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Article

Network Pharmacology Reveals Curcuma aeruginosa Roxb. Regulates MAPK and HIF-1 Pathways to Treat Androgenetic Alopecia

by
Aaron Marbyn L. Sintos
1 and
Heherson S. Cabrera
1,2,*
1
School of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, Manila 1002, Philippines
2
Department of Biology, School of Health Sciences, Mapúa University, Makati 1200, Philippines
*
Author to whom correspondence should be addressed.
Biology 2024, 13(7), 497; https://doi.org/10.3390/biology13070497
Submission received: 1 June 2024 / Revised: 20 June 2024 / Accepted: 26 June 2024 / Published: 4 July 2024
(This article belongs to the Section Bioinformatics)
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Abstract

:

Simple Summary

Androgenetic alopecia (AGA) represents the most common form of hair loss experienced by both men and women. Curcuma aeruginosa Roxb., a plant known for its medicinal properties, has shown promise in reversing this hair loss disorder for its hair growth effects and anti-androgenic effects. Despite its promising potential, the mechanism of action by which it acts remains unknown. As such, this study unveiled how this plant works against hair loss by identifying its bioactive compounds, the gene its targets, and the potential mechanism involved in the therapy of AGA using network pharmacology and molecular docking. The findings revealed insights into how C. aeruginosa can potentially prevent AGA, highlighting its potential for developing new, safe therapies for AGA, benefiting those affected by this condition.

Abstract

Androgenetic alopecia (AGA) is the most prevalent hair loss disorder worldwide, driven by excessive sensitivity or response to androgen. Herbal extracts, such as Curcuma aeruginosa Roxb., have shown promise in AGA treatment due to their anti-androgenic activities and hair growth effects. However, the precise mechanism of action remains unclear. Hence, this study aims to elucidate the active compounds, putative targets, and underlying mechanisms of C. aeruginosa for the therapy of AGA using network pharmacology and molecular docking. This study identified 66 bioactive compounds from C. aeruginosa, targeting 59 proteins associated with AGA. Eight hub genes were identified from the protein–protein interaction network, namely, CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3. Topological analysis of components–targets network revealed trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide as the core components with potential significance in AGA treatment. The molecular docking verified the binding affinity between the hub genes and core compounds. Moreover, the enrichment analyses showed that C. aeruginosa is involved in hormone response and participates in HIF-1 and MAPK pathways to treat AGA. Overall, this study contributes to understanding the potential anti-AGA mechanism of C. aeruginosa by highlighting its multi-component interactions with several targets involved in AGA pathogenesis.

1. Introduction

Androgenic alopecia (AGA) is the most prevalent hair loss disorder, causing at least 95% of all pattern hair loss cases, such as hair thinning among women and baldness in men [1]. AGA, an androgen-dependent alopecia with genetic origin, features progressive replacement of thick, long terminal hair with fine, small vellus hair. The androgens responsible for AGA are testosterone and its biologically active metabolite, dihydrotestosterone (DHT). High levels of, or hypersensitivity to, DHT miniaturizes hair follicles, causing a shortened anagen phase of the hair growth cycle and, eventually, hair loss.
Although AGA does not pose a significant health risk, it may affect an individual’s mental and social well-being, amplifying the need for treatment [2]. Currently, only two drugs are approved by the Food and Drug Administration (FDA) to treat AGA, namely, oral finasteride and topical minoxidil. Although they exhibited favorable efficacy, side effects are reported, such as sexual dysfunction from finasteride [3] and dermatitis from minoxidil [4]. Because of this, many opt for complementary and alternative medicine (CAM) interventions. One of these is the use of herbal extracts, which have been shown to stop hair loss and encourage hair growth [5].
Curcuma aeruginosa Roxb. is a rhizomatous plant rich in ethnomedicinal values for treating several ailments, exhibiting a wide spectrum of pharmacologic activities such as antioxidant [6], anti-cancer [7], antimicrobial [8], anti-HIV-1 [9], uterine-relaxant [10], and anti-androgenic effects. Phytochemically, C. aeruginosa is a rich source of sesquiterpenes, which have been shown to exhibit anti-androgenic action in vitro and in vivo by suppressing the growth of testosterone-induced human prostate cancer cells and androgen-dependent hamster flank gland model, respectively [11]. Additionally, clinical trials have demonstrated that C. aeruginosa is a promising, efficient component of AGA hair tonic for slowing hair loss and stimulating hair growth [12,13]. Despite exhibiting therapeutic effects on AGA, the mechanism by which C. aeruginosa acts remains unknown.
Therefore, the purpose of this study is to elucidate the potential mechanism of C. aeruginosa against AGA through network pharmacology and molecular docking. Network pharmacology has become a commonly used tool in drug research to unveil how drugs interact with their targets, pathways, and associated disorders [14]. Meanwhile, molecular docking is utilized to confirm the potential associations of compounds and target genes predicted in the network pharmacological analysis [15].
This study is limited to investigating the influence of C. aeruginosa on AGA using an in silico approach, as none had been reported. Initially, the bioactive compounds of the said plant were screened out and selected. The overlap between its predicted targets and AGA-linked target genes was acquired for protein–protein interaction (PPI) construction and GO and KEGG enrichment analyses. Furthermore, the components–targets–pathways network was visualized to give a general overview of the molecular mechanisms of C. aeruginosa against AGA. Finally, molecular docking was employed to verify the affinity between the components and targets.

2. Materials and Methods

2.1. Screening of Bioactive Compounds in C. aeruginosa

The Indian Medicinal Plants, Phytochemistry and Therapeutics 2.0 (IMPPAT 2.0) online database (https://cb.imsc.res.in/imppat/home (accessed on 31 January 2024) [16] was used in search of compounds present in C. aeruginosa. This database integrates the phytochemicals of Indian medicinal plants and their pharmacokinetic properties, including drug-likeness, blood–brain barrier permeation, gastrointestinal absorption, etc. Literature mining was also conducted for further collection. The physicochemical, drug-like, and pharmacokinetic parameters of the additional compounds were computed through SwissADME (http://www.swissadme.ch/ (accessed on 1 February 2024)) [17]. The compounds that met at least three drug-likeness principles and had high gastrointestinal absorption and an oral bioavailability greater than 30% were selected and classified as bioactive ones.

2.2. Target Gene Prediction in C. aeruginosa

The targets of the screened potentially bioactive compounds were determined through SwissTargetPrediction (http://www.swisstargetprediction.ch/ (accessed on 30 May 2024) [18], a web server that uses a combination of two-dimensional and three-dimensional similarity metrics with known ligands to reliably predict the protein targets of compounds. For the target genes prediction C. aeruginosa, the canonical SMILES of compounds were copied to SwissTargetPrediction. “Homo sapiens” was selected as the study species to restrict the analysis of the target genes to those within humans, ensuring the relevance to human biology.

2.3. Target Gene Prediction in Androgenetic Alopecia

The GenCLip 3 (http://ci.smu.edu.cn/genclip3/analysis.php accessed on 30 May 2024) [19], DisGenNet (https://www.disgenet.org/ accessed on 30 May 2024) [20], Online Mendelian Inheritance in Man (OMIM) (https://omim.org/ accessed on 30 May 2024) [21], and GeneCards (https://www.genecards.org/ accessed on 30 May 2024) [22] databases were utilized to find human AGA-related targets using “androgenetic alopecia” as the keyword. The search results retrieved from the mentioned databases were combined, and duplicate targets were removed to acquire the potential target genes for treating AGA.

2.4. Protein–Protein Interaction Network

The target genes for the protein–protein interaction (PPI) network were obtained by determining the common targets of C. aeruginosa and AGA through a Venn diagram plotted in Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/ accessed on 30 May 2024). The PPI network of common targets was constructed by the Search Tool for Retrieval of Interacting Genes (STRING) (https://string-db.org/ accessed on 30 May 2024) [23], a database of predicted and known protein interactions, to investigate the relationship between the imported genes. The analysis was restricted to proteins found in “Homo sapiens” species. Afterward, the PPI network was imported into the Cytoscape (version 3.10.0) software for further analysis. CytoHubba, a Cytoscape plugin, was used to identify the potential hub target genes in the network. Three algorithms were employed—maximal clique centrality (MCC), maximum neighborhood component (MNC), and degree—to rank the best 10 genes based on the scores. The results were then intersected in which the overlapped genes represent the final set of hub targets. Moreover, MCODE was used for the cluster analysis of the PPI network with parameters set to default.

2.5. Gene Ontology and Pathway Enrichment Analysis

The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to explore the biological mechanism underlying C. aeruginosa and its influence on AGA. GO is widely recognized for defining and describing genes from three aspects—biological process (BP), molecular function (MF), and cellular component (CC). KEGG, on the other hand, is a massive compilation of databases on drugs, genomes, enzymes, and biological pathways, among others. The enrichment analysis for the common targets was conducted using Metascape (https://metascape.org/gp/index.html accessed on 30 May 2024) [24], a gene annotation and analysis repository. Herein, the analysis was considered for “Homo sapiens” only, and p-value < 0.01 served as the cutoff. The results were taken as the top 20 based on descending −log10(p-value). The results were visualized in the form of a bar chart with the help of the Scientific and Research plot tool (SRplot) (http://www.bioinformatics.com.cn/SRplot accessed on 30 May 2024).

2.6. Components–Targets–Pathways Network Construction

Cytoscape (version 3.10.0) was used to plot a network for the top 20 KEGG pathways with the corresponding potentially bioactive compounds and targets associated with them to effectively display the interactions between C. aeruginosa and AGA and ultimately characterize the therapeutic mechanisms of the former on the latter.

2.7. Molecular Docking

Molecular docking was applied to validate the binding between the core compounds of C. aeruginosa and the identified hub target genes. The crystal structures of human hub genes were downloaded from the RCSB Protein Data Bank (PDB) (https://www.rcsb.org/ accessed on 30 May 2024), while the 3D structures of the C. aeruginosa core compounds were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/ accessed on 30 May 2024). The docking was performed using CB-Dock 2 (http://clab.labshare.co.uk/cb-dock/php/index.php accessed on 30 May 2024) [25], a protein–ligand blind docking tool that integrates the well-known molecular docking software Autodock Vina 1.1.2 to identify the binding site and predict binding pose.

3. Results

3.1. Bioactive Compounds in C. aeruginosa

Through IMPPAT, 93 compounds were found in the C. aeruginosa plant. Additionally, a literature search reveals a review article that described the phytochemical composition of C. aeruginosa [26], extracting 10 more compounds after eliminating the duplicates and those lacking structural information. In total, 103 compounds were screened, as shown in Table 1. However, only 66 were found to be bioactive after filtering with the parameters of an oral bioavailability greater than 30%, high gastrointestinal absorption, and adherence to three druglike rule-based filters, with one being the Lipinski rules. Interestingly, all compounds had bioavailability scores of 0.55, besides their high gastrointestinal absorption. A bioavailability score greater than 0.55 is assigned to any compound complying with Lipinski’s rules and is considered ideal as it indicates the compound’s optimal absorption [27].

3.2. Predicted Target Genes of C. aeruginosa and AGA

The 66 druglike compounds of C. aeruginosa had their target genes predicted through SwissTargetPrediction, yielding 795 human proteins after removing the duplicates (Supplementary Table S1). On the other hand, a total of 672 genes related to AGA were collected, of which 100 genes were from GenClip, 112 from DisGeNet, 452 from GeneCard, and 8 from OMIM. After the deletion of duplicates, 562 potential AGA-related targets were obtained (Supplementary Table S2).

3.3. Common Targets of C. aeruginosa and AGA

The intersection of potential C. aeruginosa target genes and AGA-related genes revealed 59 overlapping genes, as presented by the Venn diagram shown in Figure 1. The overlapped genes, as listed in Table A1 (see Appendix A), indicate the potential targets of C. aeruginosa for the therapy of AGA. They were collected for further mechanisms study of the former against the latter.

3.4. Protein–Protein Interaction (PPI) Network

The 59 targets associated with AGA and determined as targets for C. aeruginosa bioactive compounds were imported to STRING to construct a PPI network and analyze the relationship between them. An original PPI network comprising 59 nodes, of which one was disconnected, and 449 edges with an average node degree of 15.2 were produced, as shown in Figure 2. Also, it had an average local clustering coefficient of 0.629, indicating how connected the nodes were. The expected number of edges was 178, which was much lesser than the actual edges of the network, and the PPI enrichment p-value was observed to be <1.0 × 10−16. Hence, the network had significantly greater interactions than expected for the random network of similar size, and the proteins, represented by nodes, were at least partially biologically connected as a cluster.
Subsequently, the original PPI network from STRING was reconstructed in Cytoscape for visualization and further analysis. The reconstructed network, as shown in Figure 3, contained 58 nodes and 449 edges while removing one unconnected gene. The color of the node differed depending on its degree value, with darker colors reflecting greater values. The larger the degree, the more the involvement of biological functions, suggesting protein’s vital role in the network. Thus, the nodes with darker colors may serve as essential targets for the therapeutic effects of C. aeruginosa in AGA.

3.5. Identification of Hub Genes

To determine the hub targets, the CytoHubba plug-in of Cytoscape 3.10.2 was used to analyze each node by incorporating three topological algorithms, namely, maximal clique centrality (MCC), maximum neighborhood component (MNC), and degree. Figure 4 shows the top 10 genes predicted by each algorithm based on their scores. Eight genes (CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3) were screened out after intersecting the three results, as revealed in Figure 4D. These highly connected genes, as shown in Figure 4E, represent the hub target genes of C. aeruginosa in AGA treatment.

3.6. Cluster Analysis of PPI Network

MCODE plug-in was employed for cluster analysis of the PPI, yielding three cluster modules, as shown in Figure 5. Module 1 possessed 23 nodes and 186 edges; Module 2 comprised 6 nodes and 11 edges; and Module 3 comprised 3 nodes and 3 edges. Module 1 had the highest average score of 16.91, followed by Module 2 and Module 3, whose scores were 4.40 and 3.00, respectively. In the PPI network, modules with greater average scores may have more significant roles. Hence, Module 1 was the most important. Consequently, all identified hub genes were clustered in this module.

3.7. GO and KEGG Enrichment Analyses

GO and KEGG enrichment analyses of the 59 common targets in Metascape yielded 1055 biological processes, 64 molecular functions, 36 cellular components, and 150 KEGG pathway terms, wherein the top 20 terms in each category are visualized in Figure 6 and listed in Table A2, Table A3 and Table A4 (see Appendix A). Based on biological processes, the function of the bioactive compounds is mostly concentrated on response to peptides, lipids, and hormones, among others, suggesting that C. aeruginosa can modulate the hormones participating in AGA. Additionally, the majority of the genes are coded for protein in the receptor complex, transcription regulator complex, and plasma membrane protein complex, suggesting that C. aeruginosa targets protein complexes. Also, the cellular components included the cell body, which may indicate the potential interactions of C. aeruginosa bioactive compounds with cells relevant to the hair growth cycle. Molecularly, the functions of C. aeruginosa were mainly enriched in the activity of and binding to protein kinase and transcription factors, as well as hormone binding and nuclear receptor activity. This indicated that C. aeruginosa affects these proteins, which are the categories of the identified AGA-related targets of C. aeruginosa. Lastly, The KEGG enrichment showed the potential signaling pathways by which C. aeruginosa played an anti-AGA role, including MAPK and HIF-1 signaling pathways.

3.8. Components–Targets–Pathways Network

A network representation of the interaction between C. aeruginosa bioactive compounds, its potential target genes linked to AGA, and pathways associated with the targets was constructed through Cytoscape and portrayed as the components–targets–pathways network shown in Figure 7. The compounds, targets, and pathways were represented by yellow elliptical nodes, blue round rectangular nodes, and red arrow-shaped nodes, respectively. The network contained 144 nodes (66 compounds, 58 target genes, and 20 pathways) and 847 edges, showing the intricate relationships between them. Topological analysis of the components–targets network revealed trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide as core components, exhibiting degrees of 16, 15, 15, 14, and 14, respectively, which were the highest among the compounds. Generally, this network revealed the multi-components of C. aeruginosa exerting synergistic multi-targeted effects against AGA.

3.9. Molecular Docking

The hub target genes (CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3) were molecularly docked with the core components to ascertain whether the core components (trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide) of C. aeruginosa could bind to the protein targets predicted by SwissTargetPrediction. Two positive controls were also docked, namely, finasteride and minoxidil. Generally, the binding energy between the ligand compounds and receptor proteins dictates their structural stability. The lower the binding energy, the greater the affinity between the protein target and component. Based on the molecular docking studies, as shown in Figure 8, the binding energy between the core compounds and hub genes ranged from −4.6 to −8.9 kcal/mol. The results showed that the binding energies were less than 0, suggesting they spontaneously bound to one another. Binding energies less than −5 kcal/mol are believed to create more stable structures as opposed to those with greater binding energies. Hence, all molecules could bind stably to the target genes with strong affinity except for carvone and myrtenal when bound to HIF1A. AKT1 and AR, among the target genes, had the highest binding affinity with the core compounds. This may imply that targeting these proteins plays an essential role in AGA treatment by C. aeruginosa bioactive compounds. Strikingly, alpha-atlantone and isoaromandendrene epoxide demonstrated superior affinity to AR when compared to controls. The top four stable ligand-receptor complexes were selected for visualization, as shown in Figure 9, and their binding sites were tabulated in Table 2. Since both alpha-atlantone and isoaromandendrene epoxide are sesquiterpenes, they are bound to similar amino acid residue sites in the AKT1 complex.

4. Discussion

AGA is a multifactorial hair loss disorder, and those suffering from this disease have limited options for medical treatment. Drugs like finasteride and minoxidil cause side effects, restricting their long-term administration. Moreover, invasive treatments like hair transplantation and platelet-rich plasma (PRP) require repeated procedures, resulting in costly investments [28,29]. Currently, topical herbal preparations are becoming more commonly available due to their greater compliance rate, broader active spectrum, more affordable price, and lesser side effects [30,31]. Thus, they are anticipated to be extensively utilized for AGA complementary and alternative medicine. C. aeruginosa, as a topical preparation, has been shown to exhibit anti-androgenic and hair growth effects due to its phytochemical content that may target different pathways involved in AGA. Accordingly, network pharmacology fits as a valid method for elucidating its multicomponent–multitarget anti-AGA mechanism.
The network pharmacology results revealed five compounds—trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide—that might be the core components in C. aeruginosa and enable it to induce therapeutic effects against AGA. However, no studies reported their anti-androgenic and trichogenic effects yet, recommending further in vivo and in vitro studies to test their anti-AGA potential. Molecular docking analysis confirmed that the core components bound with the eight hub genes—CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3—implying potential interactions and modulation of these genes to treat AGA.
CASP3, whose role is central in executing cell apoptosis, is overexpressed in the bald area of AGA patients in the early courses of the disease, revealing the presence of inflammation and apoptosis at such stages [32]. Activation of CASP3 inhibits the PI3K/AKT signaling pathway, which mediates extracellular signals and intracellular responses and is critically involved in the regulation of cell proliferation, growth, and differentiation. This, in turn, stops hair follicles from transitioning from the resting phase to the anagen phase, blocks cell proliferation, promotes apoptosis, and finally degenerates hair follicles [33]. As such, inhibiting CASP3 activation helps reverse hair follicles’ entry to the abnormal and degenerative anagen phases and stimulates hair growth.
Likewise, AKT1, one of the relevant serine/threonine protein kinases, regulates cell apoptosis and proliferation and serves as the main downstream molecule of the PI3K/AKT pathway, whose role is necessary for de novo hair follicle regeneration [34]. In response to extracellular signals, AKT1 can either act as a positive or negative regulator of the PI3K/AKT pathway via PIK3, leading to AR expression regulation [35].
AR-bound DHT is the main cause of AGA, with DHT-AR signaling strongly associated with AGA pathogenesis [36,37,38]. AR is primarily expressed in hair follicles, particularly dermal papilla cells (DPCs) [39]. When DHT binds to AGA, expression of growth inhibition factors (i.e., DKK-1, TGF-β, and IL-6) is triggered [40,41]. IL-6 suppresses hair shaft elongation by inhibiting matrix cell proliferation and, thereby, stimulates hair follicle regression [42]. Additionally, PPARG also discourages hair growth, but by promoting mitochondrial activity [43]. Essentially, DHT-AR signaling facilitates the miniaturization of hair follicles, resulting in the apoptosis of keratinocytes [44] and DPCs [45] and, eventually, AGA progression. Therefore, treatment for AGA may benefit most from inhibiting AR expression due to the major role of AR in AGA.
Contrary to the alopecia-inducing effects of the earlier hub genes, STAT3, HIF1A, and MAPK3 are reported to counteract hair loss. STAT3 is required in the hair cycle during the onset of anagen because it activates keratinocytes for the continuation of the hair cycle [46]. Loss of STAT3 functions in keratinocytes increases apoptotic hair follicle stem cells (HFSCs), impairing the hair cycle process [47]. Contrastingly, gain of function raises progenitor cells and HFSCs above the bulge region, ensuring proper maintenance and growth of hair follicles [48]. With this, STAT3 regulation is critical in maintaining hair cycling and growth. Meanwhile, HIF1A regulates trichogenic gene expression in DPCs [49], suggesting a similar function to minoxidil, which exerts trichogenic effects ascribed by its vasodilating properties [50]. Lastly, MAPK’s role in regenerating HFSCs, inducing anagen hair cycle, and modulating root hair tip growth is important in hair growth stimulation [51,52]. Specifically, when MAPK3 was upregulated, hair growth improved [53].
The GO enrichment analysis suggested that C. aeruginosa may regulate hormones, such as androgen, estrogen, and cortisol. Androgens, like testosterone and DHT, activate AR signaling, upregulating genes involved in hair growth suppression resulting from growth inhibition factors, vascular regression around dermal papilla [54], apoptosis [55], and DPC aging [56]. On the other hand, estrogen maintains hair follicle cycling [57], encourages healthy hair growth by activating the Wnt/β-catenin signaling pathway to sustain HFSC differentiation and proliferation [58,59], and shields hair follicles from oxidative stress and eventually hair follicle aging by modulating antioxidant enzymes [60]. Lastly, corticosterone, the cortisol counterpart in mice and the main stress hormone, disallows the entry of HFSCs into the anagen phase [61]. Thus, chronic stress can quicken hair aging and loss by influencing HFSC.
On the other hand, the KEGG enrichment showed the potential signaling pathways by which C. aeruginosa played an anti-AGA role, including MAPK and HIF-1 signaling pathways. The identified hub genes were implicated in the MAPK pathway (AKT1, CASP3, and MAPK3) and the HIF-1 pathway (AKT1, GIF1A, IL6, MAPK3, and STAT3) through which core compounds may act to modulate them. The combined effects of the compounds through their regulation of MAPK and HIF-1 pathways, together with direct interaction with other hub genes, create a synergistic approach to address various aspects of AGA pathogenesis. MAPK pathway is important in regulating normal cell survival, migration, proliferation, and migration [62]. In hair, MAPK has been revealed to amplify growth factor production [63], regulate the hair cycle and quiescence of HFSC [51], and promote HFSC differentiation and proliferation [64], thereby influencing hair follicle morphogenesis and regeneration. Currently, four MAPK signal transduction pathways are known in mammalian cells: extracellular signal-regulated kinases (ERKs) which stimulate DPC proliferation and anagen phase [65,66]; and p38 MAPKs and Jun N-terminal kinases (JNKs), both of which control Wnt/β-catenin pathway [67,68], the master regulator of hair cells.
Whereas the HIF-1 pathway has been shown to govern hair regeneration, regulating the size and shape of dermal papilla [69,70]. AGA has been associated with insufficient nutrient supply and reduced blood vessels. Consequently, HIF stimulation can come into play in this by regulating neovascularization and regeneration as DPCs react to hypoxia [71]. The HIF-1 pathway is strongly linked to the mechanism of action of minoxidil attributed to its vasodilating properties [72]. Clinical trials showed that the combination of minoxidil and C. aeruginosa stimulated hair growth more effectively than minoxidil alone [12,13]. The topical application of C. aeruginosa complimented minoxidil as it increased hair growth and decreased hair shedding by enhancing penetration [12,13]. This may be caused by C. aeruginosa’s potential regulation of the HIF-1 pathway, which is said to increase vasodilation, promoting conducive conditions for hair growth. Figure 10 shows the potential mechanism of C. aeruginosa against AGA. It is essential to consider that although MAPK and HIF-1 pathways influence AGA, their roles are part of a complex cascade of events rather than a direct one.
Notably, Module 3 derived from the cluster analysis of the PPI network contained SRD5A2, SRD5A1, and CYP17A1, which are involved in androgen biosynthesis. SRD5As amplify DHT production in hair follicles of the scalp, causing AGA [73]. To date, steroidal drugs finasteride and dutasteride are used to treat AGA by acting as SRD5A inhibitors. Finasteride selectively inhibits SRD5A2 while dutasteride inhibits both SRD5A1 and SRD5A2. Accordingly, a prior study suggested that the potential mechanism of C. aeruginosa for its anti-androgenic effect is SRD5A inhibition [11]. Its sesquiterpene content, specifically germacrone, was on par with finasteride when it came to exhibiting anti-androgenic activity. It inhibited SRD5A to a similar extent as finasteride in suppressing the growth of testosterone-induced growth of human prostate cancer cells (in vitro) and hamster flank gland model (in vivo), thereby suggesting C. aeruginosa as a novel SRD5A inhibitor. Similarly, CYP17A1 is needed in DHT production, both in anterior and posterior routes [74,75]. Minoxidil suppresses CYP17A1 to inhibit AGA [76]. Therefore, synergizing this with MAPK and HIF-1 pathways may influence both anti-androgenic activities and hair growth effects, promoting healthy hair follicles and eventually preventing AGA.
While AGA affects both men and women, men are more commonly and severely impacted because of innate higher levels of androgens. Although C. aeruginosa exhibited trichogenic activities that could impact women too, men with genetic predispositions to AGA are likely to see more significant benefits from C. aeruginosa given its anti-androgenic effects. Additionally, younger men, whose AGA symptoms are not yet more pronounced, may experience greater advantages from early intervention using it to potentially prevent AGA. Further research is recommended to fully comprehend these effects across different patient populations. In terms of safety, no side effects were reported on the topical application of C. aeruginosa in clinical trials [12,13], as opposed to those associated with finasteride and minoxidil, namely, sexual dysfunction and dermatological problems. This underscores the potential of C. aeruginosa as an AGA treatment, warranting further investigation.

5. Conclusions

In summary, this study combined network pharmacology and molecular docking to elucidate the active compounds, putative targets, and potential mechanisms of C. aeruginosa in the treatment of AGA. The results pinpointed that bioactive compounds in C. aeruginosa, such as trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide, may play a crucial role in AGA by eliciting their effects on key target genes, including CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3. The molecular docking studies indicated that these bioactive components of C. aeruginosa could effectively act on these targets. Also, enrichment analyses revealed that C. aeruginosa may play its therapeutic role against AGA by modulating both HIF-1 and MAPK pathways, offering new approaches in the treatment and prevention of AGA. Substantially, the findings showed that C. aeruginosa could treat AGA via a mechanism involving multiple components, targets, and pathways. Hence, the valuable results may imply that C. aeruginosa could be a promising option in developing new drugs for AGA. As bound by the limitations of bioinformatics data and in silico network pharmacology and molecular docking analysis, however, experimental exploration and further confirmation via in vivo and in vitro studies are recommended to verify the findings of this study.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology13070497/s1, Table S1: Curcuma aeruginosa targets; Table S2: AGA-related targets.

Author Contributions

Conceptualization, A.M.L.S. and H.S.C.; methodology, A.M.L.S. and H.S.C.; software, A.M.L.S.; validation, A.M.L.S. and H.S.C.; formal analysis, A.M.L.S.; investigation, A.M.L.S.; resources, A.M.L.S.; data curation, A.M.L.S.; writing—original draft preparation, A.M.L.S.; writing—review and editing, A.M.L.S.; visualization, A.M.L.S.; supervision, H.S.C.; project administration, H.S.C.; funding acquisition, H.S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data and results presented in this study are available upon request from the first and corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The common targets of C. aeruginosa and AGA.
Table A1. The common targets of C. aeruginosa and AGA.
No.GeneFull Name
1PIK3CAphosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha
2CASP1caspase 1
3CYP2C19cytochrome P450 family 2 subfamily C member 19
4ARandrogen receptor
5NR3C2nuclear receptor subfamily 3 group C member 2
6IGF1Rinsulin-like growth factor 1 receptor
7PARP1poly (ADP-ribose) polymerase 1
8KCNE1potassium voltage-gated channel subfamily E regulatory subunit 1
9VDRvitamin D receptor
10MAPK1mitogen-activated protein kinase 1
11CYP19A1cytochrome P450 family 19 subfamily A member 1
12PREPprolyl endopeptidase
13SRD5A1steroid 5 alpha-reductase 1
14HPGDShematopoietic prostaglandin D synthase
15MC4Rmelanocortin 4 receptor
16CASP3caspase 3
17NLRP3NLR family pyrin domain containing 3
18BRD4bromodomain containing 4
19CYP17A1cytochrome P450 family 17 subfamily A member 1
20PTGESprostaglandin E synthase
21MAPK3mitogen-activated protein kinase 3
22SHBGsex-hormone-binding globulin
23SRD5A2steroid 5 alpha-reductase 2
24PPARGperoxisome proliferator-activated receptor gamma
25PPARAperoxisome proliferator-activated receptor alpha
26PTPN1protein tyrosine phosphatase non-receptor type 1
27IL6interleukin 6
28PTK2Bprotein tyrosine kinase 2 beta
29PDE5Aphosphodiesterase 5A
30GLI2GLI family zinc finger 2
31GLI1GLI family zinc finger 1
32SHHsonic hedgehog signaling molecule
33DPP4dipeptidyl peptidase 4
34HIF1Ahypoxia-inducible factor 1 subunit alpha
35NR1H2nuclear receptor subfamily 1 group H member 2
36PTGFRprostaglandin F receptor
37PTGDRprostaglandin D2 receptor
38TGFBR1transforming growth factor beta receptor 1
39IL1Binterleukin 1 beta
40CRHR1corticotropin-releasing hormone receptor 1
41LSSlanosterol synthase
42TNFtumor necrosis factor
43CDK4cyclin-dependent kinase 4
44AKT1AKT serine/threonine kinase 1
45CXCR3C-X-C motif chemokine receptor 3
46KITKIT proto-oncogene receptor tyrosine kinase
47RHOAras homolog family member A
48ABCB1ATP-binding cassette subfamily B member 1
49BRAFB-Raf proto-oncogene, serine/threonine kinase
50STAT3signal transducer and activator of transcription 3
51PTGDR2prostaglandin D2 receptor 2
52ABL1ABL proto-oncogene 1
53STSsteroid sulfatase
54NTRK2neurotrophic receptor tyrosine kinase 2
55XIAPX-linked inhibitor of apoptosis
56INSRinsulin receptor
57NFE2L2NFE2-like bZIP transcription factor 2
58HDAC4histone deacetylase 4
59HDAC9histone deacetylase 9
Table A2. GO biological processes.
Table A2. GO biological processes.
GODescriptionCount%Log (p-Value)
GO:0009725response to hormone2847.46−28.2906
GO:0032870cellular response to hormone stimulus2135.59−22.1297
GO:0048732gland development1932.20−20.4392
GO:0071396cellular response to lipid2033.90−20.0435
GO:0043434response to peptide hormone1830.51−19.8568
GO:1901652response to peptide1932.20−19.7229
GO:0071417cellular response to organonitrogen compound1932.20−17.7248
GO:1901699cellular response to nitrogen compound1932.20−17.2401
GO:1901653cellular response to peptide1525.42−16.5512
GO:0071375cellular response to peptide hormone stimulus1423.73−16.4445
GO:0007167enzyme-linked receptor protein signaling pathway1830.51−16.0099
GO:0032868response to insulin1322.03−15.5597
GO:0030335positive regulation of cell migration1728.81−15.1975
GO:0035270endocrine system development1118.64−14.8955
GO:2000147positive regulation of cell motility1728.81−14.872
GO:0030522intracellular receptor signaling pathway1220.34−14.7579
GO:0040017positive regulation of locomotion1728.81−14.7092
GO:0007169transmembrane receptor protein tyrosine kinase signaling pathway1525.42−14.6503
GO:0032869cellular response to insulin stimulus1118.64−14.1934
GO:0048545response to steroid hormone1322.03−14.1231
GO:0004672protein kinase activity1423.72−11.5209
GO:0140297DNA-binding transcription factor binding1322.03−11.1747
GO:0008134transcription factor binding1423.73−11.1417
GO:0004879nuclear receptor activity711.86−11.0367
GO:0098531ligand-activated transcription factor activity711.86−10.9758
GO:0016773phosphotransferase activity, alcohol group as acceptor1423.73−10.5067
GO:0061629RNA polymerase II-specific DNA-binding transcription factor binding1118.64−10.2395
GO:0016301kinase activity1423.73−10.0562
GO:0004955prostaglandin receptor activity46.78−8.56851
GO:0019900kinase binding1322.03−8.5368
GO:0004954prostanoid receptor activity46.78−8.37285
GO:0019901protein kinase binding1220.34−8.04403
GO:0042562hormone binding610.17−7.7874
GO:0004953icosanoid receptor activity46.78−7.75875
GO:0004674protein serine/threonine kinase activity915.25−6.86615
GO:0004713protein tyrosine kinase activity610.17−6.52868
GO:0002020protease binding610.17−6.49179
GO:0019199transmembrane receptor protein kinase activity58.47−6.32271
GO:0019904protein domain specific binding1016.95−6.32105
GO:0033218amide binding813.56−5.94186
Table A3. GO cellular components.
Table A3. GO cellular components.
GODescriptionCount%Log (p-Value)
GO:0045121membrane raft915.25−8.37279
GO:0098857membrane microdomain915.25−8.35955
GO:0005667transcription regulator complex1016.95−7.25901
GO:0043235receptor complex1016.95−7.04516
GO:0044297cell body1016.95−6.97898
GO:0043025neuronal cell body915.25−6.39411
GO:0090575RNA polymerase II transcription regulator complex610.17−4.98282
GO:0031252cell leading edge711.86−4.73873
GO:0005901caveola46.78−4.71441
GO:0030027lamellipodium58.47−4.32896
GO:0005911cell–cell junction711.86−4.20332
GO:0044853plasma membrane raft46.78−4.16185
GO:1902911protein kinase complex46.78−3.68335
GO:0030424axon711.86−3.58421
GO:0061695transferase complex, transferring phosphorus-containing groups58.47−3.47364
GO:0005788endoplasmic reticulum lumen58.47−3.44166
GO:0005635nuclear envelope610.17−3.39345
GO:0098802plasma membrane signaling receptor complex58.47−3.36715
GO:0098552side of membrane711.86−3.2816
GO:0098797plasma membrane protein complex711.86−3.25701
Table A4. KEGG pathways.
Table A4. KEGG pathways.
Pathway IDPathway NameLog (p-Value)CountGene Hits
hsa05200Pathways in cancer−23.133722ABL1, AKT1, XIAP, AR, RHOA, BRAF, CASP3, CDK4, GLI1, GLI2, HIF1A, IGF1R, IL6, KIT, NFE2L2, PIK3CA, PPARG, MAPK1, MAPK3, SHH, STAT3, TGFBR1
hsa05417Lipid and atherosclerosis−17.268814AKT1, RHOA, CASP1, CASP3, IL1B, IL6, NFE2L2, PIK3CA, PPARG, MAPK1, MAPK3, STAT3, TNF, NLRP3
hsa04933AGE-RAGE signaling pathway in diabetic complications−16.15111AKT1, CASP3, CDK4, IL1B, IL6, PIK3CA, MAPK1, MAPK3, STAT3, TGFBR1, TNF
hsa05135Yersinia infection−14.601711AKT1, RHOA, CASP1, PTK2B IL1B, IL6, PIK3CA, MAPK1 MAPK3, TNF, NLRP3
hsa05205Proteoglycans in cancer−14.24812AKT1, RHOA, BRAF, CASP3, HIF1A, IGF1R, PIK3CA, MAPK1, MAPK3, SHH, STAT3, TNF
hsa04625C-type lectin receptor signaling pathway−14.098310AKT1, RHOA, CASP1, IL1B, IL6, PIK3CA, MAPK1, MAPK3, TNF, NLRP3
hsa05161Hepatitis B−13.788611AKT1, BRAF, CASP3, PTK2B, IL6, PIK3CA, MAPK1, MAPK3, STAT3, TGFBR1, TNF
hsa05163Human cytomegalovirus infection−13.762512AKT1, RHOA, CASP3, CDK4, PTK2B, IL1B, IL6, PIK3CA, MAPK1, MAPK3, STAT3, TNF
hsa05133Pertussis−13.56229RHOA, CASP1, CASP3, IL1B, IL6, MAPK1, MAPK3, TNF, NLRP3
hsa05164Influenza A−13.527911AKT1, CASP1, CASP3, CDK4, IL1B, IL6, PIK3CA, MAPK1, MAPK3, TNF, NLRP3
hsa04068FoxO signaling pathway−13.072310AKT1, BRAF, IGF1R, IL6, INSR, PIK3CA, MAPK1, MAPK3, STAT3, TGFBR1
hsa04010MAPK signaling pathway−12.260312AKT1, BRAF, CASP3, IGF1R, IL1B, INSR, KIT NTRK2, MAPK1, MAPK3, TGFBR1, TNF
hsa05160Hepatitis C−12.24910AKT1, BRAF, CASP3, CDK4, PIK3CA, PPARA, MAPK1, MAPK3, STAT3, TNF
hsa04931Insulin resistance−12.14439AKT1, IL6, INSR, PIK3CA, PPARA, PTPN1, STAT3, TNF, NR1H2
hsa04066HIF-1 signaling pathway−12.10759AKT1, HIF1A, IGF1R, IL6, INSR, PIK3CA, MAPK1 MAPK3, STAT3
hsa04668TNF signaling pathway−11.92889AKT1, XIAP, CASP3, IL1B, IL6, PIK3CA, MAPK1, MAPK3, TNF
hsa05132Salmonella infection−11.736811AKT1, RHOA, CASP1 CASP3, IL1B, IL6, PIK3CA, MAPK1, MAPK3, TNF, NLRP3
hsa05212Pancreatic cancer−11.66788AKT1, BRAF, CDK4, PIK3CA, MAPK1, MAPK3, STAT3, TGFBR1
hsa05220Chronic myeloid leukemia−11.66788ABL1, AKT1, BRAF, CDK4, PIK3CA, MAPK1, MAPK3, TGFBR1
hsa01521EGFR tyrosine kinase inhibitor resistance−11.52878AKT1, BRAF, IGF1R, IL6, PIK3CA, MAPK1, MAPK3, STAT3

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Biology 13 00497 g001
Figure 1. The 59 overlapping targets between C. aeruginosa and AGA identified by Venn diagram.
Figure 1. The 59 overlapping targets between C. aeruginosa and AGA identified by Venn diagram.
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Biology 13 00497 g002
Figure 2. Original PPI network of 59 potential targets of C. aeruginosa in AGA constructed by STRING.
Figure 2. Original PPI network of 59 potential targets of C. aeruginosa in AGA constructed by STRING.
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Biology 13 00497 g003
Figure 3. The PPI network (58 nodes and 449 edges) showing the degree of the targets reconstructed by Cytoscape 3.10.2. The darker the color of the node, the greater its degree.
Figure 3. The PPI network (58 nodes and 449 edges) showing the degree of the targets reconstructed by Cytoscape 3.10.2. The darker the color of the node, the greater its degree.
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Biology 13 00497 g004
Figure 4. The top 10 hub gene networks of C. aeruginosa bioactive compounds against AGA from the employment of (A) maximal clique centrality, (B) maximum neighborhood component (MNC), and (C) degree. The warmer the color, the higher the score. The score correlates with the rank of genes in the network. (D) The Venn diagram intersecting the results of the three algorithms, revealing eight hub genes. (E) The PPI network of eight hub genes. Node color denotes interaction degree (red for high degree, orange for intermediate degree, and yellow for low degree).
Figure 4. The top 10 hub gene networks of C. aeruginosa bioactive compounds against AGA from the employment of (A) maximal clique centrality, (B) maximum neighborhood component (MNC), and (C) degree. The warmer the color, the higher the score. The score correlates with the rank of genes in the network. (D) The Venn diagram intersecting the results of the three algorithms, revealing eight hub genes. (E) The PPI network of eight hub genes. Node color denotes interaction degree (red for high degree, orange for intermediate degree, and yellow for low degree).
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Figure 5. The modules obtained from the cluster analysis of the PPI network.
Figure 5. The modules obtained from the cluster analysis of the PPI network.
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Figure 6. Enrichment analyses of C. aeruginosa potential targets in AGA for the top 20 GO annotations and KEGG pathways: (A) GO biological processes, (B) GO cellular components, (C) GO molecular functions, and (D) KEGG pathways.
Figure 6. Enrichment analyses of C. aeruginosa potential targets in AGA for the top 20 GO annotations and KEGG pathways: (A) GO biological processes, (B) GO cellular components, (C) GO molecular functions, and (D) KEGG pathways.
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Figure 7. The components–targets–pathways network displaying the potential mechanism of C. aeruginosa against AGA (yellow ellipses: compounds; red arrows: pathways; blue rectangles: targets).
Figure 7. The components–targets–pathways network displaying the potential mechanism of C. aeruginosa against AGA (yellow ellipses: compounds; red arrows: pathways; blue rectangles: targets).
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Figure 8. Heatmap of the molecular docking of C. aeruginosa core compounds with hub target genes. The bluer the color, the greater the binding energy and binding affinity between the ligand and the receptor.
Figure 8. Heatmap of the molecular docking of C. aeruginosa core compounds with hub target genes. The bluer the color, the greater the binding energy and binding affinity between the ligand and the receptor.
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Figure 9. The top four ligand-receptor complexes: (A) AKT1-alpha-atlantone, (B) AKT1-isoaromandendrene epoxide, (C) AR-alpha-atlantone, and (D) PPARG- isoaromandendrene epoxide.
Figure 9. The top four ligand-receptor complexes: (A) AKT1-alpha-atlantone, (B) AKT1-isoaromandendrene epoxide, (C) AR-alpha-atlantone, and (D) PPARG- isoaromandendrene epoxide.
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Figure 10. The potential mechanism of Curcuma aeruginosa Roxb. against androgenetic alopecia. Collectively, in the treatment of AGA, the MAPK pathway promotes hair follicle proliferation, differentiation, and self-renewal to maintain the hair cycle, and the HIF-1 pathway improves hair vascularization and nutrient supply conducive to hair growth.
Figure 10. The potential mechanism of Curcuma aeruginosa Roxb. against androgenetic alopecia. Collectively, in the treatment of AGA, the MAPK pathway promotes hair follicle proliferation, differentiation, and self-renewal to maintain the hair cycle, and the HIF-1 pathway improves hair vascularization and nutrient supply conducive to hair growth.
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Table 1. Bioactive compounds in C. aeruginosa.
Table 1. Bioactive compounds in C. aeruginosa.
No.CompoundMWOBGIADrug-Likeness
LipinskiGhoseVeberMueggeEgan
1Zedoarol246.310.55HighYesYesYesYesYes
2Myrcene *136.240.55LowYesYesNoYesYes
3Trans-Tagetone152.240.55HighYesYesNoYesYes
4Furanodienone230.310.55HighYesYesYesYesYes
5gamma-Terpinene *136.240.55LowYesYesNoYesYes
61-Hexen-3-OL100.160.55HighYesYesNoYesYes
7p-Cymene *134.220.55LowYesYesNoYesYes
8Curdione236.360.55HighYesYesYesYesYes
9Myrtenal150.220.55HighYesYesNoYesYes
10Germacrone218.340.55HighYesYesYesYesYes
11Isocurcumenol234.340.55HighYesYesYesYesYes
121-Hexanol102.180.55HighYesYesNoYesYes
13beta-Cubebene *204.360.55LowYesYesYesYesYes
14Eucalyptol154.250.55HighYesYesNoYesYes
15beta-Elemene *204.360.55LowNoYesYesYesYes
16Furanogermenone232.320.55HighYesYesYesYesYes
17Curzerene216.320.55HighYesYesYesYesYes
18(+)-Curcumenol234.340.55HighYesYesYesYesYes
194-Carvomenthenol154.250.55HighYesYesNoYesYes
20(1R)-2-methyl-5-propan-2-ylbicyclo[3.1.0]hex-2-ene *136.240.55LowYesYesNoYesYes
21Curzerenone230.310.55HighYesYesYesYesYes
22alpha-Selinene *204.360.55LowNoYesYesYesYes
23cis-3-Hexen-1-ol100.160.55HighYesYesNoYesYes
24d-Borneol154.250.55HighYesYesNoYesYes
25Terpinolene *136.240.55LowYesYesNoYesYes
26beta-Farnesene *204.360.55LowNoYesYesYesYes
27Curcumanolide B234.340.55HighYesYesYesYesYes
28Curcumanolides A234.340.55HighYesYesYesYesYes
292-Hexen-1-OL100.160.55HighYesYesNoYesYes
30Humulene *204.360.55LowNoYesYesYesYes
31Pulegone152.240.55HighYesYesNoYesYes
32Thujone152.240.55HighYesYesNoYesYes
33(+)-delta-Cadinene *204.360.55LowNoYesYesYesYes
34(-)-cis-Carveol152.240.55HighYesYesNoYesYes
35Camphor152.240.55HighYesYesNoYesYes
36Linalool154.250.55HighYesYesNoYesYes
37alpha-Pinene *136.240.55LowYesYesNoYesYes
38Carvone150.220.55HighYesYesNoYesYes
39beta-Pinene *136.240.55LowYesYesNoYesYes
40alpha-Fenchol154.250.55HighYesYesNoYesYes
41alpha-Terpineol154.250.55HighYesYesNoYesYes
42Sabinene *136.240.55LowYesYesNoYesYes
43trans-Pinocarveol152.240.55HighYesYesNoYesYes
44Caryophyllene oxide220.360.55HighYesYesYesYesYes
45Phytol *296.540.55LowNoYesNoNoNo
46(Z)-beta-Ocimene *136.240.55LowYesYesNoYesYes
47gamma-Elemene *204.360.55LowNoYesYesYesYes
48beta-Selinene *204.360.55LowNoYesYesYesYes
49beta-Caryophyllene *204.360.55LowNoYesYesYesYes
50(E)-beta-ocimene *136.240.55LowYesYesNoYesYes
51Camphene *136.240.55LowYesYesNoYesYes
52Limonene *136.240.55LowYesYesNoYesYes
53trans-Verbenol152.240.55HighYesYesNoYesYes
54Allo-Aromadendrene *204.360.55LowYesYesYesYesYes
552-Heptanol116.20.55HighYesYesNoYesYes
56Myrtenol152.240.55HighYesYesNoYesYes
57beta-Bisabolene *204.360.55LowNoYesYesYesYes
58Curcumenone234.430.55HighYesYesYesYesYes
592-Undecanol172.310.55HighYesYesYesYesYes
60gamma-Terpineol154.240.55HighYesYesNoYesYes
61Humuladienone220.360.55HighYesYesYesYesYes
62(-)-beta-Curcumene *204.360.55LowNoYesYesYesYes
63Dehydrocurdione234.430.55HighYesYesYesYesYes
64Tetradecanal212.380.55HighYesYesNoYesYes
65Bisacumol218.340.55HighYesYesYesYesYes
66Tricyclene *136.240.55LowYesYesNoYesYes
67Linalyl acetate196.290.55HighYesYesYesYesYes
684′-Methylacetophenone134.180.55HighYesYesNoYesYes
69Xanthorrhizol218.340.55HighYesYesYesYesYes
701-Methyl-4-(prop-1-en-2-yl)benzene *132.210.55LowYesYesNoYesYes
71Linalyl isobutyrate224.340.55HighYesYesYesYesYes
72alpha-Guaiene *204.360.55LowNoYesYesYesYes
732-Nonanol144.260.55HighYesYesNoYesYes
742-Nonanone142.240.55HighYesYesNoYesYes
753,7(11)-Eudesmadiene *204.460.55LowNoYesYesYesYes
76Camphene hydrate154.250.55HighYesYesNoYesYes
78Curcuphenol218.340.55HighYesYesYesYesYes
79Turmerol220.360.55HighYesYesYesYesYes
802-Undecanone170.30.55HighYesYesYesYesYes
81beta-Eudesmol222.370.55HighYesYesYesYesYes
82Farnesol222.370.55HighYesYesYesYesYes
83alpha-Terpinene *136.230.55LowYesYesNoYesYes
84cis-beta-Farnesene *204.360.55LowNoYesYesYesYes
85Zingiberene *204.360.55LowNoYesYesYesYes
86(+)-beta-Phellandrene *136.230.55LowYesYesNoYesYes
87alpha-Curcumene *202.340.55LowNoYesYesYesYes
88ar-Turmerone216.320.55HighYesYesYesYesYes
893-(1,5-Dimethyl-4-hexenyl)-6-methylene-1-cyclohexene *204.360.55LowYesYesNoYesYes
90Linalool oxide B170.250.55HighYesYesYesYesYes
91delta-Elemene *204.360.55LowNoYesYesYesYes
92alpha-Atlantone218.340.55HighYesYesYesYesYes
93alpha-Phellandrene *136.240.55LowYesYesNoYesYes
94Nerolidol222.370.55HighYesYesYesYesYes
95Flavone222.240.55HighYesYesYesYesYes
96Zedoalactone A266.360.55HighYesYesYesYesYes
97Zedoarondiol252.350.55HighYesYesYesYesYes
98Furanodiene216.320.55HighYesYesYesYesYes
99Zederone246.300.55HighYesYesYesYesYes
100Pyrocurzerenone212.290.55HighYesYesYesYesYes
101Dehydrochromolaenin210.270.55HighYesYesYesYesYes
102Isoaromadendrene epoxide220.350.55HighYesYesYesYesYes
103Demethoxycurcumin338.350.55HighNoYesYesYesYes
* Did not meet the screening conditions.
Table 2. Binding site interactions of top 4 ligand–protein complexes.
Table 2. Binding site interactions of top 4 ligand–protein complexes.
CompoundProteinBinding Sites
alpha-atlantoneARLeu701, Leu704, Asn705, Leu707, Gly708, Gln711, Trp741, Met742, Met745, Val746, Met749, Arg752, Phe764, Met780, Met787, Leu873, Phe876, THR877 Leu880, Phe891, Met895, Ile899
AKT1Asn53, Asn54, Ala58, Gln59, Leu78, Gln79, Trp80, Thr82, Ile84, Asn199, Val201, Ser205, Leu210, Thr211, Leu264, Lys268, Val270, Val271, Tyr272, Ile290, Thr291, Asp292
isoaromandendrene epoxideAKT1Glu17, Tyr18, Asn53, Asn54, Ser56, Ala58, Gln59, Gln79, Trp80, Thr81, Thr82, Ile84, Glu85, Arg86, Lys179, Val201, Ser205, Leu210, Thr211, Leu213, Tyr263, Leu264, Lys268, Val270, Val271, Tyr272, Arg273, Asp274, Asn279, Thr291, Asp292, Phe293, Gly294, Cys296, Lys297, Glu298, Tyr326
PPARGPhe226, Pro227, Leu228, Gly284, Cys285, Arg288, Ser289, Glu291, Ala292 Glu295, Ile296, Ile325, Ile326, Met329, Leu330, Leu333, Val339, Leu340, Ile341, Ser342, Glu343, Gly344, Met364
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Sintos, A.M.L.; Cabrera, H.S. Network Pharmacology Reveals Curcuma aeruginosa Roxb. Regulates MAPK and HIF-1 Pathways to Treat Androgenetic Alopecia. Biology 2024, 13, 497. https://doi.org/10.3390/biology13070497

AMA Style

Sintos AML, Cabrera HS. Network Pharmacology Reveals Curcuma aeruginosa Roxb. Regulates MAPK and HIF-1 Pathways to Treat Androgenetic Alopecia. Biology. 2024; 13(7):497. https://doi.org/10.3390/biology13070497

Chicago/Turabian Style

Sintos, Aaron Marbyn L., and Heherson S. Cabrera. 2024. "Network Pharmacology Reveals Curcuma aeruginosa Roxb. Regulates MAPK and HIF-1 Pathways to Treat Androgenetic Alopecia" Biology 13, no. 7: 497. https://doi.org/10.3390/biology13070497

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