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  • Original Article
  • OPEN ACCESS

Anti-photoaging Properties of Asiaticoside in Ultraviolet A-irradiated Human Dermal Fibroblasts by Activating the PI3K-AKT Pathway and Inhibiting the NF-κB Pathway

  • Jia Huang1,#,
  • Yiyi Gong1,#,
  • Ke Liu2,#,
  • Jun Chen2,*  and
  • Xiaobo Zhou2,* 
 Author information
Exploratory Research and Hypothesis in Medicine   2023;8(4):319-337

doi: 10.14218/ERHM.2023.00037

Abstract

Background and objectives

Asiaticoside reduces inflammatory reactions and oxidative stress, the primary causes of photoaging. The authors speculated that asiaticoside might contain therapeutic potential for photoaging.

Methods

Network pharmacology and molecular docking were used to explore the mechanisms of asiaticoside in treating photoaging. After achieving the targets of asiaticoside using PharmMapper, SwissTargetPrediction, CTD and BATMAN databases, as well as, targets of photoaging using GeneCards database, the co-targets interaction network was formed and a network of asiaticoside, photoaging, and common targets were constructed by Cytoscape. Next, the common targets were analyzed using GO and KEGG enrichment.

Results

The analysis highlighted 202 core targets of asiaticoside were involved in the pathogenesis of photoaging. KEGG indicated asiaticoside performed an anti-photoaging effect through inflammation- and apoptosis-related signalling pathways, especially the PI3K-AKT and NF-κB pathways. Furthermore, the anti-photoaging effect of asiaticoside was verified by human dermal fibroblasts with UVA irradiation in vitro.

Conclusion

Asiaticoside may alleviate UVA-induced cell proliferation inhibition, reverse the abnormal gene expressions involved in the PI3K-AKT and NF-κB pathways, and have a high affinity with those core targets.

Keywords

Asiaticoside, anti-photoaging, Anti-inflammation, Anti-apoptosis, Network pharmacology, Molecular docking

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 1

Primers used in qPCR analysis

GenePrimer Sequence (5′-3′)Annealing Temperature (°C)Product Size (bp)
TGF-β1Sense: AAGGACCTCGGCTGGAAGTG58136
Antisense: CCGGGTTATGCTGGTTGTA
TGF-β3Sense: GGTTTTCCGCTTCAATGTGT58119
Antisense: GCTCGATCCTCTGCTCATTC
TIMP1Sense: TGACATCCGGTTCGTCTACA58102
Antisense: TGCAGTTTTCCAGCAATGAG
SMAD4Sense: CTCATGTGATCTATGCCCGTC58146
Antisense: AGGTGATACAACTCGTTCGTAGT
SMAD7Sense: TTCCTCCGCTGAAACAGGG58116
Antisense: CCTCCCAGTATGCCACCAC
TNF-αSense: CTCGAACCCCGAGTGACAAG58159
Antisense: TGAGGTACAGGCCCTCTGAT
IL-6Sense: CCTGACCCAACCACAAATGC58157
Antisense: ATCTGAGGTGCCCATGCTAC
PTENSense: AGGGACGAACTGGTGTAATGA58100
Antisense: CTGGTCCTTACTTCCCCATAGAA
PPARγSense: ACCAAAGTGCAATCAAAGTGGA58100
Antisense: ATGAGGGAGTTGGAAGGCTCT
PIK3R1Sense: TGGACGGCGAAGTAAAGCATT58154
Antisense: AGTGTGACATTGAGGGAGTCG
BCL2L1Sense: GAGCTGGTGGTTGACTTTCTC58119
Antisense: TCCATCTCCGATTCAGTCCCT
BAXSense: CCCGAGAGGTCTTTTTCCGAG58155
Antisense: CCAGCCCATGATGGTTCTGAT
P53Sense: GAGGTTGGCTCTGACTGTACC58133
Antisense: TCCGTCCCAGTAGATTACCAC
GAPDHSense: ACAACTTTGGTATCGTGGAAGG58101
Antisense: GCCATCACGCCACAGTTTC

BAX, BCL-2-associated X protein; BCL2L1, BCL2 like 1 (human); GAPDH, glyceraldehyde 3-phosphate dehydrogenase; IL-6, interleukin 6; P53, tumor protein p53; PIK3R1, phosphoinositide-3-kinase regulatory subunit 1; PPARγ, peroxisome proliferator-activated receptor gamma; PTEN, phosphatase and tensin homolog; SMAD, suppressor of mothers against decapentaplegic; TGFβ, transforming growth factor-beta; TNF-α, tumour necrosis factor-α.

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 2

Antibodies used in western blotting

TargetsSourceDilution ratio
BAXAbcam, ab1827331:2,000
IκB-αAbcam, ab325181:5,000
P-IκBαAbcam, ab1334621:10,000
P65Abcam, ab325361:5,000
P-P65Abcam, ab862991:5,000
P53Abcam, ab261:1,000
P-53Abcam, ab338891:2,000
PTENAbcam, ab1709411:5,000
PPARγAbcam, ab1788601:1,000
AKTAbcam, ab88051:500
P-AKTAbcam, ab89331:500
GAPDHAbcam, ab82451:2,000

AKT, protein kinase B; BAX, BCL-2-associated X protein; BCL-2, b-cell lymphoma 2; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; IκBα, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha; PPARγ, peroxisome proliferator-activated receptor gamma; PTEN, phosphatase and tensin homolog.

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 3

Identification of potential targets for photoaging by GeneCards

MMP1ELANEMMP7CEMIPDNMT1ABCC9
ELNMRC2RARS1AKT1PIK3R1HRH1
FBN1FLGESR1CASP3MMP14CTSL
JUNMAPK14NFKB1RARBFN1KRT19
MC1RACACAMAPK8ALOX5HDAC3TP53BP1
PTGS2FASNESR2CREB1NOS2PRF1
OPN1SWGLB1MIFCDKN1ANFKBIATYRP1
OPN3SCDCYP1B1PIK3CGSPARCAGER
FOSTGM1HSD17B4DDB2TLR4CLOCK
BMP6FBLN2HSD17B2KRT14PRKAA2CCNA2
RARALORICRINSULT1E1PPARDIRAK1GAL
IVLCLUSULT1A1HYAL1GNAQRPS3
CCN1HSD11B1HSD17B8ANGPT1STK11CALCA
MMP3EGFGPER1SHC1RPS6KB1DCT
VCANPOLQGREB1SERPINH1CASP9ACP1
MMP9SPRR1BTNFECM1ANXA1ARNTL
MMP2FBXO40SOD2HSF1CPPOSTN
TRPV1STXBP5LMYD88HSPA1ABAXTAGLN
KRT16F2RL1NR1H2PTPRKCYCSPEX7
CTSDPDYNSIRT1ORAI1MITFRPS27A
TIMP1PLA2G4ANR1H3CRABP2ODC1TIMP2
TGFBR2XPAMMP10HYAL2LMNAIL18R1
IL1R1MSRAPPARAHBEGFMAPK3HMMR
TP53MIR155GZMBGDANFE2L2NOX4
MAPK10LMNB1KRT17KRT10VEGFAPTPRU
COL1A1GLO1SFNAREGBCL2L1FOSB
MYCHAGHS100A8HSPA4GPX4GDF15
ITGB1MSRB1SIRT4DUSP16CYP27B1MIP
SMAD4CATHYAL3MFAP2CYBBPSMC4
XDHSOD1MIR15BSAA1HIF1ATEP1
MIR146AMMP12VDRIL11KCNJ5RNASE1
PPARGSMAD2RXRACSN1S1LYZRBP1
RHOSMAD7MMP8EZH2RXRBXAB2
FBN2CD36IL1BMTORPTK2SSBP3
OPN4IL6RARGPRKCDATF2WARS1
OPN5AQP3TYRCTNNB1COL3A1DEFB4A
CTSBIL1ALOXCHUKDHCR7MIR23A
FASEGFRHAS2CASP8TCF7L2MIR101-1
MMP13PTENMFAP4MDM2TGFB3MIR377
GSRMAPK1DSPPRPS6KA3ANGMIR101-2
DCNTGFB1IL1RAPL2CDKN2ABADVTRNA2-1

ABCC9, ATP Binding Cassette Subfamily C Member 9; ACACA, Acetyl-CoA Carboxylase Alpha; ACP1, Acid Phosphatase 1, Soluble; AGER, Advanced Glycosylation End-Product Specific Receptor; AKT1, AKT Serine/Threonine Kinase 1; ALOX5, Arachidonate 5-Lipoxygenase; ANG, Angiogenin; ANGPT1, Angiopoietin 1; ANXA1, Annexin A1; AQP3, Aquaporin 3 (Gill Blood Group); AREG, Amphiregulin; ARNTL, Aryl hydrocarbon receptor nuclear translocator-like protein 1; ATF2, Activating Transcription Factor 2; BAD, BCL2 Associated Agonist Of Cell Death; BAX, BCL2 Associated X, Apoptosis Regulator; BCL2L1, BCL2 Like 1; BMP6, Bone Morphogenetic Protein 6; CALCA, Calcitonin Related Polypeptide Alpha; CASP3, Caspase 3; CASP8, Caspase 8; CASP9, Caspase 9; CAT, Catalase; CCN1, Cellular Communication Network Factor 1; CCNA2, Cyclin A2; CD36, CD36 Molecule; CDKN1A, Cyclin Dependent Kinase Inhibitor 1A; CDKN2A, Cyclin Dependent Kinase Inhibitor 2A; CEMIP, Cell Migration Inducing Hyaluronidase 1; CHUK, Component Of Inhibitor Of Nuclear Factor Kappa B Kinase Complex; CLOCK, Clock Circadian Regulator; CLU, Clusterin, COL1A1, Collagen Type I Alpha 1 Chain; COL3A1, Collagen Type 3 Alpha 1 Chain; CP, Ceruloplasmin; CRABP2, Cellular Retinoic Acid Binding Protein 2; CREB1, CAMP Responsive Element Binding Protein 1; CSN1S1, Casein Alpha S1; CTNNB1, Catenin Beta 1; CTSB, Cathepsin B; CTSD, Cathepsin D; CTSL, Cathepsin L; CYBB, Cytochrome B-245 Beta Chain; CYCS, Cytochrome C, Somatic; CYP1B1, Cytochrome P450 Family 1 Subfamily B Member 1; CYP27B1, Cytochrome P450 Family 27 Subfamily B Member 1; DCN, Decorin; DCT, Dopachrome Tautomerase; DDB2, Damage Specific DNA Binding Protein 2; DEFB4A, Defensin Beta 4A; DHCR7, 7-Dehydrocholesterol Reductase; DNMT1, DNA Methyltransferase 1; DSPP, Dentin Sialophosphoprotein; DUSP16, Dual Specificity Phosphatase 16; ECM1, Extracellular Matrix Protein 1; EGF, Epidermal Growth Factor; EGFR, Epidermal Growth Factor Receptor; ELANE, Elastase, Neutrophil Expressed; ELN, Elastin; ESR1; Estrogen Receptor 1; ESR2, Estrogen Receptor 2; EZH2, Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit; F2RL1, F2R Like Trypsin Receptor 1; FAS, Fas Cell Surface Death Receptor; FASN; Fatty Acid Synthase; FBLN2, Fibulin 2; FBN1, Fibrillin 1; FBN2, Fibrillin 2; FBXO40, F-Box Protein 40; FLG, Filaggrin, FN1, Fibronectin 1; FOS, Fos Proto-Oncogene, AP-1 Transcription Factor Subunit; FOSB, FosB Proto-Oncogene, AP-1 Transcription Factor Subunit; GAL, Galanin And GMAP Prepropeptide; GDA, Guanine Deaminase; GDF15, Growth Differentiation Factor 15; GLB1, Galactosidase Beta 1; GLO1, Glyoxalase I; GNAQ, G Protein Subunit Alpha Q; GPER1, G Protein-Coupled Estrogen Receptor 1; GPX4, Glutathione Peroxidase 4; GREB1, Growth Regulating Estrogen Receptor Binding 1; GSR, Glutathione-Disulfide Reductase; GZMB, Granzyme B; HAGH, Hydroxyacylglutathione Hydrolase; HAS2, Hyaluronan Synthase 2; HBEGF, Heparin Binding EGF Like Growth Factor; HDAC3, Histone Deacetylase 3; HIF1A, Hypoxia Inducible Factor 1 Subunit Alpha; HMMR, Hyaluronan Mediated Motility Receptor; HRH1, Histamine Receptor H1; HSD11B1, Hydroxysteroid 11-Beta Dehydrogenase 1; HSD17B2, Hydroxysteroid 11-Beta Dehydrogenase 2; SD17B4, Hydroxysteroid 11-Beta Dehydrogenase 4; HSD17B8, Hydroxysteroid 11-Beta Dehydrogenase 8; HSF1. Heat Shock Transcription Factor 1; HSPA1A, Heat Shock Protein Family A (Hsp70) Member 1A; HSPA4, Heat Shock Protein Family A (Hsp70) Member 4; HYAL1, Hyaluronidase 1; HYAL2, Hyaluronidase 2; HYAL3, Hyaluronidase 3; IL11, Interleukin 11; IL18R1, Interleukin 18 Receptor 1; IL1A, Interleukin 1 Alpha; IL1B, Interleukin 1 Beta; IL1R1, Interleukin 1 Receptor Type 1; IL1RAPL2, Interleukin 1 Receptor Accessory Protein Like 2; IL6, Interleukin 6; IRAK1, Interleukin 1 Receptor Associated Kinase 1; ITGB1, Integrin Subunit Beta 1; IVL, Involucrin; JUN, Jun proto-oncogene, AP-1 Transcription Factor Subunit; KCNJ5, Potassium Inwardly Rectifying Channel Subfamily J Member 5; KRT10, Keratin 10; KRT14, Keratin 14; KRT16, Keratin 16; KRT17, Keratin 17; KRT19, Keratin 19; LMNA, Lamin A/C; LMNB1, Lamin B1; LORICRIN, Loricrin Cornified Envelope Precursor Protein; LOX, Lysyl Oxidase, LYZ, Lysozyme; MAPK1, Mitogen-Activated Protein Kinase 1; MAPK3, Mitogen-Activated Protein Kinase 3; MAPK8, Mitogen-Activated Protein Kinase 8; MAPK10, Mitogen-Activated Protein Kinase 10; MAPK14, Mitogen-Activated Protein Kinase 14; MC1R, Melanocortin 1 receptor; MDM2, MDM2 Proto-Oncogene; MFAP2, Microfibril Associated Protein 2; MFAP4, Microfibril Associated Protein 4; MIF, Macrophage Migration Inhibitory Factor; MIP, Major Intrinsic Protein Of Lens Fiber; MIR101-1, MicroRNA 101-1; MIR101-2, MicroRNA 101-2; MIR146A, MicroRNA 146a; MIR155, MicroRNA 155; MIR15B, MicroRNA 15B; MIR23A, MicroRNA 23A; MIR377, MicroRNA 377; MITF, Melanocyte Inducing Transcription Factor; MMP1, Matrix Metallopeptidase 1; MMP2, Matrix Metallopeptidase 2, MMP3, Matrix Metallopeptidase 3, MMP7, Matrix Metallopeptidase 7; MMP8, Matrix Metallopeptidase 8; MMP9, Matrix Metallopeptidase 9; MMP10, Matrix Metallopeptidase 10; MMP12, Matrix Metallopeptidase 12; MMP13, Matrix Metallopeptidase 13; MMP14, Matrix Metallopeptidase 14; MRC2, Mannose Receptor C Type 2; MSRA, Methionine Sulfoxide Reductase A; MSRB1, Methionine Sulfoxide Reductase B1; MTOR, Mechanistic Target Of Rapamycin Kinase; MYC, MYC Proto-Oncogene, BHLH Transcription Factor; MYD88, MYD88 Innate Immune Signal Transduction Adaptor; NFE2L2, NFE2 Like BZIP Transcription Factor 2; NFKB1, Nuclear Factor Kappa B Subunit 1; NFKBIA, NFKB Inhibitor Alpha; NOS2, Nitric Oxide Synthase 2; NOX4, NADPH Oxidase 4; NR1H2, Nuclear Receptor Subfamily 1 Group H Member 2; NR1H3, Nuclear Receptor Subfamily 1 Group H Member 3; ODC1, Ornithine Decarboxylase 1; OPN1SW, Opsin 1, Short Wave Sensitive; OPN3, Opsin 3; OPN4, Opsin 4; OPN5, Opsin 5; ORAI1, ORAI Calcium Release-Activated Calcium Modulator 1; PDYN, Prodynorphin; PEX7, Peroxisomal Biogenesis Factor 7; PIK3CG, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Gamma; PIK3R1, Phosphoinositide-3-Kinase Regulatory Subunit 1; PLA2G4A, Phospholipase A2 Group IVA; POLQ DNA Polymerase Theta; POSTN, Periostin; PPARA, Peroxisome Proliferator Activated Receptor alpha; PPARD, Peroxisome Proliferator Activated Receptor delta; PPARG, Peroxisome Proliferator Activated Receptor Gamma; PRF1, Perforin 1; PRKAA2, Protein Kinase AMP-Activated Catalytic Subunit Alpha 2; PRKCD, Protein Kinase C Delta; PSMC4, Proteasome 26S Subunit, ATPase 4; PTEN, Phosphatase And Tensin Homolog; PTGS2, prostaglandin-endoperoxide synthase 2; PTK2, Protein Tyrosine Kinase 2, PTPRK, Protein Tyrosine Phosphatase Receptor Type K; PTPRU, Protein Tyrosine Phosphatase Receptor Type U; RARA, Retinoic Acid Receptor Alpha; RARB, Retinoic Acid Receptor Beta; RARG, Retinoic Acid Receptor Gamma; RARS1, Arginyl-TRNA Synthetase 1; RBP1, Retinol Binding Protein 1; RHO, Rhodopsin; RNASE1, Ribonuclease A Family Member 1, Pancreatic; RPS27A, Ribosomal Protein S27a; RPS3, Ribosomal Protein S3; RPS6KA3, Ribosomal Protein S6 Kinase A3; RPS6KB1, Ribosomal Protein S6 Kinase B1; RXRA, Retinoid X Receptor Alpha; RXRB, Retinoid X Receptor Beta; S100A8, S100 Calcium Binding Protein A8; SAA1, Serum Amyloid A1; SCD, Stearoyl-CoA Desaturase; SERPINH1, Serpin Family H Member 1; SFN, Stratifin; SHC1, SHC Adaptor Protein 1; SIRT1, Sirtuin 1; SIRT4, Sirtuin 4; SMAD2, SMAD Family Member 2; SMAD4, SMAD Family Member 4; SMAD7, SMAD Family Member 7; SOD1, Superoxide Dismutase 1; SOD2, Superoxide Dismutase 2; SPARC, Secreted Protein Acidic And Cysteine Rich; SPRR1B, Small Proline Rich Protein 1B; SSBP3, Single Stranded DNA Binding Protein 3; STK11, Serine/Threonine Kinase 11; STXBP5L, Syntaxin Binding Protein 5L; SULT1A1, Sulfotransferase Family 1A Member 1; SULT1E1. Sulfotransferase Family 1E Member 1; TAGLN, Transgelin, TCF7L2, Transcription Factor 7 Like 2; TEP1, Telomerase Associated Protein 1; TGFB1, Transforming Growth Factor Beta 1; TGFB3, Transforming Growth Factor Beta 3; TGFBR2, Transforming Growth Factor Beta Receptor 2; TGM1, Transglutaminase 1; TIMP1, Tissue Inhibitor of the Metalloproteinases 1; TIMP2, Tissue Inhibitor of the Metalloproteinases 2; TLR4, Toll Like Receptor 4; TNF, Tumor Necrosis Factor; TP53, Tumor Protein P53; TP53BP1, Tumor Protein P53 Binding Protein 1; TRPV1, Transient Receptor Potential Cation Channel Subfamily V Member 1; TYR, Tyrosinase; TYRP1, Tyrosinase Related Protein 1; VCAN, Versican; VDR; Vitamin D Receptor; VEGFA, Vascular Endothelial Growth Factor A; VTRNA2-1, Vault RNA 2-1; WARS1, Tryptophanyl-TRNA Synthetase 1; XAB2, XPA Binding Protein 2; XDH, Xanthine Dehydrogenase; XPA, DNA Damage Recognition And Repair Factor.

Examination of shared targets between photoaging and asiaticoside.
Fig. 1  Examination of shared targets between photoaging and asiaticoside.

(a) Venn diagram showing the overlap of targets related to photoaging and asiaticoside. (b) Protein-protein interaction (PPI) network of common targets between photoaging and asiaticoside. (c) Top 30 shared targets ranked by degree values. AKT1, AKT Serine/Threonine Kinase 1; CASP, Caspase; CREB1, CAMP Responsive Element Binding Protein 1;; CTNNB1, Catenin Beta 1; EGF, Epidermal Growth Factor; ESR1, Estrogen Receptor 1; FN1, Fibronectin 1; HDAC3, Histone Deacetylase 3; HIF1A, Hypoxia Inducible Factor 1 Subunit Alpha; IL1B, Interleukin 1 Beta; IL6, Interleukin 6; JUN, Jun proto-oncogene, AP-1 Transcription Factor Subunit; MAPK, Mitogen-Activated Protein Kinase; MMP9, Matrix Metallopeptidase 9; NFKB1, Nuclear Factor Kappa B Subunit 1; NFKBIA, NFKB Inhibitor Alpha; PIK3R1, Phosphoinositide-3-Kinase Regulatory Subunit 1; RPS27A, Ribosomal Protein S27a; SMAD4, SMAD Family Member 4; TGB1, Integrin Subunit Beta 1; TGFB1, Transforming Growth Factor Beta 1; TGFB3, Transforming Growth Factor Beta 3; TIMP1, Tissue Inhibitor of the Metalloproteinases 1; TNF, Tumor Necrosis Factor; TP53, Tumor Protein P53; VEGFA, Vascular Endothelial Growth Factor A.

Table 4

Co-targeted genes for photoaging and asiaticoside

BCL2L1SIRT1RPS27ASPRR1BFBN1
HSD11B1OPN3ELANEPRKAA2CDKN2A
JUNGPX4MYCHDAC3CTSL
CATNFKB1MC1RFN1MIP
MAPK14IL1R1GLO1ECM1IL11
MTORFBXO40ARNTLMFAP2HSPA4
RARGSMAD4ACACATAGLNIL6
HBEGFMAPK1PIK3CGHAS2RPS3
RARBANGLOXS100A8HYAL2
MMP1TGFB1IL1BMAPK10CTSB
MMP13CD36CASP9SOD1KRT16
CRABP2NR1H3TGFBR2HYAL3MRC2
CCNA2SFNSULT1A1SMAD2ALOX5
PPARGPTENMMP7MMP10RNASE1
MMP3CASP3GNAQBAXCOL1A1
RARASAA1CSN1S1IL1ASOD2
PRKCDMIFGDF15TYRBAD
ESR1SIRT4SPARCF2RL1KCNJ5
FASNMMP14CLUHSPA1ARBP1
ANXA1FOSBORAI1POLQPSMC4
DNMT1CYCSPOSTNHYAL1XAB2
ESR2PRF1BMP6NR1H2MYD88
TRPV1TEP1ATF2HRH1TGFB3
TNFNFKBIACYBBCPRHO
MMP9SULT1E1TGM1CDKN1AODC1
NOS2MMP12MSRASMAD7SSBP3
PLA2G4AFBLN2HSD17B4HIF1ASCD
EGFGZMBHMMRIL1RAPL2CEMIP
PTGS2MITFITGB1MMP8LMNA
DCTKRT10SERPINH1CLOCKCTSD
RXRBSTXBP5LNFE2L2IRAK1DDB2
AKT1TP53MAPK3TYRP1CHUK
GSRHSD17B2PIK3R1ABCC9PTK2
CASP8STK11TIMP1MDM2HSD17B8
KRT19KRT14XPAPPARDGAL
CYP1B1PTPRUAQP3AREGAGER
FLGTLR4ELNCREB1CTNNB1
PEX7OPN4IVLEZH2GDA
PTPRKOPN1SWMMP2VEGFADEFB4A
PDYNCOL3A1ANGPT1TCF7L2FBN2
XDHIL18R1

ABCC9, ATP Binding Cassette Subfamily C Member 9; ACACA, Acetyl-CoA Carboxylase Alpha; AGER, Advanced Glycosylation End-Product Specific Receptor; AKT1, AKT Serine/Threonine Kinase 1; ALOX5, Arachidonate 5-Lipoxygenase; ANG, Angiogenin; ANGPT1, Angiopoietin 1; ANXA1, Annexin A1; AQP3, Aquaporin 3 (Gill Blood Group); AREG, Amphiregulin; ARNTL, Aryl hydrocarbon receptor nuclear translocator-like protein 1; ATF2, Activating Transcription Factor 2; BAD, BCL2 Associated Agonist Of Cell Death; BAX, BCL2 Associated X, Apoptosis Regulator; BCL2L1, BCL2 Like 1; BMP6, Bone Morphogenetic Protein 6; CASP3, Caspase 3; CASP8, Caspase 8; CASP9, Caspase 9; CAT, Catalase; CCNA2, Cyclin A2; CD36, CD36 Molecule; CDKN1A, Cyclin Dependent Kinase Inhibitor 1A; CDKN2A, Cyclin Dependent Kinase Inhibitor 2A; CEMIP, Cell Migration Inducing Hyaluronidase 1; CHUK, Component Of Inhibitor Of Nuclear Factor Kappa B Kinase Complex; CLOCK, Clock Circadian Regulator; CLU, Clusterin, COL1A1, Collagen Type I Alpha 1 Chain; COL3A1, Collagen Type 3 Alpha 1 Chain; CP, Ceruloplasmin; CRABP2, Cellular Retinoic Acid Binding Protein 2; CREB1, CAMP Responsive Element Binding Protein 1; CSN1S1, Casein Alpha S1; CTNNB1, Catenin Beta 1; CTSB, Cathepsin B; CTSD, Cathepsin D; CTSL, Cathepsin L; CYBB, Cytochrome B-245 Beta Chain; CYCS, Cytochrome C, Somatic; CYP1B1, Cytochrome P450 Family 1 Subfamily B Member 1; DCT, Dopachrome Tautomerase; DDB2, Damage Specific DNA Binding Protein 2; DEFB4A, Defensin Beta 4A; DNMT1, DNA Methyltransferase 1; ECM1, Extracellular Matrix Protein 1; EGF, Epidermal Growth Factor; ELANE, Elastase, Neutrophil Expressed; ELN, Elastin; ESR1; Estrogen Receptor 1; ESR2, Estrogen Receptor 2; EZH2, Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit; F2RL1, F2R Like Trypsin Receptor 1; FASN; Fatty Acid Synthase; FBLN2, Fibulin 2; FBN1, Fibrillin 1; FBN2, Fibrillin 2; FBXO40, F-Box Protein 40; FLG, Filaggrin, FN1, Fibronectin 1; FOSB, FosB Proto-Oncogene, AP-1 Transcription Factor Subunit; GAL, Galanin And GMAP Prepropeptide; GDA, Guanine Deaminase; GDF15, Growth Differentiation Factor 15; GLO1, Glyoxalase I; GNAQ, G Protein Subunit Alpha Q; GPX4, Glutathione Peroxidase 4; GSR, Glutathione-Disulfide Reductase; GZMB, Granzyme B; HAS2, Hyaluronan Synthase 2; HBEGF, Heparin Binding EGF Like Growth Factor; HDAC3, Histone Deacetylase 3; HIF1A, Hypoxia Inducible Factor 1 Subunit Alpha; HMMR, Hyaluronan Mediated Motility Receptor; HRH1, Histamine Receptor H1; HSD11B1, Hydroxysteroid 11-Beta Dehydrogenase 1; HSD17B2, Hydroxysteroid 11-Beta Dehydrogenase 2; SD17B4, Hydroxysteroid 11-Beta Dehydrogenase 4; HSD17B8, Hydroxysteroid 11-Beta Dehydrogenase 8; HSF1, Heat Shock Transcription Factor 1; HSPA1A, Heat Shock Protein Family A (Hsp70) Member 1A; HSPA4, Heat Shock Protein Family A (Hsp70) Member 4; HYAL1, Hyaluronidase 1; HYAL2, Hyaluronidase 2; HYAL3, Hyaluronidase 3; IL11, Interleukin 11; IL18R1, Interleukin 18 Receptor 1; IL1A, Interleukin 1 Alpha; IL1B, Interleukin 1 Beta; IL1R1, Interleukin 1 Receptor Type 1; IL1RAPL2, Interleukin 1 Receptor Accessory Protein Like 2; IL6, Interleukin 6; IRAK1, Interleukin 1 Receptor Associated Kinase 1; ITGB1, Integrin Subunit Beta 1; IVL, Involucrin; JUN, Jun proto-oncogene, AP-1 Transcription Factor Subunit; KCNJ5, Potassium Inwardly Rectifying Channel Subfamily J Member 5; KRT10, Keratin 10; KRT14, Keratin 14; KRT16, Keratin 16; KRT19, Keratin 19; LMNA, Lamin A/C; LOX, Lysyl Oxidase, LYZ, Lysozyme; MAPK1, Mitogen-Activated Protein Kinase 1; MAPK3, Mitogen-Activated Protein Kinase 3; MAPK10, Mitogen-Activated Protein Kinase 10; MAPK14, Mitogen-Activated Protein Kinase 14; MC1R, Melanocortin 1 receptor; MDM2, MDM2 Proto-Oncogene; MFAP2, Microfibril Associated Protein 2; MIF, Macrophage Migration Inhibitory Factor; MIP, Major Intrinsic Protein Of Lens Fiber; MITF, Melanocyte Inducing Transcription Factor; MMP1, Matrix Metallopeptidase 1; MMP2, Matrix Metallopeptidase 2, MMP3, Matrix Metallopeptidase 3, MMP7, Matrix Metallopeptidase 7; MMP8, Matrix Metallopeptidase 8; MMP9, Matrix Metallopeptidase 9; MMP10, Matrix Metallopeptidase 10; MMP12, Matrix Metallopeptidase 12; MMP13, Matrix Metallopeptidase 13; MMP14, Matrix Metallopeptidase 14; MRC2, Mannose Receptor C Type 2; MSRA, Methionine Sulfoxide Reductase A; MTOR, Mechanistic Target Of Rapamycin Kinase; MYC, MYC Proto-Oncogene, BHLH Transcription Factor; MYD88, MYD88 Innate Immune Signal Transduction Adaptor; NFE2L2, NFE2 Like BZIP Transcription Factor 2; NFKB1, Nuclear Factor Kappa B Subunit 1;NFKBIA, NFKB Inhibitor Alpha; NOS2, Nitric Oxide Synthase 2; NR1H2, Nuclear Receptor Subfamily 1 Group H Member 2; NR1H3, Nuclear Receptor Subfamily 1 Group H Member 3; ODC1, Ornithine Decarboxylase 1; OPN1SW, Opsin 1, Short Wave Sensitive; OPN3, Opsin 3; OPN4, Opsin 4; ORAI1, ORAI Calcium Release-Activated Calcium Modulator 1; PDYN, Prodynorphin; PEX7, Peroxisomal Biogenesis Factor 7; PIK3CG, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Gamma; PIK3R1, Phosphoinositide-3-Kinase Regulatory Subunit 1; PLA2G4A, Phospholipase A2 Group IVA; POLQ DNA Polymerase Theta; POSTN, Periostin; PPARD, Peroxisome Proliferator Activated Receptor delta; PPARG, Peroxisome Proliferator Activated Receptor Gamma; PRF1, Perforin 1; PRKAA2, Protein Kinase AMP-Activated Catalytic Subunit Alpha 2; PRKCD, Protein Kinase C Delta; PSMC4, Proteasome 26S Subunit, ATPase 4; PTEN, Phosphatase And Tensin Homolog; PTGS2, prostaglandin-endoperoxide synthase 2; PTK2, Protein Tyrosine Kinase 2, PTPRK, Protein Tyrosine Phosphatase Receptor Type K; PTPRU, Protein Tyrosine Phosphatase Receptor Type U; RARA, Retinoic Acid Receptor Alpha; RARB, Retinoic Acid Receptor Beta; RARG, Retinoic Acid Receptor Gamma; RARS1, Arginyl-TRNA Synthetase 1; RBP1, Retinol Binding Protein 1; RHO, Rhodopsin; RNASE1, Ribonuclease A Family Member 1, Pancreatic; RPS27A, Ribosomal Protein S27a; RPS3, Ribosomal Protein S3; RXRB, Retinoid X Receptor Beta; S100A8, S100 Calcium Binding Protein A8; SAA1, Serum Amyloid A1; SCD, Stearoyl-CoA Desaturase; SERPINH1, Serpin Family H Member 1; SFN, Stratifin; SIRT1, Sirtuin 1; SIRT4, Sirtuin 4; SMAD2, SMAD Family Member 2; SMAD4, SMAD Family Member 4; SOD1, Superoxide Dismutase 1; SOD2, Superoxide Dismutase 2; SPARC, Secreted Protein Acidic And Cysteine Rich; SPRR1B, Small Proline Rich Protein 1B; SSBP3, Single Stranded DNA Binding Protein 3; STK11, Serine/Threonine Kinase 11; STXBP5L, Syntaxin Binding Protein 5L; SULT1A1, Sulfotransferase Family 1A Member 1; SULT1E1. Sulfotransferase Family 1E Member 1; TAGLN, Transgelin, TCF7L2, Transcription Factor 7 Like 2; TEP1, Telomerase Associated Protein 1; TGFB1, Transforming Growth Factor Beta 1; TGFB3, Transforming Growth Factor Beta 3; TGFBR2, Transforming Growth Factor Beta Receptor 2; TGM1, Transglutaminase 1; TIMP1, Tissue Inhibitor of the Metalloproteinases 1; TLR4, Toll-Like Receptor 4; TNF, Tumor Necrosis Factor; TP53, Tumor Protein P53; TRPV1, Transient Receptor Potential Cation Channel Subfamily V Member 1; TYR, Tyrosinase; TYRP1, Tyrosinase Related Protein 1; VEGFA, Vascular Endothelial Growth Factor A; XAB2, XPA Binding Protein 2; XDH, Xanthine Dehydrogenase; XPA, DNA Damage Recognition And Repair Factor.

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).

Enrichment analysis of the core targets.
Fig. 2  Enrichment analysis of the core targets.

(a) Relationship between photoaging, asiaticoside, and common targets, visualized using Cytoscape. Proteins are ranked by the Matthews correlation coefficient (MCC) score. (b) Gene Ontology (GO) enrichment analysis of core targets, including biological processes (BP), molecular functions (MF), and cellular components (CC). (c) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of core targets. Pathways are on the right side of the chord plot, while genes are on the left side. The corresponding colour of the gene ribbon is consistent with the colour of terms, indicating that this gene is enriched in this term. NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; PI3K-AKT, phosphatidylinositol 3 kinase-protein kinase B; PPAR, peroxisome proliferator-activated receptor; TGFβ, transforming growth factor-beta; UV, ultraviolet.

Table 5

The information of core targets by MCC analysis

RankNameScoreRankNameScore
1Photoaging96,55288ELANE108
1Asiaticoside96,55289SOD296
3TIMP191,24889ESR296
4TGFB188,81291HSPA1A76
5TGFB385,68092HSD17B472
6VEGFA84,88892NR1H372
7EGF82,73294CTSD68
8FN182,68095HBEGF64
9SPARC80,70096SOD160
10CLU80,64096MMP860
11MAPK352,20096STK1160
12AKT150,57699COL3A152
13TP5346,74099KRT1952
14JUN46,19299ANGPT152
15MAPK136,012102SPRR1B48
16ESR123,608102IVL48
17MYC23,448102LMNA48
18MMP920,464102NR1H248
19MMP220,368102S100A848
20MMP320,220102KRT1048
21MMP1320,208102KRT1448
22MMP120,172102KRT1648
23MMP1020,160102GAL48
24CREB117,440102PDYN48
25MAPK1417,240102FOSB48
26HIF1A15,364102BAX48
27RARA12,480102MMP1448
27RARB12,480102COL1A148
27RARG12,480102FLG48
30PIK3R111,112102TGM148
31RXRB10,624118PLA2G4A40
32SAA110,380118MFAP240
33GNAQ10,152120GSR24
34ANXA110,128120CD3624
35MMP710,104120GPX424
36F2RL110,080120DDB224
36OPN410,080120XPA24
36HRH110,080120DNMT124
39SMAD47,572120ARNTL24
40ATF27,488120CLOCK24
41NFKB17,180120ELN24
42RPS27A7,008120FBN224
43SMAD75,760130PRKAA220
43TGFBR25,760130GZMB20
45CDKN1A5,136132PPARD16
46CTNNB14,800133OPN1SW12
47IRAK14,128133OPN312
48MYD883,648133RHO12
49IL1A3,408133SERPINH112
50TNF3,276133XAB212
51CHUK3,264133NFE2L212
52SMAD22,988133PIK3CG12
53IL1R12,928133PTPRK12
54IL1B2,884133LOX12
55NFKBIA2,748142XDH8
56MDM22,448142CYBB8
56HDAC32,448142ACACA8
58CDKN2A2,112142FASN8
59EZH21,980142SULT1E18
60TLR41,732142IL18R18
61ITGB11,716142RBP18
62PPARG1,612142CTSB8
63IL61,488142CYP1B18
64BCL2L11,248142HSD17B28
65CASP81,080142HSD17B88
66CASP3876142DCT8
67PSMC4784142TYR8
68PTEN648142TYRP18
68BAD648156GDA4
70SFN516156PRF14
71CCNA2480156ODC14
72PRKCD456156PEX74
73NOS2420156HMMR4
74MTOR316156HYAL24
75CYCS304156HSD11B14
76FBN1300156HSPA44
77CASP9288156POSTN4
77PTK2288156TEP14
79MAPK10264156RPS34
80AGER240156CTSL4
80CP240156ALOX54
80CRABP2240156DEFB4A4
80TCF7L2240156AREG4
84MITF152156SCD4
85SIRT1144156FBLN24
86PTGS2128156FBXO404
87CAT124

ACACA, Acetyl-CoA Carboxylase Alpha; AGER, Advanced Glycosylation End-Product Specific Receptor; AKT1, AKT Serine/Threonine Kinase 1; ALOX5, Arachidonate 5-Lipoxygenase; ANGPT1, Angiopoietin 1; ANXA1, Annexin A1; AREG, Amphiregulin; ARNTL, Aryl hydrocarbon receptor nuclear translocator-like protein 1; ATF2, Activating Transcription Factor 2; BAD, BCL2 Associated Agonist Of Cell Death; BAX, BCL2 Associated X, Apoptosis Regulator; BCL2L1, BCL2 Like 1; CASP3, Caspase 3; CASP8, Caspase 8; CASP9, Caspase 9; CAT, Catalase; CCNA2, Cyclin A2; CD36, CD36 Molecule; CDKN1A, Cyclin Dependent Kinase Inhibitor 1A; CDKN2A, Cyclin Dependent Kinase Inhibitor 2A; CHUK, Component Of Inhibitor Of Nuclear Factor Kappa B Kinase Complex; CLOCK, Clock Circadian Regulator; CLU, Clusterin, COL1A1, Collagen Type I Alpha 1 Chain; COL3A1, Collagen Type 3 Alpha 1 Chain; CP, Ceruloplasmin; CRABP2, Cellular Retinoic Acid Binding Protein 2; CREB1, CAMP Responsive Element Binding Protein 1; CTNNB1, Catenin Beta 1; CTSB, Cathepsin B; CTSD, Cathepsin D; CTSL, Cathepsin L; CYBB, Cytochrome B-245 Beta Chain; CYCS, Cytochrome C, Somatic; CYP1B1, Cytochrome P450 Family 1 Subfamily B Member 1; DCT, Dopachrome Tautomerase; DDB2, Damage Specific DNA Binding Protein 2; DEFB4A, Defensin Beta 4A; DNMT1, DNA Methyltransferase 1; EGF, Epidermal Growth Factor; ELANE, Elastase, Neutrophil Expressed; ELN, Elastin; ESR1; Estrogen Receptor 1; ESR2, Estrogen Receptor 2; EZH2, Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit; F2RL1, F2R Like Trypsin Receptor 1; FASN; Fatty Acid Synthase; FBLN2, Fibulin 2; FBN1, Fibrillin 1; FBN2, Fibrillin 2; FBXO40, F-Box Protein 40; FLG, Filaggrin; FN1, Fibronectin 1; FOSB, FosB Proto-Oncogene; AP-1 Transcription Factor Subunit; GAL, Galanin And GMAP Prepropeptide; GDA, Guanine Deaminase; GNAQ, G Protein Subunit Alpha Q; GPX4, Glutathione Peroxidase 4; GSR, Glutathione-Disulfide Reductase; GZMB, Granzyme B; HBEGF, Heparin Binding EGF Like Growth Factor; HDAC3, Histone Deacetylase 3; HIF1A, Hypoxia Inducible Factor 1 Subunit Alpha; HMMR, Hyaluronan Mediated Motility Receptor; HRH1, Histamine Receptor H1; HSD11B1, Hydroxysteroid 11-Beta Dehydrogenase 1; HSD17B2, Hydroxysteroid 11-Beta Dehydrogenase 2; SD17B4, Hydroxysteroid 11-Beta Dehydrogenase 4; HSD17B8, Hydroxysteroid 11-Beta Dehydrogenase 8; HSPA1A, Heat Shock Protein Family A (Hsp70) Member 1A; HSPA4, Heat Shock Protein Family A (Hsp70) Member 4; HYAL2, Hyaluronidase 2; IL18R1, Interleukin 18 Receptor 1; IL1A, Interleukin 1 Alpha; IL1B, Interleukin 1 Beta; IL1R1, Interleukin 1 Receptor Type 1; IL6, Interleukin 6; IRAK1, Interleukin 1 Receptor Associated Kinase 1; ITGB1, Integrin Subunit Beta 1; IVL, Involucrin; JUN, Jun proto-oncogene, AP-1 Transcription Factor Subunit; KRT10, Keratin 10; KRT14, Keratin 14; KRT16, Keratin 16; KRT19, Keratin 19; LMNA, Lamin A/C; LMNB1, LOX, Lysyl Oxidase, MAPK1, Mitogen-Activated Protein Kinase 1; MAPK3, Mitogen-Activated Protein Kinase 3; MAPK10, Mitogen-Activated Protein Kinase 10; MAPK14, Mitogen-Activated Protein Kinase 14; MDM2, MDM2 Proto-Oncogene; MFAP2, Microfibril Associated Protein 2; MITF, Melanocyte Inducing Transcription Factor; MMP1, Matrix Metallopeptidase 1; MMP2, Matrix Metallopeptidase 2, MMP3, Matrix Metallopeptidase 3, MMP7, Matrix Metallopeptidase 7; MMP8, Matrix Metallopeptidase 8; MMP9, Matrix Metallopeptidase 9; MMP10, Matrix Metallopeptidase 10; MMP13, Matrix Metallopeptidase 13; MMP14, Matrix Metallopeptidase 14; MTOR, Mechanistic Target Of Rapamycin Kinase; MYC, MYC Proto-Oncogene, BHLH Transcription Factor; MYD88, MYD88 Innate Immune Signal Transduction Adaptor; NFE2L2, NFE2 Like BZIP Transcription Factor 2; NFKB1, Nuclear Factor Kappa B Subunit 1;NFKBIA, NFKB Inhibitor Alpha; NOS2, Nitric Oxide Synthase 2; NR1H2, Nuclear Receptor Subfamily 1 Group H Member 2; NR1H3, Nuclear Receptor Subfamily 1 Group H Member 3; ODC1, Ornithine Decarboxylase 1; OPN1SW, Opsin 1, Short Wave Sensitive; OPN3, Opsin 3; OPN4, Opsin 4; ORAI1, ORAI Calcium Release-Activated Calcium Modulator 1; PDYN, Prodynorphin; PEX7, Peroxisomal Biogenesis Factor 7; PIK3CG, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Gamma; PIK3R1, Phosphoinositide-3-Kinase Regulatory Subunit 1; PLA2G4A, Phospholipase A2 Group IVA; POSTN, Periostin; PPARD, Peroxisome Proliferator Activated Receptor delta; PPARG, Peroxisome Proliferator Activated Receptor Gamma; PRF1, Perforin 1; PRKAA2, Protein Kinase AMP-Activated Catalytic Subunit Alpha 2; PRKCD, Protein Kinase C Delta; PSMC4, Proteasome 26S Subunit, ATPase 4; PTEN, Phosphatase And Tensin Homolog; PTGS2, prostaglandin-endoperoxide synthase 2; PTK2, Protein Tyrosine Kinase 2, PTPRK, Protein Tyrosine Phosphatase Receptor Type K; RARA, Retinoic Acid Receptor Alpha; RARB, Retinoic Acid Receptor Beta; RARG, Retinoic Acid Receptor Gamma; RBP1, Retinol Binding Protein 1; RHO, Rhodopsin; RPS27A, Ribosomal Protein S27a; RPS3, Ribosomal Protein S3; RXRB, Retinoid X Receptor Beta; S100A8, S100 Calcium Binding Protein A8; SAA1, Serum Amyloid A1; SCD, Stearoyl-CoA Desaturase; SERPINH1, Serpin Family H Member 1; SFN, Stratifin; SIRT1, Sirtuin 1; SMAD2, SMAD Family Member 2; SMAD4, SMAD Family Member 4; SMAD7, SMAD Family Member 7; SOD1, Superoxide Dismutase 1; SOD2, Superoxide Dismutase 2; SPARC, Secreted Protein Acidic And Cysteine Rich; SPRR1B, Small Proline Rich Protein 1B; STK11, Serine/Threonine Kinase 11; SULT1E1. Sulfotransferase Family 1E Member 1; TCF7L2, Transcription Factor 7 Like 2; TEP1, Telomerase Associated Protein 1; TGFB1, Transforming Growth Factor Beta 1; TGFB3, Transforming Growth Factor Beta 3; TGFBR2, Transforming Growth Factor Beta Receptor 2; TGM1, Transglutaminase 1; TIMP1, Tissue Inhibitor of the Metalloproteinases 1; TLR4, Toll-Like Receptor 4; TNF, Tumor Necrosis Factor; TP53, Tumor Protein P53; TYR, Tyrosinase; TYRP1, Tyrosinase Related Protein 1; VEGFA, Vascular Endothelial Growth Factor A; XAB2, XPA Binding Protein 2; XDH, Xanthine Dehydrogenase; XPA, DNA Damage Recognition And Repair Factor.

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.

UVA-induced photoaging of HDFs and asiaticoside rescued the effect.
Fig. 3  UVA-induced photoaging of HDFs and asiaticoside rescued the effect.

(a) Effect of varying asiaticoside concentrations on HDFs proliferation at 48 h post-treatment. (b) Effect of different UVA doses with different doses on HDFs proliferation at 48 h post-irradiation. The influence of asiaticoside on the proliferation of UVA-irradiated HDFs using the CCK-8 assay (c) and manual cell counting (d) after 48 h treatment. Each experiment was conducted with at least 3 independent cell samples. Different lowercase letters indicate significant differences between the specified groups (p < 0.05). AS, asiaticoside; CCK-8, cell counting kit 8; HDFs, human dermal fibroblasts; UVA, ultraviolet A; VC, vitamin C.

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).

Asiaticoside reversed UVA-induced expression levels of genes and proteins related to photoaging mined by network pharmacology.
Fig. 4  Asiaticoside reversed UVA-induced expression levels of genes and proteins related to photoaging mined by network pharmacology.

(a) Asiaticoside reversed UVA-altered gene expression with significant differences as indicated. (b) Asiaticoside reversed UVA-altered protein expression. C, UVA, UVA+VC, UVA+AS represented the non-irradiated, UVA-irradiated, UVA and vitamin C treated, UVA and 25 ug/mL asiaticoside treated groups, respectively. The experiments were carried out on at least 3 separate cell samples for each assay. Different lowercase letters represent significant differences among indicated groups (p < 0.05). AKT, protein kinase B; AS, asiaticoside; BAX, BCL-2-associated X protein; BCL2L1, BCL2 like 1 (human;IL6, Interleukin 6; P53,tumor protein p53; p-AKT, phospho-AKT; PIK3R1, phosphoinositide-3-kinase regulatory subunit 1; PPAR-γ, Peroxisome Proliferator Activated Receptor gamma; PTEN, Phosphatase And Tensin Homolog; SMAD4, suppressor of mothers against decapentaplegic 4; TNF-α, tumour necrosis factor-α; UVA, ultraviolet A; VC, vitamin C.

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.

Molecular docking of asiaticoside and core proteins.
Fig. 5  Molecular docking of asiaticoside and core proteins.

Molecular docking results of asiaticoside with BAX (a), PIK3R1 (b), PTEN (c), PPARγ (d), P53 (e), TNF-α (f), TGF-β1 (g), TGF-β3 (h), SMAD4 (i) protein with asiaticoside. BAX, BCL-2-associated X protein; PIK3R1, phosphoinositide-3-kinase regulatory subunit 1; PPARγ, peroxisome proliferator-activated receptor gamma; PTEN, phosphatase and tensin homolog; SMAD, suppressor of mothers against decapentaplegic; TGFβ, transforming growth factor-beta; TNF-α, tumour necrosis factor-α.

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)

Click here for additional data file.

Table S2

KEGG enrichment analysis of core genes by Metascape.

(DOC)

Click here for additional data file.

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).

References

  1. Van Pham P, Dang LT, Dinh UT, Truong HT, Huynh BN, Van Le D, et al. In vitro evaluation of the effects of human umbilical cord extracts on human fibroblasts, keratinocytes, and melanocytes. In Vitro Cell Dev Biol Anim 2014;50(4):321-330 View Article PubMed/NCBI
  2. Li Y, Zeng N, Qin Z, Chen Y, Lu Q, Cheng Y, et al. Ultrasmall Prussian blue nanoparticles attenuate UVA-induced cellular senescence in human dermal fibroblasts via inhibiting the ERK/AP-1 pathway. Nanoscale 2021;13(38):16104-16112 View Article PubMed/NCBI
  3. Morgan MJ, Liu ZG. Crosstalk of reactive oxygen species and NF-κB signaling. Cell Res 2011;21(1):103-115 View Article PubMed/NCBI
  4. Chen K, Zhu P, Chen W, Luo K, Shi XJ, Zhai W. Melatonin inhibits proliferation, migration, and invasion by inducing ROS-mediated apoptosis via suppression of the PI3K/Akt/mTOR signaling pathway in gallbladder cancer cells. Aging (Albany NY) 2021;13(18):22502-22515 View Article PubMed/NCBI
  5. Wu Y, Wang J, Zhao T, Wei Y, Han L, Shen L, et al. LncRNAs activate longevity regulation pathway due to aging of Leydig cells caused by DEHP exposure: A transcriptome-based study. Ecotoxicol Environ Saf 2021;209:111798 View Article PubMed/NCBI
  6. Oh JH, Kim J, Karadeniz F, Kim HR, Park SY, Seo Y, et al. Santamarine Shows Anti-Photoaging Properties via Inhibition of MAPK/AP-1 and Stimulation of TGF-β/Smad Signaling in UVA-Irradiated HDFs. Molecules 2021;26(12):3585 View Article PubMed/NCBI
  7. Park MY, Sohn S, Lee ES, Kim YC. Photorejuvenation induced by 5-aminolevulinic acid photodynamic therapy in patients with actinic keratosis: a histologic analysis. J Am Acad Dermatol 2010;62(1):85-95 View Article PubMed/NCBI
  8. Li XM, Peng JH, Sun ZL, Tian HJ, Duan XH, Liu L, et al. Chinese medicine CGA formula ameliorates DMN-induced liver fibrosis in rats via inhibiting MMP2/9, TIMP1/2 and the TGF-β/Smad signaling pathways. Acta Pharmacol Sin 2016;37(6):783-793 View Article PubMed/NCBI
  9. An L, Li Z, Shi L, Wang L, Wang Y, Jin L, et al. Inflammation-Targeted Celastrol Nanodrug Attenuates Collagen-Induced Arthritis through NF-κB and Notch1 Pathways. Nano Lett 2020;20(10):7728-7736 View Article PubMed/NCBI
  10. Hwang JY, Yadav AK, Jang BC, Kim YC. Antioxidant and cytoprotective effects of Stachys riederi var. japonica ethanol extract on UVA-irradiated human dermal fibroblasts. Int J Mol Med 2019;43(3):1497-1504 View Article PubMed/NCBI
  11. Oh JH, Joo YH, Karadeniz F, Ko J, Kong CS. Syringaresinol Inhibits UVA-Induced MMP-1 Expression by Suppression of MAPK/AP-1 Signaling in HaCaT Keratinocytes and Human Dermal Fibroblasts. Int J Mol Sci 2020;21(11):3981 View Article PubMed/NCBI
  12. Myung DB, Lee JH, Han HS, Lee KY, Ahn HS, Shin YK, et al. Oral Intake of Hydrangea serrata (Thunb.) Ser. Leaves Extract Improves Wrinkles, Hydration, Elasticity, Texture, and Roughness in Human Skin: A Randomized, Double-Blind, Placebo-Controlled Study. Nutrients 2020;12(6):1588 View Article PubMed/NCBI
  13. Zussman J, Ahdout J, Kim J. Vitamins and photoaging: do scientific data support their use?. J Am Acad Dermatol 2010;63(3):507-525 View Article PubMed/NCBI
  14. Namviriyachote N, Muangman P, Chinaroonchai K, Chuntrasakul C, Ritthidej GC. Polyurethane-biomacromolecule combined foam dressing containing asiaticoside: fabrication, characterization and clinical efficacy for traumatic dermal wound treatment. Int J Biol Macromol 2020;143:510-520 View Article PubMed/NCBI
  15. Huang J, Zhou X, Xia L, Liu W, Guo F, Liu J, et al. Inhibition of hypertrophic scar formation with oral asiaticoside treatment in a rabbit ear scar model. Int Wound J 2021;18(5):598-607 View Article PubMed/NCBI
  16. Arora R, Kumar R, Agarwal A, Reeta KH, Gupta YK. Comparison of three different extracts of Centella asiatica for anti-amnesic, antioxidant and anticholinergic activities: in vitro and in vivo study. Biomed Pharmacother 2018;105:1344-1352 View Article PubMed/NCBI
  17. Wan J, Gong X, Jiang R, Zhang Z, Zhang L. Antipyretic and anti-inflammatory effects of asiaticoside in lipopolysaccharide-treated rat through up-regulation of heme oxygenase-1. Phytother Res 2013;27(8):1136-1142 View Article PubMed/NCBI
  18. Yuan H, Ma Q, Cui H, Liu G, Zhao X, Li W, et al. How Can Synergism of Traditional Medicines Benefit from Network Pharmacology?. Molecules 2017;22(7):1135 View Article PubMed/NCBI
  19. Jameel M, Jamal K, Alam MF, Ameen F, Younus H, Siddique HR. Interaction of thiamethoxam with DNA: Hazardous effect on biochemical and biological parameters of the exposed organism. Chemosphere 2020;254:126875 View Article PubMed/NCBI
  20. Li J, Luo H, Liu X, Zhang J, Zhou W, Guo S, et al. Dissecting the mechanism of Yuzhi Zhixue granule on ovulatory dysfunctional uterine bleeding by network pharmacology and molecular docking. Chin Med 2020;15:113 View Article PubMed/NCBI
  21. Gong R, Ren S, Chen M, Wang Y, Zhang G, Shi L, et al. Bioinformatics Analysis Reveals the Altered Gene Expression of Patients with Postmenopausal Osteoporosis Using Liuweidihuang Pills Treatment. Biomed Res Int 2019;2019:1907906 View Article PubMed/NCBI
  22. Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 2019;47(W1):W357-W364 View Article PubMed/NCBI
  23. Wang X, Shen Y, Wang S, Li S, Zhang W, Liu X, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 2017;45(W1):W356-W360 View Article PubMed/NCBI
  24. Grondin CJ, Davis AP, Wiegers JA, Wiegers TC, Sciaky D, Johnson RJ, et al. Predicting molecular mechanisms, pathways, and health outcomes induced by Juul e-cigarette aerosol chemicals using the Comparative Toxicogenomics Database. Curr Res Toxicol 2021;2:272-281 View Article PubMed/NCBI
  25. Paolacci S, Precone V, Acquaviva F, Chiurazzi P, Fulcheri E, Pinelli M, et al. Genetics of lipedema: new perspectives on genetic research and molecular diagnoses. Eur Rev Med Pharmacol Sci 2019;23(13):5581-5594 View Article PubMed/NCBI
  26. Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc 2019;14(2):482-517 View Article PubMed/NCBI
  27. Lu X, Yu S, Lü P, Chen H, Zhong S, Zhou B. Genome-Wide Transcriptomic Analysis Reveals a Regulatory Network of Oxidative Stress-Induced Flowering Signals Produced in Litchi Leaves. Genes (Basel) 2020;11(3):324 View Article PubMed/NCBI
  28. Zhang H, Fu Q, Shi X, Pan Z, Yang W, Huang Z, et al. Human A-to-I RNA editing SNP loci are enriched in GWAS signals for autoimmune diseases and under balancing selection. Genome Biol 2020;21(1):288 View Article PubMed/NCBI
  29. Huang B, Han W, Sheng ZF, Shen GL. Identification of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients. Cancer Cell Int 2020;20:195 View Article PubMed/NCBI
  30. Huang J, Zhou X, Shen Y, Li H, Zhou G, Zhang W, et al. Asiaticoside loading into polylactic-co-glycolic acid electrospun nanofibers attenuates host inflammatory response and promotes M2 macrophage polarization. J Biomed Mater Res A 2020;108(1):69-80 View Article PubMed/NCBI
  31. Tominaga K, Suzuki HI. TGF-β Signaling in Cellular Senescence and Aging-Related Pathology. Int J Mol Sci 2019;20(20):5002 View Article PubMed/NCBI
  32. Wan SZ, Liu C, Huang CK, Luo FY, Zhu X. Ursolic Acid Improves Intestinal Damage and Bacterial Dysbiosis in Liver Fibrosis Mice. Front Pharmacol 2019;10:1321 View Article PubMed/NCBI
  33. Liu Z, Li Y, Song H, He J, Li G, Zheng Y, et al. Collagen peptides promote photoaging skin cell repair by activating the TGF-β/Smad pathway and depressing collagen degradation. Food Funct 2019;10(9):6121-6134 View Article PubMed/NCBI
  34. Fujita K, Tokuda H, Yamamoto N, Kainuma S, Kawabata T, Sakai G, et al. Incretins amplify TNF-α-stimulated IL-6 synthesis in osteoblasts: Suppression of the IκB/NF-κB pathway. Int J Mol Med 2017;39(4):1053-1060 View Article PubMed/NCBI
  35. Kawaguchi A, Yajima N, Komohara Y, Aoki H, Tsuchiya N, Homma J, et al. Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients. Int J Oncol 2012;40(3):721-730 View Article PubMed/NCBI
  36. Duan S, Yu S, Yuan T, Yao S, Zhang L. Exogenous Let-7a-5p Induces A549 Lung Cancer Cell Death Through BCL2L1-Mediated PI3Kγ Signaling Pathway. Front Oncol 2019;9:808 View Article PubMed/NCBI
  37. Li Q, Wang X, Wu X, Rui Y, Liu W, Wang J, et al. Daxx cooperates with the Axin/HIPK2/p53 complex to induce cell death. Cancer Res 2007;67(1):66-74 View Article PubMed/NCBI
  38. Liu Y, Hwang E, Ngo HTT, Perumalsamy H, Kim YJ, Li L, et al. Protective Effects of Euphrasia officinalis Extract against Ultraviolet B-Induced Photoaging in Normal Human Dermal Fibroblasts. Int J Mol Sci 2018;19(11):3327 View Article PubMed/NCBI
  39. Kim HR, Kim S, Lee SW, Sin HS, Kim SY. Protective Effects of Fermented Paprika (Capsicum annuum L.) on Sodium Iodate-Induced Retinal Damage. Nutrients 2020;13(1):25 View Article PubMed/NCBI
  40. Lee S, Hoang GD, Kim D, Song HS, Choi S, Lee D, et al. Efficacy of Alpinumisoflavone Isolated from Maclura tricuspidata Fruit in Tumor Necrosis Factor-α-Induced Damage of Human Dermal Fibroblasts. Antioxidants (Basel) 2021;10(4):514 View Article PubMed/NCBI
  41. Kim KH, Park SJ, Lee YJ, Lee JE, Song CH, Choi SH, et al. Inhibition of UVB-induced skin damage by exopolymers from Aureobasidium pullulans SM-2001 in hairless mice. Basic Clin Pharmacol Toxicol 2015;116(2):73-86 View Article PubMed/NCBI
  42. Mimeault M, Batra SK. Recent insights into the molecular mechanisms involved in aging and the malignant transformation of adult stem/progenitor cells and their therapeutic implications. Ageing Res Rev 2009;8(2):94-112 View Article PubMed/NCBI
  43. Ingram DK, Zhu M, Mamczarz J, Zou S, Lane MA, Roth GS, et al. Calorie restriction mimetics: an emerging research field. Aging Cell 2006;5(2):97-108 View Article PubMed/NCBI
  44. Lee YR, Noh EM, Kwon KB, Lee YS, Chu JP, Kim EJ, et al. Radix clematidis extract inhibits UVB-induced MMP expression by suppressing the NF-kappaB pathway in human dermal fibroblasts. Int J Mol Med 2009;23(5):679-684 View Article PubMed/NCBI
  45. Zhang JA, Yin Z, Ma LW, Yin ZQ, Hu YY, Xu Y, et al. The protective effect of baicalin against UVB irradiation induced photoaging: an in vitro and in vivo study. PLoS One 2014;9(6):e99703 View Article PubMed/NCBI
  46. Jeon H, Kim DH, Nho YH, Park JE, Kim SN, Choi EH. A Mixture of Extracts of Kochia scoparia and Rosa multiflora with PPAR α/γ Dual Agonistic Effects Prevents Photoaging in Hairless Mice. Int J Mol Sci 2016;17(11):1919 View Article PubMed/NCBI
  47. Zhang M, Qureshi AA, Geller AC, Frazier L, Hunter DJ, Han J. Use of tanning beds and incidence of skin cancer. J Clin Oncol 2012;30(14):1588-1593 View Article PubMed/NCBI
  48. Pantke S, Fricke TC, Eberhardt MJ, Herzog C, Leffler A. Gating of the capsaicin receptor TRPV1 by UVA-light and oxidants are mediated by distinct mechanisms. Cell Calcium 2021;96:102391 View Article PubMed/NCBI
  49. Xia S, Zhang X, Zheng S, Khanabdali R, Kalionis B, Wu J, et al. An Update on Inflamm-Aging: Mechanisms, Prevention, and Treatment. J Immunol Res 2016;2016:8426874 View Article PubMed/NCBI
  50. Ramezani-Rad P, Geng H, Hurtz C, Chan LN, Chen Z, Jumaa H, et al. SOX4 enables oncogenic survival signals in acute lymphoblastic leukemia. Blood 2013;121(1):148-155 View Article PubMed/NCBI
  51. Sadidi M, Lentz SI, Feldman EL. Hydrogen peroxide-induced Akt phosphorylation regulates Bax activation. Biochimie 2009;91(5):577-585 View Article PubMed/NCBI
  52. Zhang B, Chu S, Agarwal P, Campbell VL, Hopcroft L, Jørgensen HG, et al. Inhibition of interleukin-1 signaling enhances elimination of tyrosine kinase inhibitor-treated CML stem cells. Blood 2016;128(23):2671-2682 View Article PubMed/NCBI
  53. Lin Y, Bai L, Chen W, Xu S. The NF-kappaB activation pathways, emerging molecular targets for cancer prevention and therapy. Expert Opin Ther Targets 2010;14(1):45-55 View Article PubMed/NCBI
  54. Xiao X, Yang G, Bai P, Gui S, Nyuyen TM, Mercado-Uribe I, et al. Inhibition of nuclear factor-kappa B enhances the tumor growth of ovarian cancer cell line derived from a low-grade papillary serous carcinoma in p53-independent pathway. BMC Cancer 2016;16:582 View Article PubMed/NCBI
  55. Ottley E, Gold E. microRNA and non-canonical TGF-β signalling: implications for prostate cancer therapy. Crit Rev Oncol Hematol 2014;92(1):49-60 View Article PubMed/NCBI
  56. Forget A, Martignetti L, Puget S, Calzone L, Brabetz S, Picard D, et al. Aberrant ERBB4-SRC Signaling as a Hallmark of Group 4 Medulloblastoma Revealed by Integrative Phosphoproteomic Profiling. Cancer Cell 2018;34(3):379-395.e7 View Article PubMed/NCBI
  57. Ramalingam P, Poulos MG, Lazzari E, Gutkin MC, Lopez D, Kloss CC, et al. Chronic activation of endothelial MAPK disrupts hematopoiesis via NFKB dependent inflammatory stress reversible by SCGF. Nat Commun 2020;11(1):666 View Article PubMed/NCBI
  58. Zhang WS, Pan A, Zhang X, Ying A, Ma G, Liu BL, et al. Inactivation of NF-κB2 (p52) restrains hepatic glucagon response via preserving PDE4B induction. Nat Commun 2019;10(1):4303 View Article PubMed/NCBI
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  • Exploratory Research and Hypothesis in Medicine
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Anti-photoaging Properties of Asiaticoside in Ultraviolet A-irradiated Human Dermal Fibroblasts by Activating the PI3K-AKT Pathway and Inhibiting the NF-κB Pathway

Jia Huang, Yiyi Gong, Ke Liu, Jun Chen, Xiaobo Zhou
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