalassemia using this epigenetic marker.

Hypermethylation of IGSF4 gene for noninvasive prenatal diagnosis of thalassemia

Background

For patients with pregnancy-induced thalassemia, fetal cord blood or amniotic fluid is invasively collected in the traditional diagnosis and prediction of thalassemia. However, there is no specific molecular target in the diagnosis of thalassemia using fetal DNA from the plasma of pregnant women.

Material/Methods

The promoter of cell surface adhesion molecule (IGSF4) gene was found to be down-regulated in patients with homozygous thalassemia, and the expression of IGSF4 was closely associated with the methylation of its promoter. In the present study, mass spectrometric sequencing of methylation was performed using MassARRAY to detect the 12 CpG sites in the promoter of IGSF4 gene.

Results

The methylation degree of these 12 CpG sites was significantly higher than that in healthy subjects (P<0.05). Hierarchical clustering was done in 23 patients with thalassemia and 5 healthy individuals. Results revealed the promoter of IGSF4 gene was highly methylated in thalassemia patients, which was dramatically different from that in healthy subjects (P<0.05). Methylation-specific PCR (MSP) was employed to confirm the methylation of the promoter of IGSF4 gene and results were consistence with those obtained in sequencing with MassARRAY. Real-time PCR showed, when compared with heterozygous subjects, the expression of IGSF4 was significantly down-regulated in thalassemia patients (ratio=0.18).

Conclusions

The expression of IGSF4 was closely related to the methylation of its promoter, suggesting the methylation of IGSF4 gene is tissue-specific for thalassemia. These findings provide evidence for the non-invasive prenatal diagnosis of thalassemia in terms of epigenetics.

Methods in DNA methylation profiling

Metastable and somatically heritable patterns of DNA methylation provide an important level of genomic regulation. In this article, we review methods for analyzing these genome-wide epigenetic patterns and offer a perspective on the ever-expanding literature, which we hope will be useful for investigators who are new to this area. The historical aspects that we cover will be helpful in interpreting this literature and we hope that our discussion of the newest analytical methods will stimulate future progress. We emphasize that no single approach can provide a complete view of the overall methylome, and that combinations of several modalities applied to the same sample set will give the clearest picture. Given the unexpected epigenomic patterns and new biological principles, as well as new disease markers, that have been uncovered in recent studies, it is likely that important discoveries will continue to be made using genome-wide DNA methylation profiling.

Emerging promise of epigenetics and DNA methylation for the diagnosis and management of women's cancers

Over the last two decades, survival rates from women's cancers (breast, ovarian, endometrial and cervical cancer) have all but modestly improved despite huge efforts from both research and clinical communities. In parallel with this, the field of epigenetics has grown from its infancy into a promising scientific discipline. In particular, DNA methylation analysis has been adopted by oncologists in an attempt to better understand and manage cancer. Now that the epigenetic technological base has caught up, the potential of methylation markers in cancer research is finally being realized. In this review, we present the current status of epigenetic research into women's cancers with a main focus on DNA methylation analysis. We provide an overview of technological development, current markers of risk prediction, early detection, diagnosis, prognosis and response to treatment, and highlight the progression of epigenetic therapies. Finally, we comment on the potential impact of epigenetic analyses on the future of women's health.

DNA methylation as clinically useful biomarkers-light at the end of the tunnel

A recent expansion of our knowledge about epigenetic changes strongly suggests that epigenetic rather than genetic features better reflect disease development, and consequently, can become more conclusive biomarkers for the detection and diagnosis of different diseases. In this paper we will concentrate on the current advances in DNA methylation studies that demonstrate a direct link between abnormal DNA methylation and a disease. This link can be used to develop diagnostic biomarkers that will precisely identify a particular disease. It also appears that disease-specific DNA methylation patterns undergo unique changes in response to treatment with a particular drug, thus raising the possibility of DNA methylation-based biomarkers for the monitoring of treatment efficacy, for prediction of response to treatment, and for the prognosis of outcome. While biomarkers for oncology are the most obvious applications, other fields of medicine are likely to benefit as well. This potential is demonstrated by DNA methylation-based biomarkers for neurological and psychiatric diseases. A special requirement for a biomarker is the possibility of longitudinal testing. In this regard cell-free circulating DNA from blood is especially interesting because it carries methylation markers specific for a particular disease. Although only a few DNA methylation-based biomarkers have attained clinical relevance, the ongoing efforts to decipher disease-specific methylation patterns are likely to produce additional biomarkers for detection, diagnosis, and monitoring of different diseases in the near future.

Preprocessing differential methylation hybridization microarray data

Background

DNA methylation plays a very important role in the silencing of tumor suppressor genes in various tumor types. In order to gain a genome-wide understanding of how changes in methylation affect tumor growth, the differential methylation hybridization (DMH) protocol has been developed and large amounts of DMH microarray data have been generated. However, it is still unclear how to preprocess this type of microarray data and how different background correction and normalization methods used for two-color gene expression arrays perform for the methylation microarray data. In this paper, we demonstrate our discovery of a set of internal control probes that have log ratios (M) theoretically equal to zero according to this DMH protocol. With the aid of this set of control probes, we propose two LOESS (or LOWESS, locally weighted scatter-plot smoothing) normalization methods that are novel and unique for DMH microarray data. Combining with other normalization methods (global LOESS and no normalization), we compare four normalization methods. In addition, we compare five different background correction methods.

Results

We study 20 different preprocessing methods, which are the combination of five background correction methods and four normalization methods. In order to compare these 20 methods, we evaluate their performance of identifying known methylated and un-methylated housekeeping genes based on two statistics. Comparison details are illustrated using breast cancer cell line and ovarian cancer patient methylation microarray data. Our comparison results show that different background correction methods perform similarly; however, four normalization methods perform very differently. In particular, all three different LOESS normalization methods perform better than the one without any normalization.

Conclusions

It is necessary to do within-array normalization, and the two LOESS normalization methods based on specific DMH internal control probes produce more stable and relatively better results than the global LOESS normalization method.

Identification of novel DNA methylation inhibitors via a two-component reporter gene system

Background

Targeting abnormal DNA methylation represents a therapeutically relevant strategy for cancer treatment as demonstrated by the US Food and Drug Administration approval of the DNA methyltransferase inhibitors azacytidine and 5-aza-2'-deoxycytidine for the treatment of myelodysplastic syndromes. But their use is associated with increased incidences of bone marrow suppression. Alternatively, procainamide has emerged as a potential DNA demethylating agent for clinical translation. While procainamide is much safer than 5-aza-2'-deoxycytidine, it requires high concentrations to be effective in DNA demethylation in suppressing cancer cell growth. Thus, our laboratories have embarked on the pharmacological exploitation of procainamide to develop potent DNA methylation inhibitors through lead optimization.

Methods

We report the use of a DNA methylation two-component enhanced green fluorescent protein reporter system as a screening platform to identify novel DNA methylation inhibitors from a compound library containing procainamide derivatives.

Results

A lead agent IM25, which exhibits substantially higher potency in GSTp1 DNA demethylation with lower cytotoxicity in MCF7 cells relative to procainamide and 5-aza-2'-deoxycytidine, was identified by the screening platform.

Conclusions

Our data provide a proof-of-concept that procainamide could be pharmacologically exploited to develop novel DNA methylation inhibitors, of which the translational potential in cancer therapy/prevention is currently under investigation.

Systematic evaluation of genome-wide methylated DNA enrichment using a CpG island array

Background

Recent progress in high-throughput technologies has greatly contributed to the development of DNA methylation profiling. Although there are several reports that describe methylome detection of whole genome bisulfite sequencing, the high cost and heavy demand on bioinformatics analysis prevents its extensive application. Thus, current strategies for the study of mammalian DNA methylomes is still based primarily on genome-wide methylated DNA enrichment combined with DNA microarray detection or sequencing. Methylated DNA enrichment is a key step in a microarray based genome-wide methylation profiling study, and even for future high-throughput sequencing based methylome analysis.

Results

In order to evaluate the sensitivity and accuracy of methylated DNA enrichment, we investigated and optimized a number of important parameters to improve the performance of several enrichment assays, including differential methylation hybridization (DMH), microarray-based methylation assessment of single samples (MMASS), and methylated DNA immunoprecipitation (MeDIP). With advantages and disadvantages unique to each approach, we found that assays based on methylation-sensitive enzyme digestion and those based on immunoprecipitation detected different methylated DNA fragments, indicating that they are complementary in their relative ability to detect methylation differences.

Conclusions

Our study provides the first comprehensive evaluation for widely used methodologies for methylated DNA enrichment, and could be helpful for developing a cost effective approach for DNA methylation profiling.

Profiling epigenetic alterations in disease

Nowadays, epigenetics is one of the fastest growing research areas in biomedicine. Studies have demonstrated that changes in the epigenome are not only common in cancer, but are also involved in the pathogenesis of noncancerous diseases like immunological, cardiovascular, developmental and neurological/psychiatric disorders. At the same time, during the last years, a technological revolution has taken place in the field of epigenomics, which is defined as the study of epigenetic changes throughout the whole genome. Microarray technologies and more recently, the development of next generation sequencing devices are now providing researchers with tools to draw high-resolution maps of DNA methylation and histone modifications in normal tissues and diseases. This chapter will review the currently available high-throughput techniques for studying the epigenome and their applications for characterizing human diseases.

Aberrant methylation impairs low density lipoprotein receptor-related protein 1B tumor suppressor function in gastric cancer

DNA methylation plays a significant role in tumor progression. In this study, we used CpG microarray and differential methylation hybridization approaches to identify low density lipoprotein receptor-related protein 1B (LRP1B) as a novel epigenetic target in gastric cancer. LRP1B was hypermethylated in four gastric cancer cell lines, and low LRP1B mRNA expression was associated with high methylation levels in gastric cancer cell lines. Addition of a DNA methylation inhibitor (5-Aza-dC) restored the mRNA expression of LRP1B in these cell lines, indicating that DNA methylation is involved in regulating LRP1B expression. In 45 out of 74 (61%) clinical samples, LRP1B was highly methylated; LRP1B mRNA expression was significantly lower in 15 out of 19 (79%, P < 0.001) gastric tumor tissues than in corresponding adjacent normal tissues. In addition, ectopic expression of mLRP1B4 in gastric cancer cell lines suppressed cell growth, colony formation and tumor formation in nude mice. These results collectively indicate that LRP1B is a functional tumor suppressor gene in gastric cancer and that is regulated by DNA methylation.

Glutamate receptor, ionotropic, kainate 2 silencing by DNA hypermethylation possesses tumor suppressor function in gastric cancer

Aberrant DNA methylation is considered a major mechanism for silencing tumor suppressor genes in gastric cancer. We used CpG microarray and differential methylation hybridization strategies to identify potential tumor suppressor genes and recovered glutamate receptor, ionotropic, kainate 2 (GRIK2) as a novel epigenetic target in gastric cancer. Additional experiments showed that the promoter region of GRIK2 was hypermethylated in 3 of the 4 tested gastric cancer cell lines, and its expression was restored by treatment of cells with the DNA methylation inhibitor, 5'-aza-dC. In clinical samples, the GRIK2 promoter was differentially hypermethylated in tumor tissues compared with adjacent normal tissues (p < 0.001), and this methylation was inversely correlated with the expression level of GRIK2 mRNA (r = -0.44). Functional studies further showed that GRIK2-expressing gastric cancer cell lines showed decreased colony formation and cell migration. Taken together, these results suggest that GRIK2 may play a tumor-suppressor role in gastric cancer. Future studies are warranted to examine whether DNA hypermethylation of the GRIK2 promoter can be used as a potential tumor marker for gastric cancer.

Methylated DNA immunoprecipitation and microarray-based analysis: detection of DNA methylation in breast cancer cell lines

The methylated DNA immunoprecipitation microarray (MeDIP-chip) is a genome-wide, high-resolution approach to detect DNA methylation in whole genome or CpG (cytosine base followed by a guanine base) islands. The method utilizes anti-methylcytosine antibody to immunoprecipitate DNA that contains highly methylated CpG sites. Enriched methylated DNA can be interrogated using DNA microarrays or by massive parallel sequencing techniques. This combined approach allows researchers to rapidly identify methylated regions in a genome-wide manner, and compare DNA methylation patterns between two samples with diversely different DNA methylation status. MeDIP-chip has been applied successfully for analyses of methylated DNA in the different targets including animal and plant tissues. Here we present a MeDIP-chip protocol that is routinely used in our laboratory, illustrated with specific examples from MeDIP-chip analysis of breast cancer cell lines. Potential technical pitfalls and solutions are also provided to serve as workflow guidelines.

Experimental approaches to the study of epigenomic dysregulation in ageing

In this review, we describe how normal ageing may involve the acquisition of epigenetic errors over time, akin to the accumulation of genetic mutations with ageing. We describe how such experiments are currently performed, their limitations technically and analytically and their application to ageing research.

Identifying differentially methylated genes using mixed effect and generalized least square models

Background

DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.

Results

We propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.

Conclusions

We demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.

Tissue-specific variation in DNA methylation levels along human chromosome 1

BACKGROUND

DNA methylation is a major epigenetic modification important for regulating gene expression and suppressing spurious transcription. Most methods to scan the genome in different tissues for differentially methylated sites have focused on the methylation of CpGs in CpG islands, which are concentrations of CpGs often associated with gene promoters.

RESULTS

Here, we use a methylation profiling strategy that is predominantly responsive to methylation differences outside of CpG islands. The method compares the yield from two samples of size-selected fragments generated by a methylation-sensitive restriction enzyme. We then profile nine different normal tissues from two human donors relative to spleen using a custom array of genomic clones covering the euchromatic portion of human chromosome 1 and representing 8% of the human genome. We observe gross regional differences in methylation states across chromosome 1 between tissues from the same individual, with the most striking differences detected in the comparison of cerebellum and spleen. Profiles of the same tissue from different donors are strikingly similar, as are the profiles of different lobes of the brain. Comparing our results with published gene expression levels, we find that clones exhibiting extreme ratios reflecting low relative methylation are statistically enriched for genes with high expression ratios, and vice versa, in most pairs of tissues examined.

CONCLUSION

The varied patterns of methylation differences detected between tissues by our methylation profiling method reinforce the potential functional significance of regional differences in methylation levels outside of CpG islands.

Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer

Background

Cisplatin and carboplatin are the primary first-line therapies for the treatment of ovarian cancer. However, resistance to these platinum-based drugs occurs in the large majority of initially responsive tumors, resulting in fully chemoresistant, fatal disease. Although the precise mechanism(s) underlying the development of platinum resistance in late-stage ovarian cancer patients currently remains unknown, CpG-island (CGI) methylation, a phenomenon strongly associated with aberrant gene silencing and ovarian tumorigenesis, may contribute to this devastating condition.

Methods

To model the onset of drug resistance, and investigate DNA methylation and gene expression alterations associated with platinum resistance, we treated clonally derived, drug-sensitive A2780 epithelial ovarian cancer cells with increasing concentrations of cisplatin. After several cycles of drug selection, the isogenic drug-sensitive and -resistant pairs were subjected to global CGI methylation and mRNA expression microarray analyses. To identify chemoresistance-associated, biological pathways likely impacted by DNA methylation, promoter CGI methylation and mRNA expression profiles were integrated and subjected to pathway enrichment analysis.

Results

Promoter CGI methylation revealed a positive association (Spearman correlation of 0.99) between the total number of hypermethylated CGIs and GI₅₀ values (i.e., increased drug resistance) following successive cisplatin treatment cycles. In accord with that result, chemoresistance was reversible by DNA methylation inhibitors. Pathway enrichment analysis revealed hypermethylation-mediated repression of cell adhesion and tight junction pathways and hypomethylation-mediated activation of the cell growth-promoting pathways PI3K/Akt, TGF-beta, and cell cycle progression, which may contribute to the onset of chemoresistance in ovarian cancer cells.

Conclusion

Selective epigenetic disruption of distinct biological pathways was observed during development of platinum resistance in ovarian cancer. Integrated analysis of DNA methylation and gene expression may allow for the identification of new therapeutic targets and/or biomarkers prognostic of disease response. Finally, our results suggest that epigenetic therapies may facilitate the prevention or reversal of transcriptional repression responsible for chemoresistance and the restoration of sensitivity to platinum-based chemotherapeutics.

A Robust Unified Approach to Analyzing Methylation and Gene Expression Data

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines.

Genotyping Arrays

Although the most common use of DNA microarrays is gene expression profiling, microarrays are also used for many other applications, including genotyping, resequencing, SNP analysis, and DNA methylation assays. Here we describe genotyping arrays for Influenza A subtype identification and for upper respiratory pathogen diagnostics using standard hybridization techniques and we also describe resequencing, SNP, and methylation assays using an enzyme-based strategy [25, 26].

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands

Background

Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).

Results

MLDA was programmed in R (version 2.7.0) and the package is available at CRAN [1]. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing.

Conclusion

MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

DNA methylation profiles in diffuse large B-cell lymphoma and their relationship to gene expression status

In an initial epigenetic characterization of diffuse large B-cell lymphoma (DLBCL), we evaluated the DNA methylation levels of over 500 CpG islands. Twelve CpG islands (AR, CDKN1C, DLC1, DRD2, GATA4, GDNF, GRIN2B, MTHFR, MYOD1, NEUROD1, ONECUT2 and TFAP2A) showed significant methylation in over 85% of tumors. Interestingly, the methylation levels of a CpG island proximal to FLJ21062 differed between the activated B-cell-like (ABC-DLBCL) and germinal center B-cell-like (GCB-DLBCL) subtypes. In addition, we compared the methylation and expression status of 67 genes proximal (within 500 bp) to the methylation assays. We frequently observed that hypermethylated CpG islands are proximal to genes that are expressed at low or undetectable levels in tumors. However, many of these same genes were also poorly expressed in DLBCL tumors where their cognate CpG islands were hypomethylated. Nevertheless, the proportional reductions in BNIP3, MGMT, RBP1, GATA4, IGSF4, CRABP1 and FLJ21062 expression with increasing methylation suggest that epigenetic processes strongly influence these genes. Lastly, the moderate expression of several genes proximal to hypermethylated CpG tracts suggests that DNA methylation assays are not always accurate predictors of gene silencing. Overall, further investigation of the highlighted CpG islands as potential clinical biomarkers is warranted.

Methods of DNA methylation analysis

PURPOSE OF REVIEW

To provide guidance for investigators who are new to the field of DNA methylation analysis.

RECENT FINDINGS

Epigenetics is the study of mitotically heritable alterations in gene expression potential that are not mediated by changes in DNA sequence. Recently, it has become clear that nutrition can affect epigenetic mechanisms, causing long-term changes in gene expression. This review focuses on methods for studying the epigenetic mechanism DNA methylation. Recent advances include improvement in high-throughput methods to obtain quantitative data on locus-specific DNA methylation and development of various approaches to study DNA methylation on a genome-wide scale.

SUMMARY

No single method of DNA methylation analysis will be appropriate for every application. By understanding the type of information provided by, and the inherent potential for bias and artifact associated with, each method, investigators can select the method most appropriate for their specific research needs.

MMASS: an optimized array-based method for assessing CpG island methylation

We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.

Prognostic DNA methylation biomarkers in ovarian cancer

PURPOSE

Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers.

EXPERIMENTAL DESIGN

We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron.

CONCLUSION

In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.

LUMA (LUminometric Methylation Assay)—A high throughput method to the analysis of genomic DNA methylation

Changes in genomic DNA methylation are recognized as important events in normal and pathological cellular processes, contributing both to normal development and differentiation as well as cancer and other diseases. Here, we report a novel method to estimate genome-wide DNA methylation, referred to as LUminometric Methylation Assay (LUMA). The method is based on combined DNA cleavage by methylation-sensitive restriction enzymes and polymerase extension assay by Pyrosequencing. The method is quantitative, highly reproducible and easy to scale up. Since no primary modification of genomic DNA, such as bisulfite treatment, is needed, the total assay time is only 6 h. In addition, the assay requires only 200-500 ng of genomic DNA and incorporates an internal control to eliminate the problem of varying amounts of starting DNA. The accuracy and linearity of LUMA were verified by in vitro methylated lambda DNA. In addition, DNA methylation levels were assessed by LUMA in DNA methyltransferase knock-out cell lines and after treatment with the DNA methyltransferase inhibitor (5-AzaCytidine). The LUMA assay may provide a useful method to analyze genome-wide DNA methylation for a variety of physiological and pathological conditions including etiologic, diagnostic and prognostic aspects of cancer.

Comparative isoschizomer profiling of cytosine methylation: the HELP assay

The distribution of cytosine methylation in 6.2 Mb of the mouse genome was tested using cohybridization of genomic representations from a methylation-sensitive restriction enzyme and its methylation-insensitive isoschizomer. This assay, termed HELP (HpaII tiny fragment Enrichment by Ligation-mediated PCR), allows both intragenomic profiling and intergenomic comparisons of cytosine methylation. The intragenomic profile shows most of the genome to be contiguous methylated sequence with occasional clusters of hypomethylated loci, usually but not exclusively at promoters and CpG islands. Intergenomic comparison found marked differences in cytosine methylation between spermatogenic and brain cells, identifying 223 new candidate tissue-specific differentially methylated regions (T-DMRs). Bisulfite pyrosequencing confirmed the four candidates tested to be T-DMRs, while quantitative RT-PCR for two genes with T-DMRs located at their promoters showed the HELP data to be correlated with gene activity at these loci. The HELP assay is robust, quantitative, and accurate and is providing new insights into the distribution and dynamic nature of cytosine methylation in the genome.

Microarray-based DNA methylation profiling: technology and applications

This work is dedicated to the development of a technology for unbiased, high-throughput DNA methylation profiling of large genomic regions. In this method, unmethylated and methylated DNA fractions are enriched using a series of treatments with methylation sensitive restriction enzymes, and interrogated on microarrays. We have investigated various aspects of the technology including its replicability, informativeness, sensitivity and optimal PCR conditions using microarrays containing oligonucleotides representing 100 kb of genomic DNA derived from the chromosome 22 COMT region in addition to 12 192 element CpG island microarrays. Several new aspects of methylation profiling are provided, including the parallel identification of confounding effects of DNA sequence variation, the description of the principles of microarray design for epigenomic studies and the optimal choice of methylation sensitive restriction enzymes. We also demonstrate the advantages of using the unmethylated DNA fraction versus the methylated one, which substantially improve the chances of detecting DNA methylation differences. We applied this methodology for fine-mapping of methylation patterns of chromosomes 21 and 22 in eight individuals using tiling microarrays consisting of over 340 000 oligonucleotide probe pairs. The principles developed in this work will help to make epigenetic profiling of the entire human genome a routine procedure.

Complex disease, gender and epigenetics

Gender differences in susceptibility to complex disease such as asthma, diabetes, lupus, autism and major depression, among numerous other disorders, represent one of the hallmarks of non-Mendelian biology. It has been generally accepted that endocrinological differences are involved in the sexual dimorphism of complex disease; however, specific molecular mechanisms of such hormonal effects have not been elucidated yet. This paper will review evidence that sex hormone action may be mediated via gene-specific epigenetic modifications of DNA and histones. The epigenetic modifications can explain sex effects at DNA sequence polymorphisms and haplotypes identified in gender-stratified genetic linkage and association studies. Hormone-induced DNA methylation and histone modification changes at specific gene regulatory regions may increase or reduce the risk of a disease. The epigenetic interpretation of sexual dimorphism fits well into the epigenetic theory of complex disease, which argues for the primary pathogenic role of inherited and/or acquired epigenetic misregulation rather than DNA sequence variation. The new experimental strategies, especially the high throughput microarray-based epigenetic profiling, can be used for testing the epigenetic hypothesis of gender effects in complex diseases.

Tilling the chromatin landscape: emerging methods for the discovery and profiling of protein-DNA interactions

Interactions between protein and DNA are essential for cellular function. The incremental process of developing global approaches to study chromatin began with the in vitro characterization of chromatin structural components and modifications of the versatile chromatin immunoprecipitation (ChIP) assay, capable of analyzing protein-DNA interactions in vivo. Among the emerging global approaches are ChIP cloning, ChIP display, differential chromatin scanning, ChIP-chip, DamID chromatin profiling, and chromatin array. These methods have been used to assess transcription-factor binding and (or) histone modification. This review describes these global methods and illustrates their potential in answering biological questions.

Epigenetic profiling of cutaneous T-cell lymphoma: promoter hypermethylation of multiple tumor suppressor genes including BCL7a, PTPRG, and p73

PURPOSE

To analyze the occurrence of promoter hypermethylation in primary cutaneous T-cell lymphoma (CTCL) on a genome-wide scale, focusing on epigenetic alterations with pathogenetic significance.

MATERIALS AND METHODS

DNA isolated from biopsy specimens of 28 patients with CTCL, including aggressive CTCL entities (transformed mycosis fungoides and CD30-negative large T-cell lymphoma) and an indolent entity (CD30-positive large T-cell lymphoma), were investigated. For genome-wide DNA methylation screening, differential methylation hybridization using CpG island microarrays was applied, which allows simultaneous detection of the methylation status of 8640 CpG islands. Bisulfite sequence analysis was applied for confirmation and detection of hypermethylation of eight selected tumor suppressor genes.

RESULTS

The DNA methylation patterns of CTCLs emerging from differential methylation hybridization analysis included 35 CpG islands hypermethylated in at least four of the 28 studied CTCL samples when compared with benign T-cell samples. Hypermethylation of the putative tumor suppressor genes BCL7a (in 48% of CTCL samples), PTPRG (27%), and thrombospondin 4 (52%) was confirmed and demonstrated to be associated with transcriptional downregulation. BCL7a was hypermethylated at a higher frequency in aggressive (64%) than in indolent (14%) CTCL entities. In addition, the promoters of the selected tumor suppressor genes p73 (48%), p16 (33%), CHFR (19%), p15 (10%), and TMS1 (10%) were hypermethylated in CTCL.

CONCLUSION

Malignant T cells of patients with CTCL display widespread promoter hypermethylation associated with inactivation of several tumor suppressor genes involved in DNA repair, cell cycle, and apoptosis signaling pathways. In view of this, CTCL may be amenable to treatment with demethylating agents.

Workshop on Cancer Biometrics: Identifying Biomarkers and Surrogates of Cancer in Patients

The current excitement about molecular targeted therapies has driven much of the recent dialog in cancer diagnosis and treatment. Particularly in the biologic therapy of cancer, identifiable antigenic T-cell targets restricted by MHC molecules and the related novel stress molecules such as MICA/B and Letal allow a degree of precision previously unknown in cancer therapy. We have previously held workshops on immunologic monitoring and angiogenesis monitoring. This workshop was designed to discuss the state of the art in identification of biomarkers and surrogates of tumor in patients with cancer, with particular emphasis on assays within the blood and tumor. We distinguish this from immunologic monitoring in the sense that it is primarily a measure of the tumor burden as opposed to the immune response to it. Recommendations for intensive investigation and targeted funding to enable such strategies were developed in seven areas: genomic analysis; detection of molecular markers in peripheral blood and lymph node by tumor capture and RT-PCR; serum, plasma, and tumor proteomics; immune polymorphisms; high content screening using flow and imaging cytometry; immunohistochemistry and tissue microarrays; and assessment of immune infiltrate and necrosis in tumors. Concrete recommendations for current application and enabling further development in cancer biometrics are summarized. This will allow a more informed, rapid, and accurate assessment of novel cancer therapies.

The epigenetics of ovarian cancer drug resistance and resensitization

Ovarian cancer is the most lethal of all gynecologic neoplasms. Early-stage malignancy is frequently asymptomatic and difficult to detect and thus, by the time of diagnosis, most women have advanced disease. Most of these patients, although initially responsive, eventually develop and succumb to drug-resistant metastases. The success of typical postsurgical regimens, usually a platinum/taxane combination, is limited by primary tumors being intrinsically refractory to treatment and initially responsive tumors becoming refractory to treatment, due to the emergence of drug-resistant tumor cells. This review highlights a prominent role for epigenetics, particularly aberrant DNA methylation and histone acetylation, in both intrinsic and acquired drug-resistance genetic pathways in ovarian cancer. Administration of therapies that reverse epigenetic "silencing" of tumor suppressors and other genes involved in drug response cascades could prove useful in the management of drug-resistant ovarian cancer patients. In this review, we summarize recent advances in the use of methyltransferase and histone deacetylase inhibitors and possible synergistic combinations of these to achieve maximal tumor suppressor gene re-expression. Moreover, when used in combination with conventional chemotherapeutic agents, epigenetic-based therapies may provide a means to resensitize ovarian tumors to the proven cytotoxic activities of conventional chemotherapeutics.

Using microarrays to predict resistance to chemotherapy in cancer patients

Chemotherapy resistance remains a major obstacle to successful treatment and better outcome in cancer patients. The advent of whole genome experimental strategies, such as DNA microarrays, has transformed the way researchers approach cancer research. There is considerable hope that microarray technology will lead to the identification of new targets for therapeutic intervention, a better understanding of the disease process, and, ultimately, to higher survival rates and more personalized medicine. The question at hand is what is the best approach to apply these new technologies to the study of anticancer drug resistance, and how can the results obtained in the laboratory be quickly moved to a clinical setting? This review offers an overview of the microarray technology, including its recently associated strategies, such as array comparative genomic hybridization and promoter arrays. It also highlights some recent examples of microarray studies, which represent a first step toward a better understanding of drug resistance in cancer and, ultimately, personalized medicine.