Dataset Information

Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception.

ABSTRACT: BACKGROUND:The well-established association of chronological age with changes in DNA methylation is primarily founded on the analysis of large sets of blood samples, while conclusions regarding tissue-specificity are typically based on small number of samples, tissues and CpGs. Here, we systematically investigate the tissue-specific character of age-related DNA methylation changes at the level of the CpG, functional genomic region and nearest gene in a large dataset. RESULTS:We assembled a compendium of public data, encompassing genome-wide DNA methylation data (Illumina 450k array) on 8092 samples from 16 different tissues, including 7 tissues with moderate to high sample numbers (Dataset size range 96-1202, Ntotal?=?2858). In the 7 tissues (brain, buccal, liver, kidney, subcutaneous fat, monocytes and T-helper cells), we identified 7850 differentially methylated positions that gained (gain-aDMPs; cut-offs: Pbonf???0.05, effect size ??2%/10 years) and 4,287 that lost DNA methylation with age (loss-aDMPs), 92% of which had not previously been reported for whole blood. The majority of all aDMPs identified occurred in one tissue only (gain-aDMPs: 85.2%; loss-aDMPs: 97.4%), an effect independent of statistical power. This striking tissue-specificity extended to both the functional genomic regions (defined by chromatin state segmentation) and the nearest gene. However, aDMPs did accumulate in regions with the same functional annotation across tissues, namely polycomb-repressed CpG islands for gain-aDMPs and regions marked by active histone modifications for loss-aDMPs. CONCLUSION:Our analysis shows that age-related DNA methylation changes are highly tissue-specific. These results may guide the development of improved tissue-specific markers of chronological and, perhaps, biological age.

SUBMITTER: Slieker RC

PROVIDER: S-EPMC5975493 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception.

Slieker Roderick C RC Relton Caroline L CL Gaunt Tom R TR Slagboom P Eline PE Heijmans Bastiaan T BT

Epigenetics & chromatin 20180530 1

<h4>Background</h4>The well-established association of chronological age with changes in DNA methylation is primarily founded on the analysis of large sets of blood samples, while conclusions regarding tissue-specificity are typically based on small number of samples, tissues and CpGs. Here, we systematically investigate the tissue-specific character of age-related DNA methylation changes at the level of the CpG, functional genomic region and nearest gene in a large dataset.<h4>Results</h4>We as ...[more]

PMID: 29848354

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Project description:BACKGROUND:DNA methylation (DNAm) age has been widely accepted as an epigenetic biomarker for biological aging. Emerging evidence suggests that DNAm age can be tissue-specific and female breast tissue ages faster than other parts of the body. The Horvath clock, which estimates DNAm age across multiple tissues, has been shown to be poorly calibrated in breast issue. We aim to develop a model to estimate breast tissue-specific DNAm age. METHODS:Genome-wide DNA methylation sequencing data were generated for 459 normal, 107 tumor, and 45 paired adjacent-normal breast tissue samples. We determined a novel set of 286 breast tissue-specific clock CpGs using penalized linear regression and developed a model to estimate breast tissue-specific DNAm age. The model was applied to estimate breast tissue-specific DNAm age in different breast tissue types and in tumors with distinct clinical characteristics to investigate cancer-related aging effects. RESULTS:Our estimated breast tissue-specific DNAm age was highly correlated with chronological age (r = 0.88; p = 2.9 × 10-31) in normal breast tissue. Breast tumor tissue samples exhibited a positive epigenetic age acceleration, where DNAm age was on average 7?years older than respective chronological age (p = 1.8 × 10-8). In age-matched analyses, tumor breast tissue appeared 12 and 13?years older in DNAm age than adjacent-normal and normal breast tissue (p = 4.0 × 10-6 and 1.0 × 10-6, respectively). Both HER2+ and hormone-receptor positive subtypes demonstrated significant acceleration in DNAm ages (p = 0.04 and 3.8 × 10-6, respectively), while no apparent DNAm age acceleration was observed for triple-negative breast tumors. We observed a non-linear pattern of epigenetic age acceleration with breast tumor grade. In addition, early-staged tumors showed a positive epigenetic age acceleration (p = 0.003) while late-staged tumors exhibited a non-significant negative epigenetic age acceleration (p = 0.10). CONCLUSIONS:The intended applications for this model are wide-spread and have been shown to provide biologically meaningful results for cancer-related aging effects in breast tumor tissue. Future studies are warranted to explore whether breast tissue-specific epigenetic age acceleration is predictive of breast cancer development, treatment response, and survival as well as the clinical utility of whether this model can be extended to blood samples.

Dataset Information

Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception.

Publications

Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception.

Similar Datasets

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