Age-related DNA Methylation Research Articles

Methylation-Based Age Estimation Model Construction and Its Effectiveness Evaluation.

To screen the DNA methylation loci associated with the age of Han males in northern China and to construct an age estimation model. Twenty-one candidate methylation loci were screened. The DNA methylation levels of 476 blood samples from Chinese Han males were detected for 21 amplicons using EpiTYPER technology platform, and data on 153 DNA methylation loci were obtained. After correlation analysis, 8 age-related DNA methylation loci were finally screened. CpG1, CpG2, CpG4, CpG7, CpG8 were located on TRIM59, RASSF5, Clorf132, CSNK1D, ELOVL2,CpG5, CpG6 on PDE4C, and CpG3 on chr17:21452808. Based on the 8 loci, 352 samples were used for model construction. A multivariate linear regression age estimation model was constructed （R2=0.93）, with mean absolute deviation （MAD） of 2.69 years old. When 109 samples were used for model validation, the MAD was 3.80 years old. The test was repeated 3 times in 15 new samples, with MADs of 4.08, 4.68 and 3.93 years old, respectively. The age estimation model on Han males in northern China constructed in this study can be used to estimate the age of victims and suspects and to narrow the scope of investigation, and therefore has practical application value.

A stably self-renewing adult blood-derived induced neural stem cell exhibiting patternability and epigenetic rejuvenation

Recent reports suggest that induced neurons (iNs), but not induced pluripotent stem cell (iPSC)-derived neurons, largely preserve age-associated traits. Here, we report on the extent of preserved epigenetic and transcriptional aging signatures in directly converted induced neural stem cells (iNSCs). Employing restricted and integration-free expression of SOX2 and c-MYC, we generated a fully functional, bona fide NSC population from adult blood cells that remains highly responsive to regional patterning cues. Upon conversion, low passage iNSCs display a profound loss of age-related DNA methylation signatures, which further erode across extended passaging, thereby approximating the DNA methylation age of isogenic iPSC-derived neural precursors. This epigenetic rejuvenation is accompanied by a lack of age-associated transcriptional signatures and absence of cellular aging hallmarks. We find iNSCs to be competent for modeling pathological protein aggregation and for neurotransplantation, depicting blood-to-NSC conversion as a rapid alternative route for both disease modeling and neuroregeneration.

Age-related epigenome-wide DNA methylation and hydroxymethylation in longitudinal mouse blood

ABSTRACTDNA methylation at cytosine-phosphate-guanine (CpG) dinucleotides changes as a function of age in humans and animal models, a process that may contribute to chronic disease development. Recent studies have investigated the role of an oxidized form of DNA methylation – 5-hydroxymethylcytosine (5hmC) – in the epigenome, but its contribution to age-related DNA methylation remains unclear. We tested the hypothesis that 5hmC changes with age, but in a direction opposite to 5-methylcytosine (5mC), potentially playing a distinct role in aging. To characterize epigenetic aging, genome-wide 5mC and 5hmC were measured in longitudinal blood samples (2, 4, and 10 months of age) from isogenic mice using two sequencing methods – enhanced reduced representation bisulfite sequencing and hydroxymethylated DNA immunoprecipitation sequencing. Examining the epigenome by age, we identified 28,196 unique differentially methylated CpGs (DMCs) and 8,613 differentially hydroxymethylated regions (DHMRs). Mouse blood showed a general pattern of epigenome-wide hypermethylation and hypo-hydroxymethylation with age. Comparing age-related DMCs and DHMRs, 1,854 annotated genes showed both differential 5mC and 5hmC, including one gene – Nfic – at five CpGs in the same 250 bp chromosomal region. At this region, 5mC and 5hmC levels both decreased with age. Reflecting these age-related epigenetic changes, Nfic RNA expression in blood decreased with age, suggesting that age-related regulation of this gene may be driven by 5hmC, not canonical DNA methylation. Combined, our genome-wide results show age-related differential 5mC and 5hmC, as well as some evidence that changes in 5hmC may drive age-related DNA methylation and gene expression.

Longitudinal effects of developmental bisphenol A, variable diet, and physical activity on age-related methylation in blood.

Research indicates that environmental factors can alter DNA methylation, but the specific effects of environmental exposures on epigenetic aging remain unclear. Here, using a mouse model of human-relevant exposures, we tested the hypothesis that early-life exposure to bisphenol A (BPA), variable diet, and/or changes in physical activity would modify rates of age-related methylation at several target regions, as measured from longitudinal blood samples (2, 4, and 10 months old). DNA methylation was quantified at two repetitive elements (LINE-1, IAP), two imprinted genes (Igf2, H19), and one non-imprinted gene (Esr1) in isogenic mice developmentally exposed to Control, Control + BPA (50 µg/kg diet), Western high-fat diet (WHFD), or Western + BPA diets. In blood samples, Esr1 DNA methylation increased significantly with age, but no other investigated loci showed significant age-related methylation. LINE-1 and IAP both showed significant negative environmental deflection by WHFD exposure (P < 0.05). Esr1also showed significant negative environmental deflection by WHFD exposure in female mice (P = 0.02), but not male mice. Physical activity had a non-significant positive effect on age-related Esr1 methylation in female blood, suggesting that it may partially abrogate the effects of WHFD on the aging epigenome. These results suggest that developmental nutritional exposures can modify age-related DNA methylation patterns at a gene related to growth and development. As such, environmental deflection of the aging epigenome may help to explain the growing prevalence of chronic diseases in human populations.

Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception

BackgroundThe well-established association of chronological age with changes in DNA methylation is primarily founded on the analysis of large sets of blood samples, while conclusions regarding tissue-specificity are typically based on small number of samples, tissues and CpGs. Here, we systematically investigate the tissue-specific character of age-related DNA methylation changes at the level of the CpG, functional genomic region and nearest gene in a large dataset.ResultsWe assembled a compendium of public data, encompassing genome-wide DNA methylation data (Illumina 450k array) on 8092 samples from 16 different tissues, including 7 tissues with moderate to high sample numbers (Dataset size range 96–1202, Ntotal = 2858). In the 7 tissues (brain, buccal, liver, kidney, subcutaneous fat, monocytes and T-helper cells), we identified 7850 differentially methylated positions that gained (gain-aDMPs; cut-offs: Pbonf ≤ 0.05, effect size ≥ 2%/10 years) and 4,287 that lost DNA methylation with age (loss-aDMPs), 92% of which had not previously been reported for whole blood. The majority of all aDMPs identified occurred in one tissue only (gain-aDMPs: 85.2%; loss-aDMPs: 97.4%), an effect independent of statistical power. This striking tissue-specificity extended to both the functional genomic regions (defined by chromatin state segmentation) and the nearest gene. However, aDMPs did accumulate in regions with the same functional annotation across tissues, namely polycomb-repressed CpG islands for gain-aDMPs and regions marked by active histone modifications for loss-aDMPs.ConclusionOur analysis shows that age-related DNA methylation changes are highly tissue-specific. These results may guide the development of improved tissue-specific markers of chronological and, perhaps, biological age.

Caloric restriction mitigates age-associated hippocampal differential CG and non-CG methylation.

Brain aging is marked by cognitive decline and susceptibility to neurodegeneration. Calorie restriction (CR) increases neurogenesis, improves memory function, and protects from age-associated neurological disorders. Epigenetic mechanisms, including DNA methylation, are vital to normal central nervous system cellular and memory functions and are dysregulated with aging. The beneficial effects of CR have been proposed to work through epigenetic processes, but this is largely unexplored. We therefore tested whether life long CR prevents age-related hippocampal DNA methylation changes. Hippocampal DNA from young (3months) and old (24months) male mice fed ad libitum and 24-month-old mice fed a 40% calorie-restricted diet from 3months of age were examined by genome-wide bisulfite sequencing to measure methylation with base specificity. Over 27 million CG and CH (non-CG) sites were examined. Of the ∼40,000 differentially methylated CG and ∼80,000 CH sites with aging, >1/3 were prevented by CR and were found across genomic regulatory regions and gene pathways. CR also caused alterations to CG and CH methylation at sites not differentially methylated with aging, and these CR-specific changes demonstrated a different pattern of regulatory element and gene pathway enrichment than those affected by aging. CR-specific DNA methyltransferase 1 and Tet methylcytosine dioxygenase 3 promoter hypermethylation corresponded to reduced gene expression. These findings demonstrate that CR attenuates age-related CG and CH hippocampal methylation changes, in combination with CR-specific methylation that may also contribute to the neuroprotective effects of CR. The prevention of age-related methylation alterations is also consistent with the prolongevity effects of CR working through an epigenetic mechanism.

Landscape of genome-wide age-related DNA methylation in breast tissue.

Despite known age-related DNA methylation (aDNAm) changes in breast tumors, little is known about aDNAm in normal breast tissues. Breast tissues from a cross-sectional study of 121 cancer-free women, were assayed for genome-wide DNA methylation. mRNA expression was assayed by microarray technology. Analysis of covariance was used to identify aDNAm’s. Altered methylation was correlated with expression of the corresponding gene and with DNA methyltransferase protein DNMT3A, assayed by immunohistochemistry. Publically-available TCGA-BRCA data were used for replication. 1,214 aDNAm’s were identified; 97% with increased methylation, and all on autosomes. Sites with increased methylation were predominantly in CpG lslands and non-enhancers. aDNAm’s with decreased methylation were generally located in intergenic regions, non-CpG Islands, and enhancers. Of the aDNAm’s identified, 650 are known to be involved in cancer, including ESR1 and beta-estradiol responsive genes. Expression of DNMT3A was positively associated with age. Two aDNAm’s showed borderline significant associations with DNMT3A expression; KRR1 (OR 6.57, 95% CI: 2.51–17.23) and DHRS12 (OR 6.08, 95% CI: 2.33–15.86). A subset of aDNAm’s co-localized within vulnerable regions for somatic mutations in cancers including breast cancer. Expression of C19orf48 was inversely and significantly correlated with its methylation level. In the TCGA dataset, 84% and 64% of the previously identified aDNAm’s were correlated with age in both normal-adjacent and tumor breast tissues, with differential associations by histological subtype. Given the similarity of findings in the breast tissues of healthy women and breast tumors, aDNAm’s may be one pathway for increased breast cancer risk with age.

A multi-tissue age prediction model based on DNA methylation analysis

Age related tissue-specific DNA methylation markers have been identified in many studies, which can be used to estimate the chronological age of an unknown biological sample’s donor. However, if these markers have been used on the wrong type of tissue, they will give an inaccurate age estimation. This research has therefore examined HumanMethylation450 (HM450) BeadChip-based profiles retrieved from the NCBI repository, with the aim of identifying a set of universal DNA methylation markers across forensically relevant tissues. By using elastic net regression, it was possible to identify 10 age-related (AR) DNA methylation markers across 41 samples coming from five types of tissue (whole blood, saliva, semen, menstrual blood, and vaginal secretions). The average predictive accuracy of the constructed model based on training data is 3.8 years. In an independent dataset of 24 samples from four types of tissues (blood, saliva, menstrual blood, and vaginal secretions), the mean absolute deviation for the menstrual blood and vaginal fluid is 6.9 years, 5.6 years for buccal swabs, and 7.8 years for blood. The overall multi-tissue accuracy rate based on bootstrap analysis was 7.8 years (95% Confidence Interval 6–9.7 years). The identified multi-tissue age prediction model has the potential to assist forensic investigations without the requirement to identify the sample body fluid type.

Measuring Animal Age with DNA Methylation: From Humans to Wild Animals.

DNA methylation (DNAm) is a key mechanism for regulating gene expression in animals and levels are known to change with age. Recent studies have used DNAm changes as a biomarker to estimate chronological age in humans and these techniques are now also being applied to domestic and wild animals. Animal age is widely used to track ongoing changes in ecosystems, however chronological age information is often unavailable for wild animals. An ability to estimate age would lead to improved monitoring of (i) population trends and status and (ii) demographic properties such as age structure and reproductive performance. Recent studies have revealed new examples of DNAm age association in several new species increasing the potential for developing DNAm age biomarkers for a broad range of wild animals. Emerging technologies for measuring DNAm will also enhance our ability to study age-related DNAm changes and to develop new molecular age biomarkers.

Biological age is better than chronological as predictor of 3-month outcome in ischemic stroke.

To analyze the effect of age-related DNA methylation changes in multiple cytosine-phosphate-guanine (CpG) sites (biological age [b-age]) on patient outcomes at 3 months after an ischemic stroke. We included 511 patients with first-ever acute ischemic stroke assessed at Hospital del Mar (Barcelona, Spain) as the discovery cohort. Demographic and clinical data, including chronological age (c-age), vascular risk factors, initial stroke severity, recanalization treatment, and previous and 3-month modified Rankin Scale (p-mRS and 3-mRS, respectively) were registered. B-age was estimated with an algorithm, based on DNA methylation in 71 CpGs. Bivariate analysis determined variables associated with 3-mRS for inclusion in ordinal multivariate analysis. After ordinal regressions for 3-month ischemic stroke outcome (3-mRS), b-age was associated with outcome (odds ratio 1.04 [95% confidence interval 1.01-1.07]), nullifying c-age. Stepwise regression kept b-age, basal NIH Stroke Scale, sex, p-mRS, and recanalization treatment as better explanatory variables, instead of c-age. These results were successfully replicated in an independent cohort. B-age, estimated by DNA methylation, is an independent predictor of ischemic stroke outcome regardless of chronological years.

Normal breast tissue DNA methylation differences at regulatory elements are associated with the cancer risk factor age

BackgroundThe underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies.MethodsHere we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450 K array) in cancer-free women from the Susan G. Komen Tissue Bank (n = 100). We tested the relation of established breast cancer risk factors, age, body mass index, parity, and family history of disease, with DNA methylation adjusting for potential variation in cell-type proportions.ResultsWe identified 787 cytosine-guanine dinucleotide (CpG) sites that demonstrated significant associations (Q value <0.01) with subject age. Notably, DNA methylation was not strongly associated with the other evaluated breast cancer risk factors. Age-related DNA methylation changes are primarily increases in methylation enriched at breast epithelial cell enhancer regions (P = 7.1E-20), and binding sites of chromatin remodelers (MYC and CTCF). We validated the age-related associations in two independent populations, using normal breast tissue samples (n = 18) and samples of normal tissue adjacent to tumor tissue (n = 97). The genomic regions classified as age-related were more likely to be regions altered in both pre-invasive (n = 40, P = 3.0E-03) and invasive breast tumors (n = 731, P = 1.1E-13).ConclusionsDNA methylation changes with age occur at regulatory regions, and are further exacerbated in cancer, suggesting that age influences breast cancer risk in part through its contribution to epigenetic dysregulation in normal breast tissue.

Non-linear patterns in age-related DNA methylation may reflect CD4+ T cell differentiation

ABSTRACTDNA methylation (DNAm) is an important epigenetic process involved in the regulation of gene expression. While many studies have identified thousands of loci associated with age, few have differentiated between linear and non-linear DNAm trends with age. Non-linear trends could indicate early- or late-life gene regulatory processes. Using data from the Illumina 450K array on 336 human peripheral blood samples, we identified 21 CpG sites that associated with age (P<1.03E-7) and exhibited changing rates of DNAm change with age (P<1.94E-6). For 2 of these CpG sites (cg07955995 and cg22285878), DNAm increased with age at an increasing rate, indicating that differential DNAm was greatest among elderly individuals. We observed significant replication for both CpG sites (P<5.0E-8) in a second set of peripheral blood samples. In 8 of 9 additional data sets comprising samples of monocytes, T cell subtypes, and brain tissue, we observed a pattern directionally consistent with DNAm increasing with age at an increasing rate, which was nominally significant in the 3 largest data sets (4.3E-15<P<0.039). cg07955995 and cg22285878 reside in the promoter region of KLF14, which encodes a protein involved in immune cell differentiation via the repression of FOXP3. These findings may suggest a possible role for cg07955995 and cg22285878 in immunosenescence.

An epigenetic aging clock for dogs and wolves.

Several articles describe highly accurate age estimation methods based on human DNA-methylation data. It is not yet known whether similar epigenetic aging clocks can be developed based on blood methylation data from canids. Using Reduced Representation Bisulfite Sequencing, we assessed blood DNA-methylation data from 46 domesticated dogs (Canis familiaris) and 62 wild gray wolves (C. lupus). By regressing chronological dog age on the resulting CpGs, we defined highly accurate multivariate age estimators for dogs (based on 41 CpGs), wolves (67 CpGs), and both combined (115 CpGs). Age related DNA methylation changes in canids implicate similar gene ontology categories as those observed in humans suggesting an evolutionarily conserved mechanism underlying age-related DNA methylation in mammals.

Age-associated DNA methylation changes in naive CD4+ T cells suggest an evolving autoimmune epigenotype in aging T cells.

We sought to define age-associated DNA methylation changes in naive CD4+ T cells. Naive CD4+ T cells were collected from 74 healthy individuals (age 19-66 years), and age-related DNA methylation changes were characterized. We identified 11,431 age-associated CpG sites, 57% of which were hypermethylated with age. Hypermethylated sites were enriched in CpG islands and repressive transcription factor binding sites, while hypomethylated sites showed T cell specific enrichment in active enhancers marked by H3K27ac and H3K4me1. Our data emphasize cancer-related DNA methylation changes with age, and also reveal age-associated hypomethylation in immune-related pathways, such as T cell receptor signaling, FCγR-mediated phagocytosis, apoptosis and the mammalian target of rapamycin signaling pathway. The MAPK signaling pathway was hypermethylated with age, consistent with a defective MAPK signaling in aging T cells. Age-associated DNA methylation changes may alter regulatory mechanisms and signaling pathways that predispose to autoimmunity.

Abstract 160: DNA-Methylation and Aging. Contribution of Biological Age to Recovery After Ischemic Stroke

Background: Stroke has a great impact in functional status of patients, although there are substantial interindividual differences in recovery capacity. Apart from stroke severity, age is considered an important predictor of outcome after stroke, but aging is not only due to chronological age. There are age-related DNA-methylation changes in multiple CpG sites across the genome that can be used to estimate the biological age (b-Age), and we seek to analyze the impact of this b-Age in recovery after an ischemic stroke. Methods: We include 600 individuals with acute ischemic stroke assessed in Hospital del Mar (Barcelona). Demographic and clinical data such as chronological age (c-Age), vascular risk factors, NIHSS at admission, recanalization treatment (rtPA or endovascular treatment), previous modified Rankin scale (p-mRS) and 3 months post stroke functional status (3-mRS) were registered. Biological age (b-Age) was estimated with Hannumm algorithm, based on DNA methylation in 71 CpGs. Results: The bivariate analyses for association with 3-mRS showed a significant results of NIHSS, c-Age, b-Age, p-mRS, and current smoking (all with p&lt;0.001). Recanalization treatment showed no significant differences in bivariate analysis. In multivariate ordinal models, b-Age kept its significance (p=0.025) nullifying c-Age (p=0.84). Initial NIHSS, p-mRS and recanalization treatment kept also significant results (p&lt;0.001). Conclusions: Biological Age, estimated by DNA methylation, is an independent predictor of stroke prognosis, irrespective to chronological age. "Healthy aging” affects the capacity of recovering after an ischemic stroke.

ArmDNA: A functional beta model for detecting age-related genomewide DNA methylation marks.

DNA methylation has been shown to play an important role in many complex diseases. The rapid development of high-throughput DNA methylation scan technologies provides great opportunities for genomewide DNA methylation-disease association studies. As methylation is a dynamic process involving time, it is quite plausible that age contributes to its variation to a large extent. Therefore, in analyzing genomewide DNA methylation data, it is important to identify age-related DNA methylation marks and delineate their functional relationship. This helps us to better understand the underlying biological mechanism and facilitate early diagnosis and prognosis analysis of complex diseases. We develop a functional beta model for analyzing DNA methylation data and detecting age-related DNA methylation marks on the whole genome by naturally taking sampling scheme into account and accommodating flexible age-methylation dynamics. We focus on DNA methylation data obtained through the widely used bisulfite conversion technique and propose to use a beta model to relate the DNA methylation level to the age. Adjusting for certain confounders, the functional age effect is left completely unspecified, offering great flexibility and allowing extra data dynamics. An efficient algorithm is developed for estimating unknown parameters, and the Wald test is used to detect age-related DNA methylation marks. Simulation studies and several real data applications were provided to demonstrate the performance of the proposed method.

Genome-Scale Assessment of Age-Related DNA Methylation Changes in Mouse Spermatozoa.

DNA methylation plays important roles in the production and functioning of spermatozoa. Recent studies have suggested that DNA methylation patterns in spermatozoa can change with age, but the regions susceptible to age-related methylation changes remain to be fully elucidated. In this study, we conducted genome-scale DNA methylation profiling of spermatozoa obtained from C57BL/6N mice at 8 weeks (8w), 18 weeks (18w) and 17 months of age (17m). There was no substantial difference in the global DNA methylation patterns between 18w and 17m samples except for a slight increase of methylation levels in long interspersed nuclear elements in the 17m samples. We found that maternally methylated imprinting control regions (mICRs) and spermatogenesis-related gene promoters had 5–10% higher methylation levels in 8w samples than in 18w or 17m samples. Analysis of individual sequence reads suggested that these regions were fully methylated (80–100%) in a subset of 8w spermatozoa. These regions are also known to be highly methylated in a subset of postnatal spermatogonia, which might be the source of the increased DNA methylation in 8w spermatozoa. Another possible source was contamination by somatic cells. Although we carefully purified the spermatozoa, it was difficult to completely exclude the possibility of somatic cell contamination. Further studies are needed to clarify the source of the small increase in DNA methylation in the 8w samples. Overall, our findings suggest that DNA methylation patterns in mouse spermatozoa are relatively stable throughout reproductive life.

2.6 Age-Related DNA Methylation Changes are Associated With Abnormal Behavior in Offspring

2.6 Age-Related DNA Methylation Changes are Associated With Abnormal Behavior in Offspring

Paternal age effects on sperm FOXK1 and KCNA7 methylation and transmission into the next generation.

Children of older fathers carry an increased risk for developing autism and other disorders. To elucidate the underlying mechanisms, we investigated the correlation of sperm DNA methylation with paternal age and its impact on the epigenome of the offspring. Methylation levels of nine candidate genes and LINE-1 repeats were quantified by bisulfite pyrosequencing in sperm DNA of 162 donors and 191 cord blood samples of resulting children (conceived by IVF/ICSI with the same sperm samples). Four genes showed a significant negative correlation between sperm methylation and paternal age. For FOXK1 and KCNA7, the age effect on the sperm epigenome was replicated in an independent cohort of 188 sperm samples. For FOXK1, paternal age also significantly correlated with foetal cord blood (FCB) methylation. Deep bisulfite sequencing and allele-specific pyrosequencing allowed us to distinguish between maternal and paternal alleles in FCB samples with an informative SNP. FCB methylation of the paternal FOXK1 allele was negatively correlated with paternal age, whereas maternal allele was unaffected by maternal age. Since FOXK1 duplication has been associated with autism, we studied blood FOXK1 methylation in 74 children with autism and 41 age-matched controls. The FOXK1 promoter showed a trend for accelerated demethylation in the autism group. Dual luciferase reporter assay revealed that FOXK1 methylation influences gene expression. Collectively, our study demonstrates that age-related DNA methylation changes in sperm can be transmitted to the next generation and may contribute to the increased disease risk in offspring of older fathers.

Blood-based biomarkers of age-associated epigenetic changes in human islets associate with insulin secretion and diabetes.

Aging associates with impaired pancreatic islet function and increased type 2 diabetes (T2D) risk. Here we examine whether age-related epigenetic changes affect human islet function and if blood-based epigenetic biomarkers reflect these changes and associate with future T2D. We analyse DNA methylation genome-wide in islets from 87 non-diabetic donors, aged 26–74 years. Aging associates with increased DNA methylation of 241 sites. These sites cover loci previously associated with T2D, for example, KLF14. Blood-based epigenetic biomarkers reflect age-related methylation changes in 83 genes identified in human islets (for example, KLF14, FHL2, ZNF518B and FAM123C) and some associate with insulin secretion and T2D. DNA methylation correlates with islet expression of multiple genes, including FHL2, ZNF518B, GNPNAT1 and HLTF. Silencing these genes in β-cells alter insulin secretion. Together, we demonstrate that blood-based epigenetic biomarkers reflect age-related DNA methylation changes in human islets, and associate with insulin secretion in vivo and T2D.