Introduction
Skin is the first natural defense barrier that protects our body from exogenous stimuli. With skin aging caused by malnutrition, environmental pollutants, gravity, collagen loss, and ultraviolet (UV) irradiation, disrupted integrity of the skin barrier could be observed. Photoaging caused by UV irradiation (especially UVA) has been identified as the primary factor in skin aging, which results in skin roughness, reduced collagen content, decreased skin elasticity, deep wrinkles and pigment formation, causing cosmetic disability and psychological distress.1,2 UVA leads to skin photoaging by inducing the overproduction of reactive oxygen species (ROS), which could initiate intracellular oxidative stress, apoptosis, DNA damage, autophagy and cellular signalling events.2 ROS could activate the NF-κB signalling pathway via increasing phosphorylated (p-) IκBα, causing an inflammatory “storm” by secretion of multiple pro-inflammatory cytokines.3 Additionally, ROS also have a suppressed effect on the PI3K-AKT signalling pathway via lowering the expression of p-AKT, which could contribute to cell apoptosis.4 Excessive ROS accumulation and cell aging regulate longevity regulation pathway5 and stimulation of transforming growth factor β (TGF-β) signalling in UVA-irradiated human dermal fibroblasts (HDFs).6 Photoaged skin was characterised by the loss of dermal extracellular matrix (ECM) including various types of collagens by overexpressed matrix metalloproteinases (MMPs) and down-expressed tissue inhibitor of the metalloproteinases 1 (TIMP1).7 Moreover, enhanced signalling MAPK and NF-κB, as well as, attenuated TGF-β/Smad and PI3K-AKT signalling could lead to decreased collagen and TIMP1 production under UVA irradiation.8,9 In addition, UVA irradiation could markedly downregulate the mRNA expression of BCL-2 while upregulated BAX in HDFs,10 triggering the release of inflammatory factors like interleukin-1 beta (IL-1β), interleukin 6 (IL-6), tumour necrosis factor-α (TNF-α) that eventually result in cell apoptosis and skin photodamage.11 Therefore, the authors hypothesize that an antioxidant, anti-apoptotic, and anti-inflammatory agent could be an effective candidate to alleviate UVA-induced photoaging.
Currently, the trend is to use natural plant extracts with antioxidant and immunomodulation activity as safe and effective products to prevent or treat skin photoaging.12,13 Asiaticoside is a traditional Chinese herbal monomer extracted from Centella Asiatica, which has been used for many years to treat various skin diseases like skin ulcers, and scleroderma, to promote healing.14,15Centella Asiatica has been reported to have a wide range of pharmacological effects that include anti-inflammation and anti-oxidation.16,17
Chinese medicine is used to treat deficiencies in the body by strengthening the body, and has the advantages of multiple targets, as well as, minimal side effects compared to pharmaceutical treatments. Network pharmacology is a rapidly developing field that integrates computer science with clinical medicine, and researchers aim to analyze the molecular mechanisms of drugs by constructing, as well as, visualizing interaction networks involving multiple genes, targets, and pathways. Through this approach, network pharmacology can provide insights into the complex interactions between drugs and biological systems to help develop novel drug targets, as well as, treatment strategies.18 Molecular docking is a novel technique to identify the mechanism of drug binding to the molecular by network pharmacology.19,20
The experimentation was initiated to clarify the mechanism of asiaticoside in preventing and treating UVA-induced skin photoaging through in vitro experiments with the help of network pharmacology.
Materials and methods
Identifying the targets of asiaticoside and photoaging
The targets of asiaticoside were retrieved from BATMAN, SwissTargetPrediction, PharmMapper, and comparative toxicogenomics database (CTD). A pioneer in providing an online bioinformatics tool to investigate molecular mechanisms of traditional Chinese medicine, the BATMAN-TCM database, is available at http://bionet.ncpsb.org/batman-tcm .21 The SwissTargetPrediction database (http://www.swisstargetprediction.ch/ ) predicts the targets of bioactive molecules by analyzing their chemistry.22 After importing line notations of asiaticoside into the BATMAN and SwissTargetPrediction database, information about targets was obtained. PharmMapper, which can be accessed at http://lilab.ecust.edu.cn/pharmmapper/ , is an analytical tool capable of predicting a small molecule’s biological targets by comparing it with all the experimentally determined three-dimensional protein structures available on PharmTargetDB.23 After submitting the 3D structures and using default parameters, PharmMapper predicted the potential targets of asiaticoside. Meanwhile, CTD (http://ctdbase.org/ ) is a freely accessible database that offers curated information on chemical-gene/protein interactions, chemical-related diseases, and gene-disease relationships.24 Using default settings, the CTD was used to predict potential targets of asiaticoside. The desired targets were obtained after removing duplicates. The target proteins were then standardized using the Universal Protein Resource (UniProt, http://www.uniprot.org/ ) after being screened from four databases. In parallel, a set of target proteins related to photoaging was established using the GeneCards database, an online resource for exploring gene-disease associations in humans.25
Target intersection between drugs and diseases
An intersection of Venn diagrams was used to identify the overlapping drug-disease targets by comparing the targets of asiaticoside and those related to photoaging. These targets are considered core targets for the action of asiaticoside in treating photoaging.
Constructing a network of protein-protein interactions (PPIs)
To construct the PPI network, the authors selected the core targets shared by both asiaticoside and photoaging. The STRING database (https://string-db.org/cgi/input.pl ) is an online resource that provides a comprehensive collection of PPI information from various publicly available sources. In addition to integrating experimental data, the database also incorporates computational predictions to enhance the coverage and accuracy of the PPI network. By integrating and analyzing this wealth of data, the STRING database can provide insights into the functional relationships between proteins and pathways, and facilitate the discovery of novel therapeutic targets. Subsequently, the authors used the core targets as input for STRING to construct a PPI network consisting of interactions between the physical and functional aspects. The confidence score cut-off was set at 0.9 to ensure a high level of confidence in the interactions. The degree of a node in the network, which represents the number of its connections, was used to identify and evaluate the core targets, as well as, the interaction information between nodes. The PPI network was then processed using R language (version 4.1.0), and the top 30 nodes in the network were identified and presented.
Creating the entire network
The authors selected the intersection of PPI nodes for further analysis. Drug-disease relationships and their potential targets were visualized using Cytoscape (version 3.8.0) based on the aforementioned data.26 The connections between each of the nodes represented a biological interaction between the drug, target, or disease. The size of the node was compared to the connectivity analysed by NetworkAnalyzer of Cytoscape associated with the degrees and weight value.27 A larger value indicates a greater possibility of the component being identified as the crucial target of asiaticoside for photoaging.
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis
To gain insight into the potential functions of the intersection PPI nodes, a Gene Ontology (GO) analysis was s carried out using the clusterProfiler package in the R environment. The analysis focused on three main categories: biological process (BP), molecular function (MF), and cellular component (CC). By examining the enriched GO terms associated with the target genes, the analysis can provide a deeper understanding of their potential roles in various biological processes and cellular contexts. Such an approach is commonly used in bioinformatics and functional genomics to annotate and interpret large-scale gene or protein datasets.28 Gene list annotation and analysis can be carried out on Metascape (https://metascape.org/ ). Metascape could provide a robust tool for investigating the functional properties of gene lists by incorporating gene annotation, functional enrichment, membership search, and interactome analysis. By integrating over 40 independent knowledge bases, Metascape allows users to access a wealth of information on gene function and regulation, as well as, gain insight into the biological processes and pathways that are associated with their gene lists. Such a platform is widely used in the field of bioinformatics and systems biology to analyze and interpret large-scale genomic and proteomic data.29 Moreover, Metascape can group similar terms and eliminate redundancy, therefore, it was used to carry out the KEGG analysis. To determine the statistical significance of the enrichment analysis, both the p-value and adjusted p-value were set to a threshold of <0.05. The top 10 enriched GO terms were visualized as a scatter plot and a chord plot was generated to display the 8 most enriched KEGG pathways.
Materials and cell culture
Asiaticoside (Sigma Aldrich, St. Louis, MO, USA, purity >98%) was solubilized beforehand in dimethylsulfoxide. Next, different concentrations of asiaticoside (0, 12.5, 25, 50, 100 and 200 µg/mL) were configured for cell experiments. The final dimethylsulfoxide concentration was controlled to under 0.1%. The authors obtained HDFs with informed written consent from male donors who underwent a routine circumcision process.30 The experiments were carried out using HDFs at passages 3–4 only. This study was approved by the ethics committee of Fudan University School of Medicine (Ethical approval document No: 2022-778).
UVA irradiation
HDFs were cultured in different formats, including 96-well plates (2,000 cells/well for cell proliferation assays) and 10-cm culture dishes (1 × 106 cells for other experiments), in Dulbecco’s modified eagle’s medium (DMEM, Hyclone, Logan City, UT) supplemented with 10% fetal bovine serum (FBS, Gibco, New Zealand) and 1% antibiotic-antimycotic (Gibco, Grand Island, NY), with or without asiaticoside and Vitamin C (VC; Sigma Aldrich).
After 48 hours of cultivation, the culture medium was replaced with phosphate-buffered saline (PBS) without drugs, and HDFs were exposed to a UVA tube (TL 20W/12, Philips) at various doses (12.5, 25, 50, 100, and 200 mJ/cm2) to determine the appropriate irradiation intensity. Radiation doses were calculated using an ultraviolet intensity meter (Topcon Technohouse Corporation, Tokyo, Japan). After removing the PBS, HDFs were cultured for 48 hours, with or without drugs, in a culture medium. VC of 50 µg/mL served as a positive control.
Cell proliferation assay
To assess cell proliferation, a Cell Counting Kit 8 (CCK-8) assay was performed. HDFs were exposed to varying doses of asiaticoside and subcultured for 48 hours to evaluate the proliferation of cells. Additionally, cell proliferation was measured at 48 hours after exposure to different UVA doses or drug treatments under 100 mJ/cm2 UVA irradiation. The results were expressed as the percentage of cell proliferation relative to untreated control HDFs.
RNA extraction and real-time quantitative polymerase chain reaction
RNA was isolated from HDFs 48 hours after irradiation using the EZ-press RNA Purification Kit (EZBioscience, USA). Reverse transcription was performed using a 4×Reverse Transcription Master Mix (EZBioscience) at 42°C for 15 minutes, followed by a 3-minute incubation at 95°C. Next, qPCR was carried out using the 2×SYBR Green qPCR Master Mix (EZBioscience) with an initial denaturation step at 95°C for 5 minutes. Then 40 amplification cycles were performed using a Strata Gene Mx3000p (Applied Biosystems) (10 seconds at 95°C for denaturation and 30 seconds at 60°C for annealing and extension). The primer sequences used for each gene of interest are listed in Table 1. Each gene of interest was normalized to GAPDH, and the fold change was calculated relative to the control. The qPCR assay was performed in triplicate and, as such, repeated three times.
Table 1Primers used in qPCR analysis
Gene | Primer Sequence (5′-3′) | Annealing Temperature (°C) | Product Size (bp) |
---|
TGF-β1 | Sense: AAGGACCTCGGCTGGAAGTG | 58 | 136 |
| Antisense: CCGGGTTATGCTGGTTGTA | | |
TGF-β3 | Sense: GGTTTTCCGCTTCAATGTGT | 58 | 119 |
| Antisense: GCTCGATCCTCTGCTCATTC | | |
TIMP1 | Sense: TGACATCCGGTTCGTCTACA | 58 | 102 |
| Antisense: TGCAGTTTTCCAGCAATGAG | | |
SMAD4 | Sense: CTCATGTGATCTATGCCCGTC | 58 | 146 |
| Antisense: AGGTGATACAACTCGTTCGTAGT | | |
SMAD7 | Sense: TTCCTCCGCTGAAACAGGG | 58 | 116 |
| Antisense: CCTCCCAGTATGCCACCAC | | |
TNF-α | Sense: CTCGAACCCCGAGTGACAAG | 58 | 159 |
| Antisense: TGAGGTACAGGCCCTCTGAT | | |
IL-6 | Sense: CCTGACCCAACCACAAATGC | 58 | 157 |
| Antisense: ATCTGAGGTGCCCATGCTAC | | |
PTEN | Sense: AGGGACGAACTGGTGTAATGA | 58 | 100 |
| Antisense: CTGGTCCTTACTTCCCCATAGAA | | |
PPARγ | Sense: ACCAAAGTGCAATCAAAGTGGA | 58 | 100 |
| Antisense: ATGAGGGAGTTGGAAGGCTCT | | |
PIK3R1 | Sense: TGGACGGCGAAGTAAAGCATT | 58 | 154 |
| Antisense: AGTGTGACATTGAGGGAGTCG | | |
BCL2L1 | Sense: GAGCTGGTGGTTGACTTTCTC | 58 | 119 |
| Antisense: TCCATCTCCGATTCAGTCCCT | | |
BAX | Sense: CCCGAGAGGTCTTTTTCCGAG | 58 | 155 |
| Antisense: CCAGCCCATGATGGTTCTGAT | | |
P53 | Sense: GAGGTTGGCTCTGACTGTACC | 58 | 133 |
| Antisense: TCCGTCCCAGTAGATTACCAC | | |
GAPDH | Sense: ACAACTTTGGTATCGTGGAAGG | 58 | 101 |
| Antisense: GCCATCACGCCACAGTTTC | | |
Western blot (WB) assay
Essential proteins selected based on the network pharmacology analysis and in vitro experiments were subject to WB assay for further validation. A 4–20% SDS-PAGE gel from Bio-Rad (Hercules, CA) was loaded with 20 µg of protein lysate. The protein fractions were transferred to a nitrocellulose membrane (Bio-Rad) and then the membrane was blocked with 5% bovine serum albumin for one hour. Primary antibodies were added and the membrane was incubated overnight at 4°C. After bathing with a washing buffer, secondary antibodies were added and the membrane was incubated for one hour at room temperature. Protein bands were visualized using an enhanced chemiluminescence detection kit (Thermo Scientific, 32106). To serve as a control, anti-GAPDH was used. Details about all the antibodies used can be found in Table 2.
Table 2Antibodies used in western blotting
Targets | Source | Dilution ratio |
---|
BAX | Abcam, ab182733 | 1:2,000 |
IκB-α | Abcam, ab32518 | 1:5,000 |
P-IκBα | Abcam, ab133462 | 1:10,000 |
P65 | Abcam, ab32536 | 1:5,000 |
P-P65 | Abcam, ab86299 | 1:5,000 |
P53 | Abcam, ab26 | 1:1,000 |
P-53 | Abcam, ab33889 | 1:2,000 |
PTEN | Abcam, ab170941 | 1:5,000 |
PPARγ | Abcam, ab178860 | 1:1,000 |
AKT | Abcam, ab8805 | 1:500 |
P-AKT | Abcam, ab8933 | 1:500 |
GAPDH | Abcam, ab8245 | 1:2,000 |
Asiaticoside-target molecular docking
Following network pharmacology analysis and in vitro experiments, molecular docking analysis was used to validate nine core-selected proteins. The three-dimensional structures of the nine targets were obtained from the PDB database (https://www.rcsb.org/ ), while asiaticoside was used as the ligand and the nine targets were used as receptors. The molecular docking analysis was carried out using AutoDock (version 1.5.6). Prior to the docking process, AutoDock was used to prepare the protein targets by removing water molecules, adding nonpolar hydrogen, and isolating the proteins. Gasteiger charges were also calculated to facilitate the docking process. Local Search Parameters were used in AutoDock to perform molecular docking. A conformation with the best affinity was selected, and Pymol (version 2.3) was used to visualize the results.
Statistical analysis
SPSS was used to analyze the experimental data using one-way ANOVA with Tukey’s post-hoc test. Differences were considered significant at p < 0.05.
Results
Screening of potential drug-disease targets
A total of 246 photoaging-associated targets (Table 3) and 17,292 potential targets for asiaticoside action were acquired (Table S1). After matching the asiaticoside targets with photoaging-related targets, a total of 202 potential asiaticoside targets for treating photoaging were obtained (Fig. 1a and Table 4).
Table 3Identification of potential targets for photoaging by GeneCards
MMP1 | ELANE | MMP7 | CEMIP | DNMT1 | ABCC9 |
ELN | MRC2 | RARS1 | AKT1 | PIK3R1 | HRH1 |
FBN1 | FLG | ESR1 | CASP3 | MMP14 | CTSL |
JUN | MAPK14 | NFKB1 | RARB | FN1 | KRT19 |
MC1R | ACACA | MAPK8 | ALOX5 | HDAC3 | TP53BP1 |
PTGS2 | FASN | ESR2 | CREB1 | NOS2 | PRF1 |
OPN1SW | GLB1 | MIF | CDKN1A | NFKBIA | TYRP1 |
OPN3 | SCD | CYP1B1 | PIK3CG | SPARC | AGER |
FOS | TGM1 | HSD17B4 | DDB2 | TLR4 | CLOCK |
BMP6 | FBLN2 | HSD17B2 | KRT14 | PRKAA2 | CCNA2 |
RARA | LORICRIN | SULT1E1 | PPARD | IRAK1 | GAL |
IVL | CLU | SULT1A1 | HYAL1 | GNAQ | RPS3 |
CCN1 | HSD11B1 | HSD17B8 | ANGPT1 | STK11 | CALCA |
MMP3 | EGF | GPER1 | SHC1 | RPS6KB1 | DCT |
VCAN | POLQ | GREB1 | SERPINH1 | CASP9 | ACP1 |
MMP9 | SPRR1B | TNF | ECM1 | ANXA1 | ARNTL |
MMP2 | FBXO40 | SOD2 | HSF1 | CP | POSTN |
TRPV1 | STXBP5L | MYD88 | HSPA1A | BAX | TAGLN |
KRT16 | F2RL1 | NR1H2 | PTPRK | CYCS | PEX7 |
CTSD | PDYN | SIRT1 | ORAI1 | MITF | RPS27A |
TIMP1 | PLA2G4A | NR1H3 | CRABP2 | ODC1 | TIMP2 |
TGFBR2 | XPA | MMP10 | HYAL2 | LMNA | IL18R1 |
IL1R1 | MSRA | PPARA | HBEGF | MAPK3 | HMMR |
TP53 | MIR155 | GZMB | GDA | NFE2L2 | NOX4 |
MAPK10 | LMNB1 | KRT17 | KRT10 | VEGFA | PTPRU |
COL1A1 | GLO1 | SFN | AREG | BCL2L1 | FOSB |
MYC | HAGH | S100A8 | HSPA4 | GPX4 | GDF15 |
ITGB1 | MSRB1 | SIRT4 | DUSP16 | CYP27B1 | MIP |
SMAD4 | CAT | HYAL3 | MFAP2 | CYBB | PSMC4 |
XDH | SOD1 | MIR15B | SAA1 | HIF1A | TEP1 |
MIR146A | MMP12 | VDR | IL11 | KCNJ5 | RNASE1 |
PPARG | SMAD2 | RXRA | CSN1S1 | LYZ | RBP1 |
RHO | SMAD7 | MMP8 | EZH2 | RXRB | XAB2 |
FBN2 | CD36 | IL1B | MTOR | PTK2 | SSBP3 |
OPN4 | IL6 | RARG | PRKCD | ATF2 | WARS1 |
OPN5 | AQP3 | TYR | CTNNB1 | COL3A1 | DEFB4A |
CTSB | IL1A | LOX | CHUK | DHCR7 | MIR23A |
FAS | EGFR | HAS2 | CASP8 | TCF7L2 | MIR101-1 |
MMP13 | PTEN | MFAP4 | MDM2 | TGFB3 | MIR377 |
GSR | MAPK1 | DSPP | RPS6KA3 | ANG | MIR101-2 |
DCN | TGFB1 | IL1RAPL2 | CDKN2A | BAD | VTRNA2-1 |
Table 4Co-targeted genes for photoaging and asiaticoside
BCL2L1 | SIRT1 | RPS27A | SPRR1B | FBN1 |
HSD11B1 | OPN3 | ELANE | PRKAA2 | CDKN2A |
JUN | GPX4 | MYC | HDAC3 | CTSL |
CAT | NFKB1 | MC1R | FN1 | MIP |
MAPK14 | IL1R1 | GLO1 | ECM1 | IL11 |
MTOR | FBXO40 | ARNTL | MFAP2 | HSPA4 |
RARG | SMAD4 | ACACA | TAGLN | IL6 |
HBEGF | MAPK1 | PIK3CG | HAS2 | RPS3 |
RARB | ANG | LOX | S100A8 | HYAL2 |
MMP1 | TGFB1 | IL1B | MAPK10 | CTSB |
MMP13 | CD36 | CASP9 | SOD1 | KRT16 |
CRABP2 | NR1H3 | TGFBR2 | HYAL3 | MRC2 |
CCNA2 | SFN | SULT1A1 | SMAD2 | ALOX5 |
PPARG | PTEN | MMP7 | MMP10 | RNASE1 |
MMP3 | CASP3 | GNAQ | BAX | COL1A1 |
RARA | SAA1 | CSN1S1 | IL1A | SOD2 |
PRKCD | MIF | GDF15 | TYR | BAD |
ESR1 | SIRT4 | SPARC | F2RL1 | KCNJ5 |
FASN | MMP14 | CLU | HSPA1A | RBP1 |
ANXA1 | FOSB | ORAI1 | POLQ | PSMC4 |
DNMT1 | CYCS | POSTN | HYAL1 | XAB2 |
ESR2 | PRF1 | BMP6 | NR1H2 | MYD88 |
TRPV1 | TEP1 | ATF2 | HRH1 | TGFB3 |
TNF | NFKBIA | CYBB | CP | RHO |
MMP9 | SULT1E1 | TGM1 | CDKN1A | ODC1 |
NOS2 | MMP12 | MSRA | SMAD7 | SSBP3 |
PLA2G4A | FBLN2 | HSD17B4 | HIF1A | SCD |
EGF | GZMB | HMMR | IL1RAPL2 | CEMIP |
PTGS2 | MITF | ITGB1 | MMP8 | LMNA |
DCT | KRT10 | SERPINH1 | CLOCK | CTSD |
RXRB | STXBP5L | NFE2L2 | IRAK1 | DDB2 |
AKT1 | TP53 | MAPK3 | TYRP1 | CHUK |
GSR | HSD17B2 | PIK3R1 | ABCC9 | PTK2 |
CASP8 | STK11 | TIMP1 | MDM2 | HSD17B8 |
KRT19 | KRT14 | XPA | PPARD | GAL |
CYP1B1 | PTPRU | AQP3 | AREG | AGER |
FLG | TLR4 | ELN | CREB1 | CTNNB1 |
PEX7 | OPN4 | IVL | EZH2 | GDA |
PTPRK | OPN1SW | MMP2 | VEGFA | DEFB4A |
PDYN | COL3A1 | ANGPT1 | TCF7L2 | FBN2 |
XDH | IL18R1 | | | |
PPI network of the common targets
To gain insight into asiaticoside’s method of action in the treatment of photoaging, it is important to investigate the interactions between the common targets. The authors utilized STRING to construct an interlaced network of the 202 common targets. Next, a network of 171 targets was obtained after removing disconnected nodes as depicted in Figure 1b, and the top 30 targets sorted by R based on degree value was shown in Figure 1c. The researchers postulated that asiaticoside exerts its medicinal effects and treats photoaging by modulating such co-targets.
The construction of the disease-target-drug network
To understand the relationship between the co-targets and drug/disease more intuitively, the “disease-target-drug” network diagram was mapped out using Cytoscape. By intersecting the drug-disease common targets with the PPI targets, the authors generated a condensed network consisting of 173 nodes including asiaticoside, photoaging, and 171 targets (Fig. 2a). Next, the authors used the MCC algorithm to evaluate the importance of each node and rank them according to scores (Fig. 2a and Table 5).
Table 5The information of core targets by MCC analysis
Rank | Name | Score | Rank | Name | Score |
---|
1 | Photoaging | 96,552 | 88 | ELANE | 108 |
1 | Asiaticoside | 96,552 | 89 | SOD2 | 96 |
3 | TIMP1 | 91,248 | 89 | ESR2 | 96 |
4 | TGFB1 | 88,812 | 91 | HSPA1A | 76 |
5 | TGFB3 | 85,680 | 92 | HSD17B4 | 72 |
6 | VEGFA | 84,888 | 92 | NR1H3 | 72 |
7 | EGF | 82,732 | 94 | CTSD | 68 |
8 | FN1 | 82,680 | 95 | HBEGF | 64 |
9 | SPARC | 80,700 | 96 | SOD1 | 60 |
10 | CLU | 80,640 | 96 | MMP8 | 60 |
11 | MAPK3 | 52,200 | 96 | STK11 | 60 |
12 | AKT1 | 50,576 | 99 | COL3A1 | 52 |
13 | TP53 | 46,740 | 99 | KRT19 | 52 |
14 | JUN | 46,192 | 99 | ANGPT1 | 52 |
15 | MAPK1 | 36,012 | 102 | SPRR1B | 48 |
16 | ESR1 | 23,608 | 102 | IVL | 48 |
17 | MYC | 23,448 | 102 | LMNA | 48 |
18 | MMP9 | 20,464 | 102 | NR1H2 | 48 |
19 | MMP2 | 20,368 | 102 | S100A8 | 48 |
20 | MMP3 | 20,220 | 102 | KRT10 | 48 |
21 | MMP13 | 20,208 | 102 | KRT14 | 48 |
22 | MMP1 | 20,172 | 102 | KRT16 | 48 |
23 | MMP10 | 20,160 | 102 | GAL | 48 |
24 | CREB1 | 17,440 | 102 | PDYN | 48 |
25 | MAPK14 | 17,240 | 102 | FOSB | 48 |
26 | HIF1A | 15,364 | 102 | BAX | 48 |
27 | RARA | 12,480 | 102 | MMP14 | 48 |
27 | RARB | 12,480 | 102 | COL1A1 | 48 |
27 | RARG | 12,480 | 102 | FLG | 48 |
30 | PIK3R1 | 11,112 | 102 | TGM1 | 48 |
31 | RXRB | 10,624 | 118 | PLA2G4A | 40 |
32 | SAA1 | 10,380 | 118 | MFAP2 | 40 |
33 | GNAQ | 10,152 | 120 | GSR | 24 |
34 | ANXA1 | 10,128 | 120 | CD36 | 24 |
35 | MMP7 | 10,104 | 120 | GPX4 | 24 |
36 | F2RL1 | 10,080 | 120 | DDB2 | 24 |
36 | OPN4 | 10,080 | 120 | XPA | 24 |
36 | HRH1 | 10,080 | 120 | DNMT1 | 24 |
39 | SMAD4 | 7,572 | 120 | ARNTL | 24 |
40 | ATF2 | 7,488 | 120 | CLOCK | 24 |
41 | NFKB1 | 7,180 | 120 | ELN | 24 |
42 | RPS27A | 7,008 | 120 | FBN2 | 24 |
43 | SMAD7 | 5,760 | 130 | PRKAA2 | 20 |
43 | TGFBR2 | 5,760 | 130 | GZMB | 20 |
45 | CDKN1A | 5,136 | 132 | PPARD | 16 |
46 | CTNNB1 | 4,800 | 133 | OPN1SW | 12 |
47 | IRAK1 | 4,128 | 133 | OPN3 | 12 |
48 | MYD88 | 3,648 | 133 | RHO | 12 |
49 | IL1A | 3,408 | 133 | SERPINH1 | 12 |
50 | TNF | 3,276 | 133 | XAB2 | 12 |
51 | CHUK | 3,264 | 133 | NFE2L2 | 12 |
52 | SMAD2 | 2,988 | 133 | PIK3CG | 12 |
53 | IL1R1 | 2,928 | 133 | PTPRK | 12 |
54 | IL1B | 2,884 | 133 | LOX | 12 |
55 | NFKBIA | 2,748 | 142 | XDH | 8 |
56 | MDM2 | 2,448 | 142 | CYBB | 8 |
56 | HDAC3 | 2,448 | 142 | ACACA | 8 |
58 | CDKN2A | 2,112 | 142 | FASN | 8 |
59 | EZH2 | 1,980 | 142 | SULT1E1 | 8 |
60 | TLR4 | 1,732 | 142 | IL18R1 | 8 |
61 | ITGB1 | 1,716 | 142 | RBP1 | 8 |
62 | PPARG | 1,612 | 142 | CTSB | 8 |
63 | IL6 | 1,488 | 142 | CYP1B1 | 8 |
64 | BCL2L1 | 1,248 | 142 | HSD17B2 | 8 |
65 | CASP8 | 1,080 | 142 | HSD17B8 | 8 |
66 | CASP3 | 876 | 142 | DCT | 8 |
67 | PSMC4 | 784 | 142 | TYR | 8 |
68 | PTEN | 648 | 142 | TYRP1 | 8 |
68 | BAD | 648 | 156 | GDA | 4 |
70 | SFN | 516 | 156 | PRF1 | 4 |
71 | CCNA2 | 480 | 156 | ODC1 | 4 |
72 | PRKCD | 456 | 156 | PEX7 | 4 |
73 | NOS2 | 420 | 156 | HMMR | 4 |
74 | MTOR | 316 | 156 | HYAL2 | 4 |
75 | CYCS | 304 | 156 | HSD11B1 | 4 |
76 | FBN1 | 300 | 156 | HSPA4 | 4 |
77 | CASP9 | 288 | 156 | POSTN | 4 |
77 | PTK2 | 288 | 156 | TEP1 | 4 |
79 | MAPK10 | 264 | 156 | RPS3 | 4 |
80 | AGER | 240 | 156 | CTSL | 4 |
80 | CP | 240 | 156 | ALOX5 | 4 |
80 | CRABP2 | 240 | 156 | DEFB4A | 4 |
80 | TCF7L2 | 240 | 156 | AREG | 4 |
84 | MITF | 152 | 156 | SCD | 4 |
85 | SIRT1 | 144 | 156 | FBLN2 | 4 |
86 | PTGS2 | 128 | 156 | FBXO40 | 4 |
87 | CAT | 124 | | | |
GO enrichment analyses
As shown in Figure 2b, the 10 top terms about BP, CC and MF were displayed. The color gradient from red to purple represented the trend of increasing p-values and decreasing significance for each term. The BP terms comprised responses, to radiation, light stimulus, UV, oxygen levels, as well as, cellular response to oxidative stress, and response to oxygen levels, among others (Fig. 2b, p < 0.05). CC terms included collagen−containing extracellular matrix, RNA polymerase II transcription regulator complex, cornified envelope, and melanosome, and so on (Fig. 2b, p < 0.05). Simultaneously, MF terms mainly contained DNA−binding transcription factor binding, collagen binding, nuclear receptor activity, protease binding, as well as, phosphatase binding, and others (Fig. 2b, p < 0.05). These GO enrichment results strongly implicated the therapeutic potential of asiaticoside on photoaging.
KEGG enrichment analyses
To gain a comprehensive understanding of the mechanisms underlying the effects of asiaticoside on photoaging, the authors performed KEGG pathway enrichment analyses using Metascape. The top 20 enrichment pathways were obtained and the genes involved in those pathways were listed in Table S2. Based on the results of the top 20 enriched pathways, 8 pathways most strongly associated with photoaging and genes involved in at least 3 of the 8 pathways were screened out for drawing KEGG chord plots, which included pathways in cancer, PI3K-AKT signalling pathway, longevity regulating pathway, NF-κB signalling pathway, TGF-β signalling pathway, melanogenesis, inflammatory mediator regulation of TRP channels, and PPAR signalling pathway (Fig. 2c, p < 0.05).
Selection of optimal drug concentration and exposure dosage
To detect the cytotoxicity of asiaticoside, HDFs treated with asiaticoside (0, 12.5, 25, 50, 100 and 200 µg/mL) for 48 h were detected by CCK-8 assay. As shown in Figure 3a, 12.5 and 25 µg/mL asiaticoside improved HDFs proliferation as opposed to the control group (p < 0.05), and asiaticoside of 50 µg/mL appeared to have a similar cell proliferation (%) to the control group (p > 0.05). However, asiaticoside of 100 and 200 µg/mL had obvious inhibition on the vitality of HDFs, which contributed to over half of the death compared to the control group (Fig. 3a, p < 0.05), indicating high concentrations of asiaticoside had cytotoxic effects. Therefore, asiaticoside of 0, 12.5, 25 and 50 µg/mL were selected for the following experiments.
To choose the most appropriate exposure dose of UVA, HDFs were irradiated by UVA at 0, 12.5, 25, 50, 100 and 200 mJ/cm2 respectively, and detected by CCK-8 assay at 48 h post-irradiation. As shown, UVA irradiation significantly inhibited HDFs proliferation at the doses of 25, 50, 100, and 200 mJ/cm2 in a dose-dependent manner (Fig. 3b, p < 0.05). Among such samples, nearly 50% of HDFs died after being irradiated by UVA of 100 mJ/cm2 (Fig. 3b, p < 0.05). Hence, UVA of 100 mJ/cm2 was selected for the following experiments.
Next, HDFs were segregated into six groups: (1) control group (HDFs untreated by UVA and drugs); (2) UVA group (HDFs exposed to UVA irradiation only); (3) VC group (HDFs exposed to UVA irradiation with VC treatment, serve as a positive control); (4–6) 12.5/25/50 µg/mL asiaticoside group (HDFs exposed to UVA irradiation with 12.5/25/50 µg/mL asiaticoside treatment, respectively.
Asiaticoside rescued UVA-treated HDFs from proliferation inhibition
After pre-treatment with or without VC/asiaticoside for 48 h, HDFs were irradiated by 100 mJ/cm2 UVA and then subcultured with VC/asiaticoside for another 48 h. Cell density and proliferation ratio in VC and 25 µg/mL asiaticoside groups could reach a similar level to those of the control group by CCK-8 assay and manual cell counting (Fig. 3c–d, p < 0.05).
The experimental results indicated that asiaticoside of 25 µg/mL may have a better effect on preventing adverse cellular response caused by UVA irradiation. Thus, asiaticoside of 25 µg/mL and VC were chosen for subsequent qPCR and WB assay.
Asiaticoside reversed UVA-induced expression of genes and proteins related to photoaging mined by network pharmacology
The researchers chose 13 important core genes sorted by MCC and KEGG pathway enrichment to perform the qPCR assay. TGF-β1 could enhance collagen production and inhibit collagen degradation while TGF-β3 exerts an opposite effect, with each of them playing a crucial role in cellular senescence and aging-related pathology.31 As shown in Figure 4, UVA exposure resulted in a significantly reduced TGF-β1 and increased TGF-β3 expression than the control group (p < 0.05), but a rescuing effect was found in VC and 25 µg/mL asiaticoside treatment groups. TIMP1 is a natural inhibitor of MMPs stimulating fibroblast proliferation, collagen, and ECM production.32 Activation of SMAD4 and inhibition of SMAD7 could promote TGF-β/Smad signalling pathway which prevents the cells from photoaging and promotes photoaging skin cell repair.33 Apparent decline in TIMP1 and SMAD4 gene expressions and increased gene expression of SMAD7 were displayed after UVA irradiation compared to the control group (Fig. 4a, p < 0.05). However, both VC and 25 µg/mL asiaticoside retracted the abnormal expression of TIMP1, SMAD4, and SMAD7 to a similar level compared to the control group (Fig. 4a, p > 0.05).
Activated NF-κB signalling pathway in the photoaging process results in a significant inflammatory response accelerating skin photoaging by secretion of multiple pro-inflammatory cytokines.3 IL-6 and TNF-α are the downstream signalling factors and essential activators of the NF-κB pathway.34 A qPCR analysis revealed remarkably upregulated gene expressions of IL-6 and TNF-α in HDFs exposed to UVA as opposed to the control group (Fig. 4a, p < 0.05). However, treatment of VC and asiaticoside abolished IL-6 and TNF-α expression at different levels than the UVA group (Fig. 4a, p < 0.05), and rescued the expression levels similar to the control group (Fig. 4a, p > 0.05).
PTEN and PPARγ are known significant negative regulators of PI3K-AKT signalling, which is suppressed in photoaging and accompanied by a decreased expression of p-AKT that leads to cell apoptosis.4 PIK3R1 encodes the regulatory subunit of the PI3K heterodimer which contributes to the PI3K activity.35 Pro-apoptotic BAX, P53 and anti-apoptotic BCL2L1 are strictly related to the PI3K-AKT pathway.36,37 As shown in Figure 4, UVA irradiation could markedly upregulate mRNA expression of PTEN, PPARγ, BAX, and P53 and downregulates PIK3R1 and BCL2L1 expression as opposed to the control group (p < 0.05). But the treatment of VC and 25 µg/mL asiaticoside could rescue UVA-induced gene expressions, especially the asiaticoside group that showed no significant expression difference of PTEN, PPARγ, BAX, P53, and BCL2L1, including an upregulated expression of PIK3R1 compared to the control group (Fig. 4a, p < 0.05).
WB assay was used for further validation. As shown in Figure 4b, an increased ratio of p-P65/P65 and p- IκBα α/IκBα α in HDFs was detected in UVA-irradiated HDFs implicating the activation of the NF-κB pathway. But such phenomenon can be attenuated by VC and reversed by 25 µg/mL asiaticoside treatment (Fig. 4b). Corresponding to the qPCR results, UVA irradiation stimulated the protein expression levels of BAX, PTEN, and PPARγ compared to the control group (Fig. 4b). However treatment by VC and asiaticoside could counteract the stimulation (Fig. 4b). Similarly, the downregulated ratio of p-AKT/AKT in UVA-irradiated HDFs revealed the suppression of PI3K-AKT pathway (Fig. 4b). However, the suppressed effect was reversed by VC and asiaticoside (Fig. 4b). Although no expressed difference of P53 was observed in the UVA and control groups, p-P53 which is more stable and had an increased activity compared to P53 had a significantly increased expression after UVA irradiation than that of the control group (Fig. 4b). Unexpectedly, drug treatment could rescue the upregulated p-P53 and the effect in asiaticoside group was more pronounced (Fig. 4b).
The results indicated that asiaticoside might rescue UVA-induced photoaging by preventing ECM degradation, repressing inflammatory response, and inhibiting cell apoptosis.
Results of molecular docking
A comprehensive consideration of the core targets sorted by MCC analysis and participation in the core signalling pathways was invalidated by in vitro experiments. Molecular docking analysis was conducted on a total of 9 molecules. The results indicated that asiaticoside displayed a high binding affinity with the selected targets BAX, PIK3R1, PTEN, PPARγ, P53, TNF-α, TGF-β1, TGF-β3, and SMAD4. As shown in Figure 5, in the formation of various hydrogen bonds with residues at very close distances, asiaticoside established a stable complex with the selected target proteins.
Discussion
Skin aging could be attributed to chronological aging and UV-induced photoaging. A leading cause of premature photoaging was UV (especially UVA) exposure, appearing as photodamage superimposed on the aging process, characterized by sagging, fragility, dyspigmentation, wrinkles, skin roughness, and decreased skin flexibility.1,2
A study demonstrated that inflammation and apoptosis were the major hallmarks of skin photoaging.38 UVA-induced oxidative stress triggered the overproduction of ROS and caused a variety of damage to fibroblasts including excessive inflammatory response.39 Moreover, UVA promoted photoaging and inflammatory infiltration in the skin and in turn, the inflammatory response accelerated the process of photoaging.40 Exposure to UV also led to mass cell apoptosis, both skin barrier and function disruption, as well as, accelerated photoaging.41
Currently, various strategies have been employed to maintain the structure and function of skin during photoaging, including antioxidant products, stem cell therapies, and intense pulsed light, among others.42,43 Plant extracts with proven antioxidant, anti-inflammatory and anti-apoptotic properties have been increasingly explored to prevent or reduce skin damage caused by oxidative stress, such as skin cancer and photoaging.44,45 Asiaticoside is a traditional Chinese herbal monomer that has been used for many years to treat various skin diseases like skin ulcer and scleroderma14,15 Asiaticoside possesses diverse pharmacological effects, including antioxidant activity including the ability to scavenge free radicals, as well as, anti-apoptotic, anti-inflammatory, and anti-tumor activities.16,17 Thus, it was hypothesized that asiaticoside may confer protective effects against photoaging induced by UVA radiation.
In this study, core targets and significantly enriched 8 signalling pathways were identified by network pharmacology analyses which provided a preliminary validation of asiaticoside efficacy on photoaging (Figs. 1–2). Previous studies found that UVA irradiation disrupted cellular fractions by overproduction of ROS, which induced macromolecule damage and accelerated skin aging, as well as, cancer.2 ROS accumulation and cell aging could regulate the longevity regulation pathway5 and stimulate the TGF-β/Smad pathway.6 In addition, ROS could activate NF-κB signalling pathway and result in severe inflammatory responses of affected skin.3 PI3K-AKT signalling pathway could also be suppressed by ROS, which led to cell apoptosis.4 Moreover, enhanced MAPK and PPAR signalling could lead to decreased collagen and TIMP1 production under UVA irradiation8,46 and UVA irradiation was revealed to promote the development of both nonmelanoma skin cancer, as well as, melanoma,47 including the activation of TRPV2 by oxidizing.48 Such results reported above proved the accuracy of our network pharmacology analysis.
After establishing a successful in vitro model, the authors contended that asiaticoside indeed prevented photoaging by reversing UVA-induced HDFs proliferation inhibition (Fig. 3). Moreover, 25 µg/mL asiaticoside appeared to rank the best photoprotective effect (Fig. 3). Both qPCR and WB were employed to elucidate the mechanisms of asiaticoside on photoaging. As the authors recognize, photoaged skin is marked by increased degradation and turnover of ECM with overexpressed MMPs, and down-expressed TIMP1, which is triggered by the attenuated TGF-β/Smad pathway.7 The researchers’ network pharmacology analysis revealed TGF-β1, TGF-β3, TIMP1, MMP1, MMP2, MMP3, MMP9, MMP13, SMAD4, and SMAD7 are the core targets in the drug-disease-target network and TGF-β signalling pathway was significantly enriched in KEGG pathways (Fig. 1–2, Table 5 and S2). A qPCR indicated that 25 µg/mL asiaticoside and 50 µg/mL VC could almost alter UVA-induced gene expression of TGF-β1, TGF-β3, TIMP1, SMAD4 and SMAD7 to the normal level (Fig. 4). This demonstrated the reliability of the joint effect of network pharmacology and in vitro experiments. Moreover, asiaticoside was previously shown to suppress TGF-β/Smad signalling through inducing Smad7 and PPARy in fibroblasts,15 which further confirmed our conjecture that asiaticoside could inhibit UVA-induced photodamage by activating the TGF-β/Smad pathway. HDFs exposed to UVA released a range of inflammation factors including IL-6 and TNF-α, the critical trigger for cell apoptosis and skin aging.49 IL-6 and TNF-α were significantly enriched in the NF-κB signalling pathway (Fig. 2, Table S2), which were the downstream signalling factors and essential activators for the NF-κB pathway.34 The raised gene expression of IL-6 and TNF-α in UVA-exposed HDFs indicated the activation of the NF-κB pathway. But this phenomenon can be reversed by 25 µg/mL asiaticoside treatment (Fig. 4). Suppressed PI3K-AKT signalling pathway was found in photoaged HDFs, which could accelerate cell apoptosis.4 PTEN and PPARγ are known significant negative regulators of PI3K-AKT signalling by decreasing the expression of p-AKT4 and PIK3R1 contributed to the PI3K activity.35 Anti-apoptotic BCL2L1 could directly activate PI3K-AKT pathway,50 with pro-apoptotic BAX and P53 stimulating an opposite effect.51 As reported, BCL2L1 and BAX are also the downstream targets of the NF-κB pathway,52,53 which could inhibit NF-κB activity and abrogate p53-induced apoptosis.54 This indicated that the NF-κB pathway was closely related to the PI3K-AKT pathway in the photoaging progression. Moreover, TGF-β can activate the PI3K-AKT pathway.55 MAPK could exert a synergistic effect on PI3K-AKT pathway56 and disrupt NF-κB dependent inflammatory stress.57 Longevity regulating pathway played antagonistic roles in response to the NF-κB pathway.58 Such evidence gives us a hint that PI3K-AKT and NF-κB signalling pathways may play a central role in asiaticoside-induced photoaging alleviation. Compared to the control group, exposure to UVA radiation significantly increased the expression of PTEN, PPARγ, BAX, and P53 while decreasing the expression of PIK3R1 and BCL2L1 as shown in Figure 4a (p < 0.05). However, 25 µg/mL asiaticoside treatment could significantly rescue UVA-induced those gene expressions (Fig. 4a, p < 0.05). WB assay also implicated the activation of the NF-κB pathway and suppression of PI3K-AKT in UVA-irradiated HDFs, but this effect could be regressed by 25 µg/mL asiaticoside treatments (Fig. 4b).
The molecular docking analysis revealed that asiaticoside exhibited a high binding affinity with the co-core proteins (Fig. 5). The findings suggest that asiaticoside may play a significant role in the treatment of photoaging by targeting the core proteins and related pathways.
Future directions
The study provided a thorough and systematic insight into the possible therapeutic mechanism of asiaticoside for the treatment of photoaging. Future research will be conducted to support this theory, including further confirmation by in vitro and in vivo investigations.
Conclusion
Achieving the objectives of preventing skin photoaging and improving the appearance of fine, as well as, coarse wrinkles is a crucial aim of skincare treatments. Currently, available anti-photoaging agents often target a single aspect, such as antioxidants. However, a drug that can target multiple aspects of the photoaging mechanism with minimal side effects would be a preferred therapeutic agent. Asiaticoside is a promising multi-targeting drug that has shown potential in photoprotection. It has been shown to reverse the inhibition of HDF proliferation caused by UVA irradiation, including the modulation of the expression levels of genes related to collagen degradation, inflammation, and apoptosis in UVA-irradiated HDFs. Further mechanistic studies have demonstrated that asiaticoside exerts its multi-targeting effect mainly by activating the PI3K-AKT pathway and inhibiting the NF-κB pathway, which triggers a series of anti-photoaging cascades. The findings suggest that asiaticoside could be an attractive anti-photoaging agent in the future.
Supporting information
Supplementary material for this article is available at https://doi.org/10.14218/ERHM.2023.00037 .
Table S1
Target prediction result for asiaticoside.
(DOC)
Table S2
KEGG enrichment analysis of core genes by Metascape.
(DOC)
Abbreviations
- BATMAN:
bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine
- BAX:
BCL-2-associated X protein
- BCL-2:
b-cell lymphoma 2
- BCL2L1:
BCL2 like 1 (human)
- CCK-8:
cell counting kit 8
- CTD:
comparative toxicogenomics database
- ECM:
extracellular matrix
- GAPDH:
glyceraldehyde 3-phosphate dehydrogenase
- GO:
gene ontology
- HDFs:
human dermal fibroblasts
- IκBα:
nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha
- IL-6:
interleukin 6
- KEGG:
Kyoto encyclopedia of genes and genomes
- MAPKs:
mitogen-activated protein kinases
- MCC:
Matthews correlation coefficient
- MMP(s):
matrix metalloproteinase(s)
- NF-κB:
nuclear factor kappa-light-chain-enhancer of activated B cells
- p-AKT:
phospho-AKT
- P53:
tumor protein p53
- PI3K-AKT:
phosphatidylinositol 3 kinase (PI3K)/protein kinase B (AKT)
- PIK3R1:
phosphoinositide-3-kinase regulatory subunit 1
- PPAR(s):
peroxisome proliferator-activated receptor(s)
- PPARγ:
peroxisome proliferator-activated receptor gamma
- PPI:
- protein-protein interactions:
- PTEN:
phosphatase and tensin homolog
- qPCR:
quantitative polymerase chain reaction
- ROS:
reactive oxygen species
- SMAD:
suppressor of mothers against decapentaplegic
- TGFβ:
transforming growth factor-beta
- TIMP1:
tissue inhibitor of the metalloproteinases 1
- TNF-α:
tumour necrosis factor-α
- UV:
ultraviolet
- UVA:
ultraviolet A
- VC:
vitamin C
Declarations
Acknowledgement
None to declare.
Data sharing statement
No additional data are available.
Ethical statement
All procedures performed in this study involving human participants, were in accordance with the ethical standards of the Fudan University School of Medicine (Ethical approval document No: 2022-778) and with the 1964 Helsinki declaration and its later amendments. Written and informed consent for publication was obtained from male donors.
Funding
The study was supported by the fundamental research program funding of Ninth People’s Hospital affiliated with Shanghai Jiao Tong University School of Medicine (JYZZ176) and the National Natural Science Foundation of China (grant no. 82104485)
Conflict of interest
The authors have declared no conflict of interests.
Authors’ contributions
Data curation, formal analysis, visualization and writing-original draft (JH); Investigation and Software (YYG and KL); Supervision (JC and XBZ); Conceptualization, writing–review & editing (XBZ).