An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies

Fast matrix completion in epigenetic methylation studies with informative covariates

DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, the issue of missing values is an important one, and appropriate imputation techniques are important in avoiding an unnecessary sample size reduction as well as to optimally leverage the information collected. We consider the case where relatively few samples are processed via an expensive high-density whole genome bisulfite sequencing (WGBS) strategy and a larger number of samples is processed using more affordable low-density, array-based technologies. In such cases, one can impute the low-coverage (array-based) methylation data using the high-density information provided by the WGBS samples. In this paper, we propose an efficient Linear Model of Coregionalisation with informative Covariates (LMCC) to predict missing values based on observed values and covariates. Our model assumes that at each site, the methylation vector of all samples is linked to the set of fixed factors (covariates) and a set of latent factors. Furthermore, we exploit the functional nature of the data and the spatial correlation across sites by assuming some Gaussian processes on the fixed and latent coefficient vectors, respectively. Our simulations show that the use of covariates can significantly improve the accuracy of imputed values, especially in cases where missing data contain some relevant information about the explanatory variable. We also showed that our proposed model is particularly efficient when the number of columns is much greater than the number of rows-which is usually the case in methylation data analysis. Finally, we apply and compare our proposed method with alternative approaches on two real methylation datasets, showing how covariates such as cell type, tissue type or age can enhance the accuracy of imputed values.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

DNA Methylation Changes in Blood Cells of Fibromyalgia and Chronic Fatigue Syndrome Patients

Purpose

Fibromyalgia (FM) and Chronic Fatigue Syndrome (CFS) affect 0.4% and 1% of society, respectively, and the prevalence of these pain syndromes is increasing. To date, no strong association between these syndromes and the genetic background of affected individuals has been shown. Therefore, it is plausible that epigenetic changes might play a role in the development of these syndromes.

Patients and Methods

Three previous studies have attempted to elaborate the involvement of genome-wide methylation changes in blood cells in the development of fibromyalgia and chronic fatigue syndrome. These studies included 22 patients with fibromyalgia and 127 patients with CFS, and the results of the studies were largely discrepant. Contradicting results of those studies may be attributed to differences in the omics data analysis approaches used in each study. We reanalyzed the data collected in these studies using an updated and coherent data-analysis framework.

Results

Overall, the methylation changes that we observed overlapped with previous results only to some extent. However, the gene set enrichment analyses based on genes annotated to methylation changes identified in each of the analyzed datasets were surprisingly coherent and uniformly associated with the physiological processes that, when affected, may result in symptoms characteristic of fibromyalgia and chronic fatigue syndrome.

Conclusion

Methylomes of the blood cells of patients with FM and CFS in three independent studies have shown methylation changes that appear to be implicated in the pathogenesis of these syndromes.

Peripheral blood DNA methylation and neuroanatomical responses to HDACi treatment that rescues neurological deficits in a Kabuki syndrome mouse model

BACKGROUND

Recent findings from studies of mouse models of Mendelian disorders of epigenetic machinery strongly support the potential for postnatal therapies to improve neurobehavioral and cognitive deficits. As several of these therapies move into human clinical trials, the search for biomarkers of treatment efficacy is a priority. A potential postnatal treatment of Kabuki syndrome type 1 (KS1), caused by pathogenic variants in KMT2D encoding a histone-lysine methyltransferase, has emerged using a mouse model of KS1 (Kmt2d+/βGeo). In this mouse model, hippocampal memory deficits are ameliorated following treatment with the histone deacetylase inhibitor (HDACi), AR-42. Here, we investigate the effect of both Kmt2d+/βGeo genotype and AR-42 treatment on neuroanatomy and on DNA methylation (DNAm) in peripheral blood. While peripheral blood may not be considered a "primary tissue" with respect to understanding the pathophysiology of neurodevelopmental disorders, it has the potential to serve as an accessible biomarker of disease- and treatment-related changes in the brain.

METHODS

Half of the KS1 and wildtype mice were treated with 14 days of AR-42. Following treatment, fixed brain samples were imaged using MRI to calculate regional volumes. Blood was assayed for genome-wide DNAm at over 285,000 CpG sites using the Illumina Infinium Mouse Methylation array. DNAm patterns and brain volumes were analyzed in the four groups of animals: wildtype untreated, wildtype AR-42 treated, KS1 untreated and KS1 AR-42 treated.

RESULTS

We defined a DNAm signature in the blood of KS1 mice, that overlapped with the human KS1 DNAm signature. We also found a striking 10% decrease in total brain volume in untreated KS1 mice compared to untreated wildtype, which correlated with DNAm levels in a subset KS1 signature sites, suggesting that disease severity may be reflected in blood DNAm. Treatment with AR-42 ameliorated DNAm aberrations in KS1 mice at a small number of signature sites.

CONCLUSIONS

As this treatment impacts both neurological deficits and blood DNAm in mice, future KS clinical trials in humans could be used to assess blood DNAm as an early biomarker of therapeutic efficacy.

Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes

DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.

Epigenetic Regulation of Inflammatory Mechanisms and a Psychological Symptom Cluster in Patients Receiving Chemotherapy

BACKGROUND

A psychological symptom cluster is the most common cluster identified in oncology patients. Although inflammatory mechanisms are hypothesized to underlie this cluster, epigenetic contributions are unknown.

OBJECTIVES

This study's purpose was to evaluate associations between the occurrence of a psychological symptom cluster and levels of DNA methylation for inflammatory genes in a heterogeneous sample of patients with cancer receiving chemotherapy.

METHODS

Prior to their second or third cycle of chemotherapy, 1,071 patients reported the occurrence of 38 symptoms using the Memorial Symptom Assessment Scale. A psychological cluster was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using Illumina Infinium 450K and EPIC microarrays. Expression-associated CpG (eCpG) loci in the promoter region of 114 inflammatory genes on the 450K and 112 genes on the EPIC microarray were evaluated for associations with the psychological cluster. Robust rank aggregation was used to identify differentially methylated genes across both samples. Significance was assessed using a false discovery rate of 0.05 under the Benjamini-Hochberg procedure.

RESULTS

Cluster of differentiation 40 ( CD40 ) was differentially methylated across both samples. All six promoter eCpGs for CD40 that were identified across both samples were hypomethylated in the psychological cluster group.

CONCLUSIONS

This study is the first to suggest associations between a psychological symptom cluster and differential DNA methylation of a gene involved in tissue inflammation and cell-mediated immunity. Our findings suggest that increased CD40 expression through hypomethylation of promoter eCpG loci is involved in the occurrence of a psychological symptom cluster in patients receiving chemotherapy. These findings suggest a direction for mechanistic studies.

Gastrointestinal Symptom Cluster is Associated With Epigenetic Regulation of Lymphotoxin Beta in Oncology Patients Receiving Chemotherapy

OBJECTIVES

While the gastrointestinal symptom cluster (GISC) is common in patients receiving chemotherapy, limited information is available on its underlying mechanism(s). Emerging evidence suggests a role for inflammatory processes through the actions of the nuclear factor kappa B (NF-κB) signaling pathway. This study evaluated for associations between a GISC and levels of DNA methylation for genes within this pathway.

METHODS

Prior to their second or third cycle of chemotherapy, 1071 outpatients reported symptom occurrence using the Memorial Symptom Assessment Scale. A GISC was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using EPIC (n = 925) and 450K (n = 146) microarrays. Trans expression-associated CpG (eCpG) loci for 56 NF-κB signaling pathway genes were evaluated. Loci significance were assessed using an exploratory false discovery rate (FDR) of 25% for the EPIC sample. For the validation assessment using the 450K sample, significance was assessed at an unadjusted p-value of 0.05.

RESULTS

For the EPIC sample, the GISC was associated with increased expression of lymphotoxin beta (LTB) at one differentially methylated trans eCpG locus (cg03171795; FDR = 0.168). This association was not validated in the 450K sample.

CONCLUSIONS

This study is the first to identify an association between a GISC and epigenetic regulation of a gene that is involved in the initiation of gastrointestinal immune responses. Findings suggest that increased LTB expression by hypermethylation of a trans eCpG locus is involved in the occurrence of this cluster in patients receiving chemotherapy. LTB may be a potential therapeutic target for this common cluster.

Peripheral Blood DNA Methylation-Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression: Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project

OBJECTIVE

The lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in OA clinical research. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression.

METHODS

Genome-wide buffy coat DNA methylation patterns from 554 individuals from the Osteoarthritis Biomarkers Consortium (OABC) were determined using Illumina Infinium MethylationEPIC 850K arrays. Data were divided into model development and validation sets, and machine learning models were trained to classify future OA progression by knee pain, radiographic imaging, knee pain plus radiographic imaging, and any progression (pain, radiographic, or both). Parsimonious models using the top 13 CpG sites most frequently selected during development were tested on independent samples from participants in the Johnston County Osteoarthritis (JoCo OA) Project (n = 128) and a previously published Osteoarthritis Initiative (OAI) data set (n = 55).

RESULTS

Full models accurately classified future radiographic-only progression (mean ± SEM accuracy 87 ± 0.8%, area under the curve [AUC] 0.94 ± 0.004), pain-only progression (accuracy 89 ± 0.9%, AUC 0.97 ± 0.004), pain plus radiographic progression (accuracy 72 ± 0.7%, AUC 0.79 ± 0.006), and any progression (accuracy 78 ± 0.4%, AUC 0.86 ± 0.004). Pain-only and radiographic-only progressors were not distinguishable (mean ± SEM accuracy 58 ± 1%, AUC 0.62 ± 0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in the OABC cohort and in both validation cohorts (mean ± SEM accuracy 80 ± 0.3%, AUC 0.88 ± 0.003 [using JoCo OA Project data], accuracy 80 ± 0.8%, AUC 0.89 ± 0.002 [using previous OAI data]).

CONCLUSION

Our data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further studies should focus on evaluating the pathophysiologic consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.

Childhood Trauma and Epigenetics: State of the Science and Future

PURPOSE OF REVIEW

There is a great deal of interest regarding the biological embedding of childhood trauma and social exposures through epigenetic mechanisms, including DNA methylation (DNAm), but a comprehensive understanding has been hindered by issues of limited reproducibility between studies. This review presents a summary of the literature on childhood trauma and DNAm, highlights issues in the field, and proposes some potential solutions.

RECENT FINDINGS

Investigations of the associations between DNAm and childhood trauma are commonly performed using candidate gene approaches, specifically involving genes related to neurological and stress pathways. Childhood trauma is defined in a wide range of ways in several societal contexts. However, although variations in DNAm are frequently found in stress-related genes, unsupervised epigenome-wide association studies (EWAS) have shown limited reproducibility both between studies and in relating these changes to exposures. The reproducibility of childhood trauma DNAm studies, and the field of social epigenetics in general, may be improved by increasing sample sizes, standardizing variables, making use of effect size thresholds, collecting longitudinal and intervention samples, appropriately accounting for known confounding factors, and applying causal analysis wherever possible, such as "two-step epigenetic Mendelian randomization."

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

Sensation-seeking-related DNA methylation and the development of delinquency: A longitudinal epigenome-wide study

Heightened sensation-seeking is related to the development of delinquency. Moreover, sensation-seeking, or biological correlates of sensation-seeking, are suggested as factors linking victimization to delinquency. Here, we focused on epigenetic correlates of sensation-seeking. First, we identified DNA methylation (DNAm) patterns related to sensation-seeking. Second, we investigated the association between sensation-seeking related DNAm and the development of delinquency. Third, we examined whether victimization was related to sensation-seeking related DNAm and the development of delinquency. Participants (N = 905; 49% boys) came from the Avon Longitudinal Study of Parents and Children. DNAm was assessed at birth, age 7 and age 15-17. Sensation-seeking (self-reports) was assessed at age 11 and 14. Delinquency (self-reports) was assessed at age 17-19. Sensation-seeking epigenome-wide association study revealed that no probes reached the critical significance level. However, 20 differential methylated probes reached marginal significance. With these 20 suggestive sites, a sensation-seeking cumulative DNAm risk score was created. Results showed that this DNAm risk score at age 15-17 was related to delinquency at age 17-19. Moreover, an indirect effect of victimization to delinquency via DNAm was found. Sensation-seeking related DNAm is a potential biological correlate that can help to understand the development of delinquency, including how victimization might be associated with adolescent delinquency.

Large-scale placenta DNA methylation integrated analysis reveals fetal sex-specific differentially methylated CpG sites and regions

Although male–female differences in placental structure and function have been observed, little is understood about their molecular underpinnings. Here, we present a mega-analysis of 14 publicly available placenta DNA methylation (DNAm) microarray datasets to identify individual CpGs and regions associated with fetal sex. In the discovery dataset of placentas from full term pregnancies (N = 532 samples), 5212 CpGs met genome-wide significance (p < 1E−8) and were enriched in pathways such as keratinization (FDR p-value = 7.37E−14), chemokine activity (FDR p-value = 1.56E−2), and eosinophil migration (FDR p-value = 1.83E−2). Nine differentially methylated regions were identified (fwerArea < 0.1) including a region in the promoter of ZNF300 that showed consistent differential DNAm in samples from earlier timepoints in pregnancy and appeared to be driven predominately by effects in the trophoblast cell type. We describe the largest study of fetal sex differences in placenta DNAm performed to date, revealing genes and pathways characterizing sex-specific placenta function and health outcomes later in life.

DNA Methylation Analysis of Turner Syndrome BAV

Turner Syndrome (TS) is a rare cytogenetic disorder caused by the complete loss or structural variation of the second sex chromosome. The most common cause of early mortality in TS results from a high incidence of left-sided congenital heart defects, including bicuspid aortic valve (BAV), which occurs in about 30% of individuals with TS. BAV is also the most common congenital heart defect in the general population with a prevalence of 0.5-2%, with males being three-times more likely to have a BAV than females. TS is associated with genome-wide hypomethylation when compared to karyotypically normal males and females. Alterations in DNA methylation in primary aortic tissue are associated with BAV in euploid individuals. Here we show significant differences in DNA methylation patterns associated with BAV in TS found in peripheral blood by comparing TS BAV (n = 12), TS TAV (n = 13), and non-syndromic BAV (n = 6). When comparing TS with BAV to TS with no heart defects we identified a differentially methylated region encompassing the BAV-associated gene MYRF, and enrichment for binding sites of two known transcription factor contributors to BAV. When comparing TS with BAV to euploid women with BAV, we found significant overlapping enrichment for ChIP-seq transcription factor targets including genes in the NOTCH1 pathway, known for involvement in the etiology of non-syndromic BAV, and other genes that are essential regulators of heart valve development. Overall, these findings suggest that altered DNA methylation affecting key aortic valve development genes contributes to the greatly increased risk for BAV in TS.

Brain DNA Methylation Patterns in CLDN5 Associated With Cognitive Decline

BACKGROUND

Cognitive trajectory varies widely and can distinguish people who develop dementia from people who remain cognitively normal. Variation in cognitive trajectory is only partially explained by traditional neuropathologies. We sought to identify novel genes associated with cognitive trajectory using DNA methylation profiles from human postmortem brain.

METHODS

We performed a brain epigenome-wide association study of cognitive trajectory in 636 participants from the ROS (Religious Orders Study) and MAP (Rush Memory and Aging Project) using DNA methylation profiles of the dorsolateral prefrontal cortex. To maximize our power to detect epigenetic associations, we used the recently developed Gene Association with Multiple Traits test to analyze the 5 measured cognitive domains simultaneously.

RESULTS

We found an epigenome-wide association for differential methylation of sites in the CLDN5 locus and cognitive trajectory (p = 9.96 × 10^-7) that was robust to adjustment for cell type proportions (p = 8.52 × 10^-7). This association was primarily driven by association with declines in episodic (p = 4.65 × 10^-6) and working (p = 2.54 × 10^-7) memory. This association between methylation in CLDN5 and cognitive decline was significant even in participants with no or little signs of amyloid-β and neurofibrillary tangle pathology.

CONCLUSIONS

Differential methylation of CLDN5, a gene that encodes an important protein of the blood-brain barrier, is associated with cognitive trajectory beyond traditional Alzheimer's disease pathologies. The association between CLDN5 methylation and cognitive trajectory in people with low pathology suggests an early role for CLDN5 and blood-brain barrier dysfunction in cognitive decline and Alzheimer's disease.

Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies

Adjusting cell type composition is challenging but critical in epigenome-wide association studies (EWAS). In this chapter, we describe how to apply reference-based and reference-free methods in R to impute cell type composition in whole blood samples.

Deciphering DNA Methylation in HIV Infection

With approximately 38 million people living with HIV/AIDS globally, and a further 1.5 million new global infections per year, it is imperative that we advance our understanding of all factors contributing to HIV infection. While most studies have focused on the influence of host genetic factors on HIV pathogenesis, epigenetic factors are gaining attention. Epigenetics involves alterations in gene expression without altering the DNA sequence. DNA methylation is a critical epigenetic mechanism that influences both viral and host factors. This review has five focal points, which examines (i) fluctuations in the expression of methylation modifying factors upon HIV infection (ii) the effect of DNA methylation on HIV viral genes and (iii) host genome (iv) inferences from other infectious and non-communicable diseases, we provide a list of HIV-associated host genes that are regulated by methylation in other disease models (v) the potential of DNA methylation as an epi-therapeutic strategy and biomarker. DNA methylation has also been shown to serve as a robust therapeutic strategy and precision medicine biomarker against diseases such as cancer and autoimmune conditions. Despite new drugs being discovered for HIV, drug resistance is a problem in high disease burden settings such as Sub-Saharan Africa. Furthermore, genetic therapies that are under investigation are irreversible and may have off target effects. Alternative therapies that are nongenetic are essential. In this review, we discuss the potential role of DNA methylation as a novel therapeutic intervention against HIV.

Characterization of cerebrospinal fluid DNA methylation age during the acute recovery period following aneurysmal subarachnoid hemorrhage

Background

Biological aging may occur at different rates than chronological aging due to genetic, social, and environmental factors. DNA methylation (DNAm) age is thought to be a reliable measure of accelerated biological aging which has been linked to an array of poor health outcomes. Given the importance of chronological age in recovery following aneurysmal subarachnoid hemorrhage (aSAH), a type of stroke, DNAm age may also be an important biomarker of outcomes, further improving predictive models. Cerebrospinal fluid (CSF) is a unique tissue representing the local central nervous system environment post-aSAH. However, the validity of CSF DNAm age is unknown, and it is unclear which epigenetic clock is ideal to compute CSF DNAm age, particularly given changes in cell type heterogeneity (CTH) during the acute recovery period. Further, the stability of DNAm age post-aSAH, specifically, has not been examined and may improve our understanding of patient recovery post-aSAH. Therefore, the purpose of this study was to characterize CSF DNAm age over 14 days post-aSAH using four epigenetic clocks.

Results

Genome-wide DNAm data were available for two tissues: (1) CSF for N = 273 participants with serial sampling over 14 days post-aSAH (N = 850 samples) and (2) blood for a subset of n = 72 participants at one time point post-aSAH. DNAm age was calculated using the Horvath, Hannum, Levine, and “Improved Precision” (Zhang) epigenetic clocks. “Age acceleration” was computed as the residuals of DNAm age regressed on chronological age both with and without correcting for CTH. Using scatterplots, Pearson correlations, and group-based trajectory analysis, we examined the relationships between CSF DNAm age and chronological age, the concordance between DNAm ages calculated from CSF versus blood, and the stability (i.e., trajectories) of CSF DNAm age acceleration over time during recovery from aSAH. We observed moderate to strong correlations between CSF DNAm age and chronological age (R = 0.66 [Levine] to R = 0.97 [Zhang]), moderate to strong correlations between DNAm age in CSF versus blood (R = 0.69 [Levine] to R = 0.98 [Zhang]), and stable CSF age acceleration trajectories over 14 days post-aSAH in the Horvath and Zhang clocks (unadjusted for CTH), as well as the Hannum clock (adjusted for CTH).

Conclusions

CSF DNAm age was generally stable post-aSAH. Although correlated, CSF DNAm age differs from blood DNAm age in the Horvath, Hannum, and Levine clocks, but not in the Zhang clock. Taken together, our results suggest that, of the clocks examined here, the Zhang clock is the most robust to CTH and is recommended for use in complex tissues such as CSF.

Methylmercury and Polycyclic Aromatic Hydrocarbons in Mediterranean Seafood: A Molecular Anthropological Perspective

Eating seafood has numerous health benefits; however, it constitutes one of the main sources of exposure to several harmful environmental pollutants, both of anthropogenic and natural origin. Among these, methylmercury and polycyclic aromatic hydrocarbons give rise to concerns related to their possible effects on human biology. In the present review, we summarize the results of epidemiological investigations on the genetic component of individual susceptibility to methylmercury and polycyclic aromatic hydrocarbons exposure in humans, and on the effects that these two pollutants have on human epigenetic profiles (DNA methylation). Then, we provide evidence that Mediterranean coastal communities represent an informative case study to investigate the potential impact of methylmercury and polycyclic aromatic hydrocarbons on the human genome and epigenome, since they are characterized by a traditionally high local seafood consumption, and given the characteristics that render the Mediterranean Sea particularly polluted. Finally, we discuss the challenges of a molecular anthropological approach to this topic.

Blood DNA methylation markers associated with type 2 diabetes, fasting glucose, and HbA1c levels: An epigenome-wide association study in 316 adult twin pairs

DNA methylation plays an important role in the development and etiology of type 2 diabetes; however, few epigenomic studies have been conducted on twins. Herein, a two-stage study was performed to explore the associations between DNA methylation and type 2 diabetes, fasting plasma glucose, and HbA1c. DNA methylation in 316 twin pairs from the Chinese National Twin Registry (CNTR) was measured using Illumina Infinium BeadChips. In the discovery sample, the results revealed that 63 CpG sites and 6 CpG sites were significantly associated with fasting plasma glucose and HbA1c, respectively. In the replication sample, cg19690313 in TXNIP was associated with both fasting plasma glucose (P = 1.23 × 10^-17, FDR < 0.001) and HbA1c (P = 2.29 × 10^-18, FDR < 0.001). Furthermore, cg04816311, cg08309687, and cg09249494 may provide new insight in the metabolic mechanism of HbA1c. Our study provides solid evidence that cg19690313 on TXNIP correlates with HbA1c and fasting plasma glucose levels.

Detecting Differentially Methylated Promoters in Genes Related to Disease Phenotypes Using R

DNA methylation in gene promoters plays a major role in gene expression regulation, and alterations in methylation patterns have been associated with several diseases. In this context, different software suites and statistical methods have been proposed to analyze differentially methylated positions and regions. Among them, the novel statistical method implemented in the mCSEA R package proposed a new framework to detect subtle, but consistent, methylation differences. Here, we provide an easy-to-use pipeline covering all the necessary steps to detect differentially methylated promoters with mCSEA from Illumina 450K and EPIC methylation BeadChips data. This protocol covers the download of data from public repositories, quality control, data filtering and normalization, estimation of cell type proportions, and statistical analysis. In addition, we show the procedure to compare disease vs. normal phenotypes, obtaining differentially methylated regions including promoters or CpG Islands. The entire protocol is based on R programming language, which can be used in any operating system and does not require advanced programming skills.

The role of the gut microbiome in cancer-related fatigue: pilot study on epigenetic mechanisms

PURPOSE

Recent evidence supports a key role of gut microbiome in brain health. We conducted a pilot study to assess associations of gut microbiome with cancer-related fatigue and explore the associations with DNA methylation changes.

METHODS

Self-reported Multidimensional Fatigue Inventory and stool samples were collected at pre-radiotherapy and one-month post-radiotherapy in patients with head and neck cancer. Gut microbiome data were obtained by sequencing the 16S ribosomal ribonucleic acid gene. DNA methylation changes in the blood were assessed using Illumina Methylation EPIC BeadChip.

RESULTS

We observed significantly different gut microbiota patterns among patients with high vs. low fatigue across time. This pattern was characterized by low relative abundance in short-chain fatty acid-producing taxa (family Ruminococcaceae, genera Subdoligranulum and Faecalibacterium; all p < 0.05), with high abundance in taxa associated with inflammation (genera Family XIII AD3011 and Erysipelatoclostridium; all p < 0.05) for high-fatigue group. We identified nine KEGG Orthology pathways significantly different between high- vs. low-fatigue groups over time (all p < 0.001), including pathways related to fatty acid synthesis and oxidation, inflammation, and brain function. Gene set enrichment analysis (GSEA) was performed on the top differentially methylated CpG sites that were associated with the taxa and fatigue. All biological processes from the GSEA were related to immune responses and inflammation (FDR < 0.05).

CONCLUSIONS

Our results suggest different patterns of the gut microbiota in cancer patients with high vs. low fatigue. Results from functional pathways and DNA methylation analyses indicate that inflammation is likely to be the major driver in the gut-brain axis for cancer-related fatigue.

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

Comparative analysis reveals distinctive epigenetic features of the human cerebellum

Identifying the molecular underpinnings of the neural specializations that underlie human cognitive and behavioral traits has long been of considerable interest. Much research on human-specific changes in gene expression and epigenetic marks has focused on the prefrontal cortex, a brain structure distinguished by its role in executive functions. The cerebellum shows expansion in great apes and is gaining increasing attention for its role in motor skills and cognitive processing, including language. However, relatively few molecular studies of the cerebellum in a comparative evolutionary context have been conducted. Here, we identify human-specific methylation in the lateral cerebellum relative to the dorsolateral prefrontal cortex, in a comparative study with chimpanzees (Pan troglodytes) and rhesus macaques (Macaca mulatta). Specifically, we profiled genome-wide methylation levels in the three species for each of the two brain structures and identified human-specific differentially methylated genomic regions unique to each structure. We further identified which differentially methylated regions (DMRs) overlap likely regulatory elements and determined whether associated genes show corresponding species differences in gene expression. We found greater human-specific methylation in the cerebellum than the dorsolateral prefrontal cortex, with differentially methylated regions overlapping genes involved in several conditions or processes relevant to human neurobiology, including synaptic plasticity, lipid metabolism, neuroinflammation and neurodegeneration, and neurodevelopment, including developmental disorders. Moreover, our results show some overlap with those of previous studies focused on the neocortex, indicating that such results may be common to multiple brain structures. These findings further our understanding of the cerebellum in human brain evolution.

Humans are distinguished from other species by several aspects of cognition. While much comparative evolutionary neuroscience has focused on the neocortex, increasing recognition of the cerebellum’s role in cognition and motor processing has inspired considerable new research. Comparative molecular studies, however, generally continue to focus on the neocortex. We sought to characterize potential genetic regulatory traits distinguishing the human cerebellum by undertaking genome-wide epigenetic profiling of the lateral cerebellum, and compared this to the prefrontal cortex of humans, chimpanzees, and rhesus macaque monkeys. We found that humans showed greater differential CpG methylation–an epigenetic modification of DNA that can reflect past or present gene expression–in the cerebellum than the prefrontal cortex, highlighting the importance of this structure in human brain evolution. Humans also specifically show methylation differences at genes involved in neurodevelopment, neuroinflammation, synaptic plasticity, and lipid metabolism. These differences are relevant for understanding processes specific to humans, such as extensive plasticity, as well as pronounced and prevalent neurodegenerative conditions associated with aging.

Estimands in epigenome-wide association studies

Background

In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.

Results

We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.

Conclusions

The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01083-9.

Hypomethylation mediates genetic association with the major histocompatibility complex genes in Sjögren's syndrome

Differential methylation of immune genes has been a consistent theme observed in Sjögren's syndrome (SS) in CD4+ T cells, CD19+ B cells, whole blood, and labial salivary glands (LSGs). Multiple studies have found associations supporting genetic control of DNA methylation in SS, which in the absence of reverse causation, has positive implications for the potential of epigenetic therapy. However, a formal study of the causal relationship between genetic variation, DNA methylation, and disease status is lacking. We performed a causal mediation analysis of DNA methylation as a mediator of nearby genetic association with SS using LSGs and genotype data collected from 131 female members of the Sjögren's International Collaborative Clinical Alliance registry, comprising of 64 SS cases and 67 non-cases. Bumphunter was used to first identify differentially-methylated regions (DMRs), then the causal inference test (CIT) was applied to identify DMRs mediating the association of nearby methylation quantitative trait loci (MeQTL) with SS. Bumphunter discovered 215 DMRs, with the majority located in the major histocompatibility complex (MHC) on chromosome 6p21.3. Consistent with previous findings, regions hypomethylated in SS cases were enriched for gene sets associated with immune processes. Using the CIT, we observed a total of 19 DMR-MeQTL pairs that exhibited strong evidence for a causal mediation relationship. Close to half of these DMRs reside in the MHC and their corresponding meQTLs are in the region spanning the HLA-DQA1, HLA-DQB1, and HLA-DQA2 loci. The risk of SS conferred by these corresponding MeQTLs in the MHC was further substantiated by previous genome-wide association study results, with modest evidence for independent effects. By validating the presence of causal mediation, our findings suggest both genetic and epigenetic factors contribute to disease susceptibility, and inform the development of targeted epigenetic modification as a therapeutic approach for SS.

An Exploratory Study of Epigenetic Age in Preeclamptic and Normotensive Pregnancy Reveals Differences by Self-Reported Race but Not Pregnancy Outcome

Preeclampsia is a leading cause of maternal and neonatal morbidity and mortality. Chronological age and race are associated with preeclampsia, but the role of these factors is not entirely understood. We hypothesized that DNA methylation age, a measure of biological age, would be higher in individuals with preeclampsia than in individuals with normotensive pregnancy and that DNA methylation age would differ by race across pregnancy. This was a longitudinal, exploratory study of 56 pregnant individuals (n = 28 preeclampsia cases and n = 28 normotensive controls). Genome-wide DNA methylation data were generated from trimester-specific peripheral blood samples. DNA methylation age was estimated using the "Improved Precision" clock, and ∆age, the difference between DNA methylation age and chronological age, was computed. DNA methylation age was compared with chronological age using Pearson correlations. The relationships between ∆age and preeclampsia status, self-reported race, and covariates were tested using multiple linear regression and performed both with and without consideration of cell-type heterogeneity. We observed strong correlation between chronological age and DNA methylation age across pregnancy, with significantly stronger correlation observed in White participants than in Black participants. We observed no association between ∆age and preeclampsia status. However, ∆age was higher in participants with higher pre-pregnancy body mass index in trimester 1 and lower in Black participants than in White participants in trimesters 2 and 3. Observations were largely consistent when controlling for cell-type heterogeneity. Our findings in a small sample support the need for additional studies to investigate the relationship between race and biological age, which could provide further insight into racial disparities across pregnancy. However, this study does not support an association between ∆age and preeclampsia status.

Epigenetic age associates with psychosocial stress and resilience in children of Latinx immigrants

Aim: To investigate associations of psychosocial stressors and resilience factors with DNA methylation age in saliva of Latinx children of immigrants before and after the 2016 presidential election (2015-2018). Materials & methods: We compared psychosocial exposures with four distinct measures of epigenetic age assessed in saliva of children (6-13 years, n = 71 pre-election; n = 35 post-election). Exploratory genome-wide analyses were also conducted. Results: We found distinct associations across epigenetic clocks and time points: for example, greater maternal social status pre-election and fear of parent deportation post-election both associated with decreased Hannum Age (p ≤ 0.01). Conclusion: Though limited in size, our unique study design provides novel hypotheses regarding how the social environment may influence epigenetic aging and genome-wide methylation, potentially contributing to racial/ethnic health inequalities.

Whole genome methylation and transcriptome analyses to identify risk for cerebral palsy (CP) in extremely low gestational age neonates (ELGAN)

Preterm birth remains the leading identifiable risk factor for cerebral palsy (CP), a devastating form of motor impairment due to developmental brain injury occurring around the time of birth. We performed genome wide methylation and whole transcriptome analyses to elucidate the early pathogenesis of CP in extremely low gestational age neonates (ELGANs). We evaluated peripheral blood cell specimens collected during a randomized trial of erythropoietin for neuroprotection in the ELGAN (PENUT Trial, NCT# 01378273). DNA methylation data were generated from 94 PENUT subjects (n = 47 CP vs. n = 47 Control) on day 1 and 14 of life. Gene expression data were generated from a subset of 56 subjects. Only one differentially methylated region was identified for the day 1 to 14 change between CP versus no CP, without evidence for differential gene expression of the associated gene RNA Pseudouridine Synthase Domain Containing 2. iPathwayGuide meta-analyses identified a relevant upregulation of JAK1 expression in the setting of decreased methylation that was observed in control subjects but not CP subjects. Evaluation of whole transcriptome data identified several top pathways of potential clinical relevance including thermogenesis, ferroptossis, ribosomal activity and other neurodegenerative conditions that differentiated CP from controls.

Childhood DNA methylation as a marker of early life rapid weight gain and subsequent overweight

BACKGROUND

High early postnatal weight gain has been associated with childhood adiposity; however, the mechanism remains unknown. DNA methylation is a hypothesised mechanism linking early life exposures and subsequent disease. However, epigenetic changes associated with high early weight gain have not previously been investigated. Our aim was to investigate the associations between early weight gain, peripheral blood DNA methylation, and subsequent overweight/obese. Data from the UK Avon Longitudinal study of Parents and Children (ALSPAC) cohort were used to estimate associations between early postnatal weight gain and epigenome-wide DNA CpG site methylation (Illumina 450 K Methylation Beadchip) in blood in childhood (n = 125) and late adolescence (n = 96). High weight gain in the first year (a change in weight z-scores > 0.67), both unconditional (rapid weight gain) and conditional on birthweight (rapid thrive), was related to individual CpG site methylation and across regions using the meffil pipeline, with and without adjustment for cell type proportions, and with 5% false discovery rate correction. Variation in methylation at high weight gain-associated CpG sites was then examined with regard to body composition measures in childhood and adolescence. Replication of the differentially methylated CpG sites was sought using whole-blood DNA samples from 104 children from the UK Southampton Women's Survey.

RESULTS

Rapid infant weight gain was associated with small (+ 1% change) increases in childhood methylation (age 7) for two distinct CpG sites (cg01379158 (NT5M) and cg11531579 (CHFR)). Childhood methylation at one of these CpGs (cg11531579) was also higher in those who experienced rapid weight gain and were subsequently overweight/obese in adolescence (age 17). Rapid weight gain was not associated with differential DNA methylation in adolescence. Childhood methylation at the cg11531579 site was also suggestively associated with rapid weight gain in the replication cohort.

CONCLUSIONS

This study identified associations between rapid weight gain in infancy and small increases in childhood methylation at two CpG sites, one of which was replicated and was also associated with subsequent overweight/obese. It will be important to determine whether loci are markers of early rapid weight gain across different, larger populations. The mechanistic relevance of these differentially methylated sites requires further investigation.

Epigenetics as a Mechanism of Developmental Embodiment of Stress, Resilience, and Cardiometabolic Risk Across Generations of Latinx Immigrant Families

Psychosocial stressors can become embodied to alter biology throughout the life course in ways that may have lasting health consequences. Immigrants are particularly vulnerable to high burdens of stress, which have heightened in the current sociopolitical climate. This study is an investigation of how immigration-related stress (IRS) may impact the cardiometabolic risk and epigenetic markers of Latinx immigrant mothers and children in Nashville, TN. We compared stress and resilience factors reported by Latina immigrant mothers and their children (aged 5-13) from two time points spanning the 2016 U.S. presidential election (June 2015-June 2016 baseline, n = 81; March-September 2018 follow-up, n = 39) with cardiometabolic risk markers (BMI, waist circumference, and blood pressure). We also analyzed these factors in relation to DNA methylation in saliva of stress-related candidate genes (SLC6A4 and FKBP5), generated via bisulfite pyrosequencing (complete case n's range from 67-72 baseline and 29-31 follow-up) (n's range from 80 baseline to 36 follow-up). We found various associations with cardiometabolic risk, such as higher social support and greater acculturation were associated with lower BMI in mothers; discrimination and school stress associated with greater waist circumferences in children. Very few exposures associated with FKBP5, but various stressors associated with methylation at many sites in SLC6A4, including immigrant-related stress in both mothers and children, and fear of parent deportation in children. Additionally, in the mothers, total maternal stress, health stress, and subjective social status associated with methylation at multiple sites of SLC6A4. Acculturation associated with methylation in mothers in both genes, though directions of effect varied over time. We also find DNA methylation at SLC6A4 associates with measures of adiposity and blood pressure, suggesting that methylation may be on the pathway linking stress with cardiometabolic risk. More research is needed to determine the role of these epigenetic differences in contributing to embodiment of stress across generations.

Cortical cellular diversity and development in schizophrenia

While a definitive understanding of schizophrenia etiology is far from current reality, an increasing body of evidence implicates perturbations in early development that alter the trajectory of brain maturation in this disorder, leading to abnormal function in early childhood and adulthood. This atypical development likely arises from an interaction of many brain cell types that follow distinct developmental paths. Because both cellular identity and development are governed by the transcriptome and epigenome, two levels of gene regulation that have the potential to reflect both genetic and environmental influences, mapping "omic" changes over development in diverse cells is a fruitful avenue for schizophrenia research. In this review, we provide a survey of human brain cellular composition and development, levels of genomic regulation that determine cellular identity and developmental trajectories, and what is known about how genomic regulation is dysregulated in specific cell types in schizophrenia. We also outline technical challenges and solutions to conducting cell type-specific functional genomic studies in human postmortem brain.

Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data

DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross-sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10-7 , 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P < .05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P < .05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross-sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with alcohol consumption changes in adulthood.

A Method to Investigate the Helicobacter pylori-Associated DNA Methylome

The protocol described here for methylome profiling consists of two parts. One is the experimental part for a genome-wide analysis of methylation level, and the other is the bioinformatics analysis of the methylome data. DNA methylation measurement is conducted using the commercially available array-based "Infinium Human Methylation 450K BeadChip" kit (or its updated version, Infinium MethylationEPICBeadChip). This BeadChip allows the high-throughput DNA methylation analysis suitable for genome-wide studies with large sample size. The results give intensities of the beads providing information on the unmethylated and methylated CpG sites. Bioinformatics data analysis involves reading the intensities as methylation values using R packages. Here, we provide a detailed analysis tool for each of the data analysis steps.

Abnormal placental DNA methylation variation in spontaneous preterm birth

OBJECTIVE

Preterm birth (PTB) has become a major public health concern as the leading cause of neonatal death, but little is understood about its etiology. Children born preterm are also at increased risk of long-term consequences such as neurodevelopmental disorders, adulthood hypertension and diabetes. Recent studies have indicated that DNA methylation may be involved in the occurrence of PTB as well as related adverse outcomes. The latest Infinium EPIC BeadChip extends the coverage of the genome and provides a better tool to help investigate the involvement of DNA methylation in these conditions.

METHODS

We conducted this case-control study in three Women and Children's hospitals in South China, and enrolled 32 spontaneous preterm births and 16 term births. We assessed placental DNA methylation profiling of these participants with the Infinium EPIC BeadChip. We identified PTB and gestational age (GA)-associated CpG sites with limma regression model, and applied seqlm to identify PTB-associated regions. We performed gene ontology analysis to further interpret functional enrichment of the identified differentially methylated genes in PTB.

RESULTS

We identified a total of 8 differentially methylated positions (DMPs) that were significantly associated with PTB (FDR < 0.1) and a total of 15 DMPs that were associated with GA (FDR < 0.1). In the regional analysis, one differentially methylated region in the SLC23A1 gene overlapped with PTB-associated CpG site. The differentially methylated CpG sites in PTB were mapped to the genes involving in biological processes mainly regarding neurodevelopment, regulation of inflammation and metabolism.

CONCLUSION

Our findings suggested that preterm placenta have distinct DNA methylation alterations, and these alteration patterns established at birth provide insight into the long-term consequences of preterm birth.

A novel whole blood gene expression signature for asthma, dermatitis, and rhinitis multimorbidity in children and adolescents

BACKGROUND

Allergic diseases often occur in combination (multimorbidity). Human blood transcriptome studies have not addressed multimorbidity. Large-scale gene expression data were combined to retrieve biomarkers and signaling pathways to disentangle allergic multimorbidity phenotypes.

METHODS

Integrated transcriptomic analysis was conducted in 1233 participants with a discovery phase using gene expression data (Human Transcriptome Array 2.0) from whole blood of 786 children from three European birth cohorts (MeDALL), and a replication phase using RNA Sequencing data from an independent cohort (EVA-PR, n = 447). Allergic diseases (asthma, atopic dermatitis, rhinitis) were considered as single disease or multimorbidity (at least two diseases), and compared with no disease.

RESULTS

Fifty genes were differentially expressed in allergic diseases. Thirty-two were not previously described in allergy. Eight genes were consistently overexpressed in all types of multimorbidity for asthma, dermatitis, and rhinitis (CLC, EMR4P, IL5RA, FRRS1, HRH4, SLC29A1, SIGLEC8, IL1RL1). All genes were replicated the in EVA-PR cohort. RT-qPCR validated the overexpression of selected genes. In MeDALL, 27 genes were differentially expressed in rhinitis alone, but none was significant for asthma or dermatitis alone. The multimorbidity signature was enriched in eosinophil-associated immune response and signal transduction. Protein-protein interaction network analysis identified IL5/JAK/STAT and IL33/ST2/IRAK/TRAF as key signaling pathways in multimorbid diseases. Synergistic effect of multimorbidity on gene expression levels was found.

CONCLUSION

A signature of eight genes identifies multimorbidity for asthma, rhinitis, and dermatitis. Our results have clinical and mechanistic implications, and suggest that multimorbidity should be considered differently than allergic diseases occurring alone.

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

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

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing

BACKGROUND

Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.

RESULTS

In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.

CONCLUSIONS

Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.

Smoking and blood DNA methylation: an epigenome-wide association study and assessment of reversibility

We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10-7, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10-7) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5-5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and quantifies the reversibility of smoking-induced methylation changes.

Comparison of DNA methylation profiles from saliva in Coeliac disease and non-coeliac disease individuals

BACKGROUND

Coeliac disease (CD) is a autoimmune disease characterised by mucosal inflammation in the small intestine in response to dietary gluten. Genetic factors play a key role with CD individuals carrying either the HLA-DQ2 or HLA-DQ8 haplotype, however these haplotypes are present in half the general population making them necessary but insufficient to cause CD. Epigenetic modifications, including DNA methylation that can change in response to environmental exposure could help to explain how interactions between genes and environmental factors combine to trigger disease development. Identifying changes in DNA methylation profiles in individuals with CD could help discover novel genomic regions involved in the onset and development of CD.

METHODS

The Illumina InfiniumMethylation450 Beadchip array (HM450) was used to compare DNA methylation profiles in saliva, in CD and non-CD affected individuals. CD individuals who had been diagnosed at least 2 years previously; were on a GFD; and who were currently asymptomatic; were compared to age and sex-matched non-CD affected healthy controls. Bisulphite pyrosequencing was used to validate regions found to be differentially methylated. These regions were also validated in a second larger cohort of CD and non-CD affected individuals.

RESULTS

Methylation differences within the HLA region at HLA-DQB1 were identified on HM450 but could not be confirmed with pyrosequencing. Significant methylation differences near the SLC17A3 gene were confirmed on pyrosequencing in the initial pilot cohort. Interestingly pyrosequencing sequencing of these same sites within a second cohort of CD and non-CD affected controls produced significant methylation differences in the opposite direction.

CONCLUSION

Altered DNA methylation profiles appear to be present in saliva in CD individuals. Further work to confirm whether these differences are truly associated with CD is needed.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

Background

Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking.

Results

Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell’s rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms’ performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research.

Conclusion

Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

Differential methylation and expression of genes in the hypoxia-inducible factor 1 signaling pathway are associated with paclitaxel-induced peripheral neuropathy in breast cancer survivors and with preclinical models of chemotherapy-induced neuropathic pain

BACKGROUND

Paclitaxel is an important chemotherapeutic agent for the treatment of breast cancer. Paclitaxel-induced peripheral neuropathy (PIPN) is a major dose-limiting toxicity that can persist into survivorship. While not all survivors develop PIPN, for those who do, it has a substantial negative impact on their functional status and quality of life. No interventions are available to treat PIPN. In our previous studies, we identified that the HIF-1 signaling pathway (H1SP) was perturbed between breast cancer survivors with and without PIPN. Preclinical studies suggest that the H1SP is involved in the development of bortezomib-induced and diabetic peripheral neuropathy, and sciatic nerve injury. The purpose of this study was to identify H1SP genes that have both differential methylation and differential gene expression between breast cancer survivors with and without PIPN.

METHODS

A multi-staged integrated analysis was performed. In peripheral blood, methylation was assayed using microarray and gene expression was assayed using RNA-seq. Candidate genes in the H1SP having both differentially methylation and differential expression were identified between survivors who received paclitaxel and did (n = 25) and did not (n = 25) develop PIPN. Then, candidate genes were evaluated for differential methylation and differential expression in public data sets of preclinical models of PIPN and sciatic nerve injury.

RESULTS

Eight candidate genes were identified as both differential methylation and differential expression in survivors. Of the eight homologs identified, one was found to be differential expression in both PIPN and "normal" mice dorsal root ganglia; three were differential methylation in sciatic nerve injury versus sham rats in both pre-frontal cortex and T-cells; and two were differential methylation in sciatic nerve injury versus sham rats in the pre-frontal cortex.

CONCLUSIONS

This study is the first to evaluate for methylation in cancer survivors with chronic PIPN. The findings provide evidence that the expression of H1SP genes associated with chronic PIPN in cancer survivors may be regulated by epigenetic mechanisms and suggests genes for validation as potential therapeutic targets.

The Promises and Challenges of Toxico-Epigenomics: Environmental Chemicals and Their Impacts on the Epigenome

BACKGROUND

It has been estimated that a substantial portion of chronic and noncommunicable diseases can be caused or exacerbated by exposure to environmental chemicals. Multiple lines of evidence indicate that early life exposure to environmental chemicals at relatively low concentrations could have lasting effects on individual and population health. Although the potential adverse effects of environmental chemicals are known to the scientific community, regulatory agencies, and the public, little is known about the mechanistic basis by which these chemicals can induce long-term or transgenerational effects. To address this question, epigenetic mechanisms have emerged as the potential link between genetic and environmental factors of health and disease.

OBJECTIVES

We present an overview of epigenetic regulation and a summary of reported evidence of environmental toxicants as epigenetic disruptors. We also discuss the advantages and challenges of using epigenetic biomarkers as an indicator of toxicant exposure, using measures that can be taken to improve risk assessment, and our perspectives on the future role of epigenetics in toxicology.

DISCUSSION

Until recently, efforts to apply epigenomic data in toxicology and risk assessment were restricted by an incomplete understanding of epigenomic variability across tissue types and populations. This is poised to change with the development of new tools and concerted efforts by researchers across disciplines that have led to a better understanding of epigenetic mechanisms and comprehensive maps of epigenomic variation. With the foundations now in place, we foresee that unprecedented advancements will take place in the field in the coming years. https://doi.org/10.1289/EHP6104.

methylCC: technology-independent estimation of cell type composition using differentially methylated regions

A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.

Grandmothers' smoking in pregnancy is associated with a reduced prevalence of early-onset myopia

Myopia (near sightedness) is the most common vision disorder resulting in visual impairment worldwide. We tested the hypothesis that intergenerational, non-genetic heritable effects influence refractive development, using grandparental prenatal smoking as a candidate exposure. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we found that the prevalence of myopia at age 7 was lower if the paternal grandmother had smoked in pregnancy, an association primarily found among grandsons compared to granddaughters. There was a weaker, non-sex-specific, reduction in the prevalence of myopia at age 7 if the maternal grandmother had smoked in pregnancy. For children who became myopic later (between 7 and 15 years of age) there were no associations with either grandmother smoking. Differences between early and late-onset myopia were confirmed with DNA methylation patterns: there were very distinct and strong associations with methylation for early-onset but not later-onset myopia.

DNA methylation alterations-potential cause of endometriosis pathogenesis or a reflection of tissue heterogeneity?

Alterations in the DNA methylation pattern of endometriotic lesions and endometrium of endometriosis patients have been proposed as one potential factor accompanying the endometriosis development. Although many differentially methylated genes have been associated with the pathogenesis of this disease, the overlap between the results of different studies has remained small. Among other potential confounders, the impact of tissue heterogeneity on the outcome of DNA methylation studies should be considered, as tissues are mixtures of different cell types with their own specific DNA methylation signatures. This review focuses on the results of DNA methylation studies in endometriosis from the cellular heterogeneity perspective. We consider both the studies using highly heterogeneous whole-lesion biopsies and endometrial tissue, as well as pure cell fractions isolated from lesions and endometrium to understand the potential impact of the cellular composition to the results of endometriosis DNA methylation studies. Also, future perspectives on how to diminish the impact of tissue heterogeneity in similar studies are provided.

Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data

BACKGROUND

Umbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age-appropriate cell-specific DNAm profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450 K and EPIC arrays. These datasets are biologically and technically different, and currently, there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution.

RESULTS

We first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n = 191) with matched Fluorescence-Activated Cell Sorting measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes.

CONCLUSION

These results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

Profiling DNA methylation patterns of zebrafish liver associated with parental high dietary arachidonic acid

Diet has been shown to influence epigenetic key players, such as DNA methylation, which can regulate the gene expression potential in both parents and offspring. Diets enriched in omega-6 and deficient in omega-3 PUFAs (low dietary omega-3/omega-6 PUFA ratio), have been associated with the promotion of pathogenesis of diseases in humans and other mammals. In this study, we investigated the impact of increased dietary intake of arachidonic acid (ARA), a physiologically important omega-6 PUFA, on 2 generations of zebrafish. Parental fish were fed either a low or a high ARA diet, while the progeny of both groups were fed the low ARA diet. We screened for DNA methylation on single base-pair resolution using reduced representation bisulfite sequencing (RRBS). The DNA methylation profiling revealed significant differences between the dietary groups in both parents and offspring. The majority of differentially methylated loci associated with high dietary ARA were found in introns and intergenic regions for both generations. Common loci between the identified differentially methylated loci in F0 and F1 livers were reported. We described overlapping gene annotations of identified methylation changes with differential expression, but based on a small number of overlaps. The present study describes the diet-associated methylation profiles across genomic regions, and it demonstrates that parental high dietary ARA modulates DNA methylation patterns in zebrafish liver.