Understanding Skin Characteristics through Genetic Variations

The unique attributes of an individual’s skin are influenced by a range of genetic factors that have become central to the concept of personalized skincare. The diversity of skin pigmentation across global populations arises from the expression and functionality of over 125 genes responsible for melanin production, which are crucial for protecting against harmful ultraviolet radiation (UVR) and preventing skin damage. For instance, individuals with lighter skin are more susceptible to oxidative stress, leading to a reduction in elastic fibers and thinning of the epidermis, both indicators of environmental-induced extrinsic aging. As skin ages, its natural defense mechanisms, including the ability to combat reactive oxygen species (ROS) and maintain barrier function, significantly diminish. This decline is largely attributed to diminished cellular processes such as DNA repair, cell proliferation, mitochondrial function, and metabolic activities, collectively contributing to intrinsic aging. Every skin type experiences this intrinsic aging, but external factors like UV exposure can accelerate this process. It is widely acknowledged that oxidative stress, caused by the imbalance of ROS production and detoxification by antioxidants, plays a pivotal role in skin aging. Recent research has identified various gene clusters affected by SNPs that correlate with specific aging patterns and traits found in European skin. More comprehensive studies are essential to unravel the intricate gene networks that dictate the phenotypes observed in darker skin types across diverse ethnic backgrounds, especially concerning traits such as hyper-pigmentation and compromised barrier function associated with environmental stressors.

This study focuses on identifying a collection of 37 biomarkers influenced by SNPs and examining their potential biological roles within a comprehensive gene network, utilizing advanced data mining techniques and in-silico modeling algorithms. The genes are categorized into three primary groups based on ethnic origin: European, Asian, and African, encompassing factors that govern both constitutive and facultative pigmentation as well as a range of biological activities that define skin phenotype sensitivities to environmental stress and defense mechanisms. These patterns revolve around essential processes such as DNA damage response, oxidative stress management, and the remodeling of the extracellular matrix (ECM) and barrier function, intricately linked to melanogenic factors highlighted in each specific skin type. In-depth in vitro investigations of dermal fibroblasts derived from European, Asian, and African skin reveal significant disparities in cellular reactions to UVR and oxidative stress exposure. The cells underwent treatment with a single dose of UVR or hydrogen peroxide (H₂O₂) and were analyzed for enlarged nuclei and alterations in cellular density through fluorescent imaging using DAPI staining. Additional assessments included the relative expression levels of the proliferation marker Ki67, along with DNA damage and senescence markers γH2AX and the cyclin-dependent kinase (CDK) inhibitor p16^INK4a, utilizing signal intensity from immunofluorescence images. The oxidative stress levels were quantified by measuring intracellular ROS production and total antioxidant capacity in cells through fluorescent and colorimetric probes. Furthermore, assays were conducted following pre-treatment of the cells with natural bioactive compounds and cosmetic ingredients, Resveratrol and Quercetin, to evaluate their protective effects against cellular damage and oxidative stress. Notably, Asian fibroblasts demonstrated the highest sensitivity to UVR exposure, whereas European fibroblasts exhibited significant ROS production solely due to oxidative stress. In contrast, African fibroblasts showcased robust antioxidant capabilities to mitigate ROS, yet they displayed higher levels of DNA damage in a pro-oxidant context. Treatment with Resveratrol and Quercetin during UVR exposure produced varying effects on oxidative stress responses and cellular protection, with Asian fibroblasts appearing to benefit the most. The in-silico models illustrating interaction networks between the compounds and SNP-affected genes suggest potential impacts on the clusters of oxidative stress and inflammatory factors, primarily emphasized in Asian and European skin types. A comparative analysis of Resveratrol and Quercetin targets could further reveal differences in their mechanisms of action, particularly concerning pigmentation and enhanced interactions among factors related to inflammation and barrier permeability.

Building on these findings, we propose that SNP sampling, paired with advanced database modeling through next-generation technologies like artificial intelligence (AI), combined with in vitro cellular assays, can provide a practical framework for developing innovative solutions in personalized skincare. This methodology can be expanded to encompass both individual genetic profiles and skin ethnicity, enhancing the accuracy of dermatological applications and expediting the discovery of novel active ingredients for cosmetic use.

Comprehensive Methodology for Skin Type Analysis

Research Design and Approach

This comprehensive study integrates both in-silico screening and gene network analysis with in-vitro cellular assays, employing a quantitative analytical strategy. Data collection was conducted through computational methods as well as experimental approaches, utilizing image analysis alongside quantification of fluorescence and immunofluorescence signals, particle size measurements, and molecular colorimetric assays. Statistical validation of variations between the three distinct cellular populations in response to external factors was performed using one-way ANOVA followed by post-hoc Tukey HSD tests.

Creation of In-Silico Gene Interactive Models

To construct the gene interactive models, an extensive literature review and data mining process were executed. This involved cataloging genes harboring SNPs alongside the corresponding skin types from which these genes originated, resulting in a comprehensive database.

The gene networks associated with European, Asian, and African skin types were subsequently developed through computational modeling. Interaction analyses among the biomarkers were conducted using the Hexis Lab Pro.X^® in-silico screening platform, which is based on advanced deep learning algorithms. These models facilitated the creation of interactive networks involving Resveratrol and Quercetin, derived from data mining efforts. The Hexis Lab Pro.X^® platform enabled systematic validation and prototyping of bioactive compounds, screening the genes against integrated databases for known and predicted bioactivities utilizing machine learning techniques. This platform was instrumental in identifying biological targets, mining biomedical literature, and analyzing polypharmacology and interactions among genes across various biological networks.

Isolation and Culture of Dermal Fibroblasts

Human dermal fibroblasts (HDF) from European and African skin types were procured from PromoCell (adult, C-12302). Adult dermal fibroblasts of Asian origin were sourced from the Asian Skin Biobank (ASB) at the Skin Research Institute of Singapore (SRIS). Cell cultures were initiated at early passages in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS, ThermoFisher) and maintained in a 5% CO₂, 95% humidified incubator at 37°C. For cell subculturing, fibroblasts were treated with Trypsin-EDTA buffer (Gibco) and replated at a consistent initial density of 6000 cells/cm² area. For UVR treatment, cells were exposed to a germicidal lamp (Philips TUV G30T8 30 W bulb), emitting predominantly 254-nm light, for 2 minutes, and allowed to recover for 72 hours before analysis. The UV dose (mJ/cm²) was calculated based on lamp specifications: 125 μW/cm² at a distance of 1m x exposure time in seconds, yielding a total exposure of 20 mJ/cm² for 120 seconds at 0.75 m distance. For H₂O₂ treatment, cells were incubated with 250 μM H₂O₂ for 24 hours, followed by a recovery phase in fresh media for 72 hours. During the treatment with Resveratrol and Quercetin, cells were pre-incubated with 0.1% of each compound (concentrated stock dissolved at 10% in DMSO) for 24 hours prior to UV irradiation. The 0.1% concentration was derived from a prior dose-response validation, discarding both excessively high toxic concentrations (especially Quercetin) and ineffective low concentrations. The assay samples included control cells alongside those treated with UVR, H₂O₂, and Resveratrol/Quercetin, with all tests performed in triplicates.

Immunofluorescence and Image Analysis Techniques

HDF cultures were maintained on glass coverslips at a consistent initial density of 6000 cells/cm² and were fixed in 4% formaldehyde/phosphate-buffered saline (PBS) for 15 minutes at room temperature (RT). Following fixation, permeabilization was achieved using 0.5% TRITON X-100 for 5 minutes, followed by three PBS washes. For immunostaining, samples were incubated with primary antibodies diluted in the blocking buffer (PBS + 1% FBS) at 4°C overnight, washed for 30 minutes with PBS, and subsequently incubated with secondary antibodies diluted in the blocking buffer (PBS + 1% FBS) at RT for 1 hour. After additional washing, samples were mounted in ProLong Gold Antifade Mountant with DAPI (ThermoFisher). Primary antibodies included anti-Ki67 (D3B5, Cell Signaling, 1:500), anti-γH2AX (20E3, Cell Signaling, 1:500), and anti-p16^INK4a (E6N8P, Cell Signaling, 1:500), while secondary antibodies were FITC anti-rabbit (Jackson ImmunoResearch; 1:1000). Fluorescent images were captured using a Leica DM IL LED microscope with a CCD DFC3000G camera and analyzed using LAS X 3.6.0.20104 software (Leica Microsystems) at both 10x and 20x magnification. For nuclear size and cell density measurements, three consecutive images of DAPI-stained nuclei were analyzed using ImageJ software. Cells from control and test samples were counted manually from each micrograph, and the results were recorded. Nuclear sizes were quantified in microns based on particle size analysis, with 20 pixels corresponding to 10 microns as indicated on the scale bar in the micrographs. The total count of cell nuclei for each sample was plotted against corresponding nuclear sizes. Nuclei larger than 20 microns were classified as enlarged nuclei. The percentage of enlarged nuclei in control, UVR, and UVR compound-treated samples was calculated based on a count of 100 cells per treatment. For cell density analysis, a percentage of counted nuclei was calculated relative to the control sample. Ki67-positive nuclei were recorded as a percentage of total nuclei for each UVR and UVR + compound-treated sample. To quantify the expression levels of γH2AX and p16^INK4a, the relative signal intensity was measured for control and treated samples in ImageJ, adjusted for equal cell numbers, with the control signal set to 1. All figures were compiled using GraphPad Prism 9 and PowerPoint software. Statistical analyses comparing variations across skin types for each applied factor were validated using one-way ANOVA with post-hoc Tukey HSD tests, n = 3 experimental replicates. Graphs represent Mean ± SEM, with statistically significant outputs indicated as * p < 0.05, p < 0.01, * p < 0.001.

Assessment of Intracellular ROS Production

The measurement of intracellular ROS production involved seeding HDFs onto 96-well plates at a density of 2×10³ cells per well, with analyses conducted 72 hours post-treatment. The media was removed and replaced with PBS, followed by a 30-minute incubation with a 10 μM 2’7’-dichlorofluorescein diacetate (DCF-DA) probe (Abcam). Fluorescent DCF production was induced by incubating the cells further in DMEM/10% FBS in a CO₂ incubator for 15 minutes. Cells were subsequently fixed with 4% formaldehyde/PBS, washed with PBS, and the relative fluorescence intensity was recorded using a microplate reader (SpectraMax iD5 Microplate Reader, Molecular Devices) at Ex/Em 485/535 nm, adjusted for equal cell counts. Statistical analyses comparing variations in ROS production across skin types for each factor were validated using one-way ANOVA with post-hoc Tukey HSD tests, n = 3 experimental replicates. Graphs depict Mean ± SEM, with significant outputs indicated as *p < 0.05, p < 0.01, *p < 0.001.

Evaluation of Total Antioxidant Capacity

The total antioxidant capacity of the HDF cultures was assessed following the manufacturer’s protocol (Abcam, ab65329). Briefly, cells were washed with cold PBS, homogenized in 100 μL of deionized water, incubated on ice for 10 minutes, and then centrifuged. Trolox standard solutions (1mM) were prepared for various dilutions ranging from 0–200 nmol Trolox/well. Both cell supernatants and Trolox standards were transferred to new wells and incubated with 100 μL Cu²⁺ Working Solution for 90 minutes at room temperature in the dark. The resulting colorimetric output was measured using a microplate reader (SpectraMax iD5 Microplate Reader, Molecular Devices) at OD 570 nm, normalized to an equal number of cells. Sample total antioxidant capacity (TAC) was calculated using the formula TAC = (Ts/Sn)*D, where Ts represents the TAC amount in the sample well derived from a standard curve (nmol), Sn denotes the sample volume added to the wells (μL), and D is the sample dilution factor. Statistical analyses comparing variations in TAC across skin types for each factor were validated using one-way ANOVA with post-hoc Tukey HSD tests, n = 3 experimental replicates. Graphs depict Mean ± SEM, with significant outputs indicated as *p < 0.05, p < 0.01, *p < 0.001.

Key Findings on SNP Variations and Skin Ethnicity

Insights into Single Nucleotide Polymorphisms (SNPs) Across Ethnic Skin Types

Single nucleotide polymorphisms (SNPs) are present in numerous genes essential for maintaining skin structure and biological functions. These SNPs can exist within both coding and non-coding areas of genes, often impacting their activity. Different skin types are likely to showcase various combinations of these modified genes. Additionally, these biomarkers may interact uniquely with genes involved in the melanogenesis pathway, establishing patterns of structural features and physiological responses associated with subtle pigmentation variations. To delve deeper into this, we conducted a thorough literature review of genes influenced by SNPs identified in genome-wide association studies (GWAS) across three distinct ethnic skin types: European, Asian, and African (Figure 1 and Table 1).

Table 1 Summary of the Genes Affected by SNPs in European, Asian and African Skin Types

Figure 1 Network of the genes affected by the SNPs across three different ethnic skin types based on published data.

Genes affected by SNPs in European skin are predominantly associated with melanogenesis and variations in pigmentation, including the androgen receptor (AR), attractin (ATRN), agouti-signaling protein (ASIP), dopachrome tautomerase (DCT), GATA binding protein 3 (GATA3), HECT and RLD domain containing E3 ubiquitin protein ligase 2 (HERC2), interferon regulatory factor 4 (IRF4), ligand for receptor-type protein-tyrosine kinase KIT (KITLG), melanocortin 1 receptor (MC1R), microphthalmia-associated transcription factor (MITF), solute carrier family 24 member 5 (SLC24A5), and tyrosinase-related protein 1 (TYRP1). Lighter skin reflectance often correlates with increased sensitivity to sunlight and diminished tanning abilities linked to ASIP, HERC2, IRF4, MC1R, and TYRP1. Certain SNPs may predispose individuals to pigmentation changes, such as freckles associated with MC1R and ASIP, pigment spots linked to basonculin-2 (BNC2), and solar lentigines tied to KITLG and human leukocyte antigen (HLA) variations. SNPs in melanogenic genes like MITF or TYRP1 can also influence vitamin D levels. European skin contains SNPs in genes involved in redox homeostasis, including superoxide dismutase 2 (SOD2) and acetyl-coenzyme A synthetase 2 (ACSS2), as well as hydration-related genes like aquaporin-3 (AQP3) and those regulating skin elasticity such as matrix metalloproteinase 1 (MMP1).

Asian skin is characterized by SNPs in genes that regulate constitutive pigmentation, including AR, ectodysplasin A (EDA), DCT, MC1R, major facilitator superfamily domain-containing 12 (MFSD12), oculocutaneous albinism II (OCA2), and SLC24A5, often resulting in a lighter skin appearance. Fixed alleles of melanogenic genes responding to UVR, such as damage-specific DNA binding protein 1 (DDB1) and MFSD12, are associated with darker phenotypes in Asian skin. Skin color variation is also influenced by SNPs in genes linked to hyperpigmentation, exemplified by BNC2, which is associated with facial pigmented spots. A significant cluster of genes likely revolves around innate immune responses and inflammation, involving intercellular adhesion molecule 1 (ICAM1), tumor necrosis factor (TNF), interleukin 10 (IL10), integrin alpha E (ITGAE), and Toll-like receptor 6 (TLR6). SNPs are also found in genes related to xenobiotic metabolism and oxidative stress, including aryl hydrocarbon receptor (AHR), cytochrome P450 family 2 subfamily C member 19 (CYP2C19), and high mobility group 20B (HMG20). Additional clusters comprise biomarkers regulating the structure and barrier function of the epidermis, such as filaggrin (FLG), trichohyalin (TCHH), disintegrin and metalloproteinase domain-containing protein 17 (ADAM17), ficolin-1 (FCN1), ECM integrity-related ADAM metallopeptidase with thrombospondin type 1 motif 20 (ADAMTS20), and collagen type 1 alpha 2 chain (COL1A2).

In African skin, a high melanin index correlates with fixed alleles in genes responsible for constitutive pigmentation and UVR responses, including DDB1, HERC2, HMG20, MFSD12, ASIP, and KITLG. Skin color variation can also be attributed to specific SNPs in EDA, KITLG, DDB1, HERC2, and SLC24A5, which contribute to lighter pigmentation. SNPs are present in genes involved in epidermal homeostasis, barrier permeability, and hydration, such as BRCA1 DNA repair associated (BRCA1) and FCN1, as well as genes regulating ECM remodeling, including ADAMTS20.

Biomarkers Influenced by Genetic Polymorphisms Form a Structure-Function Interaction Network

The SNP-affected genes can be aligned with biological functions pertinent to various cellular activities and skin homeostasis, as supported by published data (Table 2). Through in-silico modeling and interactive network analysis based on structure-function and protein–protein interaction machine learning (ML) algorithms, we have captured the molecular hierarchy of these genes (Figure 2).

<img alt="" src="https://www.dovepress.com/cr_data/article_full Share It: ChatGPT Perplexity WhatsApp LinkedIn X Grok Google AI Share. Facebook Twitter Pinterest LinkedIn Tumblr WhatsApp Email Sienna Blake Website Sienna Blake is a licensed aesthetic consultant and beauty writer specializing in cosmetic surgery advancements and non-invasive treatments. With a background in dermatology and over eight years of industry experience, Sienna is passionate about helping people achieve confidence through informed beauty decisions. She holds a Bachelor's degree in Health Sciences and regularly collaborates with top plastic surgeons to stay at the forefront of aesthetic innovations. Outside of her work, Sienna enjoys traveling, skincare research, and practicing Pilates. Related Posts Tropic Skincare: How She Built a Beauty Empire April 28, 2026 Foot Care Routine: Your Guide to Tropic Skincare Essentials April 1, 2026 Beyond Beauty: Tropic Skincare and Susie Ma’s Impact March 11, 2026 Skincare Formulation Insights from Susie Ma & Tropic February 26, 2026 Tropic Ambassadors: Meet Susie Ma and Her Impact February 17, 2026 Winter Skincare Routine by Susie Ma: Tips for Radiant Skin February 15, 2026 Add A Comment Comments are closed. Submit Type above and press Enter to search. Press Esc to cancel. × Check out this great offer! modal-check Java Burn Drink Coffee and Lose Weight Revolutionary dietary supplement, designed to turbocharge your coffee routine, sets a new weight loss and fat-burning standard. Check It Dismiss ad Dismiss ad This will close in 12 seconds Notice: ob_end_flush(): Failed to send buffer of zlib output compression (0) in /home/carhirec/public_html/cosmeticsurgerynews.org/wp-includes/functions.php on line 5481 Notice: ob_end_flush(): Failed to send buffer of zlib output compression (0) in /home/carhirec/public_html/cosmeticsurgerynews.org/wp-includes/functions.php on line 5481