Search

Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap between biological and chronological ages. This perspective article examines current algorithmic approaches to epigenetic clocks, explores the use of machine learning for age estimation from DNA methylation, and discusses how refining the interpretation of ML methods and tailoring their inferences for specific patient populations and cell types can amplify the utility of these technologies in age prediction. By harnessing insights from machine learning, we are well-positioned to effectively adapt, customize and personalize interventions aimed at aging.

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma., (© 2024. The Author(s).)

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors., (© 2024. The Author(s).)

Background: Among men, prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer death. Etiologic factors associated with both prostate carcinogenesis and somatic alterations in tumors are incompletely understood. While genetic variants associated with PCa have been identified, epigenetic alterations in PCa are relatively understudied. To date, DNA methylation (DNAm) and gene expression (GE) in PCa have been investigated; however, these studies did not correct for cell-type proportions of the tumor microenvironment (TME), which could confound results., Methods: The data (GSE183040) consisted of DNAm and GE data from both tumor and adjacent non-tumor prostate tissue of 56 patients who underwent radical prostatectomies prior to any treatment. This study builds upon previous studies that examined methylation patterns and GE in PCa patients by using a novel tumor deconvolution approach to identify and correct for cell-type proportions of the TME in its epigenome-wide association study (EWAS) and differential expression analysis (DEA)., Results: The inclusion of cell-type proportions in EWASs and DEAs reduced the scope of significant alterations associated with PCa. We identified 2,093 significantly differentially methylated CpGs (DMC), and 51 genes associated with PCa, including PCA3, SPINK1, and AMACR., Conclusions: This work illustrates the importance of correcting for cell types of the TME when performing EWASs and DEAs on PCa samples, and establishes a more confounding-adverse methodology. We identified a more tumor-cell-specific set of altered genes and epigenetic marks that can be further investigated as potential biomarkers of disease or potential therapeutic targets., (© 2023. The Author(s).)

Aim: This study addresses the challenge of predicting the response of head and neck squamous cell carcinoma (HNSCC) patients to immunotherapy. Methods: Using DNA methylation cytometry, we analyzed the immune profiles of six HNSCC patients who showed a positive response to immunotherapy over a year without disease progression. Results: There was an initial increase in CD8 T memory cells and natural killer cells during the first four cycles of immunotherapy, which then returned to baseline levels after a year. Baseline CD8 T cell levels were lower in HNSCC immunotherapy responders but became similar to those in healthy subjects after immunotherapy. Conclusion: These findings suggest that monitoring fluctuations in immune profiles could potentially identify biomarkers for immunotherapy response in HNSCC patients.

DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample t -test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.

Background: Seasonal variations in environmental exposures at birth or during gestation are associated with numerous adult traits and health outcomes later in life. Whether DNA methylation (DNAm) plays a role in the molecular mechanisms underlying the associations between birth season and lifelong phenotypes remains unclear., Methods: We carried out epigenome-wide meta-analyses within the Pregnancy And Childhood Epigenetic Consortium to identify associations of DNAm with birth season, both at differentially methylated probes (DMPs) and regions (DMRs). Associations were examined at two time points: at birth (21 cohorts, N = 9358) and in children aged 1-11 years (12 cohorts, N = 3610). We conducted meta-analyses to assess the impact of latitude on birth season-specific associations at both time points., Results: We identified associations between birth season and DNAm (False Discovery Rate-adjusted p values < 0.05) at two CpGs at birth (winter-born) and four in the childhood (summer-born) analyses when compared to children born in autumn. Furthermore, we identified twenty-six differentially methylated regions (DMR) at birth (winter-born: 8, spring-born: 15, summer-born: 3) and thirty-two in childhood (winter-born: 12, spring and summer: 10 each) meta-analyses with few overlapping DMRs between the birth seasons or the two time points. The DMRs were associated with genes of known functions in tumorigenesis, psychiatric/neurological disorders, inflammation, or immunity, amongst others. Latitude-stratified meta-analyses [higher (≥ 50°N), lower (< 50°N, northern hemisphere only)] revealed differences in associations between birth season and DNAm by birth latitude. DMR analysis implicated genes with previously reported links to schizophrenia (LAX1), skin disorders (PSORS1C, LTB4R), and airway inflammation including asthma (LTB4R), present only at birth in the higher latitudes (≥ 50°N)., Conclusions: In this large epigenome-wide meta-analysis study, we provide evidence for (i) associations between DNAm and season of birth that are unique for the seasons of the year (temporal effect) and (ii) latitude-dependent variations in the seasonal associations (spatial effect). DNAm could play a role in the molecular mechanisms underlying the effect of birth season on adult health outcomes., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Over the past decade, DNA methylation (DNAm)-based deconvolution methods that leverage cell-specific DNAm markers of immune cell types have been developed to provide accurate estimates of the proportions of leukocytes in peripheral blood. Immune cell phenotyping using DNAm markers, termed immunomethylomics or methylation cytometry, offers a solution for determining the body's immune cell landscape that does not require fresh blood and is scalable to large sample sizes. Despite significant advances in DNAm-based deconvolution, references at the population level are needed for clinical and research interpretation of these additional immune layers. Here we aim to provide some references for immune populations in a group of multi-ethnic post-menopausal American women., Results: We applied DNAm-based deconvolution to a large sample of post-menopausal women enrolled in the Women's Health Initiative (baseline, N = 58) or the ancillary Long Life Study (WHI-LLS, N = 1237) to determine the reference ranges of 58 immune parameters, including proportions and absolute counts for 19 leukocyte subsets and 20 derived cell ratios. Participants were 50-94 years old at the time of blood draw, and N = 898 (69.3%) self-identified as White. Using linear regression models, we observed significant associations between age at blood draw and absolute counts and proportions of naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ memory, neutrophils, and natural killer cells. We also assessed the same immune profiles in a subset of paired longitudinal samples collected 14-18 years apart across N = 52 participants. Our results demonstrate high inter-individual variability in rates of change of leukocyte subsets over this time. And, when conducting paired t tests to test the difference in counts and proportions between the baseline visit and LLS visit, there were significant changes in naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ cells and neutrophils, similar to the results seen when analyzing the association with age in the entire cohort., Conclusions: Here, we show that derived cell counts largely reflect the immune profile associated with proportions and that these novel methods replicate the known immune profiles associated with age. Further, we demonstrate the value this methylation cytometry approach can add as a potential application in epidemiological studies., (© 2023. The Author(s).)

Down syndrome (DS) is a genetic disorder caused by an extra copy of chromosome 21 that presents developmental dysfunction and intellectual disability. To better understand the cellular changes associated with DS, we investigated the cell composition in blood, brain, and buccal swab samples from DS patients and controls using DNA methylation-based cell-type deconvolution. We used genome-scale DNA methylation data from Illumina HumanMethylation450k and HumanMethylationEPIC arrays to profile cell composition and trace fetal lineage cells in blood samples (DS N = 46; control N = 1469), brain samples from various regions (DS N = 71; control N = 101), and buccal swab samples (DS N = 10; control N = 10). In early development, the number of cells from the fetal lineage in the blood is drastically lower in DS patients (Δ = 17.5%), indicating an epigenetically dysregulated maturation process for DS patients. Across sample types, we observed significant alterations in relative cell-type proportions for DS subjects compared with the controls. Cell-type proportion alterations were present in samples from early development and adulthood. Our findings provide insight into DS cellular biology and suggest potential cellular interventional targets for DS.

Background: DNA methylation patterning is cell-type-specific and altered DNA methylation is well established to occur early in breast carcinogenesis, affecting non-cancerous, histopathologically normal breast tissue. Previous work assessing risk factor-associated alterations to DNA methylation in breast tissue has been limited, with even less published research in breast milk, a noninvasively obtained biospecimen containing sloughed mammary epithelial cells that may identify early alterations indicative of cancer risk., Methods: Here, we present a novel library for the estimation of the cellular composition of breast tissue and milk and subsequent assessment of cell-type-independent alterations to DNA methylation associated with established breast cancer-risk factors in solid breast tissue (n = 95) and breast milk (n = 48) samples using genome-scale DNA methylation measures from the Illumina HumanMethylation450 array., Results: We identified 772 hypermethylated CpGs (P < 0.01) associated with age consistent between breast tissue and breast milk samples. Age-associated hypermethylated CpG loci were significantly enriched for CpG island shore regions known to be important for regulating gene expression. Among the overlapping hypermethylated loci mapping to genes, a differentially methylated region was identified in the promoter region of SFRP2, a gene observed to undergo promoter hypermethylation in breast cancer., Conclusions: Our findings suggest the potential to identify epigenetic biomarkers of breast cancer risk in noninvasively obtained, tissue-specific breast milk specimens., Impact: This work demonstrates the potential of using breast milk as a noninvasive biomarker of breast cancer risk, improving our ability to detect early-stage disease and lowering the overall disease burden., (©2023 The Authors; Published by the American Association for Cancer Research.)

DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.

Background: There is considerable evidence that epigenetic mechanisms and DNA methylation are critical drivers of immune cell lineage differentiation and activation. However, there has been limited coordinated investigation of common epigenetic pathways among cell lineages. Further, it remains unclear if long-lived memory cell subtypes differentiate distinctly by cell lineages., Results: We used the Illumina EPIC array to investigate the consistency of DNA methylation in B cell, CD4 T, and CD8 T naïve and memory cells states. In the process of naïve to memory activation across the three lineages, we identify considerable shared epigenetic regulation at the DNA level for immune memory generation. Further, in central to effector memory differentiation, our analyses revealed specific CpG dinucleotides and genes in CD4 T and CD8 T cells with DNA methylation changes. Finally, we identified unique DNA methylation patterns in terminally differentiated effector memory (TEMRA) CD8 T cells compared to other CD8 T memory cell subtypes., Conclusions: Our data suggest that epigenetic alterations are widespread and essential in generating human lymphocyte memory. Unique profiles are involved in methylation changes that accompany memory genesis in the three subtypes of lymphocytes., (© 2022. The Author(s).)

Background: Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types., Results: We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture., Conclusion: We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment., (© 2022. The Author(s).)

Background: Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450)., Results: The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays., Conclusions: We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care., (© 2022. The Author(s).)

Assessing individual responses to glucocorticoid drug therapies that compromise immune status and affect survival outcomes in neuro-oncology is a great challenge. Here we introduce a blood-based neutrophil dexamethasone methylation index (NDMI) that provides a measure of the epigenetic response of subjects to dexamethasone. This marker outperforms conventional approaches based on leukocyte composition as a marker of glucocorticoid response. The NDMI is associated with low CD4 T cells and the accumulation of monocytic myeloid-derived suppressor cells and also serves as prognostic factor in glioma survival. In a non-glioma population, the NDMI increases with a history of prednisone use. Therefore, it may also be informative in other conditions where glucocorticoids are employed. We conclude that DNA methylation remodeling within the peripheral immune compartment is a rich source of clinically relevant markers of glucocorticoid response., (© 2022. The Author(s).)

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7) . FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease., (© 2022. The Author(s).)

Background: Non-muscle-invasive bladder cancer (NMIBC) patients receive frequent monitoring because ≥ 70% will have recurrent disease. However, screening is invasive, expensive, and associated with significant morbidity making bladder cancer the most expensive cancer to treat per capita. There is an urgent need to expand the understanding of markers related to recurrence and survival outcomes of NMIBC., Methods and Results: We used the Illumina HumanMethylationEPIC array to measure peripheral blood DNA methylation profiles of NMIBC patients (N = 603) enrolled in a population-based cohort study in New Hampshire and applied cell type deconvolution to estimate immune cell-type proportions. Using Cox proportional hazard models, we identified that increasing CD4T and CD8T cell proportions were associated with a statistically significant decreased hazard of tumor recurrence or death (CD4T: HR = 0.98, 95% CI = 0.97-1.00; CD8T: HR = 0.97, 95% CI = 0.95-1.00), whereas increasing monocyte proportion and methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) were associated with the increased hazard of tumor recurrence or death (monocyte: HR = 1.04, 95% CI = 1.00-1.07; mdNLR: HR = 1.12, 95% CI = 1.04-1.20). Then, using an epigenome-wide association study (EWAS) approach adjusting for age, sex, smoking status, BCG treatment status, and immune cell profiles, we identified 2528 CpGs associated with the hazard of tumor recurrence or death (P < 0.005). Among these CpGs, the 1572 were associated with an increased hazard and were significantly enriched in open sea regions; the 956 remaining CpGs were associated with a decreased hazard and were significantly enriched in enhancer regions and DNase hypersensitive sites., Conclusions: Our results expand on the knowledge of immune profiles and methylation alteration associated with NMIBC outcomes and represent a first step toward the development of DNA methylation-based biomarkers of tumor recurrence., (© 2022. The Author(s).)

Background: Examining immunity-related DNA methylation alterations in blood could help elucidate the role of the immune response in lung cancer etiology and aid in discovering factors that are key to lung cancer development and progression. In a nested, matched case-control study, we estimated methylation-derived NLR (mdNLR) and quantified DNA methylation levels at loci previously linked with circulating concentrations of C-reactive protein (CRP). We examined associations between these measures and lung cancer risk and survival., Results: Using conditional logistic regression and further adjusting for BMI, batch effects, and a smoking-based methylation score, we observed a 47% increased risk of non-small cell lung cancer (NSCLC) for one standard deviation (SD) increase in mdNLR (n = 150 pairs; OR: 1.47, 95% CI 1.08, 2.02). Using a similar model, the estimated CRP Scores were inversely associated with risk of NSCLC (e.g., Score 1 OR: 0.57, 95% CI: 0.40, 0.81). Using Cox proportional hazards models adjusting for age, sex, smoking status, methylation-predicted pack-years, BMI, batch effect, and stage, we observed a 28% increased risk of dying from lung cancer (n = 145 deaths in 205 cases; HR: 1.28, 95% CI: 1.09, 1.50) for one SD increase in mdNLR., Conclusions: Our study demonstrates that immunity status measured with DNA methylation markers is associated with lung cancer a decade or more prior to cancer diagnosis. A better understanding of immunity-associated methylation-based biomarkers in lung cancer development could provide insight into critical pathways., (© 2021. The Author(s).)

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules., (© 2021. The Author(s).)

Background: DNA methylation has been shown to be associated with adiposity in adulthood. However, whether similar DNA methylation patterns are associated with childhood and adolescent body mass index (BMI) is largely unknown. More insight into this relationship at younger ages may have implications for future prevention of obesity and its related traits., Methods: We examined whether DNA methylation in cord blood and whole blood in childhood and adolescence was associated with BMI in the age range from 2 to 18 years using both cross-sectional and longitudinal models. We performed meta-analyses of epigenome-wide association studies including up to 4133 children from 23 studies. We examined the overlap of findings reported in previous studies in children and adults with those in our analyses and calculated enrichment., Results: DNA methylation at three CpGs (cg05937453, cg25212453, and cg10040131), each in a different age range, was associated with BMI at Bonferroni significance, P < 1.06 × 10 -7 , with a 0.96 standard deviation score (SDS) (standard error (SE) 0.17), 0.32 SDS (SE 0.06), and 0.32 BMI SDS (SE 0.06) higher BMI per 10% increase in methylation, respectively. DNA methylation at nine additional CpGs in the cross-sectional childhood model was associated with BMI at false discovery rate significance. The strength of the associations of DNA methylation at the 187 CpGs previously identified to be associated with adult BMI, increased with advancing age across childhood and adolescence in our analyses. In addition, correlation coefficients between effect estimates for those CpGs in adults and in children and adolescents also increased. Among the top findings for each age range, we observed increasing enrichment for the CpGs that were previously identified in adults (birth P enrichment  = 1; childhood P enrichment  = 2.00 × 10 -4 ; adolescence P enrichment  = 2.10 × 10 -7 )., Conclusions: There were only minimal associations of DNA methylation with childhood and adolescent BMI. With the advancing age of the participants across childhood and adolescence, we observed increasing overlap with altered DNA methylation loci reported in association with adult BMI. These findings may be compatible with the hypothesis that DNA methylation differences are mostly a consequence rather than a cause of obesity.

While changes in DNA methylation are known to occur early in breast carcinogenesis and the landscape of breast tumour DNA methylation is profoundly altered compared with normal tissue, there have been limited efforts to identify DNA methylation field cancerization effects in histologically normal breast tissue adjacent to tumour. Matched tumour, histologically normal tissue of the ipsilateral breast (ipsilateral-normal), and histologically normal tissue of the contralateral breast (contralateral-normal) were obtained from nine women undergoing bilateral mastectomy. Laser capture microdissection was used to select epithelial cells from normal tissue, and neoplastic cells from tumour for genome-scale measures of DNA methylation with the Illumina HumanMethylationEPIC array. We identified substantially more CpG loci that were differentially methylated between contralateral-normal and tumour (63,271 CpG loci q < 0.01), than between ipsilateral-normal and tumour (38,346 CpG loci q < 0.01). We identified differential methylation in ipsilateral-normal relative to contralateral-normal tissue (9,562 CpG loci p < 0.01). In this comparison, hypomethylated loci were significantly enriched for breast cancer-relevant transcription factor binding sites including those for ESR1, FoxA1, and GATA3 and hypermethylated loci were significantly enriched for CpG island shore regions. In addition, progression of shore hypermethylation was observed in tumours compared to matched ipsilateral normal tissue, and these alterations tracked to several well-established tumour suppressor genes. Our results indicate an epigenetic field effect in surrounding histologically normal tissue. This work offers an opportunity to focus investigations of early DNA methylation alterations in breast carcinogenesis and potentially develop epigenetic biomarkers of disease risk., Abbreviations: DCIS: ductal carcinoma in situ ; GO: gene ontology; OR: odds ratio; CI: confidence interval; TFBS: transcription factor binding site; LOLA: Locus Overlap Analysis.

Background: Prenatal inflammation has been proposed as an important mediating factor in several adverse pregnancy outcomes. C-reactive protein (CRP) is an inflammatory cytokine easily measured in blood. It has clinical value due to its reliability as a biomarker for systemic inflammation and can indicate cellular injury and disease severity. Elevated levels of CRP in adulthood are associated with alterations in DNA methylation. However, no studies have prospectively investigated the relationship between maternal CRP levels and newborn DNA methylation measured by microarray in cord blood with reasonable epigenome-wide coverage. Importantly, the timing of inflammation exposure during pregnancy may also result in different effects. Thus, our objective was to evaluate this prospective association of CRP levels measured during multiple periods of pregnancy and in cord blood at delivery which was available in one cohort (i.e., Effects of Aspirin in Gestation and Reproduction trial), and also to conduct a meta-analysis with available data at one point in pregnancy from three other cohorts from the Pregnancy And Childhood Epigenetics consortium (PACE). Secondarily, the impact of maternal randomization to low dose aspirin prior to pregnancy on methylation was assessed., Results: Maternal CRP levels were not associated with newborn DNA methylation regardless of gestational age of measurement (i.e., CRP at approximately 8, 20, and 36 weeks among 358 newborns in EAGeR). There also was no association in the meta-analyses (all p > 0.5) with a larger sample size (n = 1603) from all participating PACE cohorts with available CRP data from first trimester (< 18 weeks gestation). Randomization to aspirin was not associated with DNA methylation. On the other hand, newborn CRP levels were significantly associated with DNA methylation in the EAGeR trial, with 33 CpGs identified (FDR corrected p < 0.05) when both CRP and methylation were measured at the same time point in cord blood. The top 7 CpGs most strongly associated with CRP resided in inflammation and vascular-related genes., Conclusions: Maternal CRP levels measured during each trimester were not associated with cord blood DNA methylation. Rather, DNA methylation was associated with CRP levels measured in cord blood, particularly in gene regions predominately associated with angiogenic and inflammatory pathways., Trial Registration: Clinicaltrials.gov, NCT00467363, Registered April 30, 2007, http://www.clinicaltrials.gov/ct2/show/NCT00467363.

Background: DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision., Results: The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences., Conclusion: The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.

Prior candidate gene studies have shown tumor suppressor DNA methylation in breast milk related with history of breast biopsy, an established risk factor for breast cancer. To further establish the utility of breast milk as a tissue-specific biospecimen for investigations of breast carcinogenesis, we measured genome-wide DNA methylation in breast milk from women with and without a diagnosis of breast cancer in two independent cohorts. DNA methylation was assessed using Illumina HumanMethylation450k in 87 breast milk samples. Through an epigenome-wide association study we explored CpG sites associated with a breast cancer diagnosis in the prospectively collected milk samples from the breast that would develop cancer compared with women without a diagnosis of breast cancer using linear mixed effects models adjusted for history of breast biopsy, age, RefFreeCellMix cell estimates, time of delivery, array chip and subject as random effect. We identified 58 differentially methylated CpG sites associated with a subsequent breast cancer diagnosis (q-value <0.05). Nearly all CpG sites associated with a breast cancer diagnosis were hypomethylated in cases compared with controls and were enriched for CpG islands. In addition, inferred repeat element methylation was lower in breast milk DNA from cases compared to controls, and cases exhibited increased estimated epigenetic mitotic tick rate as well as DNA methylation age compared with controls. Breast milk has utility as a biospecimen for prospective assessment of disease risk, for understanding the underlying molecular basis of breast cancer risk factors and improving primary and secondary prevention of breast cancer., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Preterm birth and shorter duration of pregnancy are associated with increased morbidity in neonatal and later life. As the epigenome is known to have an important role during fetal development, we investigated associations between gestational age and blood DNA methylation in children., Methods: We performed meta-analysis of Illumina's HumanMethylation450-array associations between gestational age and cord blood DNA methylation in 3648 newborns from 17 cohorts without common pregnancy complications, induced delivery or caesarean section. We also explored associations of gestational age with DNA methylation measured at 4-18 years in additional pediatric cohorts. Follow-up analyses of DNA methylation and gene expression correlations were performed in cord blood. DNA methylation profiles were also explored in tissues relevant for gestational age health effects: fetal brain and lung., Results: We identified 8899 CpGs in cord blood that were associated with gestational age (range 27-42 weeks), at Bonferroni significance, P < 1.06 × 10 - 7 , of which 3343 were novel. These were annotated to 4966 genes. After restricting findings to at least three significant adjacent CpGs, we identified 1276 CpGs annotated to 325 genes. Results were generally consistent when analyses were restricted to term births. Cord blood findings tended not to persist into childhood and adolescence. Pathway analyses identified enrichment for biological processes critical to embryonic development. Follow-up of identified genes showed correlations between gestational age and DNA methylation levels in fetal brain and lung tissue, as well as correlation with expression levels., Conclusions: We identified numerous CpGs differentially methylated in relation to gestational age at birth that appear to reflect fetal developmental processes across tissues. These findings may contribute to understanding mechanisms linking gestational age to health effects.

Objective: Maternal gestational diabetes mellitus (GDM) has been associated with adverse outcomes in the offspring. Growing evidence suggests that the epigenome may play a role, but most previous studies have been small and adjusted for few covariates. The current study meta-analyzed the association between maternal GDM and cord blood DNA methylation in the Pregnancy and Childhood Epigenetics (PACE) consortium., Research Design and Methods: Seven pregnancy cohorts (3,677 mother-newborn pairs [317 with GDM]) contributed results from epigenome-wide association studies, using DNA methylation data acquired by the Infinium HumanMethylation450 BeadChip array. Associations between GDM and DNA methylation were examined using robust linear regression, with adjustment for potential confounders. Fixed-effects meta-analyses were performed using METAL. Differentially methylated regions (DMRs) were identified by taking the intersection of results obtained using two regional approaches: comb-p and DMRcate., Results: Two DMRs were identified by both comb-p and DMRcate. Both regions were hypomethylated in newborns exposed to GDM in utero compared with control subjects. One DMR (chr 1: 248100345-248100614) was located in the OR2L13 promoter, and the other (chr 10: 135341870-135342620) was located in the gene body of CYP2E1 . Individual CpG analyses did not reveal any differentially methylated loci based on a false discovery rate-adjusted P value threshold of 0.05., Conclusions: Maternal GDM was associated with lower cord blood methylation levels within two regions, including the promoter of OR2L13 , a gene associated with autism spectrum disorder, and the gene body of CYP2E1 , which is upregulated in type 1 and type 2 diabetes. Future studies are needed to understand whether these associations are causal and possible health consequences., (© 2019 by the American Diabetes Association.)

Background: Non-alcoholic fatty liver disease is the most common chronic liver disease in children in western countries. Adverse early-life exposures are associated with higher liver fat percentages in children. Differential DNA methylation may underlie these associations. We aimed to identify differential DNA methylation in newborns and children associated with liver fat accumulation in childhood. We also examined whether DNA methylation at 22 cytosine-phosphate-guanine sites (CpGs) associated with adult non-alcoholic fatty liver disease is associated with liver fat in children. Within a population-based prospective cohort study, we analyzed epigenome-wide DNA methylation data of 785 newborns and 344 10-year-old children in relation to liver fat fraction at 10 years. DNA methylation was measured using the Infinium HumanMethylation450 BeadChip (Illumina). We measured liver fat fraction by Magnetic Resonance Imaging. Associations of single CpG DNA methylation at the two-time points with liver fat accumulation were analyzed using robust linear regression models. We also analyzed differentially methylation regions using the dmrff package. We looked-up associations of 22 known adult CpGs at both ages with liver fat at 10 years., Results: The median liver fat fraction was 2.0% (95% range 1.3, 5.1). No single CpGs and no differentially methylated regions were associated with liver fat accumulation. None of the 22 known adult CpGs were associated with liver fat in children., Conclusions: DNA methylation at birth and in childhood was not associated with liver fat accumulation in 10-year-old children in this study. This may be due to modest sample sizes or DNA methylation changes being a consequence rather than a determinant of liver fat.

Summary: Performing highly parallelized preprocessing of methylation array data using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. We present a highly reproducible, scalable pipeline (PyMethylProcess) that can be quickly set-up and deployed through Docker and PIP., Availability and Implementation: Project Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI (pymethylprocess), Docker (joshualevy44/pymethylprocess)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Umbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age-appropriate cell-specific DNAm profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450 K and EPIC arrays. These datasets are biologically and technically different, and currently, there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution., Results: We first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n = 191) with matched Fluorescence-Activated Cell Sorting measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes., Conclusion: These results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

Background: Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues., Methods: We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of < 0.05 were considered statistically significant., Results: Across 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n = 6,795 tumor, n = 922 nontumor, P < 0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n = 740 tumor, n = 424 nontumor, P < 0.05)., Conclusions: The results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity.

Rationale: We aimed to identify differentially methylated regions (DMRs) in cord blood DNA associated with childhood lung function, asthma and chronic obstructive pulmonary disease (COPD) across the life course., Methods: We meta-analysed epigenome-wide data of 1688 children from five cohorts to identify cord blood DMRs and their annotated genes, in relation to forced expiratory volume in 1 s (FEV 1 ), FEV 1 /forced vital capacity (FVC) ratio and forced expiratory flow at 75% of FVC at ages 7-13 years. Identified DMRs were explored for associations with childhood asthma, adult lung function and COPD, gene expression and involvement in biological processes., Results: We identified 59 DMRs associated with childhood lung function, of which 18 were associated with childhood asthma and nine with COPD in adulthood. Genes annotated to the top 10 identified DMRs were HOXA5 , PAOX , LINC00602 , ABCA7 , PER3 , CLCA1 , VENTX , NUDT12 , PTPRN2 and TCL1A . Differential gene expression in blood was observed for 32 DMRs in childhood and 18 in adulthood. Genes related with 16 identified DMRs were associated with respiratory developmental or pathogenic pathways., Interpretation: Our findings suggest that the epigenetic status of the newborn affects respiratory health and disease across the life course., Competing Interests: Conflict of interest: H.T. den Dekker has nothing to disclose. Conflict of interest: K. Burrows has nothing to disclose. Conflict of interest: J.F. Felix reports funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 633595 (DynaHEALTH). The Generation R Study received funding from the European Union's Horizon 2020 research and innovation programme (733206, LIFECYCLE). Conflict of interest: L.A. Salas has nothing to disclose. Conflict of interest: I. Nedeljkovic has nothing to disclose. Conflict of interest: J. Yao has nothing to disclose. Conflict of interest: S.L. Rifas-Shiman has nothing to disclose. Conflict of interest: V. Ruiz-Arenas has nothing to disclose. Conflict of interest: N. Amin has nothing to disclose. Conflict of interest: M. Bustamante has nothing to disclose. Conflict of interest: D.L. DeMeo reports grants from National Institutes of Health, and personal fees from Novartis, outside the submitted work. Conflict of interest: A.J. Henderson reports grants from Medical Research Council and Wellcome, during the conduct of the study. Conflict of interest: C.G. Howe has nothing to disclose. Conflict of interest: M-F. Hivert has nothing to disclose. Conflict of interest: M.A. Ikram has nothing to disclose. Conflict of interest: J.C. de Jongste has nothing to disclose. Conflict of interest: L. Lahousse has nothing to disclose. Conflict of interest: P.R. Mandaviya has nothing to disclose. Conflict of interest: J.B. van Meurs has nothing to disclose. Conflict of interest: M. Pinart has nothing to disclose. Conflict of interest: G.C. Sharp has nothing to disclose. Conflict of interest: L. Stolk has nothing to disclose. Conflict of interest: A.G. Uitterlinden has nothing to disclose. Conflict of interest: J.M. Antó has nothing to disclose. Conflict of interest: A.A. Litonjua has nothing to disclose. Conflict of interest: C.V. Breton has nothing to disclose. Conflict of interest: G.G. Brusselle has nothing to disclose. Conflict of interest: J. Sunyer has nothing to disclose. Conflict of interest: G.D. Smith has nothing to disclose. Conflict of interest: C.L. Relton has nothing to disclose. Conflict of interest: V.W.V. Jaddoe reports funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 633595 (DynaHEALTH). The Generation R Study received funding from the European Union's Horizon 2020 research and innovation programme (733206, LIFECYCLE). Conflict of interest: L. Duijts reports grants from Lung Foundation Netherlands and from the European Union's Horizon 2020 research and innovation programme under grant agreement number 733206 (LIFECYCLE), and speaker fees for the CIPP2016 conference from Abbvie, during the conduct of the study., (Copyright ©ERS 2019.)

Swimming in pools during pregnancy may expose the fetus to water disinfection by-products (DBP). As yet, our understanding of the impacts on DBPs on the fetus is uncertain. Individuals with public water systems are typically exposed to DBPs through drinking, showering and bathing, whereas among those on private water systems, swimming in pools may be the primary exposure source. We analyzed the effects of maternal swimming on birth outcomes and cord blood epigenetic changes in the New Hampshire Birth Cohort Study, a cohort of pregnant women with households on private water systems. Information about swimming in pools during pregnancy was obtained from 1033 women via questionnaires. Swimming pool use and duration were modeled using linear regression with newborn weight, length, and head circumference (z-scores) and genome wide cord blood DNA methylation as the outcomes and with adjustment for potential confounders. Overall 19.7% of women reported swimming in a pool during pregnancy. Among swimmers, duration of swimming was inversely related to head circumference (-0.02 z-score per 10% increase in duration, P = 0.004). No associations were observed with birth weight, length or DNA methylation modifications. Our findings suggest swimming pool exposure may impact the developing fetus although longer-term studies are needed., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Lung macrophages are major participants in the pulmonary innate immune response. In the cystic fibrosis (CF) lung, the inability of lung macrophages to successfully regulate the exaggerated inflammatory response suggests dysfunctional innate immune cell function. In this study, we aim to gain insight into innate immune cell dysfunction in CF by investigating alterations in DNA methylation in bronchoalveolar lavage (BAL) cells, composed primarily of lung macrophages of CF subjects compared with healthy controls. All analyses were performed using primary alveolar macrophages from human subjects collected via bronchoalveolar lavage. Epigenome-wide DNA methylation was examined via Illumina MethylationEPIC (850 K) array. Targeted next-generation bisulfite sequencing was used to validate selected differentially methylated CpGs. Methylation-based sample classification was performed using the recursively partitioned mixture model (RPMM) and was tested against sample case-control status. Differentially methylated loci were identified by fitting linear models with adjustment of age, sex, estimated cell type proportions, and repeat measurement., Results: RPMM class membership was significantly associated with the CF disease status (P = 0.026). One hundred nine CpG loci were differentially methylated in CF BAL cells (all FDR ≤ 0.1). The majority of differentially methylated loci in CF were hypo-methylated and found within non-promoter CpG islands as well as in putative enhancer regions and DNase hyper-sensitive regions., Conclusions: These results support a hypothesis that epigenetic changes, specifically DNA methylation at a multitude of gene loci in lung macrophages, may participate, at least in part, in driving dysfunctional innate immune cells in the CF lung.

Stem cell maturation is a fundamental, yet poorly understood aspect of human development. We devised a DNA methylation signature deeply reminiscent of embryonic stem cells (a fetal cell origin signature, FCO) to interrogate the evolving character of multiple human tissues. The cell fraction displaying this FCO signature was highly dependent upon developmental stage (fetal versus adult), and in leukocytes, it described a dynamic transition during the first 5 yr of life. Significant individual variation in the FCO signature of leukocytes was evident at birth, in childhood, and throughout adult life. The genes characterizing the signature included transcription factors and proteins intimately involved in embryonic development. We defined and applied a DNA methylation signature common among human fetal hematopoietic progenitor cells and have shown that this signature traces the lineage of cells and informs the study of stem cell heterogeneity in humans under homeostatic conditions., (© 2018 Salas et al.; Published by Cold Spring Harbor Laboratory Press.)

Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R 2  = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms.

Background: Differentially methylated regions (DMRs) within DNA isolated from whole blood can be used to estimate the proportions of circulating leukocyte subtypes. We use the term "immunomethylomics" to describe the application of these immune lineage DMRs to studying leukocyte profiles. Here, we applied this approach to peripheral blood DNA from 72 glioma patients with molecularly defined brain tumors, representing common patient groups with defined characteristic survival times and risk factors. We first estimated the proportions of leukocyte subtypes in samples using deconvolution algorithms with reference DMR libraries from isolated leukocyte populations and Illumina 450K DNA methylation data. Then, we calculated the neutrophil to lymphocyte ratio (NLR) using methylation-derived cell composition estimates (mdNLR). The NLR is considered an indicator of immunosuppressive cells in cancer patients., Results: Elevated mdNLR scores were observed in glioma patients compared to mdNLR values of published controls. Significantly decreased survival times were associated with mdNLR ≥ 4.0 in Cox proportional hazards models adjusted for age, gender, tumor grade, and molecular subtype (HR 2.02, 95% CI, 1.11-3.69). We also identified five myeloid-related CpGs that were highly correlated with the mdNLR (adjusted R  ≥ 0.80). Each of the five myeloid CpG loci was associated with survival when adjusted for the above covariates and offer a simplified approach for utilizing fresh or archived peripheral blood samples for interrogating a very small number of methylation markers to estimate myeloid immune influences in glioma survival.2  ≥ 0.80). Each of the five myeloid CpG loci was associated with survival when adjusted for the above covariates and offer a simplified approach for utilizing fresh or archived peripheral blood samples for interrogating a very small number of methylation markers to estimate myeloid immune influences in glioma survival., Conclusions: The mdNLR (based on DNA methylation) is a novel candidate methylation biomarker that represents immunosuppressive myeloid cells within the blood of glioma patients with potential application in clinical trials and future epidemiologic studies of glioma risk and survival.

Background: We conducted an epigenome-wide association study (EWAS) of DNA methylation in placenta in relation to maternal tobacco smoking during pregnancy and examined whether smoking-induced changes lead to low birthweight., Methods: DNA methylation in placenta was measured using the Illumina HumanMethylation450 BeadChip in 179 participants from the INfancia y Medio Ambiente (INMA) birth cohort. Methylation levels across 431 311 CpGs were tested for differential methylation between smokers and non-smokers in pregnancy. We took forward three top-ranking loci for further validation and replication by bisulfite pyrosequencing using data of 248 additional participants of the INMA cohort. We examined the association of methylation at smoking-associated loci with birthweight by applying a mediation analysis and a two-sample Mendelian randomization approach., Results: Fifty CpGs were differentially methylated in placenta between smokers and non-smokers during pregnancy [false discovery rate (FDR) < 0.05]. We validated and replicated differential methylation at three top-ranking loci: cg27402634 located between LINC00086 and LEKR1, a gene previously related to birthweight in genome-wide association studies; cg20340720 (WBP1L); and cg25585967 and cg12294026 (TRIO). Dose-response relationships with maternal urine cotinine concentration during pregnancy were confirmed. Differential methylation at cg27402634 explained up to 36% of the lower birthweight in the offspring of smokers (Sobel P-value < 0.05). A two-sample Mendelian randomization analysis provided evidence that decreases in methylation levels at cg27402634 lead to decreases in birthweight., Conclusions: We identified novel loci differentially methylated in placenta in relation to maternal smoking during pregnancy. Adverse effects of maternal smoking on birthweight of the offspring may be mediated by alterations in the placental methylome., (© The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure., (Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Smoking increases the risk of many diseases and could act through changes in DNA methylation patterns. The aims of this study were to determine the association between smoking and DNA methylation throughout the genome at cytosine-phosphate-guanine (CpG) site level and genomic regions. A discovery cross-sectional epigenome-wide association study nested in the follow-up of the REGICOR cohort was designed and included 645 individuals. Blood DNA methylation was assessed using the Illumina HumanMethylation450 BeadChip. Smoking status was self-reported using a standardized questionnaire. We identified 66 differentially methylated CpG sites associated with smoking, located in 38 genes. In most of these CpG sites, we observed a trend among those quitting smoking to recover methylation levels typical of never smokers. A CpG site located in a novel smoking-associated gene (cg06394460 in LNX2) was hypomethylated in current smokers. Moreover, we validated two previously reported CpG sites (cg05886626 in THBS1, and cg24838345 in MTSS1) for their potential relation to atherosclerosis and cancer diseases, using several different approaches: CpG site methylation, gene expression, and plasma protein level determinations. Smoking was also associated with higher THBS1 gene expression but with lower levels of thrombospondin-1 in plasma. Finally, we identified differential methylation regions in 13 genes and in four non-coding RNAs. In summary, this study replicated previous findings and identified and validated a new CpG site located in LNX2 associated with smoking.

Trihalomethanes (THM) are undesired disinfection byproducts (DBPs) formed during water treatment. Mice exposed to DBPs showed global DNA hypomethylation and c-myc and c-jun gene-specific hypomethylation, while evidence of epigenetic effects in humans is scarce. We explored the association between lifetime THM exposure and DNA methylation through an epigenome-wide association study. We selected 138 population-based controls from a case-control study of colorectal cancer conducted in Barcelona, Spain, exposed to average lifetime THM levels ≤85 μg/L vs. >85 μg/L (N = 68 and N = 70, respectively). Mean age of participants was 70 years, and 54% were male. Average lifetime THM level in the exposure groups was 64 and 130 µg/L, respectively. DNA was extracted from whole blood and was bisulphite converted to measure DNA methylation levels using the Illumina HumanMethylation450 BeadChip. Data preprocessing was performed using RnBeads. Methylation was compared between exposure groups using empirical Bayes moderated linear regression for CpG sites and Gaussian kernel for CpG regions. ConsensusPathDB was used for gene set enrichment. Statistically significant differences in methylation between exposure groups was found in 140 CpG sites and 30 gene-related regions, after false discovery rate <0.05 and adjustment for age, sex, methylation first principal component, and blood cell proportion. The annotated genes were localized to several cancer pathways. Among them, 29 CpGs had methylation levels associated with THM levels (|Δβ|≥0.05) located in 11 genes associated with cancer in other studies. Our results suggest that THM exposure may affect DNA methylation in genes related to tumors, including colorectal and bladder cancers. Future confirmation studies are required.

Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.

BackgroundImmune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival.MethodsBladder cancer case peripheral blood DNA methylation was measured using the Illumina HumanMethylationEPIC array. Extended cell-type deconvolution quantified 12 immune cell-type proportions, including memory, naïve T and B cells, and granulocyte subtypes. DNA methylation clocks determined biological age. Cox proportional hazards models tested associations of immune cell profiles and age acceleration with bladder cancer outcomes. The partDSA algorithm discriminated 10-year overall survival groups from clinical variables and immune cell profiles, and a semi-supervised recursively partitioned mixture model (SS-RPMM) with DNA methylation data was applied to identify a classifier for 10-year overall survival.ResultsHigher CD8T memory cell proportions were associated with better overall survival [HR = 0.95, 95% confidence interval (CI) = 0.93-0.98], while higher neutrophil-to-lymphocyte ratio (HR = 1.36, 95% CI = 1.23-1.50), CD8T naïve (HR = 1.21, 95% CI = 1.04-1.41), neutrophil (HR = 1.04, 95% CI = 1.03-1.06) proportions, and age acceleration (HR = 1.06, 95% CI = 1.03-1.08) were associated with worse overall survival in patient with bladder cancer. partDSA and SS-RPMM classified five groups of subjects with significant differences in overall survival.ConclusionsWe identified associations between immune cell subtypes and age acceleration with bladder cancer outcomes.ImpactThe findings of this study suggest that bladder cancer outcomes are associated with specific methylation-derived immune cell-type proportions and age acceleration, and these factors could be potential prognostic biomarkers.

BACKGROUND: Seasonal variations in environmental exposures at birth or during gestation are associated with numerous adult traits and health outcomes later in life. Whether DNA methylation (DNAm) plays a role in the molecular mechanisms underlying the associations between birth season and lifelong phenotypes remains unclear. METHODS: We carried out epigenome-wide meta-analyses within the Pregnancy And Childhood Epigenetic Consortium to identify associations of DNAm with birth season, both at differentially methylated probes (DMPs) and regions (DMRs). Associations were examined at two time points: at birth (21 cohorts, N = 9358) and in children aged 1-11 years (12 cohorts, N = 3610). We conducted meta-analyses to assess the impact of latitude on birth season-specific associations at both time points. RESULTS: We identified associations between birth season and DNAm (False Discovery Rate-adjusted p values

IntroductionThe human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolved.MethodsUsing DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells.ResultsWe demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington's disease, epilepsy, and schizophrenia.DiscussionWe expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.

BackgroundMyeloid-derived suppressor cells (MDSCs), which include monocytic (mMDSCs) and granulocytic (gMDSCs) cells, are an immunosuppressive, heterogeneous population of cells upregulated in cancer and other pathologic conditions, in addition to normal conditions of stress. The origin of MDSCs is debated, and the regulatory pattern responsible for gMDSC differentiation remains unknown. Since DNA methylation (DNAm) contributes to lineage differentiation, we have investigated whether it contributes to the acquisition of the gMDSC phenotype.ResultsUsing the Illumina EPIC array to measure DNAm of gMDSCs and neutrophils from diverse neonatal and adult blood sources, we found 189 differentially methylated CpGs between gMDSCs and neutrophils with a core of ten differentially methylated CpGs that were consistent across both sources of cells. Genes associated with these loci that are involved in immune responses include VCL, FATS, YAP1, KREMEN2, UBTF, MCC-1, and EFCC1. In two cancer patient groups that reflected those used to develop the methylation markers (head and neck squamous cell carcinoma (HNSCC) and glioma), all of the CpG loci were differentially methylated, reaching statistical significance in glioma cases and controls, while one was significantly different in the smaller HNSCC group.ConclusionsOur findings indicate that gMDSCs have a core of distinct DNAm alterations, informing future research on gMDSC differentiation and function.

Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (PBonferroni

DNA methylation has been extensively utilized to study epigenetic patterns across many diseases as well as to deconvolve blood cell type proportions. This study builds upon previous studies examining methylation patterns in paediatric patients with varying stages of Crohn's disease to extend the immune profiling of these patients using a novel deconvolution approach. Compared with control subjects, we observed significantly decreased levels of CD4 memory and naive, CD8 naive, and natural killer cells and elevated neutrophil levels in Crohn's disease. In addition, Crohn's patients had a significantly elevated neutrophil-to-lymphocyte ratio. Using an epigenome-wide association approach and adjusting for potential confounders, including cell type, we observed 397 differentially methylated CpG (DMC) sites associated with Crohn's disease. The top genetic pathway associated with the DMCs was the regulation of arginine metabolic processes which are involved in the regulation of T cells. [ABSTRACT FROM AUTHOR]

Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (