A statistical framework for Illumina DNA methylation arrays

Alcohol consumption and epigenetic age acceleration across human adulthood

The alcohol-associated biological aging remains to be studied across adulthood. We conducted linear regression analyses to investigate the associations between alcohol consumption and two DNA methylation-based biological age acceleration metrics in 3823 Framingham Heart Study participants (24-92 years and 53.8% women) adjusting for covariates. We also investigated whether the two epigenetic aging metrics mediated the association of alcohol consumption with hypertension. We found that higher long-term average alcohol consumption was significantly associated with biological age acceleration assessed by GrimAge acceleration (GAA) and PhenoAge acceleration (PAA) in middle-aged (45-64 years, n = 1866) and older (65-92 years, n = 1267) participants while not in young participants (24-44 years, n = 690). For example, one additional standard drink of alcohol (~14 grams of ethanol per day) was associated with a 0.71 ± 0.15-year (p = 2.1e-6) and 0.60 ± 0.18-year (p = 7.5e-4) increase in PAA in middle-aged and older participants, respectively, but the association was not significant in young participants (p = 0.23). One additional standard serving of liquor (~14 grams of ethanol) was associated with a greater increase in GAA (0.82-year, p = 4.8e-4) and PAA (1.45-year, p = 7.4e-5) than beer (GAA: 0.45-year, p = 5.2e-4; PAA: 0.48-year, p = 0.02) and wine (GAA: 0.51-year, p = 0.02; PAA: 0.91-year, p = 0.008) in middle-aged participant group. We observed that up to 28% of the association between alcohol consumption and hypertension was mediated by GAA or PAA in the pooled sample. Our findings suggest that alcohol consumption is associated with greater biological aging quantified by epigenetic aging metrics, which may mediate the association of alcohol consumption with quantitative traits, such as hypertension.

Sex differences in the intergenerational link between maternal and neonatal whole blood DNA methylation: a genome-wide analysis in 2 birth cohorts

BACKGROUND

The mother-child inheritance of DNA methylation (DNAm) variations could contribute to the inheritance of disease susceptibility across generations. However, no study has investigated patterns of mother-child associations in DNAm at the genome-wide scale. It remains unknown whether there are sex differences in mother-child DNAm associations.

RESULTS

Using genome-wide DNAm profiling data (721,331 DNAm sites, including 704,552 on autosomes and 16,779 on the X chromosome) of 396 mother-newborn pairs (54.5% male) from the Boston Birth Cohort, we found significant sex differences in mother-newborn correlations in genome-wide DNAm patterns (Spearman's rho = 0.91-0.98; p = 4.0 × 10-8), with female newborns having stronger correlations. Sex differences in correlations were attenuated but remained significant after excluding X-chromosomal DNAm sites (Spearman's rho = 0.91-0.98; p = 0.035). Moreover, 89,267 DNAm sites (12.4% of all analyzed, including 88,051 [12.5% of analyzed] autosomal and 1,216 [7.2% of analyzed] X-chromosomal sites) showed significant mother-newborn associations in methylation levels, and the top autosomal DNAm sites had high heritability than the genome-wide background (e.g., the top 100 autosomal DNAm sites had a medium h2 of 0.92). Additionally, significant interactions between newborn sex and methylation levels were observed for 11 X-chromosomal and 4 autosomal DNAm sites that were mapped to genes that have been associated with sex-specific disease/traits or early development (e.g., EFHC2, NXY, ADCYAP1R1, and BMP4). Finally, 18,769 DNAm sites (14,482 [77.2%] on the X chromosome) showed mother-newborn differences in methylation levels that were significantly associated with newborn sex, and the top autosomal DNAm sites had relatively small heritability (e.g., the top 100 autosomal DNAm sites had a medium h2 of 0.23). These DNAm sites were mapped to 2,532 autosomal genes and 978 X-chromosomal genes with significant enrichment in pathways involved in neurodegenerative and psychological diseases, development, neurophysiological process, immune response, and sex-specific cancers. Replication analysis in the Isle of Wight birth cohort yielded consistent results.

CONCLUSION

In two independent birth cohorts, we demonstrated strong mother-newborn correlations in whole blood DNAm on both autosomes and ChrX, and such correlations vary substantially by sex. Future studies are needed to examine to what extent our findings contribute to developmental origins of pediatric and adult diseases with well-observed sex differences.

Epigenome-wide association study of mitochondrial genome copy number

We conducted cohort- and race-specific epigenome-wide association analyses of mitochondrial deoxyribonucleic acid (mtDNA) copy number (mtDNA CN) measured in whole blood from participants of African and European origins in five cohorts (n = 6182, mean age = 57-67 years, 65% women). In the meta-analysis of all the participants, we discovered 21 mtDNA CN-associated DNA methylation sites (CpG) (P < 1 × 10-7), with a 0.7-3.0 standard deviation increase (3 CpGs) or decrease (18 CpGs) in mtDNA CN corresponding to a 1% increase in DNA methylation. Several significant CpGs have been reported to be associated with at least two risk factors (e.g. chronological age or smoking) for cardiovascular disease (CVD). Five genes [PR/SET domain 16, nuclear receptor subfamily 1 group H member 3 (NR1H3), DNA repair protein, DNA polymerase kappa and decaprenyl-diphosphate synthase subunit 2], which harbor nine significant CpGs, are known to be involved in mitochondrial biosynthesis and functions. For example, NR1H3 encodes a transcription factor that is differentially expressed during an adipose tissue transition. The methylation level of cg09548275 in NR1H3 was negatively associated with mtDNA CN (effect size = -1.71, P = 4 × 10-8) and was positively associated with the NR1H3 expression level (effect size = 0.43, P = 0.0003), which indicates that the methylation level in NR1H3 may underlie the relationship between mtDNA CN, the NR1H3 transcription factor and energy expenditure. In summary, the study results suggest that mtDNA CN variation in whole blood is associated with DNA methylation levels in genes that are involved in a wide range of mitochondrial activities. These findings will help reveal molecular mechanisms between mtDNA CN and CVD.

Penalized logistic regression based on L1/2 penalty for high-dimensional DNA methylation data

BACKGROUND:

DNA methylation is a molecular modification of DNA that is vital and occurs in gene expression. In cancer tissues, the 5’–C–phosphate–G–3’(CpG) rich regions are abnormally hypermethylated or hypomethylated. Therefore, it is useful to find out the diseased CpG sites by employing specific methods. CpG sites are highly correlated with each other within the same gene or the same CpG island.

OBJECTIVE:

Based on this group effect, we proposed an efficient and accurate method for selecting pathogenic CpG sites.

METHODS:

Our method aimed to combine a L1/2 regularized solver and a central node fully connected network to penalize group constrained logistic regression model. Consequently, both sparsity and group effect were brought in with respect to the correlated regression coefficients.

RESULTS:

Extensive simulation studies were used to compare our proposed approach with existing mainstream regularization in respect of classification accuracy and stability. The simulation results show that a greater predictive accuracy was attained in comparison to previous methods. Furthermore, our method was applied to over 20000 CpG sites and verified using the ovarian cancer data generated from Illumina Infinium HumanMethylation 27K Beadchip. In the result of the real dataset, not only the indicators of predictive accuracy are higher than the previous methods, but also more CpG sites containing genes are confirmed pathogenic. Additionally, the total number of CpG sites chosen is less than other methods and the results show higher accuracy rates in comparison to other methods in simulation and DNA methylation data.

CONCLUSION:

The proposed method offers an advanced tool to researchers in DNA methylation and can be a powerful tool for recognizing pathogenic CpG sites.

Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data

Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq) or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. The integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. In this paper, we propose a coupled co-clustering-based unsupervised transfer learning algorithm (coupleCoC) for the integrative analysis of multimodal single-cell data. Our proposed coupleCoC builds upon the information theoretic co-clustering framework. In co-clustering, both the cells and the genomic features are simultaneously clustered. Clustering similar genomic features reduces the noise in single-cell data and facilitates transfer of knowledge across single-cell datasets. We applied coupleCoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data and scRNA-seq data from mouse and human. We demonstrate that coupleCoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. Our method coupleCoC is also computationally efficient and can scale up to large datasets. Availability: The software and datasets are available at https://github.com/cuhklinlab/coupleCoC.

Paternal DNA Methylation May Be Associated With Gestational Age at Birth

Background:

How epigenetic modifications of DNA are associated with gestational age at birth is not fully understood. We investigated potential effects of differential paternal DNA methylation (DNAm) on offspring gestational age at birth by conducting an epigenome-wide search for cytosine-phosphate-guanine (CpG) sites.

Methods:

Study participants in this study consist of male cohort members or partners of the F1-generation of the Isle of Wight Birth Cohort (IoWBC). DNAm levels in peripheral blood from F1-fathers (n = 92) collected around pregnancy of their spouses were analyzed using the Illumina 450K array. A 5-step statistical analysis was performed. First, a training-testing screening approach was applied to select CpG sites that are potentially associated with gestational age at birth. Second, functional enrichment analysis was employed to identify biological processes. Third, by centralizing on biologically informative genes, Cox proportional hazards models were used to assess the hazard ratios of individual paternal CpGs on gestational age adjusting for confounders. Fourth, to assess the validity of our results, we compared our CpG-gestational age correlations within a Born into Life Study in Sweden (n = 15). Finally, we investigated the correlation between the detected CpGs and differential gene expression in F2 cord blood in the IoWBC.

Results:

Analysis of DNAm of fathers collected around their partner’s pregnancy identified 216 CpG sites significantly associated with gestational age at birth. Functional enrichment pathways analyses of the annotated genes revealed 2 biological pathways significantly related to cell-cell membrane adhesion molecules. Differential methylation of 9 cell membrane adhesion pathway-related CpGs were significantly associated with gestational age at birth after adjustment for confounders. The replication sample showed correlation coefficients of 2 pathway-related CpGs with gestational age at birth within 95% confidence intervals of correlation coefficients in IoWBC. Finally, CpG sites of protocadherin (PCDH) gene clusters were associated with gene expression of PCDH in F2 cord blood.

Conclusions:

Our findings suggest that differential paternal DNAm may affect gestational age at birth through cell-cell membrane adhesion molecules. The results are novel but require future replication in a larger cohort.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

MOTIVATION

Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for.

RESULTS

We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose.

AVAILABILITY AND IMPLEMENTATION

InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia

Identification of genomic and epigenomic determinants of drug resistance provides important insights for improving cancer treatment. Using agnostic genome-wide interrogation of mRNA and miRNA expression, DNA methylation, SNPs, CNAs and SNVs/Indels in primary human acute lymphoblastic leukemia cells, we identified 463 genomic features associated with glucocorticoid resistance. Gene-level aggregation identified 118 overlapping genes, 15 of which were confirmed by genome-wide CRISPR screen. Collectively, this identified 30 of 38 (79%) known glucocorticoid-resistance genes/miRNAs and all 38 known resistance pathways, while revealing 14 genes not previously associated with glucocorticoid-resistance. Single cell RNAseq and network-based transcriptomic modelling corroborated the top previously undiscovered gene, CELSR2. Manipulation of CELSR2 recapitulated glucocorticoid resistance in human leukemia cell lines and revealed a synergistic drug combination (prednisolone and venetoclax) that mitigated resistance in mouse xenograft models. These findings illustrate the power of an integrative genomic strategy for elucidating genes and pathways conferring drug resistance in cancer cells.

Epigenetic aging is accelerated in alcohol use disorder and regulated by genetic variation in APOL2

To investigate the potential role of alcohol use disorder (AUD) in aging processes, we employed Levine's epigenetic clock (DNAm PhenoAge) to estimate DNA methylation age in 331 individuals with AUD and 201 healthy controls (HC). We evaluated the effects of heavy, chronic alcohol consumption on epigenetic age acceleration (EAA) using clinical biomarkers, including liver function test enzymes (LFTs) and clinical measures. To characterize potential underlying genetic variation contributing to EAA in AUD, we performed genome-wide association studies (GWAS) on EAA, including pathway analyses. We followed up on relevant top findings with in silico expression quantitative trait loci (eQTL) analyses for biological function using the BRAINEAC database. There was a 2.22-year age acceleration in AUD compared to controls after adjusting for gender and blood cell composition (p = 1.85 × 10^-5). This association remained significant after adjusting for race, body mass index, and smoking status (1.38 years, p = 0.02). Secondary analyses showed more pronounced EAA in individuals with more severe AUD-associated phenotypes, including elevated gamma-glutamyl transferase (GGT) and alanine aminotransferase (ALT), and higher number of heavy drinking days (all ps < 0.05). The genome-wide meta-analysis of EAA in AUD revealed a significant single nucleotide polymorphism (SNP), rs916264 (p = 5.43 × 10^-8), in apolipoprotein L2 (APOL2) at the genome-wide level. The minor allele A of rs916264 was associated with EAA and with increased mRNA expression in hippocampus (p = 0.0015). Our data demonstrate EAA in AUD and suggest that disease severity further accelerates epigenetic aging. EAA was associated with genetic variation in APOL2, suggesting potential novel biological mechanisms for age acceleration in AUD.

Non-monotone transformation of biomarkers to improve diagnostic and screening accuracy in a DNA methylation study with trichotomous phenotypes

We propose a non-monotone transformation to biomarkers in order to improve the diagnostic and screening accuracy. The proposed quadratic transformation only involves modeling the distribution means and variances of the biomarkers and is therefore easy to implement in practice. Mathematical justification was rigorously established to support the validity of the proposed transformation. We conducted extensive simulation studies to assess the performance of the proposed method and compared the new method with the traditional methods. Case studies on real biomedical and epigenetics data were provided to illustrate the proposed transformation. In particular, the proposed method improved the AUC values for a large number of markers in a DNA methylation study and consequently led to the identification of greater number of important biomarkers and biologically meaningful genetic pathways.

SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions

We report a new class of artifacts in DNA methylation measurements from Illumina HumanMethylation450 and MethylationEPIC arrays. These artifacts reflect failed hybridization to target DNA, often due to germline or somatic deletions and manifest as incorrectly reported intermediate methylation. The artifacts often survive existing preprocessing pipelines, masquerade as epigenetic alterations and can confound discoveries in epigenome-wide association studies and studies of methylation-quantitative trait loci. We implement a solution, P-value with out-of-band (OOB) array hybridization (pOOBAH), in the R package SeSAMe. Our method effectively masks deleted and hyperpolymorphic regions, reducing or eliminating spurious reports of epigenetic silencing at oft-deleted tumor suppressor genes such as CDKN2A and RB1 in cases with somatic deletions. Furthermore, our method substantially decreases technical variation whilst retaining biological variation, both within and across HM450 and EPIC platform measurements. SeSAMe provides a light-weight, modular DNA methylation data analysis suite, with a performant implementation suitable for efficient analysis of thousands of samples.

A Network-guided Association Mapping Approach from DNA Methylation to Disease

Aberrant DNA methylation may contribute to development of cancer. However, understanding the associations between DNA methylation and cancer remains a challenge because of the complex mechanisms involved in the associations and insufficient sample sizes. The unprecedented wealth of DNA methylation, gene expression and disease status data give us a new opportunity to design machine learning methods to investigate the underlying associated mechanisms. In this paper, we propose a network-guided association mapping approach from DNA methylation to disease (NAMDD). Compared with existing methods, NAMDD finds methylation-disease path associations by integrating analysis of multiple data combined with a stability selection strategy, thereby mining more information in the datasets and improving the quality of resultant methylation sites. The experimental results on both synthetic and real ovarian cancer data show that NAMDD substantially outperforms former disease-related methylation site research methods (including NsRRR and PCLOGIT) under false positive control. Furthermore, we applied NAMDD to ovarian cancer data, identified significant path associations and provided hypothetical biological path associations to explain our findings.

Frequency of heavy vehicle traffic and association with DNA methylation at age 18 years in a subset of the Isle of Wight birth cohort

Assessment of changes in DNA methylation (DNA-m) has the potential to identify adverse environmental exposures. To examine DNA-m among a subset of participants (n = 369) in the Isle of Wight birth cohort who reported variable near resident traffic frequencies. We used self-reported frequencies of heavy vehicles passing by the homes of study subjects as a proxy measure for TRAP, which were: never, seldom, 10 per day, 1-9 per hour and >10 per hour. Methylation of cytosine-phosphate-guanine (CpG) dinucleotide sequences in the DNA was assessed from blood samples collected at age 18 years (n = 369) in the F1 generation. We conducted an epigenome wide association study to examine CpGs related to the frequency of heavy vehicles passing by subjects' homes, and employed multiple linear regression models to assess potential associations. We repeated some of these analysis in the F2 generation (n = 140). Thirty-five CpG sites were associated with heavy vehicular traffic. After adjusting for confounders, we found 23 CpGs that were more methylated, and 11 CpGs that were less methylated with increasing heavy vehicular traffic frequency among all subjects. In the F2 generation, 2 of 31 CpGs were associated with traffic frequencies and the direction of the effect was the same as in the F1 subset while differential methylation of 7 of 31 CpG sites correlated with gene expression. Our findings reveal differences in DNA-m in participants who reported higher heavy vehicular traffic frequencies when compared to participants who reported lower frequencies.

CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data

The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such an approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed 'CancerDetector', using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation states of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor fraction. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor cfDNAs. In addition, the predicted tumor fraction showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1× to 10×). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

armDNA: A functional beta model for detecting age-related genomewide DNA methylation marks

DNA methylation has been shown to play an important role in many complex diseases. The rapid development of high-throughput DNA methylation scan technologies provides great opportunities for genomewide DNA methylation-disease association studies. As methylation is a dynamic process involving time, it is quite plausible that age contributes to its variation to a large extent. Therefore, in analyzing genomewide DNA methylation data, it is important to identify age-related DNA methylation marks and delineate their functional relationship. This helps us to better understand the underlying biological mechanism and facilitate early diagnosis and prognosis analysis of complex diseases. We develop a functional beta model for analyzing DNA methylation data and detecting age-related DNA methylation marks on the whole genome by naturally taking sampling scheme into account and accommodating flexible age-methylation dynamics. We focus on DNA methylation data obtained through the widely used bisulfite conversion technique and propose to use a beta model to relate the DNA methylation level to the age. Adjusting for certain confounders, the functional age effect is left completely unspecified, offering great flexibility and allowing extra data dynamics. An efficient algorithm is developed for estimating unknown parameters, and the Wald test is used to detect age-related DNA methylation marks. Simulation studies and several real data applications were provided to demonstrate the performance of the proposed method.

Aberrant DNA methylation of ADAMTS16 in colorectal and other epithelial cancers

BACKGROUND

ADAMs (a disintegrin and metalloproteinase) have long been associated with tumor progression. Recent findings indicate that members of the closely related ADAMTS (ADAMs with thrombospondin motifs) family are also critically involved in carcinogenesis. Gene silencing through DNA methylation at CpG loci around e.g. transcription start or enhancer sites is a major mechanism in cancer development. Here, we aimed at identifying genes of the ADAM and ADAMTS family showing altered DNA methylation in the development or colorectal cancer (CRC) and other epithelial tumors.

METHODS

We investigated potential changes of DNA methylation affecting ADAM and ADAMTS genes in 117 CRC, 40 lung cancer (LC) and 15 oral squamous-cell carcinoma (SCC) samples. Tumor tissue was analyzed in comparison to adjacent non-malignant tissue of the same patients. The methylation status of 1145 CpGs in 51 ADAM and ADAMTS genes was measured with the HumanMethylation450 BeadChip Array. ADAMTS16 protein expression was analyzed in CRC samples by immunohistochemistry.

RESULTS

In CRC, we identified 72 CpGs in 18 genes which were significantly affected by hyper- or hypomethylation in the tumor tissue compared to the adjacent non-malignant tissue. While notable/frequent alterations in methylation patterns within ADAM genes were not observed, conspicuous changes were found in ADAMTS16 and ADAMTS2. To figure out whether these differences would be CRC specific, additional LC and SCC tissue samples were analyzed. Overall, 78 differentially methylated CpGs were found in LC and 29 in SCC. Strikingly, 8 CpGs located in the ADAMTS16 gene were commonly differentially methylated in all three cancer entities. Six CpGs in the promoter region were hypermethylated, whereas 2 CpGs in the gene body were hypomethylated indicative of gene silencing. In line with these findings, ADAMTS16 protein was strongly expressed in globlet cells and colonocytes in control tissue but not in CRC samples. Functional in vitro studies using the colorectal carcinoma cell line HT29 revealed that ADAMTS16 expression restrained tumor cell proliferation.

CONCLUSIONS

We identified ADAMTS16 as novel gene with cancer-specific promoter hypermethylation in CRC, LC and SCC patients implicating ADAMTS16 as potential biomarker for these tumors. Moreover, our results provide evidence that ADAMTS16 may have tumor suppressor properties.

Accounting for tumor purity improves cancer subtype classification from DNA methylation data

Motivation

Tumor sample classification has long been an important task in cancer research. Classifying tumors into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. Thus, the data obtained from these samples are mixed signals. The 'tumor purity', or the percentage of cancer cells in cancer tissue sample, will bias the clustering results if not properly accounted for.

Results

In this article, we developed a model-based clustering method and an R function which uses DNA methylation microarray data to infer tumor subtypes with the consideration of tumor purity. Simulation studies and the analyses of The Cancer Genome Atlas data demonstrate improved results compared with existing methods.

Availability and implementation

InfiniumClust is part of R package InfiniumPurify , which is freely available from CRAN ( https://cran.r-project.org/web/packages/InfiniumPurify/index.html ).

Contact

hao.wu@emory.edu or xqzheng@shnu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Changes in DNA Methylation from Age 18 to Pregnancy in Type 1, 2, and 17 T Helper and Regulatory T-Cells Pathway Genes

To succeed, pregnancies need to initiate immune biases towards T helper 2 (Th2) responses, yet little is known about what establishes this bias. Using the Illumina 450 K platform, we explored changes in DNA methylation (DNAm) of Th1, Th2, Th17, and regulatory T cell pathway genes before and during pregnancy. Female participants were recruited at birth (1989), and followed through age 18 years and their pregnancy (2011-2015). Peripheral blood DNAm was measured in 245 girls at 18 years; from among these girls, the DNAm of 54 women was repeatedly measured in the first (weeks 8-21, n = 39) and second (weeks 22-38, n = 35) halves of pregnancy, respectively. M-values (logit-transformed β-values of DNAm) were analyzed: First, with repeated measurement models, cytosine-phosphate-guanine sites (CpGs) of pathway genes in pregnancy and at age 18 (nonpregnant) were compared for changes (p ≤ 0.05). Second, we tested how many of the 348 pathway-related CpGs changed compared to 10 randomly selected subsets of all other CpGs and compared to 10 randomly selected subsets of other CD4+-related CpGs (348 in each subset). Contrasted to the nonpregnant state, 27.7% of Th1-related CpGs changed in the first and 36.1% in the second half of pregnancy. Among the Th2 pathway CpGs, proportions of changes were 35.1% (first) and 33.8% (second half). The methylation changes suggest involvement of both Th1 and Th2 pathway CpGs in the immune bias during pregnancy. Changes in regulatory T cell and Th17 pathways need further exploration.

Modeling the Causal Role of DNA Methylation in the Association Between Cigarette Smoking and Inflammation in African Americans: A 2-Step Epigenetic Mendelian Randomization Study

The association between cigarette smoking and inflammation is well known. However, the biological mechanisms behind the association are not fully understood, particularly the role of DNA methylation, which is known to be affected by smoking. Using 2-step epigenetic Mendelian randomization, we investigated the role of DNA methylation in the association between cigarette smoking and inflammation. In 822 African Americans from the Genetic Epidemiology Network of Arteriopathy, phase 2 (Jackson, Mississippi; 2000-2005), study population, we examined the association of cigarette smoking with DNA methylation using single nucleotide polymorphisms identified in previous genome-wide association studies of cigarette smoking. We then investigated the association of DNA methylation with levels of inflammatory markers using cis-methylation quantitative trait loci single nucleotide polymorphisms. We found that current smoking status was associated with the DNA methylation levels (M values) of cg03636183 in the coagulation factor II (thrombin) receptor-like 3 gene (F2RL3) (M = -0.64, 95% confidence interval (CI): -0.84, -0.45) and of cg19859270 in the G protein-coupled receptor 15 gene (GPR15) (M = -0.21, 95% CI: -0.27, -0.15). The DNA methylation levels of cg03636183 in F2RL3 were associated with interleukin-18 concentration (-0.11 pg/mL, 95% CI: -0.19, -0.04). These combined negative effects suggest that cigarette smoking increases interleukin-18 levels through the decrease in DNA methylation levels of cg03636183 in F2RL3.

Bioinformatics Tools for Genome-Wide Epigenetic Research

Epigenetics play a central role in the regulation of many important cellular processes, and dysregulations at the epigenetic level could be the source of serious pathologies, such as neurological disorders affecting brain development, neurodegeneration, and intellectual disability. Despite significant technological advances for epigenetic profiling, there is still a need for a systematic understanding of how epigenetics shapes cellular circuitry, and disease pathogenesis. The development of accurate computational approaches for analyzing complex epigenetic profiles is essential for disentangling the mechanisms underlying cellular development, and the intricate interaction networks determining and sensing chromatin modifications and DNA methylation to control gene expression. In this chapter, we review the recent advances in the field of "computational epigenetics," including computational methods for processing different types of epigenetic data, prediction of chromatin states, and study of protein dynamics. We also discuss how "computational epigenetics" has complemented the fast growth in the generation of epigenetic data for uncovering the main differences and similarities at the epigenetic level between individuals and the mechanisms underlying disease onset and progression.

Consistency and Variability of DNA Methylation in Women During Puberty, Young Adulthood, and Pregnancy

Prior DNA methylation (DNA-m) analyses have identified cytosine-phosphate-guanine (CpG) sites, which show either a significant change or consistency during lifetime. However, the proportion of CpGs that are neither significantly different nor consistent over time (indifferent CpGs) is unknown. We investigated the methylation dynamics, both longitudinal changes and consistency, in women from preadolescence to late pregnancy using DNA-m of peripheral blood cells. Consistency of cell type–adjusted DNA-m between paired individuals was assessed by regressing CpGs of subsequent age on the prior, stability by intraclass correlation coefficients (>0.5), and changes by linear mixed models. In the first 2 transitions (10-18 years and 18 years to early pregnancy), 19.5% and 20.9% CpGs were consistent, but only 0.35% in the third transition (from early to late pregnancy). Significant changes in methylation were found in 0.7%, 5.6%, and 0% CpGs, respectively. Functional enrichment analyses of genes with significant changes in DNA-m in early pregnancy (5.6%) showed that the maternal DNA-m seems to reflect signaling pathways between the uterus and the trophoblast. The transition from early to late pregnancy showed low consistency/stability and no changes, suggesting the presence of a large proportion of indifferent CpGs in late pregnancy.

An epigenome-wide DNA methylation study of PTSD and depression in World Trade Center responders

Previous epigenome-wide association studies (EWAS) of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) have been inconsistent. This may be due to small sample sizes, and measurement and tissue differences. The current two EWA analyses of 473 World Trade Center responders are the largest to date for both PTSD and MDD. These analyses investigated DNA methylation patterns and biological pathways influenced by differentially methylated genes associated with each disorder. Methylation was profiled on blood samples using Illumina 450 K Beadchip. Two EWA analyses compared current versus never PTSD, and current versus never MDD, adjusting for cell types and demographic confounders. Pathway and gene set enrichment analyses were performed to understand the complex biological systems of PTSD and MDD. No significant epigenome-wide associations were found for PTSD or MDD at an FDR P<0.05. The majority of genes with differential methylation at a suggestive threshold did not overlap between the two disorders. Pathways significant in PTSD included a regulator of synaptic plasticity, oxytocin signaling, cholinergic synapse and inflammatory disease pathways, while only phosphatidylinositol signaling and cell cycle pathways emerged in MDD. The failure of the current EWA analyses to detect significant epigenome-wide associations is in contrast with disparate findings from previous, smaller EWA and candidate gene studies of PTSD and MDD. Enriched gene sets involved in several biological pathways, including stress response, inflammation and physical health, were identified in PTSD, supporting the view that multiple genes play a role in this complex disorder.

Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies

We present a set of statistical methods for the analysis of DNA methylation microarray data, which account for tumor purity. These methods are an extension of our previously developed method for purity estimation; our updated method is flexible, efficient, and does not require data from reference samples or matched normal controls. We also present a method for incorporating purity information for differential methylation analysis. In addition, we propose a control-free differential methylation calling method when normal controls are not available. Extensive analyses of TCGA data demonstrate that our methods provide accurate results. All methods are implemented in InfiniumPurify.

Tumor purity and differential methylation in cancer epigenomics

DNA methylation is an epigenetic modification of DNA molecule that plays a vital role in gene expression regulation. It is not only involved in many basic biological processes, but also considered an important factor for tumorigenesis and other human diseases. Study of DNA methylation has been an active field in cancer epigenomics research. With the advances of high-throughput technologies and the accumulation of enormous amount of data, method development for analyzing these data has gained tremendous interests in the fields of computational biology and bioinformatics. In this review, we systematically summarize the recent developments of computational methods and software tools in high-throughput methylation data analysis with focus on two aspects: differential methylation analysis and tumor purity estimation in cancer studies.

DNA methylation and genetic polymorphisms of the Leptin gene interact to influence lung function outcomes and asthma at 18 years of age

The leptin gene (LEP) plays a regulatory role in satiety, inflammation, and allergy. Prior findings linking leptin to asthma motivated us to investigate whether DNA methylation (DNA-M) of CpG (cytosine-phosphate-guanine) sites in concert with single nucleotide polymorphisms (SNPs) of LEP can explain the risk of asthma and lung function. Methylation of CpG sites was assessed using the Illumina Infinium Human Methylation 450 beadchip in blood samples collected from 10- and 18-year-old boys and girls from the Isle of Wight (IOW) birth cohort (UK). Four LEP SNPs were genotyped. Linear and log linear models were used for the analysis, adjusting for false discovery rate (FDR). The analyses were repeated in the BAMSE cohort (Sweden). In the IOW study, the interaction of cg00666422 and rs11763517 (CT vs TT and CC) was associated with FEV1 (FDR-adjusted p-value: 0.03), FEV1/FVC ratio (FDR-adjusted p-value: 0.0096), and FEF25-75% (FDR-adjusted p-value: 0.00048) such that they decreased with increasing DNA-M. The interaction of the same CpG-SNP pair was also associated with increased risk of asthma at age 18. We replicated the findings for FEV1/FVC and FEF25-75% in a smaller sample of 34 participants at age 10. Regarding the BAMSE cohort, although, the interaction of cg00666422 and rs11763517 on lung function were not significant, the direction of the effect was the same as in IOW cohort. Thus, penetrance of LEP genotype seems to be modified by methylation at cg00666422 and is linked to airway obstruction and asthma.

Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ_adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12–1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ_adjusted = 1.00–1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.

Placental DNA Methylation Related to Both Infant Toenail Mercury and Adverse Neurobehavioral Outcomes

BACKGROUND

Prenatal mercury (Hg) exposure is associated with adverse child neurobehavioral outcomes. Because Hg can interfere with placental functioning and cross the placenta to target the fetal brain, prenatal Hg exposure can inhibit fetal growth and development directly and indirectly.

OBJECTIVES

We examined potential associations between prenatal Hg exposure assessed through infant toenail Hg, placental DNA methylation changes, and newborn neurobehavioral outcomes.

METHODS

The methylation status of > 485,000 CpG loci was interrogated in 192 placental samples using Illumina's Infinium HumanMethylation450 BeadArray. Hg concentrations were analyzed in toenail clippings from a subset of 41 infants; neurobehavior was assessed using the NICU Network Neurobehavioral Scales (NNNS) in an independent subset of 151 infants.

RESULTS

We identified 339 loci with an average methylation difference > 0.125 between any two toenail Hg tertiles. Variation among these loci was subsequently found to be associated with a high-risk neurodevelopmental profile (omnibus p-value = 0.007) characterized by the NNNS. Ten loci had p < 0.01 for the association between methylation and the high-risk NNNS profile. Six of 10 loci reside in the EMID2 gene and were hypomethylated in the 16 high-risk profile infants' placentas. Methylation at these loci was moderately correlated (correlation coefficients range, -0.33 to -0.45) with EMID2 expression.

CONCLUSIONS

EMID2 hypomethylation may represent a novel mechanism linking in utero Hg exposure and adverse infant neurobehavioral outcomes.

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

NALP3 inflammasome upregulation and CASP1 cleavage of the glucocorticoid receptor cause glucocorticoid resistance in leukemia cells

Glucocorticoids are universally used in the treatment of acute lymphoblastic leukemia (ALL), and resistance to glucocorticoids in leukemia cells confers poor prognosis. To elucidate mechanisms of glucocorticoid resistance, we determined the prednisolone sensitivity of primary leukemia cells from 444 patients newly diagnosed with ALL and found significantly higher expression of CASP1 (encoding caspase 1) and its activator NLRP3 in glucocorticoid-resistant leukemia cells, resulting from significantly lower somatic methylation of the CASP1 and NLRP3 promoters. Overexpression of CASP1 resulted in cleavage of the glucocorticoid receptor, diminished the glucocorticoid-induced transcriptional response and increased glucocorticoid resistance. Knockdown or inhibition of CASP1 significantly increased glucocorticoid receptor levels and mitigated glucocorticoid resistance in CASP1-overexpressing ALL. Our findings establish a new mechanism by which the NLRP3-CASP1 inflammasome modulates cellular levels of the glucocorticoid receptor and diminishes cell sensitivity to glucocorticoids. The broad impact on the glucocorticoid transcriptional response suggests that this mechanism could also modify glucocorticoid effects in other diseases.

DNA methylation changes in the placenta are associated with fetal manganese exposure

Adequate micronutrient intake, including manganese (Mn), is important for fetal development. Both Mn deficiencies and excess exposures are associated with later-life disease, and Mn accumulates in the placenta. Placental functional alterations may alter fetal programming and lifelong health, and we hypothesized that prenatal exposures to Mn may alter placental function through epigenetic mechanisms. Using Illumina's HumanMethylation450 BeadArray, DNA methylation of >485,000 CpG loci genome-wide was interrogated in 61 placental samples and Mn associations assessed genome-wide via omnibus test (p=0.045). 713 loci were associated with Mn exposure (p<0.0001). Five significantly differentially-methylated (p<1.3×10(-7)) loci reside in neurodevelopmental, fetal growth and cancer-related genes. cg22284422, within the uncharacterized LOC284276 gene, was associated with birth weight; for every 10% increase in methylation, lower birth weights were observed, with an average decrease of 293.44g. Our observations suggest a link between prenatal micronutrient levels, placental epigenetic status and birth weight, although these preliminary results require validation.

Effects of phthalate exposure on asthma may be mediated through alterations in DNA methylation

Background

Phthalates may increase the asthma risk in children. Mechanisms underlying this association remain to be addressed. This study assesses the effect of phthalate exposures on epigenetic changes and the role of epigenetic changes for asthma. In the first step, urine and blood samples from 256 children of the Childhood Environment and Allergic diseases Study (CEAS) were analyzed. Urine 5OH-MEHP levels were quantified as an indicator of exposure, and asthma information was collected. DNA methylation (DNA-M) was measured by quantitative PCR. In the screening part of step 1, DNA-M of 21 potential human candidate genes suggested by a toxicogenomic data were investigated in 22 blood samples. Then, in the testing part of step 1, positively screened genes were tested in a larger sample of 256 children and then validated by protein measurements. In step 2, we replicated the association between phthalate exposure and gene-specific DNA-M in 54 children in the phthalate contaminated food event. In step 3, the risk of DNA-M for asthma was tested in 256 children from CEAS and corroborated in 270 children from the Isle of Wight (IOW) birth cohort.

Results

Differential methylation in three genes (AR, TNFα, and IL-4) was identified through screening. Testing in 256 children showed that methylation of the TNFα gene promoter was lower when children had higher urine 5OH-MEHP values (β = −0.138, P = 0.040). Functional validation revealed that TNFα methylation was inversely correlated with TNFα protein levels (β = −0.18, P = 0.041). In an additional sample of 54 children, we corroborated that methylation of the TNFα gene promoter was lower when urine 5OH-MEHP concentrations were higher. Finally, we found that a lower methylation of 5′CGI region of TNFα was associated with asthma in 256 CEAS children (OR = 2.15, 95% CI = 1.01 to 4.62). We replicated this in 270 children from the IOW birth cohort study. Methylation of the CpG site cg10717214 was negatively associated with asthma, when children had ‘AA’ or ‘AG’ genotype of the TNFα single nucleotide rs1800610.

Conclusions

Effects of phthalate exposure on asthma may be mediated through alterations in DNA methylation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0060-x) contains supplementary material, which is available to authorized users.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

An update on epigenetics and childhood respiratory diseases

Epigenetic mechanisms, defined as changes in phenotype or gene expression caused by mechanisms other than changes in the underlying DNA sequence, have been proposed to constitute a link between genetic and environmental factors that affect complex diseases. Recent studies show that DNA methylation, one of the key epigenetic mechanisms, is altered in children exposed to air pollutants and environmental tobacco smoke early in life. Several candidate gene studies on epigenetics have been published to date, but it is only recently that global methylation analyses have been performed for respiratory disorders such as asthma and chronic obstructive pulmonary disease. However, large-scale studies with adequate power are yet to be presented in children, and implications for clinical use remain to be evaluated. In this review, we summarize the recent advances in epigenetics and respiratory disorders in children, with a main focus on methodological challenges and analyses related to phenotype and exposure using global methylation approaches.

Comprehensive analysis of DNA methylation data with RnBeads

RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

Propensity score method for partially matched omics studies

This paper focuses on the problem of partially matched samples in the presence of confounders. We propose using propensity score matching to adjust for confounding factors for the subset of data with incomplete pairs, followed by integrating the P-values computed from the complete and incomplete paired samples, respectively. Several simulations and a case study on DNA methylation are considered to evaluate the operating characteristics of the proposed method.

Covariate adjusted differential variability analysis of DNA methylation with propensity score method

It has been proposed recently that differentially variable CpG methylation (DVC) may contribute to transcriptional aberrations in human diseases. In large scale epigenetic studies, potential confounders could affect the observed methylation variabilities and need to be accounted for. In this paper, we develop a robust statistical model for differential variability DVC analysis that accounts for potential confounding covariates by utilizing the propensity score method. Our method is based on a weighted score test on strata generated propensity score stratification. To the best of our knowledge, this is the first proposed statistical method for detecting DVCs that adjusts for confounding covariates. We show that this method is robust against model misspecification and achieves good operating characteristics based on extensive simulations and a case study.

Oral contraceptives modify the effect of GATA3 polymorphisms on the risk of asthma at the age of 18 years via DNA methylation

BACKGROUND

The prevalence of asthma in girls increases after puberty. Previous studies have detected associations between sex hormones and asthma, as well as between sex hormones and T helper 2 (Th2) asthma-typical immune responses. Therefore, we hypothesized that exogenous or endogenous sex hormone exposure (represented by oral contraceptive pill (OCP) use and early menarche, respectively) are associated with DNA methylation (DNA-M) of the Th2 transcription factor gene, GATA3, in turn affecting the risk of asthma in girls, possibly in interaction with genetic variants. Blood samples were collected from 245 female participants aged 18 years randomly selected for methylation analysis from the Isle of Wight birth cohort, UK. Information on use of OCPs, age at menarche, and concurrent asthma were assessed by questionnaire. Genome-wide DNA-M was determined using the Illumina Infinium HumanMethylation450 beadchip. In a first stage, we tested the interaction between sex hormone exposure and genetic variants on DNA-M of specific cytosine-phosphate-guanine (CpG) sites. In a second stage, we determined whether these CpG sites interact with genetic variants in GATA3 to explain the risk of asthma.

RESULTS

Interactions between OCP use and seven single nucleotide polymorphisms (SNPs) of GATA3 were analyzed for 14 CpG sites (stage 1). The interaction between OCP use and SNP rs1269486 was found to be associated with the methylation level of cg17124583 (P = 0.002, false discovery rate (FDR) adjusted P = 0.04). DNA-M of this same CpG site was also influenced by the interaction between age at menarche and rs1269486 (P = 0.0017). In stage 2, we found that cg17124583 modified the association of SNP rs422628 with asthma risk at the age of 18 years (P = 0.006, FDR adjusted P = 0.04). Subjects with genotype AG showed an increase in average risk ratio (RR) from 0.31 (95% CI: 0.10 to 0.8) to 11.65 (95% CI: 1.71 to 79.5) when methylation level increased from 0.02 to 0.12, relative to genotype AA.

CONCLUSION

A two-stage model consisting of genetic variants in the GATA3 gene, OCP use, age at menarche, and DNA-M may explain how sex hormones in women can increase the asthma prevalence after puberty.

Statistical challenges of high-dimensional methylation data

With the fast growing field of epigenetics comes the need to better understand the intricacies of DNA methylation data analysis. High-throughput profiling using techniques, such as Illumina's BeadArray assay, enable the quantitative assessment of methylation. Challenges arise from the fact that resulting methylation levels (so-called beta values) are proportions between 0 and 1, often from an asymmetric, bimodal distribution with peaks close to 0 and 1. Therefore, the majority of standard statistical approaches do not apply. The logit transformation into so-called M-values is a common approach to circumvent this problem and aims to allow the use of common statistical methods. However, it can be observed that the transformation from beta to M-values does not necessarily result in an approximately homoscedastic distribution. Often, bimodality, asymmetry and heteroscedasticity are conserved even after transformation. We give an overview and discussion of methods suggested in the recent years that attempt to address the characteristics of methylation data in univariate screening settings. In order to identify 'differential' methylation with respect to covariates of interest while adjusting for confounders, we compare parametric methods, such as linear and beta regression, and nonparametric methods, such as rank-based regression. Our goal is to sensitise researchers to the challenges and issues that arise from this type of data as well as to present possible solutions.

Epigenetic alterations in metastatic cutaneous carcinoma

BACKGROUND

Squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) are the 2 most common cutaneous carcinomas. Molecular profiles predicting metastasis of these cancers have not been identified.

METHODS

Epigenetic profiles of 37 primary cases of cutaneous SCC and BCC were quantified via the Illumina Goldengate Cancer Panel. Differential protein expression by metastatic potential was analyzed in 110 total cases by immunohistochemical (IHC) staining.

RESULTS

Unsupervised hierarchical clustering analysis revealed that metastatic BCCs had a methylation profile resembling cutaneous SCCs. Metastatic cutaneous SCCs were found to be hypermethylated at FRZB (median methylation: 46.7% vs 4.7%; p = 4 × 10(-5) ). Metastatic BCCs were found to be hypomethylated at MYCL2 (median methylation: 3.8% vs 83.4%; p = 1.9 × 10(-6) ). Immunohistochemical staining revealed few differences between metastatic and nonmetastatic cancers.

CONCLUSION

Metastatic primary BCCs and cutaneous SCCs had distinct epigenetic profiles when compared to their nonmetastatic counterparts. Epigenetic profiling may prove useful in future diagnosis and prevention of advanced nonmelanoma skin cancers.

Comparisons of non-Gaussian statistical models in DNA methylation analysis

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance.

SOCS3 promoter hypermethylation is a favorable prognosticator and a novel indicator for G-CIMP-positive GBM patients

BACKGROUND

Hypermethylation of the suppressor of cytokine signaling 3(SOCS3) promoter has been reported to predict a poor prognosis in several cancers including glioblastoma multiforme (GBM). We explored the function of SOCS3 promoter hypermethylation in GBM cohorts, including analysis of the CpG island methylator phenotype (CIMP), when a large number of gene loci are simultaneously hypermethylated.

METHODS

A whole genome promoter methylation profile was performed in a cohort of 33 GBM samples, with 13 long-term survivors (LTS; overall survival ≥ 18 months) and 20 short-term survivors (STS; overall survival ≤ 9 months). The SOCS3 promoter methylation status was compared between the two groups. In addition, we investigated the relationship of SOCS3 promoter methylation and G-CIMP status.

RESULTS

Interestingly, in our present study, we found that SOCS3 promoter methylation was statistically significantly higher in the 13 LTS than that in the 20 STS. Furthermore, high SOCS3 promoter methylation detected via pyro-sequencing predicted a better prognosis in an independent cohort containing 62 GBM patients. This correlation was validated by the dataset from the Cancer Genome Atlas(TCGA) and the Chinese Cancer Genome Atlas(CGGA). In addition, we found that hypermethylation of the SOCS3 promoter was tightly associated with the G-CIMP-positive GBM patients.

CONCLUSIONS

Using a total of 359 clinical samples, we demonstrate that SOCS3 promoter hypermethylation status has a favorable prognostic value in GBM patients because of whole genome methylation status. Particularly, the hypermethylation of the SOCS3 promoter indicates positive G-CIMP status.

Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer

BACKGROUND

Both genetic and epigenetic factors influence the development and progression of epithelial ovarian cancer (EOC). However, there is an incomplete understanding of the interrelationship between these factors and the extent to which they interact to impact disease risk. In the present study, we aimed to gain insight into this relationship by identifying DNA methylation marks that are candidate mediators of ovarian cancer genetic risk.

METHODS

We used 214 cases and 214 age-matched controls from the Mayo Clinic Ovarian Cancer Study. Pretreatment, blood-derived DNA was profiled for genome-wide methylation (Illumina Infinium HumanMethylation27 BeadArray) and single nucleotide polymorphisms (SNPs, Illumina Infinium HD Human610-Quad BeadArray). The Causal Inference Test (CIT) was implemented to distinguish CpG sites that mediate genetic risk, from those that are consequential or independently acted on by genotype.

RESULTS

Controlling for the estimated distribution of immune cells and other key covariates, our initial epigenome-wide association analysis revealed 1,993 significantly differentially methylated CpGs that between cases and controls (FDR, q < 0.05). The relationship between methylation and case-control status for these 1,993 CpGs was found to be highly consistent with the results of previously published, independent study that consisted of peripheral blood DNA methylation signatures in 131 pretreatment cases and 274 controls. Implementation of the CIT test revealed 17 CpG/SNP pairs, comprising 13 unique CpGs and 17 unique SNPs, which represent potential methylation-mediated relationships between genotype and EOC risk. Of these 13 CpGs, several are associated with immune related genes and genes that have been previously shown to exhibit altered expression in the context of cancer.

CONCLUSIONS

These findings provide additional insight into EOC etiology and may serve as novel biomarkers for EOC susceptibility.

Comparison of genome-wide DNA methylation in urothelial carcinomas of patients with and without arsenic exposure

BACKGROUND

Arsenic is a well-documented carcinogen of human urothelial carcinoma (UC) with incompletely understood mechanisms.

OBJECTIVES

This study aimed to compare the genome-wide DNA methylation profiles of arsenic-induced UC (AsUC) and non-arsenic-induced UC (Non-AsUC), and to assess associations between site-specific methylation levels and cumulative arsenic exposure.

METHODS

Genome-wide DNA methylation profiles in 14 AsUC and 14 non-AsUC were analyzed by Illumina Infinium methylation27 BeadChip and validated by bisulfite pyrosequencing. Mean methylation levels (β¯) in AsUC and non-AsUC were compared by their ratio (β¯ ratio) and difference (Δβ¯). Associations between site-specific methylation levels in UC and cumulative arsenic exposure were examined.

RESULTS

Among 27,578 methylation sites analyzed, 231 sites had β¯ ratio >2 or <0.5 and 45 sites had Δβ¯ >0.2 or <-0.2. There were 13 sites showing statistically significant (q<0.05) differences in β¯ between AsUC and non-AsUC including 12 hypermethylation sites in AsUC and only one hypermethylation site in non-AsUC. Significant associations between cumulative arsenic exposure and DNA methylation levels of 28 patients were observed in nine CpG sites of nine gens including PDGFD (Spearman rank correlation, 0.54), CTNNA2 (0.48), KCNK17 (0.52), PCDHB2 (0.57), ZNF132 (0.48), DCDC2 (0.48), KLK7 (0.48), FBXO39 (0.49), and NPY2R (0.45). These associations remained statistically significant for CpG sites in CTNNA2, KLK7, NPY2R, ZNF132 and KCNK17 in 20 non-smoking women after adjustment for tumor stage and age.

CONCLUSIONS

Significant associations between cumulative arsenic exposure and methylation level of CTNNA2, KLK7, NPY2R, ZNF132 and KCNK17 were found in smoking-unrelated urothelial carcinoma. Arsenic exposure may cause urothelial carcinomas through the hypermethylation of genes involved in cell adhesion, proteolysis, transcriptional regulation, neuronal pathway, and ion transport. The findings of this study, which are limited by its small sample size and moderate dose-response relation, remain to be validated by further studies with large sample sizes.

Distinct patterns of DNA methylation in conventional adenomas involving the right and left colon

Recent studies have shown two distinct non-CIMP methylation clusters in colorectal cancer, raising the possibility that DNA methylation, involving non-CIMP genes, may play a role in the conventional adenoma-carcinoma pathway. A total of 135 adenomas (65 left colon and 70 right colon) were profiled for epigenome-wide DNA methylation using the Illumina HumanMethylation450 BeadChip. A principal components analysis was performed to examine the association between variability in DNA methylation and adenoma location. Linear regression and linear mixed effects models were used to identify locus-specific differential DNA methylation in adenomas of right and left colon. A significant association was present between the first principal component and adenoma location (P=0.007), even after adjustment for subject age and gender (P=0.009). A total of 168 CpG sites were differentially methylated between right- and left-colon adenomas and these loci demonstrated enrichment of homeobox genes (P=3.0 × 10(-12)). None of the 168 probes were associated with CIMP genes. Among CpG loci with the largest difference in methylation between right- and left-colon adenomas, probes associated with PRAC (prostate cancer susceptibility candidate) gene showed hypermethylation in right-colon adenomas whereas those associated with CDX2 (caudal type homeobox transcription factor 2) showed hypermethylation in left-colon adenomas. A subgroup of left-colon adenomas enriched for current smokers (OR=6.1, P=0.004) exhibited a methylation profile similar to right-colon adenomas. In summary, our results indicate distinct patterns of DNA methylation, independent of CIMP genes, in adenomas of the right and left colon.

Interaction of prenatal maternal smoking, interleukin 13 genetic variants and DNA methylation influencing airflow and airway reactivity

BACKGROUND

Asthma is characterized by airflow limitation and airway reactivity (AR). Interleukin-13 (IL-13) is involved in the pathogenesis of asthma. Two functional SNPs, rs20541 and rs1800925, of the IL-13 gene (IL13) have been frequently associated with asthma-related lung functions. However, genetic variation alone does not fully explain asthma risk. DNA-methylation (DNA-M) is an epigenetic mechanism that regulates gene expression and can be influenced by both environment and genetic variants. To explore the interplay of prenatal maternal smoking, genetic variants and DNA-M, we used a two-stage model: (1) identifying cytosine phosphate guanine (CpG) sites where DNA-M is influenced by the interaction between genetic variants and maternal smoking during pregnancy (conditional methQTL (methylation quantitative trait loci)); and (2) determining the effect of the interaction between DNA-M of CpG (from stage 1) and SNPs (modifying genetic variants; modGV) on airflow limitation and AR in 245 female participants of the Isle of Wight birth cohort. DNA-M was assessed using the Illumina Infinium HumanMethylation450 BeadChip.

FINDINGS

Six CpG sites were analyzed in stage 1. DNA-M at cg13566430 was influenced by interaction of maternal smoking during pregnancy and rs20541. In stage 2, genotype at rs1800925 interacted with DNA-M at cg13566430 significantly affecting airflow limitation (P = 0.042) and AR (P = 0.01).

CONCLUSION

Both genetic variants and environment affect DNA-M. This study supports the proposed two-stage model (methQTL and modGV) to study genetic variants, environment and DNA-M interactions in asthma-related lung function.

Placental DNA methylation alterations associated with maternal tobacco smoking at the RUNX3 gene are also associated with gestational age

AIMS

The developmental origins of health and disease hypothesis states that later-life disease may be influenced by the quality of the in utero environment. Environmental toxicants can have detrimental effects on fetal development, potentially through effects on placental development and function. Maternal smoking during pregnancy is associated with low birth weight, preterm birth and other complications, and exposure to cigarette smoke in utero has been linked to gross pathologic and molecular changes to the placenta, including differential DNA methylation in placental tissue. The aim of this study was to investigate the relationship between maternal smoking during pregnancy, methylation changes in the placenta and gestational age.

MATERIALS & METHODS

We used Illumina(®)'s (CA, USA) Human Methylation27 BeadChip technology platform to investigate the methylation status of 21,551 autosomal, non-SNP-associated CpG loci in DNA extracted from 206 human placentas and examined loci whose variation in methylation was associated with maternal smoking during pregnancy.

RESULTS

We found that methylation patterns of a number of loci within the RUNX3 gene were significantly associated with smoking during pregnancy, and one of these loci was associated with decreased gestational age (p = 0.04).

CONCLUSION

Our findings, demonstrating maternal smoking-induced changes in DNA methylation at specific loci, suggest a mechanism by which in utero tobacco smoke exposure could exert its detrimental effects upon the health of the fetus.

Review of processing and analysis methods for DNA methylation array data

The promise of epigenome-wide association studies and cancer-specific somatic DNA methylation changes in improving our understanding of cancer, coupled with the decreasing cost and increasing coverage of DNA methylation microarrays, has brought about a surge in the use of these technologies. Here, we aim to provide both a review of issues encountered in the processing and analysis of array-based DNA methylation data and a summary of the advantages of recent approaches proposed for handling those issues, focusing on approaches publicly available in open-source environments such as R and Bioconductor. We hope that the processing tools and analysis flowchart described herein will facilitate researchers to effectively use these powerful DNA methylation array-based platforms, thereby advancing our understanding of human health and disease.

Epigenetic alterations and an increased frequency of micronuclei in women with fibromyalgia

Fibromyalgia (FM), characterized by chronic widespread pain, fatigue, and cognitive/mood disturbances, leads to reduced workplace productivity and increased healthcare expenses. To determine if acquired epigenetic/genetic changes are associated with FM, we compared the frequency of spontaneously occurring micronuclei (MN) and genome-wide methylation patterns in women with FM (n = 10) to those seen in comparably aged healthy controls (n = 42 (MN); n = 8 (methylation)). The mean (sd) MN frequency of women with FM (51.4 (21.9)) was significantly higher than that of controls (15.8 (8.5)) (χ² = 45.552; df = 1; P = 1.49 × 10⁻¹¹). Significant differences (n = 69 sites) in methylation patterns were observed between cases and controls considering a 5% false discovery rate. The majority of differentially methylated (DM) sites (91%) were attributable to increased values in the women with FM. The DM sites included significant biological clusters involved in neuron differentiation/nervous system development, skeletal/organ system development, and chromatin compaction. Genes associated with DM sites whose function has particular relevance to FM included BDNF, NAT15, HDAC4, PRKCA, RTN1, and PRKG1. Results support the need for future research to further examine the potential role of epigenetic and acquired chromosomal alterations as a possible biological mechanism underlying FM.

Differential DNA methylation in umbilical cord blood of infants exposed to low levels of arsenic in utero

BACKGROUND

There is increasing epidemiologic evidence that arsenic exposure in utero, even at low levels found throughout much of the world, is associated with adverse reproductive outcomes and may contribute to long-term health effects. Animal models, in vitro studies, and human cancer data suggest that arsenic may induce epigenetic alterations, specifically by altering patterns of DNA methylation.

OBJECTIVES

In this study we aimed to identify differences in DNA methylation in cord blood samples of infants with in utero, low-level arsenic exposure.

METHODS

DNA methylation of cord-blood derived DNA from 134 infants involved in a prospective birth cohort in New Hampshire was profiled using the Illumina Infinium Methylation450K array. In utero arsenic exposure was estimated using maternal urine samples collected at 24-28 weeks gestation. We used a novel cell mixture deconvolution methodology for examining the association between inferred white blood cell mixtures in infant cord blood and in utero arsenic exposure; we also examined the association between methylation at individual CpG loci and arsenic exposure levels.

RESULTS

We found an association between urinary inorganic arsenic concentration and the estimated proportion of CD8+ T lymphocytes (1.18; 95% CI: 0.12, 2.23). Among the top 100 CpG loci with the lowest p-values based on their association with urinary arsenic levels, there was a statistically significant enrichment of these loci in CpG islands (p = 0.009). Of those in CpG islands (n = 44), most (75%) exhibited higher methylation levels in the highest exposed group compared with the lowest exposed group. Also, several CpG loci exhibited a linear dose-dependent relationship between methylation and arsenic exposure.

CONCLUSIONS

Our findings suggest that in utero exposure to low levels of arsenic may affect the epigenome. Long-term follow-up is planned to determine whether the observed changes are associated with health outcomes.