Genome-wide analysis of DNA methylation patterns

Cytosine methylation is the most common covalent modification of DNA in eukaryotes. DNA methylation has an important role in many aspects of biology, including development and disease. Methylation can be detected using bisulfite conversion, methylation-sensitive restriction enzymes, methyl-binding proteins and anti-methylcytosine antibodies. Combining these techniques with DNA microarrays and high-throughput sequencing has made the mapping of DNA methylation feasible on a genome-wide scale. Here we discuss recent developments and future directions for identifying and mapping methylation, in an effort to help colleagues to identify the approaches that best serve their research interests.     

Characterization of DNA methylation change in stem cell marker genes during differentiation of human embryonic stem cells

Pluripotent human embryonic stem cells (hESCs) have the distinguishing feature of innate capacity to allow indefinite self-renewal. This attribute continues until specific constraints or restrictions, such as DNA methylation, are imposed on the genome, usually accompanied by differentiation. With the aim of utilizing DNA methylation as a sign of early differentiation, we probed the genomic regions of hESCs, particularly focusing on stem cell marker (SCM) genes to identify regulatory sequences that display differentiation-sensitive alterations in DNA methylation. We show that the promoter regions of OCT4 and NANOG, but not SOX2, REX1 and FOXD3, undergo significant methylation during hESCs differentiation in which SCM genes are substantially repressed. Thus, following exposure to differentiation stimuli, OCT4 and NANOG gene loci are modified relatively rapidly by DNA methylation. Accordingly, we propose that the DNA methylation states of OCT4 and NANOG sequences may be utilized as barometers to determine the extent of hESC differentiation.     

A comparative analysis of DNA methylation across human embryonic stem cell lines

Background  

We performed a comparative analysis of the genome-wide DNA methylation profiles from three human embryonic stem cell (HESC) lines. It had previously been shown that HESC lines had significantly higher non-CG methylation than differentiated cells, and we therefore asked whether these sites were conserved across cell lines.     

Heterogeneity and randomness of DNA methylation patterns in human embryonic stem cells

DNA methylation has been proposed to be important in many biological processes and is the subject of intense study. Traditional bisulfite genomic sequencing allows detailed high-resolution methylation pattern analysis of each molecule with haplotype information across a few hundred bases at each locus, but lacks the capacity to gather voluminous data. Although recent technological developments are aimed at assessing DNA methylation patterns in a high-throughput manner across the genome, the haplotype information cannot be accurately assembled when the sequencing reads are short or when each hybridization target only includes one or two cytosine-phosphate-guanine (CpG) sites. Whether a distinct and nonrandom DNA methylation pattern is present at a given locus is difficult to discern without the haplotype information, and the DNA methylation patterns are much less apparent because the data are often obtained only as methylation frequencies at each CpG site with some of these methods. It would facilitate the interpretation of data obtained from high-throughput bisulfite sequencing if the loci with nonrandom DNA methylation patterns could be distinguished from those that are randomly methylated. In this study, we carried out traditional genomic bisulfite sequencing using the normal diploid human embryonic stem (hES) cell lines, and utilized Hamming distance analysis to evaluate the existence of a distinct and nonrandom DNA methylation pattern at each locus studied. Our findings suggest that Hamming distance is a simple, quick, and useful tool to identify loci with nonrandom DNA methylation patterns and may be utilized to discern links between biological changes and DNA methylation patterns in the high-throughput bisulfite sequencing data sets.     

Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers

Background

Accurate outcome prediction in neuroblastoma, which is necessary to enable the optimal choice of risk-related therapy, remains a challenge. To improve neuroblastoma patient stratification, this study aimed to identify prognostic tumor DNA methylation biomarkers.

Results

To identify genes silenced by promoter methylation, we first applied two independent genome-wide methylation screening methodologies to eight neuroblastoma cell lines. Specifically, we used re-expression profiling upon 5-aza-2''-deoxycytidine (DAC) treatment and massively parallel sequencing after capturing with a methyl-CpG-binding domain (MBD-seq). Putative methylation markers were selected from DAC-upregulated genes through a literature search and an upfront methylation-specific PCR on 20 primary neuroblastoma tumors, as well as through MBD- seq in combination with publicly available neuroblastoma tumor gene expression data. This yielded 43 candidate biomarkers that were subsequently tested by high-throughput methylation-specific PCR on an independent cohort of 89 primary neuroblastoma tumors that had been selected for risk classification and survival. Based on this analysis, methylation of KRT19, FAS, PRPH, CNR1, QPCT, HIST1H3C, ACSS3 and GRB10 was found to be associated with at least one of the classical risk factors, namely age, stage or MYCN status. Importantly, HIST1H3C and GNAS methylation was associated with overall and/or event-free survival.

Conclusions

This study combines two genome-wide methylation discovery methodologies and is the most extensive validation study in neuroblastoma performed thus far. We identified several novel prognostic DNA methylation markers and provide a basis for the development of a DNA methylation-based prognostic classifier in neuroblastoma.     

Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers

ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions and epigenetic modifications. CHANCE (CHip-seq ANalytics and Confidence Estimation) is a standalone package for ChIP-seq quality control and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user's data with ENCODE's large collection of published datasets, performs multi-sample normalization, checks against quantitative PCR-validated control regions, and produces informative graphical reports. CHANCE is available at https://github.com/songlab/chance.     

Genome-wide analysis reveals DNA methylation markers that vary with both age and obesity

The combination of the obesity epidemic and an aging population presents growing challenges for the healthcare system. Obesity and aging are major risk factors for a diverse number of diseases and it is of importance to understand their interaction and the underlying molecular mechanisms. Herein the authors examined the methylation levels of 27578 CpG sites in 46 samples from adult peripheral blood. The effect of obesity and aging was ascertained with general linear models. More than one hundred probes were correlated to aging, nine of which belonged to the KEGG group map04080. Additionally, 10 CpG sites had diverse methylation profiles in obese and lean individuals, one of which was the telomerase catalytic subunit (TERT). In eight of ten cases the methylation change was reverted between obese and lean individuals. One region proved to be differentially methylated with obesity (LINC00304) independent of age. This study provides evidence that obesity influences age driven epigenetic changes, which provides a molecular link between aging and obesity. This link and the identified markers may prove to be valuable biomarkers for the understanding of the molecular basis of aging, obesity and associated diseases.     

Genome-wide screen for differential DNA methylation associated with neural cell differentiation in mouse

Cellular differentiation involves widespread epigenetic reprogramming, including modulation of DNA methylation patterns. Using Differential Methylation Hybridization (DMH) in combination with a custom DMH array containing 51,243 features covering more than 16,000 murine genes, we carried out a genome-wide screen for cell- and tissue-specific differentially methylated regions (tDMRs) in undifferentiated embryonic stem cells (ESCs), in in-vitro induced neural stem cells (NSCs) and 8 differentiated embryonic and adult tissues. Unsupervised clustering of the generated data showed distinct cell- and tissue-specific DNA methylation profiles, revealing 202 significant tDMRs (p<0.005) between ESCs and NSCs and a further 380 tDMRs (p<0.05) between NSCs/ESCs and embryonic brain tissue. We validated these tDMRs using direct bisulfite sequencing (DBS) and methylated DNA immunoprecipitation on chip (MeDIP-chip). Gene ontology (GO) analysis of the genes associated with these tDMRs showed significant (absolute Z score>1.96) enrichment for genes involved in neural differentiation, including, for example, Jag1 and Tcf4. Our results provide robust evidence for the relevance of DNA methylation in early neural development and identify novel marker candidates for neural cell differentiation.