Tissue-specific dysregulation of DNA methylation in aging

Abstract

The normal aging process is a complex phenomenon associated with physiological alterations in the function of cells and organs over time. Although an attractive candidate for mediating transcriptional dysregulation, the contribution of epigenetic dysregulation to these progressive changes in cellular physiology remains unclear. In this study, we employed the genome-wide HpaII tiny fragment enrichment by ligation-mediated PCR assay to define patterns of cytosine methylation throughout the rat genome and the luminometric methylation analysis assay to measure global levels of DNA methylation in the same samples. We studied both liver and visceral adipose tissues and demonstrated significant differences in DNA methylation with age at > 5% of sites analyzed. Furthermore, we showed that epigenetic dysregulation with age is a highly tissue-dependent phenomenon. The most distinctive loci were located at intergenic sequences and conserved noncoding elements, and not at promoters nor at CG-dinucleotide-dense loci. Despite this, we found that there was a subset of genes at which cytosine methylation and gene expression changes were concordant. Finally, we demonstrated that changes in methylation occur consistently near genes that are involved in metabolism and metabolic regulation, implicating their potential role in the pathogenesis of age-related diseases. We conclude that different patterns of epigenetic dysregulation occur in each tissue over time and may cause some of the physiological changes associated with normal aging.

Figures

Figure 1. Heatmap representation of global tissue-specific differences in DNA methylation in young and old animals

(A) A heatmap of the top 5% of tissue-specific differences is shown, with each row corresponding to data from a single locus, and each column representing an organ sample (ADIPOSE and LIVER) obtained from a single rat (several young next to several old rats for each tissue). The branching dendrogram at the top represents the result of unsupervised clustering using these tissue-specific sites. Liver and adipose tissue show clear differences in methylation, with hyper- and hypomethylation shown on a continuum from red to yellow, respectively. While the profile of older adipose tissue is relatively similar to young, cytosine methylation in older liver tissue diverges more strongly from livers of young rats. (B) This panel shows a heatmap of the most significant age-related changes in DNA methylation identified in either liver (378 loci shown below the horizontal dividing line) or adipose tissue (240 loci shown above the horizontal dividing line). The loci included in this heatmap are identical to those identified in Figure 2 (red and blue datapoints). It is apparent that the large majority of changes in cytosine methylation with age occur distinctly in either liver or adipose tissue, and rarely in both tissues concordantly.

Figure 2. Volcano plot representation to assess age-related changes in global DNA methylation measured by HELP

Volcano plots reveal differences in DNA methylation with age that are highly dependent upon the tissue in which they are found, liver (A) and adipose tissue (B). Every locus analyzed corresponds to a dot, showing the average differences between young and old (on x-axis, values below zero represent loss of methylation with increasing age, above zero hypermethylation with increasing age) with negative log-transformed significance (p-values) along the y-axis. Thresholds were determined from the magnitude of methylation or significance differences as ≥95th percentile of values. Red dots indicate those loci exceeding both the magnitude of difference and significance thresholds and therefore correspond with the most consistent differentially-methylated sites. Blue-labeled loci are those that exceed threshold values in both the liver and adipose tissue datasets.

Figure 3. Significant age-related changes in DNA methylation in liver and adipose tissue

Significance analysis of microarrays (SAM) was performed for young and old rats in two different tissues (liver and perinephric fat). (A) The panel shows a standard Q-Q plot with expected T statistic along the x-axis and observed T-statistic along the y-axis. Values were calculated using a two-class unpaired model comparing young and old liver data, with s0 (~0.59) automatically generated. The solid diagonal line indicates a 45-degree line of equivalent observed:expected ratios, and two dashed lines indicate thresholds of confidence corresponding to δ=0.688. Green and red datapoints represent significant hyper- and hypomethylation, with age, respectively. Thus, SAM identifies a large number of highly significant changes in DNA methylation that occur in liver specifically (380 total loci with estimated false discovery rate (FDR)

Figure 4. LUMA as a technology to assess age-related changes in global DNA methylation

DNA methylation was measured by the luminometric methylation assay (LUMA) in two tissues (Fat and Liver) from young and old rats. Each corresponding boxplot is a representation of the group-specific levels of methylation, shown along the y-axis (range from 0 to 100 with 0 indicating complete methylation). Within each boxplot, the solid black line indicates median methylation, with the upper and lower limits of each box corresponding to the 75% and 25% quantiles of the data. The bars associated with each box represent the extremes of the data. The distributions of methylation levels in each tissue were compared, demonstrating that liver tends to be more methylated than adipose tissue irrespective of age (p=0.09). Moreover, age-related differences were observed in liver, with relative hypomethylation in older animals (p=0.03). Note that p-values were obtained by two-group unpaired t-test.

Figure 5. Genomic distributions of DNA methylation in normal and aging tissues

HELP data were divided into five different subsets representing constitutively hypomethylated loci, constitutively hypermethylated loci, and tissue-specific differentially-methylated regions (DMR, Liver compared with Adipose) in panel (A), as well as age-related DMRs (Young compared with Old) specific to either liver or adipose tissue in panel (B). Overlap of these subsets with six different genomic features (gene bodies, promoters including 10 kb upstream of transcription start sites, intergenic regions, CpG islands, CG clusters, and conserved non-coding elements) was measured and is shown from left to right in both panels. We also show genomic distributions for the whole microarray (All Loci, both panels), and are thus able to determine if a given subset of HELP data is enriched or depleted for any given genomic feature beyond what one might expect to see by random chance. Many of the differences shown are associated with negligible probability that they might occur with random sampling of the data (tailed hypergeometric distribution; *, **, ***, and **** indicate PSupp. Table 2).

Figure 6. Identification of loci at which cytosine methylation and gene expression are both altered with aging

We focused on the loci identified in Figure 2A, plotting HELP data along the x-axis and corresponding gene expression data for these loci along the y-axis. A total of 378 loci with significant differences in cytosine methylation were analyzed, 347 of which were mapped to RefSeq genes, and 31 of which demonstrated a corresponding robust difference in gene expression with age (> 2.5 SD from the mean of the overall distribution of gene expression data). Solid gray horizontal lines indicate the 2.5 SD cutoffs for these gene expression data, while solid gray vertical lines indicate the 95th percentile (approximately 2 SD) cutoffs for cytosine methylation data. Numerical labels appear in each of the four corners of the plot, corresponding to the number of datapoints meeting the defined criteria.

Figure 7. Ingenuity Pathway Analysis (IPA) reveals that many of the most differentially-methylated genes form a molecular interaction network of particular relevance for metabolism

RefSeq identifiers for 75 of the top 102 sites (filtered from an input list of 65,000, by pABCG4, ABCG5, ABCG8, Abcg5/Abcg8, AGT, BACE2, β-estradiol, C1ORF109, CPA1, FBP1, FBP2, Fructose 1,6 Bisphosphatase, Fructose 2,6 Bisphosphatase, GLE1, GMPPB, HBS1L, HNF4A, HSPH1, IKBKB, IP6K1, KRT10, LEP, LEPROT, NOXO1, NTHL1, NUDT1, PFKL, PROZ, RNMTL1, SERPINA10, SLC22A1, SLC22A3, SLC2A2, SLC38A4, XPA.