DNA methylation age is not affected in psoriatic skin tissue

Abstract

BackgroundPsoriasis (Ps) is a common chronic inflammatory skin disease. The keratinocytes of psoriatic skin defy normal apoptosis and exhibit active cell proliferation. Aberrant DNA methylation (DNAm) has been suggested relevant through regulating the expression of Ps susceptibility genes. However, it is unclear whether the biological age inferred from DNA methylome is affected.ResultsTo address the above issue, we applied a recently developed methylation clock model to our Chinese Han population dataset, which includes DNAm data of 114 involved psoriatic skin tissues (PP) and 41 uninvolved psoriatic skin tissues (PN) from Ps patients, and 62 normal skin tissues (NN) from health controls. We first confirmed the applicability of the clock in PN and NN. We then showed that PP samples have largely unchanged DNAm age, and that no association was observed between available clinical features and DNAm age acceleration. Examination of genome-wide CpGs yielded age-associated CpGs with concordant age-association coefficients among the three groups, which was also supported by an external dataset. We also interestingly observed two clock CpGs differentially methylated between PP and PN.ConclusionsOverall, our results suggest no significant alteration in DNAm age in PN and PP. Therefore, the increase in keratinocyte proliferation and alteration in DNAm caused by Ps may not affect the biological age of psoriatic skin tissue.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0584-y) contains supplementary material, which is available to authorized users.

Anti‐tumor necrosis factor drug responses and skin‐blood DNA methylation age: Relationships in moderate‐to‐severe psoriasis

“…[21,22] We identified one prior study demonstrating no significant DNAmAge differences between psoriatic tissues from patients with psoriasis, normal tissues from patients with psoriasis, and normal tissues from healthy control subjects. [12] Given that we replicate this null finding in the present analysis, but report significant findings with SkinBloodAge suggests that methylation metrics intentionally crafted with skin tissues are most sensitive for detecting methylation differences in psoriatic processes. As previously mentioned, SkinBloodAge was developed due to the suboptimal performance of other methylation age metrics in fibroblasts.…”

“…DNAmAge was evaluated because it was used in a prior psoriasis study. [12] PhenoAge was selected because it is the most robust general methylation predictor of health status described in the literature. [16] GrimAge was selected because it is the most robust methylation predictor of mortality described in the literature.…”

“…One prior study reported a null association of psoriasis with the 353-CpG DNA methylation age metric (DNAmAge), which is primarily considered to be a predictor of chronological age. [12] Nevertheless, to the best of our knowledge, no studies have evaluated psoriasis relationships with the methylation/epigenetic clock for blood and skin (SkinBloodAge). [13] SkinBloodAge was specifically created after studies demonstrated suboptimal performance of DNAmAge in fibroblasts and other tissues that are readily available from skin biopsies.…”

Aging in psoriasis vulgaris: female patients are epigenetically older than healthy controls

“…Very little is currently known about the epigenetic age of psoriatic patients. A previous study of DNA methylation age in involved and uninvolved psoriatic skin tissue showed no significant difference [ 21 ]. For our study, we decided to use whole-blood samples instead of skin tissue because psoriasis is no longer considered a simple skin disease.…”

Identification of Potential Biomarkers for Psoriasis by DNA Methylation and Gene Expression Datasets

“…We included datasets of the same platform to reduce the heterogeneity between different datasets. Only two DNAm datasets were up to the selection criteria, genome-wide DNAm profiling array (GSE115797), containing data from 48 samples with 24 paired tissues (Chandra et al, 2018), and genome-wide DNAm profiling array (GSE73894) including 82 samples with 41 paired tissues (Zhou et al, 2016;Shen et al, 2018), both datasets were generated by the platform GPL13534 (Illumina HumanMethylation450 BeadChip) from the United States.…”