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Non-linear patterns in age-related DNA methylation may reflect CD4+ T cell differentiation.

ABSTRACT: DNA methylation (DNAm) is an important epigenetic process involved in the regulation of gene expression. While many studies have identified thousands of loci associated with age, few have differentiated between linear and non-linear DNAm trends with age. Non-linear trends could indicate early- or late-life gene regulatory processes. Using data from the Illumina 450K array on 336 human peripheral blood samples, we identified 21 CpG sites that associated with age (P<1.03E-7) and exhibited changing rates of DNAm change with age (P<1.94E-6). For 2 of these CpG sites (cg07955995 and cg22285878), DNAm increased with age at an increasing rate, indicating that differential DNAm was greatest among elderly individuals. We observed significant replication for both CpG sites (P<5.0E-8) in a second set of peripheral blood samples. In 8 of 9 additional data sets comprising samples of monocytes, T cell subtypes, and brain tissue, we observed a pattern directionally consistent with DNAm increasing with age at an increasing rate, which was nominally significant in the 3 largest data sets (4.3E-15

SUBMITTER: Johnson ND

PROVIDER: S-EPMC5501198 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Non-linear patterns in age-related DNA methylation may reflect CD4<sup>+</sup> T cell differentiation.

Johnson Nicholas D ND Wiener Howard W HW Smith Alicia K AK Nishitani Shota S Absher Devin M DM Arnett Donna K DK Aslibekyan Stella S Conneely Karen N KN

Epigenetics 20170407 6

DNA methylation (DNAm) is an important epigenetic process involved in the regulation of gene expression. While many studies have identified thousands of loci associated with age, few have differentiated between linear and non-linear DNAm trends with age. Non-linear trends could indicate early- or late-life gene regulatory processes. Using data from the Illumina 450K array on 336 human peripheral blood samples, we identified 21 CpG sites that associated with age (P<1.03E-7) and exhibited changing ...[more]

PMID: 28387568

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Project description:ObjectiveObesity is driven by modifiable lifestyle factors whose effects may be mediated by epigenetics. Therefore, we investigated lifestyle effects on blood DNA methylation in participants of the LIFE-Adult study, a well-characterised population-based cohort from Germany.Research design and methodsLifestyle scores (LS) based on diet, physical activity, smoking and alcohol intake were calculated in 4107 participants of the LIFE-Adult study. Fifty subjects with an extremely healthy lifestyle and 50 with an extremely unhealthy lifestyle (5th and 95th percentiles LS) were selected for genome-wide DNA methylation analysis in blood samples employing Illumina Infinium® Methylation EPIC BeadChip system technology.ResultsDifferences in DNA methylation patterns between body mass index groups (<25 vs. >30 kg/m2 ) were rather marginal compared to inter-lifestyle differences (0 vs. 145 differentially methylated positions [DMPs]), which identified 4682 differentially methylated regions (DMRs; false discovery rate [FDR <5%) annotated to 4426 unique genes. A DMR annotated to the glutamine-fructose-6-phosphate transaminase 2 (GFPT2) locus showed the strongest hypomethylation (∼6.9%), and one annotated to glutamate rich 1 (ERICH1) showed the strongest hypermethylation (∼5.4%) in healthy compared to unhealthy lifestyle individuals. Intersection analysis showed that diet, physical activity, smoking and alcohol intake equally contributed to the observed differences, which affected, among others, pathways related to glutamatergic synapses (adj. p < .01) and axon guidance (adj. p < .05). We showed that methylation age correlates with chronological age and waist-to-hip ratio with lower DNA methylation age (DNAmAge) acceleration distances in participants with healthy lifestyles. Finally, two identified top DMPs for the alanyl aminopeptidase (ANPEP) locus also showed the strongest expression quantitative trait methylation in blood.ConclusionsDNA methylation patterns help discriminate individuals with a healthy versus unhealthy lifestyle, which may mask subtle methylation differences derived from obesity.

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Non-linear patterns in age-related DNA methylation may reflect CD4+ T cell differentiation.

Publications

Non-linear patterns in age-related DNA methylation may reflect CD4<sup>+</sup> T cell differentiation.

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