Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

BACKGROUND

Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.

METHODS

Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.

RESULTS

Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.

CONCLUSIONS

This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Insulin-like growth factor 2 hypermethylation in peripheral blood leukocytes and colorectal cancer risk and prognosis: a propensity score analysis

Background

To comprehensively assess and validate the associations between insulin-like growth factor 2 (IGF2) gene methylation in peripheral blood leukocytes (PBLs) and colorectal cancer (CRC) risk and prognosis.

Methods

The association between IGF2 methylation in PBLs and CRC risk was initially evaluated in a case-control study and then validated in a nested case-control study and a twins' case-control study, respectively. Meanwhile, an initial CRC patient cohort was used to assess the effect of IGF2 methylation on CRC prognosis and then the finding was validated in the EPIC-Italy CRC cohort and TCGA datasets. A propensity score (PS) analysis was performed to control for confounders, and extensive sensitivity analyses were performed to assess the robustness of our findings.

Results

PBL IGF2 hypermethylation was associated with an increased risk of CRC in the initial study (OR_PS-adjusted, 2.57, 95% CI: 1.65 to 4.03, P<0.0001), and this association was validated using two independent external datasets (OR_PS-adjusted, 2.21, 95% CI: 1.28 to 3.81, P=0.0042 and OR_PS-adjusted, 10.65, 95% CI: 1.26 to 89.71, P=0.0295, respectively). CRC patients with IGF2 hypermethylation in PBLs had significantly improved overall survival compared to those patients with IGF2 hypomethylation (HR_PS-adjusted, 0.47, 95% CI: 0.29 to 0.76, P=0.0019). The prognostic signature was also observed in the EPIC-Italy CRC cohort, although the HR did not reach statistical significance (HR_PS-adjusted, 0.69, 95% CI: 0.37 to 1.27, P=0.2359).

Conclusions

IGF2 hypermethylation may serve as a potential blood-based predictive biomarker for the identification of individuals at high risk of developing CRC and for CRC prognosis.

The association between ACTB methylation in peripheral blood and coronary heart disease in a case-control study

Background

Coronary heart disease (CHD) brings a heavy burden to society worldwide. Novel and minimally invasive biomarkers for the risk evaluation of CHD are urgently needed. Previous study has revealed that blood-based hypomethylation of β-actin (ACTB) was associated with increased risk of stroke, but not reported in CHD yet.

Objectives

We aimed to explore the association between blood-based ACTB methylation and the risk of CHD in a case-control study in the Chinese population.

Methods

The methylation level of ACTB was quantitatively determined by mass spectrometry in 281 CHD patients and 272 controls. The association between ACTB methylation and CHD risk was estimated by logistic regression analyses adjusted for possible confounding effects.

Results

We found a significant association between hypermethylation of ACTB in peripheral blood and increased risk of CHD (odds ratios (ORs) per +10% methylation: 1.19–1.45, p < 0.013 for nine out of thirteen CpG sites), especially in male subjects and heart failure (HF) patients (ORs per +10% methylation: 1.20–1.43, 1.38–1.46; p < 0.030, 1.52 × 10⁻⁴, respectively). Hypermethylation of ACTB_CpG_2.3, ACTB_CpG_7.8, and ACTB_CpG_9.10 was observed in the CHD patients with minor to medium cardiac function impairment (NYHA I&II CHD cases) (ORs per +10% methylation: 1.38–1.44; p < 0.001). The combination of ACTB_CpG_2.3, ACTB_CpG_7.8, and ACTB_CpG_9.10 methylation levels could efficiently discriminate CHD cases, male CHD patients, HF and NYHA I&II CHD patients from controls (area under curve (AUC) = 0.75, 0.74, 0.73, and 0.77, respectively).

Conclusions

Our study reveals a strong association between blood-based ACTB hypermethylation and CHD risk. The combination of ACTB methylation and conventional risk factors might provide a novel strategy to improve risk assessment of CHD.

Identification of differential DNA methylation alterations of ovarian cancer in peripheral whole blood based on within-sample relative methylation orderings

Leukocyte cell proportion changes affect the detection of cancer-associated aberrant DNA methylation alterations in peripheral blood samples. We aimed to detect cellular DNA methylation changes in ovarian cancer (OVC) blood samples avoiding the above-mentioned cell-composition effects. Based on the within-sample relative methylation orderings (RMOs) of CpG loci in leukocyte subtypes, we developed the Ref-RMO method to detect aberrant methylation alterations from OVC blood samples. Stable CpG pairs with consistent RMOs in different leukocyte subtypes were determined, more than 99% of which retained their RMO patterns in peripheral whole blood (PWB) in independent datasets. Based on the stable CpG pairs, significantly reversed CpG pairs were detected from OVC PWB samples, which were relative to clinical information such as age, subtype, grade, stage, or CA125 level. Results showed 439 CpG loci were determined to be significant differential DNA methylations between OVC and healthy blood samples. They were mainly enriched in KEGG pathways, such as cytokine-cytokine receptor interaction, apoptosis, proteoglycans in cancer, and immune-associated Gene Ontology terms. STRING analysis showed that they tended to have functional interactions with cancer-associated genes recorded in the COSMIC database. Leukocyte cellular differential DNA methylations could be identified by the proposed RMO-based method from OVC PWB samples, which were cancer-associated aberrant signals against cell-composition effects.

DNA Methylation in Ovarian Cancer Susceptibility

Simple Summary

It is well established that ovarian cancer “runs in families”, where ovarian and other cancers (commonly breast cancer) occur at early ages at onset and in multiple generations. After decades of genetic studies, rare high-risk genetic mutations in cancer susceptibility genes and over 40 common genetic variants with much smaller risks have been identified. However, based on familial studies, we know that additional heritable genetic risk factors exist. It is possible that epigenetic variation—differences in how DNA is read, and which genes are actively expressed (or not) —also contributes to ovarian cancer susceptibility. This review summarizes the current collection of epidemiological studies that have investigated the role of DNA methylation—one type of epigenetic mechanism—in the risk of ovarian cancer.

Abstract

Epigenetic alterations are somatically acquired over the lifetime and during neoplastic transformation but may also be inherited as widespread ‘constitutional’ alterations in normal tissues that can cause cancer predisposition. Epithelial ovarian cancer (EOC) has an established genetic susceptibility and mounting epidemiological evidence demonstrates that DNA methylation (DNAm) intermediates as well as independently contributes to risk. Targeted studies of known EOC susceptibility genes (CSGs) indicate rare, constitutional BRCA1 promoter methylation increases familial and sporadic EOC risk. Blood-based epigenome-wide association studies (EWAS) for EOC have detected a total of 2846 differentially methylated probes (DMPs) with 71 genes replicated across studies despite significant heterogeneity. While EWAS detect both symptomatic and etiologic DMPs, adjustments and analytic techniques may enrich risk associations, as evidenced by the detection of dysregulated methylation of BNC2—a known CSG identified by genome-wide associations studies (GWAS). Integrative genetic–epigenetic approaches have mapped methylation quantitative trait loci (meQTL) to EOC risk, revealing DNAm variations that are associated with nine GWAS loci and, further, one novel risk locus. Increasing efforts to mapping epigenome variation across populations and cell types will be key to decoding both the genomic and epigenomic causal pathways to EOC.

A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets

Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.

Epigenetic Modifications as Biomarkers of Tumor Development, Therapy Response, and Recurrence across the Cancer Care Continuum

Aberrant epigenetic modifications are an early event in carcinogenesis, with the epigenetic landscape continuing to change during tumor progression and metastasis—these observations suggest that specific epigenetic modifications could be used as diagnostic and prognostic biomarkers for many cancer types. DNA methylation, post-translational histone modifications, and non-coding RNAs are all dysregulated in cancer and are detectable to various degrees in liquid biopsies such as sputum, urine, stool, and blood. Here, we will focus on the application of liquid biopsies, as opposed to tissue biopsies, because of their potential as non-invasive diagnostic tools and possible use in monitoring therapy response and progression to metastatic disease. This includes a discussion of septin-9 (SEPT9) DNA hypermethylation for detecting colorectal cancer, which is by far the most developed epigenetic biomarker assay. Despite their potential as prognostic and diagnostic biomarkers, technical issues such as inconsistent methodology between studies, overall low yield of epigenetic material in samples, and the need for improved histone and non-coding RNA purification methods are limiting the use of epigenetic biomarkers. Once these technical limitations are overcome, epigenetic biomarkers could be used to monitor cancer development, disease progression, therapeutic response, and recurrence across the entire cancer care continuum.

Possible Mediation by Methylation in Acute Inflammation Following Personal Exposure to Fine Particulate Air Pollution

Air pollution may increase cardiovascular and respiratory risk through inflammatory pathways, but evidence for acute effects has been weak and indirect. Between December 2014 and July 2015, we enrolled 36 healthy, nonsmoking college students for a panel study in Shanghai, China, a city with highly variable levels of air pollution. We measured personal exposure to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) continuously for 72 hours preceding each of 4 clinical visits that included phlebotomy. We measured 4 inflammation proteins and DNA methylation at nearby regulatory cytosine-phosphate-guanine (CpG) loci. We applied linear mixed-effect models to examine associations over various lag times. When results suggested mediation, we evaluated methylation as mediator. Increased PM2.5 concentration was positively associated with all 4 inflammation proteins and negatively associated with DNA methylation at regulatory loci for tumor necrosis factor alpha (TNF-α) and soluble intercellular adhesion molecule-1. A 10-μg/m3 increase in average PM2.5 during the 24 hours preceding blood draw corresponded to a 4.4% increase in TNF-α and a statistically significant decrease in methylation at one of the two studied candidate CpG loci for TNF-α. Epigenetics may play an important role in mediating effects of PM2.5 on inflammatory pathways.

Gene hypermethylation in blood leukocytes in humans long term after radiation exposure - Validation set

Hypermethylation of СpG islands in the promoter regions of several genes with basic protective function in blood leukocytes of individuals exposed to ionizing radiation long time ago (2-46 years), and differential effects of age and radiation exposure on hypermethylation was reported in our previous work. To validate these results, epigenetic modifications were assessed in an independent series of 49 nuclear industry workers from the "Mayak" facility (67-84 years old at sampling) with documented individual accumulated doses from the prolonged external γ-radiation exposure (95.9-409.5 cGy, end of work with radiation:0.3-39 years ago), and in 50 non-exposed persons matched by age. In addition to the genes analyzed before (RASSF1A, p16/INK4A, p14/ARF, GSTP1), four additional loci were analyzed: TP53, ATM, SOD3, ESR1. The frequency of individuals displaying promoter methylation of at least one of the 8 genes (71.4%) was significantly higher in exposed group as compared to the control group (40%), p = .002, OR = 3.75. A significantly elevated frequency of individuals with hypermethylated СpG islands in GSTP1, TP53, SOD3 promoters was revealed among exposed subjects as compared to the control group (p = .012, OR = 8.41; p = .041, OR = 4.02 and p = .009, OR = 3.42, respectively). A similar trend (p = .12, OR = 3.06) was observed for the p16/INK4A gene. As a whole, p16/INK4A and GSTP1 promoter hypermethylation in irradiated subjects from both previously and currently analyzed groups was pronounced. Thus, the direction of the effects was fully confirmed, suggesting the result reproducibility. No statistically significant correlation between promoter methylation and individual radiation dose was found. Further studies are required to create an array of blood epigenetic markers of radiation exposure associating with premature aging and age-related diseases and to accurately evaluate radiation-added effect across the range of doses.

SYNTHESIS

The results of studies of epigenetic changes in two independent samples of irradiated subjects indicated the significance of radiation factor in the induction of hypermethylation of CpG islands in gene promoters that is revealed in blood cells years and decades after exposure.

Epigenome-based cancer risk prediction: rationale, opportunities and challenges

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

The signature of liver cancer in immune cells DNA methylation

Background

The idea that changes to the host immune system are critical for cancer progression was proposed a century ago and recently regained experimental support.

Results

Herein, the hypothesis that hepatocellular carcinoma (HCC) leaves a molecular signature in the host peripheral immune system was tested by profiling DNA methylation in peripheral blood mononuclear cells (PBMC) and T cells from a discovery cohort (n = 69) of healthy controls, chronic hepatitis, and HCC using Illumina 450K platform and was validated in two validation sets (n = 80 and n = 48) using pyrosequencing.

Conclusions

The study reveals a broad signature of hepatocellular carcinoma in PBMC and T cells DNA methylation which discriminates early HCC stage from chronic hepatitis B and C and healthy controls, intensifies with progression of HCC, and is highly enriched in immune function-related genes such as PD-1, a current cancer immunotherapy target. These data also support the feasibility of using these profiles for early detection of HCC.

Circadian gene methylation in rotating-shift nurses: a cross-sectional study

Investigating the methylation status of the circadian genes may contribute to a better understanding of the shift work-related circadian disruption in individuals exposed to artificial light at night. In the present study, we determined the methylation status of the circadian genes associated with a shift work pattern among nurses and midwives participating in a cross-sectional study in Lodz, Poland. Quantitative methylation polymerase chain reaction assays were used to assess promoter CpG methylation in PER1, PER2, PER3, CRY1, CRY2, BMAL1, CLOCK, and NPAS2 in genomic DNA from whole blood of 347 women having a rotating-shift work schedule and 363 women working days only. The percentage of methylated reference (PMR) was assessed using fluorescent probes for PER1, PER2, PER3, CRY1, and NPAS2, and the percentage of gene methylation, as the methylation index (MI), using two sets of primers for BMAL1, CLOCK, and CRY2. We tested the possible association between current and lifetime rotating night-shift work characteristics and circadian gene methylation by using proportional odds regression model with blood DNA methylation, categorized into tertiles, and adjusted for age, current smoking status, folate intake and blood collection time. The findings indicated that CpG methylation in PER2 promoter was significantly decreased (P < 0.004) among nurses and midwives currently working rotating shifts, as compared with day-working nurses and midwives. The lower percentage of PER2 methylation was associated with a higher monthly frequency of current night duties (2-7 night shifts, and eight or more night shifts per month) (P = 0.012) and was associated at borderline significance (P = 0.092) with the lifetime duration of shift work (>10 ≤ 20 years and >20 ≤ 43 years of rotating-shift work) among nurses and midwives (N = 710). Moreover, women with a longer lifetime duration of shift work presented a lower status of PER1 methylation (P = 0.040) than did the women with up to 10 years of rotating-shift work. Long lifetime duration of shift work (> 10 years) among current rotating night-shift workers (N =  347) was associated with BMAL1 hypomethylation (P = 0.013). Among eight of the investigated circadian genes, only PER1, PER2, and BMAL1 showed differential methylation attributable to the rotating-shift work of nurses and midwives. The findings on blood-based DNA methylation in the circadian genes may provide a better insight into the mechanistic principles underlying the possible health effects of night-shift work but these should be verified in further studies recruiting larger populations of shift workers.

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition

Epigenome-wide association studies of prenatal exposure to different environmental factors are becoming increasingly common. These studies are usually performed in umbilical cord blood. Since blood comprises multiple cell types with specific DNA methylation patterns, confounding caused by cellular heterogeneity is a major concern. This can be adjusted for using reference data consisting of DNA methylation signatures in cell types isolated from blood. However, the most commonly used reference data set is based on blood samples from adult males and is not representative of the cell type composition in neonatal cord blood. The aim of this study was to generate a reference data set from cord blood to enable correct adjustment of the cell type composition in samples collected at birth. The purity of the isolated cell types was very high for all samples (>97.1%), and clustering analyses showed distinct grouping of the cell types according to hematopoietic lineage. We explored whether this cord blood and the adult peripheral blood reference data sets impact the estimation of cell type composition in cord blood samples from an independent birth cohort (MoBa, n = 1092). This revealed significant differences for all cell types. Importantly, comparison of the cell type estimates against matched cell counts both in the cord blood reference samples (n = 11) and in another independent birth cohort (Generation R, n = 195), demonstrated moderate to high correlation of the data. This is the first cord blood reference data set with a comprehensive examination of the downstream application of the data through validation of estimated cell types against matched cell counts.

Impact of confounding by leukocyte composition on associations of leukocyte DNA methylation with common risk factors

AIM

One concern in epigenome-wide studies investigating leukocyte DNA methylation is that observed associations may at least partly reflect differences in leukocyte composition (LC) rather than changes in methylation. We estimated the magnitude of confounding by LC for common risk factors and diseases.

MATERIALS & METHODS

Variation of LC according to sex, race, age, smoking, alcohol consumption, BMI, cardiovascular fitness, hypertension, coronary heart disease and diabetes was analyzed using blood differentials from 4117 participants of NHANES. Furthermore, leukocyte DNA methylation levels of biomarkers of smoking, BMI, diabetes, age and sex were regressed on these outcomes in a sample of 989 participants of ESTHER, and regression coefficients with and without adjustment for estimated LC were compared.

RESULTS

Aside from race and ages below 25 years, none of the investigated factors had substantial impact on LC. Adjusted and unadjusted coefficients were virtually identical.

CONCLUSION

Confounding by LC might often be a minor issue.

Cell-type deconvolution in epigenome-wide association studies: a review and recommendations

A major challenge faced by epigenome-wide association studies (EWAS) is cell-type heterogeneity. As many EWAS have already demonstrated, adjusting for changes in cell-type composition can be critical when analyzing and interpreting findings from such studies. Because of their importance, a great number of different statistical algorithms, which adjust for cell-type composition, have been proposed. Some of the methods are 'reference based' in that they require a priori defined reference DNA methylation profiles of cell types that are present in the tissue of interest, while other algorithms are 'reference free.' At present, however, it is unclear how best to adjust for cell-type heterogeneity, as this may also largely depend on the type of tissue and phenotype being considered. Here, we provide a critical review of the major existing algorithms for correcting cell-type composition in the context of Illumina Infinium Methylation Beadarrays, with the aim of providing useful recommendations to the EWAS community.

Training a model for estimating leukocyte composition using whole-blood DNA methylation and cell counts as reference

AIM

Whole-blood DNA methylation depends on the underlying leukocyte composition and confounding hereby is a major concern in epigenome-wide association studies. Cell counts are often missing or may not be feasible. Computational approaches estimate leukocyte composition from DNA methylation based on reference datasets of purified leukocytes. We explored the possibility to train such a model on whole-blood DNA methylation and cell counts without the need for purification.

MATERIALS & METHODS

Using whole-blood DNA methylation and corresponding five-part cell counts from 2445 participants from the London Life Sciences Prospective Population Study, a model was trained on a subset of 175 subjects and evaluated on the remaining.

RESULTS

Correlations between cell counts and estimated cell proportions were high (neutrophils 0.85, eosinophils 0.88, basophils 0.02, lymphocytes 0.84, monocytes 0.55) and estimated proportions explained more variance in whole-blood DNA methylation levels than counts.

CONCLUSION

Our model provided precise estimates for the common cell types.

Epigenome-wide differential DNA methylation between HIV-infected and uninfected individuals

Epigenetic control of human immunodeficiency virus-1 (HIV-1) genes is critical for viral integration and latency. However, epigenetic changes in the HIV-1-infected host genome have not been well characterized. Here, we report the first large-scale epigenome-wide association study of DNA methylation for HIV-1 infection. We recruited HIV-infected (n = 261) and uninfected (n = 117) patients from the Veteran Aging Cohort Study (VACS) and all samples were profiled for 485,521 CpG sites in DNA extracted from the blood. After adjusting for cell type and clinical confounders, we identified 20 epigenome-wide significant CpGs for HIV-1 infection. Importantly, 2 CpGs in the promoter of the NLR family, CARD domain containing gene 5 (NLRC5), a key regulator of major histocompatibility complex class I gene expression, showed significantly lower methylation in HIV-infected subjects than in uninfected subjects (cg07839457: t = −6.03, P_nominal = 4.96 × 10⁻⁹; cg16411857: t = −7.63, P_nominal = 3.07 × 10⁻¹³). Hypomethylation of these 2 CpGs was replicated in an independent sample (GSE67705: cg07839457: t = −4.44, P_nominal = 1.61 × 10⁻⁵; cg16411857: t = −5.90; P = 1.99 × 10⁻⁸). Methylation of these 2 CpGs in NLRC5 was negatively correlated with viral load in the 2 HIV-infected samples (cg07839457: P = 1.8 × 10⁻⁴; cg16411857: P = 0.03 in the VACS; and cg07839457: P = 0.04; cg164111857: P = 0.01 in GSE53840). Our findings demonstrate that differential DNA methylation is associated with HIV infection and suggest the involvement of a novel host gene, NLRC5, in HIV pathogenesis.

Epigenetic studies in Developmental Origins of Health and Disease: pitfalls and key considerations for study design and interpretation

The field of Developmental Origins of Health and Disease (DOHaD) seeks to understand the relationships between early-life environmental exposures and long-term health and disease. Until recently, the molecular mechanisms underlying these phenomena were poorly understood; however, epigenetics has been proposed to bridge the gap between the environment and phenotype. Epigenetics involves the study of heritable changes in gene expression, which occur without changes to the underlying DNA sequence. Different types of epigenetic modifications include DNA methylation, post-translational histone modifications and non-coding RNAs. Increasingly, changes to the epigenome have been associated with early-life exposures in both humans and animal models, offering both an explanation for how the environment may programme long-term health, as well as molecular changes that could be developed as biomarkers of exposure and/or future disease. As such, epigenetic studies in DOHaD hold much promise; however, there are a number of factors which should be considered when designing and interpreting such studies. These include the impact of the genome on the epigenome, the tissue-specificity of epigenetic marks, the stability (or lack thereof) of epigenetic changes over time and the importance of associating epigenetic changes with changes in transcription or translation to demonstrate functional consequences. In this review, we discuss each of these key concepts and provide practical strategies to mitigate some common pitfalls with the aim of providing a useful guide for future epigenetic studies in DOHaD.

Epigenetic drift, epigenetic clocks and cancer risk

It is well-established that the DNA methylation landscape of normal cells undergoes a gradual modification with age, termed as 'epigenetic drift'. Here, we review the current state of knowledge of epigenetic drift and its potential role in cancer etiology. We propose a new terminology to help distinguish the different components of epigenetic drift, with the aim of clarifying the role of the epigenetic clock, mitotic clocks and active changes, which accumulate in response to environmental disease risk factors. We further highlight the growing evidence that epigenetic changes associated with cancer risk factors may play an important causal role in cancer development, and that monitoring these molecular changes in normal cells may offer novel risk prediction and disease prevention strategies.

Integrative DNA methylome analysis of pan-cancer biomarkers in cancer discordant monozygotic twin-pairs

Background

A key focus in cancer research is the discovery of biomarkers that accurately diagnose early lesions in non-invasive tissues. Several studies have identified malignancy-associated DNA methylation changes in blood, yet no general cancer biomarker has been identified to date. Here, we explore the potential of blood DNA methylation as a biomarker of pan-cancer (cancer of multiple different origins) in 41 female cancer discordant monozygotic (MZ) twin-pairs sampled before or after diagnosis using the Illumina HumanMethylation450 BeadChip.

Results

We analysed epigenome-wide DNA methylation profiles in 41 cancer discordant MZ twin-pairs with affected individuals diagnosed with tumours at different single primary sites: the breast, cervix, colon, endometrium, thyroid gland, skin (melanoma), ovary, and pancreas. No significant global differences in whole blood DNA methylation profiles were observed. Epigenome-wide analyses identified one novel pan-cancer differentially methylated position at false discovery rate (FDR) threshold of 10 % (cg02444695, P = 1.8 × 10⁻⁷) in an intergenic region 70 kb upstream of the SASH1 tumour suppressor gene, and three suggestive signals in COL11A2, AXL, and LINC00340. Replication of the four top-ranked signals in an independent sample of nine cancer-discordant MZ twin-pairs showed a similar direction of association at COL11A2, AXL, and LINC00340, and significantly greater methylation discordance at AXL compared to 480 healthy concordant MZ twin-pairs. The effects at cg02444695 (near SASH1), COL11A2, and LINC00340 were the most promising in biomarker potential because the DNA methylation differences were found to pre-exist in samples obtained prior to diagnosis and were limited to a 5-year period before diagnosis. Gene expression follow-up at the top-ranked signals in 283 healthy individuals showed correlation between blood methylation and gene expression in lymphoblastoid cell lines at PRL, and in the skin tissue at AXL. A significant enrichment of differential DNA methylation was observed in enhancer regions (P = 0.03).

Conclusions

We identified DNA methylation signatures in blood associated with pan-cancer, at or near SASH1, COL11A2, AXL, and LINC00340. Three of these signals were present up to 5 years prior to cancer diagnosis, highlighting the potential clinical utility of whole blood DNA methylation analysis in cancer surveillance.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0172-y) contains supplementary material, which is available to authorized users.

Genome-wide blood DNA methylation alterations at regulatory elements and heterochromatic regions in monozygotic twins discordant for obesity and liver fat

Background

The current epidemic of obesity and associated diseases calls for swift actions to better understand the mechanisms by which genetics and environmental factors affect metabolic health in humans. Monozygotic (MZ) twin pairs showing discordance for obesity suggest that epigenetic influences represent one such mechanism. We studied genome-wide leukocyte DNA methylation variation in 30 clinically healthy young adult MZ twin pairs discordant for body mass index (BMI; average within-pair BMI difference: 5.4 ± 2.0 kg/m²).

Results

There were no differentially methylated cytosine-guanine (CpG) sites between the co-twins discordant for BMI. However, stratification of the twin pairs based on the level of liver fat accumulation revealed two epigenetically highly different groups. Significant DNA methylation differences (n = 1,236 CpG sites (CpGs)) between the co-twins were only observed if the heavier co-twins had excessive liver fat (n = 13 twin pairs). This unhealthy pattern of obesity was coupled with insulin resistance and low-grade inflammation. The differentially methylated CpGs included 23 genes known to be associated with obesity, liver fat, type 2 diabetes mellitus (T2DM) and metabolic syndrome, and potential novel metabolic genes. Differentially methylated CpG sites were overrepresented at promoters, insulators, and heterochromatic and repressed regions. Based on predictions by overlapping histone marks, repressed and weakly transcribed sites were significantly more often hypomethylated, whereas sites with strong enhancers and active promoters were hypermethylated. Further, significant clustering of differentially methylated genes in vitamin, amino acid, fatty acid, sulfur, and renin-angiotensin metabolism pathways was observed.

Conclusions

The methylome in leukocytes is altered in obesity associated with metabolic disturbances, and our findings indicate several novel candidate genes and pathways in obesity and obesity-related complications.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0073-5) contains supplementary material, which is available to authorized users.

Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective

Background

The impact of cell-composition effects in analysis of DNA methylation data is now widely appreciated. With the availability of a reference data set consisting of DNA methylation measurements on isolated cell types, it is possible to impute cell proportions and adjust for them, but there is increasing interest in methods that adjust for cell composition effects when reference sets are incomplete or unavailable.

Results

In this article we present a theoretical basis for one such method, showing that the total effect of a phenotype on DNA methylation can be decomposed into orthogonal components, one representing the effect of phenotype on proportions of major cell types, the other representing either subtle effects in composition or global effects at focused loci, and that it is possible to separate these two types of effects in a finite data set. We demonstrate this principle empirically on nine DNA methylation data sets, showing that the first few principal components generally contain a majority of the information on cell-type present in the data, but that later principal components nevertheless contain information about a small number of loci that may represent more focused associations. We also present a new method for determining the number of linear terms to interpret as cell-mixture effects and demonstrate robustness to the choice of this parameter.

Conclusions

Taken together, our work demonstrates that reference-free algorithms for cell-mixture adjustment can produce biologically valid results, separating cell-mediated epigenetic effects (i.e. apparent effects arising from differences in cell composition) from those that are not cell mediated, and that in general the interpretation of associations evident from DNA methylation should be carefully considered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0527-y) contains supplementary material, which is available to authorized users.

An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging

Recent studies have demonstrated that the DNA methylome changes with age. This epigenetic drift may have deep implications for cellular differentiation and disease development. However, it remains unclear how much of this drift is functional or caused by underlying changes in cell subtype composition. Moreover, no study has yet comprehensively explored epigenetic drift at different genomic length scales and in relation to regulatory elements.

Here we conduct an in-depth analysis of epigenetic drift in blood tissue. We demonstrate that most of the age-associated drift is independent of the increase in the granulocyte to lymphocyte ratio that accompanies aging and that enrichment of age-hypermethylated CpG islands increases upon adjustment for cellular composition. We further find that drift has only a minimal impact on in-cis gene expression, acting primarily to stabilize pre-existing baseline expression levels. By studying epigenetic drift at different genomic length scales, we demonstrate the existence of mega-base scale age-associated hypomethylated blocks, covering approximately 14% of the human genome, and which exhibit preferential hypomethylation in age-matched cancer tissue. Importantly, we demonstrate the feasibility of integrating Illumina 450k DNA methylation with ENCODE data to identify transcription factors with key roles in cellular development and aging. Specifically, we identify REST and regulatory factors of the histone methyltransferase MLL complex, whose function may be disrupted in aging.

In summary, most of the epigenetic drift seen in blood is independent of changes in blood cell type composition, and exhibits patterns at different genomic length scales reminiscent of those seen in cancer. Integration of Illumina 450k with appropriate ENCODE data may represent a fruitful approach to identify transcription factors with key roles in aging and disease.

Two well-known features of aging are the gradual decline of the body’s ability to regenerate tissues, as well as an increased incidence of diseases like cancer and Alzheimers. One of the most recent exciting findings which may underlie the aging process is a gradual modification of DNA, called epigenetic drift, which is effected by the covalent addition and removal of methyl groups, which in turn can deregulate the activity of nearby genes. However, this study presents the most convincing evidence to date that epigenetic drift acts to stabilize the activity levels of nearby genes. This study shows that instead, epigenetic drift may act primarly to disrupt DNA binding patterns of proteins which regulate the activity of many genes, and moreover identifies specific regulatory proteins with key roles in cancer and Alzheimers. The study also performs the most comprehensive analysis of epigenetic drift at different spatial scales, demonstrating that epigenetic drift on the largest length scales is highly reminiscent of those seen in cancer. In summary, this work substantially supports the view that epigenetic drift may contribute to the age-associated increased risk of diseases like cancer and Alzheimers, by disrupting master regulators of genomewide gene activity.

Aberrant DNA methylation of blood in schizophrenia by adjusting for estimated cellular proportions

DNA methylation, which is the transference of a methyl group to the 5'-carbon position of the cytosine in a CpG dinucleotide, is one of the major mechanisms of epigenetic modifications. A number of studies have demonstrated altered DNA methylation of peripheral blood cells in schizophrenia (SCZ) in previous studies. However, most of these studies have been limited to the analysis of the CpG sites in CpG islands in gene promoter regions, and cell-type proportions of peripheral leukocytes, which may be one of the potential confounding factors for DNA methylation, have not been adjusted in these studies. In this study, we performed a genome-wide DNA methylation profiling of the peripheral leukocytes from patients with SCZ and from non-psychiatric controls (N = 105; 63 SCZ and 42 control subjects) using a quantitative high-resolution DNA methylation microarray which covered across the whole gene region (485,764 CpG dinucleotides). In the DNA methylation data analysis, we first estimated the cell-type proportions of each sample with a published algorithm. Next, we performed a surrogate variable analysis to identify potential confounding factors in our microarray data. Finally, we conducted a multiple linear regression analysis in consideration of these factors, including estimated cell-type proportions, and identified aberrant DNA methylation in SCZ at 2,552 CpG loci at a 5% false discovery rate correction. Our results suggest that altered DNA methylation may be involved in the pathophysiology of SCZ, and cell heterogeneity adjustments may be necessary for DNA methylation analysis.