Online ISSN: 1945-4589
Learn about our FREE Post-Publication Promotion Services
Research Paper Volume 15, Issue 11 pp 5144—5163

Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging

Jiaxin Mo1, *, , Zunjiang Li1, *, , Hankun Chen3, , Zhongyu Lu1, , Banghan Ding1,2, , Xiaohong Yuan2, , Yuan Liu4, , Wei Zhu2, ,

  • 1 The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
  • 2 Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
  • 3 Guangzhou Qinglan Biotechnology Co. Ltd., Guangzhou Province 515000, China
  • 4 Guangzhou Huamiao Biotechnology Research Institute Co. Ltd., Guangzhou Province 510000, China
* Co-first authors

Received: February 6, 2023       Accepted: May 15, 2023       Published: June 12, 2023      

https://doi.org/10.18632/aging.204789
How to Cite

Copyright: © 2023 Mo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.

Introduction

Skin aging, a complex and unavoidable process among the elderly population, has attracted increasing attention around the world. It is mainly charactered by progressive dysfunction and decreased regenerative potentiality of skin layers [1], which appears as laxity, dryness and some facial exaggerated expression lines, xerosis [2]. Nowadays, with a high rate up to 13.7% of aged population in the world (https://population.un.org/), aging has been the principal consideration leading to the prevalence of skin aging and barrier dysfunction. However, another important exterior factor should also be taken into account, that is ultraviolet radiation (UVR) exposure, which mainly includes ultraviolet radiation A (UVA) (90–95%) and ultraviolet radiation B (UVB) (5–10%) [3]. UVR exposure could accelerate skin degradation and photoaging, leading to damage of skin appearance, physiological function, and even melanoma or non-melanoma skin cancer [4]. UVA and UVB exposure are considered to be the potent driver for photodamaged skin induced by oxidative stress injury and free radical damage [5]. The most popular approach in protecting the skin from UVR is the application of shading equipment (umbrellas, hats, long sleeved clothes etc.) and topical application of sunscreens. Although some oral and topical drugs with skin photoprotective effects were recommended, most of products still lack convincing protective functions, which unveiled the need and significance for the development and deployment of new drugs with potential effectiveness for skin aging.

Network medicine framework (NMF) has been applied for unveiling the complex role between drugs and diseases by capturing the molecular interaction between drugs and their targets [6]. The proximity between the targets of compounds and diseases with human interactome is calculated to identify their potential rational drug repurposing [7–9]. It could enhance the confidence and accuracy of predictive power by calculating the discernible adjacent regions between the targets of AAK component and the human interactome, thus the proximity between compound and disease targets could be applied for identified the therapeutic effects of AAK for skin aging. Deisy Morselli Gysi et al. published their results on the Proceedings of the National Academy of Sciences (PNAS) suggesting that the application of NMF could quickly and correctly screen new drugs with repurposing opportunities for COVID-19 [9], which greatly reduce time and economic cost of new drug development in the context of a viral pandemic. The results published on Nature Food also proved that calculating the proximity of NMF between polyphenol targets and disease proteins could precisely predict and screen polyphenols with therapeutic effects [8]. The results of Fang J et al. published in Nature Aging also successfully identified sildenafil with repurposing opportunities for Alzheimer’s disease by NMF [10]. NMF is mainly used to explore the new therapeutical effect of drugs [11], and the rug-repurposing opportunities from one disease to another [12]. Thus, the NMF exerts a significant role in correctly revealing drug-repurposing opportunities for new drugs, especially for nature products, which contain multiple compounds [8, 9, 13].

Traditional Chinese medicine (TCM) has exerted a vital role in skin aging during the past decades, it is reported that TCM has been the main source of new drugs for multiple nature compounds [14]. TCM exerts therapeutic effect on skin aging relying on complex molecular mechanisms of multi-compounds, multi-targets and multi-pathways [15], which conceals the role of single components with significant efficacy. For example, artemisinin, exploited from Artemisia annua L., was proven as an antimalarial drug against drug-resistant plasmodium falciparum [16]. This herb was employed for relieving malaria symptoms more than a thousand years ago, but its active compound (artemisinin) was only extracted and verified at the end of the last century [17]. Similarly, Angelica acutiloba (Sieb. et Zucc.) Kitagawa (AAK), a herbal medicine in TCM, has been widely used for anti-inflammation [18], alleviating pain, anti-tumor [19], regulating immunity [20], and promotion of hematopoiesis function etc. [20, 21], its active ingredient still need to be elucidated, as that it contains multiple compounds, including volatile oil, phthalides, terpenes, coumarins, flavonoids, phenolic acids, and polysaccharide etc. [22]. Previous studies pointed out AAK has the function of protecting dermal tissue damage [23], inhibiting photoaging induced factors and antioxidant activity [24] of UVA-induced photoaging. Surprisingly, the function of AAK and its extracts are proved to decrease inflammatory factors, skin interstitium tissue damage and cell apoptosis etc. However, the underlying molecular mechanisms, targets and the pharmaco-active compounds of AAK for skin aging are unexplored.

The development of new drugs will last for a decade or longer time, but it is the main procession of screening ingredients with potential pharmacological effective [9]. Skin aging is a long-term progression governed by internal or external factors interacting with each other, which causes great difficulty in finding efficient active compounds. The introduce of algorithm strategy relies on network distribution, proximity and multimodal ensemble grounded on human genome-wide interaction network [25]. A drug for a particular disease might have pharmacodynamic effects for other diseases, which will take a longer time to explore. Take berberine as example, the hypoglycemic effect of oral berberine is mainly involved in decreased biotransformation of glycodeoxycholic acid, but its repurposing efficacy of berberine on gut microbiome still lack explore until the emerge of corresponding technologies [26]. Thus, new strategy should be developed to shorten the cycle of new drug development for specific diseases, aiming to explore the effectiveness and overall mechanism of action of natural medicines.

In the present study, we intend to explore the pharmaco-active components of AAK with repurposed opportunity for skin aging and their pharmacological mechanism via the proximity of NMF and cMAP connecting analysis. Subsequently the targets and mechanism of these pharmaco-active components were verified by clinical data verification from Gene Expression Omnibus (GEO) database and molecular docking. The results aimed to provide reference for skin aging in clinical practice, a theoretical strategy for identifying repurposing drugs, and new sight for our future research.

Results

The similarity and congregate feature of AAK compound

Human PPI interactomes including 25120 unique proteins and 751939 pairs of interaction were collected from 20 protein-protein interaction (PPI) databases, (Supplementary Table 1). A total of 29 AAK compounds were selected after standardization and duplication, but only a few AAK compounds had a larger number of targets (Figure 1E) (Supplementary Table 2). The Jaccard index (JI) indicated that the pairs of targets among AAK compounds had a limited similarity (average JI = 0.0115) (Supplementary Table 3), which may due to the commonality of chemicals binding domains in the three-dimensional structure of protein targets [6] (Figure 1C). Gene ontology enrichment analysis recovered protein targets of AAK compounds were enriched in the same gene ontology categories and biological process. It indicated that AAK compounds target different processes of the same biological process to exert pharmacological effect as the low similarity between target proteins of AAK compound (Figure 1D). When map the targets of AAK to human PPI, most targets clustered in special region, prompting us to explore whether the interactome regions targeted by the AAK compounds reside within network neighborhoods associated with skin aging (Figure 1A, 1B).

The similarity and congregate feature of the compounds of AAK. (A) Proteins targeted by AAK compounds were not randomly congregated in feature-specific adjacent region of human protein-protein interactome; (B) Proteins targeted by AAK formed a large connected component (LCC) consisting of 467 proteins, and multiple small subgraphs in the human interactome; (C) The results of Jaccard index (JI) among 29 AAK compounds; (D) Top (n = 20) enriched gene ontology terms (Biological Process)among all AAK compounds’ targets; (E) Protein–protein interactions of 29 AAK compounds’ targets).

Figure 1. The similarity and congregate feature of the compounds of AAK. (A) Proteins targeted by AAK compounds were not randomly congregated in feature-specific adjacent region of human protein-protein interactome; (B) Proteins targeted by AAK formed a large connected component (LCC) consisting of 467 proteins, and multiple small subgraphs in the human interactome; (C) The results of Jaccard index (JI) among 29 AAK compounds; (D) Top (n = 20) enriched gene ontology terms (Biological Process)among all AAK compounds’ targets; (E) Protein–protein interactions of 29 AAK compounds’ targets).

AAK had potential pharmacological effect for skin aging

When mapping the targets of AAK compounds to PPI of skin aging proteins (Supplementary Table 4), it showed that the targets of AAK compound were congregated in feature-specific adjacent region of skin aging proteins in the interaction network, indicating AAK compounds may cluster in the specific regions of skin aging with specific pharmacological effect (Figure 2A, 2B). We further focused on AAK compounds with more than two targets, then we measured the size and significance of the largest connected component (LCC) formed by each AAK compounds. The results indicated 23 of 29 compounds have larger LCC than expected (|Z-score| > 1.70) (Figure 2C) (Supplementary Table 5), suggesting the targets of AAK compounds may have potential pharmacological effect on skin aging.

The proteins of the AAK compounds were congregated in the feature-specific adjacent region of skin aging proteins interactome. (A) AAK compound proteins congregated in feature-specific adjacent region of skin aging proteins interactome; (B) The random expectation of the LCC size indicates that the observed skin aging LCC; (C) Size of the LCC formed by the targets of each AAK compound in the skin aging protein interactome and the corresponding significance (|Z-score|).

Figure 2. The proteins of the AAK compounds were congregated in the feature-specific adjacent region of skin aging proteins interactome. (A) AAK compound proteins congregated in feature-specific adjacent region of skin aging proteins interactome; (B) The random expectation of the LCC size indicates that the observed skin aging LCC; (C) Size of the LCC formed by the targets of each AAK compound in the skin aging protein interactome and the corresponding significance (|Z-score|).

Proximity revealed 8 AAK compounds had therapeutic effect on skin aging

AAK compounds can be viewed as drugs in that they could exert their therapeutic effect by binding to specific proteins of skin aging, which was presented as shorter distance in the network framework. We therefore calculated the network proximity between AAK compound targets and skin aging proteins. We ranked and screened the top 8 AAK compounds with closest distance in the NMF (Figure 3A) (Supplementary Table 6), which indicated these 8 AAK compounds had pharmacological effect on skin aging, for the reason that they had shortest distance of their target cluster in the PPI network of the proteins of skin aging.

The 8 AAK compounds had potential pharmacologic efficacy for skin aging. (A) The shortest, closest and |z-shortest| calculated between compounds and skin aging by proximity and LCC analysis; (B) ROC curve of the 8 AAK compounds to skin aging and therapeutic diseases).

Figure 3. The 8 AAK compounds had potential pharmacologic efficacy for skin aging. (A) The shortest, closest and |z-shortest| calculated between compounds and skin aging by proximity and LCC analysis; (B) ROC curve of the 8 AAK compounds to skin aging and therapeutic diseases).

Then in the Comparative Toxicogenomic Database (CTD) database, we collected 74 diseases which can be targeted by 8 AAK compounds with known therapeutic efficacy. The proximity values of the 8 AAK compounds between skin aging and the 74 validated diseases were compared by ROC analysis, (Supplementary Table 7), and the results indicated AUC and performance accuracy were both greater than 0.75, which was similar as the AUC of AAK compounds with 74 validated diseases (AUC = 0.876) (P = 0.19762 > 0.05) (Figure 3B), all above results suggested that 8 AAK compounds may had therapeutic effect on skin aging.

Medicinal effect of AAK key compounds in the treatment of skin aging

In order to further identify the potential efficacy of these key compounds, we conducted genetic perturbations analysis via cMAP database. Firstly, we identified 356 differential genes (adjust P < 0.05, |LogFC| > 0.5) (fold changes, FCs) from dataset GSE192564 in the GEO database, including 144 up-regulated and 212 down-regulated genes (Figure 4A, 4B). Venn analysis screened 29 intersecting genes between the differentially expressed genes (DEGs) and targets of AAK compounds (Figure 4C), including 13 upregulated genes and 16 downregulated genes (Figure 4D). As showed in Figure 4E, it indicated the pharmacological effect of 8 key AAK compounds were analogous to QW-BI-011, Alvespimycin, VU-0418946-1, TW-37, BMY-45778, SRT-1720, BRD-K30064966, TTNPB, 1-phenylbiguanide and alfacalcidol (TOP 10 of cMAP connecting score, Figure 4F). It is reported QW-BI-011 and Alvespimycin exhibit an important role in the process of skin aging. QW-BI-011 regulates epigenetic modulatory and suppresses the expression of histone methyltransferase, G9a, which restrains the development of melanoma and prevents photoaging injury [27]. Alvespimycin is a HSP90 inhibitor, it stabilizes the proteins of fibroblasts by preventing HSP90 protein upregulated [28]. VU-0418946-1, TW-37, BMY-45778, SRT-1720, BRD-K30064966, TTNPB, 1-phenylbiguanide and alfacalcidol are also intimately involved in regulating the process of skin aging. They were mainly similar to BCL inhibitor, ATP synthase inhibitor, EGFR inhibitor, HIF activator, PKC activator, ribonucleotide reductase inhibitor, Tubulin inhibitor, T-type calcium channel blocker, EIF Proteins LOF and PKC inhibitor (Figure 4G), prompting that those AAK compounds would act by managing metastasis, growth and apoptosis of cells [29–32], restraining oxidative stress [33], modulating energy metabolism of mitochondrial function [34], modulating vasodilatation [35], alone or in combination.

Exploration on the function of key AAK compounds via GEO analysis and cMAP analysis. (A) Heatmaps of DEGs between normal skin and actinic lentigines skin; (B) Volcano plot of DEGs between normal skin and actinic lentigines skin; (C) Venn analysis on the intersection targets between DEGs and targets of key AAK compounds; (D) Circular cluster heatmap of intersection targets; (E) Identifying compounds with similar pharmacological effect as key AAK compounds; (F, G) Identifying perturbational class of key AAK compounds and which in skin cells).

Figure 4. Exploration on the function of key AAK compounds via GEO analysis and cMAP analysis. (A) Heatmaps of DEGs between normal skin and actinic lentigines skin; (B) Volcano plot of DEGs between normal skin and actinic lentigines skin; (C) Venn analysis on the intersection targets between DEGs and targets of key AAK compounds; (D) Circular cluster heatmap of intersection targets; (E) Identifying compounds with similar pharmacological effect as key AAK compounds; (F, G) Identifying perturbational class of key AAK compounds and which in skin cells).

The Hub-Target between AAK key compounds and skin aging

Although we have obtained 29 core genes, their expression level and regulatory role during the processes of skin aging were not clarified. Thus, PPI analysis screened 12 Hub-Target from the 29 core genes according to the degree of PPI network (Figure 5E). Then, we confirmed the down-regulated expressions of ABCC4, PTGER3, BCEH, HPGD, and MOXD1 in normal group, while AR, CXCR2, HSD17B2, ODC1, PI3, PLAU and THBS2 were up-regulated in skin aging group (Figure 5A, 5B). It suggested that the key compounds of AAK played a therapeutic role by inhibiting or promoting the expression of these genes. In addition, ROC curve verified that these hub-target had certain sensitivity and specificity for the diagnosis of skin aging (AUC greater than 0.5), AR, BCEH, CXCR2, HPGD and PI3 performed well especially (Figure 5C, 5D, 5F, 5G). The AUC of them were all greater than 0.9, reminding that they could be served as specific biomarkers for skin aging diagnosis. Hence, we calculated their Cutoff value, and the results showed that when AR, HPGD and PI3 expression were up-regulated to 8.221, 9.686 and 7.603 respectively, BCEH and CXCR2 expression were down-regulated to 9.948 and 7.695 respectively, they could be applied as a criterion for diagnosing skin aging.

The relationship between the Hub-target of AAK compounds and the development processes of skin aging. (A, B) The down-regulated expression of ABCC4, PTGER3, BCEH, HPGD, MOXD1 and up-regulated expression of AR, CXCR2, HSD17B2, ODC1, PI3, PLAU and THBS2 among hub-target by AAK; (C, D) ROC curves of ABCC4, AR, BCEH, PI3, BCHE, CXCR2, HPGD, HSD17B2 through dataset GSE192564 and GSE192565; (E) PPI analysis of 29 core genes; (F, G) ROC curves of MOXD1, ODC1, PLAU, PTGER3, THBS2 through dataset GSE192564 and GSE192565).

Figure 5. The relationship between the Hub-target of AAK compounds and the development processes of skin aging. (A, B) The down-regulated expression of ABCC4, PTGER3, BCEH, HPGD, MOXD1 and up-regulated expression of AR, CXCR2, HSD17B2, ODC1, PI3, PLAU and THBS2 among hub-target by AAK; (C, D) ROC curves of ABCC4, AR, BCEH, PI3, BCHE, CXCR2, HPGD, HSD17B2 through dataset GSE192564 and GSE192565; (E) PPI analysis of 29 core genes; (F, G) ROC curves of MOXD1, ODC1, PLAU, PTGER3, THBS2 through dataset GSE192564 and GSE192565).

The LncRNA-miRNA-target network of Hub-Target

microRNA (miRNA) is a type of non-coding single-stranded RNA molecules, it can directly join in post-transcriptional gene expression. LncRNAs are long noncoding transcripts and exposed function of target-mimetic, sponge or decoy on miRNA, their expression greatly links to specular disease and developmental stage [36]. Thus, we constructed lncRNA-miRNA interacting with AAK hub-targets to clarify the translated regulation and biogenesis pathways. As shown in Figure 6A, we screened out 19 regulatory miRNA targeting AR, BCEH, CXCR2, HPGD, and PI3 from 3 miRNA databases. They mainly participated in the biological process of cellular nitrogen compound metabolic process, small molecule metabolic process, cellular protein metabolic process, biosynthetic process, response to stress, cellular protein modification process and immune system process. The biological processes above were associated with the progression of skin aging (Figure 6C). Figure 6B manifested 90 IncRNAs (|Energy|>25 kCal/Mol, Score >150) participated in the regulatory process of miRNAs of Hub-Target. Transcription factor (TF) including ATF ETS1, NFE2, FOXO1, KLF11, PBX1, ATF3, BRD3 and RFX5 were principally involved in the regulatory process of these 19 miRNAs (Figure 6D). In brief, AAK compounds targeted skin aging by regulating these IncRNA-miRNA-target network.

LncRNA-miRNA-target gene network of AAK hub-targets. (A) The 19 regulated hub-targets from 3 mi RNA databases; (B) The network of LncRNA-miRNA-target gene; (C) Biological processes of 19 regulated miRNA; (D) TF-miRNAs regulatory network).

Figure 6. LncRNA-miRNA-target gene network of AAK hub-targets. (A) The 19 regulated hub-targets from 3 mi RNA databases; (B) The network of LncRNA-miRNA-target gene; (C) Biological processes of 19 regulated miRNA; (D) TF-miRNAs regulatory network).

Mechanism of AAK compounds targeting skin aging

In order to explore the mechanism of AAK compounds targeting skin aging, we performed a single-gene set enrichment analysis (GSEA) on the hub targets of AAK compounds. The results showed that apart from CXCR2, other targets (AR, BCEH, HPGD and PI3) were all linked to up-regulation of autophagy (Figure 7A–7L), and down-regulation of Phospholipase D signaling pathway, which suggested AAK compounds may exert therapeutical effect on skin aging via inhibiting autophagy process and activating the Phospholipase D signaling.

Single-gene GSEA pathway enrichment on Hub-targets of key AAK compounds. (A–D) Single-gene GSEA-KEGG pathway analysis in AR, BCEH, HPGD and PI3; (E–H) Single-gene GSEA-KEGG pathway enriched in autophagy pathway; (I–L) Single-gene GSEA-KEGG pathway enriched in Phospholipase D signaling pathway).

Figure 7. Single-gene GSEA pathway enrichment on Hub-targets of key AAK compounds. (A–D) Single-gene GSEA-KEGG pathway analysis in AR, BCEH, HPGD and PI3; (E–H) Single-gene GSEA-KEGG pathway enriched in autophagy pathway; (I–L) Single-gene GSEA-KEGG pathway enriched in Phospholipase D signaling pathway).

Molecular docking

The prerequisite for drug efficacy is the ability of binding with proteins or receptors. To identify whether the screened compounds could bind with core targets at protein conformations, we measured the affinity by combining approaches of spatial structure and molecular docking [37]. We calculated the docking score between the molecular structure of the AAK components (camphene, cyclohexane, folinic acid, indole, isoquercitrin, limonene, stearic acid, alpha-pinene) and Hub-Target (AR, CXCR2, BCEH, HPGD, PI3) (Figure 8A–8J), the main intermolecular forces are Van der Waals, Conventional Hydrogen Bond, Carbon Hydrogen Bond, Pi-Sigma, Pi-Sulfur, Amide-Pi Stacked, Pi-Alkyl. Indicated by docking score, the results showed that alpha-pinene, camphene, cyclohexane, indole and limonene could bind with PI3 better, folinic acid, isoquercitrin and stearic acid could bine with PHGD, BCEH, CXCR2 better respectively, indicating these 8 AAK compounds may exert pharmacological effect on skin aging by targeting PI3, HGD, BCEH and CXCR2, respectively.

Molecular docking analysis between AAK compounds and hub-targets. (A–E) 2D and 3D structure visualization of alpha-pinene, camphene, cyclohexane, indole, limonene interaction docked with PI3; (F) type of interaction between key compounds and hub targets; (G) 2D and 3D structure visualization of folinic acid interaction docked with PHGD; (H) 2D and 3D structure visualization of isoquercitrin docked with BCHE; (I) 2D and 3D structure visualization of stearic acid interaction docked with CXCR2; (J) hot map of docking score.

Figure 8. Molecular docking analysis between AAK compounds and hub-targets. (A–E) 2D and 3D structure visualization of alpha-pinene, camphene, cyclohexane, indole, limonene interaction docked with PI3; (F) type of interaction between key compounds and hub targets; (G) 2D and 3D structure visualization of folinic acid interaction docked with PHGD; (H) 2D and 3D structure visualization of isoquercitrin docked with BCHE; (I) 2D and 3D structure visualization of stearic acid interaction docked with CXCR2; (J) hot map of docking score.

Discussion

Nowadays, aging tendency of population is inevitable, aging and UVR exposure were the main drivers for the weakness of skin barrier, the decreased ability of repairing and regenerating [1]. Chinese medicinal herbs exert satisfactory role in anti-aging and has been applying for a long time, natural ingredients have become the main source for new drugs development, but it still lacks effective methods to explore effective compounds with newly pharmacology or repurposing opportunities for other disease [38].

Network medicine framework (NMF) was applied for exploring the therapeutic effects of drugs by quantifying the proximity between candidate compounds and disease and prioritizing the interacting nodes in human PPI network. The NMF plays a powerful role in quickly and correctly identifying ingredients with certain pharmacology from existing drugs, which could dramatically reduce the time and cost for novel drug development [9]. We conducted this comprehensive strategy to identify 8 active compounds (camphene, cyclohexane, folinic acid, indole, isoquercitrin, limonene, stearic acid, alpha-pinene) of AAK with drug repurposing opportunity for skin aging. Previous results showed that those compounds exhibited great effects in regulating autophagy [39], anti-inflammation [40], increasing cell viability [41], reducing antioxidant enzymes, phospholipase D [42] and lipid peroxidation [23]. Thus we made a preliminary proposal that the 8 AAK compounds would be responsible for the pharmacological effect of AAK for skin aging.

From cMAP analysis, we found that those 8 compounds had similar genetic perturbations as QW-BI-011, Alvespimycin, VU-0418946-1, TW-37, BMY-45778, SRT-1720, BRD-K30064966, TTNPB, 1-phenylbiguanide and alfacalcidol, indicating that 8 AAK compounds had same pharmacological effect as them. As reported, skin aging is involved in the process of cell proliferation, oxidative stress, apoptosis, inflammation and skin barrier repairing etc. It is reported that Alvespimycin suppressed the inflammasome/Caspase-1/GSDMD signal pathway [28], TW-37 obviously improved inflammation by inhibiting the proliferation of human oral cancer cell lines such as MC-3, HSC-3 [43, 44]. Alfacalcidol regulated inflammation process for weak elderly [45], which was similar to Alpha-pinene in down-regulating expression of inflammatory proteins. Camphene had strong antioxidant effects on scavenging the activity of hydroxyl and superoxide radicals [46], SRT-1720 suppressed the ROS generation against oxidative stress [47], TTNPB significantly up-regulated the expression of caspase-3, thus induced apoptosis of human melanoma cell [48], and Stearic acid could also induce apoptosis via same pathways [49]. Limonene and retinoids were the chemo-preventive agents for numerous or invasive nonmelanoma skin cancer, while TTNPB was a retinoid pathway activator, it had the similar pharmacological effects by acting on the same pathways [50]. Structurally, Folinic acid and TW-37 both had benzoyl group, thus they often had a similar role [51]. All above suggested 8 compounds of AAK also had similar effect on regulating these processes to alleviate the development and progression of skin aging.

Next, we further found that these 8 key AAK compounds would exert therapeutic effect by targeting AR, BCHE, CXCR2, HPGD and PI3. AR is an androgen receptor, and its expression in sun-protected skin is higher than the sun-unprotected [52], whose level is negatively correlated with the degree of damage; The activity of BCHE directly correlated with low susceptible to oxidative stress and detoxification [53]. Selective agonist to CXCR2 and PI3 kinase pathway promote human skin wound healing and keratinization, cell proliferation and migration [54], and reverse delayed skin healing as the degradation and inactivation of HPGD [55, 56]. The molecular docking proved that the high affinity between key compounds and these hub-targets, which also provided the evidence for efficacy validation.

In addition, we also found that 8 key compounds would exert anti-skin aging effect by inhibiting autophagy and activating phospholipase D (PLD) signaling pathway. Autophagy is a critical pathway against skin aging via removing aged proteins and subcellular organelles, maintaining homeostasis under external and internal stimulus such as UV irradiation and stress, and activating synthase kinase signaling pathway to protect epidermal cells [1]. Autophagy also inhibits hyperinflammatory skin reaction induced by inflammasome activation, and regulates the level of differentiated skin cells and the number of epidermal stem cells [39]. In detail, it is reported that isoquercitrin and stearic acid take part in the regulated process of autophagy by causing endoplasmic reticulum stress [49] and activating the AMPK/ mTOR/ p70S6K pathway [57]. Simultaneously, PLD and its enzymatic reaction product are the important protein in cell survival, which is regulated by the autophagosomes key component of autophagy, mTOR. mTOR combining with PLD enzymatic reaction product dual-directional regulates the formation and maintenance of the autophagosomes. The inhibitor of PLD also take part in accumulation of ceramide, which is assist in the survival of variety of cells, so that it can avoid the termination of the cell cycle [42].

The biological process of skin aging is closely related to oxidative stress, mitochondrial dysfunction, free radical accumulation and autophagy [47]. Meanwhile, this study revealed that 8 key AAK compounds had therapeutic activity on regulating oxidative stress, cellular proliferation and apoptosis and mitochondrial energy metabolism in our study. There are the reasons to believe that AAK exerts anti-skin aging effects through the mechanism of above pathways. However, due to lack of corresponding research, the regulating mechanism of 8 AAK key compounds for autophagy and PLD signaling pathway is still ambiguous, thus we initially proposed that 8 key compounds of AAK may exert anti-skin aging by regulating the autophagy and PLD signaling pathway, which drew new sights for our future research in next step.

Conclusions

In conclusion, this study identified 8 key compounds which would be responsible for the main pharmacological effect of AAK for skin aging, these 8 key AAK compounds had therapeutic activity on regulating oxidative stress, cellular proliferation and apoptosis and mitochondrial energy metabolism in our study, and their mechanism would be involving in regulating autophagy and activating phospholipase D signaling pathway, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.

Materials and Methods

Human PPI interactome establishment

The human interactome was established according to 20 databases containing five different types of PPIs: (1) binary PPIs root in high throughput yeast two-hybrid (Y2H) experiments from Interactome INSIDER [58], HURI, HI-union [59], Intact [60]; (2) primary document curated from BioGRID [61], PINA [62], MINT [63], LitBM, HINT [64], HIPPIE [65], InWeb_IM [66], APID [67]; (3) low throughput kinase substrate interactions experiments from Phosphositeplus [68], KinomeNetworkX [69], Human Protein Resource Database (HPRD) [70] and affinity purification followed by mass spectrometry (AP-MS) from bioplex3.0 [71] (4) three-dimension structural analysis from Instruct [72], Interactome 3D [73], Interactome INSIDER (5) signaling interactions originated from low through-put experiments documented from Signalink. These genes were normalized according to the Entrez ID and official gene symbols through the National Center for Biotechnology Information (NCBI) database.

Collecting compounds and targets of AAK

A total of 173 AAK compounds were retrieved from literatures and herb compounds database (PubMed, CNKI and HERB database). Then AAK compounds were screened according to the following criteria that (1) could be mapped in PubChem IDs, (2) were listed as having therapeutic effects on human diseases in the CTD database (3) had protein-binding information present in the STITCH and Drugbank database with experimental evidence. Finally, the targets of the screened compounds of AAK were retrieved from STITCH, Drugbank database, and ChEMBL database (on the threshold of homo sapiens, 90% confidence, active targets).

Collection of skin aging disease proteins (targets)

According to CTD database, we collected the symptoms of skin aging considering the similarity level in hierarchical branches of diseases along the MeSH tree, the involved proteins of skin aging were collected from GWAS (Genome-Wide Association Studies database) and Genecards database by inputting the MESH word of skin aging or its related symptoms, including “skin aging” and “aging, skin” etc. The latter database help to clarify the human genes information like generic name, Entrez id, official symbol in genome.

Network proximity between AAK targets and skin aging disease proteins

The proximity between skin aging and compounds of AAK were evaluated using a distance metric according to Deisy Morselli Gysi et al. [9] and Italo F. do Valle et al. [8], which takes into account the shortest path lengths between compounds targets and disease protein. The effective compounds aimed at skin aging disease proteins showed closer proximity and shorter distance. In order to evaluate the significance of proximity, we calculated the reference distance distribution and the expected distances between the proteins of compounds and diseases by randomly mapping, the mean and SD of reference distribution represented by proximity were calculated for 1000 repeats [74]. As human interactome had few nodes with high degrees due to its scale-free nature, we conducted randomly selection of nodes by stratified sampling according (binning approach) to the degree in order to avoid repeatedly selecting the same nodes with high-degree.

AKK–disease associations

We retrieved the AAK compounds–disease associations from the CTD database, we only considered the diseases in the condition that AAK compounds have therapeutical effect, observing unconnected association at network as negative cases out of true negative cases. Network proximity was calculated based on AAK compound targets with identified disease targets from CTD database and skin aging targets. Then the AUC value of 8 AAK compounds were measured for skin aging with therapeutic and non-therapeutic diseases, meanwhile, receiver operating characteristic curve (ROC curve) was applied for comparative analysis of therapeutic targets link to compounds with skin aging. So that it could verify the predictive power of network proximity limiting the items with predictive performance of AUC >0.75.

GEO data collection and identification of DEGs of skin aging

Expression profiling by high throughput sequencing with series number GSE192564 based on platform GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array (Homo sapiens)) was downloaded from the GEO database in NCBI (https://www.ncbi.nlm.nih.gov/). The dataset contained 26 actinic lentigines skin samples and 26 normal skin samples. Then the DEGs were analyzed through the online tool GEO2R according to the |log FC| >0.5 and adjust P < 0.05 [75]. Hierarchical clustering and visualization were used by Heat-map plot and Volcano plot by R software (Version 4.2.0) [76]. The verification dataset (GSE192565) was also selected from GEO database and was analyzed for DEGs through online tool GEO2R according to the |log FC| >0.5 and adjust P < 0.05.

cMAP analysis

cMAP is commonly applied for exploring the relationship among drugs, targets, and diseases by experimental verification result using the L1000 analysis platform, which could be used to explore or identified similar molecules sharing a same mechanism of action, chemicals and physiological processes etc. [77]. The intersection targets between key compounds of AAK and DGEs of dataset GSE192564 were imported into cMAP database to find out similar molecules sharing a same differential expressed genes to further infer the mechanism and pharmacological effect of the key compounds of AAK.

PPI network analysis

The intersection targets between key compounds of AAK and DGEs of dataset GSE192564 were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, http://string-db.org/; version 11.0) to construct the Protein–Protein Interaction (PPI) network, then it was introduced into Cytoscape 3.1.1 software for network analysis of the core subsystem. Key targets (Hub-Target) were screened according to the degree after network topology analysis.

Verifying the expression of key predicted targets of AAK compounds

After the hub-targets were screened, they indicated that the key compounds of AAK exerted pharmacological effect against skin aging by targeting these hub-targets. In order to validate whether these hub-targets were actually the pathogenic gene of skin aging, thus we verified the expression of hub-targets in an external cohort (the validate datasets GSE192565). The boxplot of hub-targets expression was performed using the “ggplot2” package in R software (Version 4.2).

ROC analysis

GEO dataset 129564 (training set) and external validation datasets (GSE192565) were used to validate whether hub-targets were the specific biomarker for the diagnosis of skin aging by classifying the sensitivity and specificity of the hub-targets. The calculation of area under curve (AUC) >0.9 was seen to be of high accuracy for diagnosis, providing clinical perspective in skin aging diagnosis. The R software (Version 4.2) was used to visualize the diagnostic value of each individual gene and construct the AUC curve using “pROC” package.

Identifying the IncRNA-miRNA-target gene network of AAK Hub-Target

miRNet database (https://www.mirnet.ca/) was used to predict lncRNA of miRNA, miRDB database (http://mirdb.org), miRWalk database (http://mirwalk.umm.uni-heidelberg.de), miRTarBase database (https://miRTarBase.cuhk.edu.cn/), and RNAInter database (http://www.rna-society.org/rnainter) were used to forecast the miRNA of mRNA (targets). Cytoscape 3.7.2 was used to construct IncRNA-miRNA-mRNA regulatory networks.

GSEA analysis on AAK Hub-Target

To further probe the pathway involving in the mechanism of the key compounds of AAK targeting skin aging, GSEA analysis was performed on single Hub-Targets via “clusterprofiler” package (version 3.18.1) and org.Hs.eg.db package (version 3.12.0) in R software. KEGG pathways with |NES| >1, and adjust P < 0.05 were considered to be markedly enriched.

Verification by molecular docking

The protein structures of hub-targets were downloaded from the PDB Database (https://www.rcsb.org/) and were preprocessed by Discovery studio software (Version 4.5). The structural files of the key compounds of AAK were downloaded from the PubChem Database (https://pubchem.ncbi.nlm.nih.gov/) and were preprocessed by Discovery studio software (Version 4.5). Finally, molecules and proteins were docked through the Libdock modular, the model with the highest docking score was selected, and their structural visualizations were conducted by Discovery studio software (Version 4.5).

Data and materials availability

All the data could be obtained from the Supplementary Material and contacting the corresponding authors.

Author Contributions

Wei Zhu and Xiaohong Yuan provided conceptualization and designed the research process. Hankun Chen Zhongyu Lu and Yuan Liu conducted formal analysis. Jiaxin Mo and Zunjiang Li were responsible for the methodology, conducted the final statistical analysis and graph, wrote the manuscript. Xiaohong Yuan, Wei Zhu and Banghan Ding reviewed and edited the article, Wei Zhu and Xiaohong Yuan were responsible for the decision and final check of the manuscript. All authors agreed to be accountable for all aspects of work ensuring integrity and accuracy.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by Guangzhou Science and Technology Bureau Municipal School (Institute) Joint Funding Project (202201020296); Guangdong Provincial Hospital of Traditional Chinese Medicine Science and Technology Research Project (YN2019MJ11); Foshan City Key Areas of Science and Technology (2120001008478).

References

  • 1. Csekes E, Račková L. Skin Aging, Cellular Senescence and Natural Polyphenols. Int J Mol Sci. 2021; 22:12641. https://doi.org/10.3390/ijms222312641 [PubMed]
  • 2. Khavkin J, Ellis DA. Aging skin: histology, physiology, and pathology. Facial Plast Surg Clin North Am. 2011; 19:229–34. https://doi.org/10.1016/j.fsc.2011.04.003 [PubMed]
  • 3. Nobile V, Burioli A, Yu S, Zhifeng S, Cestone E, Insolia V, Zaccaria V, Malfa GA. Photoprotective and Antiaging Effects of a Standardized Red Orange (Citrus sinensis (L.) Osbeck) Extract in Asian and Caucasian Subjects: A Randomized, Double-Blind, Controlled Study. Nutrients. 2022; 14:2241. https://doi.org/10.3390/nu14112241 [PubMed]
  • 4. Marionnet C, Tricaud C, Bernerd F. Exposure to non-extreme solar UV daylight: spectral characterization, effects on skin and photoprotection. Int J Mol Sci. 2014; 16:68–90. https://doi.org/10.3390/ijms16010068 [PubMed]
  • 5. Barua S, Jiang LI, Kononov T, Zahr AS. A case study investigating the short-term efficacy and tolerability of a daily serum composed from a unique sunflower sprout extract. J Cosmet Dermatol. 2022; 21:4410–21. https://doi.org/10.1111/jocd.15248 [PubMed]
  • 6. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015; 347:1257601. https://doi.org/10.1126/science.1257601 [PubMed]
  • 7. Lee LY, Loscalzo J. Network Medicine in Pathobiology. Am J Pathol. 2019; 189:1311–26. https://doi.org/10.1016/j.ajpath.2019.03.009 [PubMed]
  • 8. do Valle IF, Roweth HG, Malloy MW, Moco S, Barron D, Battinelli E, Loscalzo J, Barabási AL. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nat Food. 2021; 2:143–55. https://doi.org/10.1038/s43016-021-00243-7 [PubMed]
  • 9. Morselli Gysi D, do Valle Í, Zitnik M, Ameli A, Gan X, Varol O, Ghiassian SD, Patten JJ, Davey RA, Loscalzo J, Barabási AL. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc Natl Acad Sci U S A. 2021; 118:e2025581118. https://doi.org/10.1073/pnas.2025581118 [PubMed]
  • 10. Fang J, Zhang P, Zhou Y, Chiang CW, Tan J, Hou Y, Stauffer S, Li L, Pieper AA, Cummings J, Cheng F. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease. Nat Aging. 2021; 1:1175–88. https://doi.org/10.1038/s43587-021-00138-z [PubMed]
  • 11. Fang J, Zhang P, Wang Q, Chiang CW, Zhou Y, Hou Y, Xu J, Chen R, Zhang B, Lewis SJ, Leverenz JB, Pieper AA, Li B, et al. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease. Alzheimers Res Ther. 2022; 14:7. https://doi.org/10.1186/s13195-021-00951-z [PubMed]
  • 12. do Valle IF, Ferolito B, Gerlovin H, Costa L, Demissie S, Linares F, Cohen J, Gagnon DR, Gaziano JM, Begoli E, Cho K, Barabási AL. Network-medicine framework for studying disease trajectories in U.S. veterans. Sci Rep. 2022; 12:12018. https://doi.org/10.1038/s41598-022-15764-9 [PubMed]
  • 13. Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks:Opportunities, challenges and prospects. J Biosci. 2022; 47:24. https://doi.org/10.1007/s12038-022-00253-y [PubMed]
  • 14. Imenshahidi M, Hosseinzadeh H. Berberine and barberry (Berberis vulgaris): A clinical review. Phytother Res. 2019; 33:504–23. https://doi.org/10.1002/ptr.6252 [PubMed]
  • 15. Zhao Y, Lin J, Li J, Bwalya C, Xu Y, Niu Y, Zhang Y, Wu J, Xu Y, Chen J, Ye S, Lin L. RhFGF21 Protects Epidermal Cells against UVB-Induced Apoptosis through Activating AMPK-Mediated Autophagy. Int J Mol Sci. 2022; 23:12466. https://doi.org/10.3390/ijms232012466 [PubMed]
  • 16. Talman AM, Clain J, Duval R, Ménard R, Ariey F. Artemisinin Bioactivity and Resistance in Malaria Parasites. Trends Parasitol. 2019; 35:953–63. https://doi.org/10.1016/j.pt.2019.09.005 [PubMed]
  • 17. Shi Q, Xia F, Wang Q, Liao F, Guo Q, Xu C, Wang J. Discovery and repurposing of artemisinin. Front Med. 2022; 16:1–9. https://doi.org/10.1007/s11684-021-0898-6 [PubMed]
  • 18. Jang JC, Lee KM, Ko SG. Angelica acutiloba Kitagawa Extract Attenuates DSS-Induced Murine Colitis. Mediators Inflamm. 2016; 2016:9275083. https://doi.org/10.1155/2016/9275083 [PubMed]
  • 19. Yamada H, Komiyama K, Kiyohara H, Cyong JC, Hirakawa Y, Otsuka Y. Structural characterization and antitumor activity of a pectic polysaccharide from the roots of Angelica acutiloba. Planta Med. 1990; 56:182–6. https://doi.org/10.1055/s-2006-960921 [PubMed]
  • 20. Hatano R, Takano F, Fushiya S, Michimata M, Tanaka T, Kazama I, Suzuki M, Matsubara M. Water-soluble extracts from Angelica acutiloba Kitagawa enhance hematopoiesis by activating immature erythroid cells in mice with 5-fluorouracil-induced anemia. Exp Hematol. 2004; 32:918–24. https://doi.org/10.1016/j.exphem.2004.07.003 [PubMed]
  • 21. Negishi H, Sugahama S, Kawakamia A, Kondo J, Nishigaki Y, Yoshikawa M, Ueyama T, Ikeda K. Effect of Angelica acutiloba Extract on Blood flow Regulation in Stroke-prone Spontaneously Hypertensive Rats. Nat Prod Commun. 2016; 11:1497–8. [PubMed]
  • 22. Jeong SY, Kim HM, Lee KH, Kim KY, Huang DS, Kim JH, Seong RS. Quantitative analysis of marker compounds in Angelica gigas, Angelica sinensis, and Angelica acutiloba by HPLC/DAD. Chem Pharm Bull (Tokyo). 2015; 63:504–11. https://doi.org/10.1248/cpb.c15-00081 [PubMed]
  • 23. Karthikeyan R, Kanimozhi G, Madahavan NR, Agilan B, Ganesan M, Prasad NR, Rathinaraj P. Corrigendum to "Alpha-pinene attenuates UVA-induced photoaging through inhibition of matrix metalloproteinases expression in mouse skin" [Life Sci. 217 (2019) 110-118]. Life Sci. 2020; 240:117156. https://doi.org/10.1016/j.lfs.2019.117156 [PubMed]
  • 24. Lee EH, Park HJ, Kim HH, Jung HY, Kang IK, Cho YJ. Isolated isoquercitrin from Green ball apple peel inhibits photoaging in CCD-986Sk fibroblasts cells via modulation of the MMPs signaling. J Cosmet Dermatol. 2021; 20:2932–9. https://doi.org/10.1111/jocd.13903 [PubMed]
  • 25. Huang JK, Carlin DE, Yu MK, Zhang W, Kreisberg JF, Tamayo P, Ideker T. Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst. 2018; 6:484–95.e5. https://doi.org/10.1016/j.cels.2018.03.001 [PubMed]
  • 26. Zhang Y, Gu Y, Ren H, Wang S, Zhong H, Zhao X, Ma J, Gu X, Xue Y, Huang S, Yang J, Chen L, Chen G, et al. Gut microbiome-related effects of berberine and probiotics on type 2 diabetes (the PREMOTE study). Nat Commun. 2020; 11:5015. https://doi.org/10.1038/s41467-020-18414-8 [PubMed]
  • 27. Dang NN, Jiao J, Meng X, An Y, Han C, Huang S. Abnormal overexpression of G9a in melanoma cells promotes cancer progression via upregulation of the Notch1 signaling pathway. Aging (Albany NY). 2020; 12:2393–407. https://doi.org/10.18632/aging.102750 [PubMed]
  • 28. Zhou Z, Li X, Qian Y, Liu C, Huang X, Fu M. Heat shock protein 90 inhibitors suppress pyroptosis in THP-1 cells. Biochem J. 2020; 477:3923–34. https://doi.org/10.1042/BCJ20200351 [PubMed]
  • 29. Hwangbo H, Choi EO, Kim MY, Kwon DH, Ji SY, Lee H, Hong SH, Kim GY, Hwang HJ, Hong SH, Choi YH. Suppression of tumor growth and metastasis by ethanol extract of Angelica dahurica Radix in murine melanoma B16F10 cells. Biosci Trends. 2020; 14:23–34. https://doi.org/10.5582/bst.2019.01230 [PubMed]
  • 30. Wu L, Yin L, Ma L, Yang J, Yang F, Sun B, Nianzeng X. Comprehensive bioinformatics analysis of ribonucleoside diphosphate reductase subunit M2(RRM2) gene correlates with prognosis and tumor immunotherapy in pan-cancer. Aging (Albany NY). 2022; 14:7890–905. https://doi.org/10.18632/aging.204315 [PubMed]
  • 31. Wang P, Xiao X, Zhang Y, Zhang B, Li D, Liu M, Xie X, Liu C, Liu P, Ren R. A dual inhibitor overcomes drug-resistant FLT3-ITD acute myeloid leukemia. J Hematol Oncol. 2021; 14:105. https://doi.org/10.1186/s13045-021-01098-y [PubMed]
  • 32. Bezu L, Humeau J, Leduc M, Pan H, Kroemer G, Kepp O. Assessment of eIF2α phosphorylation during immunogenic cell death. Methods Cell Biol. 2022; 172:83–98. https://doi.org/10.1016/bs.mcb.2022.01.003 [PubMed]
  • 33. Ebanks B, Chakrabarti L. Mitochondrial ATP Synthase is a Target of Oxidative Stress in Neurodegenerative Diseases. Front Mol Biosci. 2022; 9:854321. https://doi.org/10.3389/fmolb.2022.854321 [PubMed]
  • 34. Ham PB 3rd, Raju R. Mitochondrial function in hypoxic ischemic injury and influence of aging. Prog Neurobiol. 2017; 157:92–116. https://doi.org/10.1016/j.pneurobio.2016.06.006 [PubMed]
  • 35. Mikkelsen MF, Björling K, Jensen LJ. Age-dependent impact of Ca(V) 3.2 T-type calcium channel deletion on myogenic tone and flow-mediated vasodilatation in small arteries. J Physiol. 2016; 594:5881–98. https://doi.org/10.1113/JP271470 [PubMed]
  • 36. Paraskevopoulou MD, Hatzigeorgiou AG. Analyzing MiRNA–LncRNA Interactions. Long Non-Coding RNAs. 2016; 1402:271–86. https://doi.org/10.1007/978-1-4939-3378-5_21
  • 37. Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci. 2019; 20:4331. https://doi.org/10.3390/ijms20184331 [PubMed]
  • 38. Shen CY, Jiang JG, Yang L, Wang DW, Zhu W. Anti-ageing active ingredients from herbs and nutraceuticals used in traditional Chinese medicine: pharmacological mechanisms and implications for drug discovery. Br J Pharmacol. 2017; 174:1395–425. https://doi.org/10.1111/bph.13631 [PubMed]
  • 39. Jeong D, Qomaladewi NP, Lee J, Park SH, Cho JY. The Role of Autophagy in Skin Fibroblasts, Keratinocytes, Melanocytes, and Epidermal Stem Cells. J Invest Dermatol. 2020; 140:1691–7. https://doi.org/10.1016/j.jid.2019.11.023 [PubMed]
  • 40. Duman N, Duman R, Tosun M, Akıcı M, Göksel E, Gökçe B, Alagöz O. Topical folinic acid enhances wound healing in rat model. Adv Med Sci. 2018; 63:347–52. https://doi.org/10.1016/j.advms.2018.04.011 [PubMed]
  • 41. Kumar KJS, Vani MG, Wang SY. Limonene protects human skin keratinocytes against UVB-induced photodamage and photoaging by activating the Nrf2-dependent antioxidant defense system. Environ Toxicol. 2022; 37:2897–909. https://doi.org/10.1002/tox.23646 [PubMed]
  • 42. Gomez-Cambronero J, Kantonen S. A river runs through it: how autophagy, senescence, and phagocytosis could be linked to phospholipase D by Wnt signaling. J Leukoc Biol. 2014; 96:779–84. https://doi.org/10.1189/jlb.2VMR0214-120RR [PubMed]
  • 43. Ahn CH, Lee WW, Jung YC, Shin JA, Hong KO, Choi S, Swarup N, Kim J, Ahn MH, Jung M, Cho SD, Jin B. Antitumor effect of TW-37, a BH3 mimetic in human oral cancer. Lab Anim Res. 2019; 35:27. https://doi.org/10.1186/s42826-019-0028-7 [PubMed]
  • 44. Mori Y, Ajay AK, Chang JH, Mou S, Zhao H, Kishi S, Li J, Brooks CR, Xiao S, Woo HM, Sabbisetti VS, Palmer SC, Galichon P, et al. KIM-1 mediates fatty acid uptake by renal tubular cells to promote progressive diabetic kidney disease. Cell Metab. 2021; 33:1042–61.e7. https://doi.org/10.1016/j.cmet.2021.04.004 [PubMed]
  • 45. Rizka A, Setiati S, Harimurti K, Sadikin M, Mansur IG. Effect of Alfacalcidol on Inflammatory markers and T Cell Subsets in Elderly with Frailty Syndrome: a Double Blind Randomized Controlled Trial. Acta Med Indones. 2018; 50:215–21. [PubMed]
  • 46. Quintans-Júnior L, Moreira JC, Pasquali MA, Rabie SM, Pires AS, Schröder R, Rabelo TK, Santos JP, Lima PS, Cavalcanti SC, Araújo AA, Quintans JS, Gelain DP. Antinociceptive Activity and Redox Profile of the Monoterpenes (+)-Camphene, p-Cymene, and Geranyl Acetate in Experimental Models. ISRN Toxicol. 2013; 2013:459530. https://doi.org/10.1155/2013/459530 [PubMed]
  • 47. Liang D, Zhuo Y, Guo Z, He L, Wang X, He Y, Li L, Dai H. SIRT1/PGC-1 pathway activation triggers autophagy/mitophagy and attenuates oxidative damage in intestinal epithelial cells. Biochimie. 2020; 170:10–20. https://doi.org/10.1016/j.biochi.2019.12.001 [PubMed]
  • 48. Niu XW, Feng J, Peng ZH, Ma HQ, Liu C, Juan JY. [Mechanism of receptor for retinoids inducing apoptosis of human melanoma cell line A375]. Sichuan Da Xue Xue Bao Yi Xue Ban. 2006; 37:538–41. [PubMed]
  • 49. Yang Y, Huang J, Li J, Yang H, Yin Y. Effects of Stearic Acid on Proliferation, Differentiation, Apoptosis, and Autophagy in Porcine Intestinal Epithelial Cells. Curr Mol Med. 2020; 20:157–66. https://doi.org/10.2174/1566524019666190917144127 [PubMed]
  • 50. Soltani-Arabshahi R, Tristani-Firouzi P. Chemoprevention of nonmelanoma skin cancer. Facial Plast Surg. 2013; 29:373–83. https://doi.org/10.1055/s-0033-1353377 [PubMed]
  • 51. Kuş C, Özer E, Korkmaz Y, Yurtcu E, Dağalp R. Benzamide and Benzamidine Compounds as New Inhibitors of Urokinasetype Plasminogen Activators. Mini Rev Med Chem. 2018; 18:1753–8. https://doi.org/10.2174/1389557518666180816110740 [PubMed]
  • 52. Seleit I, Bakry OA, El Repey HS, Ali R. Intrinsic versus Extrinsic Aging: A Histopathological, Morphometric and Immunohistochemical Study of Estrogen Receptor β and Androgen Receptor. Skin Pharmacol Physiol. 2016; 29:178–89. https://doi.org/10.1159/000446662 [PubMed]
  • 53. Haghnazari L, Vaisi-Raygani A, Keshvarzi F, Ferdowsi F, Goodarzi M, Rahimi Z, Baniamerian H, Tavilani H, Vaisi-Raygani H, Vaisi-Raygani H, Pourmotabbed T. Effect of Acetylcholinesterase and Butyrylcholinesterase on Intrauterine Insemination, Contribution to Inflammations, Oxidative Stress and Antioxidant Status; A Preliminary Report. J Reprod Infertil. 2016; 17:157–62. [PubMed]
  • 54. Chang L, Liang J, Xia X, Chen X. miRNA-126 enhances viability, colony formation, and migration of keratinocytes HaCaT cells by regulating PI3 K/AKT signaling pathway. Cell Biol Int. 2019; 43:182–91. https://doi.org/10.1002/cbin.11088 [PubMed]
  • 55. Wilkinson HN, Clowes C, Banyard KL, Matteuci P, Mace KA, Hardman MJ. Elevated Local Senescence in Diabetic Wound Healing Is Linked to Pathological Repair via CXCR2. J Invest Dermatol. 2019; 139:1171–81.e6. https://doi.org/10.1016/j.jid.2019.01.005 [PubMed]
  • 56. Seo SY, Han SI, Bae CS, Cho H, Lim SC. Effect of 15-hydroxyprostaglandin dehydrogenase inhibitor on wound healing. Prostaglandins Leukot Essent Fatty Acids. 2015; 97:35–41. https://doi.org/10.1016/j.plefa.2015.03.005 [PubMed]
  • 57. Shui L, Wang W, Xie M, Ye B, Li X, Liu Y, Zheng M. Isoquercitrin induces apoptosis and autophagy in hepatocellular carcinoma cells via AMPK/mTOR/p70S6K signaling pathway. Aging (Albany NY). 2020; 12:24318–32. https://doi.org/10.18632/aging.202237 [PubMed]
  • 58. Meyer MJ, Beltrán JF, Liang S, Fragoza R, Rumack A, Liang J, Wei X, Yu H. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods. 2018; 15:107–14. https://doi.org/10.1038/nmeth.4540 [PubMed]
  • 59. Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE, Bian W, Brignall R, Cafarelli T, Campos-Laborie FJ, Charloteaux B, Choi D, Coté AG, Daley M, et al. A reference map of the human binary protein interactome. Nature. 2020; 580:402–8. https://doi.org/10.1038/s41586-020-2188-x [PubMed]
  • 60. Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, Campbell NH, Chavali G, Chen C, del-Toro N, Duesbury M, Dumousseau M, Galeota E, et al. The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014; 42:D358–63. https://doi.org/10.1093/nar/gkt1115 [PubMed]
  • 61. Oughtred R, Stark C, Breitkreutz BJ, Rust J, Boucher L, Chang C, Kolas N, O'Donnell L, Leung G, McAdam R, Zhang F, Dolma S, Willems A, et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 2019; 47:D529–41. https://doi.org/10.1093/nar/gky1079 [PubMed]
  • 62. Du Y, Cai M, Xing X, Ji J, Yang E, Wu J. PINA 3.0: mining cancer interactome. Nucleic Acids Res. 2021; 49:D1351–7. https://doi.org/10.1093/nar/gkaa1075 [PubMed]
  • 63. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E, Castagnoli L, Cesareni G. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012; 40:D857–61. https://doi.org/10.1093/nar/gkr930 [PubMed]
  • 64. Das J, Yu H. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol. 2012; 6:92. https://doi.org/10.1186/1752-0509-6-92 [PubMed]
  • 65. Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 2017; 45:D408–14. https://doi.org/10.1093/nar/gkw985 [PubMed]
  • 66. Li T, Wernersson R, Hansen RB, Horn H, Mercer J, Slodkowicz G, Workman CT, Rigina O, Rapacki K, Stærfeldt HH, Brunak S, Jensen TS, Lage K. A scored human protein-protein interaction network to catalyze genomic interpretation. Nat Methods. 2017; 14:61–4. https://doi.org/10.1038/nmeth.4083 [PubMed]
  • 67. Alonso-López D, Campos-Laborie FJ, Gutiérrez MA, Lambourne L, Calderwood MA, Vidal M, De Las Rivas J. APID database: redefining protein-protein interaction experimental evidences and binary interactomes. Database (Oxford). 2019; 2019:baz005. https://doi.org/10.1093/database/baz005 [PubMed]
  • 68. Hornbeck PV, Kornhauser JM, Latham V, Murray B, Nandhikonda V, Nord A, Skrzypek E, Wheeler T, Zhang B, Gnad F. 15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms. Nucleic Acids Res. 2019; 47:D433–41. https://doi.org/10.1093/nar/gky1159 [PubMed]
  • 69. Cheng F, Jia P, Wang Q, Zhao Z. Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget. 2014; 5:3697–710. https://doi.org/10.18632/oncotarget.1984 [PubMed]
  • 70. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, et al. Human Protein Reference Database--2009 update. Nucleic Acids Res. 2009; 37:D767–72. https://doi.org/10.1093/nar/gkn892 [PubMed]
  • 71. Schweppe DK, Huttlin EL, Harper JW, Gygi SP. BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data. J Proteome Res. 2018; 17:722–6. https://doi.org/10.1021/acs.jproteome.7b00572 [PubMed]
  • 72. Meyer MJ, Das J, Wang X, Yu H. INstruct: a database of high-quality 3D structurally resolved protein interactome networks. Bioinformatics. 2013; 29:1577–9. https://doi.org/10.1093/bioinformatics/btt181 [PubMed]
  • 73. Mosca R, Céol A, Aloy P. Interactome3D: adding structural details to protein networks. Nat Methods. 2013; 10:47–53. https://doi.org/10.1038/nmeth.2289 [PubMed]
  • 74. Guney E, Menche J, Vidal M, Barábasi AL. Network-based in silico drug efficacy screening. Nat Commun. 2016; 7:10331. https://doi.org/10.1038/ncomms10331 [PubMed]
  • 75. Huang Y, Yang DD, Li XY, Fang DL, Zhou WJ. ZBP1 is a significant pyroptosis regulator for systemic lupus erythematosus. Ann Transl Med. 2021; 9:1773. https://doi.org/10.21037/atm-21-6193 [PubMed]
  • 76. Guo A, Wang W, Shi H, Wang J, Liu T. Identification of Hub Genes and Pathways in a Rat Model of Renal Ischemia-Reperfusion Injury Using Bioinformatics Analysis of the Gene Expression Omnibus (GEO) Dataset and Integration of Gene Expression Profiles. Med Sci Monit. 2019; 25:8403–11. https://doi.org/10.12659/MSM.920364 [PubMed]
  • 77. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006; 313:1929–35. https://doi.org/10.1126/science.1132939 [PubMed]
Download PDF
Corresponding Authors

Wei Zhu
zhuwei9201@163.com
https://orcid.org/0000-0003-1165-634X

Xiaohong Yuan
yuanxiaohong@gzucm.edu.cn

Keywords
network medicine framework drug-repurposing opportunities Angelica acutiloba (Siebold & Zucc.) Kitag. skin aging pharmaco-active compounds
Paper Sections