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Longitudinal DNA methylation dynamics as a practical indicator in clinical epigenetics.

ABSTRACT: BACKGROUND:One of the fundamental assumptions of DNA methylation in clinical epigenetics is that DNA methylation status can change over time with or without interplay with environmental and clinical conditions. However, little is known about how DNA methylation status changes over time under ordinary environmental and clinical conditions. In this study, we revisited the high frequency longitudinal DNA methylation data of two Japanese males (24 time-points within three months) and characterized the longitudinal dynamics. RESULTS:The results showed that the majority of CpGs on Illumina HumanMethylation450 BeadChip probe set were longitudinally stable over the time period of three months. Focusing on dynamic and stable CpGs extracted from datasets, dynamic CpGs were more likely to be reported as epigenome-wide association study (EWAS) markers of various traits, especially those of immune- and inflammatory-related traits; meanwhile, the stable CpGs were enriched in metabolism-related genes and were less likely to be EWAS markers, indicating that the stable CpGs are stable both in the short-term within individuals and under various environmental and clinical conditions. CONCLUSIONS:This study indicates that CpGs with different stabilities are involved in different functions and traits, and thus, they are potential indicators that can be applied for clinical epigenetic studies to outline underlying mechanisms.

SUBMITTER: Komaki S

PROVIDER: S-EPMC8670275 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent research suggests that epigenetics, especially DNA methylation, plays a mechanistic role in aging. Epigenetic clocks, which measure changes in a few hundred specific CpG sites, can accurately predict chronological age in a variety of species, including humans. These clocks are currently the best biomarkers for predicting mortality in humans. Additionally, several studies have characterized the effects of aging across the methylome in a wide variety of tissues from humans and mice. A small fraction (~2%) of the CpG sites show age-related changes, either hypermethylation or hypomethylation with aging. Evaluation of non-CpG site methylation has only been examined in a few studies, with about ~0.5% of these sites showing a change with age. Therefore, while only a small fraction of cytosines in the genome show changes in DNA methylation with age, this represents 2 to 3 million cytosines in the genome. Importantly, the only study to compare the effect of aging on DNA methylation in male and female mice and humans found that >95% of the age-related changes in DNA methylation in the hippocampus were sexually divergent, i.e., the methylation did not differ between males and females at young age but age-related changes occurred in one sex but not the other. The age-related changes in DNA methylation tend to be enriched and under-represented in specific genomic contexts, with some commonalities between tissues and species that require further investigation. The strongest evidence that the age-related changes in DNA methylation play a role in aging comes from studies of anti-aging interventions (e.g., caloric restriction, dwarfism, and rapamycin treatment) in mice. These anti-aging interventions deaccelerate the epigenetic clocks and reverse/prevent 20 to 40% of the age-related changes in DNA methylation. It will be important in the future to demonstrate that at least some of the age-related changes in DNA methylation directly lead to alterations in the transcriptome of cells/tissues that could potentially contribute to aging.

Project description:Background: Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N=1 phenotypes. Methods: Whole blood samples from 4 African American women, 4 Caucasian women, and 4 Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNASeq, miRNASeq, and Illumina Methyl-450 arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure that is among individuals, and to quantify correlations among measures and with clinical attributes related to wellness. Results: Longitudinal omic profiles are in general highly consistent over time, with an average of 67% of the variance in transcript abundance, 42% of CpG methylation level (but 88% for the most differentiated CpG per gene), and 50% of miRNA abundance among individuals, which are all comparable to 74% of the variance among individuals for 74 clinical traits. One third of the variance can be attributed to differential blood cell type abundance, which is also fairly stable over time, and a lesser amount to eQTL effects, whereas seven conserved axes of covariance that capture diverse aspects of immune function explain over half of the variance. These axes also explain a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that are significantly up- or down-regulated in each person and are in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes have individually divergent methylation levels, but these do not overlap with the transcripts, and fewer than 20% of genes have significantly correlated methylation and gene expression. Conclusions: People express an “omic personality” consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions. Whole blood samples from 12 subjects drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNASeq, miRNASeq, and Illumina Methyl-450 arrays.

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Longitudinal DNA methylation dynamics as a practical indicator in clinical epigenetics.

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