|Abstract:||Changes in the methylation status of the genome mediates differentiation and developmental programs. DNA methylation changes continue in the postnatal development phase during the adulthood.|
The DNA methylome progression demethylates from newborn over middle age to a centenarians. Centenarian DNA has a lower DNA methylation content and reduced correlation in the methylation status of neighboring CpGs throughout the genome in comparison with the more homogeneously methylated newborn DNA. More hypermethylated CpGs in centenarian DNA compared with the neonate colves all genomic compartments, like promoters, exonic, intronic, and intergenic regions. In regulatory regions, the most hypomethylated sequences in centenarian are present mainly at CpG-poor promoters and in tissue-specific genes, while a greater level of DNA methylation is present in CpG island promoters. The DNA methylome of middle aged is in the crossroad between the newborn and the nonagenarians/centenarians [22689993; Heyn et al. 2012].
Genome-wide methylation patterns represent a strong and reproducible biomarker of biological aging rate (methylation profiles from whole blood of 656 humans aged 19 to 101 were created [GSE40279]). Such patterns enable a quantitative model of the aging methylome which exhibits a high accuracy and ability to discriminate relevant factors in aging (like gender and genetic variants). The ability to apply the modeling multiple tissues suggests the possibility of a common molecular clock, regulated in part by changes in the methylome .
Based an age-related methylation changes model can be constructed able to predict the age of most individuals with high accuracy. A model based on 71 methylation markers genes is able to predict the individual age with high accurate (96%) and an error of 3.9 years. Nearly all marker in the model lay within or near genes with known functions in aging-related conditions, like Alzheimer's disease, cancer, tissue degradation, DNA damage, and oxidative stress. Gender, but not BMI have significant contributions to aging rate. Men genome appears to age approximately 4% faster than that of women . An epigenetic signal for BMI does not change with age [Feinberg et al. 2010].
There exists genetic associations with human longevity and aging phenotypes [Atzmon et al. 2006; Suh et al. 2008; Willcox et al. 2008; Wheeler et al. 2009]. None of the genetic variants are significant predictors of age itself, because the genome sequence is considered to be relatively static over the course of a lifetime .
There is an advanced aging rate in tumor tissue (40% increase) . Genes silenced in cancer exhibit a tendency to increased methylation during aging [17; 29; 31; 76-78].
Age-associated epigenetic modifications are similar to the changes in whole blood [Rakyan et al. 2010].
For a given individual, especially high or low methylome deviance is a strong predictor of aging rate, indicating that difference in aging rates account for part of methylome heterogeneity and epigenetic drift.
Shannon entropy is the loss of information content in the methylome over time [Shannon and Weaver, 1963]. An increase in entropy of a CpG maker suggests that its methylation state becomes less predictable across the cell population (i.e. its methylation fraction tends toward 50%]. Over all markers associated with a change in methylation fratcion in the sample cohort, 70% tended towards a methylation fraction of 50%. There is a highly significant methylome entropy over the sample cohort. Extreme methylome entropy of an individual is highly correlated with accelerated aging rate.
Changes in methylation are directly linked to changes in gene expression [Sun et al. 2011].
Gene expression profiles from whole blood of 488 individuals spanning an age range of 20 to 75 [Emilsson et al. 2008].
There is evidence for genes whose expression associated with age and for genes with increasing expression deviance. Genes with age-associated expression profiles are more likely to be nearby age-associated methylation markers. It is possible to measure aging-rate using expression data. There is an increased aging rates in transcriptome in men as compared to women.
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