Characterization of differential methylation in the hippocampus using whole-genome bisulfite sequencing

To assess the relationship between hippocampal age-related differential methylation and age-related transcriptional changes we first analyzed differential methylation with aging using WGBS in both male and female mice. Previous studies characterizing differential methylation in the hippocampus with aging focused on just global levels of methylation or used approaches that allowed for high-resolution analysis of portions (~ 10%) of the genome [27, 49]. Whole-genome bisulfite sequencing provides the most comprehensive analysis of gene methylation by covering the majority of CpG sites across the genome. Sequencing methods that examine smaller portions of the genomic CpG sites provide a limited and incomplete view of genic methylation (Additional file 1: Figure S1).

The average methylation level across all CpGs in young (3 months) and old (24 months) animals demonstrate no differences with aging (FY 74% ± 0.2, FO 73.5% ± 0.4, MY 74.1% ± 0.5, MO 72.5% ± 1.4, Additional file 2: Figure S2). Similarly, no difference in transposable element CpG methylation with age was evident. No differences in average methylation levels between males and females were observed. These agree with previous findings that there is no hypomethylation with aging in the murine hippocampus [49, 50].

To determine regions of differential methylation, the genome was binned to 500 bp non-overlapping windows. Windows with ≥ 10 CpGs and at least 3× coverage per CpG were retained yielding 979,603 regions analyzed for differential methylation with aging. Both males and females had roughly similar numbers of age-related differentially methylated regions (age-DMRs: 7702 in females vs 7029 in males) and showed a slight bias towards hypomethylation (Fig. 1a–d). Only 2% of all age-DMRs were common to both males and females (Fig. 1b). Of these sex-common changes, 68% were commonly regulated, e.g., hypermethylated in both males and females (χ2 test of independence p value = 1.3 × 10−6). These results demonstrate that genome-wide, age-related changes in DNA methylation are predominately sex-specific, in agreement with prior findings [27].

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) Full size image

Functional enrichment of genes containing age-DMRs revealed that although age-DMRs in males and females occurred in different genomic locations, genes containing age-related differential methylation are enriched in pathways with functional similarities, for example, genes containing age-DMRs in females are enriched in inositol phosphate metabolism, while genes containing age-DMRs in males are enriched in phospholipid metabolism and phosphoinositol metabolism (Fig. 1e, f, Additional file 3: Table S1, Additional file 4: Table S2). Generally, pathways common to both males and females are involved in glucose and lipid metabolism, neuronal interactions, and cellular integrity. These results suggest that while sex-divergence occurs at the level of the genome, the pathways affected by aging may still be functionally similar.

Age-DMRs were assessed for their enrichment across genomic features and gene regulatory elements. Over-representation of age-DMRs was observed in CpG islands and shelves, and within gene bodies (Fig. 1g, h). Generally, DMRs were not enriched in promoter regions, but when separated according whether the promoter contained a CpG island, significant enrichment of age-DMRs is observed in promoters without a CpG island. This is consistent with previous studies indicating that methylation of promoter CpG islands generally does not change with aging [53, 54]. Age-DMRs were over-represented in active and poised distal gene regulatory regions, namely active enhancers and promoter flanks. This was also evident by enrichment of age-DMRs in hippocampal H3K27ac and H3K4me1 peaks, both indicators of active and poised enhancers [55, 56] (Fig. 1e). Hypomethylated age-DMRs were also over-represented in H3K36me3, a marker of exons and transcriptional elongation [57, 58] shown to be altered with aging and associated with longevity [59, 60], and in H3K27me3, a marker associated with gene repression (Fig. 1g, h). Overall, enrichment of age-DMRs in genomic regions suggest that methylation of certain genomic regions is more susceptible to change with age as compared with others.

Association between differential gene expression and differential methylation with aging

DNA methylation functions to modulate genomic architecture and regulate gene expression. However, the relationship of differential methylation to altered steady state gene expression with aging has not been comprehensively addressed. We used RNA-sequencing to analyze transcriptional differences with aging in the same samples used for methylation analysis and correlated age-DMRs with age-related differentially expressed genes (age-DEGs) in the hippocampus. With aging 781 genes were differentially expressed with aging in males and 433 in females (multiple linear regressions, fdr < 0.05 and |FC| > 1.25) (Fig. 2a, b). Approximately 1/3 of the genes upregulated with aging were common between males and females (Fig. 2b), and only 22 downregulated genes were common between the sexes (χ2 test of independence p value < 2.2 × 10−16). This is consistent with previous findings reporting sexual divergence in transcriptional profiles in addition to a common core set of genes with aging [52].

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) Full size image

In both males and females, only a small number of age-DEGs contained an age-DMR in their promoter region (± 1 kb of the TSS). The association between age-DMRs and differentially expressed genes with aging in promoters was not significant in both males and females (Fig. 2c, f). When assessing all age-DMRs independent of their location in the gene body (TSS to TES), a weak negative correlation is observed in both males (r = − 0.13, p = 0.039) and females (r = − 0.25, p = 0.01) (Fig. 2d, g). On average, differentially expressed genes and those not changing in expression with aging had similar methylation values across their gene bodies (Additional file 5: Figure S3). Given that DNA methylation can regulate gene transcription through changes in enhancer regions we examined the correlation between age-DMRs mapped to enhancer regions (determined by H3K27ac ChIP data from cortex) and transcriptional changes of their nearby genes. A significant negative correlation was observed between age-DMRs in enhancer regions and age-DEGs in both males (r = − 0.21, p = 0.018) and females (r = − 0.25, p = 0.04) (Fig. 2e, h). Age-DMRs mapped to gene bodies or enhancers associated with genes that were not differentially expressed with aging resulted in significant, but very weak negative correlation (r < 0.1) in both males and females (Fig. 2d, e, g, h). Taken together, age-DMRs may explain a small portion of the transcriptional changes that occur with age, and generally this effect is observed in enhancers and gene bodies, but not promoters. These findings are in agreement with recent studies in the liver showing a limited inverse association between gene body methylation with aging and gene repression of genes involved in lipid metabolism and growth hormone signaling [33]. Additionally, DNA methylation changes poorly correspond with transcriptional changes in the CNS during neuronal maturation [41] or following induction of methylation in culture [61]. Therefore, while the canonical regulation of gene transcription by DNA methylation is likely to explain a portion of the age-associated differential gene expression, age-related differential methylation may potentially serve a more complex role in transcriptional regulation than simply induction and suppression of steady-state gene expression.

Age-related gene expression changes are associated with methylation profiles in early life

DNA methylation can play multiple roles in regulating gene transcription by altering protein binding occupancy [62], regulation of alternative splicing [63,64,65,66,67], and through interactions with histone marks [11, 68]. To examine relationships between DNA methylation patterns and gene expression with aging and gene body methylation levels (mean methylation from TSS to TES) (Fig. 3a, b) in early and late life were examined. Intriguingly, genes differentially expressed with aging show a moderate positive association between age-related differential mRNA expression and gene body methylation levels at both young and old age (Fig. 3a, b). Genes whose expression does not change with aging do not show a consistent positive association as observed for differentially expressed genes. That is, genes that were downregulated with aging have lower gene body methylation levels in early life, and remained lower to old age as compared to genes that were upregulated with aging (Fig. 3c, d). This relationship was consistent in both young and old animals and was not influenced by age-related changes in CpG methylation (Fig. 3c, d). This analysis was repeated for CH methylation to examine whether the relationship between early-life methylation and gene expression persists for non-CpGs. Unlike CpGs, CH methylation was comparable between upregulated genes and downregulated genes (Additional file 6: Figure S4A, B). The lack of interaction between CH methylation and changes in transcription may stem from the differences in functions between CpG and CH methylation in transcription regulation. Although transcriptional changes with aging are predominately sex-specific, this association was evident in both males and females (Fig. 3), with males showing a stronger association as compared to females.

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 Full size image

Qualitative assessment of the DNA methylation landscape of up- and downregulated genes with aging revealed that the main difference between up- and downregulated genes occurs primarily around the transcription start site (Fig. 3e, f). Therefore, we repeated the analysis focusing on promoter methylation defined as ± 1 kb of the TSS. The positive association between differentially expressed genes and baseline DNA methylation was recapitulated when examining only the promoter region (Fig. 4a, b), and was comparable in both sexes (Fig. 4c–f, Additional file 6: Figure S4C, D). Genes that do not change in expression with aging showed a weaker association that was not consistent between males and females (Fig. 4a, b). The correlation between promoter methylation levels and gene expression changes was greater compared to observed with gene body methylation and was independent of apparent age changes in methylation. Our observation reveals a relationship between age-related gene expression changes and DNA methylation that depends on the methylation patterns established early in life rather than differential methylation with aging. To determine whether the positive association between DNA methylation patterns and transcriptional changes with aging is observed in other tissues, we performed our analysis using paired WGBS and RNA-sequencing in the liver [33] (data obtained from GEO:GSE92486). A positive relationship between fold change and gene body methylation was observed with the liver data similar to that observed in the hippocampus (Additional file 7: Figure S5). The lack of whole-genome bisulfite sequencing data with aging in other tissues prevents further extension and validation of relationship at this time.

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 Full size image

Association of methylation patterns with transcriptional changes with aging is not random

Differentially expressed genes with aging appear to have a different DNA methylation profile compared to genes that are stably expressed across the lifespan (Figs. 3, 4). To determine whether this observation is unique to genes that are differentially regulated with aging, we used a random sampling approach to correlate gene body DNA methylation values with their corresponding mRNA fold change with aging. Randomly sampled sets of 500 genes (n = 10,000) showed weak correlation (r < 0.1) similar to that of genes not differentially expressed with aging and much less compared to that observed for genes differentially expressed with aging (r > 0.4) (Fig. 5a).

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 Full size image

Next we asked whether genes sets that belong to the same pathway present a similar positive association. Pathways were extracted from the Reactome pathway database [69], and used as gene sets for correlation between methylation levels at young age and mRNA fold change with aging. After filtering pathways containing < 50 genes, 368 pathways remained for the analysis (Fig. 5b). Out of all the pathways analyzed, 35 pathways showed a correlation coefficient that met or exceeded the correlation coefficient of r > 0.4 (Fig. 5c) observed between promoter methylation and genes differentially expressed with aging. For gene body methylation 32 pathways met or exceeded the correlation coefficient cutoff (Fig. 5d) and were observed only in males. Pathways that showed the highest correlation between DNA methylation patterns and transcriptional change with age were pathways previously shown to be involved with aging included inflammatory pathways (transcriptional regulation by RUNX1, MHC II signaling, interferon signaling), oxidative stress, proteolysis, cell senescence, epigenetic regulation, and estrogen signaling (Additional file 8: Table S3, Additional file 9: Table S4).

A central geroscience concept is that age-related changes intersect with those involved with disease pathogenesis, including Alzheimer’s disease [18, 70]. Therefore, we hypothesized that a positive correlation between transcriptional changes with neurodegeneration and DNA methylation profiles would be observed. To identify genes altered following neurodegeneration in the hippocampus, we used published RNA-sequencing data from two models of AD (APP and Ck-p25) and examined whether gene body and promoter DNA methylation levels in young and old animals are associated with differential gene expression observed in a neurodegenerative disease model. A significant number of genes were unique to each of the models; however, significant overlap was observed between both AD models and with genes altered with aging (APP:Aging χ2 p < 2.2 × 10−16; CK-p25:Aging χ2 p < 2.0 × 10−14; APP:CK-p25 χ2 p < 2.2 × 10−16) (Fig. 6a). As observed with genes differentially regulated with aging, upregulated genes with both APP and CK-p25 had significantly higher mean methylation in early life compared to downregulated genes (Fig. 6b, c). This was observed for gene body (Fig. 6d, f) and promoter methylation (Fig. 6e, g) as well. Differences in methylation in these models were not examined, therefore a potential difference in methylation due to AD pathology as a driving mechanism of differential gene regulation cannot be excluded; however, our findings suggest that genes differentially regulated with neurodegeneration may be more susceptible to change due to their methylation profile in a manner similar to that observed for genes differentially expressed with aging.

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) Full size image

DNA methylation-based prediction of differential expression with aging

Given the distinction in early-life methylation patterns among age-related differentially expressed genes, we investigated the early-life patterns of other epigenetic marks known to interact with DNA methylation in genes that are up and downregulated with aging. Using publicly available data sets of histone marks maps generated from the young mouse hippocampus and cortex (H2Bac, H3K27ac, H3K27me3, H3K36me3, H3K4me3, H3K9me3, and H2A.Z), we profiled each age-related differentially expressed gene’s epigenetic landscape using DNA methylation data and the gene’s calculated histone breadth of coverage. A principal component analysis (PCA) based on genes’ epigenetic profiles revealed a separation between upregulated genes and downregulated genes. Combined PC1 and PC2 explained 90% of the variance between upregulated and downregulated genes (Fig. 7a). Correlation of the first component eigenvectors with the original epigenetic variables showed strong positive correlation with DNA methylation and negative correlation with active transcription marks such as H3K27ac, an active enhancer mark and H3K4me3, an active promoter mark (Fig. 7b). This suggests that at baseline (young age), genes that undergo expression changes with aging are under different epigenetic regulation during earlier time-points. Interestingly, the second principle component (variance explained 28.7%) shown the opposite correlation as the first components and was negatively correlated with gene body methylation and active transcription marks (Fig. 7b). Together this shows that genes differentially expressed with aging have different epigenetic patterns, starting in early life. This early-life epigenetic landscape may alter these genes’ responsivity to aging. As expected, not all genes differed according to their epigenetic profile. A subset of genes showed a similar epigenetic profile regardless of their expression trajectory.

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) Full size image

Next, we set to investigate the associations between different epigenetic marks in age-related differentially expressed genes. Genes were separated by up and downregulation with aging and the interactions between the different epigenetic marks were investigated. While the baseline epigenetic profile of genes appear to differ between up and downregulated genes (Figs. 3, 4, 7a, b), the interactions between these epigenetic marks remain consistent between up and downregulated genes. Promoter and gene body methylation were positively correlated with one another in both gene groups, and as expected were negatively correlated with active enhancer and promoter marks, H3K27ac and H3K4me3 (Additional file 10: Figure S6A, B). While the interactions between epigenetic marks did not change between differentially expressed genes with aging, similar to DNA methylation levels, the baseline levels of different histone marks were different between up and downregulated genes. Genes that were downregulated with aging show higher breadth of coverage of active transcription marks compared to upregulated genes (Fig. 7c). This is consistent with the lower promoter methylation levels observed in these genes. Interestingly, the gene size of up and downregulated genes was also different between up- and down-age-related differentially expressed genes with upregulated genes significantly longer than downregulated genes (Fig. 7c). Together, these findings further demonstrate that altered epigenetic patterns may contribute to the trajectory of change of genes changing with aging.

To strengthen the potential link between differences in epigenetic landscape in young age and differential expression observed late in life we used random forest (RF) modeling to find whether early-life epigenetic patterns can predict gene expression changes with aging. The RF models were trained to predict the direction of transcriptional change with age (upregulated or downregulated) based on methylation data, gene size, relative expression in young age expressed by RPKM, and the epigenetic marks annotated in the hippocampus and cortex obtained from publicly available data sets (see methods).

The trained RF model was able to correctly classify transcriptional changes with high accuracy in both males (87%) and females (78%) (Fig. 7d, e). RF performance decreased slightly when trained based on DNA methylation means and RPKM alone, but still performed significantly better than random in both males (78%) and females (71%) (Additional file 10: Figure S6C, D). Evaluation of feature importance to each of the RF models revealed that DNA methylation and gene size are highly important for predicting gene expression in both sexes. In males, gene size, H2A.Z marks, H3K4me3, H3K27ac, and DNA methylation averages of both whole gene and promoters (Fig. 7f) contributed the most to predictive accuracy. In females, high importance features for model prediction included mean expression, DNA methylation levels and gene size (Fig. 7g). Feature importance measures of histone breadth of coverage were much lower in females compared to males. This is likely due to well-documented sex-differences in histone landscape observed in both mice and humans [71], which were not accounted for in the current analysis as most histone data available for hippocampus obtained for the analysis were collected from male animals.

It should be noted that these different epigenetic marks are not independent of each other as DNA methylation is closely associated with both H3K4me3, an active promoter mark [72], and H3K27ac, an enhancer mark [73]. Regions of H3K4me3 and H3K27ac often act coordinately with DNA methylation during gene transcription regulation [74]. Local depletion of DNA methylation is a hallmark of H3K4me3 and H3K27ac [56], and thus these marks are considered to be regulated by DNA methylation. Gene size was a significant contributor to the accuracy of the models (Fig. 7c, d), the relationship between gene length and DNA methylation is still not completely understood; however, transcription of long genes may be partially regulated by DNA methylation. For example, in the CNS transcriptional regulation of long genes is mediated through the DNA methylation binding protein MeCP2 [75]. The results presented here are in agreement with those of Benayoun et al. [76] which examined some of these marks but not DNA methylation in the cerebellum and olfactory bulb. Taken together, these results put forward the concept that epigenetic regulation at a young age may direct transcriptional change with aging.