Age-related epigenome-wide DNA methylation and hydroxymethylation in longitudinal mouse blood

ABSTRACT

DNA methylation at cytosine-phosphate-guanine (CpG) dinucleotides changes as a function of age in humans and animal models, a process that may contribute to chronic disease development. Recent studies have investigated the role of an oxidized form of DNA methylation – 5-hydroxymethylcytosine (5hmC) – in the epigenome, but its contribution to age-related DNA methylation remains unclear. We tested the hypothesis that 5hmC changes with age, but in a direction opposite to 5-methylcytosine (5mC), potentially playing a distinct role in aging. To characterize epigenetic aging, genome-wide 5mC and 5hmC were measured in longitudinal blood samples (2, 4, and 10 months of age) from isogenic mice using two sequencing methods – enhanced reduced representation bisulfite sequencing and hydroxymethylated DNA immunoprecipitation sequencing. Examining the epigenome by age, we identified 28,196 unique differentially methylated CpGs (DMCs) and 8,613 differentially hydroxymethylated regions (DHMRs). Mouse blood showed a general pattern of epigenome-wide hypermethylation and hypo-hydroxymethylation with age. Comparing age-related DMCs and DHMRs, 1,854 annotated genes showed both differential 5mC and 5hmC, including one gene – Nfic – at five CpGs in the same 250 bp chromosomal region. At this region, 5mC and 5hmC levels both decreased with age. Reflecting these age-related epigenetic changes, Nfic RNA expression in blood decreased with age, suggesting that age-related regulation of this gene may be driven by 5hmC, not canonical DNA methylation. Combined, our genome-wide results show age-related differential 5mC and 5hmC, as well as some evidence that changes in 5hmC may drive age-related DNA methylation and gene expression.

KEYWORDS:

• Epigenetics
• DNA methylation
• DNA hydroxymethylation
• aging
• mouse
• blood

Acknowledgments

We thank Dr. Claudia Lalancette at the University of Michigan Epigenomics Core for ERRBS and HMeDIP-seq DNA library preparation. We also thank the University of Michigan Bioinformatics Core for sequencing data generation and quality control.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental Material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the University of Michigan (UM) NIEHS/EPA Children’s Environmental Health and Disease Prevention Center (P01 ES022844/RD83543601), the Michigan Lifestage Environmental Exposures and Disease (M-LEEaD) NIEHS Core Center (P30 ES017885), as well as the UM NIEHS Institutional Training Grant T32 ES007062 (JJK, EHM), UM NICHD Institutional Training Grant T32 079342 (EHM), NSRA F31 ES025101 (EHM), and the University of Michigan School of Public Health Regents’ Fellowship (JJK).