DNA methylation-based age estimation in pediatric healthy tissues and brain tumors.
Ontology highlight
ABSTRACT:
SUBMITTER: Kling T
PROVIDER: S-EPMC7695434 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In wild animal conservation, knowing the age of an individual animal is extremely beneficial. However, estimating the age is difficult for many species. Recently, epigenetics-based methods of estimating age have been reported. These studies were predominantly on humans with few reports on other animals, especially wild animals. In the present study, a chimpanzee (Pan troglodytes) age prediction model was developed based on the ELOVL2, CCDC102B, and ZNF423 genes that may also have application in human age prediction. Pyrosequencing was used to measure methylation in 20 chimpanzee blood samples and correlation between age and methylation status was calculated. Age and methylation of sites in ELOVL2 and CCDC102B were significantly correlated and an age prediction model was created using these genes. In the regression equation using only ELOVL2, the highest correlation coefficient was 0.741, with a mean absolute deviation (MAD) of 5.41, compared with the combination of ELOVL2 and CCDC102B, where the highest correlation coefficient was 0.742 and the MAD was 5.41. Although larger MADs were observed in chimpanzees than in humans based on these genes, the results indicate the feasibility of estimating chimpanzee age using DNA methylation, and can have implications in understanding the ecology of chimpanzees and chimpanzee conservation.
| S-EPMC6030051 | biostudies-literature
Project description:Although DNA methylation variation of autosomal CpGs provides robust age predictive biomarkers, no male-specific age predictor exists based on Y-CpGs yet. Since sex chromosomes play an important role in aging, a Y-chromosome-based age predictor would allow studying male-specific aging effects and would also be useful in forensics. Here, we used blood-based DNA methylation microarray data of 1,057 males from six cohorts aged 15-87 and identified 75 Y-CpGs with an interquartile range of ≥0.1. Of these, 22 and six were significantly hyper- and hypomethylated with age (p(cor)<0.05, Bonferroni), respectively. Amongst several machine learning algorithms, a model based on support vector machines with radial kernel performed best in male-specific age prediction. We achieved a mean absolute deviation (MAD) between true and predicted age of 7.54 years (cor=0.81, validation) when using all 75 Y-CpGs, and a MAD of 8.46 years (cor=0.73, validation) based on the most predictive 19 Y-CpGs. The accuracies of both age predictors did not worsen with increased age, in contrast to autosomal CpG-based age predictors that are known to predict age with reduced accuracy in the elderly. Overall, we introduce the first-of-its-kind male-specific epigenetic age predictor for future applications in aging research and forensics.
| S-EPMC7993701 | biostudies-literature
Project description:In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath's results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.
| S-EPMC8228382 | biostudies-literature
Project description:DNA methylation (DNAm) plays diverse roles in human biology, but this dynamic epigenetic mark remains far from fully characterized. Although earlier studies uncovered loci that undergo age-associated DNAm changes in adults, little is known about such changes during childhood. Despite profound DNAm plasticity during embryogenesis and early development, monozygotic twins show indistinguishable childhood methylation, suggesting DNAm is highly coordinated during the pediatric period. Here we examine the methylation of 27,578 CpG dinucleotides in peripheral blood DNA from 398 boys, aged 3 to 17 years, and find significant age-associated changes in DNAm at 2,078 loci. We report a deficit of such loci on the X chromosome, a preference for specific nucleotides immediately surrounding the interrogated CpG dinucleotide, and a primary association with developmental and immune ontological functions. These pediatric age-associated loci overlap significantly with those previously identified in adults (p < 0.001) but most of the pediatric loci are unique, suggesting many are childhood-specific. Meta-analysis (n = 1080) with two adult studies reveals that the methylation changes in 29.5% of the age-associated pediatric loci follow a linear pattern from childhood into adulthood; however, we also find a three-fold higher rate of change in children compared with adults and that a higher proportion of lifelong changes are more accurately modeled as a function of logarithmic age. We therefore conclude that DNAm changes occur more rapidly during childhood and are imperfectly accounted for by statistical corrections that are linear in age, further suggesting that future DNAm studies are matched closely for age. Age associated DNA methylation was evaluated at each CpG locus using a fixed-effects linear regression model adjusting for BeadChip as a covariate to account for batch effects. Analysis of variance (ANOVA) was conducted for each CpG locus to assess the age affect at each CpG locus which was subsequently corrected for multiple hypotheses using a Benjamini-Hochberg FDR. SUPPLEMENTARY FILES: * Matrix_AllSampleDetection.txt: Detection P-values for each sample and each loci * Matrix_AllSampleSignal.txt: Signal for Probe A and Probe B for each sample and each loci * Matrix_Non-Normalized_AllSampleBeta.txt: Beta values calculated using GenomeStudio v2010.3 Methylation Module 1.8.5 as Signal of Probe B / (Signal of Probe A + Signal of Probe B + 100) * Matrix_Normalized_AllSampleBetaPrime.txt: Beta Prime values used for analysis calculated from the Probe B and Probe A signals as Beta Prime = Signal of Probe B / (Signal of Probe A + Signal of Probe A)
2012-02-01 | E-GEOD-27097 | biostudies-arrayexpress
Project description:DNA methylation (DNAm) plays diverse roles in human biology, but this dynamic epigenetic mark remains far from fully characterized. Although earlier studies uncovered loci that undergo age-associated DNAm changes in adults, little is known about such changes during childhood. Despite profound DNAm plasticity during embryogenesis, monozygotic twins show indistinguishable childhood methylation, suggesting that DNAm is highly coordinated throughout early development. Here we examine the methylation of 27,578 CpG dinucleotides in peripheral blood DNA from a cross-sectional study of 398 boys, aged 3-17 yr, and find significant age-associated changes in DNAm at 2078 loci. These findings correspond well with pyrosequencing data and replicate in a second pediatric population (N = 78). Moreover, we report a deficit of age-related loci on the X chromosome, a preference for specific nucleotides immediately surrounding the interrogated CpG dinucleotide, and a primary association with developmental and immune ontological functions. Meta-analysis (N = 1158) with two adult populations reveals that despite a significant overlap of age-associated loci, most methylation changes do not follow a lifelong linear pattern due to a threefold to fourfold higher rate of change in children compared with adults; consequently, the vast majority of changes are more accurately modeled as a function of logarithmic age. We therefore conclude that age-related DNAm changes in peripheral blood occur more rapidly during childhood and are imperfectly accounted for by statistical corrections that are linear in age, further suggesting that future DNAm studies should be matched closely for age.
| S-EPMC3317145 | biostudies-literature
Project description:DNA methylation (DNAm) plays diverse roles in human biology, but this dynamic epigenetic mark remains far from fully characterized. Although earlier studies uncovered loci that undergo age-associated DNAm changes in adults, little is known about such changes during childhood. Despite profound DNAm plasticity during embryogenesis and early development, monozygotic twins show indistinguishable childhood methylation, suggesting DNAm is highly coordinated during the pediatric period. Here we examine the methylation of 27,578 CpG dinucleotides in peripheral blood DNA from 398 boys, aged 3 to 17 years, and find significant age-associated changes in DNAm at 2,078 loci. We report a deficit of such loci on the X chromosome, a preference for specific nucleotides immediately surrounding the interrogated CpG dinucleotide, and a primary association with developmental and immune ontological functions. These pediatric age-associated loci overlap significantly with those previously identified in adults (p < 0.001) but most of the pediatric loci are unique, suggesting many are childhood-specific. Meta-analysis (n = 1080) with two adult studies reveals that the methylation changes in 29.5% of the age-associated pediatric loci follow a linear pattern from childhood into adulthood; however, we also find a three-fold higher rate of change in children compared with adults and that a higher proportion of lifelong changes are more accurately modeled as a function of logarithmic age. We therefore conclude that DNAm changes occur more rapidly during childhood and are imperfectly accounted for by statistical corrections that are linear in age, further suggesting that future DNAm studies are matched closely for age.
2012-02-01 | GSE27097 | GEO
Project description:It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure.I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance.I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
| S-EPMC4015143 | biostudies-literature
Project description:BackgroundAberrant DNA methylation is a prominent feature of many cancers, and may be especially relevant in germ cell tumors (GCTs) due to the extensive epigenetic reprogramming that occurs in the germ line during normal development.MethodsWe used the Illumina GoldenGate Cancer Methylation Panel to compare DNA methylation in the three main histologic subtypes of pediatric GCTs (germinoma, teratoma and yolk sac tumor (YST); N?=?51) and used recursively partitioned mixture models (RPMM) to test associations between methylation pattern and tumor and demographic characteristics. We identified genes and pathways that were differentially methylated using generalized linear models and Ingenuity Pathway Analysis. We also measured global DNA methylation at LINE1 elements and evaluated methylation at selected imprinted loci using pyrosequencing.ResultsMethylation patterns differed by tumor histology, with 18/19 YSTs forming a distinct methylation class. Four pathways showed significant enrichment for YSTs, including a human embryonic stem cell pluripotency pathway. We identified 190 CpG loci with significant methylation differences in mature and immature teratomas (q?<?0.05), including a number of CpGs in stem cell and pluripotency-related pathways. Both YST and germinoma showed significantly lower methylation at LINE1 elements compared with normal adjacent tissue while there was no difference between teratoma (mature and immature) and normal tissue. DNA methylation at imprinted loci differed significantly by tumor histology and location.ConclusionUnderstanding methylation patterns may identify the developmental stage at which the GCT arose and the at-risk period when environmental exposures could be most harmful. Further, identification of relevant genetic pathways could lead to the development of new targets for therapy.
| S-EPMC3701494 | biostudies-literature
Project description:Age is a key demographic in conservation biology where individual age classes show diffuse differences in terms of important population dynamics metrics such as morbidity and mortality. Furthermore, several traits including reproductive potential show clear senescence with aging. Thus, the ability to estimate the ages for the individuals of a population as part of age class assignment is critical in understanding both the current population structure as well as in modelling and predicting the future survival of species. This study explored the utility of age-related changes in methylation for six candidate genes, EDARADD, ELOVL2, FHL2, GRIA2, ITGA2B, and PENK, to create an age estimation model in captive cheetah. Gene orthologues between humans and cheetah were retrieved from NCBI containing a hundred CpG’s. Target regions were assayed for differential methylation and fragmentation patterns in fifty samples using mass array technology for a total of seventy-seven CpG clusters. Correlation analyses between CpG methylation and chronological age identified six CpG’s with an age relationship, of which four were hypomethylated and two were hypermethylated. Regression models, fitted for different combinations of CpG’s, indicated that age models using four and six CpG’s were most accurate, with the six CpG model having superior correlation and predictive power (R2 = 0.70, Mean Absolute Error = 25 months). This model was more accurate than previous attempts using methylation sensitive Polymerase Chain Reaction and performed similarly to models created using a candidate gene approach in several other mammal species, making methylation a promising tool of age estimation in cheetah.
2024-01-14 | GSE252541 | GEO
Project description:The interaction of CNS tumors with infiltrating lymphocytes plays an important role in their initiation and progression and might be related to therapeutic responses. Gene expression-based methods have been successfully used to characterize the tumor microenvironment. However, methylation data are now increasingly used for molecular diagnostics and there are currently only few methods to infer information about the microenvironment from this data type. Using an approach based on differential methylation and principal component analysis, we developed DIMEimmune (Differential Methylation Analysis for Immune Cell Estimation) to estimate CD4+ and CD8+ T cell abundance as well as tumor-infiltrating lymphocytes (TILs) scores from bulk methylation data. Well-established approaches based on gene expression data and immunohistochemistry-based lymphocyte counts were used as benchmarks. The comparison of DIMEimmune to the previously published MethylCIBERSORT and MeTIL algorithms showed an improved correlation with both gene expression-based and immunohistological results across different brain tumor types. Further, we applied our method to large datasets of glioma, medulloblastoma, atypical teratoid/rhabdoid tumors (ATRTs) and ependymoma. High-grade gliomas showed higher scores of tumor-infiltrating lymphocytes than lower-grade gliomas. There were overall only few tumor-infiltrating lymphocytes in medulloblastoma subgroups. ATRTs were highly infiltrated by lymphocytes, most prominently in the MYC subgroup. DIMEimmune-based estimates of TILs were a significant prognostic factor in the overall cohort of gliomas and medulloblastomas, but not within methylation-based diagnostic subgroups. To conclude, DIMEimmune allows for robust estimates of TIL abundance and might contribute to establishing them as a prognostic or predictive factor in future studies of CNS tumors.
| S-EPMC8216185 | biostudies-literature