Data correction and quality control of the primary screen.

<p>(A) Correction of neutral lipid values with respect to changes in cell number. Z-scores of lipid values are illustrated before and after the amount of DNA was factored in. (B) Q-Q plot of normally distributed quantiles against screening result (Z-score) quantiles (red circles = positive control/<i>PPARγ</i>-siRNA; blue circles = negative control/scrambled siRNA). A perfect fit to a normal distribution is represented by red dotted line. (C) Experiment-wide quality plot focusing on controls. Signal from positive (red dots; <i>PPARγ</i>-siRNA) and negative (blue dots; control (scrambled) siRNA) controls plotted against plate number. The distance between the two distributions was quantified by the Z′-factor (0.42). For data normalization, the method ‘normalized percent inhibition’ (NPI) was applied.</p

Effects of the <i>HTR2B</i>-antagonist RS127445 on neutral lipid accumulation during human primary (pre)adipocyte differentiation.

<p>(A) Increase in lipid concentration in maturing adipocytes after treatment with 50 µM RS127445. Neutral lipid accumulation is shown relative to untreated control cells set as 100%. Results are depicted as mean ± SD (n = 10). Significant differences are marked with an asterisk (* for p<0.0001). (B) Cell viability of differentiating adipocytes after treatment with 50 µM RS127445 is shown relative to untreated control cells set as 100%. Results are depicted as mean ± SD. (C) Fluorescence microscopy after incubation with or without 50 µM RS127445 and lipid staining (yellow: neutral lipids; scale bar: 200 µm).</p

Network of axonemal dyneins.

<p>(A) Dynein network: The green coloring indicates 4 out of 5 hits regarding axonemal dyneins. <i>DNAH7</i> (also identified in our screen) was not part of the network. Blue symbols represent dyneins which were part of the library but were not determined as hits. Dyneins colored in white could not be investigated using the druggable siRNA library. (B) IPA showed an accumulation of motor proteins including dyneins and kinesins. (C) Messenger RNA levels of <i>DNAH7</i>, <i>DNAH8</i> and <i>DNAH17</i> in the course of adipocyte differentiation. Expression data at day 0 (preadipocytes) were set as 100%. All CT-values analyzed were between 28 and 34.</p

Genes identified and validated in our siRNA-screening showing the most significant changes in gene expression during adipogenesis were again transfected with specific siRNAs to classify the corresponding phenotype.

<p>Knock-down of genes caused a decreased lipid accumulation (upper box) or increased lipid accumulation (Lower box). InCell-Western Analyses were performed using aP2- and Perilipin-specific antibodies to classify target phenotypes into the following categories: (1) target-phenotype that reduce differentiation, [aP2 and Perilipin A signals decreased ≥20% compared to controls, green arrow]; (2) target-knock-down that stimulate differentiation [aP2 and Perilipin A signals increased ≥20% compared to controls, red arrow] and (3) target-phenotype that caused no changes in differentiation [none alterations in aP2 and Perilipin signals]. For targets highlighted in grey, signals of both markers decreased or increased. For those targets under laid in yellow, none marker changed.</p

Genes identified in the primary and validated in the secondary screen displaying a strong change in mRNA expression (determined by microarray analysis) during adipogenesis on day 3 or on day 7 compared with preadipocytes (d0).

<p>Genes identified in the primary and validated in the secondary screen displaying a strong change in mRNA expression (determined by microarray analysis) during adipogenesis on day 3 or on day 7 compared with preadipocytes (d0).</p

Ingenuity Pathways Analysis of positive hits identified in the primary screen.

<p>(A) Depicted are the three most significant canonical pathways with cAMP signaling being the most prominent. (B) Top five associated network functions of related genes. Two of the five top networks are believed to be directly involved in lipid metabolism. (C) The network displaying the highest score shows <i>PPARγ</i> and <i>RXRA</i>, known regulators of adipogenesis, as central genes.</p

Knock-down of axonemal dyneins and classification of corresponding phenotypes.

<p>InCell-Western Analyses were performed using aP2- and Perilipin-specifc antibodies. (A) Depicted are immunofluorescence images showing aP2 and Perilipin stainings. (B) InCell-Western-Image using aP2 and Perilipin antibodies showing PPARγ knock-down (rimmed in red) and scrambled siRNA edged in blue. (C) Classify of ‘target phenotypes’ into the following categories: (1) target-knock-down that reduce differentiation, [aP2 and Perilipin A signals decreased ≥20% compared to controls, green arrow]; (2) target-knock-down that stimulate differentiation [aP2 and Perilipin A signals increased ≥20% compared to controls, red arrow] and (3) target-knock-down that caused no changes in differentiation [no significant changes in aP2 and Perilipin signals]. For targets highlighted in grey, signals of both markers decreased or increased. For those targets under laid in yellow, none marker changed. (D) Messenger RNA levels of adipocyte-relevant transcription factors as well as IL6 and IL1ß after DNAI2- and DNAH8 knock-down. Differentiation was initiated 3 days after transfection. The qRT-PCR was performed on day 5 post-transfection. Results are depicted as mean ± SD.</p

siRNA screening procedure.

<p>Two read-out-assays were performed in 96-well-plates to determine accumulation of neutral lipids and DNA-content of untransfected and transfected (pre)adipocytes (N = negative control: control (scrambled) siRNA; P = positive control, <i>PPARγ</i>-siRNA; T = transfection reagent, no siRNA). Outer wells were filled with PBS to reduce edge effects.</p

DataSheet3_Development of an epigenetic clock to predict visual age progression of human skin.pdf

Aging is a complex process characterized by the gradual decline of physiological functions, leading to increased vulnerability to age-related diseases and reduced quality of life. Alterations in DNA methylation (DNAm) patterns have emerged as a fundamental characteristic of aged human skin, closely linked to the development of the well-known skin aging phenotype. These changes have been correlated with dysregulated gene expression and impaired tissue functionality. In particular, the skin, with its visible manifestations of aging, provides a unique model to study the aging process. Despite the importance of epigenetic age clocks in estimating biological age based on the correlation between methylation patterns and chronological age, a second-generation epigenetic age clock, which correlates DNAm patterns with a particular phenotype, specifically tailored to skin tissue is still lacking. In light of this gap, we aimed to develop a novel second-generation epigenetic age clock explicitly designed for skin tissue to facilitate a deeper understanding of the factors contributing to individual variations in age progression. To achieve this, we used methylation patterns from more than 370 female volunteers and developed the first skin-specific second-generation epigenetic age clock that accurately predicts the skin aging phenotype represented by wrinkle grade, visual facial age, and visual age progression, respectively. We then validated the performance of our clocks on independent datasets and demonstrated their broad applicability. In addition, we integrated gene expression and methylation data from independent studies to identify potential pathways contributing to skin age progression. Our results demonstrate that our epigenetic age clock, VisAgeX, specifically predicting visual age progression, not only captures known biological pathways associated with skin aging, but also adds novel pathways associated with skin aging.</p

Table1_Identification of dihydromyricetin as a natural DNA methylation inhibitor with rejuvenating activity in human skin.pdf

Changes in DNA methylation patterning have been reported to be a key hallmark of aged human skin. The altered DNA methylation patterns are correlated with deregulated gene expression and impaired tissue functionality, leading to the well-known skin aging phenotype. Searching for small molecules, which correct the aged methylation pattern therefore represents a novel and attractive strategy for the identification of anti-aging compounds. DNMT1 maintains epigenetic information by copying methylation patterns from the parental (methylated) strand to the newly synthesized strand after DNA replication. We hypothesized that a modest inhibition of this process promotes the restoration of the ground-state epigenetic pattern, thereby inducing rejuvenating effects. In this study, we screened a library of 1800 natural substances and 640 FDA-approved drugs and identified the well-known antioxidant and anti-inflammatory molecule dihydromyricetin (DHM) as an inhibitor of the DNA methyltransferase DNMT1. DHM is the active ingredient of several plants with medicinal use and showed robust inhibition of DNMT1 in biochemical assays. We also analyzed the effect of DHM in cultivated keratinocytes by array-based methylation profiling and observed a moderate, but significant global hypomethylation effect upon treatment. To further characterize DHM-induced methylation changes, we used published DNA methylation clocks and newly established age predictors to demonstrate that the DHM-induced methylation change is associated with a reduction in the biological age of the cells. Further studies also revealed re-activation of age-dependently hypermethylated and silenced genes in vivo and a reduction in age-dependent epidermal thinning in a 3-dimensional skin model. Our findings thus establish DHM as an epigenetic inhibitor with rejuvenating effects for aged human skin.</p