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.

Evaluation of short-term epigenetic age fluctuation

“…Because the normalization method can affect the prediction accuracy and resultant degree of age fluctuation, we also evaluated epigenetic age fluctuations based on raw (unnormalized) and beta-mixture quantile-normalized datasets (Additional file 1: Methods 1), as well as the Horvath-normalized dataset mentioned above. To determine whether the fluctuation of epigenetic age resulted from biological consequences (in vivo) rather than experimental noise, we characterized the clock CpGs with higher and lower longitudinal methylation changes; in each clock, we extracted clock CpGs with greater (top 25%; variable CpGs) and smaller (bottom 25%; stable CpGs) standard deviations (SDs), and obtained the CpG and genic annotations of each CpG group as done in the previous study [6]. We then used the chi-squared test to evaluate differences in annotation proportions.…”

Reducing Virus Infection Risk in Space Environments through Nutrient Supplementation

“…DNA methylation is one of the most stable and prevalent epigenetic mechanisms and can be used to outline the underlying biological mechanisms [ 27 , 28 ]. The purpose of methylation differential analysis is to study the adverse effects of long-term, isolated and limited environments on the health of the crew during space flight.…”

“…The nutrient enrichment analysis was based on the conclusion of nutrigenomics [ 34 ], and the hypergeometric test method was used to conduct enrichment analysis of the nutrient-related gene set and the disease-related gene set [ 28 ]. The idea behind this approach is that food compounds and nutrients may exert their potential health benefits by acting on genes associated with disease [ 35 , 36 ].…”

Why epigenetics is (not) a biosocial science and why that matters

“…The lack of integration of these complex views of social–biological transitions in epigenome-wide association studies (EWAS) is a methodological gap that has found recognition only recently—including on this journal [ 10 , 12 ]. Little consideration is given also, in the EWAS literature, to the need of differentiating the degrees of specificity, stability, and reversibility of epigenetic modifications (e.g., DNA methylation differences) in the face of clinical, behavioral, or social interventions [ 10 , 11 ]. For instance, few studies have tried to dissect the age-specific associations between DNA methylation differences and cardiovascular phenotypes: The few results available suggest that epigenetic differences may be less relevant to predict cardiovascular outcomes in children than they are in adults [ 32 ].…”

“…correspond to only partially overlapping biological signatures (i.e., specific DNA methylation differences). Of note, these differences are also affected by timing (i.e., there exist more or less sensitive periods over the life course), or duration (e.g., social mobility, cumulative effects) of exposures and fluctuate longitudinally depending on disease evolution patterns specific to each patient [ 11 ]. The variation in risk and outcomes of complex diseases is, in other words, not explained by biological or environmental factors taken in isolation: rather, this results from their combination, which produces a greater effect than the sum of their separate effects.…”