Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver

Large-scale methylation domains mark a functional subset of neuronally expressed genes

DNA methylation is essential for embryonic and neuronal differentiation, but the function of most genomic DNA methylation marks is poorly understood. Generally the human genome is highly methylated (>70%) except for CpG islands and gene promoters. However, it was recently shown that the IMR90 human fetal lung fibroblast cells have large regions of the genome with partially methylated domains (PMDs, <70% average methylation), in contrast to the rest of the genome which is in highly methylated domains (HMDs, >70% average methylation). Using bisulfite conversion followed by high-throughput sequencing (MethylC-seq), we discovered that human SH-SY5Y neuronal cells also contain PMDs. We developed a novel hidden Markov model (HMM) to computationally map the genomic locations of PMDs in both cell types and found that autosomal PMDs can be >9 Mb in length and cover 41% of the IMR90 genome and 19% of the SH-SY5Y genome. Genomic regions marked by cell line specific PMDs contain genes that are expressed in a tissue-specific manner, with PMDs being a mark of repressed transcription. Genes contained within N-HMDs (neuronal HMDs, defined as a PMD in IMR90 but HMD in SH-SY5Y) were significantly enriched for calcium signaling, synaptic transmission, and neuron differentiation functions. Autism candidate genes were enriched within PMDs and the largest PMD observed in SH-SY5Y cells marked a 10 Mb cluster of cadherin genes with strong genetic association to autism. Our results suggest that these large-scale methylation domain maps could be relevant to interpreting and directing future investigations into the elusive etiology of autism.

Early in vitro differentiation of mouse definitive endoderm is not correlated with progressive maturation of nuclear DNA methylation patterns

The genome organization in pluripotent cells undergoing the first steps of differentiation is highly relevant to the reprogramming process in differentiation. Considering this fact, chromatin texture patterns that identify cells at the very early stage of lineage commitment could serve as valuable tools in the selection of optimal cell phenotypes for regenerative medicine applications. Here we report on the first-time use of high-resolution three-dimensional fluorescence imaging and comprehensive topological cell-by-cell analyses with a novel image-cytometrical approach towards the identification of in situ global nuclear DNA methylation patterns in early endodermal differentiation of mouse ES cells (up to day 6), and the correlations of these patterns with a set of putative markers for pluripotency and endodermal commitment, and the epithelial and mesenchymal character of cells. Utilizing this in vitro cell system as a model for assessing the relationship between differentiation and nuclear DNA methylation patterns, we found that differentiating cell populations display an increasing number of cells with a gain in DNA methylation load: first within their euchromatin, then extending into heterochromatic areas of the nucleus, which also results in significant changes of methylcytosine/global DNA codistribution patterns. We were also able to co-visualize and quantify the concomitant stochastic marker expression on a per-cell basis, for which we did not measure any correlation to methylcytosine loads or distribution patterns. We observe that the progression of global DNA methylation is not correlated with the standard transcription factors associated with endodermal development. Further studies are needed to determine whether the progression of global methylation could represent a useful signature of cellular differentiation. This concept of tracking epigenetic progression may prove useful in the selection of cell phenotypes for future regenerative medicine applications.

DNA methylation status predicts cell type-specific enhancer activity

Cell-selective glucocorticoid receptor (GR) binding to distal regulatory elements is associated with cell type-specific regions of locally accessible chromatin. These regions can either pre-exist in chromatin (pre-programmed) or be induced by the receptor (de novo). Mechanisms that create and maintain these sites are not well understood. We observe a global enrichment of CpG density for pre-programmed elements, and implicate their demethylated state in the maintenance of open chromatin in a tissue-specific manner. In contrast, sites that are actively opened by GR (de novo) are characterized by low CpG density, and form a unique class of enhancers devoid of suppressive effect of agglomerated methyl-cytosines. Furthermore, treatment with glucocorticoids induces rapid changes in methylation levels at selected CpGs within de novo sites. Finally, we identify GR-binding elements with CpGs at critical positions, and show that methylation can affect GR-DNA interactions in vitro. The findings present a unique link between tissue-specific chromatin accessibility, DNA methylation and transcription factor binding and show that DNA methylation can be an integral component of gene regulation by nuclear receptors.

Statistical quantification of methylation levels by next-generation sequencing

Background/Aims

Recently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout the entire human genome. With rapid reduction of sequencing costs, these technologies will enable epigenotyping of large cohorts for phenotypic association studies. Existing quantification methods for sequencing-based methylation profiling are simplistic and do not deal with the noise due to the random sampling nature of sequencing and various experimental artifacts. Therefore, there is a need to investigate the statistical issues related to the quantification of methylation levels for these emerging technologies, with the goal of developing an accurate quantification method.

Methods

In this paper, we propose two methods for Methyl-Seq quantification. The first method, the Maximum Likelihood estimate, is both conceptually intuitive and computationally simple. However, this estimate is biased at extreme methylation levels and does not provide variance estimation. The second method, based on Bayesian hierarchical model, allows variance estimation of methylation levels, and provides a flexible framework to adjust technical bias in the sequencing process.

Results

We compare the previously proposed binary method, the Maximum Likelihood (ML) method, and the Bayesian method. In both simulation and real data analysis of Methyl-Seq data, the Bayesian method offers the most accurate quantification. The ML method is slightly less accurate than the Bayesian method. But both our proposed methods outperform the original binary method in Methyl-Seq. In addition, we applied these quantification methods to simulation data and show that, with sequencing depth above 40–300 (which varies with different tissue samples) per cleavage site, Methyl-Seq offers a comparable quantification consistency as microarrays.

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.

Histone modification profiles are predictive for tissue/cell-type specific expression of both protein-coding and microRNA genes

Background

Gene expression is regulated at both the DNA sequence level and through modification of chromatin. However, the effect of chromatin on tissue/cell-type specific gene regulation (TCSR) is largely unknown. In this paper, we present a method to elucidate the relationship between histone modification/variation (HMV) and TCSR.

Results

A classifier for differentiating CD4+ T cell-specific genes from housekeeping genes using HMV data was built. We found HMV in both promoter and gene body regions to be predictive of genes which are targets of TCSR. For example, the histone modification types H3K4me3 and H3K27ac were identified as the most predictive for CpG-related promoters, whereas H3K4me3 and H3K79me3 were the most predictive for nonCpG-related promoters. However, genes targeted by TCSR can be predicted using other type of HMVs as well. Such redundancy implies that multiple type of underlying regulatory elements, such as enhancers or intragenic alternative promoters, which can regulate gene expression in a tissue/cell-type specific fashion, may be marked by the HMVs. Finally, we show that the predictive power of HMV for TCSR is not limited to protein-coding genes in CD4+ T cells, as we successfully predicted TCSR targeted genes in muscle cells, as well as microRNA genes with expression specific to CD4+ T cells, by the same classifier which was trained on HMV data of protein-coding genes in CD4+ T cells.

Conclusion

We have begun to understand the HMV patterns that guide gene expression in both tissue/cell-type specific and ubiquitous manner.

Genome-wide survey reveals dynamic widespread tissue-specific changes in DNA methylation during development

BACKGROUND

Changes in DNA methylation in the mammalian genome during development are frequent events and play major roles regulating gene expression and other developmental processes. It is necessary to identify these events so that we may understand how these changes affect normal development and how aberrant changes may impact disease.

RESULTS

In this study Methylated DNA ImmunoPrecipitation (MeDIP) was used in conjunction with a NimbleGen promoter plus CpG island (CpGi) array to identify Tissue and Developmental Stage specific Differentially Methylated DNA Regions (T-DMRs and DS-DMRs) on a genome-wide basis. Four tissues (brain, heart, liver, and testis) from C57BL/6J mice were analyzed at three developmental stages (15 day embryo, E15; new born, NB; 12 week adult, AD). Almost 5,000 adult T-DMRs and 10,000 DS-DMRs were identified. Surprisingly, almost all DS-DMRs were tissue specific (i.e. methylated in at least one tissue and unmethylated in one or more tissues). In addition our results indicate that many DS-DMRs are methylated at early development stages (E15 and NB) but are unmethylated in adult. There is a very strong bias for testis specific methylation in non-CpGi promoter regions (94%). Although the majority of T-DMRs and DS-DMRs tended to be in non-CpGi promoter regions, a relatively large number were also located in CpGi in promoter, intragenic and intergenic regions (>15% of the 15,979 CpGi on the array).

CONCLUSIONS

Our data suggests the vast majority of unique sequence DNA methylation has tissue specificity, that demethylation has a prominent role in tissue differentiation, and that DNA methylation has regulatory roles in alternative promoter selection and in non-promoter regions. Overall, our studies indicate changes in DNA methylation during development are a dynamic, widespread, and tissue-specific process involving both DNA methylation and demethylation.

Reduced dosage of the modifiers of epigenetic reprogramming Dnmt1, Dnmt3L, SmcHD1 and Foxo3a has no detectable effect on mouse telomere length in vivo

Studies carried out in cultured cells have implicated modifiers of epigenetic reprogramming in the regulation of telomere length, reporting elongation in cells that were null for DNA methyltransferase DNA methyltransferase 1 (Dnmt1), both de novo DNA methyltransferases, Dnmt3a and Dnmt3b or various histone methyltransferases. To investigate this further, we assayed telomere length in whole embryos or adult tissue from mice carrying mutations in four different modifiers of epigenetic reprogramming: Dnmt1, DNA methyltransferase 3-like, structural maintenance of chromosomes hinge domain containing 1, and forkhead box O3a. Terminal restriction fragment analysis was used to compare telomere length in homozygous mutants, heterozygous mutants and wild-type littermates. Contrary to expectation, we did not detect overall lengthening in the mutants, raising questions about the role of epigenetic processes in telomere length in vivo.

Extensive epigenetic reprogramming in human somatic tissues between fetus and adult

Background

Development of human tissue is influenced by a combination of intrinsic biological signals and extrinsic environmental stimuli, both of which are mediated by epigenetic regulation, including DNA methylation. However, little is currently known of the normal acquisition or loss of epigenetic markers during fetal and postnatal development.

Results

The DNA methylation status of over 1000 CpGs located in the regulatory regions of nearly 800 genes was evaluated in five somatic tissues (brain, kidney, lung, muscle and skin) from eight normal second-trimester fetuses. Tissue-specific differentially methylated regions (tDMRs) were identified in 195 such loci. However, comparison with corresponding data from trisomic fetuses (five trisomy 21 and four trisomy 18) revealed relatively few DNA methylation differences associated with trisomy, despite such conditions having a profound effect on development. Of interest, only 17% of the identified fetal tDMRs were found to maintain this same tissue-specific DNA methylation in adult tissues. Furthermore, 10% of the sites analyzed, including sites associated with imprinted genes, had a DNA methylation difference of >40% between fetus and adult. This plasticity of DNA methylation over development was further confirmed by comparison with similar data from embryonic stem cells, with the most altered methylation levels being linked to domains with bivalent histone modifications.

Conclusions

Most fetal tDMRs seem to reflect transient DNA methylation changes during development rather than permanent epigenetic signatures. The extensive tissue-specific and developmental-stage specific nature of DNA methylation will need to be elucidated to identify abnormal patterns of DNA methylation associated with abnormal development or disease.

Epigenetic and inflammatory marker profiles associated with depression in a community-based epidemiologic sample

BACKGROUND

Recent work suggests that epigenetic differences may be associated with psychiatric disorders. Here we investigate, in a community-based sample, whether methylation profiles distinguish between individuals with and without lifetime depression. We also investigate the physiologic consequences that may be associated with these profiles.

METHOD

Using whole blood-derived genomic DNA from a subset of participants in the Detroit Neighborhood Health Study (DNHS), we applied methylation microarrays to assess genome-wide methylation profiles for over 14 000 genes in 33 persons who reported a lifetime history of depression and 67 non-depressed adults. Bioinformatic functional analyses were performed on the genes uniquely methylated and unmethylated in each group, and inflammatory biomarkers [interleukin (IL)-6 and C-reactive protein (CRP)] were measured to investigate the possible functional significance of the methylation profiles observed.

RESULTS

Uniquely unmethylated gene sets distinguished between those with versus without lifetime depression. In particular, some processes (e.g. brain development, tryptophan metabolism) showed patterns suggestive of increased methylation among individuals with depression whereas others (e.g. lipoprotein) showed patterns suggestive of decreased methylation among individuals with depression. IL-6 and CRP levels were elevated among those with lifetime depression and, among those with depression only, IL-6 methylation showed an inverse correlation with circulating IL-6 and CRP.

CONCLUSIONS

Genome-wide methylation profiles distinguish individuals with versus without lifetime depression in a community-based setting, and show coordinated signals with pathophysiological mechanisms previously implicated in the etiology of this disorder. Examining epigenetic mechanisms in concert with other dynamic markers of physiologic functioning should improve our understanding of the neurobiology of depression.

Next generation sequencing based approaches to epigenomics

Next generation sequencing has brought epigenomic studies to the forefront of current research. The power of massively parallel sequencing coupled to innovative molecular and computational techniques has allowed researchers to profile the epigenome at resolutions that were unimaginable only a few years ago. With early proof of concept studies published, the field is now moving into the next phase where the importance of method standardization and rigorous quality control are becoming paramount. In this review we will describe methodologies that have been developed to profile the epigenome using next generation sequencing platforms. We will discuss these in terms of library preparation, sequence platforms and analysis techniques.

A user's guide to the encyclopedia of DNA elements (ENCODE)

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.

Emerging patterns of epigenomic variation

Fuelled by new sequencing technologies, epigenome mapping projects are revealing epigenomic variation at all levels of biological complexity, from species to cells. Comparisons of methylation profiles among species reveal evolutionary conservation of gene body methylation patterns, pointing to the fundamental role of epigenomes in gene regulation. At the human population level, epigenomic changes provide footprints of the effects of genomic variants within the vast nonprotein-coding fraction of the genome, and comparisons of the epigenomes of parents and their offspring point to quantitative epigenomic parent-of-origin effects confounding classical Mendelian genetics. At the organismal level, comparisons of epigenomes from diverse cell types provide insights into cellular differentiation. Finally, comparisons of epigenomes from monozygotic twins help dissect genetic and environmental influences on human phenotypes and longitudinal comparisons reveal aging-associated epigenomic drift. The development of new bioinformatic frameworks for comparative epigenome analysis is putting epigenome maps within the reach of researchers across a wide spectrum of biological disciplines.

Design and validation issues in RNA-seq experiments

The next-generation sequencing technologies are being rapidly applied in biological research. Tens of millions of short sequences generated in a single experiment provide us enormous information on genome composition, genetic variants, gene expression levels and protein binding sites depending on the applications. Various methods are being developed for analyzing the data generated by these technologies. However, the relevant experimental design issues have rarely been discussed. In this review, we use RNA-seq as an example to bring this topic into focus and to discuss experimental design and validation issues pertaining to next-generation sequencing in the quantification of transcripts.

Exposing the DNA methylome iceberg

DNA methylation was the first epigenetic modification discovered. Until recently, comprehensive coverage of the composition and distribution of methylated cytosines across the genome was lacking. Technological advances, however, are providing methylation maps that can reveal the genomic distribution of DNA methylation in different cell states or phenotypes. The emerging picture includes extensive gene body methylation that is highly conserved in eukaryotes, the presence of DNA methylation in previously unappreciated sequence contexts, and the discovery of another modified DNA base, 5-hydroxymethylcytosine. These new data point to the role of DNA methylation both in gene silencing and gene activation; reconciliation of these seemingly contradictory roles will be essential to fully unravel the biological function of DNA methylation in eukaryotes. Here we review how these recently exposed features of the DNA methylome are challenging previously held dogmas in the field.

Strategies for DNA methylation analysis in developmental studies

Developmental processes in eukaryotes are highly dependent on DNA methylation. 5-methylcytosine (m(5) C) is the most prevalent and best understood DNA modification implicated in maintenance of genomic integrity and function across species. Although m(5) C occurs almost exclusively in symmetrical CpG context in vertebrates, additional asymmetrical distribution in CpHpG and CpHpH sites has been observed in plants and embryonic stem cells. To this end, accurate and reproducible methodology for full analysis of the DNA methylome is highly demanded. Fortunately, a variety of methods enable quantitative DNA methylation mapping at a single-base resolution and in a large scale. Here, we provide a critical overview of methods applied primarily to m(5) C detection with particular emphasis on technical improvements of the classical bisulfite-conversion protocol. We further describe strategies in combination with emerging technologies that allow acquisition of highly reliable data for developmental studies.

Individual variability in the detoxification of carcinogenic arylhydroxylamines in human breast

Cytochrome b(5) (b5) and NADH cytochrome b(5) reductase (b5R) detoxify reactive hydroxylamine (NHOH) metabolites of known arylamine and heterocyclic amine mammary carcinogens. The aim of this study was to determine whether NHOH reduction for the prototypic arylamine 4-aminobiphenyl (4-ABP) was present in human breast and to determine whether variability in activity was associated with single nucleotide polymorphisms (SNPs) in the coding, promoter, and 3'untranslated region (UTR) regions of the genes encoding b5 (CYB5A) and b5R (CYB5R3). 4-ABP-NHOH reduction was readily detected in pooled human breast microsomes, with a K(m) (280μM) similar to that found with recombinant b5 and b5R, and a V(max) of 1.12 ± 0.19 nmol/min/mg protein 4-ABP-NHOH reduction varied 75-fold across 70 individual breast samples and correlated significantly with both b5 (80-fold variability) and b5R (14-fold) immunoreactive protein. In addition, wide variability in b5 protein expression was significantly associated with variability in CYB5A transcript levels, with a trend toward the same association between b5R and CYB5R3. Although a sample with a novel coding SNP in CYB5A, His22Arg, was found with low reduction and b5 expression, no other SNPs in either gene were associated with outlier activity or protein expression. We conclude that b5 and b5R catalyze the reduction of 4-ABP-NHOH in breast tissue, with very low activity, protein, and messenger RNA expression in some samples, which cannot be attributed to promoter, coding, or 3'UTR SNPs. Further studies are underway to characterize the transcriptional regulation of CYB5A and CYB5R3 and begin to understand the mechanisms of individual variability in this detoxification pathway.

Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling

Genome-wide mapping of 5-methylcytosine is of broad interest to many fields of biology and medicine. A variety of methods have been developed, and several have recently been advanced to genome-wide scale using arrays and next-generation sequencing approaches. We have previously reported reduced representation bisulfite sequencing (RRBS), a bisulfite-based protocol that enriches CG-rich parts of the genome, thereby reducing the amount of sequencing required while capturing the majority of promoters and other relevant genomic regions. The approach provides single-nucleotide resolution, is highly sensitive and provides quantitative DNA methylation measurements. This protocol should enable any standard molecular biology laboratory to generate RRBS libraries of high quality. Briefly, purified genomic DNA is digested by the methylation-insensitive restriction enzyme MspI to generate short fragments that contain CpG dinucleotides at the ends. After end-repair, A-tailing and ligation to methylated Illumina adapters, the CpG-rich DNA fragments (40-220 bp) are size selected, subjected to bisulfite conversion, PCR amplified and end sequenced on an Illumina Genome Analyzer. Note that alignment and analysis of RRBS sequencing reads are not covered in this protocol. The extremely low input requirements (10-300 ng), the applicability of the protocol to formalin-fixed and paraffin-embedded samples, and the technique's single-nucleotide resolution extends RRBS to a wide range of biological and clinical samples and research applications. The entire process of RRBS library construction takes ∼9 d.

A new FACS approach isolates hESC derived endoderm using transcription factors

We show that high quality microarray gene expression profiles can be obtained following FACS sorting of cells using combinations of transcription factors. We use this transcription factor FACS (tfFACS) methodology to perform a genomic analysis of hESC-derived endodermal lineages marked by combinations of SOX17, GATA4, and CXCR4, and find that triple positive cells have a much stronger definitive endoderm signature than other combinations of these markers. Additionally, SOX17(+) GATA4(+) cells can be obtained at a much earlier stage of differentiation, prior to expression of CXCR4(+) cells, providing an important new tool to isolate this earlier definitive endoderm subtype. Overall, tfFACS represents an advancement in FACS technology which broadly crosses multiple disciplines, most notably in regenerative medicine to redefine cellular populations.

Epigenetic methodologies for behavioral scientists

Hormones are essential regulators of many behaviors. Steroids bind either to nuclear or membrane receptors while peptides primarily act via membrane receptors. After a ligand binds, the conformational change in the receptor initiates changes in cell signaling cascades (membrane receptors) or direct alternations in DNA transcription (steroid receptors). Changes in gene transcription that result are responsible for protein production and ultimately behavioral modifications. A significant part of how hormones affect DNA transcription is via epigenetic modifications of DNA and/or the chromatin in which it is entwined. These alterations lead to transcriptional changes that ultimately define the phenotype and function of a given cell. Importantly we now know that environmental stimuli influence epigenetic marks, which in the context of neuroendocrinology can lead to behavioral changes. Importantly tracking epigenetic states and profiling the epigenome within cells require the use of epigenetic methodologies and subsequent data analysis. Here we describe the techniques of particular importance in the mapping of DNA methylation, histone modifications and occupancy of chromatin bound effector proteins that regulate gene expression. For researchers wanting to move into these levels of analysis we discuss the application of modern sequencing technologies applied in assays such as chromatin immunoprecipitation and the bioinformatics analysis involved in the rich datasets generated.

The Polycomb complex PRC2 and its mark in life

Polycomb group proteins maintain the gene-expression pattern of different cells that is set during early development by regulating chromatin structure. In mammals, two main Polycomb group complexes exist - Polycomb repressive complex 1 (PRC1) and 2 (PRC2). PRC1 compacts chromatin and catalyses the monoubiquitylation of histone H2A. PRC2 also contributes to chromatin compaction, and catalyses the methylation of histone H3 at lysine 27. PRC2 is involved in various biological processes, including differentiation, maintaining cell identity and proliferation, and stem-cell plasticity. Recent studies of PRC2 have expanded our perspectives on its function and regulation, and uncovered a role for non-coding RNA in the recruitment of PRC2 to target genes.

Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray

The Illumina Infinium HumanMethylation27 BeadChip (Illumina 27k) microarray is a high-throughput platform capable of interrogating the human DNA methylome. In a search for autosomal sex-specific DNA methylation using this microarray, we discovered autosomal CpG loci showing significant methylation differences between the sexes. However, we found that the majority of these probes cross-reacted with sequences from sex chromosomes. Moreover, we determined that 6-10% of the microarray probes are non-specific and map to highly homologous genomic sequences. Using probes targeting different CpGs that are exact duplicates of each other, we investigated the precision of these repeat measurements and concluded that the overall precision of this microarray is excellent. In addition, we identified a small number of probes targeting CpGs that include single-nucleotide polymorphisms. Overall, our findings address several technical issues associated with the Illumina 27k microarray that, once considered, will enhance the analysis and interpretation of data generated from this platform.

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.

Genome-wide distribution of DNA methylation at single-nucleotide resolution

DNA methylation, a well-known epigenetic modification in mammalian genomes, is important for development and health. Dysregulation of DNA methylation can cause abnormal gene regulation, leading to anomalous development and diseases. Until recently, the ability to understand the functions and dynamics of DNA methylation was limited by the availability of technologies for comprehensively characterizing methylation on a genome-wide scale. Rapid advances in high-throughput approaches (particularly next-generation sequencing), coupled with molecular techniques, have enabled unbiased genome-wide profiling of DNA modifications at single-base resolution and helped to elucidate their impact on gene regulation. Here, we discuss the development of genomic approaches to decipher the global methylome at single-base resolution, the challenges faced, and the emerging new insights. Our ability to decipher this important epigenetic modification and how it impacts gene expression will provide a framework for understanding numerous disease mechanisms, and suggest means to treat or prevent them in the future.

The hunt for the epiallele

Understanding the origin of phenotypic variation remains one of the principle challenges of contemporary biology. Recent genome-wide association studies have identified association between common genetic variants and complex phenotype; however, the minimal effect sizes observed in such studies highlight the potential for other causal factors to be involved in phenotypic variation. The epigenetic state of an organism (or 'epigenome') incorporates a landscape of complex and plastic molecular events that may underlie the 'missing link' that integrates genotype with phenotype. The nature of these processes has been the subject of intense scientific study over the recent years, and characterisation of epigenetic variation, in the form of 'epialleles', is providing fascinating insight into how the genome functions within a range of developmental processes, environments, and in states of health and disease. This review will discuss how and when mammalian epialleles may be generated and their interaction with genetic and environmental factors. We will outline how an epiallele has a variable relationship with phenotype, and how new technologies may be used for their detection and to facilitate an understanding of their contribution to phenotype. Finally, we will consider epialleles in population variation and their teleological role in evolution. variation and their teleological role in evolution.

Targets and dynamics of promoter DNA methylation during early mouse development

DNA methylation is extensively reprogrammed during the early phases of mammalian development, yet genomic targets of this process are largely unknown. We optimized methylated DNA immunoprecipitation for low numbers of cells and profiled DNA methylation during early development of the mouse embryonic lineage in vivo. We observed a major epigenetic switch during implantation at the transition from the blastocyst to the postimplantation epiblast. During this period, DNA methylation is primarily targeted to repress the germline expression program. DNA methylation in the epiblast is also targeted to promoters of lineage-specific genes such as hematopoietic genes, which are subsequently demethylated during terminal differentiation. De novo methylation during early embryogenesis is catalyzed by Dnmt3b, and absence of DNA methylation leads to ectopic gene activation in the embryo. Finally, we identify nonimprinted genes that inherit promoter DNA methylation from parental gametes, suggesting that escape of post-fertilization DNA methylation reprogramming is prevalent in the mouse genome.

Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data

The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revolutionized the genetics and genomics fields by allowing rapid and inexpensive sequencing of billions of bases. The rapid deployment of NGS in a variety of sequencing-based experiments has resulted in fast accumulation of massive amounts of sequencing data. To process this new type of data, a torrent of increasingly sophisticated algorithms and software tools are emerging to help the analysis stage of the NGS applications. In this article, we strive to comprehensively identify the critical challenges that arise from all stages of NGS data analysis and provide an objective overview of what has been achieved in existing works. At the same time, we highlight selected areas that need much further research to improve our current capabilities to delineate the most information possible from NGS data. The article focuses on applications dealing with ChIP-Seq and RNA-Seq.

Distinct epigenomic landscapes of pluripotent and lineage-committed human cells

Human embryonic stem cells (hESCs) share an identical genome with lineage-committed cells, yet possess the remarkable properties of self-renewal and pluripotency. The diverse cellular properties in different cells have been attributed to their distinct epigenomes, but how much epigenomes differ remains unclear. Here, we report that epigenomic landscapes in hESCs and lineage-committed cells are drastically different. By comparing the chromatin-modification profiles and DNA methylomes in hESCs and primary fibroblasts, we find that nearly one-third of the genome differs in chromatin structure. Most changes arise from dramatic redistributions of repressive H3K9me3 and H3K27me3 marks, which form blocks that significantly expand in fibroblasts. A large number of potential regulatory sequences also exhibit a high degree of dynamics in chromatin modifications and DNA methylation. Additionally, we observe novel, context-dependent relationships between DNA methylation and chromatin modifications. Our results provide new insights into epigenetic mechanisms underlying properties of pluripotency and cell fate commitment.

Quantitative comparison of genome-wide DNA methylation mapping technologies

DNA methylation is a key component of mammalian gene regulation and the most classical example of an epigenetic mark. DNA methylation patterns are mitotically heritable and stable over time, but they undergo considerable changes in response to cell differentiation, diseases and environmental influences. Several methods have been developed for DNA methylation profiling on a genomic scale. Here, we benchmark four of these methods on two sample pairs, comparing their accuracy and power to detect DNA methylation differences. The results show that all evaluated methods (MeDIP-seq: methylated DNA immunoprecipitation, MethylCap-seq: methylated DNA capture by affinity purification, RRBS: reduced representation bisulfite sequencing, and the Infinium HumanMethylation27 assay) produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.

Epigenome mapping in normal and disease States

Epigenomes are comprised, in part, of all genome-wide chromatin modifications, including DNA methylation and histone modifications. Unlike the genome, epigenomes are dynamic during development and differentiation to establish and maintain cell type-specific gene expression states that underlie cellular identity and function. Chromatin modifications are particularly labile, providing a mechanism for organisms to respond and adapt to environmental cues. Results from studies in animal models clearly demonstrate that epigenomic variability leads to phenotypic variability, including susceptibility to disease that is not recognized at the DNA sequence level. Thus, capturing epigenomic information is invaluable for comprehensively understanding development, differentiation, and disease. Herein, we provide a brief overview of epigenetic processes, how they are relevant to human health, and review studies using technologies that enable epigenome mapping. We conclude by describing feasible applications of epigenome mapping, focusing on epigenome-wide association studies (eGWAS), which have the potential to revolutionize current studies of human diseases and will likely promote the discovery of novel diagnostic, preventative, and treatment strategies.

Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage

The generation of genome-wide data derived from methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) has become a major tool for epigenetic studies in health and disease. The computational analysis of such data, however, still falls short on accuracy, sensitivity, and speed. We propose a time-efficient statistical method that is able to cope with the inherent complexity of MeDIP-seq data with similar performance compared with existing methods. In order to demonstrate the computational approach, we have analyzed alterations in DNA methylation during the differentiation of human embryonic stem cells (hESCs) to definitive endoderm. We show improved correlation of normalized MeDIP-seq data in comparison to available whole-genome bisulfite sequencing data, and investigated the effect of differential methylation on gene expression. Furthermore, we analyzed the interplay between DNA methylation, histone modifications, and transcription factor binding and show that in contrast to de novo methylation, demethylation is mainly associated with regions of low CpG densities.

MetMap enables genome-scale Methyltyping for determining methylation states in populations

The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap's inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies.

Quality control and single nucleotide resolution analysis of methylated DNA immunoprecipitation products

DNA methylation patterns are altered in many diseases, and their analysis has become of great interest. Methylated DNA immunoprecipitation (MeDIP) is a simple method to enrich the methylated fraction of the genome. However, it has been difficult to assess the quality and the detailed methylation patterns of the immunoprecipitated DNA. Here we present a simple method for the analysis of the immunoprecipitated DNA at single nucleotide resolution by bisulfite treatment and pyrosequencing of genomic regions. The presented method can be used as an initial quality measure prior to genome-wide read-out technologies such as microarrays and second-generation sequencing.

HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data

Background

Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method.

Results

Here we introduce HPeak, a Hidden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage.

Conclusions

Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.

Oxidative stress and DNA methylation in prostate cancer

The protective effects of fruits, vegetables, and other foods on prostate cancer may be due to their antioxidant properties. An imbalance in the oxidative stress/antioxidant status is observed in prostate cancer patients. Genome oxidative damage in prostate cancer patients is associated with higher lipid peroxidation and lower antioxidant levels. Oxygen radicals are associated with different steps of carcinogenesis, including structural DNA damage, epigenetic changes, and protein and lipid alterations. Epigenetics affects genetic regulation, cellular differentiation, embryology, aging, cancer, and other diseases. DNA methylation is perhaps the most extensively studied epigenetic modification, which plays an important role in the regulation of gene expression and chromatin architecture, in association with histone modification and other chromatin-associated proteins. This review will provide a broad overview of the interplay of oxidative stress and DNA methylation, DNA methylation changes in regulation of gene expression, lifestyle changes for prostate cancer prevention, DNA methylation as biomarkers for prostate cancer, methods for detection of methylation, and clinical application of DNA methylation inhibitors for epigenetic therapy.

Profiling DNA methylomes from microarray to genome-scale sequencing

DNA cytosine methylation is a central epigenetic modification which plays critical roles in cellular processes including genome regulation, development and disease. Here, we review current and emerging microarray and next-generation sequencing based technologies that enhance our knowledge of DNA methylation profiling. Each methodology has limitations and their unique applications, and combinations of several modalities may help build the entire methylome. With advances on next-generation sequencing technologies, it is now possible to globally map the DNA cytosine methylation at single-base resolution, providing new insights into the regulation and dynamics of DNA methylation in genomes.

The application of next generation sequencing in DNA methylation analysis

DNA methylation is a major form of epigenetic modification and plays essential roles in physiology and disease processes. In the human genome, about 80% of cytosines in the 56 million CpG sites are methylated to 5-methylcytosines. The methylation pattern of DNA is highly variable among cells types and developmental stages and influenced by disease processes and genetic factors, which brings considerable theoretical and technological challenges for its comprehensive mapping. Recently various high-throughput approaches based on bisulfite conversion combined with next generation sequencing have been developed and applied for the genome wide analysis of DNA methylation. These methods provide single base pair resolution, quantitative DNA methylation data with genome wide coverage. We review these methods here and discuss some technical points of special interest like the sequence depth necessary to reach conclusions, the identification of clonal DNA amplification after bisulfite conversion and the detection of non-CpG methylation. Future application of these methods will greatly facilitate the profiling of the DNA methylation in the genomes of different species, individuals and cell types under healthy and disease states.

Sequence features that drive human promoter function and tissue specificity

Promoters are important regulatory elements that contain the necessary sequence features for cells to initiate transcription. To functionally characterize a large set of human promoters, we measured the transcriptional activities of 4575 putative promoters across eight cell lines using transient transfection reporter assays. In parallel, we measured gene expression in the same cell lines and observed a significant correlation between promoter activity and endogenous gene expression (r = 0.43). As transient transfection assays directly measure the promoting effect of a defined fragment of DNA sequence, decoupled from epigenetic, chromatin, or long-range regulatory effects, we sought to predict whether a promoter was active using sequence features alone. CG dinucleotide content was highly predictive of ubiquitous promoter activity, necessitating the separation of promoters into two groups: high CG promoters, mostly ubiquitously active, and low CG promoters, mostly cell line-specific. Computational models trained on the binding potential of transcriptional factor (TF) binding motifs could predict promoter activities in both high and low CG groups: average area under the receiver operating characteristic curve (AUC) of the models was 91% and exceeded the AUC of CG content by an average of 23%. Known relationships, for example, between HNF4A and hepatocytes, were recapitulated in the corresponding cell lines, in this case the liver-derived cell line HepG2. Half of the associations between tissue-specific TFs and cell line-specific promoters were new. Our study underscores the importance of collecting functional information from complementary assays and conditions to understand biology in a systematic framework.

Introduction into the analysis of high-throughput-sequencing based epigenome data

Sequencing-based approaches now allow high-resolution, genome-scale investigation of cellular epigenetic landscapes. For example, mapping of open chromatin regions, post-translational histone modifications and DNA methylation across a whole genome is now feasible, and new non-coding regulatory RNAs can be sensitively identified via RNA sequencing. The resulting large-scale data sets promise to contribute towards a more precise and complete understanding of gene regulation and to yield insights into the interplay between genomes and the environment. In this article, I review some of the conceptual issues and currently available software tools for the analysis of sequencing-based whole-genome epigenetics data.

mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain

mRNA-seq is a paradigm-shifting technology because of its superior sensitivity and dynamic range and its potential to capture transcriptomes in an agnostic fashion, i.e., independently of existing genome annotations. Implementation of the agnostic approach, however, has not yet been fully achieved. In particular, agnostic mapping of pre-mRNA splice sites has not been demonstrated. The present study pursued dual goals: (1) to advance mRNA-seq bioinformatics toward unbiased transcriptome capture and (2) to demonstrate its potential for discovery in neuroscience by applying the approach to an in vivo model of neurological disease. We have performed mRNA-seq on the L4 dorsal root ganglion (DRG) of rats with chronic neuropathic pain induced by spinal nerve ligation (SNL) of the neighboring (L5) spinal nerve. We found that 12.4% of known genes were induced and 7% were suppressed in the dysfunctional (but anatomically intact) L4 DRG 2 wk after SNL. These alterations persisted chronically (2 mo). Using a read cluster classifier with strong test characteristics (ROC area 97%), we discovered 10,464 novel exons. A new algorithm for agnostic mapping of pre-mRNA splice junctions (SJs) achieved a precision of 97%. Integration of information from all mRNA-seq read classes including SJs led to genome reannotations specifically relevant for the species used (rat), the anatomical site studied (DRG), and the neurological disease considered (pain); for example, a 64-exon coreceptor for the nociceptive transmitter substance P was identified, and 21.9% of newly discovered exons were shown to be dysregulated. Thus, mRNA-seq with agnostic analysis methods appears to provide a highly productive approach for in vivo transcriptomics in the nervous system.

Epigenetic and immune function profiles associated with posttraumatic stress disorder

The biologic underpinnings of posttraumatic stress disorder (PTSD) have not been fully elucidated. Previous work suggests that alterations in the immune system are characteristic of the disorder. Identifying the biologic mechanisms by which such alterations occur could provide fundamental insights into the etiology and treatment of PTSD. Here we identify specific epigenetic profiles underlying immune system changes associated with PTSD. Using blood samples (n = 100) obtained from an ongoing, prospective epidemiologic study in Detroit, the Detroit Neighborhood Health Study, we applied methylation microarrays to assay CpG sites from more than 14,000 genes among 23 PTSD-affected and 77 PTSD-unaffected individuals. We show that immune system functions are significantly overrepresented among the annotations associated with genes uniquely unmethylated among those with PTSD. We further demonstrate that genes whose methylation levels are significantly and negatively correlated with traumatic burden show a similar strong signal of immune function among the PTSD affected. The observed epigenetic variability in immune function by PTSD is corroborated using an independent biologic marker of immune response to infection, CMV-a typically latent herpesvirus whose activity was significantly higher among those with PTSD. This report of peripheral epigenomic and CMV profiles associated with mental illness suggests a biologic model of PTSD etiology in which an externally experienced traumatic event induces downstream alterations in immune function by reducing methylation levels of immune-related genes.

The Genomedata format for storing large-scale functional genomics data

Summary: We present a format for efficient storage of multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. We have also developed utilities to load data into this format. We show that retrieving data from this format is more than 2900 times faster than a naive approach using wiggle files.

Availability and Implementation: Reference implementation in Python and C components available at http://noble.gs.washington.edu/proj/genomedata/ under the GNU General Public License.

Contact:william-noble@uw.edu

Optimized design and data analysis of tag-based cytosine methylation assays

Genome-wide, tag-based cytosine methylation analysis is optimized.

Using the type III restriction-modification enzyme EcoP15I, we isolated sequences flanking sites digested by the methylation-sensitive HpaII enzyme or its methylation-insensitive MspI isoschizomer for massively parallel sequencing. A novel data transformation allows us to normalise HpaII by MspI counts, resulting in more accurate quantification of methylation at >1.8 million loci in the human genome. This HELP-tagging assay is not sensitive to sequence polymorphism or base composition and allows exploration of both CG-rich and depleted genomic contexts.

Next-generation sequencing technologies for DNA methylation analyses in cancer genomics

For the first time, the development of next-generation sequencing technologies has brought about tools to investigate epigenetic alterations in an unbiased, yet genome-wide approach. The importance of this innovative technology is undeniable since it has already been established that changes in DNA methylation play an important role in cancer initiation and progression. The first methylation maps have already been created, and it is only a matter of time until the complete epigenetic maps of healthy and diseased human genomes are available. In this review, we summarize the use of next-generation sequencing for diverse epigenetic technologies, give an overview of the status quo and outline future perspectives for its application in oncology and basic research.

DNA methylation profiles of ovarian epithelial carcinoma tumors and cell lines

Background

Epithelial ovarian carcinoma is a significant cause of cancer mortality in women worldwide and in the United States. Epithelial ovarian cancer comprises several histological subtypes, each with distinct clinical and molecular characteristics. The natural history of this heterogeneous disease, including the cell types of origin, is poorly understood. This study applied recently developed methods for high-throughput DNA methylation profiling to characterize ovarian cancer cell lines and tumors, including representatives of three major histologies.

Methodology/Principal Findings

We obtained DNA methylation profiles of 1,505 CpG sites (808 genes) in 27 primary epithelial ovarian tumors and 15 ovarian cancer cell lines. We found that the DNA methylation profiles of ovarian cancer cell lines were markedly different from those of primary ovarian tumors. Aggregate DNA methylation levels of the assayed CpG sites tended to be higher in ovarian cancer cell lines relative to ovarian tumors. Within the primary tumors, those of the same histological type were more alike in their methylation profiles than those of different subtypes. Supervised analyses identified 90 CpG sites (68 genes) that exhibited ‘subtype-specific’ DNA methylation patterns (FDR<1%) among the tumors. In ovarian cancer cell lines, we estimated that for at least 27% of analyzed autosomal CpG sites, increases in methylation were accompanied by decreases in transcription of the associated gene.

Significance

The significant difference in DNA methylation profiles between ovarian cancer cell lines and tumors underscores the need to be cautious in using cell lines as tumor models for molecular studies of ovarian cancer and other cancers. Similarly, the distinct methylation profiles of the different histological types of ovarian tumors reinforces the need to treat the different histologies of ovarian cancer as different diseases, both clinically and in biomarker studies. These data provide a useful resource for future studies, including those of potential tumor progenitor cells, which may help illuminate the etiology and natural history of these cancers.