Highly accurate skin-specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging

Clin Epigenetics. 2020 Jul 13;12(1):105. doi: 10.1186/s13148-020-00899-1.

Abstract

Background: DNA methylation (DNAm) age constitutes a powerful tool to assess the molecular age and overall health status of biological samples. Recently, it has been shown that tissue-specific DNAm age predictors may present superior performance compared to the pan- or multi-tissue counterparts. The skin is the largest organ in the body and bears important roles, such as body temperature control, barrier function, and protection from external insults. As a consequence of the constant and intimate interaction between the skin and the environment, current DNAm estimators, routinely trained using internal tissues which are influenced by other stimuli, are mostly inadequate to accurately predict skin DNAm age.

Results: In the present study, we developed a highly accurate skin-specific DNAm age predictor, using DNAm data obtained from 508 human skin samples. Based on the analysis of 2,266 CpG sites, we accurately calculated the DNAm age of cultured skin cells and human skin biopsies. Age estimation was sensitive to the biological age of the donor, cell passage, skin disease status, as well as treatment with senotherapeutic drugs.

Conclusions: This highly accurate skin-specific DNAm age predictor constitutes a holistic tool that will be of great use in the analysis of human skin health status/molecular aging, as well as in the analysis of the potential of established and novel compounds to alter DNAm age.

Keywords: Aging; DNA methylation; DNAm age algorithm; Epigenetics; Fibroblasts; Molecular clock; Skin aging.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Aging / genetics
  • Algorithms
  • CpG Islands / genetics
  • DNA Methylation / genetics*
  • Epigenome / genetics*
  • Epigenomics / methods
  • Female
  • Fibroblasts / metabolism
  • Fibroblasts / pathology
  • Health Status
  • Healthy Aging / genetics*
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Skin / metabolism*
  • Skin / pathology