Cytosine-guanine Dinucleotide Research Articles

A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer.

Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysis of bounded DNA methylation data often requires data transformation, reducing biological interpretability. To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data. The BMMs achieve this through model-based clustering of CpG sites and by employing parameter constraints, facilitating application to different study settings. Inference proceeds via an expectation-maximisation algorithm, with an approximate maximization step providing tractability and computational feasibility. Performance of the BMMs is assessed through thorough simulation studies, and the BMMs are used for differential analyses of DNA methylation data from a prostate cancer study. Intuitive and biologically interpretable methylation state thresholds are inferred and DMCs are identified, including those related to genes such as GSTP1, RASSF1 and RARB, known for their role in prostate cancer development. Gene ontology analysis of the DMCs revealed significant enrichment in cancer-related pathways, demonstrating the utility of BMMs to reveal biologically relevant insights. An R package betaclust facilitates widespread use of BMMs.

UHRF1 ubiquitin ligase activity supports the maintenance of low-density CpG methylation.

The RING E3 ubiquitin ligase UHRF1 is an established cofactor for DNA methylation inheritance. The model posits that nucleosomal engagement through histone and DNA interactions directs UHRF1 ubiquitin ligase activity toward lysines on histone H3 tails, creating binding sites for DNMT1 through ubiquitin interacting motifs (UIM1 and UIM2). However, the extent to which DNMT1 relies on ubiquitin signaling through UHRF1 in support of DNA methylation maintenance remains unclear. Here, with integrative epigenomic and biochemical analyses, we reveal that DNA methylation maintenance at low-density cytosine-guanine dinucleotides (CpGs) is particularly vulnerable to disruption of UHRF1 ubiquitin ligase activity and DNMT1 ubiquitin reading activity through UIM1. Hypomethylation of low-density CpGs in this manner induces formation of partially methylated domains (PMDs), a methylation signature observed across human cancers. In contrast, UIM2 disruption completely abolishes the DNA methylation maintenance function of DNMT1 in a CpG density-independent manner. In the context of DNA methylation recovery following acute DNMT1 depletion, we further reveal a 'bookmarking' function for UHRF1 ubiquitin ligase activity in support of DNA re-methylation. Collectively, these studies show that DNMT1-dependent DNA methylation inheritance is a ubiquitin-regulated process that is partially reliant on UHRF1 and suggest a disrupted UHRF1-DNMT1 ubiquitin signaling axis contributes to PMD formation in cancers.

Biomimetic Nanovaccines Restore Immunosuppressive Tumor Antigen-Presenting Cells via the Saposin-Feeding Strategy.

Cancer cell membrane-derived biomimetic nanovaccines have shown tremendous potential in cancer immunotherapy. However, their efficacy is restricted by the insufficient cross-presentation of cell membrane-associated antigens. Saposins (SAs), which are vital for membrane vesicle disintegration and cell membrane-associated antigen presentation, are severely deficient in the antigen-presenting cells (APCs) within tumors. Herein, we propose a complementary strategy for increasing the efficacy of biomimetic nanovaccines via the use of SAs. Biomimetic nanovaccines were designed using cancer cell membrane shells to provide a comprehensive array of tumor-associated antigens and reactive oxygen species (ROS)-responsive nanoparticle cores that allowed the codelivery of cytosine-guanine dinucleotides (CpGs) and SAs. The biomimetic nanovaccines were ROS-responsive and highly internalized by APCs, which enabled the release of CpGs and SAs in the endo/lysosomes of APCs. Furthermore, biomimetic nanovaccines increased the activation of immunosuppressive APCs and enhanced T-cell priming by delivering SAs to the APCs. Consequently, biomimetic nanovaccines loaded with SAs not only suppressed tumor growth but also exhibited excellent therapeutic effects in combination with immune checkpoint blockade strategies. Overall, our study provides insights into the development of enhanced biomimetic nanovaccines via integrating SAs and offers a promising strategy for highly effective cancer immunotherapy.

An epigenome-wide study of selenium status and DNA methylation in the Strong Heart Study

BackgroundSelenium (Se) is an essential nutrient linked to adverse health endpoints at low and high levels. The mechanisms behind these relationships remain unclear and there is a need to further understand the epigenetic impacts of Se and their relationship to disease. We investigated the association between urinary Se levels and DNA methylation (DNAm) in the Strong Heart Study (SHS), a prospective study of cardiovascular disease (CVD) among American Indians adults. MethodsSelenium concentrations were measured in urine (collected in 1989–1991) using inductively coupled plasma mass spectrometry among 1,357 participants free of CVD and diabetes. DNAm in whole blood was measured cross-sectionally using the Illumina MethylationEPIC BeadChip (850 K) Array. We used epigenome-wide robust linear regressions and elastic net to identify differentially methylated cytosine-guanine dinucleotide (CpG) sites associated with urinary Se levels. ResultsThe mean (standard deviation) urinary Se concentration was 51.8 (25.1) μg/g creatinine. Across 788,368 CpG sites, five differentially methylated positions (DMP) (hypermethylated: cg00163554, cg18212762, cg11270656, and hypomethylated: cg25194720, cg00886293) were significantly associated with Se in linear regressions after accounting for multiple comparisons (false discovery rate p-value: 0.10). The top hypermethylated DMP (cg00163554) was annotated to the Disco Interacting Protein 2 Homolog C (DIP2C) gene, which relates to transcription factor binding. Elastic net models selected 425 hypo- and hyper-methylated DMPs associated with urinary Se, including three sites (cg00163554 [DIP2C], cg18212762 [MAP4K2], cg11270656 [GPIHBP1]) identified in linear regressions. ConclusionsUrinary Se was associated with minimal changes in DNAm in adults from American Indian communities across the Southwest and the Great Plains in the United States, suggesting that other mechanisms may be driving health impacts. Future analyses should explore other mechanistic biomarkers in human populations, determine these relationships prospectively, and investigate the potential role of differentially methylated sites with disease endpoints.

Development and Validation of a Novel Placental DNA Methylation Biomarker of Maternal Smoking during Pregnancy in the ECHO Program.

Maternal cigarette smoking during pregnancy (MSDP) is associated with numerous adverse health outcomes in infants and children with potential lifelong consequences. Negative effects of MSDP on placental DNA methylation (DNAm), placental structure, and function are well established. Our aim was to develop biomarkers of MSDP using DNAm measured in placentas (), collected as part of the Vitamin C to Decrease the Effects of Smoking in Pregnancy on Infant Lung Function double-blind, placebo-controlled randomized clinical trial conducted between 2012 and 2016. We also aimed to develop a digital polymerase chain reaction (PCR) assay for the top ranking cytosine-guanine dinucleotide (CpG) so that large numbers of samples can be screened for exposure at low cost. We compared the ability of four machine learning methods [logistic least absolute shrinkage and selection operator (LASSO) regression, logistic elastic net regression, random forest, and gradient boosting machine] to classify MSDP based on placental DNAm signatures. We developed separate models using the complete EPIC array dataset and on the subset of probes also found on the 450K array so that models exist for both platforms. For comparison, we developed a model using CpGs previously associated with MSDP in placenta. For each final model, we used model coefficients and normalized beta values to calculate placental smoking index (PSI) scores for each sample. Final models were validated in two external datasets: the Extremely Low Gestational Age Newborn observational study, ; and the Rhode Island Children's Health Study, . Logistic LASSO regression demonstrated the highest performance in cross-validation testing with the lowest number of input CpGs. Accuracy was greatest in external datasets when using models developed for the same platform. PSI scores in smokers only () were moderately correlated with maternal plasma cotinine levels. One CpG (cg27402634), with the largest coefficient in two models, was measured accurately by digital PCR compared with measurement by EPIC array (). To our knowledge, we have developed the first placental DNAm-based biomarkers of MSDP with broad utility to studies of prenatal disease origins. https://doi.org/10.1289/EHP13838.

Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging.

Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability-including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.

Fluorogenic RNA Aptamer-Based Amplification and Transcription Strategy for Label-free Sensing of Methyltransferase Activity in Complex Matrixes.

DNA methyltransferase is significant in cellular activities and gene expression, and its aberrant expression is closely linked to various cancers during initiation and progression. Currently, there is a great demand for reliable and label-free techniques for DNA methyltransferase evaluation in tumor diagnosis and cancer therapy. Herein, a low-background fluorescent RNA aptamer-based sensing approach for label-free quantification of cytosine-guanine (CpG) dinucleotides methyltransferase (M.SssI) is reported. The fluorogenic light-up RNA aptamers-based strategy exhibits high selectivity via restriction endonuclease, padlock-based recognition, and RNA transcription. By combining rolling circle amplification (RCA), and RNA transcription with fluorescence response of RNA aptamers of Spinach-dye compound, the proposed platform exhibited efficiently ultrahigh sensitivity toward M.SssI. Eventually, the detection can be achieved in a linear range of 0.02-100 U mL-1 with a detection limit of 1.6 × 10-3 U mL-1. Owing to these superior features, the method is further applied in serum samples spiked M.SssI, which delivers a recovery ranging from 92.0 to 107.0% and a relative standard deviation <7.0%, providing a promising and practical tool for determining M.SssI in complex biological matrices.

Integrated Bioinformatics Analysis Reveals the Aberrantly Methylated Differentially Expressed Genes in Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) causes heart failure and sudden death. Epigenetics is crucial in cardiomyopathy susceptibility and progression; however, the relationship between epigenetics, particularly DNA methylation, and DCM remains unknown. Therefore, this study identified aberrantly methylated differentially expressed genes (DEGs) associated with DCM using bioinformatics analysis and characterized their clinical utility in DCM. DNA methylation expression profiles and transcriptome data from public datasets of human DCM and healthy control cardiac tissues were obtained from the Gene Expression Omnibus public datasets. Then an epigenome-wide association study was performed. DEGs were identified in both DCM and healthy control cardiac tissues. In total, 3,353 cytosine-guanine dinucleotide sites annotated to 2,818 mRNAs were identified, and 479 DCM-related genes were identified. Subsequently, core genes were screened using logistic, least absolute shrinkage and selection operator, random forest, and support vector machine analyses. The overlapping of these genes resulted in DEGs with abnormal methylation patterns. Cross-tabulation analysis identified 8 DEGs with abnormal methylation. Real-time quantitative polymerase chain reaction confirmed the expression of aberrantly methylated DEGs in mice. In DCM murine cardiac tissues, the expressions of SLC16A9, SNCA, PDE5A, FNDC1, and HTRA1 were higher compared to normal murine cardiac tissues. Moreover, logistic regression model associated with aberrantly methylated DEGs was developed to evaluate the diagnostic value, and the area under the receiver operating characteristic curve was 0.949, indicating that the diagnostic model could reliably distinguish DCM from non-DCM samples. In summary, our study identified 5 DEGs through integrated bioinformatic analysis and in vivo experiments, which could serve as potential targets for further comprehensive investigation.

DNA methylation and expression of LGR6 gene in ankylosing spondylitis: A case-control study

ObjectiveThe objectives of the present research were to ascertain the relationship of Leucine-Rich Repeat-Containing G-Protein Coupled Receptors 6 (LGR6) methylation and transcript levels with ankylosing spondylitis (AS). MethodsTargeted bisulfite sequencing was applied to analyze LGR6 DNA methylation in 81 AS cases and 81 controls. Besides, the LGR6 transcription level of peripheral blood mononuclear cells (PBMCs) from 70 AS cases and 64 controls was measured utilizing quantitative real-time transcription-polymerase chain reaction (qRT-PCR). ResultsThe study detected the methylation levels of 43 sites in two CpG (cytosine-guanine dinucleotide) islands of LGR6 and found that LGR6 were significantly hypomethylated in AS patients (LGR6_1: P = 0.002; LGR6_2: P < 0.001). LGR6 transcript level was obviously reduced in AS (P = 0.001) and was positively related to DNA methylation level (CpG-1: P = 0.010; CpG-2: P = 0.007). Besides, the Receiver operating characteristic curve (ROC) exhibited good diagnostic performance of LGR6 methylation level (AUC = 0.676, 95% CI = 0.594–0.758, P < 0.001). Further subgroup analysis revealed that gender may affect the LGR6_1 methylation pattern. ConclusionThe present study revealed that LGR6 DNA methylation dysregulation may be involved in the pathogenesis of AS from an epigenetic perspective for the first time, with the aim of providing new directions for biomarker identification and treatment development for AS patients.

Diverse genome-wide DNA methylation alterations in canine hepatocellular tumours.

Canine hepatocellular tumours (HCTs) are common primary liver tumours. However, the exact mechanisms of tumourigenesis remain unclear. Although some genetic mutations have been reported, DNA methylation alterations in canine HCT have not been well studied. In this study, we aimed to analyse the DNA methylation status of canine HCT. Tissues from 33 hepatocellular carcinomas, 3 hepatocellular adenomas, 1 nodular hyperplasia, 21 non-tumour livers from the patients and normal livers from 5 healthy dogs were used. We analysed the DNA methylation levels of 72,367 cytosine-guanine dinucleotides (CpG sites) in all 63 samples. Although a large fraction of CpG sites that were highly methylated in the normal liver became hypomethylated in tumours from most patients, we also found some patients with less remarkable change or no change in DNA methylation. Hierarchical clustering analysis revealed that 32 of 37 tumour samples differed from normal livers, although the remaining 5 tumour livers fell into the same cluster as normal livers. In addition, the number of hypermethylated genes in tumour livers varied among tumour cases, suggesting various DNA methylation patterns in different tumour groups. However, patient and clinical parameters, such as age, were not associated with DNA methylation status. In conclusion, we found that HCTs undergo aberrant and diverse patterns of genome-wide DNA methylation compared with normal liver tissue, suggesting a complex epigenetic mechanism in canine HCT.

Exposure to unmethylated CpG oligonucleotides disrupts blood pressure circadian rhythms and placental clock gene network in pregnant rats.

Bacterial infections and impaired circulating mitochondrial DNA dynamics are associated with adverse pregnancy outcomes. Unmethylated cytosine-guanine dinucleotide (CpG) motifs are common in bacterial and mitochondrial DNA and act as potent immunostimulators. We tested the hypothesis that exposure to CpG oligonucleotides (ODN) during pregnancy would disrupt blood pressure circadian rhythms and placental molecular clock network, mediating aberrant fetoplacental growth dynamics. Rats were repeatedly treated with CpG ODN in the third trimester [gestational days (GD) 14, 16, and 18] and euthanized on GD20 (near term) or treated with a single dose of CpG ODN on GD14 and euthanized 4 h after treatment. Hemodynamic circadian rhythms were analyzed via Lomb-Scargle periodogram analysis on 24-h raw data collected continuously via radiotelemetry. A P value ≥ 0.05 indicates the absence of a circadian rhythm. Following the first treatment with CpG ODN, maternal systolic and diastolic blood pressure circadian rhythms were lost (P ≥ 0.05). Blood pressure circadian rhythm was restored by GD16 and remained unaffected after the second treatment with CpG ODN (P < 0.0001). Diastolic blood pressure circadian rhythm was again lost after the last treatment on GD18 (P ≥ 0.05). CpG ODN increased placental expression of Per2, Per3, and Tnfα (P ≤ 0.05) and affected fetoplacental growth dynamics. Reduced fetal and placental weights were disproportionately associated with increases in the number of resorptions in ODN-treated dams compared with controls. In conclusion, gestational exposure to unmethylated CpG ODN dysregulates the placental molecular clock network and fetoplacental growth dynamics and disrupts blood pressure circadian rhythms.NEW & NOTEWORTHY Gestational exposure to unmethylated CpG ODN dysregulates placental molecular clock network and fetoplacental growth dynamics and disrupts blood pressure circadian rhythms. These findings provide novel insights into the relationship between circadian rhythms and immune responses in pregnancy and propose new mechanisms by which maternal responses to immune triggers could dictate circadian rhythms of cardiovascular processes and placental clock machinery function to determine fetal growth trajectories.

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

BackgroundThe field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine–guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons.ResultsWe found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general—suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities.ConclusionsThe variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

Methylation of BDNF gene in association with episodic memory in women.

Brain-derived neurotrophic factor (BDNF) gene regulation plays an important role in long-term memory formation, and the DNA methylation (DNAm) level of BDNF promoters has been associated with episodic memory deficits. Our aim was to explore the association between DNAm levels in BDNF promoter IV with verbal learning and memory performance in healthy women. We conducted a cross-sectional study by recruiting 53 individuals. Episodic memory was assessed by using the Rey Auditory Verbal Learning Test (RAVLT). Clinical interviews, RAVLT, and blood sample collection were assessed in all participants. DNAm was measured on DNA from whole peripheral blood using pyrosequencing. According to generalized linear model (GzLM) analyses, cytosine guanine dinucleotide (CpG) site 5 showed significant associations between learning capacity (LC, p < 0.035), that is, every 1% of DNA methylation at CpG site 5 results in a 0.068 reduction in verbal learning performance. To the best of our knowledge, the current study is the first to show that BDNF DNAm plays an important role in episodic memory.

Epigenetic biomarkers for smoking cessation

Cigarette smoking has been associated with epigenetic alterations that may be reversible upon cessation. As the most-studied epigenetic modification, DNA methylation is strongly associated with smoking exposure, providing a potential mechanism that links smoking to adverse health outcomes. Here, we reviewed the reversibility of DNA methylation in accessible peripheral tissues, mainly blood, in relation to cigarette smoking cessation and the utility of DNA methylation as a biomarker signature to differentiate current, former, and never smokers and to quantify time since cessation. We summarized thousands of differentially methylated Cytosine-Guanine (CpG) dinucleotides and regions associated with smoking cessation from candidate gene and epigenome-wide association studies, as well as the prediction accuracy of the multi-CpG predictors for smoking status. Overall, there is robust evidence for DNA methylation signature of cigarette smoking cessation. However, there are still gaps to fill, including (1) cell-type heterogeneity in measuring blood DNA methylation; (2) underrepresentation of non-European ancestry populations; (3) limited longitudinal data to quantitatively measure DNA methylation after smoking cessation over time; and (4) limited data to study the impact of smoking cessation on other epigenetic features, noncoding RNAs, and histone modifications. Epigenetic machinery provides promising biomarkers that can improve success in smoking cessation in the clinical setting. To achieve this goal, larger and more-diverse samples with longitudinal measures of a broader spectrum of epigenetic marks will be essential to developing a robust DNA methylation biomarker assay, followed by meeting validation requirements for the assay before being implemented as a clinically useful tool.

Sex-based differences in placental DNA methylation profiles related to gestational age: an NIH ECHO meta-analysis

ABSTRACT The placenta undergoes many changes throughout gestation to support the evolving needs of the foetus. There is also a growing appreciation that male and female foetuses develop differently in utero, with unique epigenetic changes in placental tissue. Here, we report meta-analysed sex-specific associations between gestational age and placental DNA methylation from four cohorts in the National Institutes of Health (NIH) Environmental influences on Child Health Outcomes (ECHO) Programme (355 females/419 males, gestational ages 23–42 weeks). We identified 407 cytosine-guanine dinucleotides (CpGs) in females and 794 in males where placental methylation levels were associated with gestational age. After cell-type adjustment, 55 CpGs in females and 826 in males were significant. These were enriched for biological processes critical to the immune system in females and transmembrane transport in males. Our findings are distinct between the sexes: in females, associations with gestational age are largely explained by differences in placental cellular composition, whereas in males, gestational age is directly associated with numerous alterations in methylation levels.

Analysis of Methylation and mRNA Expression of Lin28B Gene Promoter Region in the Hypothalamus of Dolang Sheep During Pubertal Initiation.

Lin28B plays an important role in puberty initiation in sheep. This study aimed to discuss the correlation between different growth periods and the methylation status of cytosine-guanine dinucleotide (CpG) islands in the promoter region of the Lin28B gene in the Dolang sheep's hypothalamus. In this study, the sequence of the Lin28B gene promoter region in Dolang sheep was obtained by cloning and sequencing, and methyl groups of the CpG island of the Lin28B gene promoter in the hypothalamus were detected by bisulfite sequencing PCR during the three periods of prepuberty, adolescence, and postpuberty in Dolang sheep. Lin28B expression in the Dolang sheep's hypothalamus was detected by fluorescence quantitative PCR at three stages: prepuberty, puberty, and postpuberty. In this experiment, the 2993-bp Lin28B promoter region was obtained, and it was predicted that there was a CpG island containing 15 transcription factor binding sites and 12 CpG sites, which may play a role in gene expression regulation. Overall, methylation levels increased from prepuberty to postpuberty, while Lin28B expression levels decreased, indicating that Lin28B expression was negatively correlated with promoter methylation levels. Variance analysis showed significant differences in the methylation status of CpG5, CpG7, and CpG9 between pre- and postpuberty (p < 0.05). Our data show that Lin28B expression is increased by demethylation of promoter CpG islands, with CpG5, CpG7, and CpG9 implicated as critical regulatory sites.

Maternal DNA methylation signatures of arsenic exposure is associated with adult offspring insulin resistance in the Strong Heart Study

Exposure to low to moderate arsenic (As) levels has been associated with type 2 diabetes (T2D) and other chronic diseases in American Indian communities. Prenatal exposure to As may also increase the risk for T2D in adulthood, and maternal As has been associated with adult offspring metabolic health measurements. We hypothesized that T2D-related outcomes in adult offspring born to women exposed to low to moderate As can be evaluated utilizing a maternally-derived molecular biosignature of As exposure. Herein, we evaluated the association of maternal DNA methylation with incident T2D and insulin resistance (Homeostatic model assessment of insulin resistance [HOMA2-IR]) in adult offspring. For DNA methylation, we used 20 differentially methylated cytosine-guanine dinucleotides (CpG) previously associated with the sum of inorganic and methylated As species (ΣAs) in urine in the Strong Heart Study (SHS). Of these 20 CpGs, we found six CpGs nominally associated (p < 0.05) with HOMA2-IR in a fully adjusted model that included clinically relevant covariates and offspring adiposity measurements; a similar model that adjusted instead for maternal adiposity measurements found three CpGs nominally associated with HOMA2-IR, two of which overlapped the offspring adiposity model. After adjusting for multiple comparisons, cg03036214 remained associated with HOMA2-IR (q < 0.10) in the offspring adiposity model. The odds ratio of incident T2D increased with an increase in maternal DNA methylation at one HOMA2-IR associated CpG in the model adjusting for offspring adiposity, cg12116137, whereas adjusting for maternal adiposity had a minimal effect on the association. Our data suggests offspring adiposity, rather than maternal adiposity, potentially influences the effects of maternal DNAm signatures on offspring metabolic health parameters. Here, we have presented evidence supporting a role for epigenetic biosignatures of maternal As exposure as a potential biomarker for evaluating risk of T2D-related outcomes in offspring later in life.

Meta-Analysis of DNA Methylation Datasets Shows Aberrant DNA Methylation of Thyroid Development or Function Genes in Down Syndrome.

Background: In Down syndrome (DS), there is high occurrence of congenital hypothyroidism (CH) and subclinical hypothyroidism (SH) early in life. The etiology of CH and early SH in DS remains unclear. Previous research has shown genome-wide transcriptional and epigenetic alterations in DS. Thus, we hypothesized that CH and early SH could be caused by epigenetically driven transcriptional downregulation of thyroid-related genes, through promoter region hypermethylation. Methods: We extracted whole blood DNA methylation (DNAm) profiles of DS and non-DS individuals from four independent Illumina array-based datasets (252 DS individuals and 519 non-DS individuals). The data were divided into discovery and validation datasets. Epigenome-wide association analysis was performed using a linear regression model, after which we filtered results for thyroid-related genes. Results: In the discovery dataset, we identified significant associations for DS in 18 thyroid-related genes. Twenty-one of 30 significant differentially methylated positions (DMPs) were also significant in the validation dataset. A meta-analysis of the discovery and validation datasets detected 31 DMPs, including 29 promoter-associated cytosine-guanine dinucleotides (CpG) with identical direction of effect across the datasets, and two differentially methylated regions. Twenty-seven DMPs were hypomethylated and promoter associated. The mean methylation difference of hypomethylated thyroid-related DMPs decreased with age. Conclusions: Contrary to our hypothesis of generalized hypermethylation of promoter regions of thyroid-related genes-indicative of epigenetic silencing of promoters and subsequent transcriptional downregulation, causing biochemical thyroid abnormalities in DS-we found an enrichment of hypomethylated DMPs annotated to promoter regions of these genes. This suggests that CH and early SH in DS are not caused by differential methylation of thyroid-related genes. Considering that epigenetic regulation is dynamic, we hypothesize that the observed thyroid-related gene DNAm changes could be a rescue phenomenon in an attempt to ameliorate the thyroid phenotype, through epigenetic upregulation of thyroid-related genes. This hypothesis is supported by the finding of decreasing methylation difference of thyroid-related genes with age. The prevalence of early SH declines with age, so hypothetically, epigenetic upregulation of thyroid-related genes also diminishes. While this study provides interesting insights, the exact origin of CH and early SH in DS remains unknown.

Wildfire-related PM2.5 and DNA methylation: An Australian twin and family study

BackgroundWildfire-related fine particulate matter (PM2.5) has many adverse health impacts, but its impacts on human epigenome are unknown. We aimed to evaluate the associations between long-term exposure to wildfire-related PM2.5 and blood DNA methylation, and whether the associations differ from those with non-wildfire-related PM2.5. MethodsWe studied 479 Australian women comprising 132 twin pairs and 215 of their sisters. Blood-derived DNA methylation was measured using the HumanMethylation450 BeadChip array. Data on 3-year (year of blood collection and previous two years) average wildfire-related and non-wildfire-related PM2.5 at 0.01°×0.01° spatial resolution were created by combining information from satellite observations, chemical transport models, and ground-based observations. Exposure data were linked to each participant’s home address, assuming the address did not change during the exposure window. For DNA methylation of each cytosine-guanine dinucleotide (CpG), and for global DNA methylation represented by the average of all measured CpGs or CpGs in repetitive elements, we evaluated their associations with wildfire- or non-wildfire-related PM2.5 using a within-sibship analysis controlling for factors shared between siblings and other important covariates. Differentially methylated regions (DMRs) were defined by comb-p and DMRcate. ResultsThe 3-year average wildfire-related PM2.5 (range: 0.3 to 7.6 µg/m3, mean: 1.6 µg/m3) was negatively, but not significantly (p-values greater than 0.05) associated with all seven global DNA methylation measures. There were 26 CpGs and 33 DMRs associated with wildfire-related PM2.5 (Bonferroni adjusted p-value < 0.05) mapped to 47 genes enriched for pathways related to inflammatory regulation and platelet activation. These genes have been related to many human diseases or phenotypes e.g., cancer, mental disorders, diabetes, obesity, asthma, blood pressure. These CpGs, DMRs and enriched pathways did not overlap with the 1 CpG and 7 DMRs associated with non-wildfire-related PM2.5. ConclusionsLong-term exposure to wildfire-related PM2.5 was associated with various blood DNA methylation signatures in Australian women, and these were distinct from those associated with non-wildfire-related PM2.5.

Epigenome-wide association study of short-term temperature fluctuations based on within-sibship analyses in Australian females

BackgroundTemperature fluctuations can affect human health independent of the effect of mean temperature. However, no study has evaluated whether short-term temperature fluctuations could affect DNA methylation. MethodsPeripheral blood DNA methylation for 479 female siblings of 130 families were analysed. Gridded daily temperatures data were obtained, linked to each participant’s home address, and used to calculate nine different metrics of short-term temperature fluctuations: temperature variabilities (TVs) within the day of blood draw and preceding one to seven days (TV 0–1 to TV 0–7), diurnal temperature range (DTR), and temperature change between neighbouring days (TCN). Within-sibship design was used to perform epigenome-wide association analyses, adjusting for daily mean temperatures, and other important covariates (e.g., smoking, alcohol use, cell-type proportions). Differentially methylated regions (DMRs) were further identified. Multiple-testing comparisons with a significant threshold of 0.01 for cytosine-guanine dinucleotides (CpGs) and 0.05 for DMRs were applied. ResultsAmong 479 participants (mean age ± SD, 56.4 ± 7.9 years), we identified significant changes in methylation levels in 14 CpGs and 70 DMRs associated with temperature fluctuations. Almost all identified CpGs were associated with exposure to temperature fluctuations within three days. Differentially methylated signals were mapped to 68 genes that were linked to human diseases such as cancer (e.g., colorectal carcinoma, breast carcinoma, and metastatic neoplasms) and mental disorder (e.g., schizophrenia, mental depression, and bipolar disorder). The top three most significantly enriched gene ontology terms were Response to bacterium (TV 0–3), followed by Hydrolase activity, acting on ester bonds (TCN), and Oxidoreductase activity (TV 0–3). ConclusionsShort-term temperature fluctuations were associated with differentially methylated signals across the human genome, which provides evidence on the potential biological mechanisms underlying the health impact of temperature fluctuations. Future studies are needed to further clarify the roles of DNA methylation in diseases associated with temperature fluctuations.