Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling

Investigating DNA methylation as a mediator of genetic risk in childhood acute lymphoblastic leukemia

Genome-wide association studies have identified a growing number of single nucleotide polymorphisms (SNPs) associated with childhood acute lymphoblastic leukemia (ALL), yet the functional roles of most SNPs are unclear. Multiple lines of evidence suggest that epigenetic mechanisms may mediate the impact of heritable genetic variation on phenotypes. Here, we investigated whether DNA methylation mediates the effect of genetic risk loci for childhood ALL. We performed an epigenome-wide association study (EWAS) including 808 childhood ALL cases and 919 controls from California-based studies using neonatal blood DNA. For differentially methylated CpG positions (DMPs), we next conducted association analysis with 23 known ALL risk SNPs followed by causal mediation analyses addressing the significant SNP-DMP pairs. DNA methylation at CpG cg01139861, in the promoter region of IKZF1, mediated the effects of the intronic IKZF1 risk SNP rs78396808, with the average causal mediation effect (ACME) explaining ~30% of the total effect (ACME P = 0.0031). In analyses stratified by self-reported race/ethnicity, the mediation effect was only significant in Latinos, explaining ~41% of the total effect of rs78396808 on ALL risk (ACME P = 0.0037). Conditional analyses confirmed the presence of at least three independent genetic risk loci for childhood ALL at IKZF1, with rs78396808 unique to non-European populations. We also demonstrated that the most significant DMP in the EWAS, CpG cg13344587 at gene ARID5B (P = 8.61 × 10-10), was entirely confounded by the ARID5B ALL risk SNP rs7090445. Our findings provide new insights into the functional pathways of ALL risk SNPs and the DNA methylation differences associated with risk of childhood ALL.

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Background

DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors.

Results

Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations.

Conclusions

We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02793-w.

The WID-CIN test identifies women with, and at risk of, cervical intraepithelial neoplasia grade 3 and invasive cervical cancer

Background

Cervical screening is transitioning from primary cytology to primary human papillomavirus (HPV) testing. HPV testing is highly sensitive but there is currently no high-specificity triage method for colposcopy referral to detect cervical intraepithelial neoplasia grade 3 or above (CIN3+) in women positive for high-risk (hr) HPV subtypes. An objective, automatable test that could accurately perform triage, independently of sample heterogeneity and age, is urgently required.

Methods

We analyzed DNA methylation at ~850,000 CpG sites across the genome in a total of 1254 cervical liquid-based cytology (LBC) samples from cases of screen-detected histologically verified CIN1-3+ (98% hrHPV-positive) and population-based control women free from any cervical disease (100% hrHPV-positive). Samples were provided by a state-of-the-art population-based cohort biobank and consisted of (i) a discovery set of 170 CIN3+ cases and 202 hrHPV-positive/cytology-negative controls; (ii) a diagnostic validation set of 87 CIN3+, 90 CIN2, 166 CIN1, and 111 hrHPV-positive/cytology-negative controls; and (iii) a predictive validation set of 428 cytology-negative samples (418 hrHPV-positive) of which 210 were diagnosed with CIN3+ in the upcoming 1–4 years and 218 remained disease-free.

Results

We developed the WID-CIN (Women’s cancer risk IDentification-Cervical Intraepithelial Neoplasia) test, a DNA methylation signature consisting of 5000 CpG sites. The receiver operating characteristic area under the curve (AUC) in the independent diagnostic validation set was 0.92 (95% CI 0.88–0.96). At 75% specificity (≤CIN1), the overall sensitivity to detect CIN3+ is 89.7% (83.3–96.1) in all and 92.7% (85.9–99.6) and 65.6% (49.2–82.1) in women aged ≥30 and <30. In hrHPV-positive/cytology-negative samples in the predictive validation set, the WID-CIN detected 54.8% (48.0–61.5) cases developing 1–4 years after sample donation in all ages or 56.9% (47.6–66.2) and 53.5% (43.7–63.2) in ≥30 and <30-year-old women, at a specificity of 75%.

Conclusions

The WID-CIN test identifies the vast majority of hrHPV-positive women with current CIN3+ lesions. In the absence of cytologic abnormalities, a positive WID-CIN test result is likely to indicate a significantly increased risk of developing CIN3+ in the near future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01116-9.

Epigenetic and Proteomic Biomarkers of Elevated Alcohol Use Predict Epigenetic Aging and Cell-Type variation Better Than Self-Report

Excessive alcohol consumption (EAC) has a generally accepted effect on morbidity and mortality, outcomes thought to be reflected in measures of epigenetic aging (EA). As the association of self-reported EAC with EA has not been consistent with these expectations, underscoring the need for readily employable non-self-report tools for accurately assessing and monitoring the contribution of EAC to accelerated EA, newly developed alcohol consumption DNA methylation indices, such as the Alcohol T Score (ATS) and Methyl DetectR (MDR), may be helpful. To test that hypothesis, we used these new indices along with the carbohydrate deficient transferrin (CDT), concurrent as well as past self-reports of EAC, and well-established measures of cigarette smoking to examine the relationship of EAC to both accelerated EA and immune cell counts in a cohort of 437 young Black American adults. We found that MDR, CDT, and ATS were intercorrelated, even after controlling for gender and cotinine effects. Correlations between EA and self-reported EAC were low or non-significant, replicating prior research, whereas correlations with non-self-report indices were significant and more substantial. Comparing non-self-report indices showed that the ATS predicted more than four times as much variance in EA, CDT4 cells and B-cells as for both the MDR and CDT, and better predicted indices of accelerated EA. We conclude that each of the non-self-report indices have differing predictive capacities with respect to key alcohol-related health outcomes, and that the ATS may be particularly useful for clinicians seeking to understand and prevent accelerated EA. The results also underscore the likelihood of substantial underestimates of problematic use when self-report is used and a reduction in correlations with EA and variance in cell-types.

Short-Term Exposure of PM2.5 and Epigenetic Aging: A Quasi-Experimental Study

Epigenetic age (EA) is an emerging DNA methylation-based biomarker of biological aging, but whether EA is causally associated with short-term PM_2.5 exposure remains unknown. We conducted a quasi-experimental study of 26 healthy adults to test whether short-term PM_2.5 exposure accelerates seven EAs with three health examinations performed before, during, and after multiple PM_2.5 pollution waves. Seven EAs were derived from the DNA methylation profiles of the Illumina HumanMethylationEPIC BeadChip from CD4+ T-helper cells. We found that an increase of 10 μg/m³ in the 0-24 h personal PM_2.5 exposure prior to health examinations was associated with a 0.035, 0.035, 0.050, 0.055, 0.052, and 0.037-unit increase in the changes of z-scored DNA methylation age acceleration (AA,Horvath), AA (Hannum), AA (GrimAge), DunedinPoAm, mortality risk score (MS), and epiTOC, respectively (p-values < 0.05). The same increase in the 24-48 h average personal PM_2.5 exposure yielded smaller effects but was still robustly associated with the changes in AA (GrimAge), DunedinPoAm, and MS. Such acute aging effects of PM_2.5 were mediated by the changes in several circulating biomarkers, including EC-SOD and sCD40L, with up to ∼28% mediated proportions. Our findings demonstrated that short-term PM_2.5 exposure could accelerate aging reflected by DNA methylation profiles via blood coagulation, oxidative stress, and systematic inflammation.

Do Loneliness and Per Capita Income Combine to Increase the Pace of Biological Aging for Black Adults across Late Middle Age?

In a sample of 685 late middle-aged Black adults (M age at 2019 = 57.17 years), we examined the effects of loneliness and per capita income on accelerated aging using a newly developed DNA-methylation based index: the DunedinPACE. First, using linear, mixed effects regression in a growth curve framework, we found that change in DunedinPACE was dependent on age, with a linear model best fitting the data (b = 0.004, p < 0.001), indicating that average pace of change increased among older participants. A quadratic effect was also tested, but was non-significant. Beyond the effect of age, both change in loneliness (b = 0.009, p < 0.05) and change in per capita income (b = -0.016, p < 0.001) were significantly associated with change in DunedinPACE across an 11-year period, accounting for significant between person variability observed in the unconditional model. Including non-self-report indices of smoking and alcohol use did not reduce the association of loneliness or per capita income with DunedinPACE. However, change in smoking was strongly associated with change in DunedinPACE such that those reducing their smoking aged less rapidly than those continuing to smoke. In addition, both loneliness and per capita income were associated with DunedinPACE after controlling for variation in cell-types.

Aclust2.0: a revamped unsupervised R tool for Infinium methylation beadchips data analyses

MOTIVATION

A wide range of computational packages has been developed for regional DNA methylation analyses of Illumina's Infinium array data. Aclust, one of the first unsupervised algorithms, was originally designed to analyze regional methylation of Infinium's 27K and 450K arrays by clustering neighboring methylation sites prior to downstream analyses. However, Aclust relied on outdated packages that rendered it largely non-operational especially with the newer Infinium EPIC and mouse arrays.

RESULTS

We have created Aclust2.0, a streamlined pipeline that involves five steps for the analyses of human (450K and EPIC) and mouse array data. Aclust2.0 provides a user-friendly pipeline and versatile for regional DNA methylation analyses for molecular epidemiological and mouse studies.

AVAILABILITY AND IMPLEMENTATION

Aclust2.0 is freely available on Github (https://github.com/OluwayioseOA/Alcust2.0.git).

The Role of Dynamic DNA Methylation in Liver Transplant Rejection in Children

Transcriptional regulation of liver transplant (LT) rejection may reveal novel predictive and therapeutic targets. The purpose of this article is to test the role of differential DNA methylation in children with biopsy-proven acute cellular rejection after LT.

Genetic regulation of newborn telomere length is mediated and modified by DNA methylation

Telomere length at birth determines later life telomere length and potentially predicts ageing-related diseases. However, the genetic and epigenetic settings of telomere length in newborns have not been analyzed. In addition, no study yet has reported how the interplay between genetic variants and genome-wide cytosine methylation explains the variation in early-life telomere length. In this study based on 281 mother-newborn pairs from the ENVIRONAGE birth cohort, telomere length and whole-genome DNA methylation were assessed in cord blood and 26 candidate single nucleotide polymorphism related to ageing or telomere length were genotyped. We identified three genetic variants associated with cord blood telomere length and 57 cis methylation quantitative trait loci (cis-mQTLs) of which 22 mQTLs confirmed previous findings and 35 were newly identified. Five SNPs were found to have significant indirect effects on cord blood telomere length via the mediating CpGs. The association between rs911874 (SOD2) and newborn telomere length was modified by nearby DNA methylation indicated by a significant statistical interaction. Our results suggest that DNA methylation in cis might have a mediation or modification effect on the genetic difference in newborn telomere length. This novel approach warrants future follow-up studies that are needed to further confirm and extend these findings.

DNA methylation in blood cells is associated with cortisol levels in offspring of mothers who had prenatal post‐traumatic stress disorder

Maternal stress during pregnancy is associated with differential DNA methylation in offspring and disrupted cortisol secretion. This study aimed to determine methylation signatures of cortisol levels in children, and whether associations differ based on maternal post-traumatic stress disorder (PTSD). Blood epigenome-wide methylation and fasting cortisol levels were measured in 118 offspring of mothers recruited from the Kosovo Rehabilitation Centre for Torture Victims. Mothers underwent clinically administered assessment for PTSD using Diagnostic and Statistical Manual of Mental Disorders. Correlations between offspring methylation and cortisol levels were examined using epigenome-wide analysis, adjusting for covariates. Subsequent analysis focussed on a priori selected genes involved in the hypothalamic-pituitary-adrenal (HPA) axis stress signalling. Methylation at four sites were correlated with cortisol levels (cg15321696, r = -0.33, cg18105800, r = +0.33, cg00986889, r = -0.25, and cg15920527, r = -0.27). In adjusted multivariable regression, when stratifying based on prenatal PTSD status, significant associations were only found for children born to mothers with prenatal PTSD (p < 0.001). Several sites within HPA axis genes were also associated with cortisol levels in the maternal PTSD group specifically. There is evidence that methylation is associated with cortisol levels, particularly in offspring born to mothers with prenatal PTSD. However, larger studies need to be carried out to independently validate these findings.

Comprehensive methylome sequencing reveals prognostic epigenetic biomarkers for prostate cancer mortality

BACKGROUND

Prostate cancer is a clinically heterogeneous disease with a subset of patients rapidly progressing to lethal-metastatic prostate cancer. Current clinicopathological measures are imperfect predictors of disease progression. Epigenetic changes are amongst the earliest molecular changes in tumourigenesis. To find new prognostic biomarkers to enable earlier intervention and improved outcomes, we performed methylome sequencing of DNA from patients with localised prostate cancer and long-term clinical follow-up.

METHODS

We used whole-genome bisulphite sequencing (WGBS) to comprehensively map and compare DNA methylation of radical prostatectomy tissue between patients with lethal disease (n = 7) and non-lethal (n = 8) disease (median follow-up 19.5 years). Validation of differentially methylated regions (DMRs) was performed in an independent cohort (n = 185, median follow-up 15 years) using targeted multiplex bisulphite sequencing of candidate regions. Survival was assessed via univariable and multivariable analyses including clinicopathological measures (log-rank and Cox regression models).

RESULTS

WGBS data analysis identified cancer-specific methylation patterns including CpG island hypermethylation, and hypomethylation of repetitive elements, with increasing disease risk. We identified 1420 DMRs associated with prostate cancer-specific mortality (PCSM), which showed enrichment for gene sets downregulated in prostate cancer and de novo methylated in cancer. Through comparison with public prostate cancer datasets, we refined the DMRs to develop an 18-gene prognostic panel. Applying this panel to an independent cohort, we found significant associations between PCSM and hypermethylation at EPHB3, PARP6, TBX1, MARCH6 and a regulatory element within CACNA2D4. Strikingly in a multivariable model, inclusion of CACNA2D4 methylation was a better predictor of PCSM versus grade alone (Harrell's C-index: 0.779 vs. 0.684).

CONCLUSIONS

Our study provides detailed methylome maps of non-lethal and lethal prostate cancer and identifies novel genic regions that distinguish these patient groups. Inclusion of our DNA methylation biomarkers with existing clinicopathological measures improves prognostic models of prostate cancer mortality, and holds promise for clinical application.

Differentially Methylated DNA Regions and Left Ventricular Hypertrophy in African Americans: A HyperGEN Study

Left ventricular (LV) hypertrophy (LVH) is an independent risk factor for cardiovascular disease, and African Americans experience a disparate high risk of LVH. Genetic studies have identified potential candidate genes and variants related to the condition. Epigenetic modifications may continue to help unravel disease mechanisms. We used methylation and echocardiography data from 636 African Americans selected from the Hypertension Genetic Epidemiology Network (HyperGEN) to identify differentially methylated regions (DMRs) associated with LVH. DNA extracted from whole blood was assayed on Illumina Methyl450 arrays. We fit linear mixed models to examine associations between co-methylated regions and LV traits, and we then conducted single CpG analyses within significant DMRs. We identified associations between DMRs and ejection fraction (XKR6), LV internal diastolic dimension (TRAK1), LV mass index (GSE1, RPS15 A, PSMD7), and relative wall thickness (DNHD1). In single CpG analysis, CpG sites annotated to TRAK1 and DNHD1 were significant. These CpGs were not associated with LV traits in replication cohorts but the direction of effect for DNHD1 was consistent across cohorts. Of note, DNHD1, GSE1, and PSMD7 may contribute to cardiac structural function. Future studies should evaluate relationships between regional DNA methylation patterns and the development of LVH.

Site-specific decreases in DNA methylation in replicating cells following exposure to oxidative stress

Oxidative stress is a common feature of inflammation-driven cancers, and it promotes genomic instability and aggressive tumour phenotypes. It is known that oxidative stress transiently modulates gene expression through the oxidation of transcription factors and associated regulatory proteins. Neutrophils are our most abundant white blood cells and accumulate at sites of infection and inflammation. Activated neutrophils produce hypochlorous acid and chloramines, which can disrupt DNA methylation by oxidizing methionine. The goal of the current study was to determine whether chloramine exposure results in sequence-specific modifications in DNA methylation that enable long-term alterations in transcriptional output. Proliferating Jurkat T-lymphoma cells were exposed to sublethal doses of glycine chloramine and differential methylation patterns were compared using Illumina EPIC 850 K bead chip arrays. There was a substantial genome-wide decrease in methylation 4 h after exposure that correlated with altered RNA expression for 24 and 48 h, indicating sustained impacts on exposed cells. A large proportion of the most significant differentially methylated CpG sites were situated towards chromosomal ends, suggesting that these regions are most susceptible to inhibition of maintenance DNA methylation. This may contribute to epigenetic instability of chromosomal ends in rapidly dividing cells, with potential implications for the regulation of telomere length and cellular longevity.

CDH1 (E-cadherin) Gene Methylation in Human Breast Cancer: Critical Appraisal of a Long and Twisted Story

Simple Summary

Genes can be inactivated by specific modifications of DNA bases, most often by adding a methyl group to the DNA base cytosine if it is followed by guanosine (CG methylation). This modification prevents gene expression and has been reported for many different genes in nearly all types of cancer. A prominent example is the gene CDH1, which encodes the cell-adhesion molecule E-cadherin. This is an important player in the spreading of tumor cells within the body (metastasis). Particularly in human breast cancer, many different research groups have studied the inactivation of the CDH1 gene via DNA methylation using various methods. Over the last 20 years, different, in part, even contradicting results have been published for the CDH1 gene in breast cancer. This review summarizes the most important publications and explains the bewildering heterogeneity of results through careful analysis of the methods which have been used.

Abstract

Epigenetic inactivation of a tumor suppressor gene by aberrant DNA methylation is a well-established defect in human tumor cells, complementing genetic inactivation by mutation (germline or somatic). In human breast cancer, aberrant gene methylation has diagnostic, prognostic, and predictive potential. A prominent example is the hypermethylation of the CDH1 gene, encoding the adhesion protein E-Cadherin (“epithelial cadherin”). In numerous publications, it is reported as frequently affected by gene methylation in human breast cancer. However, over more than two decades of research, contradictory results concerning CDH1 gene methylation in human breast cancer accumulated. Therefore, we review the available evidence for and against the role of DNA methylation of the CDH1 gene in human breast cancer and discuss in detail the methodological reasons for conflicting results, which are of general importance for the analysis of aberrant DNA methylation in human cancer specimens. Since the loss of E-cadherin protein expression is a hallmark of invasive lobular breast cancer (ILBC), special attention is paid to CDH1 gene methylation as a potential mechanism for loss of expression in this special subtype of human breast cancer. Proper understanding of the methodological basis is of utmost importance for the correct interpretation of results supposed to demonstrate the presence and clinical relevance of aberrant DNA methylation in cancer specimens.

Four methylation-driven genes detected by linear discriminant analysis model from early-stage colorectal cancer and their methylation levels in cell-free DNA

The process of colorectal cancer (CRC) formation is considered a typical model of multistage carcinogenesis in which aberrant DNA methylation plays an important role. In this study, 752 methylation-driven genes (MDGs) were identified by the MethylMix package based on methylation and gene expression data of CRC in The Cancer Genome Atlas (TCGA). Iterative recursive feature elimination (iRFE) based on linear discriminant analysis (LDA) was used to determine the minimum MDGs (iRFE MDGs), which could distinguish between cancer and cancer-adjacent tissues. Further analysis indicated that the changes in methylation levels of the four iRFE MDGs, ADHFE1-Cluster1, CNRIP1-Cluster1, MAFB, and TNS4, occurred in adenoma tissues, while changes did not occur until stage IV in cell-free DNA. Furthermore, the methylation levels of iRFE MDGs were correlated with the genes involved in the reprogramming process of somatic cells to pluripotent stem cells, which is considered the common signature of cancer cells and embryonic stem cells. The above results indicated that the four iRFE MDGs may play roles in the early stage of colorectal carcinogenesis and highlighted the complicated relationship between tissue DNA and cell-free DNA (cfDNA).

Sex-specific and generational effects of alcohol and tobacco use on epigenetic age acceleration in the Michigan longitudinal study

Background:

Methods:

Results:

Conclusions:

Chronic stress-driven glucocorticoid receptor activation programs key cell phenotypes and functional epigenomic patterns in human fibroblasts

Chronic environmental stress can profoundly impact cell and body function. Although the underlying mechanisms are poorly understood, epigenetics has emerged as a key link between environment and health. The genomic effects of stress are thought to be mediated by the action of glucocorticoid stress hormones, primarily cortisol in humans, which act via the glucocorticoid receptor (GR). To dissect how chronic stress-driven GR activation influences epigenetic and cell states, human fibroblasts underwent prolonged exposure to physiological stress levels of cortisol and/or a selective GR antagonist. Cortisol was found to drive robust changes in cell proliferation, migration, and morphology, which were abrogated by concomitant GR blockade. The GR-driven cell phenotypes were accompanied by widespread, yet genomic context-dependent, changes in DNA methylation and mRNA expression, including gene loci with known roles in cell proliferation and migration. These findings provide insights into how chronic stress-driven functional epigenomic patterns become established to shape key cell phenotypes.

•

Physiological stress levels of cortisol drive robust changes in key cell phenotypes

•

Stress-driven changes in cell phenotypes are abrogated by concomitant GR blockade

•

GR activation induces functional and phenotypically relevant epigenomic changes

Genome-wide decrease in DNA methylation in adults with epilepsy treated with modified ketogenic diet: A prospective study

OBJECTIVE

The aim of this study was to investigate the impact of the modified ketogenic diet on DNA methylation in adults with epilepsy.

METHODS

In this prospective study, we investigated the genome-wide DNA methylation in whole blood in 58 adults with epilepsy treated with the modified ketogenic for 12 weeks. Patients were recruited from the National Center for Epilepsy, Norway, from March 1, 2011 to February 28, 2017. DNA methylation was analyzed using the Illumina Infinium MethylationEPIC BeadChip array. Analysis of variance and paired t-test were used to identify differentially methylated loci after 4 and 12 weeks of dietary treatment. A false discovery rate approach with a significance threshold of <5% was used to adjust for multiple comparisons.

RESULTS

We observed a genome-wide decrease in DNA methylation, both globally and at specific sites, after 4 and 12 weeks of dietary treatment. A substantial share of the differentially methylated positions (CpGs) were annotated to genes associated with epilepsy (n = 7), lipid metabolism (n = 8), and transcriptional regulation (n = 10). Furthermore, five of the identified genes were related to inositol phosphate metabolism, which may represent a possible mechanism by which the ketogenic diet attenuates seizures.

SIGNIFICANCE

A better understanding of the modified ketogenic diet's influence at the molecular level may be the key to unraveling the mechanisms by which the diet can ameliorate seizures and possibly to identifying novel therapeutic targets for epilepsy.

Epigenetic signature of exposure to maternal Trypanosoma cruzi infection in cord blood cells from uninfected newborns

Aims: To assess the epigenetic effects of in utero exposure to maternal Trypanosoma cruzi infection. Methods: We performed an epigenome-wide association study to compare the DNA methylation patterns of umbilical cord blood cells from uninfected babies from chagasic and uninfected mothers. DNA methylation was measured using Infinium EPIC arrays. Results: We identified a differential DNA methylation signature of fetal exposure to maternal T. cruzi infection, in the absence of parasite transmission, with 12 differentially methylated sites in B cells and CD4⁺ T cells, including eight protein-coding genes. Conclusion: These genes participate in hematopoietic cell differentiation and the immune response and may be involved in immune disorders. They also have been associated with several developmental disorders and syndromes.

The shared mother-child epigenetic signature of neglect is related to maternal adverse events

Studies of DNA methylation have revealed the biological mechanisms by which life adversity confers risk for later physical and mental health problems. What remains unknown is the “biologically embedding” of maternal adverse experiences resulting in maladaptive parenting and whether these epigenetic effects are transmitted to the next generation. This study focuses on neglectful mothering indexed by a severe disregard for the basic and psychological needs of the child. Using the Illumina Human Methylation EPIC BeadChip in saliva samples, we identified genes with differentially methylated regions (DMRs) in those mothers with (n = 51), versus those without (n = 87), neglectful behavior that present similar DMRs patterns in their children being neglected versus non-neglected (n = 40 vs. 75). Mothers reported the emotional intensity of adverse life events. After covariate adjustment and multiple testing corrections, we identified 69 DMRs in the mother epigenome and 42 DMRs in the child epigenome that were simultaneously above the α = 0.01 threshold. The common set of nine DMRs contained genes related to childhood adversity, neonatal and infant diabetes, child neurobehavioral development and other health problems such as obesity, hypertension, cancer, posttraumatic stress, and the Alzheimer’s disease; four of the genes were associated with maternal life adversity. Identifying a shared epigenetic signature of neglect linked to maternal life adversity is an essential step in breaking the intergenerational transmission of one of the most common forms of childhood maltreatment.

Genetic and Methylation Analysis of CTNNB1 in Benign and Malignant Melanocytic Lesions

Simple Summary

Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology and in 1–2% of advanced melanoma. We performed a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations and found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanoma) demonstrated a different genetic profile, grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (55%) and NRAS Q61 (34%).

Abstract

Melanocytic neoplasms have been genetically characterized in detail during the last decade. Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology. In addition, they have been identified in 1–2% of advanced melanoma. Performing a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations, we found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanomas) demonstrated a different genetic profile, instead grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (n = 21, 55%) and NRAS Q61 (n = 13, 34%). In our cohort, four of seven (58%) and one of nine (11%) patients treated with targeted therapy (BRAF and MEK Inhibitors) or immune-checkpoint therapy, respectively, showed disease control (partial response or stable disease). In summary, CTNNB1 mutations are associated with a unique melanocytic tumor type in benign tumors (nevi), which can be applied in a diagnostic setting. In advanced disease, no clear characteristics distinguishing CTNNB1-mutant from other melanomas were observed; however, studies of larger, optimally prospective, cohorts are warranted.

Schizophrenia-associated differential DNA methylation in brain is distributed across the genome and annotated to MAD1L1, a locus at which DNA methylation and transcription phenotypes share genetic variation with schizophrenia risk

DNA methylation (DNAm), the addition of a methyl group to a cytosine in DNA, plays an important role in the regulation of gene expression. Single-nucleotide polymorphisms (SNPs) associated with schizophrenia (SZ) by genome-wide association studies (GWAS) often influence local DNAm levels. Thus, DNAm alterations, acting through effects on gene expression, represent one potential mechanism by which SZ-associated SNPs confer risk. In this study, we investigated genome-wide DNAm in postmortem superior temporal gyrus from 44 subjects with SZ and 44 non-psychiatric comparison subjects using Illumina Infinium MethylationEPIC BeadChip microarrays, and extracted cell-type-specific methylation signals by applying tensor composition analysis. We identified SZ-associated differential methylation at 242 sites, and 44 regions containing two or more sites (FDR cutoff of q = 0.1) and determined a subset of these were cell-type specific. We found mitotic arrest deficient 1-like 1 (MAD1L1), a gene within an established GWAS risk locus, harbored robust SZ-associated differential methylation. We investigated the potential role of MAD1L1 DNAm in conferring SZ risk by assessing for colocalization among quantitative trait loci for methylation and gene transcripts (mQTLs and tQTLs) in brain tissue and GWAS signal at the locus using multiple-trait-colocalization analysis. We found that mQTLs and tQTLs colocalized with the GWAS signal (posterior probability >0.8). Our findings suggest that alterations in MAD1L1 methylation and transcription may mediate risk for SZ at the MAD1L1-containing locus. Future studies to identify how SZ-associated differential methylation affects MAD1L1 biological function are indicated.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer's disease

To better understand DNA methylation in Alzheimer's disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10^-5). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.

Nicotine dose-dependent epigenomic-wide DNA methylation changes in the mice with long-term electronic cigarette exposure

Epigenomic-wide DNA methylation profiling holds the potential to reflect both electronic cigarette exposure-associated risks and individual poor health outcomes. However, a systemic study in animals or humans is still lacking. Using the Infinium Mouse Methylation BeadChip, we examined the DNA methylation status of white blood cells in male ApoE-/- mice after 14 weeks of electronic cigarette exposure with the InExpose system (2 hr/day, 5 days/week, 50% PG and 50% VG) with low (6 mg/ml) and high (36 mg/ml) nicotine concentrations. Our results indicate that electronic cigarette aerosol inhalation induces significant alteration of 8,985 CpGs in a dose-dependent manner (FDR<0.05); 7,389 (82.2%) of the CpG sites are annotated with known genes. Among the top 6 significant CpG sites (P-value<1e-8), 4 CpG sites are located in the known genes, and most (3/5) of these genes have been related to cigarette smoking. The other two CpGs are close to/associated with the Phc2 gene that was recently linked to smoking in a transcriptome-wide associations study. Furthermore, the gene set enrichment analysis highlights the activation of MAPK and 4 cardiomyocyte/cardiomyopathy-related signaling pathways (including adrenergic signaling in cardiomyocytes and arrhythmogenic right ventricular cardiomyopathy) following repeated electronic cigarette use. The MAPK pathway activation correlates well with our finding of increased cytokine mRNA expression after electronic cigarette exposure in the same batch of mice. Interestingly, two pathways related to mitochondrial activities, namely mitochondrial gene expression and mitochondrial translation, are also activated after electronic cigarette exposure. Elucidating the relationship between these pathways and the increased circulating mitochondrial DNA observed here will provide further insight into the cell-damaging effects of prolonged inhalation of e-cigarette aerosols.

Diagnosis Biomarkers of Cholangiocarcinoma in Human Bile: An Evidence-Based Study

Simple Summary

A liquid biopsy has the characteristics of low trauma and easy acquisition in the diagnosis of cholangiocarcinoma. Many researchers try to find diagnostic or prognostic biomarkers of CCA through blood, urine, bile and other body fluids. Due to the close proximity of bile to the lesion and the stable nature, bile gradually comes into people’s view. The evaluation of human bile diagnostic biomarkers is not only to the benefit of screening more suitable clinical markers but also of exploring the pathological changes of the disease.

Abstract

Cholangiocarcinoma (CCA) is a multifactorial malignant tumor of the biliary tract, and the incidence of CCA is increasing in recent years. At present, the diagnosis of CCA mainly depends on imaging and invasive examination, with limited specificity and sensitivity and late detection. The early diagnosis of CCA always faces the dilemma of lacking specific diagnostic biomarkers. Non-invasive methods to assess the degree of CAA have been developed throughout the last decades. Among the many specimens looking for CCA biomarkers, bile has gotten a lot of attention lately. This paper mainly summarizes the recent developments in the current research on the diagnostic biomarkers for CCA in human bile at the levels of the gene, protein, metabolite, extracellular vesicles and volatile organic compounds.

Identification of influential probe types in epigenetic predictions of human traits: implications for microarray design

BACKGROUND

CpG methylation levels can help to explain inter-individual differences in phenotypic traits. Few studies have explored whether identifying probe subsets based on their biological and statistical properties can maximise predictions whilst minimising array content. Variance component analyses and penalised regression (epigenetic predictors) were used to test the influence of (i) the number of probes considered, (ii) mean probe variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Training and test samples comprised ≤ 4450 and ≤ 2578 unrelated individuals from Generation Scotland, respectively.

RESULTS

As the number of probes under consideration decreased, so too did the estimates from variance components and prediction analyses. Methylation QTL status and mean probe variability did not influence variance components. However, relative effect sizes were 15% larger for epigenetic predictors based on probes with known or reported methylation QTLs compared to probes without reported methylation QTLs. Relative effect sizes were 45% larger for predictors based on probes with mean Beta-values between 10 and 90% compared to those based on hypo- or hypermethylated probes (Beta-value ≤ 10% or ≥ 90%).

CONCLUSIONS

Arrays with fewer probes could reduce costs, leading to increased sample sizes for analyses. Our results show that reducing array content can restrict prediction metrics and careful attention must be given to the biological and distribution properties of CpG probes in array content selection.

Prenatal vitamin intake in first month of pregnancy and DNA methylation in cord blood and placenta in two prospective cohorts

BACKGROUND

Prenatal vitamin use is recommended before and during pregnancies for normal fetal development. Prenatal vitamins do not have a standard formulation, but many contain calcium, folic acid, iodine, iron, omega-3 fatty acids, zinc, and vitamins A, B6, B12, and D, and usually they contain higher concentrations of folic acid and iron than regular multivitamins in the US Nutrient levels can impact epigenetic factors such as DNA methylation, but relationships between maternal prenatal vitamin use and DNA methylation have been relatively understudied. We examined use of prenatal vitamins in the first month of pregnancy in relation to cord blood and placenta DNA methylation in two prospective pregnancy cohorts: the Early Autism Risk Longitudinal Investigation (EARLI) and Markers of Autism Risk Learning Early Signs (MARBLES) studies.

RESULTS

In placenta, prenatal vitamin intake was marginally associated with -0.52% (95% CI -1.04, 0.01) lower mean array-wide DNA methylation in EARLI, and associated with -0.60% (-1.08, -0.13) lower mean array-wide DNA methylation in MARBLES. There was little consistency in the associations between prenatal vitamin intake and single DNA methylation site effect estimates across cohorts and tissues, with only a few overlapping sites with correlated effect estimates. However, the single DNA methylation sites with p-value < 0.01 (EARLI cord nCpGs = 4068, EARLI placenta nCpGs = 3647, MARBLES cord nCpGs = 4068, MARBLES placenta nCpGs = 9563) were consistently enriched in neuronal developmental pathways.

CONCLUSIONS

Together, our findings suggest that prenatal vitamin intake in the first month of pregnancy may be related to lower placental global DNA methylation and related to DNA methylation in brain-related pathways in both placenta and cord blood.

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.

Prenatal and birth associations of epigenetic gestational age acceleration in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort

Gestational age (GA) is an important determinant of child health and disease risk. Two epigenetic GA clocks have been developed using DNA methylation (DNAm) patterns in cord blood. We investigate the accuracy of GA clocks and determinants of epigenetic GA acceleration (GAA), a biomarker of biological ageing. We hypothesize that prenatal and birth characteristics are associated with altered GAA, thereby disrupting foetal biological ageing. We examined 372 mother-child pairs from the Center for the Health Assessment of Mothers and Children of Salinas study of primarily Latino farmworkers in California. Chronological GA was robustly correlated with epigenetic GA (DNAm GA) estimated by the Knight (r = 0.48, p < 2.2x10^-16) and Bohlin clocks (r = 0.67, p < 2.2x10^-16) using the Illumina 450K array in cord blood samples collected at birth. GA clock performance was robust, though slightly lower, using DNAm profiles from the Illumina EPIC array in a smaller subsample (Knight: r = 0.39, p < 3.5x10^-5; Bohlin: r = 0.60, p < 7.7x10^-12). After adjusting for confounders, high maternal serum triglyceride levels (Bohlin: β = -0.01 days per mg/dL, p = 0.03), high maternal serum lipid levels (Bohlin: β = -4.31x10^-3 days per mg/dL, p = 0.04), preterm delivery (Bohlin: β = -4.03 days, p = 9.64x10^-4), greater maternal parity (Knight: β = -4.07 days, p = 0.01; Bohlin: β = -2.43 days, p = 0.01), and male infant sex (Knight: β = -3.15 days, p = 3.10x10^-3) were associated with decreased GAA.Prenatal and birth characteristics affect GAA in newborns. Understanding factors that accelerate or delay biological ageing at birth may identify early-life targets for disease prevention and improve ageing across the life-course. Future research should test the impact of altered GAA on the long-term burden of age-related diseases.

Functional correlation of genome-wide DNA methylation profiles in genetic neurodevelopmental disorders

An expanding range of genetic syndromes are characterized by genome-wide disruptions in DNA methylation profiles referred to as episignatures. Episignatures are distinct, highly sensitive and specific biomarkers that have recently been applied in clinical diagnosis of genetic syndromes. Episignatures are contained within the broader disorder-specific genome-wide DNA methylation changes which can share significant overlap amongst different conditions. In this study we performed functional genomic assessment and comparison of disorder-specific and overlapping genome-wide DNA methylation changes related to 65 genetic syndromes with previously described episignatures. We demonstrate evidence of disorder-specific and recurring genome-wide differentially methylated probes (DMPs) and regions (DMRs). The overall distribution of DMPs and DMRs across the majority of the neurodevelopmental genetic syndromes analyzed showed substantial enrichment in gene promoters and CpG islands, and under-representation of the more variable intergenic regions. Analysis showed significant enrichment of the DMPs and DMRs in gene pathways and processes related to neurodevelopment, including neurogenesis, synaptic signaling and synaptic transmission. This study expands beyond the diagnostic utility of DNA methylation episignatures by demonstrating correlation between the function of the mutated genes and the consequent genomic DNA methylation profiles as a key functional element in the molecular etiology of genetic neurodevelopmental disorders. This article is protected by copyright. All rights reserved.

DNA methylation profile of a rural cohort exposed to early-adversity and malnutrition: An exploratory analysis

Barker's hypothesis affirms that undernourishment in early-life induces metabolic reprogramming that compromises organism functions later in life, leading to age-related diseases. We are exposed to environmental and social conditions that impact our life trajectories, leading to ageing phenotypes as we grow. Epigenetic mechanisms constitute the link between both external stimuli and genetic programming. Studies have focused on describing the effect of early adverse events such as trauma, famines, or childhood labor on epigenetic markers in adulthood and the elderly. However, we lack information on epigenetic programming in individuals born in rural communities from underdeveloped countries, exposed to negative influences during fetal and postnatal development, particularly chronic malnutrition. Hence, in this exploratory analysis, we characterize the epigenome of individuals and some parents from Tlaltizapan (a rural community in Mexico originally studied almost 50 years ago) and collect anthropometric data on growth and development, as well on the living conditions of the families. Our results help build a biological hypothesis indicating that most of the epigenetic age measures of the subjects are significantly different among them. Interestingly, the most affected methylated regions correspond to pathways involved in neuronal system development, reproductive behaviour, learning and memory regulation.

Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing

Smoking and Heavy Alcohol Consumption (HAC) are established risk factors for myriad complex disorders of ageing. Yet many prior studies of Epigenetic Ageing (EA) have shown only modest effects of smoking and drinking on accelerated ageing. One potential reason for this conundrum might be the reliance of some prior EA studies on self-reported substance use, which may be unreliable in many samples. To test whether novel, non-self-reported indices would show a stronger association of smoking and HAC to EA, we used methylation sensitive digital PCR (MSdPCR) and data from 437 African American subjects from Wave 7 of the Family and Community Health Study Offspring Cohort to examine the effects of subjective and objective measures of smoking and HAC on 7 indices of EA. Because of limited overall correlations between the various EA indices, we examined patterns of association separately for each index. Consistent with expectations, MSdPCR assessments of smoking and HAC, but not self-reported alcohol consumption, were strongly correlated with accelerated EA. MSdPCR assessments of smoking and HAC accounted for 57% of GrimAge acceleration and the shared variance in GrimAge and DunedinPOAM accelerated EA. We conclude that MSdPCR assessments of smoking and HAC are valuable tools for understanding EA, represent directly targetable conditions for the prevention of premature ageing, and substantially improve upon self-reported assessment of smoking and HAC.

Association between Usual Dietary Intake of Food Groups and DNA Methylation and Effect Modification by Metabotype in the KORA FF4 Cohort

Associations between diet and DNA methylation may vary among subjects with different metabolic states, which can be captured by clustering populations in metabolically homogenous subgroups, called metabotypes. Our aim was to examine the relationship between habitual consumption of various food groups and DNA methylation as well as to test for effect modification by metabotype. A cross-sectional analysis of participants (median age 58 years) of the population-based prospective KORA FF4 study, habitual dietary intake was modeled based on repeated 24-h diet recalls and a food frequency questionnaire. DNA methylation was measured using the Infinium MethylationEPIC BeadChip providing data on >850,000 sites in this epigenome-wide association study (EWAS). Three metabotype clusters were identified using four standard clinical parameters and BMI. Regression models were used to associate diet and DNA methylation, and to test for effect modification. Few significant signals were identified in the basic analysis while many significant signals were observed in models including food group-metabotype interaction terms. Most findings refer to interactions of food intake with metabotype 3, which is the metabotype with the most unfavorable metabolic profile. This research highlights the importance of the metabolic characteristics of subjects when identifying associations between diet and white blood cell DNA methylation in EWAS.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging, and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. Apc ^Min/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This Infinium Mouse Methylation BeadChip (version MM285) is widely accessible to the research community and will accelerate high-sample-throughput studies in this important model organism.

Association of Cardiovascular Health Through Young Adulthood With Genome-Wide DNA Methylation Patterns in Midlife: The CARDIA Study

BACKGROUND

Cardiovascular health (CVH) from young adulthood is strongly associated with an individual's future risk of cardiovascular disease (CVD) and total mortality. Defining epigenomic biomarkers of lifelong CVH exposure and understanding their roles in CVD development may help develop preventive and therapeutic strategies for CVD.

METHODS

In 1085 CARDIA study (Coronary Artery Risk Development in Young Adults) participants, we defined a clinical cumulative CVH score that combines body mass index, blood pressure, total cholesterol, and fasting glucose measured longitudinally from young adulthood through middle age over 20 years (mean age, 25-45). Blood DNA methylation at >840 000 methylation markers was measured twice over 5 years (mean age, 40 and 45). Epigenome-wide association analyses on the cumulative CVH score were performed in CARDIA and compared in the FHS (Framingham Heart Study). We used penalized regression to build a methylation-based risk score to evaluate the risk of incident coronary artery calcification and clinical CVD events.

RESULTS

We identified 45 methylation markers associated with cumulative CVH at false discovery rate <0.01 (P=4.7E-7-5.8E-17) in CARDIA and replicated in FHS. These associations were more pronounced with methylation measured at an older age. CPT1A, ABCG1, and SREBF1 appeared as the most prominent genes. The 45 methylation markers were mostly located in transcriptionally active chromatin and involved lipid metabolism, insulin secretion, and cytokine production pathways. Three methylation markers located in genes SARS1, SOCS3, and LINC-PINT statistically mediated 20.4% of the total effect between CVH and risk of incident coronary artery calcification. The methylation risk score added information and significantly (P=0.004) improved the discrimination capacity of coronary artery calcification status versus CVH score alone and showed association with risk of incident coronary artery calcification 5 to 10 years later independent of cumulative CVH score (odds ratio, 1.87; P=9.66E-09). The methylation risk score was also associated with incident clinical CVD in FHS (hazard ratio, 1.28; P=1.22E-05).

CONCLUSIONS

Cumulative CVH from young adulthood contributes to midlife epigenetic programming over time. Our findings demonstrate the role of epigenetic markers in response to CVH changes and highlight the potential of epigenomic markers for precision CVD prevention, and earlier detection of subclinical CVD, as well.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

An epigenome-wide view of osteoarthritis in primary tissues

Osteoarthritis is a complex degenerative joint disease. Here, we investigate matched genotype and methylation profiles of primary chondrocytes from macroscopically intact (low-grade) and degraded (high-grade) osteoarthritis cartilage and from synoviocytes collected from 98 osteoarthritis-affected individuals undergoing knee replacement surgery. We perform an epigenome-wide association study of knee cartilage degeneration and report robustly replicating methylation markers, which reveal an etiologic mechanism linked to the migration of epithelial cells. Using machine learning, we derive methylation models of cartilage degeneration, which we validate with 82% accuracy in independent data. We report a genome-wide methylation quantitative trait locus (mQTL) map of articular cartilage and synovium and identify 18 disease-grade-specific mQTLs in osteoarthritis cartilage. We resolve osteoarthritis GWAS loci through causal inference and colocalization analyses and decipher the epigenetic mechanisms that mediate the effect of genotype on disease risk. Together, our findings provide enhanced insights into epigenetic mechanisms underlying osteoarthritis in primary tissues.

DNA methylation fingerprint of hepatocellular carcinoma from tissue and liquid biopsies

Hepatocellular carcinoma (HCC) is amongst the cancers with highest mortality rates and is the most common malignancy of the liver. Early detection is vital to provide the best treatment possible and liquid biopsies combined with analysis of circulating tumour DNA methylation show great promise as a non-invasive approach for early cancer diagnosis and monitoring with low false negative rates. To identify reliable diagnostic biomarkers of early HCC, we performed a systematic analysis of multiple hepatocellular studies and datasets comprising > 1500 genome-wide DNA methylation arrays, to define a methylation signature predictive of HCC in both tissue and cell-free DNA liquid biopsy samples. Our machine learning pipeline identified differentially methylated regions in HCC, some associated with transcriptional repression of genes related with cancer progression, that benchmarked positively against independent methylation signatures. Combining our signature of 38 DNA methylation regions, we derived a HCC detection score which confirmed the utility of our approach by identifying in an independent dataset 96% of HCC tissue samples with a precision of 98%, and most importantly successfully separated cfDNA of tumour samples from healthy controls. Notably, our risk score could identify cell-free DNA samples from patients with other tumours, including colorectal cancer. Taken together, we propose a comprehensive HCC DNA methylation fingerprint and an associated risk score for detection of HCC from tissue and liquid biopsies.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Evaluation of different computational methods for DNA methylation-based biological age

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

Comparison of DNA methylation measurements from EPIC BeadChip and SeqCap targeted bisulphite sequencing in PON1 and nine additional candidate genes

Epigenome-wide association studies (EWAS) are widely implemented in epidemiology, and the Illumina HumanMethylationEPIC BeadChip (EPIC) DNA microarray is the most-used technology. Recently, next-generation sequencing (NGS)-based methods, which assess DNA methylation at single-base resolution, have become more affordable and technically feasible. While the content of microarray technology is fixed, NGS-based approaches, such as the Roche Nimblegen, SeqCap Epi Enrichment System (SeqCap), offer the flexibility of targeting most CpGs in a gene. With the current usage of microarrays and emerging NGS-based technologies, it is important to establish whether data generated from the two platforms are comparable. We harnessed 112 samples from the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) birth cohort study and compared DNA methylation between the EPIC microarray and SeqCap for PON1 and nine additional candidate genes, by evaluating epigenomic coverage and correlations. We conducted multivariable linear regression and principal component analyses to assess the ability of the EPIC array and SeqCap to detect biological differences in gene methylation by the PON1_-108 single nucleotide polymorphism. We found an overall high concordance (r = 0.84) between SeqCap and EPIC DNA methylation, among highly methylated and minimally methylated regions. However, substantial disagreement was present between the two methods in moderately methylated regions, with SeqCap measurements exhibiting greater within-site variation. Additionally, SeqCap did not capture PON1 SNP associated differences in DNA methylation that were evident with the EPIC array. Our findings indicate that microarrays perform well for analysing DNA methylation in large cohort studies but with limited coverage.

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.

Associations between epigenetic age acceleration and infertility

STUDY QUESTION

Is the use of ART, a proxy for infertility, associated with epigenetic age acceleration?

SUMMARY ANSWER

The epigenetic age acceleration measured by Dunedin Pace of Aging methylation (DunedinPoAm) differed significantly between non-ART and ART mothers.

WHAT IS KNOWN ALREADY

Among mothers who used ART, epigenetic age acceleration may be associated with low oocyte yield and poor ovarian response. However, the difference in epigenetic age acceleration between non-ART and ART mothers (or even fathers) has not been examined.

STUDY DESIGN, SIZE, DURATION

The Norwegian Mother, Father and Child Cohort Study (MoBa) recruited pregnant women and their partners across Norway at around 18 gestational weeks between 1999 and 2008. Approximately 95 000 mothers, 75 000 fathers and 114 000 children were included. Peripheral blood samples were taken from mothers and fathers at ultrasound appointments or from mothers at childbirth, and umbilical cord blood samples were collected from the newborns at birth.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Among the MoBa participants, we selected 1000 couples who conceived by coitus and 894 couples who conceived by IVF (n = 525) or ICSI (n = 369). We measured their DNA methylation (DNAm) levels using the Illumina MethylationEPIC array and calculated epigenetic age acceleration. A linear mixed model was used to examine the differences in five different epigenetic age accelerations between non-ART and ART parents.

MAIN RESULTS AND THE ROLE OF CHANCE

We found a significant difference in the epigenetic age acceleration calculated by DunedinPoAm between IVF and non-ART mothers (0.021 years, P-value = 2.89E−06) after adjustment for potential confounders. Further, we detected elevated DunedinPoAm in mothers with tubal factor infertility (0.030 years, P-value = 1.34E−05), ovulation factor (0.023 years, P-value = 0.0018) and unexplained infertility (0.023 years, P-value = 1.39E−04) compared with non-ART mothers. No differences in epigenetic age accelerations between non-ART and ICSI fathers were found. DunedinPoAm also showed stronger associations with smoking, education and parity than the other four epigenetic age accelerations.

LIMITATIONS, REASONS FOR CAUTION

We were not able to determine the directionality of the causal pathway between the epigenetic age accelerations and infertility. Since parents’ peripheral blood samples were collected after conception, we cannot rule out the possibility that the epigenetic profile of ART mothers was influenced by the ART treatment. Hence, the results should be interpreted with caution, and our results might not be generalizable to non-pregnant women.

WIDER IMPLICATIONS OF THE FINDINGS

A plausible biological mechanism behind the reported association is that IVF mothers could be closer to menopause than non-ART mothers. The pace of decline of the ovarian reserve that eventually leads to menopause varies between females yet, in general, accelerates after the age of 30, and some studies show an increased risk of infertility in females with low ovarian reserve.

STUDY FUNDING/COMPETING INTEREST(S)

This study was partly funded by the Research Council of Norway (Women’s fertility, project no. 320656) and through its Centres of Excellence Funding Scheme (project no. 262700). M.C.M. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 947684). The authors declare no conflict of interest.

TRIAL REGISTRATION NUMBER

N/A.

Low reliability of DNA methylation across Illumina Infinium platforms in cord blood: implications for replication studies and meta-analyses of prenatal exposures

Background

There is an increasing interest in the role of epigenetics in epidemiology, but the emerging research field faces several critical biological and technical challenges. In particular, recent studies have shown poor correlation of measured DNA methylation (DNAm) levels within and across Illumina Infinium platforms in various tissues. In this study, we have investigated concordance between 450 k and EPIC Infinium platforms in cord blood. We could not replicate our previous findings on the association of prenatal paracetamol exposure with cord blood DNAm, which prompted an investigation of cross-platform DNAm differences.

Results

This study is based on two DNAm data sets from cord blood samples selected from the Norwegian Mother, Father and Child Cohort Study (MoBa). DNAm of one data set was measured using the 450 k platform and the other data set was measured using the EPIC platform. Initial analyses of the EPIC data could not replicate any of our previous significant findings in the 450 k data on associations between prenatal paracetamol exposure and cord blood DNAm. A subset of the samples (n = 17) was included in both data sets, which enabled analyses of technical sources potentially contributing to the negative replication. Analyses of these 17 samples with repeated measurements revealed high per-sample correlations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\stackrel{\mathrm{-}}{\text{R}}\hspace{0.17em}\approx$$\end{document}R-≈ 0.99), but low per-CpG correlations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\stackrel{\mathrm{-}}{\text{R}}$$\end{document}R- ≈ 0.24) between the platforms. 1.7% of the CpGs exhibited a mean DNAm difference across platforms > 0.1. Furthermore, only 26.7% of the CpGs exhibited a moderate or better cross-platform reliability (intra-class correlation coefficient ≥ 0.5).

Conclusion

The observations of low cross-platform probe correlation and reliability corroborate previous reports in other tissues. Our study cannot determine the origin of the differences between platforms. Nevertheless, it emulates the setting in studies using data from multiple Infinium platforms, often analysed several years apart. Therefore, the findings may have important implications for future epigenome-wide association studies (EWASs), in replication, meta-analyses and longitudinal studies. Cognisance and transparency of the challenges related to cross-platform studies may enhance the interpretation, replicability and validity of EWAS results both in cord blood and other tissues, ultimately improving the clinical relevance of epigenetic epidemiology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01299-3.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.

Longitudinal Study of DNA Methylation and Epigenetic Clocks Prior to and Following Test-Confirmed COVID-19 and mRNA Vaccination

The host epigenetic landscape rapidly changes during SARS-CoV-2 infection, and evidence suggest that severe COVID-19 is associated with durable scars to the epigenome. Specifically, aberrant DNA methylation changes in immune cells and alterations to epigenetic clocks in blood relate to severe COVID-19. However, a longitudinal assessment of DNA methylation states and epigenetic clocks in blood from healthy individuals prior to and following test-confirmed non-hospitalized COVID-19 has not been performed. Moreover, the impact of mRNA COVID-19 vaccines upon the host epigenome remains understudied. Here, we first examined DNA methylation states in the blood of 21 participants prior to and following test-confirmed COVID-19 diagnosis at a median time frame of 8.35 weeks; 756 CpGs were identified as differentially methylated following COVID-19 diagnosis in blood at an FDR adjusted p-value < 0.05. These CpGs were enriched in the gene body, and the northern and southern shelf regions of genes involved in metabolic pathways. Integrative analysis revealed overlap among genes identified in transcriptional SARS-CoV-2 infection datasets. Principal component-based epigenetic clock estimates of PhenoAge and GrimAge significantly increased in people over 50 following infection by an average of 2.1 and 0.84 years. In contrast, PCPhenoAge significantly decreased in people fewer than 50 following infection by an average of 2.06 years. This observed divergence in epigenetic clocks following COVID-19 was related to age and immune cell-type compositional changes in CD4+ T cells, B cells, granulocytes, plasmablasts, exhausted T cells, and naïve T cells. Complementary longitudinal epigenetic clock analyses of 36 participants prior to and following Pfizer and Moderna mRNA-based COVID-19 vaccination revealed that vaccination significantly reduced principal component-based Horvath epigenetic clock estimates in people over 50 by an average of 3.91 years for those who received Moderna. This reduction in epigenetic clock estimates was significantly related to chronological age and immune cell-type compositional changes in B cells and plasmablasts pre- and post-vaccination. These findings suggest the potential utility of epigenetic clocks as a biomarker of COVID-19 vaccine responses. Future research will need to unravel the significance and durability of short-term changes in epigenetic age related to COVID-19 exposure and mRNA vaccination.

Epigenome-wide DNA methylation in obsessive-compulsive disorder

In adult patients with obsessive-compulsive disorder (OCD), altered DNA methylation has been discerned in several candidate genes, while DNA methylation on an epigenome-wide level has been investigated in only one Chinese study so far. Here, an epigenome-wide association study (EWAS) was performed in a sample of 76 OCD patients of European ancestry (37 women, age ± SD: 33.51 ± 10.92 years) and 76 sex- and age-matched healthy controls for the first time using the Illumina MethylationEPIC BeadChip. After quality control, nine epigenome-wide significant quantitative trait methylation sites (QTMs) and 21 suggestive hits were discerned in the final sample of 68 patients and 68 controls. The top hit (cg24159721) and four other significant QTMs (cg11894324, cg01070250, cg11330075, cg15174812) map to the region of the microRNA 12136 gene (MIR12136). Two additional significant CpG sites (cg05740793, cg20450977) are located in the flanking region of the MT-RNR2 (humanin) like 8 gene (MT-RNRL8), while two further QTMs (cg16267121, cg15890734) map to the regions of the MT-RNR2 (humanin) like 3 (MT-RNRL3) and MT-RNR2 (humanin) like 2 (MT-RNRL2) genes. Provided replication of the present findings in larger samples, the identified QTMs might provide more biological insight into the pathogenesis of OCD and thereby could in the future serve as peripheral epigenetic markers of OCD risk with the potential to inform targeted preventive and therapeutic efforts.

Prenatal exposure to PM10 and changes in DNA methylation and telomere length in cord blood

BACKGROUND

Air pollution exposure in pregnancy can cause molecular level alterations that might influence later disease susceptibility.

OBJECTIVES

We investigated DNA methylation (DNAm) and telomere length (TL) in the cord blood in relation to gestational PM₁₀ exposure and explored potential gestational windows of susceptibility.

METHODS

Cord blood epigenome-wide DNAm (N = 384) and TL (N = 500) were measured in children of the Italian birth cohort Piccolipiù, using the Infinium Methylation EPIC BeadChip and qPCR, respectively. PM₁₀ daily exposure levels, based on maternal residential address, were estimated for different gestational periods using models based on satellite data. Epigenome-wide analysis to identify differentially methylated probes (DMPs) and regions (DMRs) was conducted, followed by a pathway analysis and replication analysis in an second Piccolipiù dataset. Distributed lag models (DLMs) using weekly exposures were used to study the association of PM₁₀ exposure across pregnancy with telomere length, as well as with the DMPs that showed robust associations.

RESULTS

Gestational PM₁₀ exposure was associated with the DNA methylation of more than 250 unique DMPs, most of them identified in early gestation, and 1 DMR. Out of 151 DMPs available in the replication dataset, ten DMPs showed robust associations: eight were associated with exposure during early gestation and 2 with exposure during the whole pregnancy. These exposure windows were supported by the DLM analysis. The PM₁₀ exposure between 15th and 20th gestational week seem to be associated with shorter telomeres at birth, while exposure between 24th and 29th was associated with longer telomeres.

DISCUSSION

The early pregnancy period is a potential critical window during which PM₁₀ exposure can influence cord blood DNA methylation. The results from the TL analysis were consistent with previous findings and merit further exploration in future studies. The study underlines the importance of considering gestational windows outside of the predefined trimesters that may not always overlap with biologically relevant windows of exposure.