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

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.

Fig. 1

Age estimation accuracy of the Skin-Specific DNAm age predictor. a Correlation analysis between predicted age using the elastic net model and chronological age for all samples from the testing dataset. b A correlation was evaluated considering only epidermal or whole skin samples from the testing dataset. c Performance comparison with previously published algorithms by a correlation analysis between predicted and chronological age using a novel dataset of whole skin biopsies (external validation)

Fig. 2

Effects of aging on CpGs and genes associated with the skin-specific DNAm age predictor. a Heat map of DNA methylation levels of probes associated with the model across all samples. Only probes with a SD between the second and third quartile are plotted. Color codes represent beta DNAm values after row-wise z-score transformation. Probes (rows) were clustered using Pearson correlation. Samples were ordered according to age. Features regarding tissue of origin, sun exposure, sex, and age group (age 1: < 30 years old, age 2: between 30 and 60 years old, and age 3: > 60 years old) are also shown. b Heat map of CpG-related genes expression levels associated with the model across all samples. Only genes with a SD higher than the second quartile are plotted. Color-codes represent log(normalized expression + 1) values after row-wise z-score transformation. Genes (rows) were clustered using Pearson correlation. Samples were ordered as shown in (a). c Gene ontology (GO) enrichment summary for genes associated with probes in the model. d Over representation analysis using KEGG database genes associated with probes positively correlated with age and e genes associated with probes negatively correlated with age. Dark bars represent significantly enriched pathways after controlling for false discovery rate (FDR) using the Bonferroni method

Fig. 3

Importance of predictors. a Variable importance for top 50 predictors according to the Loess r-squared variable importance given by the varImp function from caret R package. b Frequency of regions where top 50 probes are located. Blue color refers to probes positively correlated with age in the model, and red color refers to probes negatively correlated with age. c Heat map of DNA methylation levels of the top 50 probes. Color codes represent beta DNAm values after row-wise z-score transformation. Probes (rows) are ordered according to their importance. Samples were ordered according to their age. d Heat map of the top 50 CpG-related gene expression levels associated with the model across all samples. Only genes with SD higher than the second quartile are plotted. Color-codes represent log(normalized expression + 1) values after row-wise z-score transformation. Genes (rows) were clustered using Pearson correlation. Samples were ordered according to their age. Features regarding tissue of origin, sun exposure, sex, and aging group (age 1: under 30 years old, age 2: between 30 and 60 years old and age 3: over 60 years old) are also shown

Fig. 4

Skin-specific DNAm age predictor applications. a DNAm age of primary human dermal fibroblasts obtained from two healthy donors of different ages. b DNAm age of primary human dermal fibroblasts derived from an HGPS donor with different cell passage number. c DNAm age of human psoriatic (PP) and paired uninvolved psoriatic (PN) skin tissues (GSE73894). d DNAm age residuals of normal epidermis tissues, AK—actinic keratosis and cSCC—cutaneous squamous cell carcinoma epidermis samples (E-MTAB-5738)

Fig. 5

Skin-specific DNAm predictor as a tool to validate the senotherapeutic potential of different compounds. a DNAm age residuals of primary human dermal fibroblasts treated with OSKMLN reprogramming factors (GSE142439 data) and untreated control samples (Ctrl). b–d Primary human dermal fibroblasts derived from HGPS donor treated with ABT-263 (ABT) at 1.25 and 5 μM, as well as 100 nM of Rapamycin (Rapa) for 3 days. Untreated cells were considered as controls (Ctrl). b Predicted DNAm age using the new skin-specific molecular clock, c senescence-associated beta-galactosidase (SA-β-Gal) staining intensity per nuclei, and the number of ATRX foci/cell. d Relative gene expression of CDKN2A (P16) and IL6 measured by qRT-PCR compared to untreated samples using ANOVA and Bonferroni *p < 0.05; **p < 0 < 0.01; ***p < 0.001; ****p < 0.0001

Fig. 6

Effect of senotherapeutic treatments in human skin biopsies treated with 100 nM Rapamycin for 5 days. a Predicted DNAm age using the skin-specific molecular clock. b Representative images of H&E of treated and untreated (control) samples, c mRNA expression in the epidermis, and d mRNA expression in the dermis. Ctrl control, IGFBP3 insulin growth factor binding protein 3, B2M β2 microglobulin, IL8 interleukin-8, HAS2 hyaluronic acid synthase 2, COL1A1 collagen type 1 alpha 1 compared to untreated samples using ANOVA and Bonferroni, or t test. Data refer to experiments performed in triplicate