The impact of methylation quantitative trait loci (mQTLs) on active smoking-related DNA methylation changes

HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data

Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.

Epigenetics Research in Evolutionary Biology: Perspectives on Timescales and Mechanisms

Epigenetics research in evolutionary biology encompasses a variety of research areas, from regulation of gene expression to inheritance of environmentally mediated phenotypes. Such divergent research foci can occasionally render the umbrella term "epigenetics" ambiguous. Here I discuss several areas of contemporary epigenetics research in the context of evolutionary biology, aiming to provide balanced views across timescales and molecular mechanisms. The importance of epigenetics in development is now being assessed in many nonmodel species. These studies not only confirm the importance of epigenetic marks in developmental processes, but also highlight the significant diversity in epigenetic regulatory mechanisms across taxa. Further, these comparative epigenomic studies have begun to show promise toward enhancing our understanding of how regulatory programs evolve. A key property of epigenetic marks is that they can be inherited along mitotic cell lineages, and epigenetic differences that occur during early development can have lasting consequences on the organismal phenotypes. Thus, epigenetic marks may play roles in short-term (within an organism's lifetime or to the next generation) adaptation and phenotypic plasticity. However, the extent to which observed epigenetic variation occurs independently of genetic influences remains uncertain, due to the widespread impact of genetics on epigenetic variation and the limited availability of comprehensive (epi)genomic resources from most species. While epigenetic marks can be inherited independently of genetic sequences in some species, there is little evidence that such "transgenerational inheritance" is a general phenomenon. Rather, molecular mechanisms of epigenetic inheritance are highly variable between species.

Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

BACKGROUND

Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits.

RESULTS

We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19).

CONCLUSIONS

These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.

Integration of methylation quantitative trait loci (mQTL) on dietary intake on DNA methylation levels: an example of n-3 PUFA and ABCA1 gene

BACKGROUND

Epigenetic studies have reported relationships between dietary nutrient intake and methylation levels. However, genetic variants that may affect DNA methylation (DNAm) pattern, called methylation quantitative loci (mQTL), are usually overlooked in these analyses. We investigated whether mQTL change the relationship between dietary nutrient intake and leukocyte DNAm levels with an example of estimated fatty acid intake and ATP-binding cassette transporter A1 (ABCA1).

METHODS

A cross-sectional study on 231 participants (108 men, mean age: 62.7 y) without clinical history of cancer and no prescriptions for dyslipidemia. We measured leukocyte DNAm levels of 8 CpG sites within ABCA1 gene by pyrosequencing method and used mean methylation levels for statistical analysis. TaqMan assay was used for genotyping a genetic variant of ABCA1 (rs1800976). Dietary fatty acid intake was estimated with a validated food frequency questionnaire and adjusted for total energy intake by using residual methods.

RESULTS

Mean ABCA1 DNAm levels were 5% lower with the number of minor alleles in rs1800976 (CC, 40.6%; CG, 35.9%; GG, 30.6%). Higher dietary n-3 PUFA intake was associated with lower ABCA1 DNAm levels (1st (ref) vs. 4th, β [95% CI]: -2.52 [-4.77, -0.28]). After controlling for rs180076, the association between dietary n-3 PUFA intake and ABCA1 DNAm levels was attenuated, but still showed an independent association (1st (ref) vs. 4th, β [95% CI]: -2.00 [-3.84, -0.18]). The interaction of mQTL and dietary n-3 PUFA intake on DNAm levels was not significant.

CONCLUSIONS

This result suggested that dietary n-3 PUFA intake would be an independent predictor of DNAm levels in ABCA1 gene after adjusting for individual genetic background. Considering mQTL need to broaden into other genes and nutrients for deeper understanding of DNA methylation, which can contribute to personalized nutritional intervention.

Implication of DNA methylation during lifestyle mediated weight loss

Over the past 50 years, the number of overweight/obese people increased significantly, making obesity a global public health challenge. Apart from rare monogenic forms, obesity is a multifactorial disease, most likely resulting from a concerted interaction of genetic, epigenetic and environmental factors. Although recent studies opened new avenues in elucidating the complex genetics behind obesity, the biological mechanisms contributing to individual's risk to become obese are not yet fully understood. Non-genetic factors such as eating behaviour or physical activity are strong contributing factors for the onset of obesity. These factors may interact with genetic predispositions most likely via epigenetic mechanisms. Epigenome-wide association studies or methylome-wide association studies are measuring DNA methylation at single CpGs across thousands of genes and capture associations to obesity phenotypes such as BMI. However, they only represent a snapshot in the complex biological network and cannot distinguish between causes and consequences. Intervention studies are therefore a suitable method to control for confounding factors and to avoid possible sources of bias. In particular, intervention studies documenting changes in obesity-associated epigenetic markers during lifestyle driven weight loss, make an important contribution to a better understanding of epigenetic reprogramming in obesity. To investigate the impact of lifestyle in obesity state specific DNA methylation, especially concerning the development of new strategies for prevention and individual therapy, we reviewed 19 most recent human intervention studies. In summary, this review highlights the huge potential of targeted interventions to alter disease-associated epigenetic patterns. However, there is an urgent need for further robust and larger studies to identify the specific DNA methylation biomarkers which influence obesity.

Targeted DNA methylation analysis and prediction of smoking habits in blood based on massively parallel sequencing

Tobacco smoking is a frequent habit sustained by > 1.3 billion people in 2020 and the leading preventable factor for health risk and premature mortality worldwide. In the forensic context, predicting smoking habits from biological samples may allow broadening DNA phenotyping. In this study, we aimed to implement previously published smoking habit classification models based on blood DNA methylation at 13 CpGs. First, we developed a matching lab tool based on bisulfite conversion and multiplex PCR followed by amplification-free library preparation and targeted paired-end massively parallel sequencing (MPS). Analysis of six technical duplicates revealed high reproducibility of methylation measurements (Pearson correlation of 0.983). Artificially methylated standards uncovered marker-specific amplification bias, which we corrected via bi-exponential models. We then applied our MPS tool to 232 blood samples from Europeans of a wide age range, of which 90 were current, 71 former and 71 never smokers. On average, we obtained 189,000 reads/sample and 15,000 reads/CpG, without marker drop-out. Methylation distributions per smoking category roughly corresponded to previous microarray analysis, showcasing large inter-individual variation but with technology-driven bias. Methylation at 11 out of 13 smoking-CpGs correlated with daily cigarettes in current smokers, while solely one was weakly correlated with time since cessation in former smokers. Interestingly, eight smoking-CpGs correlated with age, and one displayed weak but significant sex-associated methylation differences. Using bias-uncorrected MPS data, smoking habits were relatively accurately predicted using both two- (current/non-current) and three- (never/former/current) category model, but bias correction resulted in worse prediction performance for both models. Finally, to account for technology-driven variation, we built new, joint models with inter-technology corrections, which resulted in improved prediction results for both models, with or without PCR bias correction (e.g. MPS cross-validation F₁-score > 0.8; 2-categories). Overall, our novel assay takes us one step closer towards the forensic application of viable smoking habit prediction from blood traces. However, future research is needed towards forensically validating the assay, especially in terms of sensitivity. We also need to further shed light on the employed biomarkers, particularly on the mechanistics, tissue specificity and putative confounders of smoking epigenetic signatures.

Epigenetic and Genetic Population Structure is Coupled in a Marine Invertebrate

Delineating the relative influence of genotype and the environment on DNA methylation is critical for characterizing the spectrum of organism fitness as driven by adaptation and phenotypic plasticity. In this study, we integrated genomic and DNA methylation data for two distinct Olympia oyster (Ostrea lurida) populations while controlling for within-generation environmental influences. In addition to providing the first characterization of genome-wide DNA methylation patterns in the oyster genus Ostrea, we identified 3,963 differentially methylated loci between populations. Our results show a clear coupling between genetic and epigenetic patterns of variation, with 27% of variation in interindividual methylation differences explained by genotype. Underlying this association are both direct genetic changes in CpGs (CpG-SNPs) and genetic variation with indirect influence on methylation (mQTLs). When comparing measures of genetic and epigenetic population divergence at specific genomic regions this relationship surprisingly breaks down, which has implications for the methods commonly used to study epigenetic and genetic coupling in marine invertebrates.

The potential of DNA methylation as a biomarker for obesity and smoking

DNA methylation is an epigenetic modification that has consistently been shown to be linked with a variety of human traits and diseases. Because DNA methylation is dynamic and potentially reversible in nature and can reflect environmental exposures and predict the onset of diseases, it has piqued interest as a potential disease biomarker. DNA methylation patterns are more stable than transcriptomic or proteomic patterns, and they are relatively easy to measure to track exposure to different environments and risk factors. Importantly, technologies for DNA methylation quantification have become increasingly cost effective-accelerating new research in the field-and have enabled the development of novel DNA methylation biomarkers. Quite a few DNA methylation-based predictors for a number of traits and diseases already exist. Such predictors show potential for being more accurate than self-reported or measured phenotypes (such as smoking behavior and body mass index) and may even hold potential for applications in clinics. In this review, we will first discuss the advantages and challenges of DNA methylation biomarkers in general. We will then review the current state and future potential of DNA methylation biomarkers in two human traits that show rather consistent alterations in methylome-obesity and smoking. Lastly, we will briefly speculate about the future prospects of DNA methylation biomarkers, and possible ways to achieve them.

Epigenetics of Schizophrenia

Schizophrenia (SCZ) is a chronic psychotic disorder that contributes significantly to disability, affecting behavior, thought, and cognition. It has long been known that there is a heritable component to schizophrenia; studies in both the pre-genomic and post-genomic era, however, have failed to elucidate fully the genetic basis for this complex disease. Epigenetic processes - broadly, those which contribute to changes in gene expression without altering the genetic code itself - may help to understand better the mechanisms leading to development of SCZ. The objective of this review is to synthesize current knowledge of the epigenetic mechanisms involved in schizophrenia. Specifically, DNA methylation studies in both peripheral and post-mortem brain samples in SCZ are reviewed, as are epigenetic mechanisms including histone modification. The promising role of non-coding RNA including micro-RNA (miRNA) and its role as a potential diagnostic and therapeutic biomarker is outlined, as are epigenetic age acceleration and telomere shortening. Finally, we discuss limitations in current knowledge and propose future research directions.

Prediction of clinical diagnosis of Alzheimer's disease, vascular, mixed, and all-cause dementia by a polygenic risk score and APOE status in a community-based cohort prospectively followed over 17 years

The strongest genetic risk factor for Alzheimer's disease (AD) is the ε4 allele of Apolipoprotein E (APOE) and recent genome-wide association meta-analyses have confirmed additional associated genetic loci with smaller effects. The aim of this study was to investigate the ability of an AD polygenic risk score (PRS) and APOE status to predict clinical diagnosis of AD, vascular (VD), mixed (MD), and all-cause dementia in a community-based cohort prospectively followed over 17 years and secondarily across age, sex, and education strata. A PRS encompassing genetic variants reaching genome-wide significant associations to AD (excluding APOE) from the most recent genome-wide association meta-analysis data was calculated and APOE status was determined in 5203 participants. During follow-up, 103, 111, 58, and 359 participants were diagnosed with AD, VD, MD, and all-cause dementia, respectively. Prediction ability of AD, VD, MD, and all-cause dementia by the PRS and APOE was assessed by multiple logistic regression and receiver operating characteristic curve analyses. The PRS per standard deviation increase in score and APOE4 positivity (≥1 ε4 allele) were significantly associated with greater odds of AD (OR, 95% CI: PRS: 1.70, 1.45-1.99; APOE4: 3.34, 2.24-4.99) and AD prediction accuracy was significantly improved when adding the PRS to a base model of age, sex, and education (ASE) (c-statistics: ASE, 0.772; ASE + PRS, 0.810). The PRS enriched the ability of APOE to discern AD with stronger associations than to VD, MD, or all-cause dementia in a prospective community-based cohort.

Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Background

Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects.

Results

Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements.

Conclusions

Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-021-00415-6.

Ambient temperature and genome-wide DNA methylation: A twin and family study in Australia

Little is known about the association between ambient temperature and DNA methylation, which is a potential biological process through which ambient temperature affects health. This study aimed to evaluate the association between ambient temperature and DNA methylation across human genome. We included 479 Australian women, including 132 twin pairs and 215 sisters of these twins. Blood-derived DNA methylation was measured using the HumanMethylation450 BeadChip array. Data on average ambient temperature during eight different exposure windows [lag0d (the blood draw day), lag0-7d (the current day and previous seven days prior to blood draw), lag0-14d, lag0-21d, lag0-28d, lag0-90d, lag0-180d, and lag0-365d)] was linked to each participant's home address. For each cytosine-guanine dinucleotide (CpG), we evaluated the association between its methylation level and temperature using generalized estimating equations (GEE), adjusting for important covariates. We used comb-p and DMRcate to identify differentially methylated regions (DMRs). We identified 31 CpGs at which blood DNA methylation were significantly associated with ambient temperature with false discovery rate [FDR] < 0.05. There were 82 significant DMRs identified by both comb-p (Sidak p-value < 0.01) and DMRcate (FDR < 0.01). Most of these CpGs and DMRs only showed association with temperature during one specific exposure window. These CpGs and DMRs were mapped to 85 genes. These related genes have been related to many human chronic diseases or phenotypes (e.g., diabetes, arthritis, breast cancer, depression, asthma, body height) in previous studies. The signals of short-term windows (lag0d and lag0-21d) showed enrichment in biological processes related to cell adhesion. In conclusion, short-, medium-, and long-term exposures to ambient temperature were all associated with blood DNA methylation, but the target genomic loci varied by exposure window. These differential methylation signals may serve as potential biomarkers to understand the health impacts of temperature.

Residential surrounding greenness and DNA methylation: An epigenome-wide association study

BACKGROUND

DNA methylation is a potential biological mechanism through which residential greenness affects health, but little is known about its association with greenness and whether the association could be modified by genetic background. We aimed to evaluate the association between surrounding greenness and genome-wide DNA methylation and potential gene-greenness interaction effects on DNA methylation.

METHODS

We measured blood-derived DNA methylation using the HumanMethylation450 BeadChip array (Illumina) for 479 Australian women, including 66 monozygotic, 66 dizygotic twin pairs, and 215 sisters of these twins. Surrounding greenness was represented by Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) within 300, 500, 1000 or 2000 m surrounding participants' home addresses. For each cytosine-guanine dinucleotide (CpG), the associations between its methylation level and NDVI or EVI were evaluated by generalized estimating equations, after adjusting for age, education, marital status, area-level socioeconomic status, smoking behavior, cell-type proportions, and familial clustering. We used comb-p and DMRcate to identify significant differentially methylated regions (DMRs). For each significant CpG, we evaluated the interaction effects of greenness and single-nucleotide polymorphisms (SNPs) within ±1 Mb window on its methylation level.

RESULTS

We found associations between surrounding greenness and blood DNA methylation for one CpG (cg04720477, mapped to the promoter region of CNP gene) with false discovery rate [FDR] < 0.05, and for another 9 CpGs with 0.05 ≤ FDR < 0.10. For two of these CpGs, we found 33 SNPs significantly (FDR < 0.05) modified the greenness-methylation association. There were 35 significant DMRs related to surrounding greenness that were identified by both comb-p (Sidak p-value < 0.01) and DMRcate (FDR < 0.01). Those CpGs and DMRs were mapped to genes related to many human diseases, such as mental health disorders and neoplasms as well as nutritional and metabolic diseases.

CONCLUSIONS

Surrounding greenness was associated with blood DNA methylation of many loci across human genome, and this association could be modified by genetic variations.

Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth

Maternal smoking during pregnancy (MSDP) contributes to poor birth outcomes, in part through disrupted placental functions, which may be reflected in the placental epigenome. Here we present a meta-analysis of the associations between MSDP and placental DNA methylation (DNAm) and between DNAm and birth outcomes within the Pregnancy And Childhood Epigenetics (PACE) consortium (N = 1700, 344 with MSDP). We identify 443 CpGs that are associated with MSDP, of which 142 associated with birth outcomes, 40 associated with gene expression, and 13 CpGs are associated with all three. Only two CpGs have consistent associations from a prior meta-analysis of cord blood DNAm, demonstrating substantial tissue-specific responses to MSDP. The placental MSDP-associated CpGs are enriched for environmental response genes, growth-factor signaling, and inflammation, which play important roles in placental function. We demonstrate links between placental DNAm, MSDP and poor birth outcomes, which may better inform the mechanisms through which MSDP impacts placental function and fetal growth.

Genetic Control of Human Infection with SARS-CoV-2

In 2019, the SARS-CoV-2 beta-coronavirus, which caused a pandemic of severe acute respiratory viral infection COVID-19 (from COronaVIrus Disease 2019), was first detected. The susceptibility to SARS-CoV-2 and the nature of the course of the COVID-19 clinical picture are determined by many factors, including genetic characteristics of both the pathogen and the human. The SARS-CoV-2 genome has a similarity to the genomes of other coronaviruses, which are pathogenic for humans and cause a severe course of infection: 79% to the SARS-CoV genome and 50% to the MERS-CoV genome. The most significant differences between SARS-CoV-2 and other coronaviruses are recorded in the structure of the gene of the S protein, a key protein responsible for the virus binding to the receptor of the host organism cells. In particular, substitutions in the S protein of SARS-CoV-2, leading to the formation of the furin cleavage site that is absent in other SARS-like coronaviruses, were identified, which may explain the high pathogenicity of SARS-CoV-2. In humans, the genes that are significant for the initial stages of infection include ACE2, ANPEP, DPP4 (encode receptors for coronavirus binding); TMPRSS2, FURIN, TMPRSS11D, CTSL, CTSB (encode proteases involved in the entry of the coronavirus into the cell); DDX1 (the gene of ATP-dependent RNA helicase DDX1, which promotes replication of coronaviruses); and IFITM1, IFITM2, and IFITM3 (encode interferon-induced transmembrane proteins with an antiviral effect). These genes are expressed in many tissues (including those susceptible to the effects of SARS-CoV-2); rare and frequent variants that affect the structure of the encoded protein and its properties and expression level are described in them. A number of common genetic variants with proven functional significance are characterized by the variability in the allele frequency in the world's populations, which can determine interpopulation differences in the prevalence of COVID-19 and in the clinical features of the course of this pathology. The expression level of genes that are important for the formation of the susceptibility to SARS-CoV-2 is affected by epigenetic modifications, comorbidities at the time of infection, taking medications, and bad habits.

Genetic predisposition, Aβ misfolding in blood plasma, and Alzheimer's disease

Alzheimer's disease is highly heritable and characterized by amyloid plaques and tau tangles in the brain. The aim of this study was to investigate the association between genetic predisposition, Aβ misfolding in blood plasma, a unique marker of Alzheimer associated neuropathological changes, and Alzheimer's disease occurrence within 14 years. Within a German community-based cohort, two polygenic risk scores (clinical Alzheimer's disease and Aβ42 based) were calculated, APOE genotype was determined, and Aβ misfolding in blood plasma was measured by immuno-infrared sensor in 59 participants diagnosed with Alzheimer's disease during 14 years of follow-up and 581 participants without dementia diagnosis. Associations between each genetic marker and Aβ misfolding were assessed through logistic regression and the ability of each genetic marker and Aβ misfolding to predict Alzheimer's disease was determined. The Alzheimer's disease polygenic risk score and APOE ε4 presence were associated to Aβ misfolding (odds ratio, 95% confidence interval: per standard deviation increase of score: 1.25, 1.03-1.51; APOE ε4 presence: 1.61, 1.04-2.49). No association was evident for the Aβ polygenic risk score. All genetic markers were predictive of Alzheimer's disease diagnosis albeit much less so than Aβ misfolding (areas under the curve: Aβ polygenic risk score: 0.55; AD polygenic risk score: 0.59; APOE ε4: 0.63; Aβ misfolding: 0.84). Clinical Alzheimer's genetic risk was associated to early pathological changes (Aβ misfolding) measured in blood, however, predicted Alzheimer's disease less accurately than Aβ misfolding itself. Genetic predisposition may provide information regarding disease initiation, while Aβ misfolding could be important in clinical risk prediction.

Dioxin exposure associated with fecundability and infertility in mothers and daughters of Seveso, Italy

STUDY QUESTION

Is there an association between 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure and fecundability and infertility among Seveso women and their daughters?

SUMMARY ANSWER

TCDD exposure is associated with a decrease in fecundability and increased risk of infertility in women, as well as their daughters.

WHAT IS KNOWN ALREADY

In animal studies, maternal exposure to TCDD is associated with decreased fertility in offspring. Effects of TCDD are mediated by activation of the aryl hydrocarbon receptor (AHR) pathway.

STUDY DESIGN, SIZE, DURATION

The Seveso Women's Health Study (SWHS) has followed 981 women exposed to TCDD in a 1976 accident since 1996. In 2014, we initiated the Seveso Second Generation Study to follow-up their children.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We obtained information on pregnancy history including time of trying to conceive from SWHS women and their daughters who were 18 years or older. We considered TCDD exposure as initial 1976 serum TCDD concentration and estimated TCDD at pregnancy. We examined relationships of TCDD exposure with time to pregnancy (TTP, the monthly probability of conception within the first 12 months of trying) and infertility (≥12 months of trying to conceive). We also assessed contributions of polymorphisms in the AHR pathway via genetic risk score.

MAIN RESULTS AND THE ROLE OF CHANCE

Among SWHS women (n = 446), median TTP was 3 months and 18% reported taking ≥12 months to conceive. Initial 1976 TCDD (log10) was associated with longer TTP (adjusted fecundability odds ratio = 0.82; 95% CI 0.68-0.98) and increased risk of infertility (adjusted relative risk = 1.35; 95% CI 1.01-1.79). TCDD at pregnancy yielded similar associations. Among SWHS daughters (n = 66), median TTP was 2 months and 11% reported taking ≥12 months to conceive. Daughters showed similar, but non-significant, associations with maternal TCDD exposure.

LIMITATIONS, REASONS FOR CAUTION

A limitation of this study is time to pregnancy was reported retrospectively, although previous studies have found women are able to recall time to conception with a high degree of accuracy many years after the fact. The number of SWHS daughters who had a live birth was small and we were unable to examine fecundability of SWHS sons.

WIDER IMPLICATIONS OF THE FINDINGS

Consistent with previous findings in animal studies, our study found that TCDD exposure may be associated with decreased fertility in Seveso mothers and potentially in their daughters exposed in utero. There may be susceptible genetic subgroups. The literature has largely considered the genetics of the AHR pathway in the context of male fertility but not female fertility, despite strong biological plausibility. These findings should be replicated in larger populations and of different ancestry. Future studies in Seveso should examine the sons and the grandchildren of exposed mothers given the animal literature suggesting potential heritable epigenetic effects.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by grant numbers F06 TW02075-01 from the National Institutes of Health, R01 ES07171 and 2P30-ESO01896-17 from the National Institute of Environmental Health Sciences, R82471 from the U.S. Environmental Protection Agency and #2896 from Regione Lombardia and Fondazione Lombardia Ambiente, Milan, Italy. J.A. was supported by F31ES026488 from the National Institutes of Health. The authors declare they have no actual or potential competing financial interests.

TRIAL REGISTRATION NUMBER

N/A.

Environmental temperature and human epigenetic modifications: A systematic review

The knowledge about the effects of environmental temperature on human epigenome is a potential key to understand the health impacts of temperature and to guide acclimation under climate change. We performed a systematic review on the epidemiological studies that have evaluated the association between environmental temperature and human epigenetic modifications. We identified seven original articles on this topic published between 2009 and 2019, including six cohort studies and one cross-sectional study. They focused on DNA methylation in elderly people (blood sample) or infants (placenta sample), with sample size ranging from 306 to 1798. These studies were conducted in relatively low temperature setting (median/mean temperature: 0.8-13 °C), and linear models were used to evaluate temperature-DNA methylation association over short period (≤28 days). It has been reported that short-term ambient temperature could affect global human DNA methylation. A total of 15 candidate genes (ICAM-1, CRAT, F3, TLR-2, iNOS, ZKSCAN4, ZNF227, ZNF595, ZNF597, ZNF668, CACNA1H, AIRE, MYEOV2, NKX1-2 and CCDC15) with methylation status associated with ambient temperature have been identified. DNA methylation on ZKSCAN4, ICAM-1 partly mediated the effect of short-term cold temperature on high blood pressure and ICAM-1 protein (related to cardiovascular events), respectively. In summary, epidemiological evidence about the impacts of environment temperature on human epigenetics remains scarce and limited to short-term linear effect of cold temperature on DNA methylation in elderly people and infants. More studies are needed to broaden our understanding of temperature related epigenetic changes, especially under a changing climate.

EpiSmokEr: a robust classifier to determine smoking status from DNA methylation data

Aim: Smoking strongly influences DNA methylation, with current and never smokers exhibiting different methylation profiles. Methods: To advance the practical applicability of the smoking-associated methylation signals, we used machine learning methodology to train a classifier for smoking status prediction. Results: We show the prediction performance of our classifier on three independent whole-blood datasets demonstrating its robustness and global applicability. Furthermore, we examine the reasons for biologically meaningful misclassifications through comprehensive phenotypic evaluation. Conclusion: The major contribution of our classifier is its global applicability without a need for users to determine a threshold value for each dataset to predict the smoking status. We provide an R package, EpiSmokEr (Epigenetic Smoking status Estimator), facilitating the use of our classifier to predict smoking status in future studies.

Establishing a generalized polyepigenetic biomarker for tobacco smoking

Large-scale epigenome-wide association meta-analyses have identified multiple 'signatures'' of smoking. Drawing on these findings, we describe the construction of a polyepigenetic DNA methylation score that indexes smoking behavior and that can be utilized for multiple purposes in population health research. To validate the score, we use data from two birth cohort studies: The Dunedin Longitudinal Study, followed to age-38 years, and the Environmental Risk Study, followed to age-18 years. Longitudinal data show that changes in DNA methylation accumulate with increased exposure to tobacco smoking and attenuate with quitting. Data from twins discordant for smoking behavior show that smoking influences DNA methylation independently of genetic and environmental risk factors. Physiological data show that changes in DNA methylation track smoking-related changes in lung function and gum health over time. Moreover, DNA methylation changes predict corresponding changes in gene expression in pathways related to inflammation, immune response, and cellular trafficking. Finally, we present prospective data about the link between adverse childhood experiences (ACEs) and epigenetic modifications; these findings document the importance of controlling for smoking-related DNA methylation changes when studying biological embedding of stress in life-course research. We introduce the polyepigenetic DNA methylation score as a tool both for discovery and theory-guided research in epigenetic epidemiology.

Leukocyte telomere length and epigenetic-based mortality risk score: associations with all-cause mortality among older adults

Telomere length (TL) has been established as a biomarker of aging and aging-related health outcomes, but showed only a weak or inconsistent association with all-cause mortality in previous epidemiological studies. Recently, an epigenetic 'mortality risk score' (MS) based on whole blood DNA methylation at 10 mortality-related CpG sites has been demonstrated to be strongly related to all-cause mortality at the population level. This study aimed to address the association between TL and this MS, and to assess and compare their associations with all-cause mortality. The MS was derived from the DNA methylation profiles measured by Illumina Human Methylation450K Beadchip and TL was measured by quantitative PCR at baseline among 1517 participants aged 50-75 of the German ESTHER cohort study. In cross-sectional bi- and multivariable analyses, the MS was strongly associated and showed monotonic dose-response relationships with TL (p-values <0.05). However, only the MS but not TL was associated with all-cause mortality during a median follow-up of 12.5 years. After controlling for potential covariates and TL, hazard ratios (95% CI) for all-cause mortality for low, moderate and high levels of the MS defined by 1, 2-5 and >5 CpG sites with aberrant methylation were 2.24 (1.13-4.41), 3.31 (1.76-6.22) and 6.33 (3.22-12.41) compared to a MS of 0, respectively. Our investigation shows that the epigenetic-based MS is strongly associated with TL, a broadly accepted aging biomarker, and at the same time shows much stronger associations with all-cause mortality than the latter.

Genetic-Driven Druggable Target Identification and Validation

Choosing the right biological target is the critical primary decision for the development of new drugs. Systematic genetic association testing of both human diseases and quantitative traits, along with resultant findings of coincident associations between them, is becoming a powerful approach to infer drug targetable candidates and generate in vitro tests to identify compounds that can modulate them therapeutically. Here, we discuss opportunities and challenges, and infer criteria for the optimal use of genetic findings in the drug discovery pipeline.

Association of DNA methylation in BDNF with escitalopram treatment response in depressed Chinese Han patients

PURPOSE

The neurotrophin brain-derived neurotrophic factor (BDNF) has been found to be associated with both the pathophysiology of depression and antidepressants response. Gene expression differences were partly mediated by SNP, which might be identified as a predictor of antidepressant response. In the present study, we attempt to identify whether DNA methylation, another factor known to affect gene transcription, might also predict antidepressant response.

METHODS

A total of 85 depressed Chinese Han patients were followed-up 8 weeks after initiating escitalopram treatment. Treatment response was assessed by changes in the Hamilton Depression Rating Scale-17 (HAMD-17) score. The Life Events Scale (LES) and the Childhood Trauma Questionnaire (CTQ) were utilized as the assessment of previous life stress. The bisulfate sequencing was used to assess DNA methylation. Four single nucleotide polymorphisms (SNPs) in the BDNF gene were genotyped using PCR-RFLP or PCR sequencing.

RESULTS

We identified a DNA methylation predictor (P = 0.006-0.036) and a DNA methylation by LES interaction predictor (OR = 1.442 [1.057-1.968], P = 0.021) of general antidepressant treatment response. Lower mean BDNF DNA methylation was associated with impaired antidepressant response. Furthermore, the present data indicated that age, life stress, and SNPs genotype might be likely related to DNA methylation status. Average DNA methylation of BDNF at baseline was significantly lower than that at endpoint after 8 weeks of escitalopram treatment, which was based only on a subset of cases (n = 44).

CONCLUSIONS

Our results suggest that BDNF DNA hypomethylation and its interaction with lower LES score might result in impaired antidepressant treatment response. The pharmacoepigenetic study could eventually help in finding epigenetic biomarkers of antidepressant response.

DNA methylome variation in a perinatal nurse-visitation program that reduces child maltreatment: a 27-year follow-up

This study reveals the influence of child maltreatment on DNA methylation across the genome and provides the first evidence that a psychosocial intervention program, the Nurse Family Partnership (NFP), which targets mothers at risk for abusive parenting, associates with variation in the DNA methylome in adult offspring. The 188 participants were born to women randomly assigned to control (n = 99) or nurse-visited intervention groups (n = 89) and provided blood samples and a diagnostic interview at age 27 years. Interindividual variation in the blood DNA methylome was described using principal components (PC) scores derived from principal component analysis and showed that the NFP program (PC10: p = 0.029) and a history of abuse/neglect (PC1: p = 0.029, PC2: p = 0.009) significantly associated with DNA methylome variation at 27 years of age independent of gender, ancestry, cellular heterogeneity, and a polygenic risk index for major psychiatric disorders. The magnitude of the association between child maltreatment and DNA methylation was reduced when accounting for lifestyle factors, including smoking. These findings reflect the sustained impact of both childhood adversity as well as intervention programs that target such adversity on the epigenome but highlight the need for prospective longitudinal studies of DNA methylome variation in the context of early intervention programs.

The dynamics of smoking-related disturbed methylation: a two time-point study of methylation change in smokers, non-smokers and former smokers

Background

The evidence for epigenome-wide associations between smoking and DNA methylation continues to grow through cross-sectional studies. However, few large-scale investigations have explored the associations using observations for individuals at multiple time-points. Here, through the use of the Illumina 450K BeadChip and data collected at two time-points separated by approximately 7 years, we investigate changes in methylation over time associated with quitting smoking or remaining a former smoker, and those associated with continued smoking.

Results

Our results indicate that after quitting smoking the most rapid reversion of altered methylation occurs within the first two decades, with reversion rates related to the initial differences in methylation. For 52 CpG sites, the change in methylation from baseline to follow-up is significantly different for former smokers relative to the change for never smokers (lowest p-value 3.61 x 10^-39 for cg26703534, gene AHRR). Most of these sites’ respective regions have been previously implicated in smoking-associated diseases. Despite the early rapid change, dynamism of methylation appears greater in former smokers vs never smokers even four decades after cessation. Furthermore, our study reveals the heterogeneous effect of continued smoking: the methylation levels of some loci further diverge between smokers and non-smokers, while others re-approach. Though intensity of smoking habit appears more significant than duration, results remain inconclusive.

Conclusions

This study improves the understanding of the dynamic link between cigarette smoking and methylation, revealing the continued fluctuation of methylation levels decades after smoking cessation and demonstrating that continuing smoking can have an array of effects. The results can facilitate insights into the molecular mechanisms behind smoking-induced disturbed methylation, improving the possibility for development of biomarkers of past smoking behavior and increasing the understanding of the molecular path from exposure to disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4198-0) contains supplementary material, which is available to authorized users.