Blood-based profiles of DNA methylation predict the underlying distribution of cell types

The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which this methodology can accurately predict cell-type proportions based on blood-derived DNA methylation data in a large-scale epigenome-wide association study (EWAS) has yet to be examined. We used publicly available data deposited in the Gene Expression Omnibus (GEO) database (accession number GSE37008), which consisted of both blood-derived epigenome-wide DNA methylation data assayed using the Illumina Infinium HumanMethylation27 BeadArray and complete blood cell (CBC) counts among a community cohort of 94 non-diseased individuals. Constrained projection (CP) was used to obtain predictions of the proportions of lymphocytes, monocytes and granulocytes for each of the study samples based on their DNA methylation signatures. Our findings demonstrated high consistency between the average CBC-derived and predicted percentage of monocytes and lymphocytes (17.9% and 17.6% for monocytes and 82.1% and 81.4% for lymphocytes), with root mean squared error (rMSE) of 5% and 6%, for monocytes and lymphocytes, respectively. Similarly, there was moderate-high correlation between the CP-predicted and CBC-derived percentages of monocytes and lymphocytes (0.60 and 0.61, respectively), and these results were robust to the number of leukocyte differentially methylated regions (L-DMRs) used for CP prediction. These results serve as further validation of the CP approach and highlight the promise of this technique for EWAS where DNA methylation is profiled using whole-blood genomic DNA.     

Quantitative reconstruction of leukocyte subsets using DNA methylation

Background

Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including archived samples previously precluded from such analysis. Here we assess the performance characteristics and validity of this approach.

Results

Using Illumina Infinium HumanMethylation27 and VeraCode GoldenGate Methylation Assay microarrays, we measure DNA methylation in leukocyte subsets purified from human whole blood and identify cell lineage-specific DNA methylation signatures that distinguish human T cells, B cells, NK cells, monocytes, eosinophils, basophils and neutrophils. We employ a bioinformatics-based approach to quantify these cell types in complex mixtures, including whole blood, using DNA methylation at as few as 20 CpG loci. A reconstruction experiment confirms that the approach could accurately measure the composition of mixtures of human blood leukocyte subsets. Applying the DNA methylation-based approach to quantify the cellular components of human whole blood, we verify its accuracy by direct comparison to gold standard immune quantification methods that utilize physical, optical and proteomic characteristics of the cells. We also demonstrate that the approach is not affected by storage of blood samples, even under conditions prohibiting the use of gold standard methods.

Conclusions

Cell mixture distributions within peripheral blood can be assessed accurately and reliably using DNA methylation. Thus, precise immune cell differential estimates can be reconstructed using only DNA rather than whole cells.     

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.     

Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood

Reduced levels of global DNA methylation are associated with genomic instability and are independent predictors of cancer risk. Little is known about the environmental determinants of global DNA methylation in peripheral blood. We examined the association between demographic and lifestyle factors and levels of global leukocyte DNA methylation in 161 cancer-free subjects enrolled in the North Texas Healthy Heart Study aged 45–75 years in 2008. We used in-person interviews for demographics and lifestyle factors, a self-administrated Block food frequency questionnaire for diet, and bioelectrical impedance analysis and CT-scan for body composition. We measured genomic DNA methylation using bisulfite conversion of DNA and pyrosequencing for LINE-1. Body composition measures including body mass index, waist circumference, areas of subcutaneous fat and visceral fat, percent of fat mass and fat-free mass were not associated with global genomic DNA methylation after controlling the effect of age, gender and race/ethnicity. Instead, female gender was significantly associated with a reduced level of global methylation (β = -2.77, 95% CI: -4.33, -1.22). Compared to non-Hispanic whites, non-Hispanic blacks (β = -2.02, 95% CI: -3.55, -0.50) had significantly lower levels of global methylation. No association was found with age, cigarette smoking, alcohol drinking and dietary intake of nutrients in one-carbon metabolism. Global leukocyte DNA methylation differs by gender and race/ethnicity, suggesting these variables need to be taken into consideration in studies of global DNA methylation as an epigenetic marker for cancer.     

Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood

Reduced levels of global DNA methylation are associated with genomic instability and are independent predictors of cancer risk. Little is known about the environmental determinants of global DNA methylation in peripheral blood. We examined the association between demographic and lifestyle factors and levels of global leukocyte DNA methylation in 161 cancer-free subjects enrolled in the North Texas Healthy Heart Study aged 45–75 years in 2008. We used in-person interviews for demographics and lifestyle factors, a self-administrated Block food frequency questionnaire for diet, and bioelectrical impedance analysis and CT-scan for body composition. We measured genomic DNA methylation using bisulfite conversion of DNA and pyrosequencing for LINE-1. Body composition measures including body mass index, waist circumference, areas of subcutaneous fat and visceral fat, percent of fat mass and fat-free mass were not associated with global genomic DNA methylation after controlling the effect of age, gender and race/ethnicity. Instead, female gender was significantly associated with a reduced level of global methylation (β = −2.77, 95% CI: −4.33, −1.22). Compared to non-Hispanic whites, non-Hispanic blacks (β = −2.02, 95% CI: −3.55, −0.50) had significantly lower levels of global methylation. No association was found with age, cigarette smoking, alcohol drinking and dietary intake of nutrients in one-carbon metabolism. Global leukocyte DNA methylation differs by gender and race/ethnicity, suggesting these variables need to be taken into consideration in studies of global DNA methylation as an epigenetic marker for cancer.Key words: gender, race/ethnicity, DNA methylation     

Accounting for cellular heterogeneity is critical in epigenome-wide association studies

Background

Epigenome-wide association studies of human disease and other quantitative traits are becoming increasingly common. A series of papers reporting age-related changes in DNA methylation profiles in peripheral blood have already been published. However, blood is a heterogeneous collection of different cell types, each with a very different DNA methylation profile.

Results

Using a statistical method that permits estimating the relative proportion of cell types from DNA methylation profiles, we examine data from five previously published studies, and find strong evidence of cell composition change across age in blood. We also demonstrate that, in these studies, cellular composition explains much of the observed variability in DNA methylation. Furthermore, we find high levels of confounding between age-related variability and cellular composition at the CpG level.

Conclusions

Our findings underscore the importance of considering cell composition variability in epigenetic studies based on whole blood and other heterogeneous tissue sources. We also provide software for estimating and exploring this composition confounding for the Illumina 450k microarray.     

Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation

Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.     

Global DNA Hypomethylation in Liver Cancer Cases and Controls: A Phase I Preclinical Biomarker Development Study

Background: Global genomic DNA hypomethylation is a feature of genomic DNA derived from solid and hematologic tumors in animal models and human carcinogenesis. Global genomic DNA hypomethylation may be the earliest epigenetic change from a normal to a pre-malignant cell. Objectives: To test if global hypomethylation is a good marker for early detection of cancer we used a novel quantification method of 2’-deoxynucleosides to evaluate DNA methylation in liver cancer cases and controls. Methods: Frozen tissue from liver cancer patients and controls were obtained from the Cooperative Human Tissue Network. DNA was extracted using standard methods. Genomic DNA samples were boiled and treated with nuclease P1 and alkaline phosphatase. Global genomic DNA methylation patterns were obtained using HPLC for fraction separation and mass spectrometry for quantification. A two-sample t test was performed using Welch’s approximation for samples with unequal variances. A Wilcoxon rank sum test was also performed. Results: A global genomic DNA methylation index measuring methylated cytidine relative to global cytidine in the genome was significantly lower (p-value = 0.001) for all cases, mean = 2.43 (95% CI, 2.08, 2.78), when compared to controls, mean = 3.55 (95% CI, 3.16, 3.93). Discussion: A correlation between global genomic DNA methylation patterns and type of liver tissue was observed. These results add to the accumulating body of evidence suggesting that global DNA hypomethylation may be a useful biomarker to distinguish between liver cancer cases and controls.     

Postnatal stability,tissue, and time specific effects of AHRR methylation change in response to maternal smoking in pregnancy

The intrauterine environment has the potential to “program” the developing fetus in a way that can be potentially deleterious to later health. While in utero environmental/stochastic factors are known to influence DNA methylation profile at birth, it has been difficult to assign specific examples of epigenetic variation to specific environmental exposures. Recently, several studies have linked exposure to smoking with DNA methylation change in the aryl hydrocarbon receptor repressor (AHRR) gene in blood. This includes hypomethylation of AHRR in neonatal blood in response to maternal smoking in pregnancy. The role of AHRR as a negative regulator of pathways involved in pleiotropic responses to environmental contaminants raises the possibility that smoking-induced hypomethylation is an adaptive response to an adverse in utero environmental exposure. However, the tissue specificity of the response to maternal smoking, and the stability of the methylation changes early in life remain to be determined. In this study we analyzed AHRR methylation in three cell types—cord blood mononuclear cells (CBMCs), buccal epithelium, and placenta tissue—from newborn twins of mothers who smoked throughout pregnancy and matched controls. Further, we explored the postnatal stability of this change at 18 months. Our results confirm the previous association between maternal smoking and AHRR methylation in neonatal blood. In addition, this study expands the region of AHRR methylation altered in response to maternal smoking during pregnancy and reveals the tissue-specific nature of epigenetic responses to environmental exposures in utero. Further, the evidence for postnatal stability of smoking-induced epigenetic change supports a role for epigenetics as a mediator of long-term effects of specific in utero exposures in humans. Longitudinal analysis of further specific exposures in larger cohorts is required to examine the extent of this phenomenon in humans.     

Postnatal stability,tissue, and time specific effects of AHRR methylation change in response to maternal smoking in pregnancy

The intrauterine environment has the potential to “program” the developing fetus in a way that can be potentially deleterious to later health. While in utero environmental/stochastic factors are known to influence DNA methylation profile at birth, it has been difficult to assign specific examples of epigenetic variation to specific environmental exposures. Recently, several studies have linked exposure to smoking with DNA methylation change in the aryl hydrocarbon receptor repressor (AHRR) gene in blood. This includes hypomethylation of AHRR in neonatal blood in response to maternal smoking in pregnancy. The role of AHRR as a negative regulator of pathways involved in pleiotropic responses to environmental contaminants raises the possibility that smoking-induced hypomethylation is an adaptive response to an adverse in utero environmental exposure. However, the tissue specificity of the response to maternal smoking, and the stability of the methylation changes early in life remain to be determined. In this study we analyzed AHRR methylation in three cell types—cord blood mononuclear cells (CBMCs), buccal epithelium, and placenta tissue—from newborn twins of mothers who smoked throughout pregnancy and matched controls. Further, we explored the postnatal stability of this change at 18 months. Our results confirm the previous association between maternal smoking and AHRR methylation in neonatal blood. In addition, this study expands the region of AHRR methylation altered in response to maternal smoking during pregnancy and reveals the tissue-specific nature of epigenetic responses to environmental exposures in utero. Further, the evidence for postnatal stability of smoking-induced epigenetic change supports a role for epigenetics as a mediator of long-term effects of specific in utero exposures in humans. Longitudinal analysis of further specific exposures in larger cohorts is required to examine the extent of this phenomenon in humans.     

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.     

NDRG2 and TLR7 as novel DNA methylation prognostic signatures for acute myelocytic leukemia

Acute myelocytic leukemia (AML) is an aggressive malignant tumor and typically fatal without treatment. Identification and development of novel biomarkers could be beneficial for the diagnosis and prognosis of AML patients. Here, we aimed to identify the accurate DNA methylation prognostic signatures for AML patients. The DNA methylation data of AML patients and corresponding clinical information were retrieved from The Cancer Genome Atlas database. CPG sites that correlates closely with the survival of the AML patients were identified and further combined into CPG sites pairs to screen the survival-related pairs. The prognostic signatures were identified by the C-index and forward search algorithms and validated by the verification group. Finally, the functional enrichment analysis was performed on these CPG sites. As a result, a total of 498 CPG sites associated with the overall survival of AML patients was obtained. A prognostic signature composed of 10 CPG sites pairs was obtained and validated. The functional enrichment analysis showed prognostic genes were mainly enriched in tumor protein processing, cell differentiation, blood leukocyte immunity, and platelet growth factor pathways. In summary, we identified two accurate prognostic methylation signatures (NDRG2 and TLR7), which would be served as a novel therapy target for AML.     

Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia

Background

Although aberrant DNA methylation has been observed previously in acute lymphoblastic leukemia (ALL), the patterns of differential methylation have not been comprehensively determined in all subtypes of ALL on a genome-wide scale. The relationship between DNA methylation, cytogenetic background, drug resistance and relapse in ALL is poorly understood.

Results

We surveyed the DNA methylation levels of 435,941 CpG sites in samples from 764 children at diagnosis of ALL and from 27 children at relapse. This survey uncovered four characteristic methylation signatures. First, compared with control blood cells, the methylomes of ALL cells shared 9,406 predominantly hypermethylated CpG sites, independent of cytogenetic background. Second, each cytogenetic subtype of ALL displayed a unique set of hyper- and hypomethylated CpG sites. The CpG sites that constituted these two signatures differed in their functional genomic enrichment to regions with marks of active or repressed chromatin. Third, we identified subtype-specific differential methylation in promoter and enhancer regions that were strongly correlated with gene expression. Fourth, a set of 6,612 CpG sites was predominantly hypermethylated in ALL cells at relapse, compared with matched samples at diagnosis. Analysis of relapse-free survival identified CpG sites with subtype-specific differential methylation that divided the patients into different risk groups, depending on their methylation status.

Conclusions

Our results suggest an important biological role for DNA methylation in the differences between ALL subtypes and in their clinical outcome after treatment.     

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.     

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.     

DNA methylation changes associated with cancer risk factors and blood levels of vitamin metabolites in a prospective study

Aberrant DNA methylation is a major epigenetic mechanism of gene silencing in a wide range of human cancers. Previous studies on DNA methylation typically used paired tumor and normal-appearing surrounding tissues from cancer-bearing individuals. However, genomic DNA isolated from surrogate tissues such as blood cells represents an attractive material that can be exploited in the discovery of biomarkers of exposure and tumorigenesis. Here we examined the association between lung cancer and DNA methylation patterns in a panel of candidate genes. We also investigated whether blood levels of vitamin metabolites modify DNA methylation levels in blood cells. To this end, we quantitatively determined DNA methylation levels in blood cells of nested cases and controls from a prospective study with well defined dietary habits and lifestyles. Multiple CpG sites in five genes (CDKN2A/p16, RASSF1A, GSTP1, MTHFR, and MGMT) that are frequent targets of hypermethylation in a variety of human malignancies were included in the analysis. While no clear association between DNA methylation patterns and the case/control status was found, with the exception of RASSF1A hypermethylation, methylation level changed according to serum levels of 1-carbon metabolites and vitamins B. Overall, folate was associated with increased methylation levels of RASSF1A and MTHFR and methionine was associated with decreased methylation levels of RASSF1A. The associations with folate were more pronounced among never smokers while the associations with methionine were more evident among ever-smokers. These results are consistent with the notion that blood levels of 1-carbon metabolism markers and dietary/lifestyle factors may modify DNA methylation levels in blood cells and that blood cells can be exploited for the discovery of epigenetic biomarkers of exposures, providing proof-of-principle on the use of blood samples in the context of prospective studies.