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

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.

DNA methylation profiling of central nervous system hemangioblastomas identifies two distinct subgroups

Hemangioblastomas (HBs) of the central nervous system are highly vascular neoplasms that occur sporadically or as a manifestation of von Hippel–Lindau (VHL) disease. Despite their benign nature, HBs are clinically heterogeneous and can be associated with significant morbidity due to mass effects of peritumoral cysts or tumor progression. Underlying molecular factors involved in HB tumor biology remain elusive. We investigated genome‐wide DNA methylation profiles and clinical and histopathological features in a series of 47 HBs from 42 patients, including 28 individuals with VHL disease. Thirty tumors occurred in the cerebellum, 8 in the brainstem and 8 HBs were of spinal location, while 1 HB was located in the cerebrum. Histologically, 12 HBs (26%) belonged to the cellular subtype and exclusively occurred in the cerebellum, whereas 35 HBs were reticular (74%). Unsupervised clustering and dimensionality reduction of DNA methylation profiles revealed two distinct subgroups. Methylation cluster 1 comprised 30 HBs of mainly cerebellar location (29/30, 97%), whereas methylation cluster 2 contained 17 HBs predominantly located in non‐cerebellar compartments (16/17, 94%). The sum of chromosomal regions being affected by copy‐number alterations was significantly higher in methylation cluster 1 compared to cluster 2 (mean 262 vs. 109 Mb, p = 0.001). Of note, loss of chromosome 6 occurred in 9/30 tumors (30%) of methylation cluster 1 and was not observed in cluster 2 tumors (p = 0.01). No relevant methylation differences between sporadic and VHL‐related HBs or cystic and non‐cystic HBs could be detected. Deconvolution of the bulk DNA methylation profiles revealed four methylation components that were associated with the two methylation clusters suggesting cluster‐specific cell‐type compositions. In conclusion, methylation profiling of HBs reveals 2 distinct subgroups that mainly associate with anatomical location, cytogenetic profiles and differences in cell type composition, potentially reflecting different cells of origin.

Response to supraphysiological testosterone is predicted by a distinct androgen receptor cistrome

The androgen receptor (AR) is a master transcription factor that regulates prostate cancer (PC) development and progression. Inhibition of AR signaling by androgen deprivation is the first-line therapy with initial efficacy for advanced and recurrent PC. Paradoxically, supraphysiological levels of testosterone (SPT) also inhibit PC progression. However, as with any therapy, not all patients show a therapeutic benefit, and responses differ widely in magnitude and duration. In this study, we evaluated whether differences in the AR cistrome before treatment can distinguish between SPT-responding (R) and -nonresponding (NR) tumors. We provide the first preclinical evidence to our knowledge that SPT-R tumors exhibit a distinct AR cistrome when compared with SPT-NR tumors, indicating a differential biological role of the AR. We applied an integrated analysis of ChIP-Seq and RNA-Seq to the pretreatment tumors and identified an SPT-R signature that distinguishes R and NR tumors. Because transcriptomes of SPT-treated clinical specimens are not available, we interrogated available castration-resistant PC (CRPC) transcriptomes and showed that the SPT-R signature is associated with improved survival and has the potential to identify patients who would respond to SPT. These findings provide an opportunity to identify the subset of patients with CRPC who would benefit from SPT therapy.

Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health

The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene–environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.

Effect of Prenatal Opioid Exposure on the Human Placental Methylome

Prenatal exposure to addictive drugs can lead to placental epigenetic modifications, but a methylome-wide evaluation of placental DNA methylation changes after prenatal opioid exposure has not yet been performed. Placental tissue samples were collected at delivery from 19 opioid-exposed and 20 unexposed control full-term pregnancies. Placental DNA methylomes were profiled using the Illumina Infinium HumanMethylationEPIC BeadChip. Differentially methylated CpG sites associated with opioid exposure were identified with a linear model using the ‘limma’ R package. To identify differentially methylated regions (DMRs) spanning multiple CpG sites, the ‘DMRcate’ R package was used. The functions of genes mapped by differentially methylated CpG sites and DMRs were further annotated using Enrichr. Differentially methylated CpGs (n = 684, unadjusted p < 0.005 and |∆β| ≥ 0.05) were mapped to 258 genes (including PLD1, MGAM, and ALCS2). Differentially methylated regions (n = 199) were located in 174 genes (including KCNMA1). Enrichment analysis of the top differentially methylated CpG sites and regions indicated disrupted epigenetic regulation of genes involved in synaptic structure, chemical synaptic transmission, and nervous system development. Our findings imply that placental epigenetic changes due to prenatal opioid exposure could result in placental dysfunction, leading to abnormal fetal brain development and the symptoms of opioid withdrawal in neonates.

Alpha-synuclein overexpression induces epigenomic dysregulation of glutamate signaling and locomotor pathways

Parkinson’s disease (PD) is a neurological disorder with complex interindividual etiology that is becoming increasingly prevalent worldwide. Elevated alpha-synuclein levels can increase risk of PD and may influence epigenetic regulation of PD pathways. Here, we report genome-wide DNA methylation and hydroxymethylation alterations associated with overexpression of two PD-linked alpha-synuclein variants (wild-type and A30P) in LUHMES cells differentiated to dopaminergic neurons. Alpha-synuclein altered DNA methylation at thousands of CpGs and DNA hydroxymethylation at hundreds of CpGs in both genotypes, primarily in locomotor behavior and glutamate signaling pathway genes. In some cases, epigenetic changes were associated with transcription. SMITE network analysis incorporating H3K4me1 ChIP-seq to score DNA methylation and hydroxymethylation changes across promoters, enhancers, and gene bodies confirmed epigenetic and transcriptional deregulation of glutamate signaling modules in both genotypes. Our results identify distinct and shared impacts of alpha-synuclein variants on the epigenome, and associate alpha-synuclein with the epigenetic etiology of PD.

Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays

Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionise the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the aetiology of complex disease.

Characterising sex differences of autosomal DNA methylation in whole blood using the Illumina EPIC array

BACKGROUND

Sex differences are known to play a role in disease aetiology, progression and outcome. Previous studies have revealed autosomal epigenetic differences between males and females in some tissues, including differences in DNA methylation patterns. Here, we report for the first time an analysis of autosomal sex differences in DNAme using the Illumina EPIC array in human whole blood by performing a discovery (n = 1171) and validation (n = 2471) analysis.

RESULTS

We identified and validated 396 sex-associated differentially methylated CpG sites (saDMPs) with the majority found to be female-biased CpGs (74%). These saDMP's are enriched in CpG islands and CpG shores and located preferentially at 5'UTRs, 3'UTRs and enhancers. Additionally, we identified 266 significant sex-associated differentially methylated regions overlapping genes, which have previously been shown to exhibit epigenetic sex differences, and novel genes. Transcription factor binding site enrichment revealed enrichment of transcription factors related to critical developmental processes and sex determination such as SRY and ESR1.

CONCLUSION

Our study reports a reliable catalogue of sex-associated CpG sites and elucidates several characteristics of these sites using large-scale discovery and validation data sets. This resource will benefit future studies aiming to investigate sex specific epigenetic signatures and further our understanding of the role of DNA methylation in sex differences in human whole blood.

Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities

Meningiomas are the most common primary intracranial tumors. There are no effective medical therapies for meningioma patients, and new treatments have been encumbered by limited understanding of meningioma biology. Here, we use DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, proteomic and single-cell approaches to show meningiomas are composed of three DNA methylation groups with distinct clinical outcomes, biological drivers and therapeutic vulnerabilities. Merlin-intact meningiomas (34%) have the best outcomes and are distinguished by NF2/Merlin regulation of susceptibility to cytotoxic therapy. Immune-enriched meningiomas (38%) have intermediate outcomes and are distinguished by immune infiltration, HLA expression and lymphatic vessels. Hypermitotic meningiomas (28%) have the worst outcomes and are distinguished by convergent genetic and epigenetic mechanisms driving the cell cycle and resistance to cytotoxic therapy. To translate these findings into clinical practice, we show cytostatic cell cycle inhibitors attenuate meningioma growth in cell culture, organoids, xenografts and patients.

DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD.

Investigating the relationship between melatonin patterns and methylation in circadian genes among day shift and night shift workers

OBJECTIVES

Mechanisms underlying the carcinogenicity of night shift work remain uncertain. One compelling yet understudied cancer mechanism may involve altered DNA methylation in circadian genes due to melatonin secretion patterns. The objective of this study was to explore the relationship between melatonin secretion patterns and circadian gene methylation among day and night shift workers.

METHODS

Female healthcare employees (n=38 day workers, n=36 night shift workers) for whom we had urinary 6-sulfatoxymelatonin secretion data from a previous study were recontacted. New blood samples were collected and used to measure methylation levels at 1150 CpG loci across 22 circadian genes using the Illumina Infinium MethylationEPIC beadchip. Linear regression was used to examine the association between melatonin (acrophase and mesor) and M values for each CpG site (false discovery rate, q=0.2), while testing for effect modification by shift work status.

RESULTS

Among night shift workers, a higher mesor (24 hours of mean production of melatonin) was associated with increased methylation in the body of RORA (q=0.02) and decreased methylation in the putative promoter region of MTNR1A (q=0.03). Later acrophase (ie, time of peak concentration) was associated with increased methylation in the putative promoter region of MTNR1A (q=0.20) and decreased methylation in the body of PER3 (q=0.20). No associations were identified among day workers.

CONCLUSIONS

In conclusion, patterns in melatonin secretion were associated with differential circadian gene methylation among night shift workers. Melatonin and alteration of DNA methylation in circadian genes may be one pathway towards increased cancer risk, although larger-scale studies examining multiple time points are needed.

Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues

Molecular clocks that record cell ancestry mutate too slowly to measure the short-timescale dynamics of cell renewal in adult tissues. Here, we show that fluctuating DNA methylation marks can be used as clocks in cells where ongoing methylation and demethylation cause repeated 'flip-flops' between methylated and unmethylated states. We identify endogenous fluctuating CpG (fCpG) sites using standard methylation arrays and develop a mathematical model to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than the colon, with slower stem cell replacement in the small intestine. Germline APC mutation increased the number of replacements per crypt. In blood, we measured rapid expansion of acute leukemia and slower growth of chronic disease. Thus, the patterns of human somatic cell birth and death are measurable with fluctuating methylation clocks (FMCs).

Epigenome-wide association study of incident type 2 diabetes: a meta-analysis of five prospective European cohorts

AIMS/HYPOTHESIS

Type 2 diabetes is a complex metabolic disease with increasing prevalence worldwide. Improving the prediction of incident type 2 diabetes using epigenetic markers could help tailor prevention efforts to those at the highest risk. The aim of this study was to identify predictive methylation markers for incident type 2 diabetes by combining epigenome-wide association study (EWAS) results from five prospective European cohorts.

METHODS

We conducted a meta-analysis of EWASs in blood collected 7-10 years prior to type 2 diabetes diagnosis. DNA methylation was measured with Illumina Infinium Methylation arrays. A total of 1250 cases and 1950 controls from five longitudinal cohorts were included: Doetinchem, ESTHER, KORA1, KORA2 and EPIC-Norfolk. Associations between DNA methylation and incident type 2 diabetes were examined using robust linear regression with adjustment for potential confounders. Inverse-variance fixed-effects meta-analysis of cohort-level individual CpG EWAS estimates was performed using METAL. The methylGSA R package was used for gene set enrichment analysis. Confirmation of genome-wide significant CpG sites was performed in a cohort of Indian Asians (LOLIPOP, UK).

RESULTS

The meta-analysis identified 76 CpG sites that were differentially methylated in individuals with incident type 2 diabetes compared with control individuals (p values <1.1 × 10-7). Sixty-four out of 76 (84.2%) CpG sites were confirmed by directionally consistent effects and p values <0.05 in an independent cohort of Indian Asians. However, on adjustment for baseline BMI only four CpG sites remained genome-wide significant, and addition of the 76 CpG methylation risk score to a prediction model including established predictors of type 2 diabetes (age, sex, BMI and HbA1c) showed no improvement (AUC 0.757 vs 0.753). Gene set enrichment analysis of the full epigenome-wide results clearly showed enrichment of processes linked to insulin signalling, lipid homeostasis and inflammation.

CONCLUSIONS/INTERPRETATION

By combining results from five European cohorts, and thus significantly increasing study sample size, we identified 76 CpG sites associated with incident type 2 diabetes. Replication of 64 CpGs in an independent cohort of Indian Asians suggests that the association between DNA methylation levels and incident type 2 diabetes is robust and independent of ethnicity. Our data also indicate that BMI partly explains the association between DNA methylation and incident type 2 diabetes. Further studies are required to elucidate the underlying biological mechanisms and to determine potential causal roles of the differentially methylated CpG sites in type 2 diabetes development.

Multi-Omics Integration Reveals Only Minor Long-Term Molecular and Functional Sequelae in Immune Cells of Individuals Recovered From COVID-19

The majority of COVID-19 patients experience mild to moderate disease course and recover within a few weeks. An increasing number of studies characterized the long-term changes in the specific anti-SARS-CoV-2 immune responses, but how COVID-19 shapes the innate and heterologous adaptive immune system after recovery is less well known. To comprehensively investigate the post-SARS-CoV-2 infection sequelae on the immune system, we performed a multi-omics study by integrating single-cell RNA-sequencing, single-cell ATAC-sequencing, genome-wide DNA methylation profiling, and functional validation experiments in 14 convalescent COVID-19 and 15 healthy individuals. We showed that immune responses generally recover without major sequelae after COVID-19. However, subtle differences persist at the transcriptomic level in monocytes, with downregulation of the interferon pathway, while DNA methylation also displays minor changes in convalescent COVID-19 individuals. However, these differences did not affect the cytokine production capacity of PBMCs upon different bacterial, viral, and fungal stimuli, although baseline release of IL-1Ra and IFN-γ was higher in convalescent individuals. In conclusion, we propose that despite minor differences in epigenetic and transcriptional programs, the immune system of convalescent COVID-19 patients largely recovers to the homeostatic level of healthy individuals.

Additive and Interactive Genetically Contextual Effects of HbA1c on cg19693031 Methylation in Type 2 Diabetes

Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG, p = 4.63 × 10−11, Δβ = 13%, effect size = 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10−4, Δβ = 9%, effect size = 0.38 [95% CI = 0.12, 0.56]. Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates (age, sex and BMI) and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic–epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D.

DNA methylation in newborns conceived by assisted reproductive technology

Assisted reproductive technology (ART) may affect fetal development through epigenetic mechanisms as the timing of ART procedures coincides with the extensive epigenetic remodeling occurring between fertilization and embryo implantation. However, it is unknown to what extent ART procedures alter the fetal epigenome. Underlying parental characteristics and subfertility may also play a role. Here we identify differences in cord blood DNA methylation, measured using the Illumina EPIC platform, between 962 ART conceived and 983 naturally conceived singleton newborns. We show that ART conceived newborns display widespread differences in DNA methylation, and overall less methylation across the genome. There were 607 genome-wide differentially methylated CpGs. We find differences in 176 known genes, including genes related to growth, neurodevelopment, and other health outcomes that have been associated with ART. Both fresh and frozen embryo transfer show DNA methylation differences. Associations persist after controlling for parents’ DNA methylation, and are not explained by parental subfertility.

Timing of assisted reproduction technology (ART) procedures coincides with extensive epigenetic remodeling early after conception. Here the authors identify 176 DNA methylation differences in cord blood of newborns conceived with ART. including genes related to growth, neurodevelopment, and cancer.

DNA methylation signatures in cord blood associated with birthweight are enriched for dmCpGs previously associated with maternal hypertension or pre-eclampsia, smoking and folic acid intake

Many epidemiological studies have linked low birthweight to an increased risk of non-communicable diseases (NCDs) in later life, with epigenetic proceseses suggested as an underlying mechanism. Here, we sought to identify neonatal methylation changes associated with birthweight, at the individual CpG and genomic regional level, and whether the birthweight-associated methylation signatures were associated with specific maternal factors. Using the Illumina Human Methylation EPIC array, we assessed DNA methylation in the cord blood of 557 and 483 infants from the UK Pregnancies Better Eating and Activity Trial and Southampton Women's Survey, respectively. Adjusting for gestational age and other covariates, an epigenome-wide association study identified 2911 (FDR≤0.05) and 236 (Bonferroni corrected p ≤ 6.45×10-8) differentially methylated CpGs (dmCpGs), and 1230 differentially methylated regions (DMRs) (Stouffer ≤0.05) associated with birthweight. The top birthweight-associated dmCpG was located within the Homeobox Telomere-Binding Protein 1 (HMBOX1) gene with a 195 g (95%CI: -241, -149 g) decrease in birthweight per 10% increase in methylation, while the top DMR was located within the promoter of corticotropin-releasing hormone-binding protein (CRHBP). Furthermore, the birthweight-related dmCpGs were enriched for dmCpGs previously associated with gestational hypertension/pre-eclampsia (14.51%, p = 1.37×10-255), maternal smoking (7.71%, p = 1.50 x 10-57) and maternal plasma folate levels during pregnancy (0.33%, p = 0.029). The identification of birthweight-associated methylation markers, particularly those connected to specific pregnancy complications and exposures, may provide insights into the developmental pathways that affect birthweight and suggest surrogate markers to identify adverse prenatal exposures for stratifying for individuals at risk of later NCDs.

Prenatal Exposures to Common Phthalates and Prevalent Phthalate Alternatives and Infant DNA Methylation at Birth

Phthalates are a diverse group of chemicals used in consumer products. Because they are so widespread, exposure to these compounds is nearly unavoidable. Recently, growing scientific consensus has suggested that phthalates produce health effects in developing infants and children. These effects may be mediated through mechanisms related to the epigenome, the constellation of mitotically heritable chemical marks and small compounds that guide transcription and translation. The present study examined the relationship between prenatal, first-trimester exposure of seven phthalates and epigenetics in two pregnancy cohorts (n = 262) to investigate sex-specific alterations in infant blood DNA methylation at birth (cord blood or neonatal blood spots). Prenatal exposure to several phthalates was suggestive of association with altered DNA methylation at 4 loci in males (all related to ΣDEHP) and 4 loci in females (1 related to ΣDiNP; 2 related to BBzP; and 1 related to MCPP) at a cutoff of q < 0.2. Additionally, a subset of dyads (n = 79) was used to interrogate the relationships between two compounds increasingly used as substitutions for common phthalates (ΣDINCH and ΣDEHTP) and cord blood DNA methylation. ΣDINCH, but not ΣDEHTP, was suggestive of association with DNA methylation (q < 0.2). Together, these results demonstrate that prenatal exposure to both classically used phthalate metabolites and their newer alternatives is associated with sex-specific infant DNA methylation. Research and regulatory actions regarding this chemical class should consider the developmental health effects of these compounds and aim to avoid regrettable substitution scenarios in the present and future.

Reproducibility of the Infinium methylationEPIC BeadChip assay using low DNA amounts

The Infinium MethylationEPIC BeadChip (EPIC) is a reliable method for measuring the DNA methylation of more than 850,000 CpG positions. In clinical and forensic settings, it is critical to be able to work with low DNA amounts without risking reduced reproducibility. We evaluated the EPIC for a range of DNA amounts using two-fold serial dilutions investigated on two different days. While the β-value distributions were generally unaffected by decreasing DNA amounts, the median squared Pearson’s correlation coefficient (R²) of between-days β-value comparisons decreased from 0.994 (500 ng DNA) to 0.957 (16 ng DNA). The median standard deviation of the β-values was 0.005 and up to 0.017 (median of medians: 0.014) for β-values around 0.6–0.7. With decreasing amounts of DNA from 500 ng to 16 ng, the percentage of probes with standard deviations ≤ 0.1 decreased from 99.9% to 99.4%. This study showed that high reproducibility results are obtained with DNA amounts in the range 125–500 ng DNA, while DNA amounts equal to 63 ng or below gave less reproducible results.

The Long Non-Coding RNA SNHG12 as a Mediator of Carboplatin Resistance in Ovarian Cancer via Epigenetic Mechanisms

Simple Summary

Epithelial ovarian cancer is a lethal malignancy in which recurrence and therapy resistance are the major causes of death. We investigated the transcriptome and DNA methylation profile of ovarian cancer cell lines sensitive and resistant to carboplatin, aiming to identify genes associated with therapy resistance. We focused on long non-coding RNAs (lncRNAs), known as epigenetic regulators of several cellular and biological processes. We found 11 lncRNAs associated with carboplatin resistance, including SNHG12 (small nucleolar RNA host gene 12), also confirmed in an external dataset (The Cancer Genome Atlas). SNHG12 gene silencing increased the sensitivity to carboplatin, giving evidence that this lncRNA contributes to resistance to carboplatin in ovarian cancer cell lines. We also demonstrated that SNHG12 could control the expression of nearby genes probably by altering epigenetic markers and modifying the transcript levels.

Abstract

Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of ovarian cancer (OC). Four carboplatin-sensitive OC cell lines (IGROV1, OVCAR3, OVCAR4, and OVCAR5), their derivative resistant cells, and two inherently carboplatin-resistant cell lines (OVCAR8 and Ovc316) were subjected to RNA sequencing and global DNA methylation analysis. Integrative and cross-validation analyses were performed using external (The Cancer Genome Atlas, TCGA dataset, n = 111 OC samples) and internal datasets (n = 39 OC samples) to identify lncRNA candidates. A total of 4255 differentially expressed genes (DEGs) and 14529 differentially methylated CpG positions (DMPs) were identified comparing sensitive and resistant OC cell lines. The comparison of DEGs between OC cell lines and TCGA-OC dataset revealed 570 genes, including 50 lncRNAs, associated with carboplatin resistance. Eleven lncRNAs showed DMPs, including the SNHG12. Knockdown of SNHG12 in Ovc316 and OVCAR8 cells increased their sensitivity to carboplatin. The results suggest that the lncRNA SNHG12 contributes to carboplatin resistance in OC and is a potential therapeutic target. We demonstrated that SNHG12 is functionally related to epigenetic mechanisms.

Epigenome-wide association study and epigenetic age acceleration associated with cigarette smoking among Costa Rican adults

Smoking-associated DNA methylation (DNAm) signatures are reproducible among studies of mostly European descent, with mixed evidence if smoking accelerates epigenetic aging and its relationship to longevity. We evaluated smoking-associated DNAm signatures in the Costa Rican Study on Longevity and Healthy Aging (CRELES), including participants from the high longevity region of Nicoya. We measured genome-wide DNAm in leukocytes, tested Epigenetic Age Acceleration (EAA) from five clocks and estimates of telomere length (DNAmTL), and examined effect modification by the high longevity region. 489 participants had a mean (SD) age of 79.4 (10.8) years, and 18% were from Nicoya. Overall, 7.6% reported currently smoking, 35% were former smokers, and 57.4% never smoked. 46 CpGs and five regions (e.g. AHRR, SCARNA6/SNORD39, SNORA20, and F2RL3) were differentially methylated for current smokers. Former smokers had increased Horvath’s EAA (1.69-years; 95% CI 0.72, 2.67), Hannum’s EAA (0.77-years; 95% CI 0.01, 1.52), GrimAge (2.34-years; 95% CI1.66, 3.02), extrinsic EAA (1.27-years; 95% CI 0.34, 2.21), intrinsic EAA (1.03-years; 95% CI 0.12, 1.94) and shorter DNAmTL (− 0.04-kb; 95% CI − 0.08, − 0.01) relative to non-smokers. There was no evidence of effect modification among residents of Nicoya. Our findings recapitulate previously reported and novel smoking-associated DNAm changes in a Latino cohort.

SEEMLIS: a flexible semi-automated method for enrichment of methylated DNA from low-input samples

Background

DNA methylation alterations have emerged as hallmarks of cancer and have been proposed as screening, prognostic, and predictive biomarkers. Traditional approaches for methylation analysis have relied on bisulfite conversion of DNA, which can damage DNA and is not suitable for targeted gene analysis in low-input samples. Here, we have adapted methyl-CpG-binding domain protein 2 (MBD2)-based DNA enrichment for use on a semi-automated exclusion-based sample preparation (ESP) platform for robust and scalable enrichment of methylated DNA from low-input samples, called SEEMLIS.

Results

We show that combining methylation-sensitive enzyme digestion with ESP-based MBD2 enrichment allows for single gene analysis with high sensitivity for GSTP1 in highly impure, heterogenous samples. We also show that ESP-based MBD2 enrichment coupled with targeted pre-amplification allows for analysis of multiple genes with sensitivities approaching the single cell level in pure samples for GSTP1 and RASSF1 and sensitivity down to 14 cells for these genes in highly impure samples. Finally, we demonstrate the potential clinical utility of SEEMLIS by successful detection of methylated gene signatures in circulating tumor cells (CTCs) from patients with prostate cancer with varying CTC number and sample purity.

Conclusions

SEEMLIS is a robust assay for targeted DNA methylation analysis in low-input samples, with flexibility at multiple steps. We demonstrate the feasibility of this assay to analyze DNA methylation in prostate cancer cells using CTCs from patients with prostate cancer as a real-world example of a low-input analyte of clinical importance. In summary, this novel assay provides a platform for determining methylation signatures in rare cell populations with broad implications for research as well as clinical applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01252-4.

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl with complete documentation and example analyses.

Triiodothyronine (T3) Induces Limited Transcriptional and DNA Methylation Reprogramming in Human Monocytes

Thyroid hormones have immunomodulatory roles, but their effects on the transcriptome and epigenome of innate immune cell types remain unexplored. In this study, we investigate the effects of triiodothyronine (T3) on the transcriptome and methylome of human monocytes in vitro, both in resting and lipopolysaccharide (LPS)-stimulated conditions. In resting monocytes, 5 µM T3 affected the expression of a small number of monocyte-to-macrophage differentiation-associated genes, including TLR4 (p-value < 0.05, expression fold change >1.5). T3 attenuated a small proportion of monocyte-to-macrophage differentiation-associated DNA methylation changes, while specifically inducing DNA methylation changes at several hundred differentially methylated CpG probes (DMPs) (p-value < 0.05, Δβ > 0.05). In LPS-stimulated monocytes, the presence of T3 attenuated the effect of 27% of LPS-induced DMPs (p-value < 0.05, Δβ > 0.05). Interestingly, co-stimulation with T3 + LPS induced a unique DNA methylation signature that was not observed in the LPS-only or T3-only exposure groups. Our results suggest that T3 induces limited transcriptional and DNA methylation remodeling in genes enriched in metabolism and immune processes and alters the normal in vitro LPS response. The overlap between differentially expressed genes and genes associated with DMPs was minimal; thus, other epigenetic mechanisms may underpin the expression changes. This research provides insight into the complex interplay between thyroid hormones, epigenetic remodeling, and transcriptional dynamics in monocytes.

Prenatal NeuN+ neurons of Down syndrome display aberrant integrative DNA methylation and gene expression profiles

Aim: To detect expression quantitative trait methylation (eQTM) loci within the cerebrum of prenatal Down syndrome (DS) and controls. Material & methods: DNA methylation gene expression profiles were acquired from NeuN+ nuclei, obtained from cerebrum sections of DS and controls. Linear regression models were applied to both datasets and were subsequently applied in an integrative analysis model to detect DS-associated eQTM loci. Results & conclusion: Widespread aberrant DNA methylation and gene expression were observed in DS. A substantial number of differentially methylated loci were replicated according to a previously reported study. Subsequent integrative analyses (eQTM) yielded numerous associated DS loci. the authors associated DNA methylation, gene expression and eQTM loci with DS that may underlie particular DS phenotypical characteristics.

Accelerated epigenetic aging and inflammatory/immunological profile (ipAGE) in patients with chronic kidney disease

Chronic kidney disease (CKD) is defined by a reduced estimated glomerular filtration rate (eGFR). This failure can be related to a phenotype of accelerated aging. In this work, we considered 76 patients with end-stage renal disease (ESRD) and 83 healthy controls. We concomitantly evaluated for the first time two measures that can be informative of the rate of aging, i.e., whole blood DNA methylation using the Illumina Infinium EPIC array and plasma levels of a selection of inflammatory/immunological proteins using multiplex immunoassays. First of all, we demonstrated accelerated aging in terms of the most common epigenetic age estimators in CKD patients. Moreover, we developed a new clock/predictor of age based on the inflammatory/immunological profile (ipAGE) and identified the inflammatory/immunological biomarkers differentially expressed between cases and controls. IpAGE appeared to be more sensitive than epigenetic clocks in quantifying the accelerated aging phenotype of ESRD patients. Interestingly, we did not find any correlation between the age acceleration evaluated according to the epigenetic clocks and ipAGE in either the ESRD group or the control group. On the whole, our data show a consistent accelerated aging phenotype in ESRD patients, which is better appreciated by quantifying the underlying inflammatory processes (inflammaging) by ipAGE than by using epigenetic clocks.

Risks and rewards of big-data in epigenomics research: an interview with Melanie Ehrlich

Melanie Ehrlich, PhD, is a professor in the Tulane Cancer Center, the Tulane Center for Medical Bioinformatics and Genomics and the Hayward Human Genetics Program at Tulane Medical School, New Orleans, LA. She obtained her PhD in molecular biology in 1971 from the State University of New York at Stony Brook and completed postdoctoral research at Albert Einstein College of Medicine in 1972. She has been working on various aspects of epigenetics, starting with DNA methylation, since 1973. Her group made many first findings about DNA methylation (see below). For example, in 1982 and 1983, in collaboration with Charles Gehrke at the University of Missouri, she was the first to report tissue-specific and cancer-specific differences in overall DNA methylation in humans. In 1985, Xian-Yang Zhang and Richard Wang in her lab discovered a class of human DNA sequences specifically hypomethylated in sperm. In 1998, her group was the first to describe extensive losses of DNA methylation in pericentromeric and centromeric DNA repeats in human cancer. Her lab's many publications on the prevalence of both DNA hypermethylation and hypomethylation in the same cancers brought needed balance to our understanding of the epigenetics of cancer and to its clinical implications [1]. Besides working on cancer epigenetics, her research group has helped elucidate cytogenetic and gene expression abnormalities in the immunodeficiency, centromeric and facial anomalies (ICF) syndrome, a rare recessive disease often caused by mutations in DNMT3B. Her group also studied the epigenetics and transcriptomics of facioscapulohumeral muscular dystrophy (FSHD), whose disease locus is a tandem 3.3-kb repeat at subtelomeric 4q (that happens to be hypomethylated in ICF DNA [2]). Her study of FSHD has taken her in the direction of muscle (skeletal muscle, heart and aorta) epigenetics [3-6]. Recently, she has led research that applies epigenetics much more rigorously than usual to the evaluation of genetic variants from genome-wide association studies (GWAS) of osteoporosis and obesity. In continued collaboration with Sriharsa Pradhan at New England Biolabs and Michelle Lacey at Tulane University, she has compared 5-hydroxymethylcytosine and 5-methylcytosine clustering in various human tissues [7] and is studying myoblast methylomes that they generated by a new high-resolution enzymatic technique (enzymatic methyl-seq).

Gestational Perfluoroalkyl Substance Exposure and DNA Methylation at Birth and 12 Years of Age: A Longitudinal Epigenome-Wide Association Study

BACKGROUND

DNA methylation alterations may underlie associations between gestational perfluoroalkyl substances (PFAS) exposure and later-life health outcomes. To the best of our knowledge, no longitudinal studies have examined the associations between gestational PFAS and DNA methylation.

OBJECTIVES

We examined associations of gestational PFAS exposure with longitudinal DNA methylation measures at birth and in adolescence using the Health Outcomes and Measures of the Environment (HOME) Study (2003-2006; Cincinnati, Ohio).

METHODS

We quantified serum concentrations of perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoate (PFNA), and perfluorohexane sulfonate (PFHxS) in mothers during pregnancy. We measured DNA methylation in cord blood (n=266) and peripheral leukocytes at 12 years of age (n=160) using the Illumina HumanMethylation EPIC BeadChip. We analyzed associations between log2-transformed PFAS concentrations and repeated DNA methylation measures using linear regression with generalized estimating equations. We included interaction terms between children's age and gestational PFAS. We performed Gene Ontology enrichment analysis to identify molecular pathways. We used Project Viva (1999-2002; Boston, Massachusetts) to replicate significant associations.

RESULTS

After adjusting for covariates, 435 cytosine-guanine dinucleotide (CpG) sites were associated with PFAS (false discovery rate, q<0.05). Specifically, we identified 2 CpGs for PFOS, 12 for PFOA, 8 for PFHxS, and 413 for PFNA; none overlapped. Among these, 2 CpGs for PFOA and 4 for PFNA were replicated in Project Viva. Some of the PFAS-associated CpG sites annotated to gene regions related to cancers, cognitive health, cardiovascular disease, and kidney function. We found little evidence that the associations between PFAS and DNA methylation differed by children's age.

DISCUSSION

In these longitudinal data, PFAS biomarkers were associated with differences in several CpGs at birth and at 12 years of age in or near genes linked to some PFAS-associated health outcomes. Future studies should examine whether DNA methylation mediates associations between gestational PFAS exposure and health. https://doi.org/10.1289/EHP10118.

Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies

OBJECTIVE

Maternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring, a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations.

RESEARCH DESIGN AND METHODS

To address this hypothesis, we conducted fixed-effects meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmaximum = 3,503), insulin (Nmaximum = 2,062), and area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (Nmaximum = 1,505). We performed lookup analyses for identified cytosine-guanine dinucleotides (CpGs) in independent observational cohorts to examine associations between DNAm and cardiometabolic traits as well as tissue-specific gene expression.

RESULTS

Greater maternal AUCgluc was associated with lower cord blood DNAm at neighboring CpGs cg26974062 (β [SE] -0.013 [2.1 × 10-3], P value corrected for false discovery rate [PFDR] = 5.1 × 10-3) and cg02988288 (β [SE]-0.013 [2.3 × 10-3], PFDR = 0.031) in TXNIP. These associations were attenuated in women with GDM. Lower blood DNAm at these two CpGs near TXNIP was associated with multiple metabolic traits later in life, including type 2 diabetes. TXNIP DNAm in liver biopsies was associated with hepatic expression of TXNIP. We observed little evidence of associations between either maternal glucose or insulin and cord blood DNAm.

CONCLUSIONS

Maternal hyperglycemia, as reflected by AUCgluc, was associated with lower cord blood DNAm at TXNIP. Associations between DNAm at these CpGs and metabolic traits in subsequent lookup analyses suggest that these may be candidate loci to investigate in future causal and mediation analyses.

Characterization of Early Peripheral Immune Responses in Patients with Sepsis and Septic Shock

(1) Background: Sepsis is a life-threatening condition caused by an abnormal host response to infection that produces altered physiological responses causing tissue damage and can result in organ dysfunction and, in some cases, death. Although sepsis is characterized by a malfunction of the immune system leading to an altered immune response and immunosuppression, the high complexity of the pathophysiology of sepsis requires further investigation to characterize the immune response in sepsis and septic shock. (2) Methods: This study analyzes the immune-related responses occurring during the early stages of sepsis by comparing the amounts of cytokines, immune modulators and other endothelial mediators of a control group and three types of severe patients: critically ill non-septic patients, septic and septic shock patients. (3) Results: We showed that in the early stages of sepsis the innate immune system attempts to counteract infection, probably via neutrophils. Conversely, the adaptive immune system is not yet fully activated, either in septic or in septic shock patients. In addition, immunosuppressive responses and pro-coagulation signals are active in patients with septic shock. (4) Conclusions: The highest levels of IL-6 and pyroptosis-related cytokines (IL-18 and IL-1α) were found in septic shock patients, which correlated with D-dimer. Moreover, endothelial function may be affected as shown by the overexpression of adhesion molecules such as s-ICAM1 and E-Selectin during septic shock.

How to Translate DNA Methylation Biomarkers Into Clinical Practice

Recent advances in sequencing technologies provide unprecedented opportunities for epigenetic biomarker development. Particularly the DNA methylation pattern—which is modified at specific sites in the genome during cellular differentiation, aging, and disease—holds high hopes for a wide variety of diagnostic applications. While many epigenetic biomarkers have been described, only very few of them have so far been successfully translated into clinical practice and almost exclusively in the field of oncology. This discrepancy might be attributed to the different demands of either publishing a new finding or establishing a standardized and approved diagnostic procedure. This is exemplified for epigenetic leukocyte counts and epigenetic age-predictions. To ease later clinical translation, the following hallmarks should already be taken into consideration when designing epigenetic biomarkers: 1) Identification of best genomic regions, 2) pre-analytical processing, 3) accuracy of DNA methylation measurements, 4) identification of confounding parameters, 5) accreditation as diagnostic procedure, 6) standardized data analysis, 7) turnaround time, and 8) costs and customer requirements. While the initial selection of relevant genomic regions is usually performed on genome wide DNA methylation profiles, it might be advantageous to subsequently establish targeted assays that focus on specific genomic regions. Development of an epigenetic biomarker for clinical application is a long and cumbersome process that is only initiated with the identification of an epigenetic signature.

Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research

PURPOSE OF REVIEW

Acceleration of biological processes of aging is hypothesized to drive excess morbidity and mortality in socially disadvantaged populations. DNA methylation measures of biological aging provide tools for testing this hypothesis.

RECENT FINDINGS

Next-generation DNA methylation measures of biological aging developed to predict mortality risk and physiological decline are more predictive of morbidity and mortality than the original epigenetic clocks developed to predict chronological age. These new measures show consistent evidence of more advanced and faster biological aging in people exposed to socioeconomic disadvantage and may be able to record the emergence of socially determined health inequalities as early as childhood. Next-generation DNA methylation measures of biological aging also indicate race/ethnic disparities in biological aging. More research is needed on these measures in samples of non-Western and non-White populations. New DNA methylation measures of biological aging open opportunities for refining inference about the causes of social disparities in health and devising policies to eliminate them. Further refining measures of biological aging by including more diversity in samples used for measurement development is a critical priority for the field.

Gender-affirming hormone therapy induces specific DNA methylation changes in blood

Background

DNA methylation is an epigenetic mark that is influenced by underlying genetic profile, environment, and ageing. In addition to X-linked DNA methylation, sex-specific methylation patterns are widespread across autosomal chromosomes and can be present from birth or arise over time. In individuals where gender identity and sex assigned at birth are markedly incongruent, as in the case of transgender people, feminization or masculinization may be sought through gender-affirming hormone therapy (GAHT). GAHT is a cornerstone of transgender care, yet no studies to date have investigated its effect on genome-wide methylation. We profiled genome-wide DNA methylation in blood of transgender women (n = 13) and transgender men (n = 13) before and during GAHT (6 months and 12 months into feminizing or masculinizing hormone therapy).

Results

We identified several thousand differentially methylated CpG sites (DMPs) (Δβ ≥ 0.02, unadjusted p value < 0.05) and several differentially methylated regions (DMRs) in both people undergoing feminizing and masculinizing GAHT, the vast majority of which were progressive changes over time. X chromosome and sex-specific autosomal DNA methylation patterns established in early development are largely refractory to change in association with GAHT, with only 3% affected (Δβ ≥ 0.02, unadjusted p value < 0.05). The small number of sex-specific DMPs that were affected by GAHT were those that become sex-specific during the lifetime, known as sex-and-age DMPs, including DMRs in PRR4 and VMP1 genes. The GAHT-induced changes at these sex-associated probes consistently demonstrated a shift towards the methylation signature of the GAHT-naïve opposite sex, and we observed enrichment of previously reported adolescence-associated methylation changes.

Conclusion

We provide evidence for GAHT inducing a unique blood methylation signature in transgender people. This study advances our understanding of the complex interplay between sex hormones, sex chromosomes, and DNA methylation in the context of immunity. We highlight the need to broaden the field of ‘sex-specific’ immunity beyond cisgender males and cisgender females, as transgender people on GAHT exhibit a unique molecular profile.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01236-4.

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.

Methods to probe DNA methylation in the majority of non-human mammals are lacking. Here the authors developed a Mammalian Methylation Array that includes 36k well-conserved CpGs in mammals which will facilitate cross-species comparisons. They annotate the conserved CpGs in > 200 species. The array allows one to measure methylation in all mammalian species including unsequenced ones.

The applications of DNA methylation as a biomarker in kidney transplantation: a systematic review

Background

Although kidney transplantation improves patient survival and quality of life, long-term results are hampered by both immune- and non-immune-mediated complications. Current biomarkers of post-transplant complications, such as allograft rejection, chronic renal allograft dysfunction, and cutaneous squamous cell carcinoma, have a suboptimal predictive value. DNA methylation is an epigenetic modification that directly affects gene expression and plays an important role in processes such as ischemia/reperfusion injury, fibrosis, and alloreactive immune response. Novel techniques can quickly assess the DNA methylation status of multiple loci in different cell types, allowing a deep and interesting study of cells’ activity and function. Therefore, DNA methylation has the potential to become an important biomarker for prediction and monitoring in kidney transplantation.

Purpose of the study

The aim of this study was to evaluate the role of DNA methylation as a potential biomarker of graft survival and complications development in kidney transplantation.

Material and Methods

A systematic review of several databases has been conducted. The Newcastle–Ottawa scale and the Jadad scale have been used to assess the risk of bias for observational and randomized studies, respectively.

Results

Twenty articles reporting on DNA methylation as a biomarker for kidney transplantation were included, all using DNA methylation for prediction and monitoring. DNA methylation pattern alterations in cells isolated from different tissues, such as kidney biopsies, urine, and blood, have been associated with ischemia–reperfusion injury and chronic renal allograft dysfunction. These alterations occurred in different and specific loci. DNA methylation status has also proved to be important for immune response modulation, having a crucial role in regulatory T cell definition and activity. Research also focused on a better understanding of the role of this epigenetic modification assessment for regulatory T cells isolation and expansion for future tolerance induction-oriented therapies.

Conclusions

Studies included in this review are heterogeneous in study design, biological samples, and outcome. More coordinated investigations are needed to affirm DNA methylation as a clinically relevant biomarker important for prevention, monitoring, and intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01241-7.

Placental genomics mediates genetic associations with complex health traits and disease

As the master regulator in utero, the placenta is core to the Developmental Origins of Health and Disease (DOHaD) hypothesis but is historically understudied. To identify placental gene-trait associations (GTAs) across the life course, we perform distal mediator-enriched transcriptome-wide association studies (TWAS) for 40 traits, integrating placental multi-omics from the Extremely Low Gestational Age Newborn Study. At $$P \; < \; 2.5\times {10}^{-6}$$ P < 2.5 × 10 − 6 , we detect 248 GTAs, mostly for neonatal and metabolic traits, across 176 genes, enriched for cell growth and immunological pathways. In aggregate, genetic effects mediated by placental expression significantly explain 4 early-life traits but no later-in-life traits. 89 GTAs show significant mediation through distal genetic variants, identifying hypotheses for distal regulation of GTAs. Investigation of one hypothesis in human placenta-derived choriocarcinoma cells reveal that knockdown of mediator gene EPS15 upregulates predicted targets SPATA13 and FAM214A, both associated with waist-hip ratio in TWAS, and multiple genes involved in metabolic pathways. These results suggest profound health impacts of placental genomic regulation in developmental programming across the life course.

Does DNA methylation mediate the association of age at puberty with FVC or FEV1?

Background

Age of pubertal onset is associated with lung function in adulthood. However, the underlying role of epigenetics as a mediator of this association remains unknown.

Methods

DNA methylation (DNAm) in peripheral blood was measured at age 18 years in the Isle of Wight birth cohort (IOWBC) along with data on age of pubertal events, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV₁) at 26 years. Structural equation models were applied to examine mediation effects of DNAm on the association of age at pubertal events with FVC and FEV₁. Findings were further tested in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort.

Results

In the IOWBC, for females, 21 cytosine-phosphate-guanine sites (CpGs) were shown to mediate the association of age at puberty with FVC or FEV₁ at 26 years (p<0.05). In males, DNAm at 20 CpGs was found to mediate the association of age at puberty with FVC (p<0.05). At almost all these CpGs, indirect effects (effects of age at pubertal events on FVC or FEV₁via DNAm) contributed a smaller portion to the total effects compared to direct effects (e.g. at cg08680129, ∼22% of the estimated total effect of age at menarche on FVC at age 26 was contributed by an indirect effect). Among the IOWBC-discovered CpGs available in ALSPAC, none of them was replicated in ALSPAC (p>0.05).

Conclusions

Our findings suggest that post-adolescence DNAm in peripheral blood is likely not to mediate the association of age at pubertal onset with young adulthood FVC or FEV₁.

The association between age at pubertal onset and lung function parameters FVC or FEV₁ in young adulthood is not likely to be mediated by DNA methylation in peripheral bloodhttps://bit.ly/31G8hDi