Early-life DNA methylation profiles are indicative of age-related transcriptome changes

Abstract

Background: Alterations to cellular and molecular programs with brain aging result in cognitive impairment and susceptibility to neurodegenerative disease. Changes in DNA methylation patterns, an epigenetic modification required for various CNS functions are observed with brain aging and can be prevented by anti-aging interventions, but the relationship of altered methylation to gene expression is poorly understood.

Results: Paired analysis of the hippocampal methylome and transcriptome with aging of male and female mice demonstrates that age-related differences in methylation and gene expression are anti-correlated within gene bodies and enhancers. Altered promoter methylation with aging was found to be generally un-related to altered gene expression. A more striking relationship was found between methylation levels at young age and differential gene expression with aging. Highly methylated gene bodies and promoters in early life were associated with age-related increases in gene expression even in the absence of significant methylation changes with aging. As well, low levels of methylation in early life were correlated to decreased expression with aging. This relationship was also observed in genes altered in two mouse Alzheimer's models.

Conclusion: DNA methylation patterns established in youth, in combination with other epigenetic marks, were able to accurately predict changes in transcript trajectories with aging. These findings are consistent with the developmental origins of disease hypothesis and indicate that epigenetic variability in early life may explain differences in aging trajectories and age-related disease.

Keywords: Aging; DNA methylation; Epigenetics; Gene regulation; Hippocampus.

Fig. 1

Whole-genome analysis of age-related differential methylation in males and females. a Heatmap of age-related differentially methylated regions, age-DMR (Fisher Exact Test with FDR < 0.05, n = 3/group) across all groups. Dot plot showing changes in methylation with aging relative to baseline methylation in young animals in males (b) and females (c). d Overlap between age-DMRs in males and in females and the directionality of methylation changes of common age-DMRs. Pathway enrichment of genes containing age-DMR within their gene body in females (e) and in males (f). Significant enrichment was determined by hypergeometric test (p < 0.05). g, h Over- and under-representation of age-DMRs in genic regions, CpG islands, and regulatory elements in the brain divided by their activation state, and regulatory elements annotated by specific histone marks in males and females. Over- and under-representation were determined using hypergeometric test (p < 0.05)

Fig. 2

Differential methylation with aging is anti-correlated with expression changes in gene body and enhancer regions. a Volcano plots of mRNA differential expression with aging (multiple linear regression, FDR < 0.05, |FC| > 1.25, n = 6/group) in males and females. b Venn diagrams of the overlap of upregulated and downregulated differentially expressed genes between males and females. Correlation between age-DMRs mapped to promoters (c, f), gene body (D,G) or enhancer regions (e, h) and gene expression fold change (O/Y) in statistically significant (blue) and non-statistically significant genes (red) in females (c–e) and males (f–h)

Fig. 3

Age-related differentially expressed genes are positively associated with gene body methylation. Genes downregulated with aging have lower gene body methylation at young age (Y, blue regression line) in both males (a) and females (b) compared to genes upregulated with aging. This relationship is maintained in old age (O, red regression line). Curve corresponds to polynomial regression curve across significant (red and blue) and non-significant (N.S., black) differentially expressed genes, 95% confidence intervals are shaded by the grey area. Gene body methylation was calculated as methylation of all cytosines between the transcription start site and transcription end site of a given gene. Box plot of whole gene methylation grouped by genes upregulated, non-differentially expressed, and downregulated genes in males (c) and females (d) *p < 0.001 (Kruskal–Wallis Test). Heatmaps illustrating the per-gene gene body methylation patterns of genes upregulated and downregulated with aging in young and old, male (e) and female (f) animals

Fig. 4

Age-related differentially expressed genes are positively associated with promoter methylation. Genes downregulated with aging have lower promoter methylation at young age (Y, blue) in both males (a) and females (b) compared to genes upregulated with aging. This relationship is maintained with aging (O, red). Curve corresponds to polynomial regression curve across significant (red and blue) and non-significant (N.S., black) differentially expressed genes, 95% confidence intervals are shaded by the grey area. Promoter is defined as ± 1 kb from transcription start site. Box plots of promoter methylation grouped by genes upregulated, non-differentially expressed, and downregulated genes in males (c) and females (d) *p < 0.001 (Kruskal–Wallis Test). Heatmaps illustrating promoter methylation patterns of genes upregulated and downregulated with aging in young and old in male (e) and female (f) animals

Fig. 5

The association between differential expression and DNA methylation patterns in young animals is not random. a Distribution of the correlation coefficients generated by correlating log2 fold mRNA change with gene body methylation of 500 randomly sampled genes (N = 10,000). Arrow indicates the location of the correlation coefficient of gene body methylation and differentially expressed genes in males. Snippet showing the polynomial regression curves of randomly selected gene sets compared to that observed in males (black regression line). b Correlation between age-related differential gene expression and gene body methylation of Reactome pathways gene sets (only pathways with > 50 genes are included). Regression curve through all differentially expressed genes with aging and gene body methylation in males is shown in black. Distributions of the correlation coefficients generated by correlating log2 fold mRNA change with promoter (c) or gene body methylation (d) for each Reactome pathway gene set

Fig. 6

DNA methylation patterns in hippocampus of young and old animals are associated with genes differentially regulated in models of neurodegeneration. a Venn-diagram representing the overlap between genes differentially expressed in two models of neurodegeneration (APP and CK-p25) and genes differentially regulated with aging (males and females combined). Heatmaps illustrating the per-gene gene body methylation patterns of young and old animals (females only) in genes upregulated and downregulated in two models of neurodegeneration (b APP, c CK-p25). Box plots of gene body (d, f) and promoter (e, g) methylation grouped by genes upregulated, unchanged or downregulated in APP (d, e) and CK-p25 (f, g)

Fig. 7

Direction of change of age-related differentially expressed genes can be predicated based on epigenetic marks in young age. Principle component analysis of epigenetic profiles of upregulated and downregulated genes with aging in the hippocampus (a). Correlation matrix representing the correlations between each principle component with epigenetic marks (b). Box plots comparing highly correlated epigenetic marks with the first principle component in upregulated and downregulated genes with aging (c). Area under the curve of the receive operating characteristic (ROC) curve showing the classification accuracy of differentially expressed to upregulated and downregulated genes for Random Forest model in males (d) and females (e). Feature importance of epigenetic marks for classification accuracy (mean decrease accuracy and mean decrease gini) in males (f) and females (g)