Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer

Distinct DNA methylomes of newborns and centenarians

Human aging cannot be fully understood in terms of the constrained genetic setting. Epigenetic drift is an alternative means of explaining age-associated alterations. To address this issue, we performed whole-genome bisulfite sequencing (WGBS) of newborn and centenarian genomes. The centenarian DNA had a lower DNA methylation content and a reduced correlation in the methylation status of neighboring cytosine--phosphate--guanine (CpGs) throughout the genome in comparison with the more homogeneously methylated newborn DNA. The more hypomethylated CpGs observed in the centenarian DNA compared with the neonate covered all genomic compartments, such as promoters, exonic, intronic, and intergenic regions. For regulatory regions, the most hypomethylated sequences in the centenarian DNA were present mainly at CpG-poor promoters and in tissue-specific genes, whereas a greater level of DNA methylation was observed in CpG island promoters. We extended the study to a larger cohort of newborn and nonagenarian samples using a 450,000 CpG-site DNA methylation microarray that reinforced the observation of more hypomethylated DNA sequences in the advanced age group. WGBS and 450,000 analyses of middle-age individuals demonstrated DNA methylomes in the crossroad between the newborn and the nonagenarian/centenarian groups. Our study constitutes a unique DNA methylation analysis of the extreme points of human life at a single-nucleotide resolution level.

Concurrent epigenetic silencing of wnt/β-catenin pathway inhibitor genes in B cell chronic lymphocytic leukaemia

BACKGROUND

The Wnt/β-catenin signalling is aberrantly activated in primary B cell chronic lymphocytic leukaemia (CLL). Epigenetic silencing of pathway inhibitor genes may be a mechanism for its activation. In this study, we investigated systematically and quantitatively the methylation status of 12 Wnt/β-catenin pathway inhibitor genes - CDH1, DACT1, DKK1, DKK2, DKK3, DKK4, SFRP1, SFRP2, SFRP3, SFRP4, SFRP5 and WIF1 - in the cell lines EHEB and MEC-1 as well as patient samples.

METHODS

Quantification of DNA methylation was performed by means of bisulphite pyrosequencing and confirmed by bisulphite Sanger sequencing. Gene expression was analysed by qPCR using GAPDH as internal control. E-cadherin and β-catenin protein quantification was carried out by microsphere-based immunoassays. Methylation differences observed between the patient and control groups were tested using generalised least squares models.

RESULTS

For 10 genes, a higher methylation level was observed in tumour material. Only DKK4 exhibited similarly high methylation levels in both tumour and normal specimens, while DACT1 was always essentially unmethylated. However, also for these inhibitors, treatment of cells with the demethylating agent 5-aza-2´-deoxycytidine resulted in an induction of their expression, as shown by quantitative PCR, suggesting an indirect epigenetic control of activity. While the degree of demethylation and its transcriptional consequences differed between the genes, there was an overall high correlation of demethylation and increased activity. Protein expression studies revealed that no constitutive Wnt/β-catenin signalling occurred in the cell lines, which is in discrepancy with results from primary CLL. However, treatment with 5-aza-2´-deoxycytidine caused accumulation of β-catenin. Simultaneously, E-cadherin expression was strongly induced, leading to the formation of a complex with β-catenin and thus demonstrating its epigenetically regulated inhibition effect.

CONCLUSIONS

The results suggest an epigenetic silencing mechanism of the Wnt/β-catenin pathway inhibitor genes in CLL. Hypermethylation and silencing of functionally related genes may not be completely stochastic but result from the tumour epigenome reprogramming orchestrated by Polycomb-group repressive complexes. The data are of interest in the context of epigenetic-based therapy.

Standardization and quality controls for the methylated DNA immunoprecipitation technique

MeDIP (Methylated DNA Immunoprecipitation) is a relatively recent technique aimed to enrich the methylated fraction of DNA with an antibody directed against 5-methyl-cytosine. MeDIP processed samples are suitable for investigation of the methylation status of specific genomic loci and for performing genome-wide screening when hybridized to DNA methylation microarrays or analyzed by deep sequencing. Here, we describe a standardization protocol and quality controls to assess the specificity, reproducibility and efficiency of the MeDIP procedure. These may have utility when comparing results between samples and experiments within laboratories and between laboratories.

Epigenetic inactivation of DNA repair in breast cancer

The study of epigenetic mechanisms in cancer, such as DNA methylation and histone modifications, has revealed a plethora of events that contribute to cancer through stable changes in the expression of genes critical to transformation pathways. In this mini review we look at the different epigenetic modifications prevalent in this neoplastic phenotype, focusing on breast cancer. Most encouragingly, research in epigenetics has led to improved survival of patients with certain forms of lymphoma and leukemia through the use of drugs that alter DNA methylation and histone acetylation. Thus, we look at the clinical utility of targeting epigenetic pathways. In addition, we explore numerous other clinical applications of epigenetics, in areas such as cancer screening and early detection, prevention, classification for epidemiology and prognostic purposes, and predicting outcomes after standard therapy.

A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform

Background

The 27k Illumina Infinium Methylation Beadchip is a popular high-throughput technology that allows the methylation state of over 27,000 CpGs to be assayed. While feature selection and classification methods have been comprehensively explored in the context of gene expression data, relatively little is known as to how best to perform feature selection or classification in the context of Illumina Infinium methylation data. Given the rising importance of epigenomics in cancer and other complex genetic diseases, and in view of the upcoming epigenome wide association studies, it is critical to identify the statistical methods that offer improved inference in this novel context.

Results

Using a total of 7 large Illumina Infinium 27k Methylation data sets, encompassing over 1,000 samples from a wide range of tissues, we here provide an evaluation of popular feature selection, dimensional reduction and classification methods on DNA methylation data. Specifically, we evaluate the effects of variance filtering, supervised principal components (SPCA) and the choice of DNA methylation quantification measure on downstream statistical inference. We show that for relatively large sample sizes feature selection using test statistics is similar for M and β-values, but that in the limit of small sample sizes, M-values allow more reliable identification of true positives. We also show that the effect of variance filtering on feature selection is study-specific and dependent on the phenotype of interest and tissue type profiled. Specifically, we find that variance filtering improves the detection of true positives in studies with large effect sizes, but that it may lead to worse performance in studies with smaller yet significant effect sizes. In contrast, supervised principal components improves the statistical power, especially in studies with small effect sizes. We also demonstrate that classification using the Elastic Net and Support Vector Machine (SVM) clearly outperforms competing methods like LASSO and SPCA. Finally, in unsupervised modelling of cancer diagnosis, we find that non-negative matrix factorisation (NMF) clearly outperforms principal components analysis.

Conclusions

Our results highlight the importance of tailoring the feature selection and classification methodology to the sample size and biological context of the DNA methylation study. The Elastic Net emerges as a powerful classification algorithm for large-scale DNA methylation studies, while NMF does well in the unsupervised context. The insights presented here will be useful to any study embarking on large-scale DNA methylation profiling using Illumina Infinium beadarrays.

Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype–phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes.

Epigenetic patterns vary during healthy ageing and development. Age-related DNA methylation changes have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To understand the biological mechanisms involved in potential longevity and rate of healthy ageing, we performed genome-wide association of epigenetic and genetic variation with both chronological age and age-related phenotypes. We identified hundreds of DNA methylation variants significantly associated with age and replicated these in an independent sample of young adult twins. Only a small proportion of these variants were also associated with age-related phenotypes. Therefore, the majority of age-related epigenetic changes do not contribute to rate of healthy ageing at later stages in life. Our results suggest that age-related changes in methylation occur throughout an individual's lifespan and that a proportion of these may be initiated from an early age. Intriguingly, a fraction of the age differentially methylated regions also associated with genetic variants in our sample, suggesting that DNA methylation may be a candidate mechanism of mediating not only environmental but also genetic effects on age-related phenotypes.

Postnatal development- and age-related changes in DNA-methylation patterns in the human genome

Alterations in DNA methylation have been reported to occur during development and aging; however, much remains to be learned regarding post-natal and age-associated epigenome dynamics, and few if any investigations have compared human methylome patterns on a whole genome basis in cells from newborns and adults. The aim of this study was to reveal genomic regions with distinct structure and sequence characteristics that render them subject to dynamic post-natal developmental remodeling or age-related dysregulation of epigenome structure. DNA samples derived from peripheral blood monocytes and in vitro differentiated dendritic cells were analyzed by methylated DNA Immunoprecipitation (MeDIP) or, for selected loci, bisulfite modification, followed by next generation sequencing. Regions of interest that emerged from the analysis included tandem or interspersed-tandem gene sequence repeats (PCDHG, FAM90A, HRNR, ECEL1P2), and genes with strong homology to other family members elsewhere in the genome (FZD1, FZD7 and FGF17). Our results raise the possibility that selected gene sequences with highly homologous copies may serve to facilitate, perhaps even provide a clock-like function for, developmental and age-related epigenome remodeling. If so, this would represent a fundamental feature of genome architecture in higher eukaryotic organisms.

Differential variability improves the identification of cancer risk markers in DNA methylation studies profiling precursor cancer lesions

MOTIVATION

The standard paradigm in omic disciplines has been to identify biologically relevant biomarkers using statistics that reflect differences in mean levels of a molecular quantity such as mRNA expression or DNA methylation. Recently, however, it has been proposed that differential epigenetic variability may mark genes that contribute to the risk of complex genetic diseases like cancer and that identification of risk and early detection markers may therefore benefit from statistics based on differential variability.

RESULTS

Using four genome-wide DNA methylation datasets totalling 311 epithelial samples and encompassing all stages of cervical carcinogenesis, we here formally demonstrate that differential variability, as a criterion for selecting DNA methylation features, can identify cancer risk markers more reliably than statistics based on differences in mean methylation. We show that differential variability selects features with heterogeneous outlier methylation profiles and that these play a key role in the early stages of carcinogenesis. Moreover, differentially variable features identified in precursor non-invasive lesions exhibit significantly increased enrichment for developmental genes compared with differentially methylated sites. Conversely, differential variability does not add predictive value in cancer studies profiling invasive tumours or whole-blood tissue. Finally, we incorporate the differential variability feature selection step into a novel adaptive index prediction algorithm called EVORA (epigenetic variable outliers for risk prediction analysis), and demonstrate that EVORA compares favourably to powerful prediction algorithms based on differential methylation statistics.

CONCLUSIONS

Statistics based on differential variability improve the detection of cancer risk markers in the context of DNA methylation studies profiling epithelial preinvasive neoplasias. We present a novel algorithm (EVORA) which could be used for prediction and diagnosis of precursor epithelial cancer lesions.

AVAILABILITY

R-scripts implementing EVORA are available from CRAN (www.r-project.org).

Hypomethylation of LINE-1 in primary tumor has poor prognosis in young breast cancer patients: a retrospective cohort study

Long interspersed element 1 (LINE-1), a non-coding genomic repeat sequence, methylation status can influence tumor progression. In this study, the clinical significance of LINE-1 methylation status was assessed in primary breast cancer in young versus old breast cancer patients. LINE-1 methylation index (MI) was assessed by absolute quantitative assessment of methylated alleles (AQAMA) PCR assay. Initially, LINE-1 MI was assessed in a preliminary study of 235 tissues representing different stages of ductal breast cancer development. Next, an independent cohort of 379 primary ductal breast cancer patients (median follow-up 18.9 years) was studied. LINE-1 hypomethylation was shown to occur in DCIS and invasive breast cancer. In primary breast cancer it was associated with pathological tumor stage (p = 0.026), lymph node metastasis (p = 0.022), and higher age at diagnosis (>55, p < 0.001). In multivariate analysis, LINE-1 hypomethylation was associated with decreased OS (HR 2.19, 95 % CI 1.17-4.09, log-rank p = 0.014), DFS (HR 2.05, 95 % CI 1.14-3.67, log-rank p = 0.016) and increased DR (HR 2.83, 95 % CI 1.53-5.21, log-rank p = 0.001) in younger (≤55 years), but not older patients (>55 years). LINE-1 analysis of primary breast cancer demonstrated cancer-related age-dependent hypomethylation. In patients ≤55 years, LINE-1 hypomethylation portends a high-risk of DR.

DNA methylation testing and marker validation using PCR: diagnostic applications

DNA methylation provides a fundamental epigenetic mechanism to establish and promote cell-specific gene-expression patterns, which are inherited by subsequent cell generations. Thus, the epigenome determines the differentiation into a cell lineage but can also program cells to become abnormal or malignant. In humans, different germline and somatic diseases have been linked to faulty DNA methylation. In this article, we will discuss the available PCR-based technologies to assess differences in DNA methylation levels mainly affecting 5-methylcytosine in the CpG dinucleotide context in hereditary syndromal and somatic pathological conditions. We will discuss some of the current diagnostic applications and provide an outlook on how DNA methylation-based biomarkers might provide novel tools for diagnosis, prognosis or patient stratification for diseases such as cancer.

Neonatal DNA methylation patterns associate with gestational age

Risk for adverse neonatal outcome increases with declining gestational age (GA), and changes in DNA methylation may contribute to the relationship between GA and adverse health outcomes in offspring. To test this hypothesis, we evaluated the association between GA and more than 27,000 CpG sites in neonatal DNA extracted from umbilical cord blood from two prospectively-characterized cohorts: (1) a discovery cohort consisting of 259 neonates from women with a history of neuropsychiatric disorders and (2) a replication cohort consisting of 194 neonates of uncomplicated mothers. GA was determined by obstetrician report and maternal last menstrual period. The associations between proportion of DNA methylated and GA were evaluated by fitting a separate linear mixed effects model for each CpG site, adjusting for relevant covariates including neonatal sex, race, parity, birth weight percentile and chip effects. CpG sites in 39 genes were associated with GA (false discovery rate < 0.05) in the discovery cohort. The same CpG sites in 25 of these genes replicated in the replication cohort, with each association replicating in the same direction. Notably, these CpG sites were located in genes previously implicated in labor and delivery (e.g., AVP, OXT, CRHBP and ESR1) or that may influence the risk for adverse health outcomes later in life (e.g., DUOX2, TMEM176A and CASP8). All associations were independent of method of delivery or induction of labor. These results suggest neonatal DNA methylation varies with GA even in term deliveries. The potential contribution of these changes to clinically significant postnatal outcomes warrants further investigation.

Hot topics in epigenetic mechanisms of aging: 2011

Aging is a complex process that results in compromised biological functions of the organism and increased susceptibility to disease and death. Although the molecular basis of aging is currently being investigated in many experimental contexts, there is no consensus theory to fully explain the aging process. Epigenetic factors, including DNA methylation, histone modifications, and microRNA expression, may play central roles in controlling changes in gene expression and genomic instability during aging. In this Hot Topic review, we first examine the mechanisms by which these epigenetic factors contribute to aging in diverse eukaryotic species including experimental models of yeasts, worms, and mammals. In a second section, we will emphasize in the mammalian epigenetic alterations and how they may affect human longevity by altering stem cell function and/or somatic cell decline. The field of aging epigenetics is ripe with potential, but is still in its infancy, as new layers of complexity are emerging in the epigenetic network. As an example, we are only beginning to understand the relevance of non-coding genome to organism aging or the existence of an epigenetic memory with transgenerational inheritance. Addressing these topics will be fundamental for exploiting epigenetics phenomena as markers of aging-related diseases or as therapeutic targets.

On the epigenetic origin of cancer stem cells

Epigenetic mechanisms are the key component of the dynamic transcriptional programming that occurs along the process of differentiation from normal stem cells to more specialized cells. In the development of cancer and according to the cancer stem cell model, aberrant epigenetic changes may ensure the property of cancer cells to switch cancer stem cell markers on and off in order to generate a heterogeneous population of cells. The tumour will then be composed of tumourigenic (cancer stem cells) and non-tumourigenic (the side population that constitutes the bulk of the tumour) cells. Characterizing epigenetic landscapes may thus help discriminate aberrant marks (good candidates for tumour detection) from cancer stem cell specific profiles. In this review, we will give some insights about what epigenetics can teach us about the origin of cancer stem cells. We will also discuss how identification of epigenetic reprogramming may help designing new drugs that will specifically target cancer stem cells.

Penalized logistic regression for high-dimensional DNA methylation data with case-control studies

MOTIVATION

DNA methylation is a molecular modification of DNA that plays crucial roles in regulation of gene expression. Particularly, CpG rich regions are frequently hypermethylated in cancer tissues, but not methylated in normal tissues. However, there are not many methodological literatures of case-control association studies for high-dimensional DNA methylation data, compared with those of microarray gene expression. One key feature of DNA methylation data is a grouped structure among CpG sites from a gene that are possibly highly correlated. In this article, we proposed a penalized logistic regression model for correlated DNA methylation CpG sites within genes from high-dimensional array data. Our regularization procedure is based on a combination of the l(1) penalty and squared l(2) penalty on degree-scaled differences of coefficients of CpG sites within one gene, so it induces both sparsity and smoothness with respect to the correlated regression coefficients. We combined the penalized procedure with a stability selection procedure such that a selection probability of each regression coefficient was provided which helps us make a stable and confident selection of methylation CpG sites that are possibly truly associated with the outcome.

RESULTS

Using simulation studies we demonstrated that the proposed procedure outperforms existing main-stream regularization methods such as lasso and elastic-net when data is correlated within a group. We also applied our method to identify important CpG sites and corresponding genes for ovarian cancer from over 20 000 CpGs generated from Illumina Infinium HumanMethylation27K Beadchip. Some genes identified are potentially associated with cancers.

Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

Background

Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation.

Methods

We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions.

Results

We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively).

Conclusions

We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application.

Trial registration

The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.

The influence of cis-regulatory elements on DNA methylation fidelity

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

A DNA hypermethylation module for the stem/progenitor cell signature of cancer

Many DNA-hypermethylated cancer genes are occupied by the Polycomb (PcG) repressor complex in embryonic stem cells (ESCs). Their prevalence in the full spectrum of cancers, the exact context of chromatin involved, and their status in adult cell renewal systems are unknown. Using a genome-wide analysis, we demonstrate that ~75% of hypermethylated genes are marked by PcG in the context of bivalent chromatin in both ESCs and adult stem/progenitor cells. A large number of these genes are key developmental regulators, and a subset, which we call the "DNA hypermethylation module," comprises a portion of the PcG target genes that are down-regulated in cancer. Genes with bivalent chromatin have a low, poised gene transcription state that has been shown to maintain stemness and self-renewal in normal stem cells. However, when DNA-hypermethylated in tumors, we find that these genes are further repressed. We also show that the methylation status of these genes can cluster important subtypes of colon and breast cancers. By evaluating the subsets of genes that are methylated in different cancers with consideration of their chromatin status in ESCs, we provide evidence that DNA hypermethylation preferentially targets the subset of PcG genes that are developmental regulators, and this may contribute to the stem-like state of cancer. Additionally, the capacity for global methylation profiling to cluster tumors by phenotype may have important implications for further refining tumor behavior patterns that may ultimately aid therapeutic interventions.

Epigenetic-aging-signature to determine age in different tissues

All tissues of the organism are affected by aging. This process is associated with epigenetic modifications such as methylation changes at specific cytosine residues in the DNA (CpG sites). Here, we have identified an Epigenetic-Aging-Signature which is applicable for many tissues to predict donor age. DNA-methylation profiles of various cell types were retrieved from public data depositories - all using the HumanMethylation27 BeadChip platform which represents 27,578 CpG sites. Five datasets from dermis, epidermis, cervical smear, T-cells and monocytes were used for Pavlidis Template Matching to identify 19 CpG sites that are continuously hypermethylated upon aging (R > 0.6; p-value <10⁻¹³). Four of these CpG sites (associated with the genes NPTX2, TRIM58, GRIA2 and KCNQ1DN) and an additional hypomethylated CpG site (BIRC4BP) were implemented in a model to predict donor age. This Epigenetic-Aging-Signature was tested on a validation group of eight independent datasets corresponding to several cell types from different tissues. Overall, the five CpG sites revealed age-associated DNA-methylation changes in all tissues. The average absolute difference between predicted and real chronological age was about 11 years. This method can be used to predict donor age in various cell preparations - for example in forensic analysis.

Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk

Few studies have evaluated the association between DNA methylation in white blood cells (WBC) and the risk of breast cancer. The evaluation of WBC DNA methylation as a biomarker of cancer risk is of particular importance as peripheral blood is often available in prospective cohorts and easier to obtain than tumor or normal tissues. Here, we used prediagnostic blood samples from three studies to analyze WBC DNA methylation of two ATM intragenic loci (ATMmvp2a and ATMmvp2b) and genome-wide DNA methylation in long interspersed nuclear element-1 (LINE1) repetitive elements. Samples were from a case-control study derived from a cohort of high-risk breast cancer families (KConFab) and nested case-control studies in two prospective cohorts: Breakthrough Generations Study (BGS) and European Prospective Investigation into Cancer and Nutrition (EPIC). Bisulfite pyrosequencing was used to quantify methylation from 640 incident cases of invasive breast cancer and 741 controls. Quintile analyses for ATMmvp2a showed an increased risk of breast cancer limited to women in the highest quintile [OR, 1.89; 95% confidence interval (CI), 1.36-2.64; P = 1.64 × 10(-4)]. We found no significant differences in estimates across studies or in analyses stratified by family history or menopausal status. However, a more consistent association was observed in younger than in older women and individually significant in KConFab and BGS, but not EPIC. We observed no differences in LINE1 or ATMmvp2b methylation between cases and controls. Together, our findings indicate that WBC DNA methylation levels at ATM could be a marker of breast cancer risk and further support the pursuit of epigenome-wide association studies of peripheral blood DNA methylation.

A new statistical approach to detecting differentially methylated loci for case control Illumina array methylation data

MOTIVATION

As an epigenetic alteration, DNA methylation plays an important role in epigenetic controls of gene transcription. Recent advances in genome-wide scan of DNA methylation provide great opportunities in studying the impact of DNA methylation on many human diseases including various types of cancer. Due to the unique feature of this type of data, applicable statistical methods are limited and new sophisticated approaches are desirable.

RESULTS

In this article, we propose a new statistical test to detect differentially methylated loci for case control methylation data generated by Illumina arrays. This new method utilizes the important finding that DNA methylation is highly correlated with age. The proposed method estimates the overall P-value by combining the P-values from independent individual tests each for one age group. Through real data application and simulation study, we show that the proposed test is robust and usually more powerful than other methods.

DNA methylation signatures in development and aging of the human prefrontal cortex

The human prefrontal cortex (PFC), a mastermind of the brain, is one of the last brain regions to mature. To investigate the role of epigenetics in the development of PFC, we examined DNA methylation in ∼14,500 genes at ∼27,000 CpG loci focused on 5' promoter regions in 108 subjects range in age from fetal to elderly. DNA methylation in the PFC shows unique temporal patterns across life. The fastest changes occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. At the genome level, the transition from fetal to postnatal life is typified by a reversal of direction, from demethylation prenatally to increased methylation postnatally. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes, including genes involved in brain development and in de novo DNA methylation. Our results indicate that promoter DNA methylation in the human PFC is a highly dynamic process modified by genetic variance and regulating gene transcription. Additional discovery is made possible with a stand-alone application, BrainCloudMethyl.

The dynamics and prognostic potential of DNA methylation changes at stem cell gene loci in women's cancer

Aberrant DNA methylation is an important cancer hallmark, yet the dynamics of DNA methylation changes in human carcinogenesis remain largely unexplored. Moreover, the role of DNA methylation for prediction of clinical outcome is still uncertain and confined to specific cancers. Here we perform the most comprehensive study of DNA methylation changes throughout human carcinogenesis, analysing 27,578 CpGs in each of 1,475 samples, ranging from normal cells in advance of non-invasive neoplastic transformation to non-invasive and invasive cancers and metastatic tissue. We demonstrate that hypermethylation at stem cell PolyComb Group Target genes (PCGTs) occurs in cytologically normal cells three years in advance of the first morphological neoplastic changes, while hypomethylation occurs preferentially at CpGs which are heavily Methylated in Embryonic Stem Cells (MESCs) and increases significantly with cancer invasion in both the epithelial and stromal tumour compartments. In contrast to PCGT hypermethylation, MESC hypomethylation progresses significantly from primary to metastatic cancer and defines a poor prognostic signature in four different gynaecological cancers. Finally, we associate expression of TET enzymes, which are involved in active DNA demethylation, to MESC hypomethylation in cancer. These findings have major implications for cancer and embryonic stem cell biology and establish the importance of systemic DNA hypomethylation for predicting prognosis in a wide range of different cancers.

Replicative senescence of mesenchymal stem cells causes DNA-methylation changes which correlate with repressive histone marks

Cells in culture undergo replicative senescence. In this study, we analyzed functional, genetic and epigenetic sequels of long-term culture in human mesenchymal stem cells (MSC). Already within early passages the fibroblastoid colonyforming unit (CFU-f) frequency and the differentiation potential of MSC declined significantly. Relevant chromosomal aberrations were not detected by karyotyping and SNP-microarrays. Subsequently, we have compared DNA-methylation profiles with the Infinium HumanMethylation27 Bead Array and the profiles differed markedly in MSC derived from adipose tissue and bone marrow. Notably, all MSC revealed highly consistent senescence-associated modifications at specific CpG sites. These DNA-methylation changes correlated with histone marks of previously published data sets, such as trimethylation of H3K9, H3K27 and EZH2 targets. Taken together, culture expansion of MSC has profound functional implications - these are hardly reflected by genomic instability but they are associated with highly reproducible DNA-methylation changes which correlate with repressive histone marks. Therefore replicative senescence seems to be epigenetically controlled.

Age-associated DNA methylation in pediatric populations

DNA methylation (DNAm) plays diverse roles in human biology, but this dynamic epigenetic mark remains far from fully characterized. Although earlier studies uncovered loci that undergo age-associated DNAm changes in adults, little is known about such changes during childhood. Despite profound DNAm plasticity during embryogenesis, monozygotic twins show indistinguishable childhood methylation, suggesting that DNAm is highly coordinated throughout early development. Here we examine the methylation of 27,578 CpG dinucleotides in peripheral blood DNA from a cross-sectional study of 398 boys, aged 3-17 yr, and find significant age-associated changes in DNAm at 2078 loci. These findings correspond well with pyrosequencing data and replicate in a second pediatric population (N = 78). Moreover, we report a deficit of age-related loci on the X chromosome, a preference for specific nucleotides immediately surrounding the interrogated CpG dinucleotide, and a primary association with developmental and immune ontological functions. Meta-analysis (N = 1158) with two adult populations reveals that despite a significant overlap of age-associated loci, most methylation changes do not follow a lifelong linear pattern due to a threefold to fourfold higher rate of change in children compared with adults; consequently, the vast majority of changes are more accurately modeled as a function of logarithmic age. We therefore conclude that age-related DNAm changes in peripheral blood occur more rapidly during childhood and are imperfectly accounted for by statistical corrections that are linear in age, further suggesting that future DNAm studies should be matched closely for age.

Age determines the prognostic role of the cancer stem cell marker aldehyde dehydrogenase-1 in breast cancer

Background

The purpose of this study was to compare the expression and the prognostic effect of the breast cancer stem cell marker aldehyde dehydrogenase-1 (ALDH1) in young and elderly breast cancer patients.

Methods

The study population (N = 574) consisted of all early breast cancer patients primarily treated with surgery in our center between 1985 and 1994. Median follow-up was 17.9 years (range: 0.1 to 23.5). Tissue microarray slides were immunohistochemically stained for ALDH1 expression and quantified by two independent observers who were blinded to clinical outcome. Assessment of the prognostic effect of ALDH1 expression was stratified according to age and systemic treatment.

Results

Complete lack of expression of ALDH1 was found in 40% of tumors. With increasing age more tumors showed complete absence of ALDH1 expression (P < .001). In patients aged > 65 years, ALDH1 status was not associated with any clinical outcome. Conversely, in patients aged < 65 years, ALDH1 positivity was an independent risk factor of worse outcome for relapse free period (hazard ratio = 1.71 (95% CI, 1.09 to 2.68); P = .021) and relative survival (relative excess risks of death = 2.36 (95% CI, 1.22 to 3.68); P = .016). Ten-year relative survival risk was 57% in ALDH1-positive patients compared to 83% in ALDH1-negative patients.

Conclusion

ALDH1 expression and its prognostic effect are age-dependent. Our results support the hypothesis that breast cancer biology is different in elderly patients compared to their younger counterparts and emphasizes the importance of taking into consideration age-specific interactions in breast cancer research.

The potential role of epigenetic responses to diet in ageing

Epigenetic changes may be causal in the ageing process and may be influenced by diet, providing opportunities to improve health in later life. The aim of this review is to provide an overview of several areas of research relevant to this topic and to explore a hypothesis relating to a possible role of epigenetic effects, mediated by sirtuin 1, in the beneficial effects of dietary restriction, including increased lifespan. Epigenetic features of ageing include changes in DNA methylation, both globally and at specific loci, which differ between individuals. A major focus of research on dietary influences on epigenetic status has been on nutrition in utero, because the epigenome is probably particularly malleable during this life-course window and because epigenetic marking by early exposures is a compelling mechanism underlying effects on lifelong health. We explore the potential of diet during adulthood, including the practice of dietary restriction, to affect the epigenetic architecture. We report progress with respect to deriving data to support our hypothesis that sirtuin 1 may mediate some of the effects of dietary restriction through effects on DNA methylation and note observations that resveratrol affects DNA methylation and other epigenetic features. Disentangling cause and effect in the context of epigenetic change and ageing is a challenge and requires better understanding of the underlying mechanisms and also the development of more refined experimental tools to manipulate the epigenetic architecture, to facilitate hypothesis-driven research to elucidate these links and thus to exploit them to improve health across the full life-course through dietary measures.

The value of twins in epigenetic epidemiology

During past decades, twin studies have played an important role in genetic epidemiology studies of complex traits. The strength of twin studies lies in the ability to disentangle genetic and environmental factors that contribute to a phenotype, by comparing genetically identical monozygotic twins to dizygotic twins, who share on average 50% of genetic variants. Twin studies now offer the opportunity to study epigenetic variation across the genome with two aims. First, twin studies can improve our understanding of the factors regulating epigenetic variability by assessing the heritability of epigenetic variants. Secondly, the use of twins in epigenetic research is increasingly recognized as an important approach to help unravel the complexities associated with human development and disease. The strategic use of identical twins discordant for complex disease has revealed the importance of linking epigenetic disruption to the disease-associated risk in humans. Lastly, we also discuss the possibility that epigenetic effects on disease may in part explain some of the missing heritability in genome-wide association studies. The study of human epigenetic factors in twins can inform the role of genetics, as well as in utero and postnatal environments to the establishment, maintenance and functional consequences of human epigenome variation.

Epigenetics and the environment: emerging patterns and implications

Epigenetic phenomena in animals and plants are mediated by DNA methylation and stable chromatin modifications. There has been considerable interest in whether environmental factors modulate the establishment and maintenance of epigenetic modifications, and could thereby influence gene expression and phenotype. Chemical pollutants, dietary components, temperature changes and other external stresses can indeed have long-lasting effects on development, metabolism and health, sometimes even in subsequent generations. Although the underlying mechanisms remain largely unknown, particularly in humans, mechanistic insights are emerging from experimental model systems. These have implications for structuring future research and understanding disease and development.

Epigenetics and the environment: emerging patterns and implications

Epigenetic phenomena in animals and plants are mediated by DNA methylation and stable chromatin modifications. There has been considerable interest in whether environmental factors modulate the establishment and maintenance of epigenetic modifications, and could thereby influence gene expression and phenotype. Chemical pollutants, dietary components, temperature changes and other external stresses can indeed have long-lasting effects on development, metabolism and health, sometimes even in subsequent generations. Although the underlying mechanisms remain largely unknown, particularly in humans, mechanistic insights are emerging from experimental model systems. These have implications for structuring future research and understanding disease and development.

DNA methylation: an epigenetic risk factor in preterm birth

Spontaneous preterm birth (PTB; birth prior to 37 weeks of gestation) is a complex phenotype with multiple risk factors that complicate our understanding of its etiology. A number of recent studies have supported the hypothesis that epigenetic modifications such as DNA methylation induced by pregnancy-related risk factors may influence the risk of PTB or result in changes that predispose a neonate to adult-onset diseases. The critical role of timing of gene expression in the etiology of PTB makes it a highly relevant disorder in which to examine the potential role of epigenetic changes. Because changes in DNA methylation patterns can result in long-term consequences, it is of critical interest to identify the epigenetic patterns associated with adverse pregnancy outcomes. This review examines the potential role of DNA methylation as a risk factor for PTB and discusses several issues and limitations that should be considered when planning DNA methylation studies.

The aging epigenome: DNA methylation from the cradle to the grave

Whole-genome methylation analysis of newborns and centenarians reveals widespread epigenetic alterations, and provides new insight into age-related methylation pattern changes.

Epigenetic epidemiology for cancer risk: harnessing germline epigenetic variation

Genetic epidemiology aims to use the natural variation in the genome, namely single nucleotide polymorphisms and copy number variants to look for associations between particular genotypes and disease risk or prognosis. Recent work is now aiming to look further into the genome at the natural variation present in the epigenome, in DNA methylation as well as histone modifications, which both regulate gene expression. Epigenetic epidemiology aims to address the same questions about disease risk and prognosis using the normal epigenetic variability. Some examples of rare "epimutations" that can be detected in peripheral blood DNA have been reported in the genes MLH1, MSH2 and IGF2. Other studies have reported increased cancer risk with skewed distributions of the normal pattern in cancer cases compared to controls, showing the promise of harnessing the normal variation in the epigenome. However, some confounding factors need to be considered including the relationship between the epigenome and increasing age and tissue heterogeneity. Future studies using genome-wide approaches will likely find many more novel epigenetic biomarkers for cancer risk and prognosis.

Batch effect correction for genome-wide methylation data with Illumina Infinium platform

Background

Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data.

Methods

We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average β value (QNβ); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated.

Results

Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets.

Conclusion

Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.

Epigenomics in environmental health

This review considers the emerging relationships between environmental factors and epigenetic alterations and the application of genome-wide assessments to better define these relationships. First we will briefly cover epigenetic programming in development, one-carbon metabolism, and exposures that may disrupt normal developmental programming of epigenetic states. In addition, because a large portion of epigenetic research has focused on cancer, we discuss exposures associated with carcinogenesis including asbestos, alcohol, radiation, arsenic, and air pollution. Research on other exposures that may affect epigenetic states such as endocrine disruptors is also described, and we also review the evidence for epigenetic alterations associated with aging that may reflect cumulative effects of exposures. From this evidence, we posit potential mechanisms by which exposures modify epigenetic states, noting that understanding the true effect of environmental exposures on the human epigenome will require additional research with appropriate epidemiologic studies and application of novel technologies. With a more comprehensive understanding of the affects of exposures on the epigenome, including consideration of genetic background, the prediction of the toxic potential of new compounds may be more readily achieved, and may lead to the development of more personalized disease prevention and treatment strategies.

DNA methylation: superior or subordinate in the epigenetic hierarchy?

Epigenetic modifications are heritable changes in gene expression not encoded by the DNA sequence. In the past decade, great strides have been made in characterizing epigenetic changes during normal development and in disease states like cancer. However, the epigenetic landscape has grown increasingly complicated, encompassing DNA methylation, the histone code, noncoding RNA, and nucleosome positioning, along with DNA sequence. As a stable repressive mark, DNA methylation, catalyzed by the DNA methyltransferases (DNMTs), is regarded as a key player in epigenetic silencing of transcription. DNA methylation may coordinately regulate the chromatin status via the interaction of DNMTs with other modifications and with components of the machinery mediating those marks. In this review, we will comprehensively examine the current understanding of the connections between DNA methylation and other epigenetic marks and discuss molecular mechanisms of transcriptional repression in development and in carcinogenesis.

T2DM: Why Epigenetics?

Type 2 Diabetes Mellitus (T2DM) is a metabolic disorder influenced by interactions between genetic and environmental factors. Epigenetics conveys specific environmental influences into phenotypic traits through a variety of mechanisms that are often installed in early life, then persist in differentiated tissues with the power to modulate the expression of many genes, although undergoing time-dependent alterations. There is still no evidence that epigenetics contributes significantly to the causes or transmission of T2DM from one generation to another, thus, to the current environment-driven epidemics, but it has become so likely, as pointed out in this paper, that one can expect an efflorescence of epigenetic knowledge about T2DM in times to come.

The impact of nutrition on differential methylated regions of the genome

Nutrition has always played an important role in health and disease, ranging from common diseases to its likely contribution to the fetal origins of adult disease. However, deciphering the molecular details of this role is much more challenging. The impact of nutrition on the methylome, i.e., DNA methylation, has received particular attention in more recent years. Our understanding of the complexity of the methylome is evolving as efforts to catalog the DNA methylation differences that exist between different tissues and individuals continue. We review selected examples of animal and human studies that provide evidence that, in fact, specific genes and DNA methylation sites are subject to change during development and during a lifetime as a direct response to nutrition. Investigation of the methyl donors folate, choline, and methionine provide the most compelling evidence of a role in mediating DNA methylation changes. Although a number of candidate regions/genes have been identified to date, we are just at the beginning in terms of cataloging so-called nutrient-sensitive methylation variable positions in humans.

Epigenetic alterations associated with cellular senescence: a barrier against tumorigenesis or a red carpet for cancer?

Cellular senescence is eminently characterized by a permanent cell cycle arrest and the acquisition of morphological, physiological and epigenetic changes. The establishment of cellular senescence can occur in response to telomere attrition associated with cell turnover and ageing or following oncogene activation. Although seemingly two distinct phenomena, cellular senescence and cancer share similarly altered global epigenetic profiles comprising changes in DNA methylation, involving global hypomethylation of repetitive DNA sequences and regional hypermethylation of some gene promoters, and in histone post-translational modifications. As epigenetic and genetic alterations are likely to act synergistically in cancer, anomalous epigenetic marks acquired during ageing or in response to oncogene activation might play important roles in tumorigenesis and cancer progression. These potentially tumor-promoting epigenetic alterations include transcriptional repression of genes encoding tumor suppressors or developmentally regulated proteins, expression of non-coding repetitive RNAs and acquisition of distinct heterochromatin marks that may contribute to suppress cell death by reducing DNA damage response. Cellular senescence may thus be viewed as a double-edged sword that, although acting as a potent anti-proliferative barrier, may pave the way to tumorigenesis in senescence-escaping cells by altering their epigenetic make up.

Differential immune system DNA methylation and cytokine regulation in post-traumatic stress disorder

DNA methylation may mediate persistent changes in gene function following chronic stress. To examine this hypothesis, we evaluated African American subjects matched by age and sex, and stratified into four groups by post-traumatic stress disorder (PTSD) diagnosis and history of child abuse. Total Life Stress (TLS) was also assessed in all subjects. We evaluated DNA extracted from peripheral blood using the HumanMethylation27 BeadChip and analyzed both global and site-specific methylation. Methylation levels were examined for association with PTSD, child abuse history, and TLS using a linear mixed model adjusted for age, sex, and chip effects. Global methylation was increased in subjects with PTSD. CpG sites in five genes (TPR, CLEC9A, APC5, ANXA2, and TLR8) were differentially methylated in subjects with PTSD. Additionally, a CpG site in NPFFR2 was associated with TLS after adjustment for multiple testing. Notably, many of these genes have been previously associated with inflammation. Given these results and reports of immune dysregulation associated with trauma history, we compared plasma cytokine levels in these subjects and found IL4, IL2, and TNFα levels associated with PTSD, child abuse, and TLS. Together, these results suggest that psychosocial stress may alter global and gene-specific DNA methylation patterns potentially associated with peripheral immune dysregulation. Our results suggest the need for further research on the role of DNA methylation in stress-related illnesses.

Method to detect differentially methylated loci with case-control designs using Illumina arrays

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

Epigenome-wide association studies for common human diseases

Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.

Dissecting DNA hypermethylation in cancer

There is compelling evidence to support the importance of DNA methylation alterations in cancer development. Both losses and gains of DNA methylation are observed, thought to contribute pathophysiologically by inactivating tumor suppressor genes, inducing chromosomal instability and ectopically activating gene expression. Lesser known are the causes of aberrant DNA methylation. Recent studies have pointed out that intrinsic gene susceptibility to DNA methylation, environmental factors and gene function all have an intertwined participation in this process. Overall, these data support a deterministic rather than a stochastic mechanism for de novo DNA methylation in cancer. In this review article, we discuss the technologies available to study DNA methylation and the endogenous and exogenous factors that influence the onset of de novo methylation in cancer.

Epigenetic predictor of age

From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites—in the promoters of the EDARADD, TOM1L1, and NPTX2 genes—is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.

A DNA methylation fingerprint of 1628 human samples

Most of the studies characterizing DNA methylation patterns have been restricted to particular genomic loci in a limited number of human samples and pathological conditions. Herein, we present a compromise between an extremely comprehensive study of a human sample population with an intermediate level of resolution of CpGs at the genomic level. We obtained a DNA methylation fingerprint of 1628 human samples in which we interrogated 1505 CpG sites. The DNA methylation patterns revealed show this epigenetic mark to be critical in tissue-type definition and stemness, particularly around transcription start sites that are not within a CpG island. For disease, the generated DNA methylation fingerprints show that, during tumorigenesis, human cancer cells underwent a progressive gain of promoter CpG-island hypermethylation and a loss of CpG methylation in non-CpG-island promoters. Although transformed cells are those in which DNA methylation disruption is more obvious, we observed that other common human diseases, such as neurological and autoimmune disorders, had their own distinct DNA methylation profiles. Most importantly, we provide proof of principle that the DNA methylation fingerprints obtained might be useful for translational purposes by showing that we are able to identify the tumor type origin of cancers of unknown primary origin (CUPs). Thus, the DNA methylation patterns identified across the largest spectrum of samples, tissues, and diseases reported to date constitute a baseline for developing higher-resolution DNA methylation maps and provide important clues concerning the contribution of CpG methylation to tissue identity and its changes in the most prevalent human diseases.

DNA methylation and cancer: ghosts and angels above the genes

PURPOSE OF REVIEW

To discuss recent advances in the field of DNA methylation and their impact on our understanding of the role of this epigenetic mechanism in cancer development, as well as their implications for biomarker discovery and therapy.

RECENT FINDINGS

Epigenetics is a new frontier in cancer research with tremendous impact on our thinking and understanding of biological phenomena and complex diseases, notably cancer. Over the past decade there has been remarkable progress in our knowledge of the importance of epigenetic events in the control of both normal cellular processes and abnormal events associated with tumor development and progression. DNA methylation is a major epigenetic mechanism that is most intensively studied in the context of gene regulation and unscheduled silencing in cancer cells. Although hypermethylation of gene promoters is in turn associated with gene inactivation, the precise consequences of genome-wide hypomethylation are still debated. Recent studies have shed new light on the mechanisms underlying both promoter-specific hypermethylation and global hypomethylation in cancer cells and identified potential targets for biomarker discovery and therapeutic intervention.

SUMMARY

Recent conceptual advances in the field of DNA methylation and the advent and rapid development of new technologies in epigenomics have started to unravel the mechanisms underlying aberrant DNA methylation in cancer cells and identify novel targets for diagnosis, risk assessment and therapy.

Integration of genomic and epigenomic DNA methylation data in common complex diseases by haplotype-specific methylation analysis

The analysis of complex diseases was revolutionized by the ability to genotype at a genome-wide level tagging common SNPs in sufficiently large, and therefore adequately powered, population sample sets. This technological breakthrough has led to thousands of genetic variants being robustly associated with a multitude of phenotypic traits. These findings have illuminated novel genes and previously unknown pathways in the pathogenesis of disease, although in the majority of loci the functional mechanism remains unknown. The integration of this genomic information with epigenomic and transcriptomic data from these regions is one of the next steps in unraveling their biological significance. Allele-specific methylation influences allele-specific expression; therefore, the methylation state of the haplotypes within genetically associated regions can determine epigenetic differences with potential functional effects. DNA methylation data and association-determined risk and nonrisk haplotypes can be compared by a haplotype-specific methylation analysis. These are the first forays into what will become an increasingly routine multidimensional analysis as whole-genome, epigenome and transcriptome sequencing data become easily obtainable, with existing second- and soon to be available third-generation sequencing analyzers. Concise understanding of the functional implications of these genome-wide association-derived risk factors, plus rare variants discovered from deep sequencing experiments currently underway, will enable personalized risk and prevention profiling, as well as treatment, to come to fruition.

Extensive increase of microarray signals in cancers calls for novel normalization assumptions

When using microarray data for studying a complex disease such as cancer, it is a common practice to normalize data to force all arrays to have the same distribution of probe intensities regardless of the biological groups of samples. The assumption underlying such normalization is that in a disease the majority of genes are not differentially expressed genes (DE genes) and the numbers of up- and down-regulated genes are roughly equal. However, accumulated evidences suggest gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalization assumption. Here, we analyzed 7 large Affymetrix datasets of pair-matched normal and cancer samples for cancers collected in the NCBI GEO database. We showed that in 6 of these 7 datasets, the medians of perfect match (PM) probe intensities increased in cancer state and the increases were significant in three datasets, suggesting the assumption that all arrays have the same median probe intensities regardless of the biological groups of samples might be misleading. Then, we evaluated the effects of three currently most widely used normalization algorithms (RMA, MAS5.0 and dChip) on the selection of DE genes by comparing them with LVS which relies less on the above-mentioned assumption. The results showed using RMA, MAS5.0 and dChip may produce lots of false results of down-regulated DE genes while missing many up-regulated DE genes. At least for cancer study, normalizing all arrays to have the same distribution of probe intensities regardless of the biological groups of samples might be misleading. Thus, most current normalizations based on unreliable assumptions may distort biological differences between normal and cancer samples. The LVS algorithm might perform relatively well due to that it relies less on the above-mentioned assumption. Also, our results indicate that genes may be widely up-regulated in most human cancer.

Improving human forensics through advances in genetics, genomics and molecular biology

Forensic DNA profiling currently allows the identification of persons already known to investigating authorities. Recent advances have produced new types of genetic markers with the potential to overcome some important limitations of current DNA profiling methods. Moreover, other developments are enabling completely new kinds of forensically relevant information to be extracted from biological samples. These include new molecular approaches for finding individuals previously unknown to investigators, and new molecular methods to support links between forensic sample donors and criminal acts. Such advances in genetics, genomics and molecular biology are likely to improve human forensic case work in the near future.