Spatial, temporal and interindividual epigenetic variation of functionally important DNA methylation patterns

Abstract

DNA methylation is an epigenetic modification that plays an important role in gene regulation. It can be influenced by stochastic events, environmental factors and developmental programs. However, little is known about the natural variation of gene-specific methylation patterns. In this study, we performed quantitative methylation analyses of six differentially methylated imprinted genes (H19, MEG3, LIT1, NESP55, PEG3 and SNRPN), one hypermethylated pluripotency gene (OCT4) and one hypomethylated tumor suppressor gene (APC) in chorionic villus, fetal and adult cortex, and adult blood samples. Both average methylation level and range of methylation variation depended on the gene locus, tissue type and/or developmental stage. We found considerable variability of functionally important methylation patterns among unrelated healthy individuals and a trend toward more similar methylation levels in monozygotic twins than in dizygotic twins. Imprinted genes showed relatively little methylation changes associated with aging in individuals who are >25 years. The relative differences in methylation among neighboring CpGs in the generally hypomethylated APC promoter may not only reflect stochastic fluctuations but also depend on the tissue type. Our results are consistent with the view that most methylation variation may arise after fertilization, leading to epigenetic mosaicism.

Figure 1.

Median methylation and range of methylation variation of two paternally methylated DMRs, H19 and MEG3, and four maternally methylated DMRs, LIT1, NESP55, SNRPN and PEG3, of imprinted genes as well as of promoters of one pluripotency gene, OCT4, and one tumor suppressor gene, APC. The box plots of a given gene show the distribution of DMR or promoter methylation values in 48 CVS from healthy newborns, 10 fetal and 12 adult brain samples, and 26 adult blood samples from unrelated individuals. The median is represented by a horizontal line. The bottom of the box indicates the 25th percentile, the top the 75th percentile. Outliers are shown as open circles, extreme outliers as stars.

Figure 2.

Association between age and H19, MEG3, LIT1, NESP55, SNRPN and PEG3 methylation. (A) Regression lines and data points represent measurements in blood samples of unrelated healthy individuals between 21 and 72 years. R² is the linear correlation coefficient. (B) Regression lines when removing all samples from individuals up to 25 years.

Figure 3.

Differences in gene-specific methylation levels between pairs of monozygotic (MZ) and dizygotic twins (DZ). (A) The box plots display the pairwise difference in DMR methylation of H19, MEG3, LIT1, NESP55, SNRPN and PEG3 between MZ (12 pairs) and DZ (14 pairs). The median methylation difference is represented by a horizontal line. (B) ROC curve comparing the pairwise methylation differences of MZ and DZ. The area under the curve indicates the discriminative power of pairwise methylation differences in six studied genes between the MZ and DZ groups.

Figure 4.

(A) Methylation patterns of seven neighboring CpG sites in the APC promoter region in adult blood (n = 26), adult cortex (n = 12), cultured (n = 12) and native (n = 29) CVS from first trimester abortions, and sperm (n = 45) samples. The box plots display the distribution of methylation values at each CpG site. Please note the different graduations of scale on the y-axis. (B) PAM analysis of methylation patterns across the seven CpGs. Each sample is represented by one column. PAM uses cross-validation to calculate posterior class probabilities for each sample. A classifier which is based upon this would vote for the class with the highest posterior in a given sample. Native CVS and sperm samples would be classified correctly to a large extent, whereas almost all other samples would be misclassified as sperm.